research articles on anxiety disorders

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Anxiety articles from across Nature Portfolio

Anxiety is characterized by excessive uneasiness, apprehension or dread. It can be generalized or be directed towards specific, usually imagined or exaggerated threat.

Latest Research and Reviews

Stress increases hepatic release of lipocalin 2 which contributes to anxiety-like behavior in mice

Cross talk between periphery and the central nervous system may contribute to stress associated behaviours. Here the authors identified liver-derived lipocalin 2 as a peripheral factor that elicits anxiety-like behaviours via modulating medial prefrontal neural activity.

• Fengzhen Yang

Long-term exposure to residential greenness and decreased risk of depression and anxiety

The authors examine the longitudinal relationship between residential greenness and the incidence of depression and anxiety using a sample of 409,556 participants from the UK Biobank.

• Jianing Wang
• Yudiyang Ma
• Yaohua Tian

Social defeat stress induces liver injury by modulating endoplasmic reticulum stress in C57BL/6J mice

• XiaoLei Gao
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The impact of mental health on health-related quality of life in patients with NF2-related Schwannomatosis

• Anna Freier
• Anna C. Lawson McLean
• Johannes Kruse

Feasibility of online managing cancer and living meaningfully (CALM) in Chinese patients with metastatic breast cancer: a pilot randomized control trial

• Yening Zhang

Health anxiety amplifies fearful responses to illness-related imagery

• Christoph Benke
• Laura-Marie Wallenfels
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News and Comment

Destressing microglia.

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Emotional modulation of attention

• Selen Gönül

Mapping the dysfunctome provides an avenue for targeted brain circuit therapy

Brain connections modulated by 534 deep-brain-stimulation electrodes revealed a gradient of circuits involved in dystonia, Parkinson’s disease, Tourette’s syndrome and obsessive-compulsive disorder. Together, these circuits begin to describe the human ‘dysfunctome’, a library of dysfunctional circuits that lead to various brain disorders.

Mental health inequality in young LGBT+ people demands early universal interventions

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Ibogaine therapy in TBI

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An AI chatbot for talking therapy referrals

An AI-enabled chatbot increased self-referrals to psychological therapies for common mental health disorders; but further research is needed to ensure that better access translates into high-quality treatment experiences and outcomes for everyone.

• Jacqueline Sin

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• Published: 15 May 2021

Contemporary treatment of anxiety in primary care: a systematic review and meta-analysis of outcomes in countries with universal healthcare

• Erin L. Parker 1 ,
• Michelle Banfield 2 ,
• Daniel B. Fassnacht 1 , 3 ,
• Timothy Hatfield 1 &
• Michael Kyrios 3  

BMC Family Practice volume  22 , Article number:  92 ( 2021 ) Cite this article

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Anxiety disorders are highly prevalent mental health conditions and are managed predominantly in primary care. We conducted a systematic review and meta-analysis of psychological and pharmacological treatments in countries with universal healthcare, and investigated the influence of treatment provider on the efficacy of psychological treatment.

PubMed, Cochrane, PsycINFO, CINAHL, and Scopus were searched in April 2017 for controlled studies of evidence-based anxiety treatment in adults in primary care, published in English since 1997. Searches were repeated in April 2020. We synthesised results using a combination of meta-analysis and narrative methods. Meta-analysis was conducted using a random-effects multi-level model to account for intercorrelation between effects contributed different treatment arms of the same study. Moderator variables were explored using meta-regression analyses.

In total, 19 articles (from an initial 2,247) reporting 18 studies were included. Meta-analysis including ten studies ( n  = 1,308) found a pooled effect size of g  = 1.16 (95%CI = 0.63 – 1.69) for psychological treatment compared to waitlist control, and no significant effect compared to care as usual ( p  = .225). Substantial heterogeneity was present (I 2  = 81.25). Specialist treatment produced large effects compared to both waitlist control ( g  = 1.46, 95%CI = 0.96 – 1.96) and care as usual ( g  = 0.76, 95%CI = 0.27 – 1.25). Treatment provided by non-specialists was only superior to waitlist control ( g  = 0.80, 95%CI = 0.31 – 1.28). We identified relatively few studies (n = 4) of medications, which reported small to moderate effects for SSRI/SNRI medications and hydroxyzine. The quality of included studies was variable and most studies had at least “unclear” risk of bias in one or more key domains.

Conclusions

Psychological treatments for anxiety are effective in primary care and are more effective when provided by a specialist (psychologist or clinical psychologist) than a non-specialist (GP, nurse, trainee). However, non-specialists provide effective treatment compared with no care at all. Limited research into the efficacy of pharmacological treatments in primary care needs to be considered carefully by prescribers

Trial registration

PROSPERO registration number CRD42018050659

Peer Review reports

Anxiety disorders are among the most prevalent mental health conditions globally, affecting approximately one in nine people in a given year [ 1 ]. These conditions are associated with substantial impairments in occupational and social functioning, including unemployment and under-employment, social isolation, and interpersonal and marital conflict [ 2 ]. Anxiety disorders are a leading cause of disability, accounting for more years lived with a disability than any other mental health condition, as well as many physical health conditions [ 3 ].

Anxiety disorders are managed predominantly within primary care and are one of the most common conditions seen in these settings, despite less than half of those with an anxiety disorder seeking help [ 4 , 5 , 6 ]. Treating anxiety in primary care has substantial advantages in terms of ease of access and financial cost. Indeed, integrating mental health services in primary care is considered a key component of achieving universal health coverage [ 7 ]. However, only a minority of people seeking help in primary care receive adequate treatment for their anxiety [ 8 , 9 ]. Anxiety disorders tend to have a chronic course if insufficiently treated, resulting in significant impairment for the individual and high economic costs due to repeat service use and decreased work productivity [ 3 , 10 ]. Furthermore, delayed or inadequate treatment increases the likelihood of developing common co-occurring conditions such as depression and substance use, which are associated with greater impairment [ 10 ].

Several different professionals may provide treatment for anxiety disorders in primary care (e.g., social workers, nurses, psychologists), though the majority of treatment is provided by general practitioners (GPs) [ 6 , 11 ]. Best practice treatment involves a stepped-care approach based on severity of symptoms and functional impairment, as well as consideration of co-occurring difficulties, consumer preferences, and previous treatment [ 12 , 13 ]. The specific steps vary by disorder, and include low intensity psychological interventions (e.g., guided or unguided self-help, psychoeducation groups) for milder or uncomplicated anxiety problems, and higher-intensity treatments such as individual cognitive behavioural therapy (CBT) or medications for more moderate problems, or where low-intensity interventions have been unsuccessful [ 14 , 15 ]. For complex and severe anxiety difficulties, referral to specialist mental health services outside of primary care should be considered [ 14 , 15 ]. In general, psychological interventions are recommended as first line in preference to pharmacological treatment [ 12 ]. However, pharmacological interventions are the most common treatment provided in primary care regardless of anxiety severity [ 8 , 11 ], and despite research suggesting consumers prefer psychological therapies [ 16 , 17 ].

Although GPs are not routinely able to provide high-intensity psychological treatments due to limited training and time pressures [ 18 , 19 ], they can offer low intensity interventions such as psychoeducation and self-help programs. In particular, computerised or internet-delivered CBT has been shown to be effective for treating anxiety, and may be as effective as face-to-face CBT [ 20 , 21 ]. Computerised CBT programs usually involve modules delivered by desktop, internet, or phone applications, and are suitable for provision in primary care as either guided (i.e., with support from a clinician) or unguided interventions [ 20 ].

When appropriate, higher intensity therapies can such as face-to-face CBT can also be provided in primary care by other lay providers (e.g., nurses), which has been a focus of recent research to improve access to these therapies [ 22 ]. However, financing of non-specialists to deliver psychosocial interventions remains a barrier in many countries, and may explain why GPs continue to provide the majority of care for anxiety disorders. In addition, while there is emerging evidence for psychological interventions provided by non-specialists, the majority of outcome research involves treatment provided by mental health specialists. For example, a previous systematic review and meta-analysis of psychological treatment in primary care found a moderate effect size for reducing anxiety symptoms [ 23 ]. However, the treatment in most included studies was provided by clinical psychologists, who do not typically work in primary care settings.

Medications such as selective serotonin reuptake inhibitors (SSRIs) or serotonin noradrenaline reuptake inhibitors (SNRIs) are also recommended treatments for anxiety [ 12 , 13 ] and may be cheaper and more accessible to consumers than psychological treatments. However, their effectiveness when prescribed in primary care populations, and without any combined psychological management, is unclear. Benzodiazepine medications also remain frequently prescribed for anxiety despite not being a current recommended treatment [ 24 , 25 ]. To our knowledge, no previous reviews of pharmacological anxiety interventions in primary care exist.

In this review, we aimed to synthesise contemporary evidence for the effect of psychological and pharmacological treatments for anxiety compared with control in primary care. We were interested in evidence from studies that most accurately reflected the real-world treatment settings in which they were conducted. To this end, we focused on reviewing evidence from countries with existing universal healthcare systems (i.e., where mental health services are routinely provided in primary care without significant cost to consumers). Regarding psychological treatments, our review sought to update and extend upon the review conducted by Seekles et al. [ 17 ] by a) maximising identification of studies where treatment was provided by non-specialists or GPs, and b) excluding studies of obsessive compulsive disorder (OCD) and post-traumatic stress disorder (PTSD), which are no longer considered anxiety disorders in the most recent classification systems. We also sought to investigate variables that may moderate psychological treatment effectiveness, namely treatment provider (specialist vs. non-specialist) and treatment modality (face-to-face vs. online vs. self-help).

Search strategy and selection process

This review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and was registered with the international prospective register of systematic reviews (PROSPERO; registration number CRD42018050659). Primary searching was conducted in PubMed using MeSH terms (see Table 1 ). PsycINFO, the Cochrane Central Register of Controlled Trials (CENTRAL), the Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Scopus were also searched to maximise identification of relevant studies. The full search strategy for all databases is available in additional file 1 .

We identified and removed duplicate articles using Endnote Referencing software. Two independent researchers (ELP and TH) screened titles and abstracts of retrieved articles to determine eligibility for the review. ELP and TH then screened full-text versions of all eligible studies for final inclusion. The reference lists of included articles were hand-searched to identify additional studies, and none were found. Disagreements between reviewers were resolved through post-assessment discussion at each stage of the process.

Initial searches were conducted on April 17, 2017. We re-ran searches on 22 April 2020 to identify any studies published in the period since our initial search date. The first author screened the additional records retrieved following the same process as above. Our inclusion and exclusion criteria can be seen in Table 2 .

We were interested in synthesising the most recent evidence for treating anxiety in primary care. As such, we excluded studies published prior to 1997, which was 20 years before our initial search. We included studies of participants with a primary diagnosis of an anxiety disorder according to diagnostic criteria (DSM or ICD), or clinically significant levels of anxiety on an assessment/screening measure (e.g., Beck Anxiety Inventory [BAI]; Depression Anxiety Stress Scales [DASS]). We excluded studies of OCD and PTSD, which are no longer classified as anxiety disorders. Studies focusing on mixed anxiety/depression were included due to the high rates of co-occurrence between these conditions, as long as treatment was anxiety-specific (i.e., recommended pharmacological agents for anxiety, or anxiety-focussed psychological treatment).

We defined evidence-based treatments as psychological and pharmacological interventions with an existing evidence base, as determined by current clinical practice guidelines (e.g., NICE guidelines, [ 12 ]). For psychological interventions, this included self-help, mindfulness/applied relaxation, and individual cognitive behavioural therapy [ 12 , 14 , 15 ]. Pharmacological treatments included SSRIs, SNRIs, pregabalin (generalised anxiety disorder), tricyclic antidepressants (panic disorder) and benzodiazepines in the case of short-term treatment [ 12 , 14 , 15 ].

Data extraction and synthesis

The primary outcome in this review was treatment effect size (standardised mean difference) for the reduction of anxiety symptoms in each study. Secondary outcomes were treatment effect sizes for reduction in depressive symptoms and improvement in quality of life. Included papers were coded by two independent reviewers (ELP and either TH or DBF) using a standardised data extraction form. We extracted the following variables from each study: demographic information about participants (age, gender); country in which the study was conducted; type of anxiety; treatment type; modality of treatment (e.g., self-help, online, face-to-face); treatment provider; type of control group; and outcome statistics (means and standard deviations between groups at post-treatment and follow-up, or other statistics where these were not available). Data were extracted from published reports, and study authors were contacted to obtain missing information. We assessed interrater agreement by comparing the information on each reviewer’s coding form after extraction of all items. Disagreements were resolved through discussion and review of the information in the article.

 We calculated standardised mean differences (Hedges g) [ 26 ] and standard errors at post-treatment between control and treatment groups for each study. This was calculated from means and standard deviations or other statistics (e.g., t-value, p-value) when the former were not reported. Hedge’s g was chosen over other measures of effect size as it corrects for small sample sizes [ 27 ], which was an issue for some of the studies in this review. We calculated a separate effect size for all active treatments compared with control in studies with multiple treatment arms. If an anxiety-specific measure was not the primary outcome in the study, the best (e.g., gold standard for a particular disorder, best test–retest reliability) measure of anxiety symptoms in the study was chosen to calculate these statistics. Measures from each study are reported in Table 3 .

Meta-analysis was performed on studies of psychological treatment only, and other studies were synthesised using narrative methods. We conducted meta-analysis in RStudio version 1.0.143 using the metafor package [ 28 ]. For studies with multiple treatment arms, we entered effect sizes from each active treatment compared with the control group into this analysis. A random-effects multi-level model was used to account for intercorrelation between effect sizes contributed by the same study, and meta-regression analyses were run to investigate the effects of moderator variables. We obtained the code for these analyses from the metafor package website ( www.metafor-project.org ) based on the description of meta-analysis for multiple treatment studies [ 29 ] and multivariate random and mixed-effects models [ 30 ]. We assessed variability between studies using Chi 2 tests and I 2 estimates of heterogeneity. Interpretation of I 2 values was based on guidelines from the Cochrane handbook, where 0% to 40% represents heterogeneity that may not be important; 30% to 60% may represent moderate heterogeneity; 50% to 90% may represent substantial heterogeneity; and 75% to 100% represents considerable heterogeneity [ 31 ]. Heterogeneity was explored using meta-regression to investigate the effect of moderators, as noted above.

Publication bias was investigated with Egger's regression test of funnel plot asymmetry [ 32 , 33 ] by using sampling variance as a moderator in a multi-level model. Methods of sensitivity analysis are not yet well developed for multivariate/multi-level models [ 34 ], and options (e.g., Trim and Fill) are not currently available in the metafor package for these types of models. Therefore, we conducted sensitivity analysis by calculating Cook’s distance [ 35 , 36 ] to identify influential outliers. These were defined as observations with a Cook’s distance greater than 4/n.

Risk of bias

Risk of bias for each study was assessed by ELP and DBF independently using the Cochrane Collaboration’s risk of bias tool [ 37 ]. In many psychological treatment studies, blinding of participants and personnel is not possible due to the interpersonal nature of the treatment. In these cases, we rated studies as having “unclear” risk of bias for this criterion, providing no other factors warranted a rating of “high”. Consistent with similar reviews of heterogeneous studies with complex interventions [ 38 ], we sought agreement between reviewers for all items by comparing ratings and resolved disagreements through post-assessment discussion.

Description of studies

Our initial search identified 2,151 articles (after removal of duplicates), and 207 full-text articles were screened. Eighteen articles reporting 17 studies met all inclusion criteria. Interrater agreement for extracted variables was 89.3%. Updated searching in April 2020 identified only one further study for inclusion (from an initial 95 articles published since our original search). Of the 191 articles excluded after full-text screening, 71 were excluded on the basis of being conducted in a country without universal healthcare (all from the USA). Thirty-one of these articles were publications from a single, large study of collaborative care for anxiety [ 39 ]. The full study selection process can be seen in Fig.  1 .

Study selection process using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram

A total of 19 articles reporting 18 studies met all criteria and were included in our review. Two articles reported separate steps of the same study [ 40 , 41 ], and eight studies involved more than one active treatment condition [ 19 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 ]. Across all studies, there were 28 comparisons of active treatment with a control group (placebo, waitlist control, or care as usual [CAU]). Key characteristics of the included studies are available in Table 3 .

Participants

In the included studies, 2,059 participants were randomised to an active treatment condition and 1,247 to a control condition. Participants ranged in age from 18 to 80 years, with the average age in each study between 34.2 years and 51 years. All studies had a higher proportion of women than men.

Thirteen studies investigated anxiety disorders specifically; four generalised anxiety disorder (22.2% of 18), four panic disorder with or without agoraphobia (22.2% of 18), and five investigated multiple anxiety disorders (including mixed anxiety/depression; 27.8% of 18). Five studies (27.8% of 18 studies) included participants with “common mental disorders” as their primary diagnosis, which referred to one or more of anxiety disorders, depression, mixed anxiety/depression, and stress/adjustment disorders. One study reported separate outcomes for participants with an anxiety disorder only [ 40 ] and anxiety-only data was obtained from the authors for another study [ 43 ].

Most studies reported moderate mean anxiety severity at baseline among participants, as measured by either clinician (e.g., CGI-S, HAM-A) or self-report (e.g., BAI) measures. Two studies reported mild-to-moderate anxiety severity at baseline [ 41 , 43 ], and five studies reported moderate-severe or severe anxiety [ 19 , 44 , 45 , 50 , 51 ].

Treatment and control group type

The majority of included studies were of psychological treatments (10/18, 55.5%). Four studies investigated one or more pharmacological treatments (22.2% of 18), and one study compared psychological and pharmacological treatments (and their combination). The remaining three studies investigated the effect of stepped care, which included both psychological and pharmacological treatments. Pharmacological studies tended to be older (published between 1998 and 2003) than psychological studies (published between 2000 and 2019).

In the 10 psychological treatment studies, four compared treatment with a waitlist control (i.e., no treatment) and six used a CAU control. The care received by control group participants was described in four of the six CAU-controlled studies [ 19 , 48 , 50 , 52 ], and most commonly included antidepressants, benzodiazepines, CBT, or referral for specialist mental health care. These studies reported that most control group participants received at least one of these treatments, though did not report actual numbers for the different types of care, with the exception of one study [ 50 ]. All three studies of stepped care used CAU as a control and provided descriptions of the care received by participants. At least half of control group participants in these studies received medication (antidepressants or benzodiazepines), referral to a specialist mental health professional, or both. All pharmacological treatment studies used placebo controls.

Psychological interventions

Four psychological treatment studies investigated the effects of two different treatments with a control. With the addition of the psychological treatment arm from the study of combined treatment [ 42 ] as well as the article reporting outcomes for the self-help step [ 40 ] of a stepped care study [ 41 ], there were a total of 16 comparisons of psychological treatment with either CAU or waitlist control.

Psychological treatments were predominantly CBT-based ( n  = 13, 81.2% of 16) and provided on an individual basis. One study involved group treatment [ 52 ], and one study compared individual treatment with group treatment [ 49 ]. Treatment was delivered either face-to-face with a health professional ( n  = 6, 37.5% of 16) or through self-help manuals/internet programs with support from a professional ( n  = 10, 62.5% of 16). Treatment was provided by specialists (clinical psychologists or psychologists) in six treatment conditions (37.5% of 16). In the other ten treatment conditions, treatment was provided by trainee psychologists ( n  = 2), mental health nurses ( n  = 3), GPs ( n  = 3), an unspecified clinician ( n  = 1), and the participant themselves ( n  = 1), all of whom we coded as non-specialists in this review.

Effect on anxiety disorders

We conducted meta-analysis on the studies of psychological treatment for anxiety disorders; to limit heterogeneity, we excluded the studies of common mental disorders and mixed anxiety/depression from this analysis [ 43 , 53 ]. The effect of psychological treatment on common mental disorders is instead described below using narrative synthesis. Meta-analysis included 14 comparisons of psychological treatment with a control group, taken from ten studies (Fig.  2 , Table 4 ). The model found a large effect size for psychological treatment compared to waitlist control ( g  = 1.16, 95%CI = 0.63 – 1.69), and no significant effect compared to CAU control (Z = 1.21, p  = 0.225). Considerable heterogeneity was present (I 2  = 81.25).

Forest plot for comparison of psychological treatments with control, for studies of anxiety only

Due to a lack of power, we were only able to investigate the effects of one moderator variable. Treatment provider was chosen as this variable was more relevant to the aims of the review. Meta-regression analysis found that treatment effect was significantly moderated by treatment provider (z = 2.61, p  = 0.009). Results are presented in Table 4 . The inclusion of this moderator accounted for 53% of the total amount of heterogeneity. However, the resulting test for residual heterogeneity was significant (Q E  = 36.22, df = 11, p  < 0.001).

Treatment provided by a non-specialist compared with CAU did not produce a significant effect on anxiety symptoms ( p  = 0.468). However, compared with waitlist control a large effect was found ( g  = 0.80, 95%CI = 0.31 – 1.28). Treatment provided by a specialist was associated with large effects regardless of the comparison group (CAU: g  = 0.76, 95%CI = 0.27 – 1.25; waitlist: g  = 1.46, 95%CI = 0.96 – 1.96).

Egger’s regression test showed significant funnel plot asymmetry (z = 3.70, p  < 0.001), indicating the presence of publication bias. No influential outliers were identified, though Cook’s distance for one study [ 19 ] was substantially larger (D = 0.23) than for other studies and close to the threshold of 0.29 (4/n), suggesting this study had a larger influence on the model than the other observations.

Effect on common mental disorders

One study investigated two types of psychological treatment (problem-solving and generic mental health nurse care) for common mental disorders (anxiety, depressive, stress, and adjustment disorders) and found no significant treatment effect for either compared with CAU [ 43 ]. The authors for this study also provided us with results for participants with anxiety only, which are reported in the meta-analysis above. A second study investigated online CBT for mixed anxiety and depression and found a large effect size of g  = 0.85 (95% CI = 0.43 – 1.27) compared with waitlist control [ 53 ].

Pharmacological interventions

All four pharmacological studies investigated medications for generalised anxiety disorder (GAD), with three examining the relative efficacy of two different medications. There were a total of eight comparisons of pharmacological treatment with placebo, including the pharmacological treatment arm of the study of combined treatment (which studied generalised social phobia) [ 42 ]. Meta-analysis was not possible for these comparisons due to incomplete reporting of outcome statistics in the primary articles.

Two comparisons of benzodiazepines with placebo [ 45 , 47 ] found no significant difference between groups at post-treatment. Authors in two studies [ 45 , 46 ] also reported no effect of buspirone compared with placebo. Both studies comparing hydroxyzine with placebo found a significant treatment effect; one reported a moderate effect size of g  = 0.47 (95% CI = 0.16 – 0.78) at post-treatment [ 46 ], and the other found a similar effect size of g  = 0.32 (95% CI = 0.05 – 0.60) [ 47 ]. Likewise, both studies of SSRI/SNRI medications reported a treatment effect, with small effects of g  = 0.29 (95% CI = 0.00 – 0.58) found for sertraline compared with placebo [ 42 ], and g  = 0.25 (95% CI = 0.00 – 0.50) for venlafaxine compared with placebo [ 51 ].

Combined interventions

We did not perform meta-analysis on studies of combined interventions due to the small number of studies and the clinical diversity among them. The sole study of combined psychological and pharmacological treatment investigated the relative effects of exposure therapy, sertraline, and exposure therapy plus sertraline compared with placebo [ 42 ]. The results for psychological treatment and pharmacological treatment in this study have been reported above. A significant treatment effect was also found for combined treatment compared with control, with an effect size of g  = 0.35 (95% CI = 0.07 – 0.64). Although combined treatment produced the largest effect size, this was not significantly different from the other active treatment groups.

In the three studies of stepped care [ 41 , 54 , 55 ], treatment was provided by multiple professionals, including mental health nurses and psychiatrists. Higher and more intensive steps of these interventions included medication combined with psychological therapy. Two studies found small, significant effects of stepped care compared to CAU for common mental disorders ( g  = 0.23, 95%CI = -0.13 – 0.58 [ 41 ]; g  = 0.31, 95%CI = -0.01 – 0.63 [ 55 ]). The third study investigated stepped care for anxiety only, and also found a significant effect ( g  = 0.21, 95%CI = -0.12 – 0.54) [ 54 ].

Longer-term follow-up

Follow-up of at least three months post-treatment was reported in 11 of the 18 included studies. Outcomes were difficult to synthesise due to variability in how these statistics were reported and are described below using narrative methods.

All but one of the psychological treatment studies [ 52 ] reported follow-up data. For studies where a waitlist control was used, three studies reported maintenance of gains within the treatment group at three-[ 44 , 53 ] and 10-month [ 56 ] follow up. Control group data was not recorded in these studies as control participants received the intervention after the waiting period. A fourth study, which investigated the effect of group and individual CBT, reported gains in the group CBT condition were maintained at follow-up, but the rate of clinically significant change decreased in the individual CBT condition [ 49 ].

Among studies comparing to a CAU control, four reported outcomes for both control and treatment groups at follow-up. There was no significant difference between treatment and control groups in two of these studies [ 19 , 43 ], though authors also reported that post-treatment and follow-up scores did not differ significantly in any of the groups. One study [ 50 ] reported an effect size of g = 0.31 (95%CI = 0.08 – 0.53, p  = 0.01) for self-help CBT compared with control at follow-up, and another study reported maintained rates of clinically significant change from post-treatment [ 48 ]. One further study reported sustained treatment gains in treatment group participants for whom follow-up assessments were conducted [ 57 ].

Two (out of four) studies of combined treatment reported follow-up; one reported an effect size of g = 0.37 (95%CI = 0.02 – 0.72, p  = 0.04) for stepped-care compared with CAU [ 54 ], and the other reported maintenance of gains within the treatment group, but no significant effect of stepped-care compared to CAU due to improvements in the control group at follow-up [ 55 ]. Follow-up was not reported in any of the pharmacological treatment studies.

Risk of bias in included studies

The majority of included studies had an unclear risk of bias for one or more key domains (see Fig.  3 for risk of bias in each study, and Fig.  4 for a summary of risk of bias items across all studies). Interrater agreement between authors ELP and DBF was 85.3% for risk of bias information. In psychological and combined treatment studies, the risk of performance bias was unclear in most studies, as participants were often not blinded. These studies were also at risk of detection bias due to the use of self-report measures (and unblinded participants) or unblinded outcome assessors. Risk of reporting bias was considered low for studies of psychological or combined treatment, and risk of selection bias was low-to-unclear, with most studies assessed as low risk. Studies of any treatment type tended to report equal rates of drop-out across treatment conditions and used intention-to-treat analyses.

Assessment of each study across risk of bias items. Figure produced using RevMan [ 58 ]

Assessment of each risk of bias item, presented as proportion of studies with low, unclear, and high risk of bias. Figure produced using RevMan [ 58 ]

For the majority of pharmacological treatment studies, risk of bias was unclear-to-high across domains. All four studies reported inadequate information about random sequence generation and allocation concealment. Three studies had a high risk of bias due to selective outcome reporting, as they presented results visually without reporting outcome statistics (i.e., one or more of the following were missing: means, standard deviations, results of statistical analyses). Furthermore, three of the studies were funded or partially funded by pharmaceutical companies [ 46 , 47 , 51 ] and for all four studies no conflict of interest statement was included.

Secondary outcomes

Most included studies ( n  = 15, 83.3% of 18) measured depressive symptoms as secondary outcomes, or as combined primary outcomes along with anxiety symptoms. The majority of these ( n  = 8) reported no significant difference in depressive symptoms between control and treatment groups. The seven studies that found a significant treatment effect on depressive symptoms reported effect sizes ranging from g  = 0.35 to 1.00.

Less than half of the studies ( n  = 7, 38.8% of 18) included measurements of quality of life. Three studies reported no significant difference in quality of life between groups, and four studies found significant treatment effects ranging from g  = 0.31 to 1.36.

Our review investigated both psychological and pharmacological treatments for anxiety and explored the effects of treatment provider on psychological treatment effectiveness. Studies of psychological treatment were diverse and could broadly be categorised into two subgroups – those that investigated anxiety specifically, and those that investigated common mental disorders (anxiety, depressive, stress, and adjustment disorders).

Meta-analysis demonstrated that for those with primarily anxiety-related difficulties, psychological treatments (predominantly CBT) are effective for reducing anxiety symptoms when provided in primary care. However, the magnitude of this improvement differs depending on who is providing treatment, and is relative to the comparison group. When a specialist provides treatment, large improvements are seen in anxiety symptoms regardless of the type of control group, though the effect is smaller when treatment is compared to other usual treatments than waitlist control. Treatments provided by a non-specialist are also associated with large improvements compared to waitlist control (i.e., no care at all), but were not found to improve anxiety over other usual treatments. These findings are consistent with a previous review of psychological treatment for anxiety in primary care, which demonstrated a superior treatment effect for interventions provided by specialist mental health professionals compared with non-specialists [ 23 ]. Previous research has also demonstrated that for both face-to-face CBT and computerised CBT, effect sizes are smaller when comparing to CAU (which involves active treatment) than inactive control groups such as waitlist or placebo [ 20 , 23 ].

Cognitive behaviour therapy is well documented as an effective treatment for anxiety [ 13 , 23 ], though further research is needed on long-term effectiveness in primary care. In the studies included in our review, CBT was predominantly provided via bibliotherapy or computerised methods, with varying degrees of support from a clinician. The effectiveness of self-help CBT has been demonstrated in other reviews [ 20 , 21 ], and our results provide support for the implementation of these interventions for anxiety in primary care. Computerised CBT has the additional benefit of high fidelity, as interventions can be delivered exactly as designed. This is in contrast to face-to-face therapy where fidelity is impacted by experience and training of the provider and their adherence to treatment manuals, which may be particularly relevant for non-specialist treatment providers [ 13 ].

The results for longer-term follow-up in psychological treatment studies included in our review were mixed. However, most reported treatment gains were maintained within the treatment group, and were superior to gains seen in control group participants who received other usual treatments. Limited data on long-term follow-up is a limitation in the field, though studies not specific to primary care settings have found that the effect of psychological treatment for anxiety tends to be well maintained at follow-up [ 59 , 60 ].

The studies investigating treatment for common mental disorders were summarised using narrative synthesis as there were too few studies to conduct meta-analysis. The pattern of results across these studies was similar to that of the studies on anxiety only; psychological treatments did not produce a significant effect compared with CAU control groups, though large effects of treatment were seen when compared to waitlist control.

Only a small number of included studies involved pharmacological treatment, and only two [ 42 , 51 ] involved current first-line agents for anxiety (sertraline and venlafaxine) [ 12 ]. Both medications produced small, superior effects compared to placebo, indicating they are effective for reducing anxiety symptoms in primary care. Across an additional three studies, hydroxyzine also produced small to moderate effects, while buspirone and benzodiazepines were not found to reduce anxiety compared with placebo. However, hydroxyzine and buspirone are not considered first-line agents for anxiety, and benzodiazepines are only recommended in specific conditions such as during the initiation phase of an SSRI [ 61 ]. Furthermore, the majority of pharmacological treatment studies were funded by pharmaceutical companies and had a high risk of bias due to selective outcome reporting, questioning the validity of these results. Overall, we did not find a strong body of research documenting the use of pharmacological treatments in primary care. This was true irrespective of the exclusion of studies from countries without universal healthcare, as only one additional study of medication (an SSRI) would have been included if not for this restriction.

None of the included studies of pharmacological treatment reported on longer-term follow-up, so we were not able to investigate the effectiveness of these medications beyond the acute treatment phase. Previous research has demonstrated that the risk of relapse is high when pharmacological interventions are discontinued following acute treatment, and it is therefore advised that treatment continue for between six and 24-months after remission [ 62 ]. Given pharmacological interventions are the dominant treatment strategy provided in primary care, further research is needed to determine the effectiveness of these treatments in this setting.

The combined use of medication and psychological therapy was directly investigated in only one study [ 42 ]. This demonstrated combined treatment was effective in comparison to control but no more effective than either treatment alone. Although combined treatment is commonly used in practice, there is limited evidence to indicate this leads to better outcomes [ 13 ]. Stepped care interventions, including both pharmacological and psychological treatment steps, appear effective for treating anxiety based on the three studies included in our review. Results from these studies are consistent with the emerging body of evidence for collaborative stepped care in primary care, with small to moderate effect sizes found in a previous review [ 63 ].

Limitations

Our review had several limitations. Studies were heterogeneous and meta-analytic results for the effects of psychological treatment should be interpreted with caution. Several factors may have contributed to heterogeneity in this review. For example, across the included studies there was a mixture of self-report and clinician assessed measures, and treatment was provided using a variety of modalities (e.g., online, individual face-to-face, group). Likewise, multiple anxiety disorders were investigated both within and between studies, and different disorders may have responded differently to the treatments used. Unfortunately, additional moderators, including the planned investigation of treatment modality, were not able to be explored due to the small number of included studies. The decision to pool studies using meta-analysis is based on both statistical and theoretical considerations. It is important to note the heterogeneous nature of primary care, and diversity among included studies can be considered a reflection of the real-world treatment provided in this setting. Combining studies of diverse interventions may not provide meaningful information about the individual effects of each intervention, but can be useful in answering broader questions (e.g., summarising the average effect of a class of drugs by combining studies of different drugs within that class) [ 31 ]. Although heterogeneity limits the strength of conclusions that can be drawn from our meta-analytic results, we believe our findings are useful in contributing to the broader question of how well psychological interventions work for anxiety in primary care.

Another limitation of our review is that the effect of psychological treatments compared with CAU is difficult to interpret, as CAU was poorly described in the included studies. Control group participants could receive medication, other psychological treatments, general advice, or no treatment at all, and most studies did not report the rates of different care. However, studies reported that at least half of control group participants received some form of active intervention, including referral for specialist mental health care and antidepressant medication. This may have reduced the apparent effectiveness of treatments provided by non-specialists in particular, as participants in the control condition may have received a higher intensity treatment such as specialist psychological treatment, medication, or both.

As with all systematic reviews, our search strategy and inclusion criteria may have excluded relevant studies of treatment for anxiety in primary care. This is particularly true of studies conducted in countries without universal healthcare systems (most notably, the USA), and studies that were published in languages other than English. We also identified very few studies of primary care specific pharmacological treatment, and may have identified further studies if we had searched additional biomedical databases (e.g., Embase). Unfortunately, we did not have access to Embase for this review.

Despite attempts to maximise identification of studies with non-specialist treatment providers, we identified relatively few studies of psychological treatments provided by GPs. Combined with the limited number of pharmacological treatment studies, the body of evidence identified is inconsistent with the real-world treatment of anxiety disorders in primary care [ 6 , 11 ] and limits our ability to describe the effectiveness of this treatment. The generalisability of our findings to low-income countries and high-income countries without universal health care is also limited. Finally, only one study was identified that directly compared medication and psychological treatments in primary care, making it difficult to comment on the relative effectiveness of the two. Other reviews have noted the lack of comparison between psychological and pharmacological treatments as a serious limitation in the field, particularly in the case of computerised CBT programs versus medication [ 20 ].

Implications for clinical practice

Despite the limitations, our review has several important implications for primary care. Results support previous research in this area, demonstrating that CBT-based psychological treatments for anxiety are effective, and that specialist treatment (i.e., provided by a psychologist or clinical psychologist) is preferable [ 23 ]. Our results also extend upon previous findings by providing information about treatment delivered by non-specialists, which is important given that access to specialists is not always possible in primary care. Although we did not find that psychological treatment provided by non-specialists is superior to other usual treatments, we also did not find it to be inferior. This indicates that non-specialist psychological treatment may be at least as good as other usual treatments, and an appropriate option for consumers. Additionally, our results demonstrated that non-specialist treatment is associated with significant and large improvements in anxiety compared with no treatment at all.

Although pharmacological treatments are effective for anxiety generally [ 61 ] and have advantages in terms of cost and ease of access, we did not find strong evidence for their use in primary care due to a small number of studies and high-risk of bias among those studies. Medications for anxiety disorders carry side effects [ 64 ], and benzodiazepines, which remain commonly prescribed despite no longer being a recommended first-line treatment [ 24 , 25 ], carry risks of both physiological and psychological dependence. Furthermore, benzodiazepines may in fact prolong anxiety symptoms if used alone due to their use as a safety behaviour and potential to impair fear extinction [ 65 , 66 ]. This may be particularly true when physiological anxiety sensations themselves are the feared stimuli (e.g., in panic disorder), and exposure to these symptoms is avoided through the use of benzodiazepines.

We therefore recommend that pharmacological treatments be used with caution in primary care until further research is conducted, and that CBT-based psychological treatments, including those provided online and via self-help, be offered as first-line treatments for anxiety disorders in this setting. This treatment should be provided by a specialist such as a psychologist or clinical psychologist if available and affordable for the consumer. However, non-specialists should still offer psychological treatment if specialist treatment is not possible.

Overall, our review demonstrated that, in countries with universal healthcare, a greater alignment of research and practice is needed to more effectively manage anxiety disorders. Additional research is needed to investigate the use of pharmacological treatments in primary care and to determine their relative effectiveness when compared with psychological interventions in this setting. Future research on psychological treatments should aim to more closely mirror the treatment that is delivered in real-world primary care settings (i.e., in terms of treatment provider). This research should be conducted alongside implementation science involving both provider and consumer perspectives, that explores barriers to the delivery of psychological treatments for anxiety in primary care.

Availability of data and materials

All data generated or analysed during this study are included in this published article, its additional files, and the published articles included in this review.

Abbreviations

Beck anxiety inventory

Care as usual

Cognitive behaviour therapy

Depression anxiety stress scale

Diagnostic and statistical manual of mental disorders

Generalised anxiety disorder

General practitioner

International classification of diseases

Obsessive compulsive disorder

Post-traumatic stress disorder

Serotonin noradrenaline reuptake inhibitors

Selective serotonin reuptake inhibitor

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Acknowledgements

The first author conducted this review under the supervision of the second, third, and last authors in partial fulfilment of a Doctor of Philosophy in Clinical Psychology at the Australian National University (ANU). We also thank Professor Philip Batterham for his contributions to this review.

This research received no specific grant from any funding agency, commercial or not-for-profit sectors. ELP was supported by an Australian Government Research Training Program (AGRTP) Stipend Scholarship for the duration of the review. MB is supported by a Medical Research Future Fund (MRFF) Translating Research into Practice (TRIP) Fellowship number MRF1150698, which is unrelated to the submitted work. These funders had no role in study design, data collection, data analysis, data interpretation, or writing of the report.

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Parker, E.L., Banfield, M., Fassnacht, D.B. et al. Contemporary treatment of anxiety in primary care: a systematic review and meta-analysis of outcomes in countries with universal healthcare. BMC Fam Pract 22 , 92 (2021). https://doi.org/10.1186/s12875-021-01445-5

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Cognitive-Behavioral Treatments for Anxiety and Stress-Related Disorders

• Joshua E. Curtiss , Ph.D. ,
• Daniella S. Levine , B.A. ,
• Ilana Ander , B.A. ,
• Amanda W. Baker , Ph.D.

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Cognitive-behavioral therapy (CBT) is a first-line, empirically supported intervention for anxiety disorders. CBT refers to a family of techniques that are designed to target maladaptive thoughts and behaviors that maintain anxiety over time. Several individual CBT protocols have been developed for individual presentations of anxiety. The article describes common and unique components of CBT interventions for the treatment of patients with anxiety and related disorders (i.e., panic disorder, social anxiety disorder, generalized anxiety disorder, obsessive-compulsive disorder, posttraumatic stress disorder, prolonged grief). Recent strategies for enhancing the efficacy of CBT protocols are highlighted as well.

Anxiety disorders are among the most prevalent of mental disorders and are associated with high societal burden ( 1 ). One of the most well-researched and efficacious treatments for anxiety disorders is cognitive-behavioral therapy (CBT). At its core, CBT refers to a family of interventions and techniques that promote more adaptive thinking and behaviors in an effort to ameliorate distressing emotional experiences ( 2 ). CBT differs from other therapeutic orientations in that it is highly structured and often manualized. CBT sessions often occur weekly for a limited period (e.g., 12–16 weeks), and a small number of booster sessions are sometimes offered subsequently to reinforce independent use of skills. A cognitive-behavioral conceptualization of anxiety disorders includes identification of dysfunctional thinking patterns, distressing feelings or physiological experiences, and unproductive behaviors. When each of these three components interact and mutually reinforce one another, distressing and impairing levels of anxiety can be maintained over time. Although there are several CBT interventions for different types of anxiety, some common techniques and treatment goals form the basis of the CBT philosophy.

Cognitive Interventions

One of the primary CBT strategies is cognitive intervention. In brief, CBT holds that one’s emotional experience is dictated by one’s interpretation of the events and circumstances surrounding that experience ( 2 , 3 ). Anxiety disorders are associated with negatively biased cognitive distortions (e.g., “I think it’s 100% likely I will lose my job, and no one will ever hire me again”). The objective of cognitive interventions is to facilitate more adaptive thinking through cognitive restructuring and behavioral experiments. Cognitive restructuring promotes more adaptive and realistic interpretations of events by identifying the presence of thinking traps. These cognitive traps are patterns of biased thinking that contribute to overly negative appraisals. For example, “black-and-white thinking” describes the interpretation of circumstances as either all good or all bad, without recognition of interpretations between these two extremes, and “overgeneralization” describes the making of sweeping judgments on the basis of limited experiences). Through identification of thinking traps, cognitive restructuring can be used to promote more balanced thinking, encouraging patients to consider alternative interpretations of circumstances that are more helpful and less biased by anxiety (e.g., “Maybe thinking the chance of losing my job is 100% is overestimating the likelihood that it will actually happen. And, it’s not a forgone conclusion that even if I lose my job, I will never find another one for the rest of my life.”). Similarly, behavioral experiments can be used to facilitate cognitive change. Behavioral experiments involve encouraging patients to empirically test maladaptive beliefs to determine whether there is evidence supporting extreme thinking. For example, if a patient believes that he/she/they is romantically undesirable and that asking someone on a date will cause the other person to react with disgust and disdain, then the patient would be encouraged to test this belief by asking someone on a date. Some combination of cognitive restructuring and behavioral experiments are often implemented in CBT across all anxiety disorders.

Behavioral Interventions

There are several behavioral strategies in CBT for anxiety disorders, yet the central behavioral strategy is exposure therapy. Exposure techniques rely on learning theory to explain how prolonged fear is maintained over time. Specifically, heightened anxiety and fear prompt individuals to avoid experiences, events, and thoughts that they believe will lead to catastrophic outcomes. Continued avoidance of feared stimuli and events contributes to the maintenance of prolonged anxiety. Consistent with the premises underlying extinction learning, exposure exercises are designed to encourage a patient to confront a feared situation without engaging in avoidance or subtle safety behaviors (i.e., doing something to make an anxiety-inducing situation less distressing). After repeated exposures to a feared situation (e.g., heights) without engaging in avoidance or safety behaviors (e.g., closing one’s eyes to avoid looking down), the patient will learn that such a situation is less likely to be associated with disastrous outcomes, and new experiences of safety will be reinforced. Similar to the behavioral experiments described in the cognitive intervention section above, which test whether a faulty thought is true or false, exposure exercises offer the opportunity for patients to test their negative beliefs about the likelihood of a bad outcome by exposing themselves to whatever situations they have been avoiding. Thus, cognitive approaches and exposure exercises are complementary techniques that can benefit individuals with anxiety disorders. In the following sections, different aspects of CBT will be explored and emphasized insofar as they relate to specific presentations of anxiety.

CBT for Specific Disorders

Panic disorder.

Panic disorder, as defined by the DSM-5 , is characterized by recurrent, unexpected panic attacks accompanied by worry and behavioral changes in relation to future attacks. Panic attacks are marked by acute, intense discomfort, with symptoms including heart palpitations, sweating, and shortness of breath. Individuals with panic disorder exhibit cognitive and behavioral symptoms, such as catastrophic misinterpretations of their symptoms as dangerous (e.g., “my heart pounding means I will have a heart attack”) and avoidance of situations or sensations that induce panic ( 4 ). Cognitive-behavioral treatments thus target these symptoms. For example, cognitive restructuring is used to help patients reinterpret their maladaptive thoughts surrounding panic (e.g., “if I get dizzy, I will go crazy”) to be more flexible (e.g., “if I get dizzy, it may just mean that I spun around too fast”). Behavioral treatments for panic include exposure to the situations (i.e., in-vivo exposure, which might include driving in traffic or riding the subway) and bodily sensations (i.e., interoceptive exposure, which would include physical exercises to bring on physical symptoms) that trigger panic in order to reduce the fear and anticipatory anxiety that maintain the symptoms. The aim of these exposures is to illustrate that the situations and sensations are benign and not indicative of danger.

Generalized Anxiety Disorder

Generalized anxiety disorder (GAD) is characterized by excessive and uncontrollable worry about several life domains (e.g., finances, health, career, the future in general). Treatment for GAD involves a wholesale approach to target excessive worry with a combination of cognitive and behavioral strategies ( 5 ). Although cognitive restructuring exercises are indeed emphasized throughout the treatment to target dysfunctional thoughts, usually further cognitive treatments are included to address worry behavior in addition to thought content. Individuals with GAD rarely achieve complete remission after restructuring only one of their negative thoughts. The CBT conceptualization of worry describes worry as a mental behavior or process, characterized by repetitive negative thinking about catastrophic future outcomes. To target worrying as a process, cognitive techniques, such as mindfulness, are emphasized. Rather than targeting the content of worry (e.g., “I think I will definitely lose my job if I do not prepare for this meeting”), mindfulness exercises target the worry behavior by promoting the opposite of repetitive negative thinking (i.e., nonjudgmental and nonreactive present moment awareness), thereby facilitating greater psychological distance from negative thoughts. Exposure therapy is often implemented as imaginal exposures for GAD, because individuals with GAD rarely have an external object that is feared. Such imaginal exposures will encourage patients with GAD to write a detailed narrative of their worst-case scenario or catastrophic outcome and then imagine themselves undergoing such an experience without avoiding their emotions. Cognitive restructuring and imaginal exposure exercises can benefit patients with GAD by targeting their tendency to give catastrophic interpretations to their worries, whereas mindfulness can be helpful in targeting worry as a mental behavior itself ( 5 ).

Social Anxiety Disorder

Social anxiety disorder involves a fear of negative evaluation in social situations and is accompanied by anxiety and avoidance of interpersonal interactions and performance in front of others. The primary treatment approach for social anxiety disorder consists of exposure exercises to feared social situations ( 6 ). Cognitive restructuring is used in conjunction with exposure exercises to reinforce the new learning and shift in perspective occurring through exposure therapy. Typically, exposure exercises for social anxiety disorder come in two stages ( 6 ). The first stage of exposures often targets patients’ overestimation that something bad will happen during a social interaction. For instance, patients with this disorder may fear that they will make many verbal faux pas (e.g., saying “uh” more than 30 times) during a conversation. An exposure exercise may consist of recording the patient having a 2-minute conversation and listening to the recording afterward to see whether the feared outcome actually occurred. The second stage of exposure exercises (i.e., social cost exposures) consists of having patients directly making their worst-case social anxiety scenario come true to determine how bad and intolerable it actually is. Such a social cost exposure might involve encouraging a patient to embarrass her- or himself on purpose by singing “Twinkle, Twinkle Little Star” in a crowded public street. After fully confronting a social situation that the patient predicted would be very embarrassing, the patient can then determine whether such a situation is as devastating and intolerable as predicted. After repeated social cost exposures, patients with social anxiety disorder experience less anxiety in embarrassing social situations and are more willing to adopt less catastrophic beliefs about the meaning of making mistakes in social situations.

Obsessive-Compulsive Disorder

Obsessive-compulsive disorder (OCD) is characterized by obsessions (i.e., unwanted thoughts or images that are intrusive in nature) and compulsions (i.e., actions or mental behaviors that are performed in a rule-like manner to neutralize the obsession). A CBT conceptualization of OCD considers compulsions as a form of emotional avoidance. Although both cognitive interventions and exposure exercises are helpful for individuals with OCD, the latter are often emphasized. The gold-standard CBT treatment for OCD is exposure and ritual prevention therapy ( 7 ). The primary idea underlying exposure and ritual prevention is to expose individuals with OCD to the feared circumstance associated with the obsession and prevent them from performing the compulsive ritual that gives them comfort through avoidance. For example, patients who experience frequent obsessions about whether their doors are locked or their appliances are off (e.g., “If my door is unlocked, then my house might be robbed or something bad might happen.”) will often feel compelled to perform a compulsion (e.g., ritualistic checking) to avoid the likelihood of having their obsession come true. Exposure and ritual prevention would be used to expose such patients to a feared situation, such as leaving their door unlocked on purpose, and resisting the compulsion to check the door or to lock it. During these exposures, the patients would be asked to embrace the uncertainty surrounding the possibility of the feared outcome coming true (i.e., someone entering the house). Repeated sessions of exposure and ritual prevention will facilitate corrective learning about the likelihood that feared outcomes will occur.

Posttraumatic Stress Disorder

As defined by the DSM-5 , posttraumatic stress disorder (PTSD) can arise after a traumatic event in which an individual directly experiences, witnesses, or learns about the actual or threatened death, serious injury, or sexual violence toward a loved one. After the traumatic stressor event, an individual with PTSD may experience intrusion symptoms (e.g., upsetting dreams or flashbacks of the event), avoidance of reminders of the event, changes in cognitions and affect (e.g., distorted beliefs about oneself, others, and the world), and changes in physiological arousal (e.g., jumpiness, irritability) ( 4 ). Gold-standard treatments for PTSD involve targeting the cognitive and behavioral symptoms that maintain the disorder ( 8 ). PTSD treatments target negative changes in cognition by restructuring the thoughts and beliefs surrounding the traumatic event. For example, evidence-based treatments alter persistent negative beliefs about the world (e.g., “I was assaulted; therefore, the world is dangerous”) to be more flexible (e.g., “even though I was assaulted, there are safe places for me to be”). In challenging these beliefs, the patient may be better able to foster flexible thinking, positive affect, trust, and control in their lives. PTSD treatments are also designed to help patients confront the upsetting memories and situations associated with the traumatic event. Through in-vivo exposures (i.e., approaching situations that are reminders of the trauma) and imaginal exposures (i.e., confronting upsetting memories of the trauma), the patient can begin to behaviorally approach, rather than avoid, reminders of the event to overcome their fears of the trauma and the associated symptoms.

Prolonged Grief Disorder

After losing a loved one, many individuals experience grief symptoms, such as thoughts (e.g., memories of the deceased, memories of the death), emotions (e.g., yearning, emotional pain), and behaviors (e.g., social withdrawal, avoidance of reminders). For most bereaved individuals, these symptoms decrease over time; however, some individuals experience a debilitating syndrome of persistent grief called prolonged grief disorder. This disorder is a direct consequence of the loss, thereby differentiating it from depression and PTSD. Evidence-based and efficacious treatment options for prolonged grief disorder draw from interpersonal therapy, CBT, and motivational interviewing, with additional psychoeducation components ( 9 ). These treatments aim to facilitate the natural bereavement process as individuals accept and integrate the loss. Strategies can be either loss-related or restoration-related. Specific loss-related strategies that draw from CBT include imaginal and situational revisiting (e.g., retelling the story of the loss, going to places that have been avoided since the loss) and a grief monitoring diary. Restoration-related strategies include short- and long-term planning, self-assessment and self-regulation, and rebuilding interpersonal connections.

Transdiagnostic Approaches to CBT for Anxiety Disorders

Throughout the past several decades, there has been a proliferation of CBT approaches that have been individualized to specific anxiety disorder presentations (e.g., panic disorder, specific phobias, social anxiety disorder). Each disorder-specific treatment manual is written to consider unique applications of CBT strategies for the presenting disorder. However, in recent years, there has been increased interest in considering transdiagnostic approaches to the treatment of anxiety and related disorders ( 10 ). The commonalities among individual anxiety disorders and the high levels of comorbidity have contributed to the rationale for a unified CBT approach that can target transdiagnostic mechanisms underlying all anxiety disorders. The Unified Protocol for Transdiagnostic Treatment of Emotional Disorders (UP) has been the most studied transdiagnostic treatment for anxiety disorders, and recent evidence ( 10 ) corroborates the equivalent efficacy of the UP relative to disorder-specific treatment protocols for individual anxiety disorders.

The UP consists of five core modules that target transdiagnostic mechanisms of emotional disorders, particularly neuroticism and emotional avoidance, underlying all anxiety disorders. Specifically, the modules are mindfulness of emotions, cognitive flexibility, identifying and preventing patterns of emotion avoidance, increasing tolerance of emotion-related physical sensations, and interoceptive and situational emotion-focused exposures ( 10 ). Each module may be used flexibly for individual patients. The first two modules are more cognitive in nature, whereas the latter modules are more behavioral and emphasize the treatment of avoidance. The first module emphasizes mindfulness of emotions, which consists of allowing oneself to fully and nonjudgmentally experience emotions and allow them to come and go while remaining focused on the present. The second module fosters cognitive flexibility by identifying thinking traps that lead to overly negative thoughts and interpretations and by teaching restructuring strategies to generate alternative interpretations of circumstances that are less biased and more adaptive. The third module promotes the identification of emotion-driven behaviors (i.e., actions that a given emotion compels a person to do, such as avoidance behaviors in response to fear) and the adoption of alternative actions (i.e., behaviors that are different from or the opposite of the emotion-driven behavior). For example, if social anxiety prompts an individual to avoid eye contact as an emotion-driven behavior, then an alternative action would be to intentionally maintain eye contact with another speaker to counteract this subtle form of avoidance. The final two modules consist of exposure exercises to develop better tolerance of unwanted physical symptoms produced by anxiety (e.g., increased heart rate) and to reduce fear in anxiety-provoking situations.

Because the UP contains many of the core components of disorder-specific protocols and has demonstrated equivalent efficacy, such a treatment approach may reduce the need for excessive reliance on disorder-specific protocols ( 10 ). Furthermore, the UP can be extended to other emotional disorders, such as depression.

Complementary Approaches for CBT

Mindfulness.

Mindfulness-based interventions function both as transdiagnostic adjunctive treatments to CBT for patients with anxiety and stress disorders as well as stand-alone treatments. Mindfulness is the practice of nonjudgmental awareness of the present moment experience. The aim of these interventions is to reduce emotional dysregulation and reactivity to stressors. Common mindfulness-based interventions include manualized group skills training programs called mindfulness-based stress reduction (MBSR) and mindfulness-based cognitive therapy ( 11 ). MBSR involves eight, 2–2.5-hour sessions with an instructor, in conjunction with a daylong retreat, weekly homework assignments, and practice sessions. Modules are designed to train participants in mindful meditation, interpersonal communication, sustained attention, and recognition of automatic stress reactivity. Mindfulness-based cognitive therapy has a structure similar to MBSR but includes cognitive therapy techniques to train participants to recognize and disengage from negative automatic thought patterns ( 12 ). These interventions omit aspects of traditional CBT (e.g., cognitive restructuring). Mindfulness-based interventions have been explored as both brief and Internet-delivered interventions and have been integrated into other evidence-based practices (e.g., dialectical behavior therapy and acceptance and commitment therapy).

Pharmacotherapy

There has been much interest in determining whether combination strategies of CBT and pharmacotherapy yield greater efficacy than either one alone for individuals with anxiety disorders. A comprehensive meta-analysis ( 13 ) examining this combination strategy suggested that adding pharmacotherapy to CBT may produce short-term benefit, yet such improvements diminished during 6-month follow-up. This combination strategy was more efficacious for individuals with panic disorder or GAD than for individuals with other presentations of anxiety. Moreover, the meta-analysis ( 13 ) indicated that the effect size for CBT combined with benzodiazepines was significantly greater than that for CBT combined with serotonin reuptake inhibitors (SSRIs) or tricyclic antidepressants. Another important consideration for pharmacotherapy in the treatment of individuals with anxiety disorders is to ensure that anxiolytic medications, such as benzodiazepines, are administered carefully in the context of exposure therapy. Anxiolytic medications taken to temporarily reduce anxiety may undermine quality exposure therapy sessions by preventing patients from fully learning whether they can tolerate fear without resorting to avoidance behaviors. Thus, although pharmacotherapy appears to improve outcomes in combination with CBT for patients with anxiety disorders, further research is needed to determine the durability of these effects.

D-Cycloserine in Conjunction With Exposures

One approach for improving patient outcomes is to target the extinction learning process underlying exposure exercises. There has been recent interest in cognitive enhancers, such as d-cycloserine (DCS) or methylene blue, as pharmacological adjuncts to exposure therapy ( 14 , 15 ). In preclinical studies, DCS has demonstrated evidence as a cognitive enhancer, consolidating new learning during extinction training. Specifically, the efficacy associated with DCS depends on the efficacy of the exposure exercise. For instance, during a successful exposure exercise, in which anxiety levels decrease substantially, the administration of DCS may confer additional benefit by consolidating this learning. However, if an exposure exercise was unsuccessful and fear levels never decreased, then DCS might consolidate the fear memory, thereby exacerbating the severity of the anxiety disorder ( 14 ). Recently, however, there has been evidence ( 16 ) suggesting that the efficacy of cognitive enhancers, such as DCS, has been declining, possibly because of changes in dose and dose timing. More research needs to be undertaken to understand under what circumstances (e.g., length of exposure session, amount of fear reduction, timing of dose) DCS would offer the greatest therapeutic effect in conjunction with exposure therapy.

Novel Delivery Methods

Internet-delivered CBT (I-CBT) is an alternative modality for the delivery of CBT for patients with anxiety and related disorders. I-CBT is a scalable alternative to in-person treatment, with the Internet used as an accessible and cost-effective method of delivery for evidence-based treatment. In I-CBT, CBT modules are delivered via computer or an application on a mobile device, with the support of a therapist or through a self-guided system. I-CBT has been shown ( 17 – 19 ) to be superior to waitlist and placebo conditions in the treatment of adults with a range of anxiety and trauma disorders, including anxiety and PTSD. Results ( 18 ) have indicated that I-CBT is similarly effective at reducing panic disorder symptoms as face-to-face CBT. The results of another trial ( 20 ) have indicated that I-CBT is also effective at reducing symptoms of OCD and social anxiety disorder.

In addition to Internet and mobile application platforms for CBT, virtual reality technology offers novel avenues to access cognitive-behavioral interventions ( 21 ). One key advantage is that recent advances in the sensory vividness of virtual reality platforms have facilitated more meaningful exposure exercises. For example, virtual reality flight simulators can be leveraged to expose a patient with flight phobia to several flight conditions with enhanced sensory detail (e.g., sounds of liftoff or landing, vibrations, images of clouds through a window, images of in-cabin atmosphere). This technology could obviate the need to purchase several expensive flights to participate in exposure exercises, thereby permitting more frequent exposure opportunities.

Conclusions

CBT is an effective, gold-standard treatment for anxiety and stress-related disorders. CBT uses specific techniques to target unhelpful thoughts, feelings, and behaviors shown to generate and maintain anxiety. CBT can be used as a stand-alone treatment, may be combined with standard medications for the treatment of patients with anxiety disorders (e.g., selective serotonin reuptake inhibitors), or used with novel interventions (e.g., mindfulness). Furthermore, this treatment is flexible in terms of who may benefit from it. Overall, whenever a patient is experiencing some form of emotional psychopathology (e.g., an anxiety or depression disorder) or distressing emotions that do not meet disorder threshold but cause distress or interference in daily activities, referral to a CBT provider is indicated to pursue a course of treatment to actively address such symptoms and problems.

The authors report no financial relationships with commercial interests.

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• Efficacy of cognitive behavioral therapy after the surgical treatment of women with endometriosis: A preliminary case-control study Medicine, Vol. 101, No. 51

• Anxiety and anxiety disorders
• Psychotherapy

• Research article
• Open access
• Published: 20 January 2021

The lifetime prevalence and impact of generalized anxiety disorders in an epidemiologic Italian National Survey carried out by clinicians by means of semi-structured interviews

• Antonio Preti   ORCID: orcid.org/0000-0001-9003-9838 1 , 2 ,
• Roberto Demontis 1 ,
• Giulia Cossu 1 ,
• Goce Kalcev 3 ,
• Federico Cabras 1 ,
• Maria Francesca Moro 4 ,
• Ferdinando Romano 5 ,
• Matteo Balestrieri 6 ,
• Filippo Caraci 7 , 8 ,
• Liliana Dell’Osso 9 ,
• Guido Di Sciascio 10 ,
• Filippo Drago 7 ,
• Maria Carolina Hardoy 11 ,
• Rita Roncone 12 ,
• Carlo Faravelli 13 ,
• Cesar Ivan Aviles Gonzalez 1 , 14 ,
• Matthias Angermayer 15 &
• Mauro Giovanni Carta 1  

BMC Psychiatry volume  21 , Article number:  48 ( 2021 ) Cite this article

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Metrics details

Generalized anxiety disorder (GAD) is one of the most reported diagnoses in psychiatry, but there is some discrepancy between the cases identified in community studies and those identified in tertiary care. This study set out to evaluate whether the use of clinicians as interviewers may provide estimates in a community survey close to those observed in primary or specialized care.

This is a community survey on a randomly selected sample of 2338 adult subjects. The Advanced Neuropsychiatric Tools and Assessment Schedule (ANTAS) was administered by clinicians, providing lifetime diagnosis based on the DSM-IV-TR. Health-related quality of life (HR-QoL) was measured with the Short-Form Health Survey (SF-12).

Overall, 55 (2.3%) subjects met the criteria for GAD, with greater prevalence in women (3.6%) than in men (0.9%): OR = 4.02; 95%CI: 1.96–8.26. Up to 40% of those with GAD had at least another diagnosis of mood, anxiety, or eating disorders. The mean score of SF-12 in people with GAD was 32.33 ± 6.8, with a higher attributable burden than in other conditions except for major depressive disorder.

Conclusions

We found a relatively lower lifetime prevalence of GAD than in community surveys based on lay interviewers and a structured interview. The identified cases of GAD showed a strong impact on the quality of life regardless of co-morbidity and high risk in women, suggesting a profile similar to the one identified from studies in primary and specialized care.

Peer Review reports

Generalized anxiety disorder (GAD) is one of the most reported mental disorders in primary care and emergency services [ 1 ]. Prevalence estimates varied widely across countries, with higher lifetime prevalence in high-income countries than in middle−/low-income countries (5% versus 1.5 to 3%) [ 2 ]. The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), describes the GAD as characterized by persistent, excessive, and unrealistic worry about everyday things, usually involving more than an area, such as finance, family, health, and the future [ 3 ]. Anxiety in GAD is difficult to control and is accompanied by many non-specific psychological and physical symptoms, like, among others, muscle tension, fatigue, sleep disturbances, difficulty in concentrating, and irritability (DSM-5 code: 300.02) [ 3 ]. This profile of symptoms corresponds to the profile described in the fourth edition of the DSM (DSM-IV) [ 4 ] and its text revision (DSM-IV-TR) [ 5 ], thus favoring the comparison of studies across time.

GAD manifests high comorbidity with mood and other anxiety disorders, up to 50% and over, depending on the disorder and sample [ 2 , 6 ]. GAD is often comorbid with bipolar disorder (BD), and might be associated with a more severe BD course and increased risk of suicide [ 7 ]. Role impairment is common in people with GAD and may be severe [ 2 ], as well as the association with chronic non-psychiatric diseases [ 6 ].

The etiology of GAD is unknown. A combination of genetics, environmental factors such as adverse childhood experiences, somatic disorders (including diabetes), alcohol and substance use, and the impact of stressful life events is thought to contribute to the onset, the course, and the persistence of GAD across lifetime. Some, low quality, brain imaging studies support a role in the expression of GAD symptoms of areas related to decision making, memory, cognitive flexibility, emotion appraisal and regulation, and detection of threat [ 8 , 9 ]. GAD imports a high cost-of-illness, in terms of health expenditure and lost productivity, which has been estimated to be increased by a factor of 2.60 (95%CI: 2.01–3.36) [ 10 ]. Only about half of those with GAD seek treatment [ 2 ]. Patients with GAD may benefit from pharmacotherapy [ 11 , 12 ]. In clinical practice, a combination of benzodiazepines and antidepressants is often prescribed [ 11 , 12 ]. However, current guidelines emphasize that benzodiazepines should be avoided for long-term management of GAD and should be restricted to short-term use for the risk of tolerance and dependence [ 13 , 14 ]. Pregabalin and quetiapine can be prescribed for long-term treatment of GAD [ 15 ]. Besides pharmacotherapy, cognitive behavioral therapy has been proved to be effective for GAD [ 16 ], while physical activity [ 17 ] and the application of transcranial magnetic stimulation [ 18 ] or transcranial direct current stimulation [ 19 ] may help for decreasing symptoms in GAD.

Despite GAD being one of the most reported diagnoses in psychiatry, and the validity of the phenotype received some support [ 20 ], the autonomy of the diagnosis was questioned by the findings of some epidemiological surveys [ 21 ]. For example, some of the symptoms required for major depressive disorder (e.g.., sleep difficulties, fatigue, and decreased concentration) overlap with GAD ones (being easily fatigued, difficulty concentrating, sleep disturbance). Indeed, the symptoms of GAD overlap in a large proportion with those of many other psychiatric conditions and a very small percentage of people diagnosed with GAD do not show another mental health diagnosis (about 17%) [ 21 ]. This is against the expectation of zones of rarity between syndromes [ 22 ]. Autonomous entities should show identifiable discontinuities with related conditions, with mixed conditions expected to be rarer than the pure forms [ 23 ]. Eventually, the actual diagnostic algorithm of the GAD goes into a detailed list of exclusion criteria, from obvious ones (the exclusion of the physiological effects of a prescribed or abuse substance or of a medical condition) to a cumbersome list of other mental disorders that should be assessed and whose impact on the anxiety, worry, or physical symptoms should be excluded (e.g., among others, anxiety or worry about having panic attacks in panic disorder, negative evaluation in social anxiety disorder, reminders of traumatic events in posttraumatic stress disorder, physical complaints in somatic symptom disorder, having a serious illness in illness anxiety disorder). Such a kind of detailed evaluation can be done in epidemiological survey but it is less easily conducted in the clinical setting. Moreover, studies on clinical samples provide data somewhat inconsistent with epidemiological studies, e.g. in a special anxiety unit in Göttingen, Germany, the proportion of patients seeking help had about 50% a diagnosis of panic disorder (frequency in epidemiological surveys around 2–3%) and only 7.5% a diagnosis of GAD (around 4% in epidemiological surveys) [ 24 , 25 ].

These inconsistencies might depend on the fact that the cases identified in community studies are not the same as those identified in tertiary care. Indeed, in a diagnosis in which a central symptom such as worries has a fundamental clinical relevance, the use of “lay” interviewers and structured interviews can flatten the clinical relevance of the symptom’s centrality in epidemiological surveys [ 26 ]. Conversely, in the clinical setting greater attention is paid to patients’ reporting of theirs worries. A competing explanation could be that clinicians that work in specialized and tertiary care centers may overlook milder, but still burdensome symptoms: they may actually underdiagnose more soft cases because their clinical judgment is biased towards more severe and complex mental problems. Several studies conducted in the primary medicine setting described cases of GAD, rare in general but more frequent in the elderly (unlike some epidemiological studies that found greater frequency among young people), and with a severe impairment of health-related quality of life (HR-QoL) regardless of comorbidity with other anxiety and depressive disorders [ 27 , 28 ].

The purpose of this work is to estimate the prevalence of GAD in a nationwide Italian sample. The impact of GAD and its comorbidity in terms of HR-QoL will be quantified, too. In this study, clinicians such as interviewers and semi-structured interviews (instead of lay interviewers and structured interviews like most epidemiological studies) will be used, and this might lead to the identification of a GAD profile different from that of other epidemiological studies previously conducted [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ].

This is an observational cross-sectional study (community survey).

Design and procedure

The study sample was selected by randomization after stratification in 8 cells (gender and age 18–24; 25–44; 45–64; > 64) from records of municipalities of six Italian regions (one urban, one suburban, and at least one rural municipalities each region). The selected regions were representative of geographic and socio-economic characteristics of the whole 20 Italian regions.

Trained physicians or clinical psychologists conducted the interview face to face at homes of the enrolled people. This study is a secondary research of a project whose main objective was to study the appropriateness of psychiatric diagnosis and use of prescribed drugs in the Italian population. Details on the sampling procedure and the characteristics of the sample can be found in the parent article [ 37 ].

Study tools

The psychiatric interviews were conducted by means of a semi-structured tool, the Advanced Tools and Neuropsychiatric Assessment Schedule (ANTAS) [ 37 ]. The ANTAS is a computerized tool inspired to the Structured Clinical Interview for DSM-IV (SCID) [ 38 ]. The ANTAS produces mood, anxiety and eating disorders diagnosis according to the DSM-IV-TR [ 5 ] with high cross-validity and reliability with SCID [ 37 ]. All diagnoses of psychiatric disorders were estimated as lifetime prevalence according to DSM-IV-TR criteria.

The Mood Disorder Questionnaire (MDQ) [ 39 , 40 ] was adopted to assess lifetime subthreshold hypomanic episodes. Despite low accuracy in screening DSM-defined cases of bipolar disorder [ 41 ], the tool is good at identifying subthreshold cases [ 42 ].

The 12 items Survey Short Form (SF–12) [ 43 ] was used to measure the HR-QoL. The HR-QoL is a construct encompassing the self-perception of physical and psychological health. It is currently utilized as whole outcome and of impairment indicator in chronic diseases [ 44 ].

Statistical analysis

The odds ratio (OR) in univariate analysis for DSM-IV TR GAD diagnosis and age, gender and comorbidity with DSM-IV-TR diagnosed disorders, was calculated using a single group as pivot by each table. The statistical significance of the associations was measured with the χ 2 , with or without Yates correction. The SF-12 mean scores between groups were compared with Analysis of Variance (ANOVA) one-way statistic.

The attributable burden on impairing HR-QoL of GAD was measured as difference between mean score on the SF-12 in a sample drawn from the same community survey database of people without GAD and the mean score of SF-12 of people with GAD. For this measure, the “healthy” control sample was obtained matching and randomization by blocks. For each person with GAD, a cell was created including all the people without GAD in the database of the same age and gender, thus four people for each cell were selected. The burden in impairing of HR-QoL attributable to GAD was also compared to a similar measure obtained to other diseases in previous case-control studies, which were carried out with the same methodology [ 45 , 46 , 47 , 48 , 49 , 50 , 51 ].

The study was approved by the by the ethical committee of the Italian National Health Institute (Rome) and conducted according to the Declaration of Helsinki and its revisions [ 52 ]. All participants signed a written informed consent. They all received an appropriate referral to primary (general practitioner) or tertiary care (local psychiatric services) in case they manifest symptoms related to the disorders under investigation.

Table  1 shows lifetime prevalence of GAD by sex and age, the overall lifetime prevalence in the sample was 2.3%, with a markedly higher frequency in women (3.6%) than in men (0.9%; OR = 4.02; 95%CI: 1.96–8.26) and a substantially stable frequency in age in both sexes.

The lifetime prevalence found by our research (2.3%) is lower than the one found in all other studies that were conducted through structured interviews administered by lay interviewers (Table  2 ).

With just the exception of the study of Chang in Singapore (1.6%), and the European Study of the Epidemiology of Mental Disorders (ESEMeD) [ 32 ], with estimates of 2.8%, the other studies ranged from 3.6% in Korea [ 34 ] to 10.5% in a small town in Taiwan [ 29 ].

As far as comorbidity was concerned, people with at least another diagnosis of mood, anxiety or eating disorders were 22 out 55 (40%). The most frequent diagnoses in comorbidity were: major depressive disorders (20%, OR = 5.97; 2.99–11.95), panic disorder (16.4%; OR = 17.4; 7.56–38.40), and simple phobia (16.3%; OR = 9.93; 4.58–21.55) (Table  3 ).

The level of HR-QoL in people with GAD (measured as mean score of SF-12) was 32.33 ± 6.8, without differences in people with ( N  = 22; 30.4 ± 7.0) or without comorbidities ( N  = 33; 33.6 ± 6.7): F (1;53) = 2.90; p  = 0.094. Overall, with the only exception of major depressive disorder, GAD showed an attributable burden higher to that observed for the other investigated disorders from the same database (Table  4 ).

However, if we consider the cases of GAD without comorbidity, the “attributable burden” in impairing HR-QoL becomes comparable between GAD to that of most of the other disorders considered, except for panic disorder and simple phobia that resulted less impairing.

This survey, conducted by clinical interviewers who employed a semi-structured interview, showed a lower frequency of GAD in a sample of Italian general population compared to all community surveys conducted recently with the use of lay interviewing and clinical interviews structured [ 29 , 30 , 31 , 32 , 33 , 34 , 35 ], with the only exception of the study of Chang in Singapore [ 36 ] and the ESEMeD study [ 32 ]. The Chang ‘study also showed an increase from 0.9 to 1.6% compared to a study conducted in Singapore a few years earlier [ 36 ].

It is worth noting that our study highlights lower rates than research conducted on samples that are culturally closer, such as those examined by Faravelli’s study in a center of Tuscany in Italy [ 33 ]. Compared to this study, people with GAD in our sample have a lower frequency of comorbidity with other mood, anxiety or eating disorders (40% vs 70% of the study by Faravelli et al. [ 33 ]), and are more frequently women (4/1 ratio instead of 2/1). Another peculiar characteristic of our sample is that the frequency is stable over time and there is no higher frequency in the youth population as otherwise highlighted in the other community surveys conducted with structured interviews [ 25 ].

The stability of rates in different age groups, resulting in a higher rate in the elderly population (comparing with other community surveys) and the increased frequency in women makes our sample closer to the profile of GAD described in the specialist medical setting and/or primary care [ 27 , 28 ]. It must be noted that a disorder like GAD, which should have a long course, should accumulate its frequency over time and, therefore, it would be logical to expect the lifetime rates in the elderly to be high. But this certainly applies to a disabling and high-impact disorder, less to a mild disorder that tends to be forgotten more frequently, generating higher recalling bias rates [ 53 ]. Nevertheless, several investigations noted a high prevalence of GAD in elderly people, with estimates around 10% or above [ 54 , 55 , 56 ]. A fraction of these cases were late-onset cases of GAD triggered by recent adverse life events and by chronic physical or mental (depression) health disorders. Adversities during childhood and a history of mental problems in parents were also related to recent onset GAD [ 55 ].

The GAD profile highlighted in our sample confirms that it has a severe impact on the lives of individuals, even independently of co-morbidity with other disorders, which, consistent with the cases highlighted in the primary medicine setting, defines a very well-defined pathology. Our study, therefore, seems to confirm that there may be a more clinically relevant (and less extensive) nucleus of people suffering from GAD and that the research conducted with hyper-structured methodology and using lay interviewers may produce an improper enlargement of the number of disorders that it may include people who are not properly suffering from a clinically important condition. This can be confirmed not only by the mismatch between the profile in community surveys and in health care agencies (which can be determined by barriers to access care for milder cases, although this is unlikely about primary care) but above all from the paradox of a progressive decrease over time of the lifetime frequency by age group.

The use of trained clinician interviewers is the strength of this study, together with the application of a standardized tool in community-based samples that were representative of the socio-cultural characteristics of the entire national territory. Nevertheless, some limitations must be acknowledged. The target of the original study was the lifetime prevalence of people diagnosed within the bipolar spectrum, which was estimated to involve 4% of participants. However, GAD and other anxiety disorders have lower lifetime prevalence, thus we were somehow underpowered to estimates some comorbid associations. We also lack information on somatic comorbidities, which may be relevant in GAD and reinforce the symptoms of anxiety in the disorder [ 6 , 57 ].

Our community survey conducted with a methodology that used clinical interviewers and a semi-structured interview showed a relatively low GAD frequency in the community than in other community surveys based on lay interviewers and a structured interview. The characteristics of the GADs of our sample (as a strong impact on the quality of life regardless of co-morbidity and high risk in women) indicate a disorder with characteristics very similar to those identified from studies in primary care and specialized care agencies.

It should be noted that there is no undisputable gold standard about GAD and, given the essential differences between the focus and scope of the clinician-based and lay-administered assessment methods, it cannot be decided whether the prevalence estimates of this study are more precise than those that can be derived from epidemiological studies based on lay-administered assessment methods. Only a direct comparison of the methods may consent an answer to that.

Availability of data and materials

The dataset for this article is not publicly available because the agreement shared with the partners in the planning of the study, in the presentation for the assignment of the original grant and the request for authorization to the ethics committee was that the database (with anonymized records) would be available only under the review of the project leader as guarantor. Requests to access the dataset should be directed to Professor Mauro Giovanni Carta.

Abbreviations

Analysis Of Variance

Advanced Tools and Neuropsychiatric Assessment Schedule

Bipolar disorder

Confidence interval

D iagnostic and Statistical Manual of Mental Disorders, fifth edition

Diagnostic and Statistical Manual of Mental Disorders, fourth edition, text revision

European Study of the Epidemiology of Mental Disorders

• Generalized anxiety disorder

Health related quality of life

Mood Disorder Questionnaire

National Institute for Health and Care Excellence

Structured Clinical Interview for DSM-IV

12 items Survey Short Form

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Acknowledgements

This study was supported by a grant of AIFA (Agenzia Italiana del Farmaco) Number FARM54S73S, approved in 2005. The funding body did not have had any further role in study design; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication.

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MGC, FR, MB, FC, LD, GD, FD, MCH, RR, CF, MA: Conceptualization, Methodology, Software; MGC, AP, FR, MB, FC, LD, GD, FD, MCH, RR, CF, MA, CIAG: Data curation, Writing, Original draft preparation; MGC, FR, MB, FC, LD, GD, FD, MCH, RR, CF, MFM: Visualization, Investigation; MGC, AP: Formal analysis; MGC, MA, FR, MB, FC, LD, GD, FD, MCH, RR, CF, CIAG : Supervision; AP, MGC, RD, GC, CIAG, CK, FC, MFM: Writing, Reviewing and Editing; MCG: Funding acquisition; MCG: Project administration. All authors read and approved the manuscript.

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Preti, A., Demontis, R., Cossu, G. et al. The lifetime prevalence and impact of generalized anxiety disorders in an epidemiologic Italian National Survey carried out by clinicians by means of semi-structured interviews. BMC Psychiatry 21 , 48 (2021). https://doi.org/10.1186/s12888-021-03042-3

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• Murray B. Stein , M.D.

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OBJECTIVE: Quality-of-life indices have been used in medical practice to estimate the impact of different diseases on functioning and well-being and to compare outcomes between different treatment modalities. An integrated view of the issue of quality of life in patients with anxiety disorders can provide important information regarding the nature and extent of the burden associated with these disorders and may be useful in the development of strategies to deal with it. METHOD: A review of epidemiological and clinical studies that have investigated quality of life (broadly conceptualized) in patients with panic disorder, social phobia, posttraumatic stress disorder, generalized anxiety disorder, and obsessive-compulsive disorder was conducted by searching MEDLINE and PsycLIT citations from 1984 to 1999. A summary of the key articles published in this area is presented. RESULTS: The studies reviewed portray an almost uniform picture of anxiety disorders as illnesses that markedly compromise quality of life and psychosocial functioning. Significant impairment can also be found in individuals with subthreshold forms of anxiety disorders. Effective pharmacological or psychotherapeutic treatment has been shown to improve the quality of life for patients with panic disorder, social phobia, and posttraumatic stress disorder. Limitations in current knowledge in this area are identified, and suggestions for needed future research are provided. CONCLUSIONS: It is expected that a more thorough understanding of the impact on quality of life will lead to increased public awareness of anxiety disorders as serious mental disorders worthy of further investment in research, prevention, and treatment.

Anxiety disorders were described as early as the fourth century B.C. in the writings of Hippocrates (1) , but their importance was not fully appreciated until less than 30 years ago. For complex historical reasons the first specialists in psychiatry, the alienists of the early nineteenth century, were mainly concerned with the description and classification of psychotic disorders. As a result, the development of the field of anxiety disorders (as well as the domains of somatization and conversion disorders) was left to specialists in internal medicine and neurology such as da Silva, Briquet, Beard, Charcot, and Freud (2) . The interest of mainstream psychiatry in anxiety disorders would remain limited throughout the first half of the twentieth century because of the prevailing belief that neurotic disorders were benign conditions with nonorganic causes and that their treatment should necessarily be based on some form of psychotherapy (3) .

The realization that anxiety disorders could be successfully treated by pharmacological means (4) , the development of reliable diagnostic criteria (5) , and the advent of modern psychiatric nosology set the stage for a critical reappraisal of the magnitude of the problem of anxiety disorders. Using the DSM-III criteria, the National Institute of Mental Health (NIMH) Epidemiologic Catchment Area (ECA) study showed that anxiety disorders had the highest overall prevalence rate among the mental disorders, with a 6-month rate of 8.9% and a lifetime rate of 14.6% (1) , and affected 26.9 million individuals in the United States at some point in their lives. The costs associated with anxiety disorders in 1990 were a staggering $46.6 billion, accounting for 31.5% of total expenditures in that year for mental health (6) .

Quality of Life: The Concept

It is often said that the cost of human suffering cannot be measured. This truism may no longer be accurate. Many aspects of human suffering (or its absence) can be reliably measured. One of the approaches to this difficult yet invaluable task makes use of the concept of “quality of life.” This concept, developed in the social sciences, was first applied in medical practice to determine if available cancer treatments could not only increase the survival time of patients but also improve their sense of well-being (7) . The concept of quality of life was later applied to compare several antihypertensive medications in terms of functioning, well-being, and life satisfaction (8) .

According to Patrick and Erickson (9) , life has two dimensions: quantity and quality. Quantity of life is expressed in terms of “hard” biomedical data, such as mortality rates or life expectancy. Quality of life refers to complex aspects of life that cannot be expressed by using only quantifiable indicators; it describes an ultimately subjective evaluation of life in general. It encompasses, though, not only the subjective sense of well-being but also objective indicators such as health status and external life situations (10) . Data about quality of life can be used to estimate the impact of different diseases on functioning and well-being, to compare outcomes between different treatment modalities (such as medication and surgery), and, as in the examples mentioned, to differentiate between two therapies with marginal differences in mortality and/or morbidity (11) .

No single definition of quality of life is universally accepted (12) . There is, however, a degree of consensus regarding the minimal requirements for an operational definition of quality of life for employment in health status assessment and research. First, most experts agree that the scope of the concept of quality of life should be centered on the individual’s subjective perception of the quality of his or her own life. This consensus stems from the findings of several sociological studies that have demonstrated that objective conditions of life such as education and income are only marginally related to the subjective experience of a higher quality of life (13 , 14) . Second, most authors agree that given the difficulties in assessing the relative impact of the complex experiences that ultimately determine one’s perception of quality of life, quality of life is better approached as a multidimensional construct, covering a certain number of conventionally defined domains (15) . Finally, it is recommended that we avoid the vagaries of abstract and philosophical concepts and concentrate on aspects of personal experience that are related to health and health care (health-related quality of life) (16) .

An example of a subjective multidimensional definition of health-related quality of life was proposed by Patrick and Erickson (17) : “the value assigned to the duration of life as modified by the social opportunities, perceptions, functional states, and impairments that are influenced by disease, injuries, treatments, or policies” (p. 6). Aaronson et al. (18) suggested that the assessment of quality of life should comprise at least the following four domains: 1) physical functional status, 2) disease- and treatment-related physical symptoms, 3) psychological functioning, and 4) social functioning. Additional domains that are of particular relevance to specific demographic, cultural, or clinical populations (such as sexual function, body image, or sleep) may sometimes need to be included in the assessment to increase the breadth of coverage (19) .

Approaches to Studying Quality of Life in Individuals With Anxiety Disorders

Data regarding quality of life in mental disorders in general, and in anxiety disorders in particular, derive from two types of sources. The first source is represented by epidemiological studies such as the ECA and the National Comorbidity Survey. Although these studies were not specifically designed to study the association between quality of life and mental disorders in the community, they provide a number of indicators from which quality of life can be inferred. These indicators include a subjective assessment of physical and emotional health, psychosocial functioning, and financial dependency (1 , 20 , 21) .

Clinical studies made by using specifically designed instruments represent the second major source of data concerning quality of life. These instruments may be generic (i.e., attempting to measure multiple important aspects of quality of life) or specific (i.e., focusing on aspects of health status that are specific to the area of primary interest). The latter may be specific to a disease (e.g., panic disorder), to a population (e.g., elderly patients), to a function (e.g., sleep), or to a problem (e.g., pain) (22) . The main advantage of generic measures is that they permit comparisons across conditions and populations. In contrast, specific measures are intended to detect small, meaningful changes in specific conditions to which generic measures may be insensitive.

Although quality-of-life data can be collected in interviews or through patient diaries, most studies now employ self-report questionnaires, the most cost-effective method for obtaining patient-related information (19) . For this report, a review of epidemiological and clinical studies that have investigated quality of life (broadly conceptualized) in patients with panic disorder, social phobia, posttraumatic stress disorder (PTSD), generalized anxiety disorder, and obsessive-compulsive disorder (OCD) was conducted by searching MEDLINE and PsycLIT from Jan. 1984 to Oct. 1999. The key words employed were “quality of life,” “impairment,” and “disability.” With few exceptions (to be discussed later), only clinical studies utilizing self-report instruments based on subjective, multidimensional concepts of health-related quality of life that were properly validated were considered. ( Table 1 briefly summarizes the psychometric properties of these instruments.)

Quality of Life in Individuals With Panic Disorder

Studies in community samples.

The ECA study is an important source of data regarding the epidemiology of panic disorder and the impact of panic disorder on quality of life. This study found a lifetime prevalence for panic disorder of 1.5% (21) . The domains of quality of life assessed were the subjective reporting of health, psychosocial functioning, and financial dependency. Quality-of-life measures in persons with lifetime panic disorder were compared with those of persons with lifetime major depression—a condition whose social morbidity is well documented (41 , 42) —and subjects with neither disorder (21 , 43) .

Among persons with panic disorder in the community, 35% felt they were in fair or poor physical health, and 38% felt they were in poor emotional health (21) . Individuals with major depression showed similar rates (29% and 39%, respectively), whereas those with neither disorder had significantly lower levels of negative perceptions of their physical and mental health (24% and 16%). A total of 27% of the persons with panic disorder were receiving welfare or some form of disability compensation, a significantly higher proportion than that found among persons with major depression (16%) and with neither disorder (12%).

Infrequent Panic Attacks and Quality of Life

Persons with panic attacks that did not meet the full DSM-III criteria for panic disorder because of insufficient frequency of attacks or symptoms (“infrequent” panic attacks: lifetime prevalence=3.6%) also showed substantial impairment in perceived physical and emotional health, occupational functioning, and financial independence. Klerman and colleagues (44) noted that on almost any measure, subjects with panic attacks were intermediate in severity between those who met the full criteria for panic disorder and those with no disorder. These findings were consistent with Gelder’s observation (45) that the difference between subjects with panic disorder and with panic attacks is more quantitative than qualitative. Since the lifetime prevalence of panic attacks in the general population is more than twice as high as that of panic disorder (7.3% and 3.5%, respectively, in the National Comorbidity Survey [46]), panic attacks are more likely to be associated with a higher population-attributable risk of decrements in social and vocational function than panic disorder itself (47) .

Studies in Clinical Samples

The most widely used instrument currently employed to measure quality of life is the Medical Outcomes Study 36-item Short-Form Health Survey (23) . It assesses two broad dimensions—mental health and physical health—each consisting of four specific domains (i.e., eight total): physical functioning, role limitations due to physical health problems, bodily pain, social functioning, general mental health (covering psychological distress and well-being), role limitations due to emotional problems, vitality, and general health perceptions. Norms are available for the general U.S. population as well as for several medical conditions (48) .

Sherbourne and colleagues (49) used the Short-Form Health Survey to compare the quality of life of patients with current panic disorder to that of patients with depression or chronic medical conditions such as hypertension, diabetes (type I or II), heart disease, arthritis, and chronic lung disease. In this study panic disorder emerged as associated with high psychological distress and limitations in role functioning but with relatively preserved physical functioning. In contrast, patients with depression showed limitations in all domains of functioning that were as great as or greater than the limitations associated with most chronic medical diseases.

The findings of Sherbourne and colleagues (49) are consistent with those of several other studies using the Short-Form Health Survey. Numerous investigators (47 , 50 – 54) clearly documented a decreased quality of life in patients with panic disorder compared to normal subjects. Four studies (47 , 50 , 51 , 54) found significant impairment in scores on the physical functioning subscales of the Short-Form Health Survey, as well as in scores on the mental health functioning subscales for patients with panic disorder. Schonfeld and colleagues (51) also found that major depression had a far greater impact on scores for subscales of the Short-Form Health Survey than any anxiety disorder, including panic disorder. These findings, like the epidemiologic findings cited previously, place the quality of life in patients with panic disorder as better than that of patients with major depression but still markedly lower than that of otherwise healthy individuals.

The publication of numerous validation studies, availability of general population norms, and ease of administration make the Short-Form Health Survey a very attractive option for the measurement of quality of life in persons with anxiety disorders. Nonetheless, it should be noted that making statistical comparisons by using the Short-Form Health Survey may not be straightforward (55) . Six of the eight scales of the Short-Form Health Survey have continuous variables, with scores ranging from 0 to 100. Scores on these measures in the general population, however, tend to be skewed to the left, with a majority of individuals showing a relatively high quality of life. The two remaining scales—role limitations due to physical health problems and role limitations due to emotional problems—are categorical. Thus, comparisons of scores on the Short-Form Health Survey in patient groups with population norms may be methodologically complicated, requiring the use of nonparametric tests or logarithmic or z transformations to obtain a more normal distribution. These procedures, however, have been seldom reported in the literature concerning anxiety disorders to date.

Impact of Treatment on Quality of Life in Patients With Panic Disorder

A growing number of clinical trials have incorporated quality-of-life assessment as an outcome measure in the treatment of panic disorder. The measurement of quality of life in clinical trials represents a special situation, imposing specific requirements on the instruments to be employed. For evaluative purposes it is essential to demonstrate that the instrument is capable of measuring the magnitude of the longitudinal changes on the dimension of interest in an individual or group exposed to a specific intervention. This property is called sensitivity to change, or responsiveness. For measures that are to be administered repeatedly, it is important that the instrument have very good reliability. Characteristics such as reliability and responsiveness may be difficult to reconcile in a single instrument, particularly in generic instruments intended to cover extensive domains. As shown in the descriptions of studies to follow, the Short-Form Health Survey seems to perform admirably in these respects. The sensitivity to change of the Sheehan Disability Scale (37) , Social Adjustment Scale (unpublished handbook by Weissman et al.), and Quality of Life Enjoyment and Satisfaction Questionnaire (34) have been demonstrated, which supports their use in clinical trials.

Another important characteristic of an instrument is its interpretability. For evaluative purposes, one should be able to interpret changes in the instrument’s scores in terms of their relevance (or lack thereof) for the health status of a patient. Although clinicians can easily interpret the implications of a change in the number of panic attacks per day or in the percentage of time spent worrying about having a panic attack, the meaning of a change in the score on a quality-of-life instrument may remain obscure unless some standard is provided. To our knowledge, only the Short-Form Health Survey provides standards for comparing clinical changes across several clinical conditions, as seen in the study by Jacobs et al. (56) , to be described. We will summarize results from studies of panic disorder outcomes according to the main quality-of-life measure(s) employed.

Outcome studies using the Short-Form Health Survey

Mavissakalian et al. (57) treated 110 patients with moderate-to-severe panic disorder, including agoraphobia, with a fixed regimen of imipramine, 2.25 mg/day per kg of body weight for 24 weeks. The Short-Form Health Survey was administered at pretreatment and at week 16. A total of 53% of the patients had a marked and stable response. Completers (N=59) and noncompleters (N=51) had equivalent scores on a baseline Short-Form Health Survey, except on the pain subscale, on which completers scored significantly lower than noncompleters. At week 16 the completers showed significant improvements on all subscales, particularly on role limitations (emotional), energy, social functioning, and mental health.

Jacobs et al. (56) examined the effects of clonazepam and placebo on scores for patients with panic disorder on the Short-Form Health Survey in a double-blind, controlled trial. Quality-of-life assessments were made at baseline and after 6 weeks of therapy (or at premature termination from the study). Between-group comparisons showed that clonazepam-treated patients (N=71) had a significant improvement in scores on the Short-Form Health Survey mental health component summary (which aggregates the scores of the four subscales measuring mental and emotional health) compared to placebo-treated subjects (N=68) after 6 weeks of treatment. Scores on the mental health component summary were found to be strongly related to clinical measures, with patients reporting marked improvement in avoidance and fear also showing the strongest mental health component summary score gains. The authors observed that the 8.9-point gain in scores on the mental health component summary observed in the clonazepam group was comparable to the 10.9-point improvement reported for recovered depressive individuals.

Outcome studies using the Quality of Life Enjoyment and Satisfaction Questionnaire

The Quality of Life Enjoyment and Satisfaction Questionnaire is a validated quality-of-life scale that rates eight aspects of quality of life, including physical health, subjective feelings, activities of daily living, and overall life satisfaction (34) .

Pohl et al. (58) and Pollack et al. (59) conducted a 10-week, randomized, double-blind study comparing the effects of sertraline and placebo in over 150 outpatients with a DSM-III-R diagnosis of panic disorder with or without agoraphobia. At the beginning and end of the double-blind phase the patients completed the Quality of Life Enjoyment and Satisfaction Questionnaire. In both studies, in addition to experiencing fewer panic attacks, sertraline-treated patients exhibited a statistically significant increase (change from baseline) in scores on the Quality of Life Enjoyment and Satisfaction Questionnaire for total and overall life satisfaction compared with placebo-treated patients.

Outcome studies using the Sheehan Disability Scale

The Sheehan Disability Scale is a three-item self-report that assesses impairment in work activities, social life and leisure activities, and family life and home responsibilities (38) .

Three studies have compared selective serotonin reuptake inhibitors (SSRIs) to placebo in randomized, controlled studies for the treatment of panic disorder. Hoehn-Saric et al. (60) compared fluvoxamine with placebo in 50 patients with panic disorder over 8 weeks and failed to find statistically significant group differences among scores on the Sheehan Disability Scale. The authors suggested that a longer follow-up period might be needed to detect improvements in social adjustment. Lecrubier et al. (61) compared the effects of placebo, paroxetine, and clomipramine in 367 patients with panic disorder. At week 9, patients treated with paroxetine (N=123) and clomipramine (N=121) showed significantly larger increases from baseline in scores on the three Sheehan Disability Scale items than placebo-treated subjects (N=123); there were no significant differences between scores for groups treated with paroxetine or clomipramine on any Sheehan Disability Scale items. Michelson et al. (62) compared groups receiving 10 mg/day or 20 mg/day of fluoxetine to a placebo group among 243 patients with a diagnosis of panic disorder. After 10 weeks of therapy, functional impairment, as measured by the Sheehan Disability Scale, was significantly more improved on items for family life (for groups receiving 10 or 20 mg/day of fluoxetine) and social life (for the group receiving 10 mg/day of fluoxetine) in the fluoxetine groups than in the placebo group. Reduction in the frequency of panic attacks was found to correlate poorly with ratings on the Sheehan Disability Scale and other secondary outcome measures, which suggests that the impairment associated with panic disorder may result primarily from other symptom domains, such as phobic avoidance and depression.

Outcome studies using the Social Adjustment Scale—Self-Report

The Social Adjustment Scale—Self-Report is a 42-item, self-report instrument measuring either instrumental or expressive role performance over the past 2 weeks in six major areas of functioning. It was originally designed as an outcome measurement to evaluate psychotherapy and drug treatment for depressed patients (35) .

To our knowledge, only one study has employed quality of life as an outcome measure for the treatment of patients with panic disorder with cognitive behavior therapy. Telch et al. (63) randomly assigned 156 outpatients meeting the DSM-III-R criteria for panic disorder with agoraphobia to group cognitive behavioral therapy or to a delayed-treatment control condition. An assessment battery including two measures relevant to the assessment of quality of life, the Social Adjustment Scale—Self-Report and the Sheehan Disability Scale, was administered at baseline (week 0), posttreatment (week 9), and 6-month follow-up. Consistent with results from previous studies, patients with panic disorder showed a significant impairment in quality of life at baseline. Treated subjects displayed significantly less impairment on the Social Adjustment Scale—Self-Report scale measuring work outside and inside the home, social and leisure activities, marital and extended family relationships, and overall functioning and on the Sheehan Disability Scale items measuring family and social functioning, work functioning, and global functioning. Anxiety and phobic avoidance were shown to be significantly associated with quality of life, whereas the frequency of panic attacks was not. The authors hypothesized that the infrequency and transient nature of panic attacks may lead to less impairment than the more chronic and pervasive symptoms of anxiety and agoraphobic avoidance. These conclusions are supported by the findings of other groups that employed the Sheehan Disability Scale to measure impairment in patients with panic disorder, such as Michelson et al. (62) , just mentioned, and Leon et al. (64) , who found that the frequency of panic attacks accounts for no more than 12% of the variance in impairment.

Quality of Life in Individuals With Social Phobia

Although social phobia is not a newly recognized disorder (65) , the magnitude of the problem was not fully appreciated until the late 1980s, leading to social phobia being termed a “neglected anxiety disorder” (66) . Even mental health specialists may have felt at first that this disorder, then just recently included in the DSM-III, represented an undue extension of the medical model into the domain of a naturally occurring phenomenon—shyness. Also, the first clinical studies comparing patients with social phobia and panic disorder reported that patients with social phobia tended to be men with higher educational, intellectual, social, and occupational status (67 – 69) , suggesting that social phobia was a relatively benign condition. It was not until the ECA findings were reported (70) that a different profile emerged, showing social phobia to be a common disorder associated with significant disability and impairment.

Studies in Epidemiologic Samples

Although the ECA study did not include direct measures of quality of life, many of the areas surveyed by the ECA are relevant to this issue. For example, the rate of financial dependency among subjects with uncomplicated social phobia (22.3%) was found to be significantly elevated compared with that of normal subjects (70) .

The National Comorbidity Survey (20) reinforced the perception of social phobia as a major source of disability and suffering. It found a much higher lifetime prevalence for social phobia (13.3%) than the ECA. It showed that social phobia is negatively related to education and income and is significantly elevated among never-married individuals, students, persons who are neither working nor studying, and those who live with their parents. Approximately half of the persons with social phobia reported at least one outcome indicative of severity at some time in their lives (either significant role impairment, professional help seeking, or use of medication more than once); social phobia was also associated with low social support (71) .

Subthreshold Social Phobia

Some studies suggest that the negative impact of social phobia on quality of life may be felt beyond the strict set of diagnostic criteria in DSM-III/DSM-III-R. Davidson and colleagues (72) examined the Duke University site’s ECA data to compare individuals with social phobia, subthreshold social phobia (i.e., phobic avoidance of public speaking and/or meeting strangers or eating in public not associated with significant functional interference), and nonphobic, healthy comparison subjects. Compared with nonphobic normal subjects, persons with noncomorbid subthreshold social phobia were more likely to be female and unmarried and to report less income and fewer years of education. Persons with uncomplicated subthreshold social phobia were also more likely to report poor grades and lack of a close friend—a measure of perceived social support. Davidson and colleagues (72) concluded that subthreshold social phobia, in terms of impairment, closely resembles social phobia diagnosed according to the DSM-III criteria, which is similar to Klerman and colleagues’ conclusions (44) with respect to infrequent panic attacks.

Some studies have investigated the possibility that the subtypes of social phobia may affect quality of life in different ways or degrees. Kessler and colleagues (73) used National Comorbidity Survey data to compare social phobia characterized by pure speaking fears and by other social fears. Overall, social phobia characterized by pure speaking fears was found to be less persistent, less impairing, and less highly comorbid than social phobia characterized by more generalized social fears. Thus, although even subthreshold social phobia may be associated with a reduced quality of life (72) , these findings suggest that the most pervasive functional impairment and reduced quality of life is seen in persons who suffer from generalized social phobia (74) .

Schneier and colleagues (40) examined the nature of impairment of functioning in 32 outpatients with social phobia by comparing their scores on two new rating scales—the Disability Profile and the Liebowitz Self-Rated Disability Scale—with those of 14 normal comparison subjects. The Disability Profile is a clinician-rated instrument with items assessing current (i.e., over the last 2 weeks) and most severe lifetime impairment due to emotional problems in eight domains. The Liebowitz Self-Rated Disability Scale is a patient-rated instrument with 11 items assessing current and most severe lifetime impairment due to emotional problems. More than half of all patients with social phobia reported at least moderate impairment at some time in their lives due to social anxiety and avoidance in areas of education, employment, family relationships, marriage or romantic relationships, friendships or social network, and other interests. A substantial minority reported at least moderate impairment in the areas of activities of daily living (such as shopping and personal care) and suicidal behavior or desire to live. On the Liebowitz scale, more than half of all patients reported at least moderate impairment in self-regulation of alcohol use at some time in their lives due to social phobia. Patients with social phobia were rated more impaired than normal comparison subjects on nearly all items on both measures. These findings on the Liebowitz Self-Rated Disability Scale must be considered preliminary, pending further validation of this instrument.

Wittchen and Beloch (75) measured quality of life and other indices of impairment in a group of 65 subjects with social phobia (with no significant comorbidity) and compared the results with those from a comparison group of individuals with herpes infection. The instruments employed included the Short-Form Health Survey and the Liebowitz Self-Rated Disability Scale. Compared to the matched comparison group, the group with social phobia had significantly lower scores (i.e., worse function) on most of the Short-Form Health Survey scales. Pronounced reductions in self-rated quality of life were found among the patients with social phobia in the domains of role limitation due to emotional problems, social functioning, general mental health, and vitality. Standardized summed scores for the mental health components of the Short-Form Health Survey showed that 23.1% of all subjects with social phobia were severely impaired and 24.6% were significantly impaired compared to only 4.5% of the comparison subjects. The Liebowitz Self-Rated Disability Scale showed that social phobia affected most areas of life but in particular education, career, and romantic relations.

Impact of Treatment on Quality of Life in Patients With Social Phobia

In a 12-week, double-blind, randomized, placebo-controlled trial, Stein et al. (76) had patients with social phobia (91.3% with the generalized subtype of the disorder) treated with fluvoxamine, an SSRI. At the study’s endpoint, patients taking fluvoxamine (N=34) showed a significantly greater improvement in scores on the work functioning and family life and home functioning items of the Sheehan Disability Scale compared to placebo-treated patients (N=34).

Safren and colleagues (77) studied quality of life in a group of treatment-seeking persons with social phobia who underwent cognitive behavioral therapy for anxiety disorders in a university clinic. Subjects with comorbidities were not excluded. The instrument employed to measure quality of life was the Quality of Life Inventory (78) , a 17-item scale that assesses a person’s satisfaction in a particular area of life that he or she deems important (such as health, relationships, and work). Patients with social phobia judged their overall quality of life as lower than that of a normative reference group. Quality of life was inversely associated with various measures of severity of social phobia (especially social interaction anxiety), functional impairment, and depression. Subjects with generalized social phobia had significantly lower scores on the Quality of Life Inventory than those with nongeneralized social phobia. Patients showed significant improvement in scores on the Quality of Life Inventory after completion of cognitive behavioral group therapy for social phobia. However, their posttreatment scores on the Quality of Life Inventory remained lower than those of the normative group.

These studies suggest that there may be merit to the continued inclusion of quality-of-life outcome measures in treatment studies of social phobia, although changes may turn out to be more subtle (and perhaps more difficult to measure) than those seen in panic disorder.

Quality of Life in Individuals With PTSD

Generations of military physicians described PTSD under a variety of rubrics: nostalgia (Civil War), shell shock (World War I), combat fatigue or combat exhaustion (World War II), or post-Vietnam syndrome (79 , 80) . When the diagnosis of PTSD was finally added to the official psychiatric nomenclature with the publication of the DSM-III in 1980, little was known about the role played by the disorder in civilian life. The misconception that PTSD could only result from either combat experiences or some unusually severe traumas in civilian life was incorporated into the DSM-III/DSM-III-R description of a stressor as being “outside the range of usual human experiences.” Recent appreciation of the role played by a wide range of traumas experienced in the community in the genesis of PTSD led to the suppression of this description in the DSM-IV, which in turn emphasizes the subjective experience of intense fear, helplessness, or horror resulting from a person’s exposure to real or threatened death or serious injury or to a threat to the physical integrity of self or others. This major conceptual change extended the scope of the PTSD construct well beyond its original limits. Readers must be aware that the cases defined according to the DSM-III/DSM-III-R criteria represent just part of the universe delineated by those criteria.

Epidemiologic studies (81 – 83) found a lifetime prevalence for PTSD of 7.8% to 9.2%, with the rate in women two times higher than that in men. Zatzick and colleagues (84) undertook an archival analysis of data from the National Vietnam Veterans Readjustment Study to measure the impact of PTSD on functioning and quality of life. Six domains were examined: bed days in the past 2 weeks, role functioning, subjective well-being, self-reported physical health status, current physical functioning, and perpetration of violent interpersonal acts in the past year. The study subjects consisted of a nationally representative sample of 1,200 male Vietnam veterans. Poorer outcomes were significantly more common in subjects with PTSD than in subjects without PTSD in all domains except bed days in the past 2 weeks. Even after adjusting for demographic characteristics as well as for comorbid psychiatric and other medical disorders, subjects with PTSD continued to have a significantly higher risk of diminished well-being, fair or poor physical health, current unemployment, and physical limitations than did veterans without PTSD.

Zatzick and colleagues (85) also investigated the impact of PTSD on the quality of life of female veterans. A total of 432 female veterans of the Vietnam theater, most of whom were nurses, were assessed as part of the National Vietnam Veterans Readjustment Study. Functional impairment and diminished quality of life were assessed by responses to questions covering six domains: bed days in the past 3 months, role functioning, subjective well-being, self-reported physical health status, current physical functioning, and perpetration of violent interpersonal acts in the past year. PTSD was found to be associated with significantly elevated odds of poorer functioning in all domains, except perpetration of violence in the past year. After adjustment for demographic characteristics and medical and psychiatric comorbidities, PTSD remained associated with a statistically significant elevation of the odds of poorer functioning in three domains: role functioning, self-reported physical health status, and bed days in the past 3 months. When these results were compared with their findings in male Vietnam veterans (84) , Zatzick and colleagues (85) found similar patterns of elevated odds across genders, suggesting that sex differences are minimal or absent in the extent to which PTSD is related to functional impairment.

Jordan and colleagues (86) interviewed Vietnam veterans and their spouses or co-resident partners as part of the National Vietnam Veterans Readjustment Study to assess family and marital adjustment, parenting problems, and the presence of violence. Veterans with PTSD were found to be much more likely to report marital, parental, and family adjustment problems than were veterans without PTSD. There was more violence in the families of veterans with PTSD than in the families of veterans without PTSD. The majority of the spouses and partners reported high levels of nonspecific distress, and the children of veterans with PTSD were more likely to have behavioral problems than were the children of veterans without PTSD. These data underscore that PTSD (and, by inference, other anxiety disorders, although this has been little studied) adversely affects the quality of life, not only of individuals with the disorder, but also of their families.

Stein and colleagues (87) studied the impact of full and “partial” PTSD (or subthreshold PTSD—i.e., having fewer than the required number of DSM-IV criterion C or criterion D symptoms) on the social functioning of a community sample. Persons with partial PTSD reported significantly more interference with work or education than did traumatized subjects without PTSD, but they reported significantly less interference than persons with the full disorder. Persons with full and partial PTSD reported comparable levels of interference with social and family functioning. The authors concluded that partial PTSD seems to carry a burden of disability that approaches, if not matches, that produced by full PTSD. These findings remain to be replicated by using more comprehensive and standardized measures of quality of life.

Studies in Clinical Samples and Impact of Treatment

There is presently a dearth of information about quality of life in patients with PTSD. But a study using the Short-Form Health Survey in 16 patients with PTSD who participated in a clinical trial suggests that quality of life is markedly compromised in this disorder (88) . Furthermore, pilot data from this 12-week, double-blind, placebo-controlled study of the SSRI fluoxetine suggest that significant improvement in health-related quality of life can be obtained with pharmacologic treatment (88) . These findings remain to be replicated in larger study groups and extended to other treatment modalities, but they are promising indeed.

Quality of Life in Individuals With OCD

Until 1980 obsessive-compulsive disorder was thought to be rare. The ECA study, however, found lifetime prevalences ranging from 1.94% to 3.29% (89) , although a more recent study places the current prevalence rate in a somewhat lower range (90) . Despite its well-known morbidity, few studies have attempted to measure the impact of OCD on quality of life.

Koran and colleagues (91) studied quality of life in 60 unmedicated patients with moderate-to-severe OCD using the Short-Form Health Survey and compared their scores with published norms for the general U.S. population and with patients with either depression or diabetes. Patients with OCD had higher median scores on all domains of physical health for quality of life (physical functioning, role limitation due to physical problems, and bodily pain) than patients with diabetes and depression and scored near the general population norm. In contrast, in all the domains of mental health (social functioning, role limitation due to emotional problems, and mental health), the OCD patients’ average scores were well below those of the general population. The diabetic patients’ median scores were similar to those of the depressed patients. The severity of OCD was negatively correlated with scores on social functioning (i.e., the more severe the disorder, the lower the scores). This single study, which remains to be replicated, portrayed OCD as a disorder with a marked negative impact on quality of life.

Quality of Life in Individuals With Generalized Anxiety Disorder

Probably none of the categories of anxiety disorder established in DSM-III has been more difficult to ratify than generalized anxiety disorder. After two waves of substantial revisions in the diagnostic criteria and almost 20 years of continuous research, the uncertainties concerning the nature, boundaries, and clinical implications of this nosologic entity remain as strong as ever. As Roy-Byrne and Katon (92) pointed out, “there continues to be considerable debate about whether generalized anxiety disorder is a freestanding primary disorder, a prodromal or residual phase of other disorders, a personality trait, or a comorbid condition that modifies the course, treatment response, and outcome of other diseases” (p. 34). There is increasing recognition that comorbidity is a fundamental feature in the nature and course of generalized anxiety disorder. Judd and colleagues (93) found that 80% of individuals with lifetime generalized anxiety disorder also had a comorbid mood disorder during their lifetime. This finding suggests that the ideal goal of studying “pure,” noncomorbid generalized anxiety disorder may be unattainable.

The ECA study used the DSM-III criteria for generalized anxiety disorder, which emphasize its status as a residual category, and found a reported lifetime prevalence of 4.1% to 6.6% (94) . A total of 58% to 65% of the subjects who had generalized anxiety disorder also had at least one other DSM-III disorder. Persons with generalized anxiety disorder were more often unmarried or divorced. A significantly higher proportion of persons with generalized anxiety disorder than without had received disability benefits during their lifetimes. Even when employed, individuals with generalized anxiety disorder showed indirect evidence of impairment: a significantly higher proportion of them had annual incomes of less than $10,000 (1980 dollars).

The National Comorbidity Survey (95) used the DSM-III-R criteria for generalized anxiety disorder; these emphasize the presence of excessive and/or unrealistic worry, somatic symptoms, and a duration of at least 6 months. The hierarchical exclusion rules of the DSM-III, which preclude the diagnosis of generalized anxiety disorder if a patient meets the criteria for any other mental disorder, were replaced by a less restrictive rule that required only that the diagnosis of generalized anxiety disorder could not be assigned if it occurred during the course of a mood or psychotic disorder. Generalized anxiety disorder was found to be a relatively rare current disorder (current prevalence of 1.6%) but a more frequent lifetime disorder, affecting 5.1% of the U.S. population aged 15–54 years. The vast majority of persons with generalized anxiety disorder also had at least one other disorder (current morbidity, 66.3%; lifetime morbidity, 90.4%). The most frequent comorbid disorders were affective disorder and panic disorder. “Pure” lifetime generalized anxiety disorder was found to be rare, with a lifetime prevalence of 0.5%. Wittchen and colleagues (95) found that comorbidity was associated with a significantly greater likelihood of interference with daily activities (51.2% in comorbid generalized anxiety disorder; 28.1% in “pure” generalized anxiety disorder) and made it more difficult to assess the role played by noncomorbid generalized anxiety disorder.

Massion and colleagues (96) examined the effects of generalized anxiety disorder and panic disorder on the quality of life of a group of patients from the Harvard/Brown Anxiety Disorders Research Program using questions derived from the National Comorbidity Survey. Both groups showed impairment in role functioning and social life as well as low overall life satisfaction. Generalized anxiety disorder was associated with a reduction in overall emotional health. However, the finding that the vast majority of the patients with generalized anxiety disorder had at least one other anxiety disorder led the authors to affirm that “generalized anxiety disorder virtually never occurs in isolation” and made it difficult to assess the role played by noncomorbid generalized anxiety disorder. In summary, these limited data suggest that, although relatively rare, noncomorbid generalized anxiety disorder can be found in a substantial minority of individuals and is associated with important impairment in its own right.

Comparing the Relative Decrements in Quality of Life Attributable to Different Anxiety Disorders

Most, if not all, of the studies reviewed previously involve comparisons between the decrements in quality of life associated with a specific anxiety disorder and with physical disorders or major depression. These studies, part of the first generation of investigations on the impact of anxiety disorders on quality of life, were mainly comparing this impact against well-known “gold standards” of impairment and incapacity such as depression or hypertension. Recently, some studies have shifted the focus of their investigations toward comparing the decrements in quality of life attributable to different anxiety disorders and can be considered the forerunners of a new generation of studies on quality of life in patients with anxiety disorders.

Kessler and Frank (97) used National Comorbidity Survey data to examine relationships between DSM-III-R psychiatric disorders and work impairment in the U.S. labor force. Individuals with anxiety disorders, when compared to persons without them, showed statistically significantly higher rates of work impairment. Among individuals with anxiety disorders, those with panic disorder had the highest number of days on which their productivity was reduced (mean=4.87 days per month, SD=1.56), whereas those with social phobia had the lowest (mean=1.11 days per month, SD=0.47). Data for persons with generalized anxiety disorder and PTSD fell in the intermediate range (mean=3.11 days per month, SD=1.33; mean=2.76 days per month, SD=1.00, respectively).

Schonfeld and colleagues (51) employed the Short-Form Health Survey to investigate the degree to which untreated anxiety disorders and major depressive disorder, occurring either singly or in combination, reduced functioning and well-being among 637 primary-care patients. Trained lay interviewers administered the NIMH Diagnostic Interview Schedule to this group and identified 319 patients meeting the diagnostic criteria for one or more of six anxiety disorders (generalized anxiety disorder, PTSD, simple phobia, social phobia, panic disorder or agoraphobia, and OCD) and major depression. Of this group, 137 (43%) had a single disorder, and 182 (57%) had multiple disorders. Regression models were used to estimate the relative effects of these disorders on quality of life by comparing patients with anxiety disorders to patients without anxiety. Simple phobia and OCD scores were omitted from the analysis because they almost never occurred as single disorders. The estimated effect of each single disorder on all subscales for physical, social, and emotional functioning was substantial. The effects due to major depression were the most negative of any disorder, with reductions in score of more than 20 points (on a 100-point scale) below the predicted scores for the reference group with no disorders on six of eight subscales. Among the anxiety disorders, PTSD had significant negative effects across all functioning scales and was estimated to have the second most negative burden on five of the eight subscales of the Short-Form Health Survey; the main score reductions were observed in the subscales for role limitations (emotional) (42 points), role limitations (physical health) (29.2 points), and vitality (23.1 points). For panic disorder or agoraphobia, the largest score reductions were in the subscales for role limitations (physical health) (29.7 points) and bodily pain (20.1 points). The effects of generalized anxiety disorder were mostly felt in the subscales for role limitations (emotional) (28.2 points) and role limitations (physical health) (21.1 points). The role limitations (emotional) subscale showed the largest score reduction for social phobia (22 points). These findings highlight the value of examining specific domains of functioning across the anxiety disorders, because they appear to vary considerably.

Olfson and colleagues (98 , 99) examined social and occupational disability associated with several DSM-IV mental disorders and groups of subthreshold psychiatric symptoms that did not meet the full criteria for a DSM-IV disorder (including depressive, generalized anxiety, panic, obsessive-compulsive, drug, and alcohol symptoms) in 1,001 adult primary-care outpatients in a large health maintenance organization. The assessment consisted of a structured diagnostic interview for DSM-IV given by telephone, the Sheehan Disability Scale, and three impairment items from the ECA study. After adjusting for the confounding effects of comorbid axis I disorders, other subthreshold symptoms, age, sex, race, marital status, and perceived physical health status, only subthreshold symptoms for depressive and panic disorder were found to be significantly correlated with impairment measures. Although depressive symptoms were significantly correlated with impairment in social, family, and work functioning, impairment associated with panic symptoms was restricted to loss of work and increased utilization of mental health services.

The construct of “illness intrusiveness” was described by Devins (28) as corresponding to “lifestyle disruptions, attributable to an illness and/or its treatment, that interfere with continued involvement in valued activities and interests” (p. 252). The Illness Intrusiveness Ratings Scale (29) is a multidimensional tool that examines 13 domains of functioning, each of which may be specifically affected by an illness or its treatment. Antony and colleagues (30) measured the extent to which anxiety disorders interfere with several domains of functioning by having individuals with panic disorder (N=35), social phobia (N=49), and OCD (N=51) complete the Illness Intrusiveness Ratings Scale. The three groups did not differ on total scores on the Illness Intrusiveness Ratings Scale, but significant differences in particular domains of functioning were observed. Patients with OCD reported more interference with respect to passive recreation (e.g., reading) than did patients with social phobia and more interference with respect to religious expression than did the two other groups. Patients with social phobia reported more impairment with respect to social relationships and self-expression or self-improvement than any other group. Average scores on the Illness Intrusiveness Ratings Scale for the three anxiety disorders were considerably higher than those found in other chronic illnesses. These findings are consistent with the well-known impairment of social life associated with social phobia and with the tendency of obsessions (many of them with religious content) to invade the consciousness and disrupt intentional activities.

Quality-of-Life Studies of Patients With Anxiety Disorders: Limitations and Prospects

Quality-of-life assessment has been instrumental in exposing the extent and seriousness of anxiety disorders. As summarized previously, both epidemiological and clinical studies clearly delineate the extensive reduction in quality of life associated with anxiety disorders and hint at possible differences between anxiety disorders. Significant degrees of impairment can also be found in individuals with subthreshold forms of anxiety disorders, particularly panic disorder. Preliminary evidence suggests that panic disorder and PTSD may exert a heavier toll on quality of life than other anxiety disorders. Effective pharmacological or psychotherapeutic treatments have been shown to improve the quality of life in patients with panic disorder and social phobia but have yet to be demonstrated for other anxiety disorders.

Several validated generic and specific instruments have been shown to adequately measure quality of life in patients with anxiety disorders, raising the issue of how to select the most adequate instrument for a given purpose. It has been suggested that future studies addressing quality of life should employ a combination of generic and specific instruments to maximize both sensitivity and generalizability (100) . The Short-Form Health Survey is the most extensively tested generic measure and would constitute the natural candidate for an all-purpose instrument. The choice of the accompanying specific instrument should be determined by the specific goals of the study. An alternative approach would be the modular system proposed by Aaronson et al. (18) : the Short-Form Health Survey would constitute the “generic core” to which one or several additional “specific” modules with 10–15 questions could be added. These modules would focus on domains of quality of life that are not captured by the Short-Form Health Survey but that are likely to be affected by the presence of anxiety disorders (such as sleep in PTSD) or by the treatment itself (such as the sexual function of patients medicated with SSRIs). A modular instrument, the Hepatitis Quality of Life Questionnaire (101) , has been recently validated for the assessment of quality of life in patients with chronic hepatitis C; similar measures could be developed for anxiety disorders.

Progress in the field of the assessment of quality of life in anxiety disorders has not been homogeneous. Certain areas of knowledge are in need of further scientific investigation. First, although some disorders such as panic disorder have been reasonably well studied, others such as PTSD have been largely neglected. Second, there are disagreements between epidemiological and clinical findings in some areas that need to be clarified. The causes of this disagreement are open to debate and will require further study (102) . Third, to our knowledge, only a handful of studies have attempted to compare the impact of different anxiety disorders on quality of life. Fourth, the original goal for which the concept of quality of life was first adopted in clinical research was to compare outcomes between different treatment modalities. However, we found only 11 studies—eight in panic disorder, two in social phobia, and a small pilot study on PTSD—that attempted to assess the impact of treatment on the quality of life in patients with anxiety disorders. This is surprising considering that unlike other areas of medical research, mental health has few physiological variables to employ as outcome measures and would likely benefit from an approach that has proved successful in oncology and cardiology. It is likely that therapies that are equivalent in terms of the reduction of symptoms may be qualitatively or quantitatively dissimilar with respect to effects on quality of life. Knowledge of these differences may lead to a more informed choice of treatment modality for a particular disorder and, perhaps, for individual patients. In this area, much additional research is needed.

Despite the growing number of studies undertaken during the past 15 years, the investigation of quality of life in individuals with anxiety disorders is still in its infancy. Nevertheless, the studies conducted to date almost uniformly portray a picture of anxiety disorders as illnesses that markedly compromise quality-of-life and psychosocial functioning in several functional domains. It is hoped that these findings will translate into a more accurate public (and health care policy) view of anxiety disorders as serious mental disorders worthy of further research and appropriate health care expenditures. Finally, outcome studies that incorporate quality-of-life indices will further inform us as to the efficacy of existing and new treatments to lessen the burden of illness attributable to these disorders.

Received June 18, 1999, revision received Nov. 5, 1999, accepted Dec. 10, 1999. From the Department of Psychiatry, University of California at San Diego. Address reprint requests to Dr. Stein, Department of Psychiatry, University of California at San Diego, 8950 Villa La Jolla Dr., Suite 2243, La Jolla, CA 92037; [email protected] (e-mail). Supported in part by NIMH grant MH-57835. The authors thank Leslie Wetherell for her review of the manuscript.

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Ten years of researches on generalized anxiety disorder (GAD): a scientometric review

• Published: 11 April 2024

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• Ying Zhou 1 , 2 ,
• Yulin Luo 2 ,
• Na Zhang 3 &
• Shen Liu   ORCID: orcid.org/0000-0002-6900-8831 2  

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Generalized anxiety disorders (GAD) is a chronic anxiety disorder characterized by autonomic excitability and hypervigilance. However, there was currently a lack of a quantitative synthesis of this time-varying science, as well as a measure of researchers’ networks and scientific productivity. Searching from the Web of Science Core Collection, PubMed, and Scopus on January 31st, 2024. The scientometric analysis was realized and the clinical research of GAD in recent ten years was explored. 9703 studies published from 2014 to 2023 were included, which aggregated into a well-structured network with credible clustering. It was worth studying the recent trend of productivity. Eleven clusters were identified by the co-citation reference network. The network structure was reasonable ( Q  = 0.5996) and the clustering reliability was high ( S  = 0.8378). The main trend of research is ‘’china’’, ‘’epidemic’’. These results can provide reference for the future development of funding agencies and research groups.

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Complexity, Reductionism and the Biomedical Model

Social Anxiety Disorder

Anxiety Disorders in the DSM-5: Changes, Controversies, and Future Directions

Data availability.

The datasets generated and analyzed during the current study are not publicly available. The datasets are available from the corresponding author on reasonable request when the aim is to verify the published results.

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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Acknowledgements

This study was supported by the Outstanding Youth Program of Philosophy and Social Sciences in Anhui Province (2022AH030089) and the Starting Fund for Scientific Research of High-Level Talents at Anhui Agricultural University (rc432206).

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Study design: Shen Liu, Na Zhang. Data collection, analysis and interpretation: Ying Zhou. Drafting the manuscript: Ying Zhou, Yulin Luo, Na Zhang, Shen Liu. Critical revision of the manuscript: Shen Liu, Na Zhang. Approval of the final version for publication: all co-authors.

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Zhou, Y., Luo, Y., Zhang, N. et al. Ten years of researches on generalized anxiety disorder (GAD): a scientometric review. Curr Psychol (2024). https://doi.org/10.1007/s12144-024-05872-2

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Colored boxes indicate the time period groupings, with blue indicating time period 1 (March to June 2020); green, time period 2 (July to October 2020); and gray, time period 3 (November 2020 to March 2021). A, Shaded areas indicate 95% CIs. ALSPAC indicates children in the Avon Longitudinal Study of Parents and Children; BCS70, 1970 British Cohort Study; BiB, Born in Bradford; ELSA, the English Longitudinal Study of Aging; GS, Generation Scotland; MCS, the Millennium Cohort Study; NCDS, 1958 National Child Development Study; NS, Next Steps; NSHD, 1946 National Survey of Health and Development; and USOC, Understanding Society.

Standardized mean differences measure changes across time periods (compared with prepandemic distress) for the continuous psychological distress scores (A), and relative risk measures risk of high distress scores at each time period (B). ALSPAC indicates children in the Avon Longitudinal Study of Parents and Children; BCS70, 1970 British Cohort Study; BiB, Born in Bradford; ELSA, the English Longitudinal Study of Aging; GS, Generation Scotland; MCS, the Millennium Cohort Study; NCDS, 1958 National Child Development Study; NS, Next Steps, formerly the Longitudinal Study of Young People in England; NSHD, 1946 National Survey of Health and Development; TwinsUK, Twins UK; and USOC, Understanding Society.

Stratified changes across time periods (compared with prepandemic distress). Each light-colored point represents estimates from a different included study (study-specific estimates appear in eTables 8 and 10-12 in the Supplement ).

Stratified changes across time periods (compared with prepandemic distress).

eAppendix 1. Ethics and Data Access Statements For Each Study

eAppendix 2. Details of Mental Health Measures in Each Longitudinal Study

eAppendix 3. Participant Flow Diagrams

eTable 1. Measures of General Psychological Distress in Each Study

eTable 2. Additional Measures of Anxiety by Study

eTable 3. Distribution of Participant Characteristics by Study

eTable 4. Mean Psychological Distress Scores and Percentage With High Psychological Distress, by Study and Over Time

eTable 5. Mean Additional Anxiety Scores and Percentage With Psychological Distress, by Study and Over Time

eTable 6. Mean Prepandemic Psychological Distress Scores by Sociodemographic Characteristics and Study

eTable 7. Meta-analyzed Regression Coefficients: Continuous; Unstratified

eTable 8. Meta-analyzed Regression Coefficients: Continuous; Stratified by Sex

eTable 9. Meta-analyzed Regression Coefficients: Continuous; Stratified by Age

eTable 10. Meta-analyzed Regression Coefficients: Continuous; Stratified by Education

eTable 11. Meta-analyzed Regression Coefficients: Continuous; Stratified by Education

eTable 12. Meta-analyzed Regression Coefficients: Continuous; Stratified by Country

eTable 13. Meta-analyzed Regression Coefficients: Continuous; Sensitivity; Cohorts With All Time Points

eTable 14. Meta-analyzed Regression Coefficients: Continuous; Sensitivity; Anxiety

eTable 15. Meta-analyzed Regression Coefficients: Continuous; Sensitivity; Depression

eTable 16. Meta-analyzed High Psychological Distress When Named Cohort Is Removed

eTable 17. Meta-analyzed Regression Coefficients: Binary; Unstratified

eTable 18. Meta-analyzed Regression Coefficients: Binary; Stratified by Sex

eTable 19. Meta-analyzed Regression Coefficients: Binary; Stratified by Age

eTable 20. Meta-analyzed Regression Coefficients: Binary; Stratified by Education

eTable 21. Meta-analyzed Regression Coefficients: Binary; Stratified by Ethnicity

eTable 22. Meta-analyzed Regression Coefficients: Binary; Stratified by Country

eTable 23. Meta-analyzed Regression Coefficients: Interactions; Sex

eTable 24. Meta-analyzed Regression Coefficients: Interactions; Ethnicity

eTable 25. Meta-analyzed Regression Coefficients: Interactions; Education

eTable 26. Inclusion and Exclusion Criteria by Cohort for Metaregressions and Sensitivity Analyses

eTable 27. Meta-analyzed Interaction Coefficients

eTable 28. Heterogeneity Explained by Measures Explored by Metaregression Analyses

eAppendix 4. Results of Binary High Psychological Distress Outcomes by Sociodemographic Factor

eAppendix 5. Meta-analyzed High Psychological Distress When One Cohort Is Removed

eAppendix 6. Funding Statements for Each Study

eAppendix 7. ALSPAC Further Study Information

eReferences.

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Patel K , Robertson E , Kwong ASF, et al. Psychological Distress Before and During the COVID-19 Pandemic Among Adults in the United Kingdom Based on Coordinated Analyses of 11 Longitudinal Studies. JAMA Netw Open. 2022;5(4):e227629. doi:10.1001/jamanetworkopen.2022.7629

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Psychological Distress Before and During the COVID-19 Pandemic Among Adults in the United Kingdom Based on Coordinated Analyses of 11 Longitudinal Studies

• 1 MRC Unit for Lifelong Health and Ageing, University College London, London, England, United Kingdom
• 2 MRC/CSO Social & Public Health Sciences Unit, University of Glasgow, Glasgow, Scotland
• 3 Division of Psychiatry, University of Edinburgh, Edinburgh, Scotland
• 4 MRC Integrative Epidemiology Unit, University of Bristol, Bristol, England
• 5 Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, England
• 6 Bradford Institute for Health Research, United Kingdom
• 7 Department of Epidemiology and Public Health, University College London, London, England, United Kingdom
• 8 Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, Scotland
• 9 Department of Neuroscience, Psychology and Behaviour, University of Leicester, Leicester, England
• 10 Department of Twin Research and Genetic Epidemiology, School of Life Course & Population Sciences, King’s College London, London, England
• 11 Institute of Health & Wellbeing, University of Glasgow, Glasgow, Scotland
• 12 Centre for Longitudinal Studies, University College London, London, England, United Kingdom
• 13 Centre for Medical Information, University of Edinburgh, Edinburgh, Scotland

Question   How has the mental health of the UK population changed from before to during the COVID-19 pandemic?

Findings   This cohort study of 49 993 participants in 11 longitudinal studies found that mental health has deteriorated from before the start of the COVID-19 pandemic, and this deterioration was sustained across the first year of the pandemic. Deterioration in mental health varied by sociodemographic factors, namely age, sex, and education, and did not recover when social restrictions were eased.

Meaning   The substantial deterioration in mental health during the ongoing COVID-19 pandemic observed in this study highlights the need for improved mental health care provision and broader support to minimize the risk of longer-term mental health consequences and widening health inequalities.

Importance   How population mental health has evolved across the COVID-19 pandemic under varied lockdown measures is poorly understood, and the consequences for health inequalities are unclear.

Objective   To investigate changes in mental health and sociodemographic inequalities from before and across the first year of the COVID-19 pandemic in 11 longitudinal studies.

Design, Setting, and Participants   This cohort study included adult participants from 11 UK longitudinal population-based studies with prepandemic measures of psychological distress. Analyses were coordinated across these studies, and estimates were pooled. Data were collected from 2006 to 2021.

Exposures   Trends in the prevalence of poor mental health were assessed in the prepandemic period (time period 0 [TP 0]) and at 3 pandemic TPs: 1, initial lockdown (March to June 2020); 2, easing of restrictions (July to October 2020); and 3, a subsequent lockdown (November 2020 to March 2021). Analyses were stratified by sex, race and ethnicity, education, age, and UK country.

Main Outcomes and Measures   Multilevel regression was used to examine changes in psychological distress from the prepandemic period across the first year of the COVID-19 pandemic. Psychological distress was assessed using the 12-item General Health Questionnaire, the Kessler 6, the 9-item Malaise Inventory, the Short Mood and Feelings Questionnaire, the 8-item or 9-item Patient Health Questionnaire, the Hospital Anxiety and Depression Scale, and the Centre for Epidemiological Studies–Depression across different studies.

Results   In total, 49 993 adult participants (12 323 [24.6%] aged 55-64 years; 32 741 [61.2%] women; 4960 [8.7%] racial and ethnic minority) were analyzed. Across the 11 studies, mental health deteriorated from prepandemic scores across all 3 pandemic periods, but there was considerable heterogeneity across the study-specific estimated effect sizes (pooled estimate for TP 1: standardized mean difference [SMD], 0.15; 95% CI, 0.06-0.25; TP 2: SMD, 0.18; 95% CI, 0.09-0.27; TP 3: SMD, 0.21; 95% CI, 0.10-0.32). Changes in psychological distress across the pandemic were higher in women (TP 3: SMD, 0.23; 95% CI, 0.11, 0.35) than men (TP 3: SMD, 0.16; 95% CI, 0.06-0.26) and lower in individuals with below–degree level education at TP 3 (SMD, 0.18; 95% CI, 0.06-0.30) compared with those who held degrees (SMD, 0.26; 95% CI, 0.14-0.38). Increased psychological distress was most prominent among adults aged 25 to 34 years (SMD, 0.49; 95% CI, 0.14-0.84) and 35 to 44 years (SMD, 0.35; 95% CI, 0.10-0.60) compared with other age groups. No evidence of changes in distress differing by race and ethnicity or UK country were observed.

Conclusions and Relevance   In this study, the substantial deterioration in mental health seen in the UK during the first lockdown did not reverse when lockdown lifted, and a sustained worsening was observed across the pandemic period. Mental health declines have been unequal across the population, with women, those with higher degrees, and those aged 25 to 44 years more affected than other groups.

There have been widespread concerns about the impact of the COVID-19 pandemic and related mitigation measures on population mental health. 1 , 2 Globally, there is evidence that the pandemic has resulted in poorer mental health, 3 but much of this might depend on COVID-19 rates and the varying mitigation policies implemented. Concerns exist that specific policy responses, notably so-called lockdown measures, may themselves adversely affect mental health. Examining changes from before the pandemic, but also across different pandemic periods with different restrictions in place, may help understand the factors associated with adverse mental health effects.

Reports on population mental health changes at the start of the pandemic within the United Kingdom are conflicting, with some studies indicating a widespread decline in psychological well-being early on, 4 while other studies suggest improvements or no changes in mental ill health. 5 , 6 Findings have remained inconsistent as the pandemic has progressed, with both increasing and decreasing levels of poorer mental health reported. 7 - 9

The COVID-19 pandemic has had disproportionate impacts on different age and sociodemographic groups via different mechanisms. 10 , 11 For instance, older adults were at greater risk of severe disease and were asked to stay at home and minimize face-to-face contact (shielding), while younger people, women, and racial and ethnic minority groups have been disproportionately affected by employment losses and precarity. 12 The focus of many existing studies is on population averages, which may have concealed inequalities in mental health outcomes. 3

Uncertainty remains about how mental health has changed over the pandemic, including who has been most affected and whether any observed deterioration reflects lockdown measures or other aspects of the pandemic. To examine this, we conducted coordinated analyses of 11 UK longitudinal population studies with data from before and across the pandemic. We aimed to (1) estimate the consequences of the pandemic on population mental health and how these evolved during the first year of the pandemic as lockdown restrictions changed and (2) examine inequalities in these impacts by age, sex, race and ethnicity, education level, and UK country.

The UK National Core Studies–Longitudinal Health and Well-being initiative aims to coordinate primary analyses across multiple UK longitudinal population-based studies. 13 , 14 Coordinating analyses across different data sets minimizes methodological heterogeneity and maximizes comparability, while appropriately accounting for the study design and characteristics of individual data sets. Reporting followed the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline.

Data were pooled from 11 UK longitudinal population studies that conducted surveys both before and during the COVID-19 pandemic. Details of the design, sampling frames, current age range, timing of the prepandemic and COVID-19 surveys, response rates, and analytical sample size are in the Table , 15 - 34 with further details of each analytical sample in eTable 3 in the Supplement . Ethical approvals were received for all included studies, with ethics statements described in eAppendix 1 of the Supplement . All studies collected informed consent from their participants. This study did not seek any additional institutional review board approval.

Six studies were age-homogenous cohorts (ie, similarly aged individuals): the Millennium Cohort Study (MCS) 15 ; children in the Avon Longitudinal Study of Parents and Children (ALSPAC) 17 ; Next Steps (NS; formerly known as the Longitudinal Study of Young People in England) 19 ; 1970 British Cohort Study (BCS70) 21 ; 1958 National Child Development Study (NCDS) 23 ; and 1946 National Survey of Health and Development (NSHD). 24 Five other studies had age-heterogeneous samples (ie, cohorts with multiple age groups): Understanding Society (USOC) 25 ; Generation Scotland (GS) 28 ; Twins UK (TwinsUK) 32 ; Born in Bradford (BiB) 34 ; and the English Longitudinal Study of Aging (ELSA). 26

Analytical samples included those who had valid observations of psychological distress in a prepandemic survey, at least 1 survey during the pandemic, and valid data on sex and age (participant flow diagrams for each study appear in eAppendix 3 in the Supplement ). Participants who had died or emigrated by the start of the pandemic were also excluded. Most studies were weighted to be representative of their target population, accounting for sampling design and differential nonresponse to the COVID-19 surveys. 35 - 37 Weights were not used for ALSPAC, TwinsUK, GS, and BiB.

In the following sections, we describe the variables used for analysis. Details of the specific scales and coding used within each cohort appear in eAppendix 2 in the Supplement .

Psychological distress was measured both before the pandemic and at multiple points across the pandemic using validated, continuous scales measuring symptoms of common mental health disorders, such as depression and anxiety (specific measures used appear in the Table ). Continuous scales were standardized across time points and within studies on a common SD-based scale. This enhances comparability of estimates between studies while allowing examination of changes over time within studies. We also conducted analyses with dichotomous indicators of high psychological distress using established thresholds for each scale (eTable 1 and eTable 2 in the Supplement ).

While most studies used the same measure for both prepandemic and COVID-19 surveys, GS and NSHD used different measures. For these studies, we identified comparable items to create a smaller scale consistent over time, and the threshold for the binary outcome was reweighted based on the number of items retained (eTable 1 in the Supplement ).

We identified 3 time periods (TPs 1-3) representing different stages during the course of the pandemic in the United Kingdom for comparison against prepandemic mental health (measured at TP 0). Surveys from April to June 2020 represented the first wave of high infection levels accompanied by the first lockdown measures (TP 1). Surveys taken from July to October 2020 coincided with easing of restrictions and lower rates of infection (TP 2). Following this, infection levels again increased, and lockdown measures were reintroduced; surveys taken from November 2020 to March 2021 represent this second wave of infections (TP 3). Some studies contributed multiple survey waves to some TPs, and not all studies were represented in all 3 COVID-19 TPs ( Table ).

The following covariates were adjusted for and/or used to stratify estimates: sex (male or female); age in the age-heterogeneous cohorts (coded in 10-year bands to examine nonlinearity: 16-24, 25-34, 35-44, 45-54, 55-64, 65-74, and ≥75 years); race and ethnicity (self-reported and coded for main analyses; as White [including White ethnic minorities] vs racial and ethnic minority groups ); UK country of residence (England, Scotland, Wales, or Northern Ireland); and highest educational qualification (degree vs less than degree; parental education was used for the MCS cohort, who had not all completed their full-time education). Due to small sample sizes or lack of available ethnicity breakdown, we are we are unable to report race and ethnicity findings in more detail.

Changes in continuous measures of mental health over the 3 TPs were modeled using multilevel mixed-effects models within each study to account for associations between repeated measures from the same individuals, adjusting for sex and age (in age-heterogeneous cohorts). TP was a categorical exposure, with TP 0 as the reference. In some studies, multiple survey waves were included within the same TP. Coefficients are presented as standardized mean differences (SMDs). Multilevel mixed-effects Poisson regression models with robust standard errors were used to calculate relative risks for the binary outcome. 38

Results from each study were pooled using a random-effects meta-analysis with restricted maximum likelihood. Meta-analyses were conducted separately for continuous psychological distress scores and binary high psychological distress thresholds. Heterogeneity is reported using the I 2 statistic. 39

Interactions between TP and sex, education, and race and ethnicity were estimated within each study and then meta-analyzed to formally test for effect modification (ie, to determine whether changes across time periods varied between population subgroups). Formal interactions could not be tested by age and UK country given the age-homogeneous nature of several cohorts and few studies including all UK nations. We present meta-analysis of estimates stratified by sex, education, ethnicity, age, and UK country.

Further sensitivity meta-analyses restricted analyses to include studies that only assessed anxiety specifically, that assessed depression specifically, and that included survey responses for all 3 TPs. To explore the heterogeneity in estimates, metaregression analyses were conducted, quantifying the association of time with prepandemic and postpandemic measures, measurement type, and whether study samples were representative of their target age range in the UK population (eTable 26 in the Supplement ). All meta-analyses and metaregressions were conducted using Stata version 17 (StataCorp). No prespecified level of significance was set.

Across 11 individual longitudinal studies, 49 993 participants (12 323 [24.6%] aged 55-64 years; 32 741 [61.2%] women; 4960 [8.7%] racial and ethnic minority) were analyzed, ranging from 1816 participants in NSHD to 12 437 in USOC. The proportion of women ranged between 7208 (52.1%) in USOC to 1967 (100.0%) for BiB, and racial and ethnic minority participants ranged from 26 of 4103 (0.6%) in GS to 1223 (62.2%) in BiB. Descriptive statistics for all the studies, weighted and taking account of complex survey design where relevant, are in eTable 3 in the Supplement .

Descriptive statistics appear in eTables 4, 5, and 6 in the Supplement . Figure 1 A shows that for most studies, prevalence of high psychological distress either worsened or was fairly stable over the course of the pandemic. The largest increase in prevalence of high psychological distress was observed within the ELSA study, rising from 11.5% to 28.0% over the course of the 3 TPs. The largest increase between 2 consecutive TPs was observed within the NSHD study, between the prepandemic (2015) and first pandemic TP, increasing from 11.4% to 35.0%. In 2 studies (ALSPAC and BCS70), the prevalence of distress in the final pandemic TP (TP 3) was marginally lower than in the prepandemic time period (prevalence decreased by 2.3% and 0.8% respectively).

Figure 1 B shows the sex difference in mental health over the course of the pandemic, with higher prevalence of distress among women than men in all sex-heterogeneous studies. In April and May 2020 (TP 1), sex inequalities appeared especially high, with female respondents exhibiting higher prevalence of mental distress in most studies. For example, in NSHD at TP 1, 46.0% of female respondents reported mental distress vs 23.5% of male respondents. In NS at TP 1, 43.0% of female respondents reported mental distress vs 26.1% of male respondents.

Psychological distress increased from prepandemic scores across all 3 pandemic TPs examined (observed in 8 of the 11 included cohorts when focusing on general distress or depressive symptom measures), with no clear differences in changes across the 3 pandemic TPs (TP 1: SMD, 0.15; 95% CI, 0.06-0.25; TP 2: SMD, 0.18; 95% CI, 0.09-0.27; TP 3: SMD, 0.21; 95% CI, 0.10-0.32). However, there was considerable heterogeneity between estimates from different studies ( I 2 of 99.2%, 98.6%, and 99.2% at TP 1, TP 2, and TP 3, respectively), with estimates for TP 1 ranging from an SMD of −0.08 (95% CI, −0.11 to −0.05) for ALSPAC to an SMD of 0.46 (95% CI, 0.37 to 0.55) for NSHD (individual cohort results in eTable 7 in the Supplement ). Leave one out meta-analysis found that no single cohort significantly skewed the pooled estimates (eTable 16 and eAppendix 5 in the Supplement ). Similar patterns and high levels of heterogeneity were observed when considering prevalence of psychological distress as a binary outcome (eTable 17 in the Supplement ). Estimates for both continuous and binary measures of mental distress are displayed in Figure 2 . The pooled relative risk of high mental distress was elevated at TP 1 (relative risk, 1.29; 95% CI, 1.05-1.58) and TP 2 (relative risk, 1.23; 95% CI, 1.09-1.38), with the highest risk at TP 3 (relative risk, 1.36; 95% CI, 1.14-1.62).

Meta-analysis of the study-specific interaction terms between each marker of inequity and time period (eTable 17 in the Supplement ) indicated that changes in distress were greater in women (TP 3: SMD, 0.23; 95% CI, 0.11-0.35) compared with men (TP3: SMD, 0.16; 95% CI, 0.06-0.26) (eTable 8 in the Supplement ), suggesting a further widening of sex inequalities. Changes were marginally lower at TP 1 and TP 3 for persons with a below-degree level education (TP 3: SMD, 0.18; 95% CI, 0.06-0.30) compared with those with a degree (TP 3: SMD, 0.26; 95% CI, 0.14-0.38), albeit often from a greater prepandemic inequality, indicating a slight narrowing of educational inequalities during the pandemic. We did not find evidence for trends differing by ethnicity or UK country. Heterogeneity varied across these analyses, with I 2 values ranging from 44.2% for the interaction between education and TP 1 to 88.8% for ethnicity and TP 1. Estimates stratified by sex, ethnicity, education, and UK country are shown in Figure 3 . Again, in all analyses there was large heterogeneity between study estimates (eTables 8 and 10-12 in the Supplement ).

Age-stratified results showed no monotonic pattern by age ( Figure 4 ), despite some suggestion that the consequences of the pandemic on mental health might have been greater in those aged 25 to 44 years. The pooled SMD at TP 3 for those aged 25 to 34 years was 0.49 (95% CI, 0.14-0.84) and for those aged 35 to 44 years, 0.35 (95% CI, 0.10-0.60) (eTable 9 in the Supplement ).

Sensitivity analyses were conducted to consider specific measures of mental health (depression or anxiety) and to limit data to participants with survey responses during all 3 TPs. Findings were consistent with the main analyses (eTables 13-15 in the Supplement ). We also presented pooled analyses of the binary high distress outcomes, overall and stratified in eTables 17 to 25 in the Supplement .

Given the high levels of heterogeneity across studies, we conducted metaregressions to examine whether time between prepandemic and postpandemic measures, measurement type, and representativeness of the studies for their target population helped account for some of the observed heterogeneity (eTable 28 in the Supplement ). Heterogeneity was largely unexplained by these factors; the largest explanatory factor was the representativeness of the studies, which explained 3.25% of the heterogeneity at TP 2 and suggested the deterioration in distress was less marked in representative studies. A subsequent meta-analysis including only studies with national coverage showed a worsening of mental health over the pandemic similar to the main meta-analysis (TP 1: SMD, 0.15; 95% CI, 0.02-0.29; TP 2: SMD, 0.11; 95% CI, 0.05-0.17; TP 3: SMD, 0.16; 95% CI, 0.05-0.27).

Our analyses of 11 well-established longitudinal studies provide a comprehensive picture of the evolution of mental health over the course of differing lockdown periods during the COVID-19 pandemic. Overall, our results indicate mental health has deteriorated since the onset of the pandemic and this has been sustained with no evidence of recovery, even when lockdown measures temporarily eased in the United Kingdom during the summer of 2020. Although evidence for deterioration from prepandemic levels is seen in most included studies, there was considerable heterogeneity in effect sizes estimated. Furthermore, our findings demonstrate that while aggregate population mental health deteriorated over time, not all groups were equally affected. Women, those with a degree-level education, and young adults (aged 25-34 and 35-44 years) were affected most, reporting greater increases in psychological distress during the pandemic and thereby exacerbating some prepandemic mental health inequalities.

Our findings suggest that initial declines in mental health were not a transient reaction to an unprecedented event, but an early indication of a sustained deterioration from prepandemic levels. These findings extend research conducted earlier in the pandemic, 4 , 40 replicate some research suggesting sustained effects, 9 , 41 and contradict findings from some convenience samples suggesting improvements in mental health when the initial lockdown was lifted. 7 From a policy perspective, having a wealth of longitudinal data both before and during the COVID-19 pandemic gives further information on how the pandemic has affected mental health, beyond simple convenience sampling data. While the direct mechanisms generating poorer mental health are complex, the COVID-19 pandemic resulted in considerable economic, social, and behavioral changes and an increase in physical comorbidities and bereavement; therefore, increased mental distress is perhaps unsurprising. Financial stressors, changes in social interactions, and disruptions to daily life may all help to explain our findings. 42 - 45 These results suggest that deteriorations in population mental health may be driven more by time-stable disruption and concern arising from the COVID-19 pandemic, rather than the consequences of time-specific mitigation measures such as lockdowns.

Furthermore, this deterioration suggests that avoiding lockdown measures alone may not maintain population mental health, and other factors should be considered. For example, health services in the UK were not able to meet their population’s mental health needs before the pandemic, with this situation made substantially worse during the pandemic. 46 To minimize the detrimental longer-term consequences of the pandemic, mental health care needs to encompass multiple levels of support, including investment in primary care, community mental health, and public mental health. Initiatives should target groups at greater risk of experiencing mental ill health, including ensuring rapid access to services, but also addressing the underlying drivers of poor mental health, such as mitigating risks of unemployment, sexual violence, and poverty.

Our results highlight widening gender inequalities in mental health. Women had much higher distress levels and showed greater deterioration during the pandemic than men. Possible reasons include increased childcare responsibilities that disproportionately fell to women, greater economic impacts, and reports of large increases in gender-based violence. 47 We also observed that deterioration in lockdown periods was greater in those with degree-level education, albeit from a lower prepandemic level, indicating that educational inequalities narrowed. 40 Our investigation of age differences show that all age groups have been adversely affected to some extent, but high psychological distress was greater in those aged 25 to 44 years. The mechanisms underpinning subpopulation differences remain unclear but likely include disruptions to social interactions, changes in employment or education, and shifts in parental responsibilities and/or work-life balance. 48 For example, individuals between the ages of 25 and 44 years are more likely to have school-aged children and may therefore have faced additional challenges of working from home and caring for children. Moreover, younger adults have been at an increased risk of employment disruptions 49 as well as changes in healthy behaviors, 50 , 51 which may have contributed to further deteriorations in their mental health. However, the well-documented midlife peak in psychological distress is noteworthy, 52 and may partly explain some of the deterioration we found in these age groups.

The multiple longitudinal studies included in this article highlight the wide range in the size of the estimated deterioration in distress from prepandemic levels across varied data sources that represent different populations. While we explored multiple factors (such as age, outcome measure, timing, and representativeness), we could not explain much of this heterogeneity. Other factors not considered, such as rates of COVID-19 within the samples, might also play a role.

Our study has several strengths. By harnessing high-quality existing longitudinal studies, we have robust prepandemic baseline data and multiple waves of data collection capturing different TPs during the pandemic. We investigated the potential consequences of COVID-19 policy responses, specifically the introduction and removal of lockdown measures. Our approach to data harmonization allowed us to develop comparable exposure, outcome, and covariate measures and pool estimates for similar TPs. Furthermore, we maximized the value of existing data by using multilevel models to include all available data. The baseline samples of many of these studies were representative of their target populations, and analyses were weighted to account for nonresponse. Lastly, this study combined 11 longitudinal data sources, and heterogeneity between the study-specific estimates was large, highlighting that documenting the results from multiple sources is more reliable for informing policy and health planning than relying on a single data source.

Despite these advantages, limitations should be noted. We cannot definitively attribute changes in population mental health to the COVID-19 pandemic or related policy responses, as COVID-19 was a universal exposure to everyone. However, we note that we are unaware of alternative events that would have been likely to substantially confound our analyses or their interpretation. There were differences between studies in the timing of data collection (including when prepandemic measures were collected) and the mental health survey instruments used, although this did not account for the high levels of statistical heterogeneity observed. Similarly, although weighting was used when possible to control for nonrandom response, conditioning on voluntary response may induce selection bias, as it is very plausible that the mental health of the observed differs systematically from the target population. However, the broad consistency in the direction of findings across data sets provides reassurance that the key conclusions are likely to be robust to these differences, even if the magnitude of the effect size is harder to confirm.

The findings of this study suggest that mental health has been persistently worse during the COVID-19 pandemic than before, particularly among women, those with higher degrees, and those aged 25 to 44 years. The sustained deterioration, even when lockdown measures were eased, somewhat refutes the notion that easing lockdown measures necessarily improved mental health and implies that there are myriad pathways leading to adverse mental health outcomes. Our findings highlight the need for investment in mental health support to turn the tide and improve population mental health going forward.

Accepted for Publication: February 28, 2022.

Published: April 22, 2022. doi:10.1001/jamanetworkopen.2022.7629

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2022 Patel K et al. JAMA Network Open .

Corresponding Author: Praveetha Patalay, PhD, MRC Unit for Lifelong Health and Ageing, UCL, 1-19 Torrington Place, Floor 5, London, WC1E 7HB ( [email protected] ).

Author Contributions: Drs Patalay and Katikireddi had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Drs Patel, Kwong, Griffith, and Green and Mss Robertson and Willan are joint first authors. Drs Patalay, Porteous, and Katikireddi are joint last authors.

Concept and design: Patel, Kwong, Griffith, Green, McElroy, Maddock, Niedzwiedz, Henderson, Richards, Ploubidis, Fitzsimons, Patalay, Katikireddi.

Acquisition, analysis, or interpretation of data: Patel, Robertson, Kwong, Griffith, Willan, Green, Di Gessa, Huggins, McElroy, Thompson, Henderson, Richards, Steptoe, Ploubidis, Moltrecht, Booth, Silverwood, Patalay, Porteous, Katikireddi.

Drafting of the manuscript: Patel, Robertson, Kwong, Griffith, Willan, Green, Henderson, Richards, Moltrecht, Patalay, Katikireddi.

Critical revision of the manuscript for important intellectual content: Patel, Kwong, Griffith, Green, Di Gessa, Huggins, McElroy, Thompson, Maddock, Niedzwiedz, Richards, Steptoe, Ploubidis, Moltrecht, Booth, Fitzsimons, Silverwood, Patalay, Porteous, Katikireddi.

Statistical analysis: Patel, Kwong, Griffith, Willan, Green, Di Gessa, Huggins, McElroy, Thompson, Silverwood.

Obtained funding: Henderson, Steptoe, Ploubidis, Fitzsimons, Patalay, Porteous, Katikireddi.

Administrative, technical, or material support: Robertson, Kwong, Griffith, McElroy, Thompson, Niedzwiedz, Steptoe, Booth.

Supervision: Steptoe, Patalay, Porteous, Katikireddi.

Conflict of Interest Disclosures: Ms Robertson reported receiving grants from the Medical Research Council (MRC) and the Scottish Government Chief Scientist Office during the conduct of the study. Dr Griffith reports holding a postdoctoral post funded by the MRC and receiving a postdoctoral fellowship from grants from the Economic and Social Research Council (ESRC) during the conduct of the study. Dr Green reported receiving grants from the MRC during the conduct of the study. Dr Huggins reported receiving grants from the Wellcome Trust during the conduct of the study. Dr Niedzwiedz reported receiving grants from the MRC during the conduct of the study and outside the submitted work. Dr Henderson reported grants from ESRC during the conduct of the study. Dr Katikireddi reported receiving grants from the MRC and the Scottish Government Chief Scientist Office during the conduct of the study; serving as cochair of the Scottish Government’s Expert Reference Group on Ethnicity and COVID-19; being a member of the UK Government’s Scientific Advisory Group on Emergencies subgroup on ethnicity; and being a member of the UK Cabinet Office’s International Best Practice Advisory Group. No other disclosures were reported.

Funding/Support: This work was supported by the National Core Studies, an initiative funded by UK Research and Innovation, the National Institute for Health Research, and the Health and Safety Executive. The COVID-19 Longitudinal Health and Wellbeing National Core Study was funded by the MRC (MC PC 20059). Full funding acknowledgements for each individual study can be found as part of eAppendix 6 in the Supplement .

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Additional Contributions: The contributing studies have been made possible because of the tireless dedication, commitment and enthusiasm of the many people who have taken part. We would like to thank the participants and the numerous team members involved in the studies including interviewers, technicians, researchers, administrators, managers, health professionals, and volunteers. We are additionally grateful to our funders for their financial input and support in making this research happen. Specifically, we thank Claire Steves, Ruth C. E. Bowyer, Deborah Hart, María Paz García, and Rachel Horsfall (Twins UK); Nicholas J. Timpson, Kate Northstone, and Rebecca M. Pearson (Avon Longitudinal Study of Parents and Children; more information in eAppendix 7 in the Supplement ); Drew Altschul, Chloe Fawns-Ritchie, Archie Campbell, and Robin Flaig (Generation Scotland); Michaela Benzeval (Understanding Society); Andrew Wong, Maria Popham, Karen MacKinnon, Imran Shah, and Philip Curran (1946 National Survey of Health and Development); our colleagues in survey, data, and cohort maintenance teams (the Millennium Cohort Study, Next Steps, 1970 British Cohort Study, National Child Development Study); John Wright and Dan Mason and other colleagues in cohort, survey, data maintenance teams (Born in Bradford).

Additional Information: Dr McElroy had full access to the Millenium Cohort Study, Next Steps, the 1970 British Cohort Study, and the National Child Development Study; Dr Patel, 1946 National Survey of Health and Development; Dr Kwong, Avon Longitudinal Study of Parents and Children; Dr Green, Understanding Society; Dr Di Gessa, English Longitudinal Study of Ageing; Dr Huggins, Generation Scotland; Ellen Thompson, Twins UK; and Ms Willan, Born in Bradford.

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Anxiety Disorders

We all experience anxiety. For example, speaking in front of a group can make us anxious, but that anxiety also motivates us to prepare and practice. Driving in heavy traffic is another common source of anxiety, but it helps keep us alert and cautious to avoid accidents. However, when feelings of intense fear and distress become overwhelming and prevent us from doing everyday activities, an anxiety disorder may be the cause.

Anxiety disorders are the most common mental health concern in the United States. Over 40 million adults in the U.S. ( 19.1% ) have an anxiety disorder. Meanwhile, approximately  7%  of children aged 3-17 experience issues with anxiety each year. Most people develop symptoms before age 21.

Anxiety disorders are a group of related conditions, each having unique symptoms. However, all anxiety disorders have one thing in common: persistent, excessive fear or worry in situations that are not threatening. People typically experience one or more of the following symptoms:

Emotional symptoms:

• Feelings of apprehension or dread
• Feeling tense or jumpy
• Restlessness or irritability
• Anticipating the worst and being watchful for signs of danger

Physical symptoms:

• Pounding or racing heart and shortness of breath
• Sweating, tremors and twitches
• Headaches, fatigue and insomnia
• Upset stomach, frequent urination or diarrhea

Types Of Anxiety Disorders

There are many types of anxiety disorders, each with different symptoms. The most common types of anxiety disorders include:

Generalized Anxiety Disorder (GAD)

GAD produces chronic, exaggerated worrying about everyday life. This worrying can consume hours each day, making it hard to concentrate or finish daily tasks. A person with GAD may become exhausted by worry and experience headaches, tension or nausea.

Social Anxiety Disorder

More than shyness, this disorder causes intense fear about social interaction, often driven by irrational worries about humiliation (e.g. saying something stupid or not knowing what to say). Someone with social anxiety disorder may not take part in conversations, contribute to class discussions or offer their ideas, and may become isolated. Panic attacks are a common reaction to anticipated or forced social interaction.

Panic Disorder

This disorder is characterized by panic attacks and sudden feelings of terror sometimes striking repeatedly and without warning. Often mistaken for a heart attack, a panic attack causes powerful physical symptoms including chest pain, heart palpitations, dizziness, shortness of breath and stomach upset. Many people will go to desperate measures to avoid an attack, including social isolation.

We all tend to avoid certain things or situations that make us uncomfortable or even fearful. But for someone with a phobia, certain places, events or objects create powerful reactions of strong, irrational fear. Most people with specific phobias have several things that can trigger those reactions; to avoid panic, they will work hard to avoid their triggers. Depending on the type and number of triggers, attempts to control fear can take over a person’s life.

Other anxiety disorders include:

• Agoraphobia
• Selective mutism
• Separation anxiety disorder
• Substance/medication-induced anxiety disorder, involving intoxication or withdrawal or medication treatment

Scientists believe that many factors combine to cause anxiety disorders:

• Genetics.   Studies support the evidence that anxiety disorders “run in families,” as some families have a higher-than-average amount of anxiety disorders among relatives.
• Environment.  A stressful or traumatic event such as abuse, death of a loved one, violence or prolonged illness is often linked to the development of an anxiety disorder.

Physical symptoms of an anxiety disorder can be easily confused with other medical conditions, like heart disease or hyperthyroidism. Therefore, a doctor will likely perform an evaluation involving a physical examination, an interview and lab tests. After ruling out an underlying physical illness, a doctor may refer a person to a mental health professional for evaluation.

Using the Diagnostic and Statistical Manual of Mental Disorders (DSM) a mental health professional is able to identify the specific type of anxiety disorder causing symptoms as well as any other possible disorders that may be involved. Tackling all disorders through comprehensive treatment is the best recovery strategy.

Different anxiety disorders have their own distinct sets of symptoms. This means that each type of anxiety disorder also has its own treatment plan. But there are common types of treatment that are used.

• Psychotherapy , including cognitive behavioral therapy
• Medications , including antianxiety medications and antidepressants
• Complementary health approaches , including stress and relaxation techniques

Related Conditions

Anxiety disorders can occur along with other mental health conditions, and they can often make related conditions worse. So, talk with a mental health care professional if you are experiencing anxiety and any of the following:

• Substance Use
• Attention Deficit Hyperactivity Disorder ( ADHD )
• Eating Disorders
• Trouble Sleeping

Reviewed December 2017

Once it is clear there is no underlying physical condition present or medication side effect causing your anxiety, then exploring options for mental health treatment is essential.

The types of treatment proven to be most effective for many people experiencing an anxiety disorder involve a combination of psychotherapy and medication. Your preferences in a treatment plan are essential, however, so discuss the best approaches and options with your treatment team.

Co-occurring conditions, like depression, are common when a person has anxiety. Be sure to work with your treatment team to make sure these other conditions are not overlooked.

Psychotherapy

Cognitive Behavioral Therapy (CBT)  is the most researched  psychotherapy  for anxiety disorders. In general, CBT focuses on finding the counterproductive thinking patterns that contribute to anxiety. CBT offers many constructive strategies to reduce the beliefs and behaviors that lead to anxiety.

CBT is also effective when delivered outside of the traditional in-person setting. Working with a therapist using  telehealth technology  — like video or phone calls or online learning modules that teach CBT concepts —  can be just as effective  as traditional face-to-face therapy.

CBT has the largest research base to support its effectiveness, though it can be difficult to figure out which therapists are trained in CBT. There is no single national certification program for this skill. Ask your therapist how they approach treating anxiety and their trainings in these approaches.

Exposure Response Prevention  is a psychotherapy for specific anxiety disorders like phobias and social anxiety. Its aim is to help a person develop a more constructive response to a fear. The goal is for a person to “expose” themselves to that which they fear, in an attempt to experience less anxiety over time and develop effective coping tools.

Some people find that medication is helpful in managing an anxiety disorder. Talk with your health care provider about the potential benefits, risks and side effects.

• Anti-anxiety medications . Certain medications work solely to reduce the emotional and physical symptoms of anxiety. Benzodiazepines can be effective for short-term reduction of symptoms, but can create the risk of dependence when used for a long time. Be sure to review these potential risks if you select these medicines.  Click here  for more information on these medications.
• Antidepressants . Many antidepressants may also be useful for treating anxiety. These can also be useful if your anxiety has a co-occurring depression. Be sure to check our  Medication page  for more information.

Complementary Health Approaches

More and more people have started using  complementary and alternative treatments  along with conventional treatment to help with their recovery. Some of the most common approaches for treating anxiety include:

• Self-management strategies , such as allowing for specific periods of time for worrying. Someone who becomes an expert on their condition and its triggers gains more control over their day.
• Stress and Relaxation Techniques  often combine breathing exercises and focused attention to calm the mind and body. These techniques can be an important component in treating phobias or panic disorder.
• Yoga . The combination of physical postures, breathing exercises and meditation found in yoga have helped many people improve the management of their anxiety disorder.
• Exercise . Aerobic exercise can have a positive effect on your stress and anxiety. Check with your primary care doctor before beginning an exercise plan.
• Surviving the Bed Shortage in Mental Health Treatment Facilities: A Teenager’s Experience

If you, a family member or friend is experiencing symptoms of an anxiety disorder, there is help. NAMI is here to provide you with support and information about community resources for you and your family.

Find education programs and support groups  at your local NAMI . Contact the NAMI HelpLine at 1-800-950-NAMI (6264) or  [email protected]  if you have any questions about anxiety or want help finding support and resources.

Helping Yourself

Anxiety disorders can impact even the smallest details of life. It’s important to get help and learn how to remain resilient during difficult times. Here are some ways you can help yourself move forward:

• Become an expert.  Learn about medication and  treatment options . Keep up with current research. Build a personal library of useful websites and helpful books.
• Know your triggers and stressors.  If large groups make you nervous, go to a park and sit on an out-of-the-way bench. If taking a walk outdoors reduces your anxiety before a big meeting, schedule a 10-minute walk before the meeting starts. Being mindful of triggers and stressors will help you live your life with fewer limitations.
• Partner with your health care providers.  Actively participate in your treatment by working with mental health care professionals to develop a plan that works for you. Talk with them about your goals, decide on a recovery pace you’re comfortable with and stick to your plan. Don’t quit when something doesn’t go well. Instead, talk to your doctor or therapist about possible changes.
• Get healthy.  Studies have reported that 30 minutes of vigorous, aerobic exercise can eliminate symptoms, while low-key activities like meditation, yoga or Tai Chi relieve stress. Regular exercise can reduce many symptoms. Diet is also an important factor, so try to eat healthy, balanced meals and pay attention to food sensitivities. In some people, certain foods or additives can cause unpleasant physical reactions, which may lead to irritability or anxiety.
• Avoid drugs and alcohol.  These substances may  seem  to help with anxiety at first, but can disrupt emotional balance, sleep cycles and interact with medications. Coffee, energy drinks and cigarettes worsen anxiety.
• Find support.  Share your thoughts, fears and questions with others. NAMI offers  support groups and education programs , as well as online discussion communities.

Learn more about  managing your mental health and finding support  while living with mental illness.

Helping A Family Member Or Friend

Learn about your loved one’s triggers, stressors and symptoms. By being informed and aware, you may help prevent an increase in symptoms. Look for things like rapid breathing, fidgeting or avoidance behaviors. Discuss your friend or family member’s past experiences with them so they can recognize the signs early as well.

• Play a role in treatment.  Increasingly, mental health professionals are recommending couple or family-based treatment programs. And on occasion, a therapist might enlist a loved one to help reinforce behavior modification techniques with homework. Ultimately, the work involved in recovery is the responsibility of the person with the disorder, but you can play an active, supportive role.
• Communicate.  Speak honestly and kindly. Make specific offers of help and follow through. Tell the person you care about her. Ask how she feels and don’t judge her for her anxious thoughts.
• Allow time for recovery.  Understanding and patience  need to be balanced  with pushing for progress and your expectations.
• React calmly and rationally.  Even if your loved one is in a crisis, it’s important to remain calm. Listen to him and make him feel understood, then take the next step in getting help.

Find out more about  taking care of your family member or friend  (without forgetting about yourself!).

• Tips For Easing Back-to-School Anxiety
• Being Queer is Joyful

Know the warning signs of mental illness

Learn more about common mental health conditions

NAMI HelpLine is available M-F, 10 a.m. – 10 p.m. ET. Call 800-950-6264 , text “helpline” to 62640 , or chat online. In a crisis, call or text 988 (24/7).

• Open access
• Published: 24 April 2024

Association between body mass index and mental health among nurses: a cross-sectional study in China

• Bonan Luan   ORCID: orcid.org/0000-0003-2703-3390 1 ,
• Xueyan Tian   ORCID: orcid.org/0000-0002-7933-5844 2 ,
• Chao Wang   ORCID: orcid.org/0000-0001-6097-7719 2 ,
• Ming Cao   ORCID: orcid.org/0009-0001-4858-838X 2 &
• Dongmei Liu   ORCID: orcid.org/0000-0003-1822-7580 2  

BMC Health Services Research volume  24 , Article number:  506 ( 2024 ) Cite this article

Metrics details

To examine the correlation between body mass index (BMI) and mental well-being in Chinese nurses during the COVID-19 epidemic.

This study was conducted in a tertiary hospital using a cross-sectional design. A total of 2,811 nurses were enlisted at Shengjing Hospital in China during the period from March to April, 2022. Information was gathered through a questionnaire that individuals completed themselves. The mental health of the participants was assessed using the Patient Health Questionnaire-9 and the Generalized Anxiety Disorder Assessment-7. Binary logistic regression was used to calculate adjusted odds ratios (ORs) and their corresponding 95% confidence intervals.

The prevalence of nurses experiencing depression and anxiety was 7.8% (219) and 6.7% (189), respectively. Regarding depression after adjustment, the odds ratios (ORs) for each quartile, compared to the lowest quartile, were as follows: 0.91 (95% confidence interval [CI]: 0.53, 1.56), 2.28 (95% CI: 0.98, 3.77), and 2.32 (95% CI: 1.41, 3.83). The p-value for trend was found to be 0.001. The odds ratios (ORs) for anxiety after adjustment were 2.39 (0.83, 4.36), 4.46 (0.51, 7.93), and 2.81 (1.56, 5.08) when comparing the highest quartiles to the lowest quartile. The p -value for trend was 0.009.

This study found a positive association between BMI and poor mental health among nurses during the COVID-19 pandemic, particularly in those who were overweight or obesity. The findings could assist in developing interventions and help policy-makers establish appropriate strategies to support the mental health of frontline nurses, especially those who are overweight or obesity.

Peer Review reports

Introduction

Depression and anxiety are the most common mental health illnesses worldwide [ 1 ]. Depression is a mood disorder that affects an individual’s thoughts and feelings and leads to persistent feelings of sadness and disinterest [ 2 ]. Anxiety is a group of mental disorders characterized by nervousness, apprehension, and fear [ 3 ]. Depression and anxiety disorders are major contributors to the mental health burden of adults [ 4 ]. Poor mental health often affects regular activities and probably results in poor professional performance. Given the detrimental effects of depression and anxiety on physical and mental health, it is important to explore the relevant factors, and to thereby contribute toward preventing the development of mental health disorders [ 5 ].

According to the National Institute of Health of America, body mass index (BMI) is a measure that defines individuals as underweight, normal weight, or overweight, that is calculated using their weight and height [ 6 ]. Recent research indicates that high BMI and obesity continue to relentlessly increase globally, with approximately two billion people being overweight or obese [ 7 ]. In a meta-analysis including 57 prospective studies and 900,000 adults, they found that above 25 kg/m 2 , positive associations between BMI and cardiovascular disease, hypertension, diabetes mellitus, stroke, and cancer were recorded in both sexes. Moreover, each 5 kg/m 2 higher BMI was associated with about 30% higher overall mortality [ 8 ]. Obesity-related diseases have become the fifth leading cause of death worldwide [ 9 ].

A systematic review and meta-analysis on the longitudinal relationship between BMI and mental health, they found that obesity at baseline increased the risk of onset of depression and the unadjusted ORs were 1.55 (including 15 included studies and 58,745 participants) [ 10 ]. Moreover, another meta-analysis of 8 Mendelian randomization studies indicated that obesity is a causal risk factor for elevated risk of depression (OR = 1.33) [ 11 ]. Previous studies also demonstrate a bi-directional relationship between obesity and mental health [ 12 ]. Although these existing studies address the issue of obesity and mental health, none of these studies address this issue among nurses.

As nurses fulfill an essential role among healthcare workers, they experienced a particularly high occupational burden during the peak of coronavirus disease (COVID-19) pandemic [ 13 ]. In a multi-center cross-sectional online survey, among 395 healthcare workers, there were 42.28% and 56.2% were found to have depression, and anxiety during the COVID-19 pandemic, respectively [ 14 ]. A recent study conducted in 2020 from China shows that nurses experienced more unfavorable mental health outcomes than other healthcare workers during the pandemic [ 15 ]. Furthermore, for nurses, poor mental health may influence not only themselves but also their professional performance and the quality of the health care provided, even affecting patient safety [ 16 , 17 ]. A growing body of evidence suggests that individuals with changes in BMI have experienced deteriorating symptoms, such as isolation, anxiety and depression as a result of the COVID-19 pandemic compared to previous timepoints. The increasing obesity rates may have modestly increased the prevalence of depressive symptoms in the general population [ 18 ]. However, there is currently no data to explore the association between BMI and mental health among nurses during the COVID-19 pandemic. To fill this gap, we conducted a large cross-sectional study to explore the association between BMI and mental health among nurses in China during the COVID-19 pandemic.

Study design

The present cross-sectional investigation was carried out at a Chinese hospital throughout the period from March 2022 to April 2022. The survey was conducted by the nursing department, and it included a total of 3,450 nurses who were employed at the hospital. In the end, a grand total of 2,811 individuals supplied valid and useful responses, leading to an effective response rate of 81.49%. An ensemble of web-based surveys that individuals completed themselves was utilized. Participants successfully filled out a well-organized questionnaire within a time frame of 20 to 25 min. Figure  1 provides a visual representation of the specific information using a flow chart.

Flowchart of this study

The Ethics Committee of Shengjing Hospital Affiliated China Medical University granted ethical permission (2022PS753K). All participants provided written informed consent. The procedures were carried out in accordance with the ethical guidelines outlined in the 1975 Declaration of Helsinki.

Inclusion and exclusion criteria

The criteria for inclusion were as follows: nurses who were currently employed in hospitals and actively working. The study employed the following exclusion criteria: nurses who had engaged in employment for less than three months or had not completed the psychological questionnaire in its whole were disqualified.

Measurement of covariates characteristics

This study gathered data from the nursing staff on various aspects, including demographic characteristics, dietary habits, life-related factors, work-related factors, experienced important life events, history of physical sickness, exposure to the COVID-19 pandemic, and psychological assessments.

The demographic data encompassed age, gender, and body mass index (BMI), which was self-reported by the participants and measured in kg/m 2 . The individual's dietary habits encompassed their smoking status, alcohol consumption patterns, and coffee consumption patterns. Smoking behavior was classified into three categories: current smokers (those who smoked at least one cigarette per day and had done so for at least six months), former smokers (those who had stopped smoking for at least six months), and non-smokers. Alcohol and coffee consumption patterns are classified into three categories: current drinkers (those who use alcohol or coffee at least once a day and have been doing so for at least six months), former drinkers (those who have stopped consuming alcohol or coffee for at least six months), and non-drinkers (those who do not consume alcohol or coffee).

Life-related factors encompassed various aspects such as sleep quality (measured by PSQI, Pittsburgh Sleep Quality Index scores), physical activity (assessed using IPAQ, International Physical Activity Questionnaire, in terms of Mets × hour/week), religious affiliation, marital status, presence of siblings, monthly household income (in RMB, yuan), occurrence of major life events, history of chronic disease, and frequency of visiting friends.The researchers evaluated the level of physical activity (PA) in the past week using the abbreviated version of the International Physical Activity Questionnaire [ 19 ]. The Pittsburgh sleep quality index (PSQI) [ 20 ] was used to assess sleep quality.

Work-related variables including employment, field of expertise, weekly working hours, and night shifts. Exposure to the COVID-19 pandemic pertains to nurses who may come into touch with patients suspected or confirmed to have COVID-19, or find themselves in a situation that necessitates COVID-19 quarantine.

Measurements of psychological variates

The level of organization support was measured using the Chinese version of the Perceived Organization Support Questionnaire (POS) [ 21 ]. The Cronbach's α coefficient for POS was 0.921. The Chinese version of the 24-item Psychological Capital Questionnaire (PCQ) [ 22 , 23 ] was used to assess PsyCap. The Cronbach's α coefficients for self-efficacy, hope, resilience, and optimism were 0.921, 0.936, 0.920, and 0.900, respectively.

Measurement of depression and anxiety

The assessment of depressive symptoms was conducted using clinically validated measures, specifically the PHQ09 [ 24 ]. The PHQ09 scale consists of nine items, each with a 4-point Likert-type scale answer. These responses indicate the frequency of individuals' feelings during the preceding two weeks, ranging from 0 to 3. The cumulative score spans from 0 to 27, with a higher value denoting a greater intensity of depression symptoms. A PHQ09 score of 10 or more was used to determine the presence of serious depression.

The Chinese version of the GAD07 [ 25 ] was used to assess anxiety symptoms. The GAD07 questionnaire comprises 7 items, with each item being responded to on a 4-point Likert-type scale ranging from 0 (indicating never) to 3 (indicating always). A greater score indicates a higher level of anxiety symptoms. A GAD07 standardized score of 10 or higher was used to characterize the presence of significant anxiety symptoms. The Cronbach's α coefficients for the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) were 0.951 and 0.928, respectively.

Sample size calculation

The confidence level (1-α) was 0.95; the proportion of main outcome (depression and anxiety) was 0.1; The confidence interval width (two sided) was 0.03. The confidence interval formula was Exact (Clopper-Pearson); the 2-tailed P value was 0.05. The sample size was 1,603. It was calculated by PASS 11.0 (Power Analysis and Sample Size 11.0, NCSS Inc., USA) [ 8 , 10 , 18 ].

Statistical analysis

The data were analyzed using SPSS 22.0 for Windows, developed by SPSS Inc. in Chicago, IL, USA. The continuous variables were reported as the median together with the interquartile range. The categorical variables were presented as the count (proportion). Discrete sets of data that are neither related or dependent on each other. The mean of two continuous variables that follow a normal distribution was compared using the Student's t-test. The Mann–Whitney U test was employed to compare the average values of two continuous variables that do not follow a normal distribution. On the other hand, the χ2 test or Fisher's exact test were utilized for categorical variables.

The quartiles were determined by categorizing the BMI values of all participants depending on their distribution, and these quartiles were then utilized for subsequent research. The study investigated the association between quartile categories of BMI and the presence of poor mental health, specifically depression and anxiety, using binary unconditional logistic regression analysis. The dependent variable in this study was the individual's mental health state, whereas the independent variable was their BMI. The crude odds ratio (OR) was calculated using crude data, and model 1 was further modified for age and gender. Model 2 further accounted for baseline variables that were deemed clinically significant or had a p -value < 0.10 in the univariate analysis. These variables included alcohol consumption, sleep quality, number of siblings, experience of major life events, frequency of visiting friends, years of employment, duration of work hours, psychological characteristics related to depression, age, physical activity, marital status, history of chronic disease, specialty, and psychological characteristics related to anxiety. The Model 3 was modified to account for all baseline variables. Adjusted OR and their corresponding 95% confidence intervals (95% CI) were calculated using binary unconditional logistic regression, taking into account any confounding factors. The study examined the presence of a linear trend by analyzing the median value of each quartile as a continuous variable. All P values were calculated using a two-tailed test, and the observed difference was considered statistically significant when the P value was less than 0.05.

A total of 2,811 nurses were ultimately enrolled in the study, with a median age of 35 years and a median BMI of 21.83 kg/m 2 . Female participants constituted the majority (94.20%). Out of the total, 69.9% (1,965) of the nurses had a normal weight, 6.3% (177) were underweight, 19.9% (558) were overweight, and 3.9% (111) were obese. The occurrence of depression and anxiety among nurses was 7.8% (219 out of 2,811) and 6.7% (189 out of 2,811), respectively; see details in Table  1 (the distribution of characters by outcome status) and supplementary Table  1 ( the distribution of characters by BMI status ) .

Participants with elevated BMI, impaired sleep quality, and diminished scores in perceived organizational support, efficacy, hope, resiliency, and optimism exhibited an increased likelihood of developing depression, as indicated by the univariate analysis. A greater proportion of individuals with depression exhibited concurrent alcohol consumption, had siblings, encountered significant life events, had infrequent social interactions with friends, had employment tenure exceeding five years, and worked in excess of 40 h per week. Individuals who were older, had a higher BMI, experienced poor sleep quality, engaged in lower levels of weekly physical activity, and had lower scores in perceived organizational support, efficacy, hope, resiliency, and optimism were found to have a higher likelihood of developing anxiety. A greater proportion of individuals with anxiety engaged in marriage or cohabitation, had siblings, experienced significant life events, had a background of chronic illnesses, had infrequent social interactions with friends, had a job history exceeding five years, worked for more than 40 h per week, and were employed in the surgical department. The factors stated above exhibited statistical significance in the univariate analysis, as shown in detail in Table  1 .

In order to investigate the correlation between BMI and depression, the BMI was divided into four categories based on quartiles. In comparison to the lowest quartile, the odds ratios (ORs) for the other quartiles were as follows: 0.91 (0.53, 1.56), 2.28 (0.98, 3.77), and 2.32 (1.41, 3.83) after making adjustments. Additionally, the p-value for the trend was found to be 0.001. In relation to anxiety, the odds ratios (ORs) for each quartile were as follows: 2.39 (0.83, 4.36), 4.46 (0.51, 7.93), and 2.81 (1.56, 5.08) after adjusting for other factors. Furthermore, there was a significant trend with a p -value of 0.009. Refer to the comprehensive information provided in Table  2 . We also did sensitivity analysis by excluding participants who were underweight (BMI < 18.5) and only including participants who exposed to the COVID-19 pandemic, these results were consistent with the main outcome; see details in supplementary Tables  2 and 3 .

Obesity is a major contributor to morbidity and mortality. However, no existing study has focused on the relationship between BMI and mental health among nurses during COVID-19 pandemic. Therefore, we performed a cross-sectional study on a large population of nurses in China. This study showed a positive association between BMI and poor mental health (anxiety and depression) among Chinese nurses during the COVID-19 pandemic, particularly in those who were overweight or obesity.

In line with this, A systematic review and meta-analysis on the longitudinal relationship between BMI and mental health, they found that obesity at baseline increased the risk of onset of depression and the unadjusted ORs were 1.55 (including 15 included studies and 58,745 participants) [ 10 ]. Another population-based cross-sectional study enrolled 4,361 Iranian healthcare staff; their results indicate that abdominal obesity was significantly associated with anxiety among females but not among males. It is worth noting that in the current study, most participants were female. At the same time, no significant association was discovered between abdominal obesity and psychological distress in either gender. There was, however, a weak positive association between BMI and depression [ 26 ]. Further, a meta-analysis reviewed 25 prospective studies and provided solid evidence of the link between obesity and depression, indicating a bi-directional relationship between BMI and depression [ 27 ]. A possible mechanism is the adoption of an unhealthy lifestyle, such as insufficient physical exercise and unhealthy dietary preferences, possibly leading to obesity [ 27 ].

The exact underlying pathophysiological mechanism between being overweight and poor mental health is unknown. It has been shown that immune inflammation disorder plays an essential role in mental health disorders such as depression and anxiety. Moreover, a high BMI status can lead to many pro-inflammatory factors in the peripheral circulation system crossing the blood–brain barrier, subsequently inducing depressive-like behaviors. In such cases, the risk of depression and anxiety gradually increases [ 28 , 29 ]. The association between obesity and disorders such as depression and anxiety may also be explained by hypothalamic–pituitary–adrenal (HPA) axis disorder, leptin, or microbial mechanisms [ 30 , 31 , 32 , 33 , 34 , 35 ]. The obesity might involve HPA-axis dysregulation and HPA-axis dysregulation is also well known to be involved in depression. Through HPA axis dysregulation, obesity might cause development to depression. Leptin play an important role in the signaling pathway of glutamatergic neurons for regulating depression-related behaviors, suggesting a possible association between synaptic depression and behavioral manifestations of depression. Depression is associated with decreased gut microbiota richness and diversity. Fecal microbiota transplantation from depressed patients to microbiota-depleted rats can induce behavioural and physiological features characteristic of depression in the recipient animals, including anhedonia and anxiety-like behaviours, as well as alterations in tryptophan metabolism. This suggests that the gut microbiota may play a causal role in the development of features of depression.

While this study provides interesting insights, it is important to acknowledge its various limitations. First, since this study is cross-sectional, there is a concern for reverse causation, where mental health problems may contribute to increased BMI. Future studies with a longitudinal framework are warranted to address this issue. Second, the data were gathered by self-reported questionnaires, specifically pertaining to measurements such as height and weight. It is important to note that this method may be susceptible to recall bias. In addition, given that the majority of the study sample consists of young women, there is a possibility that they may be tempted to falsely report their height and weight. Therefore, this social desirability bias is another limitation of this study. Third, it is important to note that the GAD-7 and PHQ-9 are screening questionnaires that lack the ability to provide clinical diagnosis. This limitation may have had an impact on the outcomes of our study. However, this study is the first to examine the connection between BMI and mental health in nurses during the COVID-19 epidemic while accounting for several influential factors.

Availability of data and materials

No datasets were generated or analysed during the current study.

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Acknowledgements

We give special thanks to all the colleagues of Shengjing Hospital for their help and support. We thank International Science Editing ( http://www.internationalscienceediting.com ) for editing this manuscript. The authors would like to thank all of the study participants.

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This study was financially supported by The 345 Talent Project of Shengjing Hospital (grant number: N/A).

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Luan, B., Tian, X., Wang, C. et al. Association between body mass index and mental health among nurses: a cross-sectional study in China. BMC Health Serv Res 24 , 506 (2024). https://doi.org/10.1186/s12913-024-11006-y

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ORIGINAL RESEARCH article

Diagnostic accuracy and clinical utility of the phq-2 and gad-2: a comparison with long-format measures for depression and anxiety.

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Background: Anxiety and depression are highly prevalent and often comorbid mental disorders that are encompassed within the broad category of emotional disorders. The frequent comorbidity of anxiety and depression can pose challenges for accurate diagnosis and treatment which, in turn, highlights the need for reliable measurements that are simultaneously responsive to change and prevent non-response bias. Brief measures of anxiety and depression can potentially increase response rates due to their brevity and ease of administration. This study evaluates the psychometric characteristics, discriminative accuracy, and sensitivity to change of the Generalized Anxiety Disorder 2-item scale (GAD-2) and the Patient Health Questionnaire 2-item scale (PHQ-2) within a clinical population. Method: The sample comprised treatment-seeking participants (n = 3411), screened (n = 2477) to receive an internet-based psychotherapeutic intervention (cognitivebehavioral, psychodynamic, or waitlist). Results: Brief measures can effectively detect individuals who may be eligible for a diagnosis of depression and anxiety, not only prior to but also during and following the completion of psychological treatment. The discriminative ability of the GAD-2 was significantly greater during active treatment and at post-assessment compared with pre-treatment screening, although no such differences were found for the PHQ-2. Finally, endorsing the most severe response option on the GAD-2 and PHQ-2 was associated with a high probability of presenting clinically relevant anxiety and depressive symptoms. Conclusion: Brief measures of anxiety and depression are viable instruments to screen for and monitor anxiety and depressive symptoms.

Keywords: Generalized Anxiety Disorder Questionnaire1, Patient Health Questionnaire2, Psychometric Evaluation3, Internet-Based Psychotherapy4, Receiver Operating Characteristic (ROC) Analysis5, Item-Option Characteristic Analysis6

Received: 17 Jul 2023; Accepted: 23 Apr 2024.

Copyright: © 2024 Hlynsson and Carlbring. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Per Carlbring, Department of Psychology, Faculty of Social Sciences, Stockholm University, Stockholm, SE-106 91, Stockholm, Sweden

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

• Open access
• Published: 23 April 2024

Prevalence of anxiety, depression, and post-traumatic stress disorder among Omani children and adolescents diagnosed with cancer: a prospective cross-sectional study

• Laila S. Al-Saadi 1 ,
• Moon Fai Chan 1 ,
• Amal Al Sabahi 2 ,
• Jalila Alkendi 2 ,
• Nawal Al-Mashaikhi 3 ,
• Hana Al Sumri 1 ,
• Amal Al-Fahdi 4 &
• Mohammed Al-Azri 1  

BMC Cancer volume  24 , Article number:  518 ( 2024 ) Cite this article

Metrics details

Children and adolescents diagnosed with cancer often experience psychological distress, encompassing anxiety, depression, and post-traumatic stress disorder (PTSD). This study aimed to evaluate the prevalence of these conditions among Omani children and adolescents diagnosed with cancer, alongside identifying contributing factors.

A prospective cross-sectional study was conducted from October 2021 to June 2023 among a cohort of Omani children and adolescents (6–18 years old) diagnosed with cancer at three primary cancer referral centres in Oman. Validated Arabic-language versions of the Screen for Child Anxiety Related Disorders, the Center for Epidemiologic Studies Depression Scale for Children, and the Impact of Event Scale-Revised instruments were used to assess symptoms of anxiety, depression, and PTSD, respectively. An initial assessment (T1) was undertaken within the first 3 months of diagnosis, followed by a second assessment (T2) 3–6 months later.

Of 113 eligible participants, 101 agreed to participate in the study (response rate: 95.6%), with 92 (91.0%) completing both assessments and included in the final analysis. Prevalence rates of anxiety, depression, and PTSD decreased from 43.5%, 56.5%, and 32.6%, respectively, at T1, to 38.0%, 35.9%, and 23.9% at T2. All average scores were below diagnostic cut-off points, except for the depression score at T1. Anxiety and depression scores decreased significantly ( p  = 0.043 and 0.001, respectively) between T1 and T2, as did the overall prevalence of depression ( p  = 0.004). At T1, linear regression analysis showed significant correlations between anxiety scores and the child’s age and PTSD score ( p  < 0.05); these variables were also correlated with depression scores ( p  ≤ 0.001). At T2, significant correlations were observed between anxiety scores and the child’s age and PTSD scores ( p  < 0.001). At both T1 and T2, anxiety, depression, and PTSD scores remained significantly correlated ( p  < 0.001).

Conclusions

Omani children and adolescents recently diagnosed with cancer exhibit a high prevalence of anxiety, depression, and PTSD over time. Age-appropriate communication, ongoing support, and mental health services are recommended to help this patient group cope with their diagnosis and manage their emotional wellbeing. There is a need for future research to determine the effectiveness of specific psychological interventions in reducing the frequency of these disorders.

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Cancer in childhood and adolescence ranked as the sixth leading contributor to the total global cancer burden in 2019 [ 1 ]. An estimated 429,000 individuals under 19 years of age are diagnosed with cancer every year, with 141–185 cases per million reported worldwide [ 2 , 3 ]. Approximately 100,000 children and adolescents die annually from cancer, with the vast majority of deaths (90%) occurring in low- and middle-income countries (LMICs) [ 1 ]. Furthermore, those diagnosed with cancer in LMICs have a low five-year survival rate of 30%, in stark contrast to high-income countries where survival rates exceed 80% due to significant advances in cancer treatment [ 2 , 3 ].

In Arab countries, over 18,000 children below the age of 15 years are diagnosed with cancer every year, with annual incidence rates ranging from 7.5 to 12.8 cases per 100,000 children, although variations may be due to differences in registration accuracy [ 4 ]. In Oman, approximately 31% of the total population is under 19 years of age [ 5 ]. In 2019, a total of 2,307 patients were diagnosed with cancer, of which 2,089 patients (91.5%) were of Omani nationality and 124 (5.9%) comprised children aged 0 to 14 years [ 6 ]. However, the anticipated total number of annual cancer diagnoses is projected to rise to 8,549 by the year 2040 [ 7 ].

Anxiety, depression, and post-traumatic stress disorder (PTSD) are frequent in children and adolescents with cancer, with pooled prevalence rates of 13.92%, 20.43%, and 20.90%, respectively [ 8 ]. Recent research underscores a higher incidence of anxiety and depression in paediatric cancer patients and the heightened vulnerability of this demographic to post-traumatic stress symptoms, emphasising the need for a nuanced understanding of emotional challenges throughout the cancer diagnosis, treatment, recovery, and survivorship journey [ 9 , 10 , 11 , 12 ]. In itself, a diagnosis of cancer, along with accompanying physical symptoms and treatment side-effects, can lead to excessive tension, discomfort, and fear of death [ 8 , 13 ]. Symptoms of depression, including low mood, despair, guilt, and loss of interest in usual activities, may also challenge patients’ ability to function and adhere to treatment [ 14 ].

As Oman continues to make significant improvements in healthcare delivery and medical treatment, cancer survival rates among children and adolescents have improved. However, the emotional toll of a cancer diagnosis cannot be underestimated, and an understanding of these psychological repercussions is crucial as an essential indicator of patients’ well-being to ensure the provision of comprehensive oncologic care [ 8 ]. Indeed, it has been found that that the activity of making jewelry from beads was effective in reducing the state and trait anxiety levels of children with cancer [ 15 ]. Our study therefore aimed to identify the prevalence of anxiety, depression, and PTSD among Omani children and adolescents diagnosed with cancer and their associated factors, and to describe changes occurring over time.

Study design and setting

A cross-sectional study was conducted targeting all Omani children and adolescents aged six to 19 years diagnosed with any type of cancer between 1st October 2021 and 30th June 2023. The study was conducted at the National Oncology Centre (NOC) of the Royal Hospital, the Paediatric Haematology Unit of the Sultan Qaboos University Hospital (SQUH), and the Sultan Qaboos Comprehensive Cancer Care and Research Centre (SQCCCRC). These centres, located in Muscat, the capital city of Oman, serve as the primary referral cancer centres providing integrated care for cancer patients throughout Oman.

Recruitment of participants

Participants were recruited during their visits to either the outpatient clinics of the three referral centres or upon admission to the oncology/haematology wards. Children and adolescents who were non-Omani or had cognitive and behavioural disorders (as documented in their medical records) were excluded from the study.

Data collection

An Arabic version of the Screen for Child Anxiety Related Disorders (SCARED) tool was used to screen for anxiety symptoms over the past three months [ 16 , 17 ]. It consists of various questions or items related to anxiety symptoms, and individuals are typically asked to respond based on their experiences which is valuable for understanding the child’s mental health status over a recent period [ 16 ]. This child self-report instrument includes 41 items scored on a 3-point scale (from 0 to 2), yielding five factors matching classifications outlined in the Diagnostic and Statistical Manual of Mental Disorder, fifth edition (DSM-IV) [ 16 ]. Overall, a total SCARED score of ≥ 25 may indicate the presence of an anxiety disorder, while scores of > 30 are more specific to anxiety. According to a validation study, internal consistency (Cronbach’s α) for the translated tool is 0.91, ranging between 0.65 and 0.89 for individual subscales [ 16 ].

Depressive symptoms were assessed using an Arabic version of the Center for Epidemiologic Studies Depression Scale for Children (CES-DC) [ 18 , 19 ]. It consists of a series of questions that ask about various feelings and behaviours associated with depression, such as sadness, irritability, changes in appetite or sleep patterns, and feelings of worthlessness [ 19 ]. Respondents rate how often they have experienced each symptom over a specific period, typically within the past week [ 19 ]. This self-report scale consists of 20 items scored on a 4-point scale (from 0 to 3), for a total score ranging from 0 to 60, with higher scores more indicative of depression [ 18 ]. The cut-off CES-DC score is 15, with scores of 15–60 considered indicative of significant symptoms of depression. The Arabic version of the CES-DC has previously demonstrated high internal consistency (Cronbach’s α = 0.90) [ 18 ].

An Arabic version of the Impact of Event Scale-Revised (IES-R) was used to measure symptoms of post-traumatic stress [ 20 , 21 ]. The IES-R is a self-report scale designed to assess current subjective distress for any major life event in children, adolescents, and adults, assessing the core symptom cluster of avoidance, intrusion, and hyperarousal [ 20 , 21 ]. The tool has been also used to evaluate the extent of distress experienced by individuals who have been exposed to a traumatic event such as accidents, natural disasters, combat, or other life-threatening situations [ 21 ]. The scale helps clinicians and researchers understand the psychological impact of these events on individuals [ 20 ]. The tool consists of 22 items scored on a 5-point scale (from 0 to 4), of which 14 items correspond directly to symptom criteria outlined in the DSM-IV. Total scores range from 0 to 88, with a cut-off IES-R score of 33 and above indicative of a probable diagnosis of PTSD [ 20 , 21 ]. According to previous research, the Arabic version of the self-report IES-R scale has demonstrated acceptable internal consistency (Cronbach’s α = 0.94) [ 20 ].

Arabic versions of the SCARED, CES-DC, and IES-R instruments were administered twice to assess for symptoms of anxiety, depression, and PTSD, respectively. The first assessment (T1) was conducted at any time within the first 3 months of diagnosis, while the second assessment (T2) was conducted 3 to 6 months after T1. Research assistants administered the questionnaire to participants aged 6 to 12 years, while the instruments were self-administered by participants aged 12 years or older. In both cases, a research assistant remained available to clarify any questions that the participants might have had during completion. Additional sociodemographic and clinical information was recorded by the researchers based on data gathered from the participants’ medical records or elicited from the children’s parents or primary caregivers at T1. Cancer risk was estimated based on the participant’s age at diagnosis, disease stage, tumour histology, MYCN status (amplified versus nonamplified), and tumour cell ploidy status [ 22 ].

Statistical analysis

Statistical analysis was performed using SPSS Statistics Software for Windows, version 23 (IBM Corp., Armonk, NY). Descriptive statistics, including percentages, frequencies, means, and standard deviations, were used to delineate the participants’ sociodemographic and clinical characteristics, as well as their average anxiety, depression, and PTSD scores. Paired t-tests and McNemar’s tests were utilised to assess differences in average anxiety, depression, and PTSD scores between the two time points. Analysis of variance and independent sample t-tests were employed to assess variations between the dependent variable (average anxiety, depression, or PTSD scores) and independent variables (sociodemographic and clinical characteristics). Pearson’s Chi-squared test was applied to explore associations between psychological outcomes and sociodemographic and clinical characteristics. Linear regression models were used to investigate correlations between sociodemographic and clinical characteristics and average anxiety, depression, and PTSD scores. The researchers adhered to a significance level of 5% throughout the analysis.

Characteristics of the participants

Out of the 113 Omani children and adolescents diagnosed with cancer during the study period, 101 agreed to participate, yielding a response rate of 95.6%. However, only 92 participants (91.0%) completed both T1 and T2 assessments and were included in the final analysis. Of these, 83 (90.2%) received a new diagnosis of cancer, while nine (9.8%) had suffered relapses. The mean age was 11.4 ± 3.6 years, with a median of 11.0 years. Most participants ( n  = 62; 67.4%) were children (aged 6–12 years). Males ( n  = 52; 56.5%) outnumbered females ( n  = 40; 43.5%). Leukaemia was the most frequent diagnosis ( n  = 38; 41.3%), with most participants receiving chemotherapy as the sole form of treatment ( n  = 56; 60.9%). Most participants were assessed within the first month of diagnosis ( n  = 66; 71.7%), with their cancer diagnosis not being disclosed to them ( n  = 65; 70.7%) [see Table  1 ].

Prevalence of anxiety, depression, and PTSD

Overall, 40 (43.5%) and 35 (38.0%) participants exhibited anxiety symptoms at T1 and T2, respectively, while 52 (56.5%) and 33 (35.9%) reported depressive symptoms and 30 (32.6%) and 22 (23.9%) had probable diagnoses of PTSD. There was a significant reduction in the prevalence of depression between T1 and T2 ( p  = 0.004). Similarly, average scores decreased significantly for both anxiety (23.7 ± 10.6 vs. 21.7 ± 11.0; p  = 0.043) and depression (17.67 ± 10.7 vs. 13.6 ± 8.9; p  = 0.001) during this interval [see Table  2 ].

Factors associated with anxiety, depression, and PTSD

At T1, the univariate analysis revealed a statistically significant increase in average scores for anxiety ( p  = 0.005), depression ( p  < 0.001), and PTSD ( p  < 0.001) as the child’s age advanced. Adolescents (aged 13–18 years) demonstrated significantly increased anxiety ( p  = 0.041), depression ( p  = 0.012), and PTSD ( p  = 0.001) scores compared to children (aged 6–12 years). Participants identified as having a high risk of cancer exhibited significantly increased PTSD scores ( p  = 0.001), while those aware of their cancer diagnosis showed significant increases in both anxiety ( p  = 0.003) and PTSD ( p  = 0.004) scores. Increased anxiety scores correlated with significant increases in both depression ( p  < 0.001) and PTSD ( p  < 0.001) scores; similarly, increased depression scores were associated with higher anxiety ( p  < 0.001) and PTSD ( p  < 0.001) scores, while elevated PTSD scores were associated with significant increases in both anxiety ( p  < 0.001) and depression ( p  < 0.001) scores [see Table  3 ].

At T2, the univariate analysis similarly showed significant increases in anxiety ( p  = 0.001), depression ( p  < 0.001), and PTSD ( p  < 0.001) scores as age increased, with adolescents exhibiting greater ( p  = 0.006), depression ( p  = 0.001), and PTSD ( p  = 0.002) scores compared to children. Participants with a high risk of cancer had significantly higher anxiety ( p  = 0.007) and depression ( p  = 0.007) scores, while those aware of their diagnosis demonstrated significantly higher scores for anxiety ( p  = 0.007), depression ( p  = 0.003), and PTSD ( p  = 0.005). Increased anxiety scores correlated with increased depression ( p  < 0.001) and PTSD ( p  < 0.001) scores, while increased depression scores correlated with increased anxiety ( p  < 0.001) and PTSD ( p  < 0.001) scores. Finally, increased PTSD scores were associated with significant increases in both anxiety ( p  < 0.001) and depression ( p  < 0.001) scores [see Table  4 ].

A linear regression analysis was conducted to establish links between anxiety, depression, and PTSD scores and various sociodemographic, clinical, and psychological factors. At T1, significant correlations were observed between anxiety scores and age (β = 0.762; p  < 0.001), age group (adolescents vs. children; β = -0.217; p  = 0.001), and PTSD scores (β = 0.209; p  = 0.025), with an adjusted R 2 value of 0.861. Depression scores demonstrated significant correlations with age (β = 0.460; p  = 0.001) and PTSD scores (β = 0.488; p  < 0.001), with an adjusted R 2 value of 0.849. Finally, PTSD scores were significantly correlated with cancer risk (β = 0.147; p  = 0.025), anxiety scores (β = 0.287; p  = 0.016), and depression scores (β = 0.604; p  < 0.001), with an adjusted R 2 value of 0.827 [see Table  5 ].

At T2, anxiety scores were found to be significantly correlated with age (β = 0.553; p  < 0.001), age group (adolescents vs. children; β = -0.134; p  = 0.014), and PTSD scores (β = 0.400; p  < 0.001), with an adjusted R 2 value of 0.896. Depression scores were significantly correlated with age (β = 0.297; p  = 0.018) and PTSD scores (β = 0.431; p  < 0.001), with an adjusted R 2 value of 0.837. Finally, PTSD scores showed significant correlations with both anxiety (β = 0.622; p  < 0.001) and depression (β = 0.426; p  < 0.001) scores, with an adjusted R 2 value of 0.839 [see Table  5 ].

To our knowledge, this is the first study conducted in Oman to identify the prevalence of anxiety, depression, and PTSD among Omani children and adolescents diagnosed with cancer, associated factors, and to describe changes occurring over time. Our findings revealed that a high number of children and adolescents with cancer in Oman exhibit symptoms of anxiety (43.5%), depression (56.5%), and PTSD (32.6%) within the first three months of diagnosis. Prevalence rates of these psychological disorders, especially anxiety and depression, were notably higher compared to the pooled rates reported in a recent systematic review and meta-analysis of previous literature (13.92%, 20.43%, and 20.90%, respectively) [ 8 ]. However, these differences might be attributed to variations in the measurement and screening tools used across different studies.

Alternatively, another explanation for the high prevalence rates of anxiety, depression, and PTSD symptoms observed in our study could be linked to the lack of specialized or psychosocial supportive care for cancer patients in Oman, particularly at the time of diagnosis [ 23 ]. This is likely exacerbated by the fact that, in certain Arab cultures, including in Oman, there remains considerable stigma surrounding mental health issues, posing a challenge for individuals to actively seek or obtain psychological support [ 24 , 25 ]. Moreover, limitations in healthcare resources, such as a shortage of mental health professionals, may further hinder access to psychological support services for cancer patients [ 24 ]. Finally, a lack of widespread awareness regarding the significance of psychological support for cancer patients, particularly children and adolescents, could contribute to a shortage of available programs and services [ 26 ].

We also found that symptoms of anxiety, depression, and PTSD among children and adolescents diagnosed with cancer decreased over time; these findings are supported by other longitudinal studies [ 27 , 28 , 29 ]. Other research has shown that a healthy family environment is a strong protective factor against the development of these disorders, as well as improving the quality of life of children and adolescents diagnosed with cancer [ 30 , 31 ]. In Oman, support extended by family members and friends to cancer patients has been observed to significantly reduce mental distress and alleviate the adverse side-effects associated with cancer treatment [ 23 ]. Moreover, cancer patients in Oman have been shown to develop various coping mechanisms and adaptive strategies to deal with the emotional impacts of a cancer diagnosis, including denial, optimism, withdrawal, and a strong reliance on Islamic beliefs and practices [ 32 ]. These factors likely play a role in decreasing psychological distress over time.

The results of our study indicated that the child’s age had a significant impact on their anxiety, depression, and PTSD scores, with adolescents exhibiting a higher likelihood of experiencing these conditions compared to children. Other studies have also highlighted a notable increase in major depressive episodes during the transition to adolescence [ 33 , 34 ]. This finding aligns with the understanding that adolescence is marked by hormonal changes and an enhanced ability to comprehend emotions [ 35 ]. Moreover, adolescents with cancer may face substantial disruptions to their education, potentially missing school due to the demands of treatment and recovery [ 36 ]. Repercussions may extend beyond academic skills, encompassing a range of missed opportunities, such as participation in sports, group activities, excursions, and award ceremonies, as well as the absence of daily structure and routine provided in the scholastic environment [ 37 ]. Prolonged absences from school and limited peer interaction can contribute to the development of emotional, behavioural, and psychological challenges [ 37 , 38 ].

We also found that children and adolescents who were informed of their diagnosis exhibited significantly higher anxiety, depression, and PTSD scores compared to those who remained unaware of their condition. The relationship between disclosure of a cancer diagnosis and mental health outcomes is complex, and individual reactions can vary widely. Some children and adolescents may benefit from being informed, as this allows them to be more actively involved in their own care and treatment decision-making, while others may find comfort in not knowing the full extent of their illness [ 39 ]. Fundamentally, awareness of a cancer diagnosis results in a deeper cognitive understanding of illness severity, the side-effects of treatment, social stigma, and health uncertainties, all of which can increase anxiety and stress [ 40 ]. However, in Omani culture, it is routine for some parents and family members to try to protect their loved ones or keep their hopes up by choosing to withhold knowledge of their diagnosis [ 23 ].

Our findings showed that high-risk patients had significantly higher PTSD scores during the first three months of diagnosis. Patients with more aggressive types of cancer often require more intensive and invasive treatment regimens, such as surgery and radiation, resulting in long periods of hospitalization, all of which may contribute to increased stress, anxiety, and trauma [ 41 ]. Furthermore, the aggressive nature of the cancer and its associated treatment can create a sense of uncertainty about the future, including treatment outcomes and the potential for relapse [ 42 ]. Indeed, the physical and emotional toll of aggressive cancer can be overwhelming as a result of the side-effects of treatment, including changes in physical appearance and disruptions to daily life, factors which can contribute to symptoms of depression [ 43 ].

The results of our study should be considered in the light of certain limitations. Firstly, the study involved a prospective, cross-sectional design in which Omani children and adolescents were screened for symptoms of anxiety, depression and PTSD at two separate time intervals following diagnosis. The length of time between these intervals might not have been adequate to track dynamic changes in anxiety and PTSD over time, thereby limiting our understanding of the long-term psychological effects of cancer diagnoses. An extended study period with more frequent assessments could have potentially enabled a more in-depth exploration of the psychological challenges faced by children and adolescents at different points in their cancer experiences.

Secondly, the information regarding anxiety, depression, and PTSD symptoms was self-assessed by the participants; such self-reporting is susceptible to various biases, including memory recall influenced by the passage of time, emotional states, and individual differences in cognitive processing. Thus, the participants may have unintentionally provided inaccurate or incomplete information regarding their psychological experiences, leading to potential discrepancies between reported and actual symptoms. Finally, we cannot rule out the effect of the confounding variables such as socioeconomic status that are associated with both the independent variable (the factor of interest) and the dependent variable (mental health outcome).

To our knowledge, this is the first study conducted in Oman to identify the prevalence of anxiety, depression, and PTSD symptoms, along with their associated factors, among Omani children and adolescents diagnosed with cancer. The findings indicated that children and adolescents in Oman exhibited high levels of anxiety, depression, and PTSD within the first three months of a cancer diagnosis. Implementing routine screening protocols for psychological symptoms among children and adolescents diagnosed with cancer, particularly within the first three months of diagnosis, is imperative. The early identification of mental health challenges can facilitate timely intervention and support, particularly for adolescents, as they are more likely to suffer from psychological and emotional distress.

Furthermore, integrating mental health services into standard care protocols for paediatric and adolescent cancer patients in Oman could significantly enhance outcomes and support the delivery of holistic care. An urgent need exists for the provision of additional resources and specialised training for healthcare professionals in Oman, enabling them to recognize and address the psychological needs of children and adolescents with cancer. To advance the field, future research should consider employing longitudinal interventional designs, extending assessment durations, and incorporating a more comprehensive set of psychological variables. This approach will bolster the robustness and applicability of findings concerning mental health in the context of cancer. Additionally, longitudinal designs will enable the observation of changes in self-reported symptoms over time, offering a more nuanced understanding of the evolving psychological state of individuals navigating cancer.

Data availability

The datasets supporting the conclusions of this article are available from the corresponding author upon reasonable request.

Abbreviations

Center for Epidemiologic Studies Depression Scale for Children

Diagnostic and Statistical Manual of Mental Disorder, Fifth Edition

Impact of Event Scale-Revised

Low and Middle-Income Countries

V-Myc Avian Myelocytomatosis Viral Oncogene Neuroblastoma-Derived Homolog

National Oncology Centre

Post-Traumatic Stress Disorder

Screen for Child Anxiety Related Disorders

Statistical Package for the Social Sciences

Sultan Qaboos Comprehensive Cancer Care and Research Centre

Sultan Qaboos University Hospital

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Acknowledgements

The authors would like to thank the parents and guardians of the participants for allowing their children to take part in the study. The authors also extend their gratitude to the respective authorities of the NOC, SQUH, and SQCCCRC for permitting this study to be conducted.

The authors received no financial support for the research, authorship, and/or publication of this article.

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LA, MFC, AA, JA, NA, AF, and MA contributed to the study conception and design. Data collection was performed by LAS. Data analysis was performed by LA, MFC, and HA. The first draft of the manuscript was written by LAS and MA. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Al-Saadi, L.S., Chan, M.F., Al Sabahi, A. et al. Prevalence of anxiety, depression, and post-traumatic stress disorder among Omani children and adolescents diagnosed with cancer: a prospective cross-sectional study. BMC Cancer 24 , 518 (2024). https://doi.org/10.1186/s12885-024-12272-z

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Current Diagnosis and Treatment of Anxiety Disorders

Anxiety disorders are the most prevalent mental health conditions. Although they are less visible than schizophrenia, depression, and bipolar disorder, they can be just as disabling. The diagnoses of anxiety disorders are being continuously revised. Both dimensional and structural diagnoses have been used in clinical treatment and research, and both methods have been proposed for the new classification in the Diagnostic and Statistical Manual of Mental Disorders IV (DSM-5). However, each of these approaches has limitations. More recently, the emphasis in diagnosis has focused on neuroimaging and genetic research. This approach is based partly on the need for a more comprehensive understanding of how biology, stress, and genetics interact to shape the symptoms of anxiety.

Anxiety disorders can be effectively treated with psychopharmacological and cognitive–behavioral interventions. These inter ventions have different symptom targets; thus, logical combinations of these strategies need to be further studied in order to improve future outcomes. New developments are forthcoming in the field of alternative strategies for managing anxiety and for treatment-resistant cases. Additional treatment enhancements should include the development of algorithms that can be easily used in primary care and with greater focus on managing functional impairment in patients with anxiety.

INTRODUCTION

Anxiety disorders are present in up to 13.3% of individuals in the U.S. and constitute the most prevalent subgroup of mental disorders. 1 The extent of their prevalence was first revealed in the Epidemiological Catchments Area study about 26 years ago. 2 Despite their widespread prevalence, these disorders have not received the same recognition as other major syndromes such as mood and psychotic disorders; in addition, the primary care physician is usually the principal assessor and treatment provider. 3 , 4 As a result of this management environment, anxiety disorders can be said to account for decreased productivity, increased morbidity and mortality rates, and the growth of alcohol and drug abuse in a large segment of the population. 5 – 7

Anxiety disorders currently included in the Diagnostic and Statistical Manual of Mental Disorders, 4th ed., text revision (DSM IV-TR) are listed in Table 1 . 8

Anxiety Disorders

Advances in anxiety research over the previous decade are likely to be reflected in modifications of diagnostic criteria in the upcoming DSM-5 , 9 planned for publication in May 2013. For instance, post-traumatic stress disorder (PTSD) and obsessive–compulsive disorder (OCD) have been reclassified in the separate domains of Trauma and Stressor Related Disorders and Obsessive–Compulsive and Related Disorders, respectively. 10 , 11

In this article, we review the challenges to the diagnosis of anxiety disorders, provide a model that explains how anxiety symptoms occur and change over time, highlight the neurotransmitter systems affected by these disorders, and discuss the roles and relative efficacy of pharmacological and non-pharmacological interventions.

DIAGNOSTIC DILEMMAS

Within the past 10 years or so, epidemiological data have been used in the attempt to refine the boundaries of diagnostic categories of anxiety disorders. The results of this approach have been progressively reflected from DSM III to IIIR to DSM IV-TR (see Table 1 ) and, finally, to DSM-5 . However, this effort has been hampered by the extensive presence of comorbidities in patients with anxiety, as revealed by the National Comorbidity Survey (NCS). 11 For instance, in patients with some disorders such as generalized anxiety disorder (GAD) and social anxiety disorder (SAD), the presence of comorbidities is a rule rather than the exception. 12 In clinical practice and in research, it is not unusual to find the coexistence of two or more diagnosable conditions in the same patient or at least symptomatic overlap with several subsyndromal states. This is particularly true for symptom overlap between different anxiety disorders, depression, and alcohol and drug abuse. 13

A related phenomenon is the emergence of different disorders in the same patient over a lifetime. For example, during an initial evaluation, the original diagnosis could be panic disorder that resolves after treatment, and then presents after a few years with symptoms more suitable to a diagnosis of OCD or GAD. Whether this process reflects a primary diathesis or two distinct entities is uncertain.

Another significant problem with the present classification of anxiety disorders is the absence of known etiological factors and of specific treatments for different diagnostic categories. Studying the genetic underpinnings of anxiety disorders using molecular biological techniques has failed to produce a single gene or a cluster of genes implicated as an etiologic factor for any single anxiety disorder, even though some genetic findings exist for OCD and panic disorder. 14 , 15 Despite a lack of specificity, family and twin studies point to the importance of genetic factors that are possibly shared among various anxiety disorders, depression, and alcohol and drug abuse. 16

Despite these diagnostic ambiguities, the emergence of efficacious serotonergic medications that cut across a variety of categorical disorders (e.g., mood and anxiety) has led many to suggest that a dimensional model might be more applicable in the study and treatment of these conditions. 17 In this view, the disorder is seen as a complex set of coexisting symptom dimensions (e.g., panic, social awkwardness, and obsessiveness). Each of these dimensions can vary, depending on hypothetical, biological, or genetic factors, which may dictate separate biological or psychological treatment approaches. 9 The usefulness of the dimensional versus the categorical approach remains a highly debatable topic in research and in clinical practice and is one of the bases for the introduction of DSM-5 . 18 , 19

Within psychiatry, similarities between distinct disorders has led to the emergence of the term “spectrum” disorders, a concept initially developed for OCD. 20 This conceptualization was helpful in evaluating similar responses to pharmacological and psychological treatments and has been expanded to consider many other spectra such as social anxiety, panic–agoraphobia, and post-traumatic disorders. 21 – 23 This approach, although useful, can be overly inclusive and misleading because it sometimes lumps together disorders that have little in common, such as placing pathological gambling and body dysmorphic disorder (BDD) in the same OCD spectrum. So far, few genetic or neuro-circuitry investigations have validated this concept.

Dimensional and categorical diagnosis in the DSM-IV-TR is usually produced by cross-sectional comparisons of distinct subject samples. However, diagnostic presentations in clinical practice occur in individuals treated sequentially and may therefore be better understood as part of a psychopathological process that unfolds over time. For example, although a patient might meet criteria for OCD purely on the basis of obsessions or compulsions, the latter usually arise later in the disorder as if to counteract the threat and anxiety associated with obsessive thoughts. 24

Analogous viewpoints can be found in medical disease, with symptoms usually representing a combination of a noxious agent and the body’s reaction to its presence. For instance, when the lungs are infected with the harmful organism Mycobacterium tuberculosis , they compensate by forming scars around the tissue. In the short run, this may be effective in walling off the infection (and may even elude clinical detection), but the strategy fails when pushed to the extreme, leading to respiratory compromise in some cases.

In recent years, scientists and clinicians have begun to realize that the processes underlying anxiety and fear might be similar among the various disorders. This has resulted in the implementation of uniform treatment regimens in primary care 25 and in the development of the unified theory of anxiety. 26

THE ‘ABC’ MODEL OF ANXIETY

Understanding how emotional reactivity, core beliefs, and coping strategies interact in time should lead to more precise diagnoses and better management of anxiety disorders. We recently applied a mathematical model using nonlinear dynamics to describe these processes 27 and further developed this model to cover diagnostic presentations and their underlying processes. 28 The model that we, for simplicity, call the “ABC model of anxiety” could be viewed as an interaction in space and time of a larms, b eliefs and c oping strategies ( Figure 1 ).

Schematic detailing the “ABC” model of anxiety. In this model, a variety of triggering events can elicit responses at the levels of A larm sensations, B eliefs, and associated C oping (ABC) strategies, including behaviors. Each of these processes originates in discrete brain circuits that are functionally connected. Over time, this perpetuates a vicious circle, shaping the presentation of a variety of anxiety disorders.

Alarms (A) are emotional sensations or physiological reactions to a trigger situation, sensation, or thought. A well-defined set of brain circuits rapidly processes information about the alarm.

The ensuing decision to act is made on the basis of beliefs (B) that rely heavily on previous experiences, personal and cultural background, and the information that is perceived by the sensory organs. Patients with anxiety disorders appear to process information about a supposedly dangerous situation with more focused attention compared with individuals without the disorder. 29 Accurate decision-making regarding beliefs is obscured by a flood of details, which leads to catastrophic thinking and indecision.

This, in turn, leads to coping strategies (C), for example, specific behaviors or mental activity aimed at reducing anxiety and avoiding the perceived “danger.” Coping strategies can be considered adaptive or maladaptive, based on their efficacy in reducing the target anxiety. These processes evolve over time, forming a complex picture of a particular anxiety disorder.

As a clinical example, panic disorder may start as an initial devastating panic attack driven by activation of the brain’s alarm networks. This event activates circuits that process information about danger and, when coupled with personal beliefs about the event, leads to increased concern about personal health and safety. This in turn leads to a specific attempt to decrease the danger of the situation (e.g., a medical workup that initially calms the fear).

These processes often occur in healthy people who might experience an unpleasant or dangerous situation; in patients with panic disorder, however, a regular medical workup is in-sufficient to calm them because they require a 100% assurance of “no danger.” Because this is impossible to provide, worry and anticipation of another impending attack persist. The patient subsequently increases “safety” coping behaviors such as having repeated medical examinations (seeking reassurance) and having a “safe” person around at all times.

Unfortunately, because no absolute safety is to be found, these behaviors become more extensive and chronic in the attempt to alleviate anxiety. The fact that anxiety persists induces more worry and eventually distress, thus perpetuating the vicious circle of the disorder (recurrent panic attacks). If the pattern is uninterrupted, it eventually leads to even more inappropriate coping behavior, such as avoidance of any potential triggers of panic (agoraphobia), and can result in comorbid despair and depression. Most of the anxiety disorders follow this process even though different stages may predominate in different disorders; that is, ritualistic behavior is more characteristic of OCD, and avoidance predominates in social anxiety disorder.

We have found that patients quickly recognize and interpret their symptom patterns within the ABC model. We effectively incorporate this pattern with medication and behavioral techniques, as described in the previous studies. 30 We have also found that conceptualization of clinical cases using the ABC model is particularly helpful in teaching psychiatric residents. Using this model, residents are able to understand and to begin administering cognitive–behavioral therapy (CBT) within relatively few sessions.

Interplay Between Biological and Psychological Factors

In order to treat an anxiety disorder effectively, clinicians should understand how these conditions emerge and which factors are involved in maintaining them. In recent years, we have gained a better understanding of the interplay between genetic, biological, and stress factors that shape the presentation of the disorder, although it is not clear which factors are inherited.

One possibility is that abnormal cognition could be the inherited factor. Cognitive theory assigns a primary importance to abnormal or “catastrophic” cognition as an underlying mechanism of all anxiety disorders. Most cognitive strategies for treatment and research were developed in earlier years.

The ABC model focuses on the interaction of information processing and emotional and cognitive processes that are controlled by overlapping circuits and compete for the same brain resources. 27

In most anxiety disorders, patients usually process fear-inducing information in excessive detail that overwhelms their ability to appraise it properly. They cope by separating the information into “good” and “bad” with no gray area in between. As a result, they consider the worst-case scenario (i.e., by catastrophizing about the situation) and then act to protect themselves against the perceived danger.

Stress also plays a major role in the pathology of anxiety disorders. For example, PTSD is a condition in which stress is considered the main etiological factor, although there is a high degree of co-occurring stress reported by these patients. In other anxiety disorders such as GAD and OCD, the role of stress is less apparent. Nevertheless, patients with any anxiety disorder often pinpoint the onset of their disorder in relation to a striking stressful event or to a continuous persistent stressor. Whether a cause or a consequence, increased stress reactivity sometimes accounts for relapses in chronic anxiety conditions like GAD. According to some studies, a stressful event or a persistent and chronic disorder can even cause secondary biological changes in specific brain structures. 31 , 32

The current DSM-IV-TR system does not adequately address the role of stressors. Although stressors are separately identified along Axis IV of the multiaxial system, the context for the patient is unclear. Perhaps a better way to address the patient’s anxiety would be to indicate the source and rate the persistence (i.e., immediate, intermittent, or constant) and the degree of the stress (i.e., mild, moderate, severe, or catastrophic). With this approach, we might be better able to capture the landscape and dynamic of the stress. For example, panic disorder resulting from exposure to catastrophic combat may differ clinically from panic disorder that results from a persistent work-related stress or separation from family. Exploration of how stress affects biology and the course of anxiety disorders is clearly needed.

Biological Factors

Biological factors are of primary importance in anxiety disorders. Anxiety disorders can occur in the context of medical illness, 33 and the clinician should consider an intricate relationship between medical illnesses and anxiety disorders. This relationship could be manifold.

First, metabolic or autonomic abnormalities caused by the illness can produce the syndrome of anxiety (i.e., hyperthyroidism sometimes results in panic attacks). The symptom of medical illness can be a trigger for anxiety (i.e., sensations of arrhythmia can serve as a trigger for a panic attack). Sometimes medical illness can mimic the anxiety disorder (i.e., when perseverations in mental retardation are mistaken for OCD).

Finally, medical illness and an anxiety disorder can simply coexist in the same patient. One of the most interesting interactions between medical illness and anxiety disorders is pediatric autoimmune neuropsychiatric disorder associated with streptococcal infections (PANDAS), which has been reported in a subset of OCD patients. 34

Over the previous two decades, the main thrust of biological research in anxiety disorders has shifted from peripheral measures of autonomic and neurochemical parameters to identifying reactivity and neurochemistry of the living brain directly through advances in neuroimaging technology. Anxiety disorders are an appropriate target for neuroimaging research because it is easy to provoke specific symptoms in many cases. Much of the research on neural circuits has focused on models of anxiety and fear proposed earlier by basic scientists, 35 , 36 and a synthesis of current data has been attempted for panic disorder 37 and OCD. 38

There have been some excellent reviews of neuroimaging experiments in anxiety, 39 , 40 but the picture remains incomplete, in part because of a lack of clinical trials addressing the long-term integration of threat responses. As in the dynamical model, every anxiety disorder may be viewed as an interplay of anxious feelings, abnormal processing of information, and inadequate coping strategies. In accordance with this model of anxiety, overlapping neuronal circuits are responsible for alarm reactions, processing of perceived threats, and behavioral coping (see Figure 1 ). This model attempts to simplify complex brain circuitry that needs to be studied over the next several decades before we can truly understand how the brain processes threats over time.

For simplicity we identify Alarm circuits (A), in which the amygdala is the structure of primary importance. These circuits also include periaqueductal gray matter and multiple nuclei in the brainstem. 41 The disturbance of anxiety circuits results in a lower threshold for alarm reactions that leads to spontaneous panic attacks. These circuits are possibly responsible for the quick response to a threat.

Circuits associated with Beliefs (B), responsible for processing information related to “threats,” are probably closely associated with the basal ganglia, cingulum, and corticostriatal connections, which are typically affected in OCD.

Abnormalities in Coping (C) should be governed by distributed cortical networks and are difficult to tease apart. Thus, a convenient mnemonic explaining these circuits could be A (Alarm, amygdala), B (Beliefs, basal ganglia), and C (Coping, cortex).

How Anxiety Affects Neurotransmitters

Neuronal circuits are governed by multiple neurotransmitter systems; the most extensive of these are gamma-aminobutyric acid (GABA) and glutamate. The neural systems of the three major neurotransmitter systems—serotonin, dopamine, and norepinephrine—have been extensively studied in normal and pathological anxiety states. 40 , 42 The significance of these systems in anxiety is apparent from the fact that most effective therapies for these disorders affect one or several of them. However, anxiety disorders are not simply a deficiency of one neurotransmitter or another. The networks governed by these transmitters have extensive interrelationships, multiple feedback mechanisms, and complex receptor structures. 43 This complexity helps to explain the unpredictable and sometimes paradoxical responses to medication.

Research involving other neurotransmitter systems has been fruitful in elucidating their function in anxiety but thus far has failed to produce new treatments. The primary neurotransmitter and receptor systems implicated in the pathogenesis of anxiety disorders are discussed next.

The primary serotonergic pathways originate in the raphe nuclei and project widely to numerous targets throughout the forebrain. 44 These circuits play a fundamental role in regulating brain states, including anxiety, and modulate the dopaminergic and noradrenergic pathways as well. 45 Increased serotonergic tone appears to be correlated with a reduction in anxiety; however, the mechanism underlying this correlation is not known.

There are also numerous serotonin receptor subtypes whose roles may vary, depending on location. For example, the serotonin-1a receptor serves as both a mediator and an inhibitor of serotonergic neurotransmission, depending on whether it is located on the presynaptic or the postsynaptic neuron. 46 Furthermore, not all serotonin receptor subtypes mediate anxiolytic effects; this is demonstrated by the fact that serotonin-2a receptor agonism underlies the psychedelic properties of drugs such as lysergic acid (LSD) and mescaline. 47

Despite this complexity, it is recognized that medications that inhibit the reuptake of serotonin, presumably increasing serotonergic neurotransmission, result in a reduction in symptoms of anxiety for many patients. 48

Gamma-aminobutyric Acid

GABA is the main inhibitory neurotransmitter in the central nervous system (CNS). Increases in GABA neurotransmission mediate the anxiolytic effect of barbiturates and benzodiazepines. 49 Medications in these classes do not bind directly to the GABA receptor; instead, they promote the open configuration of an associated chloride channel. Barbiturates do this by increasing the duration of the channels’ open state, whereas benzodiazepines increase the frequency of opening.

Although modulation of GABA-ergic pathways can reduce anxiety almost immediately, compensatory mechanisms associated with these circuits and the use of barbiturates and benzodiazepines can result in tolerance and potentially fatal withdrawal. 50 Further, these drugs impair memory encoding and thus may undermine the efficacy of concomitantly administered psychotherapy.

Anticonvulsant agents also alter GABA transmission and are used to treat anxiety. 51 This class of medications affects GABA transmission indirectly by blocking calcium channels, resulting in a lower potential for withdrawal and addiction. 52

The principal dopaminergic pathways originate from the midbrain in the ventral tegmental area and substantia nigra, with projections to the cortex, striatum, limbic nuclei, and infundibulum. Dopamine’s role in normal and pathological anxiety states is complex, and dopaminergic pathways may affect anxiety states in several ways. 53 It is well known that dopamine D 2 blockade, the characteristic mechanism of antipsychotic medications, is also anxiolytic. 54

This class of medications has been widely used in the treatment of anxiety. However, as a catecholamine, dopamine is up-regulated with norepinephrine in anxiety states, whereas increases in dopaminergic signaling also appear to mediate feelings of self-efficacy and confidence—which can act to reduce anxiety. 55 , 56 The result of this complexity is a variation in responses to medications that increase dopamine. Some patients with anxiety disorder respond well to pro-dopaminergic drugs such as bupropion (Wellbutrin, GlaxoSmithKline); other patients find that such agents exacerbate their symptoms.

Norepinephrine

Noradrenergic neurons originate primarily in the locus coeruleus in the pons and project widely throughout the CNS. 57 Like dopamine, norepinephrine is a catecholamine that is up-regulated in anxiety states, but it has a complex and potentially bidirectional role in mediating normal and pathological anxiety. Many of the physiological symptoms of anxiety are mediated by norepinephrine, and antagonists of various norepinephrine receptor subtypes are used to combat particular aspects of anxiety.

For example, propranolol, an antagonist of the beta 2 -norepinephrine receptor, is used to reduce the rapid heart rate, hand tremor, and quivering voice that might accompany public speaking or other activities associated with performance anxiety. 58 Although propranolol has been useful in targeting these physiological symptoms of normal anxiety, it has not been particularly effective in reducing the emotional or cognitive aspects of anxiety and is not generally used as a therapy for anxiety disorders.

Similarly, prazosin (Minipress, Pfizer), an antagonist of the alpha 1 -norepinephrine receptor, is used to reduce the intensity and frequency of nightmares associated with PTSD but has not been effective in relieving other symptoms of anxiety disorders. 59 , 60 Serotonin–norepinephrine reuptake inhibitors (SNRIs), such as venlafaxine (Effexor, Wyeth/Pfizer) and duloxetine (Cymbalta, Eli Lilly), have been effective in the treatment of anxiety disorders. 61 These medications also help to reduce neuropathic pain and may target the agonal component of anxiety.

Glutamate is the primary excitatory neurotransmitter in the CNS and is involved in virtually every neuronal pathway, including those underlying normal and pathological anxiety states. 62 , 63 The N -methyl- d -aspartate (NMDA) receptor subtype may be particularly important in anxiety disorders, as it is believed to mediate learning and memory. Activation of the NMDA receptor triggers protein synthesis, which appears to strengthen the connection between neurons when they fire concurrently. Therefore, glutamatergic pathways are probably involved in both conditioning and extinction, the processes associated with the development and treatment of anxiety disorders, respectively. 64

Preliminary evidence suggests that both augmentation and antagonism of NMDA-mediated pathways are effective in the treatment of anxiety disorders, although no glutamatergic medications have received an FDA indication for this use. d -cycloserine enhances glutamatergic neurotransmission and has been effective in augmenting the effects of exposure therapy for anxiety disorders. 65 However, the NMDA receptor antagonists memantine (Namenda, Forest) and riluzole (Rilutek, Sanofi) have evidence supporting their efficacy in the treatment of OCD. 66 Interestingly, memantine appears to be much less effective in the treatment of GAD, suggesting that different pathways may underlie different anxiety disorders. 67

Other Neurotransmitters

Many other neurotransmitter systems participate in the biological mechanisms of fear and anxiety. Neuropeptides, including substances P, N, and Y; corticotropin-releasing factor (CRF); cannabinoids; and others, modulate fear in animal models. 68 – 70 However, none of the experimental agents that utilize these systems have been translated into FDA-approved treatments. 71 Stringent criteria for approval, along with high placebo responses typical in anxiety trials, could be responsible. 72

PHARMACOLOGICAL THERAPY

Numerous neurotransmitters play a role in normal states and in pathological anxiety states. Each of these systems is a potential target for pharmacological intervention, but relatively few classes of medications are used in clinical practice for the treatment of anxiety. These drug classes are briefly discussed next.

Selective Serotonin Reuptake Inhibitors

SSRIs, usually indicated in depression, are considered to be the first line of therapy for anxiety disorders. This drug class includes fluoxetine (Prozac, Eli Lilly), sertraline (Zoloft, Pfizer), citalopram (Celexa, Forest), escitalopram (Lexapro, Forest), fluvoxamine (Luvox, Solvay), paroxetine (Paxil, GlaxoSmithKline), and vilazodone (Viibryd, Forest). 72 The essential characteristic of the medications in this class is that they inhibit the serotonin transporter and appear to cause desensitization of postsynaptic serotonin receptors, thus normalizing the activity of serotonergic pathways.

The mechanism by which this leads to amelioration of anxiety symptoms is not fully understood. Vilazodone, the most recently approved medication in this class (although indicated for major depressive disorder), also acts as a partial agonist at the serotonin-1a receptor, which may contribute to anxiolysis. 73 Buspirone (BuSpar, Bristol-Myers Squibb), which is not a serotonin reuptake inhibitor (SRI), is also a 5-HT 1a agonist and is frequently used as a single agent or as augmentation to SSRI therapy. 74

Serotonin–Norepinephrine Reuptake Inhibitors

SNRIs, which inhibit the serotonin and norepinephrine transporters, include venlafaxine, desvenlafaxine (Pristiq, Pfizer), and duloxetine. 75 Milnacipran (Savella, Cypress/Forest) is rarely, if ever, used to treat anxiety because its only FDA-approved indication is for fibromyalgia. 76 SNRIs are typically used after failure or inadequate response to an SSRI. They are used in place of augmentation to SSRIs because the combination of these two drug classes may result in serotonin syndrome.

Patient responses to SNRIs can vary widely; some patients may experience an exacerbation of the physiological symptoms of anxiety as a result of the increased norepinephrine-mediated signaling caused by inhibition of the norepinephrine transporter. For patients who do not experience this effect, the increased noradrenergic tonus may contribute to the anxiolytic efficacy of these medications.

Benzodiazepines

Although benzodiazepines were widely used in the past to treat anxiety conditions, they are no longer considered to be first-line therapies because of the risks associated with their chronic use. 75 They are very effective in reducing acute anxiety but are associated with problematic adverse effects when used for a long time in high doses, including:

• physiological and psychological dependence.
• potential fatalities upon withdrawal.
• impaired cognition and coordination.
• a potentially lethal overdose when they are mixed with alcohol or opioids.
• inhibition of memory encoding, which can interfere with the efficacy of concomitant psychotherapy.

For these reasons, the use of benzodiazepines is often restricted to the short-term treatment of acute anxiety or as therapy for refractory anxiety after failed trials of several other drugs. Of note, some subgroups of patients do well with low doses of benzodiazepines and are able to safely taper from high doses, especially when cognitive–behavioral therapy (CBT) is added. 77

Antiseizure Medications

Because of the side effects of benzodiazepines, antiepileptic agents have been used more extensively for the treatment of anxiety. Antiseizure drugs were initially used for mood stabilization in mood disorders; however, their anxiolytic properties were quickly noted. Many agents in this drug class are being used in an off-label fashion to treat anxiety, especially gabapentin (Neurontin, Pfizer) and pregabalin (Lyrica, Pfizer). 51 , 78 Less information is available for topiramate (Topamax, Janssen), lamotrigine (Lamictal, GlaxoSmithKline), and valproate (Depacon, Abbott). 79 In higher doses, the antiseizure class can produce adverse effects similar to those of the benzodiazepines. 80

Tricyclic Antidepressants

All tricyclic antidepressants (TCAs) function as norepinephrine reuptake inhibitors, and several mediate serotonin reuptake inhibition as well. Although several medications in this drug class are comparable in efficacy to the SSRIs or SNRIs for anxiety disorders, TCAs carry a greater number of adverse effects and are potentially lethal in an overdose. For this reason, TCAs are rarely used in the treatment of anxiety disorders. A notable exception is clomipramine (Anafranil, Malinckrodt), which may be more efficacious than SSRIs or SNRIs in patients with OCD. 81

Additional Medications

Hydroxyzine (Atarax, Pfizer), mirtazapine (Remeron, Organon), nefazodone (Bristol-Myers Squibb), and atypical neuroleptic agents are commonly used to treat anxiety. 82 Although all of these medications are efficacious for anxiety disorders, especially OCD, they are not considered first-line treatments and are typically used as an adjunct to an SSRI or an SNRI. Hydroxyzine is indicated for anxiety and probably achieves anxiolysis by inhibiting the histamine H 1 receptor and the serotonin-2a receptor. 83

TREATMENT STRATEGIES

Initial treatment algorithms.

During the 1990s, mainstream psychological and pharmacological treatments of anxiety disorders were developed and tested, leading to an initial algorithm that is similar for all major anxiety disorders. 84 , 85 The typical algorithm, adapted from Roy-Byrne et al., 25 is presented in Figure 2 .

Stepped-care treatment algorithm. AD = antidepressant therapy; CBT = cognitive–behavioral therapy; MED = medication; rTMS = repetitive transcranial magnetic stimulation; SSRI = selective serotonin reuptake inhibitor. (Adapted from Roy-Byrne, et al. Arch Gen Psychiatry 2005;62[3]:290–298; 3 and Roy-Byrne et al. JAMA 2010;303[19]:1921–1928. 25 )

In general, clinicians must choose between CBT and an SSRI and then try another SSRI if the first one did not work or was not tolerated. None of the SSRIs has shown superiority to another. The choice of an SSRI is usually based on the side-effect profile, pharmacokinetic and pharmacodynamic properties, and potential interactions with coadministered medications.

Several excellent reviews of SSRI therapies for anxiety disorders have been published. 86 A general principle with SSRIs is to “start low and go slow,” starting with approximately half the dose of that used for depression and slowly titrating the dose upward, with no more than a once-weekly change in the dosage.

Antidepressants with broader mechanisms of action (i.e., venlafaxine and clomipramine) have been tried in nonresponders. The rationale for this practice is that these medications affect more than one neurotransmitter system and have some, albeit weak, meta-analytic data supporting their superiority in depression and OCD. 87 Benzodiazepines are generally avoided except in acute states or treatment-resistant chronic conditions.

Few data have been published about what to do after the few initial steps of treatment, such as how long maintenance therapy should last. Based on clinical experience, we generally recommend continuing treatment until the patient has achieved marked symptom reduction for at least 6 months. More research on this topic is needed.

Further testing of combined treatments at the initial and later steps of the typical algorithm was subsequently performed. 88 , 89 In the later stages of anxiety treatment, GABA-ergic anti-epileptic drugs and atypical antipsychotic agents may be tried. Atypical neuroleptic medications have shown even better evidence of efficacy in anxiety disorders, according to some placebo-controlled trials. 90

Side Effect Profiles

Patients and physicians need to be aware of adverse drug reactions. An extensive review of the side effects of SSRIs has been published by Valuck. 91 In other studies, SSRIs and SNRIs were found to increase the risk of suicidality 92 and atypical neuroleptic agents caused tardive dyskinesia and arrhythmias. 93 All of these drugs can cause weight gain and sexual dysfunction. Because polypharmacy is becoming the rule rather than the exception, especially in complex and treatment-resistant anxiety, practitioners should be cognizant of potential drug–drug interactions. 94

Serotonin syndrome and neuroleptic malignant syndromes, although rare, should be kept in mind. Discontinuation of SSRIs has not been well studied, but a withdrawal syndrome upon abrupt discontinuation of SSRIs (and SNRIs) is common. Symptoms may include paresthesias, nonvertiginous dizziness, nausea, diaphoresis, and rebound anxiety. 95 For this reason, stopping SSRIs and SNRIs should involve a gradual tapering and should take place, if possible, in parallel with CBT.

Cognitive–Behavioral Therapy and Medications

CBT has received the greatest amount of empirical support for the psychological treatment of anxiety disorders. 96 In our treatment algorithm, CBT stands with the SSRIs as a first-line treatment choice (see Figure 2 ). Combining drug therapy and CBT has shown mixed results in favoring one approach over the other, depending on the type of anxiety disorder.

A review and meta-analysis approached the question of combination treatment over monotherapy or CBT in anxiety by hypothesizing that CBT would be more successful compared with medications; however, the medication held an advantage over CBT in depression. 97 Within the anxiety disorders, there was great heterogeneity in their responsiveness to either CBT or medication, with CBT holding an advantage over medication in patients with panic disorder. By contrast, patients with social anxiety disorder were more responsive to medication.

The choice of medication or CBT, alone or in combination, is based on several variables, including the availability of a therapist; the affordability of CBT, which costs more than medication, especially if drugs are prescribed in primary care settings; and patient preference.

Cognitive–Behavior Therapy Alone

It is generally acknowledged that the treatment of anxiety disorders is suboptimal because of a lack of CBT therapists or the availability of affordable sessions. There is a great need to distill the essence of good therapy and to bring it into the primary care setting, with an emphasis on education and staff training. 25 Oxford University Press has published many excellent manuals that include both therapist and patient guides. 98 The proliferation of the Internet-based, self-administered therapies calls for further research into the efficacy of this method of dissemination. 99 Complex anxiety disorders might not be able to be self-treated adequately, whereas a specific phobia might be self-treated alone or with the support of a friend of family member.

Koszycki et al. 100 discussed whether self-administered CBT could stand alone or could be optimized with therapist-directed CBT, self-administered CBT, or medication augmented with self-administered CBT. Their work suggested that even self-administered treatment might be an effective addition to the CBT armamentarium.

Although many treatments are effective for anxiety, not all of them can help everyone and not all of them are effective for all anxiety disorders. A simple phobia is easier to treat than a complicated case of PTSD. The most empirically supported treatments are SSRIs and CBT. Relapse rates for CBT, compared with medication, are an understudied area, although our clinical experience suggests that CBT has a longer treatment effect if the patient continues to use the skills and tools learned in therapy.

CBT shares much in common with other more dynamically based forms of psychotherapy. A patient seeks help from an expert caregiver who treats the patient in a warm and nonjudgmental relationship in an attempt to help the patient function and feel better in a reality-oriented setting. However, CBT is directive and collaborative; the therapist establishes clear and specific goals with the patient and uses evidence-based techniques to elicit the patient’s feelings and bodily sensations (Arousal, or Alarm), dysfunctional and irrational thinking (Beliefs), and subsequent behavior (Coping).

The helping relationship is less emphasized in CBT as a curative factor, but it is considered important in building trust and support, serving as a springboard for patients to consider their erroneous beliefs and behaviors that cause them anxiety and fear. The therapist is explicit about conceptualizing the patient’s disorder, with regard to the genesis, evolution, and maintenance of the disorder over time. The therapist often incorporates manuals or other psychoeducational materials and may propose daily homework to help the patient learn more adaptive ways to manage and reduce the alarm (A), change irrational and dysfunctional beliefs (B), and develop adaptive coping (C) mechanisms, often through exposure exercises. To the most appropriate extent possible, patients are taught the ABC model to help them understand the dynamic and reciprocal relationship among feelings, thoughts, and behaviors.

Patient compliance with therapy is directly proportional to the treatment’s effectiveness. Motivational interviewing, which is used to help patients examine the cost–benefit ratio of their maladaptive thoughts and behavior, often increases compliance and, subsequently, effectiveness. 101 Patients are taught self-monitoring and symptom-reduction techniques to increase their motivation to confront their anxiety. Breathing and relaxation techniques can be explained as mental hygiene to raise one’s threshold for the onset of alarm reactivity and for increasing the patient’s ability to notice whether an alarm reaction is mounting over the course of the day.

The linchpin in the CBT model of anxiety is considered to be the patient’s thoughts. 102 Misguided beliefs must change for both the alarm to down-regulate and for subsequent adaptive coping to replace avoidant and escape-based coping. Although beliefs are the linchpin, exposure to the anxiety-producing thought, image, or situation is often the essential CBT component for jogging the linchpin loose. This too is a dynamic process. Cognitive restructuring techniques aimed at reducing catastrophic thinking help to diminish irrational or exaggerated thoughts, thereby allowing patients to become more willing to test those beliefs through exercises involving exposure.

Exposure is the gradual and systematic presentation of the anxiety-inducing thought, image, or situation for a long enough time for patients to see that their anxious feelings can be decreased without engaging in avoidance or escape. For example, a patient who is afraid of dogs might first be shown a picture of a dog, then stand across the street from a pet shop, and finally hold a dog in his or her arms. The patient would engage in each of these steps repeatedly and in a concentrated but not overwhelming way.

Ideally, the patient would experience a gradual lessening of anxiety at each step before moving on to the next. The patient would experience the alarm being reduced, and the exaggerated belief that all dogs are dangerous could be modified to a more accurate belief that most pet dogs are not threatening. The hoped-for outcome would be that the patient would no longer have a phobic avoidance of all dogs.

Mindfulness (The Third Wave)

A final emerging area in the evolution of CBT is the approach based on mindfulness (acceptance). This is the “third wave” in CBT, the first wave being the strict behavioral approach and the second wave emphasizing the cognitive approach. 103

Mindfulness is a type of meditation that has been adapted from Buddhist psychology. One definition is “awareness of present experience with acceptance.” 103 These therapies owe a debt of gratitude to Jon Kabat-Zinn’s Mindfulness-Based Stress Reduction (MBSR) program, which began at the University of Massachusetts in 1979. 104

Mindfulness-based cognitive therapy (MBCT) is one component of the integration of mindfulness into CBT. 105 MBCT has been applied to the treatment of panic disorder and other anxiety disorders, but more carefully controlled research is needed in this area. 106 MBCT emphasizes the prevention of relapse through a meta-cognitive or mindful awareness that leads patients to realize that their current symptoms do not necessarily mean that they are relapsing.

Acceptance and commitment therapy involves a mindful focus; many exercises are aimed at the meta-cognitive level to help patients perceive their thinking and subsequent anxiety to be separate from, and less identified with, their sense of self. Anxiety-causing thoughts are to be observed and accepted, not to be struggled with and changed, as in more traditional CBT and Western psychological approaches. 107

Shifting Treatment to Primary Care

In today’s managed care environment, the treatment of anxiety usually takes place in the primary care setting. Given the increasing limits on primary care physicians’ time, it is not surprising that anxiety disorders are underrecognized and undertreated. At the same time, SSRIs (antidepressants) are increasingly used in primary care, and physicians in fact are the largest group of prescribers. This is a mixed blessing for several reasons:

• SSRIs are often prescribed quickly in response to emotional distress that might not meet criteria for an anxiety disorder.
• The dose and duration of therapy might be inadequate.
• Adverse effects might not be managed by any means other than by discontinuation of the treatment.

This state of affairs may partly explain why psychiatrists are seeing more patients who are disenchanted with numerous failed attempts at pharmacotherapy.

Another problem in primary care is a lack of understanding of behavioral strategies that result in low referral rates to mental health professionals. There has been a trend toward developing comprehensive treatments for panic disorder to be delivered by primary care physicians.

In one study, an algorithm was tested for the treatment of panic disorder. 108 This study reflected the trend of how psychiatrists became more like consultants to primary care physicians, assisting them with correct initial management plans and taking over the management of more severe and treatment-resistant anxiety.

Management of Treatment-Resistant Anxiety

In managing refractory anxiety, it is important to start with a re-evaluation of the patient, including the diagnosis; comorbidities; and the interplay of cognitive, stress-related, and biological factors. Inadequate coping strategies on the part of patients and their family members should be reviewed. Doses and duration of the initial treatments should be assessed.

Initially, more intensive CBT, combined with an adequate trial of SSRIs, SNRIs, or both, may be needed in refractory anxiety. After that, the treatment may progress to a combination of SSRIs with antiepileptic or atypical neuroleptic agents, especially if bipolar disorder or a psychotic disorder is suspected. 109 , 110 Later, partial hospitalization in specialized centers with more extensive CBT and medication management might be recommended. 111

Although other forms of therapy have not demonstrated efficacy in anxiety disorders, they may be helpful for addressing personality issues in chronically anxious patients.

Experimental and Off-Label Nonpharmacological Treatments

Therapies for anxiety disorders, beyond combining conventional treatments, using off-label antiepileptic and antipsychotic agents, and introducing more intensive CBT programs, are mostly experimental. Promising medications have included intravenous clomipramine, citalopram, and morphine. 109 Many other treatments targeting more specific neurotransmitter systems have failed. 72

A handful of invasive therapies have emerged. These options may be considered after several off-label pharmacotherapy and psychotherapeutic approaches have failed or when significant functional impairment remains. They are typically reserved for the most treatment-resistant cases, typically those involving severe OCD. Invasive treatments often target brain circuits implicated in the processing of fear and anxiety.

Electroconvulsive Therapy

Electroconvulsive therapy (ECT) involves the application of brief electrical impulses to the scalp to induce large-scale cortical neuronal discharges, eventually producing generalized seizure activity. Although ECT is effective in treatment-resistant mood disorders, data regarding its efficacy in anxiety disorders are limited. 112 The mechanism and focal targets of ECT have not yet been determined.

Vagal Nerve Stimulation

Initially developed as an antiepileptic treatment, vagal nerve stimulation (VNS) was used in psychiatric patients after sustained mood improvements were noted with this therapy. 113 VNS is thought to stimulate brain networks relevant to anxiety and fear processing (taking place in the amygdala, hippocampus, insula, and orbitofrontal cortex) via the afferent vagal nerve. This modality is not routinely used to treat anxiety, and evidence of its effectiveness in resistant anxiety disorders is limited. 114 To date, no randomized controlled trials have investigated this intervention further.

Repetitive Transcranial Magnetic Stimulation

Focal magnetic stimulation of the scalp is used with the goal of invoking excitation or inhibition of cortical neurons. Repetitive transcranial magnetic stimulation (rTMS) is less invasive than ECT; anesthesia induction is not required, and rTMS does not elicit generalized seizure activity in the brain. It also has the advantage of being able to target brain regions thought to be involved in anxiety disorders.

The main limitations of rTMS include the inability to penetrate deeper brain structures implicated in OCD (the caudate nucleus, thalamus, and anterior capsule fiber tracts) or in panic disorder (the amygdala, hippocampus, and anterior cingulate); there is also a lack of specificity at the site of stimulation.

rTMS has not been approved as a treatment for any anxiety disorder, probably because of the paucity of large-scale studies. There is limited evidence for efficacy in treating OCD, although larger treatment effects have been reported by altering the stimulation site. 115 , 116 rTMS has been reported to improve anxiety symptoms in PTSD and panic disorder, although the approach has not been incorporated into clinical practice. 117

A small study reported significant anxiety reductions in patients with generalized anxiety disorder (GAD) using a symptom-provocation task during functional magnetic resonance imaging (fMRI) to guide individual selection of the rTMS site. 118 No studies have investigated the role of rTMS in social anxiety disorder.

Although psychosurgery has been used for various treatment-resistant anxiety disorders such as GAD, panic disorder, and social phobia, long-term follow-up studies in these patients have revealed adverse cognitive outcomes, including apathy and frontal lobe dysfunction. 119 Consequently, surgical approaches are usually reserved for OCD, given the disproportionate functional deficits that are a hallmark in treatment-refractory cases.

Several surgical approaches have been used, including anterior capsulotomy (which targets the anterior limb of the internal capsule), anterior cingulotomy (which targets the anterior cingulate and cingulum bundle), subcaudate tractotomy (which targets the substantia innominata, just inferior to the caudate nucleus), and limbic leucotomy (which combines anterior cingulotomy with subcaudate tractotomy). 120 , 121

Cingulotomy remains the most commonly used psychosurgical procedure in North America, probably because of its clinical efficacy as well as low morbidity and mortality rates. Postsurgical effects have included transient headache, nausea, or difficulty urinating. Postoperative seizures, the most serious common side effect, have been reported from 1% to 9% of the time.

Patient outcomes cannot be fully assessed until at least 6 months to 2 years after the definitive procedure, suggesting that postoperative neural reorganization plays an important role in recovery. Direct comparisons of each lesion approach within studies are rare.

Overall, the long-term outcomes of these approaches have demonstrated significant therapeutic effects of each procedure. In general, reported response rates vary from 30% to 70% in terms of remission, response, and functional improvements in quality of life.

Deep-Brain Stimulation

Deep-brain stimulation (DBS) involves the insertion of small electrodes under precise stereotactic MRI guidance. The major advantage of DBS over ablative surgery is the ability to adjust and customize neurostimulation. 122 Following implantation, parameters of electrode stimulation (electrode polarity, intensity, frequency, and laterality) can be modified. Parameters can be optimized by a specially trained clinician during long-term follow-up.

Several studies with blinded stimulation have been conducted with moderate-to-fair results. 123 More recently, structures adjacent to the internal capsule have also been targeted. 124 , 125 In all trials, response rates have been consistently reported in the 50% range. 125

Postoperative complications (e.g., infections, lead malfunctions) occur more commonly with DBS because of the prosthetic nature of the procedure. Batteries must also be periodically explanted and replaced. Stimulation-related side effects have been reported, including mood changes (transient sadness, anxiety, euphoria, and hypomania), sensory disturbances (olfactory, gustatory, and motor sensations), and cognitive changes (confusion and forgetfulness). These side effects are typically stimulation-dependent and disappear after the stimulation parameters are altered.

Complementary and Alternative Medicine

During the 1990s, many alternative treatment strategies for anxiety disorders emerged. 126 These included herbal medications (with St. John’s wort the most frequently used), vitamins, nutritional supplements, magnetic and electroencephalographic synchronizing devices, “energy” treatments, and meditation-based therapies (see Mindfulness on page 38).

These treatments may be provided by alternative medicine practitioners within the scope of a health care model, such as acupuncture, homeopathy, Ayurvedic medicine, Reiki, and healing touch. Because of minimal FDA regulation and widespread over-the-counter availability, many of these same treatments are self-selected and used by patients. Herbs are the most commonly used complementary and alternative medicine (CAM) products and are particularly popular with those with psychiatric disorders. Anxiety is one of the strongest predictors of herbal remedy utilization, 127 and patients often use these treatments without the knowledge of their physician. Consequently, clinicians and pharmacists are advised to regularly monitor the full range of treatments used by their patients, including a thorough medication reconciliation of prescription and non-prescription products, herbs, and supplements at each visit.

Results of herbal trials for anxiety disorders have been mixed. The widespread use of Piper methysticum (Kava) for anxiolysis was curtailed by reports of hepatotoxicity, prompting government warnings and withdrawal of the product from the market in many Western countries. 128 , 129 However, a randomized placebo crossover trial using a supposedly benign aqueous formulation reported moderate reductions in anxiety symptoms in a small sample of patients with mixed anxiety disorders. 128 , 130 Both Hypericum perforatum (St. John’s wort) and Silybum marianum (milk thistle) have been used for the treatment of OCD symptoms, although no placebo-controlled trials revealed any significant differences in symptoms or adverse effects between treatment groups. 131 , 132 Lower-quality studies of CAM have reported modest treatment effects for interventions such as mindfulness meditation, yoga, and acupuncture. 133

Despite a lack of data on efficacy, many patients continue to use CAM therapies, prompting a need to monitor use for potential interactions with prescription medications. 134 For instance, St. John’s wort is known to interact with many medications because of the induction of cytochrome P450 (CYP) isoenzymes 3A4 and 2C9. Of relevance in anxiety disorders, CYP3A4 may cause a decrease in serum levels of alprazolam (Xanax, Pfizer) and clonazepam (Klonopin, Roche). Combining St. John’s wort with SSRIs also increases the risk of serotonin syndrome. Milk thistle inhibits CYP3A4 and has the potential to increase levels of other medications metabolized by this pathway. Kava has been linked with inhibition of several CYP isoenzymes, including 1A2, 2D6, 2C9, and 3A4. 135 Further exploration of the efficacy of these alternative strategies for anxiety disorders is needed.

Functional Status

Although many patients with anxiety disorders experience symptom relief with treatment, residual symptoms still have an impact on everyday functions. Even subclinical anxiety can produce disability sometimes exceeding that seen in other severe mental illnesses. 111 , 136 In addition, chronic, persistent anxiety disorders have a significant impact on patients’ lives, often leading to deficits in social and work skills. Yet there are few clear interventions or programs with a focus on rehabilitation and restoration of function in these patients.

Stress is an important factor in the emergence and maintenance of anxiety syndromes. Patients who need to return to the workforce can experience increased stress that in turn may cause re-emergence of the symptoms, again resulting in decreased productivity and even loss of employment. More research is needed to address this problem.

Anxiety disorders are treatable. Effective treatments have been developed, and algorithms have been refined. However, more work needs to be directed toward merging of our knowledge of the biological mechanisms of anxiety with treatment in order to more accurately predict and improve treatment response. Dynamic models of anxiety—such as the ABC model—can be helpful in understanding the interplay between processes responsible for development and maintenance of the symptoms over time and between biological and psychological factors affecting them.

We need to learn how to better administer existing efficacious treatments in real-world health care environments, such as in primary care, and to inform the public via media outlets. We should continue to test alternative therapies for treating and preventing anxiety disorders and to help patients whose anxiety is resistant to conventional treatments.

Finally, we need to consider the patient’s feelings about mental illness and address their responses early in treatment. All of these measures will enhance the care of patients with anxiety.

Disclosure: Dr. Bystritsky reports that he has received honoraria, research grants, and travel reimbursements from AstraZeneca, Takeda, and Brainsway. He has also served as a consultant for UpToDate, John Wiley & Sons, Brainsonix Corp., and Consumer Brands. Dr. Khalsa, Dr. Cameron, and Dr. Schi3man report that they have no financial or commercial relationships in regard to this article. This work was supported in part by a grant from the Saban Family Foundation.