case study of depression and treatment

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Patient Case Presentation

Figure 1. Blue and silver stethoscope (Pixabay, N.D.)

Ms. S.W. is a 48-year-old white female who presented to an outpatient community mental health agency for evaluation of depressive symptoms. Over the past eight weeks she has experienced sad mood every day, which she describes as a feeling of hopelessness and emptiness. She also noticed other changes about herself, including decreased appetite, insomnia, fatigue, and poor ability to concentrate. The things that used to bring Ms. S.W. joy, such as gardening and listening to podcasts, are no longer bringing her the same happiness they used to. She became especially concerned as within the past two weeks she also started experiencing feelings of worthlessness, the perception that she is a burden to others, and fleeting thoughts of death/suicide.

Ms. S.W. acknowledges that she has numerous stressors in her life. She reports that her daughter’s grades have been steadily declining over the past two semesters and she is unsure if her daughter will be attending college anymore. Her relationship with her son is somewhat strained as she and his father are not on good terms and her son feels Ms. S.W. is at fault for this. She feels her career has been unfulfilling and though she’d like to go back to school, this isn’t possible given the family’s tight finances/the patient raising a family on a single income.

Ms. S.W. has experienced symptoms of depression previously, but she does not think the symptoms have ever been as severe as they are currently. She has taken antidepressants in the past and was generally adherent to them, but she believes that therapy was more helpful than the medications. She denies ever having history of manic or hypomanic episodes. She has been unable to connect to a mental health agency in several years due to lack of time and feeling that she could manage the symptoms on her own. She now feels that this is her last option and is looking for ongoing outpatient mental health treatment.

Past Medical History

Hypertension, diagnosed at age 41

Past Surgical History

Wisdom teeth extraction, age 22

Pertinent Family History

Mother with history of Major Depressive Disorder, treated with antidepressants
Maternal grandmother with history of Major Depressive Disorder, Generalized Anxiety Disorder
Brother with history of suicide attempt and subsequent inpatient psychiatric hospitalization,
Brother with history of Alcohol Use Disorder
Father died from lung cancer (2012)

Pertinent Social History

Works full-time as an enrollment specialist for Columbus City Schools since 2006
Has two children, a daughter age 17 and a son age 14
Divorced in 2015, currently single
History of some emotional abuse and neglect from mother during childhood, otherwise denies history of trauma, including physical and sexual abuse
Smoking 1/2 PPD of cigarettes
Occasional alcohol use (approximately 1-2 glasses of wine 1-2 times weekly; patient had not had any alcohol consumption for the past year until two weeks ago)

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Research in Context: Treating depression

Finding better approaches.

While effective treatments for major depression are available, there is still room for improvement. This special Research in Context feature explores the development of more effective ways to treat depression, including personalized treatment approaches and both old and new drugs.

Woman standing on a road between a bleak, desolate area and a lush, green area.

Everyone has a bad day sometimes. People experience various types of stress in the course of everyday life. These stressors can cause sadness, anxiety, hopelessness, frustration, or guilt. You may not enjoy the activities you usually do. These feelings tend to be only temporary. Once circumstances change, and the source of stress goes away, your mood usually improves. But sometimes, these feelings don’t go away. When these feelings stick around for at least two weeks and interfere with your daily activities, it’s called major depression, or clinical depression.

In 2021, 8.3% of U.S. adults experienced major depression. That’s about 21 million people. Among adolescents, the prevalence was much greater—more than 20%. Major depression can bring decreased energy, difficulty thinking straight, sleep problems, loss of appetite, and even physical pain. People with major depression may become unable to meet their responsibilities at work or home. Depression can also lead people to use alcohol or drugs or engage in high-risk activities. In the most extreme cases, depression can drive people to self-harm or even suicide.

The good news is that effective treatments are available. But current treatments have limitations. That’s why NIH-funded researchers have been working to develop more effective ways to treat depression. These include finding ways to predict whether certain treatments will help a given patient. They're also trying to develop more effective drugs or, in some cases, find new uses for existing drugs.

Finding the right treatments

The most common treatments for depression include psychotherapy, medications, or a combination. Mild depression may be treated with psychotherapy. Moderate to severe depression often requires the addition of medication.

Several types of psychotherapy have been shown to help relieve depression symptoms. For example, cognitive behavioral therapy helps people to recognize harmful ways of thinking and teaches them how to change these. Some researchers are working to develop new therapies to enhance people’s positive emotions. But good psychotherapy can be hard to access due to the cost, scheduling difficulties, or lack of available providers. The recent growth of telehealth services for mental health has improved access in some cases.

There are many antidepressant drugs on the market. Different drugs will work best on different patients. But it can be challenging to predict which drugs will work for a given patient. And it can take anywhere from 6 to 12 weeks to know whether a drug is working. Finding an effective drug can involve a long period of trial and error, with no guarantee of results.

If depression doesn’t improve with psychotherapy or medications, brain stimulation therapies could be used. Electroconvulsive therapy, or ECT, uses electrodes to send electric current into the brain. A newer technique, transcranial magnetic stimulation (TMS), stimulates the brain using magnetic fields. These treatments must be administered by specially trained health professionals.

“A lot of patients, they kind of muddle along, treatment after treatment, with little idea whether something’s going to work,” says psychiatric researcher Dr. Amit Etkin.

One reason it’s difficult to know which antidepressant medications will work is that there are likely different biological mechanisms that can cause depression. Two people with similar symptoms may both be diagnosed with depression, but the causes of their symptoms could be different. As NIH depression researcher Dr. Carlos Zarate explains, “we believe that there’s not one depression, but hundreds of depressions.”

Depression may be due to many factors. Genetics can put certain people at risk for depression. Stressful situations, physical health conditions, and medications may contribute. And depression can also be part of a more complicated mental disorder, such as bipolar disorder. All of these can affect which treatment would be best to use.

Etkin has been developing methods to distinguish patients with different types of depression based on measurable biological features, or biomarkers. The idea is that different types of patients would respond differently to various treatments. Etkin calls this approach “precision psychiatry.”

One such type of biomarker is electrical activity in the brain. A technique called electroencephalography, or EEG, measures electrical activity using electrodes placed on the scalp. When Etkin was at Stanford University, he led a research team that developed a machine-learning algorithm to predict treatment response based on EEG signals. The team applied the algorithm to data from a clinical trial of the antidepressant sertraline (Zoloft) involving more than 300 people.

Young woman undergoing electroencephalography.

EEG data for the participants were collected at the outset. Participants were then randomly assigned to take either sertraline or an inactive placebo for eight weeks. The team found a specific set of signals that predicted the participants’ responses to sertraline. The same neural “signature” also predicted which patients with depression responded to medication in a separate group.

Etkin’s team also examined this neural signature in a set of patients who were treated with TMS and psychotherapy. People who were predicted to respond less to sertraline had a greater response to the TMS/psychotherapy combination.

Etkin continues to develop methods for personalized depression treatment through his company, Alto Neuroscience. He notes that EEG has the advantage of being low-cost and accessible; data can even be collected in a patient’s home. That’s important for being able to get personalized treatments to the large number of people they could help. He’s also working on developing antidepressant drugs targeted to specific EEG profiles. Candidate drugs are in clinical trials now.

“It’s not like a pie-in-the-sky future thing, 20-30 years from now,” Etkin explains. “This is something that could be in people's hands within the next five years.”

New tricks for old drugs

While some researchers focus on matching patients with their optimal treatments, others aim to find treatments that can work for many different patients. It turns out that some drugs we’ve known about for decades might be very effective antidepressants, but we didn’t recognize their antidepressant properties until recently.

One such drug is ketamine. Ketamine has been used as an anesthetic for more than 50 years. Around the turn of this century, researchers started to discover its potential as an antidepressant. Zarate and others have found that, unlike traditional antidepressants that can take weeks to take effect, ketamine can improve depression in as little as one day. And a single dose can have an effect for a week or more. In 2019, the FDA approved a form of ketamine for treating depression that is resistant to other treatments.

But ketamine has drawbacks of its own. It’s a dissociative drug, meaning that it can make people feel disconnected from their body and environment. It also has the potential for addiction and misuse. For these reasons, it’s a controlled substance and can only be administered in a doctor’s office or clinic.

Another class of drugs being studied as possible antidepressants are psychedelics. These include lysergic acid diethylamide (LSD) and psilocybin, the active ingredient in magic mushrooms. These drugs can temporarily alter a person’s mood, thoughts, and perceptions of reality. Some have historically been used for religious rituals, but they are also used recreationally.

In clinical studies, psychedelics are typically administered in combination with psychotherapy. This includes several preparatory sessions with a therapist in the weeks before getting the drug, and several sessions in the weeks following to help people process their experiences. The drugs are administered in a controlled setting.

Dr. Stephen Ross, co-director of the New York University Langone Health Center for Psychedelic Medicine, describes a typical session: “It takes place in a living room-like setting. The person is prepared, and they state their intention. They take the drug, they lie supine, they put on eye shades and preselected music, and two therapists monitor them.” Sessions last for as long as the acute effects of the drug last, which is typically several hours. This is a healthcare-intensive intervention given the time and personnel needed.

In 2016, Ross led a clinical trial examining whether psilocybin-assisted therapy could reduce depression and anxiety in people with cancer. According to Ross, as many as 40% of people with cancer have clinically significant anxiety and depression. The study showed that a single psilocybin session led to substantial reductions in anxiety and depression compared with a placebo. These reductions were evident as soon as one day after psilocybin administration. Six months later, 60-80% of participants still had reduced depression and anxiety.

Psychedelic drugs frequently trigger mystical experiences in the people who take them. “People can feel a sense…that their consciousness is part of a greater consciousness or that all energy is one,” Ross explains. “People can have an experience that for them feels more ‘real’ than regular reality. They can feel transported to a different dimension of reality.”

About three out of four participants in Ross’s study said it was among the most meaningful experiences of their lives. And the degree of mystical experience correlated with the drug’s therapeutic effect. A long-term follow-up study found that the effects of the treatment continued more than four years later.

If these results seem too good to be true, Ross is quick to point out that it was a small study, with only 29 participants, although similar studies from other groups have yielded similar results. Psychedelics haven’t yet been shown to be effective in a large, controlled clinical trial. Ross is now conducting a trial with 200 people to see if the results of his earlier study pan out in this larger group. For now, though, psychedelics remain experimental drugs—approved for testing, but not for routine medical use.

Unlike ketamine, psychedelics aren’t considered addictive. But they, too, carry risks, which certain conditions may increase. Psychedelics can cause cardiovascular complications. They can cause psychosis in people who are predisposed to it. In uncontrolled settings, they have the risk of causing anxiety, confusion, and paranoia—a so-called “bad trip”—that can lead the person taking the drug to harm themself or others. This is why psychedelic-assisted therapy takes place in such tightly controlled settings. That increases the cost and complexity of the therapy, which may prevent many people from having access to it.

Better, safer drugs

Despite the promise of ketamine or psychedelics, their drawbacks have led some researchers to look for drugs that work like them but with fewer side effects.

Depression is thought to be caused by the loss of connections between nerve cells, or neurons, in certain regions of the brain. Ketamine and psychedelics both promote the brain’s ability to repair these connections, a quality called plasticity. If we could understand how these drugs encourage plasticity, we might be able to design drugs that can do so without the side effects.

Dr. David Olson at the University of California, Davis studies how psychedelics work at the cellular and molecular levels. The drugs appear to promote plasticity by binding to a receptor in cells called the 5-hydroxytryptamine 2A receptor (5-HT2AR). But many other compounds also bind 5-HT2AR without promoting plasticity. In a recent NIH-funded study, Olson showed that 5-HT2AR can be found both inside and on the surface of the cell. Only compounds that bound to the receptor inside the cells promoted plasticity. This suggests that a drug has to be able to get into the cell to promote plasticity.

Moreover, not all drugs that bind 5-HT2AR have psychedelic effects. Olson’s team has developed a molecular sensor, called psychLight, that can identify which compounds that bind 5-HT2AR have psychedelic effects. Using psychLight, they identified compounds that are not psychedelic but still have rapid and long-lasting antidepressant effects in animal models. He’s founded a company, Delix Therapeutics, to further develop drugs that promote plasticity.

Meanwhile, Zarate and his colleagues have been investigating a compound related to ketamine called hydroxynorketamine (HNK). Ketamine is converted to HNK in the body, and this process appears to be required for ketamine’s antidepressant effects. Administering HNK directly produced antidepressant-like effects in mice. At the same time, it did not cause the dissociative side effects and addiction caused by ketamine. Zarate’s team has already completed phase I trials of HNK in people showing that it’s safe. Phase II trials to find out whether it’s effective are scheduled to begin soon.

“What [ketamine and psychedelics] are doing for the field is they’re helping us realize that it is possible to move toward a repair model versus a symptom mitigation model,” Olson says. Unlike existing antidepressants, which just relieve the symptoms of depression, these drugs appear to fix the underlying causes. That’s likely why they work faster and produce longer-lasting effects. This research is bringing us closer to having safer antidepressants that only need to be taken once in a while, instead of every day.

—by Brian Doctrow, Ph.D.

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Mental Health Case Study: Understanding Depression through a Real-life Example

Imagine feeling an unrelenting heaviness weighing down on your chest. Every breath becomes a struggle as a cloud of sadness engulfs your every thought. Your energy levels plummet, leaving you physically and emotionally drained. This is the reality for millions of people worldwide who suffer from depression, a complex and debilitating mental health condition.

Understanding depression is crucial in order to provide effective support and treatment for those affected. While textbooks and research papers provide valuable insights, sometimes the best way to truly comprehend the depths of this condition is through real-life case studies. These stories bring depression to life, shedding light on its impact on individuals and society as a whole.

In this article, we will delve into the world of mental health case studies, using a real-life example to explore the intricacies of depression. We will examine the symptoms, prevalence, and consequences of this all-encompassing condition. Furthermore, we will discuss the significance of case studies in mental health research, including their ability to provide detailed information about individual experiences and contribute to the development of treatment strategies.

Through an in-depth analysis of a selected case study, we will gain insight into the journey of an individual facing depression. We will explore their background, symptoms, and initial diagnosis. Additionally, we will examine the various treatment options available and assess the effectiveness of the chosen approach.

By delving into this real-life example, we will not only gain a better understanding of depression as a mental health condition, but we will also uncover valuable lessons that can aid in the treatment and support of those who are affected. So, let us embark on this enlightening journey, using the power of case studies to bring understanding and empathy to those who need it most.

Understanding Depression

Depression is a complex and multifaceted mental health condition that affects millions of people worldwide. To comprehend the impact of depression, it is essential to explore its defining characteristics, prevalence, and consequences on individuals and society as a whole.

Defining depression and its symptoms

Depression is more than just feeling sad or experiencing a low mood. It is a serious mental health disorder characterized by persistent feelings of sadness, hopelessness, and a loss of interest in activities that were once enjoyable. Individuals with depression often experience a range of symptoms that can significantly impact their daily lives. These symptoms include:

1. Persistent feelings of sadness or emptiness. 2. Fatigue and decreased energy levels. 3. Significant changes in appetite and weight. 4. Difficulty concentrating or making decisions. 5. Insomnia or excessive sleep. 6. feelings of guilt, worthlessness, or hopelessness. 7. Loss of interest or pleasure in activities.

Exploring the prevalence of depression worldwide

Depression knows no boundaries and affects individuals from all walks of life. According to the World Health Organization (WHO), an estimated 264 million people globally suffer from depression. This makes depression one of the most common mental health conditions worldwide. Additionally, the WHO highlights that depression is more prevalent among females than males.

The impact of depression is not limited to individuals alone. It also has significant social and economic consequences. Depression can lead to impaired productivity, increased healthcare costs, and strain on relationships, contributing to a significant burden on families, communities, and society at large.

The impact of depression on individuals and society

Depression can have a profound and debilitating impact on individuals’ lives, affecting their physical, emotional, and social well-being. The persistent sadness and loss of interest can lead to difficulties in maintaining relationships, pursuing education or careers, and engaging in daily activities. Furthermore, depression increases the risk of developing other mental health conditions, such as anxiety disorders or substance abuse.

On a societal level, depression poses numerous challenges. The economic burden of depression is significant, with costs associated with treatment, reduced productivity, and premature death. Moreover, the social stigma surrounding mental health can impede individuals from seeking help and accessing appropriate support systems.

Understanding the prevalence and consequences of depression is crucial for policymakers, healthcare professionals, and individuals alike. By recognizing the significant impact depression has on individuals and society, appropriate resources and interventions can be developed to mitigate its effects and improve the overall well-being of those affected.

The Significance of Case Studies in Mental Health Research

Case studies play a vital role in mental health research, providing valuable insights into individual experiences and contributing to the development of effective treatment strategies. Let us explore why case studies are considered invaluable in understanding and addressing mental health conditions.

Why case studies are valuable in mental health research

Case studies offer a unique opportunity to examine mental health conditions within the real-life context of individuals. Unlike large-scale studies that focus on statistical data, case studies provide a detailed examination of specific cases, allowing researchers to delve into the complexities of a particular condition or treatment approach. This micro-level analysis helps researchers gain a deeper understanding of the nuances and intricacies involved.

The role of case studies in providing detailed information about individual experiences

Through case studies, researchers can capture rich narratives and delve into the lived experiences of individuals facing mental health challenges. These stories help to humanize the condition and provide valuable insights that go beyond a list of symptoms or diagnostic criteria. By understanding the unique experiences, thoughts, and emotions of individuals, researchers can develop a more comprehensive understanding of mental health conditions and tailor interventions accordingly.

How case studies contribute to the development of treatment strategies

Case studies form a vital foundation for the development of effective treatment strategies. By examining a specific case in detail, researchers can identify patterns, factors influencing treatment outcomes, and areas where intervention may be particularly effective. Moreover, case studies foster an iterative approach to treatment development—an ongoing cycle of using data and experience to refine and improve interventions.

By examining multiple case studies, researchers can identify common themes and trends, leading to the development of evidence-based guidelines and best practices. This allows healthcare professionals to provide more targeted and personalized support to individuals facing mental health conditions.

Furthermore, case studies can shed light on potential limitations or challenges in existing treatment approaches. By thoroughly analyzing different cases, researchers can identify gaps in current treatments and focus on areas that require further exploration and innovation.

In summary, case studies are a vital component of mental health research, offering detailed insights into the lived experiences of individuals with mental health conditions. They provide a rich understanding of the complexities of these conditions and contribute to the development of effective treatment strategies. By leveraging the power of case studies, researchers can move closer to improving the lives of individuals facing mental health challenges.

Examining a Real-life Case Study of Depression

In order to gain a deeper understanding of depression, let us now turn our attention to a real-life case study. By exploring the journey of an individual navigating through depression, we can gain valuable insights into the complexities and challenges associated with this mental health condition.

Introduction to the selected case study

In this case study, we will focus on Jane, a 32-year-old woman who has been struggling with depression for the past two years. Jane’s case offers a compelling narrative that highlights the various aspects of depression, including its onset, symptoms, and the treatment journey.

Background information on the individual facing depression

Before the onset of depression, Jane led a fulfilling and successful life. She had a promising career, a supportive network of friends and family, and engaged in hobbies that brought her joy. However, a series of life stressors, including a demanding job, a breakup, and the loss of a loved one, began to take a toll on her mental well-being.

Jane’s background highlights a common phenomenon – depression can affect individuals from all walks of life, irrespective of their socio-economic status, age, or external circumstances. It serves as a reminder that no one is immune to mental health challenges.

Presentation of symptoms and initial diagnosis

Jane began noticing a shift in her mood, characterized by persistent feelings of sadness and a lack of interest in activities she once enjoyed. She experienced disruptions in her sleep patterns, appetite changes, and a general sense of hopelessness. Recognizing the severity of her symptoms, Jane sought help from a mental health professional who diagnosed her with major depressive disorder.

Jane’s case exemplifies the varied and complex symptoms associated with depression. While individuals may exhibit overlapping symptoms, the intensity and manifestation of those symptoms can vary greatly, underscoring the importance of personalized and tailored treatment approaches.

By examining this real-life case study of depression, we can gain an empathetic understanding of the challenges faced by individuals experiencing this mental health condition. Through Jane’s journey, we will uncover the treatment options available for depression and analyze the effectiveness of the chosen approach. The case study will allow us to explore the nuances of depression and provide valuable insights into the treatment landscape for this prevalent mental health condition.

The Treatment Journey

When it comes to treating depression, there are various options available, ranging from therapy to medication. In this section, we will provide an overview of the treatment options for depression and analyze the treatment plan implemented in the real-life case study.

Overview of the treatment options available for depression

Treatment for depression typically involves a combination of approaches tailored to the individual’s needs. The two primary treatment modalities for depression are psychotherapy (talk therapy) and medication. Psychotherapy aims to help individuals explore their thoughts, emotions, and behaviors, while medication can help alleviate symptoms by restoring chemical imbalances in the brain.

Common forms of psychotherapy used in the treatment of depression include cognitive-behavioral therapy (CBT), interpersonal therapy (IPT), and psychodynamic therapy. These therapeutic approaches focus on addressing negative thought patterns, improving relationship dynamics, and gaining insight into underlying psychological factors contributing to depression.

In cases where medication is utilized, selective serotonin reuptake inhibitors (SSRIs) are commonly prescribed. These medications help rebalance serotonin levels in the brain, which are often disrupted in individuals with depression. Other classes of antidepressant medications, such as serotonin-norepinephrine reuptake inhibitors (SNRIs) or tricyclic antidepressants (TCAs), may be considered in specific cases.

Exploring the treatment plan implemented in the case study

In Jane’s case, a comprehensive treatment plan was developed with the intention of addressing her specific needs and symptoms. Recognizing the severity of her depression, Jane’s healthcare team recommended a combination of talk therapy and medication.

Jane began attending weekly sessions of cognitive-behavioral therapy (CBT) with a licensed therapist. This form of therapy aimed to help Jane identify and challenge negative thought patterns, develop coping strategies, and cultivate more adaptive behaviors. The therapeutic relationship provided Jane with a safe space to explore and process her emotions, ultimately helping her regain a sense of control over her life.

In conjunction with therapy, Jane’s healthcare provider prescribed an SSRI medication to assist in managing her symptoms. The medication was carefully selected based on Jane’s specific symptoms and medical history, and regular follow-up appointments were scheduled to monitor her response to the medication and adjust the dosage if necessary.

Analyzing the effectiveness of the treatment approach

The effectiveness of treatment for depression varies from person to person, and it often requires a period of trial and adjustment to find the most suitable intervention. In Jane’s case, the combination of cognitive-behavioral therapy and medication proved to be beneficial. Over time, she reported a reduction in her depressive symptoms, an improvement in her overall mood, and increased ability to engage in activities she once enjoyed.

It is important to note that the treatment journey for depression is not always linear, and setbacks and challenges may occur along the way. Each individual responds differently to treatment, and adjustments might be necessary to optimize outcomes. Continuous communication between the individual and their healthcare team is crucial to addressing any concerns, monitoring progress, and adapting the treatment plan as needed.

By analyzing the treatment approach in the real-life case study, we gain insights into the various treatment options available for depression and how they can be tailored to meet individual needs. The combination of psychotherapy and medication offers a holistic approach, addressing both psychological and biological aspects of depression.

The Outcome and Lessons Learned

After undergoing treatment for depression, it is essential to assess the outcome and draw valuable lessons from the case study. In this section, we will discuss the progress made by the individual in the case study, examine the challenges faced during the treatment process, and identify key lessons learned.

Discussing the progress made by the individual in the case study

Throughout the treatment process, Jane experienced significant progress in managing her depression. She reported a reduction in depressive symptoms, improved mood, and a renewed sense of hope and purpose in her life. Jane’s active participation in therapy, combined with the appropriate use of medication, played a crucial role in her progress.

Furthermore, Jane’s support network of family and friends played a significant role in her recovery. Their understanding, empathy, and support provided a solid foundation for her journey towards improved mental well-being. This highlights the importance of social support in the treatment and management of depression.

Examining the challenges faced during the treatment process

Despite the progress made, Jane faced several challenges during her treatment journey. Adhering to the treatment plan consistently proved to be difficult at times, as she encountered setbacks and moments of self-doubt. Additionally, managing the side effects of the medication required careful monitoring and adjustments to find the right balance.

Moreover, the stigma associated with mental health continued to be a challenge for Jane. Overcoming societal misconceptions and seeking help required courage and resilience. The case study underscores the need for increased awareness, education, and advocacy to address the stigma surrounding mental health conditions.

Identifying the key lessons learned from the case study

The case study offers valuable lessons that can inform the treatment and support of individuals with depression:

1. Holistic Approach: The combination of psychotherapy and medication proved to be effective in addressing the psychological and biological aspects of depression. This highlights the need for a holistic and personalized treatment approach.

2. Importance of Support: Having a strong support system can significantly impact an individual’s ability to navigate through depression. Family, friends, and healthcare professionals play a vital role in providing empathy, understanding, and encouragement.

3. Individualized Treatment: Depression manifests differently in each individual, emphasizing the importance of tailoring treatment plans to meet individual needs. Personalized interventions are more likely to lead to positive outcomes.

4. Overcoming Stigma: Addressing the stigma associated with mental health conditions is crucial for individuals to seek timely help and access the support they need. Educating society about mental health is essential to create a more supportive and inclusive environment.

By drawing lessons from this real-life case study, we gain insights that can improve the understanding and treatment of depression. Recognizing the progress made, understanding the challenges faced, and implementing the lessons learned can contribute to more effective interventions and support systems for individuals facing depression.In conclusion, this article has explored the significance of mental health case studies in understanding and addressing depression, focusing on a real-life example. By delving into case studies, we gain a deeper appreciation for the complexities of depression and the profound impact it has on individuals and society.

Through our examination of the selected case study, we have learned valuable lessons about the nature of depression and its treatment. We have seen how the combination of psychotherapy and medication can provide a holistic approach, addressing both psychological and biological factors. Furthermore, the importance of social support and the role of a strong network in an individual’s recovery journey cannot be overstated.

Additionally, we have identified challenges faced during the treatment process, such as adherence to the treatment plan and managing medication side effects. These challenges highlight the need for ongoing monitoring, adjustments, and open communication between individuals and their healthcare providers.

The case study has also emphasized the impact of stigma on individuals seeking help for depression. Addressing societal misconceptions and promoting mental health awareness is essential to create a more supportive environment for those affected by depression and other mental health conditions.

Overall, this article reinforces the significance of case studies in advancing our understanding of mental health conditions and developing effective treatment strategies. Through real-life examples, we gain a more comprehensive and empathetic perspective on depression, enabling us to provide better support and care for individuals facing this mental health challenge.

As we conclude, it is crucial to emphasize the importance of continued research and exploration of mental health case studies. The more we learn from individual experiences, the better equipped we become to address the diverse needs of those affected by mental health conditions. By fostering a culture of understanding, support, and advocacy, we can strive towards a future where individuals with depression receive the care and compassion they deserve.

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Published: 17 August 2022

The role of immunomodulators in treatment-resistant depression: case studies

Charles W. Beckett ORCID: orcid.org/0000-0003-4201-3451 1 &
Maria Victoria Niklison-Chirou ORCID: orcid.org/0000-0002-2147-370X 2

Cell Death Discovery volume 8 , Article number: 367 ( 2022 ) Cite this article

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Chronic inflammation
Neuroimmunology
Pharmacodynamics
Target identification

Depression is a common mental disorder affecting more than 264 million people worldwide. The first-line treatment for most cases of depression are selective serotonin reuptake inhibitors (SSRIs), such as sertraline, reboxetine and fluoxetine. Recently, it has been found that one-quarter of depressed patients have excessive activation of the immune system. This potentially warrants sub-categorisation of depressed patients into inflammatory and non-inflammatory subtypes. Such a sub-category of depression already exists for those not responding to various traditional antidepressants and is known as treatment-resistant depression. Those with treatment-resistant depression are far more likely to have raised inflammatory markers relative to those whose depression is treatment-responsive. Chronic, low-level inflammation seems to trigger depression via a multitude of mechanisms. These include kynurenine pathway and microglial cell activation, resulting in a reduction in hippocampal volume. Raised inflammatory cytokines also cause perturbations in monoaminergic signalling, which perhaps explains the preponderance of treatment resistance in those patients with inflammatory depression. Therefore, if treatment-resistant depression and inflammatory depression are semi-synonymous then it should follow that anti-inflammatory drugs will display high efficacy in both sub-types. Ketamine is a drug recently approved for use in depression in the USA and displays a particularly good response rate in those patients with treatment resistance. It has been suggested that the antidepressant efficacy of ketamine results from its anti-inflammatory effects. Ketamine seems to produce anti-inflammatory effects via polarisation of monocytes to M2 macrophages. Furthermore, another anti-inflammatory drug with potential use in treatment-resistant depression is Celecoxib. Celecoxib is a long-acting, selective COX-2 inhibitor. Early clinical trials show that Celecoxib has an adjuvant effect with traditional antidepressants in treatment-resistant patients. This paper highlights the importance of classifying depressed patients into inflammatory and non-inflammatory subtypes; and how this may lead to the development of more targeted treatments for treatment-resistant depression.

Psilocybin microdosers demonstrate greater observed improvements in mood and mental health at one month relative to non-microdosing controls

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Introduction.

Depression is a psychiatric disorder that affects mood, behaviour and overall health. The treatment for depression is a combination of counselling and pharmacotherapy. Depression treatment can be challenging due to drug side effects. Importantly, there is a group of depressed patients who do not respond adequately to multiple courses of appropriate pharmacotherapy, patients with treatment-resistant depression. Lack of response to antidepressants increases the risk of suicide, prolongs unnecessary suffering and is a large healthcare burden [ 1 ]. This makes finding alternative treatments for treatment-resistant patients a healthcare imperative. In this perspective, we will discuss the latest discoveries in treatment-resistant depression and how immunomodulatory drugs could be used to improve treatment responses.

Why would immunomodulators be useful in depression?

A comprehensive literature shows that depression is linked to an activation of the immune system. The primary evidence for this comes from measures of immune cytokines. Cytokines associated with Th1 activation, including tumour necrosis factor (TNF)-α, interleukin (IL)-1β and IL-6, are raised in the cerebrospinal fluid and plasma of depressed patients. Furthermore, activated microglial cells are found in patients during a depressive episode, relative to healthy control [ 2 ], and other biomarkers of inflammatory states, such as low serum iron and raised body temperature, are seen in major depression [ 3 ].

Worthy of inquiry is in which direction the relationship between inflammation and depression is causal. It is commonly known that depression-like behaviour can be induced in animals by the injection of lipopolysaccharide (LPS), which is highly pro-inflammatory [ 4 ]. In addition to this, clinical use of interferons in multiple sclerosis, among other disorders, can cause depression as a side effect. This implies that the inflammatory response can be causal in depression [ 5 ].

Further evidence for an immune system-depression link is provided by genomics data. Many genetic polymorphisms have been associated with depression but the existence of genetic roots for depression is somewhat perplexing. Through evolutionary history, depression would have increased one’s risk of death from suicide [ 6 , 7 ]. This begs the question, therefore, as to why these genes would continue to persist in the gene pool? One answer is that depression-associated genes could provide a dual function. Single nucleotide polymorphisms identified by candidate genes and genome-wide association studies to be correlated with depression risk are regularly found in immune-related genes. Indeed, 8 of the 10 gene variants that most strongly increase depression risk also have immune or inflammatory function [ 3 ]. Such evolutionary trade-offs are common, as is the case with the malaria-protective effects of the haemoglobin mutation that causes sickle cell disease for example [ 8 ]. The fact that inflammation appears to trigger depression is likely more than just an evolutionary trade-off, however, as it has been proposed that depression is a prolonged form of sickness behaviour. Indeed, the symptoms of major depression are remarkably akin to sickness behaviour. Both are characterised by sleep pattern change (insomnia, hypersomnia), appetite changes, reduced sociability (social withdrawal), irritability, low mood and reduced motivation and interest in daily activities (anhedonia) [ 3 ].

The primary mechanism proposed to mediate Th1 immune response-triggered depression is the activation of indoleamine 2,3-dioxygenase (IDO). IDO is an enzyme involved in tryptophan metabolism and is up-regulated and activated by Th1-associated cytokines, particularly IFNγ [ 9 , 10 ]. It generates kynurenine from tryptophan, diverting tryptophan away from serotonin synthesis. Kynurenine and its downstream metabolites are neurotoxic. This is due to the direct agonism of N -methyl- d -aspartate (NMDA) receptors and activation of glutamate and reactive oxygen species release from microglial cells. The resultant excitotoxicity and oxidative stress cause tissue damage, particularly in the hippocampus. Reduced hippocampal volume is a well-characterised marker seen in the brains of depressed people [ 11 , 12 , 13 , 14 ]. Further support for this hypothesis is found in the fact that LPS was unable to engender depression-like behaviour in IDO1 knockout mice, suggesting inflammation-induced depression is IDO-dependent [ 15 ].

In addition to activation of the kynurenine pathway causing hippocampal neurotoxicity, inflammation also seems to attenuate hippocampal neurogenesis. IL-6, IL-1 and TNFα, for example, all suppress the neurotrophin, brain-derived neurotrophic factor (BDNF). BDNF is known to ameliorate depressive symptoms, in part by increasing hippocampal neurogenesis, which is consistently decreased in depressed individuals [ 16 ]. Also, TNF-α increases the activity of nuclear factor kappa B, which further suppresses neurogenesis [ 17 ]. Furthermore, the inflammation affects monoaminergic signalling in the brain via mechanisms beyond those associated with IDO-induced tryptophan depletion. IL-1 and TNF-α increase phosphorylation of the serotonin transporter (SERT). This leads to increased translocation of SERT into the neuronal membrane, resulting in increased serotonin reuptake and reduced response to SERT-blocking antidepressants [ 18 ]. Furthermore, chronic, low-grade inflammation seems to reduce dopamine synthesis, packaging and release. Reduced dopamine neurotransmission increases depression risk, particularly symptoms of anhedonia, fatigue and psychomotor retardation [ 19 ].

Despite the link between inflammation and depression being incontrovertible, there is a large degree of heterogeneity between patients regarding inflammatory status. Around one-quarter of patients with depression have raised low-level inflammation [ 20 ]. This suggest that depressed patient can be categorised into non-inflammatory and inflammatory subtypes [ 21 ]. Furthermore, akin to the heterogeneity observed regarding the presence of inflammation in depressed populations, traditional antidepressants display a large degree of heterogeneity in their efficacy. On average, traditional antidepressants seem to show around a 25% remission rate, 75% response rate and 25% non-response rate, though this differs substantially from study to study [ 1 , 22 , 23 ]. Interestingly, treatment-resistant depression is far more often accompanied by increased Th1 cytokines relative to that which is treatment-responsive [ 24 , 25 ]. This data suggests that the inflammatory depression subtype is also characterised by resistance to traditional antidepressants and, therefore, investigation of the efficacy of immunomodulators in treatment-resistant depression would be prudent.

The salience of an inflammatory depression subtype, appropriately targeted by immunosuppressives, is further supported by a number of other noteworthy facts from the literature. Firstly, depression that is co-morbid with previous childhood ill-treatment is associated with an increase in inflammation and is more often treatment-resistant [ 26 ]. That is, relative to depressed patients with a more normative parental environment. Similarly, acute stress seems to increase immune function, in direct contrast to chronic stress [ 27 ]. This is important in social stress, which increases pro-inflammatory cytokines in both humans and rodents [ 28 , 29 ] (Fig. 1B ). Furthermore, trials of cytokine inhibitor use in depression, such as infliximab (anti-TNFα) and sirukumab (anti-IL-6), selectively display efficacy in patients with low- inflammation levels prior to treatment [ 30 , 31 ]. This selective efficacy will be a theme when discussing other immunomodulators (ketamine and celecoxib) during the rest of the paper.

A A diagram comparing M1 and M2 type macrophages. M1 macrophages are activated by pro-inflammatory signals, including LPS, Th1 cytokines (IFNγ, TNFα) and Th1 cell surface proteins (CD40L). M2 macrophages are activated by Th2 cytokines (IL-4, IL-10, IL-13, IL-21) and by ketamine. M1 macrophages produce pro-inflammatory cytokines, as well as nitric oxide (NO) and neurotoxic kynurenine and reactive oxygen species and thus mediate the Th1 response. Ketamine causes the polarisation of monocytes to M2 macrophages. This acts to promote the release of anti-inflammatory TGFβ, IL-10 and ornithine. It also reduces monocyte differentiation to M1 macrophages, hampering the effects of Th1 dominance. B A schematic showing the possible causes of Th1 dominance in patients with treatment-resistant depression and the downstream consequences of this excessive Th1 immune activation. Indoleamine 2,3-dioxygenase (IDO) activation is central to inflammation-induced depression via its effects on tryptophan metabolism and generation of neurotoxicity. IDO-mediated changes in tryptophan metabolism reduce serotonin, as does phosphorylation of serotonin transporters (SERT). Brain-derived neurotrophic factor (BDNF) exacerbates the negative effects of IDO activation on hippocampal volume by reducing hippocampal neurogenesis, as does nuclear factor kappa B (NFkB) induction.

Ketamine is a drug that has been used for many years as a fast-acting non-barbiturate general anaesthetic. It also has well-established use in hypotensive shock, reactive airway disease, analgesia and procedural sedation. It is primarily an NMDA receptor antagonist, but its pharmacodynamics are complex. It also shows interactions with opioid, cholinergic, purinergic and adrenergic receptors, as well as with ion channels not gated by endogenous ligands [ 32 ]. More recently, ketamine has found its purpose in depression, being approved by the US food and drug administration in 2019 [ 33 ].

Ketamine has a remarkably high response rate in treatment-resistant depression (~65%) but the exact mechanisms underlying its antidepressant effects are unknown, particularly as other NMDA receptor antagonists possess non-comparable effects [ 34 , 35 ]. While a multiplicity of mechanisms have been proposed, such as effects on opioid and AMPA receptors [ 36 , 37 , 38 ], ketamine seems to produce a number of its effects via immunomodulatory mechanisms. A systematic review of 9 human studies and 22 animal studies [ 39 ] found that ketamine consistently produced reductions in IL-1β, IL-6 and TNFα. Furthermore, in all but one study in which it was measured ketamine was found to decrease IDO activity and reduce the prevalence of downstream neurotoxic metabolites [ 39 ]. Moreover, the magnitude of reduction in IL-6 and IL-1β is associated with the magnitude of ketamine’s antidepressant effect [ 40 ].

Little is known about ketamine’s immunomodulatory mechanism. It is likely to be the result of some direct effect on leucocytes [ 41 ]. This is evidenced by the fact that ketamine can reduce pro-inflammatory cytokine production in isolated human blood. One current hypothesis as to how ketamine does this, is that it causes macrophage polarisation to an M2 phenotype [ 42 ]. Macrophages can be categorised into M1 and M2 type macrophages. Th1 cells induce differentiation to M1 macrophages via the expression of IFN-γ and CD40 ligand. These macrophages are highly pro-inflammatory. Meanwhile, Th2 cells induced differentiation to anti-inflammatory M2 macrophages via the expression of IL-4 and IL-13. M1 macrophages preferentially produce many of the pro-inflammatory immune cytokines seen in depression, such as IL-6 and TNF-α, as well as pro-inflammatory nitric oxide (NO) and reactive oxygen species (Fig. 1A ). Therefore, by ketamine polarising macrophages to the M2 type, it may be opposing the downstream effects of the Th1 dominance observed in depression [ 43 , 44 ]. Furthermore, IDO is more highly expressed in M1 macrophages, which may explain how ketamine reduces its activity [ 45 ]. The exact mechanism via which ketamine causes monocytes to differentiate into M2 macrophages is unclear but it seems to occur via an mTOR-dependent mechanism and involved increased expression of CD163 and MERTK [ 42 ].

Microglia also possess an M1 and M2 bifurcation in phenotype and LPS induces differentiation of microglia to an M1 type via activation of toll-like receptors, hence its neuroinflammatory effect [ 46 ]. Ketamine blocks LPS-induced M1 differentiation in both peripheral and central nervous system macrophages [ 47 , 48 ]. Furthermore, M2 microglia preferentially produce transforming growth factor (TGF)-1β, which produces anti-inflammatory effects in the brain. Ketamine can prevent reductions in TGF-1β levels induced by chronic social defeat stress in mice (Fig. 1B ). Meanwhile, the use of an anti-TGF-1β antibody in mice experiencing chronic social defeat stress blocks the antidepressant effects of ketamine [ 49 ].

Celecoxib is a long-acting, selective cyclooxygenase-2 inhibitor. It is of similar potency to ibuprofen but is used in patients with mild to moderate pain and/or with arthritis, who cannot tolerate the gastrointestinal side effects of traditional non-steroidal anti-inflammatory drugs (NSAIDs). Celecoxib works by inhibiting pro-inflammatory protein synthesis [ 50 ]. Downstream effects of this protein synthesis inhibition are facilitated by alterations in cell-cell interactions, vascular tone and permeability, cytokine production and receptor expression and leucocyte maturation, migration and survival [ 51 ].

Several studies had shown that Celecoxib is useful in treatment-resistant and inflammatory depression. Greater over-the-counter NSAID use is associated with reduced depression rates in the Danish population, suggesting NSAIDs may be antidepressant per se [ 52 ]. Furthermore, depression is associated with raised body temperature, perhaps suggesting a role of prostaglandin E2 in its pathophysiology [ 43 , 53 ]. The effects of NSAIDs on immune cytokines also indicate potential efficacy. As mentioned previously, IL-6 is the most commonly raised immune cytokine in depression. The synthesis of IL-6, downstream of CD40 activation in B cells, is dependent upon COX-2 [ 54 ]. This, among other mechanisms, means that NSAIDs reliably reduce levels of IL-1 and IL-6, leading to a reduction in the Th1 response [ 55 , 56 ]. Lastly, one of the ten gene polymorphisms most strongly associated with depression is found is adcy3 , which codes for adenylate cyclase 3. Adenylate cyclase 3 plays an integral role in the signal transduction downstream of prostaglandin receptors, further providing evidence of a role for COX-2 inhibition in depression [ 3 ].

Multiple clinical trials have been conducted using celecoxib in depressed individuals. In 2014, a meta-analysis of celecoxib trials was conducted [ 57 ]. Monotherapy and therapy adjunctive to traditional antidepressants in depressed individuals with and without co-morbid inflammatory disease were found to be superior to placebo and to traditional antidepressants alone [ 57 ]. It is worth noting that it is difficult to draw conclusions from trials in patients that had a co-morbid inflammatory disease (6 out of 11 of those included in the meta-analysis). This is because there is no mechanism to control for the potential that the antidepressant effect resulted indirectly via amelioration of discomfort (swelling, pain, etc.) rather than via some direct action. Saying this, more trials have been conducted since 2014 with some promising results. The effect of sertraline, a common drug to treat depression, with celecoxib versus sertraline alone in drug-naive depressed women was assessed [ 58 ]. Although no statistically significant difference in the mean reduction of Hamilton Depression Rating Scale scores was observed at eight weeks, the response rate and remission rate were much higher in the celecoxib group relative to the sertraline-only group. The response rate was 100% in the group taking both sertraline and celecoxib compared with 78% in the group taking sertraline alone. Furthermore, and perhaps most worthy of attention, the remission rate in the celecoxib group was 57% relative to 11% in the placebo group. This trial suggests that Celecoxib directly targets individuals who would normally be treatment-resistant (Fig. 2 ) Also, Celecoxib adjunct therapy was found to reduce serum IL-6 in depressed patients and the magnitude of the IL-6 reduction predicted the magnitude of the antidepressant effect [ 59 ]. Moreover, Celecoxib seems to target the IDO pathway. Higher kynurenine serum concentrations prior to treatment were found to predict remission in patients given Celecoxib adjunct therapy [ 60 ]. Lastly, Celecoxib monotherapy is better than placebo in rat models of depression and its use is associated with reduced levels of immune cytokines [ 61 ]. It also potentiates the effect of reboxetine and fluoxetine, common drugs to treat depression, on cortical noradrenaline and serotonin output in rats [ 62 ]. Considering this data, Celecoxib seems to be targeting treatment-resistant depression via the Th1-associated immune pathways.

A bar chart showing the percentage response rate, remission rate and non-response rate to antidepressant treatment. Responses to three drug treatments are compared: the average first-line selective serotonin reuptake inhibitor (SSRI) (numbers derived from analysis of the wider literature [ 1 , 22 , 23 ], shown on the left, sertraline in drug-naive women (data taken from [ 58 ]), shown in the centre, and sertraline in combination with celecoxib in drug-naive women [ 58 ], shown on the right. The addition of celecoxib to SSRI treatment dramatically increases the percentage rate of response (100% taking celecoxib vs 75–78% taking placebo/nothing) and rate of remission (57% taking celecoxib vs 11–25% taking placebo/nothing). This provides evidence as to the efficacy of celecoxib as an adjunctive treatment and suggests it may actively target the treatment-resistant depression subtype.

Unfortunately, there are contradictory results within the literature. For example, the only long-term trial of Celecoxib monotherapy in depression conducted to date, found no treatment effect [ 57 , 63 ]. This could be due to the lack of selection for individuals with inflammatory depression, rather than a lack of drug effect. On the other hand, the dearth of long-term proven efficacy and tolerability of Celecoxib in depression is concerning. While the absence of COX1 inhibition obviates the risk of gastrointestinal side effects to Celecoxib use [ 50 ], its selectivity could increase the risk of blood clotting due to preferential inhibition of prostacyclin vs thromboxane A2 synthesis [ 64 ]. Saying this, in the 2014 meta-analysis [ 57 ], the authors found no significant increase in cardiovascular incidence when using Celecoxib in depression, but the lack of long-term trials included in the study makes this finding unconvincing at this point. Also noteworthy is that while NSAID use is correlated with antidepressant effects, this is not a black and white finding. Chronic use of over-the-counter NSAIDs or the use of high doses of aspirin was associated with increased depression incidence [ 52 ]. While this is only a correlative finding, it could indicate that Celecoxib would not be applicable to long-term depression treatment.

Conclusions and perspectives

Depression is a complex condition. It is well accepted that inflammation can be a core feature of depression. Treatment-resistant depression is a subtype of depression, frequently characterised by enhancement of the Th1 cell-mediated and inflammatory immune responses. This subtype of depression could be amenable to improvement with the use of immunomodulatory drugs. Th1 dominance seems to cause treatment-resistant depression by causing reductions in hippocampal volume, mediated by IDO activation, microglial activation and reduction in neurogenesis, and perturbations in monoaminergic neurotransmission. Clinical trials of immunomodulators in depression thus far have been thwarted by the lack of an inflammatory depression subtype classification but have still shown promising results nonetheless.

Ketamine is an exemplar of how effective a drug with immunomodulatory mechanisms can be in treatment-resistant depression. It seems to act via M2 polarisation of macrophages, which results in attenuation of the Th1 immune response. Celecoxib is a pharmacotherapy in earlier stages of development for treatment-resistant depression, which has shown exciting results. This is especially true when used as an ancillary to traditional antidepressants, notably increasing the response and remission rate. Further research is needed to elucidate the exact mechanism by which ketamine causes monocytes to differentiate into M2 macrophages and to what extent its efficacy in treatment-resistant depression is the result of immunomodulation. Moreover, future research should focus on confirming the efficacy, safety and tolerability of Celecoxib in long-term clinical trials in individuals with inflammatory depression.

Data availability

All the data used to support the arguments in this study are included within the article.

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Beckett, C.W., Niklison-Chirou, M.V. The role of immunomodulators in treatment-resistant depression: case studies. Cell Death Discov. 8 , 367 (2022). https://doi.org/10.1038/s41420-022-01147-6

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Case 1: Newly Diagnosed Treatment Resistant Depression

Lisa Harding, MD, and Angelos Halaris, MD, PhD, APA, ACNP, CINP, review a case of a 26-year-old male patient who was recently diagnosed with treatment resistant depression

EP: 1 . Case 1: Newly Diagnosed Treatment Resistant Depression

Ep: 2 . case 1: an overview of treatment resistant depression, ep: 3 . case 2: prescribing intranasal esketamine, ep: 4 . case 2: starting a patient on intranasal esketamine, ep: 5 . targeting the glutamatergic system for treatment resistant depression, ep: 6 . phase 3 transform trial, ep: 7 . intransal esketamine and rems, ep: 8 . health care requirements to be aware of for intranasal esketamine, ep: 9 . advising clinicians on using intranasal esketamine.

Lisa Harding, MD: Welcome to this Psychiatric Times® Case-Based Psych Perspectives titled “Managing Patients With Treatment-Resistant Depression.” I’m Dr Lisa Harding, a board-certified psychiatrist and a clinical instructor of psychiatry at the Yale School of Medicine in New Haven, Connecticut. Joining me is the esteemed Dr Angelos Halaris, a board-certified psychiatrist and a professor of psychiatry at Loyola University Chicago’s Stritch School of Medicine in Maywood, Illinois. The goal of our discussion is to share insights in diagnosing treatment-resistant depression [TRD] and reasons for inadequate treatment response with antidepressant therapy, as well as to provide a brief overview of available treatment options and to offer recommendations on how treat patients with this disorder. Welcome, Dr Halaris.

Angelos Halaris, MD, PhD, APA, ACNP, CINP: Thank you so much for inviting me. I’m honored by your invitation and glad to be here.

Lisa Harding, MD: It’s nice to see you again. We’ll start by reviewing a couple of case scenarios. The first case presentation, No. 1, is a patient newly diagnosed with treatment-resistant depression. A 26-year-old man with a history of major depressive disorder for over 7 years presents with complaints of trouble sleeping as well as feeling unhappy, worried, and fatigued. He gradually developed sleeping difficulty as well as low mood and loss of interest. He tried multiple treatments, including escitalopram, fluoxetine, venlafaxine, and bupropion. However, his symptoms weren’t fully relieved. He was subsequently diagnosed with treatment-resistant depression. The patient reports having an average childhood, being an average student, and having good relationships with coworkers and no problems at work. He was always involved in psychotherapy, and denied any drug or alcohol use.

My overall impression of this case is this is a young patient who’s supposed to be living the life actuation part of his life, and he has now tried and failed more than 2 antidepressants. One of the things coming to mind was, is he stopping these antidepressants because of adverse effects, as I see in my clinical practice? What was the adequate dose of the adequate trial in terms of these medications that he was prescribed? Dr Halaris, what are your overall impressions of the case?

Angelos Halaris, MD, PhD, APA, ACNP, CINP: Much like what you just said, Lisa, as presented, this brief case scenario leaves many more unanswered questions, some of which you already touched upon. I’d like to reinforce your own questions and add a few of mine as well. First and foremost, what kind of work-up was done prior to diagnosing the patient and then treating him with the list of mainly SSRIs [selective serotonin reuptake inhibitors] and SNRIs [serotonin and norepinephrine reuptake inhibitors]? By that, I mean where other factors that are known to contribute to depression, and especially TRD, had they been carefully assessed by means of a thorough psychiatric diagnostic evaluation and the pretty much established blood work that we know is essential, such as ruling out endocrinopathies, assessing HPA [hypothalamic-pituitary-adrenal] function, looking at diabetes, inflammatory conditions, any chronic medical illnesses that invariably lead to chronic inflammation, including neuroinflammation. Because if there’s an inflammatory focus elsewhere in the body, these pro-inflammatory substances known as cytokines invariably make their way into the brain parenchyma and stimulate microglia and astrocytes to also become inflamed. So we have a relocation of the peripheral inflammation into the brain leading to neuroinflammation. These are all factors that I’d like to see addressed.

Other issues are obviously vitamin deficiencies, notably vitamin D, especially during the winter months. But this also happens in summer months in susceptible individuals. I’m amazed at the frequency of vitamin D deficiency, including in young people. Unless we make a point to check these issues routinely at the initial evaluation, some of the symptoms of vitamin D deficiency resemble symptoms of depressive disorder with anxiety, low energy, low motivation, sense of desperation, attention-focusing issues and so on. The good news is that it’s fixable by administering the right supplementation of vitamin D.

This transcript has been edited for clarity.

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ORIGINAL RESEARCH article

A qualitative investigation of the montgomery–åsberg depression rating scale: discrepancies in rater perceptions and data trends in remote assessments of rapid-acting antidepressants in treatment resistant depression.

1 WCG Clinical Endpoint Solutions, Princeton, NJ, United States
2 Seton Hall University, South Orange, NJ, United States
3 Department of Psychiatry, Whanganui District Health Board, Whanganui, New Zealand
4 Chelsea and Westminster Hospital NHS Foundation Trust, London, United Kingdom
5 The PANSS Institute, New York, NY, United States

Introduction: Despite the development of many successful pharmaceutical interventions, a significant subset of patients experience treatment-resistant depression (TRD). Ketamine and its derivatives constitute a novel therapeutic approach to treat TRD; however, standard tools, such as the Montgomery–Åsberg Depression Rating Scale (MADRS) are still being used to measure symptoms and track changes.

Methods: The aim of this study was to review item-level differences between rate of data change (MADRS score) and rater-weighted perception of the most useful items for assessing change in symptoms while remotely conducting the 10-item version of the MADRS in TRD in a clinical trial of rapid-acting antidepressants. Two studies of rapid-acting antidepressants in the treatment of TRD were used to identify item-scoring trends when MADRS is administered remotely and repeatedly (733 subjects across 10 visits). Scoring trends were evaluated in tandem to a rater survey completed by 75 raters. This was completed to gain insight on MADRS items’ perceived level of helpfulness when assessing change of symptoms in rapid-acting antidepressant trials.

Results: MADRS items ‘Reduced sleep’, ‘Apparent sadness’, and ‘Pessimistic thoughts’ were found to have the greatest average data change by visit, while raters ranked ‘Reported sadness’, ‘Lassitude’ and ‘Apparent sadness’ as the most helpful items when assessing symptom change.

Discussion: The diversion between rate of data-change ranking and rater perception of helpfulness could be related to difficulty in assessing specific items, to the novel treatment itself, and/or to the sensitivity to symptom change to which raters are accustomed in traditional antidepressant treatments.

1 Introduction

In addressing the challenge of treatment-resistant depression (TRD), this manuscript focuses on the evolving landscape of depression treatment and the role of rapid-acting antidepressants (RAAD), such as ketamine and its derivative, esketamine. These novel treatments, approved by the FDA in 2019 for TRD ( 1 ), constitute a significant advance in the pharmacotherapy of depression, but pose new questions about the effectiveness of traditional assessment tools like the Montgomery–Åsberg Depression Rating Scale (MADRS) in evaluating rapid symptom changes. The 10-item MADRS, a standard in clinical research and practice, is used to measure symptom severity and changes in depressive syndromes, yet its suitability for novel, rapid-acting treatments remains under-explored, particularly in the context of remote administration ( 2 , 3 ).

Depression, a leading contributor to global disability, affects about 322 million people worldwide, with an 18.4% increase in prevalence between 2005 and 2015 ( 4 ). Traditional treatments have evolved significantly since the 1950s, from tricyclic antidepressants and monoamine oxidase inhibitors to second-generation antidepressants like SSRIs and SNRIs. However, for a substantial subset of patients, these treatments fail to provide full or partial remission, leading to the classification of TRD ( 5 – 7 ). The complexity of defining and measuring treatment resistance, as highlighted by studies like the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial, underscores the need for effective treatment options and reliable assessment methods ( 8 – 10 ). Rapid-acting antidepressants, a departure from the monoaminergic focus of traditional treatments, offer a promising avenue for treating TRD. Ketamine, antagonizing the NMDA receptor, induces a glutamate surge, fostering brain adaptability and pathway creation ( 11 , 12 ). Its rapid symptom relief, observed within 24 to 72 hours post-administration, marks a stark contrast to the gradual effects of conventional antidepressants ( 13 – 15 ). However, the long-term effects and optimal dosing of ketamine remain areas for further research ( 16 – 18 ).

The MADRS, designed to sensitively capture treatment-induced symptom changes in depression, has historically been administered in-person but is increasingly used in remote settings. This shift raises concerns, particularly for items like ‘Apparent sadness,’ which rely on observational assessment ( 19 – 24 ). Despite its widespread use and proven interrater reliability across various languages, the appropriateness of MADRS for rapid-acting antidepressants, especially in remote settings, warrants examination ( 25 – 35 ). In addition to that RAADs can generate responses within hours or days, rather than weeks or months, while our rating instruments were designed to assess mood symptoms over a 7-day time frame, typically. Consequently, adaptative approaches are required for existing scales to meet the need of adjustment. Yavorsky et al. provide an excellent summary on adaptations to the standard rating instruments allowing to reflect short-term changes in which RAADs act, as well as implementing novel rating measures. Critically, they also discuss limitations and challenges to the currently used rating measures including any conceptual biases of raters ( 36 ).

This publication, therefore, seeks to analyze the item-level differences in MADRS scores and the raters’ perception of their helpfulness in determining symptom severity change in TRD during clinical trials of rapid-acting antidepressants. The study hypothesizes that while some MADRS items may align with rater perceptions, discrepancies are expected due to the unique nature of rapid-acting antidepressant treatment and the challenges of remote assessment. This investigation is crucial for ensuring that depression assessment tools remain relevant and effective in the rapidly evolving landscape of antidepressant therapy.

2 Materials and methods

2.1 data change on madrs items in two protocols of a phase 3 study.

We have established the rate of change of all items of MADRS for the entirety of two separate protocols of a Phase 3 study of rapid-acting antidepressants using a similar design, but varying in terms visit frequency (between 24 hours and 7 days). We used deidentified datasets only containing the visit date, but no other information that would be considered protected health information, only to perform a qualitative, exploratory analysis looking at individual MADRS items and changes in their score across visits. Rate of change was calculated by first determining between-visit MADRS data change by item; we then divided the per-item change by the number of days that had passed between an individual visit and the previous visit to establish change per day. We rank-ordered the per-visit change for a given item. The data was solely used to create the rankings discussed, and no other quantitative analysis has been completed for the purpose of this study.

2.2 Remote rater-experience survey

We conducted a survey of clinical research professionals who have participated in the above-mentioned Phase 3 programs of rapid-acting antidepressants utilizing new modalities for the assessment of depressive symptoms, including remote evaluation technologies (telepsychiatry) and versions of MADRS that have been adapted for use over shorter recall periods, e.g. last 24 hours. A total of 75 experienced raters from 13 countries were recruited for this survey study, all of whom participated in at least one TRD clinical trial for rapid-acting antidepressants. All survey participants were polled about which MADRS items they considered most and least helpful for assessing changes in symptoms in two protocols of a Phase 3 study with the same rapid-acting agent to treat TRD. Many raters participated in these studies on almost a daily basis, with a rotation of subjects. Items considered as most useful as endorsed by raters for assessing TRD related symptom changes were ranked as most important, second most important, and so on.

We compared the data change on MADRS items and the raters’ rankings to gain additional insight into a.) the rater experience of each item’s helpfulness in determining depression severity and b.) the rate of change in MADRS scores in a clinical trial for rapid-acting antidepressants in TRD.

Surveyed raters conducted assessments via telephone to ensure the integrity of blinding in these trials. Raters were selected from studies with more than 700 subjects combined (N = 733) across 20 countries and 16 different languages. In the two examined protocols of a Phase 3 study with the same rapid-acting agent to treat TRD, 733 subjects were included in the dataset across 10 visits (excluding early termination; 12 in visits in total). MADRS total scores ranged from 0 to 53 across visits for each protocol. Mean total MADRS score was 30.39. Individual item scores ranged from 0 to 6 for each item. Ranking of items was determined by rater perception of helpfulness in assessing symptom change, and by average MADRS score change in between-visit data. Rater ranking order (from 10 to 1) correlates with an increasing level of perceived helpfulness in determining symptom change, with 1 being the most helpful. Data ranking of MADRS items based on the deidentified dataset (from 10 to 1) correlates with a greater average rate of MADRS item-level score change between visits, with 1 representing the item with the greatest average between-visit MADRS item score change. Helpfulness of MADRS items in assessing symptom change, ranked according to average rate of data change and by rater perception of helpfulness is shown in Figure 1 , with a ranking in the first position representing the MADRS item with either the highest rate of symptom change according to greatest average between-visit MADRS data change (‘By data change’) or as being perceived as the most helpful by raters (‘By rater perception’) when assessing symptoms.

Figure 1 Ranking of MADRS items according to rater perception and data change. a Ranking by data change: ranking from tenth position to first position correlates with the average rate of data change between visits, with first position representing the MADRS item with the highest rate of symptom change according to data. b Ranking by rater perception: ranking from tenth position to first position correlates with the degree of helpfulness in assessing symptoms of depression, with first position representing the MADRS item perceived as the most helpful when assessing symptom change.

‘Reduced sleep’, ‘Apparent sadness’, and ‘Pessimistic thoughts’ were found to have the highest rate of data change by visit, however raters ranked ‘Reduced sleep’ in the ninth position of helpfulness, with ‘Apparent sadness’ and ‘Pessimistic thoughts’ placed third and fifth in terms of perceived helpfulness. In contrast, ‘Reported sadness’ was perceived as being the most helpful item for raters in assessing symptoms of depression but ranked seventh according to rate of data change. ‘Reduced appetite’ was ranked as the least useful item by rater perception, while rate of data change places this in the sixth position of helpfulness. ‘Suicidal thoughts’ was ranked sixth according to rater perception but placed tenth by rate of data change. The low ranking according to data change was caused by the smallest average change between visits for this item. ‘Inability to feel’ is ranked as the fourth most helpful item in terms of rater perception and shows the second lowest average change between visits in the data, ranking ninth according to data change.

4 Discussion

When examining the first item of the MADRS, ‘Reported sadness’, the account of symptoms and depression by subjects is the most important evidence one can receive. However, several factors including age of subject, age at onset of first depressive episode, education, length of illness, etc. could influence how sadness is being reported by subjects ( 37 ), and these may be due to differences in how depression has been conceptualized ( 38 ). Therefore, relying heavily on clinical background can be imperative for the clinician when administering and scoring the MADRS. ‘Apparent sadness’ also appears to be of critical interest, as it would have been presumed as a difficult item to assess in remote assessments. Nevertheless, both earlier studies by Kobak et al. ( 32 ) and a recent study by Sumiyoshi et al. conducted in Japan ( 39 ) found excellent reliability for the MADRS in remotely interviewing patients with MDD showing high consistency between remote and in-person interviews. These studies also emphasize the key importance of well-trained raters, which might be critical for accurately assigning ratings for more challenging items such as ‘apparent sadness’.

Appetite changes are not shared symptoms across all subjects with depression, and there can be marked increases or decreases in appetite in patients diagnosed with depression. Simmons et al. ( 40 ) report that only 48% of subjects experience reduced appetite, leaving 52% unaccounted for. Raters may be noticing this disconnect, with the focus of appetite in MADRS relying on reduction as opposed to bidirectional change and weighting the importance of this item accordingly.

We believe the discrepancy between rater-perceived ranking and ranking based on rate of data change for ‘Reduced sleep’ is due to the variability in the data itself. Sleep as a construct may be moving too quickly to be meaningful to raters more used to conducting traditional assessments. Additionally, sleep can move in more than one direction (i.e., it can both increase and decrease), and can be attributed to multiple factors not including symptoms of depression or drug treatment effects (environmental, pain, change in caffeine consumption, etc.), potentially deprioritizing sleep to raters and encouraging them to rely more heavily on other items.

Ranking for raters on ‘Suicidal thoughts’ (6 th on perceived helpfulness, but last on data change order) could demonstrate the importance of this item when it is reported, although the frequency of report may be low, and overall severity in most cases may also be low. When patients report suicidal thoughts or behaviors during MADRS interviews, the weight of this item may increase for raters, although the frequency with which it is seen in our dataset was also low.

In assessing depression, ‘Inability to feel’, involving changes in emotions, may become one of the most useful items to assess overall depression when reported, however, it may again be an item that infrequently shows large variability over shorter assessment periods.

The performance of traditional assessments to obtain ratings/scores for studies of novel treatments for depression is a critically important matter. It is imperative to reliably measure changes in symptoms—particularly for treatments that may carry unique side-effect profiles and safety risks. Researchers have used assessments on the widely agreed upon core symptoms of depression for decades. MADRS, though widely accepted by regulatory agencies and used by clinicians and researchers, may have a different value and clinical significance in the context of rapid-acting antidepressants. Using MADRS in short-interval, remote evaluations, with repeated assessments performed within 24 hours, might present challenges in accurately capturing symptom change. Certainly, not all depressed individuals have the same depressive symptoms at baseline and the dynamic nature of a therapeutic response to RAADs could potentially result in a rapid alleviation of certain symptoms, e.g. improvements in subjective mood, while leaving some other functional aspects less improved, resulting in a heterogeneity of symptom resolution and a sense of uncertainty in raters ( 36 ).

As research increasingly supports the use of rapid-acting antidepressants, and as their market approval increases, it is incumbent on clinicians to review and refine assessment processes. The rapid change in symptoms presents a challenge for clinicians, especially if the assessment is administered remotely. This study has compared how the rate of between-visit data changes for MADRS items relate to those that raters have identified as being more helpful or more challenging. The diversion between rate of data-change and rater perception of helpfulness could be related to several factors, one being difficulty in assessing specific items. A depression rater training study by Sajatovic et al. ( 41 ) showed no significant difference between raters based on country, level of experience with diagnosis, or previous training in terms of the items they identified as the most difficult to rate, namely ‘Apparent sadness’, ‘Inner tension’, ‘Concentration difficulties’, ‘Lassitude’, and ‘Inability to feel’. Similar results have been shown when comparing rater training using MADRS to other mood rating scales, such as the Hamilton Depression Rating Scale (HAM-D) and Young Mania Rating Scale (YMRS) in a bipolar disorder trial ( 42 ). We see the same MADRS items (listed above) identified as most difficult to rate with no significant difference for raters based on country, experience, diagnosis, or previous training, suggesting the items themselves present difficulty to raters ( 43 ). MADRS has also been noted as a more difficult scale to utilize when compared to other commonly used depressive symptom rating scales (HAM-D and YMRS), thus it could benefit from further insight and qualitative analysis ( 41 ).

A recent factor analysis conducted on two esketamine trials has taken an interesting approach to explore potential symptomatic clusters grouped around the rate of symptom change as detected by MADRS items ( 44 ). Three factors were identified labelled as affective/anhedonic symptoms (apparent sadness, reported sadness, lassitude, inability to feel); anxiety and vegetative symptoms (inner tension, reduced sleep, reduced appetite, concentration difficulties); and hopelessness (pessimistic thoughts, suicidal thoughts). Strikingly, our results on rater perception of items follows exactly these clusters with the affective/anhedonic factor listed as the most helpful for raters, followed by the hopelessness factor, and lastly the anxiety and vegetative factor symptom group, which is probably prone to the highest variability and heterogeneity overall.

Thus, another aspect potentially explaining the diversion between MADRS score changes and raters’ perceptions could be the novel treatment itself or the sensitivity to the consequent symptom change to which raters are accustomed. An example for this is the fluctuation of sleep on a day-to-day basis that can affect daily ratings when assessing change in symptoms with the MADRS. Research indicates that MADRS item responses - those related to sleep, most specifically - change in relation to patient experience and when compared to the prior administered assessment. Sleep, however, also influences memory and emotional memory in subjects with depression ( 45 ) and this can present additional challenges when ratings are conducted based on subject’s report every day or every other day, as is common in clinical trials of rapid-acting antidepressants.

There are inherent complications in the assessment of rapid change in TRD over short periods of time and rapid (i.e., between-visit) symptom changes at the item level can pose a challenge to ranking and assessing for severity. Singh et al. ( 14 ) examined the efficacy of both full and abbreviated MADRS scores in evaluating the response to intravenous esketamine. They conducted assessments of depressive symptoms at 2-, 4-, and 24-hours post-infusion, opting to omit the sleep and appetite items for the shorter 2 and 4-hour assessments. Johnson and colleagues also investigated the MADRS’s suitability within a 24-hour recall period, finding comparable content validity and high internal consistency and test-retest reliability ( 46 ). While most participants deemed a 24-hour recall period sufficient for assessing meaningful changes in depression symptoms, reduced sleep and appetite were noted exceptions, echoing Singh et al.’s decision to exclude these items. Yavorsky et al. analyzed esketamine trial data, revealing the MADRS’s sensitivity to change over short periods, albeit with limited responsiveness in the sleep and suicide items ( 47 ). These findings underscore the challenge of effectively capturing clinical change within a 24-hour timeframe, particularly for traditional depressive symptoms like mood, appetite, and sleep disturbances. Novel treatments for TRD have yet to yield novel assessments that are sensitive to change over such short periods of time.

Each item discussed and present in the MADRS is important in assessing overall depression and symptomology of the diagnoses, and clinical experience brings clarity to differences in items and scores, such as the importance of suicidal thoughts and sleep deprivation.

5 Conclusion

Rapid-acting antidepressants, such as ketamine and its derivatives, appear to induce more rapid changes in depression symptoms, which presents a challenge in the accurate capture of symptom change when using conventional rating scales. Our findings indicate a diversion between the rate of data change as measured by MADRS scores vs the raters’ perception of helpfulness of specific MADRS items in determining clinical improvement and depression severity change. While each MADRS item remains important in an assessment of depression symptomatology, it may be beneficial to refine raters’ sensitivity to changes in depressive symptoms over short periods of time, and to the specific side effects associated with novel treatment approaches. Further to this, gaining experience in the use of rapid-acting antidepressants, and in the ability to measure short and long-term effects of these novel treatments, might influence our notion of defining criteria for treatment resistance in depression.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

Ethical review and approval was not required for the study of human participants in accordance with the local legislation and institutional requirements. Written informed consent from the participants was not required to participate in this study in accordance with the national legislation and the institutional requirements.

Author contributions

GC: Writing – review & editing, Writing – original draft. RB: Writing – review & editing, Writing – original draft. AM: Writing – review & editing, Writing – original draft. SN: Writing – review & editing, Writing – original draft. MO: Writing – review & editing, Writing – original draft.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Conflict of interest

GC, RB and SN are employed by WCG Clinical Endpoint Solutions. AM is a contractor of WCG Clinical Endpoint Solutions and a part-time employee of Whanganui District Health Board, Whanganui, NZ. MO is employed by WCG Clinical Endpoint Solutions; has received royalties from Pearson Inc. from sales of the PANSS Manual; and has received past grant funding from NIH, Brain & Behavior Foundation, the Stanley Medical Research Institute, and the Qatar National Research Fund.

The reviewer WL declared a past co-authorship with the author MO to the handling editor.

Publisher’s note

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.

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Keywords: depression, MADRS = Montgomery-Asberg depression rating scale, rater perception, rapid acting antidepressants, structured interview guide for the MADRS (SIGMA)

Citation: Capodilupo G, Blattner R, Must A, Navarro SG and Opler M (2024) A qualitative investigation of the Montgomery–Åsberg depression rating scale: discrepancies in rater perceptions and data trends in remote assessments of rapid-acting antidepressants in treatment resistant depression. Front. Psychiatry 15:1289630. doi: 10.3389/fpsyt.2024.1289630

Received: 06 September 2023; Accepted: 16 April 2024; Published: 01 May 2024.

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Copyright © 2024 Capodilupo, Blattner, Must, Navarro and Opler. 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) and the copyright owner(s) 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: Mark Opler, [email protected]

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.

Research article
Open access
Published: 22 April 2024

Impact of COVID-19 pandemic on depression incidence and healthcare service use among patients with depression: an interrupted time-series analysis from a 9-year population-based study

Vivien Kin Yi Chan 1 na1 ,
Yi Chai 1 , 2 na1 ,
Sandra Sau Man Chan 3 ,
Hao Luo 4 ,
Mark Jit 5 , 7 ,
Martin Knapp 4 , 6 ,
David Makram Bishai 7 ,
Michael Yuxuan Ni 7 , 8 , 9 ,
Ian Chi Kei Wong 1 , 10 , 11 , 13 &
Xue Li ORCID: orcid.org/0000-0003-4836-7808 1 , 10 , 12 , 13

BMC Medicine volume 22 , Article number: 169 ( 2024 ) Cite this article

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Most studies on the impact of the COVID-19 pandemic on depression burden focused on the earlier pandemic phase specific to lockdowns, but the longer-term impact of the pandemic is less well-studied. In this population-based cohort study, we examined the short-term and long-term impacts of COVID-19 on depression incidence and healthcare service use among patients with depression.

Using the territory-wide electronic medical records in Hong Kong, we identified all patients aged ≥ 10 years with new diagnoses of depression from 2014 to 2022. We performed an interrupted time-series (ITS) analysis to examine changes in incidence of medically attended depression before and during the pandemic. We then divided all patients into nine cohorts based on year of depression incidence and studied their initial and ongoing service use patterns until the end of 2022. We applied generalized linear modeling to compare the rates of healthcare service use in the year of diagnosis between patients newly diagnosed before and during the pandemic. A separate ITS analysis explored the pandemic impact on the ongoing service use among prevalent patients with depression.

We found an immediate increase in depression incidence (RR = 1.21, 95% CI: 1.10–1.33, p < 0.001) in the population after the pandemic began with non-significant slope change, suggesting a sustained effect until the end of 2022. Subgroup analysis showed that the increases in incidence were significant among adults and the older population, but not adolescents. Depression patients newly diagnosed during the pandemic used 11% fewer resources than the pre-pandemic patients in the first diagnosis year. Pre-existing depression patients also had an immediate decrease of 16% in overall all-cause service use since the pandemic, with a positive slope change indicating a gradual rebound over a 3-year period.

Conclusions

During the pandemic, service provision for depression was suboptimal in the face of increased demand generated by the increasing depression incidence during the COVID-19 pandemic. Our findings indicate the need to improve mental health resource planning preparedness for future public health crises.

Peer Review reports

The COVID-19 pandemic that began in 2020 has resulted in an unprecedented public health crisis, with 771 million confirmed cases and over 6 million deaths across the globe as of September 2023 [ 1 ]. To curb the spread and reduce the mortality of SARS-CoV-2 infections, governments worldwide enacted stringent measures to contain its spread, including social mobility restrictions, mask-wearing, massive screenings, and lockdowns. Despite their effectiveness in limiting viral spread, these measures may have created a macro-environment of fear, social exclusion of individuals who contracted the virus, and reduced community cohesion [ 2 , 3 , 4 ]. The pandemic and the ensuing measures also led to economic disruption and created financial hardship for millions of families [ 4 , 5 ]. The combined pandemic stresses may have exacerbated the risk factors for mental health conditions including depression. Among patients with pre-existing depression, the government effort re-prioritized for outbreak control may have also led to disrupted non-emergency services and unmet care need in mental health [ 6 ].

A meta-analysis estimated an additional 53 million cases of depression and a 27.6% increase in its global prevalence in 2020 due to COVID-19-related illnesses and reduced mobility [ 7 ], which affected individuals across age groups [ 8 , 9 , 10 ]. In Hong Kong, a survey showed a consistent mental health crisis with a two-fold increase in depression symptoms and a 28.3% rise in the stress level even during the well-managed small-scale outbreaks [ 11 ]. Conversely, other studies reported that the pandemic reduced the risk of depression and self-harm because of the emotional security provided by timely government intervention, but these findings were confounded by increased barriers to seek medical help [ 12 , 13 , 14 ]. In the emergency phase of the pandemic, it was reported that lockdowns significantly reduced healthcare service use for both outpatient and inpatient services [ 15 , 16 , 17 ]. Studies also found an elevated risk of depression relapse and use of antidepressants [ 18 , 19 ].

Literature exploring pandemic impact on depression has mostly focused on the earlier phase of the pandemic (2020–2021) when short-term lockdown orders were in place. There are fewer studies and more mixed results for the post-emergency phase. Hong Kong followed the “dynamic zero-COVID policy” of China with strict border control, contact tracing, and quarantine before cases spread until the end of 2022 and so recorded a low number of SARS-CoV-2 cases for most of the time before a major Omicron outbreak [ 20 ]. It did not experience full lockdown, although stringent infection control and social measures were deployed for an entire 3-year-long period. This context thus enables us to evaluate the longer-term pandemic impact apart from a focus on lockdowns. In the late pandemic period, it is also useful to understand any potential decline in depression incidence and rebound in health service utilization. Using interrupted time series (ITS) analysis with a cohort study, we examined the changes in depression incidence and healthcare service use due to the pandemic, aiming to measure both the short-term (immediately after pandemic onset) and long-term (3 years since the outbreak) impacts on the burden of depression. We aimed to facilitate better preparedness in mental health resource planning for future public health crises.

Data source

We analyzed the Clinical Data Analysis and Reporting System (CDARS), the territory-wide routine electronic medical record (EMR) developed by the Hospital Authority, which manages all public healthcare services in Hong Kong and provides publicly funded healthcare services to all eligible residents (> 7.6 million). CDARS covers real-time anonymized patient-level data, including demographics, deaths, attendances, and all-cause diagnoses coded based on the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM), since 1993 across outpatient, inpatient, and emergency settings for research and auditing purposes in the public sector. The quality and accuracy of CDARS have been demonstrated in population-based studies on COVID-19 [ 21 , 22 ] and depression [ 23 , 24 ]. In Hong Kong, the public healthcare is heavily subsidized at a highly affordable price, while the private sector is financed mainly by non-compulsory medical insurance and out-of-pocket payments. The Hospital Authority thus manages 76% of chronic medical conditions including mental health illnesses despite a dual-track public and private system [ 25 ].

Study design and participants

This study consisted of both a quasi-experimental design with ITS analyses and a population-based retrospective study. We first identified all patients who received new clinical diagnoses of major depressive disorder or dysthymia (ICD-9-CM codes: 296.2, 300.4, 311) between January 2014 and December 2022. Patients aged below 10 were excluded to avoid confusion with maternal depression in the coding system. We performed an ITS analysis to evaluate changes in medically attended depression incidence during 36 quarters of data observations. The data cut point was the first quarter of 2020, leaving 24 quarters as pre-cut points and 12 quarters as post-cut points. ITS analysis is a valuable tool to assess the impact of population-level interventions or major macro-environmental changes and widely used in various health policy assessments [ 26 ]. Since patients who received incident diagnoses in different years could have different disease durations and care needs, we divided all patients into nine “incident cohorts” (2014 to 2022 cohorts) based on year of depression incidence. All patients were followed up until the end of 2022 for their service use patterns across outpatient, inpatient, and emergency settings.

An exploratory trend analysis showed that use of healthcare resources was the greatest at the beginning of the disease course before stabilizing. Recognizing this feature, we separately investigated the pandemic impact on the (1) initial and (2) ongoing healthcare service use. Respectively, we compared the rates of healthcare service use during the first calendar year following diagnosis, which potentially represents the most care-demanding phase, among patients newly diagnosed during the pandemic (2020 to 2022, the exposure groups) with those diagnosed before the pandemic (2014 to 2019, the reference groups) using a generalized linear model. To study the ongoing resource utilization among the relatively stable prevalent patients, defined as having a disease duration for at least 3 years by the start of the pandemic (i.e., represented by all patients in the 2014–2016 cohorts), we conducted another ITS analysis to compare their rates of service use before and during the pandemic until the end of 2022. The data points before the third calendar year of diagnosis were excluded in the analysis. The linkage between the three parts of analyses is illustrated in Additional file 1 : Figure S1.

Exposure and outcomes of interest

Our study defined the exposure as the macro-environment with the implementation of containment measures in response to the pandemic. Based on the COVID-19 Stringency Index by the Oxford COVID-19 Government Response Tracker, the Hong Kong government introduced relevant policies since January 2020 and announced the lifting of most mandates by December 2022 [ 27 ]. With quarterly data, we operationally defined the exposure period starting from the first quarter of 2020 until December 2022 (the intervention period). The reference period (the pre-pandemic period) was between the first quarter of 2014 and the last quarter of 2019.

The first outcome of interest was quarterly incidence of medically attended depression, defined as the number of patients who received depression diagnosis in the current quarter but without history of depression divided by the local eligible population, with age standardization using 5-year age bands based on the 2021 mid-year population. The second outcome was quarterly or yearly rates of attendance episodes or bed-days by incident cohort and service setting, defined by the total visit episodes or bed-days in the current period divided by the number of patients with depression whose observation period (from their first diagnosis to death or end of study) fell within the same period. We further stratified the outpatient attendance into “all-cause” (all outpatient services) and “psychiatric-related” (psychiatric specialist clinic, day hospital, and community nursing) use. Stratified data were unavailable in the inpatient and emergency settings.

Statistical analysis

In the ITS analyses, we applied segmented quasi-Poisson regression models since the data showed signs of overdispersion [ 28 ]. We included a continuous time variable in quarters, a binary indicator for the pandemic period (the exposure period) to represent level change (immediate effect) and the interaction of the two to measure slope change (gradual effect) [ 29 ], offsetting the logarithm of the local population or patients with depression. We adjusted the quarters of the data points to account for seasonality. Residual plots, autocorrelation function, and partial autocorrelation function suggested very little evidence of autocorrelation [ 28 , 30 ]. We then used Newey-West method to obtain robust standard errors and address autocorrelation up to the largest lag [ 31 , 32 ]. In the comparison of the initial healthcare service use between patients newly diagnosed during and before the pandemic, we fitted the rates of service use in the year of diagnosis between cohorts using a generalized linear model with negative binomial log link function. The model adjusted for a binary indicator of whether the diagnosis year occurred before or during the pandemic (the exposure period) and offset the logarithm of incident patients with depression in each cohort. In all analyses, we excluded data points related to major local social movements in 2014 and 2019 to address confounding due to changes in socio-political environment [ 33 , 34 , 35 ].

Subgroup and sensitivity analyses

In the ITS analysis to evaluate changes in depression incidence, we further stratified the analysis into three age groups: adolescents (10–24), adults (25–64), and the older population (65 +) to explore whether these population subgroups were differentially susceptible to a new depression diagnosis as a result of the pandemic.

During the first quarter of 2022, there was an unprecedented abrupt increase of SARS-CoV-2 cases due to the Omicron variant, marking the start of “fifth-wave outbreak” in Hong Kong [ 20 ]. In contrast to the earlier waves of smaller-scale outbreaks (below 13,000 cumulative cases before 2022), the public healthcare services were overwhelmed at the beginning of this wave, which possibly strained diagnostic capacity and caused the number of depression diagnoses to be lower than usual. We therefore performed sensitivity analyses for the ITS analyses for depression incidence and healthcare service use by adjusting a variable indicating the relevant quarter to validate the results. In addition, since outpatient service reception may be subject to long waiting time, we conducted an additional sensitivity analysis with a 6-month lag for the pandemic period by adding a binary indicator for the transition period and re-defining the pandemic to start from the third quarter of 2020. Lastly, we also performed sensitivity analyses for the pandemic impact on ongoing healthcare resource utilization by changing the defined disease duration of 3 years as stable patients into 2 years.

All data were analyzed using R version 4.0.3 and cross-validated by two investigators.

Over the 9-year study period, we identified 85,111 patients with new depression diagnosis, who generated 4,433,558 attendance or admission episodes across all diagnosis settings and 1,327,424 inpatient bed-days. For these patients, the mean age was 48.6 (SD:19.8) with 71.6% being female. Detailed demographic characteristics of the patients diagnosed in each year are summarized in Additional file 2 : Table S1.

Incidence of medically attended depression

Figure 1 illustrates the trends of the observed and model-implied quarterly incidence of medically attended depression between 2014 and 2022. The average quarterly incidence rates were 3.44 and 3.59 per 10,000 population before and during the pandemic (Additional file 2 : Table S2), respectively. After adjusting for major social movements, ITS analysis showed a small but marginally significant decline in the population incidence in the pre-pandemic period (risk ratio, RR = 0.995, 95% CI: 0.99–1.00, p = 0.042). Since the pandemic, however, there was a significant immediate increase in incidence indicated by level change (RR = 1.21, 95% CI: 1.10–1.33, p < 0.001), with a non-significant slope change (Fig. 1 A).

Interrupted time series analysis plot of pandemic impact on depression incidence

Stratifying by age groups, ITS analysis showed a slow but significant decline in incidence in the pre-pandemic period among adults and the older population (RR = 0.99, 95% CI: 0.99–0.99) but a significant increase over the time among adolescents (RR = 1.04, 95% CI: 1.04–1.05) before the pandemic. Since the pandemic, we found significant level increases indicating immediate effects of the pandemic among adults (RR = 1.19, 95% CI: 1.09–1.29) and the older population (RR = 1.33, 95% CI: 1.29–1.38, all p < 0.001), but not adolescents. The slope changes remained non-significant in all subgroups (Fig. 1 B–D).

In the sensitivity analysis which accounted for the fifth-wave outbreak, we found a similar level change (RR = 1.20, 95% CI: 1.10–1.32, p < 0.001) as the main analysis, with a significant but slowly declining pre-pandemic trend (RR = 0.995, 95% CI: 0.990–0.999, p = 0.039). Using a 6-month transition window showed a consistent level change (RR = 1.28, 95% CI: 1.22–1.34, p < 0.001) and pre-pandemic trend (RR = 0.995, 95% CI: 0.994–0.996, p < 0.001). The slope changes in both sensitivity analyses remained non-significant.

Healthcare service use

In each incident cohort, the patterns followed the natural disease history such that the greatest service demand consistently occurred within the first 2 years of a depression diagnosis, followed by gradual decline subsequently (Fig. 2 ). During the pandemic, service utilization appeared to decrease further across all diagnosis settings except for inpatient bed-days. All counts and rates of healthcare service use are listed in Additional file 2 : Tables S3–S12.

Trend of healthcare resource utilization from 2014 to 2022

Pandemic impact on initial healthcare service use

Table 1 details the rates of healthcare service use in the year of diagnosis stratified by incident cohort and the regression results across diagnosis settings. Annual rates of overall all-cause visits per patient in the year of diagnosis were 10.5 to 10.8 episodes among patients diagnosed between 2015 and 2018, in contrast to 9.0 to 10.2 episodes among those diagnosed between 2020 and 2022. Adjusting for major social movements, the negative binomial model showed that the pandemic was associated with significantly reduced utilization in inpatient bed-days (RR = 0.78, 95% CI: 0.70–0.85), outpatient all-cause visits (RR = 0.89, 95% CI: 0.85–0.93), outpatient psychiatric visits (RR = 0.82, 95% CI: 0.76–0.88), and overall all-cause visits (RR = 0.89, 95% CI: 0.85–0.94, all p < 0.001). Being diagnosed during the pandemic was not significantly associated with changes in rates of emergency and inpatient admission episodes.

Pandemic impact on ongoing healthcare service use

For the combined 2014–2016 cohorts, the mean rate of overall all-cause visits counting from their third year of diagnosis was 3.38 episodes per patient in the pre-pandemic period, which dropped to 2.25 episodes per patient in the pandemic period. Adjusting for social movements, the ITS analysis showed significant decreases in the original trends of ongoing service use in all diagnosis settings (RRs ranged from 0.96 to 0.99, all p < 0.01) before the pandemic (Table 2 and Fig. 3 ). When the pandemic began, there were immediate decreases in service use indicated by significant level changes in inpatient admission episodes (RR = 0.91, 95% CI: 0.83–0.99, p = 0.024), inpatient bed-days (RR = 0.87, 95% CI: 0.78–0.96, p = 0.017), outpatient all-cause visits (RR = 0.83, 95% CI: 0.76–0.91, p < 0.001), outpatient psychiatric visits (RR = 0.77, 95% CI: 0.74–0.83, p < 0.001), and overall all-cause visits (RR = 0.84, 95% CI: 0.76–0.92, p < 0.001), but not emergency visits. Regarding gradual effects, there were significant but small slope changes during the pandemic across all diagnosis settings except inpatient bed-days, with RRs ranging from 1.02 to 1.03, indicating a gradual rebound over time (Table 2 and Fig. 3 ).

Impact of the pandemic on the ongoing healthcare resource utilization among the 2014–2016 cohorts

In the sensitivity analyses accounting for the fifth-wave outbreak and changing definition of disease duration prior to the pandemic, effect sizes were largely consistent with those in the main analysis (Additional file 2 : Tables S13–S14).

Using a 9-year population-based study with a quasi-experimental design, we present the immediate and long-term impacts of 3 years of the pandemic on depression burden. We found a 21% immediate increase in incidence of medically attended depression, with 19% and 33% increases among adults and the older population during the 3-year period. There was no significant slope change during the pandemic, indicating a sustained effect towards the end of 2022. Though the pandemic did not affect incidence among adolescents, the incidence had been rising significantly in this subgroup over time even before the pandemic. Despite the increasing overall incidence, patients newly diagnosed during the pandemic used 11% fewer resources in their year of diagnosis than the pre-pandemic patients. Patients with pre-existing depression also had an immediate decrease by 16% in overall all-cause visits, with a positive slope change which suggests a gradual rebound over 3 years.

Rising incidence of medically attended depression

Our findings are largely consistent with the previous literature that has reported an increased prevalence of depressive mood during the pandemic [ 7 , 8 , 9 , 10 , 11 ]. However, the results from EMR-based studies that focused on clinically confirmed incident diagnoses were mixed. A cohort study based on the UK Biobank reported a 2.0- to 3.1-fold increase in new diagnoses of depressive or anxiety disorders compared to the pre-pandemic period, especially in the first 6 months of the pandemic [ 36 ]. Another Israeli time-series analysis observed a 36% increase in depression incidence among youth [ 37 ]. Conversely, population-based time-series and cohort studies in the UK found a 28% to 43% decline in recorded depression incidence with a gradual return towards pre-pandemic rates [ 38 , 39 ]. One explanation for such discrepancies is service disruption during lockdowns that led to under-diagnoses in primary care systems. Alternatively, the nature of social measures may have contributed to the trends differently. Costa-Font et al. highlighted that a “preventive lockdown” when there was low mortality appeared to increase depressive symptoms, but it was the opposite when lockdowns were in a high-mortality context [ 40 ]. This echoes with our findings in Hong Kong, where control measures were mostly preventive following the “dynamic zero-COVID” approach while maintaining low case load most of the time.

In our subgroup analysis, we found that adults and the older population were prone to developing depression due to the pandemic, but adolescents were not. However, prior studies tend to report consistent risks across age groups: adults were likely to suffer from job insecurity and increased caregiver responsibilities, older adults were susceptible to prolonged isolation, fear of illness, and grief of losing the loved ones, while adolescents faced school closures, reduced peer interactions, and outdoor activities [ 37 , 41 , 42 , 43 , 44 ]. Between 2014 and 2019, we found the incidence among Hong Kong adolescents was already increasing, with rates doubling within 5 years and overtaking the incidence among adults and the older population. This pre-existing rising trend might explain why the pandemic, despite being an additional risk factor, did not have as comparable impact as in other age groups due to a potential diminishing marginal effect. The earlier rise in adolescent depression may have stemmed from existing contextual forces including social unrest and other unknown stressors [ 35 ]. Our findings suggest that resources for depression care among adults and the older population are needed to prepare for future pandemic threats. However, policymakers should be aware of the worrying mental health situation in adolescents. As the rising incidence was minimally linked to the pandemic in this subgroup, it implies that the mental health crisis could persist in the future regardless of the pandemic. Further investigation is needed to confirm the stressors behind the recent trend and ways to reverse the deterioration in adolescent mental health.

Declining use of healthcare resources

Given the increased demand for depression care during the pandemic, evaluating the pattern in healthcare service use in this critical period is important to identify potential unmet care needs, optimize strategies of service provision, and strengthen the preparedness for future pandemics. Despite the rising incidence, we found that the pandemic substantially reduced the use of inpatient and outpatient services among both newly diagnosed and pre-existing patients. This is consistent with the previous studies in South Africa, South Korea, the United States, and the UK, which estimated 15% to 51% reductions in healthcare resource utilization depending on diagnosis settings [ 15 , 17 , 45 , 46 ]. Most of them were conducted during the early phase of the pandemic with a focus on lockdowns. This may explain the generally greater decline in service use compared with our observations for Hong Kong. Among the pre-existing patients, the reductions in service use were unlikely to represent an immediate improvement in depression outcomes but rather the limited capacity of the system even without mobility restriction to access. This also affected the care delivery for the rising number of new patients during the pandemic, who need the greatest care in the first years of diagnosis but accessed less care than historical controls. The findings therefore revealed a suboptimal service provision in response to the extra care demand generated by the pandemic.

In our study, the service types most impacted by the pandemic were the inpatient bed-days for newly diagnosed patients and outpatient psychiatric visits for pre-existing patients. This is consistent with the observation that most inpatient care occurred at the beginning of the disease course, while outpatient follow-ups became more common as patients stabilized. During the pandemic, however, inpatient resources were reserved for outbreak control, leaving the new patients with inadequate service access. Among pre-existing patients, reluctance to visit clinics owing to fear of getting infected may have discouraged them from attending regular appointments [ 47 ]. Video consultations for SARS-CoV-2 infected cases have been initiated since July 2022, which led to 214,900 consultations for quarantined patients [ 48 ]. “Tele-psychiatry” in the post-pandemic era is worth investigation for its feasibility and effectiveness in extending continuity of care, as it enables follow-ups after hospital discharge and ensures ongoing patient access even without physical attendance.

Strengths and limitations

One of the major strengths of our study is the use of territory-wide longitudinal data with a large sample size, which allowed a quasi-experimental study design. This enabled us to investigate the population-level impact of the pandemic and validate prior findings from smaller community-based studies. The context of Hong Kong also enabled us to study the longer-term impact of the entire pandemic apart from a focus on lockdowns. When studying healthcare service use, our study differed from previous studies by separating patients into nine incident cohorts before analyzing their rates of service use during the follow-up. This allowed us to differentiate the pandemic impact more clearly on new and pre-existing patients, unlike most of the previous studies.

There are also limitations to our study. Firstly, patients’ decision to seek treatment mediates whether their condition is recorded. Systematic differences between age groups in the propensity to seek treatment during different periods rather than differences in the underlying population-level burden may have driven the trends before and after 2020. Secondly, we were unable to stratify the patterns of service use into all-cause and psychiatric-related use in the emergency and inpatient settings since such information was not available in the raw data. Thirdly, though the public sector provides the majority of local healthcare services, patients may have sought help from private doctors especially when the public healthcare system was overwhelmed at the start of the fifth-wave outbreak, possibly leading to underestimated incidence and healthcare service use. Patients who were diagnosed in private clinics before seeking care in the public sector may also be labeled as incident cases later than actual diagnosis date. We therefore performed sensitivity analyses but found no change in the conclusion. Lastly, the findings represent the mixed overall effect of the pandemic macro-environment, but the current time-series study was unable to disentangle the effects of specific contributing factors.

Using ITS analyses from a 9-year cohort study, we found a persistent increase in incidence of medically attended depression over the pandemic period in the overall population, adults, and the older population. However, patients newly diagnosed during the pandemic used fewer resources in their first year of diagnosis than pre-pandemic patients. Pre-existing patients also had immediate decreases in healthcare service use following the pandemic in all diagnosis settings, with a gradual rebound over 3 years. Our findings highlight the need to improve the preparedness in mental health resource planning for future public health crises.

Availability of data and materials

We are unable to directly share the data used in this study since the data custodian, the Hong Kong Hospital Authority who manages the Clinical Data Analysis and Reporting System (CDARS), has not given permission. However, CDARS data can be accessed via the Hospital Authority Data Sharing Portal for research purpose. The relevant information can be found online ( https://www3.ha.org.hk/data ).

Abbreviations

Clinical Data Analysis and Reporting System

Electronic medical records

Interrupted time-series

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Acknowledgements

We thank Ms. Qiwen Fang and Ms. Yin Zhang for assistance in data retrieval; Dr. Deliang Yang and Ms. Jin Lee for statistical advice and support; we also thank Ms. Lisa Lam for English proofreading.

The study was jointly supported by the Collaborative Research Fund (ACESO, C7154-20GF), the Research Impact Fund (SCAN-2030, R7007-22) granted by the Research Grant Council, University Grants Committee, and the Health and Medical Research Fund (COVID19F04; COVID19F11) granted by the Health Bureau, The Government of the Hong Kong Special Administrative Region, and the Laboratory of Data Discovery for Health (D 2 4H) funded by the Innovation and Technology Commission for data during the pandemic, modeling depression burden between 2014 and 2022. The funders had no active role in the design and conduct of the work and in the analysis, interpretation, and preparation of study reports.

Author information

Vivien Kin Yi Chan and Yi Chai are co-first authors with equal contribution.

Authors and Affiliations

Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China

Vivien Kin Yi Chan, Yi Chai, Ian Chi Kei Wong & Xue Li

The Hong Kong Jockey Club Centre for Suicide Research and Prevention, The University of Hong Kong, Hong Kong SAR, China

Department of Psychiatry, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China

Sandra Sau Man Chan

Department of Social Work and Social Administration, Faculty of Social Sciences, The University of Hong Kong, Hong Kong SAR, China

Hao Luo & Martin Knapp

Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK

Care Policy and Evaluation Centre, Department of Health Policy, London School of Economics and Political Science, London, UK

Martin Knapp

School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China

Mark Jit, David Makram Bishai & Michael Yuxuan Ni

The State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong SAR, China

Michael Yuxuan Ni

Urban Systems Institute, The University of Hong Kong, Hong Kong SAR, China

Laboratory of Data Discovery for Health (D24H), Hong Kong SAR, China

Ian Chi Kei Wong & Xue Li

School of Pharmacy, Aston University, London, UK

Ian Chi Kei Wong

Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China

Advanced Data Analytics for Medical Science (ADAMS) Limited, Hong Kong SAR, China

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Contributions

X Li and ICK Wong conceived the study idea and study design. VKY Chan and Y Chai gathered the data and performed data analyses. All authors provided clinical, statistical, and epidemiological advice and interpreted the results. VKY Chan and X Li wrote and revised the drafts with all authors’ critical comments and revisions. All authors agree to be accountable for all aspects of the work. X Li and ICK Wong obtained the funding and supervised the study conduct. The corresponding authors confirm that all co-authors meet authorship criteria. All authors read and approved the final manuscript.

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Vivien Kin Yi Chan: @vivienkinyichan

Martin Knapp: @knappem

Hao Luo: @HaoLuo429

Ian Chi Kei Wong: @Ian_HKU

Xue Li: @snowly12191

HKU Pharmacy: @HkuPharm

Corresponding authors

Correspondence to Ian Chi Kei Wong or Xue Li .

Ethics declarations

Ethics approval and consent to participate.

This study received ethics approval from the Institutional Review Board of The University of Hong Kong/Hospital Authority Hong Kong Western Cluster (UW 20-218). Informed consent has been waived as the study utilized de-identified data.

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Competing interests

X Li received research grants from the Hong Kong Health and Medical Research Fund (HMRF, HMRF Fellowship Scheme, HKSAR), Research Grants Council Early Career Scheme (RGC/ECS, HKSAR), Janssen, and Pfizer; internal funding from the University of Hong Kong; and consultancy fees from Merck Sharp & Dohme and Pfizer; she is also a non-executive director of Advanced Data Analytics for Medical Science (ADAMS) Limited Hong Kong; all are unrelated to this work. ICK Wong received research funding outside the submitted work from Amgen, Bristol-Myers Squibb, Pfizer, Janssen, Bayer, GSK, Novartis, Takeda, the Hong Kong Research Grants Council, the Hong Kong Health and Medical Research Fund, National Institute for Health Research in England, European Commission, National Health and Medical Research Council in Australia, and the European Union’s Seventh Framework Programme for research and technological development. He has also received consulting fees from IQVIA, the WHO, and expert testimony for Appeal Court in Hong Kong over the past 3 years. He is an advisory member of Pharmacy and Poisons Board, Expert Committee on Clinical Events Assessment Following COVID-19 Immunization, and the Advisory Panel on COVID-19 Vaccines of the Hong Kong Government. He is also a non-executive director of Jacobson Medical Hong Kong, Advanced Data Analytics for Medical Science (ADAMS) Limited, and OCUS Innovation Limited (Hong Kong, Ireland, and UK), and the founder and a director of Therakind Limited (UK). Other authors declared no competing interests related to this study.

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Supplementary Information

Additional file 1: figure s1..

Study schema to illustrate the linkage between analyses.

Additional file 2: Table S1.

Age and sex distribution of patients newly diagnosed with depression between 2014 and 2022. Table S2. Quarterly age-standardized incidence and counts of patients newly diagnosed with depression between 2014 to 2022. Table S3. Quarterly counts of accident & emergency visit among incident cohorts between 2014 and 2022. Table S4. Quarterly counts of inpatient admission among incident cohorts between 2014 and 2022. Table S5. Quarterly counts of inpatient stay among incident cohorts between 2014 and 2022. Table S6. Quarterly counts of outpatient all-cause visit among incident cohorts between 2014 and 2022. Table S7. Quarterly counts of outpatient psychiatric-related visit among incident cohorts between 2014 and 2022. Table S8. Quarterly rates of accident & emergency visit among incident cohorts between 2014 and 2022. Table S9. Quarterly rates of inpatient admission among incident cohorts between 2014 and 2022. Table S10. Quarterly rates of inpatient stay among incident cohorts between 2014 and 2022. Table S11. Quarterly rates of outpatient all-cause visit among incident cohorts between 2014 and 2022. Table S12. Quarterly rates of outpatient psychiatric-related visit among incident cohorts between 2014 and 2022. Table S13. Sensitivity analysis results of ITS analysis of pandemic impact on the ongoing healthcare resource utilization among the 2014-2016 cohorts by adjusting for the fifth-wave outbreak. Table S14. Sensitivity analysis results of ITS analysis of pandemic impact on the ongoing healthcare resource utilization among the 2014-2017 cohorts (changing the defined disease duration prior to the pandemic from 3 years to 2 years).

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Chan, V.K.Y., Chai, Y., Chan, S.S.M. et al. Impact of COVID-19 pandemic on depression incidence and healthcare service use among patients with depression: an interrupted time-series analysis from a 9-year population-based study. BMC Med 22 , 169 (2024). https://doi.org/10.1186/s12916-024-03386-z

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In psychedelic therapy, clinician-patient bond may matter most

Study links relationship strength to reduced depression for up to 1 year.

Drug effects have dominated the national conversation about psychedelics for medical treatment, but a new study suggests that when it comes to reducing depression with psychedelic-assisted therapy, what matters most is a strong relationship between the therapist and study participant.

Researchers analyzed data from a 2021 clinical trial that found psilocybin (magic mushrooms) combined with psychotherapy in adults was effective at treating major depressive disorder.

Data included depression outcomes and participant reports about their experiences with the drugs and their connection with therapists. Results showed that the stronger the relationship between a participant and clinician -- called a therapeutic alliance -- the lower the depression scores were one year later.

"What persisted the most was the connection between the therapeutic alliance and long-term outcomes, which indicates the importance of a strong relationship," said lead author Adam Levin, a psychiatry and behavioral health resident in The Ohio State University College of Medicine.

Past research has consistently found that as mental health treatments changed, a trusting relationship between clients and clinicians has remained key to better outcomes, said senior author Alan Davis, associate professor and director of the Center for Psychedelic Drug Research and Education in The Ohio State University College of Social Work.

"This concept is not novel. What is novel is that very few people have explored this concept as part of psychedelic-assisted therapy," Davis said. "This data suggests that psychedelic-assisted therapy relies heavily on the therapeutic alliance, just like any other treatment."

The study was published recently in the journal PLOS ONE .

Twenty-four adults who participated in the trial received two doses of psilocybin and 11 hours of psychotherapy. Participants completed the therapeutic alliance questionnaire, assessing the strength of the therapist-participant relationship, three times: after eight hours of preparation therapy and one week after each psilocybin treatment.

Participants also completed questionnaires about any mystical and psychologically insightful experiences they had during the drug treatment sessions. Their depression symptoms were assessed one week, four weeks, and up to one year after the trial's end.

The analysis showed that the overall alliance score increased over time and revealed a correlation between a higher alliance score and more acute mystical and/or psychologically insightful experiences from the drug treatment. Acute effects were linked to lower depression at the four-week point after treatment, but were not associated with better depression outcomes a year after the trial.

"The mystical experience, which is something that is most often reported as related to outcome, was not related to the depression scores at 12 months," Davis said. "We're not saying this means acute effects aren't important -- psychological insight was still predictive of improvement in the long term. But this does start to situate the importance and meaning of the therapeutic alliance alongside these more well-established effects that people talk about."

That said, the analysis showed that a stronger relationship during the final therapy preparation session predicted a more mystical and psychologically insightful experience -- which in turn was linked to further strengthening the therapeutic alliance.

"That's why I think the relationship has been shown to be impactful in this analysis -- because, really, the whole intervention is designed for us to establish the trust and rapport that's needed for someone to go into an alternative consciousness safely," Davis said.

Considering that psychedelics carry a stigma as Schedule I drugs under the Controlled Substances Act, efforts to minimize negative experiences in future studies of their therapeutic potential should be paramount -- and therapy is critical to creating a supportive environment for patients, the authors said.

This study ideally will help clearly position psychedelics treatment as a psychotherapeutic intervention moving forward -- rather than its primary purpose being administration of a drug, Levin said.

"This isn't a case where we should try to fit psychedelics into the existing psychiatric paradigm -- I think the paradigm should expand to include what we're learning from psychedelics," Levin said. "Our concern is that any effort to minimize therapeutic support could lead to safety concerns or adverse events. And what we showed in this study is evidence for the importance of the alliance in not just preventing those types of events, but also in optimizing therapeutic outcomes."

This work was supported by the Center for Psychedelic and Consciousness Research, funded by the Steven & Alexandra Cohen Foundation, the RiverStyx Foundation and private donors. It was also supported by the Center for Psychedelic Drug Research and Education (CPDRE), funded by anonymous donors.

Additional co-authors are Rafaelle Lancelotta, Nathan Sepeda and Theodore Wagener of Ohio State, and Natalie Gukasyan, Sandeep Nayak, Frederick Barrett and Roland Griffiths of the Center for Psychedelic and Consciousness Research at Johns Hopkins University, where Davis is an affiliate.

Mental Health Research
Personalized Medicine
Gene Therapy
Pharmacology
Psychedelic Drugs
Mental Health
Spirituality
Psychotherapy
Psychedelic mushroom
Personalized medicine
Maggot therapy
Psychopharmacology

Story Source:

Materials provided by Ohio State University . Original written by Emily Caldwell. Note: Content may be edited for style and length.

Journal Reference :

Adam W. Levin, Rafaelle Lancelotta, Nathan D. Sepeda, Natalie Gukasyan, Sandeep Nayak, Theodore L. Wagener, Frederick S. Barrett, Roland R. Griffiths, Alan K. Davis. The therapeutic alliance between study participants and intervention facilitators is associated with acute effects and clinical outcomes in a psilocybin-assisted therapy trial for major depressive disorder . PLOS ONE , 2024; 19 (3): e0300501 DOI: 10.1371/journal.pone.0300501

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Participatory Research In Depression and Autism (PRIDA)

Examining the experiences, strategies, enablers and barriers professionals (including workforce trainees), and the autistic community face when applying NICE guidance for the treatment of depression.

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Why would immunomodulators be useful in depression?

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Case 1: Newly Diagnosed Treatment Resistant Depression

EP: 1 . Case 1: Newly Diagnosed Treatment Resistant Depression

ORIGINAL RESEARCH article

1 Introduction

2 Materials and methods

2.2 Remote rater-experience survey

4 Discussion

5 Conclusion

Data availability statement

Ethics statement

Author contributions

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Publisher’s note

Impact of COVID-19 pandemic on depression incidence and healthcare service use among patients with depression: an interrupted time-series analysis from a 9-year population-based study

Conclusions

Data source

Study design and participants

Exposure and outcomes of interest

Statistical analysis

Subgroup and sensitivity analyses