essay on impact of covid 19 on mental health

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The coronavirus ( COVID ‐19) pandemic's impact on mental health

Bilal javed.

1 Faculty of Sciences, PMAS Arid Agriculture University, Rawalpindi Pakistan

2 Roy & Diana Vagelos Laboratories, Department of Chemistry, University of Pennsylvania, Philadelphia Pennsylvania, USA

Abdullah Sarwer

3 Nawaz Sharif Medical College, University of Gujrat, Gujrat Pakistan

4 Department of General Medicine, Allama Iqbal Memorial Teaching Hospital, Sialkot Pakistan

Erik B. Soto

5 Graduate School of Public Health, University of Pittsburgh, Pittsburgh Pennsylvania, USA

Zia‐ur‐Rehman Mashwani

Throughout the world, the public is being informed about the physical effects of SARS‐CoV‐2 infection and steps to take to prevent exposure to the coronavirus and manage symptoms of COVID‐19 if they appear. However, the effects of this pandemic on one's mental health have not been studied at length and are still not known. As all efforts are focused on understanding the epidemiology, clinical features, transmission patterns, and management of the COVID‐19 outbreak, there has been very little concern expressed over the effects on one's mental health and on strategies to prevent stigmatization. People's behavior may greatly affect the pandemic's dynamic by altering the severity, transmission, disease flow, and repercussions. The present situation requires raising awareness in public, which can be helpful to deal with this calamity. This perspective article provides a detailed overview of the effects of the COVID‐19 outbreak on the mental health of people.

1. INTRODUCTION

A pandemic is not just a medical phenomenon; it affects individuals and society and causes disruption, anxiety, stress, stigma, and xenophobia. The behavior of an individual as a unit of society or a community has marked effects on the dynamics of a pandemic that involves the level of severity, degree of flow, and aftereffects. 1 Rapid human‐to‐human transmission of the SARS‐CoV‐2 resulted in the enforcement of regional lockdowns to stem the further spread of the disease. Isolation, social distancing, and closure of educational institutes, workplaces, and entertainment venues consigned people to stay in their homes to help break the chain of transmission. 2 However, the restrictive measures undoubtedly have affected the social and mental health of individuals from across the board. 3

As more and more people are forced to stay at home in self‐isolation to prevent the further flow of the pathogen at the societal level, governments must take the necessary measures to provide mental health support as prescribed by the experts. Professor Tiago Correia highlighted in his editorial as the health systems worldwide are assembling exclusively to fight the COVID‐19 outbreak, which can drastically affect the management of other diseases including mental health, which usually exacerbates during the pandemic. 4 The psychological state of an individual that contributes toward the community health varies from person‐to‐person and depends on his background and professional and social standings. 5

Quarantine and self‐isolation can most likely cause a negative impact on one's mental health. A review published in The Lancet said that the separation from loved ones, loss of freedom, boredom, and uncertainty can cause a deterioration in an individual's mental health status. 6 To overcome this, measures at the individual and societal levels are required. Under the current global situation, both children and adults are experiencing a mix of emotions. They can be placed in a situation or an environment that may be new and can be potentially damaging to their health. 7

2. CHILDREN AND TEENS AT RISK

Children, away from their school, friends, and colleagues, staying at home can have many questions about the outbreak and they look toward their parents or caregivers to get the answer. Not all children and parents respond to stress in the same way. Kids can experience anxiety, distress, social isolation, and an abusive environment that can have short‐ or long‐term effects on their mental health. Some common changes in children's behavior can be 8 :

• Excessive crying and annoying behavior
• Increased sadness, depression, or worry
• Difficulties with concentration and attention
• Changes in, or avoiding, activities that they enjoyed in the past
• Unexpected headaches and pain throughout their bodies
• Changes in eating habits

To help offset negative behaviors, requires parents to remain calm, deal with the situation wisely, and answer all of the child's questions to the best of their abilities. Parents can take some time to talk to their children about the COVID‐19 outbreak and share some positive facts, figures, and information. Parents can help to reassure them that they are safe at home and encourage them to engage in some healthy activities including indoor sports and some physical and mental exercises. Parents can also develop a home schedule that can help their children to keep up with their studies. Parents should show less stress or anxiety at their home as children perceive and feel negative energy from their parents. The involvement of parents in healthy activities with their children can help to reduce stress and anxiety and bring relief to the overall situation. 9

3. ELDERS AND PEOPLE WITH DISABILITIES AT RISK

Elderly people are more prone to the COVID‐19 outbreak due to both clinical and social reasons such as having a weaker immune system or other underlying health conditions and distancing from their families and friends due to their busy schedules. According to medical experts, people aged 60 or above are more likely to get the SARS‐CoV‐2 and can develop a serious and life‐threatening condition even if they are in good health. 10

Physical distancing due to the COVID‐19 outbreak can have drastic negative effects on the mental health of the elderly and disabled individuals. Physical isolation at home among family members can put the elderly and disabled person at serious mental health risk. It can cause anxiety, distress, and induce a traumatic situation for them. Elderly people depend on young ones for their daily needs, and self‐isolation can critically damage a family system. The elderly and disabled people living in nursing homes can face extreme mental health issues. However, something as simple as a phone call during the pandemic outbreak can help to console elderly people. COVID‐19 can also result in increased stress, anxiety, and depression among elderly people already dealing with mental health issues.

Family members may witness any of the following changes to the behavior of older relatives 11 ;

• Irritating and shouting behavior
• Change in their sleeping and eating habits
• Emotional outbursts

The World Health Organization suggests that family members should regularly check on older people living within their homes and at nursing facilities. Younger family members should take some time to talk to older members of the family and become involved in some of their daily routines if possible. 12

4. HEALTH WORKERS AT RISK

Doctors, nurses, and paramedics working as a front‐line force to fight the COVID‐19 outbreak may be more susceptible to develop mental health symptoms. Fear of catching a disease, long working hours, unavailability of protective gear and supplies, patient load, unavailability of effective COVID‐19 medication, death of their colleagues after exposure to COVID‐19, social distancing and isolation from their family and friends, and the dire situation of their patients may take a negative toll of the mental health of health workers. The working efficiency of health professionals may decrease gradually as the pandemic prevails. Health workers should take short breaks between their working hours and deal with the situation calmly and in a relaxed manner. 5

5. STIGMATIZATION

Generally, people recently released from quarantine can experience stigmatization and develop a mix of emotions. Everyone may feel differently and have a different welcome by society when they come out of quarantine. People who recently recovered may have to exercise social distancing from their family members, friends, and relatives to ensure their family's safety because of unprecedented viral nature. Different age groups respond to this social behavior differently, which can have both short‐ and long‐term effects. 1

Health workers trying to save lives and protect society may also experience social distancing, changes in the behavior of family members, and stigmatization for being suspected of carrying COVID‐19. 6 Previously infected individuals and health professionals (dealing pandemic) may develop sadness, anger, or frustration because friends or loved ones may have unfounded fears of contracting the disease from contact with them, even though they have been determined not to be contagious. 5

However, the current situation requires a clear understanding of the effects of the recent outbreak on the mental health of people of different age groups to prevent and avoid the COVID‐19 pandemic.

6. TAKE HOME MESSAGE

• Understanding the effects of the COVID‐19 outbreak on the mental health of various populations are as important as understanding its clinical features, transmission patterns, and management.
• Spending time with family members including children and elderly people, involvement in different healthy exercises and sports activities, following a schedule/routine, and taking a break from traditional and social media can all help to overcome mental health issues.
• Public awareness campaigns focusing on the maintenance of mental health in the prevailing situation are urgently needed.

CONFLICT OF INTEREST

The authors declare no potential conflict of interest.

AUTHOR CONTRIBUTIONS

B.J. and A.S. devised the study. B.J. collected and analyzed the data and wrote the first draft. E.B.S. edited and revised the manuscript. A.S. and Z.M. provided useful information. All the authors contributed to the subsequent drafts. The authors reviewed and endorsed the final submission.

Javed B, Sarwer A, Soto EB, Mashwani Z‐R. The coronavirus (COVID‐19) pandemic's impact on mental health . Int J Health Plann Mgmt . 2020; 35 :993–996. 10.1002/hpm.3008 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

• COVID-19 and your mental health

Worries and anxiety about COVID-19 can be overwhelming. Learn ways to cope as COVID-19 spreads.

At the start of the COVID-19 pandemic, life for many people changed very quickly. Worry and concern were natural partners of all that change — getting used to new routines, loneliness and financial pressure, among other issues. Information overload, rumor and misinformation didn't help.

Worldwide surveys done in 2020 and 2021 found higher than typical levels of stress, insomnia, anxiety and depression. By 2022, levels had lowered but were still higher than before 2020.

Though feelings of distress about COVID-19 may come and go, they are still an issue for many people. You aren't alone if you feel distress due to COVID-19. And you're not alone if you've coped with the stress in less than healthy ways, such as substance use.

But healthier self-care choices can help you cope with COVID-19 or any other challenge you may face.

And knowing when to get help can be the most essential self-care action of all.

Recognize what's typical and what's not

Stress and worry are common during a crisis. But something like the COVID-19 pandemic can push people beyond their ability to cope.

In surveys, the most common symptoms reported were trouble sleeping and feeling anxiety or nervous. The number of people noting those symptoms went up and down in surveys given over time. Depression and loneliness were less common than nervousness or sleep problems, but more consistent across surveys given over time. Among adults, use of drugs, alcohol and other intoxicating substances has increased over time as well.

The first step is to notice how often you feel helpless, sad, angry, irritable, hopeless, anxious or afraid. Some people may feel numb.

Keep track of how often you have trouble focusing on daily tasks or doing routine chores. Are there things that you used to enjoy doing that you stopped doing because of how you feel? Note any big changes in appetite, any substance use, body aches and pains, and problems with sleep.

These feelings may come and go over time. But if these feelings don't go away or make it hard to do your daily tasks, it's time to ask for help.

Get help when you need it

If you're feeling suicidal or thinking of hurting yourself, seek help.

• Contact your healthcare professional or a mental health professional.
• Contact a suicide hotline. In the U.S., call or text 988 to reach the 988 Suicide & Crisis Lifeline , available 24 hours a day, seven days a week. Or use the Lifeline Chat . Services are free and confidential.

If you are worried about yourself or someone else, contact your healthcare professional or mental health professional. Some may be able to see you in person or talk over the phone or online.

You also can reach out to a friend or loved one. Someone in your faith community also could help.

And you may be able to get counseling or a mental health appointment through an employer's employee assistance program.

Another option is information and treatment options from groups such as:

• National Alliance on Mental Illness (NAMI).
• Substance Abuse and Mental Health Services Administration (SAMHSA).
• Anxiety and Depression Association of America.

Self-care tips

Some people may use unhealthy ways to cope with anxiety around COVID-19. These unhealthy choices may include things such as misuse of medicines or legal drugs and use of illegal drugs. Unhealthy coping choices also can be things such as sleeping too much or too little, or overeating. It also can include avoiding other people and focusing on only one soothing thing, such as work, television or gaming.

Unhealthy coping methods can worsen mental and physical health. And that is particularly true if you're trying to manage or recover from COVID-19.

Self-care actions can help you restore a healthy balance in your life. They can lessen everyday stress or significant anxiety linked to events such as the COVID-19 pandemic. Self-care actions give your body and mind a chance to heal from the problems long-term stress can cause.

Take care of your body

Healthy self-care tips start with the basics. Give your body what it needs and avoid what it doesn't need. Some tips are:

• Get the right amount of sleep for you. A regular sleep schedule, when you go to bed and get up at similar times each day, can help avoid sleep problems.
• Move your body. Regular physical activity and exercise can help reduce anxiety and improve mood. Any activity you can do regularly is a good choice. That may be a scheduled workout, a walk or even dancing to your favorite music.
• Choose healthy food and drinks. Foods that are high in nutrients, such as protein, vitamins and minerals are healthy choices. Avoid food or drink with added sugar, fat or salt.
• Avoid tobacco, alcohol and drugs. If you smoke tobacco or if you vape, you're already at higher risk of lung disease. Because COVID-19 affects the lungs, your risk increases even more. Using alcohol to manage how you feel can make matters worse and reduce your coping skills. Avoid taking illegal drugs or misusing prescriptions to manage your feelings.

Take care of your mind

Healthy coping actions for your brain start with deciding how much news and social media is right for you. Staying informed, especially during a pandemic, helps you make the best choices but do it carefully.

Set aside a specific amount of time to find information in the news or on social media, stay limited to that time, and choose reliable sources. For example, give yourself up to 20 or 30 minutes a day of news and social media. That amount keeps people informed but not overwhelmed.

For COVID-19, consider reliable health sources. Examples are the U.S. Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO).

Other healthy self-care tips are:

• Relax and recharge. Many people benefit from relaxation exercises such as mindfulness, deep breathing, meditation and yoga. Find an activity that helps you relax and try to do it every day at least for a short time. Fitting time in for hobbies or activities you enjoy can help manage feelings of stress too.
• Stick to your health routine. If you see a healthcare professional for mental health services, keep up with your appointments. And stay up to date with all your wellness tests and screenings.
• Stay in touch and connect with others. Family, friends and your community are part of a healthy mental outlook. Together, you form a healthy support network for concerns or challenges. Social interactions, over time, are linked to a healthier and longer life.

Avoid stigma and discrimination

Stigma can make people feel isolated and even abandoned. They may feel sad, hurt and angry when people in their community avoid them for fear of getting COVID-19. People who have experienced stigma related to COVID-19 include people of Asian descent, health care workers and people with COVID-19.

Treating people differently because of their medical condition, called medical discrimination, isn't new to the COVID-19 pandemic. Stigma has long been a problem for people with various conditions such as Hansen's disease (leprosy), HIV, diabetes and many mental illnesses.

People who experience stigma may be left out or shunned, treated differently, or denied job and school options. They also may be targets of verbal, emotional and physical abuse.

Communication can help end stigma or discrimination. You can address stigma when you:

• Get to know people as more than just an illness. Using respectful language can go a long way toward making people comfortable talking about a health issue.
• Get the facts about COVID-19 or other medical issues from reputable sources such as the CDC and WHO.
• Speak up if you hear or see myths about an illness or people with an illness.

COVID-19 and health

The virus that causes COVID-19 is still a concern for many people. By recognizing when to get help and taking time for your health, life challenges such as COVID-19 can be managed.

• Mental health during the COVID-19 pandemic. National Institutes of Health. https://covid19.nih.gov/covid-19-topics/mental-health. Accessed March 12, 2024.
• Mental Health and COVID-19: Early evidence of the pandemic's impact: Scientific brief, 2 March 2022. World Health Organization. https://www.who.int/publications/i/item/WHO-2019-nCoV-Sci_Brief-Mental_health-2022.1. Accessed March 12, 2024.
• Mental health and the pandemic: What U.S. surveys have found. Pew Research Center. https://www.pewresearch.org/short-reads/2023/03/02/mental-health-and-the-pandemic-what-u-s-surveys-have-found/. Accessed March 12, 2024.
• Taking care of your emotional health. Centers for Disease Control and Prevention. https://emergency.cdc.gov/coping/selfcare.asp. Accessed March 12, 2024.
• #HealthyAtHome—Mental health. World Health Organization. www.who.int/campaigns/connecting-the-world-to-combat-coronavirus/healthyathome/healthyathome---mental-health. Accessed March 12, 2024.
• Coping with stress. Centers for Disease Control and Prevention. www.cdc.gov/mentalhealth/stress-coping/cope-with-stress/. Accessed March 12, 2024.
• Manage stress. U.S. Department of Health and Human Services. https://health.gov/myhealthfinder/topics/health-conditions/heart-health/manage-stress. Accessed March 20, 2020.
• COVID-19 and substance abuse. National Institute on Drug Abuse. https://nida.nih.gov/research-topics/covid-19-substance-use#health-outcomes. Accessed March 12, 2024.
• COVID-19 resource and information guide. National Alliance on Mental Illness. https://www.nami.org/Support-Education/NAMI-HelpLine/COVID-19-Information-and-Resources/COVID-19-Resource-and-Information-Guide. Accessed March 15, 2024.
• Negative coping and PTSD. U.S. Department of Veterans Affairs. https://www.ptsd.va.gov/gethelp/negative_coping.asp. Accessed March 15, 2024.
• Health effects of cigarette smoking. Centers for Disease Control and Prevention. https://www.cdc.gov/tobacco/data_statistics/fact_sheets/health_effects/effects_cig_smoking/index.htm#respiratory. Accessed March 15, 2024.
• People with certain medical conditions. Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/people-with-medical-conditions.html. Accessed March 15, 2024.
• Your healthiest self: Emotional wellness toolkit. National Institutes of Health. https://www.nih.gov/health-information/emotional-wellness-toolkit. Accessed March 15, 2024.
• World leprosy day: Bust the myths, learn the facts. Centers for Disease Control and Prevention. https://www.cdc.gov/leprosy/world-leprosy-day/. Accessed March 15, 2024.
• HIV stigma and discrimination. Centers for Disease Control and Prevention. https://www.cdc.gov/hiv/basics/hiv-stigma/. Accessed March 15, 2024.
• Diabetes stigma: Learn about it, recognize it, reduce it. Centers for Disease Control and Prevention. https://www.cdc.gov/diabetes/library/features/diabetes_stigma.html. Accessed March 15, 2024.
• Phelan SM, et al. Patient and health care professional perspectives on stigma in integrated behavioral health: Barriers and recommendations. Annals of Family Medicine. 2023; doi:10.1370/afm.2924.
• Stigma reduction. Centers for Disease Control and Prevention. https://www.cdc.gov/drugoverdose/od2a/case-studies/stigma-reduction.html. Accessed March 15, 2024.
• Nyblade L, et al. Stigma in health facilities: Why it matters and how we can change it. BMC Medicine. 2019; doi:10.1186/s12916-019-1256-2.
• Combating bias and stigma related to COVID-19. American Psychological Association. https://www.apa.org/topics/covid-19-bias. Accessed March 15, 2024.
• Yashadhana A, et al. Pandemic-related racial discrimination and its health impact among non-Indigenous racially minoritized peoples in high-income contexts: A systematic review. Health Promotion International. 2021; doi:10.1093/heapro/daab144.
• Sawchuk CN (expert opinion). Mayo Clinic. March 25, 2024.

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How did COVID-19 affect Americans’ well-being and mental health?

Subscribe to global connection, emily dobson , emily dobson ph.d. student - university of maryland carol graham , carol graham senior fellow - economic studies @cgbrookings tim hua , and tim hua student - middlebury college, former intern - global economy and development sergio pinto sergio pinto doctoral student, university of maryland.

April 8, 2022

COVID-19 has justifiably raised widespread public concern about mental health worldwide. In the U.S., the pandemic was an unprecedented shock to society at a time when the nation was already coping with a crisis of despair and related deaths from suicides, overdoses, and alcohol poisoning. Meanwhile, COVID-19’s impact was inequitable: Deaths were concentrated among the elderly and minorities working in essential jobs, groups who up to the pandemic had been reporting better mental health. We still do not fully understand how the shock has affected society’s well-being and mental health.

In a recent paper in which we compared trends in 2019-2020 using several nationally representative datasets, we found a variety of contrasting stories. While data from the 2019 National Health Interview Survey (NHIS) and the 2020 Household Pulse Survey (HPS) containing the same mental health screening questions for depression and anxiety show that both increased significantly, especially among young and low-income Americans in 2020, we found no such changes when analyzing alternative depression questions that are also asked in a consistent manner in the 2019-2020 Behavioral Risk Factor Surveillance System (BRFSS) and the 2019-2020 NHIS. Despite the differences in trends, the basic determinants of mental health were similar in three data sets in the same two years.

Our findings raise questions about the robustness of the many studies claiming unprecedented increases in depression and anxiety among the young compared to older cohorts. Many of them, due to the urgency created by COVID-19 and a paucity of good, consistent data, matched datasets and used different questions therein in their attempt to identify changes in the trends between the two years. The inconsistency in the outcome changes over time across datasets points to the need for caution in drawing conclusions, as well as in relying too heavily on a single study; results generated from different data may differ considerably.

Given the paucity of comparable data and the usual one-year lag in the release of the final mortality data from the Centers for Disease Control and Prevention (CDC), we also tried to get a handle on changes in patterns in mental health by examining emergency medical services (EMS) data calls related to behavior, overdoses, suicide attempts, and gun violence. The EMS data has the advantage of using the same methods and samples over the two-year period. We found an increase in gun violence and opioid overdose calls in 2020 after lockdowns, but surprisingly, a sharp decrease in behavioral health calls and no change in suicide-related EMS activations. The latter trend is a puzzle, but possible explanations include opioid overdose deaths increasing markedly and possibly substituting for suicide deaths. Alternatively, many older men—who are the demographic groups with the most suicide deaths—died of COVID-19 in that same period; another tragic “substitution” effect.

Finally, we looked at whether over the long run there is a relationship between poor mental health and later deaths of despair in micropolitan and metropolitan statistical areas (MMSAs). We found modest support for that possibility. Based on mental health reports in the BRFSS and CDC mortality data, we find that two-to-three-year-lagged bad mental health days (at the individual level) are associated with higher rates of deaths of despair (at the MMSA level), and that the two-to-four-year-lagged rates of deaths of despair are associated with a higher number of bad mental health days in later years. We cannot establish a direction of causality, but it is possible that there are vicious circles at play with individual trends in mental health contributing to broader community distress, and communities with more despair-related deaths likely to have more mental health problems later as a result.

Our analysis, based on many different datasets and indicators of despair, does not contradict other studies in that despair is an ongoing problem in the U.S., as reflected by both mental health reports and trends in EMS activations. However, we do find that the effects of the COVID-19 pandemic are mixed, and that while some trends, such as opioid overdose deaths, worsened in 2020 compared to 2019, others, such as in some mental health reports and in suicide rates, improved slightly. Our work does not speak to the longer-term mental health consequences of the pandemic, but it does suggest that there were deep pockets of both despair and resilience throughout it. It also suggests that caution is necessary in drawing policy implications from any one study.

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• Volume 76, Issue 2
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• http://orcid.org/0000-0003-1512-4471 Emily Long 1 ,
• Susan Patterson 1 ,
• Karen Maxwell 1 ,
• Carolyn Blake 1 ,
• http://orcid.org/0000-0001-7342-4566 Raquel Bosó Pérez 1 ,
• Ruth Lewis 1 ,
• Mark McCann 1 ,
• Julie Riddell 1 ,
• Kathryn Skivington 1 ,
• Rachel Wilson-Lowe 1 ,
• http://orcid.org/0000-0002-4409-6601 Kirstin R Mitchell 2
• 1 MRC/CSO Social and Public Health Sciences Unit , University of Glasgow , Glasgow , UK
• 2 MRC/CSO Social and Public Health Sciences Unit, Institute of Health & Wellbeing , University of Glasgow , Glasgow , UK
• Correspondence to Dr Emily Long, MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow G3 7HR, UK; emily.long{at}glasgow.ac.uk

This essay examines key aspects of social relationships that were disrupted by the COVID-19 pandemic. It focuses explicitly on relational mechanisms of health and brings together theory and emerging evidence on the effects of the COVID-19 pandemic to make recommendations for future public health policy and recovery. We first provide an overview of the pandemic in the UK context, outlining the nature of the public health response. We then introduce four distinct domains of social relationships: social networks, social support, social interaction and intimacy, highlighting the mechanisms through which the pandemic and associated public health response drastically altered social interactions in each domain. Throughout the essay, the lens of health inequalities, and perspective of relationships as interconnecting elements in a broader system, is used to explore the varying impact of these disruptions. The essay concludes by providing recommendations for longer term recovery ensuring that the social relational cost of COVID-19 is adequately considered in efforts to rebuild.

• inequalities

Data availability statement

Data sharing not applicable as no data sets generated and/or analysed for this study. Data sharing not applicable as no data sets generated or analysed for this essay.

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/ .

https://doi.org/10.1136/jech-2021-216690

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Introduction

Infectious disease pandemics, including SARS and COVID-19, demand intrapersonal behaviour change and present highly complex challenges for public health. 1 A pandemic of an airborne infection, spread easily through social contact, assails human relationships by drastically altering the ways through which humans interact. In this essay, we draw on theories of social relationships to examine specific ways in which relational mechanisms key to health and well-being were disrupted by the COVID-19 pandemic. Relational mechanisms refer to the processes between people that lead to change in health outcomes.

At the time of writing, the future surrounding COVID-19 was uncertain. Vaccine programmes were being rolled out in countries that could afford them, but new and more contagious variants of the virus were also being discovered. The recovery journey looked long, with continued disruption to social relationships. The social cost of COVID-19 was only just beginning to emerge, but the mental health impact was already considerable, 2 3 and the inequality of the health burden stark. 4 Knowledge of the epidemiology of COVID-19 accrued rapidly, but evidence of the most effective policy responses remained uncertain.

The initial response to COVID-19 in the UK was reactive and aimed at reducing mortality, with little time to consider the social implications, including for interpersonal and community relationships. The terminology of ‘social distancing’ quickly became entrenched both in public and policy discourse. This equation of physical distance with social distance was regrettable, since only physical proximity causes viral transmission, whereas many forms of social proximity (eg, conversations while walking outdoors) are minimal risk, and are crucial to maintaining relationships supportive of health and well-being.

The aim of this essay is to explore four key relational mechanisms that were impacted by the pandemic and associated restrictions: social networks, social support, social interaction and intimacy. We use relational theories and emerging research on the effects of the COVID-19 pandemic response to make three key recommendations: one regarding public health responses; and two regarding social recovery. Our understanding of these mechanisms stems from a ‘systems’ perspective which casts social relationships as interdependent elements within a connected whole. 5

Social networks

Social networks characterise the individuals and social connections that compose a system (such as a workplace, community or society). Social relationships range from spouses and partners, to coworkers, friends and acquaintances. They vary across many dimensions, including, for example, frequency of contact and emotional closeness. Social networks can be understood both in terms of the individuals and relationships that compose the network, as well as the overall network structure (eg, how many of your friends know each other).

Social networks show a tendency towards homophily, or a phenomenon of associating with individuals who are similar to self. 6 This is particularly true for ‘core’ network ties (eg, close friends), while more distant, sometimes called ‘weak’ ties tend to show more diversity. During the height of COVID-19 restrictions, face-to-face interactions were often reduced to core network members, such as partners, family members or, potentially, live-in roommates; some ‘weak’ ties were lost, and interactions became more limited to those closest. Given that peripheral, weaker social ties provide a diversity of resources, opinions and support, 7 COVID-19 likely resulted in networks that were smaller and more homogenous.

Such changes were not inevitable nor necessarily enduring, since social networks are also adaptive and responsive to change, in that a disruption to usual ways of interacting can be replaced by new ways of engaging (eg, Zoom). Yet, important inequalities exist, wherein networks and individual relationships within networks are not equally able to adapt to such changes. For example, individuals with a large number of newly established relationships (eg, university students) may have struggled to transfer these relationships online, resulting in lost contacts and a heightened risk of social isolation. This is consistent with research suggesting that young adults were the most likely to report a worsening of relationships during COVID-19, whereas older adults were the least likely to report a change. 8

Lastly, social connections give rise to emergent properties of social systems, 9 where a community-level phenomenon develops that cannot be attributed to any one member or portion of the network. For example, local area-based networks emerged due to geographic restrictions (eg, stay-at-home orders), resulting in increases in neighbourly support and local volunteering. 10 In fact, research suggests that relationships with neighbours displayed the largest net gain in ratings of relationship quality compared with a range of relationship types (eg, partner, colleague, friend). 8 Much of this was built from spontaneous individual interactions within local communities, which together contributed to the ‘community spirit’ that many experienced. 11 COVID-19 restrictions thus impacted the personal social networks and the structure of the larger networks within the society.

Social support

Social support, referring to the psychological and material resources provided through social interaction, is a critical mechanism through which social relationships benefit health. In fact, social support has been shown to be one of the most important resilience factors in the aftermath of stressful events. 12 In the context of COVID-19, the usual ways in which individuals interact and obtain social support have been severely disrupted.

One such disruption has been to opportunities for spontaneous social interactions. For example, conversations with colleagues in a break room offer an opportunity for socialising beyond one’s core social network, and these peripheral conversations can provide a form of social support. 13 14 A chance conversation may lead to advice helpful to coping with situations or seeking formal help. Thus, the absence of these spontaneous interactions may mean the reduction of indirect support-seeking opportunities. While direct support-seeking behaviour is more effective at eliciting support, it also requires significantly more effort and may be perceived as forceful and burdensome. 15 The shift to homeworking and closure of community venues reduced the number of opportunities for these spontaneous interactions to occur, and has, second, focused them locally. Consequently, individuals whose core networks are located elsewhere, or who live in communities where spontaneous interaction is less likely, have less opportunity to benefit from spontaneous in-person supportive interactions.

However, alongside this disruption, new opportunities to interact and obtain social support have arisen. The surge in community social support during the initial lockdown mirrored that often seen in response to adverse events (eg, natural disasters 16 ). COVID-19 restrictions that confined individuals to their local area also compelled them to focus their in-person efforts locally. Commentators on the initial lockdown in the UK remarked on extraordinary acts of generosity between individuals who belonged to the same community but were unknown to each other. However, research on adverse events also tells us that such community support is not necessarily maintained in the longer term. 16

Meanwhile, online forms of social support are not bound by geography, thus enabling interactions and social support to be received from a wider network of people. Formal online social support spaces (eg, support groups) existed well before COVID-19, but have vastly increased since. While online interactions can increase perceived social support, it is unclear whether remote communication technologies provide an effective substitute from in-person interaction during periods of social distancing. 17 18 It makes intuitive sense that the usefulness of online social support will vary by the type of support offered, degree of social interaction and ‘online communication skills’ of those taking part. Youth workers, for instance, have struggled to keep vulnerable youth engaged in online youth clubs, 19 despite others finding a positive association between amount of digital technology used by individuals during lockdown and perceived social support. 20 Other research has found that more frequent face-to-face contact and phone/video contact both related to lower levels of depression during the time period of March to August 2020, but the negative effect of a lack of contact was greater for those with higher levels of usual sociability. 21 Relatedly, important inequalities in social support exist, such that individuals who occupy more socially disadvantaged positions in society (eg, low socioeconomic status, older people) tend to have less access to social support, 22 potentially exacerbated by COVID-19.

Social and interactional norms

Interactional norms are key relational mechanisms which build trust, belonging and identity within and across groups in a system. Individuals in groups and societies apply meaning by ‘approving, arranging and redefining’ symbols of interaction. 23 A handshake, for instance, is a powerful symbol of trust and equality. Depending on context, not shaking hands may symbolise a failure to extend friendship, or a failure to reach agreement. The norms governing these symbols represent shared values and identity; and mutual understanding of these symbols enables individuals to achieve orderly interactions, establish supportive relationship accountability and connect socially. 24 25

Physical distancing measures to contain the spread of COVID-19 radically altered these norms of interaction, particularly those used to convey trust, affinity, empathy and respect (eg, hugging, physical comforting). 26 As epidemic waves rose and fell, the work to negotiate these norms required intense cognitive effort; previously taken-for-granted interactions were re-examined, factoring in current restriction levels, own and (assumed) others’ vulnerability and tolerance of risk. This created awkwardness, and uncertainty, for example, around how to bring closure to an in-person interaction or convey warmth. The instability in scripted ways of interacting created particular strain for individuals who already struggled to encode and decode interactions with others (eg, those who are deaf or have autism spectrum disorder); difficulties often intensified by mask wearing. 27

Large social gatherings—for example, weddings, school assemblies, sporting events—also present key opportunities for affirming and assimilating interactional norms, building cohesion and shared identity and facilitating cooperation across social groups. 28 Online ‘equivalents’ do not easily support ‘social-bonding’ activities such as singing and dancing, and rarely enable chance/spontaneous one-on-one conversations with peripheral/weaker network ties (see the Social networks section) which can help strengthen bonds across a larger network. The loss of large gatherings to celebrate rites of passage (eg, bar mitzvah, weddings) has additional relational costs since these events are performed by and for communities to reinforce belonging, and to assist in transitioning to new phases of life. 29 The loss of interaction with diverse others via community and large group gatherings also reduces intergroup contact, which may then tend towards more prejudiced outgroup attitudes. While online interaction can go some way to mimicking these interaction norms, there are key differences. A sense of anonymity, and lack of in-person emotional cues, tends to support norms of polarisation and aggression in expressing differences of opinion online. And while online platforms have potential to provide intergroup contact, the tendency of much social media to form homogeneous ‘echo chambers’ can serve to further reduce intergroup contact. 30 31

Intimacy relates to the feeling of emotional connection and closeness with other human beings. Emotional connection, through romantic, friendship or familial relationships, fulfils a basic human need 32 and strongly benefits health, including reduced stress levels, improved mental health, lowered blood pressure and reduced risk of heart disease. 32 33 Intimacy can be fostered through familiarity, feeling understood and feeling accepted by close others. 34

Intimacy via companionship and closeness is fundamental to mental well-being. Positively, the COVID-19 pandemic has offered opportunities for individuals to (re)connect and (re)strengthen close relationships within their household via quality time together, following closure of many usual external social activities. Research suggests that the first full UK lockdown period led to a net gain in the quality of steady relationships at a population level, 35 but amplified existing inequalities in relationship quality. 35 36 For some in single-person households, the absence of a companion became more conspicuous, leading to feelings of loneliness and lower mental well-being. 37 38 Additional pandemic-related relational strain 39 40 resulted, for some, in the initiation or intensification of domestic abuse. 41 42

Physical touch is another key aspect of intimacy, a fundamental human need crucial in maintaining and developing intimacy within close relationships. 34 Restrictions on social interactions severely restricted the number and range of people with whom physical affection was possible. The reduction in opportunity to give and receive affectionate physical touch was not experienced equally. Many of those living alone found themselves completely without physical contact for extended periods. The deprivation of physical touch is evidenced to take a heavy emotional toll. 43 Even in future, once physical expressions of affection can resume, new levels of anxiety over germs may introduce hesitancy into previously fluent blending of physical and verbal intimate social connections. 44

The pandemic also led to shifts in practices and norms around sexual relationship building and maintenance, as individuals adapted and sought alternative ways of enacting sexual intimacy. This too is important, given that intimate sexual activity has known benefits for health. 45 46 Given that social restrictions hinged on reducing household mixing, possibilities for partnered sexual activity were primarily guided by living arrangements. While those in cohabiting relationships could potentially continue as before, those who were single or in non-cohabiting relationships generally had restricted opportunities to maintain their sexual relationships. Pornography consumption and digital partners were reported to increase since lockdown. 47 However, online interactions are qualitatively different from in-person interactions and do not provide the same opportunities for physical intimacy.

Recommendations and conclusions

In the sections above we have outlined the ways in which COVID-19 has impacted social relationships, showing how relational mechanisms key to health have been undermined. While some of the damage might well self-repair after the pandemic, there are opportunities inherent in deliberative efforts to build back in ways that facilitate greater resilience in social and community relationships. We conclude by making three recommendations: one regarding public health responses to the pandemic; and two regarding social recovery.

Recommendation 1: explicitly count the relational cost of public health policies to control the pandemic

Effective handling of a pandemic recognises that social, economic and health concerns are intricately interwoven. It is clear that future research and policy attention must focus on the social consequences. As described above, policies which restrict physical mixing across households carry heavy and unequal relational costs. These include for individuals (eg, loss of intimate touch), dyads (eg, loss of warmth, comfort), networks (eg, restricted access to support) and communities (eg, loss of cohesion and identity). Such costs—and their unequal impact—should not be ignored in short-term efforts to control an epidemic. Some public health responses—restrictions on international holiday travel and highly efficient test and trace systems—have relatively small relational costs and should be prioritised. At a national level, an earlier move to proportionate restrictions, and investment in effective test and trace systems, may help prevent escalation of spread to the point where a national lockdown or tight restrictions became an inevitability. Where policies with relational costs are unavoidable, close attention should be paid to the unequal relational impact for those whose personal circumstances differ from normative assumptions of two adult families. This includes consideration of whether expectations are fair (eg, for those who live alone), whether restrictions on social events are equitable across age group, religious/ethnic groupings and social class, and also to ensure that the language promoted by such policies (eg, households; families) is not exclusionary. 48 49 Forethought to unequal impacts on social relationships should thus be integral to the work of epidemic preparedness teams.

Recommendation 2: intelligently balance online and offline ways of relating

A key ingredient for well-being is ‘getting together’ in a physical sense. This is fundamental to a human need for intimate touch, physical comfort, reinforcing interactional norms and providing practical support. Emerging evidence suggests that online ways of relating cannot simply replace physical interactions. But online interaction has many benefits and for some it offers connections that did not exist previously. In particular, online platforms provide new forms of support for those unable to access offline services because of mobility issues (eg, older people) or because they are geographically isolated from their support community (eg, lesbian, gay, bisexual, transgender and queer (LGBTQ) youth). Ultimately, multiple forms of online and offline social interactions are required to meet the needs of varying groups of people (eg, LGBTQ, older people). Future research and practice should aim to establish ways of using offline and online support in complementary and even synergistic ways, rather than veering between them as social restrictions expand and contract. Intelligent balancing of online and offline ways of relating also pertains to future policies on home and flexible working. A decision to switch to wholesale or obligatory homeworking should consider the risk to relational ‘group properties’ of the workplace community and their impact on employees’ well-being, focusing in particular on unequal impacts (eg, new vs established employees). Intelligent blending of online and in-person working is required to achieve flexibility while also nurturing supportive networks at work. Intelligent balance also implies strategies to build digital literacy and minimise digital exclusion, as well as coproducing solutions with intended beneficiaries.

Recommendation 3: build stronger and sustainable localised communities

In balancing offline and online ways of interacting, there is opportunity to capitalise on the potential for more localised, coherent communities due to scaled-down travel, homeworking and local focus that will ideally continue after restrictions end. There are potential economic benefits after the pandemic, such as increased trade as home workers use local resources (eg, coffee shops), but also relational benefits from stronger relationships around the orbit of the home and neighbourhood. Experience from previous crises shows that community volunteer efforts generated early on will wane over time in the absence of deliberate work to maintain them. Adequately funded partnerships between local government, third sector and community groups are required to sustain community assets that began as a direct response to the pandemic. Such partnerships could work to secure green spaces and indoor (non-commercial) meeting spaces that promote community interaction. Green spaces in particular provide a triple benefit in encouraging physical activity and mental health, as well as facilitating social bonding. 50 In building local communities, small community networks—that allow for diversity and break down ingroup/outgroup views—may be more helpful than the concept of ‘support bubbles’, which are exclusionary and less sustainable in the longer term. Rigorously designed intervention and evaluation—taking a systems approach—will be crucial in ensuring scale-up and sustainability.

The dramatic change to social interaction necessitated by efforts to control the spread of COVID-19 created stark challenges but also opportunities. Our essay highlights opportunities for learning, both to ensure the equity and humanity of physical restrictions, and to sustain the salutogenic effects of social relationships going forward. The starting point for capitalising on this learning is recognition of the disruption to relational mechanisms as a key part of the socioeconomic and health impact of the pandemic. In recovery planning, a general rule is that what is good for decreasing health inequalities (such as expanding social protection and public services and pursuing green inclusive growth strategies) 4 will also benefit relationships and safeguard relational mechanisms for future generations. Putting this into action will require political will.

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Not required.

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Twitter @karenmaxSPHSU, @Mark_McCann, @Rwilsonlowe, @KMitchinGlasgow

Contributors EL and KM led on the manuscript conceptualisation, review and editing. SP, KM, CB, RBP, RL, MM, JR, KS and RW-L contributed to drafting and revising the article. All authors assisted in revising the final draft.

Funding The research reported in this publication was supported by the Medical Research Council (MC_UU_00022/1, MC_UU_00022/3) and the Chief Scientist Office (SPHSU11, SPHSU14). EL is also supported by MRC Skills Development Fellowship Award (MR/S015078/1). KS and MM are also supported by a Medical Research Council Strategic Award (MC_PC_13027).

Competing interests None declared.

Provenance and peer review Not commissioned; externally peer reviewed.

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• Published: 07 March 2024

The impact of COVID-19 lockdowns on mental health patient populations in the United States

• Ibtihal Ferwana 1 &
• Lav R. Varshney   ORCID: orcid.org/0000-0003-2798-5308 1  

Scientific Reports volume  14 , Article number:  5689 ( 2024 ) Cite this article

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During the start of the COVID-19 pandemic in 2020, lockdowns and movement restrictions were thought to negatively impact population mental health, since depression and anxiety symptoms were frequently reported. This study investigates the effect of COVID-19 mitigation measures on mental health across the United States, at county and state levels using difference-in-differences analysis. It examines the effect on mental health facility usage and the prevalence of mental illnesses, drawing on large-scale medical claims data for mental health patients joined with publicly available state- and county-specific COVID-19 cases and lockdown information. For consistency, the main focus is on two types of social distancing policies, stay-at-home and school closure orders. Results show that lockdown has significantly and causally increased the usage of mental health facilities in regions with lockdowns in comparison to regions without such lockdowns. Particularly, resource usage increased by 18% in regions with a lockdown compared to 1% decline in regions without a lockdown. Also, female populations have been exposed to a larger lockdown effect on their mental health. Diagnosis of panic disorders and reaction to severe stress significantly increased by the lockdown. Mental health was more sensitive to lockdowns than to the presence of the pandemic itself. The effects of the lockdown increased over an extended time to the end of December 2020.

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Introduction.

As the COVID-19 pandemic began, confirmed cases rose, and mandated policy responses were enacted, mental health concerns started to be alarming 1 , 2 , 3 . The deterioration of mental health was observed during the first few months of the COVID-19 pandemic, March–June 2020 4 , 5 , especially among women and college students 6 , 7 , 8 . Further, people with preexisting psychiatric disorders 9 , 10 and people that encountered COVID-19 itself 4 developed more mental health issues during the pandemic.

In the early stage of the COVID-19 pandemic, people voluntarily stayed at home and limited their trips for weeks before public policy interventions were imposed 11 . Subsequently, social distancing policies were issued globally as a form of non-pharmaceutical intervention, including limiting people’s gatherings, closing schools, and fully restricting movements by lockdown orders (also called stay-at-home or shelter-in-place orders) 12 , so as to contain virus spread in light of the increasing number of COVID-19 cases and fatalities.

Given that various intertwined events took place during the COVID-19 pandemic, the cause of mental health deterioration is not clear. One possible explanation is the increased severity of COVID-19 which led to increased anxiety, worry, and depression 13 . Another explanation is that policy responses to the pandemic, particularly the lockdown orders, contributed to worsening mental health.

Previous studies observing the decline in mental health have faced a challenge in determining possible causes or selecting direct measures. For example, Refs. 14 , 15 found that depression and anxiety symptoms almost quadrupled from 2019 to June 2020, but could not infer causality given the study design. Other studies found that reduced physical activity resulting from restricted mobility led to higher rates of depression during the pandemic, but could not establish causality since they lacked pre-COVID-19 data 10 , 16 , 17 . Two other important studies by Refs. 18 , 19 used Google search data and found that the timing of lockdown policies has been significantly associated with searches of terms related to worry , sadness , and boredom revealing negative feelings. A recent study established causality of the effect of lockdown restrictions on worsening mental health using a clinical mental health questionnaire in Europe 20 . Although these studies considered pre-COVID-19 trends and have established causality on the lockdown orders, they lacked measures that reflect the rising need for mental health treatment and lacked a large representative population.

Examining the use of mental health resources and the prevalence of mental illnesses would further help in measuring the actual cost of COVID-19 lockdowns on mental health and inform mental health treatment resource planning for future lockdowns. Mental disorders have been more economically costly than any other disease, in which mental disorders were the leading segment of healthcare spending in the United States 21 , with the potential cause of a global economic burden 22 . Mental health has been related to social capital on individual and community levels 23 , 24 . Indeed, good social capital plays a role in promoting healthier public behaviors, especially during COVID-19 25 . The risk of mental health degradation goes beyond to impact the advantage of social capital in the face of viral diseases. Given these consequences of poor mental health on health care systems 26 , it has been essential to mitigate additional mental degradation and avoid potential future economic and social costs.

In this work, we consider measures that reflect the actual seeking of mental health services covering a large fraction of the United States population. To the best of our knowledge, there is no large-scale study that has investigated the effect of lockdown on the usage of mental health resources across the country. We empirically estimate the causal effect of COVID-19 social distancing policies on mental health across counties and states in the United States by comparing the differences in changes between locked and non-locked down regions using a large-scale medical claims dataset that covers most hospitals in the country. Specifically, we are interested to know whether the increase in mental health patients can be explained by COVID-19 lockdowns. Causal inference gives us the tools to uncover causal relationships rather than correlational relationships 27 , in order to understand the impact of COVID-19 policies on mental health.

We use the daily number of patients who visit mental health facilities as a measure for the usage of mental health resources, and we consider emergency department (ED) visits for mental health issues as a proxy for the development of new mental diseases, here, so severe that treatment could not be avoided. We consider ED visits to reflect the utilization of hospital resources under the shortage of medical staff. During COVID-19 there were patients with acute conditions reaching ED in which they have not been in regular outpatient visits 28 . Also, given the shortage in in-patient beds during the pandemic, mental health patients were admitted to ED instead 29 . Therefore, ED visits were of interest to indicate unmet mental health needs. The usage of mental health resources can further trigger analysis of economic costs borne by health care systems and the country as a whole. Mental health ED treatment visits might further reflect the mental health cost on an individual level.

Our results show that extended lockdown measures significantly increase the usage of mental health resources and ED visits. In particular, mental health resource usage in regions with lockdown orders has significantly increased compared to regions without a lockdown. The effect size of lockdowns was not only positive and significant but was also increasing till the end of December 2020. Our results further imply that mental health is more sensitive to policy interventions rather than the evolution of the pandemic itself.

The University of Illinois Urbana-Champaign Institutional Review Board declared this work to be exempt from review. The University of Illinois Urbana-Champaign Institutional Review Board waived the need for informed consent for the current study. All methods were carried out in accordance with relevant guidelines and regulations.

We used three sets of data to conduct our study: mental health claims data including emergency department (ED) claims, COVID-19 cases data, and lockdown dates data.

The mental health data is a large de-identified medical claims corpus provided by Change Healthcare for years 2019 and 2020. Change Healthcare serves 1 million providers covering 5500 hospitals with 220 million patients (which is roughly two-thirds of the US population) and represents over 50% of private insurance claims across the United States. It covers 51 states/territories and a total of 3141 counties (and equivalent jurisdictions like parishes). The data set includes millions of claims per month from the private insurance marketplace, and some Medicare Advantage programs and Medicaid programs using private insurance carriers, excluding Medicare and Medicaid indemnity claims, which is a limitation in the dataset coverage.

Given that different age and gender groups were affected differently during the pandemic 6 , 7 , 8 , we consider a variety of population subgroups in our analysis. Specifically, we consider subgroups of different age, gender, and mental health conditions. Not only do we look at the total mental health claims, but we also select specific mental health conditions, such as anxiety disorders, major depressive disorder, bipolar disorder. Our selected mental health conditions have been also been examined by others 30 during the COVID-19 pandemic. More details on the used clinical codes of mental health records are found in Supplementary Appendix Table   1 . We show summary statistics of the data and its subset representing gender, age, and mental disorders in Table  1 .

For COVID-19 cases, we considered state-level and county-level cases reported in the United States taken from the New York Times database 31 from the first case date in late January 2020 to December 31, 2020, covering 3218 counties in 51 states/territories. Given that reported cases depend on the testing results, thus, the data is limited by the fact that there was a widespread shortage of available tests in different regions at different times. The undercounts of COVID-19 cases used in this study would only weaken the effect we present, and so fixing the data would only strengthen the resultant effect.

For lockdown data, we used the data from the COVIDVis project URL: https://covidvis.berkeley.edu/  led by the University of California Berkeley to track policy interventions on state and county levels, in which they depended on government pandemic responses to construct the dataset. We considered the dates of two order types, shelter-in-place and K-12 school closure at state and county levels. The earliest and latest shelter-in-place orders were on March 14 and April 7, 2020, covering 2598 counties in 43 states. The earliest K-12 school closure was on March 10 and the latest was on April 28, 2020, covering 2465 counties in 39 states. The data is comprehensive, in which states and counties that do not appear in the dataset are considered without officially imposed lockdown. We focus on the impact of the initial shutdowns to avoid complications related to re-opening and repeated closures. Given that in some regions people tend to voluntarily isolate themselves at home and limit their trips before official lockdown orders 11 , therefore, lockdown dates might be limited to reflect the actual social distancing behavior across regions during the pandemic. However, lockdown dates would better reflect the beginning of persistent social distancing behaviors for a larger population group, which is useful to our study, unlike voluntary behaviors.

Difference-in-differences analysis

To estimate the effects of COVID-19 mitigation policies on mental health patients at county and state levels, we conducted a difference-in-differences (DID) analysis, which allows for inferring causality based on parallel trends assumption. For DID analysis we considered daily mental health patients’ visits from the date of September 1, 2019, till December 31, 2020, to observe the prolonged effects since mental health disorders may appear sometime after a trauma 32 . We aim to have balanced periods for pre- and post-lockdown interventions, and this is achievable with this selected range of dates. We used two outcomes, weighted and raw numbers of daily patient visits, weighted outcomes are normalized by the region population.

Our approach leveraged the variation of policy-mandated dates in different counties or states with 8 states that did not declare an official lockdown. Accordingly, we constructed both treated and control groups to implement the analysis. We estimated the following regression as our main equation:

where \(Y_{cd}\) is the outcome in a given region c (county or state) on a date d , \(policy_{jcd}\) indicates whether a policy j has been mandated for a region c on a date d , \(\beta _j\) is the DID interaction coefficient, representing the effect of introducing policy j , and \(\delta _c\) and \(\delta _{d}\) are fixed effects for region and date respectively. The region fixed-effect is included to adjust for time-invariant (independent of time) unobserved regional characteristics that might affect the outcome. For example, each county/state has its local health care system, social capital index, age profile, and socioeconomic status that the fixed effect controls for. Further, the date fixed effect \(\delta _{d}\) is included to adjust for factors that vary over time, such as COVID-19 rates or social behavioral change.

Control by the evolution of COVID-19 cases

Even though DID avoids the bias encountered in time-invariant factors, the bias of time-varying confounders may still be present 33 . Therefore, we consider the COVID-19 confirmed cases \(x_{cd}\) as a main confounder factor in counties or states and we control for it. We follow 34 to use a time-varying adjusted (TVA) model, based on the assumption that the confounding variable affects both treated and untreated groups regardless of policy intervention. We measured the interaction of time and the confounding \(x_{cd}\) covariate at county- and state-levels

Therefore, to mitigate the effect of potential confounders, e.g. socio-economic status and COVID-19 growth, we use several techniques from econometrics 35 . Specifically, we use the fixed effects \(\delta _c\) and \(\delta _d\) in ( 1 ) to adjust for time-invariant confounders related to location and time. Additionally, we use TVA 34 to adjust for time-varying confounders such as COVID-19 growth.

Event-study model

DID models rely on the assumption of parallel pre-treatment trends to exist in both treated and untreated groups. Hence, in the absence of a policy, treated counties or states would evolve similarly as untreated counties or states. To assess equal pre-policy trends, we designed an event-study type model 36 . We calculated k periods before policy implementation and used an event-study coefficient to indicate whether an outcome in specific date d and county/state c is within k periods before the policy implementation 18 , 37 . We estimated the following regression model:

where \(policy_{hsd}^k\) , a dummy variable, equals 1 if policy h took place k periods before the mandate, and zero otherwise. Period k is calculated in months, \(k=\{- 6, - 5, - 4, - 2, - 1, 0\}\) months, and the month of the policy implementation ( \(k=0\) ) is considered as the omitted category. Here, \(\beta _h^k\) is the event-study coefficient and we included all control variables as defined in ( 1 ).

Descriptive analysis

Before we delve into the causal DID inference, we report some statistics to describe the data of mental health patients. Among 16.7 million mental health patients in the United States, the mean age was 38.7 years and 56% were female. As seen in Fig. 1 , the distribution of mental health patients in states and counties shifted between 2019 and 2020. The total increase is 22% of all mental health patients of any mental health disorder as seen in Table 2 in the Supplementary Appendix.

Distributions of mental health patients weighted by regions’ populations in years of 2019 and 2020 in counties ( A ) and states ( B ). The total population increase is 22% in 2020.

Figure 2 shows the increasing trend of the number of mental health daily patients’ visits, though it decreased between March and April 2020, during lockdown mandates.

An obvious increase was during June 2020, which can be attributed to telemedicine options or relaxed lockdown measures.

Mental health patients over time.

Parallel trend assumption

To apply DID, first, we validate the pre-policy parallel trends assumption. We tested the equality of pre-policy trends for counties and states using ( 3 ). We plot the event-study coefficients for 6 months before policy implementation from the models of stay-at-home and school-closure orders and the corresponding 95 % confidence intervals. Figure 1 (in Supplementary Appendix) shows that the event-study coefficients are generally non-significant, therefore we cannot reject the null hypothesis of parallel trends. Accordingly, the key assumption of parallel trends of DID is satisfied for both counties and states.

Correlation to COVID-19

Given the possibility that COVID-19 increasing cases act as a confounding factor to the increasing mental health burden, we adjusted our main DID regression to COVID-19 cases using the TVA model in  ( 2 ). First, we validate that a correlation exists between mental health visits number and COVID-19 increasing cases. Figure 3 shows that a significant correlation between COVID-19 and mental health patients populations (R \(^2\) = 0.77, p-value < 2 \(\times 10^{-16}\) ) with an increase of 0.043 mental health visits for each new COVID-19 confirmed case. Adjusting for the COVID-19 cases acts as a proxy for adjusting for the pandemic effect itself.

Correlation of mental health daily visits and COVID-19 confirmed cases in a log-log plot with an increase of 0.043 mental health visits for each confirmed COVID-19 case in counties (R \(^2\) = 0.77, p-value < \(2 \times 10^{-16}\) ).

Effects on the usage of mental health resources

We consider daily visits of mental health patients for the causal DID inference model from September 1, 2019, to December 31, 2020. Figure 4 shows the monthly average mental health visits in counties with stay-at-home orders and without. In general, there is an increase in monthly visits in months after COVID-19 lockdowns in regions with enacted lockdowns. There is also a clear similar trend of visits between regions with and without lockdowns. This pre-COVID-19 trend has been validated in the previously mentioned event study. Figure 2 (in Supplementary Appendix) shows the monthly average visits in counties with and without school closure orders. Similarly, Figs. 3 and 4 (in Supplementary Appendix) show the average monthly visits at the state level.

We further investigate the causality relationship between daily visits and lockdown measures. In Tables   2 and 3 we summarize the estimated effects of COVID-19 lockdown measures on the weighted outcomes for counties and states respectively for different population groups with the adjusted results after controlling for COVID-19 cases. Tables 5 and 6 (in Supplementary Appendix) summarize the raw outcomes. Along with regression estimates, we include significance measures of p-value, 95% confidence intervals of standard errors, and R-squared ( \(R^2\) ). We will further discuss results for each population group in both counties and states in the following sections.

Tables 11 and 12 (in Supplementary Appendix) summarize the estimated effects of Eq. ( 1 ) at different periods of time k where k = {1, 5, 9}-months after lockdowns, to show the dynamic effect of stay-at-home and school closures in counties and states respectively.

Average number of mental health patients over time (September 2019–December 2020) in counties with stay-at-home orders and without. Vertical lines show the first stay-at-home order on 3/14/2020 and last on 4/07/2020 across United States. Difference-in-differences estimates are included for each population. (Detailed average percentage changes are listed in Table 3 ). \(***p < 0.01\) , \(**p < 0.05\) , \(*p < 0.1\) .

Effects on total population

We consider the overall mental health population including all mental health disorders with clinical codes defined in Supplementary Table 1 . Based on Table 2 there is a significant positive effect of stay-at-home order across counties on the weighted population of mental health patients’ daily visits, with a mean difference of 1 in 10,000 daily patient visits between counties with stay-at-home orders and counties without. On average, mental health patients increased by 18.7% but declined by 1% in counties without lockdown (Fig. 4 ). Adjusting for COVID-19 confounding effect preserves the positive effect significant on the mental health population with a similar effect size. School closure has also a significant, but a lower effect on the mental health patient population (estimated mean difference = 8.8 in 100,000 population), with a percentage increase of 17% and 16% in counties with closed schools and without respectively (Table 3 in Supplementary Appendix), with significant similar size effect while adjusted for COVID-19 cases.

Similar results are found at the state level, Table 3 shows that the effect of stay-at-home order is positively significant for total mental health patients (difference estimate is 8.8 and 8.6 when adjusted in 10 \(^5\) population) with 22% increase by December 2020 as compared to less than 2% increase in states without lockdown (Table 4 in Supplementary Appendix). However, school closures have no significant effect at the state level.

We further investigate whether the effect on mental health differs if we shorten the period of observation after lockdown interventions. We applied our main regression model ( 1 ) on outcomes after a 1-month of lockdown (maximum mid-May) and 5-month of lockdown (maximum mid-August) for each region. The sizes of the lockdown effects are positive and significant at different times. Also, they keep increasing from the first month after the lockdown date until the end of the year 2020, for both stay-at-home orders and school closures in counties (Table 11 in Supplementary Appendix) and states (Table 12 in Supplementary Appendix).

We further examined the sensitivity of our DID results by sequentially adding controls to the baseline DID model. Table 7 in the Supplementary Appendix shows results are robust and neither COVID-19 growth nor the social capital index contributed to the effect of lockdowns on mental health populations.

Gender effects

In counties, the estimated effects of stay-at-home orders on both women and men are 6.8 (6.6 when adjusted) and 5.7 (5.7 when adjusted) respectively (Table 2 ). Female patients’ daily visits increased by 24% in counties with stay-at-home orders in comparison with 3% in counties without (Table 3 in Supplementary Appendix). Male patients declined by 5% in counties without stay-at-home orders. Whereas the estimated effects of school closures are negative for females (mean difference = − 1.67, and − 3.89 when adjusted) and significant when adjusted. While for men, school closure effects were significantly positive (mean difference = 4.5 and 3.4 when adjusted) (Table 2 ). This implies that women have been affected more by stay-at-home orders than by school closures across counties.

Similarly in states, the estimated mean difference for women is 5.1 (5.6 when adjusted) and for men is 3.8 (4.1 when adjusted) in 10 \(^5\) population (Table 3 ). Female patients’ daily visits increased by 29% and 6% in states with stay-at-home orders and without respectively, while male patients’ daily visits decreased in states without stay-at-home lockdown (Table 4 in Supplementary Appendix). School closure did not show significant effects on women or men at the state level.

Even at an early stage of the COVID-19 lockdown, mental health visits for female and male patients were larger than in non-locked regions, which they were increasing significantly throughout the year 2020 in counties and states (Tables 11 , 12 in Supplementary Appendix)

Diagnosis effects

We selected the top five mental disorders (e.g. panic disorder ) that peaked in 2020, and other disorders of interest ( insomnia and life management difficulty ) to investigate the effect of lockdowns on patient populations for specific diagnosis. We provide the definition of each considered mental condition in Table  1 in Supplementary Appendix.

In counties, all disorders were positively and significantly affected by stay-at-home orders and by school closures with lower effect sizes. Patients diagnosed with panic disorder (ICD-10: F41) had the largest difference among other mental illnesses and increased in both county groups (31.8% vs 8.88%) with an estimated effect of 3.3 (3.2 when adjusted in 10 \(^5\) population). Patients with attention-deficit hyperactivity disorder (ICD-10: F90) decreased in counties without stay-at-home orders by − 13.6% with an estimated effect of 3.2 (3.1 when adjusted) in 10 \(^5\) population.

Unlikely, patients with insomnia , with a significant estimated effect of \(-\,0.053\) in 10 \(^5\) population when adjusted, increased more in counties without school closures by 24% compared to 17% in counties with closures, which implies that insomnia was more in counties without school closures. Patients diagnosed with life management difficulty disorder increased more in counties without school closures as well by 127.85% compared with 94.64% with closures, and the estimated effect is − 0.6 (in 10 \(^5\) population) when adjusted (Tables 2 , 3 in Supplementary Appendix).

Similarly, at the state level, panic disorder (ICD-10: F41) increased by 38.4% in states with stay-at-home orders (Table 4 in Supplementary Appendix) and had the largest difference effect size with a mean difference of 2 in 10 \(^{5}\) population, similarly when adjusted (Table 3 ). Daily visits of patients with life management difficulty increased more in states without a school closure by 161.49% compared to 123.36% in states with closures with a significant estimated effect of \(-\,0.2\) (in 10 \(^{5}\) population) similarly when adjusted.

Over time, the effect of stay-at-home order kept increasing significantly for all selected mental disorders across counties (Table 11 in Supplementary Appendix) and states (Table 12 in Supplementary Appendix). While school closure effect is significantly increasing for most diagnoses except for life management difficulty diagnosis where the effect kept declining.

Age effects

At the county level, all age groups, both lockdowns have positive significant effects on the mental health patients’ daily visits. Based on Table 2 , the two largest significant differences were for adults between 31 and 40 years old and adults between 21 and 30 years old. Adults in their thirties increased by 20.47% in counties with stay-at-home orders but declined by − 0.1% in counties without, with a mean difference of 3.2 (in 10 \(^5\) population, similarly when adjusted). Adults in their twenties increased more in counties with stay-at-home orders by 30.01% compared to 11% in counties without, with an estimated effect of 1.5 (in 10 \(^5\) population, similarly when adjusted). Daily visits of young patients under 11 and adolescent patients under 21 are lower in counties without stay-at-home orders with significant positive effects of stay-at-home lockdown (Table 2 ).

Similarly, school closures affected patients in their thirties but with lower mean differences of 1.9 in 10 \(^5\) population (not significant when adjusted) (Table 3 ). They increased by 18.75 vs. 18.62 in regions with and without closures respectively. While daily visits of teenagers and adolescent (11 to 20) patients increased more in counties with school closures by 27.16%, compared to 19.17% in counties without closures, with estimated effect 2.2 in 10 \(^5\) population (not significant when adjusted) (Fig. 2 in Supplementary Appendix).

Similar observations are found at the state-level based on Table 3 . For most age groups both stay-at-home and school closure orders show significant positive effects, with the largest effect size for people in their thirties. Mental health patients who are in their thirties increased by 28% and 1% in states with stay-at-home orders and without respectively. Similarly, patients in their twenties increased by 40% and 15% in states with stay-at-home order and without respectively (Table 4 in Supplementary Appendix).

The effect sizes of both lockdowns on most age groups kept increasing significantly throughout the year of 2020. Children less than 11 years old had the largest change of estimation size, which indicates a greater effect on children appeared later on in counties with stay-at-home orders (Table 11 in Supplementary Appendix).

Effects on urgent treatment-seeking

We consider daily emergency department (ED) visits to reflect the emergent need to seek a mental health facility during the COVID-19 pandemic such that the condition is so severe to avoid treatment. The ED visits are defined according to the codes in Table 1 in Supplementary Appendix.

Average number of mental health ED visits over time (September 2019–December 2020) in counties with stay-at-home orders and without. Vertical lines show the first stay-at-home order on 3/14/2020 and last on 4/07/2020 across United States. Difference-in-differences estimates are included for each population. \(***p < 0.01\) , \(**p < 0.05\) , \(*p < 0.1\) .

ED visits decreased at the beginning of the pandemic, with a further finding that only patients with serious medical conditions were seeking care in ED 38 . One reason is that some patients were more willing to self-treat a variety of medical conditions than risk being exposed to COVID-19 in emergency rooms 39 . Given the role played by the ED during the first few months of the pandemic, it is linked with acute conditions for which patients could not avoid treatment

ED visits show a similar increasing positive trend in response to the lockdown measures (see Fig.  5 ). We also investigated ED visits outcomes on different population groups and the trend is consistent (Fig.  5 in Supplementary Appendix).

The effect of stay-at-home order on the overall ED visits is positive and significant with a magnitude of 0.29 weighted by population on state-level, and 0.32 when adjusted to the pandemic factor. Similarly, the effect of school closure is positive and significant with a value of 0.12 weighted by state population, same when adjusted (see Table  9 in Supplementary Appendix). Women and men groups show similar effect sizes with regard to ED visits, with an effect size of 0.2 for both groups even with adjusting for the pandemic factor. Similarly for psychiatric diagnosis, the effects are positive and significant with the largest effect size on panic disorder patients with a magnitude of 0.1 and 0.09 when adjusted. Age groups also show a similar trend of increasing daily ED visits with the largest effect size on the 21–30 age group of 0.07 and 0.05 when adjusted. Younger group ages did not show a significant effect on daily ED visits (Table  9 in Supplementary Appendix). Similar results appear for the school closures and county-level outcomes (Table  8 in Supplementary Appendix).

Robustness check

Given the differences in regions with respect to the number of hospitals, facilities, and patients, we conducted robustness checks of our main analysis to show that dropping multiple states does not change the estimates and that our results are not driven by specific regions. We dropped New York and Ohio states which were two states with the largest patient volume relative to population, and we apply our DID regression model to the weighted outcomes in states. The estimates remained robust, significant, and positive (Table  4 ). We also added all 2019 samples to expand the control group and the pre-intervention period. The relationship inferred from our analysis stayed significant and positive with this expansion.

We also conducted a similar check for ED analysis and found a similar observation of consistent robustness (Table  10 in Supplementary Appendix).

Early in March 2020, non-pharmaceutical interventions, such as social distancing policies, were imposed around the world to contain the spread of COVID-19 and proved to reduce the number of COVID-19 cases and fatalities 3 , 40 , 41 . Mitigation policies come with both costs and benefits, which may be further analyzed to help determine the optimal time to release or stop a policy intervention 42 . Prior research showed significant mental health degradation associated with the COVID-19 pandemic 6 , 7 , 18 , 19 , however, no research investigated the causal relation between COVID-19 mitigation policies and the usage of mental health resources. Yet the effects on the usage of mental health resources can further reflect the economic and health costs brought by the pandemic interventions. In our study, using large-scale medical claims data, we estimated the effects of lockdowns on the usage of mental health facilities and the prevalence of mental health issues at the state- and county levels in the United States.

Our findings demonstrate a statistically significant causal effect of lockdown measures (stay-at-home and school closure orders) on the usage of mental health facilities represented by an increasing number of issued medical claims for mental health appointments during COVID-19 pandemic. Also, ED visits were statistically significant and positive in locked-down regions which reflects the increase in emergent mental help-seeking due to the COVID-19 lockdowns. Results further emphasize the cost brought by extra months of lockdowns, in which effect sizes keep increasing through the end of 2020 in both mental health visits and ED visits. Some sub-population groups were exposed to a larger deterioration effect than other groups, such as women and adolescent groups.

Some mental health conditions were of particular interest to investigate during the COVID-19 lockdown. For example, sleep disturbance have been widely observed 43 specifically being a large concern in Italy 44 and China 45 during COVID-19 lockdown. Our results showed a similar observation, in which insomnia visits increased in counties with lockdowns. Similarly, burnout has been observed among health providers 46 and some working parents 47 during lockdown measures. Life-management difficulty disorder reflects burn-out and mental health issues in the workplace. Although this is not classified as a medical condition, but rather as an occupational phenomenon 48 , it is certainly a public health challenge 49 . Our results show that life management difficulty disorder, including burnout, increased with lockdowns at the state-level.

There have been several observations on the relation of school closures with increased mental health risks. Specifically, it was observed that some children were more likely to suffer from attention-deficit hyperactivity disorder (ADHD) symptoms during the COVID-19 pandemic 50 . This further confirms our findings of increased ADHD visits with school closures.

Our findings were observed at two granularity levels, county and state levels, with very similar trends of observations of increasing daily patient visits to mental health facilities. This further strengthens the established relationship of the effect of lockdowns on the mental health population with controlled possible sources of confoundedness. We also note our results stay the same when controlling for the evolution of the pandemic. This adds to the validity and robustness of the effects of lockdown measures on mental health despite the presence of the pandemic. It also implies that mental health is more sensitive to policy measures rather than to the evolution of the pandemic.

Given the various intertwined events and causes during the COVID-19 pandemic, our analysis is limited by several factors. First, it is important to point out that the adoption of lockdowns across states did not happen at random. Differences in shutdown orders’ timings and adoption across regions were associated with the differences in COVID-19 confirmed cases and fatality rates across those regions 51 , 52 and the differences in their health systems capacity 53 . Also, there exist other political, economical, and institutional factors that affect the adoption of COVID-19 measures and their strictness level across countries 54 . Even though the lockdown timing may be affected by regional factors related to the virus, such as the number of cases or institutional factors, however, there is no reason to believe that lockdown timing was affected by the prevalence of mental health in regions. Given that, we have also encountered regional fixed effects in our model to adjust for regional differences. Second, though mental illnesses have a negative economic impact 55 , the opposite is true as well, in which economic disadvantage may lead to a greater mental illness 56 . During COVID-19, there have been negative consequences on individuals in different industry sectors who were more likely to lose their jobs due to the lockdown measures 57 with significant employment loss in occupations that require interpersonal contact 58 . Therefore, the loss of employment due to shutdowns may have a confounding effect on increased mental health issues.

In addition, the medical claims used in this study do not cover Medicare and Medicaid health insurance programs which creates a limitation on our data. Medicare covers most aged and disabled populations across the US, while Medicare covers a wider range of populations including low-income beneficiaries covering 30% of US population 59 . This limitation would impact the representativeness of results since our data misses some population groups in the US. We also note that our medical claims dataset does not provide demographics information such as race and ethnicity. This limitation restricts our analysis to only age and gender demographics information.

Despite the mentioned limitations, our results provide important policy implications from economic and social impacts. There is a notable mental health cost brought by non-pharmaceutical interventions, especially interventions that are extended to longer duration. Our results suggest that there should be considerations to the mental health cost through ensuring mental health treatment capacity.

Furthermore, we showed that number of patients’ daily visits had dropped right after lockdowns and then progressively increased in June and July 2020, supporting the findings of Refs. 60 , 61 . This suggests that people with mental health afflictions did not have the ability to seek immediate care during restrictive lockdowns. Findings suggest that policy interventions should be accompanied by strategies that facilitate mental health treatment reachability despite restrictive lockdowns, in order to avoid the exacerbated effect of delayed treatment.

Data availability

There is a Research Data Access and Services Agreement between Change Healthcare Operations, LLC and the Board of Trustees of the University of Illinois, through which data access was granted. This work is exempt from review, as per the University of Illinois Urbana-Champaign institutional review board process. Medical claims data analyzed during the current study are not publicly available because it is under the agreement between Change Healthcare, LLC and the University of Illinois Urbana-Champaign. The NYTimes data analyzed during the current study is available in the NYTiems repository, https://github.com/nytimes/covid-19-data . The COVID-19 data analyzed during the current study is available in the COVIDVis repository, https://github.com/covidvis/covid19-vis/tree/master/data .

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The authors thank the Change Healthcare team, Craig Midgett, Mina Atia, Andrew Harris, Anil Konda, Tim Suther, and Jaideep Kulkarni for facilitating our access to medical claims data and for their help in large-scale analysis.

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Ferwana, I., Varshney, L.R. The impact of COVID-19 lockdowns on mental health patient populations in the United States. Sci Rep 14 , 5689 (2024). https://doi.org/10.1038/s41598-024-55879-9

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• Published: 11 April 2023

Effects of the COVID-19 pandemic on mental health, anxiety, and depression

• Ida Kupcova 1 ,
• Lubos Danisovic 1 ,
• Martin Klein 2 &
• Stefan Harsanyi 1  

BMC Psychology volume  11 , Article number:  108 ( 2023 ) Cite this article

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The COVID-19 pandemic affected everyone around the globe. Depending on the country, there have been different restrictive epidemiologic measures and also different long-term repercussions. Morbidity and mortality of COVID-19 affected the mental state of every human being. However, social separation and isolation due to the restrictive measures considerably increased this impact. According to the World Health Organization (WHO), anxiety and depression prevalence increased by 25% globally. In this study, we aimed to examine the lasting effects of the COVID-19 pandemic on the general population.

A cross-sectional study using an anonymous online-based 45-question online survey was conducted at Comenius University in Bratislava. The questionnaire comprised five general questions and two assessment tools the Zung Self-Rating Anxiety Scale (SAS) and the Zung Self-Rating Depression Scale (SDS). The results of the Self-Rating Scales were statistically examined in association with sex, age, and level of education.

A total of 205 anonymous subjects participated in this study, and no responses were excluded. In the study group, 78 (38.05%) participants were male, and 127 (61.69%) were female. A higher tendency to anxiety was exhibited by female participants (p = 0.012) and the age group under 30 years of age (p = 0.042). The level of education has been identified as a significant factor for changes in mental state, as participants with higher levels of education tended to be in a worse mental state (p = 0.006).

Conclusions

Summarizing two years of the COVID-19 pandemic, the mental state of people with higher levels of education tended to feel worse, while females and younger adults felt more anxiety.

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Introduction

The first mention of the novel coronavirus came in 2019, when this variant was discovered in the city of Wuhan, China, and became the first ever documented coronavirus pandemic [ 1 , 2 , 3 ]. At this time there was only a sliver of fear rising all over the globe. However, in March 2020, after the declaration of a global pandemic by the World Health Organization (WHO), the situation changed dramatically [ 4 ]. Answering this, yet an unknown threat thrust many countries into a psycho-socio-economic whirlwind [ 5 , 6 ]. Various measures taken by governments to control the spread of the virus presented the worldwide population with a series of new challenges to which it had to adjust [ 7 , 8 ]. Lockdowns, closed schools, losing employment or businesses, and rising deaths not only in nursing homes came to be a new reality [ 9 , 10 , 11 ]. Lack of scientific information on the novel coronavirus and its effects on the human body, its fast spread, the absence of effective causal treatment, and the restrictions which harmed people´s social life, financial situation and other areas of everyday life lead to long-term living conditions with increased stress levels and low predictability over which people had little control [ 12 ].

Risks of changes in the mental state of the population came mainly from external risk factors, including prolonged lockdowns, social isolation, inadequate or misinterpreted information, loss of income, and acute relationship with the rising death toll. According to the World Health Organization (WHO), since the outbreak of the COVID-19 pandemic, anxiety and depression prevalence increased by 25% globally [ 13 ]. Unemployment specifically has been proven to be also a predictor of suicidal behavior [ 14 , 15 , 16 , 17 , 18 ]. These risk factors then interact with individual psychological factors leading to psychopathologies such as threat appraisal, attentional bias to threat stimuli over neutral stimuli, avoidance, fear learning, impaired safety learning, impaired fear extinction due to habituation, intolerance of uncertainty, and psychological inflexibility. The threat responses are mediated by the limbic system and insula and mitigated by the pre-frontal cortex, which has also been reported in neuroimaging studies, with reduced insula thickness corresponding to more severe anxiety and amygdala volume correlated to anhedonia as a symptom of depression [ 19 , 20 , 21 , 22 , 23 ]. Speaking in psychological terms, the pandemic disturbed our core belief, that we are safe in our communities, cities, countries, or even the world. The lost sense of agency and confidence regarding our future diminished the sense of worth, identity, and meaningfulness of our lives and eroded security-enhancing relationships [ 24 ].

Slovakia introduced harsh public health measures in the first wave of the pandemic, but relaxed these measures during the summer, accompanied by a failure to develop effective find, test, trace, isolate and support systems. Due to this, the country experienced a steep growth in new COVID-19 cases in September 2020, which lead to the erosion of public´s trust in the government´s management of the situation [ 25 ]. As a means to control the second wave of the pandemic, the Slovak government decided to perform nationwide antigen testing over two weekends in November 2020, which was internationally perceived as a very controversial step, moreover, it failed to prevent further lockdowns [ 26 ]. In addition, there was a sharp rise in the unemployment rate since 2020, which continued until July 2020, when it gradually eased [ 27 ]. Pre-pandemic, every 9th citizen of Slovakia suffered from a mental health disorder, according to National Statistics Office in 2017, the majority being affective and anxiety disorders. A group of authors created a web questionnaire aimed at psychiatrists, psychologists, and their patients after the first wave of the COVID-19 pandemic in Slovakia. The results showed that 86.6% of respondents perceived the pathological effect of the pandemic on their mental status, 54.1% of whom were already treated for affective or anxiety disorders [ 28 ].

In this study, we aimed to examine the lasting effects of the COVID-19 pandemic on the general population. This study aimed to assess the symptoms of anxiety and depression in the general public of Slovakia. After the end of epidemiologic restrictive measures (from March to May 2022), we introduced an anonymous online questionnaire using adapted versions of Zung Self-Rating Anxiety Scale (SAS) and Zung Self-Rating Depression Scale (SDS) [ 29 , 30 ]. We focused on the general public because only a portion of people who experience psychological distress seek professional help. We sought to establish, whether during the pandemic the population showed a tendency to adapt to the situation or whether the anxiety and depression symptoms tended to be present even after months of better epidemiologic situation, vaccine availability, and studies putting its effects under review [ 31 , 32 , 33 , 34 ].

Materials and Methods

This study utilized a voluntary and anonymous online self-administered questionnaire, where the collected data cannot be linked to a specific respondent. This study did not process any personal data. The questionnaire consisted of 45 questions. The first three were open-ended questions about participants’ sex, age (date of birth was not recorded), and education. Followed by 2 questions aimed at mental health and changes in the will to live. Further 20 and 20 questions consisted of the Zung SAS and Zung SDS, respectively. Every question in SAS and SDS is scored from 1 to 4 points on a Likert-style scale. The scoring system is introduced in Fig.  1 . Questions were presented in the Slovak language, with emphasis on maintaining test integrity, so, if possible, literal translations were made from English to Slovak. The questionnaire was created and designed in Google Forms®. Data collection was carried out from March 2022 to May 2022. The study was aimed at the general population of Slovakia in times of difficult epidemiologic and social situations due to the high prevalence and incidence of COVID-19 cases during lockdowns and social distancing measures. Because of the character of this web-based study, the optimal distribution of respondents could not be achieved.

Categories of Zung SAS and SDS scores with clinical interpretation

During the course of this study, 205 respondents answered the anonymous questionnaire in full and were included in the study. All respondents were over 18 years of age. The data was later exported from Google Forms® as an Excel spreadsheet. Coding and analysis were carried out using IBM SPSS Statistics version 26 (IBM SPSS Statistics for Windows, Version 26.0, Armonk, NY, USA). Subject groups were created based on sex, age, and education level. First, sex due to differences in emotional expression. Second, age was a risk factor due to perceived stress and fear of the disease. Last, education due to different approaches to information. In these groups four factors were studied: (1) changes in mental state; (2) affected will to live, or frequent thoughts about death; (3) result of SAS; (4) result of SDS. For SAS, no subject in the study group scored anxiety levels of “severe” or “extreme”. Similarly for SDS, no subject depression levels reached “moderate” or “severe”. Pearson’s chi-squared test(χ2) was used to analyze the association between the subject groups and studied factors. The results were considered significant if the p-value was less than 0.05.

Ethical permission was obtained from the local ethics committee (Reference number: ULBGaKG-02/2022). This study was performed in line with the principles of the Declaration of Helsinki. All methods were carried out following the institutional guidelines. Due to the anonymous design of the study and by the institutional requirements, written informed consent for participation was not required for this study.

In the study, out of 205 subjects in the study group, 127 (62%) were female and 78 (38%) were male. The average age in the study group was 35.78 years of age (range 19–71 years), with a median of 34 years. In the age group under 30 years of age were 34 (16.6%) subjects, while 162 (79%) were in the range from 31 to 49 and 9 (0.4%) were over 50 years old. 48 (23.4%) participants achieved an education level of lower or higher secondary and 157 (76.6%) finished university or higher. All answers of study participants were included in the study, nothing was excluded.

In Tables  1 and 2 , we can see the distribution of changes in mental state and will to live as stated in the questionnaire. In Table  1 we can see a disproportion in education level and mental state, where participants with higher education tended to feel worse much more than those with lower levels of education. Changes based on sex and age did not show any statistically significant results.

In Table  2 . we can see, that decreased will to live and frequent thoughts about death were only marginally present in the study group, which suggests that coping mechanisms play a huge role in adaptation to such events (e.g. the global pandemic). There is also a possibility that living in times of better epidemiologic situations makes people more likely to forget about the bad past.

Anxiety and depression levels as seen in Tables  3 and 4 were different, where female participants and the age group under 30 years of age tended to feel more anxiety than other groups. No significant changes in depression levels based on sex, age, and education were found.

Compared to the estimated global prevalence of depression in 2017 (3.44%), in 2021 it was approximately 7 times higher (25%) [ 14 ]. Our study did not prove an increase in depression, while anxiety levels and changes in the mental state did prove elevated. No significant changes in depression levels go in hand with the unaffected will to live and infrequent thoughts about death, which were important findings, that did not supplement our primary hypothesis that the fear of death caused by COVID-19 or accompanying infections would enhance personal distress and depression, leading to decreases in studied factors. These results are drawn from our limited sample size and uneven demographic distribution. Suicide ideations rose from 5% pre-pandemic to 10.81% during the pandemic [ 35 ]. In our study, 9.3% of participants experienced thoughts about death and since we did not specifically ask if they thought about suicide, our results only partially correlate with suicidal ideations. However, as these subjects exhibited only moderate levels of anxiety and mild levels of depression, the rise of suicide ideations seems unlikely. The rise in suicidal ideations seemed to be especially true for the general population with no pre-existing psychiatric conditions in the first months of the pandemic [ 36 ]. The policies implemented by countries to contain the pandemic also took a toll on the population´s mental health, as it was reported, that more stringent policies, mainly the social distancing and perceived government´s handling of the pandemic, were related to worse psychological outcomes [ 37 ]. The effects of lockdowns are far-fetched and the increases in mental health challenges, well-being, and quality of life will require a long time to be understood, as Onyeaka et al. conclude [ 10 ]. These effects are not unforeseen, as the global population suffered from life-altering changes in the structure and accessibility of education or healthcare, fluctuations in prices and food insecurity, as well as the inevitable depression of the global economy [ 38 ].

The loneliness associated with enforced social distancing leads to an increase in depression, anxiety, and posttraumatic stress in children in adolescents, with possible long-term sequelae [ 39 ]. The increase in adolescent self-injury was 27.6% during the pandemic [ 40 ]. Similar findings were described in the middle-aged and elderly population, in which both depression and anxiety prevalence rose at the beginning of the pandemic, during the pandemic, with depression persisting later in the pandemic, while the anxiety-related disorders tended to subside [ 41 ]. Medical professionals represented another specific at-risk group, with reported anxiety and depression rates of 24.94% and 24.83% respectively [ 42 ]. The dynamic of psychopathology related to the COVID-19 pandemic is not clear, with studies reporting a return to normal later in 2020, while others describe increased distress later in the pandemic [ 20 , 43 ].

Concerning the general population, authors from Spain reported that lockdowns and COVID-19 were associated with depression and anxiety [ 44 ]. In January 2022 Zhao et al., reported an elevation in hoarding behavior due to fear of COVID-19, while this process was moderated by education and income levels, however, less in the general population if compared to students [ 45 ]. Higher education levels and better access to information could improve persons’ fear of the unknown, however, this fact was not consistent with our expectations in this study, as participants with university education tended to feel worse than participants with lower education. A study on adolescents and their perceived stress in the Czech Republic concluded that girls are more affected by lockdowns. The strongest predictor was loneliness, while having someone to talk to, scored the lowest [ 46 ]. Garbóczy et al. reported elevated perceived stress levels and health anxiety in 1289 Hungarian and international students, also affected by disengagement from home and inadequate coping strategies [ 47 ]. Wathelet et al. conducted a study on French University students confined during the pandemic with alarming results of a high prevalence of mental health issues in the study group [ 48 ]. Our study indicated similar results, as participants in the age group under 30 years of age tended to feel more anxious than others.

In conclusion, we can say that this pandemic changed the lives of many. Many of us, our family members, friends, and colleagues, experienced life-altering events and complicated situations unseen for decades. Our decisions and actions fueled the progress in medicine, while they also continue to impact society on all levels. The long-term effects on adolescents are yet to be seen, while effects of pain, fear, and isolation on the general population are already presenting themselves.

The limitations of this study were numerous and as this was a web-based study, the optimal distribution of respondents could not be achieved, due to the snowball sampling strategy. The main limitation was the small sample size and uneven demographic distribution of respondents, which could impact the representativeness of the studied population and increase the margin of error. Similarly, the limited number of older participants could significantly impact the reported results, as age was an important risk factor and thus an important stressor. The questionnaire omitted the presence of COVID-19-unrelated life-changing events or stressors, and also did not account for any preexisting condition or risk factor that may have affected the outcome of the used assessment scales.

Data Availability

The datasets generated and analyzed during the current study are not publicly available due to compliance with institutional guidelines but they are available from the corresponding author (SH) on a reasonable request.

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Acknowledgements

We would like to provide our appreciation and thanks to all the respondents in this study.

This research project received no external funding.

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Authors and affiliations.

Institute of Medical Biology, Genetics and Clinical Genetics, Faculty of Medicine, Comenius University in Bratislava, Sasinkova 4, Bratislava, 811 08, Slovakia

Ida Kupcova, Lubos Danisovic & Stefan Harsanyi

Institute of Histology and Embryology, Faculty of Medicine, Comenius University in Bratislava, Sasinkova 4, Bratislava, 811 08, Slovakia

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Contributions

IK and SH have produced the study design. All authors contributed to the manuscript writing, revising, and editing. LD and MK have done data management and extraction, SH did the data analysis. Drafting and interpretation of the manuscript were made by all authors. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Stefan Harsanyi .

Ethics declarations

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Ethical permission was obtained from the Ethics Committee of the Institute of Medical Biology, Genetics and Clinical Genetics, Faculty of Medicine, Comenius University in Bratislava (Reference number: ULBGaKG-02/2022). The need for informed consent was waived by the Ethics Committee of the Institute of Medical Biology, Genetics and Clinical Genetics, Faculty of Medicine, Comenius University in Bratislava due to the anonymous design of the study. This study did not process any personal data and the dataset does not contain any direct or indirect identifiers of participants. This study was performed in line with the principles of the Declaration of Helsinki. All methods were carried out following the institutional guidelines.

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Kupcova, I., Danisovic, L., Klein, M. et al. Effects of the COVID-19 pandemic on mental health, anxiety, and depression. BMC Psychol 11 , 108 (2023). https://doi.org/10.1186/s40359-023-01130-5

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• Mental health

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A rapid review of the impact of COVID-19 on the mental health of healthcare workers: implications for supporting psychological well-being

• Johannes H. De Kock   ORCID: orcid.org/0000-0002-2468-5572 1 , 2 ,
• Helen Ann Latham 3 ,
• Stephen J. Leslie 4 ,
• Mark Grindle 1 ,
• Sarah-Anne Munoz 1 ,
• Liz Ellis 1 ,
• Rob Polson 1 &
• Christopher M. O’Malley 1  

BMC Public Health volume  21 , Article number:  104 ( 2021 ) Cite this article

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Health and social care workers (HSCWs) have carried a heavy burden during the COVID-19 crisis and, in the challenge to control the virus, have directly faced its consequences. Supporting their psychological wellbeing continues, therefore, to be a priority. This rapid review was carried out to establish whether there are any identifiable risk factors for adverse mental health outcomes amongst HSCWs during the COVID-19 crisis.

We undertook a rapid review of the literature following guidelines by the WHO and the Cochrane Collaboration’s recommendations. We searched across 14 databases, executing the search at two different time points. We included published, observational and experimental studies that reported the psychological effects on HSCWs during the COVID-19 pandemic.

The 24 studies included in this review reported data predominantly from China (18 out of 24 included studies) and most sampled urban hospital staff. Our study indicates that COVID-19 has a considerable impact on the psychological wellbeing of front-line hospital staff. Results suggest that nurses may be at higher risk of adverse mental health outcomes during this pandemic, but no studies compare this group with the primary care workforce. Furthermore, no studies investigated the psychological impact of the COVID-19 pandemic on social care staff. Other risk factors identified were underlying organic illness, gender (female), concern about family, fear of infection, lack of personal protective equipment (PPE) and close contact with COVID-19. Systemic support, adequate knowledge and resilience were identified as factors protecting against adverse mental health outcomes.

Conclusions

The evidence to date suggests that female nurses with close contact with COVID-19 patients may have the most to gain from efforts aimed at supporting psychological well-being. However, inconsistencies in findings and a lack of data collected outside of hospital settings, suggest that we should not exclude any groups when addressing psychological well-being in health and social care workers. Whilst psychological interventions aimed at enhancing resilience in the individual may be of benefit, it is evident that to build a resilient workforce, occupational and environmental factors must be addressed. Further research including social care workers and analysis of wider societal structural factors is recommended.

Peer Review reports

Health and social care workers (HSCWs) continue to play a vital role in our response to the COVID-19 pandemic. It is known that HSCWs exhibit high rates of pre-existing mental health (MH) disorders [ 1 , 2 , 3 ] which can negatively impact on the quality of patient care [ 4 ].

Studies from previous infectious outbreaks [ 5 , 6 ] suggest that this group may be at risk of experiencing worsening MH during an outbreak. Current evidence examining the psychological impact on similar groups [ 7 , 8 , 9 ], suggest that this group may be at risk of experiencing poor MH as a direct result of the COVID-19 pandemic. Compounding the concerns about these data are that HSCWs will be likely to not only be at a higher risk for experiencing MH problems during the pandemic, but also in its aftermath [ 5 ].

There are some specific features of the COVID-19 pandemic that may specifically heighten its potential to impact on the MH of HSCWs.

Firstly, the scale of the pandemic in terms of cases and the number of countries affected has left all with an impression that ‘no-one is safe’. Media reporting of the pandemic has repeatedly focused on the number of deaths in HSCWs and the spread of the disease within health and social care facilities which is likely to have amplified the negative effects on the MH of HSCWs.

Secondly, usual practice has been significantly disrupted and many staff have been asked to work outside of their usual workplace and have been redeployed to higher risk front line jobs.

Finally, the intense focus on personal protective equipment (PPE) is likely to have specifically heightened the impact of COVID-19 on the MH of HSCWs due to the uncertainty surrounding the quantity and quality of equipment, the frequently changing guidance on what PPE is appropriate in specific clinical situations and the uncertainty regarding the absolute risk of transmission posed. While other workers will have been impacted by COVID-19, it is highly likely that the above factors will have disproportionately affected the MH of HSCWs [ 9 , 10 ]. Indeed a British Medical Association survey on the 14th May 2020 during the pandemic showed that 45% of UK doctors are suffering from depression, anxiety, stress, burnout or other mental health conditions relating to, or made worse by, the COVID-19 crisis [ 11 ].

Although evidence based psychological interventions are available for this population [ 12 ], there is a paucity of evidence about interventions for the MH of HSCWs during pandemics. Recent calls to action mandated the need to provide high quality data on the psychological impacts of the COVID-19 pandemic [ 13 , 14 ]. This pandemic has rapidly changed the functioning of society at many levels which suggests that these data are not only needed swiftly, but also with caution and scientific rigour [ 13 , 14 ].

These data are needed in order to equip HSCWs to do their job effectively – high levels of stress and anxiety have been shown to decrease staff morale, increase absenteeism, lower levels of work satisfaction and quality of care [ 6 , 15 ]. It is therefore a priority to understand the psychological needs of our HSCWs in order to provide them with the appropriate tools to mitigate the negative effects of dealing with the COVID-19 pandemic.

While HSCWs have been identified as vulnerable to the negative psychological impact from the current pandemic, they do not form a homogeneous population. It may therefore be appropriate to identify particularly vulnerable groups within the larger population of HSCWs and target psychological support to them. This review seeks to understand whether any group of HSCWs could be confidently excluded from psychological support interventions because they are deemed to be at a low risk. Holmes et al. [ 14 ] have warned that a one-size-fits-all approach to supporting HSCWs might not be effective. This, together with the lack of evidence around tailoring psychological interventions during pandemics [ 1 ], highlights the importance of identifying vulnerable groups, to ensure appropriately personalised interventions are made available.

Aim of the review

The aim of this review is to identify the psychological impact of the COVID-19 pandemic on the health and social care professions, more specifically to identify which sub-groups are most vulnerable to psychological distress and to identify the risk and protective factors associated with this population’s mental health.

This review, looking exclusively at the psychological impact of the COVID-19 pandemic on HSCWs will therefore contribute to informing where mental health interventions, together with organisational and systemic efforts to support this population’s mental health could be focussed in an effort to support psychological well-being [ 14 ]. Rapid but robust gathering of evidence to inform health decision-makers is vital and in circumstances such as these, the WHO recommends rapid reviews [ 16 ].

Search strategy

Planning, conducting and reporting of this study was based on the guidelines for rapid reviews [ 17 ], set by the WHO [ 16 ] and the recent COVID-19 Cochrane Collaboration’s recommendations [ 18 ].

Data sources and searches

Two authors (CoM & RP) searched across a broad range of databases to capture research from potentially relevant fields, including health, mental health and health management. Within the OVID platform of databases Medline, EMBase, HMIC and PsychInfo were searched. Within the EbscoHost platform of databases, CINAHL, Medline, APA PsychInfo, Business Source Elite, Health Source and Academic Search Complete were searched. Beyond the OVID and EbscoHost platforms, SCOPUS, the King’s Fund Library, Social Care Online, PROSPERO and Google Advanced were also searched, making 16 databases searched (14 unique databases and two having been searched twice on separate platforms).

Owing to the rapidly changing landscape of the COVID-19 pandemic, and in an effort to include as many eligible papers as possible, the search strategy was executed on 23 April 2020 and again 2 weeks later on 6 May 2020 using a combination of subject headings and keyword searching (see Additional file 1 ). The bibliographical database was created with EndNote X7™.

Search criteria

The design of the search criteria was intended to draw together research both for this rapid review, and to contribute to the design of a digital mental health intervention to enhance the psychological well-being of HSCWs. The design of the search criteria is discussed in further detail in the Additional file 1 .

Types of participants

Participants were restricted to HSCWs during the COVID-19 pandemic.

Types of studies included

Published observational and experimental studies that reported the psychological effects on HSCWs during the COVID-19 pandemic were included. The study designs included quantitative and qualitative primary studies. Studies relating to previous pandemics and epidemics (such as SARS, MERS, H1N1, H5N1, Zika, Ebola, West Nile Fever) were excluded as these results have been reported elsewhere [ 7 ]. Reviews, theses, position papers, protocol papers, and studies published in languages other than English were excluded.

Screening and selection of studies

Searches were screened according to the selection criteria by JDK. The full text of potentially relevant papers was retrieved for closer examination. The reviewer erred on the side of inclusion where there was any doubt, to ensure no potentially relevant papers were missed. The inclusion criteria were then applied against full text versions of the papers (where available) independently by JDK and HL. Disagreements regarding eligibility of studies were resolved by discussion and consensus. Where the two reviewers were still uncertain about inclusion, the other reviewers (RP, CoM) were asked to provide input to reach consensus.

Data extraction and quality assessment

Relevant data were extracted into structured tables including country, setting, population, study design, number of participants, mental health conditions and their measurement tools and main study results. Where available, we extracted risk factors and protective factors. HL, LE and JDK extracted all the data while JDK checked for accuracy and completeness.

Table  2 presents an overview of the validated tools used per study type to assess study quality and risk of bias. JDK and HL assessed the quality of cross-sectional studies with the Joanna Briggs Institute tool [ 48 ] and JDK assessed their risk of bias using the Evidence Partners [ 49 ] appraisal tool. JDK assessed the risk of bias for the longitudinal study with the Critical Appraisal Skills Programme (CASP) appraisal tool [ 50 ] and the uncontrolled before-after study with the ROBINS – I [ 51 ]. SAM utilised Joanna Briggs Institute tool to assess the qualitative studies [ 38 ] and the Mixed methods appraisal tool (MMAT) [ 41 ] to assess mixed methods studies.

Data synthesis and analysis

Current best practice guided the tabulated and narrative synthesis of the results [ 52 , 53 ]. The studies’ outcomes were categorised according to the psychological impact of COVID-19 on HSCWs of:

general psychological impacts

the risk factors associated with adverse mental health outcomes

the protective factors against adverse mental health outcomes

Previous studies’ logical syntheses [ 6 ] were adapted by organising the risk and protective factors into psychosocial, occupational, sociodemographic and environmental categories. The GRADE method from the Cochrane Collaboration [ 54 ] was used to assess the quality of evidence of outcomes included in this rapid review. Varied study quality, together with study type and outcome heterogeneity precluded performing a meta-analysis.

Patient and public involvement

Some members of the author team are frontline healthcare staff during the COVID-19 pandemic and contributed to the design of the review.

Search results

The 677 records of interest were found from the two searches (429 in search 1 and 529 in search 2). After 148 duplicates were removed, 529 records were screened. Of these, 82 full texts of potentially relevant studies were assessed for eligibility (see Fig.  1 ). Twenty-four published studies met the inclusion criteria for the rapid review.

Prisma Flow Diagram

Study characteristics

The 24 studies included in this review consisted of 18 cross-sectional, 2 mixed methods, 2 qualitative, 1 longitudinal and 1 uncontrolled before-after study. The total number of participants in these studies was 13,731. In the cross-sectional studies, participant numbers ranged between 59 and 2299. Participant numbers in the two mixed method studies were 37 and 222 respectively, whilst the qualitative studies included 10 and 20 participants, respectively. The longitudinal study included 120 participants and the uncontrolled before-after study, 27 participants. See Table  1 for sampling methods within the included papers. The majority of papers utilised non-probability sampling methods, limiting generalisability of findings. One exception was Lai et al., who used region stratified 2-stage cluster sampling.

Eighteen of the studies were from China, of which 8 were based in Wuhan, where the COVID-19 outbreak began. The rest were from America (1), Israel (1), UK (1), Singapore (1), Pakistan (1), multicentre - Singapore & India (1), Global (1). Several validated measures were used to assess anxiety, depression, insomnia, stress and burnout. Table 1 provides an overview of the included studies.

Risk of bias assessment

The quality of the cross-sectional studies was fair, with 16 studies scoring 6 or higher on the JBI appraisal tool and eleven scoring 7 or higher (a score of 7 and above is an indicator of study quality). The majority of the studies indicated a low risk of bias when assessed with the Evidence Partners’ appraisal tool. The uncontrolled before-after study indicated a high risk of bias. The qualitative studies indicated a good level of quality (JBI scores of 9 & 10 respectively) while mixed methods studies showed varied quality. In the cross sectional studies, the most common problem affecting study quality was failure to deal with confounding factors. Failure to locate the researcher culturally or theoretically affected the qualitative papers, whilst the two mixed methods papers’ study quality was affected by lack of explicitly articulated research questions. A summary of the risk of bias and quality assessments are provided in Table 2 .

Psychological toll on healthcare workers

Of the 24 studies included, 22 directly assessed the psychological toll on healthcare workers and all found levels of anxiety, depression, insomnia, distress or Obsessive Compulsive Disorder (OCD) symptoms [ 24 , 25 , 26 , 27 , 29 , 30 , 31 , 33 , 34 , 35 , 36 , 37 , 39 , 40 , 42 , 43 , 44 , 46 , 47 , 58 , 59 , 60 ].

Psychological symptoms were assessed using various validated measures as outlined in Table  3 – the summary of included studies. The most common outcomes assessed were sleep, anxiety and depression. The prevalence of depressive symptoms varied greatly, ranging between 8.9% [ 39 ] to 50.4% [ 31 ]. These findings suggest marked differences in the prevalence of depressive symptoms across the studies. The prevalence of anxiety in cross-sectional studies ranged between 14.5% [ 39 ] to 44.6% [ 31 ]. Sleep was also assessed in several studies. Lai et al. [ 31 ] found the prevalence of sleep disturbances to be 34%, whilst another, nationwide survey in China found that HCWs had significantly worse sleep than the general population [ 29 ].

Risk factors associated with adverse mental health outcomes

Table 3 provides the GRADE evidence profile of the certainty of evidence for the risk factors associated with adverse MH outcomes during the COVID-19 pandemic identified through the review. These risk factors can be grouped into the three thematic areas of i) occupational, ii) psychosocial, iii) environmental.

Occupational factors

Medical hcws.

Two studies showed that medical HCWs (nurses and doctors) had significantly higher levels of MH risk in comparison to non-medical HCWs [ 34 , 47 ]. Zhang et al. [ 47 ] found that medical HCWs had significantly higher levels of insomnia, anxiety, depression, somatization and OCD symptoms in comparison to non-medical HCWs. This was also reflected in a large study in Fujian province, China, in which medical staff had significantly higher anxiety than admin staff [ 34 ]. In contrast, Tan et al. [ 39 ] found that in a population of 470 HCWs in Singapore, the prevalence of anxiety was significantly higher among non-medical HCWs than medical.

Healthcare groups

In three studies nurses were found to be at risk of worse MH outcomes than doctors [ 24 , 26 , 31 ]. One large study in China found nurses were at significant risk of more severe depression and anxiety than doctors [ 31 ]. Another found that nurses had significantly higher financial concerns than doctors and felt significantly more anxious on the ward when compared with other groups. There was no significant difference between professionals regarding stopping work or work overload [ 24 ]. A mixed method paper also showed that nurses had a higher rate of depressive symptoms than doctors. Whilst this was a small sample size, it echoes the findings from larger studies [ 26 ].

With regard to other HCWs, there were two studies which assessed dentists and other dental workers and found them to be at risk of anxiety and elevated distress. Neither study found any difference based on gender or educational level [ 36 , 59 ]. There were no studies comparing dental workers to other HCWs. We did not find any studies that focussed on the primary care workforce or that assessed social care workers.

With regard to seniority, one paper found that having an intermediate technical title was associated with more severe MH symptoms [ 31 ].

Frontline staff/direct contact with COVID-19

Four high-quality studies found being in a ‘frontline’ position or having direct contact with COVID-19 patients was associated with higher levels of psychological distress [ 30 , 31 , 34 , 42 ].

Increased direct exposure to COVID-19 patients increased the mental health risks in health care workers in one study in Wuhan [ 30 ]. This finding is backed by Lai et al. [ 31 ], who found that being a frontline worker was independently associated with more severe depression, anxiety and insomnia scores. In addition, a cross sectional survey of staff in a paediatric centre found that contact with COVID-19 patients was independently associated with increased risk of sleep disturbance [ 42 ]. Lu et al. [ 34 ] found that medical HCWs in direct contact with COVID-19 patients had almost twice the risk of anxiety and depression than non-medical staff with low risk of contact with COVID-19.

There were conflicting results found in two studies. A study in a cancer hospital in Wuhan found burnout frequency to be lower in frontline staff [ 43 ]. The authors identified confounding factors which may have led to this result, but it is of interest as it is one of the only studies that assessed HCWs outside of the acute general medicine setting. Li et al. [ 32 ], also found that frontline nurses had significantly lower levels of vicarious trauma scores than non-frontline workers and the general population.

Personal protective equipment (PPE)

PPE concerns were the most common theme brought up voluntarily in free-text feedback in a study by Chung & Yeung [ 60 ], and a survey in Pakistan revealed that 80% of participants expected provision of PPE [ 40 ]. H.Cai et al. [ 24 ] also found that PPE was protective when adequate, but a risk factor for stress when inadequate. This finding appears to be bolstered by a qualitative study of frontline nurses in Wuhan, which found that physical health and safety was one of their primary needs. This study also reported PPE as a protective factor [ 46 ].

Heavy workload

Longer working time per week was found to be a risk factor in a study by Mo et al. [ 35 ] This, together with increased work intensity or patient load per hour, were themes in a mixed methods study of 37 staff of a clinic in Beijing [ 26 ] and a qualitative study of nurses in China [ 37 ], also suggesting heavy workload as a risk factor.

Psychosocial factors

Fear of infection.

A fear of infection was a highlighted in a qualitative study by Cao et al., (2020, 31), and brought up as a theme in free-text feedback in a cross sectional survey by Chung & Yeung [ 60 ]. Ahmed et al. [ 59 ] found that 87% of dentists surveyed described a fear of being infected with COVID-19 from either a patient or a co-worker.

Concern about family

This was brought up as one of the main stress factors in a study by H.Cai et al. [ 24 ], particularly amongst staff in the 31–40 year age-group. Knowing that their family was safe was also the greatest stress reliever [ 24 ], whilst fear of infecting family was identified in 79.7% of 222 participants in a study in Pakistan [ 40 ]. It was also a theme highlighted in the qualitative data [ 26 , 37 ].

Sociodemographic factors

Younger age.

One Chinese web-based survey which included the general population and HCWs, showed that younger people had significantly higher anxiety and depression scores, but no difference in sleep quality. Conversely, the same study found that HCWs were significantly more likely to have poor sleep quality, but found no difference in anxiety or depressive symptoms based on occupation. The study did not examine the effect of age group on HCWs [ 29 ].

H. Cai et al. [ 24 ] suggested that age was more complex. They found that all age groups had concerns, but that the focus of their anxieties were different (for example: older staff were more likely to be anxious due to exhaustion from long hours and lack of PPE while younger staff were more likely to worry about their families).

Women were found to be at higher risk for depression, anxiety and insomnia by Lai et al. [ 31 ] This was also found to be an independent risk factor for anxiety in another large nationwide Chinese study [ 47 ]. However, a global survey of dentists found no differences based on gender [ 59 ].

Underlying illness

We found two studies which identified that having an underlying organic illness as an independent risk factor for poor psychological outcomes. A study of dentists in Israel found an increase in psychological distress in those with background illnesses as well as an increased fear of contracting COVID-19 and higher subjective overload [ 36 ]. In medical HCWs in China, organic illness was found to be an independent risk factor for insomnia, anxiety, OCD, somatising symptoms and depression in medical HCWs [ 47 ].

Being an only child

This was independently associated with sleep disturbance in paediatric HCWs in Wuhan [ 42 ]. Being an only child was also found to be significantly associated with stress by Mo et al. [ 35 ].

There was also a significant association between physical symptoms and poor psychological outcomes in a large multicentre study based in India and Singapore. It is unclear if this represented somatization or organic illness and the authors suggest the relationship between physical symptoms and psychological aspects was bi-directional [ 27 ].

Environmental factors

Point in pandemic curve.

One longitudinal study carried out in China in a surgical department, found that anxiety and depression scores during the ‘outbreak’ period were significantly higher when compared to a similar group assessed after the outbreak period [ 58 ]. This was a small sample of 120 and only assessed surgical staff, but this longitudinal data was supported by a qualitative study in China which suggested that anxiety peaks at the start of the outbreak and reduces with time [ 37 ].

Living in a rural area was only assessed by one study which showed that it was an independent risk factor for insomnia and anxiety in medical HCWs [ 47 ]. This may reflect a need to further investigate the effect of rurality on psychological wellbeing during this pandemic.

Protective factors against adverse mental health outcomes

The review identified protective factors against adverse mental health outcomes during COVID-19. Table  4 provides the GRADE evidence profile of the certainty of evidence for this. The protective factors can be grouped into the three thematic areas of: i) occupational, ii) psychosocial and iii) environmental.

W. Cai et al. [ 25 ] found that previous experience in a public health emergency (PHE) was protective against adverse mental health outcomes. Staff that had no previous experience were also more likely to have low rates of resilience, and social support.

A small cohort study of 27 surgeons, who were given pre and post training surveys, suggested that training alleviates psychological stress [ 22 ]. Good hospital guidance was identified to relieve stress in a study by H.Cai et al. [ 24 ], and increasing self-knowledge was a coping strategy deployed by staff. Dissemination of knowledge was also mentioned in a qualitative study by Yin & Zeng [ 46 ]; participants described subjective stress reduction after their seniors explained relevant knowledge to them.

Adequate PPE

As mentioned above, PPE was found to be a protective factor when adequate and a risk factor for poor mental health outcomes when deemed to be inadequate [ 24 , 46 ].

One study assessed self-efficacy in dental staff and found that it was a protective factor [ 36 ]. Self-efficacy was also found to improve sleep quality by Xiao et al. [ 44 ], whilst W.Cai et al. [ 25 ] measured resilience using a validated measure and found it to be a protective factor against adverse MH outcomes.

Being in a committed relationship

This was found to be protective by Shacham et al. [ 36 ] This was not directly assessed in other studies.

Safety of family

This had the biggest impact in reducing stress in a cross-sectional study by H. Cai et al. [ 24 ] This was also not assessed in other studies.

Support and recognition from the health care team, government and community was identified as a protective theme in several studies. Social support, measured using the Social Support Rate Scale (SSRS) was found to indirectly affect sleep by directly reducing anxiety and stress and increasing self-efficacy [ 44 ].

Team support was identified as a protective factor in a qualitative study by Sun et al. [ 37 ] Good hospital guidance was also identified as a stress reliever by H. Cai et al. [ 24 ], who found that HCWs expected recognition from the hospital authorities. This was echoed in a qualitative study of nurses in Wuhan where the desire for community concern was a strong need and tightly linked to the need for PPE and knowledge [ 46 ]:

‘ To be honest, I was very apprehensive before coming to the infectious department as support staff, but on the first day here, the head nurse personally explained relevant knowledge such as disinfection and quarantine, and that helped me calm down a lot . ”

“I hope that our society and government pay more attention to lack of personal protective equipment’ [ 46 ] .

As a communicable disease, and now a global public health emergency (PHE), COVID-19 places a unique challenge on our health and social care workforce that will disrupt not just their usual workplace duties but also their social context [ 62 ]. As we adjust to new ways of living and working, HSCWs are likely to continue to face challenges ahead. Our review confirms that the psychological impact of COVID-19 on health care workers is considerable, with significant levels of anxiety, depression, insomnia and distress. Studies revealed a prevalence of depressive symptoms between 8.9–50.4% and anxiety rates between 14.5–44.6% [ 31 , 39 ]. This is in keeping with other reviews and findings from previous viral outbreaks [ 7 , 8 , 63 ]. The majority of studies published to date come from China, particularly Wuhan - the epicentre of COVID-19. There is minimal evidence published to date on the psychological impact on HCWs in Europe or the US, which have been highly impacted by the pandemic. The studies included in this review were predominantly concerned with hospital settings – we found no studies relating to social care staff or primary care staff. This is a concern, as we have increasing evidence that a large proportion of Western deaths are happening in the community and specifically in care homes [ 64 ].

Our review aimed to identify whether there were any groups particularly vulnerable to poor mental health outcomes during COVID-19. We found some evidence that nurses may be at a higher risk than doctors [ 24 , 26 , 31 ]. This is similar to findings which take into account previous viral outbreaks [ 7 ]. Confounding factors were not robustly addressed however, and there were no studies that compared nurses with the primary care workforce or social care workers. There was some evidence that clinical HCWs may be at higher risk of psychological distress than non-clinical HCWs [ 34 , 47 ], but this was not absolute. Tan et al. [ 39 ] found a higher prevalence of anxiety among non-medical HCWs in Singapore. The prevalence of poor MH outcomes varied between countries. Chew et al. [ 27 ] revealed that in data from India and Singapore, there was an overall lower prevalence of anxiety and depression than similar cross-sectional data from China [ 27 , 31 , 39 , 60 ]. This suggests that different contexts and cultures may reveal different findings. It is possible that being at different points in their respective countries’ outbreak curve may have played a part, as there was evidence that this may be influential [ 58 ]. Tan et al. [ 39 ] postulated that the medical HCWs in Singapore had experienced a SARS outbreak in the past and thus were well prepared for COVID-19 both psychologically and in their infection control measures. What we can deduce is that context and cultural factors are likely to play a role, not just cadre or role of healthcare worker. It also highlights the importance of reviewing the evidence as more data emerges from other countries.

Several risk factors emerged, many in keeping with what has been found in other reviews [ 7 , 8 ]. Those with the strongest evidence were inadequate PPE [ 24 , 40 , 46 , 60 ], fear of infection [ 26 , 59 , 60 ] and heavy workload [ 26 , 35 , 37 ]. Consistent with prior outbreak data [ 7 , 63 ], there was also good evidence that close contact with COVID-19 cases was a predictor of higher levels of anxiety, depression and insomnia [ 30 , 31 , 34 , 42 ], although two studies appeared to show conflicting results [ 32 , 43 ]. Studies suggested that being younger in age [ 24 , 29 , 33 ] or being female [ 31 , 47 , 59 ] may be a risk factor, however this should be treated with caution. An alternative explanation for this study’s findings may be greater risk of frontline exposure amongst women, who are predominantly employed in lower status roles within healthcare globally according to the WHO [ 65 ]. It is important to note that respondents to all studies, when disaggregated by gender, were predominantly female and this may have impacted findings. The consistently higher mortality rate and risk of severe COVID-19 disease amongst men would suggest that the full picture regarding gender and MH during this pandemic is incomplete [ 66 , 67 ]. Although other risk factors were also identified, their certainty of evidence was deemed to be low.

The majority of cross-sectional studies focussed on measuring adverse MH outcomes which explains the lack of quantitative data on protective factors or coping mechanisms. Of the studies that did assess this, there were protective factors which were associated with adaptive psychological outcomes. Experience of prior infectious disease outbreaks and training were protective against poor mental health outcomes [ 22 , 24 , 25 , 46 ]. Adequate PPE was a protective factor when adequate and a risk factor when inadequate [ 24 , 46 , 60 ]. There was good evidence that resilience (measured by self-efficacy or resilience scales) was protective against poor mental health outcomes [ 25 , 36 , 44 ]. This is of importance when assessing how to positively contribute to reducing the psychological burden on our health and social care staff. There was strong evidence that community support was a protective factor [ 24 , 37 , 44 , 46 ]. Community support was important in a number of studies, referring to social support as well as recognition and support from the healthcare team, government and wider community [ 24 , 37 , 44 , 46 , 68 ]. Other adaptive behaviours emerged from qualitative data, including gratitude and the ability to find purpose and growth from the situation [ 37 ]. These findings are in keeping with a recent study which identified key domains of risk for burnout in healthcare. They highlighted that being part of a supportive team community is a strong protective factor as are clear values and meaningful work [ 69 ]. They advise that organisational-level interventions creating a healthy workplace are the key to preventing burnout [ 69 ]. This is echoed in a recent systematic review and meta-analysis of the effectiveness of interventions designed to reduce symptoms and prevalence of MH disorders and suicidal behaviour among physicians. This review concluded that, whilst individually directed interventions are associated with some reduction in symptoms of common MH disorders, there needs to be increased focus on organisational-level interventions that improve the work environment [ 2 ].

Whilst our findings showed evidence that occupational and environmental factors at the workplace level played a key role for MH outcomes, there was no mention of wider societal structural issues that have been emerging during this pandemic. Of particular importance is the evidence that black and ethnic minority people of all ages in the global north are at greater risk of contracting and dying from COVID-19 [ 70 , 71 , 72 ]. A recent large study in the US found that non-white HCWs were at increased risk of contracting COVID-19 and were disproportionately affected by inadequate PPE and close exposure to COVID-19 patients [ 3 ]. This suggests wider structural factors are at play and need to be investigated.

The paucity of empirical studies investigating the mental health of social care and primary care staff during the COVID-19 pandemic should also be rectified. With the majority of studies taking place in China, where ageing in place rather than residential care is the norm [ 73 ], it is unsurprising that none investigated care homes, where it is estimated around 40–50% of all deaths related to COVID-19 occur in Europe and the US [ 64 ]. Moreover, there is evidence that front-line HCWs who work in nursing homes are among the highest at risk of contracting the virus [ 3 ]. With the majority of studies taking place in urban hospital settings, and particularly in Wuhan – the epicentre of the outbreak – the generalizability of findings to other settings may be limited, particularly as countries pass through different points in the outbreak curve. However, this review does highlight the considerable psychological impact that COVID-19 has played so far on health care workers and, therefore, adds to the recent calls to take notice of this important issue [ 14 ]. Yet the evidence also suggests that, although predictors for psychological distress exist, these are not absolute and context may play an important role on the manifestation of adverse MH outcomes.

Strengths and limitations

This rapid review has synthesized and discussed the current literature on the psychological impact of the COVID-19 pandemic on health and social care workers. A major limitation was that no empirical studies investigating this impact on social care workers could be found – limiting generalisability to the population reviewed. Recent evidence also suggests that having an ongoing connection to a paid job, may be protective against poor MH outcomes during the pandemic [ 74 ]. It would therefore be useful to compare MH outcomes amongst HCWs, or the general population, who were not actively employed during the pandemic. Unfortunately, none of the studies included this data. Furthermore, job retention schemes have varied widely between countries worldwide, thus limiting the generalisability of findings if this data had been available [ 75 ].

However, to our knowledge, this is the first review investigating this population group in the context of COVID-19, without including prior viral outbreaks in its analysis and synthesis. We see this as a strength because this outbreak is different, and worth assessing in its own right. It has affected every country across the globe and disrupted everyday living in a way no other outbreak has in living memory [ 14 ]. A major strength of our review is that it endeavoured towards greater inclusion, during the rapidly changing COVID-19 landscape, by completing two runs of the search strategy spaced 2 weeks apart. Whilst we adhered to high methodological standards by assessing study quality and risk of bias, together with using the GRADE approach to evaluate the certainty evidence and following best practice principles [ 52 , 53 ] to present a narrative and tabulated synthesis, our review remains a rapid one with further clear limitations. The majority of the studies included in this review, for example, were from China and our selection criteria did not include studies from low-income countries or studies in languages other than English - limiting the generalizability of our findings. Being a rapid review, the protocol was not registered on PROSPERO and only one reviewer was responsible for the initial screening of papers and for several of the quality assessments. Finally, as the current review’s searches were carried out early in the pandemic, it will be valuable to consider emerging research from the global arena in the light of this review’s findings.

This rapid review confirms that front line HCWs are at risk of significant psychological distress as a direct result of the COVID-19 pandemic. Published studies suggest that symptoms of anxiety, depression, insomnia, distress and OCD are found within the healthcare workforce. However, most studies draw only from work in secondary care and none draw from the primary care or social care setting. Published studies so far are predominantly from China (18 out of 24 included studies) and most of these have sampled hospital staff in Wuhan - the epicentre. Findings in this review suggest that the study of different contexts and cultures may reveal different findings and we recommend more research in primary care and social care settings and to monitor rapidly emerging evidence from across the world. This should include analysis of wider societal factors including gender, racial and socio-economic disparities that may influence mental health outcomes in HCWs.

Although risk factors did emerge that were in keeping with evidence from other infectious disease outbreaks, our findings were not absolute. This review suggests that nurses may be at higher risk of adverse MH outcomes during this pandemic, but there were no studies comparing them with social care workers or the primary care workforce. Other risk factors that recurred in the data were heavy workload, lack of PPE, close contact with COVID-19, being female and underlying organic illness. Inconsistencies in findings and lack of data on staff outside hospital settings, suggest that targeting a specific group within health and social care staff with psychological interventions may be misplaced – as both presence of psychological distress and risk factors are spread across the healthcare workforce, rather than associated with particular sub-groups.

A recent call to action for mental health science during COVID-19 recommends research be undertaken to identify interventions that can be delivered under pandemic conditions to mitigate deteriorations in psychological well-being and support mental health. This call to action advised that personalised psychological approaches are likely to be a key [ 14 ]. Data from this review suggests that interventions which bolster psychological resilience may be of benefit because this was found to protect against adverse mental health outcomes. Due to the nature of the pandemic which prevents face-to-face interventions, this is likely to be digitally based. A recent systematic review, pre-dating COVID-19, suggested that individualised interventions can have modest effect on reducing adverse mental health outcomes amongst physicians [ 2 ]. However, our findings suggest that occupational and environmental factors in the workplace play a key role as risk factors and protective factors for mental health outcomes during this pandemic. Heavy workload, proximity to COVID-19 and inadequate PPE were risk factors for poor mental health, whereas good knowledge of COVID-19, a supportive work environment and adequate PPE were protective factors. It would appear from our findings that adequate PPE may be protective not just against infection, but also against adverse mental health outcomes. Individually targeted digital interventions are unlikely to address these factors [ 2 ]. We postulate that strengthening psychological resilience in a personalised approach may be effective in protecting our health and social care workers from adverse mental health outcomes but this must not defer responsibility from wider organisations and systems. We suggest that a holistic approach to HCWs psychological wellbeing is needed that includes personalised interventions alongside necessary structural changes to create a healthy, safe and supportive work environment. Further research including social care workers and analysis of wider societal structural factors is recommended.

Availability of data and materials

The datasets during and/or analysed during the current study available from the corresponding author on reasonable request.

Abbreviations

Connor-Davidson Resilience Scale

Centre for Epidemiologic Studies Depression Scale (CES-D)

Coronavirus disease 2019

Depression, Anxiety and Stress Scale

Generalised Anxiety Disorder Questionnaire

The Grades of Recommendation, Assessment, Development and Evaluation Working Group

Generalised self-efficacy scale

Hamilton Anxiety Rating Scale

Hamilton Depression Rating Scale

Healthcare workers

Health and social care workers

Impact of Event Scale

Insomnia Severity Index

Maslach Burnout Inventory (MBI)

• Mental health

Public Health Emergency

Patient Health Questionnaire-4

Patient Health Questionnaire

Personal protective equipment

Pittsburgh Sleep Quality Index

Zung Self-Rating Anxiety Scale

The Stanford Acute Stress Reaction questionnaire

Symptom checklist depression scale

The Symptom Checklist-90-R

Zung Self-Rating Depression Scale

Short Form Health Survey (SF-36)

Stress Overload Scale

Social Support Rating Scale

World Health Organisation

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Acknowledgements

Thank you to Abbie Oman (University of Aberdeen) for critically reviewing our manuscript.

This project is funded by the Chief Science Office of the Scottish Government: RAPID RESEARCH IN COVID-19 PROGRAMME REF: COV/UHI/Portfolio. The funding sources had no role in the design or conduct of the study nor in the collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

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JDK, SAM and HL had the idea for the study. JDK, RP, CoM designed the search strategy. JDK, HL, LE screened abstracts and full texts. JDK, HL, LE, SAM, acquired data, and assessed risk of bias in studies. MG contributed to interpreting the data. JDK, HL and SL wrote the manuscript. SL made substantial contributions to the revision of the manuscript. The corresponding author attests that all listed authors meet authorship criteria. All authors have approved the submitted version and have agreed both to be personally accountable for the author’s own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature.

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De Kock, J.H., Latham, H.A., Leslie, S.J. et al. A rapid review of the impact of COVID-19 on the mental health of healthcare workers: implications for supporting psychological well-being. BMC Public Health 21 , 104 (2021). https://doi.org/10.1186/s12889-020-10070-3

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Mental health and the pandemic: What U.S. surveys have found

The coronavirus pandemic has been associated with worsening mental health among people in the United States and around the world . In the U.S, the COVID-19 outbreak in early 2020 caused widespread lockdowns and disruptions in daily life while triggering a short but severe economic recession that resulted in widespread unemployment. Three years later, Americans have largely returned to normal activities, but challenges with mental health remain.

Here’s a look at what surveys by Pew Research Center and other organizations have found about Americans’ mental health during the pandemic. These findings reflect a snapshot in time, and it’s possible that attitudes and experiences may have changed since these surveys were fielded. It’s also important to note that concerns about mental health were common in the U.S. long before the arrival of COVID-19 .

Three years into the COVID-19 outbreak in the United States , Pew Research Center published this collection of survey findings about Americans’ challenges with mental health during the pandemic. All findings are previously published. Methodological information about each survey cited here, including the sample sizes and field dates, can be found by following the links in the text.

The research behind the first item in this analysis, examining Americans’ experiences with psychological distress, benefited from the advice and counsel of the COVID-19 and mental health measurement group at Johns Hopkins Bloomberg School of Public Health.

At least four-in-ten U.S. adults (41%) have experienced high levels of psychological distress at some point during the pandemic, according to four Pew Research Center surveys conducted between March 2020 and September 2022.

Young adults are especially likely to have faced high levels of psychological distress since the COVID-19 outbreak began: 58% of Americans ages 18 to 29 fall into this category, based on their answers in at least one of these four surveys.

Women are much more likely than men to have experienced high psychological distress (48% vs. 32%), as are people in lower-income households (53%) when compared with those in middle-income (38%) or upper-income (30%) households.

In addition, roughly two-thirds (66%) of adults who have a disability or health condition that prevents them from participating fully in work, school, housework or other activities have experienced a high level of distress during the pandemic.

The Center measured Americans’ psychological distress by asking them a series of five questions on subjects including loneliness, anxiety and trouble sleeping in the past week. The questions are not a clinical measure, nor a diagnostic tool. Instead, they describe people’s emotional experiences during the week before being surveyed.

While these questions did not ask specifically about the pandemic, a sixth question did, inquiring whether respondents had “had physical reactions, such as sweating, trouble breathing, nausea, or a pounding heart” when thinking about their experience with the coronavirus outbreak. In September 2022, the most recent time this question was asked, 14% of Americans said they’d experienced this at least some or a little of the time in the past seven days.

More than a third of high school students have reported mental health challenges during the pandemic. In a survey conducted by the Centers for Disease Control and Prevention from January to June 2021, 37% of students at public and private high schools said their mental health was not good most or all of the time during the pandemic. That included roughly half of girls (49%) and about a quarter of boys (24%).

In the same survey, an even larger share of high school students (44%) said that at some point during the previous 12 months, they had felt sad or hopeless almost every day for two or more weeks in a row – to the point where they had stopped doing some usual activities. Roughly six-in-ten high school girls (57%) said this, as did 31% of boys.

On both questions, high school students who identify as lesbian, gay, bisexual, other or questioning were far more likely than heterosexual students to report negative experiences related to their mental health.

Mental health tops the list of worries that U.S. parents express about their kids’ well-being, according to a fall 2022 Pew Research Center survey of parents with children younger than 18. In that survey, four-in-ten U.S. parents said they’re extremely or very worried about their children struggling with anxiety or depression. That was greater than the share of parents who expressed high levels of concern over seven other dangers asked about.

While the fall 2022 survey was fielded amid the coronavirus outbreak, it did not ask about parental worries in the specific context of the pandemic. It’s also important to note that parental concerns about their kids struggling with anxiety and depression were common long before the pandemic, too . (Due to changes in question wording, the results from the fall 2022 survey of parents are not directly comparable with those from an earlier Center survey of parents, conducted in 2015.)

Among parents of teenagers, roughly three-in-ten (28%) are extremely or very worried that their teen’s use of social media could lead to problems with anxiety or depression, according to a spring 2022 survey of parents with children ages 13 to 17 . Parents of teen girls were more likely than parents of teen boys to be extremely or very worried on this front (32% vs. 24%). And Hispanic parents (37%) were more likely than those who are Black or White (26% each) to express a great deal of concern about this. (There were not enough Asian American parents in the sample to analyze separately. This survey also did not ask about parental concerns specifically in the context of the pandemic.)

Looking back, many K-12 parents say the first year of the coronavirus pandemic had a negative effect on their children’s emotional health. In a fall 2022 survey of parents with K-12 children , 48% said the first year of the pandemic had a very or somewhat negative impact on their children’s emotional well-being, while 39% said it had neither a positive nor negative effect. A small share of parents (7%) said the first year of the pandemic had a very or somewhat positive effect in this regard.

White parents and those from upper-income households were especially likely to say the first year of the pandemic had a negative emotional impact on their K-12 children.

While around half of K-12 parents said the first year of the pandemic had a negative emotional impact on their kids, a larger share (61%) said it had a negative effect on their children’s education.

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John Gramlich is an associate director at Pew Research Center

How Americans View the Coronavirus, COVID-19 Vaccines Amid Declining Levels of Concern

Online religious services appeal to many americans, but going in person remains more popular, about a third of u.s. workers who can work from home now do so all the time, how the pandemic has affected attendance at u.s. religious services, economy remains the public’s top policy priority; covid-19 concerns decline again, most popular.

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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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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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Xue Li: @snowly12191

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Correspondence to Ian Chi Kei Wong or Xue Li .

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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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ORIGINAL RESEARCH article

The covid-19 pandemic, psychologists’ professional quality of life and mental health.

• Department of Psychology and Neuroscience, Auckland University of Technology, Auckland, New Zealand

Background: Psychologists are at known risk of work-related stress, secondary trauma, and burnout. The COVID-19 pandemic increased stress and anxiety for communities worldwide and corresponded with an increased demand for mental health services. Our study investigated the impact of COVID-19 on psychologists’ professional quality of life, psychological symptoms, and work-related stress in Aotearoa, New Zealand (NZ).

Method: Ninety-nine registered psychologists were recruited via NZ professional psychology organizations, representing 3% of the total workforce. Survey data collected included symptoms of compassion fatigue and satisfaction, psychological symptoms, COVID-19-related stress and resilience, and professional and personal circumstances during the third year of the pandemic, 2022.

Results: Seventy percent reported that their work stress had increased, and 60% reported that their caseload intensity had increased during the COVID-19 pandemic. Psychologists reported receiving little to no additional personal or professional support, while 55% reported increased personal responsibilities during the pandemic (for example, closed childcare and schools during lockdowns). High rates of compassion fatigue (burnout and secondary traumatic stress) and low resilience were reported. We observed that psychological distress was higher than the community averages before the pandemic and comparable with frontline healthcare professionals. Compassion fatigue was associated with COVID-related stress, psychological distress, years in practice, and more frequent supervision, but not with working with at-risk clients, levels of personal support, or having children at home. Despite these difficulties, high Compassion Satisfaction scores were also reported, with over 90% indicating they had no intention of leaving the profession in the foreseeable future.

Conclusion: Psychologists’ compassion fatigue appears to have worsened during the COVID-19 pandemic, as have their symptoms of psychological distress. Increased workplace and clinical demands, telehealth difficulties, stress relating to the pandemic, inadequate support, and increased personal responsibilities were reported by psychologists. Mental health workforces are not immune to the personal and professional impacts of crises and are at risk of burnout and secondary traumatic stress. We hope that increased awareness and understanding of psychologists’ own difficulties during COVID-19 can be used to better tackle future crises and support mental health professionals.

Introduction

During the pandemic, mental health service demand increases were reported worldwide ( Saha et al., 2020 ; Benton et al., 2022 ; Deng et al., 2023 ; Sicouri et al., 2023 ), specifically in Aotearoa, New Zealand (NZ; Every-Palmer et al., 2020 ; Freeman et al., 2021 ; Gasteiger et al., 2021 ; Officer et al., 2022 ). Healthcare professionals (HCPs) faced the unusual challenge of sharing stressful situations with their patients, as navigating uncertain health risks, lockdowns, travel restrictions, and financial disruptions coincided with increased client distress and severity. In NZ, government-mandated restrictions were among the strictest and most effective in reducing the spread and mortality of COVID-19, such that widespread community transmission was not seen until the 2022 Omicron outbreak. At the time of this study in 2022, extended lockdowns had only recently ended, vaccination rates were high, and community transmission was increasing ( Ministry of Health, 2022 ). Many psychologists continued to work remotely via telehealth.

The impact of the pandemic on HCPs has been reported by frontline medical workers, with rates of stress and anxiety particularly noted ( Buselli et al., 2020 ; Bell et al., 2021 ). However, little research has been conducted on mental health professionals. During the pandemic, psychologists reported clients needing care as much or more than before, as the stress of the circumstances compounded existing challenges ( British Psychological Society, 2020 ). Unprecedented numbers of people attended NZ hospitals for mental health emergencies, particularly among young people ( Every-Palmer et al., 2020 ; Freeman et al., 2021 ; Gasteiger et al., 2021 ; Officer et al., 2022 ). However, the effects on psychologists went largely unexamined, with little consideration given to these professionals working to support their communities.

Pre-pandemic research established a significant risk of work-related stress, vicarious trauma, and burnout symptoms among psychologists. Estimates were that between 20 and 67% of psychologists experienced symptoms of burnout ( Morse et al., 2012 ; McCormack et al., 2018 ; O’Connor et al., 2018 ; Simpson et al., 2019 ). In 2021, the second year of the COVID-19 pandemic, NZ psychologists reported significantly higher rates of burnout and secondary traumatic stress than caring professionals internationally and in earlier NZ-based studies ( Kercher and Gossage, 2023 ). Psychologists also reported difficulties, mainly working with high-risk clients, stress, and depression symptoms, which were linked with compassion fatigue. However, the specific and prolonged effects of the pandemic were not clear, which was the motivation for the current study.

During the pandemic, psychologists and other mental health professionals saw the challenge of therapy moving online, a platform many had rarely used, and which brought ethical, legal, and technical difficulties ( British Psychological Society, 2020 ). The benefits of ongoing connections and support for clients were numerous, with clients accessing services from home. Clinical practices have since changed, with services and training programs focusing more on online delivery than ever. Anecdotally, psychologists, like many others, reported challenges from juggling remote work with children schooling at home and other caring responsibilities, but this was not being measured formally. The current study sought to assess the challenges faced by psychologists during the pandemic and developed a questionnaire for this purpose ( Rahman et al., 2024 ).

The current study focused on the effects of COVID-19 on psychologists in NZ. Psychologists undergo prescribed training and practice under comprehensive codes of conduct and ethics and are thus a relatively homogenous and standardized sample of mental health practitioners. By the third year of the pandemic, themes were beginning to emerge in anecdotal discussions among psychologists—telehealth fatigue, personal demands, client changes, and systemic challenges. This research investigated these factors and sought to understand the effects of COVID-related stressors on psychologists’ psychological symptoms and levels of compassion fatigue.

Participants

Online surveys were conducted with 110 registered psychologists in NZ. Of these, 99 completed the study, with 11 largely incomplete responses excluded. This represented approximately 3% of NZ’s registered psychologists ( New Zealand Psychologists Board, 2021 ). Similar to the profession’s demographic makeup, 82% were identified as Pākeha (of European descent), 1% as Māori, 1% as Pasifika, 5% as Asian, and 11% from other backgrounds, with 92% female, 8% male, and no non-binary respondents, and a median age range of 41–45 years. The majority (84%) were married or in de facto relationships, 48% reported no children under 18 living in their home, 41% had one to two children, and 11% had three or more. Nine percent reported additional caregiving responsibilities (e.g., relatives with disabilities or illnesses). More than half (55%) received little to no support with personal commitments, while 23% reported adequate support and 22% good support. Approximately one-third (33%) reported additional personal stressors during the survey (e.g., health problems, housing or financial hardships, and domestic violence).

Professional characteristics varied, as shown in Appendix A . Participants reported an average of 11–15 years in practice, with 90% receiving the required monthly supervision. The NZ Psychologists Board Guidelines on Supervision recommend regular sessions, wherein discussions with a respected colleague include self-reflection, professional issues, and feedback on all elements of practice, with a focus on the quality of service, improving practice, and managing the impacts of professional work upon the supervi(see New Zealand Psychologists Board, 2021 ). Approximately half worked in publicly funded roles in health or hospital settings, and nearly half in private practice. Psychologists worked with varied client groups, including clients at risk of self-harm and suicide. The majority reported no intention to leave soon, with over 90% intending to remain in practice for more than 5 years.

Professional quality of life scale (ProQOL)

The Professional Quality of Life Scale (ProQOL; Stamm, 2010 ) is a widely used measure of the positive and negative aspects of mental health work, comprising three subscales. Thirty items are answered on a Likert Scale (from 1 = never to 5 = very often). Compassion satisfaction (CS) represents the feeling of satisfaction and reward derived from one’s work, a positive outcome. Burnout (BO) incorporates feelings of disconnection, hopelessness, and ineffectiveness in one’s role, while Secondary Traumatic Stress (STS) assesses vicarious trauma symptoms, including fear and overwhelm. These two negative outcomes are summed to represent compassion fatigue (CF), the negative impact of caring work ( Stamm, 2002 , 2010 ; Larsen and Stamm, 2008 ). Given the high level of collinearity between these two subscales, this composite negative outcome was used in multivariate analyses to allow the exploration of other variables. At the same time, BO and STS are considered compared to previous studies that reported these separate constructs.

The ProQOL has strong psychometric properties, with each subscale showing good construct validity and internal consistency (α from 0.75 to 0.88, Stamm, 2010 ). In the current study, Cronbach’s alpha was 0.77 for BO, 0.83 for STS, and 0.89 for CS.

Depression, anxiety, stress scale (DASS-21)

Symptoms of psychological distress were measured using the DASS-21, a commonly used questionnaire ( Lovibond and Lovibond, 1995 ). Twenty-one items are answered on a four-point Likert Scale (0 to 3), with seven items assessing symptoms of depression, anxiety, and stress, respectively. Scores are doubled to allow comparisons with the original DASS-42 instrument ( Crawford et al., 2011 ). The DASS-21 is widely recognized for its robust psychometric properties ( Medvedev et al., 2020 ). In the current study, Cronbach’s alpha was 0.85 (depression), 0.77 (anxiety), and 0.76 (stress). Overall symptoms of psychological distress can be measured from the total DASS-21 symptom score ( Alfonsson et al., 2017 ; Zanon et al., 2021 ), which was used here in multivariate analyses to allow consideration of other variables without the multicollinearity between DASS-21 subscales.

Connor–Davidson resilience scale (CD-RISC-10)

The 10-item Connor–Davidson Resilience Scale (CD-RISC-10) measures resilience ( Connor and Davidson, 2003 ; Davidson, 2018 ). A Likert scale (0 = not true at all, 4 = true nearly all the time) assesses stress coping abilities, with a final score of the sum of responses (0–40) and higher scores indicating higher resilience. The CD-RISC-10 has good psychometric properties ( Campbell-Sills et al., 2009 ; Davidson, 2018 ), with Cronbach’s alpha here of 0.80.

COVID-19 related stress (CVRS)

The CVRS was developed for the current study specifically to assess the negative impact of the COVID-19 pandemic on those working in the mental healthcare sector. Five questions were based on the findings of a qualitative report by the British Psychological Society (2020) , which reported experiences of the pandemic among psychologists, with two additional questions created by the authors. The measure presents seven statements on a five-point Likert scale (1 = not true at all, 5 = true nearly all the time; see Appendix B ). A higher score indicates a greater level of COVID-19-related stress. The reliability of this scale was established by Rahman et al. (2024) , with Cronbach’s alpha of 0.83 indicating high internal consistency. In addition, the Kaiser–Meyer–Olkin (KMO) tests and Bartlett’s sphericity tests were supported using exploratory factor analysis (EFA), which confirmed a one-factor solution.

Survey questions

The questionnaire also surveyed psychologists’ professional and personal circumstances, including types of work and client presentations, frequency of work with at-risk clients (from 0 = ‘never’ to 3 = ‘very often’), therapeutic practices, supervision and professional support, demographics, family and caring responsibilities, and personal support.

Participants were recruited via social media and email invitations shared by the New Zealand Psychological Society and New Zealand College of Clinical Psychologists. Almost all psychologists in NZ are members of one of these organizations. Participants gave informed consent and completed the survey via the online research platform Qualtrics, with no identifying information recorded. Ethical approval was granted by the Auckland University of Technology Ethics Committee (21/54, 8th April 2022).

Data analysis

Analyses were conducted using the software package Jamovi (v.1.6.23) and online t-test calculators. 1 Reliability analyses were conducted for all scales before analyses (as above). The properties of the CVRS were investigated through exploratory factor analysis (EFA) using principal axis factoring extraction and Obliman rotation (see Appendix C ). Based on eigenvalues of more than one, the unidimensional one-factor solution was justified with all factor loadings larger than 0.40.

Independent t-tests were performed to compare our sample with previous studies of psychologists and health professionals. Welch’s t-tests were used where the variances were unequal ( Delacre et al., 2017 ). Spearman’s rho correlations were calculated to investigate the associations between the key variables. Multicollinearity was investigated due to the correlation between variables. However, the variance inflation factors (VIFs) were less than 5, suggesting that the multicollinearity is not strong enough to prevent a multiple linear regression (MLR). Bivariate correlations were conducted. An MLR was then conducted to investigate the impact of CVRS, distress, and workplace characteristics on CF.

Descriptive statistics and comparisons

Distress and compassion fatigue.

Descriptive statistics for the distress (DASS-21), resilience (CD-RISC), COVID-19-related stress (CVRS) and burnout, secondary traumatic stress, and compassion satisfaction (ProQOL) scores are presented in Table 1 .

Table 1 . Descriptive statistics for the current sample ( N  = 99) and comparisons with previous studies.

Independent sample t-tests compared our sample with previous norms (see Table 1 ). Notably, NZ psychologists reported significantly higher average depression, anxiety, and stress than pre-pandemic community norms and higher average burnout and secondary traumatic stress than pre-pandemic psychologists. Distress averages were comparable to those reported by frontline HCPs during the pandemic’s peak, except for anxiety, which was higher for medical personnel. Resilience was lower than pre-pandemic psychologists and community norms. Compassion satisfaction was comparable with previous samples of psychologists and frontline HCPs.

Effects of the COVID-19 pandemic

Using our new COVID-19-related stress (CVRS) scale measure, psychologists reported a range of scores from 7 to 33 ( M  = 19.2, SD = 5.5). During the pandemic, most psychologists also reported increased stress or concern about their work (69.9%), increased caseload intensity (60.2%), and increased personal responsibilities (54.8%); however, nearly all reported no increase in professional support (90.3% the same or decreased) or personal support (97% the same or decreased; see Figure 1 ).

Figure 1 . Effects of COVID-19 lockdowns and restrictions on New Zealand psychologists ( N  = 99).

Bivariate analyses

Due to collinearity between the subscales, the composite Compassion Fatigue and total DASS-21 distress scores were used in multivariate analyses. Correlations were considered based on the hypotheses, with expected relationships between stressors (children at home, low support in the workplace or home, low experience, and more frequent work with at-risk clients) and experiences of distress (CVRS, DASS-21, CF). In contrast, supervision and high support were expected to be protective, and resilience and CS would show the opposite effects (see Table 2 ). These hypotheses were supported—CF is strongly associated with psychological distress, and both are strongly related to CVRS. Having more children at home was associated with increased COVID-related stress. Interestingly, CF positively correlates with increased supervision but not with years in practice. Having more experience appears slightly protective against distress. Those working with at-risk clients report higher resilience and supervision but not higher CF or distress. No significant relationships were found for other client presentation types (mental health severity, trauma, child/adolescent, Māori, and Pasifika, p  > 0.05).

Table 2 . Bivariate correlations ( N  = 99).

There are clear relationships between indicators of distress, stress, and CF, which were further explored in multivariate analyses. One-way ANOVA was conducted to investigate differences between public sector and private practice psychologists. However, differences in CVRS, CF, and DASS-21 total were not significant ( p  > 0.05).

Multivariate analyses

Predictors of compassion fatigue.

Multiple linear regression analyses were conducted to investigate the predictors of CF, considering the role of COVID-related stress and other factors hypothesized to be related. This model was significant, with 45% of the variance in CF explained by the variables listed in Table 3 ( F (7, 82) = 9.75, p  < 0.001. R 2  = 0.45). Higher levels of psychological distress (DASS-21 total symptoms), COVID-related stress (CVRS), supervision frequency, and years in practice all predicted increases in compassion fatigue, measured during the pandemic. Having children at home, providing personal support, and working with at-risk clients were not significant predictors of CF.

Table 3 . Multiple linear regression results: predictors of compassion fatigue ( N  = 99).

Psychologists in Aotearoa, NZ, reported high rates of psychological distress, burnout, and secondary traumatic stress symptoms during the third year of the pandemic (2022). While a great deal of attention has rightly focused on frontline medical workers’ wellbeing and the risk of burnout during the pandemic, our study shows that psychologists have been experiencing the same difficulties.

Psychologists had higher average scores for depression, anxiety, and stress than pre-pandemic community norms ( Crawford et al., 2011 ) and significantly higher average burnout and secondary traumatic stress than pre-pandemic psychologists in NZ ( McCormick, 2014 ). Notably, NZ psychologists’ average distress was comparable with symptoms reported by frontline HCPs during the peak of the pandemic in Australia ( Hammond et al., 2021 ), except for anxiety, which was higher for medical personnel—likely due to the intensity of emergency room work during this time which would drive autonomic nervous system arousal, as measured by the DASS-21 anxiety scale.

Psychologists in NZ reported higher average burnout and secondary traumatic stress than Italian frontline healthcare professionals during the peak of the pandemic, in intensely stressful and distressing conditions ( Buselli et al., 2020 ). This may be because burnout is a slow-onset phenomenon, culminating in work-related issues over a long period ( Stamm, 2002 ), whereas the Italian medical community was experiencing more acute and intensive stress. However, secondary traumatic stress can have a sudden onset, so, it is surprising that our rates were higher than the Italian HCPs on this scale.

Resilience was lower than pre-pandemic NZ psychologists ( McCormick, 2014 ) and general community norms ( Davidson, 2018 ). The latter is particularly surprising, as mental healthcare professionals typically show high rates of resilience ( Davidson, 2018 ) and have a professional understanding of coping strategies. Interestingly, psychologists working with more at-risk clients reported higher resilience, though this was not significantly related to compassion fatigue or other measures of distress. It could be that psychologists self-select their work, and those with higher resilience elect to work with higher-risk presentations. On the other hand, compassion satisfaction scores were comparable with previous samples of NZ psychologists and frontline HCPs, suggesting that psychologists enjoy their work and find it rewarding. These results were comparable with our earlier sample of NZ psychologists in 2021 ( Kercher and Gossage, 2023 ), so measurement errors are unlikely—psychologists have repeatedly reported elevated distress. Similar difficulties were reported for frontline HCPs ( Bell et al., 2021 ) and psychiatrists in NZ during the pandemic ( Chambers and Frampton, 2022 ), but with different measures preventing direct comparisons.

The current study sought to understand the role of COVID-19-related stress, among other workplace and personal factors, in contributing to the reported levels of compassion fatigue among NZ psychologists. We found that COVID-19-related stress was a predictor of CF, over and above psychological distress, years in practice, supervision, and non-significant predictors, including personal support, having children at home, and working with at-risk clients. Interestingly, receiving more supervision was associated with increased CF—perhaps those at risk of CF are actively seeking more support or working in settings where this is offered. However, working in public or private settings was not significantly associated with CF. Those with more years of practice experience reported higher CF in a model containing the other predictors. This is unusual—often, there is a “survivorship effect” seen in burnout, where those prone to experience it leave their roles early in their careers, and those with more years of experience appear more resilient to burnout ( Rupert and Morgan, 2005 ).

Psychologists reported increases in stress and concern about work, caseload intensity, and personal responsibilities during the pandemic, with more than 90% reporting no increases in personal or professional support. While the pandemic has since eased, with the government of Aotearoa, NZ, removing the last of the public health orders ( Government NZ, 2023 ), ongoing pressures continue for the mental health sector. Frequent reports emphasize shortages of psychologists ( Psychology Workforce Task Group, 2016 ; Skirrow, 2021 ), psychiatrists ( Thabrew et al., 2017 ), increases in demand ( Every-Palmer et al., 2022 ), difficulties with access ( Officer et al., 2022 ), and waitlists ( Cardwell, 2021 ). Arguably, this is causing a worsening cycle of severity in the community—services triage patients and see the most severe cases first, leaving those with mild-to-moderate concerns without help. Without intervention, many psychological conditions worsen over time ( Ghio et al., 2015 ) and are associated with an increased risk of suicide ( Maslow et al., 2015 ). Clients receive treatment when their symptoms worsen ( Blayney and Kercher, 2023 ). As a result, psychologists report increasing severity, intensity, and concern about their work, although this was not directly associated with CF in the current study.

Clearly, the mental health sector requires increased funding and resourcing ahead of future crises. The challenges of the pandemic exacerbated workforce shortages and increased demand on a sector already under strain and the psychologists working to support their communities. Given the increasing frequency of natural disasters and other challenges in Aotearoa, NZ, and worldwide, the mental health sector needs to be better prepared for such difficulties in the future. Learning from the impact of the pandemic on psychologists, we need to focus on better supporting our health and support services and improve the resilience of mental health systems in the future.

The cross-sectional nature of our survey was a limitation of this study. Due to anonymity, we could not compare responses with the survey conducted in 2021. Still, we can track average rates of distress and professional quality of life, which were comparable across the two samples. Although our sample size ( N  = 99) was modest, we recruited only registered, practicing psychologists who undergo extensive training and practice under standardized codes of conduct and practice in Aotearoa, New Zealand, thus providing a homogenous sample. However, self-selection bias is possible, whereby those under most stress or most at risk of CF may not take the time to answer a survey. The survey was also limited in socio-cultural diversity, with most respondents from European NZ backgrounds identifying as female participants. Invitations were extended to target Māori and Pasifika psychologists’ groups; however, response rates were low. It will be important for future research to engage better tangata whenua psychologists, who are at known risk of burnout ( Levy, 2002 ; Hemopo, 2004 ).

Encouragingly, our respondents reported good average rates of compassion satisfaction. Additionally, more than 90% reported that they had no intention to leave the profession soon (in contrast with NZ psychiatrists, nearly half of whom reported intention to leave, Chambers et al., 2022 ). Psychologists report finding their work rewarding and satisfying, reflected in the reported sense of purpose and reward both here and internationally during the pandemic ( British Psychological Society, 2020 ).

The strong implication of this study is that psychologists face significant challenges in their roles. Combined with workforce and health system data indicating continual increases in demand and insufficient resources, it is vital that the shortage of psychologists is addressed with increased training and that the mental healthcare sector in Aotearoa, NZ, receives increased resources. While supervision and workplace support were not protective against CF here, almost all respondents received the required minimum of monthly supervision sessions—arguably, without this, distress could be even worse. Relatively few psychologists received more supervision than this despite guidelines recommending additional sessions for less experienced practitioners, new areas of work, or client crises. Supervision and professional development are generally protective against burnout and distress for mental health professionals ( Yang and Hayes, 2020 ), while supportive workplaces and manageable demands are also critical ( Maslach and Leiter, 2016 ). A larger sample of psychologists would allow better investigation of these potential protective factors, wherein a focus on different types of resilience and the potential role of supervision is suggested. Workplaces, the healthcare sector, and psychologists’ organizations should consider screening psychologists for burnout and secondary traumatic stress and address both demands and support within their roles.

For 2 years in a row, psychologists in Aotearoa, NZ, have reported high average scores of burnout and secondary traumatic stress, as well as psychological distress and low resilience. The current study found that COVID-related stress was predictive of compassion fatigue, over and above the additional effects of psychological distress (depression, anxiety, and stress symptoms), years in practice, and supervision. Supervision, workplace support, and years in practice were not protective, and personal factors did not contribute to the risk of CF over and above the impact of COVID-related stress. In the future, it is important to assess the ongoing risk of burnout, secondary traumatic stress, and psychological distress among psychologists. Given the ongoing increases in mental health demand worldwide and the impact of the pandemic on psychologists, priority should be given to increasing resources in mental health sectors and better supporting our caring professionals.

Data availability statement

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

Ethics statement

The studies involving humans were approved by Auckland University of Technology Ethics Committee. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided informed consent to participate in this study.

Author contributions

AK: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing. JR: Data curation, Investigation, Project administration, Writing – original draft. MP: Formal analysis, Writing – review & editing.

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

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2024.1339869/full#supplementary-material

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Keywords: psychologists, pandemic, compassion fatigue, professional quality of life, depression, stress

Citation: Kercher A, Rahman J and Pedersen M (2024) The COVID-19 pandemic, psychologists’ professional quality of life and mental health. Front. Psychol . 15:1339869. doi: 10.3389/fpsyg.2024.1339869

Received: 16 November 2023; Accepted: 20 February 2024; Published: 25 April 2024.

Reviewed by:

Copyright © 2024 Kercher, Rahman and Pedersen. 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: Amy Kercher, [email protected]

† ORCID: Amy Kercher, https://orcid.org/0000-0003-0257-4406 Mangor Pedersen, https://orcid.org/0000-0002-9199-1916

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

• Open access
• Published: 25 April 2024

“Access to healthcare is a human right”: a constructivist study exploring the impact and potential of a hospital-community partnered COVID-19 community response team for Toronto homeless services and congregate living settings

• Vivetha Thambinathan 1 ,
• Suvendrini Lena 1 , 2 ,
• Jordan Ramnarine 1 ,
• Helen Chuang 1 ,
• Luwam Ogbaselassie 3 ,
• Marc Dagher 1 , 4 ,
• Elaine Goulbourne 1 ,
• Sheila Wijayasinghe 1 ,
• Jessica Bawden 1 ,
• Logan Kennedy 1 &
• Vanessa Wright 1  

BMC Health Services Research volume  24 , Article number:  526 ( 2024 ) Cite this article

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Individuals experiencing homelessness face unique physical and mental health challenges, increased morbidity, and premature mortality. COVID -19 creates a significant heightened risk for those living in congregate sheltering spaces. In March 2020, the COVID-19 Community Response Team formed at Women’s College Hospital, to support Toronto shelters and congregate living sites to manage and prevent outbreaks of SARS-CoV-2 using a collaborative model of onsite mobile testing and infection prevention. From this, the Women’s College COVID-19 vaccine program emerged, where 14 shelters were identified to co-design and support the administration of vaccine clinics within each shelter. This research seeks to evaluate the impact of this partnership model and its future potential in community-centered integrated care through three areas of inquiry: (1) vaccine program evaluation and lessons learned; (2) perceptions on hospital/community partnership; (3) opportunities to advance hospital-community partnerships.

Constructivist grounded theory was used to explore perceptions and experiences of this partnership from the voices of shelter administrators. Semi-structured interviews were conducted with administrators from 10 shelters using maximum variation purposive sampling. A constructivist-interpretive paradigm was used to determine coding and formation of themes: initial, focused, and theoretical.

Data analysis revealed five main categories, 16 subcategories, and one core category. The core category “access to healthcare is a human right; understand our communities” emphasizes access to healthcare is a consistent barrier for the homeless population. The main categories revealed during a time of confusion, the hospital was seen as credible and trustworthy. However, the primary focus of many shelters lies in housing, and attention is often not placed on health resourcing, solidifying partnerships, accountability, and governance structures therein. Health advocacy, information sharing tables, formalized partnerships and educating health professionals were identified by shelter administrators as avenues to advance intersectoral relationship building.

Hospital-community programs can alleviate some of the ongoing health concerns faced by shelters – during a time of COVID-19 or not. In preparation for future pandemics, access to care and cohesion within the health system requires the continuous engagement in relationship-building between hospitals and communities to support co-creation of innovative models of care, to promote health for all.

Peer Review reports

Introduction

Individuals experiencing homelessness face unique physical and mental health challenges, increased morbidity and premature mortality [ 1 , 2 ]. In Canada, it is estimated that 235,000 individuals experience homelessness annually, and 180,000 use emergency shelters each night [ 3 ]. Emergency shelters in Canada, also known as homeless shelters and congregate living facilities, support a diverse population of men, women, families, youth, newcomers, LGBTQ2S individuals and elderly [ 4 ]. Crowding within shared living spaces in shelters creates heightened risk for infectious disease outbreaks [ 5 ].

This heightened risk was demonstrated early in the COVID-19 pandemic when cases of COVID-19 surged among homeless people in Toronto. At one Toronto shelter, 40% of shelter residents tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) in a single onsite testing event in April 2020 [ 6 ]. People who are homeless are also at risk of more severe outcomes from COVID-19 infection. Individuals with a recent history of homelessness diagnosed with COVID-19, were 20 times more likely to be admitted to hospital, ten times more likely to require critical care and five times more likely to die of COVID-19, than those housed in Ontario communities [ 7 ]. Access to preventative population health measures demonstrate similar health disparity for the underhoused population. In September 2021, six months after COVID vaccines had become available to the Ontario population, 61.4% of individuals with a recent history of homelessness had received at least one COVID-19 vaccine, compared to 86.6% of the general population [ 8 ].

Primary care is the critical entry point for care related and unrelated to COVID-19 [ 9 ]. Access to primary care is essential to improving the health of those who are underhoused, yet a survey conducted in 2011, revealed less than half of Toronto’s homeless population identified having a primary care provider [ 10 ]. Models of primary care delivery for Toronto’s underhoused population include onsite clinics within homeless shelters, drop-in centres and mobile buses, all with varied funding models, catchments and ties to an array of organizational health partners, such as Inner City Health Associates, Parkdale Queen West Community Health Centre, and Safe Spaces & Care for the Homeless [ 11 ]. Despite the foundational role primary care plays within the health system, it was not identified as a formal platform for the provision of COVID-19 testing, infection control and COVID-19 vaccinations in the Toronto homeless population. Public Health Ontario privileged local public health authorities with vaccine delivery, rather than relying on existing primary care providers; this decision was controversial [ 12 , 13 , 14 ]. Rather, hospital, community health and social care organizations joined together in March 2020 and formed the Shelter and Congregate Support Coordination Table (SCSCT), created by Ontario Health, the regional health authority, in response to the surge of COVID-19 in homeless shelters.

Women’s College Hospital (WCH) is an ambulatory care facility situated in downtown Toronto. In March 2020, WCH set up one of Toronto’s 14 COVID-19 assessment centres (CACs) to facilitate and support free testing for SARS-CoV-2. The COVID-19 Community Response Team (CRT) was formed by a group of health care providers at WCH, in April 2020, as an extension of the CAC. The CRT routinely participated in SCSCT meetings and underwent cycles of adaptation to improve the model as the pandemic evolved [ 15 ]. The primary goal of the CRT was to support Toronto shelters, congregate living settings and supporting organizations across Toronto to manage and prevent outbreaks of SARS-CoV-2 using a comprehensive collaborative model through onsite mobile testing; supporting the management and prevention of outbreaks; and providing infection prevention and control training and guidance [ 15 ]. In total, CRT utilized this model of care through engaging with 49 shelter and congregate living sites from April 2020 to April 2021.

Over the course of the pandemic, SCSCT continued to provide operational guidance for health partners, particularly as COVID-19 vaccines became available. Geographic boundaries were eventually assigned for Toronto health partners to work within, based on their location. WCH was assigned to the mid-west part of Toronto. Fourteen shelter partners were identified for the WCH vaccine delivery program. Ten of these shelters had collaborative relationships with WCH prior to the pandemic.

Local vaccine procurement, administrative supports and processes were supported by a regional hospital partner, University Health Network. The WCH COVID-19 shelter vaccine program ran from March 2021 to September 2021 offering first, second and booster vaccine doses. Clinic teams ranged from utilizing eight clinicians to administer over 300 vaccines in a single clinic, to a solo clinician administering less than 20 vaccines, all within homeless shelters. Clinic frequency ranged from every one to three months. In total, 2300 vaccines were administered across the 14 sites. Clinic preparation was supported by the CRT shelter lead who liaised with a shelter administrator in advance of each clinic to set dates, review clinic flow, support any questions and, if possible, conduct a site visit in advance. Each supporting clinician (vaccinator) attended a single training on trauma informed care and cultural sensitivity before engaging in COVID-19 vaccine outreach. In addition, clinicians were briefed in advance of each clinic on shelter demographics, languages spoken, layout and relationship with WCH.

Studies examining hospital partnerships for community or population health have increased in the past five years [ 16 ]. Qualitative study findings in a systematic review on hospital-community partnerships for population health suggest these partnerships hold promise for breaking down silos, improving communication across sectors, and ensuring appropriate interventions for specific populations [ 16 ]. For example, implementation of a COVID-19 community-academic partnership model, in predominantly Black, Latinx, and otherwise racialized and/or low-income communities in San Francisco, California, was shown to be effective in creating a shared leadership and facilitating sustained linkages between partners [ 17 ]. Moreover, offering COVID-19 vaccines for the underhoused through known, hyperlocal and low barrier approaches, like community health workers and drop-in centres, has demonstrated increased trust among vaccine providers and recipients and vaccine uptake.

In this regard, the establishment of hospital-shelter partnerships offers a unique opportunity to provide culturally relevant, needs-based healthcare services to transitional housing settings. This research seeks to evaluate the impact and importance of the partnership model and its future potential. The overarching question addressed in this study is: What were the overall perceptions of shelter workers in this hospital-community partnership and strategy? Additional questions for analysis include: What were barriers, facilitators, and lessons learned throughout the process? And how can this partnership between the hospital and shelters be sustained in the future to fulfill needs beyond COVID-19?

Theoretical approach & study design

Epistemically, we approach this research with a health equity orientation, understanding that we have a responsibility as health professionals to provide expanded support to under-resourced individuals within the healthcare system. Through conversations with health partners, WCH acknowledged this need to deliver partnered programming (WCH shelter vaccine program) to combat the disproportionate effects of COVID-19 on homeless populations. Thus, this project is rooted from a social accountability standpoint; our research team believes hospitals should prioritize community partnerships to identify and deliver care based upon people’s needs within communities served.

This research project is located within a constructivist/interpretivist paradigm. In this paradigm, ontological assumptions are treated as ‘knowledge’ obtained by participating subjectively in a world of meanings created by individuals; all findings are seen as co-creations by both participant and researcher [ 18 ]. Constructivist grounded theory (CGT) is a qualitative research methodology used to understand and explore perceptions and construct theories about a social phenomenon, grounded in participants’ own experiences and words [ 19 ]. Theoretical data on the perceptions of hospital-community COVID-19 partnership are limited. In this study involving the voices of shelter administrators and staff, we chose CGT to explore and deepen analyses around perceptions and experiences of this hospital-community COVID-19 partnership strategy. CGT methodology is equipped to support a theory formation process most appropriately fitting the participants’ statements [ 20 , 21 ]. CGT also considers and works to minimize the power asymmetries between researcher and participant, as well as showcase the knowledge asymmetries on both ends.

Participant recruitment

This research was conducted with staff and administrators from shelters and congregate settings, whose organizations partnered with the WCH vaccine program to provide COVID vaccination for shelters between March 2021 to September 2021. As previously mentioned, prior on the onset of the WCH shelter vaccine program, COVID testing and infection, prevention and control education was also provided at many of these congregate living sites. Depending on the shelter organization, provision of COVID vaccines was either limited to clients only or directed at both clients and staff. To provide data richness and diversify participants’ experiences, we used maximum variation purposive sampling in our study [ 22 ]. The principal investigator (VW) reached out to shelter staff and administrators through email, inviting them to take part in a key informant interview with the research coordinator (VT). A total of 10 participants from 10 different organizations took part in this study. As a project involving quality improvement and program evaluation, this project was formally reviewed by institutional authorities at Women’s College Hospital (the Assessment Process for Quality Improvement Projects – APQIP) and has received Research Ethics Board approval.

Data collection

Data collection occurred between April and August of 2022. After the initial participant recruitment email by the primary investigator, all further communication regarding the interview took place between the research coordinator (VT) and the participants. To prevent power differentials and seek raw answers about the WCH pandemic program, VT conducted all interviews, as they were not involved in design or delivery of the program. Participants who expressed interest were re-informed about the study by VT and emailed a consent form to read prior to the interview. A data collection form was also sent out to participants to complete, which captured demographic characteristics and WCH vaccine program-related data about the shelters (# of shelters with primary care partner on-site, number of residents, resident capacity before and after COVID (though it is still on-going), # of staff before and after COVID, referral mechanism to WCH vaccine program, whether vaccine education sessions were provided by WCH or other organizations, and vaccine uptake % as of October 2021).This was collected separately from the interview sessions to allow shelter administrators time to access their internal documents to gather data. Verbal consent was acquired at the beginning of the interview session. The 45-min sessions, conducted by VT, took place via Zoom© and were audio-recorded, transcribed, and de-identified using pseudonyms. To facilitate these sessions, VT used semi-structured interview guides developed by the research team. This guide centred around three key areas of inquiry: (1) Vaccine program evaluation and lessons learned (e.g., What were barriers, facilitators, and lessons throughout the process? How were shelter staff and clients impacted?); (2) Perceptions on hospital/community partnership (e.g., What were overall perceptions of this partnership and strategy?); (3) Opportunities forward (e.g., How can this partnership between hospitals and shelters be sustained in the future to fulfill needs beyond COVID-19. By the time of the last interview, theoretical saturation was reached, as no new conceptual information emerged.

Data analysis

Aligned with the CGT data analysis method developed by Charmaz [ 23 ], this study approached data analysis through three stages: initial, focused, and theoretical. All data were imported into NVIVO 12 software for coding. VT and JR worked together to complete the first stage of analysis: initial coding. This is where line-by-line coding is performed, and information is gained inductively to create codes [ 23 ]. VT and JR individually worked through the 10 transcripts and tagged codes, which led to a creation of a joint codebook. VT and JR then came together to work on this codebook, discussing similarities and differences and potential conceptual groupings of the data. To clarify concepts and engage with the data in-depth, VT and JR made constant comparisons with questions such as “What is said, what do they mean, why is that said” [ 20 ]. This codebook was also shared with the research team (VT, JR, VW, SL) for feedback. After this, VT and JR coded three transcripts using the revised codebook to compare notes for meaningful coherence and interrater reliability. All transcripts were then coded using the codebook. Focused coding, the second phase of data analysis, consisted of VT and JR reviewing their codes and jointly identifying the emergence of analytically meaningful codes obtained from initial coding [ 19 ]. A meeting was held at this stage with the research team to debrief and discuss themes for further reflection and refinement. Lastly, VT, VW, and SL engaged in theoretical coding to further conceptualize the relationship between codes and partake in theory formation [ 23 ]. Memo notes were exchanged as a point of discussion about what stood out and what kept recurring to everyone as the core concept of the overall data analysis. This emerged as the core category “access to healthcare is a human right; understand our communities”. VT wrote the results, using assigned numbers for all participants.

Reflexivity and quality criteria

To promote ongoing reflexivity throughout this research process and maintaining high quality rigour, the research team adopted the universal guidelines for reflexive thematic analysis [ 24 ]. This guideline outlines twenty critical questions to encourage deliberate reflection and engagement, specifically during data collection and analysis. The second (SL) and last authors (VW) were involved in the WCH pandemic program as a physician and nurse practitioner working at WCH. They have built relationships with the shelter organizations and have an innate attachment to this program as they were involved at the outset, with a passionate drive to deliver this community-based program during the COVID-19 crisis. Conscious of this, the research team had raw, reflexive conversations throughout this project to capture the impacts of the two authors’ experiences and how their positionality in this work may influence this study’s data analysis. Informed by CGT and our understanding of researchers’ roles within projects, we utilized the support of continuous research team debriefing, memo writing, and reflexive dialogue to reflect on how our codes are influenced by our team’s knowledge, beliefs, and experiences [ 19 ]. These practices enhanced credibility, consistency, and resonance of our study’s findings with respect to the participants’ experiences and overall context [ 21 ].

Results & discussion

As described previously, a staff member from each shelter ( n  = 10) each filled out a data collection form, describing shelter demographic characteristics and the data related to the hospital vaccine program. Table 1 displays this compiled information, which is representative of data known at the time of interviews (April-August 2021).

Data analysis of the research findings revealed five main categories, 16 subcategories, and one core category. The core category is “access to healthcare is a human right; understand our communities”. The main categories are expanded COVID-19 response capacity, challenges identifying and managing outbreaks, barriers to the vaccine program, community-centred immediate shelter needs, and avenues for intersectoral relationship strengthening. Table 2 shows the categories, definitions, and subcategories of the hospital-community COVID-19 partnership model and strategy.

Core category: access to healthcare is a human right; understand our communities

The core category demonstrated the underlying perception that all shelter and congregate settings users experienced disproportionate effects of the COVID-19 pandemic. Shelter staff and administrators emphasized that access to healthcare is a consistent barrier for homeless populations, although it is a human right. The statement of the participant “Access to healthcare is a human right; understand our communities.” [P4] effectively captured the essence of all participants’ perspectives and therefore emerged as the core category of this study. Overall, participants explained that understanding the diverse communities and their needs is a requirement for care partners and for ensuring that hospitals work internally and externally to provide access to healthcare for all. With the pandemic, the barrier to accessing healthcare was exacerbated, but this hospital-based pandemic program helped alleviate some of its negative effects, as shown in the following categories below.

Main category: expanded COVID-19 response capacity

The Expanded COVID-19 Response Capacity category includes three subcategories: increased access to resources, increased health knowledge, and a go-to trusted, credible partner. In this category, participants assessed the hospital partner’s efficacy in supporting their shelter to successfully respond to the COVID-19 pandemic. Participants discussed the impact of the hospital partner’s support, in terms of what this hospital-community collaboration equipped them with, in order to respond to COVID-19. Ontario’s vaccination rollout, with an emphasis in Toronto, was slower than average, with confusing messaging and inconsistencies throughout various public health units [ 25 , 26 ]. Under such provincial public health complexities, this local hospital-community partnership allowed for an increased access to resources, such as administering vaccines to clients within the shelter and receiving vaccines more quickly.

Allowed us to offer our space for people to get and promote vaccines. Everyone (clients and staff) felt very confident and comfortable. There were doctors and nurses there to answer questions. It felt like they had all the support and information they needed. People would not have gone to get vaccines otherwise, unless we had physically accompanied each person [to an external location]. [P7]

We probably received vaccines quicker because of [the] partnership... A lot of the refugee homes aren’t city shelters; we’re independent NGOs that do this refugee home model; we’re not always on the city’s radar when it comes to public health initiatives. Ourhospital partner pushed to have vaccines delivered to refugee homes and it helped us getpeople vaccinated quicker. [P8]

The hospital-community partnership model between shelters and WCH also increased shelter staffs’ health knowledge, with respect to IPAC (Infection Prevention and Control) support and isolation policies.

“ X sent photos of other clinic set-ups to show how they can be adaptable to any space.” [P7].

“Helped with IPAC support. Some staff brought up stuff during staff meetings. Anything I couldn’t answer, I asked via a quick email to our hospital partner, and they did their best to help with those pieces.” [P9].

“Increased our ability to be informed about COVID-19, so we can inform others. We reach out anytime we have questions about isolation policies and stuff, seek advice from health professionals, so this support is helpful in that sense.” [P6].

During a time of confusion around infection prevention protocols and isolation practices, information originating from hospitals were seen as credible and trustworthy. Essentially, homeless shelters are considered housing facilities and resultingly, exist in a public health vacuum [ 4 ]. Despite not being included in IPAC standards, these shelters carry essential health functions that if not upheld would be detrimental to their inhabitants [ 4 ]. In many cases, shelters’ COVID-19 response capacity was strengthened by having a hospital partner as a first point of contact to answer shelters’ COVID-19-related information.

“Good to receive COVID-19 information from a hospital. I feel it was a more trusted source, especially at the beginning of pandemic.” [P8]

“Made it a lot easier to run our shelter and get up to fuller capacity, less fear. Appreciative of our connection and their support.”  [P5].

“Confidence in the vaccine because the hospital partner’s staff were well-educated and trustworthy people to the residents, so they were able to answer lots of questions. It promoted vaccine uptake for sure.”  [P7].

Main category: challenges identifying and managing outbreaks

During the COVID-19 pandemic, shelter staff highlighted outbreak identification and management as a primary component of this hospital-community partnership strategy. Outbreaks were anticipated in this situation, yet it was unclear how shelters were to navigate through them, despite the hospital-community partnership strategy. Outbreak identification and management consist of two subcategories: isolation challenges and movement of people in precarious circumstances. Participants cited limited infrastructure and wait times for test results as contributing factors to isolation challenges.

When we had the outbreak, it was really hard to contain it. It went on for a month –more and more people getting COVID. The set up that we have is such that it is hard to contain people for an extended period of time: for e.g., family of 4 sharing one hotel room. [P3].

The results at the hospital partner’s testing centre took very long. It was a challenge for a congregate setting, when we don’t know what to do when you don’t know results for a couple of days. We don't have isolation on site for clients that are symptomatic and waiting for results. We cannot refer families until we have a positive result. That's why so important to have test results very soon. [P10].

Shelters have difficulty controlling the movement of people in precarious circumstances. This was crucial to limiting the spread of COVID-19 but proved impossible for families and people who must continue to go in-person to work and school for their livelihoods.

One of the biggest challenges is, especially with pandemic, it’s very hard to control the movement of people (as a family shelter) - especially when school is open for example. Most of our clients are susceptible to getting infected with COVID-19 – managing this risk as a shelter is hard for us to control. We can’t control where and how people move around. [P3]

Main category: barriers to the vaccine program

The broad services offered by CRT over the course of the pandemic came with their own unique set of partnership challenges, particularly its shortcomings around vaccination and testing for the shelters’ clients. This category refers to the ways in which the program was inaccessible for the shelter population and certain groups therein. In addition to the frustrating testing wait times experienced by shelters, participants noted 4 main issues: inconsistent testing schedules offered onsite at the hospital, barriers to vaccine delivery, structural barriers for specific populations, and vaccine hesitancy. It should be highlighted here that these issues play out against a backdrop of ongoing systemic issues within the country’s healthcare environment. Though no individual hospital program may fix a structural issue, it is important to consider where special attention may be required when designing and carrying out such programs. For example, the hospital partner’s variability in testing schedules were disruptive for people in precarious circumstances and discouraged individuals’ participation.

Often, testing hours change [at the hospital], and we wouldn’t know until after we sent someone…. difficult and confusing, would have been great to know in advance. There were challenging situations sometimes… [the hospital testing site] also might not have a very open/positive response to receive families who come near the end of the day.” [P10].

With respect to vaccine delivery, many shelters had limited space and personnel and found it difficult to figure out the appropriate place to set up the vaccination site. When hospital partners arrived, participants said it created group gatherings, since many clients were curious with questions about COVID-19 and the vaccines. Shelter staff were needed to manage traffic flow in and out of the vaccination site, due to its inconvenient location in places like dining rooms. Some participants felt a pre-vaccine site visit would have helped alleviate this tension. All participants also felt this vaccine delivery process was unnecessarily administration-heavy on the shelters, who were expected to send paperwork with lists of client names for vaccination beforehand.

I would say the capacity and manpower to organize the vaccine clinics is hard. The hospital partner has been great to come on very short notice, but it’s difficult at times. That’s what kept me back from hosting and organizing more clinics.” [P1]

The administration part, in terms of having the names and health card numbers, was heavy and challenging. Working with the vulnerable homeless population, it’s hard to pinpoint who was exactly going to be there in a drop-in situation on a given day. There was paperwork, locked key, and it had to be done X hours before and sent to X number of people. [P2].

Contextualizing the shelter populations’ broader environmental concerns and recognizing the structural barriers individuals from specific populations face is critical to the overall success of hospital-community partnerships. Some unanticipated structural barriers for specific populations led to roadblocks and impeded shelter clients’ access to vaccination and care. Some affected groups were undocumented individuals, international students, out-of-province people – those without an Ontario Health Insurance Plan (OHIP) card.

People without an OHIP card were afraid they’ll get sick with the vaccine, and then [they] don’t have support/immediate healthcare, or long-term supports… People who don't have OHIP, they don’t have access to emergency rooms right now because the Ministry of Health is not providing free services. [P4].

Poor health care access is a long-standing fear for those who are undocumented or do not have OHIP as demonstrated in this comment above. It is important to recognize the depth of this commonly held belief in the context of COVID 19, where this understanding prevailed despite the Ontario government expanding health care entitlement to all people living in Ontario, Canada, with or without publicly funded health coverage on March 21, 2020, 10 days after COVID 19 was declared a global pandemic [ 27 ]. This perception speaks to the reality on the ground demonstrating practical access barriers; this may be an area where hospital partners could have made access issues clearer to their partners.

For clients with a history of trauma, participants hoped there could have been a more flexible approach to vaccination but understood the limitations within the current institutional structure that did not allow for this.

Having flexibility in care would be ideal in helping/supporting certain clients with trauma. It would have been nice to make settings as non-clinical as possible would’ve been great because of people’s past negative experiences with the healthcare system. If we could offer vaccines at people’s bedspaces, that would’ve been great. [P1].

Finally, vaccine hesitancy was the last subcategory of barriers to the vaccine program. Though the vaccine mandate motivated staff to get vaccinated, many clients were fearful and reluctant about getting the vaccine. Almost all participants agreed that the hospital partner’s vaccine education, specifically the 1-on-1 sit-downs with health professionals and webinars, supported increased vaccine uptake in their organizations. However, as of October 21 st , 2022, 1.5 years after vaccine rollout, only 4/10 shelters reported an organizational vaccine uptake higher than 90% (Table 1 ). In shelters or congregate settings, movement cannot be strictly managed; thus there is a higher risk of infection. Therefore, vaccine uptake is a key preventative measure in managing the spread of COVID-19. Participants described the significance of understanding the historical discrimination clients faced by the healthcare system, and associated distrust in Western medicine and medical authorities.

Like any other organization dealing with people from diverse groups, we are dealing with [a] lack of trust in vaccines, peoples’ experiences of medical discrimination, and so some clients are hesitant about getting vaccinated. It’s an ongoing, continuous discourse that all our partners must battle to minimize it. [P6].

We still need more education to combat vaccine hesitancy with our client groups, who are vulnerable and have complex medical issues. They’re afraid to take the vaccine… they’re transient, so they may say they’ll take the vaccine today, but they’re somewhere else tomorrow when the vaccines come. [P2].

Main category: community-centred immediate shelter needs

While speaking to participants about the efficacy of the vaccine program, several unaddressed, urgent shelter needs emerged. These community-centred exigencies were existing gaps that compounded shelters’ day-to-day pandemic requirements. Many shelters shared similar needs, which are categorized below into three main subcategories: continuing pandemic support, mental health services, and healthcare system navigation and access support. Continuing pandemic support, as requested by shelters, speaks to the unclear COVID-19 protocols since moving into fourth/fifth waves of the pandemic. Many participants spoke about looking for support in keeping up with changing protocols, at a time where pandemic fatigue is high and when there is still different information coming from different sources.

Lots of questions and not enough clarity around COVID restrictions and what similar organizations should be doing (especially since we are not a shelter but still fall under congregate care). It was really easy before with simple provincial guidelines… now, grey areas around keeping people healthy and safe. [The hospital partner] could continue to offer consultations for those who are making those decisions… serve as consultants to review policies and make suggestions on policies and/or guidelines. [P7].

“Priority testing would be great for all shelters and congregate settings.” [P10]

A challenge is continuing to screen residents regularly. We don’t have enough staff to screen people. It got better in the middle, but now it sort of got degraded again with the fatigue. With fluctuating numbers and changing rules with COVID, it’s hard to know what we should be doing with COVID. [P8].

The pandemic, through social isolation and widespread anxiety and depression, has detrimentally affected peoples’ mental health. Discussing the realities of how shelters serve many people with pre-existing mental health and addiction needs, participants called for mental health crisis support and intervention on-site to combat the increased number of crisis incidents during the pandemic. Many shelters specifically identified needing clinical intervention support, rather than counsellors.

We need mental health supports – not just case managers or counsellors, more like clinical counselling and psychotherapy. A lot of the time, people are connected to counsellors when asked, but our staff are already counsellors. It’s the clinical piece that’s missing. Medical students will be coming on-site monthly to see residents and address any medical concerns they may have, but mental health supports are still needed. [P9].

Many service care providers were forced to change their scope of functioning during the pandemic and shelters were no exception. However, this change wasn’t legitimized within the healthcare system. For instance, one participant recalled frustrating experiences during the pandemic, having to deal with individuals inappropriately discharged from hospitals to their shelter.

“We are a shelter, but it also feels like a gateway to the shelter system at times, even though it’s not supposed to be anymore. COVID changes who we are and what we do; throughout the system, that’s it isn’t recognized exactly, so we get a huge number of inappropriate drop-offs and discharges from hospitals. We have clients sent in a taxi at midnight from the hospital wearing a hospital gown and no shoes, showing up clearly not well… And we end up sending them back by taxi. Anything that could help in terms of that process of medical discharges to the shelter system would be good; that’s a big system ask we have. [P2].

Shelters also brought up the lack of information available around navigating the healthcare system and accessing supports, when it comes to both urgent and non-urgent care. This was underlined as key referral points hospitals, peer navigators, and healthcare professionals should share with local shelters in their geographic areas. The primary focus of many shelters lies in housing, and there is often little attention placed on health resourcing, solidifying partnerships, accountability and governance structures therein [ 4 ]. Recognizing this and working towards geographical alignment, with health partners, to meet these immediate shelter needs are essential to keeping communities’ needs at the centre of hospital-community partnerships.

Main category: avenues for intersectoral relationship strengthening

Lastly, the final main category brings up long-term, systems-level avenues where shelters, hospitals, and the general community can build stronger intersectoral relationships. Hospital-community partnerships require sustainable change, commitment, and lasting support to strengthen their relationships to serve communities holistically. Four pivotal avenues were shared by participants: health advocacy for individuals without OHIP, information-sharing tables, official partnerships, and educating health professionals about systemic health inequities. Health advocacy for individuals without OHIP arose as a top ‘ask’ for many shelters. This could be related to the fact that the hospital partner had a specific clinical program that provides comprehensive medical services to newly arrived refugee clients. With COVID-19, there has been an increase in waiting times for attaining an appointment at this clinic and many refugee claimants not having a family doctor to depend on. Prioritization of this issue illustrates the value that health is a human right, not a status-based right.

“Try to help us with encouragement and advocacy with different organizations, especially on behalf of people without OHIP.” [P4]

“Non-insured is always a challenge, and we have many women with no status in our shelter. We refer them to community health centres, but funding they have for this population always gets exhausted at the end of each year.” [P9].

Almost all shelters outlined the potential of information-sharing tables as a tool for intersectoral relationship strengthening. Shelters wanted hospital engagement on a quarterly basis to check-in, ask about evolving community needs, and share relevant information that could support communities’ health needs, including safe injection sites, monkeypox anxieties, etc. With respect to information-sharing between hospitals and community partners, one participant eloquently detailed the need to not individually visualize each community shelter organization as a siloed entity.

In addition to direct relationships, all of the refugee organizations have a really strong network and collaborate amongst us. When you think about collaboration between the hospital partner and our community, think more broadly than just individual organizations, think of us as a whole collective. A lot of the covid protocols and information that we got was collaborating amongst the various refugee houses. [P7].

The need for formalized partnerships also came up as an avenue for stronger intersectoral relationships. This demonstrates a level of commitment that communities can expect from hospitals and provide a means for accountability. Some participants brainstormed the idea of designating an official liaison role between shelter and hospital systems, instead of participants having to haphazardly reach out to health professionals they knew from previous collaborative work.

Connecting directly back to the core category of understanding communities that hospitals serve, this final avenue consists of educating health professionals about systemic health inequities. Many participants are shocked by the lack of awareness health providers have about their clients’ realities. Without the knowledge of the landscape in which the healthcare system works and the inequities it perpetuates, participants explain that health professionals are not equipped to adequately support shelter clients.

Helpful to have staff from the hospital partner who know this is a congregate setting and understand/be aware of challenges of the population they’re working with, to make sure they recognize the importance of situations & know limited access/availability to healthcare system for our clients. Perhaps then they could refer them accordingly or try ways to access other services even if not directly covered, or even collaborate to get more healthcare services. [P10].

It would be really amazing to have the support and all the hospitals to know what we are doing. We have had calls with social workers from hospitals who do not understand how we provide health care for people without OHIP. When doctors have the knowledge about lack of access to healthcare to people with OHIP, then doctors are more conscious/informed about how difficult it is for them. Knowledge is power. [P4].

This study is unique in showcasing community perspectives in an official hospital-community partnership program during COVID-19. This hospital-based pandemic program demonstrates the importance of centring community voices in hospital-based community programs, as well as the breadth of knowledge gained by service providers when doing so.

During the COVID-19 pandemic, it was found that hospital service areas across the United States with a greater number of community partnerships (schools, community-based organizations, local agencies) had reduced case-fatality rates than those with fewer partnerships [ 28 ]. Our study similarly demonstrates increased access to resources and response capacity, as the organizational vaccine uptake across the 14 shelters in the study ranged from 76–100%. There is literature that has examined and identified successful factors for responding to COVID-19 in shelter-hospital partnerships, including an increase in resources, such as rapid access to testing, as well as support with restructuring physical spaces in line with IPAC and isolation policies (the latter being what we refer to as health knowledge in our study) [ 29 ]. However, our study explicitly lists the shelter workers’ perception and value of hospitals as a go-to trusted, credible health partner as crucial to this partnership and program, especially at a time of chaos and confusion during the pandemic. This building of trust and respect within an existing or new hospital-community partnership is important to a successful model.

As shown in a study in England investigating COVID-19 among people experiencing homelessness, outbreaks in homeless and congregate settings can lead to a high attack rate among the population, even when incidence remains low in the general population [ 30 ]. This means avoiding deaths is dependent on preventing transmission within such settings. Aligned with this, our study found that there were challenges identifying and managing outbreaks, despite the partnership in place. There was limited infrastructure and space for individuals to isolate while waiting on their test results. A review article on the prevention and mitigation strategies of respiratory infectious disease outbreaks among people experiencing homelessness suggests that interventions centered on reducing homelessness through income interventions, targeting macroeconomic factors, and the provision of adequate housing [ 31 ]. Hospital-community partnerships can minimize poor health outcomes and encourage bridging across sectors to promote health for all, but it remains clear that they are unable to eradicate broader system-level concerns without necessary structural change.

Building on the topic of existing system-level concerns, our study also revealed barriers to the vaccine program in ways in which the program was inaccessible for the shelter population and certain groups therein. Although our study’s barriers focused on the absence of an OHIP card for undocumented individuals and international students, and its impediment on their access to vaccination and care, other studies have shown that this is not an Ontario or even Canada-specific problem. A study in Rome detailed bureaucratic and organizational obstacles as similar impediments and showcased alternative approaches to cost-effective models that can reduce existing structural barriers to access diagnostic and preventive services for the homeless and undocumented population [ 32 ]. Thus, when designing and carrying out such hospital-community partnered programs, there must also be considerations of how to pay special attention to such gaps and simultaneously lobby for change in these areas.

Recently, a Toronto COVID-19 study examining the perspectives of people experiencing homelessness, healthcare workers, and shelter workers who cared for them, revealed how COVID-19 exacerbated the existing healthcare barriers for populations experiencing homelessness, including reduced shelter capacity, public closures, and lack of isolation options [ 33 ]. Our findings build on this by outlining current community-centred shelter needs and future avenues for intersectoral relationship strengthening. These suggestions should be taken into consideration when planning future hospital-community programs, with the recognition that the perspectives of shelter populations may differ depending on demographic context and location. Our findings highlight that the current Ontario healthcare system has many gaps and shortcomings when it comes to serving populations who are homeless. At a time of high anxiety and many health unknowns, hospitals were viewed as a trusted source for information, and this partnership model certainly provided benefits to siloed shelters without many institutional supports. Though structural change is necessary, hospital-based community programs in collaboration with shelters, can alleviate some of the ongoing health concerns faced by shelter populations – during a time of COVID-19 or not. For example, mental health crisis support and intervention on-site is a major community-centred immediate shelter need to combat the increased number of crisis incidents during the pandemic. A UK qualitative study exploring access to mental health and substance use support among individuals experiencing homelessness during COVID-19 noted that individuals experienced many forms of exclusion that were exacerbated during the pandemic, coupled with heightened mental health needs during this time of adversity [ 34 ].

In preparation for future pandemics and unanticipated health emergencies, access to care and cohesion within the health system requires the continuous engagement in relationship-building between hospitals and communities to support co-creation of innovative models of care, to promote health for all. The primary focus of many shelters lies in housing, and there is often little attention placed on health resourcing, solidifying partnerships, accountability and governance structures therein [ 35 ]. Recognizing this and working towards geographical alignment, with health partners, to meet these immediate shelter needs are essential to keeping communities’ needs at the centre of hospital-community partnerships. Hospital-community partnerships require sustainable change, commitment, and lasting support to strengthen their relationships to serve communities holistically.

No different from other research, this study has limitations. This research was conducted a year after the hospital-community partnered vaccine program took place, which was longer than an ideal follow-up time for interviews to take place. Participants had trouble remembering certain events; some individuals who had taken notes in a journal were able to better describe scenarios and share experiences than others. Additionally, despite the interviewer not having been involved with the vaccine program itself, their affiliation with WCH may have prevented participants to not be completed honest about the shortcomings of the program/partnership, especially since future collaborations were not yet formally established. With regards to the shelter demographic characteristics and the hospital vaccine program-related data compiled from participants’ data collection forms, it would have been interesting to learn more about how these characteristics impacted participants’ experiences and perceptions of the vaccine program. For instance, it would be helpful to know how a shelter’s referral mechanism to the hospital vaccine program may have impacted their overall perceptions and how this vaccine program may have impacted the shelter differently, depending on whether they had a primary care partner onsite. In our sample, 10/14 shelters had a relationship with WCH prior to the pandemic and this is important in contextualizing the presented information. Lastly, as a constructivist grounded theory study, there are limitations on the generalizability of knowledge constructed beyond this social context. These findings should be viewed as part of a larger puzzle and can be used to generate points of inquiry for further research in the field. Sampling from other geographical locations might further enhance our understanding of the phenomena explored in this study.

Conclusions: policy and practice implications

In this study and through the process learnings of exploring the overall perceptions of shelter workers of a hospital-community partnership model, three key takeaways emerged for health(care) policy and practice:

‘Health as a human right’ framework is an organizing principle in shelters but not necessarily in hospitals. How can hospitals adopt and integrate this framework at the policy level for internal and external operations/practice for a more equity-based approach to care?

For hospitals, there are gaps in knowledge about community and shelter realities. Ongoing formal partnering between hospitals and communities, through information-sharing tables and regular check-ins, is one way to bridge this gap.

Empowering shelter staff is crucial to the success of hospital-partnered programs and clinical interventions. This occurs through bidirectional knowledge transfer. Shelter workers regard hospital staff as knowledge holders. Hospital staff/clinicians are asked to recognize shelter workers as knowledge holders so to build a foundation for information exchange and co-design of interventions.

We hope this hospital-community partnership strategy adds perspective and inspires action in hospital administrators, healthcare professionals, and policymakers to move forward in a way that serves your communities’ population health needs. Finally, this project calls attention to the urgent context-specific exploration needed to advance official hospital-community partnerships, where there is an everlasting commitment and accountability.

Availability of data and materials

The results/data/figures in this manuscript have not been published elsewhere, nor are they under consideration by another publisher. Anonymized datasets used and/or analyzed during the current study may be made available from the corresponding author on reasonable request.

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Acknowledgements

The authors thank the shelter workers, physicians, nurses, and healthcare providers who led pandemic efforts, including leaders at Women’s College Hospital and community shelters for coming together for the initiatives by the COVID-19 Community Response Team (CRT). Finally, the authors thank the participants who shared their time and experiences with the research team.

Financial disclosure statement

This project has received funding for this work from The Foundation at Women’s College hospital.

This study was supported by a grant from the The Foundation at Women’s College Hospital. The study sponsor had no role in study design, data collection, analysis, interpretation of data, manuscript preparation or the decision to submit for publication.

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Vivetha Thambinathan, Suvendrini Lena, Jordan Ramnarine, Helen Chuang, Marc Dagher, Elaine Goulbourne, Sheila Wijayasinghe, Jessica Bawden, Logan Kennedy & Vanessa Wright

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V.W. & S.L. conceived and designed the study. V.T. led data collection and analysis. V.T. theoretically conceptualized the manuscript and wrote the methods, results, and discussion sections. V.T. prepared Table 1 . S.L. wrote the implications section. J.R. and V.W. co-wrote the introduction. V.T. conducted the interviews and V.T. & J.R. co-analyzed the data. V.T., S.L., and V.W. revised the manuscript. V.T., S.L., V.W., J.R., H.C., L.O., M.D., E.G., S.W., J.B. & L.K. gave final approval of the version to be published and agreed to be accountable for all aspects of the work.

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Thambinathan, V., Lena, S., Ramnarine, J. et al. “Access to healthcare is a human right”: a constructivist study exploring the impact and potential of a hospital-community partnered COVID-19 community response team for Toronto homeless services and congregate living settings. BMC Health Serv Res 24 , 526 (2024). https://doi.org/10.1186/s12913-023-10140-3

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DOI : https://doi.org/10.1186/s12913-023-10140-3

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COVID-19’s impact on drug overdose fatalities and urgent mental health care demand in the US

• Original Paper
• Published: 25 April 2024

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• Izuru Inose 1 &
• Yoshiyasu Takefuji   ORCID: orcid.org/0000-0002-1826-742X 1  

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The purpose of this study is to scrutinize the repercussions of the COVID-19 pandemic on the incidence of drug overdose fatalities in the US.

The study utilizes datasets from the CDC and employs a linear regression model to calculate the time-series of excessive deaths spanning from 2020 to 2022. An extensive literature review focusing on overdoses during the pandemic period is also conducted.

The findings reveal that the influence of COVID-19 on overdose fatalities in 2020, 2021, and 2022 were 1.18, 1.36, and 1.38 times higher, respectively. The observed demand for urgent mental health care has seen a lesser decline compared to the overall need for emergency services.

Conclusions

This study offers critical insights into the correlation between the COVID-19 pandemic and drug overdose deaths in the US, which could serve as a valuable resource for future research and policy-making decisions. Consequently, it is imperative for emergency departments to be equipped and ready to deliver crucial care for adolescents confronted with mental health crises.

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COVID-19 significantly impacted drug overdose deaths from 2020 to 2022.

Largest impact and excessive deaths with drug overdose occurred in 2022.

Impacts of COVID-19 on overdose deaths in 2020, 2021, and 2022 were 1.18, 1.36, 1.38.

Urgent mental health care demand remains high; readiness is crucial.

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Inose, I., Takefuji, Y. COVID-19’s impact on drug overdose fatalities and urgent mental health care demand in the US. Health Technol. (2024). https://doi.org/10.1007/s12553-024-00876-1

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