research topic about mental health during pandemic

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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.

Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395:497–506.

Article   PubMed   PubMed Central   Google Scholar  

Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, et al. A novel coronavirus from patients with Pneumonia in China, 2019. N Engl J Med. 2020;382:727–33.

Liu Y-C, Kuo R-L, Shih S-R. COVID-19: the first documented coronavirus pandemic in history. Biomed J. 2020;43:328–33.

Advice for the public on COVID-19 – World Health Organization. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/advice-for-public . Accessed 13 Nov 2022.

Osterrieder A, Cuman G, Pan-Ngum W, Cheah PK, Cheah P-K, Peerawaranun P, et al. Economic and social impacts of COVID-19 and public health measures: results from an anonymous online survey in Thailand, Malaysia, the UK, Italy and Slovenia. BMJ Open. 2021;11:e046863.

Article   PubMed   Google Scholar  

Mofijur M, Fattah IMR, Alam MA, Islam ABMS, Ong HC, Rahman SMA, et al. Impact of COVID-19 on the social, economic, environmental and energy domains: Lessons learnt from a global pandemic. Sustainable Prod Consum. 2021;26:343–59.

Article   Google Scholar  

Vlachos J, Hertegård E, Svaleryd B. The effects of school closures on SARS-CoV-2 among parents and teachers. Proc Natl Acad Sci U S A. 2021;118:e2020834118.

Ludvigsson JF, Engerström L, Nordenhäll C, Larsson E, Open Schools. Covid-19, and child and teacher morbidity in Sweden. N Engl J Med. 2021;384:669–71.

Miralles O, Sanchez-Rodriguez D, Marco E, Annweiler C, Baztan A, Betancor É, et al. Unmet needs, health policies, and actions during the COVID-19 pandemic: a report from six european countries. Eur Geriatr Med. 2021;12:193–204.

Onyeaka H, Anumudu CK, Al-Sharify ZT, Egele-Godswill E, Mbaegbu P. COVID-19 pandemic: a review of the global lockdown and its far-reaching effects. Sci Prog. 2021;104:368504211019854.

The Lancet null. India under COVID-19 lockdown. Lancet. 2020;395:1315.

Lo Coco G, Gentile A, Bosnar K, Milovanović I, Bianco A, Drid P, et al. A cross-country examination on the fear of COVID-19 and the sense of loneliness during the First Wave of COVID-19 outbreak. Int J Environ Res Public Health. 2021;18:2586.

COVID-19 pandemic. triggers 25% increase in prevalence of anxiety and depression worldwide. https://www.who.int/news/item/02-03-2022-covid-19-pandemic-triggers-25-increase-in-prevalence-of-anxiety-and-depression-worldwide . Accessed 14 Nov 2022.

Bueno-Notivol J, Gracia-García P, Olaya B, Lasheras I, López-Antón R, Santabárbara J. Prevalence of depression during the COVID-19 outbreak: a meta-analysis of community-based studies. Int J Clin Health Psychol. 2021;21:100196.

Hajek A, Sabat I, Neumann-Böhme S, Schreyögg J, Barros PP, Stargardt T, et al. Prevalence and determinants of probable depression and anxiety during the COVID-19 pandemic in seven countries: longitudinal evidence from the european COvid Survey (ECOS). J Affect Disord. 2022;299:517–24.

Piumatti G, Levati S, Amati R, Crivelli L, Albanese E. Trajectories of depression, anxiety and stress among adults during the COVID-19 pandemic in Southern Switzerland: the Corona Immunitas Ticino cohort study. Public Health. 2022;206:63–9.

Korkmaz H, Güloğlu B. The role of uncertainty tolerance and meaning in life on depression and anxiety throughout Covid-19 pandemic. Pers Indiv Differ. 2021;179:110952.

McIntyre RS, Lee Y. Projected increases in suicide in Canada as a consequence of COVID-19. Psychiatry Res. 2020;290:113104.

Funkhouser CJ, Klemballa DM, Shankman SA. Using what we know about threat reactivity models to understand mental health during the COVID-19 pandemic. Behav Res Ther. 2022;153:104082.

Landi G, Pakenham KI, Crocetti E, Tossani E, Grandi S. The trajectories of anxiety and depression during the COVID-19 pandemic and the protective role of psychological flexibility: a four-wave longitudinal study. J Affect Disord. 2022;307:69–78.

Holt-Gosselin B, Tozzi L, Ramirez CA, Gotlib IH, Williams LM. Coping strategies, neural structure, and depression and anxiety during the COVID-19 pandemic: a longitudinal study in a naturalistic sample spanning clinical diagnoses and subclinical symptoms. Biol Psychiatry Global Open Sci. 2021;1:261–71.

McCracken LM, Badinlou F, Buhrman M, Brocki KC. The role of psychological flexibility in the context of COVID-19: Associations with depression, anxiety, and insomnia. J Context Behav Sci. 2021;19:28–35.

Talkovsky AM, Norton PJ. Negative affect and intolerance of uncertainty as potential mediators of change in comorbid depression in transdiagnostic CBT for anxiety. J Affect Disord. 2018;236:259–65.

Milman E, Lee SA, Neimeyer RA, Mathis AA, Jobe MC. Modeling pandemic depression and anxiety: the mediational role of core beliefs and meaning making. J Affect Disorders Rep. 2020;2:100023.

Sagan A, Bryndova L, Kowalska-Bobko I, Smatana M, Spranger A, Szerencses V, et al. A reversal of fortune: comparison of health system responses to COVID-19 in the Visegrad group during the early phases of the pandemic. Health Policy. 2022;126:446–55.

Holt E. COVID-19 testing in Slovakia. Lancet Infect Dis. 2021;21:32.

Stalmachova K, Strenitzerova M. Impact of the COVID-19 pandemic on employment in transport and telecommunications sectors. Transp Res Procedia. 2021;55:87–94.

Izakova L, Breznoscakova D, Jandova K, Valkucakova V, Bezakova G, Suvada J. What mental health experts in Slovakia are learning from COVID-19 pandemic? Indian J Psychiatry. 2020;62(Suppl 3):459–66.

Rabinčák M, Tkáčová Ľ, VYUŽÍVANIE PSYCHOMETRICKÝCH KONŠTRUKTOV PRE, HODNOTENIE PORÚCH NÁLADY V OŠETROVATEĽSKEJ PRAXI. Zdravotnícke Listy. 2019;7:7.

Google Scholar  

Sekot M, Gürlich R, Maruna P, Páv M, Uhlíková P. Hodnocení úzkosti a deprese u pacientů se zhoubnými nádory trávicího traktu. Čes a slov Psychiat. 2005;101:252–7.

Lipsitch M, Krammer F, Regev-Yochay G, Lustig Y, Balicer RD. SARS-CoV-2 breakthrough infections in vaccinated individuals: measurement, causes and impact. Nat Rev Immunol. 2022;22:57–65.

Accorsi EK, Britton A, Fleming-Dutra KE, Smith ZR, Shang N, Derado G, et al. Association between 3 doses of mRNA COVID-19 vaccine and symptomatic infection caused by the SARS-CoV-2 Omicron and Delta Variants. JAMA. 2022;327:639–51.

Barda N, Dagan N, Cohen C, Hernán MA, Lipsitch M, Kohane IS, et al. Effectiveness of a third dose of the BNT162b2 mRNA COVID-19 vaccine for preventing severe outcomes in Israel: an observational study. Lancet. 2021;398:2093–100.

Magen O, Waxman JG, Makov-Assif M, Vered R, Dicker D, Hernán MA, et al. Fourth dose of BNT162b2 mRNA Covid-19 vaccine in a nationwide setting. N Engl J Med. 2022;386:1603–14.

Dubé JP, Smith MM, Sherry SB, Hewitt PL, Stewart SH. Suicide behaviors during the COVID-19 pandemic: a meta-analysis of 54 studies. Psychiatry Res. 2021;301:113998.

Kok AAL, Pan K-Y, Rius-Ottenheim N, Jörg F, Eikelenboom M, Horsfall M, et al. Mental health and perceived impact during the first Covid-19 pandemic year: a longitudinal study in dutch case-control cohorts of persons with and without depressive, anxiety, and obsessive-compulsive disorders. J Affect Disord. 2022;305:85–93.

Aknin LB, Andretti B, Goldszmidt R, Helliwell JF, Petherick A, De Neve J-E, et al. Policy stringency and mental health during the COVID-19 pandemic: a longitudinal analysis of data from 15 countries. The Lancet Public Health. 2022;7:e417–26.

Prochazka J, Scheel T, Pirozek P, Kratochvil T, Civilotti C, Bollo M, et al. Data on work-related consequences of COVID-19 pandemic for employees across Europe. Data Brief. 2020;32:106174.

Loades ME, Chatburn E, Higson-Sweeney N, Reynolds S, Shafran R, Brigden A, et al. Rapid systematic review: the impact of social isolation and loneliness on the Mental Health of Children and Adolescents in the Context of COVID-19. J Am Acad Child Adolesc Psychiatry. 2020;59:1218–1239e3.

Zetterqvist M, Jonsson LS, Landberg Ã, Svedin CG. A potential increase in adolescent nonsuicidal self-injury during covid-19: a comparison of data from three different time points during 2011–2021. Psychiatry Res. 2021;305:114208.

Mooldijk SS, Dommershuijsen LJ, de Feijter M, Luik AI. Trajectories of depression and anxiety during the COVID-19 pandemic in a population-based sample of middle-aged and older adults. J Psychiatr Res. 2022;149:274–80.

Sahebi A, Nejati-Zarnaqi B, Moayedi S, Yousefi K, Torres M, Golitaleb M. The prevalence of anxiety and depression among healthcare workers during the COVID-19 pandemic: an umbrella review of meta-analyses. Prog Neuropsychopharmacol Biol Psychiatry. 2021;107:110247.

Stephenson E, O’Neill B, Kalia S, Ji C, Crampton N, Butt DA, et al. Effects of COVID-19 pandemic on anxiety and depression in primary care: a retrospective cohort study. J Affect Disord. 2022;303:216–22.

Goldberg X, Castaño-Vinyals G, Espinosa A, Carreras A, Liutsko L, Sicuri E et al. Mental health and COVID-19 in a general population cohort in Spain (COVICAT study).Soc Psychiatry Psychiatr Epidemiol. 2022;:1–12.

Zhao Y, Yu Y, Zhao R, Cai Y, Gao S, Liu Y, et al. Association between fear of COVID-19 and hoarding behavior during the outbreak of the COVID-19 pandemic: the mediating role of mental health status. Front Psychol. 2022;13:996486.

Furstova J, Kascakova N, Sigmundova D, Zidkova R, Tavel P, Badura P. Perceived stress of adolescents during the COVID-19 lockdown: bayesian multilevel modeling of the Czech HBSC lockdown survey. Front Psychol. 2022;13:964313.

Garbóczy S, Szemán-Nagy A, Ahmad MS, Harsányi S, Ocsenás D, Rekenyi V, et al. Health anxiety, perceived stress, and coping styles in the shadow of the COVID-19. BMC Psychol. 2021;9:53.

Wathelet M, Duhem S, Vaiva G, Baubet T, Habran E, Veerapa E, et al. Factors Associated with Mental Health Disorders among University students in France Confined during the COVID-19 pandemic. JAMA Netw Open. 2020;3:e2025591.

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

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Ida Kupcova, Lubos Danisovic & Stefan Harsanyi

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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.

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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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• Volume 10, Issue 12
• Impact of the COVID-19 pandemic on mental health and well-being of communities: an exploratory qualitative study protocol
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• http://orcid.org/0000-0003-0180-0213 Anam Shahil Feroz 1 , 2 ,
• Naureen Akber Ali 3 ,
• Noshaba Akber Ali 1 ,
• Ridah Feroz 4 ,
• Salima Nazim Meghani 1 ,
• Sarah Saleem 1
• 1 Community Health Sciences , Aga Khan University , Karachi , Pakistan
• 2 Institute of Health Policy, Management and Evaluation , University of Toronto , Toronto , Ontario , Canada
• 3 School of Nursing and Midwifery , Aga Khan University , Karachi , Pakistan
• 4 Aga Khan University Institute for Educational Development , Karachi , Pakistan
• Correspondence to Ms Anam Shahil Feroz; anam.sahyl{at}gmail.com

Introduction The COVID-19 pandemic has certainly resulted in an increased level of anxiety and fear in communities in terms of disease management and infection spread. Due to fear and social stigma linked with COVID-19, many individuals in the community hide their disease and do not access healthcare facilities in a timely manner. In addition, with the widespread use of social media, rumours, myths and inaccurate information about the virus are spreading rapidly, leading to intensified irritability, fearfulness, insomnia, oppositional behaviours and somatic complaints. Considering the relevance of all these factors, we aim to explore the perceptions and attitudes of community members towards COVID-19 and its impact on their daily lives and mental well-being.

Methods and analysis This formative research will employ an exploratory qualitative research design using semistructured interviews and a purposive sampling approach. The data collection methods for this formative research will include indepth interviews with community members. The study will be conducted in the Karimabad Federal B Area and in the Garden (East and West) community settings in Karachi, Pakistan. The community members of these areas have been selected purposively for the interview. Study data will be analysed thematically using NVivo V.12 Plus software.

Ethics and dissemination Ethical approval for this study has been obtained from the Aga Khan University Ethical Review Committee (2020-4825-10599). The results of the study will be disseminated to the scientific community and to the research subjects participating in the study. The findings will help us explore the perceptions and attitudes of different community members towards the COVID-19 pandemic and its impact on their daily lives and mental well-being.

• mental health
• public health

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Strengths and limitations of this study

The mental health impact of the COVID-19 pandemic is likely to last much longer than the physical health impact, and this study is positioned well to explore the perceptions and attitudes of community members towards the pandemic and its impact on their daily lives and mental well-being.

This study will guide the development of context-specific innovative mental health programmes to support communities in the future.

One limitation is that to minimise the risk of infection all study respondents will be interviewed online over Zoom and hence the authors will not have the opportunity to build rapport with the respondents or obtain non-verbal cues during interviews.

The COVID-19 pandemic has affected almost 180 countries since it was first detected in Wuhan, China in December 2019. 1 2 The COVID-19 outbreak has been declared a public health emergency of international concern by the WHO. 3 The WHO estimates the global mortality to be about 3.4% 4 ; however, death rates vary between countries and across age groups. 5 In Pakistan, a total of 10 880 cases and 228 deaths due to COVID-19 infection have been reported to date. 6

The worldwide COVID-19 pandemic has not only incurred massive challenges to the global supply chains and healthcare systems but also has a detrimental effect on the overall health of individuals. 7 The pandemic has led to lockdowns and has created destructive impact on the societies at large. Most company employees, including daily wage workers, have been prohibited from going to their workplaces or have been asked to work from home, which has caused job-related insecurities and financial crises in the communities. 8 Educational institutions and training centres have also been closed, which resulted in children losing their routine of going to schools, studying and socialising with their peers. Delay in examinations is likewise a huge stressor for students. 8 Alongside this, parents have been struggling with creating a structured milieu for their children. 9 COVID-19 has hindered the normal routine life of every individual, be it children, teenagers, adults or the elderly. The crisis is engendering burden throughout populations and communities, particularly in developing countries such as Pakistan which face major challenges due to fragile healthcare systems and poor economic structures. 10

The COVID-19 pandemic has certainly resulted in an increased level of anxiety and fear in communities in terms of disease management and infection spread. 8 Further, the highly contagious nature of COVID-19 has also escalated confusion, fear and panic among community residents. Moreover, social distancing is often an unpleasant experience for community members and for patients as it adds to mental suffering, particularly in the local setting where get-togethers with friends and families are a major source of entertainment. 9 Recent studies also showed that individuals who are following social distancing rules experience loneliness, causing a substantial level of distress in the form of anxiety, stress, anger, misperception and post-traumatic stress symptoms. 8 11 Separation from family members, loss of autonomy, insecurity over disease status, inadequate supplies, inadequate information, financial loss, frustration, stigma and boredom are all major stressors that can create drastic impact on an individual’s life. 11 Due to fear and social stigma linked with COVID-19, many individuals in the community hide their disease and do not access healthcare facilities in a timely manner. 12 With the widespread use of social media, 13 rumours, myths and inaccurate information about COVID-19 are also spreading rapidly, not only among adults but are also carried on to children, leading to intensified irritability, fearfulness, insomnia, oppositional behaviours and somatic complaints. 9 The psychological symptoms associated with COVID-19 at the community level are also manifested as anxiety-driven panic buying, resulting in exhaustion of resources from the market. 14 Some level of panic also dwells in the community due to the unavailability of essential protective equipment, particularly masks and sanitisers. 15 Similarly, mental health issues, including depression, anxiety, panic attacks, psychotic symptoms and even suicide, were reported during the early severe acute respiratory syndrome outbreak. 16 17 COVID-19 is likely posing a similar risk throughout the world. 12

The fear of transmitting the disease or a family member falling ill is a probable mental function of human nature, but at some point the psychological fear of the disease generates more anxiety than the disease itself. Therefore, mental health problems are likely to increase among community residents during an epidemic situation. Considering the relevance of all these factors, we aim to explore the perceptions and attitudes towards COVID-19 among community residents and the impact of these perceptions and attitude on their daily lives and mental well-being.

Methods and analysis

Study design.

This study will employ an exploratory qualitative research design using semistructured interviews and a purposive sampling approach. The data collection methods for this formative research will include indepth interviews (IDIs) with community members. The IDIs aim to explore perceptions of community members towards COVID-19 and its impact on their mental well-being.

Study setting and study participants

The study will be conducted in two communities in Karachi City: Karimabad Federal B Area Block 3 Gulberg Town, and Garden East and Garden West. Karimabad is a neighbourhood in the Karachi Central District of Karachi, Pakistan, situated in the south of Gulberg Town bordering Liaquatabad, Gharibabad and Federal B Area. The population of this neighbourhood is predominantly Ismailis. People living here belong mostly to the middle class to the lower middle class. It is also known for its wholesale market of sports goods and stationery. Garden is an upmarket neighbourhood in the Karachi South District of Karachi, Pakistan, subdivided into two neighbourhoods: Garden East and Garden West. It is the residential area around the Karachi Zoological Gardens; hence, it is popularly known as the ‘Garden’ area. The population of Garden used to be primarily Ismailis and Goan Catholics but has seen an increasing number of Memons, Pashtuns and Baloch. These areas have been selected purposively because the few members of these communities are already known to one of the coinvestigators. The coinvestigator will serve as a gatekeeper for providing entrance to the community for the purpose of this study. Adult community members of different ages and both genders will be interviewed from both sites, as mentioned in table 1 . Interview participants will be selected following the eligibility criteria.

• View inline

Study participants for indepth interviews

IDIs with community members

We will conduct IDIs with community members to explore the perceptions and attitudes of community members towards COVID-19 and its effects on their daily lives and mental well-being. IDI participants will be identified via the community WhatsApp group, and will be invited for an interview via a WhatsApp message or email. Consent will be taken over email or WhatsApp before the interview begins, where they will agree that the interview can be audio-recorded and that written notes can be taken. The interviews will be conducted either in Urdu or in English language, and each interview will last around 40–50 min. Study participants will be assured that their information will remain confidential and that no identifying features will be mentioned on the transcript. The major themes will include a general discussion about participants’ knowledge and perceptions about the COVID-19 pandemic, perceptions on safety measures, and perceived challenges in the current situation and its impact on their mental well-being. We anticipate that 24–30 interviews will be conducted, but we will cease interviews once data saturation has been achieved. Data saturation is the point when no new themes emerge from the additional interviews. Data collection will occur concurrently with data analysis to determine data saturation point. The audio recordings will be transcribed by a transcriptionist within 24 hours of the interviews.

An interview guide for IDIs is shown in online supplemental annex 1 .

Supplemental material

Eligibility criteria.

The following are the criteria for inclusion and exclusion of study participants:

Inclusion criteria

Residents of Garden (East and West) and Karimabad Federal B Area of Karachi who have not contracted the disease.

Exclusion criteria

Those who refuse to participate in the study.

Those who have experienced COVID-19 and are undergoing treatment.

Those who are suspected for COVID-19 and have been isolated/quarantined.

Family members of COVID-19-positive cases.

Data collection procedure

A semistructured interview guide has been developed for community members. The initial questions on the guide will help to explore participants’ perceptions and attitudes towards COVID-19. Additional questions on the guide will assess the impact of these perceptions and attitude on the daily lives and mental health and well-being of community residents. All semistructured interviews will be conducted online via Zoom or WhatsApp. Interviews will be scheduled at the participant’s convenient day and time. Interviews are anticipated to begin on 1 December 2020.

Patient and public involvement

No patients were involved.

Data analysis

We will transcribe and translate collected data into English language by listening to the audio recordings in order to conduct a thematic analysis. NVivo V.12 Plus software will be used to import, organise and explore data for analysis. Two independent researchers will read the transcripts at various times to develop familiarity and clarification with the data. We will employ an iterative process which will help us to label data and generate new categories to identify emergent themes. The recorded text will be divided into shortened units and labelled as a ‘code’ without losing the main essence of the research study. Subsequently, codes will be analysed and merged into comparable categories. Lastly, the same categories will be grouped into subthemes and final themes. To ensure inter-rater reliability, two independent investigators will perform the coding, category creation and thematic analyses. Discrepancies between the two investigators will be resolved through consensus meetings to reduce researcher bias.

Ethics and dissemination

Study participants will be asked to provide informed, written consent prior to participation in the study. The informed consent form can be submitted by the participant via WhatsApp or email. Participants who are unable to write their names will be asked to provide a thumbprint to symbolise their consent to participate. Ethical approval for this study has been obtained from the Aga Khan University Ethical Review Committee (2020-4825-10599). The study results will be disseminated to the scientific community and to the research subjects participating in the study. The findings will help us explore the perceptions and attitudes of different community members towards the COVID-19 pandemic and its impact on their daily lives and mental well-being.

The findings of this study will help us to explore the perceptions and attitudes towards the COVID-19 pandemic and its impact on the daily lives and mental well-being of individuals in the community. Besides, an indepth understanding of the needs of the community will be identified, which will help us develop context-specific innovative mental health programmes to support communities in the future. The study will provide insights into how communities are managing their lives under such a difficult situation.

• World Health Organization
• Nielsen-Saines K , et al
• Worldometer
• Ebrahim SH ,
• Gozzer E , et al
• Snoswell CL ,
• Harding LE , et al
• Nargis Asad
• van Weel C ,
• Qidwai W , et al
• Brooks SK ,
• Webster RK ,
• Smith LE , et al
• Tripathy S ,
• Kar SK , et al
• Schwartz J ,
• Maunder R ,

Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

• Data supplement 1

ASF and NAA are joint first authors.

Contributors ASF and NAA conceived the study. ASF, NAA, RF, NA, SNM and SS contributed to the development of the study design and final protocols for sample selection and interviews. ASF and NAA contributed to writing the manuscript. All authors reviewed and approved the final version of the paper.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Patient consent for publication Not required.

Provenance and peer review Not commissioned; externally peer reviewed

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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One Year In: COVID-19 and Mental Health

By Joshua Gordon

April 9, 2021

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It has been just over a year now since the coronavirus (COVID-19) pandemic struck the United States full force. A year of hunkering down and Zooming in, teleworking and telepsychiatry, economic and social upheaval, and steady scientific progress. Looking back to last March, we knew this would be difficult. But we didn’t know how difficult. And we certainly didn’t know that the challenge of COVID-19 would last this long.

This year has been a challenging one on multiple fronts. For many Americans, this challenge has been overwhelming, affecting their mental health. Understanding the impact of the pandemic on mental health, and on those with serious mental illness, is critical to the National Institute of Mental Health (NIMH) mission of responding with research that will pave the way for prevention, recovery, and cure.

From prior research on disasters and epidemics we mostly knew what to expect. In the immediate wake of a traumatic experience, large numbers of affected people report distress, including new or worsening symptoms of depression, anxiety, and insomnia. Most people will recover, though that recovery can take some time. A notable fraction of people will develop chronic symptoms severe enough to meet criteria for a mental illness, such as post-traumatic stress disorder (PTSD) or major depressive disorder. People who experience more severe stressors, such as exposure to the dead or dying, and people with more prolonged disruptions are more likely to experience enduring symptoms that would benefit from intervention. We also know that people are more likely to develop chronic or severe reactions if they have one or more risk factors, such as poor social supports, financial difficulties, food or housing instability, or a history of mental illness. Receiving economic or social supports and using coping strategies can lower these risks and maximize a person’s chances for recovery.

It seems that much of what we have learned from past disasters and epidemics is holding true in the context of the COVID-19 pandemic. Several surveys, including those collected by the Centers for Disease Control (CDC), have shown substantial increases in self-reported behavioral health symptoms. According to one CDC report  , which surveyed adults across the U.S. in late June of 2020, 31% of respondents reported symptoms of anxiety or depression, 13% reported having started or increased substance use, 26% reported stress-related symptoms, and 11% reported having serious thoughts of suicide in the past 30 days. These numbers are nearly double the rates we would have expected before the pandemic. As in prior studies, this survey showed that risk factors for reporting anxiety symptoms or suicidal ideation included food insufficiency, financial concerns, and loneliness.

The  CDC  , NIMH , and numerous other government agencies and non-profit organizations have been spreading the message that physical distancing doesn’t mean we must stop supporting one another. In fact, research shows that helping others is a coping strategy that can reduce the mental health impacts of the pandemic. We also know that addressing people’s basic needs can help alleviate their psychiatric symptoms. For example, one study showed that food insufficiency was independently associated with all symptoms of poor mental health, but that association was mitigated for those who received free groceries or meals.

Early in the pandemic, there were concerns that suicide rates would increase. So far, data from the CDC  suggest that overall suicide death rates have remained steady or have even fallen during the pandemic.

Yet, there is also cause for concern in the emerging data. There is clear evidence that the pandemic has not affected all Americans equally. As is often the case, unfortunately, the most vulnerable among us are also feeling the mental health effects most intensely. Job loss, housing instability, food insecurity, and other risk factors for poor outcomes have disproportionately hit minority communities  . And while overall suicide rates may have remained steady, data from states such as Maryland   and Connecticut   suggest that, early in the pandemic, the number of African Americans dying by suicide increased.

Emerging data also indicate that people with schizophrenia and other serious mental illnesses have also been hard hit by the pandemic. Individuals with schizophrenia, for instance, are nearly 10 times more likely to contract COVID-19 and are nearly three times more likely to die from it if they do fall ill, compared with individuals who do not have a mental illness. Finally, deaths due to opioid overdose rose substantially in the context of the pandemic. These data remind us that we need to work hard to address long-standing disparities and ensure access to life-saving medical and psychiatric care is available for all Americans.

Indeed, the pandemic has raised awareness of mental health symptoms and service needs. Crisis intervention services such as SAMHSA’s Disaster Distress Helpline (1-800-985-5990)  and the Crisis Text Line (text HOME to 741741)   reported substantial increases in volume early in the pandemic, reflecting anxiety and distress brought on by COVID-19’s many uncertainties. And although data indicate the volume of mental health and suicide risk visits to emergency departments initially dropped when states issued stay-at-home orders, these visits increased again after stay-at-home restrictions were lifted. 

The CDC, NIMH, and other agencies have been working hard to raise public awareness of the resources that are available to support people’s immediate mental health needs, including the Disaster Distress Helpline, the Crisis Text Line, and the Suicide Prevention Lifeline (1-800-273-TALK)   . In addition, many mental health care providers made a rapid transition to phone- and computer-based telehealth, with widespread adoption across both private and public mental health systems. 

The mental health impacts of COVID-19 continue. From all that we know, it is clear these impacts will outlive the pandemic itself. Therefore, it is crucial that we work together to apply evidence-based strategies to support the mental health needs of all Americans and to make these strategies broadly available, especially in vulnerable communities.

Bray, M. J. C., Daneshvari, N. O., Radhakrishnan, I., Cubbage, J., Eagle, M., Southall, P., & Nestadt, P. S. (2020). Racial differences in statewide suicide mortality trends in Maryland during the coronavirus disease 2019 (COVID-19) pandemic. JAMA Psychiatry . http://dx.doi.org/10.1001/jamapsychiatry.2020.3938  

Czeisler, M. É., Lane, R. I., Petrosky E., Wiley, J. F., Christensen, A., Njai, R., Weaver, M. D., Robbins, R., Facer-Childs, E. R., Barger, L. K., Czeisler, C. A., Howard, M. E., & Rajaratnam, S. M. (2020). Mental health, substance use, and suicidal ideation during the COVID-19 pandemic — United States, June 24–30, 2020. Morbidity Mortality Weekly Report (MMWR) , 69, 1049–1057. http://dx.doi.org/10.15585/mmwr.mm6932a1external icon  

Mason, M. Welch, S. B., Arunkumar, P., Post, L. A., & Feinglass, J. M. (2021). Opioid overdose deaths before, during, and after an 11-week COVID-19 stay-at-home order — Cook County, Illinois, January 1, 2018–October 6, 2020. MMWR Morbidity Mortality Weekly Report, 70 , 362-363. http://dx.doi.org/10.15585/mmwr.mm7010a3  

McKnight-Eily, L. R., Okoro, C. A., Strine, T. W., Verlenden, J., Hollis, N. D., Njai, R., Mitchell, E. W., Board, A., Puddy, R., & Thomas, C. (2021). Racial and ethnic disparities in the prevalence of stress and worry, mental health conditions, and increased substance use among adults during the COVID-19 pandemic — United States, April and May 2020. MMWR Morbidity Mortality Weekly Report,70 , 162–166. http://dx.doi.org/10.15585/mmwr.mm7005a3external icon   .

Mitchell, T. O., & Li, L. (2021). State-level data on suicide mortality during COVID-19 quarantine: Early evidence of a disproportionate impact on minorities. Psychiatry Research, 295. https://doi.org/10.1016/j.psychres.2020.113629   .

Nagata, J. M., Ganson, K. T., Whittle, H. J., Chu, J., Harris, O. O., Tsai, A. C., Weiser, S. D. (2021). Food insufficiency and mental health in the U.S. during the COVID-19 pandemic. American Journal of Preventive Medicine. https://doi.org/10.1016/j.amepre.2020.12.004   .

Nemani, K., Li, C., Olfson, M., Blessing, E. M., Razavian, N., Chen, J., Petkova, E., & Goff, D. C. (2021). Association of psychiatric disorders with mortality among patients with COVID-19.  JAMA Psychiatry.   http://dx.doi.org/10.1001/jamapsychiatry.2020.4442  

Wang, Q., Xu, R., & Volkow, N. D. (2021). Increased risk of COVID-19 infection and mortality in people with mental disorders: Analysis from electronic health records in the United States. World Psychiatry . https://doi.org/10.1002/wps.20806  

• Research article
• Open access
• Published: 22 April 2024

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

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

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

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

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

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

Conclusions

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

Peer Review reports

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

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

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

Data source

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

Study design and participants

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

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

Exposure and outcomes of interest

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

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

Statistical analysis

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

Subgroup and sensitivity analyses

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

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

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

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

Incidence of medically attended depression

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

Interrupted time series analysis plot of pandemic impact on depression incidence

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

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

Healthcare service use

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

Trend of healthcare resource utilization from 2014 to 2022

Pandemic impact on initial healthcare service use

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

Pandemic impact on ongoing healthcare service use

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

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

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

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

Rising incidence of medically attended depression

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

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

Declining use of healthcare resources

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

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

Strengths and limitations

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

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

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

Availability of data and materials

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

Abbreviations

Clinical Data Analysis and Reporting System

• Electronic medical records

Interrupted time-series

World Health Organization. WHO Coronavirus (COVID-19) dashboard. https://covid19.who.int . Published 2023. Accessed 29 Mar 2023.

Borkowska M, Laurence J. Coming together or coming apart? Changes in social cohesion during the COVID-19 pandemic in England. Eur Soc. 2021;23(sup1):S618–36.

Article   Google Scholar  

Chew CC, Lim XJ, Chang CT, Rajan P, Nasir N, Low WY. Experiences of social stigma among patients tested positive for COVID-19 and their family members: a qualitative study. BMC Public Health. 2021;21(1):1623.

Article   CAS   PubMed   PubMed Central   Google Scholar  

ElTohamy A, Hyun S, Macaranas AR, Chen JA, Stevens C, Liu CH. Testing positive, losing a loved one, and financial hardship: real-world impacts of COVID-19 on US college student distress. J Affect Disord. 2022;314:357–64.

Article   PubMed   PubMed Central   Google Scholar  

Beland LP, Brodeur A, Wright T. The short-term economic consequences of COVID-19: exposure to disease, remote work and government response. PLoS ONE. 2023;18(3):e0270341.

World Health Organization. Third round of the global pulse survey on continuity of essential health services during the COVID-19 pandemic: interim report—November-December 2021. Geneva: World Health Organisation; 2022.

Covid-Mental Disorders Collaborators. Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic. Lancet. 2021;398(10312):1700–12.

Hawes MT, Szenczy AK, Klein DN, Hajcak G, Nelson BD. Increases in depression and anxiety symptoms in adolescents and young adults during the COVID-19 pandemic. Psychol Med. 2022;52(14):3222–30.

Article   PubMed   Google Scholar  

Kim SJ, Sohn S, Choi YK, et al. Time-series trends of depressive levels of Korean adults during the 2020 COVID-19 pandemic in South Korea. Psychiatry Investig. 2023;20(2):101–8.

Mooldijk SS, Dommershuijsen LJ, de Feijter M, Luik AI. Trajectories of depression and anxiety during the COVID-19 pandemic in a population-based sample of middle-aged and older adults. J Psychiatr Res. 2022;149:274–80.

Zhao SZ, Wong JYH, Luk TT, Wai AKC, Lam TH, Wang MP. Mental health crisis under COVID-19 pandemic in Hong Kong, China. Int J Infect Dis. 2020;100:431–3.

Lee Y, Lui LMW, Chen-Li D, et al. Government response moderates the mental health impact of COVID-19: a systematic review and meta-analysis of depression outcomes across countries. J Affect Disord. 2021;290:364–77.

Zhai Y, Du X. Trends and prevalence of suicide 2017–2021 and its association with COVID-19: interrupted time series analysis of a national sample of college students in the United States. Psychiatry Res. 2022;316:114796.

Steeg S, Bojanic L, Tilston G, et al. Temporal trends in primary care-recorded self-harm during and beyond the first year of the COVID-19 pandemic: time series analysis of electronic healthcare records for 2.8 million patients in the Greater Manchester Care Record. EClinicalMedicine. 2021;41:101175.

Liberman JN, Bhattacharjee S, Rui P, et al. Impact of the COVID-19 pandemic on healthcare resource utilization in individuals with major depressive disorder. Health Serv Res Manag Epidemiol. 2022;9:23333928221111864.

PubMed   PubMed Central   Google Scholar  

Lear-Claveras A, Claveria A, Couso-Viana S, Nabbe P, Olivan-Blazquez B. Analysis of drug and health resource use before and after COVID-19 lockdown in a population undergoing treatment for depression or anxiety. Front Psychol. 2022;13:861643.

Wettstein A, Tlali M, Joska JA, et al. The effect of the COVID-19 lockdown on mental health care use in South Africa: an interrupted time-series analysis. Epidemiol Psychiatr Sci. 2022;31:e43.

MacNeil A, Birk S, Villeneuve PJ, Jiang Y, de Groh M, Fuller-Thomson E. Incident and recurrent depression among adults aged 50 years and older during the COVID-19 pandemic: a longitudinal analysis of the Canadian longitudinal study on aging. Int J Environ Res Public Health. 2022;19(22):15032.

Frangou S, Travis-Lumer Y, Kodesh A, et al. Increased incident rates of antidepressant use during the COVID-19 pandemic: interrupted time-series analysis of a nationally representative sample. Psychol Med. 2023;53(11):4943–51.

Burki T. Hong Kong’s fifth COVID-19 wave-the worst yet. Lancet Infect Dis. 2022;22(4):455–6.

Luo H, Lau WCY, Chai Y, et al. Rates of antipsychotic drug prescribing among people living with dementia during the COVID-19 pandemic. JAMA Psychiat. 2023;80(3):211–9.

Huang C, Wei Y, Yan VKC, et al. Vaccine effectiveness of BNT162b2 and CoronaVac against SARS-CoV-2 omicron infection and related hospital admission among people with substance use disorder in Hong Kong: a matched case-control study. Lancet Psychiatry. 2023;10(6):403–13.

Chan VK, Cheung EC, Chan SS, et al. Mortality-causing mechanisms and healthcare resource utilisation of treatment-resistant depression: a six-year population-based cohort study. Lancet Reg Health West Pac. 2022;22:100426.

Chan VKY, Luo H, Chan SSM, et al. Treatment-resistant depression and risk of autoimmune diseases: evidence from a population-based cohort and nested case-control study. Transl Psychiatry. 2023;13(1):76.

Census and Statistics Department. Thematic household survey report - report no. 58 - health status of Hong Kong residents. https://www.censtatd.gov.hk/en/data/stat_report/product/B1130201/att/B11302582015XXXXB0100.pdf . Published 2015. Accessed 21 Dec 2023.

Lagarde M. How to do (or not to do) … Assessing the impact of a policy change with routine longitudinal data. Health Policy Plan. 2012;27(1):76–83.

Hale T, Angrist N, Goldszmidt R, et al. A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker). Nat Hum Behav. 2021;5(4):529–38.

Bernal JL, Cummins S, Gasparrini A. Interrupted time series regression for the evaluation of public health interventions: a tutorial. Int J Epidemiol. 2017;46(1):348–55.

PubMed   Google Scholar  

Xiao H, Augusto O, Wagenaar BH. Reflection on modern methods: a common error in the segmented regression parameterization of interrupted time-series analyses. Int J Epidemiol. 2021;50(3):1011–5.

Bhaskaran K, Gasparrini A, Hajat S, Smeeth L, Armstrong B. Time series regression studies in environmental epidemiology. Int J Epidemiol. 2013;42(4):1187–95.

Bottomley C, Scott JA, Isham V. Analysing interrupted time series with a control. Epidemiol Methods. 2019;8(1):20180010.

Newey WK, West KD. Automatic lag selection in covariance-matrix estimation. Rev Econ Stud. 1994;61(4):631–53.

Wan KM, Ka-Ki Ho L, Wong NWM, Chiu A. Fighting COVID-19 in Hong Kong: the effects of community and social mobilization. World Dev. 2020;134:105055.

Hou WK, Li TW, Liang L, et al. Trends of depression and anxiety during massive civil unrest and COVID-19 in Hong Kong, 2019–2020. J Psychiatr Res. 2021;145:77–84.

Ni MY, Yao XI, Leung KSM, et al. Depression and post-traumatic stress during major social unrest in Hong Kong: a 10-year prospective cohort study. Lancet. 2020;395(10220):273–84.

Wang Y, Ge F, Wang J, et al. Trends in incident diagnoses and drug prescriptions for anxiety and depression during the COVID-19 pandemic: an 18-month follow-up study based on the UK Biobank. Transl Psychiatry. 2023;13(1):12.

Bilu Y, Flaks-Manov N, Bivas-Benita M, et al. Data-driven assessment of adolescents’ mental health during the COVID-19 pandemic. J Am Acad Child Adolesc Psychiatry. 2023;62:920–37.

Qi C, Osborne T, Bailey R, et al. Effect of the COVID-19 pandemic on incidence of long-term conditions in Welsh residents: a population linkage study. Lancet. 2022;400 Suppl 1:S69.

Carr MJ, Steeg S, Webb RT, et al. Effects of the COVID-19 pandemic on primary care-recorded mental illness and self-harm episodes in the UK: a population-based cohort study. Lancet Public Health. 2021;6(2):e124–35.

Costa-Font J, Knapp M, Vilaplana-Prieto C. The ‘welcomed lockdown’ hypothesis? Mental wellbeing and mobility restrictions. Eur J Health Econ. 2023;24(5):679–99.

Grolli RE, Mingoti MED, Bertollo AG, et al. Impact of COVID-19 in the mental health in elderly: psychological and biological updates. Mol Neurobiol. 2021;58(5):1905–16.

de Figueiredo CS, Sandre PC, Portugal LCL, et al. COVID-19 pandemic impact on children and adolescents’ mental health: biological, environmental, and social factors. Prog Neuropsychopharmacol Biol Psychiatry. 2021;106:110171.

Webb LM, Chen CY. The COVID-19 pandemic’s impact on older adults’ mental health: Contributing factors, coping strategies, and opportunities for improvement. Int J Geriatr Psychiatr. 2022;37:1–7.

Bafail DA. Mental health issues associated with COVID-19 among the elderly population: a narrative review. Cureus. 2022;14(12):e33081.

Seo JH, Kim SJ, Lee M, Kang JI. Impact of the COVID-19 pandemic on mental health service use among psychiatric outpatients in a tertiary hospital. J Affect Disord. 2021;290:279–83.

Mansfield KE, Mathur R, Tazare J, et al. Indirect acute effects of the COVID-19 pandemic on physical and mental health in the UK: a population-based study. Lancet Digit Health. 2021;3(4):e217–30.

Einav S, Tankel J. The unseen pandemic: treatment delays and loss to follow-up due to fear of COVID. J Anesth Analg Crit Care. 2022;2(1):5.

The Government of the Hong Kong Special Administrative Region. LCQ19: Designated clinics and tele-consultation service under the Hospital Authority [Press release]. https://www.info.gov.hk/gia/general/202302/15/P2023021500405.htm . Assessed 21 Dec 2023.

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Acknowledgements

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

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

Author information

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

Authors and Affiliations

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

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

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

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

Sandra Sau Man Chan

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

Hao Luo & Martin Knapp

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

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

Martin Knapp

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

Mark Jit, David Makram Bishai & Michael Yuxuan Ni

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

Michael Yuxuan Ni

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

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

Ian Chi Kei Wong & Xue Li

School of Pharmacy, Aston University, London, UK

Ian Chi Kei Wong

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

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

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Contributions

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

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Xue Li: @snowly12191

HKU Pharmacy: @HkuPharm

Corresponding authors

Correspondence to Ian Chi Kei Wong or Xue Li .

Ethics declarations

Ethics approval and consent to participate.

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

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Competing interests

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

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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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Mental Health Research During the COVID-19 Pandemic: Focuses and Trends

Affiliations.

• 1 Law School, Changsha University, Changsha, China.
• 2 Department of Psychology, University of Toronto St. George, Toronto, ON, Canada.
• 3 Centre for Mental Health and Education, Central South University, Changsha, China.
• PMID: 35958839
• PMCID: PMC9360762
• DOI: 10.3389/fpubh.2022.895121

Background: The COVID-19 pandemic has profoundly influenced the world. In wave after wave, many countries suffered from the pandemic, which caused social instability, hindered global growth, and harmed mental health. Although research has been published on various mental health issues during the pandemic, some profound effects on mental health are difficult to observe and study thoroughly in the short term. The impact of the pandemic on mental health is still at a nascent stage of research. Based on the existing literature, we used bibliometric tools to conduct an overall analysis of mental health research during the COVID-19 pandemic.

Method: Researchers from universities, hospitals, communities, and medical institutions around the world used questionnaire surveys, telephone-based surveys, online surveys, cross-sectional surveys, systematic reviews and meta-analyses, and systematic umbrella reviews as their research methods. Papers from the three academic databases, Web of Science (WOS), ProQuest Academic Database (ProQuest), and China National Knowledge Infrastructure (CNKI), were included. Their previous research results were systematically collected, sorted, and translated and CiteSpace 5.1 and VOSviewers 1.6.13 were used to conduct a bibliometric analysis of them.

Result: Authors with papers in this field are generally from the USA, the People's Republic of China, the UK, South Korea, Singapore, and Australia. Huazhong University of Science and Technology, Hong Kong Polytechnic University, and Shanghai Jiao Tong University are the top three institutions in terms of the production of research papers on the subject. The University of Toronto, Columbia University, and the University of Melbourne played an important role in the research of mental health problems during the COVID-19 pandemic. The numbers of related research papers in the USA and China are significantly larger than those in the other countries, while co-occurrence centrality indexes in Germany, Italy, England, and Canada may be higher.

Conclusion: We found that the most mentioned keywords in the study of mental health research during the COVID-19 pandemic can be divided into three categories: keywords that represent specific groups of people, that describe influences and symptoms, and that are related to public health policies. The most-cited issues were about medical staff, isolation, psychological symptoms, telehealth, social media, and loneliness. Protection of the youth and health workers and telemedicine research are expected to gain importance in the future.

Keywords: COVID-19; bibliometric analysis; focuses; keyword clustering; mental health; trends.

Copyright © 2022 Liang, Sun and Tan.

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Examining Coping Skills, Anxiety, and Depression Dynamics Amidst the COVID-19 Pandemic

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This cross-sectional study, conducted amid the COVID-19 pandemic, delves into the intricate connections between coping strategies and levels of anxiety and depression, presenting vital implications for medical, clinical, and broader societal contexts. As crises like the pandemic highlight the importance of adaptive coping, this investigation underscores the imperative to comprehend and address maladaptive coping strategies. The study utilized a diverse sample of 386 participants during the pandemic's peak, employing online platforms for recruitment and ensuring broad demographic representation. Data were collected through self-report measures, including the Patient Health Questionnaire-4 (PHQ-4) for depression and anxiety symptoms and the Brief Coping Orientation to Problems Experienced (COPE) inventory to assess coping skills across various domains. The coping skills assessment measured strategies such as Self-Distraction, Active Coping, Denial, Substance Use, Emotional and Instrumental Support, Behavioral Disengagement, Venting, Positive Reframing, Planning, Humor, Acceptance, Religion, and Self-Blame. The Colorado Multiple Institutional Review Board prioritized and approved ethical considerations, and participants provided informed consent. Data analysis involved rigorous cleaning, recoding, and quantitative analysis using SPSS. Descriptive statistics, regression analyses, and correlation analyses were employed to uncover nuanced relationships between coping strategies and mental health outcomes, contributing to understanding the phenomena under investigation within the context of the pandemic. The findings highlight the pivotal role of individualized approaches and the potential of humor as an essential coping mechanism, emphasizing the need for tailored interventions during crises.

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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 .

Thunström, L., Newbold, S. C., Finnoff, D., Ashworth, M. & Shogren, J. F. The benefits and costs of using social distancing to flatten the curve for COVID-19. J. Benefit-Cost Anal. 11 , 179–195. https://doi.org/10.1017/bca.2020.12 (2020).

Article   Google Scholar  

Anderson, R. M., Heesterbeek, H., Klinkenberg, D. & Hollingsworth, T. D. How will country-based mitigation measures influence the course of the COVID-19 epidemic? The Lancet 395 , 931–934. https://doi.org/10.1016/S0140-6736(20)30567-5 (2020).

Article   CAS   Google Scholar  

Fowler, J. H., Hill, S. J., Levin, R. & Obradovich, N. The effect of stay-at-home orders on COVID-19 cases and fatalities in the United States. MedRxiv 1 , 628. https://doi.org/10.1101/2020.04.13.20063628 (2020).

Pfefferbaum, B. & North, C. S. Mental health and the Covid-19 pandemic. N. Engl. J. Med. 383 , 510–512. https://doi.org/10.1056/NEJMp2008017 (2020).

Article   CAS   PubMed   Google Scholar  

File, T. & Marlay, M. Living Alone has More Impact on Mental Health of Young Adults than Older Adults (United States Census Bureau, 2021).

Google Scholar  

Elmer, T., Mepham, K. & Stadtfeld, C. Students under lockdown: Comparisons of students’ social networks and mental health before and during the COVID-19 crisis in Switzerland. PLoS ONE 15 , e0236337. https://doi.org/10.1371/journal.pone.0236337 (2020).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Adams-Prassl, A., Boneva, T., Golin, M. & Rauh, C. The Impact of the Coronavirus Lockdown on Mental Health: Evidence from the US. Cambridge Working Papers in Economics 2037 (University of Cambridge, 2020).

Wathelet, M. et al. Factors associated with mental health disorders among university students in France confined during the COVID-19 pandemic. JAMA Netw. Open 3 , e2025591. https://doi.org/10.1001/jamanetworkopen.2020.25591 (2020).

Article   PubMed   PubMed Central   Google Scholar  

Zhu, Y. et al. The risk and prevention of novel coronavirus pneumonia infections among inpatients in psychiatric hospitals. Neurosci. Bull. 36 , 299–302. https://doi.org/10.1007/s12264-020-00476-9 (2020).

Melamed, O. C. et al. Physical health among people with serious mental illness in the face of COVID-19: Concerns and mitigation strategies. Gen. Hosp. Psychiatry 66 , 30–33. https://doi.org/10.1016/j.genhosppsych.2020.06.013 (2020).

Lee, M. et al. Human mobility trends during the early stage of the COVID-19 pandemic in the United States. PLoS ONE 15 , 1–15. https://doi.org/10.1371/journal.pone.0241468 (2020).

Musinguzi, G. & Asamoah, B. O. The science of social distancing and total lock down: Does it work? Whom does it benefit? Electron. J. Gen. Med. 17 , 230. https://doi.org/10.29333/ejgm/7895 (2020).

Le, K. & Nguyen, M. The psychological burden of the covid-19 pandemic severity. Econom. Hum. Biol. 41 , 100979 (2021).

Czeisler, M. É. et al. Mental Health, Substance Use, and Suicidal Ideation During the COVID-19 Pandemic—United States, June 24–30, 2020. Morbidity and Mortality Weekly Report 32 (Centers for Disease Control and Prevention, 2020).

Abbott, A. COVID’s mental-health toll: How scientists are tracking a surge in depression. Nature 590 , 194–195. https://doi.org/10.1038/d41586-021-00175-z (2021).

Article   CAS   PubMed   ADS   Google Scholar  

Giuntella, O., Hyde, K., Saccardo, S. & Sadoff, S. Lifestyle and mental health disruptions during COVID-19. Proc. Natl. Acad. Sci. U.S.A. 118 , e2016632118. https://doi.org/10.1073/pnas.2016632118 (2021).

Devaraj, S. & Patel, P. C. Change in psychological distress in response to changes in reduced mobility during the early 2020 COVID-19 pandemic: Evidence of modest effects from the US. Soc. Sci. Med. 270 , 113615. https://doi.org/10.1016/j.socscimed.2020.113615 (2021).

Article   PubMed   Google Scholar  

Farkhad, B. F. & Albarracín, D. Insights on the implications of COVID-19 mitigation measures for mental health. Econom. Hum. Biol. 40 , 100963. https://doi.org/10.1016/j.ehb.2020.100963 (2021).

Brodeur, A., Clark, A. E., Fleched, S. & Powdthavee, N. COVID-19, lockdowns and well-being: Evidence from Google Trends. J. Public Econ. 193 , 104346. https://doi.org/10.1016/j.jpubeco.2020.104346 (2021).

Serrano-Alarcón, M., Kentikelenis, A., Mckee, M. & Stuckler, D. Impact of covid-19 lockdowns on mental health: Evidence from a quasi-natural experiment in England and Scotland. Health Econ. 31 , 284–296 (2022).

Roehrig, C. Mental disorders top the list of the most costly conditions in the United States: \$201 billion. Health Aff. 35 , 1130–1135. https://doi.org/10.1377/hlthaff.2015.1659 (2016).

Bloom, D. et al. The Global Economic Burden of Noncommunicable Diseases. Geneva. Tech. Rep (World Economic Forum, 2011).

McKenzie, K. W. S. & Whitley, R. Social capital and mental health. Br. J. Psychiatry 181 , 280–283 (2002).

Berkman, L. F. & Syme, S. L. Social networks, host resistance, and mortality: A nine-year follow-up study of Alameda County residents. Am. J. Epidemiol. 109 , 186–204 (1979).

Ferwana, I. & Varshney, L. R. Social capital dimensions are differentially associated with COVID-19 vaccinations, masks, and physical distancing. PLoS ONE 16 , e0260818 (2021).

Simon, N. M., Saxe, G. N. & Marmar, C. R. Mental health disorders related to COVID-19-related deaths. JAMA 324 , 1493–1494 (2020).

Ross, L. & Bassett, D. Causation in neuroscience: Keeping mechanism meaningful. Nat. Rev. Neurosci. 1 , 81–90 (2024).

Bommersbach, T. J., McKean, A. J., Olfson, M. & Rhee, T. G. National trends in mental health-related emergency department visits among youth, 2011–2020. JAMA 329 , 1469–1477 (2023).

Kuehn, B. M. Clinician shortage exacerbates pandemic-fueled “mental health crisis’’. JAMA 327 , 2179–2181 (2022).

Cantor, J. H., McBain, R. K., Ho, P. C., Bravata, D. M. & Whaley, C. Telehealth and in-person mental health service utilization and spending, 2019 to 2022. JAMA Health Forum 4 , e232645. https://doi.org/10.1001/jamahealthforum.2023.2645 (2023).

Times, T. N. Y. Coronavirus (Covid-19) Data in the United States. Dataset (2021).

Brooks, S. K. et al. The psychological impact of quarantine and how to reduce it: Rapid review of the evidence. The Lancet 395 , 912–920. https://doi.org/10.1016/S0140-6736(20)30460-8 (2020).

Keele, L. J., Small, D. S., Hsu, J. Y. & Fogarty, C. B. Patterns of effects and sensitivity analysis for differences-in-differences. http://arxiv.org/abs/1901.01869 (2019).

Zeldow, B. & Hatfield, L. A. Confounding and regression adjustment in difference-in-differences studies. Health Serv. Res. 56 , 932–941 (2021).

Angrist, J. D. & Pischke, J.-S. Mostly Harmless Econometrics: An Empiricist’s Companion (Princeton University Press, 2008).

Book   Google Scholar  

Goodman-Bacon, A. & Marcus, J. Using difference-in-differences to identify causal effects of COVID-19 policies. Survey Res. Methods 14 , 153–158. https://doi.org/10.18148/srm/2020.v14i2.7723 (2020).

Rambachan, A. & Roth, J. An Honest Approach to Parallel Trends. Unpublished (2020).

Hartnett, K. P. et al. Impact of the covid-19 pandemic on emergency department visits—United States, January 1, 2019–May 30, 2020. Morb. Mortal. Wkly. Rep. 23 , 699–704 (2020).

Kocher, K. E. & Macy, M. L. Emergency department patients in the early months of the coronavirus disease 2019 (covid-19) pandemic-what have we learned? JAMA Health Forum 6 , e200705 (2020).

Friedson, A. I., McNichols, D., Sabia, J. J. & Dave, D. Did California’s Shelter-in-Place Order Work? Early Coronavirus-Related Public Health Effects. Working Paper 26992 (National Bureau of Economic Research, 2020).

Cucinotta, D. WHO declares COVID-19 a pandemic. Acta Bio-med. 91 , 157–160. https://doi.org/10.23750/abm.v91i1.9397 (2020).

Layard, R. et al. When to Release the Lockdown? A Wellbeing Framework for Analysing Costs and Benefits IZA. Discussion Papers 13186 (Institute of Labor Economics, 2020).

Gupta, R. et al. Changes in sleep pattern and sleep quality during COVID-19 lockdown. Indian J. Psychiatry 62 , 370–378. https://doi.org/10.4103/psychiatry.IndianJPsychiatry_523_20 (2020).

Gualano, M. R., Lo Moro, G., Voglino, G., Bert, F. & Siliquini, R. Effects of Covid-19 lockdown on mental health and sleep disturbances in Italy. Int. J. Environ. Res. Public Health 17 , 4779. https://doi.org/10.3390/ijerph17134779 (2020).

Xiao, H., Zhang, Y., Kong, D., Li, S. & Yang, N. The effects of social support on sleep quality of medical staff treating patients with coronavirus disease 2019 (COVID-19) in January and February 2020 in China. Med. Sci. Monit. 26 , 1–8. https://doi.org/10.12659/MSM.923549 (2020).

Joshi, G. & Sharma, G. Burnout: A risk factor amongst mental health professionals during COVID-19. Asian J. Psychiatry 54 , 102300 (2020).

Aguiar, J. et al. Parental burnout and the COVID-19 pandemic: How Portuguese parents experienced lockdown measures. Fam. Relat. 70 , 927–938 (2021).

Murthy, V. H. Confronting health worker burnout and well-being. N. Engl. J. Med. 387 , 577–579 (2022).

Bailhache, M., Monnier, M. & Moulin, F. E. A. Emotional and attention-deficit/hyperactivity disorder symptoms of preterm vs full-term children during covid-19 pandemic restrictions. Pediatr. Res. 92 , 1749–1756 (2022).

Huang, X. et al. The impact of lockdown timing on COVID-19 transmission across US counties. EClinicalMedicine 38 , 101035 (2021).

Loewenthal, G. et al. COVID-19 pandemic-related lockdown: Response time is more important than its strictness. EMBO Mol. Med. 12 , e13171. https://doi.org/10.15252/emmm.202013171 (2020).

Gutkowski, V. A. Lockdown Responses to COVID-19. Tech. Rep. (Federal Reserve Bank of St. Louis, 2021).

Ferraresi, M., Kotsogiannis, C., Rizzod, L. & Secomandi, R. The ‘Great Lockdown’ and its determinants. Econ. Lett. 197 , 109628. https://doi.org/10.1016/j.econlet.2020.109628 (2020).

JoAnn, P., Dooley, D. & Huh, J. Income volatility and psychological depression. Am. J. Community Psychol. 43 , 57–70. https://doi.org/10.1007/s10464-008-9219-3 (2009).

Knapp, M. & Wong, G. Economics and mental health: The current scenario. World Psychiatry 19 , 3–14 (2020).

Fana, M., Pérez, S. T. & Fernández-Macías, E. Employment impact of Covid-19 crisis: From short term effects to long terms prospects. J. Ind. Bus. Econom. 47 , 391–410. https://doi.org/10.1007/s40812-020-00168-5 (2020).

Montenovo, L. et al. Determinants of Disparities in COVID-19 Job Losses NBER. Working Paper 27132 (National Bureau of Economic Research, 2021).

Klees, B. S. & Curtis, C. A. Brief Summaries of Medicare & Medicaid. Summary Report (Centers for Medicare & Medicaid Services, Department of Health and Human Services, 2020).

Trinkl, J. & Muñoz del Río, A. Effect of COVID-19 Pandemic on Visit Patterns for Anxiety and Depression (2020).

Patel, S. Y. et al. Trends in outpatient care delivery and telemedicine during the COVID-19 pandemic in the US. JAMA Intern. Med. 181 , 388–391. https://doi.org/10.1001/jamainternmed.2020.5928 (2020).

Article   PubMed Central   Google Scholar  

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Acknowledgements

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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Connolly SL , Miller CJ , Gifford AL , Charness ME. Perceptions and Use of Telehealth Among Mental Health, Primary, and Specialty Care Clinicians During the COVID-19 Pandemic. JAMA Netw Open. 2022;5(6):e2216401. doi:10.1001/jamanetworkopen.2022.16401

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Perceptions and Use of Telehealth Among Mental Health, Primary, and Specialty Care Clinicians During the COVID-19 Pandemic

• 1 Center for Healthcare Organization and Implementation Research, VA Boston Healthcare System, Boston, Massachusetts
• 2 Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
• 3 Boston University School of Medicine, Boston, Massachusetts
• 4 Boston University School of Public Health, Boston, Massachusetts
• 5 VA Boston Healthcare System, Boston, Massachusetts
• 6 Harvard Medical School, Boston, Massachusetts

Question   Are clinician perceptions of telehealth quality associated with use?

Findings   In this survey study of 866 mental health (MH), primary care (PC), and specialty care (SC) clinicians, MH clinicians rated the quality of video care the highest and were more likely to prefer video over phone when providing care for patients remotely; PC and SC clinicians were more likely to endorse challenges of video care. Findings aligned with utilization rates, with MH clinicians conducting significantly more video visits than PC and SC clinicians.

Meaning   These findings suggest that specialty-specific differences in clinician perceptions of telehealth were associated with actual use.

Importance   Clinician attitudes toward telehealth may impact utilization rates, and findings may differ based on specialty.

Objective   To determine whether clinician beliefs regarding telehealth quality and ease of use were associated with the proportion of care delivered via video, phone, and in-person across specialties.

Design, Setting, and Participants   This survey study used a voluntary, anonymous survey conducted from August to September 2021 in the Department of Veterans Affairs New England Healthcare System (VANEHS). Mental health (MH), primary care (PC), and specialty care (SC) clinicians were invited to participate. Data were analyzed from October 2021 to January 2022.

Exposures   Participation in a 32-item survey.

Main Outcomes and Measures   The main outcomes were clinicians’ views on relative quality of video, phone, and in-person care; factors contributing to clinicians’ modality choice; telehealth challenges; and clinician modality preferences and utilization when treating new and established patients.

Results   There were 866 survey respondents (estimated 64% response rate); 52 respondents reported no video or phone telehealth use in the 3 months prior to survey completion and were excluded, resulting in a final sample of 814 respondents. Respondents were divided among MH (403 respondents [49.5%]), PC (153 respondents [18.8%]), and SC (258 respondents [31.7%]). Compared with PC and SC clinicians, MH clinicians rated the quality of video care the highest (eg, compared with in-person care with masks when treating new patients: χ 2  = 147.8; P  < .001) and were more likely to prefer video over phone when treating both new (χ 2  = 26.6; P  < .001) and established (χ 2  = 100.4; P  < .001) patients remotely. PC and SC clinicians were more likely to rate phone care as being at least equivalent in quality to video for both new (χ 2  = 26.3; P  < .001) and established (χ 2  = 33.5; P  < .001) patients. PC and SC clinicians were also more likely to endorse challenges of video care, including patient barriers and the inability to conduct a physical examination (χ 2  = 292.0; P  < .001). Most PC and SC clinicians either had no preference (46 PC respondents [36.2%]; 59 SC respondents [28.4%]) or preferred phone (36 PC respondents [28.3%]; 67 SC respondents [32.2%]) for remote care of established patients. Findings aligned with utilization rates within VANEHS, with MH clinicians conducting significantly more of their encounters via video (36 734 encounters [40.3%]) than PC (3201 encounters [3.9%]) and SC (1157 encounters [4.9%]) clinicians.

Conclusions and Relevance   These findings suggest that clinician attitudes regarding telehealth quality and ease of use were associated with utilization rates. Moving forward, clinician use of telehealth may be impacted by additional data regarding the relative effectiveness of modalities as well as improvements in video telehealth workflows.

Rates of video and phone telehealth use skyrocketed during the COVID-19 pandemic to protect patients and clinicians from infection. 1 , 2 While telehealth was most commonly used for mental health (MH) treatment prior to the pandemic, adoption increased dramatically within primary care (PC), specialty care (SC), and MH during COVID-19. 3 , 4 This rapid transformation has provided an unprecedented opportunity to examine differences in telehealth utilization across specialties.

Video visits are more difficult to conduct than audio-only phone visits because they require that patients and clinicians own a video-enabled device, have internet connectivity, and know how to navigate a video telehealth platform. 5 However, emerging evidence suggests that compared with phone visits, video visits may be more clinically effective 6 , 7 and preferred by patients. 8 , 9 Clinician-related factors may have greater impacts on the relative rates of video and phone use than patient factors 10 ; indeed, clinicians have often been referred to as the gatekeepers of telehealth . 11 , 12

Clinician perceptions of the quality and ease of use of virtual care modalities may contribute to variation in utilization. 13 In a survey conducted early in the pandemic within the Veterans Health Administration (VHA), we found significant specialty-level variability in clinician perceptions, including that PC and SC clinicians may be more likely to prefer phone over video care, while MH clinicians were more likely to prefer video. We also found that clinicians expressed greater comfort in using video and phone telehealth to treat established patients compared with new patients. 14 The survey was conducted shortly after the sudden shift to virtual care, during a time of rapid change for clinicians and health care systems.

More than 1 year later, we administered a survey with 4 objectives, aiming to understand (1) how VHA clinicians evaluate the quality of telehealth care they had delivered; (2) the factors that contribute to their choice of modality; (3) the challenges of telehealth care; and (4) their preferences for care delivery when treating both new and established patients. We also examined how utilization of in-person, phone, and video care varied by specialty and compared this variation with differences in clinician perceptions. Informed by our prior survey findings, we hypothesized that MH clinicians would have more positive perceptions of video care and would have higher rates of video use than PC and SC clinicians. This work, conducted within the largest integrated health care system in the United States, seeks to identify key clinician-level factors that may be associated with utilization of telehealth care across specialties.

This survey study was reviewed by the VA Boston Research and Development Committee; the project was classified as quality improvement and was therefore exempt from institutional review board review. Given this status, the need for informed consent from participants was waived. This study followed the American Association for Public Opinion Research ( AAPOR ) reporting guideline for web-based surveys to the extent possible given our study design, in which the exact number of eligible clinicians reached by the survey invitation was unknown.

A voluntary and anonymous electronic survey was emailed to MH, PC, and SC clinicians (ie, physicians, psychologists, social workers, nurse practitioners, pharmacists, physician assistants, and podiatrists) (eAppendix 1 in the Supplement ) across the 8 medical centers of the VA New England Healthcare System (VANEHS), a 6-state regional health care system serving approximately 260 000 veterans annually. Among these, 4 medical centers are in urban or suburban locations and 4 medical centers are in rural locations (eAppendix 2 in the Supplement ).

Survey questions were informed by literature reviews of clinician attitudes toward telehealth, 15 - 18 including a prior survey of VANEHS clinicians conducted in 2020. 14 We assessed telehealth experience, perceptions of telehealth quality (using the National Academy of Medicine definition of quality 19 ), factors contributing to the choice of care modality, challenges of telehealth care, and modality preference for remote care during the 3 months prior to survey completion. The final survey contained 32 multiple-choice questions and took approximately 10 minutes to complete (eAppendix 3 in the Supplement ). A survey link was distributed by medical center chiefs of staff through clinical service chiefs to clinicians. The survey remained open from August 4, 2021, until September 20, 2021.

Data for completed outpatient visits for the months of August and September 2021 were extracted from the VHA corporate data warehouse (VHA’s national clinical and administrative database 20 ) and sorted according to specialty: MH, PC, and SC. Within each specialty, visits were sorted based on encounter type: in-person, video, or phone. Other care modalities, including electronic consultations, represented less than 2% of the total number of encounters and were not considered further. We analyzed data specific to the VANEHS, the region selected for surveying clinician perceptions. We also conducted secondary analyses examining outpatient visits nationally across VHA, to determine the extent to which VANEHS was representative of the VHA.

Descriptive statistics and χ 2 tests were conducted using SPSS Statistics for Windows, version 26.0 (IBM). Since the exact number of eligible participants was unknown, we derived an estimated response rate (eAppendix 4 in the Supplement ). P values were 2-sided, and statistical significance was set at P  < .05. Data were analyzed from October 2021 to January 2022.

There were 866 survey respondents across all specialties (estimated 64% response rate) (eAppendix 4 in the Supplement ); 52 respondents reported no video or phone telehealth use in the 3 months prior to survey completion and were excluded from subsequent analyses. The final sample included 814 clinicians divided among MH (403 respondents [49.5%]), PC (153 respondents [18.8%]), and SC (258 respondents [31.7%]). Respondent occupations included physician (328 respondents [40.3%]), psychologist (197 respondents [24.2%]), social worker (107 respondents [13.1%]), nurse practitioner (107 respondents [13.1%]), physician assistant (36 respondents [4.4%]), pharmacist (34 respondents [4.2%]), and podiatrist (5 respondents [0.6%]). Table 1 shows the distribution of professions among specialties.

During the 3 months prior to survey completion, 701 clinicians (86.1%) had conducted a phone appointment and 720 clinicians (88.5%) had conducted a video appointment, as measured via self-report. MH clinicians were significantly more likely to have completed a video appointment (386 MH clinicians [95.8%]; 137 PC clinicians [89.5%]; 197 SC clinicians [76.4%]; χ 2  = 58.3; P  < .001) and significantly less likely than PC or SC clinicians to have completed a phone appointment (316 MH clinicians [78.4%]; 149 PC clinicians [97.4%]; 236 SC clinicians [91.5%]; χ 2  = 42.5; P  < .001) in the past 3 months.

Clinicians were significantly more likely to rate video care and phone care as equivalent to or higher in quality than in-person care with masks when treating established patients compared with new patients (video: χ 2  = 72.0, P  < .001; phone: χ 2  = 99.6; P  < .001) ( Table 2 ). Quality ratings of video care for new patients varied markedly across specialties, being highest for MH, intermediate for PC, and lowest for SC (χ 2  = 147.8; P  < .001). The perceived quality gap between video care for new and established patients was smallest for MH, intermediate for PC, and largest for SC. Compared with these video ratings, quality ratings of phone care vs in-person care for both new and established patients were lower and demonstrated less variation across specialties ( Table 2 ).

Less than one-third of clinicians (207 clinicians [32.1%]) rated phone as equivalent to or higher in quality than video when treating new patients ( Table 2 ). Endorsement increased significantly when considering established patients (χ 2  = 68.3; P  < .001). PC and SC clinicians were significantly more likely than MH clinicians to rate phone care as equivalent or higher quality than video care for new (χ 2  = 26.3; P  < .001) and established (χ 2  = 33.5; P  < .001) patients.

Clinicians were asked to endorse the major contributors to their decision to choose video, phone, or in-person care (ie, modality choice) ( Table 3 ). Overall, patient preference was the most frequently endorsed factor (531 respondents [73.3%]) followed by clinical judgment (408 respondents [56.7%]) and leadership guidance (214 respondents [30.7%]). There were some notable differences across specialties; PC clinicians were more likely than MH or SC clinicians to describe scheduling staff as having a major influence on modality choice (χ 2  = 36.6; P  < .001). MH clinicians were more likely to report that choices were impacted by leadership guidance (χ 2  = 27.4; P  < .001) and available data regarding the relative effectiveness of the modalities (χ 2  = 56.4; P  < .001). SC clinicians were more likely to endorse clinical judgment (χ 2  = 18.1; P  = .001) and less likely to endorse patient preference (χ 2  = 15.4; P  = .004) as contributors.

SC clinicians were more likely than PC or MH clinicians to endorse significant challenges of phone appointments, including the inability to assess physical health status (χ 2  = 127.1; P  < .001), conduct an adequate physical examination (χ 2  = 398.3; P  < .001), and receive full workload credit (χ 2  = 32.4; P  < .001). With regards to video appointments, PC and SC clinicians generally endorsed challenges at higher rates than MH clinicians ( Table 3 ). Among these were clinician challenges, such as the inability to conduct an adequate physical examination (χ 2  = 292.0; P  < .001), and patient challenges, such as patient difficulty using their device or telehealth platform (χ 2  = 58.2; P  < .001), lack of technical support and training for patients (χ 2  = 60.6; P  < .001), and inadequate internet connectivity (χ 2  = 32.4; P  < .001) ( Table 3 ).

Overall, when asked to select the modality they would prefer to use while delivering remote care, most clinicians expressed a preference for video over phone, particularly for new vs established (χ 2  = 80.3; P  < .001) patients ( Table 4 ). MH clinicians showed the strongest preference for video over phone for both new (χ 2  = 26.6; P  < .001) and established (χ 2  = 100.4; P  < .001) patients and the smallest distinction between new and established patients. In contrast, most PC and SC clinicians either had no preference (46 PC respondents [36.2%]; 59 SC respondents [28.4%]), or preferred phone (36 PC respondents [28.3%]; 67 SC respondents [32.2%]) when treating established patients ( Table 4 ).

The survey provided specialty-specific assessments of quality and preferences for the use of in-person, video, and phone care. We next determined the actual use of these care modalities over the 2 months when the survey was conducted. During this time, VANEHS recorded 402 989 completed visits (91 314 MH visits [22.7%]; 82 946 PC visits [20.6%]; 228 729 SC visits [56.8%]), of which 358 470 visits (89.0%) were for established patients, including 89 236 MH visits (97.7%), 79 113 PC visits (95.4%), and 190 121 SC visits (83.1%).

Across specialties, new patients received care in-person at higher rates than established patients (χ 2  = 4494.8; P  < .001). VANEHS data also revealed significant, specialty-specific differences in the proportions of video, phone, and in-person encounters ( Table 5 ). MH provided the highest percentage of video visits for both new (χ 2  = 3987.4; P  < .001) and established (χ 2  = 81 345.0; P  < .001) patients, with video encounters accounting for 36 734 (40.3%) of all MH encounters, compared with 3201 PC encounters (3.9%) and 11 157 SC encounters (4.9%). When considering established patients, SC provided the most in-person care, while PC demonstrated the highest rates of phone care across specialties (χ 2  = 81345.0; P  < .001). Utilization rates and specialty-level differences observed within VANEHS were very similar to those seen nationally across VHA (eTable in the Supplement ).

This survey study of VHA clinicians found substantial specialty-level differences in clinician beliefs regarding the quality of video and phone telehealth, major contributors to their modality choice, challenges of telehealth use, and modality preferences when providing care remotely. These findings may in part explain observed differences in actual video, phone, and in-person care utilization across specialties.

MH clinicians, who provided the greatest proportion of video visits at the time of the survey, rated the quality of video care the highest and were more likely to prefer video over phone when providing care for patients remotely. These findings align with prior work reporting high MH clinician satisfaction with video telehealth, particularly as they gain experience with the modality. 15 , 21 MH clinicians were also more likely to report that their selection of care modalities was influenced by leadership guidance and data regarding the relative effectiveness of video, phone, and in-person care. Indeed, given that telehealth was being used for MH care well before the onset of the COVID-19 pandemic, there is a strong body of evidence demonstrating that video care is noninferior to in-person MH services, 22 - 24 as well as an emerging literature suggesting that phone care may sometimes be inferior in quality to video care. 7 , 25 , 26 Findings from this survey suggest that clinician and perhaps leadership decision-making has been influenced by these data. There is less literature regarding telehealth effectiveness in PC and SC, but publication of high-quality studies has increased since the start of the pandemic 27 ; this work will be critical in informing PC and SC clinicians’ and leadership’s decision-making regarding the choice of care modalities.

PC and SC clinicians, who conducted substantially less video care than MH, had multiple similarities in their responses across the survey. These clinicians were more likely to rate phone care as being at least equivalent in quality to video. They were also more likely to endorse challenges of video care, including patient barriers to use and the inability to conduct an adequate physical examination. Importantly, most PC and SC clinicians either had no preference or preferred phone for remote care of established patients. However, there were some notable differences between PC and SC clinicians. SC clinicians were more likely to endorse their clinical judgment as influencing modality choice. They also were more likely to rate video and phone care as being lower in quality than in-person care when treating new patients. In addition, SC clinicians endorsed more challenges with phone visits compared with PC clinicians, including an inability to assess health status.

These findings may partly explain why SC clinicians conducted the highest proportion of in-person visits across all clinician groups. SC clinicians may be more likely to view both video and phone care as inferior to in-person care because of the limited ability to conduct physical examinations and assess patient health status. Indeed, other clinician surveys have identified difficulties in conducting physical examinations and performing procedures as barriers to remote SC. 28 - 30

PC clinicians conducted the highest proportion of phone visits when providing care for established patients. This could be owing, in part, to their increased likelihood of endorsing challenges of video care coupled with a tendency to believe that video and phone care are equivalent in quality, particularly for established patients. Indeed, most PC clinicians either preferred phone or had no preference between phone and video for the remote care of established patients. This finding underscores the importance of complexity in influencing adoption of new technologies 13 ; if PC clinicians believe that phone and video care are equivalent in quality, ease of use may then drive the choice of phone over video, particularly when treating patients whom they have already seen in-person.

Most clinicians across specialties endorsed patient preference as a major contributor to modality choice. Yet while patient preference for video over phone visits becomes increasingly apparent, 8 , 9 utilization data reveal a large portion of remote visits continue to be conducted by phone. The extent to which patient preference is in fact a post hoc rationalization for clinician preference is unknown. Importantly, research conducted during the pandemic has underlined the substantial role of clinicians in influencing rates of video use. One study found that one-third of Medicare enrollees were only offered phone visits, and not video, for remote appointments, despite the fact that most of them owned a video-enabled device. 31 Another study demonstrated that practice- and clinician-level factors explained significantly more of the variation in video visit utilization than did patient-level factors. 10 The findings of these studies 10 , 31 highlight the need to more closely examine the extent to which patient preference is being fully incorporated into the decision-making process when choosing a care modality.

Likewise, it is unclear how often what we refer to as patient preference is instead a measure of patient readiness for telehealth (ie, that the patient owns a video-enabled device or is comfortable navigating a telehealth platform). A patient without a smartphone may be viewed as preferring a phone appointment because they do not have access to the appropriate technologies. Indeed, COVID-19 has revealed a stark digital divide in which patients who are older and/or have lower income are less likely to be video-ready. 32 - 35 These findings highlight the importance of increasing patient access to video-enabled devices and broadband connectivity to ensure that they are able to successfully engage in video visits. The VHA’s tablet distribution program 36 and the Federal Communications Commissions’ Lifeline program, 37 which offers discounted broadband to individuals with low income, are important steps in this direction. Increasing technical support staff to help patients troubleshoot technology will be critical, particularly for clinicians with large caseloads and short appointment times. Broadly, processes and workflows must be streamlined to ensure that video visits are as simple and accessible as possible for both patients and clinicians.

This study has some limitations, including its use of a regional sample of VHA clinicians. Whereas utilization patterns within VANEHS closely mirrored national VHA data, it is possible that attitudes may differ across regions. Given that this survey study was conducted within a national integrated health care system, results may not fully generalize to alternative settings. However, it is important to note that VHA’s financial model is also a strength of this study. Because clinician choices in VHA are not driven by fee-for-service reimbursement schedules, they may more closely reflect intrinsic clinician preferences. 38 - 40 In addition, we did not collect demographic information from respondents, including sex, age, or race or ethnicity, in an effort to keep the length of the survey manageable and to ensure anonymity. However, this information could provide additional insights and is worth examining in future work.

This survey study found significant specialty-level differences in clinician attitudes toward video and phone telehealth care, many of which aligned with observed differences in actual utilization of these modalities. Our findings suggest that in the absence of financial incentives, clinician beliefs, particularly regarding the quality and ease of use of telehealth, played an important role in the care modalities that were ultimately used with patients. There is a need for additional data regarding the relative effectiveness of video and phone telehealth as well as improved processes to better integrate video telehealth into clinician workflows. Such advances will be critical in influencing clinician attitudes and ensuring the provision of high-quality care at the right place and the right time.

Accepted for Publication: April 23, 2022.

Published: June 7, 2022. doi:10.1001/jamanetworkopen.2022.16401

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

Corresponding Author: Samantha L. Connolly, PhD, Center for Healthcare Organization and Implementation Research, VA Boston Healthcare System, 150 S Huntington Ave, Boston, MA 02130 ( [email protected] ).

Author Contributions : Dr. Connolly had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: All authors.

Acquisition, analysis, or interpretation of data: Connolly, Miller.

Drafting of the manuscript: Connolly.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Connolly, Charness.

Obtained funding: Connolly.

Administrative, technical, or material support: Miller, Gifford, Charness.

Supervision: Miller, Gifford.

Conflict of Interest Disclosures: None reported.

Funding/Support: Dr Connolly was supported by grants from the Department of Veterans Affairs, Veterans Health Administration (grant No. VA HSR&D QUE 20-026 and VA HSR&D COR 20-199).

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

Disclaimer: The content of this study is solely the responsibility of the authors and does not necessarily represent the official views of the US Department of Veterans Affairs or the US Government.

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Reasons for hope

Solutions for the mental health crisis emerge through innovative research, diagnostics and treatments

By Nina Bai

Illustration by Jules Julien

Photography by Leslie Williamson

It’s the spring of hope for mental health, astir with novel discoveries, life-changing therapies and more openness than ever before — yet, for many, it feels like the winter of despair. The pandemic years, that crucible of stress, isolation and uncertainty, fueled and exposed mental health problems. In 2022, nearly 1 in 4 American adults (about 59 million people) said they experienced a mental illness in the previous year, but only half of those afflicted reported receiving any mental health treatment.

Among children and adolescents, the prevalence of mental illness, which had been steadily creeping upward, jumped during the pandemic, according to the U.S. Substance Abuse and Mental Health Services Administration. In 2019, 15.7% of American adolescents aged 12-17 reported experiencing a major depressive episode in the past year. In 2022, that number was 19.5%. That same year, 13.4% of adolescents — just over 1 in 8 — seriously thought about killing themselves.   

And even as the pandemic has stoked demand for mental health care, it also has worn down the mental health workforce, already short-handed, with early retirements and widespread burnout. Access to affordable, effective interventions remains a daunting barrier. People face long waiting lists and lack of insurance coverage. Many treatable conditions remain undiagnosed because people lack a way to obtain assessments. 

Yet, below this perfect storm of mental health crisis, there is a strong undercurrent of hope that begins in the lab. Research is leading the way toward treatments that are more effective, more personalized and more accessible.

“The manner in which we know the brain now, compared with what we knew in previous decades, is incredibly different,” said Victor Carrión , MD, the John A. Turner, MD, Endowed Professor for Child and Adolescent Psychiatry and vice chair of the department of psychiatry and behavioral sciences.

Direct impact on patients

New imaging technologies allow researchers to see the neural circuitry that goes awry in neuropsychiatric disorders, lab-grown clumps of brain tissue — known as organoids — can simulate the impact of genetics in autism, and artificial intelligence can surmise signals that predict the onset of depression and anxiety.

Moreover, these discoveries, rather than moving slowly through specialist silos, can now rapidly inform new treatments. “Collaboration is vital for translation, and our departmental awards and programs promote and emphasize synergy between research and clinical practice,” said Laura Roberts , MD, the Katharine Dexter McCormick and Stanley McCormick Memorial Professor and chair of the department of psychiatry and behavioral sciences.

“Our bench scientists doing tremendous research also work alongside our clinicians to make sure that new knowledge translates to the clinical setting and has a direct impact on patient care,” she said.

Researchers developing transcranial magnetic stimulation, for example, work with clinicians who treat patients with severe depression to design clinical trials, and their techniques are informed by teams inventing new ways to measure the flow of brain signals and those building virtual reality models of the brain.

A clearer understanding of the biology of mental health disorders not only leads to breakthrough treatments — but just as powerfully, helps dissipate stigma.

“There’s been a large shift in stigma in the past 25 years,” said Heather Gotham , PhD, clinical professor of psychiatry and behavioral sciences, who leads the coordination of a nationwide network of centers dedicated to implementing evidence-based mental health care.

The Mental Health Technology Transfer Center Network, funded by the Substance Abuse and Mental Health Services Administration, offers training in preventing school violence, substance use in the workplace, adolescent depression and more, and it offers support for mental health providers seeing refugees and asylum seekers.

“Collaboration is vital for translation, and our departmental awards and programs promote and emphasize synergy between research and clinical practice.” Laura Roberts, the Katharine Dexter McCormick and Stanley McCormick Memorial Professor and chair of the department of psychiatry and behavioral sciences

“One thing that’s made a difference is the greater understanding that mental health disorders and substance use disorders are chronic, relapsing disorders of the body, just like diabetes and heart disease,” Gotham said.

With this new awareness, more people want to be mental health literate. In the past few years, Gotham has seen a surge of interest, from a broader community, in the network’s online courses — from teachers, for example, who want to be more responsive to the needs of students and reduce stigma in the classroom.

Less stigma also means more money for research and mental health services. Funding for mental health has become a rare bipartisan issue. In 2022, Congress passed the Bipartisan Safer Communities Act, which has provided $245 million to fund mental health services like training for school personnel, first responders and law enforcement and expanding the 988 suicide and crisis lifeline.

Stanford Medicine researchers know that to make the most impact with their discoveries they must reach those who need help the most — through online symptom screenings, virtual therapy, group therapy, inclusive clinical trials and community interventions.

They are training mental health professionals locally and globally in new evidence-based techniques. Providers in more than 38 countries, for example, have been trained in cue-centered therapy, a 15-week treatment program developed at Stanford Medicine to help children and teens recover from chronic trauma. Recently, pro bono training in cue-centered therapy was provided to clinicians in Ukraine.

What gives Roberts hope is that a more open conversation on mental health is drawing together experts from different fields with a shared purpose. “It used to be that clinicians would stay in their clinical practice and refer to journals for new research, and researchers would stay in the lab and never see a patient — and we don’t have that now,” she said. “I see more openness and more flexibility from the current generation of researchers and clinicians.”

Read on in this issue of Stanford Medicine to learn about some of the ways Stanford Medicine researchers and clinicians are advancing the understanding of mental health and sharing that knowledge.

Nina Bai is a science writer in the Stanford Medicine Office of Communications.

Email the author

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

1. ^ www.graphpad.com

Alfonsson, S., Wallin, E., and Maathz, P. (2017). Factor structure and validity of the depression, anxiety and stress Scale-21 in Swedish translation. J. Psychiatr. Ment. Health Nurs. 24, 154–162. doi: 10.1111/jpm.12363

PubMed Abstract | Crossref Full Text | Google Scholar

Bell, C., Williman, J., Beaglehole, B., Stanley, J., Jenkins, M., Gendall, P., et al. (2021). Challenges facing essential workers: a cross-sectional survey of the subjective mental health and well-being of New Zealand healthcare and ‘other’ essential workers during the COVID-19 lockdown. BMJ Open 11:e048107. doi: 10.1136/bmjopen-2020-048107

Benton, T., Njoroge, W. F., and Ng, W. Y. (2022). Sounding the alarm for children’s mental health during the COVID-19 pandemic. JAMA Pediatr. 176:e216295. doi: 10.1001/jamapediatrics.2021.6295

Blayney, M., and Kercher, A. (2023). Psychologists’ experiences of burnout in Aotearoa, New Zealand: a nationwide qualitative survey. N. Z. J. Psychol. 52, 57–65.

Google Scholar

British Psychological Society. (2020). Impact of COVID-19 on the well-being of psychologists. www.bps.org.uk

Buselli, R., Corsi, M., Baldanzi, S., Chiumiento, M., Del Lupo, E., Dell’Oste, V., et al. (2020). Professional quality of life and mental health outcomes among health care workers exposed to Sars-Cov-2 (Covid-19). Int. J. Environ. Res. Public Health 17:6180. doi: 10.3390/ijerph17176180

Campbell-Sills, L., Forde, D., and Stein, M. B. (2009). Demographic and childhood environmental predictors of resilience in a community sample. J. Psychiatr. Res. 43, 1007–1012. doi: 10.1016/j.jpsychires.2009.01.013

Cardwell, H. (2021). Shortage of psychologists leaving patients on waitlist for 9 to 12 months RNZ. Available at: https://www.rnz.co.nz/news/political/451062/shortage-of-psychologists-leaving-patients-on-waitlist-for-9-to-12-months#:~:text=1000%20psychologists%20short%2C%20not%20enough,months%20on%20treatment%20waiting%20lists

Chambers, C. N. L., and Frampton, C. M. A. (2022). Burnout, stress and intentions to leave work in New Zealand psychiatrists; a mixed methods cross sectional study. BMC Psychiatry 22:380. doi: 10.1186/s12888-022-03980-6

Connor, K. M., and Davidson, J. R. T. (2003). Development of a new resilience scale: The Connor-Davidson Resilience Scale (CD-RISC). Depression and anxiety , vol. 18, 76–82. Available at: https://doi.org/10.1002/da.10113

Crawford, J., Cayley, C., Lovibond, P. F., Wilson, P. H., and Hartley, C. (2011). Percentile norms and accompanying interval estimates from an Australian general adult population sample for self-report mood scales (BAI, BDI, CRSD, CES-D, DASS, DASS-21, STAI-X, STAI-Y, SRDS, and SRAS). Aust. Psychol. 46, 3–14. doi: 10.1111/j.1742-9544.2010.00003.x

Crossref Full Text | Google Scholar

Davidson, JRT. Connor-Davidson Resilience Scale (CD-RISC) Manual (2018). Unpublished, editor. www.cd-risc.com .

Delacre, M., Lakens, D., and Leys, C. (2017). Why psychologists should by default use Welch’s t-test instead of Student’s t-test. International Review of Social Psychology , 30, 92–101. doi: 10.5334/irsp.82

Deng, J., Zhou, F., Hou, W., Heybati, K., Lohit, S., Abbas, U., et al. (2023). Prevalence of mental health symptoms in children and adolescents during the COVID-19 pandemic: a meta-analysis. Ann. N. Y. Acad. Sci. 1520, 53–73. doi: 10.1111/nyas.14947

Every-Palmer, S., Grant, M. L., and Thabrew, H. (2022). Young people don’t tend to ask for help more than once: child and adolescent psychiatrists’ views on ailing mental health services for young new Zealanders. Australas. Psychiatry 30, 684–688. doi: 10.1177/10398562221115624

Every-Palmer, S., Jenkins, M., Gendall, P., Hoek, J., Beaglehole, B., Bell, C., et al. (2020). Psychological distress, anxiety, family violence, suicidality, and wellbeing in New Zealand during the COVID-19 lockdown: a cross-sectional study. PLoS One 15:e0241658. doi: 10.1371/journal.pone.0241658

Freeman, C., Ergler, C., Kearns, R., and Smith, M. (2021). Covid-19 in New Zealand and the Pacific: implications for children and families. Child. Geograph. 20, 459–468. doi: 10.1080/14733285.2021.1907312

Gasteiger, N., Vedhara, K., Massey, A., Jia, R., Ayling, K., Chalder, T., et al. (2021). Depression, anxiety and stress during the COVID-19 pandemic: results from a New Zealand cohort study on mental well-being. BMJ Open 11:e045325. doi: 10.1136/bmjopen-2020-045325

Ghio, L., Gotelli, S., Cervetti, A., Respino, M., Natta, W., Marcenaro, M., et al. (2015). Duration of untreated depression influences clinical outcomes and disability. J. Affect. Disord. 175, 224–228. doi: 10.1016/j.jad.2015.01.014

Government NZ. COVID-19 requirements (2023) Available at: https://covid19.govt.nz/

Hammond, N. E., Crowe, L., Abbenbroek, B., Elliott, R., Tian, D. H., Donaldson, L. H., et al. (2021). Impact of the coronavirus disease 2019 pandemic on critical care healthcare workers’ depression, anxiety, and stress levels. Aust. Crit. Care 34, 146–154. doi: 10.1016/j.aucc.2020.12.004

Hemopo, H. (2004). Training, recruitment and retention strategies for psychologists in child and adolescent mental health services .: A Maori perspective. Auckland Werry Centre. (2004). Retrieved from: http://www.werrycentre.org.nz/site_resources/library/Workforce_Development_Publications/Training__Recruitment_and_Retention_Strategies_for_Psychologists_in_CAMHS_A_Maori_Perspective.pdf

Kercher, A., and Gossage, L. (2023). Identifying risk factors for compassion fatigue in psychologists in Aotearoa, New Zealand during the COVID-19 pandemic. Prof. PsychoL. Res. Pract. 55, 28–38. Available at: https://doi.org/10.1037/pro0000538

Larsen, D., and Stamm, B. H. (2008). “Professional quality of life and trauma therapists” in Trauma, recovery, and growth: positive psychological perspectives on posttraumatic stress (Hoboken, NJ, US: John Wiley & Sons Inc), 275–293.

Levy, M. P. (2002). “Barriers and incentives to Māori participation in the profession of psychology,” in The Proceedings of the National Māori Graduates of Psychology Symposium 2002: Making a difference . Eds. L. W. Nikora, M. Levy, B. Masters, W. Waitoki, N. Te Awekotuku, and R. J. M. Etheredge Hamilton, New Zealand: University of Waikato. 27–35.

Lovibond, S. H., and Lovibond, P. F.. Manual for the depression anxiety stress scales . (2nd. Ed.). Sydney: Psychology Foundation; (1995).

Maslach, C., and Leiter, M. P. (2016). “Burnout” in Stress: Concepts, cognition, emotion, and behavior . ed. G. Fink (San Diego: Academic Press), 351–357.

Maslow, G. R., Dunlap, K., and Chung, R. J. (2015). Depression and suicide in children and adolescents. Pediatr. Rev. 36, 299–310. doi: 10.1542/pir.36.7.299

McCormack, H. M., MacIntyre, T. E., O’Shea, D., Herring, M. P., and Campbell, M. J. (2018). The prevalence and cause(s) of burnout among applied psychologists: a systematic review. Front. Psychol. 9:1897. doi: 10.3389/fpsyg.2018.01897

McCormick, K. (2014). Restoring and strengthening resilience in the psychology profession: The RIO model of resilience for psychologists . [Doctor of Clinical Psychology thesis] Auckland, NZ: University of Auckland.

Medvedev, O. N., Krägeloh, C. U., Titkova, E. A., and Siegert, R. J. (2020). Rasch analysis and ordinal-to-interval conversion tables for the depression, anxiety and stress scale. J. Health Psychol. 25:1374. doi: 10.1177/1359105318755261

Ministry of Health. COVID-19: Vaccine data (2022). Available at: https://www.health.govt.nz/covid-19-novel-coronavirus/covid-19-data-and-statistics/covid-19-vaccine-data .

Morse, G., Salyers, M. P., Rollins, A. L., Monroe-DeVita, M., and Pfahler, C. (2012). Burnout in mental health services: a review of the problem and its remediation. Adm. Policy Ment. Health Ment. Health Serv. Res. 39, 341–352. doi: 10.1007/s10488-011-0352-1

New Zealand Psychologists Board. Guidelines on Supervision (2021) Available at: https://psychologistsboard.org.nz/wp-content/uploads/2023/08/BPG-Supervision-June-2021.pdf .

New Zealand Psychologists Board. Annual Report . Wellington: NZPB; (2021).

O’Connor, K., Neff, D. M., and Pitman, S. (2018). Burnout in mental health professionals: a systematic review and meta-analysis of prevalence and determinants. Eur. Psychiatry 53, 74–99. doi: 10.1016/j.eurpsy.2018.06.003

Officer, T., Imlach, F., McKinlay, E., Kennedy, J., Pledger, M., Russell, L., et al. (2022). COVID-19 pandemic lockdown and wellbeing: experiences from Aotearoa New Zealand in 2020. Int. J. Environ. Res. Public Health 19:2269. doi: 10.3390/ijerph19042269

Psychology Workforce Task Group. (2016). The Aotearoa New Zealand psychology workforce survey . Available at: https://www.nzccp.co.nz/assets/Aotearoa-NZ-Psychology-Workforce-Survey-2016.pdf .

Rahman, J., Kercher, A., and Pedersen, M. (2024). The COVID-Related Stress Scale: development and Initial Validation with psychologists. PsyArXiv . doi: 10.31234/osf.io/2jz6t

Rupert, P. A., and Morgan, D. J. (2005). Work setting and burnout among professional psychologists. Prof. Psychol. Res. Pract. 36, 544–550. doi: 10.1037/0735-7028.36.5.544

Saha, K., Torous, J., Caine, E. D., and De Choudhury, M. (2020). Psychosocial effects of the COVID-19 pandemic: large-scale quasi-experimental study on social media. J. Med. Internet Res. 22:e22600. doi: 10.2196/22600

Sicouri, G., March, S., Pellicano, E., de Young, A. C., Donovan, C. L., Russell, J. K., et al. (2023). Mental health symptoms in children and adolescents during COVID-19 in Australia. Aust. N. Z. J. Psychiatry 57, 213–229. doi: 10.1177/00048674221090174

Simpson, S., Simionato, G., Smout, M., van Vreeswijk, M. F., Hayes, C., Sougleris, C., et al. (2019). Burnout amongst clinical and counselling psychologist: the role of early maladaptive schemas and coping modes as vulnerability factors. Clin. Psychol. Psychother. 26, 35–46. doi: 10.1002/cpp.2328

Skirrow, P. Clinical Psychologists in Private Practice ‘Overwhelmed’ with Referrals: NZ College of Clinical Psychologists; (2021). Available at: https://www.nzccp.co.nz/clinical-psychologists-in-private-practice-overwhelmed-with-referrals/

Stamm, B. H. Measuring compassion satisfaction as well as fatigue: Developmental history of the compassion satisfaction and fatigue test. Treating compassion fatigue . Psychosocial stress series, no. 24. New York, NY, US: Brunner-Routledge; (2002). p. 107–19.

Stamm, B. H. The concise ProQOL manual , 2nd Ed. Pocatello, ID: ProQOL.org (2010).

Thabrew, H., Henderson, S., Hazell, P., Moor, S., Kowalenko, N., and Kenn, F. (2017). Is it time for child psychiatry to grow up? Aust. N. Z. J. Psychiatry 51, 971–973. doi: 10.1177/0004867417718946

Yang, Y., and Hayes, J. A. (2020). Causes and consequences of burnout among mental health professionals: a practice-oriented review of recent empirical literature. Psychotherapy 57, 426–436. doi: 10.1037/pst0000317

Zanon, C., Brenner, R. E., Baptista, M. N., Vogel, D. L., Rubin, M., Al-Darmaki, F. R., et al. (2021). Examining the dimensionality, reliability, and invariance of the depression, anxiety, and stress scale–21 (DASS-21) across eight countries. Assessment 28, 1531–1544. doi: 10.1177/1073191119887449

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.

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In Their Own Words, Americans Describe the Struggles and Silver Linings of the COVID-19 Pandemic

The outbreak has dramatically changed americans’ lives and relationships over the past year. we asked people to tell us about their experiences – good and bad – in living through this moment in history..

Pew Research Center has been asking survey questions over the past year about Americans’ views and reactions to the COVID-19 pandemic. In August, we gave the public a chance to tell us in their own words how the pandemic has affected them in their personal lives. We wanted to let them tell us how their lives have become more difficult or challenging, and we also asked about any unexpectedly positive events that might have happened during that time.

The vast majority of Americans (89%) mentioned at least one negative change in their own lives, while a smaller share (though still a 73% majority) mentioned at least one unexpected upside. Most have experienced these negative impacts and silver linings simultaneously: Two-thirds (67%) of Americans mentioned at least one negative and at least one positive change since the pandemic began.

For this analysis, we surveyed 9,220 U.S. adults between Aug. 31-Sept. 7, 2020. Everyone who completed the survey is a member of Pew Research Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories.  Read more about the ATP’s methodology . 

Respondents to the survey were asked to describe in their own words how their lives have been difficult or challenging since the beginning of the coronavirus outbreak, and to describe any positive aspects of the situation they have personally experienced as well. Overall, 84% of respondents provided an answer to one or both of the questions. The Center then categorized a random sample of 4,071 of their answers using a combination of in-house human coders, Amazon’s Mechanical Turk service and keyword-based pattern matching. The full methodology  and questions used in this analysis can be found here.

In many ways, the negatives clearly outweigh the positives – an unsurprising reaction to a pandemic that had killed  more than 180,000 Americans  at the time the survey was conducted. Across every major aspect of life mentioned in these responses, a larger share mentioned a negative impact than mentioned an unexpected upside. Americans also described the negative aspects of the pandemic in greater detail: On average, negative responses were longer than positive ones (27 vs. 19 words). But for all the difficulties and challenges of the pandemic, a majority of Americans were able to think of at least one silver lining. 

Both the negative and positive impacts described in these responses cover many aspects of life, none of which were mentioned by a majority of Americans. Instead, the responses reveal a pandemic that has affected Americans’ lives in a variety of ways, of which there is no “typical” experience. Indeed, not all groups seem to have experienced the pandemic equally. For instance, younger and more educated Americans were more likely to mention silver linings, while women were more likely than men to mention challenges or difficulties.

Here are some direct quotes that reveal how Americans are processing the new reality that has upended life across the country.

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COVID-19-induced financial hardships reveal mental health struggles

30 April 2024

Now economic researchers at the University of South Australia have examined the mental health effects on people who experienced immediate or expected financial setbacks during the height of the pandemic.

Data gathered from China, Japan and South Korea during the early phases of the pandemic revealed that the severe economic shocks induced by COVID-19 caused significant effects to people’s mental health including anxiety, sleeping troubles, boredom and loneliness.

UniSA’s Associate Professor of Economics Tony Cavoli says the anticipation of future income loss had a more profound impact on people’s mental wellbeing compared to the actual decreases in their income.

“Our research shows that mental health issues are more likely to occur due to anticipated income losses rather than actual losses. It was also interesting to find that although women are generally more likely to experience mental health problems, in many instances in our study we found that men were more likely to experience anxiety than women in response to either actual or expected losses in their income,” he says.

“There are a couple of reasons as to why this might be the case. Firstly, in societies that are perhaps seen as more traditional in terms of household and familial structures, it is possible that males feel greater societal pressure to remain employed. Secondly, those industries for which there was a higher likelihood of experiencing income reductions were more likely to have greater participation by males.”

The initial impacts of COVID-19 lockdowns led to decreased demand, a reduction in hours worked and significant job losses. The Australian Bureau of Statistics predicted the pandemic caused a $47 billion hit to the country’s economy.

Assoc Prof Cavoli says his study presents an important insight into people’s anxieties arising from economic turbulence and uncertainties.

“We have an insight into how people dealt with income shocks during stressful times, and this is a really important opportunity for future policy implications, particularly around the design of government support and other interventions,” he says.

“Responses from governments, for example, early in times of crisis may help individuals manage possibility anxieties arising from economic uncertainties."

For more information:

Akbar Zamanzadeh, Tony Cavoli, Matina Ghasemi, Ladan Rokni, The effect of actual and expected income shocks on mental wellbeing: Evidence from three East Asian countries during COVID-19 , Economics & Human Biology, Volume 53, 2024, 101378, ISSN 1570-677X, https://doi.org/10.1016/j.ehb.2024.101378 .

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Media contact: Melissa Keogh, Communications Officer, UniSA Media M: +61 403 659 154 E: [email protected]

Researcher contact: Associate Professor Tony Cavoli, UniSA, E: [email protected]

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