What this is
- This research investigates the mental health impacts of the COVID-19 pandemic across three distinct stages: stable, recurrence, and end-of-emergency.
- It evaluates anxiety, depression, , and insomnia symptoms in a large sample of the general population in China.
- The study identifies key factors associated with mental health symptoms, revealing significant shifts in prevalence at different pandemic stages.
Essence
- Mental health symptoms increased during the recurrence of the COVID-19 pandemic, with anxiety, depression, and levels remaining elevated even after the declared end of the emergency. Centralized quarantine, frontline work, and living in initially infected areas were consistently linked to higher mental health risks.
Key takeaways
- Anxiety, depression, and insomnia symptoms rose from 13.7–16.4% at the stable stage to 17.3–22.2% during the recurrence stage, then decreased to 14.5–18.6% at the end-of-emergency stage. symptoms continuously increased from 5.1% to 9.2% across the same periods.
- Centralized quarantine was a persistent risk factor for anxiety, depression, , and insomnia across all stages. Frontline workers consistently exhibited higher risks for these symptoms.
- Stage-specific risk factors included lack of outdoor activity linked to anxiety and depression during stable and recurrence stages, and family or friends' deaths associated with and depression at the end of the emergency stage.
Caveats
- Selection bias may exist due to the inability to conduct random sampling during pandemic restrictions. The cross-sectional design limits insights into long-term mental health changes.
- The study's findings are based on self-reported measures, which may not fully represent clinical diagnoses of mental health conditions.
Definitions
- PTSD: Post-traumatic stress disorder; a mental health condition triggered by experiencing or witnessing a traumatic event.
AI simplified
Introduction
Coronavirus disease 2019 (COVID-19) quickly emerged in late 2019 and was quickly designated a global pandemic (Chen et al., 2020; Stein et al., 2022). As an acute global emergency, COVID-19 is very contagious (Tan et al., 2023) and leads to multisystem symptoms, which can even be life threatening for some patients (Parotto et al., 2023; Wiersinga et al., 2020), with more than 770 million cases globally over the past 3 years (Marks and Gulick, 2023; World Health Organization, 2023). The latest Global Burden of Disease Study 2021 estimates that COVID-19 is the leading cause of disability-adjusted life-years globally (GBD 2021 Diseases and Injuries Collaborators, 2024), with 15.9 million excess deaths from 2020 to 2021(GBD 2021 Demographics Collaborators, 2024). In May 2023, the World Health Organization (WHO) announced that COVID-19 ‘no longer constitutes a public health emergency of international concern’, considering that it has not been an unusual or unexpected event (Harris, 2023). However, COVID-19 has been and will continue evolving to influence global health through long symptoms, new mutations, and frequent recurrence, which necessitates the attention of global health (El-Shabasy et al., 2022; Yisimayi et al., 2024).
During the COVID-19 pandemic, multiple stress factors, such as worries about exposure and infection, social isolation and physical inactivity, and unavailable psychosocial support, could exacerbate psychological distress (Shi et al., 2020; Wang et al., 2020, 2021) and impact mental health, such as anxiety, depression, sleep problems, post-traumatic stress disorder (PTSD) and other symptoms (Alimoradi et al., 2021; Cénat et al., 2021; Salanti et al., 2022). This implication has been of global concern, as growing evidence has revealed an increased mental health burden (COVID-19 Mental Disorders Collaborators, 2021; Kola et al., 2021; Prime et al., 2020). However, major gaps and concerns remain regarding the shifts in mental health impacts and associated factors during different pandemic periods, and limited information is available on post-acute mental health symptoms (Penninx et al., 2022; Pirkis et al., 2021; Raina et al., 2021). Additionally, the results from many studies have shown substantial heterogeneity among their study populations and times (Hossain et al., 2020; Penninx et al., 2022) and have limited methodological quality due to small sample sizes and convenience sampling, as well as unclear representativeness and generalizability(Cénat et al., 2021; Salanti et al., 2022). To more reliably estimate shifts in mental health impacts and to identify factors associated with symptoms at different pandemic stages, we conducted this large-sample multicentre study with a repeated cross-sectional design at 3 representative stages (stable, recurrence, and end-of-emergency) in the general Chinese population with our previous experience during the initial COVID-19 wave (Wang et al., 2020) and return-to-work period (Wang et al., 2021; Zhang et al., 2020). This study can also provide timely references for the post-COVID-19 era and future potential pandemics and recurrence, helping in more appropriately mediating health policies and identifying and protecting individuals at risk and promoting long-term resilience; thus, providing important information for policy makers, practicing clinicians, researchers and other stakeholders (Penninx et al., 2022).
Materials and methods
Study design and sampling process
This repeated cross-sectional study was conducted through 3 surveys from 2021 to 2023. The ethics committee of Shandong Provincial Hospital Affiliated to Shandong First Medical University approved this study. All participants provided online or oral informed consent. This study followed the 1964 Helsinki Declaration and its later amendments and reports according to the Statement of Reporting of Observational studies in Epidemiology (STROBE; von Elm et al., 2014). The surveys were anonymous, and the confidentiality of the data was ensured.
Pandemic periods classification, key time points and health policies were collected from epidemic reports by the National Health Commission of China (NHC; www.nhc.gov.cn↗). Specifically, from late 2019 to March 2020, China experienced initial epidemic wave and applied ‘nationwide restriction’ policy. This initial wave was basically controlled from February 2020 and the ‘normalized prevention and control’ during this stable stage. However, the epidemic recurrent from March 2022, during which the ‘dynamic zero’ policy were applied. In December 2022, the Chinese government declared the end of the emergency of the COVID-19 epidemic and announced the ‘scientific and precise control’ (Fig. 1).
Additionally, the NHC database and the WHO dashboard (World Health Organization, 2023) were queried to stratify the infection risks of these provinces during different pandemic periods in exploring its potential influences on mental health at different pandemic stages. Pandemic area I (initial wave) was stratified according to cumulative confirmed cases between January 2020 and March 2020 in capturing regional characteristics of the initial waves; pandemic area II (recurrence) was stratified according to cumulative confirmed cases between March 2022 and May 2022 to classified risks of the first COVID-19 recurrence waves; and pandemic area III (end-of-emergency, summary of the pandemic) was stratified according to cumulative confirmed cases between January 2020 and December 2022 in summarizing the entire pandemic situation until the end of emergency. The detailed regional divisions are shown in Supplementary Table 1.

Flow diagram showing the study procedure (A), sketch map showing the region divisions (B), and timeline showing the COVID-19 stages (). COVID-19, coronavirus disease 2019; NEPD, National Economic Population Division. Region division was classified based on socio-geographical characteristics or the influence of COVID-19 on regional features and infection risk at different pandemic stages, refers tofor detailed regional division for provinces; socio-geographic region was stratified based on NEPD, the National Bureau of Statistics for the normal period; COVID-19 pandemic area I was stratified according to cumulative confirmed cases between January 2020 and March 2020 (initial wave, 2020) and data from the National Health Commission, China; COVID-19 pandemic area II was stratified according to cumulative confirmed cases between March 2022 and May 2022 (recurrence, 2022) and data from the National Health Commission, China; and COVID-19 pandemic area III was stratified according to cumulative confirmed cases between January 2020 and December 2022 (end-of-emergency, 2023). C Supplementary Table 1
Study population
This study assessed the mental health of the general population. Considering ethical issues and measurement scales, only adults (≥18 years) were included. The target sample size for recruitment was calculated in PASS software version 2021 (NCSS LLC) for multiple (3) comparisons of proportions (Chow et al., 2008), in which the statistical power was set to 90.0% and the overall α = 0.05 (Bonferroni adjusted α = 0.0167). Based on previous studies (Wang et al., 2020, 2021; Xiong et al., 2020; Zhang et al., 2020), the proportion of mental health symptoms was estimated to be 15%, and the acceptable margin of differences was set to 1%. When the dropout rate was set to 20%, the dropout-inflated enrolment sample size was 41,864 for each stage. Considering the convenience of calculating the required quotas, we increased the sample size to 42,000 for each survey stage.
| No. (%) | ||||
|---|---|---|---|---|
| Subject | Stable stage (sampled 2021) | Recurrence stage (sampled 2022) | End-of-emergency stage (sampled 2023) | valueP |
| Overall | 36,218 (100.0) | 36,097 (100.0) | 36,306 (100.0) | |
| (Response rate), recruited participants= 42,000n | (86.2) | (85.9) | (86.4) | 0.11 |
| Region division 2045796025100243 | ||||
| Socio-geographic region (NEPD, normal period) | 0.89 | |||
| Eastern region | 14,554 (40.2) | 14,583 (40.4) | 14,611 (40.2) | |
| Middle region | 8,976 (24.8) | 8,893 (24.6) | 8,970 (24.7) | |
| Western region | 9,759 (26.9) | 9,724 (26.9) | 9,716 (26.8) | |
| Northeast region | 2,929 (8.1) | 2,897 (8.1) | 3,009 (8.3) | |
| COVID-19 pandemic area I (initial wave, 2020) | ||||
| Widely-infected area (≥10,000 confirmed cases) | 1,463 (4.0) | 1,465 (4.1) | 1,471 (4.1) | >0.999 |
| Moderate-infected area (≥500 confirmed cases) | 20,098 (55.5) | 20,005 (55.4) | 20,130 (55.4) | |
| Less-infected area (<500 confirmed cases) | 14,657 (40.5) | 14,627 (40.5) | 14,705 (40.5) | |
| COVID-19 pandemic area II (recurrence, 2022) | ||||
| High-risk area (≥10,000 confirmed cases) | NA | 1,395 (3.9) | 1,419 (3.9) | 0.93 |
| Moderate-risk area (≥500 confirmed cases) | NA | 19,434 (53.8) | 19,567 (53.9) | |
| Low-risk area (<500 confirmed cases) | NA | 15,268 (42.3) | 15,320 (42.2) | |
| COVID-19 pandemic area III (end-of-emergency, 2023) | ||||
| Severe affected area (≥10,000 confirmed cases) | NA | NA | 9,273 (25.5) | NA |
| Moderate affected area (≥5,000 confirmed cases) | NA | NA | 10,814 (29.8) | |
| Mild affected area (<5,000 confirmed cases) | NA | NA | 16,219 (44.7) | |
| Socio-demographic characteristic | ||||
| Gender | 0.65 | |||
| Male | 18,556 (51.2) | 18,392 (51.0) | 18,611 (51.3) | |
| Female | 17,662 (48.8) | 17,705 (49.0) | 17,695 (48.7) | |
| Age, years | 0.47 | |||
| 18−34 | 9,940 (27.4) | 9,881 (27.4) | 10,027 (27.6) | |
| 35−49 | 10,424 (28.8) | 10,327 (28.6) | 10,338 (28.5) | |
| 50−64 | 9,668 (26.7) | 9,549 (26.5) | 9,751 (26.9) | |
| ≥65 | 6,186 (17.1) | 6,340 (17.5) | 6,190 (17.0) | |
| Place of residence | 0.74 | |||
| Urban | 19,656 (54.3) | 19,682 (54.5) | 19,707 (54.3) | |
| Rural | 16,562 (45.7) | 16,415 (45.5) | 16,599 (45.7) | |
| Education level | ||||
| Less than college | 28,332 (78.2) | 28,274 (78.3) | 28,622 (78.8) | 0.1 |
| College degree or higher | 7,886 (21.8) | 7,823 (21.7) | 7,684 (21.2) | |
| Marriage status | 0.61 | |||
| Unmarried | 6,960 (19.2) | 6,929 (19.2) | 7,023 (19.3) | |
| Married | 26,403 (72.9) | 26,397 (73.1) | 26,393 (72.7) | |
| Divorced/Widowed | 2,855 (7.9) | 2,771 (7.7) | 2,890 (8.0) | |
| History of chronic diseases | 0.72 | |||
| Yes | 3,255 (9.0) | 3,258 (9.0) | 3,299 (9.1) | |
| No | 31,835 (87.9) | 31,777 (88.0) | 31,916 (87.9) | |
| Unknown | 1128 (3.1) | 1062 (3.0) | 1091 (3.0) | |
| History of psychiatric disorders | 0.2 | |||
| Yes | 413 (1.1) | 428 (1.2) | 429 (1.2) | |
| No | 34,708 (95.9) | 34,610 (95.9) | 34,704 (95.6) | |
| Unknown | 1097 (3.0) | 1059 (2.9) | 1173 (3.2) | |
| Occupation | 0.57 | |||
| Students, full-time | 1,723 (4.8) | 1,681 (4.7) | 1,691 (4.7) | |
| Technicians and associate professionals | 3,600 (9.9) | 3,679 (10.2) | 3,613 (10.0) | |
| Government and clerical support workers | 3,229 (8.9) | 3,226 (8.9) | 3,249 (8.9) | |
| Social and life service workers | 9,516 (26.3) | 9,534 (26.4) | 9,684 (26.7) | |
| Agricultural, forestry and fishery workers | 7,006 (19.3) | 7,071 (19.6) | 7,097 (19.5) | |
| Production and manufacture workers | 8,989 (24.8) | 8,884 (24.6) | 8,793 (24.2) | |
| Other unclassified occupations | 105 (0.3) | 97 (0.3) | 103 (0.3) | |
| Freelance or inoccupation | 2,050 (5.7) | 1,925 (5.3) | 2,076 (5.7) | |
| Yearly family income, CNY 2045796025100243 | 0.86 | |||
| <40,000 | 7,497 (20.7) | 7,537 (20.9) | 7,557 (20.8) | |
| 40,000−99,999 | 22,725 (62.7) | 22,690 (62.9) | 22,803 (62.8) | |
| ≥100,000 | 5,996 (16.6) | 5,870 (16.2) | 5,946 (16.4) | |
| Activity and work/study status | ||||
| Outside activity/once | <0.001 * | |||
| 1−7 days | 19,230 (53.1) | 7,837 (21.7) 2045796025100243 | 22,947 (63.2) 2045796025100243 | |
| 8−14 days | 10,982 (30.3) | 11,904 (33.0) 2045796025100243 | 10,061 (27.7) 2045796025100243 | |
| 15−29 days | 3,908 (10.8) | 8,920 (24.7) 2045796025100243 | 2,532 (7.0) 2045796025100243 | |
| ≥30 days | 2,098 (5.8) | 7,436 (20.6) 2045796025100243 | 766 (2.1) 2045796025100243 | |
| Work/study status | <0.001 * | |||
| On-site work/study | 22,391 (61.8) | 10,001 (27.7) 2045796025100243 | 28,847 (79.5) 2045796025100243 | |
| Off-site work/study | 7,301 (20.2) | 16,581 (45.9) 2045796025100243 | 4,810 (13.2) 2045796025100243 | |
| Not back to work/study | 6,526 (18.0) | 9,515 (26.4) 2045796025100243 | 2,649 (7.3) 2045796025100243 | |
| Experience related to COVID-19 | ||||
| Current COVID-19 identity | <0.001 * | |||
| Current infected | 402 (1.1) | 4,322 (12.0) 2045796025100243 | 2,257 (6.2) 2045796025100243 | |
| Previous infected | 3,349 (9.2) | 6,291 (17.4) 2045796025100243 | 24,821 (68.4) 2045796025100243 | |
| Suspect infected | 557 (1.5) | 5,299 (14.7) 2045796025100243 | 2,783 (7.7) 2045796025100243 | |
| Not infected | 31,910 (88.2) | 20,185 (55.9) 2045796025100243 | 6,445 (17.7) 2045796025100243 | |
| Frontline workers during COVID-19 2045796025100243 | <0.001 * | |||
| Yes | 6,038 (16.7) | 6,698 (18.6) 2045796025100243 | 7,976 (22.0) 2045796025100243 | |
| No | 30,180 (83.3) | 29,399 (81.4) 2045796025100243 | 28,330 (78.0) 2045796025100243 | |
| Experience of hospitalization for COVID-19 | <0.001 * | |||
| Yes | 2,710 (7.5) | 4,515 (12.5) 2045796025100243 | 8,176 (22.5) 2045796025100243 | |
| No | 33,508 (92.5) | 31,582 (87.5) 2045796025100243 | 28,130 (77.5) 2045796025100243 | |
| Experience of quarantine during COVID-19 | ||||
| Centralized | 3,788 (10.5) | 6,989 (19.4) 2045796025100243 | 10,125 (27.9) 2045796025100243 | <0.001 * |
| At home | 6,219 (17.2) | 10,294 (28.5) 2045796025100243 | 16,997 (46.8) 2045796025100243 | |
| None | 26,211 (72.3) | 18,814 (52.1) 2045796025100243 | 9,184 (25.3) 2045796025100243 | |
| Families/friends hospitalization related to COVID-19 | <0.001 * | |||
| Yes | 5,487 (15.1) | 9,111 (25.2) 2045796025100243 | 16,435 (45.3) 2045796025100243 | |
| No | 30,731 (84.9) | 26,986 (74.8) 2045796025100243 | 19,871 (54.7) 2045796025100243 | |
| Families/friends death related to COVID-19 | <0.001 * | |||
| Yes | 837 (2.3) | 3,731 (10.3) 2045796025100243 | 5,350 (14.7) 2045796025100243 | |
| No | 35,381 (97.7) | 32,366 (89.7) 2045796025100243 | 30,956 (85.3) 2045796025100243 | |
| Psychological intervention during COVID-19 | ||||
| Psychological intervention during COVID-19 | <0.001 * | |||
| Yes | 9,266 (25.6) | 10,678 (29.6) 2045796025100243 | 12,594 (34.7) 2045796025100243 | |
| Public psychological education only | 7,997 (86.3) | 9,196 (86.1) | 10,803 (85.8) | 0.52 |
| With individual counselling | 1269 (13.7) | 1482 (13.9) | 1,791 (14.2) | |
| No | 26,952 (74.4) | 25,419 (70.4) 2045796025100243 | 23,712 (65.3) 2045796025100243 | |
Measurements and covariates
The same questionnaire design was applied for the 3 surveys. Participants preferred to answer online via WJX links (Ranxing LLC) with an IP address restriction to prevent repeated answers from the same person in the survey. For those who could not finish the online survey, telephone interviews read by investigators were provided with the same content. Additionally, participants were asked to answer only one type of survey to avoid duplication. A guiding webpage or oral introduction of the informed consent was provided prior to the survey. The participants were informed about their free decision to participate or not participate, provided informed consent or not, and could terminate the survey at any time.
The self-designed survey consisted of 4 sections and required approximately 15 minutes to complete. The first section collected socio-demographic information, including sex, age (division according to previous epidemiological studies [Huang et al., 2019; Wang et al., 2020, 2021; Zhang et al., 2020] for comparisons), place of residence (urban vs rural), education level, marital status, history of chronic disease and psychiatric disorders, occupation (classified by the NPC 2020), and yearly family income. The second section asked pandemic-related questions about current activity (outside activity frequency) and work/study status (on-site, off-site or not), experience related to COVID-19 (current identity, frontline workers or not, experience of hospitalization and quarantine, and hospitalization and death of family/friends), and psychological interventions during COVID-19 (with or without; and public psychological education only, i.e., provided by governments, societies, communities and others, which was delivered once or scattered times without specialized guiding, or with individual counselling, i.e., provided by professional psychologists with systematic terms and individualized treatments).
The third section included 4 standardized screening scales, including the Generalized Anxiety Disorder-7 scale (GAD-7, scores from 0-21, Cronbach’s α coefficient = 0.93)(Zhang et al., 2021), Patient Health Questionnaire-9 (PHQ-9, scores from 0-27, Cronbach’s α coefficient = 0.90)(Wang et al., 2014; Zhang et al., 2013), Impact of Events Scale-Revised (IES-R, scores from 0-88, Cronbach’s α coefficient = 0.95)(Creamer et al., 2003; Wu and Chan, 2003), and Insomnia Severity Index (ISI, scores from 0-28, Cronbach’s α coefficient = 0.91)(Chung et al., 2011; Thorndike et al., 2011), which measured anxiety, depression, PTSD, and insomnia symptoms, respectively. These scales are all validated Chinese versions and have been widely used in previous epidemiological investigations (Chen et al., 2023; Lai et al., 2020; Shi et al., 2020; Xiong et al., 2020). Higher scores on these scales indicate more severe symptoms. In the present study, the cut-off scores for detecting symptoms were ≥10, ≥10, ≥33 and ≥15, and scores ≥15, ≥15, ≥37 and ≥22 indicated severe symptoms for the GAD-7, PHQ-9, IES-R and ISI, respectively. These cut-off values were determined according to Chinese norms and previous studies of the Chinese population (Chen et al., 2023; Wang et al., 2020, 2021; Xiong et al., 2020; Zhang et al., 2020), which has been widely recognized and further reviewed by a consensus of neuropsychologists.
For the fourth section, two trust test questions were designed: ‘I answered truthfully (yes or no)’ and ‘What is ten plus ten?’ Surveys without consent, with incomplete answers, with a failure of any trust question, or with an outrange of time (i.e., <1 min or >2 hours) were regarded as invalid questionnaires.
Statistical analysis
Categorical variables are reported as numbers and percentages. The prevalence of symptoms was calculated, and 95% confidence intervals (CIs) were determined by exact binomial methods. Pearson’s x2 test was used to compare categorical variables. For pairwise comparisons of multiple groups, post hoc z-tests were applied after adjusting by Bonferroni correction. Considering the incomplete answers were missing randomly and variables in regression analysis, all analyses were based on complete data (Graham, 2009).
Logistic regression was used to explore potential factors (such as region divisions, socio-demographic characteristics, activity and work/study status, experience related to COVID-19, and psychological interventions) associated with symptoms. All factors with significance in the univariable unadjusted logistic analyses, which might convey important information, were then entered into multivariable logistic regression (backward) to adjust for confounding effects of other variables in the model. The contrast was used as an indicator of the subgroup with the lowest prevalence to explore potential risk factors. The adjusted odds ratios (aORs), 95% CIs and P values of the risk factors are provided. Additionally, multicollinearity diagnostics tested by variance inflation factors were verified with < 10 in the final model, suggesting the independence of these factors.
In this study, all the statistical tests were two-sided, and the significance level was set at α = 0.05. All analyses were performed in SPSS software version 27 (IBM Crop) and R software version 4 (R Foundation), and the figures were drawn using GraphPad Prism version 10 (GraphPad Software LLC).
Results
Socio-demographic characteristics
Data from a total of 36,218, 36,097, and 36,306 participants were included in the final analysis at the stable, recurrence and end-of-emergency stages, respectively, with a response rate comparable to 85.9–86.4%. Generally, 93.9–95.0% and 5.0–6.1% of the participants were recruited from quota and snowball sampling; and 87.6–89.0% and 11.0–12.1% of them were surveyed online or through telephone, respectively. Participants recruited from quota or snowball sampling, as well as surveyed online or through telephone were compared with nonsignificant difference in characteristics or outcomes, confirming their equivalence (all P > 0.05). Baseline characteristics of the included participants (subregions, genders, ages and occupations) were compared with the designed quotas and the NPC 2020, and no significant difference was revealed (all P > 0.05), suggesting that the included participants had sufficient representativeness of the general population and inconsequential influences of the missing data.
No significant difference was found in the distribution of regional populations among these 3 surveys, and their socio-demographic characteristics were comparable (Table 1), indicating good comparability. Among these participants at each survey stage, 18,392–18,611 (51.0–51.3%) participants were reported as male, aged 18–87 years (IQR 32–58, the same for 3 surveys), and 19,656–19,707 (54.3–54.5%) participants were urban residents. Most participants had an educational level less than college (28,274–28,622 [78.2–78.8%]) and were married (26,393–26,403 [72.7–73.1%]). A significant difference was found in pandemic-related variables. Increased participants have previously infected (3,349 [9.2%] vs 6,291 [17.4%] vs 24,821 [68.4%]), served as frontline workers (6,038 [16.7%] vs 6,698 [18.6%] vs 7,976 [22.0%]), experienced hospitalization (2,710 [7.5%] vs 4,515 [12.5%] vs 8,176 [22.5%]), quarantine (centralized, 3,788 [10.5%] vs 6,989 [19.4%] vs 10,125 [27.9%]), and at home, 6,219 [17.2%] vs 10,294 [28.5%] vs 16,997 [46.8%]), and had family/friends’ hospitalization (5,487 [15.1%] vs 9,111 [25.2%] vs 16,435 [45.3%]) and death (837 [2.3%] vs 3,731 [10.3%] vs 5,350 [14.7%]) related to COVID-19 from the stable stage to recurrence and end-of-emergency stages.

Line chart showing trends in the prevalence of mental health symptoms () and sector chart showing the proportions of participants who received psychological intervention () and the distribution of intervention types (; public psychological education only or with individual counselling) during the COVID-19 pandemic. COVID-19, coronavirus disease 2019; PTSD, post-traumatic stress disorder;, Not significant.(Bonferroni) adjusted< 0.05 compared with the stable stage (post hoc-test for pairwise comparisons, adjusted by Bonferroni correction);(Bonferroni) adjusted< 0.05 compared with the recurrence stage (post hoc-test for pairwise comparisons, adjusted by Bonferroni correction). A B C n.S. P z P z A b
Prevalence of mental health symptoms at different pandemic stages
| No. % (95% CI) | ||||
|---|---|---|---|---|
| Subject | Stable stage (sampled 2021) | Recurrence stage (sampled 2022) | End-of-emergency stage (sampled 2023) | valueP |
| Assessment scale/mental health symptom | ||||
| GAD-7 | ||||
| Anxiety symptom (scores ≥10) | 5,467 | 7,410 2045796025100243 | 5,788 2045796025100243 | <0.001 * |
| Prevalence, % (95% CI) | 15.1 (14.7−15.5) | 20.5 (20.1−20.9) | 15.9 (15.6−16.3) | |
| Severe anxiety symptom (scores ≥15) | 567 | 1,177 2045796025100243 | 784 2045796025100243 | <0.001 * |
| Prevalence, % (95% CI) | 1.6 (1.4−1.7) | 3.3 (3.1−3.4) | 2.2 (2.0−2.3) | |
| PHQ-9 | ||||
| Depression symptom (scores ≥10) | 4,968 | 6,258 2045796025100243 | 5,266 2045796025100243 | <0.001 * |
| Prevalence, % (95% CI) | 13.7 (13.4−14.1) | 17.3 (16.9−17.7) | 14.5 (14.1−14.9) | |
| Severe depression symptom (scores ≥15) | 433 | 862 2045796025100243 | 724 2045796025100243 | <0.001 * |
| Prevalence, % (95% CI) | 1.2 (1.1−1.3) | 2.4 (2.2−2.5) | 2.0 (1.9−2.1) | |
| IES-R | ||||
| PTSD symptom (scores ≥33) | 1,853 | 2,757 2045796025100243 | 3,344 2045796025100243 | <0.001 * |
| Prevalence, % (95% CI) | 5.1 (4.9−5.3) | 7.6 (7.4−7.9) | 9.2 (8.9−9.5) | |
| Severe PTSD symptom (scores ≥37) | 295 | 509 2045796025100243 | 697 2045796025100243 | <0.001 * |
| Prevalence, % (95% CI) | 0.8 (0.7−0.9) | 1.4 (1.3−1.5) | 1.9 (1.8−2.1) | |
| ISI | ||||
| Insomnia symptom (scores ≥15) | 5,925 | 8,007 2045796025100243 | 6,768 2045796025100243 | <0.001 * |
| Prevalence, % (95% CI) | 16.4 (16.0−16.7) | 22.2 (21.8−22.6) | 18.6 (18.2−19.0) | |
| Severe insomnia symptom (scores ≥22) | 615 | 1,471 2045796025100243 | 1,074 2045796025100243 | <0.001 * |
| Prevalence, % (95% CI) | 1.7 (1.6−1.8) | 4.1 (3.9−4.3) | 3.0 (2.8−3.1) | |
Factors associated with mental health symptoms

Factors associated with mental health at stable (A), recurrence (B) and end-of-emergency (C) COVID-19 pandemic stages. COVID-19, coronavirus disease 2019; GAD-7, Generalized Anxiety Disorder-7 scale; PHQ-9, Patient Health Questionnaire-9; IES-R, Impact of Events Scale-Revised; PTSD, post-traumatic stress disorder; ISI, Insomnia Severity Index; CI, confidence interval. The factors with significance in the univariable analyses (refer to) were then entered into the multivariable logistic regression in a backward fashion to adjust for confounding effects of other factors included in the model. The contrast was set as an indicator determined by the group with the lowest prevalence (proportions) of anxiety, depression, PTSD, and insomnia symptoms to identify potential risk factors for mental health symptoms. The multicollinearity diagnostics showed that variables that were included in the multivariable analyses did not have significant multicollinearity (all variance inflation factors, VIF < 10). *< 0.05 (multivariable logistic regression); **< 0.01 (multivariable logistic regression). Supplementary Tables 2–5 P P
| Stable stage (sampled 2021) | Recurrence stage (sampled 2022) | End-of-emergency stage (sampled 2023) | |||||
|---|---|---|---|---|---|---|---|
| Scale /intervention | Factor | aOR (95% CI) | valueP | aOR (95% CI) | valueP | aOR (95% CI) | valueP |
| GAD-7 (anxiety symptom) | Pandemic area II: High-risk area | NA | NA | 1.42 (1.13−1.87) | 0.04 * | NA | NA |
| Outside activity ≥30 days/once | 2.32 (1.37−3.47) | 0.002 ** | 1.55 (1.18−2.51) | 0.01 * | NA | NA | |
| Suspect infected with COVID-19 | NA | NA | 2.82 (1.41−4.40) | 0.002 ** | 1.42 (1.10−1.92) | 0.02 * | |
| Frontline workers during COVID-19 | 1.98 (1.26−2.85) | 0.007 ** | 2.69 (1.38−3.95) | 0.003 ** | 1.93 (1.19−2.71) | 0.009 ** | |
| Experience of centralized quarantine | 1.47 (1.15−2.34) | 0.03 * | 3.09 (1.78−5.62) | <0.001 ** | 1.25 (1.21−1.53) | 0.04 * | |
| PHQ-9 (depression symptom) | Pandemic area I: Widely infected area | 1.27 (1.04−1.49) | 0.04 * | NA | NA | 1.19 (0.92−1.72) | 0.16 |
| Outside activity ≥30 days/once | 2.03 (1.34−2.97) | 0.01 * | 2.18 (1.35−3.02) | 0.004 ** | NA | NA | |
| Frontline workers during COVID -19 | 1.36 (1.29−1.64) | 0.03 * | 1.43 (1.18−1.74) | 0.02 * | 1.49 (1.23−2.08) | 0.01 * | |
| Experience of centralized quarantine | 2.14 (1.52−3.08) | 0.003 ** | 2.79 (1.78−3.98) | <0.001 ** | 1.96 (1.36−2.93) | 0.001 ** | |
| Families/friends death related to COVID-19 | NA | NA | 1.24 (0.96−1.57) | 0.12 | 1.81 (1.28− 2.44) | 0.006 ** | |
| IES-R (PTSD symptom) | Pandemic area I: Widely infected area | 2.58 (1.62− 3.66) | <0.001 ** | 1.46 (1.25 − 1.88) | 0.03 * | 1.37 (1.20−1.64) | 0.03 * |
| Experience of centralized quarantine | 1.39 (1.22−1.70) | 0.003 ** | 2.93 (1.43−4.78) | <0.001 ** | 2.62 (1.38−4.05) | <0.001 ** | |
| Families/friends death related to COVID-19 | NA | NA | 2.54 (1.87−3.50) | <0.001 ** | 3.12 (1.75−4.19) | <0.001 ** | |
| ISI (insomnia symptom) | Pandemic area II: High-risk area | NA | NA | 1.31 (1.14−1.60) | 0.03 * | NA | NA |
| Outside activity ≥30 days/Once | 2.06 (1.32−2.98) | <0.001 * | 1.70 (1.39−2.18) | 0.02 * | NA | NA | |
| Suspect infected with COVID-19 | NA | NA | 3.16 (1.82−5.20) | <0.001 ** | 1.21 (0.96−1.57) | 0.07 | |
| Frontline workers during COVID-19 | 1.55 (1.14−2.05) | 0.02 * | 2.65 (1.35−3.95) | 0.002 ** | 1.88 (1.24−2.38) | 0.005 ** | |
| Experience of centralized quarantine | 1.34 (1.09−1.88) | 0.04 * | 2.94 (1.78−4.14) | <0.001 ** | 1.49 (1.28−1.91) | 0.03* | |
| Families/friends death related to COVID-19 | NA | NA | 1.16 (0.91−2.48) | 0.17 | 2.05 (1.39−2.76) | <0.001 ** | |
Discussion
Up to date, this is the largest nationwide repeated cross-sectional study on mental health symptoms and associated factors at different COVID-19 pandemic periods for general population, of which dataset can have important contributions to the global scientific community. In total, 36,218, 36,097 and 36,306 participants with sufficient national representativeness were enrolled at 3 crucial stages: stable, recurrence and end of emergency, respectively. The prevalence of anxiety, depression and insomnia symptoms exhibited a similar trend, increasing from 13.7–16.4% at the stable stage to 17.3–22.2% at the recurrence stage. Although the prevalence decreased to 14.5–18.6% at the end-of-emergency stage, it was still higher than that at the stable stage. The prevalence of PTSD symptoms continuously increased from 5.1% at the stable stage to 7.6% and 9.2% at the recurrence and end-of-emergency stages, respectively. Several factors were also associated with a greater risk of mental symptoms, including centralized quarantine, frontline workers, and initial wave widely infected area at all 3 stages; lack of outside activity at the stable and recurrence stages; high-risk areas at the recurrence stage; and suspected infection and family/friends’ deaths at the recurrence and end-of-emergency stages. These findings could more reliably inform public health policies and population-specific strategies and could be an important reference for future potential pandemics and recurrence (Aknin et al., 2022).
Estimates of mental health status are closely related to the measurement scales and their cut-off thresholds (Salanti et al., 2022; Salari et al., 2020; Xiong et al., 2020). In the present study, we applied the cut-offs that optimally prompt probable clinical diagnoses of anxiety, depression, PTSD and insomnia as scores above moderate symptoms (the best cut-off for a probable diagnosis) to avoid overestimation (mild preclinical symptoms) or underestimation (severe symptoms) (Chung et al., 2011; Creamer et al., 2003; Thorndike et al., 2011; Wang et al., 2014; Wu and Chan, 2003; Zhang et al., 2013, 2021). In addition, this stratification also improved the compatibility of the current findings with those of pre-COVID-19 studies on mental disorders (Huang et al., 2019; Lu et al., 2021), other COVID-19 studies (Chen et al., 2023; Salanti et al., 2022; Salari et al., 2020; Xiong et al., 2020), and our previous estimates(Wang et al., 2020, 2021; Zhang et al., 2020). However, it should also be noted that the current samples are all from Chinese populations, and the scales and cut-off values are based on Chinese norms. Although theoretically, the severity classification standards represented by these cut-off values are consistent with those of international samples, further research is needed to validate their international applicability and comparability. Generally, compared with the pre-COVID-19 prevalence of mental disorders (3.6–5.0% for anxiety, depression and other disorders; and 15.0% for insomnia) in the general population in China (Cao et al., 2017; Huang et al., 2019; Kola et al., 2021), there is a clear indication of an upward burden of mental health problems across different pandemic periods, even after the end of emergency.
During the COVID-19 outbreak, an overall 11.0–31.9% prevalence of mental health symptoms was reported (Chen et al., 2023; Ettman et al., 2020; Salanti et al., 2022; Salari et al., 2020; Xiong et al., 2020). A great number of national and regional governments have applied drastic outside activity restrictions and strict interpersonal isolation, while panic (Hossain et al., 2020; Xiong et al., 2020), quarantine (Jin et al., 2021; Kelly, 2021), hospitalization (Patel et al., 2022), physical distancing (Shi et al., 2020; Wang et al., 2020) and policy stringency (Aknin et al., 2022) have contributed to serious psychiatric epidemics co-occurring with COVID-19(Hossain et al., 2020). After the initial waves of the pandemic, changes in mental health symptoms varied substantially across studies (Patel et al., 2022; Salanti et al., 2022). For the Chinese government, work resumption and the ‘normalized prevention and control’ policy were announced in February 2020. A slight increase in the prevalence of anxiety, depression, and insomnia symptoms was observed at 14.9–18.3% (versus 11.0–13.3% with a similar design for the initial wave) at the beginning of work resumption (Wang et al., 2021; Zhang et al., 2020), and a lower prevalence of symptoms (10.8–16.4%) was reported in later surveys and this study (Tan et al., 2020), as the pandemic was gradually controlled with vaccines(Fiolet et al., 2022; Polack et al., 2020). A time series analysis of electronic healthcare records also suggested reductions in primary care-recorded self-harm following the onset of the pandemic (Pirkis et al., 2021). The present study captured a crucial pandemic recurrence period when new virus mutations occurred and caused recurrent infections(El-Shabasy et al., 2022; Yisimayi et al., 2024), during which the Chinese government announced the ‘dynamic zero’ policy (Bai et al., 2022), a strategy similar to the test-trace-quarantine strategy but still widely restricted cross-regional activities(Kerr et al., 2021). There was a significant increase in mental health symptoms (17.3–22.2%) at the recurrence stage, suggesting the synchronous nature of the mental health crisis. In December 2022, the Chinese national government announced the end-of-emergency of the pandemic and abolished restrictions. Our survey at 6 months after this time point suggested that mental health symptoms decreased but remained in 14.5–18.6% of the population, suggesting potential time lag effects and post-acute symptoms (Penninx et al., 2022). Experience from other epidemics, such as severe acute respiratory syndrome (SARS), suggested long-term mental health consequences, which could last for more than 3 years (Liu et al., 2012; Wu et al., 2009). The impacts of chronic mental health necessitate the attention of the global health community and require future studies. The continuous increase in PTSD symptoms is noteworthy. During the recurrence and end-of-emergency periods, the rapid spread of the virus and insufficient medical resources have led to more individuals experiencing quarantine and deaths from family/friends, which could result in traumatic experiences and cause PTSD symptoms (Cao et al., 2022; Cénat et al., 2021; Chamaa et al., 2021; Chen et al., 2023; Dubey et al., 2020; Jafri et al., 2022), requiring timely preventive and treatment measures. Together, our findings systematically traced the temporalities of the mental health impacts of different COVID-19 pandemic periods and highlighted the increases in mental health symptoms when the pandemic recurred and symptoms remained even after the end of the emergency, especially PTSD symptoms.
Centralized quarantine, frontline workers, and initial wave widely infected area were persistent risk factors for mental health symptoms at all 3 stages. Under centralized quarantine, people might experience multiple pressures, such as infection concerns and interpersonal isolation(Jin et al., 2021; Kelly, 2021; Shi et al., 2020). An epidemiological study of the general population in China during the COVID-19 outbreak also revealed that quarantine was associated with depression, anxiety, insomnia, and acute stress symptoms(Shi et al., 2020), while the current study further confirmed the long-term existence of this risk factor. They also reported that home quarantine contributed to poor mental health (Shi et al., 2020), which was not observed in this study. We assume that this discrepancy is related to expanded knowledge of the virus after the initial waves and more easily accessed psychosocial support at home (Parotto et al., 2023). In addition, this study, together with previous studies, suggested long-lasting negative effects, such as PTSD symptoms, requiring sufficient management (Brooks et al., 2020; Dubey et al., 2020). Frontline workers also reported sustained symptoms of anxiety, depression and insomnia, which was also suggested in studies during the outbreak (Dubey et al., 2020; Lai et al., 2020; Wang et al., 2020, 2021). Frontline workers experienced greater occupational exposure risks, increased work overtime and nightshifts, and more frequently witnessed suffering and death of infected patients, requiring particular attention to their mental health and fostering a resilient work environment (Berkhout et al., 2021; Labrague, 2021; Lai et al., 2020). Another prominent finding was that residents of the initially wide infected area (i.e., Hubei) had a persistently greater risk of PTSD symptoms. Populations in Hubei (including Wuhan city), which experienced early transmission of COVID-19 in China, experienced the most worries and uncertainty about the pandemic and the initial strict outside activity restrictions and had the highest rates of infection and deaths during the outbreak (Huang et al., 2020; Zhu et al., 2020). In addition to many studies during the initial waves reporting a high risk of psychological distress in Hubei (Lai et al., 2020; Shi et al., 2020; Wang et al., 2020, 2021), this study also revealed post-acute symptoms even after the end of emergency, indicating that persistent symptoms are still challenging for residents in this area.
Some stage-specific risk factors were also identified. A lack of outside activity was identified as a risk factor for anxiety, depression and insomnia symptoms during the previous outbreak and return-to-work periods of COVID-19 (Creese et al., 2021; Wang et al., 2020, 2021; Zhang et al., 2020), while current findings suggest that engaging in outside activity once in ≥ 30 days also increases the risk of anxiety, depression and insomnia symptoms at the stable and recurrence stages. As restrictions and isolation controls were abolished after the end of the emergency, the frequency of outside activities was quickly restored, and it no longer served as risk factors. At the recurrence stage, living in a high-risk area was also associated with increased odds of anxiety and insomnia symptoms, suggesting the rationality of mediating mental health policy when a pandemic occurs. With growing populations and their families or friends having experienced the infection, participants with suspected infections and who experienced family/friends’ deaths were at a greater risk of symptoms at the recurrence and end-of-emergency stages. These negative experiences and events were also found to have impacts on mental health during the outbreak (Hossain et al., 2020; Shi et al., 2020; Wang et al., 2020, 2021), inspiring us to protect vulnerable individuals in a timely manner(Penninx et al., 2022). These identified risk factors could contribute to the delivery of effective targeted interventions for those at risk for mental health symptoms(Penninx et al., 2022; Shi et al., 2020; Wang et al., 2020). In addition, demographic factors such as sex and age were also previously reported but with substantial heterogeneity and controversial results (Lai et al., 2020; Penninx et al., 2022; Shi et al., 2020), while the findings of the current study were not significant. This variation might be related to the sample, location and culture, and future controlled studies are needed.
Finally, although a significant increase in the proportion of patients receiving psychological intervention was observed, only limited of them have received individual counselling, which were still not enough to cover potential mental health burdens (Hossain et al., 2020; Kola et al., 2021; Salari et al., 2020). Public psychological education has its advantages as providing some knowledge in relieving psychological distress and promoting those with potential symptoms to pursue professional interventions. But for people who have already developed mental health symptoms, especially severe symptoms, public education is not enough, while individual counselling provided by professional psychologists with systematic terms and individualized treatments could be more effectively (Hossain et al., 2020; Labrague, 2021; Wang et al., 2021). While pandemic during an infectious could be a non-negligible barrier for face-to-face psychological interventions, an important lesson was the growing evidence of remotely computerized or videoconferencing delivered interventions during COVID-19, which guides more safety measures (Bryant et al., 2022; Liu et al., 2021). However, their efficacy, stability, acceptability and applicability still need future comparisons with traditional well developed measures. To better address changes in the mental health burdens of society, psychosocial crisis prevention and multipronged intervention models should be urgently developed at the level of individualization by the government, healthcare providers, and other stakeholders in preparation for future potential pandemics and recurrences (Dubey et al., 2020; Dzinamarira et al., 2024). Furthermore, considering potential post-acute and long-term symptoms, prolonged evidence-based interventions should be applied to address mental health and unfavourable socio-environmental factors for at-risk populations (Ettman et al., 2020; Penninx et al., 2022).
Our study has some limitations. First, because of pandemic restrictions, random sampling was unavailable, which might introduce selection bias. Second, due to the nature of cross-sectional surveys, the present study did not follow the same group due to sensitivity concerns. Additionally, the participants in the current study were surveyed only 6 months after the end of emergency. A longitudinal cohort study with longer follow-ups would be better for exploring changes in and long-term effects of mental health symptoms. Third, comparisons of the current findings with those of other studies could reveal heterogeneity in sample and methodology (especially for the assessment scales used and their cut-offs), and findings from the current study still need to be verified in other countries/regions and international collaboration, which could be a future research direction. Finally, the measurement of mental health symptoms was based on self-reported screening tools, which cannot represent clinical diagnoses.
Conclusions
The prevalence of anxiety, depression, and insomnia symptoms increased from 13.7–16.4% at the stable stage of the COVID-19 pandemic to 17.3–22.2% at the recurrence stage. Although the prevalence decreased to 14.5–18.6% at the end-of-emergency stage, it was still higher than that at the stable stage. The prevalence of PTSD symptoms continuously increased from 5.1% at the stable stage to 7.6% and 9.2% at the recurrence and end-of-emergency stages, respectively. Several key factors and their variations were identified at different pandemic stages. Centralized quarantine, frontline workers and initial wave-widely infected areas had a persistent increase in the risk of symptoms, while stage-specific risk factors included a lack of outside activity, high-risk recurrence areas, suspected infections and family/friend deaths, suggesting potential differences in at-risk populations. Current individual counselling still does not cover those potentially experienced mental health symptoms enough.