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

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

2Reports on population mental health changes at the start of the pandemic within the United Kingdom are conflicting, with some studies indicating a widespread decline in psychological well-being early on, while other studies suggest improvements or no changes in mental ill health., Findings have remained inconsistent as the pandemic has progressed, with both increasing and decreasing levels of poorer mental health reported.,,

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

4Uncertainty remains about how mental health has changed over the pandemic, including who has been most affected and whether any observed deterioration reflects lockdown measures or other aspects of the pandemic. To examine this, we conducted coordinated analyses of 11 UK longitudinal population studies with data from before and across the pandemic. We aimed to (1) estimate the consequences of the pandemic on population mental health and how these evolved during the first year of the pandemic as lockdown restrictions changed and (2) examine inequalities in these impacts by age, sex, race and ethnicity, education level, and UK country.

5The UK National Core Studies–Longitudinal Health and Well-being initiative aims to coordinate primary analyses across multiple UK longitudinal population-based studies., Coordinating analyses across different data sets minimizes methodological heterogeneity and maximizes comparability, while appropriately accounting for the study design and characteristics of individual data sets. Reporting followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.

6Data were pooled from 11 UK longitudinal population studies that conducted surveys both before and during the COVID-19 pandemic. Details of the design, sampling frames, current age range, timing of the prepandemic and COVID-19 surveys, response rates, and analytical sample size are in the Table,,,,,,,,,,,,,,,,,,,, with further details of each analytical sample in eTable 3 in the Supplement. Ethical approvals were received for all included studies, with ethics statements described in eAppendix 1 of the Supplement. All studies collected informed consent from their participants. This study did not seek any additional institutional review board approval.

7Six studies were age-homogenous cohorts (ie, similarly aged individuals): the Millennium Cohort Study (MCS); children in the Avon Longitudinal Study of Parents and Children (ALSPAC); Next Steps (NS; formerly known as the Longitudinal Study of Young People in England); 1970 British Cohort Study (BCS70); 1958 National Child Development Study (NCDS); and 1946 National Survey of Health and Development (NSHD). Five other studies had age-heterogeneous samples (ie, cohorts with multiple age groups): Understanding Society (USOC); Generation Scotland (GS); Twins UK (TwinsUK); Born in Bradford (BiB); and the English Longitudinal Study of Aging (ELSA).

8Analytical samples included those who had valid observations of psychological distress in a prepandemic survey, at least 1 survey during the pandemic, and valid data on sex and age (participant flow diagrams for each study appear in eAppendix 3 in the Supplement). Participants who had died or emigrated by the start of the pandemic were also excluded. Most studies were weighted to be representative of their target population, accounting for sampling design and differential nonresponse to the COVID-19 surveys.,, Weights were not used for ALSPAC, TwinsUK, GS, and BiB.

9In the following sections, we describe the variables used for analysis. Details of the specific scales and coding used within each cohort appear in eAppendix 2 in the Supplement.

10Psychological distress was measured both before the pandemic and at multiple points across the pandemic using validated, continuous scales measuring symptoms of common mental health disorders, such as depression and anxiety (specific measures used appear in the Table). Continuous scales were standardized across time points and within studies on a common SD-based scale. This enhances comparability of estimates between studies while allowing examination of changes over time within studies. We also conducted analyses with dichotomous indicators of high psychological distress using established thresholds for each scale (eTable 1 and eTable 2 in the Supplement).

11While most studies used the same measure for both prepandemic and COVID-19 surveys, GS and NSHD used different measures. For these studies, we identified comparable items to create a smaller scale consistent over time, and the threshold for the binary outcome was reweighted based on the number of items retained (eTable 1 in the Supplement).

12We identified 3 time periods (TPs 1-3) representing different stages during the course of the pandemic in the United Kingdom for comparison against prepandemic mental health (measured at TP 0). Surveys from April to June 2020 represented the first wave of high infection levels accompanied by the first lockdown measures (TP 1). Surveys taken from July to October 2020 coincided with easing of restrictions and lower rates of infection (TP 2). Following this, infection levels again increased, and lockdown measures were reintroduced; surveys taken from November 2020 to March 2021 represent this second wave of infections (TP 3). Some studies contributed multiple survey waves to some TPs, and not all studies were represented in all 3 COVID-19 TPs (Table).

13The following covariates were adjusted for and/or used to stratify estimates: sex (male or female); age in the age-heterogeneous cohorts (coded in 10-year bands to examine nonlinearity: 16-24, 25-34, 35-44, 45-54, 55-64, 65-74, and ≥75 years); race and ethnicity (self-reported and coded for main analyses; as White [including White ethnic minorities] vs racial and ethnic minority groups ); UK country of residence (England, Scotland, Wales, or Northern Ireland); and highest educational qualification (degree vs less than degree; parental education was used for the MCS cohort, who had not all completed their full-time education). Due to small sample sizes or lack of available ethnicity breakdown, we are we are unable to report race and ethnicity findings in more detail.

14Changes in continuous measures of mental health over the 3 TPs were modeled using multilevel mixed-effects models within each study to account for associations between repeated measures from the same individuals, adjusting for sex and age (in age-heterogeneous cohorts). TP was a categorical exposure, with TP 0 as the reference. In some studies, multiple survey waves were included within the same TP. Coefficients are presented as standardized mean differences (SMDs). Multilevel mixed-effects Poisson regression models with robust standard errors were used to calculate relative risks for the binary outcome.

15Results from each study were pooled using a random-effects meta-analysis with restricted maximum likelihood. Meta-analyses were conducted separately for continuous psychological distress scores and binary high psychological distress thresholds. Heterogeneity is reported using the I2 statistic.

16Interactions between TP and sex, education, and race and ethnicity were estimated within each study and then meta-analyzed to formally test for effect modification (ie, to determine whether changes across time periods varied between population subgroups). Formal interactions could not be tested by age and UK country given the age-homogeneous nature of several cohorts and few studies including all UK nations. We present meta-analysis of estimates stratified by sex, education, ethnicity, age, and UK country.

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

18Across 11 individual longitudinal studies, 49 993 participants (12 323 [24.6%] aged 55-64 years; 32 741 [61.2%] women; 4960 [8.7%] racial and ethnic minority) were analyzed, ranging from 1816 participants in NSHD to 12 437 in USOC. The proportion of women ranged between 7208 (52.1%) in USOC to 1967 (100.0%) for BiB, and racial and ethnic minority participants ranged from 26 of 4103 (0.6%) in GS to 1223 (62.2%) in BiB. Descriptive statistics for all the studies, weighted and taking account of complex survey design where relevant, are in eTable 3 in the Supplement.

19Descriptive statistics appear in eTables 4, 5, and 6 in the Supplement. Figure 1A shows that for most studies, prevalence of high psychological distress either worsened or was fairly stable over the course of the pandemic. The largest increase in prevalence of high psychological distress was observed within the ELSA study, rising from 11.5% to 28.0% over the course of the 3 TPs. The largest increase between 2 consecutive TPs was observed within the NSHD study, between the prepandemic (2015) and first pandemic TP, increasing from 11.4% to 35.0%. In 2 studies (ALSPAC and BCS70), the prevalence of distress in the final pandemic TP (TP 3) was marginally lower than in the prepandemic time period (prevalence decreased by 2.3% and 0.8% respectively).

20Figure 1B shows the sex difference in mental health over the course of the pandemic, with higher prevalence of distress among women than men in all sex-heterogeneous studies. In April and May 2020 (TP 1), sex inequalities appeared especially high, with female respondents exhibiting higher prevalence of mental distress in most studies. For example, in NSHD at TP 1, 46.0% of female respondents reported mental distress vs 23.5% of male respondents. In NS at TP 1, 43.0% of female respondents reported mental distress vs 26.1% of male respondents.

21Psychological distress increased from prepandemic scores across all 3 pandemic TPs examined (observed in 8 of the 11 included cohorts when focusing on general distress or depressive symptom measures), with no clear differences in changes across the 3 pandemic TPs (TP 1: SMD, 0.15; 95% CI, 0.06-0.25; TP 2: SMD, 0.18; 95% CI, 0.09-0.27; TP 3: SMD, 0.21; 95% CI, 0.10-0.32). However, there was considerable heterogeneity between estimates from different studies (I2 of 99.2%, 98.6%, and 99.2% at TP 1, TP 2, and TP 3, respectively), with estimates for TP 1 ranging from an SMD of −0.08 (95% CI, −0.11 to −0.05) for ALSPAC to an SMD of 0.46 (95% CI, 0.37 to 0.55) for NSHD (individual cohort results in eTable 7 in the Supplement). Leave one out meta-analysis found that no single cohort significantly skewed the pooled estimates (eTable 16 and eAppendix 5 in the Supplement). Similar patterns and high levels of heterogeneity were observed when considering prevalence of psychological distress as a binary outcome (eTable 17 in the Supplement). Estimates for both continuous and binary measures of mental distress are displayed in Figure 2. The pooled relative risk of high mental distress was elevated at TP 1 (relative risk, 1.29; 95% CI, 1.05-1.58) and TP 2 (relative risk, 1.23; 95% CI, 1.09-1.38), with the highest risk at TP 3 (relative risk, 1.36; 95% CI, 1.14-1.62).

22Meta-analysis of the study-specific interaction terms between each marker of inequity and time period (eTable 17 in the Supplement) indicated that changes in distress were greater in women (TP 3: SMD, 0.23; 95% CI, 0.11-0.35) compared with men (TP3: SMD, 0.16; 95% CI, 0.06-0.26) (eTable 8 in the Supplement), suggesting a further widening of sex inequalities. Changes were marginally lower at TP 1 and TP 3 for persons with a below-degree level education (TP 3: SMD, 0.18; 95% CI, 0.06-0.30) compared with those with a degree (TP 3: SMD, 0.26; 95% CI, 0.14-0.38), albeit often from a greater prepandemic inequality, indicating a slight narrowing of educational inequalities during the pandemic. We did not find evidence for trends differing by ethnicity or UK country. Heterogeneity varied across these analyses, with I2 values ranging from 44.2% for the interaction between education and TP 1 to 88.8% for ethnicity and TP 1. Estimates stratified by sex, ethnicity, education, and UK country are shown in Figure 3. Again, in all analyses there was large heterogeneity between study estimates (eTables 8 and 10-12 in the Supplement).

23Age-stratified results showed no monotonic pattern by age (Figure 4), despite some suggestion that the consequences of the pandemic on mental health might have been greater in those aged 25 to 44 years. The pooled SMD at TP 3 for those aged 25 to 34 years was 0.49 (95% CI, 0.14-0.84) and for those aged 35 to 44 years, 0.35 (95% CI, 0.10-0.60) (eTable 9 in the Supplement).

24Sensitivity analyses were conducted to consider specific measures of mental health (depression or anxiety) and to limit data to participants with survey responses during all 3 TPs. Findings were consistent with the main analyses (eTables 13-15 in the Supplement). We also presented pooled analyses of the binary high distress outcomes, overall and stratified in eTables 17 to 25 in the Supplement.

25Given the high levels of heterogeneity across studies, we conducted metaregressions to examine whether time between prepandemic and postpandemic measures, measurement type, and representativeness of the studies for their target population helped account for some of the observed heterogeneity (eTable 28 in the Supplement). Heterogeneity was largely unexplained by these factors; the largest explanatory factor was the representativeness of the studies, which explained 3.25% of the heterogeneity at TP 2 and suggested the deterioration in distress was less marked in representative studies. A subsequent meta-analysis including only studies with national coverage showed a worsening of mental health over the pandemic similar to the main meta-analysis (TP 1: SMD, 0.15; 95% CI, 0.02-0.29; TP 2: SMD, 0.11; 95% CI, 0.05-0.17; TP 3: SMD, 0.16; 95% CI, 0.05-0.27).

26Our analyses of 11 well-established longitudinal studies provide a comprehensive picture of the evolution of mental health over the course of differing lockdown periods during the COVID-19 pandemic. Overall, our results indicate mental health has deteriorated since the onset of the pandemic and this has been sustained with no evidence of recovery, even when lockdown measures temporarily eased in the United Kingdom during the summer of 2020. Although evidence for deterioration from prepandemic levels is seen in most included studies, there was considerable heterogeneity in effect sizes estimated. Furthermore, our findings demonstrate that while aggregate population mental health deteriorated over time, not all groups were equally affected. Women, those with a degree-level education, and young adults (aged 25-34 and 35-44 years) were affected most, reporting greater increases in psychological distress during the pandemic and thereby exacerbating some prepandemic mental health inequalities.

27Our findings suggest that initial declines in mental health were not a transient reaction to an unprecedented event, but an early indication of a sustained deterioration from prepandemic levels. These findings extend research conducted earlier in the pandemic,, replicate some research suggesting sustained effects,, and contradict findings from some convenience samples suggesting improvements in mental health when the initial lockdown was lifted. From a policy perspective, having a wealth of longitudinal data both before and during the COVID-19 pandemic gives further information on how the pandemic has affected mental health, beyond simple convenience sampling data. While the direct mechanisms generating poorer mental health are complex, the COVID-19 pandemic resulted in considerable economic, social, and behavioral changes and an increase in physical comorbidities and bereavement; therefore, increased mental distress is perhaps unsurprising. Financial stressors, changes in social interactions, and disruptions to daily life may all help to explain our findings.,,, These results suggest that deteriorations in population mental health may be driven more by time-stable disruption and concern arising from the COVID-19 pandemic, rather than the consequences of time-specific mitigation measures such as lockdowns.

28Furthermore, this deterioration suggests that avoiding lockdown measures alone may not maintain population mental health, and other factors should be considered. For example, health services in the UK were not able to meet their population’s mental health needs before the pandemic, with this situation made substantially worse during the pandemic. To minimize the detrimental longer-term consequences of the pandemic, mental health care needs to encompass multiple levels of support, including investment in primary care, community mental health, and public mental health. Initiatives should target groups at greater risk of experiencing mental ill health, including ensuring rapid access to services, but also addressing the underlying drivers of poor mental health, such as mitigating risks of unemployment, sexual violence, and poverty.

29Our results highlight widening gender inequalities in mental health. Women had much higher distress levels and showed greater deterioration during the pandemic than men. Possible reasons include increased childcare responsibilities that disproportionately fell to women, greater economic impacts, and reports of large increases in gender-based violence. We also observed that deterioration in lockdown periods was greater in those with degree-level education, albeit from a lower prepandemic level, indicating that educational inequalities narrowed. Our investigation of age differences show that all age groups have been adversely affected to some extent, but high psychological distress was greater in those aged 25 to 44 years. The mechanisms underpinning subpopulation differences remain unclear but likely include disruptions to social interactions, changes in employment or education, and shifts in parental responsibilities and/or work-life balance. For example, individuals between the ages of 25 and 44 years are more likely to have school-aged children and may therefore have faced additional challenges of working from home and caring for children. Moreover, younger adults have been at an increased risk of employment disruptions as well as changes in healthy behaviors,, which may have contributed to further deteriorations in their mental health. However, the well-documented midlife peak in psychological distress is noteworthy, and may partly explain some of the deterioration we found in these age groups.

30The multiple longitudinal studies included in this article highlight the wide range in the size of the estimated deterioration in distress from prepandemic levels across varied data sources that represent different populations. While we explored multiple factors (such as age, outcome measure, timing, and representativeness), we could not explain much of this heterogeneity. Other factors not considered, such as rates of COVID-19 within the samples, might also play a role.

31Our study has several strengths. By harnessing high-quality existing longitudinal studies, we have robust prepandemic baseline data and multiple waves of data collection capturing different TPs during the pandemic. We investigated the potential consequences of COVID-19 policy responses, specifically the introduction and removal of lockdown measures. Our approach to data harmonization allowed us to develop comparable exposure, outcome, and covariate measures and pool estimates for similar TPs. Furthermore, we maximized the value of existing data by using multilevel models to include all available data. The baseline samples of many of these studies were representative of their target populations, and analyses were weighted to account for nonresponse. Lastly, this study combined 11 longitudinal data sources, and heterogeneity between the study-specific estimates was large, highlighting that documenting the results from multiple sources is more reliable for informing policy and health planning than relying on a single data source.

32Despite these advantages, limitations should be noted. We cannot definitively attribute changes in population mental health to the COVID-19 pandemic or related policy responses, as COVID-19 was a universal exposure to everyone. However, we note that we are unaware of alternative events that would have been likely to substantially confound our analyses or their interpretation. There were differences between studies in the timing of data collection (including when prepandemic measures were collected) and the mental health survey instruments used, although this did not account for the high levels of statistical heterogeneity observed. Similarly, although weighting was used when possible to control for nonrandom response, conditioning on voluntary response may induce selection bias, as it is very plausible that the mental health of the observed differs systematically from the target population. However, the broad consistency in the direction of findings across data sets provides reassurance that the key conclusions are likely to be robust to these differences, even if the magnitude of the effect size is harder to confirm.

33The findings of this study suggest that mental health has been persistently worse during the COVID-19 pandemic than before, particularly among women, those with higher degrees, and those aged 25 to 44 years. The sustained deterioration, even when lockdown measures were eased, somewhat refutes the notion that easing lockdown measures necessarily improved mental health and implies that there are myriad pathways leading to adverse mental health outcomes. Our findings highlight the need for investment in mental health support to turn the tide and improve population mental health going forward.