Mental health responses to the COVID-19 pandemic: a latent class trajectory analysis using longitudinal UK data
Introduction
- Xiong J
- Lipsitz O
- Nasri F
- et al.
Using a random sample with pre-pandemic data, we previously reported that the prevalence of clinically significant levels of mental distress was 50% higher than before the pandemic a month after lockdown measures were introduced in the UK (April, 2020).
- Pierce M
- Hope H
- Ford T
- et al.
- Fancourt D
- Steptoe A
- Bu F
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- Bu F
- Steptoe A
- Fancourt D
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- Bu F
- Dissing AS
- et al.
However, these studies have methodological problems relating to sampling, adjustment, and mental health measures. First, these studies used convenience samples, which means they cannot be adjusted properly for sampling bias and are thus considered poor tools for estimating population statistics.
- Pierce M
- McManus S
- Jessop C
- et al.
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- Benzeval M
- Burton J
- Crossley TF
- et al.
Second, many studies have considerable attrition over time, and individuals with poor mental health are more susceptible to dropout,
- Czeisler MÉ
- Wiley JF
- Czeisler CA
- Rajaratnam SMW
- Howard ME
resulting in an overoptimistic assessment of mental health trends. Third, many studies used mental health indicators that were limited to symptoms occurring only in the past week.
- Saunders R
- Buckman JEJ
- Fonagy P
- Fancourt D
A clinical diagnosis of anxiety disorder or a depressive episode require symptoms to be consistently present for at least the past 2 weeks—otherwise, fluctuation in psychological distress commonly seen in healthy populations might become overstated as clinical illness. Most studies do not have comparable pre-pandemic data,
- Thombs BD
- Bonardi O
- Rice DB
- et al.
which is important to understand whether the acute increases in mental distress in the population returned to pre-pandemic levels after the initial shock of its onset. Furthermore, the average trajectory for the whole population could mask varied responses to the pandemic—some groups might have remained or become increasingly vulnerable.
Evidence before this study
We searched Embase, PsychINFO, and MEDLINE for articles published in English between Jan 1 2020, and Jan 31, 2021, using search terms relating to mental health (‘psychiatr*’ or ‘mental’ or ‘distress’ or ‘depression’ or ‘anxiety’), COVID (‘covid’ or ‘coronavirus’ or ‘sars-cov-2), and longitudinal analysis (‘trajector*’ or ‘longitudinal’ or ‘latent curve’). Of 496 studies retrieved, only 13 conducted a trend analysis. Studies with a pre-pandemic baseline showed that population mental health deteriorated with the onset of the pandemic in the UK, USA, China, and European countries. Most studies were drawn from convenience samples, where participants are recruited according to ease of access. UK studies on trends since the beginning of the pandemic indicated a pattern of immediate recovery in the population overall and in all subgroups (regardless of gender, age, employment status, and other deprivation measures). However, studies that rely on follow-up from convenience samples might be biased towards a positive trend in mental health because study attrition is more likely among those with poor or deteriorating mental health.
Added value of this study
Using a longitudinal, probability sample survey to map mental health in the first 6 months of the COVID-10 pandemic in the UK, we found the elevated rates of poor mental health immediately after the onset of the pandemic (April 24–30, 2020) were sustained, with significant improvements occurring only from July, 2020 (when UK schools reopened, infection rates fell, and substantial relaxation of lockdown measures occurred). This study revealed that, although most of the population either remained resilient or reacted and recovered within the first 6 months of the pandemic, there are two groups of individuals of continuing concern. In one group, individuals’ mental health deteriorated rapidly at the onset of the pandemic and showed no sign of recovery; the other group comprised people whose mental health progressively worsened month on month during the pandemic. Infection with SARS-CoV-2, previous physical or mental health conditions, and financial difficulties predicted subsequent deterioration in mental health during the pandemic.
Implications of all the available evidence
Socioeconomic pressures—both area-level deprivation and individual financial struggles—emerged as risk factors for deteriorating mental health during the pandemic, highlighting the need for policies aimed at socioeconomic inequalities in the recovery response. Confirmed infection with SARS-CoV-2 also strongly predicted a subsequent decline in mental health. These findings provide valuable information for policy makers and planners about the likelihood of changing needs for mental health services because of the pandemic.
- Xiong J
- Lipsitz O
- Nasri F
- et al.
Most of these risk factors were associated with poor mental health before COVID-19. In the early phases of the pandemic, young people, women, and parents living with preschool children saw greater than average decreases in mental health (measured by the 12-item General Health Questionnaire [GHQ-12]) compared with results of pre-pandemic studies.
- Pierce M
- Hope H
- Ford T
- et al.
Whether these groups and characteristics are associated with sustained psychological distress as the pandemic has continued remains unclear. Additionally, although some of the determinants of worsening mental health might have receded after the early shock of pandemic onset and initial easing of national lockdown, some might have persisted, for example, infection with SARS-CoV-2,
- Taquet M
- Geddes JR
- Husain M
- Luciano S
- Harrison PJ
localised containment measures, and financial insecurity.
- Chandola T
- Kumari M
- Booker CL
- Benzeval M
We used a large, longitudinal panel sample, which was representative of the adult UK general population, with the overall aim of describing population trends in mental health during the first 6 months of the pandemic, overall and by age and gender.
We aimed to identify distinct trajectories in mental health over this period, describe the characteristics of individuals within each distinct mental health trajectory, and identify adversities that predict worsening mental health during the pandemic.
Methods
Study design and participants
Understanding society: waves 1-9, 2009–2018 and Harmonised BHPS: waves 1–18, 1991–2009 [data collection].
The sample is representative of the UK population, comprising clustered, stratified samples of households in England, Scotland, and Wales and a non-clustered, systematic random sample in Northern Ireland. Areas with proportionately large migrant and ethnic minority populations were oversampled. The questionnaires were available in English and Welsh.
- Burton J
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with monthly, and then bi-monthly data collections from July, 2020. Panel members who took part in waves 8 or 9 (between Jan 1, 2016, and May 21, 2019) were invited to complete a series of web-based data collections in the last week of each month: April 24 to 30, May 27 to June 2, June 25 to July 1, July 24 to 31, and Sept 24 to Oct 1, 2020.
All household members aged 16 years or older were invited to participate, except for those unable to make an informed decision, because of incapacity, and those with unknown postal addresses or addresses abroad. Those aged 16 years in April, 2020, were not eligible to complete the UKHLS at previous waves, but participated in the COVID-19 survey if they were from eligible households (ie, those with at least one participant in the two most recent waves of the main survey).
Understanding society COVID-19 user guide. Version 5.0.
and provided with the September wave. Unweighted and weighted statistics for each wave and patterns of non-response to the COVID-19 web surveys are provided in the appendix (pp 1–5).
Individuals gave oral informed consent for participation in the study. Ethics approval was granted by the University of Essex Ethics Committee for the COVID-19 web and telephone surveys (ETH1920-1271).
Procedures
The GHQ-12 was administered by self-completion in wave 10 and in each of the five COVID-19 web survey waves. The items refer to difficulties with sleep, concentration, problems in decision making, strain, feeling overwhelmed, and other indicators of distress. GHQ-12 items were scored as follows: 0, not at all; 1, no more than usual; 2, rather more than usual; or 3, much more than usual. A total score was derived for each wave (0–36). In addition to the total score, used when generating a mean score, a binary measure was derived identifying those reporting distress in at least four of the 12 items. A score of 4 or more is used to indicate a level of mental distress that is clinically relevant.
Sociodemographic variables included gender (women vs men), age (16–24 years, 25–34 years, 35–44 years, 45–54 years, 55–69 years, and ≥70 years) and ethnicity (White British, White other, mixed, Asian, Black, or other). Household structure captured indicators of whether the participant lived with a partner (yes vs no) and the age of the youngest child living in the household (no children, 0–5 years, or 6–15 years). Area-level context was captured with geography (Wales, Scotland, Northern Ireland, and English region) and quintiles based on ranked Index of Multiple Deprivation (IMD) scores, an area-level deprivation measure mapped to lower layer super output areas (median population size of 1500) that was only available for England. Thus, analyses by IMD quintile were done in the sample of residents in England only.
Coronavirus: local lockdowns.
A list of local authorities that had local lockdown restrictions, their implementation dates, and a description of lockdown measures in the UK are provided in the appendix (p 6).
Statistical analysis
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- Skrondal A
- Pickles A
These models included fixed effects for time (parameterised as time since first COVID-19 data collection) and discrete random variables for the latent classes. A four-latent class model was initially fitted to determine whether a squared term for time and a random intercept and slope were a good fit for the data (as indicated by a likelihood ratio test). Once the functional form of the model was determined, models were fitted with one to seven latent classes. Each model with two or more classes used random starting values from the model with one fewer class and a grid-search technique was used (with 50 iterations) to avoid the model identifying local maxima.
and the entropy statistic value was normalised.
In addition to these fit statistics, models were compared graphically to examine whether a larger number of latent classes provided a clearer theoretical interpretation of the data.
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- Brown CH
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- Rown CHB
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which involved creating 10 imputed datasets with class membership determined using a random variable created from the posterior probabilities from the mixed model. Next, a univariable multinomial logistic regression model was fitted to each imputed dataset, with class membership as the dependent variable. The p-value for the association between covariates and class membership was determined using Rubin’s rules to combine the f-test from each model. The latent class trajectory analysis was reported in line with the Guidelines for Reporting on Latent Trajectory Studies checklist (appendix p 7).
Fixed-effect models were fitted to individuals’ repeated GHQ-12 scores to ascertain which of three COVID-19 adversity variables were associated with a change in GHQ-12 score. These were confirmed or suspected infection with SARS-CoV-2 infection; local lockdown measures; and reported problems paying bills. These models included parameters for time since first wave of data collection (as a continuous variable and its square, both with p<0·01 from Wald test), subject-specific effects that captured all time-invariant confounders, and time-dependent adversity variables.
In a sensitivity analysis, the GHQ-12 total for each participant was recalculated removing the question “have you recently been able to enjoy your normal day-to-day activities?,” which was considered potentially to be indicative of pandemic-related restrictions rather than mental health. Population trends and fixed-effects models were then refitted on this adapted version of the GHQ-12.
All analyses accounted for sampling probability weights. Cross-tabulations and calculations of means also accounted for clustered and stratified sampling using the svy suite of commands in Stata. Analyses were done using Stata (version 14) and graphs were produced using R package ggplot2.
Role of the funding source
There was no funding source for this study.
Results
Figure 1Overall mean GHQ-12 score by month of data collection
The dashed horizontal line represents the pre-pandemic mean (from 2018–19). Error bars represent 95% CIs. GHQ-12=General Health Questionnaire.
Figure 2Mean GHQ-12 score by month of data collection, by age group and gender
The dashed horizontal lines represent the pre-pandemic mean (from 2018–19). Error bars represent 95% CIs. GHQ-12=General Health Questionnaire.
Figure 3Observed mean GHQ-12 score from five class-specific trajectories across five waves of data collection during the pandemic
The dashed line indicates the change from the pre-pandemic score. Error bars represent 95% CIs. GHQ-12=General Health Questionnaire.
Table 1Membership in each latent class group according to key demographics
Numbers relate to the absolute frequency and percentages relate to the proportion after weighting. NHS=UK National Health Service.
Table 2Fixed-effect model of effect of dynamic time-dependent variables on within-subject change in GHQ-12
Discussion
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). Mental health continued to improve through to October, 2020, although not to pre-pandemic levels. This overall view masks the very different experiences encountered by people as the pandemic progressed, which we identified using latent class analysis. Five distinct trajectories emerged. Around three quarters of participants had either consistently very good or good mental health throughout the pandemic; a substantial minority of participants reported a very different experience, with very poor or steadily worsening mental health and, by October, 2020, had far more mental health symptoms than before the pandemic. These trajectories were not equally distributed within the population. Living in a deprived neighbourhood, shielding for health reasons, and self-reporting a previous mental illness were all significantly more common in individuals whose mental health worsened between April and October, 2020. Men, older age groups, and those living in affluent areas were most likely to have maintained good mental health throughout the pandemic.
- Pierce M
- Hope H
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- et al.
in this update, women were over-represented in the recovered group. Notably, this was also the case for parents of young children and for young people, many of whom suffered precipitous decline in their mental health at the beginning of the pandemic,
- Pierce M
- Hope H
- Ford T
- et al.
but who appear to have better mental health by October, 2020. Several factors might play a part in the improving mental health of these individuals over this period. For example, easing of national containment measures, school re-openings, summer holidays, and falling infection and death rates. Although socioeconomic context was not a predictor of larger increases in distress initially, over the course of the pandemic this factor gained predictive power. Similarly, Asian, Black and mixed ethnicity individuals did not have elevated levels of distress early in the pandemic, but in this analysis Asian and mixed ethnicity individuals were overly represented in the very poor or deteriorating groups, indicating that minority ethnic groups might need ongoing support during the pandemic.
- Taquet M
- Geddes JR
- Husain M
- Luciano S
- Harrison PJ
Longer-term follow-up of patients with confirmed SARS-CoV-2 infection is required to assess who is most affected and whether this translates into long-term clinical need for mental health services. Also, for the first time to our knowledge, we observed that local lockdown measures were negatively affecting mental health.
- Fancourt D
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- Bu F
Our results are also consistent with most reports from the USA
and across Europe,
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- et al.
showing improvement in mental health in populations since the initial deterioration at the beginning of the pandemic. However, whereas these reports find that improvements in mental health occurred almost immediately after the start of the pandemic, we found that recovery in overall population mental health did not occur in the UK until July, 2020, coinciding with lifting of the national lockdown measures. Other studies might have overstated the pace of recovery for three reasons. First, surveys using convenience samples are unrepresentative, even after demographic adjustments;
- Benzeval M
- Burton J
- Crossley TF
- et al.
second, high-frequency online data collection, with no supplementary telephone interviews, can lead to particular loss of participants with poor or declining mental health, resulting in assessment of trends which are biased towards better mental health;
- Czeisler MÉ
- Wiley JF
- Czeisler CA
- Rajaratnam SMW
- Howard ME
and third, use of wellbeing and measures relating to short periods (such as yesterday or the past week) are likely to show more volatility (and less clinical relevance) than measures relating to the past 2 weeks.
- Chandola T
- Kumari M
- Booker CL
- Benzeval M
report of deteriorating mental health in those with financial stressors using the same COVID-19 Understanding Society dataset up to July, 2020. However, this study
- Chandola T
- Kumari M
- Booker CL
- Benzeval M
did not find an effect of having problems paying bills, which might result from the lower statistical power of shorter follow-up and a dichotomised outcome measure. The five distinct trajectories that we identified are strikingly similar to those reported across six years of data collection in the UK, albeit using a different measure (the 12-item Short Form).
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We might conclude from this comparison that the pandemic has resulted in an acceleration of the rate of change of mental health among UK adults. The fact that those in the lowest income areas were more likely to have mental health declines suggests that existing mental health inequalities are being accentuated.
- Pierce M
- McManus S
- Jessop C
- et al.
Second, as well as including multiple timepoints after the onset of the COVID-19 pandemic in the UK, unlike many other mental health surveys during the pandemic, our sample includes pre-pandemic data, allowing us to understand whether individuals’ mental health recovered to pre-pandemic levels. The longitudinal nature of the data enabled discrete trajectories of change to be discerned. Lastly, the large sample size and rich set of covariates provide sufficient statistical power to identify latent class trajectories and characteristics that were associated with them.
However, drawbacks associated with the fixed-effects model are outweighed by the fact that all time-invariant confounders are accounted for.
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Although the GHQ-12 score is a validated measure of mental health,
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it is not equivalent to a clinical diagnosis. A previous mental illness diagnosis was ascertained from self-report, and the estimated prevalence (6·6%) was lower than expected, indicating underreporting. This might be because of socially desirable responding or could indicate non-response bias that was not accounted for in the sample weights, potentially leading to underestimation of the prevalence of deteriorating or consistently poor mental health. These data might normally be ascertained from routinely collected clinical contacts; however, there has been a decrease in visits to primary care for mental illness,
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even though individuals’ mental health was apparently worsening. We have not adjusted for seasonal variation in population mental health.
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Using data from the same survey, we and others
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have previously found that any effects of seasonal and year-to-year variation on mental health were minimal and unlikely to account for changes in population mental health during the pandemic. Finally, our study only includes data up to the beginning of October, 2020, before the second and third waves of COVID-19 restrictions in the UK. National survey data reported that post-pandemic anxiety was at its lowest in July, 2020, and increased again up to January, 2021.
Coronavirus and the social impacts on Great Britain.
Compared with previous rapid convenience surveys, which suggested the mental health of individuals in the UK adjusted quickly to the social changes surrounding the pandemic, our results imply that a more prolonged deterioration in mental health occurred, with relatively little psychological adjustment or habituation, until July, 2020, coinciding with the revocation of national lockdown measures. We also found an effect of localised lockdowns on levels of mental distress. We might anticipate similar effects to have occurred during subsequent national lockdowns in November, 2020, and January, 2021.
Therefore, socioeconomic policies should be central to post-pandemic recovery programmes to address the mental health effects seen in low-income communities and the further likely effects of school closures, financial hardship, job insecurity, and local restrictions. Mental health services might also expect to see increased referrals from the around 10% of individuals recovering from COVID-19
The prevalence of long COVID symptoms and COVID-19 complications.
who develop features of so-called long COVID, including psychiatric illness.
- Taquet M
- Geddes JR
- Husain M
- Luciano S
- Harrison PJ
Preventive interventions might usefully be targeted at the vulnerable groups of people whom we have identified. In advance of further lockdowns or future pandemics, public mental health should be a priority and support should be focussed on deprived communities, while local authority public health measures and social welfare should target deprived families and individuals.
MP, KMA, and SM devised the study concept. MP wrote the initial analysis plan with input from KMA, SM, and MH. MP did the data analysis and produced the figures. KMA, MP, and SM wrote the first draft of the manuscript and all authors contributed to editing and commenting on the final version. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.
Declaration of interests
MH reports grants from Innovative Medicines Initiative, outside the submitted work. SLH reports grants from the National Institute for Health Research, grants from the Economic and Social Research Council (ESRC), grants from Wellcome Trust, grants from the Medical Research Council, and grants from Guy’s and St Thomas’ Charity, outside the submitted work; is a member of the following advisory groups: ethnic inequalities in health care among people with multiple conditions (University of Sussex), Advisory Board NHS Race and Health Observatory, Co-Chair Academic Reference Group and Board Member The Royal Foundation, Mental Health Research Group NHS England and NHS Improvement, The Mental Health Equalities Data Quality and Research Subgroup NHS England and NHS Improvement, Patient and Carers Race Equalities Framework Steering Group NHS England and NHS Improvement, Advancing Mental Health Equalities Taskforce Health Education England, Mental Health Workforce Equalities Subgroup Maudsley Learning, Maudsley Learning Advisory Board South London and Maudsley NHS Foundation Trust (SLaM), Independent Advisory Groups, the SLaM Partnership Group, Lambeth Public Health, Serious Youth Violence Public Health Task and Finish Group Thrive London, Thrive London Advisory Board Black Thrive, Black Thrive Advisory Board NHS England and NHS Improvement, The Mental Health Workforce Equalities Subgroup Commissions: Welsh Government’s Race Equality Plan; and contributed to the evidence review for Health and Social Care and Employment and Income policy areas. All other authors declare no competing interests.