Modeling mental health trajectories during the COVID-19 pandemic using UK-wide data in the presence of sociodemographic variables

TL;DR

The paper analyzes mental health trajectories in the UK during the COVID-19 pandemic using nine waves of Understanding Society data and GHQ36 as the outcome. It employs two Generalized Additive Models (GAM-abs and GAM-diff) to capture nonlinear time trends and the impact of sociodemographic factors on mental health, both in absolute terms and relative to a pre-pandemic baseline. Key findings show that women, younger adults, those not living with a partner, individuals with long-term illness, and lower-income groups experienced greater deterioration, with regional differences and persistent inequalities. The study provides policy-relevant insight into which groups bore the brunt of pandemic-related mental health impacts and demonstrates the value of GAMs for modeling complex, time-varying public health signals.

Abstract

Background: The negative effects of the COVID-19 pandemic on the mental health and well-being of populations are an important public health issue. Our study aims to determine the underlying factors shaping mental health trajectories during the COVID-19 pandemic in the UK. Methods: Data from the Understanding Society COVID-19 Study were utilized and the core analysis focussed on GHQ36 scores as the outcome variable. We used GAMs to evaluate trends over time and the role of sociodemographic variables, i.e., age, sex, ethnicity, country of residence (in UK), job status (employment), household income, living with a partner, living with children under age 16, and living with a long-term illness, on the variation of mental health during the study period. Results: Statistically significant differences in mental health were observed for age, sex,ethnicity, country of residence (in UK), job status (employment), household income, living with a partner, living with children under age 16, and living with a long-term illness. Women experienced higher GHQ36 scores relative to men with the GHQ36 score expected to increase by 1.260 (95%CI: 1.176, 1.345). Individuals living without a partner were expected to have higher GHQ36 scores, of 1.050 (95%CI: 0.949, 1.148) more than those living with a partner, and age groups 16-34, 35-44, 45-54, 55-64 experienced higher GHQ36 scores relative to those who were 65+. Individuals with relatively lower household income were likely to have poorer mental health relative to those who were more well off. Conclusion: This study identifies key demographic determinants shaping mental health trajectories during the COVID-19 pandemic in the UK. Policies aiming to reduce mental health inequalities should target women, youth, individuals living without a partner, individuals living with children under 16, individuals with a long-term illness, and lower income families.

Figures (5)

• Figure 1: Trend plots for all nine variables in GAMabs model
• Figure 2: Trend plots around baseline value accompanied by $95\%$ confidence intervals for variables studied.
• Figure 3: Forest plots for the GAM models
• Figure 4: Plots of splines from the GAM-abs model
• Figure 5: Plots of splines from the GAM-diff model