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

2026-01-15

AI Summary

This study investigates the trajectories of mental health among the UK population during the COVID-19 pandemic and examines associated sociodemographic disparities. Leveraging data from the Understanding Society COVID-19 survey, the authors employ generalized additive models (GAMs) to analyze longitudinal changes in psychological well-being, using the 12-item General Health Questionnaire (GHQ-12) as the primary mental health indicator. The findings reveal significantly deteriorated mental health among women, younger individuals, those without a partner, low-income groups, and people with chronic illnesses, underscoring the pandemic’s disproportionate impact on vulnerable populations. These results provide empirical evidence and targeted insights to inform the development of precise public health interventions aimed at mitigating mental health inequities exacerbated by the crisis.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters

Application Category

• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

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.

Problem

• mental health trajectories
• COVID-19 pandemic
• sociodemographic variables
• health inequalities
• UK population

Innovation

• Generalized Additive Models
• mental health trajectories
• sociodemographic determinants
• longitudinal analysis
• GHQ-12