{
  "annotation_id": "bd74fb67-7c3c-4a01-896a-7fd7d4b143c1",
  "date_created": "2026-02-17T05:53:24.336000Z",
  "date_modified": "2026-02-17T05:53:24.336000Z",
  "file_hash": "6ba09572b2460ad6ebb99b50adfc2596c07491f7d28381f3578927d9ac58b71d",
  "private": false,
  "record": {
    "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.",
    "arxiv_id": "2601.10445",
    "authors": [
      "Glenna Nightingale",
      "Karthik Mohan",
      "Eloi Ribe",
      "Valentin Popov",
      "Shakes Wang",
      "Clara Calia",
      "Luciana Brondi",
      "Sohan Seth"
    ],
    "categories": [
      "stat.AP"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Modeling mental health trajectories during the COVID-19 pandemic using UK-wide data in the presence of sociodemographic variables",
    "url": "https://arxiv.org/abs/2601.10445",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "6fae3b84-7098-4ded-986e-bd61790f6d34",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}