Journal of clinical psychology · 2023
Predicting anxiety and depression over 12 months of the COVID-19 pandemic: A machine learning study.
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Bailey Brooklynn, Strunk Daniel R
Аннотация
OBJECTIVES: The coronavirus disease 2019 (COVID-19) pandemic was associated with substantial increases in anxiety and depressive symptoms. To understand individual risk, we examined a large set of potential risk factors for anxiety and depression in the pandemic context.
METHODS: Adults in the United States (N = 1200) completed eight online self-report assessments over 12 months of the COVID-19 pandemic. Area under the curve scores summarized cumulative experiences of anxiety and depression over the assessment period. A machine learning approach to elastic net regularized regression was used to select predictors of cumulative anxiety and depression severity from a set of 68 sociodemographic, psychological, and pandemic-related baseline variables.
RESULTS: Cumulative anxiety severity was most strongly explained by stress and depression-related variables (such as perceived stress) and select sociodemographic characteristics. Cumulative depression severity was predicted by psychological variables, including generalized anxiety and depressive symptom reactivity. Being immunocompromised or having a medical condition were also important.
CONCLUSIONS: By considering many predictors, findings provide a more complete view than previous studies focused on specific predictors. Important predictors included psychological variables suggested by prior research and variables more specific to the pandemic context. We discuss how such findings can be used in understanding risk and planning interventions.
METHODS: Adults in the United States (N = 1200) completed eight online self-report assessments over 12 months of the COVID-19 pandemic. Area under the curve scores summarized cumulative experiences of anxiety and depression over the assessment period. A machine learning approach to elastic net regularized regression was used to select predictors of cumulative anxiety and depression severity from a set of 68 sociodemographic, psychological, and pandemic-related baseline variables.
RESULTS: Cumulative anxiety severity was most strongly explained by stress and depression-related variables (such as perceived stress) and select sociodemographic characteristics. Cumulative depression severity was predicted by psychological variables, including generalized anxiety and depressive symptom reactivity. Being immunocompromised or having a medical condition were also important.
CONCLUSIONS: By considering many predictors, findings provide a more complete view than previous studies focused on specific predictors. Important predictors included psychological variables suggested by prior research and variables more specific to the pandemic context. We discuss how such findings can be used in understanding risk and planning interventions.