Factors associated with psychological distress during the coronavirus disease 2019 (COVID-19) pandemic on the predominantly general population: A systematic review and meta-analysis

Abstract

Background: The Coronavirus Disease 2019 (COVID-19) outbreak has escalated the burden of psychological distress. We aimed to evaluate factors associated with psychological distress among the predominantly general population during the COVID-19 pandemic.

Methods: We searched PubMed, EMBASE, Scopus, Cochrane Library, PsycINFO, and World Health Organization COVID-19 databases (Dec 2019-15 July 2020). We included cross-sectional studies that reported factors associated with psychological distress during the COVID-19 pandemic. Primary outcomes were self-reported symptoms of anxiety and depression. Random-effects models were used to pool odds ratios (OR) and 95% confidence intervals (CI). The protocol was registered in PROSPERO (#CRD42020186735).

Findings: We included 68 studies comprising 288,830 participants from 19 countries. The prevalence of anxiety and depression was 33% (95% CI: 28%-39%) and 30% (26%-36%). Women versus men (OR: 1.48 [95% CI: 1.29-1.71; I2 = 90.8%]), younger versus older (< versus ≥35 years) adults (1.20 [1.13-1.26]; I2 = 91.7%), living in rural versus urban areas (1.13 [1.00-1.29]; I2 = 82.9%), lower versus higher socioeconomic status (e.g. lower versus higher income: 1.45 [1.24-1.69; I2 = 82.3%]) were associated with higher anxiety odds. These factors (except for residential area) were also associated with higher depression odds. Furthermore, higher COVID-19 infection risk (suspected/confirmed cases, living in hard-hit areas, having pre-existing physical or mental conditions) and longer media exposure were associated with higher odds of anxiety and depression.

Interpretation: One in three adults in the predominantly general population have COVID-19 related psychological distress. Concerted efforts are urgently needed for interventions in high-risk populations to reduce urban-rural, socioeconomic and gender disparities in COVID-19 related psychological distress.

Fig 1. Study selection of the meta-analysis.

Fig 2. Daily new cases of COVID-19 and publication dates of included papers by by six World Health Organization regions and time.

Fig 3

Forest plot of the association between gender (women versus men) and A) anxiety and B) depression. The size of the data markers indicates the weight of the study, which is the inverse variance of the effect estimate. The diamond data markers indicate the pooled ORs.

Fig 4

Forest plot of the association between age (younger vs. older) and A) anxiety and B) depression. The size of the data markers indicates the weight of the study, which is the inverse variance of the effect estimate. The diamond data markers indicate the pooled ORs.

Fig 5

Forest plot of associations between education (lower vs. higher) and A) anxiety and B) depression. The size of the data markers indicates the weight of the study, which is the inverse variance of the effect estimate. The diamond data markers indicate the pooled ORs.

Fig 6

Forest plot of associations between income (lower vs. higher) and A) anxiety and B) depression. The size of the data markers indicates the weight of the study, which is the inverse variance of the effect estimate. The diamond data markers indicate the pooled ORs.

Fig 7

Forest plot of associations between employment (yes vs. no) and A) anxiety and B) depression. The size of the data markers indicates the weight of the study, which is the inverse variance of the effect estimate. The diamond data markers indicate the pooled ORs.

Fig 8

Forest plot of the association between residential area (rural vs. urban) and A) anxiety and B) depression. The size of the data markers indicates the weight of the study, which is the inverse variance of the effect estimate. The diamond data markers indicate the pooled ORs.