Levels of depression, anxiety, and psychological distress among Ugandan adults during the first wave of the COVID-19 pandemic: cross-sectional evidence from a mobile phone-based population survey.

Objectives: Policy measures to slow the spread of coronavirus disease 2019 (COVID-19), such as curfews and business closures, may have negative effects on mental health. Populations in low- and middle-income countries (LMICs) may be particularly affected due to high rates of poverty and less comprehensive welfare systems, but the evidence is scarce. We evaluated predictors of depression, anxiety, and psychological distress in Uganda, which implemented one of the world's most stringent lockdowns. Methods: We conducted a mobile phone-based cross-sectional survey from December 2020 through April 2021 among individuals aged 18 years or over in Uganda. We measured depression, anxiety, and psychological distress using the Patient Health Questionnaire (PHQ)-2, the Generalized Anxiety Disorder (GAD)-2, and the PHQ-4. We applied linear regression to assess associations between experiences of COVID-19 (including fear of infection, social isolation, income loss, difficulty accessing medical care, school closings, and interactions with police) and PHQ-4 score, adjusted for sociodemographic characteristics. Results: 29.2% of 4066 total participants reported scores indicating moderate psychological distress, and 12.1% reported scores indicating severe distress. Distress was most common among individuals who were female, had lower levels of education, and lived in households with children. Related to COVID-19, PHQ-4 score was significantly associated with difficulty accessing medical care, worries about COVID-19, worries about interactions with police over lockdown measures, and days spent at home. Conclusions: There is an urgent need to address the significant burden of psychological distress associated with COVID-19 and policy responses in LMICs. Pandemic mitigation strategies must consider mental health consequences.

Impact Evaluation of Coronavirus Disease 2019 Policy: A Guide to Common Design Issues.

Policy responses to coronavirus disease 2019 (COVID-19), particularly those related to nonpharmaceutical interventions, are unprecedented in scale and scope. However, evaluations of policy impacts require a complex combination of circumstance, study design, data, statistics, and analysis. Beyond the issues that are faced for any policy, evaluation of COVID-19 policies is complicated by additional challenges related to infectious disease dynamics and a multiplicity of interventions. The methods needed for policy-level impact evaluation are not often used or taught in epidemiology, and they differ in important ways that may not be obvious. Methodological complications of policy evaluations can make it difficult for decision-makers and researchers to synthesize and evaluate the strength of the evidence in COVID-19 health policy papers. Here we 1) introduce the basic suite of policy-impact evaluation designs for observational data, including cross-sectional analyses, pre-/post- analyses, interrupted time-series analysis, and difference-in-differences analysis; 2) demonstrate key ways in which the requirements and assumptions underlying these designs are often violated in the context of COVID-19; and 3) provide decision-makers and reviewers with a conceptual and graphical guide to identifying these key violations. Our overall goal is to help epidemiologists, policy-makers, journal editors, journalists, researchers, and other research consumers understand and weigh the strengths and limitations of evidence.