Gendered socio-economic and mental health effects of the COVID-19 pandemic among adults living in selected informal settlements in Kenya: an intersectional analysis
Bibliographic record
Abstract
Background COVID-19 pandemic had devastating socio-economic and health effects, including mental health. This study examines the intersectionality between gender and mental health outcomes among Kenyan adults in informal settlements of Nairobi, Kisumu, and Kilifi Counties during the COVID-19 crisis. This is necessary to inform mental health response in case of another pandemic. Methods We analyzed data collected in a longitudinal survey between July 2020 (fourth round) and February 2021 (fifth round). The data covered COVID-19-related effects on job loss, food insecurity, access to health services, and mental health. Participants were randomly sampled from existing cohorts at the Population Council. The outcomes of interest were depressive and anxiety disorders, combined into a binary composite outcome variable. Descriptive statistics included means for continuous variables and frequencies and proportions for categorical variables. Chi-square tests were used to examine the differences between groups. The relationship between the gendered COVID-19 socio-economic effects and participants’ mental health was examined using modified Poisson regression. Results A total of 4,050 participants were interviewed, 66% female and median age 38 [interquartile range (IQR): 29–46]. Complete income loss was strongly associated with negative mental health outcomes in multiple intersections with varied magnitudes. The association was larger among older females (≥50 years) (PR = 1.33, 95% CI = 1.17–1.51, p < 0.001) than older males (PR = 1.22, 95% CI = 1.17–1.27, p < 0.001). Partial loss of income was protective against negative mental health outcomes among young males (18–29 years) (PR = 0.81, 95% CI = 0.76–0.87, p < 0.001) but linked to negative outcomes among middle-aged males (30–49 years old) (PR = 1.14, 95% CI = 1.12–1.16, p < 0.001). Skipping meals was associated with negative mental health outcomes for both genders particularly the married (married male: PR = 1.49, 95% CI = 1.22–1.83, p < 0.001; married female: PR = 1.42, 95% CI = 1.26–1.60, p < 0.001). Conclusion We observed significant gender differences in the prevalence of depressive symptoms and anxiety disorders during the COVID-19 pandemic, underscoring the importance of socio-economic factors and health services access in shaping mental health outcomes. Interventions targeting pandemic-related mental health issues should be gender-sensitive and address economic vulnerabilities such as job losses and food insecurity. Policies to mitigate these effects, especially for at-risk groups are crucial for reducing mental health burden in future crises.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".