Pre- and during -COVID-19 pandemic mortality trends and drivers in rural, coastal Kenya: findings from the Kaloleni–Rabai Health and Demographic Surveillance System
Bibliographic record
Abstract
BACKGROUND: There is contradicting information regarding the effect of COVID-19 on mortality in African settings. Knowledge of the complete direct and indirect burden of COVID-19 on mortality is heavily reliant on the availability of a population-based surveillance system. Here we provide robust data on the effect of COVID-19 on mortality trends in a rural, coastal, Kenyan community. METHODS: A historical cohort study using data from the Kaloleni Rabai Health and Demographic Surveillance System was conducted with special focus on two discernible time periods representing the pre-COVID-19 (2018-2019) and COVID-19 (2020-2021) periods. Mortality rates were estimated as the total number of deaths divided by the person-time (years) at risk, accounting for attrition, and calculated separately for the two periods. A cox proportional hazards model was used to estimate the impact of COVID-19 on mortality. RESULTS: 1191 deaths occurred between 2018 and 2021. There was no significant change in overall mortality rates between pre-COVID-19 and COVID-19 periods (3.7 and 3.6 per 1000 person years at risk respectively, p = 0.74). Older age was significantly associated with mortality (a_HR: 1.05, 95% CI: 1.05-1.06; p < 0.001). However, an interaction term between age and time-period appeared to reverse this association (a_HR: 0.99, 95% CI: 0.99-1.00; p < 0.001). CONCLUSIONS: Our findings suggest that although overall COVID-19 did not directly impact mortality rates within this rural population, the onset of the pandemic did appear to reverse and/or attenuate the impact of several risk factors on mortality. It is possible that COVID-19 brought health and wellness into sharp focus, making people more vigilant about their health, hygiene and associated preventive measures.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".