Socioeconomic, demographic and environmental factors associated with under-five mortality among children in Kenya: analysis of the 2022 Kenya demographic and health survey
Bibliographic record
Abstract
BACKGROUND: Under-five mortality remains a critical public health challenge globally, particularly in low- and middle-income countries such as Kenya. Despite recent declines in mortality rates, Kenya continues to face a high burden of child deaths due to preventable or treatable conditions such as pneumonia, malaria, and malnutrition. This study aimed to assess the predictors of under-five mortality in Kenya using the 2022 Kenya Demographic and Health Survey (KDHS) to inform evidence-based interventions and policies. METHODS: This cross-sectional study utilised the 2022 Kenya Demographic and Health Survey (KDHS) dataset, focusing on variables from women's and household questionnaires relevant to under-five mortality. Data were cleaned and analysed using STATA 17 software. Descriptive statistics, univariate, bivariate, and multivariate logistic regression analyses were performed at p < 0.05 with 95% confidence intervals, accounting for the complex survey design. RESULTS: A total of 19,530 children under five years were included in the analysis, with 694 reported deaths. Second-born twins had a significantly higher mortality risk than first-born twins (AOR = 0.19, 95% CI: 0.05-0.68), and mothers using modern contraceptives had 3.31 times higher odds of child mortality compared to those using folkloric methods (AOR = 3.31, 95% CI: 1.97-5.55). Mothers with 1-4 antenatal care (ANC) visits had 2.55 times higher odds of child mortality (AOR = 2.55, 95% CI: 1.04-6.23) compared to those with no visits. Mothers with two or three births in the last five years had increased mortality odds (AOR = 2.59, 95% CI: 1.26-5.32; AOR = 6.15, 95% CI: 1.57-24.03, respectively), highlighting the risks of short birth intervals. CONCLUSION: This study provides important insights into the factors influencing child mortality in Kenya. These findings suggest the need for targeted interventions, such as scaling up Kangaroo Mother Care for twins, integrating family planning counselling into Kenya's Linda Mama program to promote optimal birth spacing, and improving ANC quality to address high-risk pregnancies. Our research contributes to the broader understanding of child mortality determinants and offers a foundation for future studies aimed at mitigating this critical public health issue.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".