Sociodemographic and Environmental Factors for Ill Health in Rwanda: Results from the 2016 Integrated Household Living Conditions Survey
Bibliographic record
Abstract
Introduction A quarter of deaths are due to environmental factors worldwide, in Africa, and in Rwanda. Integrated Household Living Conditions Surveys (EICV) assess the living conditions of households. We sought to increase evidence on environmental factors in Rwanda. Materials and Methods We used a cross-sectional design to analyze the fifth EICV 5 conducted from October 2016 to October 2017, and conducted a multiple logistic regression to assess the prevalence of illness and environmental factors associated with disease in Rwanda. Results One-third of participants reported illness. Females (OR 1.26, 95% CI 1.22-1.33), over 65 years (OR 3.75, 95% CI 3.30-4.26), and affiliation with military medical insurance (OR=1.60, 95% CI: 1.14-2.23, p=0.006) indicated greater odds of disease. Using unimproved water sources (OR=1.47, 95% CI: 1.20-1.79, p<0.001), firewood for lighting (OR=1.28, 95% CI: 1.06-1.53, p=0.008), unimproved sanitation services (OR=1.72, 95% CI: 1.28-2.31, p<0.001), and occurrence of an environmental shock (OR=1.18, 95% CI: 1.18-1.39, p<0.001) showed higher odds of disease. Discussion Biological and social factors contribute to poorer health among females. Increased illness with age may be explained by aging-related changes. Higher odds of disease among military personnel might be due to the nature of their work. Increased odds of disease associated with non-improved drinking water sources reflect the drinking water ladder. We confirmed the harmful effects of wood consumption, the increased risk of illness across the sanitation ladder, and the association between environmental shocks and poor health. Conclusion Increased access to improved water sources, high-level sanitation services, and clean energy, reinforced disaster preparedness, and longitudinal studies are needed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".