The Uganda housing modification study - association between housing characteristics and malaria burden in a moderate to high transmission setting in Uganda
Bibliographic record
Abstract
BACKGROUND: Scale up of proven malaria control interventions has not been sufficient to control malaria in Uganda, emphasizing the need to explore innovative new approaches. Improved housing is one such promising strategy. This paper describes housing characteristics and their association with malaria burden in a moderate to high transmission setting in Uganda. METHODS: Between October and November 2021, a household survey was conducted in 1500 randomly selected households in Jinja and Luuka districts. Information on demographics, housing characteristics, use of malaria prevention measures, and proxy indicators of wealth were collected for each household. A finger-prick blood sample was obtained for thick blood smears for malaria from all children aged 6 months to 14 years in the surveyed households. Febrile children had a malaria rapid diagnostics test (RDT) done; positive cases were managed according to national treatment guidelines. Haemoglobin was assessed in children aged < 5 years. Households were stratified as having modern houses (defined as having finished materials for roofs, walls, and floors and closed eaves) or traditional houses (those not meeting the definition of modern house). Associations between malaria burden and house type were estimated using mixed effects models and adjusted for age, wealth, and bed net use. RESULTS: Most (65.5%) of the households surveyed lived in traditional houses. Most of the houses had closed eaves (85.5%), however, the use of other protective features like window/vent screens and installed ceilings was limited (0.4% had screened windows, 2.8% had screened air vents, and 5.2% had ceiling). Overall, 3,443 children were included in the clinical survey, of which 31.4% had a positive smear. RDT test positivity rate was 56.6% among children with fever. Participants living in modern houses had a significantly lower parasite prevalence by microscopy (adjusted prevalence ratio [aPR = 0.80]; 95% confidence interval [CI] 0.71 - 0.90), RDT test positivity rate (aPR = 0.90, 95%CI 0.81 - 0.99), and anaemia (aPR = 0.80, 95%CI 0.65 - 0.97) compared to those in traditional houses. CONCLUSION: The study found that even after adjusting for wealth, higher quality housing had a moderate protective effect against malaria, on top of the protection already afforded by recently distributed nets.
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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 | 0.001 |
| 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 | 0.001 |
| 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".