Spatial Clustering and Risk Factors for Malaria Infections and Marker of Recent Exposure to Plasmodium falciparum from a Household Survey in Artibonite, Haiti
Bibliographic record
Abstract
Targeting malaria interventions in elimination settings where transmission is heterogeneous is essential to ensure the efficient use of resources. Identifying the most important risk factors among persons experiencing a range of exposure can facilitate such targeting. A cross-sectional household survey was conducted in Artibonite, Haiti, to identify and characterize spatial clustering of malaria infections. Household members (N = 21,813) from 6,962 households were surveyed and tested for malaria. An infection was defined as testing positive for Plasmodium falciparum by either a conventional or novel highly sensitive rapid diagnostic test. Seropositivity to the early transcribed membrane protein 5 antigen 1 represented recent exposure to P. falciparum. Clusters were identified using SaTScan. Associations among individual, household, and environmental risk factors for malaria, recent exposure, and living in spatial clusters of these outcomes were evaluated. Malaria infection was detected in 161 individuals (median age: 15 years). Weighted malaria prevalence was low (0.56%; 95% CI: 0.45-0.70%). Serological evidence of recent exposure was detected in 1,134 individuals. Bed net use, household wealth, and elevation were protective, whereas being febrile, over age 5 years, and living in either households with rudimentary wall material or farther from the road increased the odds of malaria. Two predominant overlapping spatial clusters of infection and recent exposure were identified. Individual, household, and environmental risk factors are associated with the odds of individual risk and recent exposure in Artibonite; spatial clusters are primarily associated with household-level risk factors. Findings from serology testing can further strengthen the targeting of interventions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".