Spatial and temporal village-level prevalence of Plasmodium infection and associated risk factors in two districts of Meghalaya, India
Bibliographic record
Abstract
BACKGROUND: Despite declining incidence over the past decade, malaria remains an important health burden in India. This study aimed to assess the village-level temporal patterns of Plasmodium infection in two districts of the north-eastern state of Meghalaya and evaluate risk factors that might explain these patterns. METHODS: Primary Health Centre passive malaria case data from 2014 to 2018 were analysed to characterize village-specific annual incidence and temporal trends. Active malaria case detection was undertaken in 2018 and 2019 to detect Plasmodium infections using PCR. A questionnaire collected socio-demographic, environmental, and behavioural data, and households were spatially mapped via GPS. Adult mosquitoes were sampled at a subset of subjects' houses, and Anopheles were identified by PCR and sequencing. Risk factors for Plasmodium infection were evaluated using bivariate and multivariate logistic regression analysis, and spatial cluster analysis was undertaken. RESULTS: The annual malaria incidence from PHC-based passive surveillance datasets in 2014-2018 was heterogenous but declining across villages in both districts. Active surveillance in 2018 enrolled 1468 individuals from 468 households (West Jaintia Hills) and 1274 individuals from 359 households (West Khasi Hills). Plasmodium falciparum prevalence per 100 people varied from 0 to 4.1% in the nine villages of West Jaintia Hills, and from 0 to 10.6% in the 12 villages of West Khasi Hills. Significant clustering of P. falciparum infections [observed = 11, expected = 2.15, Relative Risk (RR) = 12.65; p < 0.001] was observed in West Khasi Hills. A total of 13 Anopheles species were found at 53 houses in five villages, with Anopheles jeyporiensis being the most abundant. Risk of infection increased with presence of mosquitoes and electricity in the households [Odds Ratio (OR) = 1.19 and 1.11], respectively. Households with reported animals had reduced infection risk (OR = 0.91). CONCLUSION: Malaria incidence during 2014-2018 declined in all study villages covered by the passive surveillance data, a period that includes the first widespread insecticide-treated net campaign. The survey data from 2018 revealed a significant association between Plasmodium infection and certain household characteristics. Since species of Plasmodium-competent mosquito vectors continue to be abundant, malaria resurgence remains a threat, and control efforts should continue.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".