Bayesian Spatiotemporal Modeling of Routinely Collected Data to Assess the Effect of Health Programs in Malaria Incidence During Pregnancy in Burkina Faso
Bibliographic record
Abstract
Control of malaria in pregnancy (MiP) remains a major challenge in Burkina Faso. Surveillance of the burden due to MiP based on routinely collected data at a fine-scale level, followed by an appropriate analysis and interpretation, may be crucial for evaluating and improving the effectiveness of existing control measures. We described the spatio-temporal dynamics of MiP at the community-level and assessed health program effects, mainly community-based health promotion, results-based financing, and intermittent-preventive-treatment with sulphadoxine-pyrimethamine (IPTp-SP). Community-aggregated monthly MiP cases were downloaded from Health Management Information System and combined with covariates from other sources. The MiP spatio-temporal pattern was decomposed into three components: overall spatial and temporal trends and space-time interaction. Bayesian hierarchical spatio-temporal Poisson models were used to fit the MiP incidence rate and assess health program effects. The overall annual incidence increased between 2015 and 2017. The findings reveal spatio-temporal heterogenicity throughout the year, which peaked during rainy season. From the model without covariates, 96 communities located mainly in the Cascades, South-West, Center-West, Center-East, and Eastern regions, exhibited significant relative-risk levels. The combined effect (significant reducing effect) of RBF, health promotion and IPTp-SP strategies was greatest in 17.7% (17/96) of high burden malaria communities. Despite intensification of control efforts, MiP remains high at the community-scale. The provided risk maps are useful tools for highlighting areas where interventions should be optimized, particularly in high-risk communities.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".