Key result
Enumeration-aware Bayesian optimization accurately predicts screening detection yield with negligible exposure bias.
Why the study?
Community health worker blood pressure screening programmes in low- and middle-income countries routinely operate on incomplete household enumeration lists, and the resulting coverage bias is rarely modelled when planning routes.
Does Bayesian optimisation with enumeration-aware routing improve healthcare detection yield in simulated community health worker blood pressure screening programmes?
Does Bayesian optimisation with enumeration-aware routing improve healthcare detection yield in simulated community health worker blood pressure screening programmes?
This computational modeling study demonstrates that Bayesian optimisation can be applied to community health worker blood pressure screening routing to account for incomplete household enumeration lists with negligible estimation bias.
May improve CHW BP screening equity in LMICs; leaves open real-world validation before adoption.
Community health worker (CHW) blood pressure screening programmes in low- and middle-income countries routinely operate on household enumeration lists that are incomplete, and the resulting coverage bias is rarely modelled when routes are planned. The framework couples a Gaussian-process surrogate over enumeration coverage with a constrained acquisition function that trades expected detections against travel cost and workload equity, and it is evaluated in a replication-based replication of 600 computed enumeration surfaces at N = 500 households per surface, using a declared data-generating process with intercept 12.0, exposure 0.42, group -3.1, severity 1.35 and error SD 4.2. The estimator behaviour is characterised across replications: the intercept bias was -0.02031 with RMSE 0.9213, the exposure bias was 0.00064 with RMSE 0.01609, and nominal 95% interval coverage was 1.0 for both parameters. The results indicate that enumeration-aware routing changes the ordering of candidate routes relative to naive coverage-maximising plans, and that the detection-yield criterion is estimable with negligible bias under the stated process. The contribution is a formalisation of enumeration coverage as a decision-relevant uncertainty field and a demonstration that Bayesian optimisation can be applied to CHW routing without requiring complete household lists.
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Akplogan et al. (2026) studied Hypertension / Blood pressure screening. Enumeration-aware Bayesian optimisation routing framework vs. Naive coverage-maximising routing plans was evaluated on Healthcare detection-yield estimator performance (intercept and exposure bias, RMSE, and 95% interval coverage). Enumeration-aware Bayesian optimisation reordered candidate screening routes and estimated detection yield with negligible bias (exposure bias 0.00064, RMSE 0.01609; 95% CI coverage 1.0).
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