Estimating Plasmodium falciparum Parasite Rate using Test Positivity Rate from 2016-2024: Health Management Information Systems in Uganda

Abstract Background Malaria transmission in Uganda is heterogenous, so the national malaria program needs information about the distribution of malaria to develop appropriate policies. While population-based community surveys estimate Plasmodium falciparum parasite rate ( Pf PR), they are too infrequent and sparse for routine malaria management. Health facility data is routinely collected and covers a large geographic scope, but the data is collected passively, variable in quality, and potentially highly biased. We aimed to triangulate test positivity rate (TPR) from health facility data to survey estimated Pf PR data in Uganda to create monthly, high-resolution Pf PR estimates. Methods Using matched health facility and survey data, we fit a multi-level logistic regression model that accounted for clustering at the district and region level, to predict Pf PR from TPR. Additional covariates were explored to select a final model that reduced bias while prioritizing its utility for programmatic tasks. Model predictions were validated against observed Pf PR and used to generate monthly district-level prevalence estimates from 2016 to 2024. Regional and national level estimates were made by weighting district level estimates by population. Results The final model included a smoothed TPR term and proportion of severe malaria cases at a district-month level. Predicted Pf PR was strongly positively correlated with the observed survey Pf PR (Pearson’s rank correlation rho =0.79, p