Applying a zero-corrected, gravity model estimator reduces bias due to heterogeneity in healthcare utilization in community-scale, passive surveillance datasets of endemic diseases

Applying a zero-corrected, gravity model estimator reduces bias due to heterogeneity in healthcare utilization in community-scale, passive surveillance datasets of endemic diseases Evans M. V. auteur aut IRD Ihantamalala F. A. auteur aut IRD Randriamihaja M. auteur aut IRD Aina A. T. auteur aut IRD Bonds M. H. auteur aut IRD Finnegan K. E. auteur aut IRD Rakotonanahary R. J. L. auteur aut IRD Raza-Fanomezanjanahary M. auteur aut IRD Razafinjato B. auteur aut IRD Raobela O. auteur aut IRD Raholiarimanana S. H. auteur aut IRD Randrianavalona T. H. auteur aut IRD Garchitorena Andres auteur aut IRD text journalArticle eng text/pdf reformatted digital access Data on population health are vital to evidence-based decision making but are rarely adequately localized or updated in continuous time. They also suffer from low ascertainment rates, particularly in rural areas where barriers to healthcare can cause infrequent touch points with the health system. Here, we demonstrate a novel statistical method to estimate the incidence of endemic diseases at the community level from passive surveillance data collected at primary health centers. The zero-corrected, gravity-model (ZERO-G) estimator explicitly models sampling intensity as a function of health facility characteristics and statistically accounts for extremely low rates of ascertainment. The result is a standardized, real-time estimate of disease incidence at a spatial resolution nearly ten times finer than typically reported by facility-based passive surveillance systems. We assessed the robustness of this method by applying it to a case study of field-collected malaria incidence rates from a rural health district in southeastern Madagascar. The ZERO-G estimator decreased geographic and financial bias in the dataset by over 90% and doubled the agreement rate between spatial patterns in malaria incidence and incidence estimates derived from prevalence surveys. The ZERO-G estimator is a promising method for adjusting passive surveillance data of common, endemic diseases, increasing the availability of continuously updated, high quality surveillance datasets at the community scale. specialized MADAGASCAR 050 020 Scientific Reports - Nature 13 1 21288 [15 p.] 2023 2045-2322 https://www.documentation.ird.fr/hor/fdi:010089562 10.1038/s41598-023-48390-0 2045-2322 [F B010089562] https://www.documentation.ird.fr/hor/fdi:010089562 https://horizon.documentation.ird.fr/exl-doc/pleins_textes/2024-04/010089562.pdf IRD - Base Horizon / Pleins textes 2024-04-05 2025-02-24 fdi:010089562 fre