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

Abstract 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...

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Bibliographic Details
Main Authors: Michelle V. Evans, Felana A. Ihantamalala, Mauricianot Randriamihaja, Andritiana Tsirinomen’ny Aina, Matthew H. Bonds, Karen E. Finnegan, Rado J. L. Rakotonanahary, Mbolatiana Raza-Fanomezanjanahary, Bénédicte Razafinjato, Oméga Raobela, Sahondraritera Herimamy Raholiarimanana, Tiana Harimisa Randrianavalona, Andres Garchitorena
Format: Article
Language:English
Published: Nature Portfolio 2023-12-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-023-48390-0

Internet

https://doi.org/10.1038/s41598-023-48390-0

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