@article{fdi:010089562,
  title = {{A}pplying a zero-corrected, gravity model estimator reduces bias due to heterogeneity in healthcare utilization in community-scale, passive surveillance datasets of endemic diseases},
  author = {{E}vans, {M}. {V}. and {I}hantamalala, {F}. {A}. and {R}andriamihaja, {M}. and {A}ina, {A}. {T}. and {B}onds, {M}. {H}. and {F}innegan, {K}. {E}. and {R}akotonanahary, {R}. {J}. {L}. and {R}aza-{F}anomezanjanahary, {M}. and {R}azafinjato, {B}. and {R}aobela, {O}. and {R}aholiarimanana, {S}. {H}. and {R}andrianavalona, {T}. {H}. and {G}architorena, {A}ndres},
  editor = {},
  language = {{ENG}},
  abstract = {{D}ata on population health are vital to evidence-based decision making but are rarely adequately localized or updated in continuous time. {T}hey also suffer from low ascertainment rates, particularly in rural areas where barriers to healthcare can cause infrequent touch points with the health system. {H}ere, 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. {T}he 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. {T}he 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. {W}e 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 {M}adagascar. {T}he {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. {T}he {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.},
  keywords = {{MADAGASCAR}},
  booktitle = {},
  journal = {{S}cientific {R}eports - {N}ature},
  volume = {13},
  numero = {1},
  pages = {21288 [15 p.]},
  ISSN = {2045-2322},
  year = {2023},
  DOI = {10.1038/s41598-023-48390-0},
  URL = {https://www.documentation.ird.fr/hor/fdi:010089562},
}