{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Hegde ST"],"funding":["Research and Policy for Infectious Disease Dynamics","FIC NIH HHS","National Science Foundation/NIH Ecology and Evolution of Infectious Diseases","National Institutes of Health","NIH","RAPIDD"],"pagination":["1219-1227"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC6693802"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["48(4)"],"pubmed_abstract":["Background
Understanding the true burden of emergent diseases is critical for assessing public-health impact. However, surveillance often relies on hospital systems that only capture a minority of cases. We use the example of Nipah-virus infection in Bangladesh, which has a high case-fatality ratio and frequent person-to-person transmission, to demonstrate how healthcare-seeking data can estimate true burden.Methods
We fit logistic-regression models to data from a population-based, healthcare-seeking study of encephalitis cases to characterize the impact of distance and mortality on attending one of three surveillance hospital sites. The resulting estimates of detection probabilities, as a function of distance and outcome, are applied to all observed Nipah outbreaks between"],"journal":["International journal of epidemiology"],"pubmed_title":["Using healthcare-seeking behaviour to estimate the number of Nipah outbreaks missed by hospital-based surveillance in Bangladesh."],"pmcid":["PMC6693802"],"funding_grant_id":["2R01-TW005869","R01 TW005869","07–015-0712–52200"],"pubmed_authors":["Daszak P","Luby SP","Sazzad HMS","Nichol ST","Hossain MJ","Hegde ST","Rahman M","Klena JD","Gurley ES","Salje H"],"additional_accession":[]},"is_claimable":false,"name":"Using healthcare-seeking behaviour to estimate the number of Nipah outbreaks missed by hospital-based surveillance in Bangladesh.","description":"Background
Understanding the true burden of emergent diseases is critical for assessing public-health impact. However, surveillance often relies on hospital systems that only capture a minority of cases. We use the example of Nipah-virus infection in Bangladesh, which has a high case-fatality ratio and frequent person-to-person transmission, to demonstrate how healthcare-seeking data can estimate true burden.Methods
We fit logistic-regression models to data from a population-based, healthcare-seeking study of encephalitis cases to characterize the impact of distance and mortality on attending one of three surveillance hospital sites. The resulting estimates of detection probabilities, as a function of distance and outcome, are applied to all observed Nipah outbreaks between","dates":{"release":"2019-01-01T00:00:00Z","publication":"2019 Aug","modification":"2025-04-05T15:17:50.182Z","creation":"2025-04-05T15:17:50.182Z"},"accession":"S-EPMC6693802","cross_references":{"pubmed":["30977803"],"doi":["10.1093/ije/dyz057"]}}