Using healthcare-seeking behaviour to estimate the number of Nipah outbreaks missed by hospital-based surveillance in Bangladesh

• Published in: International journal of epidemiology Vol. 48; no. 4; pp. 1219 - 1227
• Main Authors: Hegde, Sonia T, Salje, Henrik, Sazzad, Hossain M S, Hossain, M Jahangir, Rahman, Mahmudur, Daszak, Peter, Klena, John D, Nichol, Stuart T, Luby, Stephen P, Gurley, Emily S
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01-08-2019
• Subjects: Bangladesh - epidemiology; Disease Outbreaks; Henipavirus Infections - diagnosis; Henipavirus Infections - epidemiology; Hospitals - statistics & numerical data; Humans; Infectious Disease; Logistic Models; Patient Acceptance of Health Care - statistics & numerical data; Risk Factors; Sentinel Surveillance; Bangladesh; Bangladesh; Nipah virus; spatial statistics; surveillance; healthcare access
• ISSN: 0300-5771; 1464-3685
• DOI: 10.1093/ije/dyz057

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 2007 and 2014 to estimate the true burden.
Results
The probability of attending a surveillance hospital fell from 82% for people with fatal encephalitis living 10 km away from a surveillance hospital to 54% at 50 km away. The odds of attending a surveillance hospital are 3.2 (95% confidence interval: 1.6, 6.6) times greater for patients who eventually died (i.e. who were more severely ill) compared with those who survived. Using these probabilities, we estimated that 119 Nipah outbreaks (95% confidence interval: 103, 140)—an average of 15 outbreaks per Nipah season—occurred during 2007–14; 62 (52%) were detected.
Conclusions
Our findings suggest hospital-based surveillance missed nearly half of all Nipah outbreaks. This analytical method allowed us to estimate the underlying burden of disease, which is important for emerging diseases where healthcare access may be limited.