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A Bayesian approach for estimating typhoid fever incidence from large-scale facility-based passive surveillance data
Maile T Phillips1, James E Meiring2,3,4, Merryn Voysey2,3
1Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, USA.
Statistics in Medicine
|August 24, 2021
Summary
Accurate typhoid fever incidence estimates are crucial for effective prevention. This study developed a Bayesian model to correct for under-detection, revealing significantly higher actual typhoid rates than previously reported, aiding better control strategies.
Area of Science:
- Infectious Disease Epidemiology
- Public Health Surveillance
- Biostatistics
Background:
- Typhoid fever prevention relies on accurate incidence data, which is challenging to obtain due to diagnostic limitations and incomplete datasets.
- Underestimation of typhoid incidence hinders effective public health decision-making for control and prevention strategies.
Purpose of the Study:
- To develop and validate a Bayesian statistical model for estimating age-specific typhoid fever incidence.
- To account for under-detection in case reporting by integrating demographic, healthcare utilization, and surveillance data.
- To provide more accurate typhoid incidence estimates for informing prevention and control efforts.
Main Methods:
- Integrated demographic censuses, healthcare utilization surveys, and facility-based and serological surveillance data from Malawi, Nepal, and Bangladesh.
- Developed a Bayesian approach to adjust reported blood-culture-positive typhoid cases for detection and healthcare-seeking probabilities, varying by age.
- Validated the model using simulated data and compared adjusted incidence rates with seroincidence limits.
Main Results:
- The model revealed significant underestimation of typhoid fever incidence, with observed to adjusted rate ratios of 7.7 in Malawi, 14.4 in Nepal, and 7.0 in Bangladesh.
- Factors influencing case under-detection, such as blood culture collection and healthcare seeking, varied by age and country.
- Adjusted incidence rates were within or below seroincidence rate limits, suggesting the model captures true infection rates.
Conclusions:
- Standard reporting of blood-culture-confirmed typhoid fever considerably underestimates true incidence.
- The developed Bayesian approach synthesizes multiple data sources to provide adjusted typhoid incidence estimates with uncertainty quantification.
- Accurate typhoid incidence data are essential for informed decision-making in disease prevention and control programs.
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