Publication: A Bayesian approach for estimating typhoid fever incidence from large-scale facility-based passive surveillance data
Issued Date
2021-11-20
Resource Type
ISSN
10970258
02776715
02776715
DOI
Other identifier(s)
2-s2.0-85113302049
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Mahidol University
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SCOPUS
Bibliographic Citation
Statistics in Medicine. Vol.40, No.26 (2021), 5853-5870
Suggested Citation
Maile T. Phillips, James E. Meiring, Merryn Voysey, Joshua L. Warren, Stephen Baker, Buddha Basnyat, John D. Clemens, Christiane Dolecek, Sarah J. Dunstan, Gordon Dougan, Melita A. Gordon, Deus Thindwa, Robert S. Heyderman, Kathryn E. Holt, Firdausi Qadri, Andrew J. Pollard, Virginia E. Pitzer A Bayesian approach for estimating typhoid fever incidence from large-scale facility-based passive surveillance data. Statistics in Medicine. Vol.40, No.26 (2021), 5853-5870. doi:10.1002/sim.9159 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/77377
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Title
A Bayesian approach for estimating typhoid fever incidence from large-scale facility-based passive surveillance data
Other Contributor(s)
NIHR Oxford Biomedical Research Centre
Oxford University Clinical Research Unit
Department of Medicine
Malawi-Liverpool-Wellcome Trust Clinical Research Programme
London School of Hygiene & Tropical Medicine
University of Melbourne
University College London
University of Liverpool
Faculty of Medicine, Nursing and Health Sciences
Mahidol University
International Centre for Diarrhoeal Disease Research Bangladesh
Nuffield Department of Medicine
Yale University
University of Oxford Medical Sciences Division
Oxford University Clinical Research Unit
Department of Medicine
Malawi-Liverpool-Wellcome Trust Clinical Research Programme
London School of Hygiene & Tropical Medicine
University of Melbourne
University College London
University of Liverpool
Faculty of Medicine, Nursing and Health Sciences
Mahidol University
International Centre for Diarrhoeal Disease Research Bangladesh
Nuffield Department of Medicine
Yale University
University of Oxford Medical Sciences Division
Abstract
Decisions about typhoid fever prevention and control are based on estimates of typhoid incidence and their uncertainty. Lack of specific clinical diagnostic criteria, poorly sensitive diagnostic tests, and scarcity of accurate and complete datasets contribute to difficulties in calculating age-specific population-level typhoid incidence. Using data from the Strategic Typhoid Alliance across Africa and Asia program, we integrated demographic censuses, healthcare utilization surveys, facility-based surveillance, and serological surveillance from Malawi, Nepal, and Bangladesh to account for under-detection of cases. We developed a Bayesian approach that adjusts the count of reported blood-culture-positive cases for blood culture detection, blood culture collection, and healthcare seeking—and how these factors vary by age—while combining information from prior published studies. We validated the model using simulated data. The ratio of observed to adjusted incidence rates was 7.7 (95% credible interval [CrI]: 6.0-12.4) in Malawi, 14.4 (95% CrI: 9.3-24.9) in Nepal, and 7.0 (95% CrI: 5.6-9.2) in Bangladesh. The probability of blood culture collection led to the largest adjustment in Malawi, while the probability of seeking healthcare contributed the most in Nepal and Bangladesh; adjustment factors varied by age. Adjusted incidence rates were within or below the seroincidence rate limits of typhoid infection. Estimates of blood-culture-confirmed typhoid fever without these adjustments results in considerable underestimation of the true incidence of typhoid fever. Our approach allows each phase of the reporting process to be synthesized to estimate the adjusted incidence of typhoid fever while correctly characterizing uncertainty, which can inform decision-making for typhoid prevention and control.