A Bayesian approach for estimating typhoid fever incidence from large-scale facility-based passive surveillance data
Loading...
Citations
Altmetric:
Altmetric::
Download
Associated Records
Authors
File Licence
Editors
Date
2021-11-20
Source Title
Statistics in Medicine
Journal Title
Journal ISSN
Volume Title
Publisher
WILEY
Melbourne Authors
Affiliation
Biochemistry and Pharmacology
Infectious Diseases
Infectious Diseases
Document Type
Journal Article
Subject Code
Citation
Phillips, M. T., Meiring, J. E., Voysey, M., Warren, J. L., Baker, S., Basnyat, B., Clemens, J. D., Dolecek, C., Dunstan, S. J., Dougan, G., Gordon, M. A., Thindwa, D., Heyderman, R. S., Holt, K. E., Qadri, F., Pollard, A. J. & Pitzer, V. E. (2021). A Bayesian approach for estimating typhoid fever incidence from large-scale facility-based passive surveillance data. Statistics in Medicine, 40 (26), pp.5853-5870. https://doi.org/10.1002/sim.9159.
Access Status
Open Access
DOI
Access URL
Open Access URL
Open Access at PMC
Linked Resource URL
NHMRC Grant Code
ARC Grant Code
Description
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.