Facility-based disease surveillance and Bayesian hierarchical modeling to estimate endemic typhoid fever incidence, Kilimanjaro Region, Tanzania, 2007-2018.

Abstract

Growing evidence suggests considerable variation in endemic typhoid fever incidence at some locations over time, yet few settings have multi-year incidence estimates to inform typhoid control measures. We sought to describe a decade of typhoid fever incidence in the Kilimanjaro Region of Tanzania. Cases of blood culture confirmed typhoid were identified among febrile patients at two sentinel hospitals during three study periods: 2007-08, 2011-14, and 2016-18. To account for under-ascertainment at sentinel facilities, we derived adjustment multipliers from healthcare utilization surveys done in the hospital catchment area. Incidence estimates and credible intervals (CrI) were derived using a Bayesian hierarchical incidence model that incorporated uncertainty of our observed typhoid fever prevalence, of healthcare seeking adjustment multipliers, and of blood culture diagnostic sensitivity. Among 3,556 total participants, 50 typhoid fever cases were identified. Of typhoid cases, 26 (52%) were male and the median (range) age was 22 (<1-60) years; 4 (8%) were aged <5 years and 10 (20%) were aged 5 to 14 years. Annual typhoid fever incidence was estimated as 61.5 (95% CrI 14.9-181.9), 6.5 (95% CrI 1.4-20.4), and 4.0 (95% CrI 0.6-13.9) per 100,000 persons in 2007-08, 2011-14, and 2016-18, respectively. There were no deaths among typhoid cases. We estimated moderate typhoid incidence (≥10 per 100 000) in 2007-08 and low (<10 per 100 000) incidence during later surveillance periods, but with overlapping credible intervals across study periods. Although consistent with falling typhoid incidence, we interpret this as showing substantial variation over the study periods. Given potential variation, multi-year surveillance may be warranted in locations making decisions about typhoid conjugate vaccine introduction and other control measures.

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Citation

Published Version (Please cite this version)

10.1371/journal.pntd.0010516

Publication Info

Cutting, Elena R, Ryan A Simmons, Deng B Madut, Michael J Maze, Nathaniel H Kalengo, Manuela Carugati, Ronald M Mbwasi, Kajiru G Kilonzo, et al. (2022). Facility-based disease surveillance and Bayesian hierarchical modeling to estimate endemic typhoid fever incidence, Kilimanjaro Region, Tanzania, 2007-2018. PLoS neglected tropical diseases, 16(7). p. e0010516. 10.1371/journal.pntd.0010516 Retrieved from https://hdl.handle.net/10161/25551.

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Scholars@Duke

Madut

Deng Madut

Assistant Professor of Medicine

I am an infectious diseases doctor who specializes in the care of patients with general infectious diseases, including persons living with HIV. My research is focused on improving the diagnosis and treatment of infectious diseases among populations living in low-resource settings.

Carugati

Manuela Carugati

Associate Professor of Medicine
Hertz

Julian T Hertz

Associate Professor of Emergency Medicine

Julian Hertz, MD, MSc, is an Associate Professor of Emergency Medicine & Global Health. He graduated summa cum laude from Princeton University and attended medical school at Duke University, where he received the Dean's Merit Scholarship and the Thomas Jefferson Award for leadership. He completed his residency training in emergency medicine at Vanderbilt University Medical Center and his fellowship in Global Health at Duke.

Dr. Hertz's primary interests include global health, implementation science, and undergraduate and graduate medical education. Dr. Hertz's research focuses on using implementation science methods to improve cardiovascular care both locally and globally. His current projects involve developing interventions to improve acute myocardial infarction care in Tanzania, to improve management of hypertension among Tanzanians with HIV, and to improve post-hospital care among patients with multimorbidity in East Africa.

Dr. Hertz has received numerous awards for clinical, educational, and research excellence, including the Duke Emergency Medicine Faculty Teacher of the Year Award, the Duke Emergency Medicine Faculty Clinician of the Year Award, and the Duke Emergency Medicine Faculty Researcher of the Year Award. He has also received the Golden Apple Teaching Award from the Duke medical student body, the Duke Master Clinician/Teacher Award, and the Global Academic Achievement Award from the Society of Academic Emergency Medicine.

Turner

Elizabeth Louise Turner

Associate Professor of Biostatistics & Bioinformatics

Dr. Turner is Associate Professor of Biostatistics and Global Health and serves as Director of the Research Design and Analysis Core of the Duke Global Health Institute. Her primary methodological focus is on the design and analysis of randomized controlled trials, particularly those that involve clustering such as cluster randomized trials (CRTs), stepped wedge CRTs and individually-randomized group treatment trials. She is expert in the implementation of trials in low resource settings, with a substantive focus on malaria, mental health and cardiovascular disease.

Dr. Turner joined the Department of Biostatistics & Bioinformatics and Duke Global Health Institute in March 2012 following four years as Research Fellow in the Department of Medical Statistics at the London School of Hygiene and Tropical Medicine (LSHTM). Since then, she has continued to hold a joint position with Duke's Global Health Institute (DGHI) where she serves as faculty statistician and collaborates with faculty and affiliates. Dr. Turner earned her undergraduate honors degree in Mathematics from the University of Warwick, UK, during which she spent an intercalated year at the Universite of Pierre et Marie Curie, Paris, France. She then earned her MSc and PhD in Statistics from McGill University, Canada, with her doctoral studies funded by the prestigious Commonwealth Scholarship.

Thanks to her participation in multi-disciplinary projects, Dr. turner has a great appreciation for the importance of good study design and data collection and is well aware that no fancy statistical analyses can save researchers from the scourge of bad data. Through those experiences and her teaching in different settings, including the UK, Canada, France and Tanzania, she is aware that statisticians and their collaborators sometimes “speak a different language”. As a result, her approach is very much one of translation, pragmatism and collaboration.

Further information about the Global Health Research Design and Analysis Core service can be found at:
https://globalhealth.duke.edu/dghi-research-design-analysis-core

Rubach

Matthew P. Rubach

Associate Professor of Medicine

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