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Home / The International Journal of Tuberculosis and Lung Disease, Volume 26, Number 4

Open Access Finding gaps in routine TB surveillance activities in Bangladesh

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BACKGROUND: TB was the leading cause of death from a single infectious pathogen globally between 2014 and 2019. Fine-scale estimates of TB prevalence and case notifications can be combined to guide priority-setting for strengthening routine surveillance activities in high-burden countries. We produce policy-relevant estimates of the TB epidemic at the second administrative unit in Bangladesh.

METHODS: We used a Bayesian spatial framework and the cross-sectional National TB Prevalence Survey from 2015–2016 in Bangladesh to estimate prevalence by district. We used case notifications to calculate prevalence-to-notification ratio, a key metric of under-diagnosis and under-reporting.

RESULTS: TB prevalence rates were highest in the north-eastern districts and ranged from 160 cases per 100,000 (95% uncertainty interval [UI] 80–310) in Jashore to 840 (UI 690–1020) in Sunamganj. Despite moderate prevalence rates, the Rajshahi and Dhaka Divisions presented the highest prevalence-to-notification ratios due to low case notifications. Resolving subnational disparities in case detection could lead to 26,500 additional TB cases (UI 8,500–79,400) notified every year.

CONCLUSION: This study is the first to produce and map subnational estimates of TB prevalence and prevalence-to-notification ratios, which are essential to target prevention and treatment efforts in high-burden settings. Reaching TB cases currently missing from care will be key to ending the TB epidemic.

Keywords: National Tuberculosis Control Programme; SDG-3; case notification; geospatial modeling; spatial analysis; survey methods; tuberculosis; tuberculosis prevalence survey

Document Type: Research Article

Affiliations: 1: Department of Global Health, University of Washington, Seattle, WA, Institute for Health Metrics and Evaluation, Seattle, WA, USA 2: International Centre for Diarrhoeal Disease Research, Bangladesh (icddr, b), Dhaka, Bangladesh 3: Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA 4: Institute for Health Metrics and Evaluation, Seattle, WA, USA 5: Institute for Health Metrics and Evaluation, Seattle, WA, USA, Big Data Institute, University of Oxford, Oxford, UK 6: Institute for Health Metrics and Evaluation, Seattle, WA, USA, Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA 7: Department of Global Health, University of Washington, Seattle, WA, Department of Medicine, Division of Allergy and Infectious Diseases, University of Washington, Seattle, WA, USA

Publication date: 01 April 2022

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