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. 2022 Apr 1;26(4):356-362.
doi: 10.5588/ijtld.21.0624.

Finding gaps in routine TB surveillance activities in Bangladesh

A Allorant  1 , S Biswas  2 , S Ahmed  2 , K E Wiens  3 , K E LeGrand  4 , M M Janko  4 , N J Henry  5 , W J Dangel  4 , A Watson  4 , B F Blacker  4 , H H Kyu  6 , J M Ross  7 , M S Rahman  2 , S I Hay  6 , R C Reiner  6
Affiliations

Finding gaps in routine TB surveillance activities in Bangladesh

A Allorant et al. Int J Tuberc Lung Dis. .

Abstract
in English, French

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.

INTRODUCTION :: La TB était la première cause de décès dus à un agent infectieux unique à l’échelle internationale entre 2014 et 2019. Les estimations fines de la prévalence de la TB et les notifications de cas peuvent être assemblées afin d’établir des priorités permettant de renforcer les activités de surveillance de routine dans les pays à forte prévalence. Nous avons effectué des estimations de l’épidémie de TB pertinentes pour l’élaboration de politiques dans la deuxième unité administrative du Bangladesh.

MÉTHODES :: Nous avons utilisé un modèle spatial Bayésien ainsi que l’enquête transversale de prévalence nationale de la TB réalisée au Bangladesh en 2015–2016 pour estimer la prévalence de la maladie par district. Nous avons utilisé les notifications de cas afin de calculer un ratio prévalence-notifications, un indicateur clé du sous-diagnostic et de la sous-notification.

RÉSULTATS :: Les taux de prévalence de la TB étaient plus élevés dans les districts du nord-est et variaient de 160 cas pour 100 000 habitants (intervalle d’incertitude à 95% [UI] 80–310) à Jessore à 840 (UI 690–1020) à Sunamganj. Malgré des taux de prévalence modérés, les divisions de Râjshâhî et Dhâkâ avaient les plus forts ratios prévalence-notifications en raison d’une faible notification de cas. En gommant les disparités infranationales en matière de détection de cas, 26 500 cas supplémentaires de TB (UI 8 500–79 400) pourraient être notifiés chaque année.

CONCLUSION :: Cette étude est la première à fournir et à cartographier des estimations infranationales de la prévalence de la TB et des ratios prévalencenotifications. Ces estimations sont essentielles afin de cibler les efforts de prévention et de traitement dans un pays à forte prévalence. Réussir à atteindre les cas de TB qui passent actuellement entre les mailles du filet est crucial pour mettre fin à l’épidémie de TB.

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Conflict of interest statement

Conflicts of interest: none declared.

Figures

Figure 1
Figure 1
Distribution of surveyed clusters across Bangladesh’s divisions and observed prevalence of TB at these clusters.
Figure 2
Figure 2
A) Estimated mean TB prevalence-to-notification ratio, and B) 97.5th, and C) 2.5th percentiles.
Figure 3
Figure 3
A) Estimated TB mean prevalence (per 100,000), and B) 97.5th, and C) 2.5th percentiles.
Figure 4
Figure 4
Estimated mean TB additional cases that could be detected if every district achieved at least A) the national prevalence-to-notification ratio, and B) 97.5th and C) 2.5th percentiles.
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References

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