Cargando…

Quantifying geographic heterogeneity in TB incidence and the potential impact of geographically targeted interventions in South and North City Corporations of Dhaka, Bangladesh: a model-based study

In rapidly growing and high-burden urban centres, identifying tuberculosis (TB) transmission hotspots and understanding the potential impact of interventions can inform future control and prevention strategies. Using data on local demography, TB reports and patient reporting patterns in Dhaka South...

Descripción completa

Detalles Bibliográficos
Autores principales: Shrestha, Sourya, Reja, Mehdi, Gomes, Isabella, Baik, Yeonsoo, Pennington, Jeffrey, Islam, Shamiul, Jamil Faisel, Abu, Cordon, Oscar, Roy, Tapash, Suarez, Pedro G., Hussain, Hamidah, Dowdy, David W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cambridge University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8161375/
https://www.ncbi.nlm.nih.gov/pubmed/33866998
http://dx.doi.org/10.1017/S0950268821000832
_version_ 1783700496078340096
author Shrestha, Sourya
Reja, Mehdi
Gomes, Isabella
Baik, Yeonsoo
Pennington, Jeffrey
Islam, Shamiul
Jamil Faisel, Abu
Cordon, Oscar
Roy, Tapash
Suarez, Pedro G.
Hussain, Hamidah
Dowdy, David W.
author_facet Shrestha, Sourya
Reja, Mehdi
Gomes, Isabella
Baik, Yeonsoo
Pennington, Jeffrey
Islam, Shamiul
Jamil Faisel, Abu
Cordon, Oscar
Roy, Tapash
Suarez, Pedro G.
Hussain, Hamidah
Dowdy, David W.
author_sort Shrestha, Sourya
collection PubMed
description In rapidly growing and high-burden urban centres, identifying tuberculosis (TB) transmission hotspots and understanding the potential impact of interventions can inform future control and prevention strategies. Using data on local demography, TB reports and patient reporting patterns in Dhaka South City Corporation (DSCC) and Dhaka North City Corporation (DNCC), Bangladesh, between 2010 and 2017, we developed maps of TB reporting rates across wards in DSCC and DNCC and identified wards with high rates of reported TB (i.e. ‘hotspots’) in DSCC and DNCC. We developed ward-level transmission models and estimated the potential epidemiological impact of three TB interventions: active case finding (ACF), mass preventive therapy (PT) and a combination of ACF and PT, implemented either citywide or targeted to high-incidence hotspots. There was substantial geographic heterogeneity in the estimated TB incidence in both DSCC and DNCC: incidence in the highest-incidence wards was over ten times higher than in the lowest-incidence wards in each city corporation. ACF, PT and combined ACF plus PT delivered to 10% of the population reduced TB incidence by a projected 7%–9%, 13%–15% and 19%–23% over five years, respectively. Targeting TB hotspots increased the projected reduction in TB incidence achieved by each intervention 1.4- to 1.8-fold. The geographical pattern of TB notifications suggests high levels of ongoing TB transmission in DSCC and DNCC, with substantial heterogeneity at the ward level. Interventions that reduce transmission are likely to be highly effective and incorporating notification data at the local level can further improve intervention efficiency.
format Online
Article
Text
id pubmed-8161375
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Cambridge University Press
record_format MEDLINE/PubMed
spelling pubmed-81613752021-06-07 Quantifying geographic heterogeneity in TB incidence and the potential impact of geographically targeted interventions in South and North City Corporations of Dhaka, Bangladesh: a model-based study Shrestha, Sourya Reja, Mehdi Gomes, Isabella Baik, Yeonsoo Pennington, Jeffrey Islam, Shamiul Jamil Faisel, Abu Cordon, Oscar Roy, Tapash Suarez, Pedro G. Hussain, Hamidah Dowdy, David W. Epidemiol Infect Original Paper In rapidly growing and high-burden urban centres, identifying tuberculosis (TB) transmission hotspots and understanding the potential impact of interventions can inform future control and prevention strategies. Using data on local demography, TB reports and patient reporting patterns in Dhaka South City Corporation (DSCC) and Dhaka North City Corporation (DNCC), Bangladesh, between 2010 and 2017, we developed maps of TB reporting rates across wards in DSCC and DNCC and identified wards with high rates of reported TB (i.e. ‘hotspots’) in DSCC and DNCC. We developed ward-level transmission models and estimated the potential epidemiological impact of three TB interventions: active case finding (ACF), mass preventive therapy (PT) and a combination of ACF and PT, implemented either citywide or targeted to high-incidence hotspots. There was substantial geographic heterogeneity in the estimated TB incidence in both DSCC and DNCC: incidence in the highest-incidence wards was over ten times higher than in the lowest-incidence wards in each city corporation. ACF, PT and combined ACF plus PT delivered to 10% of the population reduced TB incidence by a projected 7%–9%, 13%–15% and 19%–23% over five years, respectively. Targeting TB hotspots increased the projected reduction in TB incidence achieved by each intervention 1.4- to 1.8-fold. The geographical pattern of TB notifications suggests high levels of ongoing TB transmission in DSCC and DNCC, with substantial heterogeneity at the ward level. Interventions that reduce transmission are likely to be highly effective and incorporating notification data at the local level can further improve intervention efficiency. Cambridge University Press 2021-04-19 /pmc/articles/PMC8161375/ /pubmed/33866998 http://dx.doi.org/10.1017/S0950268821000832 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Paper
Shrestha, Sourya
Reja, Mehdi
Gomes, Isabella
Baik, Yeonsoo
Pennington, Jeffrey
Islam, Shamiul
Jamil Faisel, Abu
Cordon, Oscar
Roy, Tapash
Suarez, Pedro G.
Hussain, Hamidah
Dowdy, David W.
Quantifying geographic heterogeneity in TB incidence and the potential impact of geographically targeted interventions in South and North City Corporations of Dhaka, Bangladesh: a model-based study
title Quantifying geographic heterogeneity in TB incidence and the potential impact of geographically targeted interventions in South and North City Corporations of Dhaka, Bangladesh: a model-based study
title_full Quantifying geographic heterogeneity in TB incidence and the potential impact of geographically targeted interventions in South and North City Corporations of Dhaka, Bangladesh: a model-based study
title_fullStr Quantifying geographic heterogeneity in TB incidence and the potential impact of geographically targeted interventions in South and North City Corporations of Dhaka, Bangladesh: a model-based study
title_full_unstemmed Quantifying geographic heterogeneity in TB incidence and the potential impact of geographically targeted interventions in South and North City Corporations of Dhaka, Bangladesh: a model-based study
title_short Quantifying geographic heterogeneity in TB incidence and the potential impact of geographically targeted interventions in South and North City Corporations of Dhaka, Bangladesh: a model-based study
title_sort quantifying geographic heterogeneity in tb incidence and the potential impact of geographically targeted interventions in south and north city corporations of dhaka, bangladesh: a model-based study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8161375/
https://www.ncbi.nlm.nih.gov/pubmed/33866998
http://dx.doi.org/10.1017/S0950268821000832
work_keys_str_mv AT shresthasourya quantifyinggeographicheterogeneityintbincidenceandthepotentialimpactofgeographicallytargetedinterventionsinsouthandnorthcitycorporationsofdhakabangladeshamodelbasedstudy
AT rejamehdi quantifyinggeographicheterogeneityintbincidenceandthepotentialimpactofgeographicallytargetedinterventionsinsouthandnorthcitycorporationsofdhakabangladeshamodelbasedstudy
AT gomesisabella quantifyinggeographicheterogeneityintbincidenceandthepotentialimpactofgeographicallytargetedinterventionsinsouthandnorthcitycorporationsofdhakabangladeshamodelbasedstudy
AT baikyeonsoo quantifyinggeographicheterogeneityintbincidenceandthepotentialimpactofgeographicallytargetedinterventionsinsouthandnorthcitycorporationsofdhakabangladeshamodelbasedstudy
AT penningtonjeffrey quantifyinggeographicheterogeneityintbincidenceandthepotentialimpactofgeographicallytargetedinterventionsinsouthandnorthcitycorporationsofdhakabangladeshamodelbasedstudy
AT islamshamiul quantifyinggeographicheterogeneityintbincidenceandthepotentialimpactofgeographicallytargetedinterventionsinsouthandnorthcitycorporationsofdhakabangladeshamodelbasedstudy
AT jamilfaiselabu quantifyinggeographicheterogeneityintbincidenceandthepotentialimpactofgeographicallytargetedinterventionsinsouthandnorthcitycorporationsofdhakabangladeshamodelbasedstudy
AT cordonoscar quantifyinggeographicheterogeneityintbincidenceandthepotentialimpactofgeographicallytargetedinterventionsinsouthandnorthcitycorporationsofdhakabangladeshamodelbasedstudy
AT roytapash quantifyinggeographicheterogeneityintbincidenceandthepotentialimpactofgeographicallytargetedinterventionsinsouthandnorthcitycorporationsofdhakabangladeshamodelbasedstudy
AT suarezpedrog quantifyinggeographicheterogeneityintbincidenceandthepotentialimpactofgeographicallytargetedinterventionsinsouthandnorthcitycorporationsofdhakabangladeshamodelbasedstudy
AT hussainhamidah quantifyinggeographicheterogeneityintbincidenceandthepotentialimpactofgeographicallytargetedinterventionsinsouthandnorthcitycorporationsofdhakabangladeshamodelbasedstudy
AT dowdydavidw quantifyinggeographicheterogeneityintbincidenceandthepotentialimpactofgeographicallytargetedinterventionsinsouthandnorthcitycorporationsofdhakabangladeshamodelbasedstudy