TY - JOUR
T1 - Incorporating patient reporting patterns to evaluate spatially targeted TB interventions
AU - Gomes, Isabella
AU - Reja, Mehdi
AU - Shrestha, Sourya
AU - Pennington, Jeffrey
AU - Jo, Youngji
AU - Baik, Yeonsoo
AU - Islam, Shamiul
AU - Khan, Ahmadul Hasan
AU - Faisel, Abu Jamil
AU - Cordon, Oscar
AU - Roy, Tapash
AU - Suarez, Pedro
AU - Hussain, Hamidah
AU - Dowdy, David
N1 - Funding Information:
Funding: The Global Health Bureau, Office of Infectious Disease, U.S. Agency for International Development, financially supported this work through Challenge TB (CTB) Project under the terms of agreement no. AID-OAA-A-14-00029 . This work is made possible by the generous support of the American people through the U.S. Agency for International Development (USAID). The contents are the responsibility of the CTB Bangladesh and JHU-based investigators and do not necessarily reflect the views of USAID or the U.S. government.
Publisher Copyright:
© 2020 Elsevier Inc.
PY - 2021/2
Y1 - 2021/2
N2 - Purpose: Tuberculosis (TB) is geographically heterogeneous, and geographic targeting can improve the impact of TB interventions. However, standard TB notification data may not sufficiently capture this heterogeneity. Better understanding of patient reporting patterns (discrepancies between residence and place of presentation) may improve our ability to use notifications to appropriately target interventions. Methods: Using demographic data and TB reports from Dhaka North City Corporation and Dhaka South City Corporation, we identified wards of high TB incidence and developed a TB transmission model. We calibrated the model to patient-level data from selected wards under four different reporting pattern assumptions and estimated the relative impact of targeted versus untargeted active case finding. Results: The impact of geographically targeted interventions varied substantially depending on reporting pattern assumptions. The relative reduction in TB incidence, comparing targeted with untargeted active case finding in Dhaka North City Corporation, was 1.20, assuming weak correlation between reporting and residence, versus 2.45, assuming perfect correlation. Similar patterns were observed in Dhaka South City Corporation (1.03 vs. 2.08). Conclusions: Movement of individuals seeking TB diagnoses may substantially affect ward-level TB transmission. Better understanding of patient reporting patterns can improve estimates of the impact of targeted interventions in reducing TB incidence. Incorporating high-quality patient-level data is critical to optimizing TB interventions.
AB - Purpose: Tuberculosis (TB) is geographically heterogeneous, and geographic targeting can improve the impact of TB interventions. However, standard TB notification data may not sufficiently capture this heterogeneity. Better understanding of patient reporting patterns (discrepancies between residence and place of presentation) may improve our ability to use notifications to appropriately target interventions. Methods: Using demographic data and TB reports from Dhaka North City Corporation and Dhaka South City Corporation, we identified wards of high TB incidence and developed a TB transmission model. We calibrated the model to patient-level data from selected wards under four different reporting pattern assumptions and estimated the relative impact of targeted versus untargeted active case finding. Results: The impact of geographically targeted interventions varied substantially depending on reporting pattern assumptions. The relative reduction in TB incidence, comparing targeted with untargeted active case finding in Dhaka North City Corporation, was 1.20, assuming weak correlation between reporting and residence, versus 2.45, assuming perfect correlation. Similar patterns were observed in Dhaka South City Corporation (1.03 vs. 2.08). Conclusions: Movement of individuals seeking TB diagnoses may substantially affect ward-level TB transmission. Better understanding of patient reporting patterns can improve estimates of the impact of targeted interventions in reducing TB incidence. Incorporating high-quality patient-level data is critical to optimizing TB interventions.
KW - Tuberculosis heterogeneity
KW - Tuberculosis in Dhaka
KW - Tuberculosis patient-level reporting
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U2 - 10.1016/j.annepidem.2020.11.003
DO - 10.1016/j.annepidem.2020.11.003
M3 - Article
C2 - 33166716
AN - SCOPUS:85097202181
SN - 1047-2797
VL - 54
SP - 7
EP - 10
JO - Annals of epidemiology
JF - Annals of epidemiology
ER -