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

Abstract

Background: In rapidly growing, high burden urban centers, identifying tuberculosis (TB) transmission hotspots and understanding potential impact of interventions can inform future control and prevention strategies. Methods: 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. hotspot) 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. Results: 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. Conclusions: 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.

Sub-district level correlation between tuberculosis notifications and socio-demographic factors in Dhaka City corporation, Bangladesh

“…The estimates were then adjusted for observed correlations between TB notifications and actual residence to reflect that 50% of the randomly selected TB cases reported that they resided outside of the ward of their TB diagnosis and treatment. [2] Demographic health survey in Bangladesh Sociodemographic factors including age, sex, education level and wealth index, were extracted from the BDHS conducted in 2013-2014, which was the most recent publicly available data at the time of analysis. The BDHS uses a two-stage stratified sampling design that divides the population of Bangladesh into 600 enumeration areas (EAs, 'clusters').…”

“…each EA should consist of a certain number of wards), this process was imprecise given the rapid changes and urbanisation occurring in Dhaka during this time period. The BDHS sampled 20-30 households per EA, covering all the seven divisions and 64 districts of Bangladesh, based on the 2011 Population and Housing Censusbut did not include wards in the sampling frame per se [2]. DCC contains 23 EA-level clusters, including surveys of 610 households and 2641 individuals.…”

“…Bangladesh ranks sixth globally in terms of tuberculosis (TB) incidence, with 357 000 estimated cases (221 per 100 000 population) and 80 000 TB deaths in 2018 [1]. While TB incidence is often heterogeneous and influenced by various sociodemographic factors, TB notifications in low-and middle-income countries often lack the specificity sufficient to enable targeting of interventions based on these heterogeneities [2]. Most models, however, use standard TB notification data to estimate the spatial distribution of TB cases in a population.…”

High-resolution characterization of recent tuberculosis transmission in Botswana using geospatial and genomic data – the Kopanyo Study

“…For example, spatial clusters of closely related Mtb strains may indicate local areas of ongoing transmission, which could be targeted for public health interventions such as active case finding (3)(4)(5)10). A growing body of evidence suggests that geographically targeted interventions may be effective and cost-efficient in high-burden, low-resource settings, and could be instrumental in accelerating progress toward TB elimination (4,(11)(12)(13).…”