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

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.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.

Assessing the quality of tuberculosis care using routine surveillance data: a process evaluation employing the Zero TB Indicator Framework in Mongolia

“…41 These higher quality diagnostics remain prohibitively costly, even in middle-income countries; continued efforts are needed to increase financial commitments to advance TB elimination efforts by implementing internationally recommended diagnostics. Also, the absolute number of participants reached and the additional cases identified by these small, short-term active case-finding initiatives were modest, pointing to the need to better target active case-finding towards TB ‘hotspots’ 42 and to sustain screening efforts over time. 4 In contrast, the yield of active TB case-finding among household contacts in the Prevent Cascade was an order of magnitude higher and contact investigation also facilitated TB prevention efforts, potentially offering much greater value.…”

Quantifying the potential epidemiological impact of a 2-year active case finding for tuberculosis in rural Nepal: a model-based analysis

“…In other words, ACF in some VDCs could be two times as impactful as in others. This suggests that being able to tailor interventions locally may help increase the efficiency and impact of ACF22 35 and optimise cost-effectiveness and access to care; however, notification data alone may not be sufficient to quantify risks accurately.…”

“…This approach of developing VDC-level models allowed us to capture heterogeneity in local TB dynamics and to model interventions implemented at local levels. Each VDC-level model followed a common model structure, a relatively parsimonious compartmental model of the ilk that has been developed previously 20–22. We calibrated the models to VDC-specific TB prevalence in each of the four districts.…”

Effectiveness of Using AI Driven Hotspot Mapping for Active Case Finding of Tuberculosis in South-Western Nigeria

“…Also, the risk of TB transmission, delayed diagnosis and treatment is determined by several contextual factors like population demographics, socio economic conditions, nutritional status, access to health services and environmental conditions, which are important to take into account along with notification data [9][10][11]. Therefore there is value in leveraging data and available technology to make evidence based decisions for routine programmatic activities and improve effectiveness of interventions [12].…”