ArticleAnnals of epidemiology2021
Incorporating patient reporting patterns to evaluate spatially targeted TB interventions.
Article in Annals of epidemiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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Who cites it
2 citing papers in PubMed.
- A spatial analysis of TB cases and abnormal X-rays detected through active case-finding in Karachi, Pakistan.Scientific reports · 2023Article
- Sub-district level correlation between tuberculosis notifications and socio-demographic factors in Dhaka City corporation, Bangladesh.Epidemiology and infection · 2021Article
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
purposeTuberculosis (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.
methodsUsing 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.
resultsThe 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).
conclusionsMovement 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.
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