ArticlePloS one2025
Spatial analysis of drug resistant tuberculosis (DRTB) incidence and relationships with determinants in Rio de Janeiro state, 2010 to 2022.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Socioeconomic dynamics and tuberculosis mortality in São Paulo: a population-level analysis using spatial modeling.Lancet regional health. Americas · 2026Article
- Spatial analysis of under-five children mortality and associated determinants in Ghana: Mapping regional disparities for targeted interventions.PLOS global public health · 2026Article
- Space-time clustering and temporal trend analysis of pulmonary tuberculosis in Brazil, 2010-2023.PloS one · 2026Article
- Projecting tuberculosis control progress in metropolitan and non-metropolitan areas of Brazil, 2001-2035: a Bayesian age-period-cohort analysis.Infectious diseases of poverty · 2025Article
- Global trajectory and spatiotemporal epidemiological landscape of multidrug-resistant tuberculosis of spanning 46 years (1990-2035): implications for achieving global end TB goals.Frontiers in public health · 2025Article
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Authors and funding
6 authors.
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Abstract
backgroundThe aim of this study was to assess the spatial distribution of drug-resistant tuberculosis (DRTB) cases in Rio de Janeiro state and its association with demographics, socioeconomic and health determinants.
methodsAn ecological study based on real-world DRTB data from 2010 to 2022, in the Rio de Janeiro state, using data from the Special Tuberculosis Treatment Information System (SITE-TB) and demographic census. Crude incidence rates (CIR) of DRTB per 100,000 inhabitants and smoothed rates through the Global and Local Empirical Bayesian (BEG and BEL) methods were calculated. Spatial autocorrelation was explored using Moran's I statistic, Local Indicators of Spatial Association (LISA), and the Getis-Ord statistics. The SCAN method was also used to identify spatial-time clusters. To analyze the association of DRTB and determinants, we used LISA bivariate for spatial correlation and four explanatory statistical models were listed.
resultsFrom 2010 to 2022, 2,709 new cases of DRTB were reported (CIR 16.9/100,000 inhabitants). The municipalities in the metropolitan region of Rio de Janeiro state had the highest rates. Despite 41% of municipalities reporting no new cases, BEG and BEL suggested higher rates than CIR, indicating underreporting. Spatial heterogeneity was observed, and spatial and spatial-temporal clusters and hotspots were detected in metropolitan region. Family health strategy coverage was identified as protection factor, however a not expected negative spatial autocorrelation between CIR and health strategy coverage, primary care and healthcare agent coverage was found. The variables identified as risk factors were population aged ≥18 years old with Elementary School completed (OR:1.10; CI95%:1.04-1.16), demographic density (OR: 1.00; CI95%:1.00-1.01), HIV-TB coinfection (OR: 1.18; CI95%:1.06-1.31).
conclusionThe identification of areas of risk for DRTB, spatial correlation and association between incidence and determinants, demonstrates that the DRTB transmission dynamics is related to the perpetuation of social inequality and urban spatial organization.
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