Author
Listed:
- Paula Cristina Pungartnik
- Paulo Victor de Souza Viana
- Jefferson Pereira Caldas dos Santos
- Laylla Ribeiro Macedo
- Thais Zamboni Berra
- Natália Santana Paiva
Abstract
Background: The 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. Methods: An 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. Results: From 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). Conclusion: The 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.
Suggested Citation
Paula Cristina Pungartnik & Paulo Victor de Souza Viana & Jefferson Pereira Caldas dos Santos & Laylla Ribeiro Macedo & Thais Zamboni Berra & Natália Santana Paiva, 2025.
"Spatial analysis of drug resistant tuberculosis (DRTB) incidence and relationships with determinants in Rio de Janeiro state, 2010 to 2022,"
PLOS ONE, Public Library of Science, vol. 20(5), pages 1-17, May.
Handle:
RePEc:plo:pone00:0321553
DOI: 10.1371/journal.pone.0321553
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0321553. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.