Geospatial analysis of tuberculosis incidence in relation to socio-economic and environmental indicators in Romania

Bibliographic Data

ID22070897
AuthorsJenna Zabroski (Centre de recherche en Epidémiologie et Santé des Populations), Daniel Peptenatu (0000-0002-4473-8969, University of Bucharest), Beatrice Mahler (0000-0002-9240-922X, Carol Davila University of Medicine and Pharmacy), Amr S Soliman (0000-0003-2284-360X, City University of New York), Amr Soliman, Andreea Karina Gruia (0000-0001-6690-1483, University of Bucharest), Alexandra Grecu (0000-0002-8821-4163, University of Bucharest, corresponding author), Ioana Munteanu (0000-0001-7986-8731, Titu Maiorescu University), Aurel Bǎloi, Aurel Mihail Băloi (University of Bucharest)
Year2025
Volume13
Pages1711489-1711489
Publication date2025-12-09
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2025.1711489
PMID41446517
OpenAlexW4417146206
LanguageEN
References cited55

Introduction Tuberculosis (TB) is a global public health burden. Romania experiences some of the highest rates of TB compared to other European countries. TB incidence is spatially clustered throughout Romania, and the reasoning behind this is likely linked to various environmental and socio-economic factors. This study aimed to investigate the spatial distribution of TB incidence per 1,000 population between 2015 and 2021 and examined its correlation with particulate matter (PM2.5) levels, living conditions, and level of educational indicators. Methods TB incidence data from 2015 to 2021 aggregated at the level of 3,181 administrative territorial units (UATs) in Romania were analyzed. Pearson correlation coefficients examined linear associations among total TB incidence and exploratory indicators. A Principal Component Analysis (PCA) was conducted due to the presence of multicollinearity in the Pearson correlation matrix, which grouped key housing indicators into one hybrid living condition index (PCA1). Bivariate Moran's I analyses assessed localized spatial clustering between TB incidence and two exploratory indicators: PM2.5 levels and PCA1. A spatial lag regression model accounted for spatial dependence between TB incidence and three explanatory predictors: PM2.5 levels, the percentage of the population that is illiterate, and PCA1. All significance tests were conducted at a threshold of p < 0.05. Results TB incidence is spatially clustered in Romania. PM2.5 levels ( r = 0.261, p < 0.0001) and PCA1 ( r = 0.338, p < 0.0001) are positively and significantly associated with TB incidence. Illiteracy showed no significant association with TB incidence. The spatial lag model confirmed spatial autocorrelation (Rho = 0.436, p < 0.0001) and explains 28.4% of the variation in TB incidence across Romania. Discussion/Conclusion This is the first geospatial study in Romania that explores the link between TB incidence and exploratory indicators of air pollution, living conditions, and education level using epidemiological data obtained in the national tuberculosis surveillance and control program. Greater exposure to air pollution and worsened living conditions are correlated with higher TB incidence rates. Policymakers should highlight the need for geographically targeted interventions to improve TB control and screening in Romania

Geospatial analysis · Psychological intervention · Public health · Spatial epidemiology · Tuberculosis · Data-Driven Disease Surveillance · Healthcare Facilities Design and Sustainability · Tuberculosis Research and Epidemiology · Epidemiology

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