Evidence map›Paper›PMID 41664002›Full record

ArticleBMC infectious diseases2026

Spatio-temporal analysis of tuberculosis prevalence in Iran.

Behnam Khodadoust, Fatemeh Sarvi, Shahnaz Rimaz, Mahmoud Khodadost, Mahshid Nasehi

Abstract read
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Article in BMC infectious diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Behnam KhodadoustDepartment of Epidemiology, School of Public Health, Iran University of Medical Sciences, Tehran, Iran.
Fatemeh SarviDepartment of Public Health, School of Health, Larestan University of Medical Sciences, Larestan, Iran. f.sarvi.2000@gmail.com.
Shahnaz RimazRadiation Biology Research Center, Department of Epidemiology, School of Public Health, Iran University of Medical Sciences, Tehran, Iran.
Mahmoud KhodadostDepartment of Public Health, School of Health, Larestan University of Medical Sciences, Larestan, Iran.
Mahshid NasehiDepartment of Epidemiology, School of Public Health, Iran University of Medical Sciences, & Ministry of Health and Medical Education, Center for Control of Communicable Diseases, Tehran, Iran. mnasehi@yahoo.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIran has one of the highest tuberculosis (TB) incidence rates in the Middle East, but a comprehensive analysis of its spatiotemporal distribution is lacking. This study aims to identify patterns of TB incidence across Iranian counties from 2014 to 2019, using advanced spatio-temporal methods, to guide targeted interventions for better disease control.

methodsThis study was done by utilizing National data on confirmed TB cases in counties of Iran from 2014 to 2019. After cleaning the data and calculating the annual TB incidence rates per 100,000 population a descriptive map of annual incidence was created. The Getis-Ord Gi* and Anselin Local Moran’s I were used to determine the disease’s spatial hotspots. In addition, the spatial scan statistic was used for both spatiotemporal and spatial variation in temporal trends. The null hypothesis, which states that there are no clusters, was rejected in every case at p ≤ 0.05.

resultsOver six years, 56,748 TB cases dropped from 13.44 per 100,000 in 2014 to 9.89 in 2019. The Gi* statistic showed that eastern, southeastern, northern, and northeastern Iran had the greatest TB rates and hotspots throughout the research. Northern hotspots have grown during the research period, affecting Tehran’s bordering counties. Additionally, a temporal study showed a high-rate cluster distribution in early 2014–2015. Space-time analysis identified 7 high-rate TB clusters with a 50% scanning window size across time and locales (p < 0.05). Spatial variation in temporal trends found additive tendency TB clusters in the research.

conclusionThe study found high-risk TB areas in Iran’s northern, eastern, and southeastern regions. Despite a decreasing trend in TB incidence, targeted prevention, screening, and treatment programs are crucial. Spatiotemporal analyses offer insights for optimizing TB control strategies in Iran, with significant clusters identified in Tehran’s neighboring counties. CLINICAL TRIAL NUMBER : Not applicable.

Indexed as

TuberculosisHumansIncidenceIranPrevalenceSpatio-Temporal AnalysisEpidemiological trendGeographical information systemIranSaTScanSpatio-temporalTuberculosis

Identifiers

PMID41664002
PMCPMC12983547

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.