Spatial and temporal patterns of tuberculosis from 2014 to 2023 in Malaysia
Abstract
BackgroundTuberculosis (TB) continues to be one of the deadliest infectious diseases in Malaysia. This nationwide retrospective ecological study aimed to determine the spatial and temporal patterns of TB with the association of sociodemographic and environmental factors in Malaysia.MethodsThe sociodemographic data of 301,822 TB cases from 2014 to 2023 were collected from the Tuberculosis Information System (TBIS). Environmental data were acquired from the Department of Environment Malaysia and the Malaysia Meteorological Department. The Choropleth mapping and heatmapping were used to assess the trend of TB cases, while the Global Moran’s I, Local Indicators of Spatial Association (LISA), and Getis-Ord Gi* were used to assess the spatial pattern of TB cases. Ordinary least squares (OLS) and geographically weighted regression (GWR) models were employed to examine the spatial association between sociodemographic and environmental factors and TB incidence.ResultsGlobal spatial autocorrelation analysis found that the cases were clustered (p < 0.05) over the 10 years, while local spatial autocorrelation detected that the high-high cluster was concentrated in several districts in Selangor and Kuala Lumpur, with in the districts of Klang, Kuala Selangor, Sabak Bernam, Jelebu, Kuala Lumpur, Gombak, Temerloh, Bentong, Sepang, and Hulu Langat. The hotspot districts were consistently in these two states. This could be attributed to the growing rapid urbanization, demographic expansion, migration, and persistent socio-economic differences. Aged 14 years old and younger, aged 65 years old and older, non-Malaysian, Malay, Chinese, Aborigines in Peninsular Malaysia, completed secondary school, completed STPM / diploma / matriculation / foundation, smoking, SO2, and NO2 were associated with TB incidence (p < 0.05). The GWR models identified the spatially varying association in the distribution of TB incidence and its respective combinatory sets of explanatory variables.ConclusionSpatial heterogeneity of TB incidence and persistent hotspot districts throughout Malaysia were explored using spatial analysis. This study emphasizes the importance of integrate spatial data to assist targeted monitoring, surveillance, early case detection, treatment, and the allocation of public health resources in high-burden regions.
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BibTeX
@article{mohidem2026spatial,
title = {{Spatial and temporal patterns of tuberculosis from 2014 to 2023 in Malaysia}},
author = {Nur Adibah Mohidem and Sharifah Norkhadijah Syed Ismail},
journal = {Frontiers in Public Health},
year = {2026},
month = nov,
doi = {10.3389/fpubh.2026.1932501},
url = {https://www.frontiersin.org/articles/10.3389/fpubh.2026.1932501},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Spatial and temporal patterns of tuberculosis from 2014 to 2023 in Malaysia
AU - Nur Adibah Mohidem
AU - Sharifah Norkhadijah Syed Ismail
PY - 2026
DA - 2026/11/02
JO - Frontiers in Public Health
DO - 10.3389/fpubh.2026.1932501
UR - https://www.frontiersin.org/articles/10.3389/fpubh.2026.1932501
AB - BackgroundTuberculosis (TB) continues to be one of the deadliest infectious diseases in Malaysia. This nationwide retrospective ecological study aimed to determine the spatial and temporal patterns of TB with the association of sociodemographic and environmental factors in Malaysia.MethodsThe sociodemographic data of 301,822 TB cases from 2014 to 2023 were collected from the Tuberculosis Information System (TBIS). Environmental data were acquired from the Department of Environment Malaysia and the Malaysia Meteorological Department. The Choropleth mapping and heatmapping were used to assess the trend of TB cases, while the Global Moran’s I, Local Indicators of Spatial Association (LISA), and Getis-Ord Gi* were used to assess the spatial pattern of TB cases. Ordinary least squares (OLS) and geographically weighted regression (GWR) models were employed to examine the spatial association between sociodemographic and environmental factors and TB incidence.ResultsGlobal spatial autocorrelation analysis found that the cases were clustered (p < 0.05) over the 10 years, while local spatial autocorrelation detected that the high-high cluster was concentrated in several districts in Selangor and Kuala Lumpur, with in the districts of Klang, Kuala Selangor, Sabak Bernam, Jelebu, Kuala Lumpur, Gombak, Temerloh, Bentong, Sepang, and Hulu Langat. The hotspot districts were consistently in these two states. This could be attributed to the growing rapid urbanization, demographic expansion, migration, and persistent socio-economic differences. Aged 14 years old and younger, aged 65 years old and older, non-Malaysian, Malay, Chinese, Aborigines in Peninsular Malaysia, completed secondary school, completed STPM / diploma / matriculation / foundation, smoking, SO2, and NO2 were associated with TB incidence (p < 0.05). The GWR models identified the spatially varying association in the distribution of TB incidence and its respective combinatory sets of explanatory variables.ConclusionSpatial heterogeneity of TB incidence and persistent hotspot districts throughout Malaysia were explored using spatial analysis. This study emphasizes the importance of integrate spatial data to assist targeted monitoring, surveillance, early case detection, treatment, and the allocation of public health resources in high-burden regions.
SN - 2296-2565
ER -