Spatial Clustering of Tuberculosis Incidence and its Spatial Association with Population Density and Primary Health Care Distribution in Semarang City, Indonesia
Aug 2026· Liaquat National Journal of Primary Care· 0 citations
TL;DR
Tuberculosis incidence in Semarang City exhibited a clustered spatial pattern, particularly in densely populated areas, which support geographically targeted TB control strategies.
Abstract
Background: While tuberculosis persists as a public health threat in urban Indonesia, the spread of the disease is uneven. It is believed that rapid urbanization and unequal access to medical services have led to varying disease burdens across regions.
Objectives: To examine the spatial distribution and clustering of tuberculosis incidence and its association with population density and primary health care distribution in Semarang City.
Methods: An ecological study was conducted using a spatial analysis approach based on secondary data on TB cases, population, and health facilities in Semarang City for the years 2022-2023. Both global and local spatial autocorrelation analyses were conducted to examine clustering, followed by bivariate analyses to test their relationships with other variables.
Results: Significant spatial clustering of TB incidence was observed in both years (Moran's I = 0.339 in 2022 and 0.465 in 2023; p < 0.001). High-incidence clusters were mainly located in the central and eastern urban areas. Population density showed a significant positive spatial association with TB incidence (Moran's I=0.415 in 2022 and 0.522 in 2023; p=0.001), whereas no significant association was found for PHC distribution.
Conclusion: Tuberculosis incidence in Semarang City exhibited a clustered spatial pattern, particularly in densely populated areas. These findings support geographically targeted TB control strategies. However, because the analysis was based on aggregated ecological data, the results should not be interpreted at the individual level.
The results emphasize the need for geographically targeted, municipality-focused interventions to advance Nepal’s progress toward the End TB Strategy and Sustainable Development Goal 3.
Spatial regression showed that socioeconomic vulnerability indicators influence the outcome, positively or negatively, depending on the region, which calls for intensified prevention and control efforts in those areas.
Maria Izabel Félix Rocha, Thatiana Araujo Maranhão, Maria Madalena Cardoso da Frota et al.· Cogitare Enfermagem· 0 citations
Summary Background Tuberculosis remains a major cause of preventable mortality in Brazil, marked by pronounced social and territorial inequalities. Evidence remains limited on how socioeconomic conditions and health system characteristics are associated with tuberculosis mortality across age groups in high-burden settings such as São Paulo state. This study aimed to examine the spatial and temporal associations of these factors with tuberculosis mortality across municipalities in the State of São Paulo, Brazil. Methods We conducted a population-based ecological study in São Paulo, Brazil. All TB deaths reported to the Mortality Information System from 2020 to 2024 were included. Socioeconomic, demographic and health system factors were selected based on a conceptual framework. Variables associated with TB mortality were assessed using Generalized Additive Models for Location, Scale, and Shape, with spatial smoothing. Findings The analysis included 38,700 municipality-month observations. In the final spatial GAMLSS model, proportions of household crowding (>2 residents/bedroom) and elderly population (>59 years) were associated with 3.25% and 5.92% increases in expected TB mortality for each one percentage-point increase, respectively. In contrast, each one percentage-point increase in primary health care coverage was associated with a 0.30% decrease in expected TB mortality. The fitted spatial effect indicated higher expected TB mortality along coastal and central regions, and lower expected mortality in the northwestern region. Interpretation Tuberculosis mortality in São Paulo is shaped by persistent socioeconomic and territorial inequalities, with distinct patterns across age groups. These findings highlight the need for strategies that address structural vulnerability and strengthen primary health care to reduce avoidable tuberculosis-related deaths. Funding This work was supported by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior–Brasil and São Paulo State Research Foundation (FAPESP).
Y. M. Alves, Reginaldo Bazon Vaz Tavares, Nathália Zini et al.· The Lancet Regional Health -...· 0 citations
We collected data on tuberculosis incidence and influencing factors (demographic factors, socioeconomic factors, medical resources, population mobility, and air pollutants) in 31 provinces of China from 2012 to 2020. Using a Bayesian spatio-temporal model, we studied the spatio-temporal distribution and its influencing factors of tuberculosis in various regions of China from 2012 to 2020. The spatial clustering effect of tuberculosis incidence in China showed an overall increasing trend during the study period, but there was a significant decline in 2018, followed by a continued increase. High-risk areas for tuberculosis incidence are primarily concentrated in the northwest and southwest regions, while low-risk areas are mainly distributed in the eastern coastal provinces. The number of healthcare institutions (NHI), illiteracy rate (IR), rural residents' disposable income (PDIR), passenger flow (PRV), and sulfur dioxide emissions (SO2) are statistically correlated with tuberculosis incidence. The IR,PRV, and SO2 are positively correlated with tuberculosis incidence, with relative risks of 2.191, 1.759, and 1.126, respectively.
Ruquan Yuan, Yantao Luo, Tingting Zheng· International Journal of Env...· 0 citations
Background Comorbidities may complicate the treatment of pulmonary tuberculosis and exacerbate both the clinical and economic burden. We aimed to examine comorbidity patterns, spatial clustering, and economic burden in hospitalised pulmonary tuberculosis (PTB) patients in Guangdong Province, supporting targeted prevention strategies. Methods Based on the Hospital Discharge Records Database of Guangdong Province from 2016 to 2024, we collected data on hospitalised PTB patients to analyse the characteristics and temporal trends of comorbidity spectra. We used Global Moran’s I and local indicators of spatial association analyses to examine the spatial clustering patterns of major comorbidity pairs. Generalised linear models evaluated the impact of comorbidity count and spatial clustering patterns on economic burden among hospitalised PTB patients. Results A total of 646,114 hospitalised PTB patients were included from 2016 to 2024, of whom 66.42% had at least one comorbidity. Hypertension (HTN), diabetes mellitus (DM), and chronic obstructive pulmonary disease (COPD) were the top three comorbidities among hospitalised PTB patients. Spatial analysis indicated that high-high clusters of PTB-HTN were mainly concentrated in the central Pearl River Delta; high-high clusters of PTB-DM in eastern Guangdong and low-low clusters in western Guangdong. High-high clusters of PTB-COPD were primarily located in eastern and northern Guangdong, while low-low clusters remained relatively stable in several cities of the Pearl River Delta region. Among hospitalised PTB patients, the number of comorbidities was significantly positively associated with both direct and indirect economic burden (P < 0.001), with economic burden progressively increasing as the number of comorbidities increased, and the impact of spatial clustering patterns on economic burden exhibited heterogeneity across different comorbidity types. Conclusions The burden of comorbidities among hospitalised PTB patients in Guangdong Province is substantial, with significant spatial clustering characteristics closely linked to direct and indirect economic burden. It is recommended to incorporate regional comorbidity epidemiological characteristics into PTB comorbidity management and resource allocation optimisation. In addition, both direct medical costs and productivity losses should be considered in prevention and control strategies to reduce the socioeconomic burden associated with PTB.
Lian-Rong Ji, Liang Chen, Yunlian Xue et al.· Journal of Global Health· 0 citations
BACKGROUND
Colorectal cancer (CRC) incidence varies in relation to social determinants of health and health system capacity, particularly in settings with territorial inequalities. This study aims to estimate CRC incidence at the municipal level in Brazil and assess its association with social, demographic, and health system determinants, accounting for spatial heterogeneity.
METHODS
Population-based cross-sectional ecological study with spatial analysis. Municipal CRC incidence for 2024 was estimated using corrected mortality data (1980-2023) and state-level incidence-mortality ratios from Population-Based Cancer Registries (PBCR). Estimated age-standardized incidence rates (estimated ASR) were calculated using the Segi-Doll standard population. Spatial autocorrelation was assessed using Global and Local Moran's I, while associations were examined using Ordinary Least Squares (OLS), spatial autoregressive (SAR), and Multiscale Geographically Weighted Regression (MGWR) models.
RESULTS
Higher estimated ASRs were observed in the South, Southeast, and parts of the Central-West, while lower rates predominated in the North and Northeast. In the SAR model, positive associations were observed for the Municipal Human Development Index (β = 1.48; p < 0.001), hospital beds (β = 0.023; p = 0.002), premature mortality due to neoplasms (β = 0.002; p < 0.001), and ultra-processed food consumption (β = 0.027; p < 0.001). Negative associations were found for under-1-year mortality (β = -0.009; p = 0.004), dependency ratio (β = -0.013; p < 0.001), and hospital admission rates (β = -0.002; p < 0.001). MGWR showed spatial heterogeneity, with MHDI and ultra-processed food consumption significant in all municipalities and dependency ratio and under-1-year mortality in over 98%, with stronger effects in the North and Northeast.
CONCLUSION
Estimated CRC ASRs showed marked territorial heterogeneity and was associated with socioeconomic, dietary, and health-system characteristics. Because the estimates were derived from mortality and state-assigned I/M ratios, the observed patterns may reflect a combination of disease occurrence, healthcare access, data quality, and model assumptions. These findings may support territorially tailored cancer surveillance and control planning.
Luís Ricardo Santos de Melo, Júlio dos Santos Pereira, L. A. Andrade et al.· Cancer Epidemiology· 0 citations
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