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.
Abstract
Tuberculosis (TB) remains a major public health challenge in Nepal, marked by substantial geographic heterogeneity. Despite ongoing control efforts, spatial clustering patterns and socio-environmental determinants at the municipal level are not well understood. This study assessed spatial clustering of TB notification rates and their association with sociodemographic, housing, and environmental factors across all 753 municipalities of Nepal.
Data of notified TB cases were extracted from National Tuberculosis Control Centre from FY 2019/20 to FY 2023/24. Spatial autocorrelation analyses were conducted using annual TB notification rates, while spatial regression models were based on five-year averaged data.
Spatial autocorrelation and clustering were examined using Global Moran’s I, Getis-Ord Gi*, and Local Indicators of Spatial Association (LISA). Associations between TB notification rates and socio-demographic, housing, and environmental factors were evaluated using Ordinary Least Squares, Spatial Lag, and Spatial Error Models.
A total of 172,155 TB cases were reported over the five-year period (FY 2019/20-2023/24), with national notification rates increasing from 92 to 139 per 100,000 population. Significant and persistent positive spatial autocorrelation was observed annually (Moran’s I: 0.42-0.53; p < 0.001). High-High clusters were consistently concentrated in the densely populated Terai municipalities of Madhesh and Lumbini Provinces, whereas Low-Low clusters dominated the remote mountain regions of Karnali and Sudurpashchim. The Spatial Error Model (SEM) provided the best fit (Pseudo-R² = 0.62), revealing that population density (β=0.005, p<0.001), liquefied petroleum gas use (β=60.85, p<0.001), and nighttime land surface temperature (β=1.69, p<0.05) were significantly associated with higher TB notification rates. Traditional housing materials (mud walls: β=-38.40, p<0.001) and cow dung fuel use (β=-48.43, p<0.05) showed negative associations, this may reflect diagnostic access barriers rather than lower disease incidence.
Tuberculosis in Nepal demonstrates significant and persistent spatial clustering at the local municipal level, driven by population density, housing, energy access, and climatic conditions. These results emphasize the need for geographically targeted, municipality-focused interventions to advance Nepal’s progress toward the End TB Strategy and Sustainable Development Goal 3.3
Not applicable.
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.
Muhammad Auliya Rahman, Muhammad Ashraff Zurkarnain, S. Sulistiyani et al.· Liaquat National Journal of...· 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
ABSTRACT Objective: To analyze the spatial and temporal patterns and factors associated with tuberculosis treatment interruption in Brazil from 2010 to 2020. Method: Ecological study using geoprocessing. The Joinpoint method was used for temporal analysis. Spatial autocorrelation and scan statistics identified clusters. Spatial and non-spatial regression models, considering p < .05, detected factors associated with the outcome. Results: A stationary trend in tuberculosis treatment interruption was observed across the country, with increases in the Central-West and North regions. Associated socioeconomic indicators included the Gini index, household density > 2, retreatment rate, social vulnerability index, illiteracy rate, percentage of individuals in extreme poverty, and Family Health Strategy coverage. Conclusion: Treatment interruption showed a stationary trend. 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
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
BACKGROUND
Since the onset of COVID-19 pandemic, multiple individual-level factors, including socioeconomic determinants have been associated with infection risk and disease severity. However, public health policies implemented at national level did not consider social determinants at territorial and community levels. In collaboration with the Regional Health Agency of Provence-Alpes-Côte d'Azur (PACA) in France, we analysed COVID-19 hospitalisations together with incidence and testing data, at a fine geographical scale, in order to contextualise the epidemic, and assess the impact of area-level socioeconomic and demographic characteristics on disease severity.
METHODS
We conducted a fine scale ecological study of COVID-19 hospitalisation rates in the PACA region, during the second and third epidemic waves (September 2020-June 2021). French census areas (IRIS), the smallest spatial units available for socioeconomic and population-based analysis, were classified into six socio-demographic profiles. We characterized COVID-19 with indicators of incidence and severity relative to total population (incidence and hospitalisation rates) and proportional to tests or cases (proportion of positive tests and proportion of hospitalised cases). Associations between these profiles and COVID-19 indicators were assessed using generalised additive models, adjusting for testing rates, healthcare access, retirement home presence and population age structure. Spatial autocorrelation between areas was accounted for in the models.
RESULTS
The most socially deprived IRIS had the highest COVID-19 incidence and hospitalisation rates both in conventional settings and in intensive care units (ICU). Decreasing social deprivation was associated with a gradient of decreasing incidence and hospitalisation rates. Complementary models examining proportion of hospitalisation among confirmed cases indicated that excess hospitalisation in very socially deprived area reflected both higher incidence and greater severity of the disease. IRIS profiles corresponding to remote, rural areas displayed an isolated increase in conventional hospitalisation ratios without a corresponding rise in ICU hospitalisation ratios.
CONCLUSION
Socioeconomic deprivation was strongly associated with both higher infection spread and greater severity of COVID-19 at the territorial level, underscoring the need to prioritize prevention efforts in socially deprived areas to mitigate future health crisis. Remote areas also exhibited higher conventional hospitalisation rates, possibly reflecting clinical decisions influenced by remoteness rather than increased disease severity.
Pierre Garneret, G. Gaubert, S. Nauleau et al.· International Journal of Inf...· 0 citations
Depressive disorders impose a substantial global burden, yet the spatiotemporal distribution of depression-related service use in Thailand remains unexamined. This study aimed to examine the spatiotemporal trends and geographic clustering of depression-related mental health service attendance across Thailand’s 77 provinces from 2018 to 2023.This retrospective study analysed province-level mental health service attendance rates (ICD-10: F32, F33, F34.1, F38, F39) across all 77 Thai provinces from 2018 to 2023. Attendance rates per 100,000 population were derived from Department of Mental Health administrative data. Temporal trends were assessed using Friedman and Wilcoxon signed-rank tests; and spatial structure using global Moran’s I and Local Indicators of Spatial Association (LISA) with a queen contiguity weights matrix (99,999 permutations) in GeoDa. National mean attendance rose 58.3% from the 2019 pre-pandemic baseline (429 per 100,000) to 679 in 2023 (Friedman χ²(5) = 204.9, p < 0.001), with attendance increasing even during COVID-19 restrictions. Global Moran’s I shifted from non-significant positive clustering (2018-2021) to significant spatial dispersion in 2022 (I = -0.155, p = 0.031). LISA revealed a transition from mild northern hotspots toward a post-pandemic checkerboard of spatial outliers, with Low-Low clusters disappearing entirely by 2022. These findings indicate substantial service expansion alongside emerging geographic fragmentation, underscoring the need for spatially targeted mental health policy in Thailand.
Tay Zar Lin, V. Punyapornwithaya, Pallop Siewchaisakul et al.· BIO Web of Conferences· 0 citations
