Jul 2026· J Statistika· Vol 19, pp. 1113-1126· 0 citations
TL;DR
It is suggested that controlling leprosy requires social and economic interventions, particularly poverty reduction efforts, as well as the SAR model, which was selected as the best model.
Abstract
Leprosy remains a public health issue in Indonesia, including in East Java, which has a relatively high number of cases. Differences in social, economic, and health characteristics between regions can cause variations in leprosy cases and spatial clustering. Unlike previous studies, which generally used absolute case numbers and applied one spatial regression model, this study compares spatial autoregressive (SAR) and spatial error (SEM) models using the new case detection rate (NCDR) as the response variable. The study uses Queen Contiguity weighting to analyze the factors that influence the NCDR of leprosy in East Java in 2025. The predictor variables include population density, the percentage of households without toilet facilities, the percentage of impoverished residents, and the number of health centers. Secondary data from 38 districts/cities in East Java were used for the analysis. The analysis revealed significant positive spatial autocorrelation, with a Moran's I value of 0.6059. Of the predictor variables, the percentage of impoverished residents was the only one that significantly affected the NCDR of leprosy. Based on Akaike's information criterion (AIC) and log-likelihood values, the SAR model was selected as the best model. These findings suggest that controlling leprosy requires social and economic interventions, particularly poverty reduction efforts.
Leprosy incidence exhibited non-normal, right-skewed distribution and spatial heterogeneity, requiring a model accommodating both characteristics. This study aimed to develop and apply the Mixed Geographically Weighted Three-Parameter Log-Logistic Regression (MGWLL3R), representing the first integration of the Mixed Geographically Weighted Regression framework with the Three-Parameter Log-Logistic distribution, to identify factors influencing leprosy incidence using cross-sectionaldata from 35 regencies/cities in Central Java, based on 2024 data obtained from BPS of Central Java. Parameters were estimated using Maximum Likelihood Estimation with the Berndt-Hal-Hall-Hausman algorithm, and the optimal bandwidth was selected using minimum Cross Validation. The best model employed a Fixed Gaussian kernel with bandwidth 0.37 and achieved the lowest AICc of 90.70 compared with the LL3R and GWLL3R models. Proper sanitation access was the only global predictor, whereas unemployment, safe drinking water access, population growth, and mean years of schooling showed spatial variation. These findings support region-specific leprosy control through global and local effects.
Nuranisa, Purhadi, Santi Wulan Purnami· Journal of Mathematics and S...· 0 citations
Abstract Leprosy, caused by Mycobacterium leprae, remains a public health challenge in Brazil, characterized by persistent transmission, physical disabilities, and a strong association with social inequalities. This study aimed to describe the sociodemographic and clinical profile of cases, analyze temporal trends, and identify spatial and spatiotemporal patterns from 2001 to 2024. Spatial distribution was analyzed using maps of crude rates and rates smoothed by the Local Empirical Bayesian Estimator. Temporal trends were assessed across four periods (2001-2024). Spatial dependence and clustering were identified using global and local Moran’s indices, with a 5% significance level. This is an ecological study with time-series and spatial analyses, including 91,138 new cases reported in the Notifiable Diseases Information System and demographic data from the Brazilian Institute of Geography and Statistics. A predominance was observed among males (56.9%), young adults aged 15-29 years (24.8%), individuals of mixed race ethnicity (58.4%), and those with low educational attainment (51.5%), with a higher frequency of multibacillary (65.8%) and borderline (42.7%) forms. Spatial analysis revealed persistent hyperendemic areas in the northern, northeastern, and eastern regions of the state, as well as active transmission foci among individuals under 15 years of age. Spatiotemporal scan analysis identified high-risk clusters in the central region (2006-2015; RR = 2.24) and in the São Luís metropolitan area (2010-2019; RR = 1.77). In conclusion, leprosy maintains a high burden in Maranhão, requiring strengthened primary health care, expanded active case finding, and the integration of spatial analyses into surveillance, alongside intersectoral policies aimed at reducing social inequalities.
L. V. Oliveira, R. S. Oliveira, K. Pimentel et al.· Brazilian Journal of Biology· 0 citations
Stunting remains a crucial issue in Indonesia, with a prevalence of 21.5\% in 2023. This study aims to model the number of stunting cases in toddlers in 34 provinces in Indonesia (2020--2022) using Spatial Panel Regression to address the weaknesses of traditional regression that ignore the effects of spatial and temporal dependencies. Predictor variables analyzed include the percentage of malnutrition, underweight, poor population, access to basic health facilities, and access to drinking water services. The selection of the best model specification was carried out using the Chow test, Hausman, and the Bayesian log-marginal posterior probabilities approach. The results of the diagnostic test confirmed the existence of spatial and temporal autocorrelation in stunting cases. Based on the Bayesian analysis, the Spatial Autoregressive (SAR) Fixed Effect (FEM) model was selected as the most optimal model with a log-marginal value of -21.360, a posterior probability of 0.630, and a coefficient of determination ($R^2$) of 0.880. Impact analysis shows that the percentage of underweight children and access to health facilities have a significant direct effect on stunting in a region. However, no significant indirect spillover effect from neighboring provinces was found. Therefore, policymakers are advised to formulate stunting management strategies that focus on precisely addressing local determinants in each region.
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 is a development of classical regression that considers the influence of location between regions. This study aims to determine the factors that influence poverty in Lampung Province in 2024, determine the best model through a comparison of the Spatial Autoregressive (SAR) and Spatial Error Model (SEM) methods, and identify spatial patterns of poverty in Lampung Province. Model parameter estimation is carried out at each observation location using a spatial weighting matrix of Rook contiguity to capture the proximity between regions. Based on the Lagrange Multiplier test, the SAR model is identified as the best model, because it is able to capture the influence of interregional linkages through significant spatial coefficients with an AIC value of 136.3521. The SAR parameter estimation results show that the population (X_3) with a p-value of <2.2e-16 and the human development index (X_4) with a p-value of 0.006990, with a significance level or α <0.10, have a significant effect on the number of poor people in Lampung Province, while gross regional domestic product (X_1) and TPAK (X_2) have no significant effect. The Local Indicators of Spatial Association (LISA) analysis also revealed the existence of High-High and Low-Low poverty clusters indicating spatial grouping in several regencies/cities in Lampung Province. However, after SAR modeling, the residual map of the SAR model shows a scattered spatial pattern and does not form a geographic cluster, indicating that the influence between regions has been successfully captured by the model. This finding confirms that poverty in Lampung Province is not only influenced by the internal characteristics of the region, but is also influenced by the conditions of the surrounding areas. Therefore, policy actions need to consider the spatial effects between regions so that poverty alleviation programs are more targeted and effective.
Kurnia Sari, Mahfuz Hudori, Siti Qomariyah et al.· International Journal of Sci...· 0 citations
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