Spatial Analysis of Child Violence in West Java Using a Geographically Weighted Negative Binomial Regression Approach

Objective: This study aims to model the number of Tuberculosis cases in West Java Province in 2021 using the Negative Binomial Regression approach. Methods: This study used quantitative analysis uses secondary data from the Central Bureau of Statistics website and the Health Office of West Java Province. 27 West Java districts/cities were studied. The number of tuberculosis cases was assumed to be affected by population density, poverty, sanitation, and health complaints in the past month. Negative Binomial Regression was used to analyse data. Results: The results showed that Poisson Regression caused overdispersion, which was solved using the Negative Binomial Regression approach. The Negative Binomial Regression model passed a detailed test. The partial test showed that only the variable percentage of low-income persons and the variable percentage of people with health concerns significantly affected the model with regression coefficients of 0.8755 and 1.0318, respectively. The final Negative Binomial Regression model with the lowest Akaike Information Criterion value of 491.9 is best for this investigation. Conclusion: The most suitable model for modelling the number of Tuberculosis cases in West Java Province in 2021 is the Negative Binomial Regression model with independent variables that significantly influence the model, namely the percentage of poor people and the percentage of people who have had complaints recently.

Stunting is a childhood growth and development disorder characterized by below-normal height. West Java, with its stunting rate of 24.5 percent, is one of the provinces included in the top 12 priority provinces in implementing the National Action Plan to Accelerate Stunting. Stunting cases are count data and their occurrence is rare. The analysis for the count data is Poisson regression with the assumption that equidispersion must be met. One way to overcome overdispersion is to use negative binomial regression. This study aimed to determine predictors/factors affecting stunting cases in West Java province in 2021 using negative binomial spatial regression. The data in this study comes from the publication of the West Java Health Service and the West Java Central Statistics Agency in 2021 with districts/cities as the object of observation. There is a spatial effect in the stunting data, so the spatial regression model is suitable. The results show that there is an overdispersion in the Poisson regression. The spatial effect test shows that there is a spatial dependence on the response variable and some predictors. The negative spatial autoregressive binomial is the best model with the lowest AIC value. The factors that have a significant effect are the percentage of infants aged less than six months who are breastfed, the percentage of food processing establishments that meet the requirements, and the percentage of infants with low birth weight.

The number of leper in West Java is an example of the count data case. The analyzes commonly used in count data is Poisson regression. This research will determine the variables that influence the number of leper in West Java. The data used is the number of leper in West Java in 2019. This data has an overdispersion condition and spatial heterogenity. To handle overdispersion, the negative binomial regression model can be employed. While spatial heterogenity is overcome by adding adaptive bisquare kernel weight. This research resulted Geographically Weighted Negative Binomial Regression (GWNBR) with a weighting adaptive bisquare kernel classifies regency/city in West Java into ten groups based on the variables that sigfinicantly influence the number of leper. In general, the variable in the percentage of households with Clean and Healthy Behavior (PHBS) has a significant effect in all regency/city in West Java. Especially for Bogor Regency, Depok City, Bogor City, and Pangandaran Regency, the variable of the percentage of people poverty does not have a significant effect on the number leper.

The realization of "Indonesia Emas 2045" is significantly determined by the current Golden Generation. It is expected that this generation possesses high competence, quality, and innovation. As the most populous province in Indonesia, West Java plays a crucial role in achieving the vision of "Indonesia Emas 2045," particularly in the Human Development pillar through the enhancement of health standards. However, infectious diseases have become a major barrier to realizing this vision, with tuberculosis (TB) being one of the most prevalent. In 2023, there were 204,934 reported TB cases in West Java. Therefore, this study aims to identify the factors influencing the number of TB cases in West Java. The data type for TB cases is count data, thus the approach used in this study is Poisson regression, with Negative Binomial regression as an alternative to address overdispersion. The results indicate that the number of community health centers (X1), the percentage of standard-compliant drinking water facilities (X2), and the Air Quality Index (X4) have a significant impact on the number of TB cases in West Java. This is demonstrated by the p-value of each variable, where variables X1, X2, and X4 all have a p-value of 0.0, except for the percentage of households with adequate sanitation (X3), which has a p-value of 0.063 in the Negative Binomial regression. Based on these factors, it is hoped that the government can optimize related programs and facilities to reduce the number of TB cases in West Java and contribute to realizing one of the pillars of "Indonesia Emas 2045."

Introduction: Dengue fever, a vector-borne disease caused by the dengue virus (DENV) and transmitted by Aedes aegypti and Aedes albopictus mosquitoes, has become a major public health problem in West Java, Indonesia. Objective: This study aimed to map high-risk zones of dengue fever in West Java from 2020 to 2024 using spatial analysis techniques. Method: The study used confirmed dengue case data obtained from the West Java Health Profile and applied ArcMap version 10.5 for spatial mapping and analysis, including Global Moran’s Index, Getis-Ord Gi*, Local Indicators of Spatial Association (LISA), and hotspot analysis. Result and Discussion: The results showed a shift in the spatial distribution pattern of dengue cases from random in 2020 to significantly clustered in subsequent years. LISA analysis consistently identified high-high clusters in Bandung Regency, Bandung City, Bogor Regency, Bogor City, Depok City, and Bekasi City, indicating persistent spatial hotspots. Getis-Ord Gi* analysis further confirmed these hotspots with varying levels of statistical significance throughout the study period. Conclusion: These findings indicate the presence of endemic pockets and underscore the need for targeted public health interventions in high-risk areas. This study highlights the value of spatial analysis in understanding disease patterns and in informing evidence-based dengue control strategies in West Java.

Indonesia is focusing on preparing for Indonesia’s Golden 2045 through the Golden Generation. The Golden Generation is expected to be the country's quality and superior human resource. The province of West Java, with the largest population in Indonesia, plays an important role in realizing Indonesia's Golden 2045 ambition. However, the high HIV/AIDS incidence in West Java is a major challenge that could potentially hinder Indonesia’s achievement of the 2045 Gold Plan. The Ministry of Health has launched the Three Zero HIV/AIDS concept to meet one of Indonesia's Golden 2045 pillars. Based on this, the study aims to identify factors that influence the number of HIV/AIDS cases in West Java. The HIV/AIDS case is a data count, so the approach used in this study is Poisson Regression. The results of the study showed that the number of cases of violence (X1), the percentage of use of condom contraceptives among members of the Family Planning (KB) program (X2), population density (X3), the proportion of poor population (X4), and number of health facilities per 100,000 inhabitants (X5) were related to cases of HIV/AIDS in the West Java Province. It is seen from the p-value of each variable X is 0.0 except the population density is worth 0.003. Having identified significant factors influencing cases of HIV/AIDS in West Java, the government is expected to enhance cooperation with relevant agencies in reducing the risk of the number of cases in Western Java. Thus, Three Zero in West Java can be achieved by 2030.

West Java, Indonesia's most populous province, faces escalating urological healthcare demands due to rapid population growth and an aging demographic. This study evaluates the adequacy and distribution of the urology workforce in West Java and examines whether current workforce trends are sufficient to meet projected population needs. A cross-sectional study utilized secondary data from the Indonesian Central Bureau of Statistics (BPS), the Indonesian Urology Collegium, and the Indonesian Urology Association (West Java section). The distribution of urologists, urologist-to-population ratios, age structure, and workforce growth trends from 2009 to 2023 were analyzed. Descriptive statistics, ratio calculations, and spatial analysis were employed to assess workforce adequacy and distribution disparities. Urologists were heavily concentrated in urban centers, particularly Bandung City, with marked shortages in rural and suburban regions. The overall urologist-to-population ratio remained far below levels considered adequate for specialist care. Although the annual growth of urologists exceeded population growth, the absolute number remained insufficient. Projections indicated a future decline in the proportion of urologists aged 30-45 years, suggesting an impending workforce gap. A strong positive correlation was observed between urologist availability and the West Java Health Index. West Java faces a substantial mismatch between urological healthcare needs and workforce availability. Uneven distribution and projected shortages threaten equitable access to care. Strategic workforce planning, expansion of specialist training capacity, targeted deployment policies, and telemedicine integration are essential to ensure sustainable and equitable urological services in this rapidly growing province.

In line with the Sustainable Development Goals (SDGs), the Infant Mortality Rate (AKB) is a very important health indicator, especially in neonatal and perinatal care. West Java Province consistently ranks third nationally in terms of infant mortality in 2020 and 2021. This study analyzes the factors influencing infant mortality in West Java in 2022 using secondary data from the 2022 West Java Provincial Health Profile. The response variable is the number of infant deaths, while the predictor variables include the percentage of K-4 coverage (X1), high-risk pregnancy (X2), family with PHBS (X3), exclusive breastfeeding (X4), and complete immunization coverage (X5). Given the count-based nature of the data, Poisson regression was used, which assumes equidispersion where the variance is equal to the mean. However, the analysis found overdispersion, where the variance significantly exceeds the mean, making Poisson regression unsuitable. To address this, Generalized Poisson Regression (GPR) was applied, as GPR introduces a dispersion parameter that accounts for overdispersion, thus better fitting the data. The initial Poisson regression results showed that X1, X2, X4, and X5 significantly influenced infant mortality, while the GPR model showed that only X2 and X3 were significant factors, with a dispersion parameter of -3.116. The GPR model shows that every additional one high-risk pregnancy increases the infant mortality rate by 1.00006, while an increase of one unit of clean and healthy living practices reduces the mortality rate by 2.66%. Model evaluation using AIC, BIC, and RMSE confirmed that the GPR model better described the relationship between predictor variables and infant mortality rates compared to Poisson regression. These findings emphasize the need to use GPR to model cases with overdispersion in count data, so as to provide more reliable information for policy and intervention strategies.

Dengue Hemorrhagic Fever (DHF) is an infectious disease that remains a serious public health problem in Indonesia, particularly in West Java Province. This study aims to model the number of DHF cases using Gaussian, Poisson, and Compound Poisson Tweedie regression methods based on 2024 data sourced from the West Java Central Statistics Agency (BPS) and the West Java Provincial Health Office. The predictor variables used include humidity, rainfall, real per capita expenditure, poverty ratio, population density, percentage of households with clean and healthy lifestyles, health facility ratio, and average altitude above sea level. The analysis results show that Gaussian and Poisson regressions are less suitable because they cannot handle overdispersion in the data. The Compound Poisson Tweedie model provides the best performance with the smallest Akaike Information Criterion (AIC) value. Two variables that significantly influence the number of DHF cases are population density and the ratio of health facilities. The model equation can be expressed as follows: the expected number of DHF cases equals the exponential of (18.380 plus 0.00008 times population density minus 0.285 times health facility ratio).

Background: Tuberculosis (TB) remains a major public health problem in Indonesia, with West Java reporting 229,683 cases in 2024. The geographic clustering distribution of TB cases requires spatial analysis to identify transmission patterns and determinants. Objective: This study aimed to analyze spatial autocorrelation of TB incidence and its relationships with demographic, health service, environmental, and economic factors in West Java in 2024. Method: Quantitative design with an ecological approach across 27 districts/cities in West Java using data from the West Java Health Profile and Statistics Agency 2025. Spatial autocorrelation analysis employed Global Moran's I and univariate–bivariate LISA with a Queen Contiguity weighting matrix. Variables included TB incidence, population size, population density, health facility ratio, adequate sanitation, non-earth floors, and poor population. Analysis used GeoDa 1.22.0.21 with α = 0.05 and 999 permutations. Result: TB incidence showed significant global spatial autocorrelation (Moran's I = 0.3514, p = 0.001). Univariate LISA identified High-High clusters in the Bogor–Bekasi–Karawang metropolitan corridor and Low-Low clusters in Ciamis–Tasikmalaya–Majalengka. Bivariate autocorrelation revealed significant positive relationships with health facility ratio (I= 0.3207, p = 0.005), population size (I = 0.2449, p = 0.014), and population density (I = 0.2088, p = 0.044). Negative autocorrelation with poor population (I = −0.2950, p = 0.006) indicated an urban paradox. Conclusion: TB incidence distribution demonstrates significant geographic clustering with spatial heterogeneity. Demographic and health service factors show positive correlations, while economic factors exhibit an urban paradox. Intervention priorities should focus on metropolitan High-High clusters with spatial data integration and cross-sectoral collaboration.

Pneumonia is a major cause of mortality among children under five, especially in developing countries like Indonesia. In 2023, West Java recorded over 18,000 cases, making it one of the provinces with the highest burden. This study analyzes the spatial distribution of pneumonia in children under five across 27 districts/cities in West Java and examines its association with individual and environmental determinants. Data were obtained from the 2023 Indonesia Health Survey (Survei Kesehatan Indonesia or SKI), Statistics Indonesia (Badan Pusat Statistik or BPS), and the West Java Health Office. Descriptive analysis (mean, median, standard deviation) was conducted using SPSS, and geospatial mapping was performed using QGIS. The results revealed notable spatial clusters of pneumonia incidence among children under five in several districts. High-High clusters, indicating areas with high pneumonia rates surrounded by similarly high-risk districts, were prominently observed in regions with elevated prevalence of household tobacco smoking and use of wood fuel for cooking, such as Bogor, Sukabumi, Cianjur, and Garut. Conversely, Low-Low clusters, representing low pneumonia incidence surrounded by similarly low-risk districts, were identified in areas with better population density management and lower tobacco exposure, such as Majalengka, Cirebon, and Kuningan. Significant gaps in basic immunization coverage and exclusive breastfeeding practices were also spatially evident. Districts like Karawang and Purwakarta demonstrated Low-Low clusters for basic immunization, highlighting regional disparities potentially due to limited healthcare accessibility. Likewise, Majalengka and Indramayu showed Low-Low clusters for exclusive breastfeeding practices, signaling inadequate maternal and community support. This spatial epidemiological analysis highlights critical hotspots and underscores the importance of geographically targeted health policies, including intensified immunization campaigns, promotion of exclusive breastfeeding, and tobacco control initiatives, to effectively reduce pneumonia risks among vulnerable children in West Java.

One of the key factors shaping the religious landscape in Indonesia is the strategic policies implemented by the New Order Government. This article examines how these policies influenced the geographic distribution of mosques and the subsequent religious dynamics in West Java. Additionally, it investigates the impact of government policies on the formation and development of religious organizations and Islamic communities across different regions, with a specific focus on West Java. This study employs a historical method by analyzing government archives, historical documents, and mosque establishment data. Spatial analysis is used to correlate mosque locations with centers of social, economic, and political activity. The findings indicate that the New Order government's mosque construction policy directly shaped the spatial distribution of mosques in West Java. The placement of mosques during the New Order era prioritized areas with concentrated Muslim population, aligning with the government's strategy to monitor religious organizations. By strategically placing mosques, which often serve as the center of life for Muslim communities, the New Order aimed to control Muslim activities. This article contributes to a deeper understanding of the complex interplay between state power, religion, and spatial politics in Indonesia.

Background: Dengue incidence has increased since 2000, in 2013 Incidence Rate of Dengue reached 50.55%o.Oneof the factors increase on the cases of disease is a residential community environment health. This study was aimed toanalize housing environment health effects on the Incidence Rate of DHF at West Java in 2013. Methods: Analysis ofsecondary data using Poisson regression test.The population in study is all common household residing in the 27 districtof West Java, and the samples that is 958 BS with 23.694 household (RR 98,9%). Results: of the analysis of environmenthealth component effects of the incidence rate of DHF is waste handling manner, waste water disposal, container driningmanner and with models Y = exp.(5,290 + 0,023 P7 + 0,006 P8–0,008 P11). Physical environment components thataffects the incidence rate of DHF is the use of the living room, and its ventilation with models Y = exp.(2,088 + 0,073 P24 +0,023 P27). Conclusion: According to GPR model physical environment education is assosiated to incidence of Dengue.Recommendation: We needs to educate people about the physical environment in residential areas in terms of arrangementof the room, ventilation and administration of environmental health that dengue cases in West Java may be on tap.

This work is motivated by the HEALing Communities Study, which is a post-test only cluster randomized trial in which communities are randomized to two different trial arms. The primary interest is in reducing opioid overdose fatalities, which will be collected as a count outcome at the community level. Communities range in size from thousands to over one million residents, and fatalities are expected to be rare. Traditional marginal modeling approaches in the cluster randomized trial literature include the use of generalized estimating equations with an exchangeable correlation structure when utilizing subject-level data, or analogously quasi-likelihood based on an over-dispersed binomial variance when utilizing community-level data. These approaches account for and estimate the intra-cluster correlation coefficient, which should be provided in the results from a cluster randomized trial. Alternatively, the coefficient of variation or R coefficient could be reported. In this article, we show that negative binomial regression can also be utilized when communities are large and events are rare. The objectives of this article are (1) to show that the negative binomial regression approach targets the same marginal regression parameter(s) as an over-dispersed binomial model and to explain why the estimates may differ; (2) to derive formulas relating the negative binomial overdispersion parameter k with the intra-cluster correlation coefficient, coefficient of variation, and R coefficient; and (3) analyze pre-intervention data from the HEALing Communities Study to demonstrate and contrast models and to show how to report the intra-cluster correlation coefficient, coefficient of variation, and R coefficient when utilizing negative binomial regression. Negative binomial and over-dispersed binomial regression modeling are contrasted in terms of model setup, regression parameter estimation, and formulation of the overdispersion parameter. Three specific models are used to illustrate concepts and address the third objective. The negative binomial regression approach targets the same marginal regression parameter(s) as an over-dispersed binomial model, although estimates may differ. Practical differences arise in regard to how overdispersion, and hence the intra-cluster correlation coefficient is modeled. The negative binomial overdispersion parameter is approximately equal to the ratio of the intra-cluster correlation coefficient and marginal probability, the square of the coefficient of variation, and the R coefficient minus 1. As a result, estimates corresponding to all four of these different types of overdispersion parameterizations can be reported when utilizing negative binomial regression. Negative binomial regression provides a valid, practical, alternative approach to the analysis of count data, and corresponding reporting of overdispersion parameters, from community randomized trials in which communities are large and events are rare.

Latar Belakang: Pemanfaatan data rutin di bidang kesehatan salah satunya untuk mengestimasi beban suatu penyakit termasuk determinannya. Tuberculosis (TB) masih menjadi masalah kesehatan global yang menginfeksi 10,6 juta orang di seluruh dunia pada tahun 2021, dimana Indonesia menjadi penyumbang beban kasus tertinggi kedua. Jawa Barat merupakan provinsi dengan jumlah temuan kasus TB terbanyak di Indonesia dalam 5 tahun terakhir Metode: Data bersumber dari Badan Pusat Statistik (BPS) Provinsi Jawa Barat tahun 2022 dan Statistik Perumahan Provinsi Jawa Barat 2021. Analisis deskriptif, autokorelasi spasial, dan analisis Geographically Weighted Regression (GWR) dilakukan menggunakan perangkat lunak pengolahan data, GeoDa dan GWR4. Hasil disajikan dalam bentuk peta menggunakan aplikasi QGIS. Analisis spasial dilakukan untuk melihat persentase kasus TB dengan faktor-faktor risiko TB.Hasil: Hasil dari penelitian ini menunjukkan adanya autokorelasi spasial positif yang berpengaruh signifikan terhadap jumlah kasus TB di Provinsi Jawa Barat yang artinya sebaran kasus membentuk pola mengelompok. Adapun kabupaten/kota yang menjadi hotspot dan merupakan wilayah prioritas intervensi penanganan kasus TB di Provinsi Jawa Barat adalah Kabupaten Bekasi, Kabupaten Bogor, Kabupaten Karawang, Kabupaten Purwakarta, Kabupaten Sukabumi, Kota Bekasi, Kota Bogor dan Kota Depok. Model GWR menemukan faktor risiko yang memiliki pengaruh berbeda di tiap wilayah kabupaten/kota yaitu penduduk miskin, suhu dan ketinggian wilayah, sehingga bentuk intervensi kesehatan yang dilakukan juga berbeda. Kesimpulan: Pemanfaatan data rutin dengan pendekatan spasial ini diharapkan dapat menjadi pendukung pengambilan keputusan (decision making support) terkait program dan kebijakan intervensi kesehatan yang spesifik wilayah sehingga tepat sasaran dan mampu menurunkan jumlah kasus TB.Kata kunci: Analisis spasial, Faktor risiko, GWR, Pemanfaatan data rutin, Tuberkulosis
 Background: One of the uses of routine data in the health sector is to estimate the burden of a disease including its determinants. TB remains a global health problem that infected 10.6 million people worldwide in 2021, and Indonesia has the second highest TB caseload globally. West Java is the province with the highest number of TB case findings in Indonesia in the last five years. Method: Data sourced from 2022 West Java Province Central Statistics Agency and 2021 West Java Province Housing Statistics. Descriptive analysis, spatial autocorrelation, and GWR analysis were carried out using SPSS, GeoDa, and GWR4. Results were presented in map form using QGIS application. Spatial analysis was carried out to know the percentage of TB cases with TB risk factors.Result: The results of this study indicate a positive spatial autocorrelation that has a significant effect on the number of TB cases in West Java, which means that the distribution of cases forms a clustered pattern. The regencies/cities that have become hotspots and priority areas for intervention in handling TB cases in West Java were Bekasi Regency, Bogor Regency, Karawang Regency, Purwakarta Regency, Sukabumi Regency, Bekasi City, Bogor City and Depok City. The GWR model found risk factors that have different effects in each regency/city area, specifically the poor population, temperature, and altitude so the forms of health interventions carried out were also different.Conclusion: The utilization of routine data with a spatial approach is expected to be decision-making support related to region-specific health intervention programs and policies so that they are targeted and able to reduce the number of TB cases. Keywords: GWR, Risk factor, Routine data utilization, Spatial analysis, Tuberculosis