{"title":"印度尼西亚rokan hilir地区五岁以下儿童肺炎危险因素的空间建模。","authors":"Yusdiana Yusdiana, Sukendi Sukendi, Siregar Yusni Ikhwan, Afandi Dedi","doi":"10.21010/Ajidv19i2.3","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Under-five pneumonia remains a critical health issue in Indonesia. Identifying risk factors using spatial models is crucial for developing effective disease-prevention strategies. This study aimed to identify risk factors and create a spatial model for under-five pneumonia distribution based on regional vulnerability.</p><p><strong>Materials and methods: </strong>This study used a mixed-method approach that integrated mathematical models and GIS to identify risk factors using generalized Poisson regression (GPR) and developed a GIS-based spatial model with inverse distance weighted (IDW) and natural break methods.</p><p><strong>Results: </strong>The GPR model revealed significant associations between under-five pneumonia and population density (β = 0.004, Z<sub>-score</sub> = 6.118), rainfall (β = 0.002, Z<sub>-score</sub> = 6.031), malnutrition (β = 1.786, Z<sub>-score</sub> = 3.696), and health facilities (β = 0.073, Z<sub>-score</sub> = 13.527). Protective factors included exclusive breastfeeding (β = -0.004, Z<sub>-score</sub> = -2.874), healthy homes (β = -0.021, Z<sub>-score</sub> = -9.532), and under-five health service coverage (β = -0.003, Z<sub>-score</sub> = -2.225). Spatial modeling classified regions into high-risk (5 subdistricts), medium-risk (11 subdistricts), and low-risk (3 subdistricts).</p><p><strong>Conclusion: </strong>This study identified key risk factors and mapped spatial vulnerability for under-five pneumonia. Targeted, integrated interventions in high-risk areas are essential to reduce pneumonia incidence below 12 cases per 1,000 children under five by 2030, aligning with global health goals.</p>","PeriodicalId":39108,"journal":{"name":"African Journal of Infectious Diseases","volume":"19 2","pages":"15-32"},"PeriodicalIF":0.0000,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12102673/pdf/","citationCount":"0","resultStr":"{\"title\":\"SPATIAL MODELING OF RISK FACTORS FOR UNDER-FIVE PNEUMONIA IN ROKAN HILIR DISTRICT, INDONESIA.\",\"authors\":\"Yusdiana Yusdiana, Sukendi Sukendi, Siregar Yusni Ikhwan, Afandi Dedi\",\"doi\":\"10.21010/Ajidv19i2.3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Under-five pneumonia remains a critical health issue in Indonesia. Identifying risk factors using spatial models is crucial for developing effective disease-prevention strategies. This study aimed to identify risk factors and create a spatial model for under-five pneumonia distribution based on regional vulnerability.</p><p><strong>Materials and methods: </strong>This study used a mixed-method approach that integrated mathematical models and GIS to identify risk factors using generalized Poisson regression (GPR) and developed a GIS-based spatial model with inverse distance weighted (IDW) and natural break methods.</p><p><strong>Results: </strong>The GPR model revealed significant associations between under-five pneumonia and population density (β = 0.004, Z<sub>-score</sub> = 6.118), rainfall (β = 0.002, Z<sub>-score</sub> = 6.031), malnutrition (β = 1.786, Z<sub>-score</sub> = 3.696), and health facilities (β = 0.073, Z<sub>-score</sub> = 13.527). Protective factors included exclusive breastfeeding (β = -0.004, Z<sub>-score</sub> = -2.874), healthy homes (β = -0.021, Z<sub>-score</sub> = -9.532), and under-five health service coverage (β = -0.003, Z<sub>-score</sub> = -2.225). Spatial modeling classified regions into high-risk (5 subdistricts), medium-risk (11 subdistricts), and low-risk (3 subdistricts).</p><p><strong>Conclusion: </strong>This study identified key risk factors and mapped spatial vulnerability for under-five pneumonia. Targeted, integrated interventions in high-risk areas are essential to reduce pneumonia incidence below 12 cases per 1,000 children under five by 2030, aligning with global health goals.</p>\",\"PeriodicalId\":39108,\"journal\":{\"name\":\"African Journal of Infectious Diseases\",\"volume\":\"19 2\",\"pages\":\"15-32\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-04-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12102673/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"African Journal of Infectious Diseases\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.21010/Ajidv19i2.3\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q4\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"African Journal of Infectious Diseases","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21010/Ajidv19i2.3","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q4","JCRName":"Medicine","Score":null,"Total":0}
SPATIAL MODELING OF RISK FACTORS FOR UNDER-FIVE PNEUMONIA IN ROKAN HILIR DISTRICT, INDONESIA.
Background: Under-five pneumonia remains a critical health issue in Indonesia. Identifying risk factors using spatial models is crucial for developing effective disease-prevention strategies. This study aimed to identify risk factors and create a spatial model for under-five pneumonia distribution based on regional vulnerability.
Materials and methods: This study used a mixed-method approach that integrated mathematical models and GIS to identify risk factors using generalized Poisson regression (GPR) and developed a GIS-based spatial model with inverse distance weighted (IDW) and natural break methods.
Results: The GPR model revealed significant associations between under-five pneumonia and population density (β = 0.004, Z-score = 6.118), rainfall (β = 0.002, Z-score = 6.031), malnutrition (β = 1.786, Z-score = 3.696), and health facilities (β = 0.073, Z-score = 13.527). Protective factors included exclusive breastfeeding (β = -0.004, Z-score = -2.874), healthy homes (β = -0.021, Z-score = -9.532), and under-five health service coverage (β = -0.003, Z-score = -2.225). Spatial modeling classified regions into high-risk (5 subdistricts), medium-risk (11 subdistricts), and low-risk (3 subdistricts).
Conclusion: This study identified key risk factors and mapped spatial vulnerability for under-five pneumonia. Targeted, integrated interventions in high-risk areas are essential to reduce pneumonia incidence below 12 cases per 1,000 children under five by 2030, aligning with global health goals.
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