Suppr超能文献

使用地理加权多元泊松回归对南苏拉威西省的发育迟缓、消瘦和体重不足情况进行细分。

Segmentation of stunting, wasting, and underweight in Southeast Sulawesi using geographically weighted multivariate Poisson regression.

作者信息

Fadmi Fitri Rachmillah, Otok Bambang Widjanarko, Melaniani Soenarnatalina, Sriningsih Riry

机构信息

Doctoral Program of Public Health, Faculty of Public Health, Airlangga University, Surabaya, Indonesia.

Department of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia.

出版信息

MethodsX. 2024 Apr 29;12:102736. doi: 10.1016/j.mex.2024.102736. eCollection 2024 Jun.

Abstract

The health profile of Southeast Sulawesi Province in 2021 shows that the prevalence of stunting is 11.69 %, wasting 5.89 % and underweight 7.67 %. This relatively high figure should be immediately reduced to zero because it greatly affects the quality of human resources. Cases of stunting, wasting and underweight are an iceberg phenomenon, especially in Southeast Sulawesi. Therefore, it is necessary to research the number of cases of stunting, wasting and underweight in Southeast Sulawesi using GWMPR. The research results show that there is a trivariate correlation between the number of cases of stunting, wasting and underweight. The GWMPR model provides better results in modeling the number of stunting, wasting and underweight cases than the MPR model. The models produced for each sub-district are different from each other based on the predictor variables that have a significant effect and the estimated parameter values ​​for each sub-district. The segmentation of the number of stunting cases consists of 21 regional groups with 10 significant predictor variables, while the number of wasting cases consists of 10 regional groups with 9 significant predictor variables, while the number of underweight cases consists of 37 regional groups with 11 significant predictor variables. Therefore, policies on stunting, wasting, and underweight should be based on local conditions. 3 important components of this study: 1. GWMPR is the development of GWPR model when there are 2 or more response variables that are correlated. 2. GWMPR is a spatial model that considers geography. 3. Application of GWMPR to the analysis of the number of stunting, wasting, and underweight in Southeast Sulawesi province.

摘要

2021年东南苏拉威西省的健康状况表明,发育迟缓患病率为11.69%,消瘦率为5.89%,体重不足率为7.67%。这一相对较高的数字应立即降至零,因为它对人力资源质量有很大影响。发育迟缓、消瘦和体重不足病例是一种冰山现象,尤其是在东南苏拉威西。因此,有必要使用地理加权多响应模型(GWMPR)研究东南苏拉威西发育迟缓、消瘦和体重不足的病例数。研究结果表明,发育迟缓、消瘦和体重不足病例数之间存在三变量相关性。与多响应模型(MPR)相比,GWMPR模型在模拟发育迟缓、消瘦和体重不足病例数方面提供了更好的结果。根据具有显著影响的预测变量和每个分区的估计参数值,为每个分区生成的模型彼此不同。发育迟缓病例数的划分包括21个区域组,有10个显著预测变量,而消瘦病例数包括10个区域组,有9个显著预测变量,体重不足病例数包括37个区域组,有11个显著预测变量。因此,关于发育迟缓、消瘦和体重不足的政策应基于当地情况。本研究的3个重要组成部分:1. GWMPR是当存在2个或更多相关响应变量时GWPR模型的发展。2. GWMPR是一个考虑地理因素的空间模型。3. GWMPR在东南苏拉威西省发育迟缓、消瘦和体重不足病例数分析中的应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d494/11109871/4bebea9ddd7d/ga1.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验