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

被引:1
|
作者
Fadmi, Fitri Rachmillah [1 ]
Otok, Bambang Widjanarko [2 ]
Kuntoro [3 ]
Melaniani, Soenarnatalina [3 ]
Sriningsih, Riry [4 ]
机构
[1] Airlangga Univ, Fac Publ Hlth, Doctoral Program Publ Hlth, Surabaya, Indonesia
[2] Inst Teknol Sepuluh Nopember, Fac Sci & Data Analyt, Dept Stat, Surabaya 60111, Indonesia
[3] Airlangga Univ, Fac Publ Hlth, Program Publ Hlth, Surabaya, Indonesia
[4] Univ Negeri Padang, Fac Math & Nat Sci, Dept Math, West Sumatera, Indonesia
关键词
Stunting; Wasting; Underweight; Southeast Sulawesi; GWMPR;
D O I
10.1016/j.mex.2024.102736
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
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页数:21
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