Evidence map›Paper›PMID 38779443›Full record

ArticleMethodsX2024

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

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

Abstract read
In one paragraph

Article in MethodsX, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

5 authors.

Fitri Rachmillah FadmiDoctoral Program of Public Health, Faculty of Public Health, Airlangga University, Surabaya, Indonesia.
Bambang Widjanarko OtokDepartment of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia.
KuntoroProgram of Public Health, Faculty of Public Health, Airlangga University, Surabaya, Indonesia.
Soenarnatalina MelanianiProgram of Public Health, Faculty of Public Health, Airlangga University, Surabaya, Indonesia.
Riry SriningsihDepartment of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Negeri Padang, West Sumatera, Indonesia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Geographically Weighted Multivariate Poisson Regression (GWMPR)GWMPRSoutheast SulawesiStuntingUnderweightWasting

Identifiers

PMID38779443
PMCPMC11109871

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