Evidence map›Paper›PMID 38416735›Full record

ArticlePloS one2024

Spatial regression models to assess variations of composite index for anthropometric failure across the administrative zones in Ethiopia.

Haile Mekonnen Fenta, Temesgen Zewotir, Essey Kebede Muluneh

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Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.9field-weighted citation impact, top 30% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 3 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 2 countries.

Haile Mekonnen FentaDepartment of Statistics, College of Science, Bahir Dar University, Bahir Dar, Ethiopia.ORCID 0000-0002-3919-2762
Temesgen ZewotirSchool of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban, South Africa.
Essey Kebede MulunehSchool of public health, Bahir Dar University, Bahir Dar, Ethiopia.
Bahir Dar University · ETUniversity of KwaZulu-Natal · ZA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThere are a number of previous studies that investigated undernutrition and its determinants in Ethiopia. However, the national average in the level of undernutrition conceals large variation across administrative zones of Ethiopia. Hence, this study aimed to determine the geographic distribution of composite index for anthropometric failure (CIAF) and identify the influencing factors it' might be more appropriate.

methodsWe used the zonal-level undernutrition data for the under-five children in Ethiopia from the Ethiopian Demographic and Health Survey (EDHS) dataset. Different spatial models were applied to explore the spatial distribution of the CIAF and the covariates.

resultsThe Univariate Moran's I statistics for CIAF showed spatial heterogeneity of undernutrition in Ethiopian administrative zones. The spatial autocorrelation model (SAC) was the best fit based on the AIC criteria. Results from the SAC model suggested that the CIAF was positively associated with mothers' illiteracy rate (0.61, pvalue 0.001), lower body mass index (0.92, pvalue = 0.023), and maximum temperature (0.2, pvalue = 0.0231) respectively. However, the CIAF was negatively associated with children without any comorbidity (-0.82, pvalue = 0.023), from families with accessibility of improved drinking water (-0.26, pvalue = 0.012), and minimum temperature (-0.16).

conclusionThe CIAF across the administrative zones of Ethiopia is spatially clustered. Improving women's education, improving drinking water, and improving child breast feeding can reduce the prevalence of undernutrition (CIAF) across Ethiopian administrative zones. Moreover, targeted intervention in the geographical hotspots of CIAF can reduce the burden of CIAF across the administrative zones.

Indexed as

Drinking WaterMalnutritionChildEthiopiaFemaleHumansMothersSpatial AnalysisSpatial RegressionDrinking Water

Identifiers

PMID38416735
PMCPMC10901317
OpenAlexW4392230920

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.