Evidence map›Paper›PMID 38694630›Full record

ArticleCureus2024

Spatial Clusters of High Prevalences of Overweight and Obesity Among Children in Indonesia.

Sofi Oktaviani, Mayumi Mizutani, Ritsuko Nishide, Susumu Tanimura

Abstract read
In one paragraph

Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Article
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

4 authors.

Sofi OktavianiDepartment of Public Health Nursing, Graduate School of Medicine, Mie University, Tsu, JPN.
Mayumi MizutaniDepartment of Integrated Health Sciences, Graduate School of Medicine, Nagoya University, Nagoya, JPN.
Ritsuko NishideDepartment of Nursing, Nagoya University of Arts and Sciences, Nagoya, JPN.
Susumu TanimuraDepartment of Public Health Nursing, Graduate School of Medicine, Mie University, Tsu, JPN.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective Childhood obesity has emerged as a pressing health concern in both high-income and lower-middle-income countries, including Indonesia. The prevalence of overweight and obesity among school children aged 5-12 years has been increasing in Indonesia, with 20% of Indonesian children identified as obese or overweight in 2018. Therefore, addressing this problem will be challenging. This study aims to identify district- and city-level clusters with high prevalences of overweight and obese children aged 5-12 years in Indonesia. Methodology This is an ecological study that utilizes secondary data from the 2018 Basic Health Research report conducted by the Indonesian Ministry of Health. We included 514 districts and cities to detect district- and city-level clusters. Spatial cluster analysis was performed using restricted flexible scan statistics to identify clusters with high prevalences of childhood overweight and obesity in Indonesian districts and cities. Results The findings reveal that childhood overweight and obesity are not randomly distributed. The study detected 20 clusters with high prevalences of childhood overweight and 36 clusters of obesity, with a particular concentration in Western Indonesia. A primary cluster of childhood overweight occurred in Sijunjung, Tanah Datar, Agam, Pasaman, South Solok, Dharmasraya, West Pasaman, Sawah Lunto City, Padang Panjang City, and Kampar. A primary cluster of obesity occurred in Mandailing Natal, South Tapanuli, Central Tapanuli, North Tapanuli, Labuhan Batu, North Padang Lawas, Padang Lawas, North Labuhan Batu, West Pasaman, and Rokan Hilir. Conclusions This study found 20 clusters with high prevalences of childhood overweight and 36 clusters of obesity in Indonesia. Implementing health promotion programs in the identified cluster regions will be crucial to effectively addressing the growing problem of childhood obesity in Indonesia.

Indexed as

childindonesiaobesityoverweightspatial clusters

Identifiers

PMID38694630
PMCPMC11061772

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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.