Evidence map›Paper›PMID 31444183›Full record

ArticleBMJ open2019

An exploratory spatial analysis of overweight and obesity among children and adolescents in Shandong, China.

Wenzhe Qin, Lu Wang, Lingzhong Xu, Long Sun, Jiajia Li, Jiao Zhang, Hui Shao

Abstract read
In one paragraph

Article in BMJ open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

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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

7 authors.

Wenzhe Qin *School of Public Health, Shandong University, Jinan, China.
Lu Wang *Shandong Provincial Medical Examination Office, Shandong Province Health and Family Planning Commission, Jinan, China.
Lingzhong XuSchool of Public Health, Shandong University, Jinan, China lzxu@sdu.edu.cn.
Long SunSchool of Public Health, Shandong University, Jinan, China.ORCID 0000-0002-3070-8427
Jiajia LiSchool of Public Health, Shandong University, Jinan, China.
Jiao ZhangSchool of Public Health, Shandong University, Jinan, China.
Hui ShaoDepartment of Global Health Systems and Development School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveIdentifying the spatial patterns of childhood overweight/obesity (OW/OB) can help to guide resource allocation for preventive intervention in China. This study aims to estimate rates of childhood OW/OB across counties within Shandong Province, using geographic techniques to identify sex-specific spatial patterns of childhood OW/OB as well as the presence of spatial clusters.

designCross-sectional study.

settingShandong Province in China. PARTICIPANTS AND

methodsData on 6 216 076 children and adolescents aged 7-18 years from the Primary and Secondary Schoolchildren Physical Examination Database for Shandong Province were used in this study. Spatial patterns of sex-specific prevalence of childhood OW/OB were mapped. Global autocorrelation statistic (Moran's

resultsThe overall prevalence of childhood OW/OB in Shandong province were 15.05% and 9.23%, respectively. Maps of the sex-specific prevalence of OW/OB demonstrate a marked geographical variation of childhood OW/OB in different regions. Prevalence of childhood OW/OB had a significant positive spatial autocorrelation among both boys and girls. LISA analysis identified significant clusters (or 'hot spots') of childhood OW/OB in the eastern coastal region, central region and southwestern region.

conclusionsThe prevalence of childhood OW/OB is highly spatially clustered. Geographically focused appropriate intervention should be introduced in current childhood OW/OB prevention and control strategy.

Indexed as

AdolescentChildChinaCross-Sectional StudiesFemaleHumansMaleOverweightPediatric ObesityPrevalenceSpatial Analysischild and adolescentgeographic locationobesityoverweightspatial analysis

Identifiers

PMID31444183
PMCPMC6707763

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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.