Evidence map›Paper›PMID 33732676›Full record

ArticleFrontiers in public health2021

Curse or Blessing? Obesity and Income-Related Inequality in the Chinese Labor Force.

Chengxiang Tang, Xiaocong Yang, Fei Peng, Xianglian Hu

Open access · goldAbstract read
In one paragraph

Article in Frontiers in public health, 2021. 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
0.2field-weighted citation impact, top 40% 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. 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 at 3 institutions in 1 country.

Chengxiang TangDepartment of Government Administration, School of Public Administration, Guangzhou University, Guangzhou, China.
Xiaocong YangDepartment of Government Administration, School of Public Administration, Guangzhou University, Guangzhou, China.
Fei PengDepartment of Economics, School of International Economics and Trade, Shanghai Lixin University of Accounting and Finance, Shanghai, China.
Xianglian HuCenter for Chinese Public Administration Research, School of Government, Sun Yat-sen University, Guangzhou, China.
Guangzhou University · CNShanghai Lixin University of Accounting and Finance · CNSun Yat-sen University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

China owns a huge labor force of around half billion workers in 2018. However, little is known about the prevalence of obesity and the association between obesity and economic status in this special population. By employing the concentration index (CI) and decomposition analysis, this paper addresses this knowledge gap by using the most recent nationally representative dataset. In specific, this study examines the prevalence of obesity and the socioeconomic gradient in the probability of obesity among Chinese workers between 16 and 65. Our results show that the prevalence of obesity is completely different by using a different measure: the overall prevalence of being general obesity (measured by body mass index, BMI ≥ 28) varies by gender and residency from a minimum of 5.88% to a maximum of 9.46%, whereas abdominal obesity (measured by waist circumference, WCmale ≥ 85 cm & WCfemale ≥ 80 cm) prevalence presents a socking level from 64.53% to 67.69%. Moreover, the results show a pro-rich distribution of obesity (general and abdominal) among male workers (CI

Indexed as

IncomeObesityChinaEmploymentFemaleHumansMaleSocioeconomic FactorsChineseincome-relatedinequalitylabor forceobesity

Identifiers

PMID33732676
PMCPMC7959819
OpenAlexW3134499889

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.