Evidence map›Paper›PMID 25885218›Full record

ArticleBMC public health2015

Common risk factors for chronic non-communicable diseases among older adults in China, Ghana, Mexico, India, Russia and South Africa: the study on global AGEing and adult health (SAGE) wave 1.

Fan Wu, Yanfei Guo, Somnath Chatterji, Yang Zheng, Nirmala Naidoo, Yong Jiang, Richard Biritwum, Alfred Yawson, Nadia Minicuci, Aaron Salinas-Rodriguez and 8 more

Abstract read
In one paragraph

Article in BMC public health, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 92 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
92citing papers in PubMed, 3 pooled it
–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

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

92 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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32 more citing papers are in PubMed but not listed here.

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

18 authors.

Fan WuShanghai Municipal Centre for Disease Control (Shanghai CDC), 1380 Zhongshan Rd (W), Shanghai, 200336, P.R. China. wufan@scdc.sh.cn.
Yanfei GuoShanghai Municipal Centre for Disease Control (Shanghai CDC), 1380 Zhongshan Rd (W), Shanghai, 200336, P.R. China. guoyanfei@scdc.sh.cn.
Somnath ChatterjiWorld Health Organization, HIS/HSI/MCS, Geneva, Switzerland. chatterjis@who.int.
Yang ZhengShanghai Municipal Centre for Disease Control (Shanghai CDC), 1380 Zhongshan Rd (W), Shanghai, 200336, P.R. China. zhengyang@scdc.sh.cn.
Nirmala NaidooWorld Health Organization, HIS/HSI/MCS, Geneva, Switzerland. naidoon@who.int.
Yong JiangNational Center for Chronic and Noncommunicable Disease Control and Prevention (NCNCD), Chinese Center for Disease Control and Prevention (China CDC), Beijing, P.R. China. jy78@vip.sina.com.
Richard BiritwumDepartment of Community Health, University of Ghana Medical School, Accra, Ghana. biritwum@africaonline.com.gh.
Alfred YawsonDepartment of Community Health, University of Ghana Medical School, Accra, Ghana. aeyawson@yahoo.com.
Nadia MinicuciDepartment of Community Health, University of Ghana Medical School, Accra, Ghana. nadia.minicuci@unipd.it.
Aaron Salinas-RodriguezNational Institute of Public Health, Mexico City, Mexico. asalinas@insp.mx.
Betty Manrique-EspinozaNational Institute of Public Health, Mexico City, Mexico. bmanrique@insp.mx.
Tamara MaximovaRussian Academy of Medical Sciences, Moscow, Russian Federation. tmaximova@mail.ru.
Karl PeltzerUniversity of Limpopo, Turfloop, South Africa. karl.pel@mahidol.ac.th.
Nancy PhaswanamafuyaHuman Sciences Research Council, Port Elizabeth/Pretoria, South Africa. NPhaswanamafuya@hsrc.ac.za.
James J SnodgrassDepartment of Anthropology, University of Oregon, Eugene, Oregon, USA. jjosh@uoregon.edu.
Elizabeth Thiele, Atlanta, GA, USA. lizthiele747@gmail.com.
Nawi NgEpidemiology and Global Health, Umeå University, Umeå, Sweden. Nawi.Ng@epiph.umu.se.
Paul KowalWorld Health Organization, HIS/HSI/MCS, Geneva, Switzerland. paul.r.kowal@gmail.com.

Funding

Towards improving the measurement of health and disability trends, the compressioR01AG034479 · NIA · WORLD HEALTH ORGANIZATION · PI CHATTERJI, SOMNATH · 2010 to 2014
$5.3M
NIA NIH HHS R01 AG034479NIA NIH HHS R01-AG034479World Health Organization 001
6 · The paper itself

Abstract

backgroundBehavioral risk factors such as tobacco use, unhealthy diet, insufficient physical activity and the harmful use of alcohol are known and modifiable contributors to a number of NCDs and health mediators. The purpose of this paper is to describe the distribution of main risk factors for NCDs by socioeconomic status (SES) among adults aged 50 years and older within a country and compare these risk factors across six lower- and upper-middle income countries.

methodsThe study population in this paper draw from SAGE Wave 1 and consisted of adults aged 50-plus from China (N=13,157), Ghana (N=4,305), India (N=6,560), Mexico (N=2,318), the Russian Federation (N=3,938) and South Africa (N=3,836). Seven main common risk factors for NCDs were identified: daily tobacco use, frequent heavy drinking, low level physical activity, insufficient vegetable and fruit intake, high risk waist-hip ratio, obesity and hypertension. Multiple risk factors were also calculated by summing all these risk factors.

resultsThe prevalence of daily tobacco use ranged from 7.7% (Ghana) to 46.9% (India), frequent heavy drinker was the highest in China (6.3%) and lowest in India (0.2%), and the highest prevalence of low physical activity was in South Africa (59.7%). The highest prevalence of respondents with high waist-to-hip ratio risk was 84.5% in Mexico, and the prevalence of self-reported hypertension ranging from 33% (India) to 78% (South Africa). Obesity was more common in South Africa, the Russia Federation and Mexico (45.2%, 36% and 28.6%, respectively) compared with China, India and Ghana (15.3%, 9.7% and 6.4%, respectively). China, Ghana and India had a higher prevalence of respondents with multiple risk factors than Mexico, the Russia Federation and South Africa. The occurrence of three and four risk factors was more prevalent in Mexico, the Russia Federation and South Africa.

conclusionThere were substantial variations across countries and settings, even between upper-middle income countries and lower-middle income countries. The baseline information on the magnitude of the problem of risk factors provided by this study can help countries and health policymakers to set up interventions addressing the global non-communicable disease epidemic.

Indexed as

Health BehaviorAgedAged, 80 and overAgingAlcoholismBody Weights and MeasuresChinaChronic DiseaseDietExerciseGhanaHumansIndiaMaleMexicoMiddle Aged

Identifiers

PMID25885218
PMCPMC4335695

What OpenQuestion holds

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LicenceCC BY
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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.