Evidence map›Paper›PMID 26617987›Full record

ArticleBMC obesity2015

Obesity trends and risk factors in the South African adult population.

Annibale Cois, Candy Day

Abstract read
In one paragraph

Article in BMC obesity, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
64citing papers in PubMed, 1 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

64 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. The Magnitude of NCD Risk Factors in Ethiopia: Meta-Analysis and Systematic Review of Evidence.International journal of environmental research and public health · 2022
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  12. The Impact of Human Immunodeficiency Virus and Menopause on Bone Mineral Density: A Longitudinal Study of Urban-Dwelling South African Women.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2023
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4 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

2 authors.

Annibale CoisDivision of Epidemiology and Biostatistics, School of Public Health and Family Medicine, University of Cape Town, Anzio Road, Observatory, Cape Town, 7925 South Africa ; Health Systems Trust, 34 Essex Terrace, Westville, Durban, 3630 South Africa.
Candy DayHealth Systems Trust, 34 Essex Terrace, Westville, Durban, 3630 South Africa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundObesity prevalence is increasing globally and contributes substantially to the burgeoning burden of non-communicable diseases. South Africa is particularly affected by this increasing trend and cross-sectional evidence suggests socioeconomic and behavioural variables as possible drivers. However, no large scale longitudinal study has attempted the direct identification of risk factors for progression towards obesity.

methodsThis study analysed data on 10,100 South African adults (18 years and over) randomly selected in 2008 and successfully recontacted in 2010 and 2012. Latent Growth Modelling was used to estimate the average rate of change in body mass index (BMI) during the study period, and to identify baseline characteristics associated with different trajectories.

resultsThe overall rate of change in BMI during the study period was +1.57 kg/m(2) per decade (95 % CI: 0.93 -2.22), and it was higher among women (+ 1.82 kg/m(2) per decade, 95 % CI: 1.06 -2.58) than among men (+ 1.03 kg/m(2) per decade; 95 % CI: 0.14 -1.93). Female gender, younger age, larger waist circumference, white population group and higher household income per capita were baseline characteristics associated with higher rates of change. The association between tobacco use and obesity was complex. Smoking was associated with greater waist circumference at baseline but lower rates of increase in BMI during the study period. Quitting smoking was an independent predictor of BMI increase among subjects with normal weight at baseline. Among subjects with baseline BMI lower than 25 kg/m(2), rates of changes were higher in rural than urban areas, and inversely related to the frequency of physical exercise.

conclusionsA strong positive trend in BMI remains in South Africa and obesity prevalence is likely to increase. Trends are not homogeneous, and high risk groups (subjects with high socioeconomic status, rural dwellers, young women) and modifiable risk factors (physical inactivity) can be targeted. Subjects quitting smoking should receive additional weight-loss support in order that the numerous health benefits of cessation are not reduced by increasing BMI. Centrally obese subjects should be targeted in campaigns.

Indexed as

Body mass indexLatent growth modellingObesitySub-saharan Africa

Identifiers

PMID26617987
PMCPMC4603579

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.