Overweight and obesity in non-pregnant women of childbearing age in South Africa: subgroup regression analyses of survey data from 1998 to 2017
Bibliographic record
Abstract
BACKGROUND: Overweight and obesity in adults are increasing globally and in South Africa (SA), contributing substantially to deaths and disability from non-communicable diseases. Compared to men, women suffer a disproportionate burden of obesity, which adversely affects their health and that of their offspring. This study assessed the changing patterns in prevalence and determinants of overweight and obesity among non-pregnant women in SA aged 15 to 49 years (women of childbearing age (WCBA)) between 1998 and 2017. METHODS: This paper conducts secondary data analysis of seven consecutive nationally representative household surveys-the 1998 and 2016 SA Demographic and Health Surveys, 2008, 2010-2011, 2012, 2014-2015 and 2017 waves of the National Income Dynamics Survey, containing anthropometric and sociodemographic data. The changing patterns of the overweight and obesity prevalence were assessed across key variables. The inferential assessment was based on a standard t-test for the prevalence. Adjusted odds ratios from logistic regression analysis were used to examine the factors associated with overweight and obesity at each time point. RESULTS: Overweight and obesity prevalence among WCBA in SA increased from 51.3 to 60.0% and 24.7 to 35.2%, respectively, between 1998 and 2017. The urban-rural disparities in overweight and obesity decreased steadily between 1998 and 2017. The prevalence of overweight and obesity among WCBA varied by age, population group, location, current smoking status and socioeconomic status of women. For most women, the prevalence of overweight and/or obesity in 2017 was significantly higher than in 1998. Significant factors associated with being overweight and obese included increased age, self-identifying with the Black African population group, higher educational attainment, urban area residence, and wealthier socioeconomic quintiles. Smoking was inversely related to being overweight and obese. CONCLUSIONS: The increasing trend in overweight and obesity in WCBA in SA demands urgent public health attention. Increased public awareness is needed about obesity and its health consequences for this vulnerable population. Efforts are needed across different sectors to prevent excessive weight gain in WCBA, focusing on older women, self-identified Black African population group, women with higher educational attainment, women residing in urban areas, and wealthy women.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".