Determinants of Obesity and Associated Population Attributability, South Africa: Empirical Evidence from a National Panel Survey, 2008-2012
Bibliographic record
Abstract
BACKGROUND: Obesity is a major risk factor for emerging non-communicable diseases (NCDS) in middle income countries including South Africa (SA). Understanding the multiple and complex determinants of obesity and their true population attributable impact is critical for informing and developing effective prevention efforts using scientific based evidence. This study identified contextualised high impact factors associated with obesity in South Africa. METHODS: Analysis of three national cross sectional (repeated panel) surveys, using a multilevel logistic regression and population attributable fraction estimation allowed for identification of contextualised high impact factors associated with obesity (BMI>30 kg/m2) among adults (15 years+). RESULTS: Obesity prevalence increased significantly from 23.5% in 2008 to 27.2% in 2012, with a significantly (p-value<0.001) higher prevalence among females (37.9% in 2012) compared to males (13.3% in 2012). Living in formal urban areas, white ethnicity, being married, not exercising and/or in higher socio-economic category were significantly associated with male obesity. Females living in formal or informal urban areas, higher crime areas, African/White ethnicity, married, not exercising, in a higher socio-economic category and/or living in households with proportionate higher spending on food (and unhealthy food options) were significantly more likely to be obese. The identified determinants appeared to account for 75% and 43% of male and female obesity respectively. White males had the highest relative gain in obesity from 2008 to 2012. CONCLUSIONS: The rising prevalence of obesity in South Africa is significant and over the past 5 years the rising prevalence of Type-2 diabetes has mirrored this pattern, especially among females. Targeting young adolescent girls should be a priority. Addressing determinants of obesity will involve a multifaceted strategy and requires at individual and population levels. With rising costs in the private and public sector to combat obesity related NCDS, this analysis can inform culturally sensitive mass communications and wellness campaigns. Knowledge of social determinants is critical to develop "best buys".
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".