Estimating the changing burden of disease attributable to alcohol use in South Africa for 2000, 2006 and 2012
Bibliographic record
Abstract
BACKGROUND: Alcohol use was one of the leading contributors to South Africa (SA)'s disease burden in 2000, accounting for 7% of deaths and disability-adjusted life years (DALYs) in the first South African Comparative Risk Assessment Study (SACRA1). Since then, patterns of alcohol use have changed, as has the epidemiological evidence pertaining to the role of alcohol as a risk factor for infectious diseases, most notably HIV/AIDS and tuberculosis (TB). OBJECTIVES: To estimate the burden of disease attributable to alcohol use by sex and age group in SA in 2000, 2006 and 2012. METHODS: The analysis follows the World Health Organization (WHO)'s comparative risk assessment methodology. Population attributable fractions (PAFs) were calculated from modelled exposure estimated from a systematic assessment and synthesis of 17 nationally representative surveys and relative risks based on the global review by the International Model of Alcohol Harms and Policies. PAFs were applied to the burden of disease estimates from the revised second South African National Burden of Disease Study (SANBD2) to calculate the alcohol-attributable burden for deaths and DALYs for 2000, 2006 and 2012. We quantified the uncertainty by observing the posterior distribution of the estimated prevalence of drinkers and mean use among adult drinkers (≥15 years old) in a Bayesian model. We assumed no uncertainty in the outcome measures. RESULTS: The alcohol-attributable disease burden decreased from 2000 to 2012 after peaking in 2006, owing to shifts in the disease burden, particularly infectious disease and injuries, and changes in drinking patterns. In 2012, alcohol-attributable harm accounted for an estimated 7.1% (95% uncertainty interval (UI) 6.6 - 7.6) of all deaths and 5.6% (95% UI 5.3 - 6.0) of all DALYs. Attributable deaths were split three ways fairly evenly across major disease categories: infectious diseases (36.4%), non-communicable diseases (32.4%) and injuries (31.2%). Top rankings for alcohol-attributable DALYs for specific causes were TB (22.6%), HIV/AIDS (16.0%), road traffic injuries (15.9%), interpersonal violence (12.8%), cardiovascular disease (11.1%), cancer and cirrhosis (both 4%). Alcohol remains an important contributor to the overall disease burden, ranking fifth in terms of deaths and DALYs. CONCLUSION: Although reducing overall alcohol use will decrease the burden of disease at a societal level, alcohol harm reduction strategies in SA should prioritise evidence-based interventions to change drinking patterns. Frequent heavy episodic (i.e. binge) drinking accounts for the unusually large share of injuries and infectious diseases in the alcohol-attributable burden of disease profile. Interventions should focus on the distal causes of heavy drinking by focusing on strategies recommended by the WHO's SAFER initiative.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".