Developing a method to derive alcohol-attributable fractions for HIV/AIDS mortality based on alcohol's impact on adherence to antiretroviral medication
Bibliographic record
Abstract
BACKGROUND: Alcohol consumption is causally linked to nonadherence to antiretroviral treatment that in turn causes an increase in HIV/AIDS mortality. This article presents a method to calculate the percentage of HIV/AIDS deaths attributable to alcohol consumption and the associated uncertainty. METHODS: By combining information on risk relations from a number of published sources, we estimated alcohol-attributable fractions (AAFs) of HIV/AIDS in a stepwise procedure. First, we estimated the effect of alcohol consumption on adherence to antiretroviral treatment, and then we combined this estimate with the impact of nonadherence on death. The 95% uncertainty intervals were computed by estimating the variance of the AAFs using Taylor series expansions of one and multiple variables. AAFs were determined for each of the five Global Burden of Disease regions of Africa, based on country-specific treatment and alcohol consumption data from 2005. RESULTS: The effects of alcohol on HIV/AIDS in the African Global Burden of Disease regions range from 0.03% to 0.34% for men and from 0% to 0.17% for women, depending on region and age category. The detrimental effect of alcohol consumption was statistically significant in every region and age category except for the North Africa/Middle East region. CONCLUSIONS: Although the method has its limitations, it was shown to be feasible and provided estimates of the impact of alcohol use on the mortality outcome of HIV/AIDS.
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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 | 0.014 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".