Simulation of Alcohol Control Policies for Health Equity (SIMAH) Project: Study Design and First Results
Bibliographic record
Abstract
Since about 2010, life expectancy at birth in the United States has stagnated and begun to decline, with concurrent increases in the socioeconomic divide in life expectancy. The Simulation of Alcohol Control Policies for Health Equity (SIMAH) Project uses a novel microsimulation approach to investigate the extent to which alcohol use, socioeconomic status (SES), and race/ethnicity contribute to unequal developments in US life expectancy and how alcohol control interventions could reduce such inequalities. Representative, secondary data from several sources will be integrated into one coherent, dynamic microsimulation to model life-course changes in SES and alcohol use and cause-specific mortality attributable to alcohol use by SES, race/ethnicity, age, and sex. Markov models will be used to inform transition intensities between levels of SES and drinking patterns. The model will be used to compare a baseline scenario with multiple counterfactual intervention scenarios. The preliminary results indicate that the crucial microsimulation component provides a good fit to observed demographic changes in the population, providing a robust baseline model for further simulation work. By demonstrating the feasibility of this novel approach, the SIMAH Project promises to offer superior integration of relevant empirical evidence to inform public health policy for a more equitable future.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".