Drink like a man? Modified Poisson analysis of adolescent binge drinking in the US, 1976–2022
Bibliographic record
Abstract
This study estimates temporal trajectories and sociodemographic disparities in underage adolescent binge drinking in the United States over the past four decades. By compiling 47 waves of national representative data from the Monitoring the Future (MTF) study between 1976 and 2022, we analyzed two types of adolescent binge drinking behaviors, past-two-week excessive drinking and drunkenness in the past 30 days, using the innovative modified Poisson (mixture) approach to grouped and right-censored counts (GRC). The overall decrease in incidence rates was attributable to substantial reductions in the risks of excessive drinking (45.77% in 1980 and 12.62% in 2022) and drunkenness (35.12% in 1998 and 14.81% in 2022). However, at-risk adolescents only showed mild reductions in incidence rates over time. While males consistently drank more often and were at a higher risk of binge drinking and drunkenness than females, the sex disparities tended to converge over time. The modified Poisson approach is a useful tool to estimate incidence, risk, and at-risk incidence in epidemiological studies with GRC counts. The alarming high incidence rates of at-risk adolescents, especially males, warrant further investigation. • Modified Poisson regression directly models grouped and right-censored counts. • Incidence of adolescent binge drinking in the United States shows an overall decline. • The decline is attributable to less adolescents at risk of binge drinking. • At-risk adolescents only showed mild reductions in incidence rates over time. • Salient sex disparities tended to converge over time.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".