How to quantify deaths averted derived from interrupted time-series analyses
Bibliographic record
Abstract
Abstract Background Interrupted time series (ITS) are an important tool for determining whether alcohol control policies, as well as other policy interventions, are successful over and above secular trends or chance. Subsequent to estimating whether a policy has had an effect, quantifying the key outcomes, such as the number of prevented deaths, is of primary practical importance. The current paper compares the results of two different methodological approaches to quantify deaths averted using different two standard populations. Methods Time series methodologies were used to estimate the effect size in deaths averted of a substantial increase in excise taxation in Lithuania in 2017. We compare the impact of a) using ITS methodology vs. fitting the trend before the intervention to predict the following 12 months and comparing the predicted monthly estimates of deaths with the actual numbers; and b) adjusting the time series either using the World Health Organization standard or the age distribution of Lithuania in the month before the intervention. The effect was estimated by sex. Results The increase in excise taxation was associated with a substantial decrease in all-cause mortality in all models considered. ITS methodology and using the age-distribution of Lithuania were consistently associated with higher estimates of deaths averted. Although confidence and prediction intervals were highly overlapping, the point estimates differed substantially. The taxation increase was associated with 1,155 deaths averted in the year following the intervention (95% prediction interval: 729, 1,582), corresponding to 2.80% of all deaths in Lithuania in the respective year, for the model selected as best for planning policy interventions in Lithuania. Conclusions Fitting a time series model for the time until the intervention, and then comparing the predicted time points with the actual mortality, standardizing to country-specific weights, was chosen as the best way to derive practically relevant effect sizes.
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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 | 0.066 | 0.235 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".