Improving alcohol management in primary health care in Mexico: A return‐on‐investment analysis
Bibliographic record
Abstract
INTRODUCTION: Alcohol screening, brief advice and referral to treatment (SBIRT) in primary health care is an effective strategy to decrease alcohol consumption at population level. However, there is relatively scarce evidence regarding its economic returns in non-high-income countries. The current paper aims to estimate the return-on-investment of implementing a SBIRT program in Mexican primary health-care settings. METHODS: Empirical data was collected in a quasi-experimental study, from 17 primary health-care centres in Mexico City regarding alcohol screening delivered by 145 health-care providers. This data was combined with data from a simulation study for a period of 10 years (2008 to 2017). Economic investments were calculated from a public sector health-care perspective as clinical consultation costs (salary and material costs) and program costs (set-up, adaptation, implementation strategies). Economic return was calculated as monetary gains in the public sector health-care, estimated via simulated reductions in alcohol consumption, dependent on population coverage of alcohol interventions delivered to primary health-care patients. RESULTS: Results showed that scaling up a SBIRT program in Mexico over a 10-year period would lead to positive return-on-investment values ranging between 21% in scenario 4 (confidence interval -8.6%, 79.5%) and 110% in scenario 5 (confidence interval 51.5%, 239.8%). Moreover, over the 10-year period, up to 16,000 alcohol-related deaths could be avoided as a result of implementing the program. DISCUSSION AND CONCLUSIONS: SBIRT implemented at national level in Mexico may lead to substantial financial gains from a public sector health-care perspective.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".