Exploring potential underestimates in fatal overdose mortality across major metropolitan areas in Mexico between 2005 and 2019
Bibliographic record
Abstract
BACKGROUND: In Mexico, low numbers of overdose deaths reported by government agencies, coupled with evidence of increased substance use in certain regions, suggest underreporting of overdose mortality. We explore the potential miscoding and underreporting of overdose deaths. METHODS: We assembled a mortality dataset using a combination of publicly available data and systematic freedom of information requests to Mexican state jurisdictions. Using data from post-mortem toxicology analyses, we identified proxy International Classification of Diseases (ICD)-10 codes for causes of death that were significantly more common (using p < 0.05 as a threshold) in decedents with positive toxicology for opioids, methamphetamine, cocaine, or benzodiazepines compared to decedents with negative toxicology for these substances. We then estimated annual crude overdose mortality rates, for individuals aged 20 to 39 with both standard overdose-specific codes (i.e., X40-44, X60-64, X85, and Y60-64), and derived proxy ICD-10 codes. RESULTS: We identified acute myocardial infarction (I21), acute pulmonary edema (J81), and acute respiratory failure (J96) as proxy ICD-10 codes for potential overdose deaths. Using these codes, the estimated crude overdose mortality rate per 100,000 aged 20 to 39 in Mexico increased from 4.14 in 2005 to 6.92 in 2019, compared to estimates of 0.31 to 0.37 using standard overdose codes for the same period. CONCLUSIONS: Our findings suggest a potential underestimate of overdose deaths due to possible misclassification and underscore the critical need for improved forensic capabilities to more accurately identify overdose deaths.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".