Abstract P115: Better Methodologies to Compare Coronary Heart Disease Mortality Trends in Latin American Countries: Argentina, Colombia and Mexico From 1985 to 2012
Bibliographic record
Abstract
Introduction: Coronary heart disease (CHD) is the leading cause of death worldwide. CHD mortality rates have decreased in most high-income countries, but increased in some Latin-American countries. CHD mortality rates have plateaued among young adults (<55 years) in the US, UK and Australia, but trends in Latin-America are largely unknown. Few studies have analyzed trends in age and sex-specific CHD mortality rates in this region, and almost none has assured data quality by correcting for garbage codes (ICD codes for nonspecific causes of death) as recommended by WHO. Objective: To describe and compare standardized, age-specific and garbage-code corrected mortality trends for coronary heart disease (CHD) from 1985 to 2012 in three contrasting Latin American countries. Methods: CHD deaths (1985-2012) in Argentina, Colombia and Mexico were grouped by calendar year, sex and 10-year age bands to calculate the overall age-adjusted and age and sex specific mortality rates for adults aged ≥25 years. We corrected for garbage-codes for the three countries (1997-2012) using the Global Burden of Disease (GBD) methodology. We then fitted Joinpoint regression models, using the original and garbage code corrected rates, to estimate the annual percent change (APC) and detect points in time when significant changes in the trends occurred. Results: In 2012, age-standardised mortality rates per 100,000 were 65.3 in Argentina, 132.4 in Colombia and 130.3 in Mexico. Compared to 1985, by 2012 mortality fell by 17.5% in Colombia and 52.5% in Argentina. The largest annual decreases in mortality rates were observed in Argentina from 1988-1994 (APC=-5.7 p-value<0.01). The declines in Colombia were constant and smaller for the full period (APC=-0.4 p-value<0.01). CHD mortality rose by 48.9% in Mexico, particularly after 2000. Mortality rates increased in both men and women, particularly in younger men (<39 years) and older women (>60 years). Application of the garbage code corrections produced dramatic increases in mortality rates, more in women than men, and particularly in Argentina: approximately 80 additional deaths per 100,000 (compared with just 14 additional deaths per 10 5 in Colombia and 13 per 10 5 in Mexico). Conclusions: Different Latin American countries demonstrate dramatically different CHD epidemiology. Mortality rates increased after correcting for garbage code misclassification. Although CHD mortality is falling in Argentina, the modest falls in Colombia and substantial rises in Mexico highlight the region’s urgent need for effective, population-wide prevention policies.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".