Disease Incidence and Mortality Among Older Americans and Europeans
Bibliographic record
Abstract
Recent research has shown a widening gap in life expectancy at age 50 between the United States and Europe as well as large differences in the prevalence of diseases at older ages. Little is known about the processes determining international differences in the prevalence of chronic diseases. Higher prevalence of disease could result from either higher incidence or longer disease-specific survival. This article uses comparable longitudinal data from 2004 and 2006 for populations aged 50 to 79 from the United States and from a selected group of European countries to examine age-specific differences in prevalence and incidence of heart disease, stroke, lung disease, diabetes, hypertension, and cancer as well as mortality associated with each disease. Not surprisingly, we find that Americans have higher disease prevalence. For heart disease, diabetes, and cancer, incidence is lower in Europe when we control for sociodemographic and health behavior differences in risk, and these differences explain much of the prevalence gap at older ages. On the other hand, incidence is higher in Europe for lung disease and not different between Europe and the United States for hypertension and stroke. Our findings do not suggest a survival advantage conditional on disease in Europe compared with the United States. Therefore, the origin of the higher disease prevalence at older ages in the United States is to be found in higher prevalence earlier in the life course and, for some conditions, higher incidence between ages 50 and 79.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".