Comparisons of disease cluster patterns, prevalence and health factors in the USA, Canada, England and Ireland
Bibliographic record
Abstract
BACKGROUND: Identification of those who are most at risk of developing specific patterns of disease across different populations is required for directing public health policy. Here, we contrast prevalence and patterns of cross-national disease incidence, co-occurrence and related risk factors across population samples from the U.S., Canada, England and Ireland. METHODS: Participants (n = 62,111) were drawn from the US Health and Retirement Study (n = 10,858); the Canadian Longitudinal Study on Ageing (n = 36,647); the English Longitudinal Study of Ageing (n = 7938) and The Irish Longitudinal Study on Ageing (n = 6668). Self-reported lifetime prevalence of 10 medical conditions, predominant clusters of multimorbidity and their specific risk factors were compared across countries using latent class analysis. RESULTS: The U.S. had significantly higher prevalence of multimorbid disease patterns and nearly all diseases when compared to the three other countries, even after adjusting for age, sex, BMI, income, employment status, education, alcohol consumption and smoking history. For the U.S. the most at-risk group were younger on average compared to Canada, England and Ireland. Socioeconomic gradients for specific disease combinations were more pronounced for the U.S., Canada and England than they were for Ireland. The rates of obesity trends over the last 50 years align with the prevalence of eight of the 10 diseases examined. While patterns of disease clusters and the risk factors related to each of the disease clusters were similar, the probabilities of the diseases within each cluster differed across countries. CONCLUSIONS: This information can be used to better understand the complex nature of multimorbidity and identify appropriate prevention and management strategies for treating multimorbidity across countries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".