The relative impact of 13 chronic conditions across three different outcomes
Bibliographic record
Abstract
STUDY OBJECTIVE: Previous estimates of individual and population attributable risks for adverse outcomes due to chronic conditions have considered only a limited number of conditions and outcomes, with some studies using inappropriate formulae or methods of estimation. This study re-examines the magnitude of individual and population attributable risks for a wide range of conditions and various health outcomes. DESIGN: Log-Poisson regression was used to calculate prevalence ratios as an indicator of individual risk and population-associated fractions of 13 chronic conditions, examining activity limitations, self-rated health and physician visits. The effect of multimorbidity on prevalence ratios was examined. SETTING: Canada, 2000-01. PARTICIPANTS: Nationally representative sample of Canadians aged 12+ years (n _ 130 880). MAIN RESULTS: At the individual level, fibromyalgia/chronic fatigue syndrome and cancer, and to a lesser extent stroke and heart disease, were associated with an increased risk of both activity limitations and a self-rated health status of fair or poor; high blood pressure was associated with four or more physician visits in the previous 12 months. In contrast, population attributable fractions were substantial for arthritis/rheumatism, heart disease, back problems and high blood pressure across all outcomes. Adjustment for multimorbidity resulted in a marked decreases in prevalence ratios. CONCLUSIONS: Differences in the ranking of individual risks and population attributable fractions for different diseases and outcomes are substantial. This needs to be taken into account when setting priorities, as interventions may need to be targeted to different conditions depending on which aspects of health are being considered, and whether the focus is on individuals, such as in clinical care, or improving the health of the population.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".