How Much do Preexisting Chronic Conditions Contribute to Age Differences in Health Care Expenditures After a Work-related Musculoskeletal Injury?
Bibliographic record
Abstract
OBJECTIVES: To estimate the contribution of preexisting chronic conditions on age differences in health care expenditures for the management of work-related musculoskeletal injuries in British Columbia. METHODS: A secondary analysis of workers' compensation claims submitted over the 5-year period between January 1, 2002 and December 31, 2006 (N = 55,827 claims among men and 32,141 claims among women). Path models examined the relationships between age and health care expenditures, and the extent to which age differences in health care expenditures were mediated by preexisting chronic conditions. Models were adjusted for individual, injury, occupational, and industrial covariates. RESULTS: The relationship between age and health care expenditures differed for men and women, with a stronger age gradient observed among men. Preexisting osteoarthritis and coronary heart disease were associated with elevated health care expenditures among men and women. Diabetes was associated with elevated health care expenditures among men only, and depression was associated with elevated health care expenditures among women only. The percentage of the age effect on health care expenditures that was mediated through preexisting chronic conditions increased from 12.4% among 25-34-year-old men (compared with 15-24 y) to 26.6% among 55+-year-old men; and 14.6% among 25-34-year-old women to 35.9% among women 55 and older. CONCLUSIONS: The results of this study demonstrate that differences in preexisting chronic conditions have an impact on the relationship between older age and greater health care expenditures after a work-related musculoskeletal injury. The differing prevalence of preexisting osteoarthritis, coronary heart disease, and to a lesser extent diabetes (among men) and depression (among women) across age groups explain a nontrivial proportion of the age effect in health care expenditures after injury. However, approximately two thirds or more of the age effect in health care expenditures remains unexplained.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".