Impact of pre-existing chronic conditions on age differences in sickness absence after a musculoskeletal work injury: A path analysis approach
Bibliographic record
Abstract
OBJECTIVES: This study aims to examine the extent to which a greater prevalence of pre-existing chronic conditions among older workers explains why older age is associated with longer duration of sickness absence (SA) following a musculoskeletal work-related injury in British Columbia. METHODS: A secondary analysis of workers' compensation claims in British Columbia over three time periods (1997-1998; 2001-2002, and 2005-2006), the study comprised 102 997 and 53 882 claims among men and women, respectively. Path models examined the relationships between age and days of absence and the relative contribution of eight different pre-existing chronic conditions (osteoarthritis, rheumatoid arthritis, hypertension, coronary heart disease, diabetes, thyroid conditions, hearing problems, and depression) to this relationship. Models were adjusted for individual, injury, occupational, and industrial covariates. RESULTS: The relationship between age and length of SA was stronger for men than women. A statistically significant indirect effect was present between older age, diabetes, and longer days of SA among both men and women. Indirect effects between age and days of SA were also present through osteoarthritis, among men but not women, and coronary heart disease, among women but not men. Depression was associated with longer duration of SA but was most prevalent among middle-aged claimants. Approximately 70-78% of the effect of age on days of SA remained unexplained after accounting for pre-existing conditions. CONCLUSIONS: Pre-existing chronic conditions, specifically diabetes, osteoarthritis and coronary heart disease, represent important factors that explain why older age is associated with more days of SA following a musculoskeletal injury. Given the increasing prevalence of chronic conditions among labor market participants (and subsequently injured workers) moderate reductions in age differences in SA could be gained by better understanding the mechanisms linking these conditions to longer durations of SA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".