Reciprocal associations between depression, anxiety and work-related injury
Bibliographic record
Abstract
OBJECTIVE: To examine the reciprocal longitudinal associations between depression or anxiety with work-related injury (WRI) at a large employer in the southwestern United States. METHOD: Three administrative datasets (2011-2013) were merged: employee eligibility, medical and prescription claims, and workers' compensation claims. The sample contained 69 066 active employees. Depression and anxiety were defined as episodes of medical visits care (ie, claims) with corresponding ICD-9-CM codes. For an individual's consecutive claims, a new case of depression or anxiety was defined if more than 8 weeks have passed since the prior episode. The presence of a workers' compensation injury claim was used to identify WRI. Three-wave (health plan years 2011 or T1, 2012 or T2, and 2013 or T3) autoregressive cross-lagged models were used to estimate whether depression or anxiety predicted WRI, also if WRI predicted depression or anxiety in the following year(s). RESULTS: Depression predicted injury from T1 to T2 (β=0.127, p<0.001) and from T2 to T3 (β=0.092, p=0.001). Injury predicted depression from T1 to T3 (β=0.418, p<0.001). Effects of anxiety on WRI were small and inconsistent, from T1 to T2 (β=0.013, p=0.622) and from T2 to T3 (β=-0.043, p=0.031). T1 injury had a protective effect on T3 anxiety (β=-0.273, p<0.001). CONCLUSIONS: We found evidence of reciprocal effects for depression with WRI after adjustment for prior injuries and depression. The evidence for the relationship between anxiety and WRI is less clear. WRI prevention and management programmes should incorporate depression prevention and management.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".