The relationship between chronic conditions and absenteeism and associated costs in Canada
Bibliographic record
Abstract
OBJECTIVES: This study aimed to measure and compare the relationship between chronic diseases and the number of absent workdays due to health problems and the associated costs among working Canadians. METHODS: The study sample included respondents to the 2010 Canadian Community Health Survey between aged 15-75 years who reported employment in the past three months. Respondents reported their number of absent workdays due to health problems and chronic conditions. A negative binomial regression was used to estimate the incremental absent workdays associated with having a particular chronic condition (of 16 conditions), conditional on other chronic conditions and confounders. For each condition, we calculated the incremental number of absent workdays, the incremental productivity loss attributed to absenteeism per employee, and the overall productivity loss in the population. RESULTS: The final sample consisted of 28 678 respondents representing 15 468 788 employed Canadians. The average number of absent workdays due to health problems was 1.35 days over a 3-month period. The three conditions with the greatest association with absent workdays were mood disorders, heart disease, and bowel disorders. They were associated with 1.17, 0.81, and 0.80 additional absent workdays, respectively, compared to workers without this condition, holding other conditions and confounders at their means. At the national working population level, back problems (CAD$621 million), mood disorders (CAD$299 million) and migraine (CAD$245 million) accounted for the largest incremental productivity loss. CONCLUSIONS: Chronic conditions, especially mood disorders and back problems, are associated with substantial work productivity loss. The study findings can help policy-makers and employers prioritize their programs and resources aimed at reducing absenteeism among the working population with chronic conditions.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".