Economic Evaluations of Occupational Health Interventions from a Company’s Perspective: A Systematic Review of Methods to Estimate the Cost of Health-Related Productivity Loss
Bibliographic record
Abstract
OBJECTIVES: To investigate the methods used to estimate the indirect costs of health-related productivity in economic evaluations from a company's perspective. METHODS: The primary literature search was conducted in Medline and Embase. Supplemental searches were conducted in the Cochrane NHS Economic Evaluation Database, the National Institute for Occupational Safety and Health database, the Ryerson International Labour, Occupational Safety and Health Index database, scans of reference lists and researcher's own literature database. Article selection was conducted independently by two researchers based on title, keywords, and abstract, and if needed, full text. Differences were resolved by a consensus procedure. Articles were selected based on seven criteria addressing study population, type of intervention, comparative intervention, outcome, costs, language and perspective, respectively. Characteristics of the measurement and valuation of health-related productivity were extracted and analyzed descriptively. RESULTS: A total of 34 studies were included. Costs of health-related productivity were estimated using (a combination of) data related to sick leave, compensated sick leave, light or modified duty or work presenteeism. Data were collected from different sources (e.g. administrative databases, worker self-report, supervisors) and by different methods (e.g. questionnaires, interviews). Valuation varied in terms of reported time units, composition and source of the corresponding price weights, and whether additional elements, such as replacement costs, were included. CONCLUSIONS: Methods for measuring and valuing health-related productivity vary widely, hindering comparability of results and decision-making. We provide suggestions for improvement.
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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 | 0.062 | 0.238 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.016 |
| Bibliometrics | 0.022 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".