Incorporating productivity loss in health economic evaluations: a review of guidelines and practices worldwide for research agenda in China
Bibliographic record
Abstract
INTRODUCTION: Productivity loss may contribute to a large proportion of costs of health conditions in an economic evaluation from a societal perspective, but there is currently a lack of methodological consensus on how productivity loss should be measured and valued. Despite the research progress surrounding this issue in other countries, it has been rarely discussed in China. METHODS: We reviewed the official guidelines on economic evaluations in different countries and regions and screened the literature to summarise the extent to which productivity loss was incorporated in economic evaluations and the underlying methodological challenges. RESULTS: A total of 48 guidelines from 46 countries/regions were included. Although 32 (67%) guidelines recommend excluding productivity loss in the base case analysis, 23 (48%) guidelines recommend including productivity loss in the base case or additional analyses. Through a review of systematic reviews and the economic evaluation studies included in these reviews, we found that the average probability of incorporating productivity loss in an economic evaluation was 10.2%. Among the economic evaluations (n=478) that explicitly considered productivity loss, most (n=455) considered losses from paid work, while only a few studies (n=23) considered unpaid work losses. Recognising the existing methodological challenges and the specific context of China, we proposed a practical research agenda and a disease list for progress on this topic, including the development of the disease list comprehensively consisting of health conditions where the productivity loss should be incorporated into economic evaluations. CONCLUSION: An increasing number of guidelines recommend the inclusion of productivity loss in the base case or additional analyses of economic evaluation. We optimistically expect that more Chinese researchers notice the importance of incorporating productivity loss in economic evaluations and anticipate guidelines that may be suitable for Chinese practitioners and decision-makers that facilitate the advancement of research on productivity loss measurement and valuation.
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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 | 0.278 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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; both teacher heads agree on what is shown here.
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".