Productivity Losses Associated With Diabetes in the U.S.
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to estimate the cost of productivity losses in the U.S. attributable to diabetes, with regard to specific demographic and disease-related characteristics in the U.S.. RESEARCH DESIGN AND METHODS: We used the 1989 National Health Interview Survey, a random survey of individuals in the U.S. that included a diabetes supplement. Data on individuals were obtained for labor force participation, hours of work, demographic and occupational characteristics, self-reported health status, and several variables that indicated the presence, duration, and severity (complications) of diabetes. Using multivariate regression analyses, we estimated the association of independent variables (e.g., demographics, health, and diabetes status) with labor force participation, hours of work lost, and the economic value of lost work attributable to diabetes and its complications and duration. RESULTS: In general, the presence of diabetes and complications were found to be related to workforce participation variables. The magnitude of the lost-productivity costs depended on personal characteristics and on the presence and status of diabetes. In general, the loss of yearly earnings amounted to about a one-third reduction in earnings and ranged from $3,700 to $8,700 per annum. CONCLUSIONS: Diabetes has a considerable net effect on earnings, and the complications and duration of diabetes have compound effects. Our findings have implications for the cost-effectiveness of diabetes control; the presence of complicating factors is the single most important predictive factor in lost productivity costs attributable to diabetes, and thus the avoidance or retardation of complications will have an impact on indirect health-related costs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".