The burden of morbidity, productivity and earnings
Bibliographic record
Abstract
People of working age affected by a severe health condition earn less than they would do otherwise. They work fewer hours a week, or fewer weeks a year, or have to make do with lower hourly wages. This paper focuses on the relation between the degree of severity of a health condition and the degree to which this has a depressive effect on earnings. The authors construct a measure for the overall state of health of an individual by looking at the intensity with which the individual interacts with the health care system. This includes the number of visits to general practitioners or specialists, the number of prescriptions filled, the duration of hospital admissions, the days of leave of absence as prescribed by general practitioners. To do so, the paper makes use of data derived from health and employment records of individuals (N = 185,761) having continuously kept residence in Lower Austria from 2006 to 2016 and have participated in labour market activities each year. The HCI-Index (Health Care Interaction Index) derived from the intensity of interaction with the health service system is a measure for the severity of the health condition. It ranges from 0 to 600 among the individuals of the population, with a high concentration between 0 and 10, i.e. little burden of morbidity. About a quarter of the population scores index values of 20 and more. The index scores are used to augment a standard earnings equation. This yields the following results: About half of the population is only burdened with health conditions of a very common kind (HCI score below 10) that hardly depress their annual earnings; a quarter of the population incurs losses between EUR 827 and EUR 1572; a quarter of the population of more than EUR 1572.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".