Exploring the labour market outcomes of the risk factors for non-communicable diseases: A systematic review
Bibliographic record
Abstract
Objective: To look at the associations between labour market outcomes and major risk factors for non-communicable diseases (NCDs) (smoking, heavy alcohol consumption), key metabolic changes resultant of the risk factors (overweight and obesity, hypertension, type 2 diabetes), and major depressive disorder, and examine any gender differences. Design: Systematic review of cohort and longitudinal studies, to establish causality between exposures and outcomes. Methods: A systematic literature search was conducted in MEDLINE (Ovid), Embase (Ovid), EconLit (EBSCO), EconPapers, and Cochrane Database of Systematic Reviews, from inception to July 2022 for all peer-reviewed literature published, guided by the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) framework. Results: 109 studies were eligible for this review. All studies were published in English. 96% of the studies were conducted in high-income countries with 63% from Europe and Central Asia. High BMI was the most frequently reported exposure (reported by 46% of the studies), while income was the most studied outcome (reported by 33% of studies). Though not all estimates presented in the literature can be interpreted as causal impacts, 77% of the studies reported significant (p < 0.05) adverse associations between the exposures and outcomes. Conclusions: All of the studies included in this review that looked at plausible causal relationships between NCD risk factors and labour market outcomes were from high-income and upper-middle-income countries (USA, northern European countries, and South Korea). Based on these studies, we found that individuals with overweight or obesity, diabetes, hypertension, depressive disorders, excessive alcohol use, and cigarette use are more likely to have lower rates of employment, lower income, and higher rates of sickness absence and disability pension.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".