Long-term local area employment rates as predictors of individual mortality and morbidity: a prospective study in England, spanning more than two decades
Bibliographic record
Abstract
BACKGROUND: Although long-term trends in local labour market conditions are likely to influence health, few studies have assessed whether this is so. This paper examines whether (1) trends in local employment rates have relevance for mortality and morbidity outcomes in England and (2) trends are stronger predictors of these outcomes than employment rates measured at one point in time. METHODS: Using latent class growth models, local areas were classified into eight groups following distinct trends in employment rates between 1981 and 2008. Areas were also categorised in 'octile' groups by rank of employment rates in 2001. These area groupings were linked to a sample of 207,959 individuals from the Office of National Statistics Longitudinal Study. Associations between area groupings and risk of all-cause mortality and of reporting a limiting long-term illness at the end of the period were measured using logistic regression. Models were adjusted for individuals' socio-demographic characteristics measured in 1981 and for their residential mobility between 1981 and 2001. RESULTS: Compared to areas with continuously high employment rates over the period, risk of mortality and morbidity was higher in areas with persistently low or declining employment rates. Findings suggest that long-term trends in local employment rates are useful as predictors of mortality and morbidity differences. These are not so clearly distinguished by only considering employment rates at one point in time. CONCLUSION: Poor health outcomes are associated with long-term economic disadvantage in some areas of England, reflected in employment rates, underlining the importance of efforts to improve health in areas with especially 'deep-seated' deprivation.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".