Health costs and benefits associated with economic transitions: Linking records of address change, property value, and self‐reported health
Bibliographic record
Abstract
Abstract Deprivation is a major risk to population health, in particular when experienced during childhood. Poor health in the early years accumulates and is expressed in adult health inequalities. Policy makers may aim to mitigate against the ill effects of deprivation by trying to increase social mobility and facilitating moves towards better earnings and living conditions or by protecting against the effects of downward moves and the experience of deprivation. This paper uses address change and property value data at the individual and family level to examine whether poor health outcomes occur more frequently among people who move between addresses and particularly those who move to properties with lower property values. We use the Northern Ireland Longitudinal Study, linking health and demographic data from 2001 and 2011 Censuses to house valuations for a representative 28% of the population aged 10–64 years (N = 342,681). Young persons (aged 10–15 years) living in a house valued at over £160,000 were half as likely to be reported as having mental ill health as those living in a house valued under £75,000 (OR = 0.49, CI: 0.31–0.78). There was no strong evidence that upward or downward mobility affected mental health or physical health for young people, but ill health of working aged persons showed a strong association with moving to houses of lower value. Results are discussed in terms of their implications for understanding the dynamics of social mobility and health and in terms of how various policies towards poverty may influence population health.
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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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".