A comparison of individual and area-based socio-economic data for monitoring social inequalities in health.
Bibliographic record
Abstract
BACKGROUND: Area-based indicators are commonly used to measure and track health outcomes by socioeconomic group. This is largely because of the absence of socio-economic information about individuals in health administrative databases. The literature shows that the magnitude of differences in health outcomes varies depending on whether the socio-economic indicators are at the individual level or are area-based. This study compares the two types of indicators. DATA AND METHODS: The data are from a file linking the results of the 1991 Census with deaths that occurred from 1991 to 2000--a 15% sample of the Canadian population aged 25 or older. The socio-economic indicator used for comparison is a material and social deprivation index, in individual and area-based versions. The health indicators are life expectancy and disability-free life expectancy, and risks of mortality and disability. RESULTS: The individual version of the deprivation index yields wider gaps in life expectancy and disability-free life expectancy than does the area-based version. These gaps vary by sex and geographic setting. However, both versions are associated with inequalities in mortality and disability, independent of each other. INTERPRETATION: Despite some limitations, area-based socioeconomic indicators are useful in assessing inequalities in health. The inequalities that they identify are significant, consistent and reliable and can be tracked through time and for different geographic settings.
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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 | 0.014 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".