Agreement between area- and individual-level income measures in a population-based cohort: Implications for population health research
Bibliographic record
Abstract
Socioeconomic status is an important determinant of health, the measurement of which is of great significance to population health research. However, individual-level socioeconomic factors are absent from much health administrative data, resulting in widespread use of area-level measures in their place. This study aims to clarify the role of individual- and area-level socioeconomic status in Ontario, Canada, through comparison of income measures. Using data from four cycles (2005-2012) of the Canadian Community Health Survey, we assessed concordance between individual- and area-level income quintiles using percent agreement and Kappa statistics. Individual-level characteristics were compared at baseline. Cumulative adult premature mortality was calculated for 5-years following interview. Rates were calculated separately for area-level and individual-level income, and jointly for each combination of income groups. Multivariable negative binomial models were fit to estimate associations between area- and individual-level income quintile and premature mortality after adjustment for basic demographics (age, sex, interview cycle) and key risk factors (alcohol, smoking, physical activity, and body mass index). Agreement between individual- and area-level income measures was low. Kappa statistics for same and similar (i.e. ±1 quintile) measures were 0.11 and 0.48, indicating low and moderate agreement, respectively. Socioeconomic disparities in premature mortality were greater for individual-level income than area-level income. When rates were stratified by both area- and individual-level income quintiles simultaneously, individual-level income gradients persisted within each area-level income group. The association between income and premature mortality was significant for both measures, including after full adjustment. Area-level socioeconomic status is an inappropriate proxy for missing individual-level data. The low agreement between area- and individual-level income measures and differences in demographic profile indicate that the two socioeconomic status measures do not capture the same population groups. However, our findings demonstrate that both individual- and area-level income measures are associated with premature mortality, and describe unique socioeconomic inequities.
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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 | 0.132 | 0.226 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".