Ailing inequality: the impact of economic inequality on health in Canadian cities 2001-2011
Bibliographic record
Abstract
Background: Previous research investigating the relationship between economic inequality and health finds mixed results, both within Canada and other nations.This may be because the effect is mixed in more equal countries, such as Canada; that most Canadian empirical studies do not use multilevel modeling to separate area and individual effects; and that the association is often only tested at one point in time.This thesis tests the hypothesis that there is an inverse relationship between economic inequality and health using two representative datasets and a multilevel modeling approach at three time points.Methods: Data from the Canadian Census/National Household Survey and the Canadian Community Health Survey (CCHS) were merged so that more than 125,000 CCHS respondents were nested in 130 cities for census years 2001, 2006, and 2011.The association between economic inequality and self-rated health (SRH) was tested using multilevel logistic regression, adjusted for individual-level and city-level covariates.Economic inequality was calculated using the Gini coefficient of weighted census respondents aged 25-64, with over $1,000 in annual employment income.Other measures of inequality were also considered (Theil Index, 90/10 percentile ratio), along with provincial fixed effects to examine the robustness of the results.Results: A one unit increase in the Gini coefficient was associated with an increased likelihood of poorer SRH (OR: 1.02; 95% CI 1.00-1.04) in 2001 and 2011, but not in 2006.Compared to those living in cities with lower levels of inequality, respondents living in cities characterized by higher levels of inequality had modestly higher odds of rating their health poorer, except in 2006.Further analysis show that the association between economic inequality and SRH is fairly robust to the choice of inequality metric and but sensitive to the consideration of provincial/territorial effects. Conclusion:We find that there is variation in economic inequality between cities, but that this variation is not systematically associated with variation in SRH.Higher economic inequality is moderately associated with poorer SRH in Canadian cities in 2001 and 2011, but not in 2006 or when including provincial/territorial effects.Further research should investigate provincial/territorial effects and the differing results for 2006.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".