Trends in Socioeconomic Inequalities in Hypertension in Ontario, Canada, 2000-2012
Bibliographic record
Abstract
IntroductionHypertension is leading risk factor for cardiovascular disease and mortality. Low socioeconomic position (e.g., income or high material deprivation) is an important risk factor for hypertension. However, there is limited evidence monitoring the extent to which socioeconomic inequalities in hypertension exist and are changing over time in Ontario. Objectives and ApproachThe study objective was to estimate socioeconomic trends in prevalent hypertension by household income and material deprivation in Ontario from 2000 to 2012. A pooled cross-sectional study was conducted using data from 6 Canadian Community Health Surveys linked to the Discharge Abstract Database and Ontario Health Insurance Plan data (n=121,390 over 35 years, 54\% female). Relative-weighted Poisson regression models were used to estimate hypertension rates (adjusted for age, sex, ethnicity and immigration) across quintiles of equivalized household income and area-level material deprivation. Socioeconomic inequalities were estimated using the slope index of inequality (SII) and relative index of inequality (RII). ResultsSocioeconomic inequalities in hypertension were observed across income quintiles on both absolute (SII: 1428 per 10,000, 95\%CI:1126,1730) and relative (RII:1.74, 95\%CI:1.53,1.94) scales in 2000, decreasing by 2012 (SII:297 per 10,000, 95%CI: -82,676; RII:1.19, 95%CI:0.93,1.45). A similar pattern was observed across material deprivation quintiles, however with smaller inequalities in 2000 (SII:595 per 10,000, 95%CI:306,884; RII:1.25, 95%CI:1.11,1.39) and 2012 (SII:389 per 10,000, 95%CI:17,761; RII:1.24, 95%CI:0.99,1.49). Conclusion/ImplicationsSocioeconomic inequalities in hypertension were observed in Ontario, with decreasing trends between 2000 and 2012. Area-level material deprivation underestimated individual-level socioeconomic inequalities in hypertension.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".