Relationship between income and mortality in a Canadian family practice cohort.
Bibliographic record
Abstract
OBJECTIVE: To examine the relationship between household income and mortality in a primary care cohort while using personal information obtained from clinical records and administrative data linkages to adjust for confounders. DESIGN: Survey and analysis of data from several administrative databases. SETTING: Toronto, Ont. PARTICIPANTS: Patients of an urban academic family practice who were aged between 45 and 74 and who had made at least 3 visits to the clinic between 1996 and 1997. MAIN OUTCOME MEASURES: Patients' self-reported household income. Patients' personal information obtained from clinical records and linkages to administrative data to adjust for confounders; personal information was used to adjust for health factors as confounders of the relationship between income and mortality risk. RESULTS: Of the 1064 patients who received surveys, 519 (49%) responded. There was no significant difference in the mortality rate between nonresponders and responders. Mortality rates were lower than those in the general population throughout follow-up. Within the patient cohort, mortality rates were elevated for smokers, those who had not consumed alcohol in the year before the survey, and those who had been diagnosed with diabetes, hypertension, or cancer before the survey. After all adjustments were made, mortality rates were lower among patients in the higher-income categories than among those with lower household incomes. CONCLUSION: Universal health care does not eliminate income-related differentials in mortality. Differences in health-related behaviour factors are not sufficient to explain the socioeconomic mortality differences within an area. These data suggest that it is not solely personal choice related to health behaviour but that other explanations must be invoked to account for the relationship between lower household income and increased mortality rates.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".