Inequities in Ambulatory Care and the Relationship Between Socioeconomic Status and Respiratory Hospitalizations: A Population-Based Study of a Canadian City
Bibliographic record
Abstract
PURPOSE: Individuals of lower socioeconomic status have higher rates of hospitalization due to ambulatory care-sensitive conditions, particularly chronic obstructive pulmonary disease and asthma. We examined whether differences in patient demographics, ambulatory care use, or physician characteristics could explain this disparity in avoidable hospitalizations. METHODS: Using administrative data from the city of Winnipeg, Manitoba, Canada, we identified all adults aged 18 to 70 years with chronic obstructive pulmonary disease or asthma, grouped together as obstructive airway disease. We divided patients into census-derived income quintiles using average household income. We performed a series of multivariate logistic regression analyses to determine how the association of socioeconomic status with the risk of obstructive airway disease-related hospitalizations changed after controlling for blocks of covariates related to patient demographics (socioeconomic status, age, sex, and comorbidity), ambulatory care use (continuity influenza vaccination and specialist referral), and characteristics of the patient's usual physician (eg, payment mechanism, sex, years in practice). RESULTS: We included 34,741 patients with obstructive airway disease, 729 (2.1%) of whom were hospitalized with a related diagnosis during a 2-year period. Patients having a lower income were more likely to be hospitalized than peers having the highest income, and this effect of socioeconomic status remained virtually unchanged after controlling for every other variable studied. In a fully adjusted model, patients in the lowest income quintile had approximately 3 times the odds of hospitalization relative to counterparts in the highest income quintile (odds ratio = 2.93; 95% confidence limits: 2.19, 3.93). CONCLUSIONS: In the setting of universal health care, the income-based disparity in hospitalizations for respiratory ambulatory care-sensitive conditions cannot be explained by factors directly related to the use of ambulatory services that can be measured using administrative data. Our findings suggest that we look beyond the health care system at the broader social determinants of health to reduce the number of avoidable hospitalizations among the poor.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".