Avoidable hospitalizations among racialized groups in Canada: Results from the 2016 Canadian Census Health and Environment Cohort
Bibliographic record
Abstract
Background: Ambulatory care sensitive conditions (ACSCs) are illnesses that can be effectively treated and managed in primary care settings. Hospitalizations for ACSCs are therefore considered avoidable and may indicate poor access to quality primary care. This study examined trends in avoidable hospitalizations in Canada among racialized groups. Data and methods: The 2016 Canadian Census Health and Environment Cohort was used to estimate annual age-standardized hospitalization rates (ASHRs) for ACSCs among people aged 10 to 74 from 2016/2017 to 2021/2022. ASHRs were disaggregated by sex and racialized group. Rate ratios (RRs) and 95% confidence intervals (CIs) were calculated to assess relative inequality. Logistic regression models were run, adjusting for age, sex, immigrant status, household income, and education. Results: Across all study years, the odds of avoidable hospitalizations were significantly higher among males, Black people, and non-immigrants, and significantly lower among Chinese people and people in the category "other racialized groups not included elsewhere." In 2020/2021, during the COVID-19 pandemic, RRs for Black females compared with non-racialized females decreased (2019/2020: RR=1.12, 95% CI=1.07 to 1.61; 2020/2021: RR=0.99, 95% CI=0.94 to 1.04), while they significantly increased for Black males compared with non-racialized males (2019/2020: RR=1.30, 95% CI=1.25 to 1.35; 2020/2021: RR=1.63, 95% CI=1.41 to 1.88). Interpretation: This study reveals inequalities in avoidable hospitalizations in Canada, pronounced for the Black population compared with the non-racialized population, especially during the pandemic (2020/2021 and 2021/2022). Future studies examining the factors driving these inequalities (e.g., access to primary care, most prevalent conditions, geography) may inform targeted interventions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| 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".