Investigation of rural–urban differences in hospitalization for ambulatory care‐sensitive conditions: Analysis of linked survey, hospitalization, and tax data from Canada
Bibliographic record
Abstract
BACKGROUND: Hospitalizations for ambulatory care sensitive conditions (ACSCs) reflect the efficiency of the primary care system, as these are preventable with timely and effective management of chronic conditions. We examined ACSC hospitalization trends in Canada's rural and urban areas, excluding Quebec, from 2007 to 2019. METHODS: The data came from Canadian Community Health Surveys linked with hospitalizations and household income tax records. The study focused on adults aged 18-74 years and used logit and zero-inflated Poisson models to analyze ACSC hospitalizations and costs. A non-linear decomposition method quantified explained and unexplained rural-urban gaps in ACSC hospitalizations and costs. RESULTS: We found persistent disparities in ACSC hospitalizations between rural and urban areas, although the gap has narrowed since 2010. Even after adjusting for socio-demographic factors, chronic conditions, and risky health behaviors, rural-urban disparities in ACSC rates remained, highlighting unequal access to primary care in rural areas. The decomposition results revealed that the disparities were driven mainly by differences in the observed characteristics. Further investigation revealed that disparities were due to populations with lower income and education, and residents in Atlantic provinces. CONCLUSIONS: This study underscores the importance of a strong primary care system to minimize ACSC-related hospitalizations in rural Canada. Our results highlight the benefits of primary care reforms undertaken by provinces over the past decade in reducing rural-urban gaps in ACSC hospitalizations. Future policy interventions targeting disadvantaged populations, such as those with lower income and education, are vital for reducing avoidable hospitalizations and enhancing population health outcomes in rural areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".