Inequalities in Ambulatory Care Sensitive Conditions: An International Comparison of High-Income Countries
Bibliographic record
Abstract
ObjectiveTo examine the differences in inequalities in Ambulatory Care Sensitive Conditions (ACSC) hospitalizations relative to socioeconomic status across nine countries, namely Australia, Canada, England, Finland, France, New Zealand, Spain, Switzerland, and the United States (US). ApproachThe International Collaborative on Costs, Outcomes, and Needs in Care (ICCONIC) research collaborative developed national data sets with hospitalization and sociodemographic data aggregated to small-area levels and pooled across countries using a common data model. Inequalities were assessed using both the slope index of inequality and the relative index of inequality. ResultsThis study developed a common definition and set of codes for ACSC hospitalizations that proved to be comparable across countries and regions. Consistent socioeconomic gradients were observed in all countries with higher ACSC hospitalization rates for individuals in most disadvantaged areas. The greatest difference in hospitalizations between the highest and lowest income quintiles were observed in New Zealand (1603 per 100,000), Finland (1802 per 100,000) and England (1955 per 100,000). ConclusionsThe high-income countries included in this study displayed many commonalities in inequalities in ACSC hospitalizations. As a proxy measure of primary care access and quality, the observed inequalities may implicate disparities in health care quality and access for the lowest socioeconomic status groups. ImplicationsBarriers to health care access in low socioeconomic groups remain a complex and multifaceted issue. Future research should investigate the underlying contributing factors to the observed ACSC hospitalization equity gradients, including primary care delivery model, remuneration approach, and population structure.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 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".