Are ambulatory care sensitive conditions a valid indicator for quality of primary health care?
Bibliographic record
Abstract
Abstract Background Hospitalisations due to ambulatory care sensitive conditions (ACSCs) have been used for assessing access to and quality of primary health care (PHC) in many countries. To assess the validity of ACSCs for assessing PHC performance we carried out a series of studies on regional and sociodemographic variations and time trends in ACSC hospitalisations and related mortality. Methods Hospitalisations due to ACSCs in Finland in 1992-2013 came from the national Hospital Discharge Register. The data were linked to population at risk data and individual sociodemographic indicators from Statistics Finland, and subsequently to area indicators of population health and socioeconomics, and health care organisation. Depending on study questions, we analysed ACSCs divided into acute, chronic and vaccine-preventable causes using appropriate statistical methods, such as multilevel Poisson models and trajectory modelling. Results We found ACSC hospitalisations to be highly associated to subsequent mortality with 4-10-fold excess 1-year mortality compared to the general population. ACSC hospitalisations showed substantial regional variations which declined over the study period due to decreasing variations in hospitalisations related to chronic ACSCs. The variations were mainly attributed to the hospital district level. In detailed analyses, about a quarter of the variance in ACSC hospitalisations was explained by individual level socioeconomic and health factors. In addition, population health indicators and factors related to hospital care organisation explained up to one third of the variance. Conclusions At patient level a hospitalisation due to ACSC is a sentinel event and associated to a high risk of poor health outcomes. However, using ACSC for benchmarking PHC providers should be addressed with caution and differences in sociodemographic factors and (co)morbidity of populations at risk, and regional heath and hospital care arrangements should be taken into account. Key messages Variations in hospitalisations due to ambulatory care sensitive conditions may mainly be linked to other factors than access to and quality of primary health care. More research is needed to validate ambulatory care sensitive conditions for use in assessing primary health care.
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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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".