How multiple chronic conditions increase the risk of ACSC hospitalizations
Bibliographic record
Abstract
Introduction: The high financial burden of avoidable hospitalizations has led to an increase of the study of hospitalizations for ambulatory care sensitive conditions (ACSC). Although patients’ social and demographic characteristics are frequently identified in the literature as factors that increase the risk of an avoidable hospitalization, there is little information on the impact of multiple chronic conditions on these hospitalizations. Our study looks for the relationship between multiple chronic conditions and ACSC hospitalizations.Theory/Methods: Data consists of all hospitalizations in the portuguese health system between 2008 and 2012. Avoidable hospitalizations were identified according to the Canadian Institute for Healthcare Information and chronic conditions were identified according to criteria set by the Agency for Healthcare Research and Quality. A retrospective study analyzing all patients hospitalized for an ACSC and all patients hospitalized for non-ACSC was made, using different multiple logistic regression models to identify the impact of chronic conditions in the risk of admission.Results: We found that the population with ACSC hospitalizations when compared to the population with non-ACSC hospitalizations has in average more chronic conditions (3.93 to 1.98, p=0.008) and chronic conditions in more body systems (2.50 to 1.49, p=0.00419). We observed that the probability of an ACSC hospitalization increases 1.35 times (p<0.001) with each additional chronic condition and 1.55 times (p<0.001) with each additional body system with chronic conditions.Conclusion: The number of chronic conditions and the type of body systems affected increases the risk of admission for ACSC. Further investigation needs to follow in order to develop risk models that help both identify these patients in higher risk of an avoidable hospitalization and promote a more assertive use of ACSC as measure of access to 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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".