The impact of multiple chronic diseases on hospitalizations for ambulatory care sensitive conditions
Bibliographic record
Abstract
BACKGROUND: The high financial burden of avoidable hospitalizations has led to an increase of the study of hospitalizations for ambulatory care sensitive conditions (ACSC). There is limited information on the impact of secondary diagnoses on these hospitalizations, although patients' social and demographic characteristics, as well as the coexistence of multiple diseases are often identified in the literature as risk factors for avoidable hospitalizations. This study explores the impact of chronic conditions on the likelihood of hospitalizations for ACSC. METHODS: Data were extracted from the Portuguese hospital discharge database. 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 analysing all patients hospitalized for an ACSC and all patients hospitalized for non-ACSC was made, using multiple logistic regression models to identify the impact of chronic conditions on the risk of admission. RESULTS: The risk of an avoidable hospitalization increases by a factor of 1.35 (95 % CI [1.34;1.35]) for each additional chronic condition, and 1.55 (95 % CI [1.55;1.56]) for each additional body system affected. The respiratory and circulatory systems have the most impact on the risk of ACSC, increasing the risk by 8.72 (95 % CI [8.58;8.86]) and 3.01 (95 % CI [2.95;3.06]), respectively. CONCLUSIONS: The number of chronic conditions and the body systems affected increase the risk of hospital admissions for ACSC.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 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".