The effect of physical multimorbidity, mental health conditions and socioeconomic deprivation on unplanned admissions to hospital: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Multimorbidity, the presence of more than 1 long-term disorder, is associated with increased use of health services, but unplanned admissions to hospital may often be undesirable. Furthermore, socioeconomic deprivation and mental health comorbidity may lead to additional unplanned admissions. We examined the association between unplanned admission to hospital and physical multimorbidity, mental health and socioeconomic deprivation. METHODS: We conducted a retrospective cohort study using data from 180 815 patients aged 20 years and older who were registered with 40 general practices in Scotland. Details of 32 physical and 8 mental health morbidities were extracted from the patients' electronic health records (as of Apr. 1, 2006) and linked to hospital admission data. We then recorded the occurrence of unplanned or potentially preventable unplanned acute (nonpsychiatric) admissions to hospital in the subsequent 12 months. We used logistic regression models, adjusting for age and sex, to determine associations between unplanned or potentially preventable unplanned admissions to hospital and physical multimorbidity, mental health and socioeconomic deprivation. RESULTS: We identified 10 828 (6.0%) patients who had at least 1 unplanned admission to hospital and 2037 (1.1%) patients who had at least 1 potentially preventable unplanned admission to hospital. Both unplanned and potentially preventable unplanned admissions were independently associated with increasing physical multimorbidity (for ≥4 v. 0 conditions, odds ratio [OR] 5.87 [95% confidence interval (CI) 5.45-6.32] for unplanned admissions, OR 14.38 [95% CI 11.87-17.43] for potentially preventable unplanned admissions), mental health conditions (for ≥1 v. 0 conditions, OR 2.01 [95% CI 1.92-2.09] for unplanned admissions, OR 1.80 [95% CI 1.64-1.97] for potentially preventable unplanned admissions) and socioeconomic deprivation (for most v. least deprived quintile, OR 1.56 [95% CI 1.43-1.70] for unplanned admissions, OR 1.98 [95% CI 1.63-2.41] for potentially preventable unplanned admissions). INTERPRETATION: Physical multimorbidity was strongly associated with unplanned admission to hospital, including admissions that were potentially preventable. The risk of admission to hospital was exacerbated by the coexistence of mental health conditions and socioeconomic deprivation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".