Readmission and associated clinical factors among individuals admitted with bipolar affective disorder at a psychiatry facility in Uganda
Bibliographic record
Abstract
BACKGROUND: Bipolar affective disorder (BAD) is a common severe mental health condition with a relapsing course that may include periods of hospital re-admissions. With recurrent relapses and admissions, the course, prognosis, and patient's overall quality of life can be affected negatively. This study aims to explore the rates and clinical factors associated with re-admission among individuals with BAD. METHOD: This study used data from a retrospective chart review of all records of patients with BAD admitted in 2018 and followed up their hospital records for four years till 2021 at a large psychiatric unit in Uganda. Cox regression analysis was used to determine the clinical characteristics associated with readmission among patients diagnosed with BAD. RESULTS: A total of 206 patients living with BAD were admitted in 2018 and followed up for four years. The average number of months to readmission was 9.4 (standard deviation = 8.6). The incidence of readmission was 23.8% (n = 49/206). Of those readmitted during the study period, 46.9% (n = 23/49) and 28.6% (n = 14/49) individuals were readmitted twice and three times or more, respectively. The readmission rate in the first 12 months following discharge was 69.4% (n = 34/49) at first readmission, 78.3% (n = 18/23) at second readmission, and 87.5% (n = 12/14) at third or more times. For the next 12 months, the readmission rate was 22.5% (n = 11/49) for the first, 21.7% (n = 5/23) for the second, and 7.1% (n = 1/14) for more than two readmissions. Between 25 and 36 months, the readmission rate was 4.1% (n = 2/49) for the first readmission and 7.1% (n = 1/14) for the third or more times. Between 37 and 48 months, the readmission rate was 4.1% (n = 2/49) for those readmitted the first time. Patients who presented with poor appetite and undressed in public before admission were at increased risk of being readmitted with time. However, the following symptoms/clinical presentations, were protective against having a readmission with time, increased number of days with symptoms before admission, mood lability, and high energy levels. CONCLUSION: The incidence of readmission among individuals living with BAD is high, and readmission was associated with patients' symptoms presentation on previous admission. Future studies looking at BAD using a prospective design, standardized scales, and robust explanatory model are warranted to understand causal factors for hospital re-admission and inform management strategies.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".