Characterizing social and policy determinants of hospital length of stay among paediatric inpatients with diabetes using linked population-based data
Bibliographic record
Abstract
BACKGROUND: Evidence is limited on the non-medical factors influencing hospital length of stay (LOS) among paediatric inpatients with diabetes, notably potential social and policy correlates. This study aimed to characterize the associations of socioeconomic status and health policy environment with diabetes-attributable LOS to help inform accountability monitoring of a provincial comprehensive diabetes strategy aiming to minimize time in hospital among this high-risk population. DATA AND METHODS: This retrospective population-based study drew on multiple linked administrative and geospatial databases among all children aged 18 and under with a diabetes-related hospitalization in the province of New Brunswick, Canada, during the four-year period following implementation of an insulin pump funding program. Multiple linear regression was used to assess the role of access to the public insulin pump resourcing scheme and relative neighbourhood deprivation as predictors of days spent in acute care, controlling for age, sex, and place of residence. RESULTS: Among the paediatric inpatient population (N = 386), 21% had accessed social resources made available through the insulin pump funding policy and 42% resided in the most materially deprived neighbourhoods. Diabetes-related hospital stays averaged 3.87 days. Paediatric inpatients having accessed resources through the social insurance policy spent significantly fewer days in hospital (1.34 days less [95% CI: 0.63-2.05]) than those who had not, all else being equal. Observed differences in LOS by neighbourhood socioeconomic deprivation were not found to be statistically significant in the multivariate analysis. CONCLUSION: Findings from this context of universal medical coverage suggested that public policy for supplemental financing of assistive technologies among children with diabetes may be associated with reduced burden to the hospital system. The causes of socioenvironmental disparities in LOS require further investigation to inform interventions to mitigate preventable patient-level variations in hospital-based health outcomes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".