Socioeconomic and Racial Disparities in Diabetic Ketoacidosis Admissions in Youth With Type 1 Diabetes
Bibliographic record
Abstract
OBJECTIVE: We sought to determine whether census tract poverty, race, and insurance status were associated with the likelihood and severity of diabetic ketoacidosis (DKA) hospitalization among youth with type 1 diabetes (T1D). METHODS: We conducted a retrospective population-based cohort study using Cincinnati Children's Hospital electronic medical record (EMR) data from January 1, 2011, to December 31, 2017, for T1D patients ≤18 years old. The primary outcome was admission for DKA. Secondary outcomes included DKA severity, defined by initial pH and bicarbonate, and length of stay. Exposures were the poverty rate for the youth's home census tract, parent-reported race, and insurance status. We used multivariable logistic regression to analyze effects on odds of admission. RESULTS: We identified 439 patients with T1D; 152 were hospitalized. The cohort was 48% female, 25% Black, and 36% publicly insured; the median age was 14 years. For every 10% increase in a youth's census tract poverty rate, the adjusted odds of admission increased by 22% (95% CI, 1.03-1.47). Public insurance status was associated with DKA admission (adjusted odds ratio [AOR], 2.71, 95% CI, 1.62-4.55) while race was not. There were no clinically meaningful differences in pH or bicarbonate by census tract poverty, race, or insurance status; however, Black patients experienced differences in care (eg, longer length of stay). CONCLUSION: Youth with T1D living in high poverty areas and on public insurance were significantly more likely to be admitted for DKA. Severity upon presentation was similar across exposures. Understanding contextual mechanisms by which disparities emerge will inform changes aimed at equitably improving 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".