Socioeconomic Inequalities Increase the Probability of Ketoacidosis at Diagnosis of Type 1 Diabetes: A 2014–2016 Nationwide Study of 2,679 Italian Children
Bibliographic record
Abstract
Aim of this study was to compare the frequency of Diabetic Ketoacidosis (DKA) at diagnosis in 2014-2016 with the one previously reported in 2004-2013; and to assess the association between family socioeconomic status and risk of DKA at type 1 diabetes (T1D) diagnosis in children <15 years of age from 2014-2016.This nationwide, population-based, observational study included 2679 children diagnosed with T1D from 54 Italian centers for pediatric diabetes during 2014-2016. The ISPAD criteria for DKA were used as a standard reference. The overall and by age frequency of DKA between the two time periods were compared. The association between family socioeconomic status and risk of DKA was assessed using multiple logistic regression analysis. Overall 989 children had DKA (36.9%, 95%CI:35.1-38.8). The frequency of DKA was significantly lower in 2014-2016 in comparison to 2004-2013 (40.3%, 95%CI:39.3-41.4, p=0.002).The probability of having DKA at diagnosis was lower in mothers with high level of education (OR=0.69, 95%CI:0.51-0.93) or high level of occupation (OR=0.76, 95%CI:0.58-0.99), and in fathers with high level of occupation (OR=0.72, 95%CI:0.55-0.94). Children living in Southern Italy had a higher probability of diagnosis with severe DKA than children living in Central Italy. In conclusion, there was a decrease in the frequency of DKA in children diagnosed with T1D under 15 years of age during 2014-2016. However, DKA frequency remains unacceptably high. This study demonstrated that socioeconomic inequalities, measured as low education and occupational levels, were associated with an increased probability of DKA at type 1 diabetes diagnosis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".