Mediating Effects of Technology-Based Therapy on the Relationship Between Socioeconomic Status and Glycemic Management in Pediatric Type 1 Diabetes
Bibliographic record
Abstract
Background: Socioeconomic disparities exist related to accessibility and uptake of diabetes technologies that impact glycemic management. The aims of this study were to describe diabetes technology use (continuous subcutaneous insulin infusion [CSII] and continuous glucose monitoring [CGM]) in children with type 1 diabetes (T1D) and assess the mediating effects of each technology on the relationship between socioeconomic status (SES) and glycemic management. Methods: Single-center retrospective cross-sectional study of children aged 0–18 years ( n = 813) with T1D and valid postal codes between 2018 and 2020. Extracted data were linked to validated census-based material deprivation (MD) quintiles. Exposures included MD and technology use (CSII, CGM), whereas the primary outcome was glycemic management (HbA1c). Results: Of 813 patients included, 379 (46.6%) and 246 (30.3%) individuals used CGM and CSII, respectively. Real-time CGM (rtCGM) and CSII were associated with both MD and HbA1c, but intermittently scanned CGM (isCGM) was not. There was a difference in HbA1c of +1.17% between patients from the most (Q5) and least deprived (Q1) MD quintile ( P < 0.0001), and significant mediating effects for rtCGM and CSII use, but not isCGM. rtCGM use and CSII use accounted for 0.14% ( P < 0.0001) and 0.25% ( P < 0.0001) of the difference in HbA1c between patients from Q1 and Q5 quintiles (indirect effects), representing 12.0% and 23.1% of this difference, respectively. Conclusions: CSII and rtCGM use partially mediated the significant discrepancies observed with SES and glycemic management, highlighting potential benefits of broader access to these technologies to improve diabetes outcomes and help mitigate the negative impact of deprivation on diabetes management.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".