Evaluating health disparities in a pediatric population with type 1 diabetes in Quebec
Bibliographic record
Abstract
Few Canadian studies have examined the relationship between socioeconomic status (SES) and glycemic control while considering other equity measures such as ethnicity, immigration, and other factors that modify the relationship between SES and glycemic control. My main objective was to determine the association between SES and glycemic control (HbA1c) in children ages 0-18 years with type 1 diabetes (T1D) followed at the Montreal Children’s Hospital (MCH). My secondary objectives were to 1)determine whether insulin pump use, processes of care (number of diabetes-related clinic visits), and depression were effect modifiers of this relationship and 2)to determine the association between ethnicity or immigration status with mean HbA1c. Retrospective cohort study using MCH’s Pediatric Diabetes Database with data on children ages 0-18 years with T1D. A Diabetes Clinical Intake Form was used for demographic information such as ethnicity, immigration status, and depression. We included children diagnosed with T1D for at least a year with an index visit between November 1st 2019 and October 31st 2020. The main outcome was mean HbA1c in the year following the index visit. The main exposure was SES measured by the Material and Social Deprivation Index. SES was also measured by the Canadian Index of Multiple Deprivation (CIMD) which examines residential instability, situational vulnerability, economic dependency, and ethnocultural composition (a measure of ethnic density). SES was defined as Q1-Q2(least deprived), Q3(moderately deprived), and Q4-Q5(most deprived). I used multivariable linear regression to determine the association between SES and mean HbA1c adjusting for age, sex, and diabetes duration, insulin pump use, and processes of care. Effect modification by insulin pump, processes of care (number of diabetes-related clinic visits in the past year) and depression was assessed with interaction terms in three separate regression models. We used multivariable linear regression analysis to determine the association between ethnicity, immigration and mean HbA1c adjusted for SES, age, sex, diabetes duration, insulin pump use, and processes of care. A total of 203 children were included in the main analysis. Mean age was 13.5 years; 53.2% males, and mean diabetes duration was 4.9 years. The sample consisted of 47.3%, 26.6%, and 26.1% in the least, moderately, and most deprived quintiles, respectively. Children in the most deprived quintiles had a higher mean HbA1c compared to those in the least and moderately deprived (p=.05). In the adjusted analysis, HbA1c in the most deprived quintiles was 0.5% higher compared to the least deprived (95% Confidence Interval (95% CI) 0.05-0.97). Effect modification by insulin pump, processes of care, and depression was not significant. The CIMD assigned quintiles for 208 children. The ethnocultural composition was associated with lower mean HbA1c in moderately and most diverse quintiles compared to least diverse quintiles (β =-1.1, 95%CI -1.9,-0.4, β=-0.8, 95% CI -1.5,-0.2). In terms of ethnicity, the racialized compared to the non-racialized group had a higher mean HbA1c (β=0.7, 95%CI 0.2-1.2). Consistent with previous findings, lower SES was associated with higher HbA1c; effect modification by insulin pump, processes of care, and depression was not observed. We observed ethnic disparities in HbA1c levels, which is consistent with previous data from the UK and the U.S. Although the racialized compared to the non-racialized group was associated with higher HbA1c, the most diverse compared to the least diverse neighborhood-level ethnocultural composition was associated with lower mean HbA1c. This suggests that other health-promoting factors in areas of high ethnocultural composition may affect HbA1c, such as social cohesion and community support. The associations between SES and glycemic control are important for further research to understand drivers that contribute to disparities in a Canadian context
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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 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".