Article

Social Determinants of Health on Glycemic Control in Pediatric Type 1 Diabetes

Division of Endocrinology, The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada. Electronic address: .
The Journal of pediatrics (Impact Factor: 3.74). 01/2013; 162(4). DOI: 10.1016/j.jpeds.2012.12.010
Source: PubMed

ABSTRACT OBJECTIVE: To evaluate the relationship between the social determinants of health (SDH) and glycemic control in a large pediatric type 1 diabetes (T1D) population. STUDY DESIGN: Deprivation Indices (DI) were used to ascertain population-level measures of socioeconomic status, family structure, and ethnicity in patients with T1D followed at The Hospital for Sick Children August 2010-2011 (n = 854). DI quintile scores were determined for individual patients based on de-identified postal codes, and linked to mean patient A1Cs as a measure of glycemic control. We compared mean A1C between the most and least deprived DI quintiles. Associations were estimated controlling for age and sex, and repeated for insulin pump use. RESULTS: The T1D population evaluated in this study was most concentrated in the least and most deprived quintiles of the Material DI. A1C levels were highest in patients with the greatest degree of deprivation (fifth vs first quintile) on the Material DI (9.2% vs 8.3%, P < .0001), Social DI (9.1% vs 8.3%, P < .0001), and Ethnic Concentration Index (8.9% vs 8.4%, P = .03). These relationships between measures of the SDH and A1C were not evident for patients on insulin pumps. On regression analysis, higher A1C was predicted by older age, female sex, not using pump therapy, and being in the most deprived quintile for Material and Social Deprivation, but not Ethnic Concentration. CONCLUSIONS: Measures of the SDH comprising Material and Social Deprivation were significantly associated with suboptimal glycemic control in our pediatric T1D cohort. Use of insulin pump therapy also predicted A1C and may have a moderating effect on these relationships.

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    09/2014; 31(7). DOI:10.1002/pdi.1883
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    ABSTRACT: Objectives To estimate the excess in admissions associated with type1 diabetes in childhood. Design Matched-cohort study using anonymously linked hospital admission data. Setting Brecon Group Register of new cases of childhood diabetes in Wales linked to hospital admissions data within the Secure Anonymised Information Linkage Databank. Population 1577 Welsh children (aged between 0 and 15 years) from the Brecon Group Register with newly-diagnosed type-1 diabetes between 1999–2009 and 7800 population controls matched on age, sex, county, and deprivation, randomly selected from the local population. Main outcome measures Difference in all-cause hospital admission rates, 30-days post-diagnosis until 31 May 2012, between participants and controls. Results Children with type-1 diabetes were followed up for a total of 12 102 person years and were at 480% (incidence rate ratios, IRR 5.789, (95% CI 5.34 to 6.723), p<0.0001) increased risk of hospital admission in comparison to matched controls. The highest absolute excess of admission was in the age group of 0–5 years, with a 15.4% (IRR 0.846, (95% CI 0.744 to 0.965), p=0.0061) reduction in hospital admissions for every 5-year increase in age at diagnosis. A trend of increasing admission rates in lower socioeconomic status groups was also observed, but there was no evidence of a differential rate of admissions between men and women when adjusted for background risk. Those receiving outpatient care at large centres had a 16.1% (IRR 0.839, (95% CI 0.709 to 0.990), p=0.0189) reduction in hospital admissions compared with those treated at small centres. Conclusions There is a large excess of hospital admissions in paediatric patients with type-1 diabetes. Rates are highest in the youngest children with low socioeconomic status. Factors influencing higher admission rates in smaller centres (eg, “out of hours resources”) need to be explored with the aim of targeting modifiable influences on admission rates.
    BMJ Open 04/2015; 5(4):e005644. DOI:10.1136/bmjopen-2014-005644 · 2.06 Impact Factor