Impact of deprivation on glycaemic control in youth with type 1 diabetes in the southwestern region of France.

OBJECTIVES: The objective of this multicenter cross-sectional study was to determine predictors of poor glycaemic control in children with type 1 diabetes mellitus (T1DM), particularly with respect to socioeconomic status (SES). METHODS: Our study population consisted of 1154 children who attended T1DM follow-up consultation with a pediatric diabetes specialist. Clinical and demographic data were retrieved retrospectively from patients' records. Individual deprivation was defined by an EPICES (Evaluation of the Deprivation and Inequalities of Health in Healthcare Centers) score ≥ 30. Patients were assigned to quintiles of the European Deprivation Index (EDI) based on their area deprivation scores. We used multivariable linear regression models to detect potential associations between glycaemic control and indicators of low SES. RESULTS: In total, 33% (n = 376) of patients had an EPICES score ≥ 30 and 23% (n = 268) were in the 5th EDI quintile. Multivariable linear regression analysis showed that poor glycaemic control was associated with both individual (ß 0.38; 95%CI 0.26-0.5; p < 0.001) and area deprivation (ß 0.26; 95%CI 0.08-0.43; p = 0.004). Demographic factors, body mass index (BMI) and insulin regimen were also independently associated with poor glycaemic control (p < 0.001). Interestingly, access to diabetes technologies was not related to SES or either glycaemic control. CONCLUSION: Low SES is associated with a higher risk of poor glycaemic control, independently of insulin regimen. BMI, age at the time of consultation, duration of diabetes, and insulin regimen. Also have an impact on HbA1c. These parameters need to be considered when developing novel treatment strategies for children with T1DM to better target at-risk patients.

Consensus Recommendations for the Use of Simulation in Therapeutic Patient Education.

INTRODUCTION: Simulation is rarely used to help individuals with chronic diseases develop skills. The aim of the study was to provide recommendations for the use of simulation in therapeutic patient education (S-TPE). METHODS: Expert consensus was achieved with the participation of the following 3 groups of experts: (a) expert patients and caregivers; (b) health professionals specialized in therapeutic patient education (TPE); and (c) simulation experts. Each expert received a list of questions by e-mail in 3 iterations. The synthesis of the 2 first questionnaires resulted in 34 first recommendations voted during the consensus conference meeting. Each recommendation was subject to an extensive literature review. The quality of the evidence and the strength of the recommendations were assessed through the evaluation, development, and evaluation criteria categories (GRADE criteria). The third questionnaire selected and illustrated recommendations more specific to the use of S-TPE. RESULTS: At the end of the process, the experts identified 26 recommendations specific to the use of S-TPE. They proposed examples of skills in different diseases and stressed the importance of adapting the conditions of use (location, equipment, time of the care) to the circumstances of the patient learner and skills to be developed. Experts should exercise great caution as this technique presents ethical considerations related to patient care. CONCLUSIONS: These recommendations underline the fact that simulation could bring added value to TPE. They provide a framework and examples for the experimental use of simulation in TPE. Research into feasibility and acceptability is needed.