No abstract

Background The performance of automated algorithms for childhood diabetes case ascertainment and type classification may differ by demographic characteristics. Objective This study evaluated the potential of administrative and electronic health record (EHR) data from a large academic care delivery system to conduct diabetes case ascertainment in youth according to type, age and race/ethnicity. Subjects 57,767 children aged <20 years as of December 31, 2011 seen at University of North Carolina Health Care System in 2011 were included. Methods Using an initial algorithm including billing data, patient problem lists, laboratory test results and diabetes related medications between July 1, 2008 and December 31, 2011, presumptive cases were identified and validated by chart review. More refined algorithms were evaluated by type (type 1 versus type 2), age (<10 versus ≥10 years) and race/ethnicity (non-Hispanic white versus “other”). Sensitivity, specificity and positive predictive value were calculated and compared. Results The best algorithm for ascertainment of diabetes cases overall was billing data. The best type 1 algorithm was the ratio of the number of type 1 billing codes to the sum of type 1 and type 2 billing codes ≥0.5. A useful algorithm to ascertain type 2 youth with “other” race/ethnicity was identified. Considerable age and racial/ethnic differences were present in type-non-specific and type 2 algorithms. Conclusions Administrative and EHR data may be used to identify cases of childhood diabetes (any type), and to identify type 1 cases. The performance of type 2 case ascertainment algorithms differed substantially by race/ethnicity.

Background The incidence of type 1 diabetes mellitus (T1DM) in children is increasing, resulting in higher burden of cardiovascular diseases due to diabetes mellitus–related vascular dysfunction. Methods and Results We examined cardiovascular risk factors ( CVRF s) and arterial parameters in 1809 youth with T1DM. Demographics, anthropometrics, blood pressure, and laboratory data were collected at T1DM onset and 5 years later. Pulse wave velocity and augmentation index were collected with tonometry. ANOVA or chi‐square tests were used to test for differences in measures of arterial parameters by CVRF . Area under the curve of CVRF s was entered in general linear models to explore determinants of accelerate vascular aging. Participants at the time of arterial measurement were 17.6±4.5 years old, 50% female, 76% non‐Hispanic white, and duration of T1DM was 7.8±1.9 years. Glycemic control was poor (glycated hemoglobin, 9.1±1.8%). All arterial parameters were higher in participants with glycated hemoglobin ≥9% and pulse wave velocity was higher with lower insulin sensitivity or longer duration of diabetes mellitus. Differences in arterial parameters were found by sex, age, and presence of obesity, hypertension, or dyslipidemia. In multivariable models, higher glycated hemoglobin, lower insulin sensitivity, body mass index, blood pressure, and lipid areas under the curve were associated with accelerated vascular aging. Conclusions In young people with T1DM, persistent poor glycemic control and higher levels of traditional CVRF s are independently associated with arterial aging. Improving glycemic control and interventions to lower CVRF s may prevent future cardiovascular events in young individuals with T1DM.

EHR data may be used to establish an efficient approach for large-scale surveillance for childhood diabetes by type, although some manual effort is still needed.

Background Pediatric diabetes clinics around the world rapidly adapted care in response to COVID‐19. We explored provider perceptions of care delivery adaptations and challenges for providers and patients across nine international pediatric diabetes clinics. Methods Providers in a quality improvement collaborative completed a questionnaire about clinic adaptations, including roles, care delivery methods, and provider and patient concerns and challenges. We employed a rapid analysis to identify main themes. Results Providers described adaptations within multiple domains of care delivery, including provider roles and workload, clinical encounter and team meeting format, care delivery platforms, self‐management technology education, and patient‐provider data sharing. Providers reported concerns about potential negative impacts on patients from COVID‐19 and the clinical adaptations it required, including fears related to telemedicine efficacy, blood glucose and insulin pump/pen data sharing, and delayed care‐seeking. Particular concern was expressed about already vulnerable patients. Simultaneously, providers reported 'silver linings' of adaptations that they perceived as having potential to inform care and self‐management recommendations going forward, including time‐saving clinic processes, telemedicine, lifestyle changes compelled by COVID‐19, and improvements to family and clinic staff literacy around data sharing. Conclusions Providers across diverse clinical settings reported care delivery adaptations in response to COVID‐19—particularly telemedicine processes—created challenges and opportunities to improve care quality and patient health. To develop quality care during COVID‐19, providers emphasized the importance of generating evidence about which in‐person or telemedicine processes were most beneficial for specific care scenarios, and incorporating the unique care needs of the most vulnerable patients.