Current state of type 1 diabetes treatment in the U.S.: updated data from the T1D Exchange clinic registry.

Real-World Diabetes Technology: Overcoming Barriers and Disparities.

Type 1 Diabetes in Children and Adolescents.

The use of continuous glucose monitors (CGM) and insulin pumps has revolutionized the care of patients with type 1 diabetes (T1D). Few data are available regarding the use of diabetes technology use in the pregnant T1D population. This study was conducted to evaluate temporal trends of diabetes technology use and predictors of use among pregnant individuals with TID in the United States from 2009 to 2020.MarketScan Research Databases from 2009 to 2020 were used to identify pregnant individuals with T1D who were and were not using CGM and/or insulin pumps. Joinpoint regression analysis was used to estimate the average annual percent change (AAPC) in diabetes technology use over time. Unadjusted and adjusted log-linear Poisson regression models were developed to assess the associations between the outcomes of CGM and insulin pump use and demographic and clinical predictors. Associations were reported as adjusted risk ratios (ARR) with 95% confidence intervals (CI).Among 9,201 pregnancies with T1D, CGM use increased from 2.3% in 2009 to 13.7% in 2020 (AAPC: 13.9%; 95% CI: 11.7-17.1), while insulin pump use remained unchanged from 10.9% in 2009 to 11.8% in 2020 (AAPC: -2.4%; 95% CI: -4.4 to 0.4). Medicaid insurance and obesity were associated with a lower likelihood of CGM use and insulin pump use, while a high obstetric comorbidity index score was associated with a higher likelihood of insulin pump use (ARR: 1.26; 95% CI: 1.05-1.51).From 2009 to 2020, CGM use among pregnant individuals with T1D increased, while insulin pump use remained unchanged. Use varied by patient demographic and clinical factors, most notable for lower likelihood of CGM use and insulin pump use with Medicaid insurance. Although CGM use increased over time, overall CGM use remained lower than expected despite the known benefits of CGM use in improving neonatal outcomes in pregnancies complicated by T1D. · CGM use in pregnant individuals with T1D increased from 2.3 to 13.7%, but pump use was stable.. · Medicaid and obesity were associated with lower CGM and pump use in pregnant individuals with T1D.. · Low CGM use in pregnant T1D individuals highlights barriers and the need for equitable access..

Diabetic retinopathy (DR) is a complication of diabetes that can lead to vision loss. Outcomes of continuous glucose monitoring (CGM) and insulin pump use in DR are not well understood. To assess the use of CGM, insulin pump, or both, and DR and proliferative diabetic retinopathy (PDR) in adults with type 1 diabetes (T1D). A retrospective cohort study of adults with T1D in a tertiary diabetes center and ophthalmology center was conducted from 2013 to 2021, with data analysis performed from June 2022 to April 2023. Use of diabetes technologies, including insulin pump, CGM, and both CGM and insulin pump. The primary outcome was development of DR or PDR. A secondary outcome was the progression of DR for patients in the longitudinal cohort. Multivariable logistic regression models assessed for development of DR and PDR and association with CGM and insulin pump use. A total of 550 adults with T1D were included (median age, 40 [IQR, 28-54] years; 54.4% female; 24.5% Black or African American; and 68.4% White), with a median duration of diabetes of 20 (IQR, 10-30) years, and median hemoglobin A1c (HbA1c) of 7.8% (IQR, 7.0%-8.9%). Overall, 62.7% patients used CGM, 58.2% used an insulin pump, and 47.5% used both; 44% (244 of 550) of the participants had DR at any point during the study. On univariate analysis, CGM use was associated with lower odds of DR and PDR, and CGM with pump was associated with lower odds of PDR (all P < .05), compared with no CGM use. Multivariable logistic regression adjusting for age, sex, race and ethnicity, diabetes duration, microvascular and macrovascular complications, insurance type, and mean HbA1c, showed that CGM was associated with lower odds of DR (odds ratio [OR], 0.52; 95% CI, 0.32-0.84; P = .008) and PDR (OR, 0.42; 95% CI, 0.23-0.75; P = .004), compared with no CGM use. In the longitudinal analysis of participants without baseline PDR, 79 of 363 patients (21.8%) had progression of DR during the study. In this cohort study of adults with T1D, CGM use was associated with lower odds of developing DR and PDR, even after adjusting for HbA1c. These findings suggest that CGM may be useful for diabetes management to mitigate risk for DR and PDR.

To assess the frequency of continuous glucose monitoring (CGM) device use, factors associated with its use, and the relationship of CGM with diabetes outcomes (HbA1c, severe hypoglycemia [SH], and diabetic ketoacidosis [DKA]). Survey questions related to CGM device use 1 year after enrollment in the T1D Exchange clinic registry were completed by 17,317 participants. Participants were defined as CGM users if they indicated using real-time CGM during the prior 30 days. Nine percent of participants used CGM (6% of children <13 years old, 4% of adolescents 13 to <18 years, 6% of young adults 18 to <26 years, and 21% of adults ≥26 years). CGM use was more likely with higher education, higher household income, private health insurance, longer duration of diabetes, and use of insulin pump (P < 0.01 all factors). CGM use was associated with lower HbA1c in children (8.3% vs. 8.6%, P < 0.001) and adults (7.7% vs. 7.9%, P < 0.001). In adults, more frequent use of CGM (≥6 days/week) was associated with lower mean HbA1c. Only 27% of users downloaded data from their device at least once per month, and ≤15% of users reported downloading their device at least weekly. Among participants who used CGM at baseline, 41% had discontinued within 1 year. CGM use is uncommon but associated with lower HbA1c in some age-groups, especially when used more frequently. Factors associated with discontinuation and infrequent use of retrospective analysis of CGM data should be considered in developing next-generation devices and education on CGM use.

AimsWe evaluated associations between social disadvantage and insulin pump and continuous glucose monitor (CGM) use among adults with type 1 diabetes (T1D) in Quebec, Canada, where public funding is available for CGM but not for insulin pumps.Materials and MethodsWe conducted a cross‐sectional analysis using self‐reported survey data collected from April 2019 to October 2023. Primary exposures were social disadvantage indicators (Race, income, education, employment, insurance, immigration, rural/urban location). Primary outcomes were insulin pump and CGM use. Logistic regression was used to assess associations between social disadvantage indicators and the odds of insulin pump and CGM use.ResultsAmong 2380 adults with T1D, 37.4% used insulin pumps and 82.5% used CGM. Insulin pump use was lower among those with income <$80 000 (odds ratio [OR] 0.64 [95% confidence interval 0.50–0.82]), no post‐secondary education (OR 0.62 [0.46–0.85]), non‐White Race (OR 0.47 [0.30–0.73]) and public insurance (OR 0.47 [0.35–0.62]). CGM use was lower only among those with income <$80 000 (OR 0.61 [0.45–0.83]) and public insurance (OR 0.61 [0.45–0.83]). Odds of insulin pump and CGM use were successively lower with an increasing number of social disadvantage indicators. Insulin pump and CGM use were both associated with lower HbA1c but not severe hypoglycaemia or diabetes hospitalisation.ConclusionsSocial disadvantage is associated with lower uptake of insulin pumps and CGM among Quebec adults with T1D, though public funding partially mitigates disparities in CGM use. Given the benefits and increasing recommendations for automated insulin delivery, strategies to increase the uptake of diabetes technologies among socially disadvantaged individuals are required.Plain Language SummarySocial disadvantage is linked to lower use of insulin pumps and CGM in adults with T1D in Quebec. Public funding narrows CGM disparities, but broader equity strategies are needed.

Foster et al. provide a superb and timely analysis of the current state of treatment of people with type 1 diabetes (T1D) in the United States in the years 2016-2018 using extensive data from the T1D Exchange Registry. 1This study is a follow-up to an analysis by Miller et al., utilizing a similar source of data for 2010-2012. 2 This analysis of a rich data set for 22,697 individuals from 81 pediatric and adult endocrinology clinics and practices in the United States provides an update on progress and obstacles facing the entire diabetes community. 1ome of the major findings include:

Background: Recent studies highlight racial-ethnic disparities in insulin pump and continuous glucose monitor (CGM) use in people with type 1 diabetes (T1D), but drivers of disparities remain poorly understood beyond socioeconomic status (SES). Methods: We recruited a diverse sample of young adults (YA) with T1D from six diabetes centers across the United States, enrolling equal numbers of non-Hispanic (NH) White, NH Black, and Hispanic YA. We used multivariate logistic regression to examine to what extent SES, demographics, health care factors (care setting, clinic attendance), and diabetes self-management (diabetes numeracy, self-monitoring of blood glucose, and Self-Care Inventory score) explained insulin pump and CGM use in each racial-ethnic group. Results: We recruited 300 YA with T1D, aged 18-28 years. Fifty-two percent were publicly insured, and the mean hemoglobin A1c was 9.5%. Large racial-ethnic disparities in insulin pump and CGM use existed: 72% and 71% for NH White, 40% and 37% for Hispanic, and 18% and 28% for NH Black, respectively. After multiple adjustment, insulin pump and CGM use remained disparate: 61% and 53% for NH White, 49% and 58% for Hispanic, and 20 and 31% for NH Black, respectively. Conclusions: Insulin pump and CGM use was the lowest in NH Black, intermediate in Hispanic, and highest in NH White YA with T1D. SES was not the sole driver of disparities nor did additional demographic, health care, or diabetes-specific factors fully explain disparities, especially between NH Black and White YA. Future work should examine how minority YA preferences, provider implicit bias, systemic racism, and mistrust of medical systems help to explain disparities in diabetes technology use.

To evaluate the contemporary prevalence of diabetic peripheral neuropathy (DPN) in participants with type 1 diabetes in the T1D Exchange Clinic Registry throughout the U.S. DPN was assessed with the Michigan Neuropathy Screening Instrument Questionnaire (MNSIQ) in adults with ≥5 years of type 1 diabetes duration. A score of ≥4 defined DPN. Associations of demographic, clinical, and laboratory factors with DPN were assessed. Among 5,936 T1D Exchange participants (mean ± SD age 39 ± 18 years, median type 1 diabetes duration 18 years [interquartile range 11, 31], 55% female, 88% non-Hispanic white, mean glycated hemoglobin [HbA1c] 8.1 ± 1.6% [65.3 ± 17.5 mmol/mol]), DPN prevalence was 11%. Compared with those without DPN, DPN participants were older, had higher HbA1c, had longer duration of diabetes, were more likely to be female, and were less likely to have a college education and private insurance (all P < 0.001). DPN participants also were more likely to have cardiovascular disease (CVD) (P < 0.001), worse CVD risk factors of smoking (P = 0.008), hypertriglyceridemia (P = 0.002), higher BMI (P = 0.009), retinopathy (P = 0.004), reduced estimated glomerular filtration rate (P = 0.02), and Charcot neuroarthropathy (P = 0.002). There were no differences in insulin pump or continuous glucose monitor use, although DPN participants were more likely to have had severe hypoglycemia (P = 0.04) and/or diabetic ketoacidosis (P < 0.001) in the past 3 months. The prevalence of DPN in this national cohort with type 1 diabetes is lower than in prior published reports but is reflective of current clinical care practices. These data also highlight that nonglycemic risk factors, such as CVD risk factors, severe hypoglycemia, diabetic ketoacidosis, and lower socioeconomic status, may also play a role in DPN development.

Marked Increases in CGM Use Has Not Prevented Increases in HbA1c Levels in Participants in the T1D Exchange (T1DX) Clinic Network

ObjectiveTo examine the use of multiple daily injections (MDI), insulin pumps, self-measured blood glucose (SMBG), and continuous glucose monitoring (CGM) systems, and their association with glycated hemoglobin (HbA1c), diabetic ketoacidosis (DKA), and severe hypoglycemia.MethodsIn a pediatric population-based nationwide cross-sectional study, we analyzed data from 2623 participants up to 18 years of age with type 1 diabetes, using 2017 annual data from the Norwegian Childhood Diabetes Registry. HbA1c was adjusted for age, gender, and diabetes duration. Using a linear mixed-effects model, we assessed HbA1c and the incidence of DKA and severe hypoglycemia according to the use of MDI, insulin pumps, SMBG, and CGM.ResultsWe observed that 74.7% of participants were using an insulin pump and 52.6% were using a CGM system. Mean HbA1c was 7.8% (62 mmol/mol). The HbA1c of pump users was 0.14 percentage points (pp) higher than that of MDI users. Fewer pump users than MDI users achieved an HbA1c of < 7.5% (38.3 vs. 41.6%). CGM users had a 0.18 pp lower HbA1c than SMBG users, with 40.5 and 38.0%, respectively, achieving an HbA1c of < 7.5%. The incidence of severe hypoglycemia or hospitalization due to DKA was not different in pump and CGM users compared with nonusers. Compared with other insulin pumps, patch pump use was associated with a significantly lower odds ratio for DKA.ConclusionsDespite the broad use of diabetes technology, as many as 61% of our pediatric cohort did not reach the HbA1c target recommended by the International Society for Pediatric and Adolescent Diabetes (ISPAD). Lower HbA1c was associated with CGM use but not with insulin pump use. Acute complications were not less frequent in the groups using insulin pumps or CGM compared with those using MDI and SMBG. Further research is required to explore the lower incidence of DKA among patch pump users.Trial RegistrationClinicalTrials.gov identifier NCT04201171.Supplementary InformationThe online version contains supplementary material available at 10.1007/s13300-021-01127-6.

The impressive progress achieved in recent years in diabetes technologies has made diabetes technological devices such as continuous subcutaneous insulin infusion (CSII) and continuous glucose monitoring (CGM) a significant part of diabetes treatment. Many studies conducted in recent years emphasized the advantages of using these technologies. The concept of the “human factor” in diabetes technologies as discussed in this chapter has several different aspects. First, it can refer to the way patients are satisfied with the use of the device and whether it is perceived convenient or inconvenient. For example, is the device perceived as “user friendly” (easy to learn and to operate, comfortable, does not cause many hassles). Second, there is the issue of effectiveness of the technology as it relates to their day-to-day diabetes management. For example, there is an improvement in glycemic control when one diabetes treatment regimen is compared to another (i.e., CSII vs. multiple daily injections (MDI)). Those two fundamental aspects may have different meanings for different groups. For example, different age groups (toddlers, children, adolescents, young adults, adults, and older people) can see different advantages and disadvantages in technological devices. The feasibility and utility of technological devices also need to fit the environments in which they will be used, such as school, the work place, and/or home. Specific subgroups such as diabetic youth with eating disorders can have unique interactions with diabetes technologies. In addition, diabetes technologies can be used as a measurement device, providing more rich and accurate data about patients' self-care that can contribute to our understanding of concepts such as adherence and satisfaction, and they can provide measurement tools to assess how glycemic control can effect cognition and intelligence. The present chapter will review articles published in the last year that have studied some of these issues.

Since the discovery of insulin 100 years ago, there have been continual advances in medications and technologies for the management of type 1 diabetes (T1D). Despite these innovations, many people struggle to meet hemoglobin A1c (HbA1c) goals, and the gap is larger in individuals from minority groups1 or lower socioeconomic status. Quality improvement (QI) efforts have been shown to improve outcomes for patients with T1D.2 The T1D Exchange Quality Improvement Collaborative (T1DX-QI) was established in 2016 with 10 pilot centers, primarily consisting of pediatric sites.3, 4 The collaborative has now grown to 41 participating centers (28 pediatric and 13 adult centers) across the United States (Figure 1). In this commentary, we describe the goals of the T1DX-QI and highlight QI and population health conference abstracts recently presented at the 2021 T1DX-QI conference. The T1DX-QI has five core goals: 1. Collecting the right kind of data to drive real-world improvements. 2. Using data to make the best decisions for the population of people with T1D.5 3. Designing and testing initiatives to improve clinical and T1D population health outcomes.6, 7 4. Quantifying factors that are contributing to driving positive outcomes for the T1D population.8, 9 5. Generating real-world insights.5, 9 These goals are summarized by a population health improvement framework to improve diabetes clinical processes (Figure 2). At the T1DX-QI 2021 conference, 21 QI and population health abstracts were shared that highlight how endocrinology centers are addressing the five goals of the T1DX-QI network. Common themes from the abstracts involved infrastructural support of QI, developing population health initiatives, improving psychosocial support, and closing inequities in diabetes technology access and use. A strong QI culture is important for implementing and sustaining change. A QI culture self-assessment completed during the orientation phase of joining T1DX-QI provided the framework for T1DX-QI to develop center-specific QI coaching and support skill growth.10 At the cornerstone of T1DX-QI is an electronic medical record (EMR) database used for collecting and benchmarking population health real-world data. Benchmarking reports help identify areas for center-specific QI efforts.11 The data shared from benchmarking reports help individual centers identify needs at their institutions and implement QI initiatives to improve diabetes care. These initiatives are supported by improvement coaches, and the outcomes are shared frequently with all the centers, contributing to more rapid success. EMR-based flowsheets can be used by diabetes team members to help standardize diabetes care and collect database metrics. The collected data are mapped from each center's EMR to the T1DX-QI database, following a unified data schema. The quality of data within the EMR database is dependent on validation of data at the site level. An open-source lightweight schema and data validation framework was added to the data extraction, mapping, and validation pipeline by one center to reduce the number of errors in the submitted data.12 By sharing successful learnings such as these, new centers can more rapidly map their data to the T1DX-QI database and more fully participate in QI initiatives. The sharing of data in the T1DX-QI database informs QI-based population health interventions to improve outcomes. Examples include the 4T Program (Teamwork, Targets, Technology, and Tight Control), which incorporated technology and intensified education to improve outcomes during the first year after diabetes diagnosis in a pediatric population,13 and the report from Nudrat Noor et al that described obesity trends over 5 years for people with T1D.14 In addition to using these data to provide insights for designing QI interventions, T1DX-QI QI teams also use them to support policy changes, for example, Medicaid coverage of continuous glucose monitors (CGMs) in Texas as reported by McCann-Crosby et al15 and to predict HbA1c rise in a pediatric population using a machine learning algorithm.16 These learnings and successes can be used by other centers to support people with newly diagnosed or established diabetes. T1D is associated with greater psychosocial challenges.17 The presence of depression, anxiety, and diabetes distress can affect the individual's ability to self-manage their diabetes. The American Diabetes Association Standards of Medical Care recommend routine screening for depression.18 One of the early projects of the T1DX-QI was to assess and improve rates of depression screening. Centers have used the Patient Health Questionnaire screening tools with EMR-based tracking and have successfully increased the percent of their T1D population who receive annual depression screening.19 Because diabetes-specific psychosocial distress is associated with increased rates of diabetic ketoacidosis (DKA), Dei-Tutu et al20 implemented annual depression screening along with an EMR-based risk score for DKA admission for their pediatric population. Those who met criteria were enrolled in an extra-care program to prevent DKA admissions. This QI intervention reduced DKA admissions from baseline 9.5% to 6.02% in 2020. One of the biggest challenges facing youth with T1D is the transition from pediatric care to adult care.21 This transition is associated with an increased incidence of acute complications.22 Given the importance of transition education and preparation, several centers have implemented QI efforts to improve transition discussions and readiness. Standardizing and documenting the processes, including incorporating transition assessments, individualized education sessions, and action plans, led to significant improvements in the number of transition discussions and identification of knowledge gaps with adolescents in the centers to improve diabetes self-management.23 Advances in diabetes technology, such as the availability and use of CGMs, have greatly improved the lives of people with T1D. Unfortunately, there are inequities in the use of and access to these devices.24, 25 In the T1DX-QI EMR database, individuals who identified as Hispanic or Non-Hispanic Black had lower rates of pump usage compared to individuals who identified as Non-Hispanic White.26 Several centers have benchmarked rates of CGM and insulin pump use in their own center and found similar trends. These data show the need for identifying factors associated with technology use by individuals of minority status and developing targeted interventions to close these gaps. One of the barriers to technology access is the absence of insurance coverage for those on public insurance. McCann-Crosby et al15 used QI methodology to track CGM usage and presented the data to state Medicaid to obtain CGM coverage for those with public insurance. Other sites conducted semistructured interviews with parents of Non-Hispanic Black youth with T1D to understand barriers to technology use. A pediatric center developed interventions to increase provider education, patient support, and advocacy to increase CGM uptake in those with Medicaid and those with HbA1c > 9%. An adult center increased CGM use by developing provider training in CGMs and inequity in access while streamlining prescribing workflows. The gaps identified by these centers and the interventions to improve equity can be used by other clinics to bolster QI efforts. Benchmarking of T1D quality metrics identifies gaps in diabetes care that are important targets for QI interventions. T1DX-QI data examined by race and ethnicity exposed inequities in care. This has led to site-specific data analyses and implementation of QI interventions focused on decreasing inequities in care by implementing interventions including increasing use of CGMs, improving psychosocial support, and assessing their effectiveness with frequent data collection and analyses. Sharing of these data in the context of a learning health system can accelerate improvements in diabetes care and health equity. We appreciate all members of the T1DX-QI Collaborative, patients, and partners. We are grateful to the Helmsley Charitable Trust that funds the T1DX-QI Collaborative. RR is associate editor for the Journal of Diabetes. RSW participates in multicenter clinical trials, through her institution, sponsored by Medtronic, Insulet, Eli Lilly, Novo Nordisk, Boehringer Ingelheim, and Kowa. Dexcom has contributed CGMs for other studies. OE is a member of the Medtronic Diabetes Health Equity Advisory Board; He is the principal investigator for research projects funded by Eli Lilly, Medtronic Diabetes, Abbot, and Dexcom. All the funds for these industry-funded projects and board roles are paid directly through his organization, T1D Exchange. All other authors report no conflicts.

756-P: Socioeconomic and Self-Management Factors Do Not Explain Racial/Ethnic Disparities in Advanced Diabetes Technology Use among Young Adults (YA) with Type 1 Diabetes (T1D)

828-P: Inpatient Provider Experience with Diabetes Technology Use on General Medicine Wards: Continuous Subcutaneous Insulin Infusion (CSII) and Continuous Glucose Monitors (CGM)