Real-World Diabetes Technology: Overcoming Barriers and Disparities.

Insulin Pumps

The Official Journal of ATTD Advanced Technologies & Treatments for Diabetes Conference 22‐25 February 2023 I Berlin & Online

BackgroundInsulin therapy is required to prevent diabetic ketoacidosis and to optimise blood glucose levels in order to to minimise vascular complications in all Children and Young People with Type 1...

Disclosure: E.E. Bell-Sambataro: None. J. Kilbarger: None. A. Lahoti: None. J.M. Ladd: None. Background: Diabetes technology (continuous glucose monitors (CGM) and insulin pumps), is associated with improved glycemic control and quality of life in children with type 1 diabetes (T1D), but technology use is not equitable. We explored whether rural/urban disparities exist in CGM and pump use among pediatric patients with T1D enrolled in an Accountable Care Organization (ACO) and thus on Medicaid. Methods: We conducted a retrospective cohort study using claims data from the central Ohio ACO database from January 1, 2018 to September 30, 2022. Individuals aged 1 to <21years were identified using T1D-specific billing codes. CGM or pump use was defined using established prescription codes. Rurality was based on zip codes of residence linked to RUCA codes. For descriptive analyses, we calculated proportions, means and standard deviations. T-tests were used to compare continuous variables; chi-square tests were used to compare categorical variables. Multivariable Poisson regression was used to model the association between rurality and CGM or pump use, adjusted for age, sex, race, and era (pre-/post-COVID pandemic). Results: We identified 1079 enrolled individuals with T1D during the study period; 878 (81.4%) were using CGM and 259 (24%) were using insulin pumps. In descriptive analysis, CGM users were younger than non-users (users 11.4±4.4years vs non-users 15.3±3.8years, p<0.01). The distribution of rurality between CGM users and non-users was similar (users 73.5% urban, 26.3% rural vs non-users 66.6% urban, 33.3% rural, p=0.05). There were no age or rurality differences between pump users and non-users (users 12.2±3.8years vs non-users 12.1±4.8years, p=0.86; users 72.6% urban vs non-users 72.1% urban, p=0.92). In regression modeling, rurality was not significantly associated with CGM use (adjusted risk ratio (aRR) 0.95, 95% confidence interval (CI) 0.89-1.02) whereas age, race, era, and pump use were significantly associated. Rurality was significantly associated with pump use (aRR 0.79, 95% CI 0.63-0.99) as were age, race and era. Conclusions: In this cohort limited to pediatric patients with T1D on Medicaid, rural residence did not have a significant effect on CGM use but did make pump use less likely. Those of older age or non-White race were significantly less likely to use CGM and pumps. Future efforts targeting these disparities should be undertaken to increase use of these standard of care technologies among all children. Presentation: 6/2/2024

State of Type 1 Diabetes Care in the United States in 2016-2018 from T1D Exchange Registry Data.

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..

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.

Abstracts from ATTD 20158th International Conference on Advanced Technologies &amp; Treatments for DiabetesParis, France—February 18–21, 2015

Diabetes Technology and the Human Factor.

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.

Abstracts from ATTD 2016 9th International Conference on Advanced Technologies & Treatments for Diabetes Milan, Italy-February 3-6, 2016.

Self-monitoring of blood glucose is essential to optimise glycaemic control in type 1 diabetes mellitus. Continuous glucose monitoring (CGM) systems measure interstitial fluid glucose levels to provide semi-continuous information about glucose levels, which identifies fluctuations that would not have been identified with conventional self-monitoring. Two types of CGM systems can be defined: retrospective systems and real-time systems. Real-time systems continuously provide the actual glucose concentration on a display. Currently, the use of CGM is not common practice and its reimbursement status is a point of debate in many countries. To assess the effects of CGM systems compared to conventional self-monitoring of blood glucose (SMBG) in patients with diabetes mellitus type 1. We searched The Cochrane Library, MEDLINE, EMBASE and CINAHL for the identification of studies. Last search date was June 8, 2011. Randomised controlled trials (RCTs) comparing retrospective or real-time CGM with conventional self-monitoring of blood glucose levels or with another type of CGM system in patients with type 1 diabetes mellitus. Primary outcomes were glycaemic control, e.g. level of glycosylated haemoglobin A1c (HbA1c) and health-related quality of life. Secondary outcomes were adverse events and complications, CGM derived glycaemic control, death and costs. Two authors independently selected the studies, assessed the risk of bias and performed data-extraction. Although there was clinical and methodological heterogeneity between studies an exploratory meta-analysis was performed on those outcomes the authors felt could be pooled without losing clinical merit. The search identified 1366 references. Twenty-two RCTs meeting the inclusion criteria of this review were identified. The results of the meta-analyses (across all age groups) indicate benefit of CGM for patients starting on CGM sensor augmented insulin pump therapy compared to patients using multiple daily injections of insulin (MDI) and standard monitoring blood glucose (SMBG). After six months there was a significant larger decline in HbA1c level for real-time CGM users starting insulin pump therapy compared to patients using MDI and SMBG (mean difference (MD) in change in HbA1c level -0.7%, 95% confidence interval (CI) -0.8% to -0.5%, 2 RCTs, 562 patients, I(2)=84%). The risk of hypoglycaemia was increased for CGM users, but CIs were wide and included unity (4/43 versus 1/35; RR 3.26, 95% CI 0.38 to 27.82 and 21/247 versus 17/248; RR 1.24, 95% CI 0.67 to 2.29). One study reported the occurrence of ketoacidosis from baseline to six months; there was however only one event. Both RCTs were in patients with poorly controlled diabetes.For patients starting with CGM only, the average decline in HbA1c level six months after baseline was also statistically significantly larger for CGM users compared to SMBG users, but much smaller than for patients starting using an insulin pump and CGM at the same time (MD change in HbA1c level -0.2%, 95% CI -0.4% to -0.1%, 6 RCTs, 963 patients, I(2)=55%). On average, there was no significant difference in risk of severe hypoglycaemia or ketoacidosis between CGM and SMBG users. The confidence interval however, was wide and included a decreased as well as an increased risk for CGM users compared to the control group (severe hypoglycaemia: 36/411 versus 33/407; RR 1.02, 95% CI 0.65 to 1.62, 4 RCTs, I(2)=0% and ketoacidosis: 8/411 versus 8/407; RR 0.94, 95% CI 0.36 to 2.40, 4 RCTs, I(2)=0%).Health-related quality of life was reported in five of the 22 studies. In none of these studies a significant difference between CGM and SMBG was found. Diabetes complications, death and costs were not measured.There were no studies in pregnant women with diabetes type 1 and in patients with hypoglycaemia unawareness. There is limited evidence for the effectiveness of real-time continuous glucose monitoring (CGM) use in children, adults and patients with poorly controlled diabetes. The largest improvements in glycaemic control were seen for sensor-augmented insulin pump therapy in patients with poorly controlled diabetes who had not used an insulin pump before. The risk of severe hypoglycaemia or ketoacidosis was not significantly increased for CGM users, but as these events occurred infrequent these results have to be interpreted cautiously.There are indications that higher compliance of wearing the CGM device improves glycosylated haemoglobin A1c level (HbA1c) to a larger extent.

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 understand differences between individuals with type 1 diabetes (T1D) and type 2 diabetes (T2D) in utilization of continuous glucose monitoring (CGM) data to adjust insulin therapy, either continuous subcutaneous insulin infusion (CSII) or multiple daily insulin injections (MDI). We surveyed 300 individuals who regularly used real-time CGM, using 70 questions to obtain information about general CGM use and response to glucose rate of change (ROC) arrows. The survey was completed by 222 T1D and 78 T2D respondents treated with intensive insulin therapy. T1D respondents included CSII (n = 166) and MDI (n = 56) users. T2D respondents were more balanced: 34 (44%) versus 44 (56%), respectively. A larger percentage of T1D then T2D respondents reported a constant use of CGM (85% vs 61%, P < .001). T1D and T2D respondents reported similar substantial increases in correction dosages in response to rapidly increasing glucose (>3 mg/dL/min; 2 arrows up): +140% versus +136%, P = .4534. However, T1D respondents reported making smaller correction dosage reductions than T2D respondents in response to rapidly decreasing glucose (-42% vs -80%, P < .001). Differences between T1D and T2D respondents were also observed in mealtime dosage adjustments in response to rapidly increasing glucose compared to when glucose is stable (flat arrow) at 110 mg/dl: +81% versus +108%, respectively (P = .003). Although these adjustments are statistically different, both are large. CGM users often rely on ROC information when determining insulin doses and tend to be more aggressive in their insulin adjustments despite differences in type of diabetes.