Association of Real-time Continuous Glucose Monitoring With Glycemic Control and Acute Metabolic Events Among Patients With Insulin-Treated Diabetes.

Importance: Continuous glucose monitoring (CGM) is recommended for patients with type 1 diabetes; observational evidence for CGM in patients with insulin-treated type 2 diabetes is lacking. Objective: To estimate clinical outcomes of real-time CGM initiation. Design, Setting, and Participants: Exploratory retrospective cohort study of changes in outcomes associated with real-time CGM initiation, estimated using a difference-in-differences analysis. A total of 41 753 participants with insulin-treated diabetes (5673 type 1; 36 080 type 2) receiving care from a Northern California integrated health care delivery system (2014-2019), being treated with insulin, self-monitoring their blood glucose levels, and having no prior CGM use were included. Exposures: Initiation vs noninitiation of real-time CGM (reference group). Main Outcomes and Measures: Ten end points measured during the 12 months before and 12 months after baseline: hemoglobin A1c (HbA1c); hypoglycemia (emergency department or hospital utilization); hyperglycemia (emergency department or hospital utilization); HbA1c levels lower than 7%, lower than 8%, and higher than 9%; 1 emergency department encounter or more for any reason; 1 hospitalization or more for any reason; and number of outpatient visits and telephone visits. Results: The real-time CGM initiators included 3806 patients (mean age, 42.4 years [SD, 19.9 years]; 51% female; 91% type 1, 9% type 2); the noninitiators included 37 947 patients (mean age, 63.4 years [SD, 13.4 years]; 49% female; 6% type 1, 94% type 2). The prebaseline mean HbA1c was lower among real-time CGM initiators than among noninitiators, but real-time CGM initiators had higher prebaseline rates of hypoglycemia and hyperglycemia. Mean HbA1c declined among real-time CGM initiators from 8.17% to 7.76% and from 8.28% to 8.19% among noninitiators (adjusted difference-in-differences estimate, -0.40%; 95% CI, -0.48% to -0.32%; P < .001). Hypoglycemia rates declined among real-time CGM initiators from 5.1% to 3.0% and increased among noninitiators from 1.9% to 2.3% (difference-in-differences estimate, -2.7%; 95% CI, -4.4% to -1.1%; P = .001). There were also statistically significant differences in the adjusted net changes in the proportion of patients with HbA1c lower than 7% (adjusted difference-in-differences estimate, 9.6%; 95% CI, 7.1% to 12.2%; P < .001), lower than 8% (adjusted difference-in-differences estimate, 13.1%; 95% CI, 10.2% to 16.1%; P < .001), and higher than 9% (adjusted difference-in-differences estimate, -7.1%; 95% CI, -9.5% to -4.6%; P < .001) and in the number of outpatient visits (adjusted difference-in-differences estimate, -0.4; 95% CI, -0.6 to -0.2; P < .001) and telephone visits (adjusted difference-in-differences estimate, 1.1; 95% CI, 0.8 to 1.4; P < .001). Initiation of real-time CGM was not associated with statistically significant changes in rates of hyperglycemia, emergency department visits for any reason, or hospitalizations for any reason. Conclusions and Relevance: In this retrospective cohort study, insulin-treated patients with diabetes selected by physicians for real-time continuous glucose monitoring compared with noninitiators had significant improvements in hemoglobin A1c and reductions in emergency department visits and hospitalizations for hypoglycemia, but no significant change in emergency department visits or hospitalizations for hyperglycemia or for any reason. Because of the observational study design, findings may have been susceptible to selection bias.

Telemedicine and urban diabetes during COVID-19 pandemic in Milano, Italy during lock-down: epidemiological and sociodemographic picture.

BACKGROUND: Since 2010, more than half of World population lives in Urban Environments. Urban Diabetes has arisen as a novel nosological entity in Medicine. Urbanization leads to the accrual of a number of factors increasing the vulnerability to diabetes mellitus and related diseases. Herein we report clinical-epidemiological data of the Milano Metropolitan Area in the contest of the Cities Changing Diabetes Program. Since the epidemiological picture was taken in January 2020, on the edge of COVID-19 outbreak in the Milano Metropolitan Area, a perspective addressing potential interactions between diabetes and obesity prevalence and COVID-19 outbreak, morbidity and mortality will be presented. To counteract lock-down isolation and, in general, social distancing a pilot study was conducted to assess the feasibility and efficacy of tele-monitoring via Flash Glucose control in a cohort of diabetic patients in ASST North Milano. METHODS: Data presented derive from 1. ISTAT (National Institute of Statistics of Italy), 2. Milano ATS web site (Health Agency of Metropolitan Milano Area), which entails five ASST (Health Agencies in the Territories). A pilot study was conducted in 65 screened diabetic patients (only 40 were enrolled in the study of those 36 were affected by type 2 diabetes and 4 were affected by type 1 diabetes) of ASST North Milano utilizing Flash Glucose Monitoring for 3 months (mean age 65 years, HbA1c 7,9%. Patients were subdivided in 3 groups using glycemic Variability Coefficient (VC): a. High risk, VC > 36, n. 8 patients; Intermediate risk 20 < VC < 36, n. 26 patients; Low risk VC < 20, n. 4 patients. The control group was constituted by 26 diabetic patients non utilizing Flash Glucose monitoring. RESULTS: In a total population of 3.227.264 (23% is over 65 y) there is an overall prevalence of 5.65% with a significant difference between Downtown ASST (5.31%) and peripheral ASST (ASST North Milano, 6.8%). Obesity and overweight account for a prevalence of 7.8% and 27.7%, respectively, in Milano Metropolitan Area. We found a linear relationship (R = 0.36) between prevalence of diabetes and aging index. Similarly, correlations between diabetes prevalence and both older people depending index and structural dependence index (R = 0.75 and R = 0.93, respectively), were found. A positive correlation (R = 0.46) with percent of unoccupied people and diabetes prevalence was also found. A reverse relationship between diabetes prevalence and University level instruction rate was finally identified (R = - 0.82). Our preliminary study demonstrated a reduction of Glycated Hemoglobin (p = 0.047) at 3 months follow-up during the lock-down period, indicating Flash Glucose Monitoring and remote control as a potential methodology for diabetes management during COVID-19 lock-down. HYPOTHESIS AND DISCUSSION: The increase in diabetes and obesity prevalence in Milano Metropolitan Area, which took place over 30 years, is related to several environmental factors. We hypothesize that some of those factors may have also determined the high incidence and virulence of COVID-19 in the Milano area. Health Agencies of Milano Metropolitan Area are presently taking care of diabetic patients facing the new challenge of maintaining sustainable diabetes care costs in light of an increase in urban population and of the new life-style. The COVID-19 pandemic will modify the management of diabetic and obese patients permanently, via the implementation of approaches that entail telemedicine technology. The pilot study conducted during the lock-down period indicates an improvement of glucose control utilizing a remote glucose control system in the Milano Metropolitan Area, suggesting a wider utilization of similar methodologies during the present "second wave" lock-down.

Impact of COVID-19 on Health Economics and Technology of Diabetes Care: Use Cases of Real-Time Continuous Glucose Monitoring to Transform Health Care During a Global Pandemic.

Background: The coronavirus disease 2019 (COVID-19) pandemic has exposed vulnerabilities and placed tremendous financial pressure on nearly all aspects of the U.S. health care system. Diabetes care is an example of the confluence of the pandemic and heightened importance of technology in changing care delivery. It has been estimated the added total direct U.S. medical cost burden due to COVID-19 to range between $160B (20% of the population infected) and $650B (80% of the population infected) over the course of the pandemic. The corresponding range for the population with diabetes is between $16B and $65B, representing between 5% and 20% of overall diabetes expenditure in the United States. We examine the evidence to support allocating part of this added spend to infrastructure capabilities to accelerate remote monitoring and management of diabetes. Methods and Results: We reviewed recent topical literature and COVID-19-related analyses in the public health, health technology, and health economics fields in addition to databases and surveys from government sources and the private sector. We summarized findings on use cases for real-time continuous glucose monitoring in the community, for telehealth, and in the hospital setting to highlight the successes and challenges of accelerating the adoption of a digital technology out of necessity during the pandemic and beyond. Conclusions: One critical and lasting consequence of the pandemic will be the accelerated adoption of digital technology in health care delivery. We conclude by discussing ways in which the changes wrought by COVID-19 from a health care, policy, and economics perspective can add value and are likely to endure postpandemic.

Effects of real-time continuous glucose monitoring in type 1 diabetes: a meta-analysis of randomized controlled trials.

AIMS: Self-monitoring of blood glucose (SMBG) represented a major breakthrough in the treatment of type 1 diabetes. The aim of the present meta-analysis is to assess the effect of continues glucose monitoring (CGM) and flash glucose monitoring (FGM), on glycemic control in type 1 diabetes. MATERIALS AND METHODS: The present analysis includes randomized clinical trials comparing CGM or FGM with SMBG, with a duration of at least 12 weeks, identified in Medline or clinicaltrials.gov. The principal endpoint was HbA1c at the end of the trial. A secondary endpoint was severe hypoglycemia. Mean and 95% confidence intervals for HbA1c and Mantel-Haenzel odds ratio [MH-OR] for severe hypoglycemia were calculated, using random effect models. A sensitivity analysis was performed using fixed effect models. In addition, the following secondary endpoints were explored, using the same methods: time in range, health-related quality of life, and treatment satisfaction. Separate analyses were performed for trials comparing CGM with SMBG, and those comparing CGM + CSII and SMBG + MDI and CGM-regulated insulin infusion system (CRIS) and CSII + SMBG. RESULTS: CGM was associated with a significantly lower HbA1c at endpoint in comparison with SMBG (- 0.24 [- 0.34, - 0.13]%); CGM was associated with a significantly lower risk of severe hypoglycemia than SMBG. Treatment satisfaction and quality of life were not measured, or not reported, in the majority of studies. FGM showed a significant reduction in the incidence of mild hypoglycemia and an increased treatment satisfaction, but no significant results are shown in HbA1c. CGM + CSII in comparison with SMBG + MDI was associated with a significant reduction in HbA1c. Only two trials with a duration of at least 12 weeks compared a CRIS with SMBG + CSII; HbA1c between the two treatment arms was not statistically significant (difference in means: - 0.23 [- 0.91; 0.46]%; p = 0.52). CONCLUSION: GCM compared to SMBG has showed a reduction in HbA1c and severe hypoglycemia in patient with type 1 diabetes. The comparison between CGM + CSII and SMBG + MDI showed a large reduction in HbA1c; it is conceivable that the effects of CSII + CGM on glycemic control additives. The only comparison available between FGM and SMBG was conducted in patients in good control.

Uninterrupted continuous glucose monitoring access is associated with a decrease in HbA1c in youth with type 1 diabetes and public insurance.

OBJECTIVE: Continuous glucose monitor (CGM) use is associated with improved glucose control. We describe the effect of continued and interrupted CGM use on hemoglobin A1c (HbA1c) in youth with public insurance. METHODS: We reviewed 956 visits from 264 youth with type 1 diabetes (T1D) and public insurance. Demographic data, HbA1c and two-week CGM data were collected. Youth were classified as never user, consistent user, insurance discontinuer, and self-discontinuer. Visits were categorized as never-user visit, visit before CGM start, visit after CGM start, visit with continued CGM use, visit with initial loss of CGM, visit with continued loss of CGM, and visit where CGM is regained after loss. Multivariate regression adjusting for age, sex, race, diabetes duration, initial HbA1c, and body mass index were used to calculate adjusted mean and delta HbA1c. RESULTS: Adjusted mean HbA1c was lowest for the consistent user group (HbA1c 8.6%;[95%CI 7.9,9.3]). Delta HbA1c (calculated from visit before CGM start) was lower for visit after CGM start (-0.39%;[95%CI -0.78,-0.02]) and visit with continued CGM use (-0.29%;[95%CI -0.61,0.02]), whereas it was higher for visit with initial loss of CGM (0.40%;[95%CI -0.06,0.86]), visit with continued loss of CGM (0.46%;[95%CI 0.06,0.85]), and visit where CGM is regained after loss (0.57%;[95%CI 0.06,1.10]). CONCLUSIONS: Youth with public insurance using CGM have improved HbA1c, but only when CGM use is uninterrupted. Interruptions in use, primarily due to gaps in insurance coverage of CGM, were associated with increased HbA1c. These data support both initial and ongoing coverage of CGM for youth with T1D and public insurance.

Racial-Ethnic Inequity in Young Adults With Type 1 Diabetes.

CONTEXT: Minority young adults (YA) currently represent the largest growing population with type 1 diabetes (T1D) and experience very poor outcomes. Modifiable drivers of disparities need to be identified, but are not well-studied. OBJECTIVE: To describe racial-ethnic disparities among YA with T1D and identify drivers of glycemic disparity other than socioeconomic status (SES). DESIGN: Cross-sectional multicenter collection of patient and chart-reported variables, including SES, social determinants of health, and diabetes-specific factors, with comparison between non-Hispanic White, non-Hispanic Black, and Hispanic YA and multilevel modeling to identify variables that account for glycemic disparity apart from SES. SETTING: Six diabetes centers across the United States. PARTICIPANTS: A total of 300 YA with T1D (18-28 years: 33% non-Hispanic White, 32% non-Hispanic Black, and 34% Hispanic). MAIN OUTCOME: Racial-ethnic disparity in HbA1c levels. RESULTS: Non-Hispanic Black and Hispanic YA had lower SES, higher HbA1c levels, and much lower diabetes technology use than non-Hispanic White YA (P < 0.001). Non-Hispanic Black YA differed from Hispanic, reporting higher diabetes distress and lower self-management (P < 0.001). After accounting for SES, differences in HbA1c levels disappeared between non-Hispanic White and Hispanic YA, whereas they remained for non-Hispanic Black YA (+ 2.26% [24 mmol/mol], P < 0.001). Diabetes technology use, diabetes distress, and disease self-management accounted for a significant portion of the remaining non-Hispanic Black-White glycemic disparity. CONCLUSION: This study demonstrated large racial-ethnic inequity in YA with T1D, especially among non-Hispanic Black participants. Our findings reveal key opportunities for clinicians to potentially mitigate glycemic disparity in minority YA by promoting diabetes technology use, connecting with social programs, and tailoring support for disease self-management and diabetes distress to account for social contextual factors.

Diabetes Healthcare Professionals Use Multiple Continuous Glucose Monitoring Data Indicators to Assess Glucose Management.

BACKGROUND: Continuous glucose monitoring (CGM) offers multiple data features that can be leveraged to assess glucose management. However, how diabetes healthcare professionals (HCPs) actually assess CGM data and the extent to which they agree in assessing glycemic management are not well understood. METHODS: We asked HCPs to assess ten de-identified CGM datasets (each spanning seven days) and rank order each day by relative glycemic management (from "best" to "worst"). We also asked HCPs to endorse features of CGM data that were important in making such assessments. RESULTS: In the study, 57 HCPs (29 endocrinologists; 28 diabetes educators) participated. Hypoglycemia and glycemic variance were endorsed by nearly all HCPs to be important (91% and 88%, respectively). Time in range and daily lows and highs were endorsed more frequently by educators (all Ps < .05). On average, HCPs endorsed 3.7 of eight data features. Overall, HCPs demonstrated agreement in ranking days by relative glycemic control (Kendall's W = .52, P < .001). Rankings were similar between endocrinologists and educators (R2 = .90, Cohen's kappa = .95, mean absolute error = .4 [all Ps < .05]; Mann-Whitney U = 41, P = .53). CONCLUSIONS: Consensus in the endorsement of certain data features and agreement in assessing glycemic management were observed. While some practice-specific differences in feature endorsement were found, no differences between educators and endocrinologists were observed in assessing glycemic management. Overall, HCPs tended to consider CGM data holistically, in alignment with published recommendations, and made converging assessments regardless of practice.

Diabetes care: addressing psychosocial well-being in young adults with a newly developed assessment tool.

BACKGROUND: Psychosocial assessment should be part of clinic visits for people with diabetes mellitus (DM). AIMS: To assess the usage and acceptance of a diabetes psychosocial assessment tool (DPAT) and to profile the clinical and psychosocial characteristics of young people with diabetes. METHODS: Over a 12-month period, young adults (18-25 years) attending diabetes clinic were offered DPAT. The tool embeds validated screening tools including the Problem Areas in Diabetes 20 (PAID-20) questionnaire, the Patient Health Questionnaire-4 (PHQ-4) and the World Health Organization Well-Being Index-5 (WHO-5). Baseline clinical data were collected and questions regarding social support, body image, eating concerns, hypoglycaemia and finances were included. RESULTS: Over the 12 month, the form was offered to 155 participants (64.6% of eligible attendees). The majority (96.1%) had type 1 DM with a mean duration of 10.5 (±5.3 SD) years. Average glycated haemoglobin (HbA1c) was 8.7% (±1.5 SD) (or 71.2 mmol/mol ±16.5 SD). Severe diabetes-related distress (PAID-20 ≥ 40) was found in 19.4%. Low WHO-5 scores (28-50 points) were seen in 14.8%. PHQ-4 identified 25.8% with anxiety and 16.1% with depression. Significant weight, shape and eating concerns were identified in 27.1, 26.6 and 28.4%, respectively. Serious hypoglycaemia concerns were raised by 4.5%. CONCLUSION: DPAT revealed a high prevalence of psychosocial stress among young adults with DM. The tool was easy to use and accepted by patients and may aid streamlining referrals to relevant members of a multidisciplinary team.

Simple Linear Support Vector Machine Classifier Can Distinguish Impaired Glucose Tolerance Versus Type 2 Diabetes Using a Reduced Set of CGM-Based Glycemic Variability Indices.

BACKGROUND: Many glycemic variability (GV) indices exist in the literature. In previous works, we demonstrated that a set of GV indices, extracted from continuous glucose monitoring (CGM) data, can distinguish between stages of diabetes progression. We showed that 25 indices driving a logistic regression classifier can differentiate between healthy and nonhealthy individuals; whereas 37 GV indices and four individual parameters, feeding a polynomial-kernel support vector machine (SVM), can further distinguish between impaired glucose tolerance (IGT) and type 2 diabetes (T2D). The latter approach has some limitations to interpretability (complex model, extensive index pool). In this article, we try to obtain the same performance with a simpler classifier and a parsimonious subset of indices. METHODS: We analyzed the data of 62 subjects with IGT or T2D. We selected 17 interpretable GV indices and four parameters (age, sex, BMI, waist circumference). We trained a SVM on the data of a baseline visit and tested it on the follow-up visit, comparing the results with the state-of-art methods. RESULTS: The linear SVM fed by a reduced subset of 17 GV indices and four basic parameters achieved 82.3% accuracy, only marginally worse than the reference 87.1% (41-features polynomial-kernel SVM). Cross-validation accuracies were comparable (69.6% vs 72.5%). CONCLUSION: The proposed SVM fed by 17 GV indices and four parameters can differentiate between IGT and T2D. Using a simpler model and a parsimonious set of indices caused only a slight accuracy deterioration, with significant advantages in terms of interpretability.

Assessment of a Precision Medicine Analysis of a Behavioral Counseling Strategy to Improve Adherence to Diabetes Self-management Among Youth: A Post Hoc Analysis of the FLEX Trial.

Importance: The Flexible Lifestyles Empowering Change (FLEX) trial, an 18-month randomized clinical trial testing an adaptive behavioral intervention in adolescents with type 1 diabetes, showed no overall treatment effect for its primary outcome, change in hemoglobin A1c (HbA1c) percentage of total hemoglobin, but demonstrated benefit for quality of life (QoL) as a prespecified secondary outcome. Objective: To apply a novel statistical method for post hoc analysis that derives an individualized treatment rule (ITR) to identify FLEX participants who may benefit from intervention based on changes in HbA1c percentage (primary outcome), QoL, and body mass index z score (BMIz) (secondary outcomes) during 18 months. Design, Setting, and Participants: This multisite clinical trial enrolled 258 adolescents aged 13 to 16 years with type 1 diabetes for 1 or more years, who had literacy in English, HbA1c percentage of total hemoglobin from 8.0% to 13.0%, a participating caregiver, and no other serious medical conditions. From January 5, 2014, to April 4, 2016, 258 adolescents were recruited. The post hoc analysis excluded adolescents missing outcome measures at 18 months (2 participants [0.8%]) or continuous glucose monitoring data at baseline (40 participants [15.5%]). Data were analyzed from April to December 2018. Interventions: The FLEX intervention included a behavioral counseling strategy that integrated motivational interviewing and problem-solving skills training to increase adherence to diabetes self-management. The control condition entailed usual diabetes care. Main Outcomes and Measures: Subgroups of FLEX participants were derived from an ITR estimating which participants would benefit from intervention, which would benefit from control conditions, and which would be indifferent. Multiple imputation by chained equations and reinforcement learning trees were used to estimate the ITR. Subgroups based on ITR pertaining to changes during 18 months in 3 univariate outcomes (ie, HbA1c percentage, QoL, and BMIz) and a composite outcome were compared by baseline demographic, clinical, and psychosocial characteristics. Results: Data from 216 adolescents in the FLEX trial were reanalyzed (166 [76.9%] non-Hispanic white; 108 teenaged girls [50.0%]; mean [SD] age, 14.9 [1.1] years; mean [SD] diabetes duration, 6.3 [3.7] years). For the univariate outcomes, a large proportion of FLEX participants had equivalent predicted outcomes under intervention vs usual care settings, regardless of randomization, and were assigned to the muted group (HbA1c: 105 participants [48.6%]; QoL: 63 participants [29.2%]; BMIz: 136 participants [63.0%]). Regarding the BMIz univariate outcome, mean baseline BMIz of participants assigned to the muted group was lower than that of those assigned to the intervention and control groups (muted vs intervention: mean difference, 0.48; 95% CI, 0.21 to 0.75; P = .002; muted vs control: mean difference, 0.86; 95% CI, 0.61 to 1.11; P < .001); this group also had a higher proportion of individuals with underweight or normal weight using weight status cutoffs (95 [69.9%] in muted group vs 24 [54.6%] in intervention group and 11 [30.6%] in control group; χ24 = 24.67; P < .001). The approach identified subgroups estimated to benefit based on HbA1c percentage (54 participants [25.0%]), QoL (89 participants [41.2%]), and BMIz (44 participants [20.4%]). Regarding the HbA1c percentage outcome, participants expected to benefit from the intervention did not have significantly higher baseline HbA1c percentages than those expected to benefit from usual care (9.4% vs 9.2%; difference, 0.2%; 95% CI, -0.16% to 0.56%; P = .44). However, participants in the muted group had higher mean HbA1c percentages at baseline than those assigned to the intervention or control groups (muted vs intervention: 9.9% vs 9.4%; difference, 0.5%; 95% CI, 0.13% to 0.89%; P = .02; muted vs control; 9.9% vs 9.2%; difference, 0.7%; 95% CI, 0.34% to 1.08%; P = .001). No significant differences were found between subgroups estimated to benefit in terms of the composite outcome from the FLEX intervention (91 participants [42.1%]) vs usual care (125 participants [57.9%]). Conclusions and Relevance: The precision medicine approach represents a conceptually and analytically novel approach to post hoc subgroup identification. More work is needed to understand markers of positive response to the FLEX intervention. Trial Registration: ClinicalTrial.gov identifier: NCT01286350.

Defining High Glycemic Variability in Type 1 Diabetes: Comparison of Multiple Indexes to Identify Patients at Risk of Hypoglycemia.

Background: International consensus on the use of continuous glucose monitoring (CGM) recommends coefficient of variation (CV) as the metric of choice to express glycemic variability (GV) with a cutoff of 36% to define unstable diabetes. Even though, CV is associated with hypoglycemia in type 2 diabetes patients, the evidence on the use of one particular measure of GV in type 1 diabetes (T1DM) patients as a predictor of hypoglycemia is limited. Methods: A cohort of T1DM ambulatory patients was evaluated using CGM. Number and incidence rate of events <54 and <70 mg/dL were calculated. Bivariate and multivariate analysis of different glycemic indexes and clinical variables were performed to identify those associated with hypoglycemia. Receiver operating characteristic (ROC) curve analysis for each of the glycemic indexes was performed to define the best index and its optimal cutoff threshold to discriminate patients with events of hypoglycemia. Results: Seventy-three patients were included. A total of 128 events <54 mg/dL were recorded in 34 patients, and 350 events <70 mg/dL were registered in 51 patients. CV was the only variable significantly associated with hypoglycemia <54 mg/dL in the multivariate analysis (adjusted relative risk [aRR] 1.44, 95% confidence interval [CI]: 1.10-1.88, P = 0.008). CV, HbA1c (glycated hemoglobin), and mean glucose were associated with events <70 mg/dL. ROC curve analysis showed that, among GV metrics, CV had the best performance to discriminate patients with events <54 mg/dL (area under the curve [AUC] 0.87, 95% CI: 0.79-0.95) and events <70 mg/dL (AUC 0.79, 95% CI: 0.68-0.90) with optimal cutoff thresholds values of 34% and 31%, respectively. Among glycemic risk (GR) indexes, low blood glucose index (LBGI) showed the best performance. Conclusions: This analysis shows that CV is the best GV index, and LBGI the best GR index, to identify patients at risk of clinically significant hypoglycemia and hypoglycemia alert events in T1DM patients.

Socioeconomic status moderates the association between patient satisfaction with community health service and self-management behaviors in patients with type 2 diabetes: A cross-sectional survey in China.

The objective of this study was to examine the association between patient satisfaction with community health service (CHS) and self-management behaviors in patients with type 2 diabetes mellitus (T2DM).In all, 1691 patients with T2DM from 8 community health centers in 5 provinces in China participated in the present study. The dependent variables included 4 measures of self-management behaviors: regular self-monitoring of blood glucose (SMBG), prescribed medication adherence, recommended dietary changes, and regular exercise. The independent variable was patient satisfaction with CHS. Multivariable logistic regression models were performed to examine the association between patient satisfaction with CHS and self-management behaviors.The mean satisfaction score in the participants was 3.14 (out of a maximum of 5). After adjusting for covariates including demographic factors, health status, health knowledge, and socioeconomic status (SES), diabetic patients with high CHS satisfaction had better medication adherence (odds ratio [OR] 1.25, 95% confidence interval [CI] 1.02-1.55), increased exercise management (OR 1.19, 95% CI 1.06-1.35), and more SMBG (OR 1.16, 95% CI 1.03-1.32); all these associations varied across SES groups. The association between satisfaction and medication adherence was significant among participants younger than 65 years with lower education (OR 2.15, 95% CI 1.37-3.37), income (OR 1.62, 95% CI 1.13-2.32), and lower-status occupations (OR 1.69, 95% CI 1.16-2.47). Among participants younger than 65 years and had lower education attainment, the association between satisfaction and diet management was observed. There were positive associations between satisfaction and regular exercise among subgroups of participants younger than 65 years, except for lower education group. A significant association between satisfaction and SMBG among participants ≥65 years old, who also had lower SES and higher-status occupations, was also observed.The study findings suggested that T2DM patient satisfaction with CHS was moderate. High satisfaction with CHS indicated better medication adherence, exercise management, and SMBG, and these associations varied by SES.

Flash Glucose Monitoring Accepted in Daily Life of Children and Adolescents with Type 1 Diabetes and Reduction of Severe Hypoglycemia in Real-Life Use.

Background: Flash glucose monitoring (FGM) is covered by the Belgian public health insurance for type 1 diabetes since 2016. The objective of this study was to describe the use of FGM and diabetes outcomes in type 1 diabetic children and adolescents 1 year after reimbursement. Methods: All patients had the choice to convert to FGM or to continue with self-monitoring of blood glucose (SMBG). Clinical data were collected at baseline, at the next visit, and after 12 months; glucose profiles at next visit and after 12 months. Regression analyses were performed to identify predictors of FGM acceptance and changes in metabolic control. Results: A total of 334 subjects were included, of whom 278 (83.2%) switched to FGM. They were younger (13.6 vs. 15.2 years; P = 0.012) and performed more SMBG testing at baseline than patients who did not switch (4.3 vs. 4.1 tests daily; P = 0.008). At the end of follow-up, the rate of severe hypoglycemia decreased by 53% in the group of FGM users (P = 0.012) while it remained stable in SMBG users. Median glycated hemoglobin did not change significantly in both groups. Among subjects who switched to FGM, 15.8% reverted to SMBG after a median use of 5.3 months. Adverse events, diabetes duration, and FGM utilization were independent predictors of the risk for reverting. FGM-related adverse events were associated with a fivefold increased risk to revert to SMBG (hazard ratio = 5.12; P < 0.0001). Conclusions: FGM is relatively well accepted and decreases the risk of severe hypoglycemic events in our pediatric population. FGM is more often discontinued in patients experiencing adverse events and with longer diabetes duration.

Evaluation of health utility values for diabetic complications, treatment regimens, glycemic control and other subjective symptoms in diabetic patients using the EQ-5D-5L.

AIMS: This study aimed to reveal health utility values for diabetic complications and treatment regimens with adjustment for glycemic control and other clinical manifestations in a diabetic population. METHODS: The EuroQol 5-Dimension 5-Level (EQ-5D-5L) health utility values for 4963 Japanese diabetic patients were analyzed using a multivariate regression model including major complications and treatment regiments (minimally adjusted model), and that additionally included glycemic control and other subjective symptoms (musculoskeletal, dental, respiratory, gastrointestinal, urinary, and cutaneous symptoms, and hearing impairment) (further adjusted model). RESULTS: The mean utility value was 0.901 ± 0.137. In the minimally adjusted model, blindness, overt nephropathy, regular dialysis, cardiac symptom, sequelae of stroke, symptomatic peripheral neuropathy, decreased sensation, claudication, foot ulcer/gangrene, major amputation, and complex treatment regimens were significantly associated with lower utility values, whereas proliferative retinopathy without blindness, coronary artery disease without cardiac symptom, sequela-free cerebrovascular disease, asymptomatic peripheral artery disease, and minor amputation were not. Major complications and treatment regimens that showed significant association in the minimally adjusted model still presented significant impact on the utility decrement in the further adjusted model. However, most of their regression coefficients were lower in absolute value compared to those in the minimally adjusted model. CONCLUSIONS: The utility decrement related to each diabetic complication varied with its severity and accompanying symptoms. Complex treatment regimens were independently associated with lower utility values. The utility decrement associated with diabetic complication and complex treatment regimens would be overestimated in the analysis without adjustment for glycemic control or other subjective symptoms.

Diabetes self care activities among adults 20 years and above residing in a resettlement colony in East Delhi.

BACKGROUND: Self-care activities are the cornerstone of diabetes care that ensures patients participation to achieve optimal glycemic control and to prevent complications. OBJECTIVE: The aim of this study is to find the level of self-care activities among diabetics aged ≥20 years residing in a resettlement colony in East Delhi and its association with sociodemographic factors, disease, and treatment profile. METHODS: Using cross-sectional survey, 168 known diabetic patients were selected from Nand Nagri, a resettlement colony in East Delhi. Data were collected using Hindi translation of revised version-Summary of Diabetic Self Care Activities along with a pretested semi-open-ended questionnaire. Self-care was assessed on six parameters as follows: (a) general diet, (b) specific diet, (c) exercise, (d) blood sugar testing, (e) foot-care, and (f) smoking. The study period was from November 2014 to April 2016. RESULTS: Nearly 35.1% of respondents belonged to 60-69 years age group. About 52.4% of respondents were female. Fifty-two diabetics (31%) reported having practised diet control on all 7 days in the past 1 week. Nearly 39.3% of patients did not perform any physical activity. The blood test was not practised by 92.3% of respondents. Foot-care was practised by only 19% of patients. There was a significant association between general diet among diabetics with family support (P = 0.020), place of diagnosis (P = 0.033), and treatment funds (P = 0.017). The exercise score among diabetics who were below the poverty line was higher than those above poverty line (P = 0.029). Younger age (P = 0.005) and treatment with insulin (P = 0.008) were positively associated with blood glucose testing. The foot-care practice was better in patients aware of complications and foot-care practices (P < 0.001). CONCLUSION: Self-care activities among diabetic patients were very poor. Self-management educational programs at hospitals along with information, education, and communication activities at the community level and one-to-one counseling are recommended.

The management of type 2 diabetes with fixed-ratio combination insulin degludec/liraglutide (IDegLira) versus basal-bolus therapy (insulin glargine U100 plus insulin aspart): A short-term cost-effectiveness analysis in the UK setting.

AIM: To evaluate the cost-effectiveness of IDegLira versus basal-bolus therapy (BBT) with insulin glargine U100 plus up to 4 times daily insulin aspart for the management of type 2 diabetes in the UK. METHODS: A Microsoft Excel model was used to evaluate the cost-utility of IDegLira versus BBT over a 1-year time horizon. Clinical input data were taken from the treat-to-target DUAL VII trial, conducted in patients unable to achieve adequate glycaemic control (HbA1c <7.0%) with basal insulin, with IDegLira associated with lower rates of hypoglycaemia and reduced body mass index (BMI) in comparison with BBT, with similar HbA1c reductions. Costs (expressed in GBP) and event-related disutilities were taken from published sources. Extensive sensitivity analyses were performed. RESULTS: IDegLira was associated with an improvement of 0.05 quality-adjusted life years (QALYs) versus BBT, due to reductions in non-severe hypoglycaemic episodes and BMI with IDegLira. Costs were higher with IDegLira by GBP 303 per patient, leading to an incremental cost-effectiveness ratio (ICER) of GBP 5924 per QALY gained for IDegLira versus BBT. ICERs remained below GBP 20 000 per QALY gained across a range of sensitivity analyses. CONCLUSIONS: IDegLira is a cost-effective alternative to BBT with insulin glargine U100 plus insulin aspart, providing equivalent glycaemic control with a simpler treatment regimen for patients with type 2 diabetes inadequately controlled on basal insulin in the UK.

Diabetes Preventive Care Practices in North Carolina, 2000-2015.

This analysis assessed trends in measures of diabetes preventive care overall and by race/ethnicity and socioeconomic status in the North Carolina Behavioral Risk Factor Surveillance System (2000-2015). We found increasing trends in 5 measures: diabetes self-management education (DSME), daily blood glucose self-monitoring, hemoglobin A1c tests, foot examinations, and flu shots. Non-Hispanic black and non-Hispanic white respondents showed increases in blood glucose self-monitoring, and a significant time-by-race interaction was observed for annual flu shots. Predisposing, enabling, and need factors were significantly associated with most measures. DSME was positively associated with 7 measures. Expanding access to health insurance and health care providers is key to improving diabetes management, with DSME being the gateway to optimal care.

Trends in Blood Glucose Test Strip Utilization: A Population-Wide Analysis in Saskatchewan, Canada.

OBJECTIVES: To describe trends in blood glucose test strip (TS) utilization and cost in Saskatchewan. METHODS: A retrospective analysis of TS use between January 1, 1996, and December 31, 2013, was conducted using population-based health administrative databases in Saskatchewan. The prescription drug database was used to describe the annual number of TS dispensations, the number of strips dispensed, the number of unique beneficiaries and the total costs. A patient-level analysis was also carried out to describe the patterns of TS use (i.e. light, moderate or heavy) by the entire cohort and by diabetes treatments. Potential cost savings due to a newly implemented restriction policy were estimated based on the most recent data (2013). RESULTS: TS utilization increased dramatically between 1996 and 2013 in terms of the number of users and the average number of TSs received. The percentage of TS users receiving fewer than 4 TSs per week (i.e. light users) decreased by 20%, while the percentage of heavy users (i.e. those receiving more than 8 TSs per week) increased by 19%. During the same period, the use of high-risk oral hypoglycemic medications declined by 30% among all TS users. Heavy TS use was observed in at least one-third of all users, irrespective of treatment type. CONCLUSIONS: If Saskatchewan's newly imposed coverage limits had been applied in 2013, the costs of strips exceeding those limits would have totalled $2.5 million. Although TS use aligns with chronic disease care paradigms, the substantial costs and lack of evidence of patient outcomes demand better strategies to help reduce unnecessary use.