Type 1 and Covid-19: Diagnosis, Clinical Care, and Health Outcomes during the Pandemic.

The coronavirus disease 2019 (COVID-19) pandemic disrupted health care, creating challenges for people with diabetes and health care systems. Diabetes was recognized as a risk factor for severe disease early in the pandemic. Subsequently, risk factors specific for people with type 1 diabetes were identified, including age, hemoglobin A1c level, and lack of continuous glucose monitoring . Telemedicine, especially when accompanied by diabetes data, allowed effective remote care delivery. However, pre-existing racial disparities in access to diabetes technology persisted and were associated with worse outcomes. Events of the COVID-19 pandemic underscore the importance of continuing to develop flexible and more equitable health care delivery systems.

Increasing Social Determinants of Health Screening Rates Among Six Endocrinology Centers Across the United States: Results From the T1D Exchange Quality Improvement Collaborative.

Social determinants of health (SDOH) are strongly associated with outcomes for people with type 1 diabetes. Six centers in the T1D Exchange Quality Improvement Collaborative applied quality improvement principles to design iterative Plan-Do-Study-Act cycles to develop and expand interventions to improve SDOH screening rates. The interventions tested include staff training, a social risk index, an electronic health record patient-facing portal, partnerships with community organizations, and referrals to community resources. All centers were successful in improving SDOH screening rates, with individual site improvements ranging from 41 to 70% and overall screening across the six centers increasing from a baseline of 1% to 70% in 27 months.

The Design of the Electronic Health Record in Type 1 Diabetes Centers: Implications for Metrics and Data Availability for a Quality Collaborative.

BACKGROUND: Systematic and comprehensive data acquisition from the electronic health record (EHR) is critical to the quality of data used to improve patient care. We described EHR tools, workflows, and data elements that contribute to core quality metrics in the Type 1 Diabetes Exchange Quality Improvement Collaborative (T1DX-QI). METHOD: We conducted interviews with quality improvement (QI) representatives at 13 T1DX-QI centers about their EHR tools, clinic workflows, and data elements. RESULTS: All centers had access to structured data tools, nine had access to patient questionnaires and two had integration with a device platform. There was significant variability in EHR tools, workflows, and data elements, thus the number of available metrics per center ranged from four to 17 at each site. Thirteen centers had information about glycemic outcomes and diabetes technology use. Seven centers had measurements of additional self-management behaviors. Centers captured patient-reported outcomes including social determinants of health (n = 9), depression (n = 11), transition to adult care (n = 7), and diabetes distress (n = 3). Various stakeholders captured data including health care professionals, educators, medical assistants, and QI coordinators. Centers that had a paired staffing model in clinic encounters distributed the burden of data capture across the health care team and was associated with a higher number of available data elements. CONCLUSIONS: The lack of standardization in EHR tools, workflows, and data elements captured resulted in variability in available metrics across centers. Further work is needed to support measurement and subsequent improvement in quality of care for individuals with type 1 diabetes.

Diabetes status and other factors as correlates of risk for thrombotic and thromboembolic events during SARS-CoV-2 infection: A nationwide retrospective case-control study using Cerner Real-World Data™.

OBJECTIVES: We sought to examine in individuals with SARS-CoV-2 infection whether risk for thrombotic and thromboembolic events (TTE) is modified by presence of a diabetes diagnosis. Furthermore, we analysed whether differential risk for TTEs exists in type 1 diabetes mellitus (T1DM) versus type 2 diabetes mellitus (T2DM). DESIGN: Retrospective case-control study. SETTING: The December 2020 version of the Cerner Real-World Data COVID-19 database is a deidentified, nationwide database containing electronic medical record (EMR) data from 87 US-based health systems. PARTICIPANTS: We analysed EMR data for 322 482 patients >17 years old with suspected or confirmed SARS-CoV-2 infection who received care between December 2019 and mid-September 2020. Of these, 2750 had T1DM; 57 811 had T2DM; and 261 921 did not have diabetes. OUTCOME: TTE, defined as presence of a diagnosis code for myocardial infarction, thrombotic stroke, pulmonary embolism, deep vein thrombosis or other TTE. RESULTS: Odds of TTE were substantially higher in patients with T1DM (adjusted OR (AOR) 2.23 (1.93-2.59)) and T2DM (AOR 1.52 (1.46-1.58)) versus no diabetes. Among patients with diabetes, odds of TTE were lower in T2DM versus T1DM (AOR 0.84 (0.72-0.98)). CONCLUSIONS: Risk of TTE during COVID-19 illness is substantially higher in patients with diabetes. Further, risk for TTEs is higher in those with T1DM versus T2DM. Confirmation of increased diabetes-associated clotting risk in future studies may warrant incorporation of diabetes status into SARS-CoV-2 infection treatment algorithms.

Time, Technology, Social Support, and Cardiovascular Health of Emerging Adults With Type 1 Diabetes.

BACKGROUND: Emerging adults with Type 1 diabetes (T1DM) face an increased risk of cardiovascular disease; however, there are both barriers and facilitators to achieving ideal cardiovascular health in this stage of their lives. OBJECTIVES: The aim of this study was to qualitatively explore the barriers and facilitators of achieving ideal levels of cardiovascular health in a sample of emerging adults with T1DM ages 18-26 years. METHODS: A sequential mixed-methods design was used to explore achievement of ideal cardiovascular health using the seven factors defined by the American Heart Association (smoking status, body mass index, physical activity, healthy diet, total cholesterol, blood pressure, and hemoglobin A1C [substituted for fasting blood glucose]). We assessed the frequency of achieving ideal levels of each cardiovascular health factor. Using Pender's health promotion model as a framework, qualitative interviews explored the barriers and facilitators of achieving ideal levels of each factor of cardiovascular health. RESULTS: The sample was mostly female. Their age range was 18-26 years, with a diabetes duration between 1 and 20 years. The three factors that had the lowest achievement were a healthy diet, physical activity at recommended levels, and hemoglobin A1C of <7%. Participants described lack of time as a barrier to eating healthy, being physically active, and maintaining in-range blood glucose levels. Facilitators included the use of technology in helping to achieve in-range blood glucose and social support from family, friends, and healthcare providers in maintaining several healthy habits. DISCUSSION: These qualitative data provide insight into how emerging adults attempt to manage their T1DM and cardiovascular health. Healthcare providers have an important role in supporting these patients in establishing ideal cardiovascular health at an early age.

Comorbidities increase COVID-19 hospitalization in young people with type 1 diabetes.

OBJECTIVES: We evaluated COVID-19 outcomes in children and young adults with type 1 diabetes (T1D) to determine if those with comorbidities are more likely to experience severe COVID-19 compared to those without. RESEARCH DESIGN AND METHODS: This cross-sectional study included questionnaire data on patients <25 years of age with established T1D and laboratory-confirmed COVID-19 from 52 sites across the US between April 2020 and October 2021. We examined patient factors and COVID-19 outcomes between those with and without comorbidities. Multivariate logistic regression analysis examined the odds of hospitalization among groups, adjusting for age, HbA1c, race and ethnicity, insurance type and duration of diabetes. RESULTS: Six hundred fifty-one individuals with T1D and COVID-19 were analyzed with mean age 15.8 (SD 4.1) years. At least one comorbidity was present in 31%, and more than one in 10%. Obesity and asthma were the most frequently reported comorbidities, present in 19% and 17%, respectively. Hospitalization occurred in 17% of patients and 52% of hospitalized patients required ICU level care. Patients with at least one comorbidity were almost twice as likely to be hospitalized with COVID-19 than patients with no comorbidities (Odds ratio 2.0, 95% CI: 1.3-3.1). This relationship persisted after adjusting for age, HbA1c, race and ethnicity (minority vs nonminority), insurance type (public vs. private), and duration of diabetes. CONCLUSIONS: Our findings show that comorbidities increase the risk for hospitalization with COVID-19 in children and young adults highlighting the need for tailored COVID-19 prevention and treatment strategies in T1D.

Trends in Type 1 Diabetic Ketoacidosis During COVID-19 Surges at 7 US Centers: Highest Burden on non-Hispanic Black Patients.

CONTEXT: The impact of the COVID-19 pandemic on individuals with type 1 diabetes remains poorly defined. OBJECTIVE: We examined United States trends in diabetic ketoacidosis (DKA) among individuals with type 1 diabetes (T1D) during the COVID-19 pandemic at 7 large US medical centers and factors associated with these trends. METHODS: We compared DKA events among children and adults with T1D during COVID-19 surge 1 (March-May 2020) and COVID-19 surge 2 (August-October 2020) to the same periods in 2019. Analysis was performed using descriptive statistics and chi-square tests. RESULTS: We found no difference in the absolute number of T1D patients experiencing DKA in 2019 vs 2020. However, a higher proportion of non-Hispanic Black (NHB) individuals experienced DKA in 2019 than non-Hispanic White (NHW) individuals (44.6% vs 16.0%; P < .001), and this disparity persisted during the COVID-19 pandemic (48.6% vs 18.6%; P < .001). DKA was less common among patients on continuous glucose monitor (CGM) or insulin pump in 2020 compared to 2019 (CGM: 13.2% vs 15.0%, P < .001; insulin pump: 8.0% vs 10.6%, P < .001). In contrast to annual DKA totals, a higher proportion of patients had DKA during COVID-19 surges 1 and 2 compared to the same months in 2019 (surge 1: 7.1% vs 5.4%, P < .001; surge 2: 6.6% vs 5.7%, P = .001). CONCLUSION: DKA frequency increased among T1D patients during COVID-19 surges with highest frequency among NHB patients. DKA was less common among patients using CGM or insulin pumps. These findings highlight the urgent need for improved strategies to prevent DKA among patients with T1D-not only under pandemic conditions, but under all conditions-especially among populations most affected by health inequities.

Age and Hospitalization Risk in People With Type 1 Diabetes and COVID-19: Data From the T1D Exchange Surveillance Study.

CONTEXT: COVID-19 morbidity and mortality are increased in type 1 diabetes (T1D), but few data focus on age-based outcomes. OBJECTIVE: This work aimed to quantify the risk for COVID-19-related hospitalization and adverse outcomes by age in people with T1D. METHODS: For this observational, multisite, cross-sectional study of patients with T1D and laboratory-confirmed COVID-19 from 56 clinical sites in the United States, data were collected from April 2020 to March 2021. The distribution of patient factors and outcomes across age groups (0-18, 19-40, and > 40 years) was examined. Descriptive statistics were used to describe the study population, and multivariate logistic regression models were used to analyze the relationship between age, adverse outcomes, and hospitalization. The main outcome measure was hospitalization for COVID-19. RESULTS: A total of 767 patients were analyzed. Fifty-four percent (n = 415) were aged 0 to 18 years, 32% (n = 247) were aged 19 to 40 years, and 14% (n = 105) were older than 40 years. A total of 170 patients were hospitalized, and 5 patients died. Compared to the 0- to 18-years age group, those older than 40 years had an adjusted odds ratio of 4.2 (95% CI, 2.28-7.83) for hospitalization after adjustment for sex, glycated hemoglobin A1c, race, insurance type, and comorbidities. CONCLUSION: Age older than 40 years is a risk factor for patients with T1D and COVID-19, with children and younger adults experiencing milder disease and better prognosis. This indicates a need for age-tailored treatments, immunization, and clinical management of individuals affected by T1D.

Cardiovascular health in emerging adults with type 1 diabetes.

AIMS: Individuals with type 1 diabetes (T1D) face increased risk for cardiovascular disease (CVD). Controlling individual cardiovascular risk factors can prevent or slow the onset of CVD. Ideal cardiovascular health is associated with a lower incidence of CVD. Identifying areas of suboptimal cardiovascular health can help guide CVD prevention interventions. To assess cardiovascular health and explore the barriers and facilitators to achieving ideal cardiovascular health in a sample of young adults with T1D. METHODS AND RESULTS: We used a sequential mixed-method design to assess the seven factors of cardiovascular health according to American Heart Association. Qualitative interviews, guided by Pender's Health Promotion Model, were used to discuss participant's cardiovascular health results and the barriers and facilitators to achieving ideal cardiovascular health. We assessed the frequency of ideal levels of each factor. The qualitative data were analysed using content analysis. Qualitative and quantitative data were integrated in the final analysis phase. The sample (n = 50) was majority female (70%), White (86%), with a mean age of 22 ± 2.4 and diabetes duration of 10.7 ± 5.5 years. Achievement of the seven factors of cardiovascular health were: non-smoking (96%); cholesterol <200 mg/dL (76%); body mass index <25 kg/m2 (54%); blood pressure <120/<80 mmHg (46%); meeting physical activity guidelines (38%); haemoglobin A1c <7% (40%); and healthy diet (14%). Emerging qualitative themes related to the perceived benefits of action, interpersonal influences on their diabetes self-management, and perceived self-efficacy. CONCLUSION: We found areas of needed improvement for cardiovascular health. However, these young adults expressed a strong interest in healthy habits which can be supported by their healthcare providers.

Pediatric Smart Insulin Pen Use: The Next Best Thing.

In the pediatric population, insulin pump therapy, or CSII, is often considered the gold standard for intensive diabetes management. Insulin pump technology offers families and caregivers many beneficial features including a calculator for insulin dosing and the ability to review diabetes management data to provide data-driven diabetes management. However, for those who find CSII challenging or choose to use multiple daily injections (MDI) there is an option that offers similar features called the Smart Insulin Pen (SIP). Even though SIP technology provides a safe and data-driven diabetes self-management tool for the pediatric population using MDI, there is limited pediatric specific literature. This article will describe current options, data-driven diabetes management, benefits, challenges and clinical use of SIP technology in the pediatric population.

Diabetic ketoacidosis drives COVID-19 related hospitalizations in children with type 1 diabetes.

BACKGROUND: Diabetes is a risk factor for poor COVID-19 outcomes, but pediatric patients with type 1 diabetes are poorly represented in current studies. METHODS: T1D Exchange coordinated a US type 1 diabetes COVID-19 registry. Forty-six diabetes centers submitted pediatric cases for patients with laboratory confirmed COVID-19. Associations between clinical factors and hospitalization were tested with Fisher's Exact Test. Logistic regression was used to calculate odds ratios for hospitalization. RESULTS: Data from 266 patients with previously established type 1 diabetes aged <19 years with COVID-19 were reported. Diabetic ketoacidosis (DKA) was the most common adverse outcome (n = 44, 72% of hospitalized patients). There were four hospitalizations for severe hypoglycemia, three hospitalizations requiring respiratory support (one of whom was intubated and mechanically ventilated), one case of multisystem inflammatory syndrome in children, and 10 patients who were hospitalized for reasons unrelated to COVID-19 or diabetes. Hospitalized patients (n = 61) were more likely than nonhospitalized patients (n = 205) to have minority race/ethnicity (67% vs 39%, P < 0.001), public insurance (64% vs 41%, P < 0.001), higher A1c (11% [97 mmol/mol] vs 8.2% [66 mmol/mol], P < 0.001), and lower insulin pump and lower continuous glucose monitoring use (26% vs 54%, P < 0.001; 39% vs 75%, P < 0.001). Age and gender were not associated with risk of hospitalization. Higher A1c was significantly associated with hospitalization, with an odds ratio of 1.56 (1.34-1.84) after adjusting for age, gender, insurance, and race/ethnicity. CONCLUSIONS: Higher A1c remained the only predictor for hospitalization with COVID-19. Diabetic ketoacidosis is the primary concern among this group.

Inequities in Diabetic Ketoacidosis Among Patients With Type 1 Diabetes and COVID-19: Data From 52 US Clinical Centers.

OBJECTIVE: We examined whether diabetic ketoacidosis (DKA), a serious complication of type 1 diabetes (T1D) was more prevalent among Non-Hispanic (NH) Black and Hispanic patients with T1D and laboratory-confirmed coronavirus disease 2019 (COVID-19) compared with NH Whites. METHOD: This is a cross-sectional study of patients with T1D and laboratory-confirmed COVID-19 from 52 clinical sites in the United States, data were collected from April to August 2020. We examined the distribution of patient factors and DKA events across NH White, NH Black, and Hispanic race/ethnicity groups. Multivariable logistic regression analysis was performed to examine the odds of DKA among NH Black and Hispanic patients with T1D as compared with NH White patients, adjusting for potential confounders, such as age, sex, insurance, and last glycated hemoglobin A1c (HbA1c) level. RESULTS: We included 180 patients with T1D and laboratory-confirmed COVID-19 in the analysis. Forty-four percent (n = 79) were NH White, 31% (n = 55) NH Black, 26% (n = 46) Hispanic. NH Blacks and Hispanics had higher median HbA1c than Whites (%-points [IQR]: 11.7 [4.7], P < 0.001, and 9.7 [3.1] vs 8.3 [2.4], P = 0.01, respectively). We found that more NH Black and Hispanic presented with DKA compared to Whites (55% and 33% vs 13%, P < 0.001 and P = 0.008, respectively). After adjusting for potential confounders, NH Black patients continued to have greater odds of presenting with DKA compared with NH Whites (OR [95% CI]: 3.7 [1.4, 10.6]). CONCLUSION: We found that among T1D patients with COVID-19 infection, NH Black patients were more likely to present in DKA compared with NH White patients. Our findings demonstrate additional risk among NH Black patients with T1D and COVID-19.

Increased DKA at presentation among newly diagnosed type 1 diabetes patients with or without COVID-19: Data from a multi-site surveillance registry.

Highlights Our multicenter study reports a higher proportion of diabetic ketoacidosis presentation of over 60% in newly diagnosed patients with type 1 diabetes with or without confirmed coronavirus disease 2019 (COVID-19) at diagnosis. This finding is suggestive of delays in seeking care during the COVID-19 pandemic.

Concordance and Discordance in the Geographic Distribution of Childhood Obesity and Pediatric Type 2 Diabetes in New York City.

OBJECTIVE: As rates of childhood obesity and pediatric type 2 diabetes (T2D) increase, a better understanding is needed of how these 2 conditions relate and which subgroups of children are more likely to develop diabetes with and without obesity. METHODS: To compare hotspots of childhood obesity and pediatric T2D in New York City, we performed geospatial clustering analyses on obesity estimates obtained from surveys of school-aged children and diabetes estimates obtained from health care claims data, from 2009 to 2013. Analyses were performed at the Census tract level. We then used multivariable regression analysis to identify sociodemographic and environmental factors associated with these hotspots. RESULTS: We identified obesity hotspots in Census tracts with a higher proportion of Black or Hispanic residents, with low median household income, or located in a food swamp. Total 51.1% of pediatric T2D hotspots overlapped with obesity hotspots. For pediatric T2D, hotspots were identified in Census tracts with a higher proportion of Black residents and a lower proportion of Hispanic residents. CONCLUSIONS: Non-Hispanic Black neighborhoods had a higher probability of being hotspots of both childhood obesity and pediatric T2D. However, we identified a discordance between hotspots of childhood obesity and pediatric diabetes in Hispanic neighborhoods, suggesting either under-detection or under-diagnosis of diabetes, or that obesity may influence diabetes risk differently in these 2 populations. These findings warrant further investigation of the relationship between childhood obesity and pediatric diabetes among different racial and ethnic groups, and may help guide pediatric public health interventions to specific neighborhoods.

Identifying Geographic Disparities in Diabetes Prevalence Among Adults and Children Using Emergency Claims Data.

Geographic surveillance can identify hotspots of disease and reveal associations between health and the environment. Our study used emergency department surveillance to investigate geographic disparities in type 1 and type 2 diabetes prevalence among adults and children. Using all-payer emergency claims data from 2009 to 2013, we identified unique New York City residents with diabetes and geocoded their location using home addresses. Geospatial analysis was performed to estimate diabetes prevalence by New York City Census tract. We also used multivariable regression to identify neighborhood-level factors associated with higher diabetes prevalence. We estimated type 1 and type 2 diabetes prevalence at 0.23% and 10.5%, respectively, among adults and 0.20% and 0.11%, respectively, among children in New York City. Pediatric type 1 diabetes was associated with higher income (P = 0.001), whereas adult type 2 diabetes was associated with lower income (P < 0.001). Areas with a higher proportion of nearby restaurants categorized as fast food had a higher prevalence of all types of diabetes (P < 0.001) except for pediatric type 2 diabetes. Type 2 diabetes among children was only higher in neighborhoods with higher proportions of African American residents (P < 0.001). Our findings identify geographic disparities in diabetes prevalence that may require special attention to address the specific needs of adults and children living in these areas. Our results suggest that the food environment may be associated with higher type 1 diabetes prevalence. However, our analysis did not find a robust association with the food environment and pediatric type 2 diabetes, which was predominantly focused in African American neighborhoods.

Identifying monogenic diabetes in a pediatric cohort with presumed type 1 diabetes.

OBJECTIVE: Monogenic diabetes (MD) is rare and can often be confused with type 1 diabetes (T1D) in a pediatric cohort. We sought to determine clinical criteria that could optimally identify candidates for genetic testing of two common forms of MD that alter therapy: glucokinase (GCK) and hepatocyte nuclear factor 1 alpha (HNF1α). RESEARCH DESIGN AND METHODS: We performed a retrospective chart review of 939 patients with a presumed diagnosis of T1D, 6 months-20 yr of age, and identified four clinical criteria that were unusual for T1D and could warrant further evaluation for MD: (i) negative pancreatic autoantibodies, (ii) evidence of prolonged endogenous insulin production, or (iii) strong family history of diabetes in multiple generations. One hundred and twenty-one patients were identified as having one or more of these high-risk clinical criteria and were offered screening for mutations in GCK and HNF1α; 58 consented for genetic testing. RESULTS: Of 58 patients with presumed T1D who underwent genetic testing, four were found to have GCK and one had HNF1α. No patients with only one high-risk feature were found to have MD. Of 10 patients who had two or more high risk criteria, five had MD (50%). CONCLUSION: A high frequency of MD from mutations in GCK/HNF1α may be identified among pediatric diabetic patients originally considered to have T1D by performing genetic testing on those patients with multiple clinical risk factors for MD.