An Integrative Review of Social Determinants of Glycemic Targets Achievement in Adults with Type 2 Diabetes in the United States.

Several individual social determinants of health have been identified as significant factors contributing to achieving glycemic targets (glycated hemoglobin < 7). However, it remains unclear how these social variables individually or collectively contribute to glycemic targets among adults with type 2 diabetes (T2D) in the United States (U.S.) The purpose of the current integrative review (IR) was to describe and synthesize findings from studies on social determinants of glycemic target achievement in adults with T2D in the U.S. and integrate them into the United States Department of Health and Human Services Conceptual Framework. The databases searched included PubMed, CINAHL Plus with Full Text, Medline with Full Text [EBSCO], Google Scholar, bibliography, and hand searching. A total of 948 records were identified. After excluding duplicates and irrelevant studies based on inclusion and exclusion criteria through title, abstract, and full-text screening, 13 studies were finally included in this IR. The results revealed that race/ethnicity, economic access and stability, educational access and quality, healthcare access and quality, neighborhood and built environment, and social and community context contribute to glycemic target achievement among adult patients with T2D in the U.S. Integrating findings from key studies on social determinants of glycemic health may contribute to developing interventions aimed at reducing and eventually eradicating health disparities for individuals with and at risk for T2D in the U.S.

Sleep Health Among Adults in Outpatient Opioid Use Disorder Treatment: A Systematic Review.

The current systematic review synthesized available original research on objective and self-reported sleep health dimensions among adults aged 18 to 50 years in outpatient treatment for opioid use disorder (OUD). A comprehensive search was conducted using multiple electronic databases followed by screening 2,738 records published in English from the inception of each database to September 14, 2021. Quality was assessed with the Mixed Methods Appraisal Tool (version 2001). Fifty nine studies-50 descriptive (21 longitudinal, 18 cross-sectional, and 11 case control), seven interventional (five non-randomized), and two mixed/multi method designs-were included, comprising 18,195 adults with mean ages ranging from 23 to 49 years (mean age = 37.5 [SD = 5.9] years; 54.4% female) with OUD and 604 comparison participants without OUD. Studies were predominantly observational with various designs with self-report and objective measures with participants at various points in treatment. More work is needed to understand the multidimensional depth of sleep health in adults with OUD. Optimizing sleep health in adults with OUD may improve their addiction trajectory and should be a priority in practice and research. [Journal of Psychosocial Nursing and Mental Health Services, 62(1), 19-26.].

Substance Use, Sleep Duration, and Health Among Adults in Ohio.

Introduction: Substance use affects approximately 46.3 million people aged 12 years or older (16.5% of the US population) and is associated with poor sleep health overall. Methods: We conducted a cross-sectional secondary analysis of data from the 2020 Behavioral Risk Factor Surveillance System survey in Ohio. The sample comprised 14,676 adults. We examined associations between the use of 2 types of substances (marijuana and nonprescribed prescription pain medication) and short sleep duration (<6 hours per night) and overall health (mental, physical, and general). We used linear and logistic regression modeling while adjusting for individual-level (age, sex, race and ethnicity, education, income, and body mass index) and area-level (socioeconomic deprivation) covariates. Results: Of survey respondents who answered questions, 9.2% (1,140 of 12,362) reported using marijuana, and 1.4% (111 of 8,203) used nonprescribed prescription pain medication. Respondents who used marijuana used it an average 17.3 days per month. In adjusted logistic regression models, the odds of reporting short sleep duration were 2.4 times greater among respondents who used nonprescribed prescription pain medication (vs those who did not). The odds of reporting short sleep duration, poor mental health, poor physical health, and poor general health were 1.5, 1.3, 2.1, and 1.9 times greater, respectively, among respondents who reported marijuana use (vs those who did not). In the linear regression models (adjusted), more days of marijuana use were associated with longer sleep duration, worse mental health, and worse general health. Conclusion: Understanding the connection between substance use and health outcomes is needed to improve trajectories of substance use and recovery. Sleep duration is often underassessed among people who use substances. Expanding diagnostics and treatment options for those who use substances may result in lower levels of substance use and improved overall health.

Environmental exposures and blood pressure in adolescents and adults in the T1D exchange clinic registry.

AIMS: To examine the associations between environmental determinants of health and blood pressure and whether age, sex, or race moderated the associations among 18,754 adolescents and adults from the type 1 diabetes (T1D) Exchange Clinic Registry. METHODS: We used multivariable linear regression. Environmental determinants included exposure to ambient fine particulate matter (PM2.5, obtained from an integrated model), nitrogen dioxide (NO2), noise and light pollution, and the normalized difference vegetation index (NDVI, a marker of green space) at the ZIP code level of residence. RESULTS: Higher exposure to PM2.5 and NO2, and lower NDVI, was associated with higher systolic and diastolic blood pressure, and higher light pollution exposure were similarly associated with higher diastolic blood pressure. These associations between environmental exposures and blood pressure remained significant after accounting for other covariates (age, sex, race/ethnicity, BMI, and T1D duration). With aging, the negative association between NDVI and blood pressure weakened. CONCLUSIONS: These findings emphasize the significance of minimizing exposure to environmental pollutants, including PM2.5 and NO2, as well as ensuring access to areas with higher NDVI, to promote cardiovascular health in individuals with T1D.

The Role of Perceived Sleep Quality in Cardiovascular Health Factors and Behaviors Among Young Adults With Type 1 Diabetes.

PURPOSE: The purpose of this study was to determine the associations between perceived sleep quality and individual cardiovascular health (CVH) factors (A1C and body mass index [BMI]) and CVH behaviors (physical activity and dietary diabetes self-management) in young adults ages 18 to 25 years with type 1 diabetes (T1D). METHODS: Associations among perceived sleep quality and CVH factors and behaviors were examined using multivariable linear regression in 69 participants ages 18 to 25 years (mean age 21.4 ± 2.0, mean T1D duration 9.7 ± 5.6 years). RESULTS: Lower perceived sleep quality was associated with multiple lower CVH factors and behaviors (higher A1C and BMI, lower physical activity, poorer diet) even after adjusting for covariates (age, T1D duration, sex assigned at birth). CONCLUSION: Experimental studies are needed to better understand the impact of modifying sleep habits on both short- and long-term CVH in adults with T1D.

Internet-Based Recruitment and Retention of Young Adults With Type 1 Diabetes: Cross-Sectional Study.

BACKGROUND: Multiple research strategies are required to recruit and engage a representative cohort of young adults in diabetes research. In this report, we describe an approach for internet-based recruitment for a repeated-measures descriptive study. OBJECTIVE: The objective of this cross-sectional study was to determine whether internet-based recruitment through multiple social media platforms, a clinical research platform, and cooperation with community partnerships-College Diabetes Network and Beyond Type 1-would serve as an effective way to recruit a representative sample of young adults aged 18-25 years with type 1 diabetes (T1D). METHODS: We conducted a repeated-measures descriptive study. We captured enrollment rates and participant characteristics acquired from each social media platform through survey data and Facebook analytics. This study was advertised via paid postings across a combination of different social media platforms (eg, Facebook, Instagram, Twitter, and Reddit). We used quarterly application postings, quarterly newsletters, and participation in the ResearchMatch registry to identify potentially eligible participants from February 3, 2021, to June 6, 2022. RESULTS: ResearchMatch proved to be the most cost-effective strategy overall, yielding the highest gender and racial diversity compared to other internet platforms (eg, Facebook, Instagram, Twitter, and Reddit), application postings (eg, Beyond Type 1), and newsletters (eg, College Diabetes Network and a local area college). However, we propose that the combination of these approaches yielded a larger, more diverse sample compared to any individual strategy. Our recruitment cost was US $16.69 per eligible participant, with a 1.27% conversion rate and a 30% eligibility rate. CONCLUSIONS: Recruiting young adults with T1D across multiple internet-based platforms was an effective strategy to yield a moderately diverse sample. Leveraging various recruitment strategies is necessary to produce a representative sample of young adults with T1D. As the internet becomes a larger forum for study recruitment, participants from underrepresented backgrounds may continue engaging in research through advertisements on the internet and other internet-based recruitment platforms.

Longitudinal Associations Among Symptoms of Family Intensive Care Unit Syndrome.

BACKGROUND: Family intensive care unit (ICU) syndrome, a comorbid response to another person's stay in the ICU, is characterized by emotional distress, poor sleep health, and decision fatigue. OBJECTIVES: This pilot study examined associations among symptoms of emotional distress (anxiety and depression), poor sleep health (sleep disturbance), and decision fatigue in a sample of family members of patients in the ICU. METHODS: The study used a repeated-measures, correlational design. Participants were 32 surrogate decision makers of cognitively impaired adults who had at least 72 consecutive hours of mechanical ventilation within the neurological, cardiothoracic, and medical ICUs at an academic medical center in northeast Ohio. Surrogate decision makers with a diagnosis of hypersomnia, insomnia, central sleep apnea, obstructive sleep apnea, or narcolepsy were excluded. Severity of symptoms of family ICU syndrome was measured at 3 time points in 1 week. Zero-order Spearman correlations of the study variables were interpreted at baseline and partial Spearman correlations of study variables were interpreted 3 days and 7 days after baseline. RESULTS: The study variables showed moderate to large associations at baseline. Baseline anxiety and depression were associated with each other and with decision fatigue at day 3. Baseline sleep disturbance was associated with anxiety, depression, and decision fatigue at day 7. CONCLUSIONS: Understanding the temporal dynamics and mechanisms of the symptoms of family ICU syndrome can inform clinical, research, and policy initiatives that enhance the provision of family-centered critical care.

Hospital Experiences of Dignity in People Who Inject Drugs.

BACKGROUND: Dignity is a complex concept necessary for the adequate treatment of patients in the healthcare setting. Autonomy, self-sufficiency, respect, and equality are concepts used to define dignity. Dignity has not been studied in people who inject drugs (PWID). OBJECTIVE: This study aimed to determine how PWID maintain or have their dignity threatened during hospitalization in an acute care unit. DESIGN: The qualitative descriptive study was a deductive thematic analysis of secondary data on PWID experiences with received nursing care. In-depth, semistructured interviews were conducted and transcribed verbatim with a constant comparison method for thematic analysis using NVivo. A priori codes of dignity concepts from the literature and dignity scales were used as a guide to examine the nine qualitative transcripts. FINDINGS: The three most common threats to dignity during hospitalization were lack of equality compared with other patients, not feeling valued as an individual, and not feeling respected by the healthcare workers providing care. The three most common protectors of dignity were feeling respected by healthcare workers, having autonomy in treatment choices, and feeling valued as an individual. CONCLUSIONS: Protecting dignity significantly affects whether PWID are willing to seek healthcare in the hospital setting. Preserving dignity in PWID during their hospitalizations can encourage this population to seek care earlier. Nurses must be experienced in caring for PWID and provide nonjudgmental care for this population.

Resilience as a moderator of role overload and sleep disturbance among caregivers of persons with dementia.

The purpose of this study was to examine if resilience moderates the association between role overload and sleep disturbance among caregivers of persons with dementia. This was a secondary analysis of data on 437 informal caregivers (mean age=61.77 years, SD=13.69) of persons with dementia in the United States. Data from the 2017 wave of the National Study of Caregiving were analyzed using multiple regression with interaction terms to evaluate the moderation effect of resilience, while controlling for caregivers' age, race, gender, education, self-rated health, caregiving hours, and primary caregiving status. Higher role overload was associated with greater sleep disturbance and this association was attenuated among caregivers with higher levels of resilience. Our findings highlight the stress-buffering effects of resilience in the context of sleep disturbance in dementia caregivers. Interventions to improve caregivers' ability to recover, resist, and rebound during challenging situations may mitigate role overload and optimize sleep health.

Cognitive Behavioral Sleep Self-Management Intervention for Young Adults With Type 1 Diabetes (NCT04975230).

The purpose of this study was to explore perceptions of the first dose of a cognitive behavioral sleep self-management intervention (CB-sleep) among young adults aged 18 to 25 years with type 1 diabetes (T1D). We used a qualitative descriptive approach to conduct in-depth semi-structured focused interviews with a purposive sample of 16 young adults with T1D, transitioning from adolescence to early adulthood. Interviews were audio-recorded, transcribed verbatim, and analyzed using qualitative content analysis. Participants described their sleep knowledge (previous, new, and additional), sleep health goals, along with barriers and facilitators of the CB-sleep intervention. Based on these results, we suggest CB-sleep is a useful modality with the potential to support sleep self-management in young adults with T1D during this complex life transition. Furthermore, CB-sleep could be incorporated into an existing diabetes self-management education and support program after pilot testing and determining efficacy to improve sleep and glycemic health.

Risk factors and geographic disparities in premature cardiovascular mortality in US counties: a machine learning approach.

Disparities in premature cardiovascular mortality (PCVM) have been associated with socioeconomic, behavioral, and environmental risk factors. Understanding the "phenotypes", or combinations of characteristics associated with the highest risk of PCVM, and the geographic distributions of these phenotypes is critical to targeting PCVM interventions. This study applied the classification and regression tree (CART) to identify county phenotypes of PCVM and geographic information systems to examine the distributions of identified phenotypes. Random forest analysis was applied to evaluate the relative importance of risk factors associated with PCVM. The CART analysis identified seven county phenotypes of PCVM, where high-risk phenotypes were characterized by having greater percentages of people with lower income, higher physical inactivity, and higher food insecurity. These high-risk phenotypes were mostly concentrated in the Black Belt of the American South and the Appalachian region. The random forest analysis identified additional important risk factors associated with PCVM, including broadband access, smoking, receipt of Supplemental Nutrition Assistance Program benefits, and educational attainment. Our study demonstrates the use of machine learning approaches in characterizing community-level phenotypes of PCVM. Interventions to reduce PCVM should be tailored according to these phenotypes in corresponding geographic areas.

A composite measure of sleep health is associated with glycaemic target achievement in young adults with type 1 diabetes.

We investigated whether sleep health (each individual dimension and a composite measure) was associated with better glycaemia among a cohort of young adults with type 1 diabetes (mean age 21.5 years, mean body mass index 24.55 kg m-2 ). Multiple validated self-report questionnaires were completed, and raw continuous glucose monitor data were shared. One self-reported sleep characteristic for each of the five sleep health dimensions was selected. A composite score was calculated by summing the number of "good" sleep health dimensions. We evaluated the associations between sleep health and glycaemia, and whether covariates, including age, type 1 diabetes duration and sleep apnea risk, influenced the relationships among the study variables using multivariable linear regression. Individual dimensions of sleep satisfaction (ß = 0.380, p = 0.019; ß = -0.414, p = 0.010), timing (ß = 0.392, p = 0.015; ß = -0.393, p = 0.015) and sleep efficiency (ß = 0.428, p = 0.007) were associated with higher achievement of glycaemic targets (J-index and time in range); however, these associations did not persist after considering covariates. A better Sleep Health Composite score was associated with higher achievement of glycaemic targets even after considering covariates. Using a multidimensional framework can guide future research on causal pathways between sleep and diabetes health, interventions to target sleep health profiles, and may improve sleep screening in routine diabetes care.

Neighborhood-level Social Vulnerability and Prevalence of Cardiovascular Risk Factors and Coronary Heart Disease.

Social determinants of health are implicated in the geographic variation in cardiovascular diseases (CVDs). The social vulnerability index (SVI) is an estimate of a neighborhood's potential for deleterious outcomes when faced with natural disasters or disease outbreaks. We sought to investigate the association of the SVI with cardiovascular risk factors and the prevalence of coronary heart disease (CHD) in the United States at the census tract level. We linked census tract SVI with prevalence of census tract CVD risk factors (smoking, high cholesterol, diabetes, high blood pressure, low physical activity and obesity), and prevalence of CHD obtained from the behavioral risk factor surveillance system. We evaluated the association between SVI, its sub-scales, CVD risk factors and CHD prevalence using linear regression. Among 72,173 census tracts, prevalence of all cardiovascular risk factors increased linearly with SVI. A higher SVI was associated with a higher CHD prevalence (R2 = 0.17, P < 0.0001). The relationship between SVI and CHD was stronger when accounting for census-tract median age (R2 = 0.57, P < 0.0001). A multivariable linear regression model including 4 SVI themes separately explained considerably more variation in CHD prevalence than the composite SVI alone (50.0% vs 17.3%). Socioeconomic status and household composition and disability were the SVI themes most closely associated with cardiovascular risk factors and CHD prevalence. In the United States, social vulnerability can explain significant portion of geographic variation in CHD, and its risk factors. Neighborhoods with high social vulnerability are at disproportionately increased risk of CHD and its risk factors. Social determinants of health are implicated in the geographic variation in cardiovascular diseases (CVDs). We investigated the association of social vulnerability index (SVI) with cardiovascular risk factors and the prevalence of coronary heart disease (CHD) in the United States at the census tract level. We show that cardiovascular risk factors and CHD were more common with higher SVI. A multivariable linear regression model including 4 SVI themes separately explained considerably more variation in CHD prevalence than the composite SVI alone (50.0% vs 17.3%). Socioeconomic status and household composition and/or disability were the SVI themes most closely associated with cardiovascular risk factors and CHD prevalence.

Nursing Students' Use of Harm Reduction in the Clinical Setting.

BACKGROUND: The use and awareness of harm reduction education strategies are missing from nursing care of people with substance use disorder (SUD). PURPOSE: To determine whether targeted training improved nursing students' application of harm reduction education in the clinical setting. METHODS: This was an experimental 2-group repeated-measures survey study. Nursing students in a baccalaureate program received targeted training for 3 semesters. Training included presentations by people with lived experience, an addiction-trained physician, and a role-play exercise. Students received harm reduction education and reported on the application of this education. RESULTS: Students in the traditional track cared for more people with SUD than students in the accelerated track. Referral for treatment was the most common education provided, and information related to injection drug use was the least commonly taught education. CONCLUSIONS: Targeted training increased the number of harm reduction options nursing students presented to people with SUD in the clinical setting.

Sleep health dimensions are associated with next-day symptoms in young adults with type 1 diabetes.

OBJECTIVE: We investigated intra-individual reciprocal associations between sleep health dimensions (individual and composite) and symptoms among young adults with type 1 diabetes (T1D). DESIGN AND MEASUREMENTS: Cross-lagged multilevel models were used to analyze electronic diary-reported sleep and symptom patterns over 7 days at waketime in 42 young adults with T1D. Sleep health dimensions included regularity, satisfaction, alertness, timing, efficiency (percentage of time spent asleep), and duration (total sleep time) and symptoms included mood, fatigue, and pain. Covariates included biological sex and age. SETTING AND PARTICIPANTS: We recruited young adults (mean age 21.5 ± 2.1 years, HbA1c 6.8%, 85% female, 10% gender minority) with T1D for at least 6 months and no other major medical or psychiatric comorbidity from social media platforms, the College Diabetes Network, and ResearchMatch. RESULTS: On days with a better sleep health composite, participants reported lower next-day symptoms (higher mood, lower fatigue, and lower pain) and on days when participants reported lower symptoms, participants reported better sleep health (as a composite). Several individual sleep health dimensions led to lower next-day symptoms (eg, higher satisfaction, alertness, and efficiency and higher mood); however, symptoms were no longer predictive of next-day sleep when controlling for prior day sleep. CONCLUSIONS: Optimal sleep health is an antecedent of fewer next day symptoms. Sleep health dimensions likely have positive additive effects on lower symptoms as some of the individual sleep health dimensions were not significantly associated with some symptoms among young adults with T1D.

Food insecurity and atherosclerotic cardiovascular disease risk in adults with diabetes.

OBJECTIVES: Adults with diabetes are at an increased risk of atherosclerotic cardiovascular disease (ASCVD), and food insecurity may be a major and underappreciated risk compounder in this population. We sought to analyze the prevalence of food insecurity and its association with ASCVD in adults with diabetes. METHODS: A total of 6424 participants with diabetes were included from the 2019 and 2020 National Health Interview Survey. Food insecurity was determined with a 10-question U.S. Adult Food Security Survey Module, and classified as high, marginal, low, and very low. ASCVD was defined as a self-reported history of coronary artery disease, myocardial infarction, or stroke. RESULTS: Of the 6424 included participants (weighted: n = 21 690 217), 5 405 543 (24.4%) reported a history of ASCVD and 2 946 061 (13.3%) were identified as food insecure (low or very low food security). Adults with food insecurity were more likely to have ASCVD than adults who were food secure (28.9% vs 23.7%; P = 0.008). In the multivariate analyses adjusted for traditional cardiovascular risk factors, all levels of food insecurity were associated with ASCVD compared with food-secure adults (marginal security: odds ratio [OR]: 1.60; 95% confidence interval [CI], 1.18-2.18]; P = 0.003; low security: OR: 2.09; 95% CI, 1.58-2.74]; P < 0.001; very low security: OR: 1.69; 95% CI, 1.22-2.34]; P = 0.001). The association persisted when adjusted for income, location, education, and insurance status. In adults with diabetes and ASCVD, income was a negative factor for food insecurity (OR: 0.71; 95% CI, 0.62-0.80; P < 0.001), but female sex and smoking were positive factors (OR: 1.90; 95% CI, 1.29-2.80; P = 0.001; and OR: 1.97; 95% CI, 1.23-3.18; P = 0.005; respectively). At younger ages, the prevalence of food insecurity increased, especially in adults with ASCVD. CONCLUSIONS: We showed that 13% of U.S. adults with diabetes are food insecure, which was associated with ASCVD independent of traditional and socioeconomic risk factors. Our findings emphasize the importance of recognizing food insecurity as a driver of ASCVD in adults with diabetes, and encourage future efforts at reducing this disparity.

Socioeconomic deprivation and cardiometabolic risk factors in individuals with type 1 diabetes: T1D exchange clinic registry.

AIMS: Social determinants of health (SDOH) influence cardiovascular health in the general population; however, the degree to which this occurs in individuals with type 1 diabetes (T1D) is not well understood. We evaluated associations among socioeconomic deprivation and cardiometabolic risk factors (hemoglobin A1c, low-density lipoprotein, blood pressure, body mass index, physical activity) in individuals with T1D from the T1D Clinic Exchange Registry. METHODS: We evaluated the association between the social deprivation index (SDI) and cardiometabolic risk factors using multivariable and logistic regression among 18,754 participants ages 13 - 90 years (mean 29.2 ± 17) in the T1D Exchange clinic registry from 6,320 zip code tabulation areas (2007-2017). RESULTS: SDI was associated with multiple cardiometabolic risk factors even after adjusting for covariates (age, biological sex, T1D duration, and race/ethnicity) in the multivariable linear regression models. Those in the highest socially deprived areas had 1.69 (unadjusted) and 1.78 (adjusted) times odds of a triple concomitant risk burden of poor glycemia, dyslipidemia, and hypertension. CONCLUSIONS: Persistent SDOH differences could account for a substantial degree of poor achievement of cardiometabolic targets in individuals with T1D. Our results suggest the need for a broader framework to understand the association between T1D and adverse cardiometabolic outcomes.

Socioeconomic Deprivation, Sleep Duration, and Mental Health during the First Year of the COVID-19 Pandemic.

The coronavirus disease 2019 (COVID-19) has had a rapid and sustained negative impact on sleep and mental health in the United States with disproportionate morbidity and mortality among socioeconomically deprived populations. We used multivariable and logistic regression to evaluate the associations among sleep duration, mental health, and socioeconomic deprivation (social deprivation index) in 14,676 Ohio residents from 1101 zip code tabulation areas from the 2020 Behavioral Risk Factor Surveillance System (BRFSS) survey. Higher socioeconomic deprivation was associated with shorter sleep and poorer mental health after adjusting for covariates (age, sex, race, education, income, and body mass index) in the multivariable linear regression models. Those in the highest socioeconomically deprived areas had 1.6 and 1.5 times higher odds of short sleep (duration < 6 h) and poor mental health (>14 poor mental health days), respectively, in the logistic regression models. Previous researchers have focused on limited socio-environmental factors such as crowding and income. We examined the role of a composite area based measure of socioeconomic deprivation in sleep duration and mental health during the first year of COVID-19. Our results suggest the need for a broader framework to understand the associations among socioeconomic deprivation, sleep duration, and mental health during a catastrophic event.

Racial and Ethnic Cardiometabolic Risk Disparities in the Type 1 Diabetes Exchange Clinic Registry Cohort.

OBJECTIVE: To determine whether individuals from a historically underrepresented racial group have a higher cardiometabolic risk than historically represented individuals with type 1 diabetes (T1D) considering socioeconomic deprivation. METHODS: We used the multivariable logistic and linear regression models to examine socioeconomic deprivation (upper 10th percentile) by race/ethnicity interaction for each cardiometabolic risk factor and cardiometabolic risk burden score, respectively, across 6320 zip code tabulation areas. We also determined the age-adjusted prevalence of low, moderate, and high cardiometabolic risks defined as 0, 1 to 2, and 3 or more risk factors for hypertension, obesity, dyslipidemia, and off-target glycemia for non-Hispanic White (n = 15 746), non-Hispanic Black (n = 1019), Hispanic (n = 1115), and other (n = 887), respectively. RESULTS: The sample comprised 18 767 adolescents and adults with T1D. Those identifying as non-Hispanic Black were more likely to have a high cardiometabolic risk profile, including a 4.5-fold increase in the odds of off-target glycemia, a twofold increase in the odds of systolic hypertension, and 0.29 (unadjusted) and 0.46 (adjusted) increases in a higher cardiometabolic risk burden compared with non-Hispanic White individuals (P < .01). Those identifying as Hispanic had a 3.4-fold increase in the odds of off-target glycemia but were less likely to be overweight/obese or have systolic hypertension compared with non-Hispanic White. However, the lower likelihood of overweight/obesity and hypertension did not persist after considering covariates. CONCLUSION: There is a need to investigate additional determinants of racially/ethnically underrepresented cardiometabolic health, including structural racism and implicit bias in cardiometabolic care for individuals with T1D.