OBJECTIVE: The Diabetes in Children, Adolescents, and Young Adults (DiCAYA) network seeks to create a nationwide electronic health record (EHR)-based diabetes surveillance system. This study aimed to develop a DiCAYA-wide EHR-based computable phenotype (CP) to identify prevalent cases of diabetes. RESEARCH DESIGN AND METHODS: We conducted network-wide chart reviews of 2,134 youth (aged <18 years) and 2,466 young adults (aged 18 to <45 years) among people with possible diabetes. Within this population, we compared the performance of three alternative CPs, using diabetes diagnoses determined by chart review as the gold standard. CPs were evaluated based on their accuracy in identifying diabetes and its subtype. RESULTS: The final DiCAYA CP requires at least one diabetes diagnosis code from clinical encounters. Subsequently, diabetes type classification was based on the ratio of type 1 diabetes (T1D) or type 2 diabetes (T2D) diagnosis codes in the EHR. For both youth and young adults, the sensitivity, specificity, and positive and negative predictive values (PPV and NPV, respectively) in finding diabetes cases were >90%, except for the specificity and NPV in young adults, which were slightly lower at 83.8% and 80.6%, respectively. The final DiCAYA CP achieved >90% sensitivity, specificity, PPV, and NPV in classifying T1D, and demonstrated lower but robust performance in identifying T2D, consistently maintaining >80% across metrics. CONCLUSIONS: The DiCAYA CP effectively identifies overall diabetes and T1D in youth and young adults, though T2D misclassification in youth highlights areas for refinement. The simplicity of the DiCAYA CP enables broad deployment across diverse EHR systems for diabetes surveillance.

INTRODUCTION: The association between various disparity factors and cardiovascular disease (CVD) prevalence among older US adults with diabetes has not been comprehensively explored. We examined disparities in CVD prevalence among Medicare beneficiaries with diabetes. METHODS: Data were from the 2015-2019 Medicare Current Beneficiary Survey. Diabetes and CVD conditions - myocardial infarction (MI), stroke, and heart failure - were self-reported. We estimated the adjusted prevalence ratios (APRs) of CVD by race and ethnicity, education, income-to-poverty ratio (IPR), urbanicity, food insecurity, and social vulnerability using logistic regressions that controlled for these factors as well as age and sex. RESULTS: Annually, an estimated 9.2 million Medicare beneficiaries aged 65 years or older had diabetes. Among them, 16.7% had MI, 13.7% had stroke, and 12.5% had heart failure. Beneficiaries who were food insecure, socially vulnerable, with an IPR less than or equal to 135%, and residing in rural areas had a higher crude CVD prevalence. After controlling for other factors, low IPR and food insecurity were linked to a higher prevalence of CVD. Hispanic beneficiaries had lower stroke and heart failure prevalence than non-Hispanic (NH) White and NH Black beneficiaries. NH Black beneficiaries had lower MI prevalence but higher heart failure prevalence compared with NH White beneficiaries. Female respondents with an IPR less than or equal to 135% had higher MI and stroke prevalence; this was not seen in male respondents. CONCLUSION: Low IPR and food insecurity were associated with higher MI, stroke, and heart failure prevalence among Medicare beneficiaries with diabetes. Our findings can inform targeted interventions to reduce CVD disparities in these populations.

Government statistical offices worldwide are under pressure to produce statistics rapidly and for more detailed geographies, to compete with unofficial estimates available from web-based big data sources or from private companies. Commonly suggested sources of improved health information are electronic health records and medical claims data. These data sources are collectively known as real-world data (RWD) because they are generated from routine health care processes, and they are available for millions of patients. It is clear that RWD can provide estimates that are more timely and less expensive to produce—but a key question is whether or not they are very accurate. To test this, we took advantage of a unique health data source that includes a full range of sociodemographic variables and compared estimates using all of those potential weighting variables versus estimates derived when only age and sex are available for weighting (as is common with most RWD sources). We show that not accounting for other variables can produce misleading and quite inaccurate health estimates. © 2024 American Association for Public Opinion Research. All rights reserved.

Introduction: People with diabetes were among the populations that experienced the most profound impacts during the COVID-19 pandemic. The authors estimated changes in healthcare utilization and expenditures for commercially insured adults aged 18–64 years with diabetes during the pandemic. Methods: Medical claims data were from IQVIA PharMetrics Plus. Linear regressions were used to estimate the changes in utilization (per 1,000 individuals) for inpatient stays, emergency room visits, physician office visits, and ambulatory surgery center procedures. Changes in expenditures, in total and out of pocket, were estimated using generalized linear models. Expenditures were adjusted to 2021 U.S. dollars using the Consumer Price Index. Results: Utilization was reduced significantly for all service types during the pandemic. Although the largest reduction occurred between March 2020 and May 2020, the decrease persisted throughout 2021. During March 2020–May 2020, ambulatory surgery center procedures were reduced by 4.7 visits per 1,000 individuals. The reduction ranged between 0.4 and 1.3 visits per 1,000 individuals subsequently. Expenditures declined for all service types during March 2020–May 2020. However, after May 2020, the reduction remained statistically significant only for physician office visits for all months, with varying changes in expenditures for other service types. Conclusions: Healthcare utilization and expenditures reduced among commercially insured adults with diabetes during the COVID-19 pandemic. © 2024

BACKGROUND: Hypertension prevalence among the overall US adult population has been relatively stable during the last two decades. However, whether this stabilization has occurred across rural-urban communities and across different geographic regions is unknown, particularly among older adults with diabetes who are likely to have concomitant cardiovascular risk factors. METHODS: This serial cross-sectional analysis used the 5% national sample of Medicare administrative claims data (n = 3,516,541) to examine temporal trends (2005-2017) in diagnosed hypertension among older adults with diabetes, across urban-rural communities and US census regions (Northeast, Midwest, South, and West). Joinpoint regression was used to obtain annual percent change (APC) in hypertension prevalence across rural-urban communities and geographic regions, and multivariable adjusted regression was used to assess associations between rural-urban communities and hypertension prevalence. RESULTS: The APC in the prevalence of hypertension was higher during 2005-2010, and there was a slowdown in the increase during 2011-2017 across all regions, with significant variations across rural-urban communities within each of the regions. In the regression analysis, in the adjusted model, older adults living in non-core (most rural) areas in the Midwest (PR = 0.988, 95% CI: 0.981-0.995) and West (PR = 0.935, 95% CI: 0.923-0.946) had lower hypertension prevalence than their regional counterparts living in large central metro areas. CONCLUSIONS: Although the magnitudes of these associations are small, differences in hypertension prevalence across rural-urban areas and geographic regions may have implications for targeted interventions to improve chronic disease prevention and management.

Estimates of county-level disease prevalence have a variety of applications. Such estimation is often done via model-based small-area estimation using survey data. However, for conditions with low prevalence (i.e., rare diseases or newly diagnosed diseases), counties with a high fraction of zero counts in surveys are common. They are often more common than the model used would lead one to expect; such zeros are called 'excess zeros'. The excess zeros can be structural (there are no cases to find) or sampling (there are cases, but none were selected for sampling). These issues are often addressed by combining multiple years of data. However, this approach can obscure trends in annual estimates and prevent estimates from being timely. Using single-year survey data, we proposed a Bayesian weighted Binomial Zero-inflated (BBZ) model to estimate county-level rare diseases prevalence. The BBZ model accounts for excess zero counts, the sampling weights and uses a power prior. We evaluated BBZ with American Community Survey results and simulated data. We showed that BBZ yielded less bias and smaller variance than estimates based on the binomial distribution, a common approach to this problem. Since BBZ uses only a single year of survey data, BBZ produces more timely county-level incidence estimates. These timely estimates help pinpoint the special areas of county-level needs and help medical researchers and public health practitioners promptly evaluate rare diseases trends and associations with other health conditions. © 2023 The Author(s).

OBJECTIVE: To compare total and out-of-pocket (OOP) medical expenditures between pre-COVID-19 (March 2019 to February 2020) and COVID-19 (March 2020 to February 2022) periods among Medicare beneficiaries with diabetes. RESEARCH DESIGN AND METHODS: Data were from 100% Medicare fee-for-service claims. Diabetes was identified using ICD-10 codes. We calculated quarterly total and OOP medical expenditures at the population and per capita level in total and by service type. Per capita expenditures were calculated by dividing the population expenditure by the number of beneficiaries with diabetes in the same quarter. Changes in expenditures were calculated as the differences in the same quarters between the prepandemic and pandemic years. RESULTS: Population total expenditure fell to $33.6 billion in the 1st quarter of the pandemic from $41.7 billion in the same prepandemic quarter; it then bounced back to $36.8 billion by the 4th quarter of the 2nd pandemic year. The per capita total expenditure fell to $5,356 in the 1st quarter of the pandemic from $6,500 in the same prepandemic quarter. It then increased to $6,096 by the 4th quarter of the 2nd pandemic year, surpassing the same quarter in the prepandemic year ($5,982). Both population and per capita OOP expenditures during the pandemic period were lower than the prepandemic period. Changes in per capita expenditure between the pre-COVID-19 and COVID-19 periods by service type varied. CONCLUSIONS: COVID-19 had a significant impact on both total and per capita medical expenditures among Medicare beneficiaries with diabetes. The COVID-19 pandemic was associated with lower OOP expenditures.

INTRODUCTION: Screening for prediabetes and type 2 diabetes may allow earlier detection, diagnosis, and treatment. The US Preventive Services Task Force recommends screening every 3 years for abnormal blood glucose among adults aged 40 to 70 years with overweight or obesity. Using IQVIA Ambulatory Electronic Medical Records, we estimated the proportion of adults aged 40 to 70 years with overweight or obesity who received blood glucose testing within 3 years from baseline in 2016. METHODS: We identified 1,338,509 adults aged 40 to 70 years with overweight or obesity in 2016 and without pre-existing diabetes. We included adults whose records were present in the data set for at least 2 years before their index body mass index (BMI) in 2016 and 3 years after the index BMI (2017-2019), during which we examined the occurrence of blood glucose testing. We calculated the unadjusted and adjusted prevalence of receiving blood glucose testing. RESULTS: The unadjusted prevalence of receiving blood glucose testing was 33.4% when it was defined as having a hemoglobin A(1c) or fasting plasma glucose measure. The unadjusted prevalence was 74.3% when we expanded the definition of testing to include random plasma glucose and unspecified glucose measures. Adults with obesity were more likely to receive the test than those with overweight. Men (vs women) and adults aged 50 to 59 years (vs other age groups) had higher testing rates. CONCLUSION: Our findings could inform clinical and public health promotion efforts to improve screening for blood glucose levels among adults with overweight or obesity.

Background Hypertension and diabetes are associated with increased COVID-19 severity. The association between level of control of these conditions and COVID-19 severity is less well understood. Methods and Results This retrospective cohort study identified adults with COVID-19, March 2020 to February 2022, in 43 US health systems in the National Patient-Centered Clinical Research Network. Hypertension control was categorized as blood pressure (BP) <130/80, 130 to 139/80 to 89, 140 to 159/90 to 99, or ≥160/100 mm Hg, and diabetes control as glycated hemoglobin <7%, 7% to <9%, ≥9%. Adjusted, pooled logistic regression assessed associations between hypertension and diabetes control and severe COVID-19 outcomes. Among 1 494 837 adults with COVID-19, 43% had hypertension and 12% had diabetes. Among patients with hypertension, the highest baseline BP was associated with greater odds of hospitalization (adjusted odds ratio [aOR], 1.30 [95% CI, 1.23-1.37] for BP ≥160/100 versus BP <130/80), critical care (aOR, 1.30 [95% CI, 1.21-1.40]), and mechanical ventilation (aOR, 1.32 [95% CI, 1.17-1.50]) but not mortality (aOR, 1.08 [95% CI, 0.98-1.12]). Among patients with diabetes, the highest glycated hemoglobin was associated with greater odds of hospitalization (aOR, 1.61 [95% CI, 1.47-1.76] for glycated hemoglobin ≥9% versus <7%), critical care (aOR, 1.42 [95% CI, 1.31-1.54]), mechanical ventilation (aOR, 1.12 [95% CI, 1.02-1.23]), and mortality (aOR, 1.18 [95% CI, 1.09-1.27]). Black and Hispanic adults were more likely than White adults to experience severe COVID-19 outcomes, independent of comorbidity score and control of hypertension or diabetes. Conclusions Among 1.5 million patients with COVID-19, higher BP and glycated hemoglobin were associated with more severe COVID-19 outcomes. Findings suggest that adults with poorest control of hypertension or diabetes might benefit from efforts to prevent and initiate early treatment of COVID-19.

BACKGROUND: Exposure to particulate matter ≤2.5 μm in diameter (PM(2.5)) and ozone (O(3)) has been linked to numerous harmful health outcomes. While epidemiologic evidence has suggested a positive association with type 2 diabetes (T2D), there is heterogeneity in findings. We evaluated exposures to PM(2.5) and O(3) across three large samples in the US using a harmonized approach for exposure assignment and covariate adjustment. METHODS: Data were obtained from the Veterans Administration Diabetes Risk (VADR) cohort (electronic health records [EHRs]), the Reasons for Geographic and Racial Disparities in Stroke (REGARDS) cohort (primary data collection), and the Geisinger health system (EHRs), and reflect the years 2003-2016 (REGARDS) and 2008-2016 (VADR and Geisinger). New onset T2D was ascertained using EHR information on medication orders, laboratory results, and T2D diagnoses (VADR and Geisinger) or report of T2D medication or diagnosis and/or elevated blood glucose levels (REGARDS). Exposure was assigned using pollutant annual averages from the Downscaler model. Models stratified by community type (higher density urban, lower density urban, suburban/small town, or rural census tracts) evaluated likelihood of new onset T2D in each study sample in single- and two-pollutant models of PM(2.5) and O(3). RESULTS: In two pollutant models, associations of PM(2.5), and new onset T2D were null in the REGARDS cohort except for in suburban/small town community types in models that also adjusted for NSEE, with an odds ratio (95% CI) of 1.51 (1.01, 2.25) per 5 μg/m(3) of PM(2.5). Results in the Geisinger sample were null. VADR sample results evidenced nonlinear associations for both pollutants; the shape of the association was dependent on community type. CONCLUSIONS: Associations between PM(2.5), O(3) and new onset T2D differed across three large study samples in the US. None of the results from any of the three study populations found strong and clear positive associations.

Background Growing evidence suggests incident cardiovascular disease (CVD) may be a long-term outcome of COVID-19 infection, and chronic diseases, such as diabetes, may influence CVD risk associated with COVID-19. We evaluated the postacute risk of CVD >30 days after a COVID-19 diagnosis by diabetes status. Methods and Results We included adults ≥20 years old with a COVID-19 diagnosis from March 1, 2020 through December 31, 2021 in a retrospective cohort study from the IQVIA PharMetrics Plus insurance claims database. A contemporaneous control group comprised adults without recorded diagnoses for COVID-19 or other acute respiratory infections. Two historical control groups comprised patients with or without an acute respiratory infection. Cardiovascular outcomes included cerebrovascular disorders, dysrhythmia, inflammatory heart disease, ischemic heart disease, thrombotic disorders, other cardiac disorders, major adverse cardiovascular events, and any CVD. The total sample comprised 23 824 095 adults (mean age, 48.4 years [SD, 15.7 years]; 51.9% women; mean follow-up, 8.5 months [SD, 5.8 months]). In multivariable Cox regression models, patients with a COVID-19 diagnosis had a significantly greater risk of all cardiovascular outcomes compared with patients without a diagnosis of COVID-19 (hazard ratio [HR], 1.66 [1.62-1.71], with diabetes; HR, 1.75 [1.73-1.78], without diabetes). Risk was attenuated but still significant for the majority of outcomes when comparing patients with COVID-19 to both historical control groups. Conclusions In patients with COVID-19 infection, postacute risk of incident cardiovascular outcomes is significantly higher than among controls without COVID-19, regardless of diabetes status. Therefore, monitoring for incident CVD may be essential beyond the first 30 days after a COVID-19 diagnosis.

IMPORTANCE: The coronavirus 2019 (COVID-19) pandemic abruptly impacted health care service delivery and utilization. However, the impact on older adults with diabetes in the United States is unclear. OBJECTIVE: To estimate changes in health care utilization among older adults with diabetes during the initial 2 years of the COVID-19 pandemic compared to the 2 years before, and to examine the variation in utilization changes by demographic and socioeconomic characteristics. DESIGN SETTING AND PARTICIPANTS: In this study, we analyzed changes in utilization, measured by the average use of health care services per 1,000 persons with diabetes, using medical claims for Medicare fee-for-service beneficiaries aged 67 years and above. Utilization changes by setting (acute inpatient, emergency room [ER], hospital outpatient, physician office, and ambulatory surgery center [ASC]) and by media (telehealth and in-person) were examined for 22 months of the pandemic (03/2020-12/2021) compared with pre-pandemic period (03/2018-12/2019). We also estimated utilization changes by beneficiaries' age group, sex, race/ethnicity, and residential urbanicity. RESULTS: The study sample consisted of approximately 6 million beneficiaries with diabetes each month. In the first 2 years of the pandemic, the average use of health care services by setting was 5-17% lower than the pre-pandemic level for all types of services. Phase 1 (03/2020-05/2020) had the largest decrease in utilization: physician office visits changed by -51.2% (95% CI, -55.0% to -47.5%), ASC procedures by -45.1% (95% CI, -49.8% to -40.4%), ER visits by -36.9% (95% CI, -39.0% to -34.7%), acute inpatient stays by -31.5% (95% CI, -33.6% to -29.3%), and hospital outpatient visits by -27% (95% CI, -29.3% to -24.8%). The reduction in utilization varied by sociodemographic subgroup. During the pandemic, the use of telehealth visits increased by 511.1% (95% CI, 502.2% to 520.0%) compared to the pre-pandemic period. The increase was smaller among rural residents. CONCLUSIONS AND RELEVANCE: Medicare beneficiaries with diabetes experienced a reduction in the use of health care services during the COVID-19 pandemic, some of which persisted through two years into the pandemic. Telehealth visits increased, but not enough to overcome decreases in in-person visits. Understanding these patterns may help to optimize the use of health care resources for diabetes management in the post-pandemic era and during future emergencies.

AIMS: The overall prevalence of diabetes has increased over the past two decades in the United States, disproportionately affecting low-income populations. We aimed to examine the trends in income-related inequalities in diabetes prevalence and to identify the contributions of determining factors. METHODS: We estimated income-related inequalities in diagnosed diabetes during 2001-2018 among US adults aged 18 years or older using data from the National Health Interview Survey (NHIS). The concentration index was used to measure income-related inequalities in diabetes and was decomposed into contributing factors. We then examined temporal changes in diabetes inequality and contributors to those changes over time. RESULTS: Results showed that income-related inequalities in diabetes, unfavorable to low-income groups, persisted throughout the study period. The income-related inequalities in diabetes decreased during 2001-2011 and then increased during 2011-2018. Decomposition analysis revealed that income, obesity, physical activity levels, and race/ethnicity were important contributors to inequalities in diabetes at almost all time points. Moreover, changes regarding age and income were identified as the main factors explaining changes in diabetes inequalities over time. CONCLUSIONS: Diabetes was more prevalent in low-income populations. Our study contributes to understanding income-related diabetes inequalities and could help facilitate program development to prevent type 2 diabetes and address modifiable factors to reduce diabetes inequalities.

INTRODUCTION: Hypertension and diabetes are associated with increased COVID-19 severity, yet less is known about COVID-19 outcomes across levels of disease control for these conditions. METHODS: All adults aged 20 years with COVID-19 between March 1, 2020 and March 15, 2021 in 42 healthcare systems in National Patient-Centered Clinical Research Network were identified. RESULTS: Among 656,049 adults with COVID-19, 41% had hypertension, and 13% had diabetes. Of patients with classifiable hypertension, 35% had blood pressure <130/80 mmHg, 40% had blood pressure of 130139/8089 mmHg, 21% had blood pressure of 140159/9099 mmHg, and 6% had blood pressure 160/100 mmHg. Severe COVID-19 outcomes were more prevalent among those with blood pressure of 160/100 than among those with blood pressure of 130-139/80-89, including hospitalization (23.7% [95% CI=23.0, 24.4] vs 11.7% [95% CI=11.5, 11.9]), receipt of critical care (5.5% [95% CI=5.0, 5.8] vs 2.4% [95% CI=2.3, 2.5]), receipt of mechanical ventilation (3.0% [95% CI=2.7, 3.3] vs 1.2% [95% CI=1.1, 1.3]), and 60-day mortality (4.6% [95% CI=4.2, 4.9] vs 1.8% [95% CI=1.7, 1.9]). Of patients with classifiable diabetes, 44% had HbA1c <7%, 35% had HbA1c 7% to <9%, and 21% had HbA1c 9%. Hospitalization prevalence was 31.3% (95% CI=30.7, 31.9) among those with HbA1c <7% vs 40.2% (95% CI=39.4, 41.1) among those with HbA1c 9%; other outcomes did not differ substantially by HbA1c. CONCLUSIONS: These findings highlight the importance of appropriate management of hypertension and diabetes, including during public health emergencies such as the COVID-19 pandemic.

BACKGROUND: Although treatment and control of diabetes can prevent complications and reduce morbidity, few data sources exist at the state level for surveillance of diabetes comorbidities and control. Surveys and electronic health records (EHRs) offer different strengths and weaknesses for surveillance of diabetes and major metabolic comorbidities. Data from self-report surveys suffer from cognitive and recall biases, and generally cannot be used for surveillance of undiagnosed cases. EHR data are becoming more readily available, but pose particular challenges for population estimation since patients are not randomly selected, not everyone has the relevant biomarker measurements, and those included tend to cluster geographically. METHODS: We analyzed data from the National Health and Nutritional Examination Survey, the Health and Retirement Study, and EHR data from the DARTNet Institute to create state-level adjusted estimates of the prevalence and control of diabetes, and the prevalence and control of hypertension and high cholesterol in the diabetes population, age 50 and over for five states: Alabama, California, Florida, Louisiana, and Massachusetts. RESULTS: The estimates from the two surveys generally aligned well. The EHR data were consistent with the surveys for many measures, but yielded consistently lower estimates of undiagnosed diabetes prevalence, and identified somewhat fewer comorbidities in most states. CONCLUSIONS: Despite these limitations, EHRs may be a promising source for diabetes surveillance and assessment of control as the datasets are large and created during the routine delivery of health care. TRIAL REGISTRATION: Not applicable.

INTRODUCTION: Geographic disparities in diabetes burden exist throughout the United States (US), with many risk factors for diabetes clustering at a community or neighborhood level. We hypothesized that the likelihood of new onset type 2 diabetes (T2D) would differ by community type in three large study samples covering the US. RESEARCH DESIGN AND METHODS: We evaluated the likelihood of new onset T2D by a census tract-level measure of community type, a modification of RUCA designations (higher density urban, lower density urban, suburban/small town, and rural) in three longitudinal US study samples (REGARDS [REasons for Geographic and Racial Differences in Stroke] cohort, VADR [Veterans Affairs Diabetes Risk] cohort, Geisinger electronic health records) representing the CDC Diabetes LEAD (Location, Environmental Attributes, and Disparities) Network. RESULTS: In the REGARDS sample, residing in higher density urban community types was associated with the lowest odds of new onset T2D (OR [95% CI]: 0.80 [0.66, 0.97]) compared to rural community types; in the Geisinger sample, residing in higher density urban community types was associated with the highest odds of new onset T2D (OR [95% CI]: 1.20 [1.06, 1.35]) compared to rural community types. In the VADR sample, suburban/small town community types had the lowest hazard ratios of new onset T2D (HR [95% CI]: 0.99 [0.98, 1.00]). However, in a regional stratified analysis of the VADR sample, the likelihood of new onset T2D was consistent with findings in the REGARDS and Geisinger samples, with highest likelihood of T2D in the rural South and in the higher density urban communities of the Northeast and West regions; likelihood of T2D did not differ by community type in the Midwest. CONCLUSIONS: The likelihood of new onset T2D by community type varied by region of the US. In the South, the likelihood of new onset T2D was higher among those residing in rural communities.

Given that the prevalence of hypertension increases with age and is more common among adults with diabetes than those without diabetes, the objective of this study was to examine trends in hypertension prevalence by geographic region among older adults with and without diabetes. Among older adults with diabetes, hypertension prevalence generally increased from 2005 to 2017 across all regions, although the annual percent change was lower from 2011 to 2017 than 2005-2011 for all regions.

The COVID-19 pandemic has disproportionately affected people with diabetes, who are at increased risk of severe COVID-19.* Increases in the number of type 1 diabetes diagnoses (1,2) and increased frequency and severity of diabetic ketoacidosis (DKA) at the time of diabetes diagnosis (3) have been reported in European pediatric populations during the COVID-19 pandemic. In adults, diabetes might be a long-term consequence of SARS-CoV-2 infection (4-7). To evaluate the risk for any new diabetes diagnosis (type 1, type 2, or other diabetes) >30 days(†) after acute infection with SARS-CoV-2 (the virus that causes COVID-19), CDC estimated diabetes incidence among patients aged <18 years (patients) with diagnosed COVID-19 from retrospective cohorts constructed using IQVIA health care claims data from March 1, 2020, through February 26, 2021, and compared it with incidence among patients matched by age and sex 1) who did not receive a COVID-19 diagnosis during the pandemic, or 2) who received a prepandemic non-COVID-19 acute respiratory infection (ARI) diagnosis. Analyses were replicated using a second data source (HealthVerity; March 1, 2020-June 28, 2021) that included patients who had any health care encounter possibly related to COVID-19. Among these patients, diabetes incidence was significantly higher among those with COVID-19 than among those 1) without COVID-19 in both databases (IQVIA: hazard ratio [HR] = 2.66, 95% CI = 1.98-3.56; HealthVerity: HR = 1.31, 95% CI = 1.20-1.44) and 2) with non-COVID-19 ARI in the prepandemic period (IQVIA, HR = 2.16, 95% CI = 1.64-2.86). The observed increased risk for diabetes among persons aged <18 years who had COVID-19 highlights the importance of COVID-19 prevention strategies, including vaccination, for all eligible persons in this age group,(§) in addition to chronic disease prevention and management. The mechanism of how SARS-CoV-2 might lead to incident diabetes is likely complex and could differ by type 1 and type 2 diabetes. Monitoring for long-term consequences, including signs of new diabetes, following SARS-CoV-2 infection is important in this age group.

IMPORTANCE: Diabetes causes substantial morbidity and mortality among adults in the US, yet its incidence varies across the country, suggesting that neighborhood factors are associated with geographical disparities in diabetes. OBJECTIVE: To examine the association between neighborhood food environment and risk of incident type 2 diabetes across different community types (high-density urban, low-density urban, suburban, and rural). DESIGN, SETTING, AND PARTICIPANTS: This is a national cohort study of 4 100 650 US veterans without type 2 diabetes. Participants entered the cohort between 2008 and 2016 and were followed up through 2018. The median (IQR) duration of follow-up was 5.5 (2.6-9.8) person-years. Data were obtained from Veterans Affairs electronic health records. Incident type 2 diabetes was defined as 2 encounters with type 2 diabetes International Classification of Diseases, Ninth Revision or Tenth Revision codes, a prescription for diabetes medication other than metformin or acarbose alone, or 1 encounter with type 2 diabetes International Classification of Diseases Ninth Revision or Tenth Revision codes and 2 instances of elevated hemoglobin A1c (≥6.5%). Data analysis was performed from October 2020 to March 2021. EXPOSURES: Five-year mean counts of fast-food restaurants and supermarkets relative to other food outlets at baseline were used to generate neighborhood food environment measures. The association between food environment and time to incident diabetes was examined using piecewise exponential models with 2-year interval of person-time and county-level random effects stratifying by community types. RESULTS: The mean (SD) age of cohort participants was 59.4 (17.2) years. Most of the participants were non-Hispanic White (2 783 756 participants [76.3%]) and male (3 779 555 participants [92.2%]). The relative density of fast-food restaurants was positively associated with a modestly increased risk of type 2 diabetes in all community types. The adjusted hazard ratio (aHR) was 1.01 (95% CI, 1.00-1.02) in high-density urban communities, 1.01 (95% CI, 1.01-1.01) in low-density urban communities, 1.02 (95% CI, 1.01-1.03) in suburban communities, and 1.01 (95% CI, 1.01-1.02) in rural communities. The relative density of supermarkets was associated with lower type 2 diabetes risk only in suburban (aHR, 0.97; 95% CI, 0.96-0.99) and rural (aHR, 0.99; 95% CI, 0.98-0.99) communities. CONCLUSIONS AND RELEVANCE: These findings suggest that neighborhood food environment measures are associated with type 2 diabetes among US veterans in multiple community types and that food environments are potential avenues for action to address the burden of diabetes. Tailored interventions targeting the availability of supermarkets may be associated with reduced diabetes risk, particularly in suburban and rural communities, whereas restrictions on fast-food restaurants may help in all community types.

BACKGROUND: Given the paucity of studies for low- or middle-income countries, we aim to provide the first ever estimations of lifetime risk of diabetes, years of life spent and lost among those with diabetes for Brazilians. Estimates of Brazil´s diabetes burden consist essentially of reports of diabetes prevalence from national surveys and mortality data. However, these additional metrics are at times more meaningful ways to characterize this burden. METHODS: We joined data on incidence of physician-diagnosed diabetes from the Brazilian risk factor surveillance system, all-cause mortality from national statistics, and diabetes mortality rate ratios from ELSA-Brasil, an ongoing cohort study. To calculate lifetime risk of developing diabetes, we applied an illness-death state model. To calculate years of life lost for those with diabetes and years lived with the disease, we additionally calculated the mortality rates for those with diabetes. RESULTS: A 35-year-old white adult had a 23.4% (95% CI = 22.5%-25.5%) lifetime risk of developing diabetes by age 80 while a same-aged black/brown adult had a 30.8% risk (95% confidence interval (CI) = 29.6%-33.2%). Men diagnosed with diabetes at age 35 would live 32.9 (95% CI = 32.4-33.2) years with diabetes and lose 5.5 (95% CI = 5.1-6.1) years of life. Similarly-aged women would live 38.8 (95% CI = 38.3-38.9) years with diabetes and lose 2.1 (95% CI = 1.9-2.6) years of life. CONCLUSIONS: Assuming maintenance of current rates, one-quarter of young Brazilians will develop diabetes over their lifetimes, with this number reaching almost one-third among young, black/brown women. Those developing diabetes will suffer a decrease in life expectancy and will generate a considerable cost in terms of medical care.

Background: While there are known individual-level risk factors for kidney disease at time of type 2 diabetes diagnosis, little is known regarding the role of community context. We evaluated the association of community socioeconomic deprivation (CSD) and community type with estimated glomerular filtration rate (eGFR) when type 2 diabetes is diagnosed. Method(s): This was a retrospective cohort study of 13,144 adults with newly diagnosed type 2 diabetes in Pennsylvania. The outcome was the closest eGFR measurement within one year prior to and two weeks after type 2 diabetes diagnosis, calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-Epi) equation. We used adjusted multinomial regression models to estimate associations of CSD (quartile 1, least deprivation) and community type (township, borough, city) with eGFR and used adjusted generalized estimating equation models to evaluate whether community features were associated with the absence of diabetes screening in the years prior to type 2 diabetes diagnosis. Result(s): Of the participants, 1279 (9.7%) had hyperfiltration and 1377 (10.5%) had reduced eGFR. Women were less likely to have hyperfiltration and more likely to have reduced eGFR. Black (versus White) race was positively associated with hyperfiltration when the eGFR calculation was corrected for race but inversely associated without the correction. Medical Assistance (ever versus never) was positively associated with reduced eGFR. Higher CSD and living in a city were each positively associated (odds ratio [95% confidence interval]) with reduced eGFR (CSD quartiles 3 and 4 versus quartile 1, 1.23 [1.04, 1.46], 1.32 [1.11, 1.58], respectively; city versus township, 1.38 [1.15, 1.65]). These features were also positively associated with the absence of a type 2 diabetes screening measure. Conclusion(s): In a population-based sample, more than twenty percent had hyperfiltration or reduced eGFR at time of type 2 diabetes diagnosis. Individual- and community-level factors were associated with these outcomes. Copyright © 2021 The Authors

OBJECTIVE: Diabetes is associated with poor oral health, but incremental expenditures for dental care associated with diabetes in the U.S. are unknown. We aimed to quantify these incremental expenditures per person and for the nation. RESEARCH DESIGN AND METHODS: We analyzed data from 46,633 noninstitutionalized adults aged ≥18 years old who participated in the 2016-2017 Medical Expenditure Panel Survey. We used two-part models to estimate dental expenditures per person in total, by payment source, and by dental service type, controlling for sociodemographic characteristics, health status, and geographic variables. Incremental expenditure was the difference in predicted expenditure for dental care between adults with and without diabetes. The total expenditure for the U.S. was the expenditure per person multiplied by the estimated number of people with diabetes. Expenditures were adjusted to 2017 USD. RESULTS: The mean adjusted annual diabetes-associated incremental dental expenditure was $77 per person and $1.9 billion for the nation. Of this incremental expenditure, 51% ($40) and 39% ($30) were paid out of pocket and by private insurance, 69% ($53) of the incremental expenditure was for restorative/prosthetic/surgical services, and adults with diabetes had lower expenditure for preventive services than those without (incremental, -$7). Incremental expenditures were higher in older adults, non-Hispanic Whites, and people with higher levels of income and education. CONCLUSIONS: Diabetes is associated with higher dental expenditures. These results fill a gap in the estimates of total medical expenditures associated with diabetes in the U.S. and highlight the importance of preventive dental care among people with diabetes.

Estimates based on the National Inpatient Sample (NIS) showed that diabetes-related nontraumatic lower- extremity amputation (NLEA) rates declined among hospitalized patients between 2000 and 2009, followed by an increasing NLEA rate between 2009 and 2015 (1). The increase was largely observed in young and middle-aged adults (1). However, the NIS dataset includes only inpatient admissions; minor amputation surgeries that were performed in ambulatory settings were not included in these estimates. In addition, NIS data are event-based, and multiple amputations in the same person are considered as a new event each time. In the current study, we used the IBM MarketScan Commercial Database to examine NLEA occurring in inpatient and outpatient settings among privately insured adults 18–64 years of age with diabetes.

Salutogenic effects of living near aquatic areas (blue space) remain underexplored, particularly in non-coastal and non-urban areas. We evaluated associations of residential proximity to inland freshwater blue space with new onset type 2 diabetes (T2D) in central and northeast Pennsylvania, USA, using medical records to conduct a nested case-control study. T2D cases (n = 15,888) were identified from diabetes diagnoses, medication orders, and laboratory test results and frequency-matched on age, sex, and encounter year to diabetes-free controls (n = 79,435). We calculated distance from individual residences to the nearest lake, river, tributary, or large stream, and residence within the 100-year floodplain. Logistic regression models adjusted for community socioeconomic deprivation and other confounding variables and stratified by community type (townships [rural/suburban], boroughs [small towns], city census tracts). Compared to individuals living ≥ 1.25 miles from blue space, those within 0.25 miles had 8% and 17% higher odds of T2D onset in townships and boroughs, respectively. Among city residents, T2D odds were 38–39% higher for those living 0.25 to < 0.75 miles from blue space. Residing within the floodplain was associated with 16% and 14% higher T2D odds in townships and boroughs. A post-hoc analysis demonstrated patterns of lower residential property values with nearer distance to the region's predominant waterbody, suggesting unmeasured confounding by socioeconomic disadvantage. This may explain our unexpected findings of higher T2D odds with closer proximity to blue space. Our findings highlight the importance of historic and economic context and interrelated factors such as flood risk and lack of waterfront development in blue space research.

AIMS: To estimate incidence of type 1 diabetes (T1D) and to develop a T1D prediction model among young adults. METHODS: Adults 20-45 years newly-diagnosed with diabetes in 2017 were identified within Kaiser Permanente's healthcare systems in California and invited for diabetes autoantibody (DAA) testing. Multiple imputation was conducted to assign missing DAA status. The primary outcome for incidence rates (IR) and the prediction model was T1D defined by ≥1 positive DAA. RESULTS: Among 2,347,989 persons at risk, 7,862 developed diabetes, 2,063 had DAA measured, and 166 (8.0%) had ≥1 positive DAA. T1D IR (95% CI) per 100,000 person-years was 15.2 (10.2-20.1) for ages 20-29 and 38.2 (28.6-47.8) for ages 30-44 years. The age-standardized IRs were 32.5 (22.2-42.8) for men and 27.2 (21.0-34.5) for women. The age/sex-standardized IRs were 30.1 (23.5-36.8) overall; 41.4 (25.3-57.5) for Hispanics, 37.0 (11.6-62.4) for Blacks, 21.4 (14.3-28.6) for non-Hispanic Whites, and 19.4 (8.5-30.2) for Asians. Predictors of T1D among cases included female sex, younger age, lower BMI, insulin use and having T1D based on diagnostic codes. CONCLUSIONS: T1D may account for up to 8% of incident diabetes cases among young adults. Follow-up is needed to establish the clinical course of patients with one DAA at diagnosis.

BACKGROUND: To better understand the relation between sleep problems and CKD, we examined temporal trends in the prevalence of self-reported sleep problems in adults in the United States and their associations with CKD and all-cause mortality. METHODS: Using data from 27,365 adult participants in five biannual National Health and Examination Surveys (2005-2006 through 2013-2014), we studied five self-reported sleep problems-trouble sleeping, sleep disorder, nocturia (urinating ≥2 times/night), inadequate sleep (<7 hours/night), and excessive sleep (>9 hours/night)-plus a composite index. We conducted three types of analysis: temporal trends in the prevalence of each sleep measure by CKD status, using model-based standardization; cross-sectional analysis of associations between four CKD measures and each sleep measure, using logistic regression; and survival analysis of the association between each sleep measure and mortality, using Cox regression. RESULTS: The prevalence of trouble sleeping and sleep disorder increased over the five surveys by 4% and 3%, respectively, whereas the other sleep problems remained relatively stable. All sleep problems, except inadequate sleep, were more common during the study period among adults with CKD than without CKD (40% versus 21% for nocturia; 5% versus 2% for excessive sleep; 30% versus 25% for trouble sleeping; 12% versus 8% for sleep disorder). Both eGFR <30 ml/min per 1.73 m(2) and albuminuria were positively associated with nocturia and excessive sleep. Excessive sleep and nocturia were also associated with higher mortality (adjusted hazard ratio for >9 versus 7-9 hours/night=1.7; 95% CI, 1.3 to 2.1; and for nocturia=1.2; 95% CI, 1.1 to 1.4). CONCLUSIONS: The high prevalence of sleep problems among persons with CKD and their associations with mortality suggest their potential importance to clinical practice. Future work could examine the health effects of identifying and treating sleep problems in patients with CKD.

Diabetes increases the risk for developing cardiovascular, neurologic, kidney, eye, and other complications. Diabetes and related complications also pose a huge economic cost to society: in 2017, the estimated total economic cost of diagnosed diabetes was $327 billion in the United States (1). Diabetes complications can be prevented or delayed through the management of blood glucose (measured by hemoglobin A1C), blood pressure (BP), and non-high-density lipoprotein cholesterol (non-HDL-C) levels, and by avoiding smoking; these are collectively known as the ABCS goals (hemoglobin A1C, Blood pressure, Cholesterol, Smoking) (2-5). Assessments of achieving ABCS goals among adults with diabetes are available at the national level (4,6); however, studies that assess state-level prevalence of meeting ABCS goals have been lacking. This report provides imputed state-level proportions of adults with self-reported diabetes meeting ABCS goals in each of the 50 U.S. states and the District of Columbia (DC). State-level estimates were created by raking and multiple imputation methods (7,8) using data from the 2009-2018 National Health and Nutrition Examination Survey (NHANES), 2017-2018 American Community Survey (ACS), and 2017-2018 Behavioral Risk Factor Surveillance System (BRFSS). Among U.S. adults with diabetes, an estimated 26.4% met combined ABCS goals, and 75.4%, 70.4%, 55.8%, and 86.0% met A1C <8%, BP <140/90 mmHg, non-HDL-C <130 mg/dL and nonsmoking goals, respectively. Public health departments could use these data in their planning efforts to achieve ABCS goal levels and reduce diabetes-related complications at the state level.

BACKGROUND: The Longitudinal Epidemiologic Assessment of Diabetes Risk (LEADR) study uses a novel Electronic Health Record (EHR) data approach as a tool to assess the epidemiology of known and new risk factors for type 2 diabetes mellitus (T2DM) and study how prevention interventions affect progression to and onset of T2DM. We created an electronic cohort of 1.4 million patients having had at least 4 encounters with a healthcare organization for at least 24-months; were aged ≥18 years in 2010; and had no diabetes (i.e., T1DM or T2DM) at cohort entry or in the 12 months following entry. EHR data came from patients at nine healthcare organizations across the U.S. between January 1, 2010-December 31, 2016. RESULTS: Approximately 5.9% of the LEADR cohort (82,922 patients) developed T2DM, providing opportunities to explore longitudinal clinical care, medication use, risk factor trajectories, and diagnoses for these patients, compared with patients similarly matched prior to disease onset. CONCLUSIONS: LEADR represents one of the largest EHR databases to have repurposed EHR data to examine patients' T2DM risk. This paper is first in a series demonstrating this novel approach to studying T2DM. IMPLICATIONS: Chronic conditions that often take years to develop can be studied efficiently using EHR data in a retrospective design. LEVEL OF EVIDENCE: While much is already known about T2DM risk, this EHR's cohort's 160 M data points for 1.4 M people over six years, provides opportunities to investigate new unique risk factors and evaluate research hypotheses where results could modify public health practice for preventing T2DM.

OBJECTIVE: Diabetes is a leading cause of nontraumatic lower-extremity amputation (NLEA) in the U.S. After a period of decline, some national U.S. data have shown that diabetes-related NLEAs have recently increased, particularly among young and middle-aged adults. However, the trend for older adults is less clear. RESEARCH DESIGN AND METHODS: To examine NLEA trends among older adults with diabetes (≥67 years), we used 100% Medicare claims for beneficiaries enrolled in Parts A and B, also known as fee for service (FFS). NLEA was defined as the highest-level amputation per patient per calendar year. Annual NLEA rates were estimated from 2000 to 2017 and stratified by age-group, sex, race/ethnicity, NLEA level (toe, foot, below-the-knee amputation [BKA], above-the-knee amputation [AKA]), and state. All rates were age and sex standardized to the 2000 Medicare population. Trends over time were assessed using Joinpoint regression and annual percent change (APC) reported. RESULTS: NLEA rates (per 1,000 people with diabetes) decreased by half from 8.5 in 2000 to 4.4 in 2009 (APC -7.9, P < 0.001). However, from 2009 onward, NLEA rates increased to 4.8 (APC 1.2, P < 0.01). Trends were similar across most age, sex, and race/ethnic groups, but absolute rates were highest in the oldest age-groups, blacks, and men. By NLEA type, overall increases were driven by increases in rates of toe and foot NLEAs, while BKA and AKA continued to decline. The majority of U.S. states showed recent increases in NLEA, similar to national estimates. CONCLUSIONS: This study of the U.S. Medicare FFS population shows that recent increases in diabetes-related NLEAs are also occurring in older populations but at a less severe rate than among younger adults (<65 years) in the general population. Preventive foot care has been shown to reduce rates of NLEA among adults with diabetes, and the findings of the study suggest that those with diabetes-across the age spectrum-could benefit from increased attention to this strategy.

OBJECTIVES: Surveys for U.S. diabetes surveillance do not reliably distinguish between type 1 and type 2 diabetes, potentially obscuring trends in type 1 among adults. To validate survey-based algorithms for distinguishing diabetes type, we linked survey data collected from adult patients with diabetes to a gold standard diabetes type. RESEARCH DESIGN AND METHODS: We collected data through a telephone survey of 771 adults with diabetes receiving care in a large healthcare system in North Carolina. We tested 34 survey classification algorithms utilizing information on respondents' report of physician-diagnosed diabetes type, age at onset, diabetes drug use, and body mass index. Algorithms were evaluated by calculating type 1 and type 2 sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) relative to a gold standard diagnosis of diabetes type determined through analysis of EHR data and endocrinologist review of selected cases. RESULTS: Algorithms based on self-reported type outperformed those based solely on other data elements. The top-performing algorithm classified as type 1 all respondents who reported type 1 and were prescribed insulin, as "other diabetes type" all respondents who reported "other," and as type 2 the remaining respondents (type 1 sensitivity 91.6%, type 1 specificity 98.9%, type 1 PPV 82.5%, type 1 NPV 99.5%). This algorithm performed well in most demographic subpopulations. CONCLUSIONS: The major federal health surveys should consider including self-reported diabetes type if they do not already, as the gains in the accuracy of typing are substantial compared to classifications based on other data elements. This study provides much-needed guidance on the accuracy of survey-based diabetes typing algorithms.