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).

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.

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.

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.

Bayesian hierarchical regression (BHR) is often used in small area estimation (SAE). BHR conditions on the samples. Therefore, when data are from a complex sample survey, neither survey sampling design nor survey weights are used. This can introduce bias and/or cause large variance. Further, if non-informative priors are used, BHR often requires the combination of multiple years of data to produce sample sizes that yield adequate precision; this can result in poor timeliness and can obscure trends. To address bias and variance, we propose a design assisted model-based approach for SAE by integrating adjusted sample weights. To address timeliness, we use historical data to define informative priors (power prior); this allows estimates to be derived from a single year of data. Using American Community Survey data for validation, we applied the proposed method to Behavioral Risk Factor Surveillance System data. We estimated the prevalence of disability for all U.S. counties. We show that our method can produce estimates that are both more timely than those arising from widely-used alternatives and are closer to ACS' direct estimates, particularly for low-data counties. Our method can be generalized to estimate the county-level prevalence of other health related measurements.

Background: Data on mortality burden and excess deaths attributable to diabetes are sparse and frequently unreliable, particularly in low and middle-income countries. Estimates in Brazil to date have relied on death certificate data, which do not consider the multicausal nature of deaths. Our aim was to combine cohort data with national prevalence and mortality statistics to estimate the absolute number of deaths that could have been prevented if the mortality rates of people with diabetes were the same as for those without. In addition, we aimed to estimate the increase in burden when considering undiagnosed diabetes. Methods: We estimated self-reported diabetes prevalence from the National Health Survey (PNS) and overall mortality from the national mortality information system (SIM). We estimated the diabetes mortality rate ratio (rates of those with vs without diabetes) from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), an ongoing cohort study. Joining estimates from these three sources, we calculated for the population the absolute number and the fraction of deaths attributable to diabetes. We repeated our analyses considering both self-reported and unknown diabetes, the latter estimated based on single point-in-time glycemic determinations in ELSA-Brasil. Finally, we compared results with diabetes-related mortality information from death certificates. Results: In 2013, 65 581 deaths, 9.1% of all deaths between the ages of 35-80, were attributable to known diabetes. If cases of unknown diabetes were considered, this figure would rise to 14.3%. In contrast, based on death certificates only, 5.3% of all death had diabetes as the underlying cause and 10.4% as any mentioned cause. Conclusions: In this first report of diabetes mortality burden in Brazil using cohort data to estimate diabetes mortality rate ratios and the prevalence of unknown diabetes, we showed marked underestimation of the current burden, especially when unknown cases of diabetes are also considered.

Importance: Individuals with prediabetes are at increased risk of developing type 2 diabetes, chronic kidney disease, and cardiovascular disease. The incidence and prevalence of type 2 diabetes in the US adolescent population have increased in the last decade. Therefore, it is important to monitor the prevalence of prediabetes and varying levels of glucose tolerance to assess the future risk of type 2 diabetes in the youngest segment of the population. Objective: To examine the prevalence of impaired fasting glucose (IFG), impaired glucose tolerance (IGT), and increased glycated hemoglobin A1c (HbA1c) levels in US adolescents (aged 12-18 years) and young adults (aged 19-34 years) without diabetes. Design, Setting, and Participants: This cross-sectional analyses of the 2005-2016 National Health and Nutrition Examination Survey assessed a population-based sample of adolescents and young adults who were not pregnant, did not have diabetes, and had measured fasting plasma glucose, 2-hour plasma glucose after a 75-g oral glucose tolerance test, and HbA1c levels. Analysis began in April 2017. Main Outcomes and Measures: Impaired fasting glucose was defined as fasting plasma glucose of 100 mg/dL to less than 126 mg/dL, IGT as 2-hour plasma glucose of 140 mg/dL to less than 200 mg/dL, and increased HbA1c level as HbA1c level between 5.7% and 6.4%. The prevalence of IFG, isolated IFG, IGT, isolated IGT, increased HbA1c level, isolated increased HbA1c level, and prediabetes (defined as having IFG, IGT, or increased HbA1c level) were estimated. Fasting insulin levels and cardiometabolic risk factors across glycemic abnormality phenotypes were also compared. Obesity was defined as having age- and sex-specific body mass index (calculated as weight in kilograms divided by height in meters squared) in the 95th percentile or higher in adolescents or 30 or higher in young adults. Results: Of 5786 individuals, 2606 (45%) were adolescents and 3180 (55%) were young adults. Of adolescents, 50.6% (95% CI, 47.6%-53.6%) were boys, and 50.6% (95% CI, 48.8%-52.4%) of young adults were men. Among adolescents, the prevalence of prediabetes was 18.0% (95% CI, 16.0%-20.1%) and among young adults was 24.0% (95% CI, 22.0%-26.1%). Impaired fasting glucose constituted the largest proportion of prediabetes, with prevalence of 11.1% (95% CI, 9.5%-13.0%) in adolescents and 15.8% (95% CI, 14.0%-17.9%) in young adults. In multivariable logistic models including age, sex, race/ethnicity, and body mass index, the predictive marginal prevalence of prediabetes was significantly higher in male than in female individuals (22.5% [95% CI, 19.5%-25.4%] vs 13.4% [95% CI, 10.8%-16.5%] in adolescents and 29.1% [95% CI, 26.4%-32.1%] vs 18.8% [95% CI, 16.5%-21.3%] in young adults). Prediabetes prevalence was significantly higher in individuals with obesity than in those with normal weight (25.7% [95% CI, 20.0%-32.4%] vs 16.4% [95% CI, 14.3%-18.7%] in adolescents and 36.9% [95% CI, 32.9%-41.1%] vs 16.6% [95% CI, 14.2%-19.4%] in young adults). Compared with persons with normal glucose tolerance, adolescents and young adults with prediabetes had significantly higher non-high-density lipoprotein cholesterol levels, systolic blood pressure, central adiposity, and lower insulin sensitivity (P < .05 for all). Conclusions and Relevance: In the United States, about 1 of 5 adolescents and 1 of 4 young adults have prediabetes. The adjusted prevalence of prediabetes is higher in male individuals and in people with obesity. Adolescents and young adults with prediabetes also present an unfavorable cardiometabolic risk profile, putting them both at increased risk of type 2 diabetes and cardiovascular diseases.

Diabetes affects approximately 12% of the U.S. adult population and approximately 25% of adults aged >/=65 years. From 2009 to 2017, there was no significant change in diabetes prevalence overall or among persons aged 65-79 years (1). However, these estimates were based on survey data with <5,000 older adults. Medicare administrative data sets, which contain claims for millions of older adults, afford an opportunity to explore both trends over time and heterogeneity within an older population. Previous studies have shown that claims data can be used to identify persons with diagnosed diabetes (2). This study estimated annual prevalence and incidence of diabetes during 2001-2015 using Medicare claims data for beneficiaries aged >/=68 years and found that prevalence plateaued after 2012 and incidence decreased after 2006. In 2015 (the most recent year estimated) prevalence was 31.6%, and incidence was 3.0%. Medicare claims can serve as an important source of data for diabetes surveillance for the older population, which can inform prevention and treatment strategies.

OBJECTIVE: Guidelines on the standard care of diabetes recommend that glycemic treatment goals for older adults consider the patient's complications and life expectancy. In this study, we examined the influence of diabetes complications and associated life expectancies on the cost-effectiveness (CE) of HbA1c treatment goals. RESEARCH DESIGN AND METHODS: We used data from the 2011 to 2016 National Health and Nutrition Examination Survey (NHANES) to generate nationally representative subgroups of older individuals with diabetes with various health states. We used the Centers for Disease Control and Prevention-RTI International diabetes CE model to estimate the long-term consequences of two treatment goals-a stringent control goal (HbA1c <7.5%) and a moderate control goal (HbA1c <8.5%)-on health and cost. Our simulation population represented typical patients, and all individuals in each health subgroup had average characteristics, which did not account for person-level variations. The CE study was conducted from a health system perspective and followed the study samples over a lifetime. We used $50,000 per quality-adjusted life year (QALY) as the incremental CE threshold. RESULTS: A stringent goal was, on average, cost-effective for individuals with no complications ($10,007 per QALY) or only microvascular complications (excluding renal failure; $19,621 per QALY), but it was not cost-effective for individuals with one or more macrovascular complications (all >$82,413 per QALY). Further, a stringent goal was not cost-effective when an individual had less than 7 years of life remaining. CONCLUSIONS: Our findings support the guideline recommendation that glycemic goals for older adults should consider the complexity of their complications and their life expectancy from a CE perspective.

Importance: Coordinated efforts by national organizations in the United States to implement evidence-based lifestyle modification programs are under way to reduce type 2 diabetes (hereinafter referred to as diabetes) and cardiovascular risks. Objective: To provide a status report on the reach and use of diabetes prevention services nationally. Design, Setting, and Participants: This nationally representative, population-based cross-sectional analysis of 2016 and 2017 National Health Interview Survey data was conducted from August 3, 2017, through November 15, 2018. Nonpregnant, noninstitutionalized, civilian respondents 18 years or older at high risk for diabetes, defined as those with no self-reported diabetes diagnosis but with diagnosed prediabetes or an elevated American Diabetes Association (ADA) risk score (>5), were included in the analysis. Analyses were conducted for adults with (and in sensitivity analyses, for those without) elevated body mass index. Main Outcomes and Measures: Absolute numbers and proportions of adults at high risk with elevated body mass index receiving advice about diet, physical activity guidance, referral to weight loss programs, referral to diabetes prevention programs, or any of these, and those affirming engagement in each (or any) activity in the past year were estimated. To identify where gaps exist, a prevention continuum diagram plotted existing vs desired goal achievement. Variation in risk-reducing activities by age, sex, race/ethnicity, educational attainment, insurance status, history of gestational diabetes mellitus, hypertension, or body mass index was also examined. Results: This analysis included 50912 respondents (representing 223.0 million adults nationally) 18 years or older (mean [SE] age, 46.1 [0.2] years; 48.1% [0.3%] male) with complete data and no self-reported diabetes diagnosis by their health care professional. Of the represented population, 36.0% (80.0 million) had either a physician diagnosis of prediabetes (17.9 million), an elevated ADA risk score (73.3 million), or both (11.3 million). Among those with diagnosed prediabetes, 73.5% (95% CI, 71.6%-75.3%) reported receiving advice and/or referrals for diabetes risk reduction from their health care professional, and, of those, 35.0% (95% CI, 30.5%-39.8%) to 75.8% (95% CI, 73.2%-78.3%) reported engaging in the respective activity or program in the past year. Half of adults with elevated ADA risk scores but no diagnosed prediabetes (50.6%; 95% CI, 49.5%-51.8%) reported receiving risk-reduction advice and/or referral, of whom 33.5% (95% CI, 30.1%-37.0%) to 75.2% (95% CI, 73.4%-76.9%) reported engaging in activities and/or programs. Participation in diabetes prevention programs was exceedingly low. Advice from a health care professional, age range from 45 to 64 years, higher educational attainment, health insurance status, gestational diabetes mellitus, hypertension, and obesity were associated with higher engagement in risk-reducing activities and/or programs. Conclusions and Relevance: Among adults at high risk for diabetes, major gaps in receiving advice and/or referrals and engaging in diabetes risk-reduction activities and/or programs were noted. These results suggest that risk perception, health care professional referral and communication, and insurance coverage may be key levers to increase risk-reducing behaviors in US adults. These findings provide a benchmark from which to monitor future program availability and coverage, identification of prediabetes, and referral to and retention in programs.

BACKGROUND: In the United States, diabetes has increased rapidly, exceeding prior predictions. Projections of the future diabetes burden need to reflect changes in incidence, mortality, and demographics. We applied the most recent data available to develop an updated projection through 2060. METHODS: A dynamic Markov model was used to project prevalence of diagnosed diabetes among US adults by age, sex, and race (white, black, other). Incidence and current prevalence were from the National Health Interview Survey (NHIS) 1985-2014. Relative mortality was from NHIS 2000-2011 follow-up data linked to the National Death Index. Future population estimates including birth, death, and migration were from the 2014 Census projection. RESULTS: The projected number and percent of adults with diagnosed diabetes would increase from 22.3 million (9.1%) in 2014 to 39.7 million (13.9%) in 2030, and to 60.6 million (17.9%) in 2060. The number of people with diabetes aged 65 years or older would increase from 9.2 million in 2014 to 21.0 million in 2030, and to 35.2 million in 2060. The percent prevalence would increase in all race-sex groups, with black women and men continuing to have the highest diabetes percent prevalence, and black women and women of other race having the largest relative increases. CONCLUSIONS: By 2060, the number of US adults with diagnosed diabetes is projected to nearly triple, and the percent prevalence double. Our estimates are essential to predict health services needs and plan public health programs aimed to reduce the future burden of diabetes.

Currently 23 million U.S. adults have been diagnosed with diabetes (1). The two most common forms of diabetes are type 1 and type 2. Type 1 diabetes results from the autoimmune destruction of the pancreas's beta cells, which produce insulin. Persons with type 1 diabetes require insulin for survival; insulin may be given as a daily shot or continuously with an insulin pump (2). Type 2 diabetes is mainly caused by a combination of insulin resistance and relative insulin deficiency (3). A small proportion of diabetes cases might be types other than type 1 or type 2, such as maturity-onset diabetes of the young or latent autoimmune diabetes in adults (3). Although the majority of prevalent cases of type 1 and type 2 diabetes are in adults, national data on the prevalence of type 1 and type 2 in the U.S. adult population are sparse, in part because of the previous difficulty in classifying diabetes by type in surveys (2,4,5). In 2016, supplemental questions to help distinguish diabetes type were added to the National Health Interview Survey (NHIS) (6). This study used NHIS data from 2016 to estimate the prevalence of diagnosed diabetes among adults by primary type. Overall, based on self-reported type and current insulin use, 0.55% of U.S. adults had diagnosed type 1 diabetes, representing 1.3 million adults; 8.6% had diagnosed type 2 diabetes, representing 21.0 million adults. Of all diagnosed cases, 5.8% were type 1 diabetes, and 90.9% were type 2 diabetes; the remaining 3.3% of cases were other types of diabetes. Understanding the prevalence of diagnosed diabetes by type is important for monitoring trends, planning public health responses, assessing the burden of disease for education and management programs, and prioritizing national plans for future type-specific health services.

States bear substantial responsibility for addressing the rising rates of diabetes and prediabetes in the United States. However, accurate state-level estimates of diabetes and prediabetes prevalence that include undiagnosed cases have been impossible to produce with traditional sources of state-level data. Various new and nontraditional sources for estimating state-level prevalence are now available. These include surveys with expanded samples that can support state-level estimation in some states and administrative and clinical data from insurance claims and electronic health records. These sources pose methodologic challenges because they typically cover partial, sometimes nonrandom subpopulations; they do not always use the same measurements for all individuals; and they use different and limited sets of variables for case finding and adjustment. We present an approach for adjusting new and nontraditional data sources for diabetes surveillance that addresses these limitations, and we present the results of our proposed approach for 2 states (Alabama and California) as a proof of concept. The method reweights surveys and other data sources with population undercoverage to make them more representative of state populations, and it adjusts for nonrandom use of laboratory testing in clinically generated data sets. These enhanced diabetes and prediabetes prevalence estimates can be used to better understand the total burden of diabetes and prediabetes at the state level and to guide policies and programs designed to prevent and control these chronic diseases.