Regional and rural-urban patterns in the prevalence of diagnosed hypertension among older U.S. adults with diabetes, 2005-2017.

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.

Associations between PM2.5 and O3 exposures and new onset type 2 diabetes in regional and national samples in the United States.

BACKGROUND: Exposure to particulate matter ≤2.5 µm in diameter (PM2.5) and ozone (O3) 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 PM2.5 and O3 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 PM2.5 and O3. RESULTS: In two pollutant models, associations of PM2.5, 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/m3 of PM2.5. 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 PM2.5, O3 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.

Prevalence of Testing for Diabetes Among US Adults With Overweight or Obesity, 2016-2019.

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 A1c 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.

Risk for Newly Diagnosed Diabetes >30 Days After SARS-CoV-2 Infection Among Persons Aged <18 Years - United States, March 1, 2020-June 28, 2021.

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.

State-level metabolic comorbidity prevalence and control among adults age 50-plus with diabetes: estimates from electronic health records and survey data in five states.

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.

Proximity to freshwater blue space and type 2 diabetes onset: the importance of historical and economic context.

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.

Imputed State-Level Prevalence of Achieving Goals To Prevent Complications of Diabetes in Adults with Self-Reported Diabetes - United States, 2017-2018.

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.

Diabetes Prevalence and Incidence Among Medicare Beneficiaries - United States, 2001-2015.

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.

Prevalence of Diagnosed Diabetes in Adults by Diabetes Type - United States, 2016.

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.

State-level estimation of diabetes and prediabetes prevalence: Combining national and local survey data and clinical data.

Many statisticians and policy researchers are interested in using data generated through the normal delivery of health care services, rather than carefully designed and implemented population-representative surveys, to estimate disease prevalence. These larger databases allow for the estimation of smaller geographies, for example, states, at potentially lower expense. However, these health care records frequently do not cover all of the population of interest and may not collect some covariates that are important for accurate estimation. In a recent paper, the authors have described how to adjust for the incomplete coverage of administrative claims data and electronic health records at the state or local level. This article illustrates how to adjust and combine multiple data sets, namely, national surveys, state-level surveys, claims data, and electronic health record data, to improve estimates of diabetes and prediabetes prevalence, along with the estimates of the method's accuracy. We demonstrate and validate the method using data from three jurisdictions (Alabama, California, and New York City). This method can be applied more generally to other areas and other data sources.

Projection of the future diabetes burden in the United States through 2060.

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.

Changes in diabetes-related complications in the United States, 1990-2010.

BACKGROUND: Preventive care for adults with diabetes has improved substantially in recent decades. We examined trends in the incidence of diabetes-related complications in the United States from 1990 through 2010. METHODS: We used data from the National Health Interview Survey, the National Hospital Discharge Survey, the U.S. Renal Data System, and the U.S. National Vital Statistics System to compare the incidences of lower-extremity amputation, end-stage renal disease, acute myocardial infarction, stroke, and death from hyperglycemic crisis between 1990 and 2010, with age standardized to the U.S. population in the year 2000. RESULTS: Rates of all five complications declined between 1990 and 2010, with the largest relative declines in acute myocardial infarction (-67.8%; 95% confidence interval [CI], -76.2 to -59.3) and death from hyperglycemic crisis (-64.4%; 95% CI, -68.0 to -60.9), followed by stroke and amputations, which each declined by approximately half (-52.7% and -51.4%, respectively); the smallest decline was in end-stage renal disease (-28.3%; 95% CI, -34.6 to -21.6). The greatest absolute decline was in the number of cases of acute myocardial infarction (95.6 fewer cases per 10,000 persons; 95% CI, 76.6 to 114.6), and the smallest absolute decline was in the number of deaths from hyperglycemic crisis (-2.7; 95% CI, -2.4 to -3.0). Rate reductions were larger among adults with diabetes than among adults without diabetes, leading to a reduction in the relative risk of complications associated with diabetes. When expressed as rates for the overall population, in which a change in prevalence also affects complication rates, there was a decline in rates of acute myocardial infarction and death from hyperglycemic crisis (2.7 and 0.1 fewer cases per 10,000, respectively) but not in rates of amputation, stroke, or end-stage renal disease. CONCLUSIONS: Rates of diabetes-related complications have declined substantially in the past two decades, but a large burden of disease persists because of the continued increase in the prevalence of diabetes. (Funded by the Centers for Disease Control and Prevention.).

Comparison of surveillance-based metrics for the assessment and monitoring of disease detection: simulation study about type 2 diabetes.

BACKGROUND: Screening and detection of cases are a common public health priority for treatable chronic conditions with long subclinical periods. However, the validity of commonly-used metrics from surveillance systems for rates of detection (or case-finding) have not been evaluated. METHODS: Using data from a Danish diabetes register and a recently developed illness-death model of chronic diseases with subclinical conditions, we simulate two scenarios of different performance of case-finding. We report different epidemiological indices to assess case-finding in both scenarios and compare the validity of the results. RESULTS: The commonly used ratio of detected cases over total cases may lead to misleading conclusions. Instead, the ratio of undetected cases over persons without a diagnosis is a more valid index to distinguish the quality of case-finding. However, incidence-based measures are preferable to prevalence based indicators. CONCLUSION: Prevalence-based indices for assessing case-finding should be interpreted with caution. If possible, incidence-based indices should be preferred.

Novel Methods and Data Sources for Surveillance of State-Level Diabetes and Prediabetes Prevalence.

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.

Using multi-year national survey cohorts for period estimates: an application of weighted discrete Poisson regression for assessing annual national mortality in US adults with and without diabetes, 2000-2006.

BACKGROUND: Monitoring national mortality among persons with a disease is important to guide and evaluate progress in disease control and prevention. However, a method to estimate nationally representative annual mortality among persons with and without diabetes in the United States does not currently exist. The aim of this study is to demonstrate use of weighted discrete Poisson regression on national survey mortality follow-up data to estimate annual mortality rates among adults with diabetes. METHODS: To estimate mortality among US adults with diabetes, we applied a weighted discrete time-to-event Poisson regression approach with post-stratification adjustment to national survey data. Adult participants aged 18 or older with and without diabetes in the National Health Interview Survey 1997-2004 were followed up through 2006 for mortality status. We estimated mortality among all US adults, and by self-reported diabetes status at baseline. The time-varying covariates used were age and calendar year. Mortality among all US adults was validated using direct estimates from the National Vital Statistics System (NVSS). RESULTS: Using our approach, annual all-cause mortality among all US adults ranged from 8.8 deaths per 1,000 person-years (95% confidence interval [CI]: 8.0, 9.6) in year 2000 to 7.9 (95% CI: 7.6, 8.3) in year 2006. By comparison, the NVSS estimates ranged from 8.6 to 7.9 (correlation = 0.94). All-cause mortality among persons with diabetes decreased from 35.7 (95% CI: 28.4, 42.9) in 2000 to 31.8 (95% CI: 28.5, 35.1) in 2006. After adjusting for age, sex, and race/ethnicity, persons with diabetes had 2.1 (95% CI: 2.01, 2.26) times the risk of death of those without diabetes. CONCLUSION: Period-specific national mortality can be estimated for people with and without a chronic condition using national surveys with mortality follow-up and a discrete time-to-event Poisson regression approach with post-stratification adjustment.

Changes in Utilization and Expenditures Among Commercially Insured U.S. Adults With Diabetes During the COVID-19 Pandemic: Preliminary Findings.

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.

Impact of the COVID-19 Pandemic on Medical Expenditures Among Medicare Fee-for-Service Beneficiaries Aged ≥67 Years With Diabetes.

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.

Americans' use of dietary supplements that are potentially harmful in CKD.

BACKGROUND: The prevalence in the United States of dietary supplement use that may be harmful to those with chronic kidney disease (CKD) is unknown. We sought to characterize potentially harmful supplement use by individual CKD status. STUDY DESIGN: Cross-sectional national survey (National Health and Nutrition Examination Survey, 1999-2008). SETTING & PARTICIPANTS: Community-based survey of 21,169 nonpregnant noninstitutionalized US civilian adults (aged ≥20 years). PREDICTOR: CKD status (no CKD, at risk of CKD [presence of diabetes, hypertension, and/or cardiovascular disease], stages 1/2 [albuminuria only (albumin-creatinine ratio ≥30 mg/g)], or stages 3/4 [estimated glomerular filtration rate of 15-59 mL/min/1.73 m(2)]). OUTCOME: Self-reported use of dietary supplements containing any of 37 herbs the National Kidney Foundation identified as potentially harmful in the setting of CKD. MEASUREMENTS: Albuminuria and estimated glomerular filtration rate assessed from urine and blood samples; demographics and comorbid conditions assessed by standardized questionnaire. RESULTS: An estimated 8.0% of US adults reported potentially harmful supplement use within the last 30 days. A lower crude estimated prevalence of potentially harmful supplement use was associated with higher CKD severity (no CKD, 8.5%; at risk, 8.0%; stages 1/2, 6.1%; and stages 3/4, 6.2%; P < 0.001). However, after adjustment for confounders, those with or at risk of CKD were as likely to use a potentially harmful supplement as those without CKD: at-risk OR, 0.93 (95% CI, 0.79-1.09); stages 1/2 OR, 0.83 (95% CI, 0.64-1.08); and stages 3/4 OR, 0.87 (95% CI, 0.63-1.18); all versus no CKD. LIMITATIONS: Herb content was not available and the list of potentially harmful supplements examined is unlikely to be exhaustive. CONCLUSIONS: The use of dietary supplements potentially harmful to people with CKD is common regardless of CKD status. Health care providers should discuss the use and potential risks of supplements with patients with and at risk of CKD.

Modeling County-Level Rare Disease Prevalence Using Bayesian Hierarchical Sampling Weighted Zero-Inflated Regression.

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.

Changes in health care utilization among Medicare beneficiaries with diabetes two years into the COVID-19 pandemic.

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.