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

INTRODUCTION: We investigated 2018 gestational diabetes mellitus (GDM) prevalence estimates in three surveillance systems (National Vital Statistics System, State Inpatient Database, and Pregnancy Risk Assessment Monitoring Survey). METHODS: We calculated GDM prevalence for jurisdictions represented in each system; a subset of data was analyzed for people 18-39 years old in 22 jurisdictions present in all three systems to observe dataset-specific demographics and GDM prevalence using comparable categories. RESULTS: GDM prevalence estimates varied widely by data system and within the data subset despite comparable demographics. DISCUSSION: Understanding the differences between GDM surveillance data systems can help researchers better identify people and places at higher risk of GDM.

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 is an important risk factor for cardiovascular diseases. Electronic health records (EHRs) may augment chronic disease surveillance. We aimed to develop an electronic phenotype (e-phenotype) for hypertension surveillance. METHODS: We included 11,031,368 eligible adults from the 2019 IQVIA Ambulatory Electronic Medical Records-US (AEMR-US) dataset. We identified hypertension using three criteria, alone or in combination: diagnosis codes, blood pressure (BP) measurements, and antihypertensive medications. We compared AEMR-US estimates of hypertension prevalence and control against those from the National Health and Nutrition Examination Survey (NHANES) 2017-18, which defined hypertension as BP ≥ 130/80mmHg or ≥ 1 antihypertensive medication. RESULTS: The study population had a mean (SD) age of 52.3 (6.7) years, and 56.7% were women. The selected three-criteria e-phenotype (≥1 diagnosis code, ≥2 BP measurements of ≥130/80mmHg, or ≥1 antihypertensive medication) yielded similar trends in hypertension prevalence as NHANES: 42.2% (AEMR-US) vs. 44.9% (NHANES) overall, 39.0% vs. 38.7% among women, and 46.5% vs. 50.9% among men. The pattern of age-related increase in hypertension prevalence was similar between AEMR-US and NHANES. The prevalence of hypertension control in AEMR-US was 31.5% using the three-criteria e-phenotype, which was higher than NHANES (14.5%). CONCLUSIONS: Using an EHR dataset of 11 million adults, we constructed a hypertension e-phenotype using three criteria, which can be used for surveillance of hypertension prevalence and control.

PURPOSE: To study contemporary trends in the diagnosed prevalence and incidence of age-related eye diseases among Medicare fee-for-service (FFS) beneficiaries. DESIGN: Analysis of Medicare administrative claims data. PARTICIPANTS: Medicare FFS beneficiaries aged 68 and older from 2005-2020. Those included were continuously enrolled in both Part A and Part B for 3 years, including the index year and 2-year lookback period. METHODS: Annual cross-sectional diagnosed prevalence and incidence rates were calculated. Age-standardization was performed using the direct standardization method to account for changes in the age-structure of the study population. Rates stratified by demographics (age, sex, race, and ethnicity) were also calculated. MAIN OUTCOME MEASURES: Annual prevalence and incidence of diagnosed age-related macular degeneration (AMD), diabetic retinopathy (DR) (among those with diabetes), and glaucoma. RESULTS: At baseline, in 2005, 60% of included beneficiaries were female, 20% were aged ≥85, 86% were non-Hispanic White, and one-quarter had diabetes. From 2005-2019, the prevalence of a diagnosis of any of the conditions studied increased from 15.0% (N=3,312,812) to 17.9% (N=3,731,281). Diagnosed incidence decreased over this period from 4.7% (N=917,846) in 2005 to 4.2% in 2019 (N=757,696). The diagnosed prevalence of AMD increased from 6.8% (N=1,504,770) to 9.4% (N=1,965,176); the diagnosed prevalence of any DR among those with diabetes decreased from 9.3% (N= 504,135) to 9.0% (N=532,859), though the diagnosed prevalence of vision-threatening DR increased from 2.0% to 3.4%; and the diagnosed prevalence of any diagnosed glaucoma decreased from 8.8% (N= 1,951,141) to 8.1% (N= 1,692,837). In 2020, the diagnosed prevalence and incidence of all diagnoses decreased. During the study period we detected demographic differences in the prevalence and incidence of diagnosis of each condition. CONCLUSIONS: This study presents updated data on the prevalence and incidence of diagnosed major chronic, age-related eye diseases among Medicare FFS beneficiaries. Compared to older epidemiological estimates, we found that the diagnosed prevalence of each condition studied was higher in more recent years. These findings may inform public health and policy planning and resource allocation to address the eye health of an increasingly older US population.

IMPORTANCE: Diabetic retinopathy (DR) is a common microvascular complication of diabetes and a leading cause of blindness among working-age adults in the US. OBJECTIVE: To update estimates of DR and vision-threatening diabetic retinopathy (VTDR) prevalence by demographic factors and US county and state. DATA SOURCES: The study team included data from the National Health and Nutrition Examination Survey (2005 to 2008 and 2017 to March 2020), Medicare fee-for-service claims (2018), IBM MarketScan commercial insurance claims (2016), population-based studies of adult eye disease (2001 to 2016), 2 studies of diabetes in youth (2021 and 2023), and a previously published analysis of diabetes by county (2012). The study team used population estimates from the US Census Bureau. STUDY SELECTION: The study team included relevant data from the US Centers for Disease Control and Prevention's Vision and Eye Health Surveillance System. DATA EXTRACTION AND SYNTHESIS: Using bayesian meta-regression methods, the study team estimated the prevalence of DR and VTDR stratified by age, a nondifferentiated sex and gender measure, race, ethnicity, and US county and state. MAIN OUTCOMES AND MEASURES: The study team defined individuals with diabetes as those who had a hemoglobin A1c level at 6.5% or more, took insulin, or reported ever having been told by a physician or health care professional that they have diabetes. The study team defined DR as any retinopathy in the presence of diabetes, including nonproliferative retinopathy (mild, moderate, or severe), proliferative retinopathy, or macular edema. The study team defined VTDR as having, in the presence of diabetes, severe nonproliferative retinopathy, proliferative retinopathy, panretinal photocoagulation scars, or macular edema. RESULTS: This study used data from nationally representative and local population-based studies that represent the populations in which they were conducted. For 2021, the study team estimated 9.60 million people (95% uncertainty interval [UI], 7.90-11.55) living with DR, corresponding to a prevalence rate of 26.43% (95% UI, 21.95-31.60) among people with diabetes. The study team estimated 1.84 million people (95% UI, 1.41-2.40) living with VTDR, corresponding to a prevalence rate of 5.06% (95% UI, 3.90-6.57) among people with diabetes. Prevalence of DR and VTDR varied by demographic characteristics and geography. CONCLUSIONS AND RELEVANCE: US prevalence of diabetes-related eye disease remains high. These updated estimates on the burden and geographic distribution of diabetes-related eye disease can be used to inform the allocation of public health resources and interventions to communities and populations at highest risk.

IMPORTANCE: Diagnostic information from administrative claims and electronic health record (EHR) data may serve as an important resource for surveillance of vision and eye health, but the accuracy and validity of these sources are unknown. OBJECTIVE: To estimate the accuracy of diagnosis codes in administrative claims and EHRs compared to retrospective medical record review. DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study compared the presence and prevalence of eye disorders based on diagnostic codes in EHR and claims records vs clinical medical record review at University of Washington-affiliated ophthalmology or optometry clinics from May 2018 to April 2020. Patients 16 years and older with an eye examination in the previous 2 years were included, oversampled for diagnosed major eye diseases and visual acuity loss. EXPOSURES: Patients were assigned to vision and eye health condition categories based on diagnosis codes present in their billing claims history and EHR using the diagnostic case definitions of the US Centers for Disease Control and Prevention Vision and Eye Health Surveillance System (VEHSS) as well as clinical assessment based on retrospective medical record review. MAIN OUTCOME AND MEASURES: Accuracy was measured as area under the receiver operating characteristic curve (AUC) of claims and EHR-based diagnostic coding vs retrospective review of clinical assessments and treatment plans. RESULTS: Among 669 participants (mean [range] age, 66.1 [16-99] years; 357 [53.4%] female), identification of diseases in billing claims and EHR data using VEHSS case definitions was accurate for diabetic retinopathy (claims AUC, 0.94; 95% CI, 0.91-0.98; EHR AUC, 0.97; 95% CI, 0.95-0.99), glaucoma (claims AUC, 0.90; 95% CI, 0.88-0.93; EHR AUC, 0.93; 95% CI, 0.90-0.95), age-related macular degeneration (claims AUC, 0.87; 95% CI, 0.83-0.92; EHR AUC, 0.96; 95% CI, 0.94-0.98), and cataracts (claims AUC, 0.82; 95% CI, 0.79-0.86; EHR AUC, 0.91; 95% CI, 0.89-0.93). However, several condition categories showed low validity with AUCs below 0.7, including diagnosed disorders of refraction and accommodation (claims AUC, 0.54; 95% CI, 0.49-0.60; EHR AUC, 0.61; 95% CI, 0.56-0.67), diagnosed blindness and low vision (claims AUC, 0.56; 95% CI, 0.53-0.58; EHR AUC, 0.57; 95% CI, 0.54-0.59), and orbital and external diseases (claims AUC, 0.63; 95% CI, 0.57-0.69; EHR AUC, 0.65; 95% CI, 0.59-0.70). CONCLUSION AND RELEVANCE: In this cross-sectional study of current and recent ophthalmology patients with high rates of eye disorders and vision loss, identification of major vision-threatening eye disorders based on diagnosis codes in claims and EHR records was accurate. However, vision loss, refractive error, and other broadly defined or lower-risk disorder categories were less accurately identified by diagnosis codes in claims and EHR data.

OBJECTIVE: To determine the prevalence, progression, and modifiable risk factors associated with the development of diabetic retinopathy (DR) in a population-based cohort of youth-onset diabetes. RESEARCH DESIGN AND METHODS: We conducted a multicenter, population-based prospective cohort study (2002-2019) of youth and young adults with youth-onset type 1 diabetes (n = 2,519) and type 2 diabetes (n = 447). Modifiable factors included baseline and change from baseline to follow-up in BMI z score, waist/height ratio, systolic and diastolic blood pressure z score, and A1C. DR included evidence of mild or moderate nonproliferative DR or proliferative retinopathy. Prevalence estimates were standardized to estimate the burden of DR, and inverse probability weighting for censoring was applied for estimating risk factors for DR at two points of follow-up. RESULTS: DR in youth-onset type 1 and type 2 diabetes is highly prevalent, with 52% of those with type 1 diabetes and 56% of those with type 2 diabetes demonstrating retinal changes at follow-up (mean [SD] 12.5 [2.2] years from diagnosis). Higher baseline A1C, increase in A1C across follow-up, and increase in diastolic and systolic blood pressure were associated with the observation of DR at follow-up for both diabetes types. Increase in A1C across follow-up was associated with retinopathy progression. BMI z score and waist/height ratio were inconsistently associated, with both positive and inverse associations noted. CONCLUSIONS: Extrapolated to all youth-onset diabetes in the U.S., we estimate 110,051 cases of DR developing within ∼12 years postdiagnosis. Tight glucose and blood pressure management may offer the opportunity to mitigate development and progression of DR in youth-onset diabetes.

IMPORTANCE: Recent evidence suggests that social determinants of health (SDOH) affect vision loss, but it is unclear whether estimated associations differ between clinically evaluated and self-reported vision loss. OBJECTIVE: To identify associations between SDOH and evaluated vision impairment and to assess whether these associations hold when examining self-reported vision loss. DESIGN, SETTING, AND PARTICIPANTS: This population-based cross-sectional comparison included participants 12 years and older in the 2005 to 2008 National Health and Nutrition Examination Survey (NHANES), participants of all ages (infants and older) in the 2019 American Community Survey (ACS), and adults 18 years and older in the 2019 Behavioral Risk Factor Surveillance System (BRFSS). EXPOSURES: Five domains of SDOH that are based on Healthy People 2030: economic stability, education access and quality, health care access and quality, neighborhood and built environment, and social and community context. MAIN OUTCOMES AND MEASURES: Presenting vision impairment of 20/40 or worse in the better-seeing eye (NHANES) and self-reported blindness or serious difficulty seeing, even with glasses (ACS and BRFSS). RESULTS: Of 3 649 085 included participants, 1 873 893 were female (51.1%) and 2 504 206 were White (64.4%). SDOH across domains of economic stability, educational attainment, health care access and quality, neighborhood and built environment, and social context were significant predictors of poor vision. For example, higher income (poverty to income ratio [NHANES]: OR, 0.91; 95% CI, 0.85-0.98; [ACS]: OR, 0.93; 95% CI, 0.93-0.94; categorical income [BRFSS:<$15 000 reference]: $15 000-$24 999; OR, 0.91; 95% CI, 0.91-0.91; $25 000-$34 999: OR, 0.80; 95% CI, 0.80-0.80; $35 000-$49 999: OR, 0.71; 95% CI, 0.71-0.72; ≥$50 000: OR, 0.49; 95% CI, 0.49-0.49), employment (BRFSS: OR, 0.66; 95% CI, 0.66-0.66; ACS: OR, 0.55; 95% CI, 0.54-0.55), and owning a home (NHANES: OR, 0.85; 95% CI, 0.73-1.00; BRFSS: OR, 0.82; 95% CI, 0.82-0.82; ACS: OR, 0.79; 95% CI, 0.79-0.79) were associated with lower odds of vision loss. The study team identified no differences in the general direction of the associations when using either clinically evaluated or self-reported vision measures. CONCLUSIONS AND RELEVANCE: The study team found evidence that associations between SDOH and vision impairment track together when using either clinically evaluated or self-reported vision loss. These findings support the use of self-reported vision data in a surveillance system to track trends in SDOH and vision health outcomes within subnational geographies.

Background: Youth with excess weight are at risk of developing type 2 diabetes (T2DM). Guidelines recommend screening for prediabetes and/or T2DM after 10 years of age or after puberty in youth with excess weight who have ≥1 risk factor(s) for T2DM. Electronic health records (EHRs) offer an opportunity to study the use of tests to detect diabetes in youth. Methods: We examined the frequency of (1) diabetes testing and (2) elevated test results among youth aged 10-19 years with at least one BMI measurement in an EHR from 2019 to 2021. We examined the presence of hemoglobin A1C (A1C), fasting plasma glucose (FPG), or oral glucose tolerance test (2-hour plasma glucose [2-hrPG]) results and, among those tested, the frequency of elevated values (A1C ≥6.5%, FPG ≥126 mg/dL, or 2-hrPG ≥200 mg/dL). Patients with pre-existing diabetes (n = 6793) were excluded. Results: Among 1,024,743 patients, 17% had overweight, 21% had obesity, including 8% with severe obesity. Among patients with excess weight, 10% had ≥1 glucose test result. Among those tested, elevated values were more common in patients with severe obesity (27%) and obesity (22%) than in those with healthy weight (8%), and among Black youth (30%) than White youth (13%). Among patients with excess weight, >80% of elevated values fell in the prediabetes range. Conclusions: In youth with excess weight, the use of laboratory tests for prediabetes and T2DM was infrequent. Among youth with test results, elevated FPG, 2hrPG, or A1C levels were most common in those with severe obesity and Black youth.

BACKGROUND: Self-reported questions on blindness and vision problems are collected in many national surveys. Recently released surveillance estimates on the prevalence of vision loss used self-reported data to predict variation in the prevalence of objectively measured acuity loss among population groups for whom examination data are not available. However, the validity of self-reported measures to predict prevalence and disparities in visual acuity has not been established. OBJECTIVE: This study aimed to estimate the diagnostic accuracy of self-reported vision loss measures compared to best-corrected visual acuity (BCVA), inform the design and selection of questions for future data collection, and identify the concordance between self-reported vision and measured acuity at the population level to support ongoing surveillance efforts. METHODS: We calculated accuracy and correlation between self-reported visual function versus BCVA at the individual and population level among patients from the University of Washington ophthalmology or optometry clinics with a prior eye examination, randomly oversampled for visual acuity loss or diagnosed eye diseases. Self-reported visual function was collected via telephone survey. BCVA was determined based on retrospective chart review. Diagnostic accuracy of questions at the person level was measured based on the area under the receiver operator curve (AUC), whereas population-level accuracy was determined based on correlation. RESULTS: The survey question, "Are you blind or do you have serious difficulty seeing, even when wearing glasses?" had the highest accuracy for identifying patients with blindness (BCVA ≤20/200; AUC=0.797). The highest accuracy for detecting any vision loss (BCVA <20/40) was achieved by responses of "fair," "poor," or "very poor" to the question, "At the present time, would you say your eyesight, with glasses or contact lenses if you wear them, is excellent, good, fair, poor, or very poor" (AUC=0.716). At the population level, the relative relationship between prevalence based on survey questions and BCVA remained stable for most demographic groups, with the only exceptions being groups with small sample sizes, and these differences were generally not significant. CONCLUSIONS: Although survey questions are not considered to be sufficiently accurate to be used as a diagnostic test at the individual level, we did find relatively high levels of accuracy for some questions. At the population level, we found that the relative prevalence of the 2 most accurate survey questions were highly correlated with the prevalence of measured visual acuity loss among nearly all demographic groups. The results of this study suggest that self-reported vision questions fielded in national surveys are likely to yield an accurate and stable signal of vision loss across different population groups, although the actual measure of prevalence from these questions is not directly analogous to that of BCVA.

OBJECTIVE: Examine the 10-year trend in the prevalence and treatment of diabetic macular edema (DME) and vision-threatening diabetic retinopathy (VTDR) among commercially insured adults with diabetes. RESEARCH DESIGN AND METHODS: We analyzed the 10-year trend (2009-2018) in health care claims for adults aged 18-64 years using the IBM MarketScan Database, a national convenience sample of employer-sponsored health insurance. We included patients continuously enrolled in commercial fee-for-service health insurance for 24 months who had a diabetes ICD-9/10-CM code on one or more inpatient or two or more different-day outpatient claims in the index year or previous calendar year. We used diagnosis and procedure codes to calculate the annual prevalence of patients with one or more claims for 1) any DME, 2) either DME or VTDR, and 3) antivascular endothelial growth factor (anti-VEGF) injections and laser photocoagulation treatment, stratified by any DME, VTDR with DME, and VTDR without DME. We calculated the average annual percent change (AAPC). RESULTS: From 2009 to 2018, there was an increase in the annual prevalence of patients with DME or VTDR (2.1% to 3.4%; AAPC 7.5%; P < 0.001) and any DME (0.7% to 2.6%; AAPC 19.8%; P < 0.001). There were sex differences in the annual prevalence of DME or VTDR and any DME, with men having a higher prevalence than women. Annual claims for anti-VEGF injections increased among patients with any DME (327%) and VTDR with DME (206%); laser photocoagulation decreased among patients with any DME (-68%), VTDR with DME (-54%), and VTDR without DME (-62%). CONCLUSIONS: Annual claims for DME or VTDR and anti-VEGF injections increased whereas those for laser photocoagulation decreased among commercially insured adults with diabetes.

INTRODUCTION: Adults with vision impairment may have unique needs when accessing health care to maintain good health. Our study examined the relationship between vision status and access to and use of health care. METHODS: We analyzed data on adults aged 18 years or older who participated in the 2018 Behavioral Risk Factor Surveillance System. Vision impairment was identified by a yes response to the question "Are you blind or do you have serious difficulty seeing, even when wearing glasses?" Survey questions assessed health care access over the past year (having health insurance coverage, a usual health care provider, or unmet health care needs because of cost) and use of health care during that period (routine checkup and dental visit). We estimated age-adjusted prevalence of our outcomes of interest and used bivariate analyses to compare estimates of the outcomes by vision impairment status. RESULTS: The prevalence of self-reported vision impairment was 5.3%. Compared with adults without impaired vision, adults with vision impairment had a lower prevalence of having health insurance coverage (80.6% vs 87.6%), a usual health care provider (71.9% vs 75.7%), or a dental visit in the past year (52.9% vs 67.2%) and a higher prevalence of having an unmet health care need in the past year because of cost (29.2% vs 12.6%). CONCLUSION: Adults with vision impairment reported lower access to and use of health care than those without. Further research can better identify and understand barriers to care to improve access to and use of health care among this population.

IMPORTANCE: Age-related macular degeneration (AMD) is a leading cause of vision loss and blindness. AMD prevalence has not been estimated for the US in over a decade and early-stage AMD prevalence estimates are scarce and inconsistently measured. OBJECTIVE: To produce estimates of early- and late-stage AMD prevalence overall and by age, gender, race and ethnicity, county, and state. DESIGN, SETTING, AND PARTICIPANTS: The study team conducted a bayesian meta-regression analysis of relevant data sources containing information on the prevalence of AMD among different population groups in the US. DATA SOURCES: We included data from the American Community Survey (2019), the National Health and Nutrition Examination Survey (2005-2008), US Centers for Medicare & Medicaid Services claims for fee-for-service beneficiaries (2018), and population-based studies (2004-2016). STUDY SELECTION: We included all relevant data from the US Centers for Disease Control and Prevention's Vision and Eye Health Surveillance System. DATA EXTRACTION AND SYNTHESIS: The prevalence of early- and late-stage AMD was estimated and stratified when possible by factors including county, age group, gender, and race and ethnicity. Data analysis occurred from June 2021 to April 2022. MAIN OUTCOMES OR MEASURES: The prevalence of early- (defined as retinal pigment epithelium abnormalities or the presence of drusen 125 or more microns in diameter in either eye) and late-stage (defined as choroidal neovascularization and/or geographic atrophy in either eye) manifestations of AMD. RESULTS: This study used data from nationally representative and local population-based studies that represent the populations in which they were conducted. For 2019, we estimated that there were 18.34 million people 40 years and older (95% uncertainty interval [UI], 15.30-22.03) living with early-stage AMD, corresponding to a crude prevalence rate of 11.64% (95% UI, 9.71-13.98). We estimated there were 1.49 million people 40 years and older (95% UI, 0.97-2.15) living with late-stage AMD, corresponding to a crude prevalence rate of 0.94% (95% UI, 0.62-1.36). Prevalence rates of early- and late-stage AMD varied by demographic characteristics and geography. CONCLUSIONS AND RELEVANCE: We estimated a higher prevalence of early-stage AMD and a similar prevalence of late-stage AMD as compared with earlier studies. State-level and county-level AMD estimates may help guide public health practice.

BACKGROUND: To evaluate the effect of diabetes comorbidities by baseline healthcare utilization on receipt of recommended eye examinations. METHODS: Retrospective analysis of 310691 nonelderly adults with type 2 diabetes in the IBM MarketScan Commercial Database from 2016 to 2019. Patients were grouped based on diabetes-concordant (related) or -discordant (unrelated) comorbidities. Logistic regression was used to estimate the prevalence ratio (PR) for eye examinations by comorbidity status, healthcare utilization, and an interaction between comorbidities and utilization, controlling for age, sex, region, and major eye disease. RESULTS: Prevalence of biennial eye examinations varied by the four comorbidity groups: 43.5% (diabetes only), 52.7% (concordant+discordant comorbidities), 48.0% (concordant comorbidities only), and 45.3% (discordant comorbidities only). In the lowest healthcare utilization tertile, the concordant-only and concordant+discordant groups had lower prevalence of examinations compared to diabetes only (PR 0.95 [95% CI 0.92-0.98] and PR 0.91 [95% CI 0.88-0.95], respectively). In the medium utilization tertile, the discordant-only and concordant+discordant groups had lower prevalence of examinations (PR 0.89 [0.83-0.95] and PR 0.94 [0.90-0.98], respectively). In the highest utilization tertile, the concordant-only and concordant+discordant groups had higher prevalence of examinations. CONCLUSIONS: Among patients with low healthcare utilization, having comorbid conditions is associated with lower prevalence of eye examinations. Among those with medium healthcare utilization, patients with diabetes-discordant comorbidities are particularly vulnerable. This study highlights populations of diabetes patients who would benefit from increased assistance in receiving vision-preserving eye examinations.

INTRODUCTION: Adults with vision impairment (VI) have a higher prevalence of cardiovascular disease (CVD) compared with those without VI. We estimated the prevalence of CVD and CVD risk factors by VI status in US adults. METHODS: We used nationally representative data from the 2018 National Health Interview Survey (N = 22,890 adults aged 18 years). We estimated the prevalence of self-reported diagnosis of CVD (coronary heart disease [including angina and myocardial infarction], stroke, or other heart disease) by VI status. We used separate logistic regression models to generate adjusted prevalence ratios (aPRs), controlling for sociodemographic covariates, for those with VI (reference group, no VI) for CVD and CVD risk factors: current smoking, physical inactivity, excessive alcohol intake, obesity, hypertension, high cholesterol, and diabetes. RESULTS: Overall, 12.9% (95% CI, 12.3-13.5) of the sample had VI. The prevalence of CVD was 26.6% (95% CI, 24.7-28.6) in people with VI versus 12.2% (95% CI, 11.7-12.8) in those without VI (aPR = 1.65 [95% CI, 1.51-1.80]). Compared with adults without VI, those with VI had a higher prevalence of all risk factors examined: current smoking (aPR = 1.40 [95% CI, 1.27-1.53]), physical inactivity (aPR = 1.14 [95% CI, 1.06-1.22]), excessive alcohol intake (aPR = 1.29 [95% CI, 1.08-1.53]), obesity (aPR = 1.28 [95% CI, 1.21-1.36]), hypertension (aPR = 1.29 [95% CI, 1.22-1.36]), high cholesterol (aPR = 1.21 [95% CI, 1.14-1.29]), and diabetes (aPR = 1.54 [95% CI, 1.38-1.72]). CONCLUSION: Adults with VI had a higher prevalence of CVD and CVD risk factors compared with those without VI. Effective clinical and lifestyle interventions, adapted to accommodate VI-related challenges, may help reduce CVD risk in adults with VI.

This cross-sectional study evaluates vision loss and blindness prevalence in the US at the county level.

IMPORTANCE: While diabetes prevalence among US adults has increased in recent decades, few studies document trends in diabetes-related eye disease. OBJECTIVE: To examine 10-year trends (2009-2018) in annual prevalence of Medicare beneficiaries with diabetes with a diagnosis of diabetic macular edema (DME) or vision-threatening diabetic retinopathy (VTDR) and trends in treatment. DESIGN, SETTING, AND PARTICIPANTS: In this cross-sectional study using Centers for Medicare & Medicaid Services research identifiable files, data for patients 65 years and older were analyzed from claims. Beneficiaries were continuously enrolled in Medicare Part B fee-for-service (FFS) insurance for the calendar year and had a diagnosis of diabetes on 1 or more inpatient claims or 2 or more outpatient claims during the calendar year or a 1-year look-back period. MAIN OUTCOMES AND MEASURES: Using diagnosis and procedure codes, annual prevalence was determined for beneficiaries with 1 or more claims for (1) any DME, (2) either DME or VTDR, and (3) anti-vascular endothelial growth factor (VEGF) injections, laser photocoagulation, or vitrectomy, stratified by any DME, VTDR with DME, and VTDR without DME. Racial and ethnic disparities in diagnosis and treatment are presented for 2018. RESULTS: In 2018, 6 960 823 beneficiaries (27.4%) had diabetes; half were aged 65 to 74 years (49.7%), half (52.7%) were women, and 75.7% were non-Hispanic White. From 2009 to 2018, there was an increase in the annual prevalence of beneficiaries with diabetes who had 1 or more claims for any DME (1.0% to 3.3%) and DME/VTDR (2.8% to 4.3%). Annual prevalence of anti-VEGF increased, particularly among patients with any DME (15.7% to 35.2%) or VTDR with DME (20.2% to 47.6%). Annual prevalence of laser photocoagulation decreased among those with any DME (45.5% to 12.5%), VTDR with DME (54.0% to 20.3%), and VTDR without DME (22.5% to 5.8%). Among all 3 groups, prevalence of vitrectomy in 2018 was less than half that in 2009. Prevalence of any DME and DME/VTDR was highest among Hispanic beneficiaries (5.0% and 7.0%, respectively) and Black beneficiaries (4.5% and 6.2%, respectively) and lowest among non-Hispanic White beneficiaries (3.0% and 3.8%, respectively). Among those with DME/VTDR, anti-VEGF was most prevalent among non-Hispanic White beneficiaries (30.3%). CONCLUSIONS AND RELEVANCE: From 2009 to 2018, prevalence of DME or VTDR increased among Medicare Part B FFS beneficiaries alongside an increase in anti-VEGF treatment and a decline in laser photocoagulation and vitrectomy.

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.

PURPOSE: The purpose of the study is to assess self-reported receipt of diabetes education among people with diabetes and its association with following recommended self-care and clinical preventive care practices. METHODS: We analyzed data from the 2017 and 2018 Behavioral Risk Factor Surveillance System for 61 424 adults (18years) with self-reported diabetes in 43 states and Washington, DC. Diabetes education was defined as ever taking a diabetes self-management class. The association of diabetes education with self-care practices (daily glucose testing, daily foot checks, smoking abstention, and engaging in leisure-time physical activity) and clinical practices (pneumococcal vaccination, biannual A1C test, and an annual dilated eye exam, influenza vaccination, health care visit for diabetes, and foot exam by a medical professional) was assessed. Multivariable logistic regression with predicted margins was used to predict the probability of following these practices, by diabetes education, controlling for sociodemographic factors. RESULTS: Of adults with diabetes, only half reported receiving diabetes education. Results indicate that receipt of diabetes education is associated with following self-care and clinical preventive care practices. Those who did receive diabetes education had a higher predicted probability for following all 4 self-care practices (smoking abstention, daily glucose testing, daily foot check, and engaging in leisure-time physical activity) and all 6 clinical practices (pneumonia vaccination, biannual A1C test, and an annual eye exam, flu vaccination, health care visit, and medical foot exam). CONCLUSIONS: The prevalence of adults with diabetes receiving diabetes education remains low. Increasing receipt of diabetes education may improve diabetes-related preventive care.

PURPOSE: To estimate the economic burden of vision loss (VL) in the United States and by state. DESIGN: Analysis of secondary data sources (American Community Survey [ACS], American Time Use Survey, Bureau of Labor Statistics, Medical Expenditure Panel Survey [MEPS], National and State Health Expenditure Accounts, and National Health Interview Survey [NHIS]) using attributable fraction, regression, and other methods to estimate the incremental direct and indirect 2017 costs of VL. PARTICIPANTS: People with a yes response to a question asking if they are blind or have serious difficulty seeing even when wearing glasses in the ACS, MEPS, or NHIS. MAIN OUTCOME MEASURES: We estimated the direct costs of medical, nursing home (NH), and supportive services and the indirect costs of absenteeism, lost household production, reduced labor force participation, and informal care by age group, sex, and state in aggregate and per person with VL. RESULTS: We estimated an economic burden of VL of $134.2 billion: $98.7 billion in direct costs and $35.5 billion in indirect costs. The largest burden components were NH ($41.8 billion), other medical care services ($30.9 billion), and reduced labor force participation ($16.2 billion), all of which accounted for 66% of the total. Those with VL incurred $16 838 per year in incremental burden. Informal care was the largest burden component for people 0 to 18 years of age, reduced labor force participation was the largest burden component for people 19 to 64 years of age, and NH costs were the largest burden component for people 65 years of age or older. New York, Connecticut, Massachusetts, Rhode Island, and Vermont experienced the highest costs per person with VL. Sensitivity analyses indicate total burden may range between $76 and $218 billion depending on the assumptions used in the model. CONCLUSIONS: Self-reported VL imposes a substantial economic burden on the United States. Burden accrues in different ways at different ages, leading to state differences in the composition of per-person costs based on the age composition of the population with VL. Information on state variation can help local decision makers target resources better to address the burden of VL.

PURPOSE: To estimate the prevalence of diagnosis of major eye disorders and their associated payments, in total and per-person diagnosed, among Medicare fee-for-service (FFS) beneficiaries in 2018. METHODS: We analyzed 100% Medicare Part B FFS claims and Part D Events among beneficiaries continuously enrolled for 12 months in 2018 to calculate the proportion of beneficiaries with ≥1 claim indicating age-related macular degeneration (AMD), cataract, diabetic retinopathy (DR), or glaucoma, and their associated payments, including Medicare and patient out-of-pocket. Eye disease and eye care services were identified using case definitions from the Centers for Disease Control and Prevention's (CDC) Vision & Eye Health Surveillance System (VEHSS). Outcomes are reported by disease overall and by age group (0-39, 40-64, 65-84, 85+ years), sex, race/ethnicity, and U.S. state. RESULTS: Among nearly 30 million Medicare Part B FFS beneficiaries in 2018, over 41% (12.4 million) had a claim containing a diagnosis of at least one of the four eye disorders; 33.7% with cataract, 13.3% with glaucoma, 9.2% with AMD and 3.2% with DR. Payments for eye care services and drugs associated with these four conditions were $10.1billion; $3.6 billion for cataract, $3.5 billion for AMD, $2.2 billion for glaucoma and $0.8 billion for DR. The average cost per beneficiary diagnosed was $816: $1,290 for AMD, $781 for DR, $543 for glaucoma, and $360 for cataract. CONCLUSIONS: Major eye disorders are common among Medicare FFS beneficiaries and account for approximately 4.3% of Medicare Part B and 1% of Medicare Part D spending.

IMPORTANCE: Globally, more than 250 million people live with visual acuity loss or blindness, and people in the US fear losing vision more than memory, hearing, or speech. But it appears there are no recent empirical estimates of visual acuity loss or blindness for the US. OBJECTIVE: To produce estimates of visual acuity loss and blindness by age, sex, race/ethnicity, and US state. DATA SOURCES: Data from the American Community Survey (2017), National Health and Nutrition Examination Survey (1999-2008), and National Survey of Children's Health (2017), as well as population-based studies (2000-2013), were included. STUDY SELECTION: All relevant data from the US Centers for Disease Control and Prevention's Vision and Eye Health Surveillance System were included. DATA EXTRACTION AND SYNTHESIS: The prevalence of visual acuity loss or blindness was estimated, stratified when possible by factors including US state, age group, sex, race/ethnicity, and community-dwelling or group-quarters status. Data analysis occurred from March 2018 to March 2020. MAIN OUTCOMES OR MEASURES: The prevalence of visual acuity loss (defined as a best-corrected visual acuity greater than or equal to 0.3 logMAR) and blindness (defined as a logMAR of 1.0 or greater) in the better-seeing eye. RESULTS: For 2017, this meta-analysis generated an estimated US prevalence of 7.08 (95% uncertainty interval, 6.32-7.89) million people living with visual acuity loss, of whom 1.08 (95% uncertainty interval, 0.82-1.30) million people were living with blindness. Of this, 1.62 (95% uncertainty interval, 1.32-1.92) million persons with visual acuity loss are younger than 40 years, and 141 000 (95% uncertainty interval, 95 000-187 000) persons with blindness are younger than 40 years. CONCLUSIONS AND RELEVANCE: This analysis of all available data with modern methods produced estimates substantially higher than those previously published.

Purpose: Examine the relationship between vision impairment and psychological distress in adults ≥18 years.Methods: Using the 2016-2017 cross-sectional, U.S. National Health Interview Survey, we analyzed self-reported data (n = 57,644) on: Kessler psychological distress scores; general vision impairment (GVI), defined as difficulty seeing even when wearing glasses or contacts; and visual function impairment (VFI), measured using six visual function questions. Multinomial logistic regression was used to estimate adjusted odds ratios (aOR) for mild/moderate and serious psychological distress, by GVI and VFI status, and identify predictors of psychological distress among those with GVI or VFI.Results: Among adults, 10.6% (95% CI: 10.2, 11.0) had GVI; 11.6% (CI: 11.1, 12.0) had VFI. One in four adults with GVI had psychological distress (14.9% [CI: 13.8, 16.0] reported mild/moderate and 11.2% [CI: 10.2, 12.3] reported serious). Individuals with GVI, versus those without, had higher odds of mild/moderate (aOR = 2.24; CI: 2.00, 2.52) and serious (aOR = 3.41; CI: 2.96, 3.93) psychological distress; VFI had similar findings. Among adults with GVI, odds of serious psychological distress were higher for those aged 18-39 (aOR = 4.46; CI: 2.89, 6.90) or 40-64 (aOR = 6.09; CI: 4.33, 8.57) versus ≥65 years; smokers (aOR = 2.45; CI: 1.88, 3.18) versus non-smokers; physically inactive (aOR = 1.61; CI: 1.22, 2.11) versus active; and with arthritis (aOR = 2.18; CI: 1.66, 2.87) or chronic obstructive pulmonary disease (aOR = 1.65; CI: 1.15, 2.37) versus without.Conclusion: Adults with self-reported vision impairment had higher odds of psychological distress. These findings may inform screening interventions to address psychological distress, particularly among younger working-age adults vision impairment.

OBJECTIVE: The Mind, Exercise, Nutrition, Do It! 7-13 (MEND 7-13) program was adapted in 2016 by five Denver Health federally qualified health centers (DH FQHC) into MEND+, integrating clinician medical visits into the curriculum and tracking health measures within an electronic health record (EHR). We examined trajectories of body mass index (BMI, kg/m(2)) percentile, and systolic and diastolic blood pressures (SBP & DBP) among MEND+ attendees in an expanded age range of 4-17 years, and comparable non-attendees. METHODS: Data from April 2015 to May 2018 were extracted from DH FQHC EHR for children eligible for MEND+ referral (BMI ≥85(th) percentile). The sample included 347 MEND+ attendees and 21,061 non-attendees. Mixed-effects models examined average rate of change for BMI percent of the 95(th) percentile (%BMIp95), SBP, and DBP, after completion of the study period. RESULTS: Most children were ages 7-13 years, half were male, and most were Hispanic. An average of 4.2 MEND+ clinical sessions were attended. Before MEND+, %BMIp95 increased by 0.247 units/month among MEND+ attendees. After attending, %BMIp95 decreased by 0.087 units/month (p<0.001). Eligible non-attendees had an increase of 0.084/month in %BMIp95. Before MEND+ attendance, SBP and DBP increased by 0.041 and 0.022/month, respectively. After MEND+ attendance, SBP and DBP decreased by 0.254 /month (p<0.001) and 0.114/month (p<0.01), respectively. SBP and DBP increased by 0.032 and 0.033/month in eligible non-attendees, respectively. CONCLUSIONS: %BMIp95, SBP, and DBP significantly decreased among MEND+ attendees when implemented in community-based clinical practice settings at DH FQHC.

PURPOSE: Determine prevalence of overweight and obesity as reported in Head Start Program Information Reports. DESIGN: Serial cross-sectional census reports from 2012-2018. SETTING: Head Start programs countrywide, aggregated from program level to state and national level. SUBJECTS: Population of children enrolled in Head Start with reported weight status data. MEASURES: Prevalence of overweight (body mass index [BMI] ≥85th percentile to <95th percentile) and obesity (BMI ≥95th percentile). ANALYSIS: Used descriptive statistics to present the prevalence of overweight and obesity by state. Performed unadjusted regression analysis to examine annual trends or average annual changes in prevalence. RESULTS: In 2018, the prevalence of overweight was 13.7% (range: 8.9% in Alabama to 20.4% in Alaska). The prevalence of obesity was 16.6% (range: 12.5% in South Carolina to 27.1% in Alaska). In the unadjusted regression model, 34 states and the District of Columbia did not have a linear trend significantly different from zero. There was a statistically significant positive trend in obesity prevalence for 13 states and a negative trend for 3 states. CONCLUSION: The prevalence of obesity and overweight in Head Start children remained stable but continues to be high. Head Start reports may be an additional source of surveillance data to understand obesity prevalence in low-income young children.

BACKGROUND: Childhood obesity is linked with adverse health outcomes and associated costs. Current information on the relationship between childhood obesity and inpatient costs is limited. OBJECTIVE: The objective of this study was to describe trends and quantify the link between childhood obesity diagnosis and hospitalization length of stay (LOS), costs, and charges. RESEARCH DESIGN: We use the National Inpatient Sample data from 2006 to 2016. SUBJECTS: The sample includes hospitalizations among children aged 2-19 years. The treatment group of interest includes child hospitalizations with an obesity diagnosis. MEASURES: Hospital LOS, charges, and costs associated with a diagnosis of obesity. RESULTS: We find increases in obesity-coded hospitalizations and associated charges and costs during 2006-2016. Obesity as a primary diagnosis is associated with a shorter hospital LOS (by 1.8 d), but higher charges and costs (by $20,879 and $6049, respectively); obesity as a secondary diagnosis is associated with a longer LOS (by 0.8 d), and higher charges and costs of hospitalizations (by $3453 and $1359, respectively). The most common primary conditions occurring with a secondary diagnosis of obesity are pregnancy conditions, mood disorders, asthma, and diabetes; the effect of a secondary diagnosis of obesity on LOS, charges, and costs holds across these conditions. CONCLUSIONS: Childhood obesity diagnosis-related hospitalizations, charges, and costs increased substantially during 2006-2016, and obesity diagnosis is associated with higher hospitalization charges and costs. Our findings provide clinicians and policymakers with additional evidence of the economic burden of childhood obesity and further justify efforts to prevent and manage the disease.

OBJECTIVE: Lay the groundwork for future survey validation and harmonization efforts by comparing prevalence rates of self-reported visual impairment (VI) and blindness measured across federally-funded national surveys by age-groups and to prevalence rates of presenting impairment and blindness measured by physical examination. DESIGN: Cross-sectional comparison of national surveys. PARTICIPANTS: Participants in: 2016 American Community Survey, the 2016 Behavioral Risk Factor Surveillance System, the 2016 National Health Interview Survey, the 1999-2008 National Health and Nutrition Examination Survey (NHANES), and the 2016 National Survey of Children's Health. METHODS: We estimated VI and blindness prevalence rates and confidence intervals for each survey measure and age-group using the Clopper-Pearson method. We then estimated weighted self-reported VI and blindness prevalence rates across survey measures by age-group using inverse variance weighting, fitted trend lines to age-group estimates, and used the trend-line equations to estimate the number of U.S. persons with VI and blindness in 2016. We compared these self-report estimates to those generated from NHANES physical evaluations of presenting VI and blindness. MAIN OUTCOME MEASURES: The variability of prevalence estimates of VI and blindness RESULTS: Survey response estimates of blindness varied between 0.1% and 5.6% for age-groups <65 years and between 0.6% to 16.6% for ages >=65. Estimates of VI varied between 1.6% and 24.8% for age-groups <65 years and between 2.2% and 26.6% for age-groups >=65. For summarized survey results and NHANES physical evaluation, prevalence rates for VI increased significantly with age-group. Blindness prevalence increased significantly with age-group for summarized survey responses but not for NHANES physical examination. Based on extrapolations of NHANES physical examination data to all ages, we estimated that in 2016, 23.4 million persons in the U.S. (7.2%) had VI or blindness, an evaluated visual acuity of 20/40 or worse in the better-seeing eye before correction; based on weighted self-reported surveys, we estimated 24.8 million persons (7.7%) had presenting VI or blindness. CONCLUSIONS: Prevalence rates of VI and blindness obtained from national survey measures varied widely across surveys and age-groups. Additional research is needed to validate the ability of survey self-report measures of VI and blindness to replicate results obtained through clinical exam by an eye health professional.

OBJECTIVES: Frequent sugar-sweetened beverage (SSB) intake is associated with weight gain in women, and pre-pregnancy overweight and excessive gestational weight gain are linked to adverse pregnancy outcomes. SSB intake information for women of reproductive age (WRA) is limited. We described SSB intake among non-pregnant and pregnant WRA and identified correlates of daily intake. METHODS: Using 2017 Behavioral Risk Factor Surveillance System data, we analyzed SSB intake (regular soda, fruit drinks, sweet tea, sports/energy drinks) for 11,321 non-pregnant and 392 pregnant WRA (18-49 years) in 12 states and D.C. Adjusted odds ratios (aOR) for daily (>/= 1 time/day) SSB intake (reference: < 1 time/day) by characteristics were estimated using multivariable logistic regression. RESULTS: Overall, 27.3% of non-pregnant and 21.9% of pregnant women reported consuming SSBs >/= 1 time/day; 12.6% and 9.7%, respectively, consumed SSBs >/= 2 times/day. Among non-pregnant women, odds of daily SSB intake were higher for women who were non-Hispanic black (aOR 2.04, 95% CI 1.55-2.69) vs. non-Hispanic white; had </= high school education (aOR 2.79, CI 2.26-3.44) or some college (aOR 1.85, CI 1.50-2.27) vs. college graduates; lived in nonmetropolitan counties (aOR 1.35, CI 1.11-1.63) vs. metropolitan; had no physical activity (aOR 1.72, CI 1.43-2.07) vs. some; were former (aOR 1.51, CI 1.17-1.94) or current (aOR 3.48, CI 2.82-4.28) smokers vs. nonsmokers. Among pregnant women, those not married had higher odds (aOR 2.81, CI 1.05-7.51) for daily SSB intake than married women. CONCLUSIONS: One in five pregnant women and one in four non-pregnant women of reproductive age consumed SSBs at least once per day. Sociodemographic and behavioral correlates of daily SSB intake were identified.

The objective of this study was to describe the prevalence of sugar-sweetened beverage (SSB) intake among US adults (n = 68,896) residing in metropolitan and nonmetropolitan counties, by state, using data from the Behavioral Risk Factor Surveillance System. We used multinomial logistic regression to calculate adjusted prevalence ratios for daily (>/=1 time per day) SSB intake. Overall, 26.0% of respondents reported daily SSB intake, with significantly higher prevalence in nonmetropolitan counties (30.9%) than in metropolitan counties (24.8%) (adjusted prevalence ratio = 1.32, 95% confidence interval, 1.26-1.39). This same pattern was significant in 5 of 11 states with metropolitan and nonmetropolitan counties. These findings could inform efforts to reduce frequent SSB intake in nonmetropolitan areas.