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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.

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.

To evaluate the effect of diabetes comorbidities by baseline healthcare utilization on receipt of recommended eye examinations.Retrospective analysis of 310 691 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.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.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.背景: 通过基础医疗利用评估糖尿病合并症对接受推荐眼科检查的影响。 方法: 对IBM® MarketScan® 商业数据库2016-2019年的310691例非老年2型糖尿病患者进行回顾性分析。根据糖尿病相关或无关合并症对患者进行分组。在控制年龄, 性别, 地区和主要眼病的情况下，采用Logistic回归方法估计眼科检查的患病率(PR)，通过合并症状况, 医疗保健利用度, 以及合并症和利用度之间的相互作用来评估。 结果: 两年一次的眼科检查的患病率在四个合并症组中有所不同:43.5%(单纯糖尿病组), 52.7%(相关和无关合并症并存组), 48.0%(仅相关合并症组)和45.3%(仅无关合并症组)。在卫生保健利用率最低三分位数中，仅相关组和并存组的检查率低于仅糖尿病组(PR分别为0.95[95%CI 0.92~0.98]和PR 0.91[0.88~0.95])。在中等利用率组中，仅无关组和并存组的检查率较低(PR分别为0.89[0.83~0.95]和0.94[0.90~0.98])。在利用率最高的三分位数中，仅相关组和并存组的检查患病率较高。 结论: 在医疗保健利用率低的患者中，合并疾病与较低的眼科检查患病率相关。在那些医疗保健利用率中等的人中，患有无关共病的糖尿病患者特别容易受到伤害。这项研究强调了糖尿病患者人群将受益于接受保护视力的眼科检查.

ImportanceDiagnostic 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.ObjectiveTo estimate the accuracy of diagnosis codes in administrative claims and EHRs compared to retrospective medical record review.Design, Setting, and ParticipantsThis 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.ExposuresPatients 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 MeasuresAccuracy 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.ResultsAmong 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 RelevanceIn 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.

Importance Approximately 8 million Americans self-report vision loss and blindness (VL), potentially resulting in a significant economic burden. Objective Estimate the economic burden of VL in the US 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 (N/SHEA), 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 indirect costs of absenteeism, lost household production, reduced labor force participation, and informal care by age group (0-18 years, 19-64 years, 65+ years), sex (male, female), and state in aggregate and per person with VL. Results We estimated an economic burden of VL of $134.2bn, $98.7 bn in direct costs, and $35.5 bn in indirect costs. The most substantial burden components were NH ($41.8 bn), medical care services other than inpatient, ambulatory, and prescription care ($30.9 bn), and reduced labor force participation ($16.2 bn) which accounted for 66% of the total. Persons with VL incurred $16,838 per year in incremental burden. Informal care was the largest burden component for people ages 0-18 years, reduced labor force participation for people ages 19-64 years, and NH costs for people ages 65+ years. 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 bn depending on assumptions used in the model. Conclusions and Relevance Self-reported VL imposes a substantial economic burden on the US. Burden accrues in different ways at different ages, leading to state differences in the composition of per-person costs based on their VL population's age composition. Information on state variation can help local decision-makers better target resources to address the burden of VL.

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

Objective: Examine the ten-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 ten-year trend (2009–2018) in healthcare claims for adults 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 ≥1 inpatient or ≥2 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 ≥1 claims for: 1) any DME, 2) either DME/VTDR, and 3) anti-vascular endothelial growth factor injections [anti-VEGF] and laser photocoagulation treatment, stratified by any DME, VTDR with DME, and VTDR without DME. We calculated Average Annual Percent Change (AAPC). Results: From 2009–2018, there was an increase in the annual prevalence of patients with DME/VTDR (2.1% to 3.4%; AAPC=7.5%; pany DME (0.7% to 2.6%; AAPC=19.8%; pDME/VTDR and any DME, with males having a higher prevalence than females. Annual claims for anti-VEGF increased among those 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/VTDR and anti-VEGF injections increased while laser photocoagulation decreased among commercially insured adults with diabetes.

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 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.

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.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.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%).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.

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.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) VisionEye 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.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.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.

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.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.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]).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.

Examine the relationship between vision impairment and psychological distress in adults ≥18 years.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.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.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.

ImportanceRecent 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.ObjectiveTo identify associations between SDOH and evaluated vision impairment and to assess whether these associations hold when examining self-reported vision loss.Design, Setting, and ParticipantsThis 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).ExposuresFive 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 MeasuresPresenting 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).ResultsOf 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:Conclusions and RelevanceThe 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.

Purpose To support survey validation efforts by comparing prevalence rates of self-reported and examination evaluated presenting visual impairment (VI) and blindness measured across national surveys. Design Cross-sectional comparison. Participants Participants in the 2016 American Community Survey, the 2016 Behavioral Risk Factor Surveillance System, the 2016 National Health Interview Survey, the 2005–2008 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 used inverse variance weighting to estimate the central tendency across measures by age-group, fitted trend lines to age-group estimates, and used the trend-line equations to estimate the number of United States persons with VI and blindness in 2016. We compared self-report estimates with those from NHANES physical evaluations of presenting VI and blindness. Main Outcome Measures Variability of prevalence estimates of VI and blindness. Results Self-report estimates of blindness varied between 0.1% and 5.6% for those younger than 65 years and from 0.6% to 16.6% for those 65 or older. Estimates of VI varied between 1.6% and 24.8% for those younger than 65 years and between 2.2% and 26.6% for those 65 years or older. For summarized survey results and NHANES physical evaluation, prevalence rates for VI increased significantly with increasing age group. Blindness prevalence increased significantly with increasing 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 United States (7.2%) had VI or blindness, an evaluated presenting visual acuity of 20/40 or worse in the better-seeing eye before correction. Based on weighted self-reported surveys, we estimated that 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 examination by an eye health professional.

An estimated 13% of all U.S. adults aged ≥18 years have diabetes, and 34.5% meet criteria for having prediabetes. Screening adults at high risk for prediabetes and diabetes may allow earlier detection, diagnosis, and treatment, thereby improving health outcomes. The U.S. Preventive Services Task Force (USPSTF) recommends screening every three years for abnormal blood glucose in adults aged 40-70 years who have overweight or obesity (this recommendation was updated to adults aged 35-70 years in August of 2021) . Using IQVIA Ambulatory Electronic Medical Records (EMR) via the IQVIA E360 platform, we aimed to estimate the proportion of adults aged 40-70 years with overweight or obesity who received a test for blood glucose in a 3-year period from 2016-2019. We defined overweight or obesity based on body mass index (BMI of 25-29.9 kg/m2 and ≥30 kg/m2, respectively) calculated using measured height and weight. Receipt of a blood glucose test was defined as having a hemoglobin A1c or fasting plasma glucose measured, as recommended by USPSTF. Our analysis identified adults aged 40-70 years with overweight or obesity in 2016 (the index year) and without preexisting diabetes, defined using a modified SUPREME-DM algorithm for EMR data[1]. We included adults who were present in the dataset for at least two years before their index BMI in 2016 (to exclude those with prevalent diabetes) and three years after the index BMI, during which we examined the occurrence of blood glucose testing. Among 1,360,095 adults who had overweight or obesity, 33.4% received a test for blood glucose in 2016-2019. The testing rate was 28.7% among adults with overweight and 37.3% among adults with obesity. Our findings could help inform clinical and public health promotion efforts to improve screening for blood glucose levels among adults with overweight or obesity. Future studies may identify factors associated with receiving timely blood glucose screening. [1]https://pubmed.ncbi.nlm.nih.gov/25515167/ Disclosure Y.Chen: None. E.A.Lundeen: None. A.Koyama: None. L.Kompaniyets: None. L.J.Andes: None. S.R.Benoit: None. G.Imperatore: None. D.B.Rolka: None.

Previous research found a decline in vision impairment (VI) prevalence among US adults with diabetes mellitus (DM) from 1997-2010. However, from 2007-20to 2015-2018, there was a decrease in glycemic control in US adults with DM, potentially leading to a worsening of ocular complications that can result in VI. We examined 20-year trends (1999-2018) in VI prevalence among adults ≥18 years with diagnosed DM, using self-reported data from the U.S. National Health Interview Survey. Annual prevalence is presented as single years and a 3-year moving average, age-standardized to the 20U.S. Census. Trends were analyzed using Joinpoint regression with single years of data. Annual Percent Change (APC) is presented. Using the 3-year moving average, the prevalence of adults with DM who reported VI decreased from 21.5% in 1999 to 20.7% in 2018. This trend was not statistically significant (APC: -0.47; p=0.2) . However, Joinpoint regression found distinct trends, with VI prevalence decreasing significantly from 1999-2012 (APC: -1.93; p Disclosure E.A.Lundeen: None. M.Kim: None. J.R.Ehrlich: Consultant; MetLife.

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.

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 (≥18 years) 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.

Approximately 30 million persons in the United States have diabetes.* Persons with diabetes are at risk for vision loss from diabetic retinopathy and other eye diseases (1). Diabetic retinopathy, the most common diabetes-related eye disease, affects 29% of U.S. adults aged ≥40 years with diabetes (2) and is the leading cause of incident blindness among working-age adults (1). It is caused by chronically high blood glucose damaging blood vessels in the retina.† Annual dilated eye exams are recommended for persons with diabetes because early detection and timely treatment of diabetic eye diseases can prevent irreversible vision loss§,¶ (3,4). Studies have documented prevalence of annual eye exams among U.S. adults with diabetes (5,6); however, a lack of recent state-level data limits identification of geographic disparities in adherence to this recommendation. Medicare claims from the 50 states, the District of Columbia (DC), Puerto Rico, and U.S. Virgin Islands (USVI) were examined to assess the prevalence of eye exams in 2017 among beneficiaries with diabetes who were continuously enrolled in Part B fee-for-service insurance, which covers annual eye exams for beneficiaries with diabetes.** This report also examines disparities, by state and race/ethnicity, in receipt of eye exams. Nationally, 54.1% of beneficiaries with diabetes had an eye exam in 2017. Prevalence ranged from 43.9% in Puerto Rico to 64.8% in Rhode Island. Fewer than 50% of beneficiaries received an eye exam in seven states (Alabama, Alaska, Kentucky, Louisiana, Nevada, West Virginia, and Wyoming) and Puerto Rico. Non-Hispanic white (white) beneficiaries had a higher prevalence of receiving an eye exam (55.6%) than did non-Hispanic blacks (blacks) (48.9%) and Hispanics (48.2%). Barriers to receiving eye care (e.g., suboptimal clinical care coordination and referral, low health literacy, and lack of perceived need for care) might limit Medicare beneficiaries' ability to follow this preventive care recommendation. Understanding and addressing these barriers might prevent irreversible vision loss among persons with diabetes.

Purpose: To examine associations of adolescent sugar-sweetened beverage (SSB) intake with parent SSB intake and parent and adolescent attitudes about limiting SSB and junk food (SSB/JF) intake. Design: Quantitative, cross-sectional study. Setting: The 2014 Family Life, Activity, Sun, Health, and Eating study. Sample: Parent–adolescent dyads (N = 1555). Measures: The outcome was adolescent SSB intake. Exposure variables were parent SSB intake, sociodemographics, and parent and adolescent attitudes about SSB/JF intake (responses: agree, neither, or disagree). Analysis: Multinomial logistic regressions estimated adjusted odds ratios (aOR) and 95% confidence intervals (CIs). Results: Half (49.5%) of adolescents and 33.7% of parents consumed SSB ≥1 time/day. Parent daily SSB intake was associated with adolescent daily SSB intake (aOR = 8.9; CI = 4.6-17.3) [referent: no consumption]. Adolescents who disagreed on having confidence to limit SSB/JF intake had higher odds of daily SSB intake (aOR = 3.5; CI = 1.8-6.8), as did those who disagreed they felt bad about themselves if they did not limit SSB/JF intake (aOR = 1.9; CI=1.1-3.3), compared to adolescents who agreed with these attitudes. No parental attitudes were significant. Conclusion: Higher odds of daily SSB intake among adolescents was associated with parent SSB intake and adolescent attitudes about confidence in, and feeling bad about, limiting SSB/JF intake. Parent attitudes were not associated with daily adolescent SSB intake. Efforts to reduce adolescent SSB intake could consider strategies geared toward improving adolescent attitudes and dietary behaviors and parental SSB intake.

While diabetes prevalence in U.S. adults has increased in the last three decades, less is known about trends in ocular complications of diabetes, particularly in adults aged Disclosure E. A. Lundeen: None. M. Kim: None. D. B. Rein: None. J. Wittenborn: None. C. Cai: None. J. B. Saaddine: None.

Key Points Question How many people in the US are living with uncorrectable visual acuity loss or blindness? Findings This bayesian meta-analysis generated an estimate that, in 2017, there were 7.08 million people living with visual acuity loss, of whom 1.08 million were living with blindness. Meaning Per this study, uncorrectable visual acuity loss and blindness are even larger drivers of health burden in the US than was previously known., This meta-analysis combines population-based surveys and data from the US Centers for Disease Control and Prevention’s Vision and Eye Health Surveillance System to produce estimates of visual acuity loss and blindness by age, sex, race/ethnicity, and US state., 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.

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.

Background: Diabetic retinopathy is the leading cause of incident blindness among working age U.S. adults. Only slightly more than half of Medicare beneficiaries with diabetes receive an annual eye exam, and this rate is lower among Hispanic and black beneficiaries compared to white beneficiaries. Objective: We examine racial/ethnic differences in diabetes-related eye disease and treatment claims, among Medicare beneficiaries with diabetes. Methods: We analyzed 2017 Medicare claims for 8,341,000 beneficiaries (both ≥65 years and Results: In 2017, 28% of FFS beneficiaries had diabetes. Of these, 9.6% had a claim for any diabetes-related eye disease; this annual rate was higher among Hispanic (13.1%), non-Hispanic black (11.2%), and Asian/Pacific Islander (11.4%) beneficiaries than among white beneficiaries (8.7%). Early/mild diabetic retinopathy was the most prevalent form (6.3%) and diabetic macular edema was the least prevalent (0.6%). Of beneficiaries with diabetes-related eye disease, 15.2% had at least one type of treatment; treatment types received were anti-vascular endothelial growth factor injections (11.8%), laser photocoagulation (4.7%), vitrectomy (1.3%), and retinal detachment repair (0.7%). Annual prevalence of any treatment was higher among Hispanic (19.0%) and non-Hispanic black (15.5%) beneficiaries compared to white beneficiaries (14.7%); it was lowest among Asian/Pacific Islander beneficiaries (12.9%). Conclusions: Annually, around one-in-ten Medicare Part B FFS beneficiaries with diabetes has a claim for diabetes-related eye disease, and this rate is higher among Hispanic and black beneficiaries. Disclosure E.A. Lundeen: None. D.B. Rein: None. J. Wittenborn: None. J.B. Saaddine: None.

Background: Diabetes self-management education and support services empower people with diabetes to follow preventive care practices that reduce disease complications. Objective: We describe 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 Behavioral Risk Factor Surveillance System for 42,631 adults (≥18 years) with self-reported diabetes (all types) in 38 states, D.C., and Guam. Diabetes education was defined as ever taking a diabetes self-management class. We assessed four self-care practices (daily glucose testing, daily foot checks, smoking abstention, and regular physical activity), and six clinical practices (pneumococcal vaccination, biannual HbA1c test, and an annual dilated eye exam, influenza vaccination, healthcare visit for diabetes, and foot exam). We used multivariable logit regression with predicted margins to predict the probability of following these practices, by diabetes education, controlling for sociodemographic factors. Results: Of adults with diabetes, 52.3% reported receiving diabetes education; those who did had a higher (p Conclusions: Diabetes education was positively associated with following preventive care practices. Developing strategies to improve receipt of education can be informed by studies that elucidate barriers to education. Disclosure E.A. Lundeen: None. I. Mendez: None. M.M. Saunders: None. J.B. Saaddine: None.

To support survey validation efforts by comparing prevalence rates of self-reported and examination evaluated presenting visual impairment (VI) and blindness measured across national surveys.Cross-sectional comparison.Participants in the 2016 American Community Survey, the 2016 Behavioral Risk Factor Surveillance System, the 2016 National Health Interview Survey, the 2005-2008 National Health and Nutrition Examination Survey (NHANES), and the 2016 National Survey of Children's Health.We estimated VI and blindness prevalence rates and confidence intervals for each survey measure and age group using the Clopper-Pearson method. We used inverse variance weighting to estimate the central tendency across measures by age-group, fitted trend lines to age-group estimates, and used the trend-line equations to estimate the number of United States persons with VI and blindness in 2016. We compared self-report estimates with those from NHANES physical evaluations of presenting VI and blindness.Variability of prevalence estimates of VI and blindness.Self-report estimates of blindness varied between 0.1% and 5.6% for those younger than 65 years and from 0.6% to 16.6% for those 65 or older. Estimates of VI varied between 1.6% and 24.8% for those younger than 65 years and between 2.2% and 26.6% for those 65 years or older. For summarized survey results and NHANES physical evaluation, prevalence rates for VI increased significantly with increasing age group. Blindness prevalence increased significantly with increasing 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 United States (7.2%) had VI or blindness, an evaluated presenting visual acuity of 20/40 or worse in the better-seeing eye before correction. Based on weighted self-reported surveys, we estimated that 24.8 million persons (7.7%) had presenting VI or blindness.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 examination by an eye health professional.

Congregate work and residential locations are at increased risk for infectious disease transmission including respiratory illness outbreaks. SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19), is primarily spread person to person through respiratory droplets. Nationwide, the meat and poultry processing industry, an essential component of the U.S. food infrastructure, employs approximately 500,000 persons, many of whom work in proximity to other workers (1). Because of reports of initial cases of COVID-19, in some meat processing facilities, states were asked to provide aggregated data concerning the number of meat and poultry processing facilities affected by COVID-19 and the number of workers with COVID-19 in these facilities, including COVID-19-related deaths. Qualitative data gathered by CDC during on-site and remote assessments were analyzed and summarized. During April 9-27, aggregate data on COVID-19 cases among 115 meat or poultry processing facilities in 19 states were reported to CDC. Among these facilities, COVID-19 was diagnosed in 4,913 (approximately 3%) workers, and 20 COVID-19-related deaths were reported. Facility barriers to effective prevention and control of COVID-19 included difficulty distancing workers at least 6 feet (2 meters) from one another (2) and in implementing COVID-19-specific disinfection guidelines.* Among workers, socioeconomic challenges might contribute to working while feeling ill, particularly if there are management practices such as bonuses that incentivize attendance. Methods to decrease transmission within the facility include worker symptom screening programs, policies to discourage working while experiencing symptoms compatible with COVID-19, and social distancing by workers. Source control measures (e.g., the use of cloth face covers) as well as increased disinfection of high-touch surfaces are also important means of preventing SARS-CoV-2 exposure. Mitigation efforts to reduce transmission in the community should also be considered. Many of these measures might also reduce asymptomatic and presymptomatic transmission (3). Implementation of these public health strategies will help protect workers from COVID-19 in this industry and assist in preserving the critical meat and poultry production infrastructure (4).

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. 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

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.

In 2009, the Ministry of Health of Kyrgyzstan launched a national Infant and Young Child Nutrition (IYCN) program which included point-of-use fortification of foods with micronutrient powders (MNP) containing iron, vitamin A, and other micronutrients. Caretakers of children aged 6–23 months were given 30 sachets of MNP every 2 months. Micronutrient surveys were conducted in 2009 and 2013. The objective of the study was to compare the prevalence of anemia and deficiencies of iron and vitamin A among children aged 6–29 months prior to the MNP program (2009) with those after full implementation (2013). Cross-sectional national surveys were conducted in 2009 (n = 666) and 2013 (n = 2150). Capillary blood samples were collected to measure hemoglobin, iron (ferritin and soluble transferrin receptor [sTfR]) and vitamin A (retinol binding protein [RBP]) status, and inflammation status (C-reactive protein [CRP] and α-1-acid glycoprotein [AGP]). Ferritin, sTfR, and RBP were adjusted for inflammation; hemoglobin was adjusted for altitude. The prevalence of anemia was non-significantly lower in 2013 compared to 2009 (32.7% vs. 39.0%, p = 0.076). Prevalence of inflammation-adjusted iron deficiency (54.8% vs. 74.2%, p

Approximately 46 million persons (14%) in the United States live in nonmetropolitan counties.* Compared with metropolitan residents, nonmetropolitan residents have a higher prevalence of obesity-associated chronic diseases such as diabetes (1), coronary heart disease (1), and arthritis (2). The 2005-2008 National Health and Nutrition Examination Survey (NHANES) found a significantly higher obesity prevalence among adults in nonmetropolitan (39.6%) than in metropolitan (33.4%) counties (3). However, this difference has not been examined by state. Therefore, CDC examined state-level 2016 Behavioral Risk Factor Surveillance System (BRFSS) data and found that the prevalence of obesity (body mass index [BMI] ≥30 kg/m2) was 34.2% among U.S. adults living in nonmetropolitan counties and 28.7% among those living in metropolitan counties (p

Purpose: To examine associations of adolescent sugar-sweetened beverage (SSB) intake with parent SSB intake and parent and adolescent knowledge of SSB-related health risks. Design: Quantitative, cross-sectional. Setting: 2014 SummerStyles survey. Subjects: Nine hundred and ninety parent and adolescent (12-17 years) pairs. Measures: The outcome was self-reported adolescent intake (0, >0 to 0 to Analysis: Separate multinomial logistic regression models were used to estimate adjusted odds ratios (aORs) for adolescent SSB intake ≥1 time/day (ref: 0 times/day), according to (1) parent SSB intake and (2) parent and (3) adolescent knowledge. Results: About 31% of adolescents consumed SSBs ≥1 time/day, and 43.2% of parents consumed SSBs ≥2 times/day. Adolescent and parent knowledge that SSB intake is related to health conditions ranged from 60.7% to 80.4%: weight gain (75.0% and 80.4%, respectively), diabetes (60.7% and 71.4%, respectively), and dental caries (77.5% and 72.9%, respectively). In adjusted models, adolescent SSB intake ≥1 time/day was associated with parent intake ≥2 times/day (aOR = 3.30; 95% confidence interval = 1.62-6.74) but not with parent or adolescent knowledge of health risks. Conclusion: Parental SSB intake may be an important factor in understanding adolescent behavior; knowledge of SSB-related health conditions alone may not influence adolescent SSB behavior.

Purpose: To compare the performance of a 1-question survey screener measuring total sugar-sweetened beverage (SSB) intake to a screener measuring SSB types separately using 4 questions. Design: Cross-sectional. Setting: Web-based 2014 SummerStyles survey. Participants: A total of 4167 US adults (≥18 years). Measures: Frequency of SSB intake measured using a 1-question screener was compared to frequency using a 4-question screener (regular soda, fruit drinks, sports/energy drinks, sweetened coffee/tea). SSB intake (number of time/day) was categorized as 0, >0 to Analysis: Paired t tests were used, and agreement was evaluated using weighted κ and Lin’s concordance correlation coefficient (CCC). Results: Mean SSB intake was 1.7 (95% confidence interval [CI]: 1.65-1.79) times/day using 4 questions and 0.6 (95% CI: 0.56-0.62) times/day using 1 question ( P < .001). Intake frequency based on 4 questions versus 1 was 16.0% versus 38.5% for 0 time/day, 15.6% versus 42.5% for >0 to Conclusion: Daily SSB intake was significantly lower using a 1-question screener versus a 4-question screener. Researchers should assess SSB types separately or consider that daily SSB intake is likely underestimated with 1 question.

Objective Although the Centers for Disease Control and Prevention (CDC) growth charts are widely used, BMI-for-age z-Scores (BMIz) are known to be uninformative above the 97th percentile. This study compared the relations of BMIz and other BMI metrics (%BMIp95, percent of 95th percentile, and ΔBMIp95, BMI minus 95th percentile) to circumferences, skinfolds, and fat mass. We were particularly interested in the differences among children with severe obesity (%BMIp95 ≥ 120). Methods Data was used from 30,003 2- to 19-year-olds who were examined from 1999-2000 through 2013-2014 in the National Health and Nutrition Examination Survey (NHANES). Results The theoretical maximum BMIz based on the growth charts varied by more than threefold across ages. The BMI metrics were strongly intercorrelated, but BMIz was less strongly related to the adiposity measures than were ΔBMIp95 and %BMIp95. Among children with severe obesity, circumferences and triceps skinfold showed almost no association with BMIz (r ≤ 0.10), whereas associations with %BMIp95 and ΔBMIp95 ranged from r = 0.32 to 0.79. Corresponding associations with fat mass ÷ height2 ranged from r = 0.40 (BMIz) to r =0.82 (%BMIp95) among 8- to 19-year-olds. Conclusions Among children with severe obesity, BMIz is only weakly associated with other measures of body fatness. Very high BMIs should be expressed relative to the CDC 95th percentile, particularly in studies that evaluate obesity interventions.

Objective Although the CDC growth charts are widely used, BMIz is known to be uninformative above the 97th percentile. We compared the relations of BMIz and other BMI metrics (%BMIp95, percent of 95th percentile, and ΔBMIp95, BMI minus 95th percentile) to circumferences, skinfolds and fat mass. We were particularly interested in the differences among children with severe obesity (%BMIp95 ≥ 120). Methods We used data from 30,003 2- to 19-year-olds who were examined from 1999-2000 through 2013-14 in NHANES. Results The theoretical maximum BMIz based on the growth charts varied by more than 3-fold across ages. The BMI metrics were strongly intercorrelated, but BMIz was less strongly related to the adiposity measures than were ΔBMIp95 and %BMIp95. Among children with severe obesity, circumferences and triceps skinfold showed almost no association with BMIz (r ≤ 0.10), whereas associations with %BMIp95 and ΔBMIp95 ranged from r=0.32 to 0.79. Corresponding associations with fat mass ÷ height2 ranged from r=0.40 (BMIz) to r=0.82 (%BMIp95) among 8- to 19-year-olds. Conclusions Among children with severe obesity, BMIz is only weakly associated with other measures of body fatness. Very high BMIs should be expressed relative to the CDC 95th percentile, particularly in studies that evaluate obesity interventions.

Added sugars intake increases risk of obesity, hypertension, dyslipidemia, and cardiovascular diseases (CVD) and may impact disease management. We examined factors related to added sugars intake among US adults with or without CVD and risk factors using 2015 National Health Interview Survey (NHIS) (n=30616). NHIS used the National Cancer Institute (NCI) Dietary Screener Questionnaire (DSQ) to assess individual’s dietary intake. The DSQ used 8 questions to assess added sugar intake over the last 30 days. We used the NCI’s scoring algorithm to convert screener responses to estimate total added sugars intake (tsp/day) by characteristics within 3 strata—adults with a history of heart disease/stroke (CVD, n=4658), adults with hypertension/high cholesterol but without CVD (HTHC, n=10476), and adults without CVD or HTHC (no CVD, n=15482). Because our goal was not to look at association between added sugar and CVD risk, comparisons among the groups were not made. Characteristics included age, sex, race/ethnicity, education, income, marital status, smoking, and weight status. We used multiple linear regression to assess independent associations between intake and characteristics. Added sugars intake was similar in magnitude across all 3 strata (CVD 16.9, HTHC 17.0, and no CVD 17.3 tsp, respectively). We found increased added sugars intake with younger age, lower education, lower income, and smoking among adults regardless of CVD status. Having obesity was positively related to increased intake for adults without CVD, but no association was found between the weight status and intake in adults with CVD or HTHC. Improving diet may prevent CVD and aid disease self-management. Product reformulation to reduce added sugars and strategies to counsel and support individuals to reduce dietary added sugars may be needed to optimize heart health.

Background: Height and adiposity track over childhood, but few studies, to our knowledge, have longitudinally examined the mediating relation of the timing and progression of puberty. Objective: We assessed interrelations between prepubertal height and body mass index, the progression through puberty, and young adult height and adiposity. Methods: We analyzed data from the Birth to Twenty Plus study (females, n = 823; males, n = 765). Serial measures of anthropometry and pubertal development were obtained between ages 9 and 16 y. We used latent class growth analysis to categorize pubertal development with respect to pubic hair (females and males), breasts (females), and genitalia (males) development. Adult height and weight were obtained at ages 18 to 20 y. Results: Among females, higher latent class (earlier initiation and faster progression through puberty) was associated with an increased risk of obesity [pubic hair class 3 compared with class 1: RR, 3.41 (95% CI: 1.57, 7.44)] and inconsistent associations with height. Among males, higher latent class was associated with increased adult height [pubic hair development class 3 compared with class 1: 2.43 cm (95% CI: 0.88, 4.00)] and increased risk of overweight/obesity [pubic hair development class 3 compared with class 1: OR, 3.44 (95% CI: 1.44, 8.20)]. In females, the association with adult height became inverse after adjusting for prepubertal height [pubic hair development class 3 compared with class 1: females, 21.31 cm (95% CI: 22.32, 20.31)]; in males, the association with height was attenuated with this adjustment [20.56 cm (95% CI: 21.63, 0.52)]. Associations with adiposity were attenuated after adjusting for prepubertal adiposity. Conclusions: Progression throughpubertymodifies the relation between prepubertal and adult anthropometry. Screening for early or rapid progression of puberty might identify children at an increased risk of becoming overweight or obese adults. J Nutr doi: 10.3945/jn.116.231076.

We analyzed data from the Birth-to-Twenty study, a birth cohort initiated in 1990 in Soweto, the largest urban township in South Africa, adjacent to Johannesburg. Detailed information on this cohort has been published elsewhere (29). The study was designed to track the growth, health, and well-being of children. The cohort enrolled 3273 children who were born during a 6-wk period in early 1990. Participants were predominantly Black, and from families with a low socioeconomic status. Ethical approval was obtained from the University of the Witwatersrand Committee for Research on Human Subjects (approval identification no. M010556), and participants or their caregivers provided written informed consent throughout the study. Weight at birth, and weight and length/height at subsequent annual rounds were measured with the use of standard procedures (30). BMI was calculated as weight (kilograms) divided by height squared (meters squared). Height and BMI measures were converted to height-for-age z scores (HAZs)8 and BMI-for-age z scores (BMIZs) with the use of the WHO standards (31, 32). At birth or soon thereafter, data on demographic and socioeconomic variables, including the child’s ethnicity and maternal monthly income, were collected. Income was categorized into quintiles. At 9–16 y of age, pubertal development was assessed with the Tanner sexual maturation scale (SMS), a commonly used standard for evaluating pubertal stage in both clinical and research settings (33). The SMS consists of drawings of progressive stages in the development of secondary sexual characteristics—breasts in girls, genitals in boys, and pubic hair in both sexs (34, 35). The drawings are accompanied by written descriptions of each stage. The stages range from 1 (prepubertal) to 5 (postpubertal), with stage 2 considered to be the onset of puberty. From 9 to 11 y of age, a trained health care provider administered the SMS in a subset of Birth-to-Twenty participants. In a comparable group of South African adolescents, self-assessment was validated against expert assessment, both with the use of the SMS, and a substantial degree of concordance was found between the 2 ratings (36). Based on these findings, pubertal self-rating with the use of SMS was carried out at 12–16 y of age in the entire cohort. Participants were included in the present analysis if they had at least one SMS measurement, which resulted in an analytical sample of 2195 participants. Missing data were accounted for with the use of the Full Information Maximum Likelihood technique (37). Data management. We used latent class growth analysis (LCGA) in order to group participants into distinct classes based on common developmental trajectories for the SMS indicators of pubertal stage (Supplemental Figure 1). LCGA is an extension of latent growth curve modeling, which estimates a latent intercept (initial level of the outcome variable) and latent slope (rate of outcome change over time) for the entire population. However, instead of assuming that one growth trajectory adequately describes the entire population, LCGA identifies multiple growth trajectories and groups observations into distinct classes based on similarities in their growth patterns. We conducted separate analyses for girls (breast and pubic hair development), and for boys (genital and pubic hair development). We determined the optimal model in terms of the number of classes based on model fit statistics, with a focus on minimizing the value for the Bayesian Information Criteria, as well as parsimony of the model and identifying classes that had distinct differences in their developmental trajectories (37). We used ANOVA to assess the significance of differences in HAZ and BMIZ at ages 5 and 8 y between classes. We used multinomial logistic regression to determine whether HAZ and BMIZ at ages 5 and 8 y were associated with class membership. To address the strong correlation between repeated measures of HAZ and BMIZ in the same individuals, we computed sex-specific conditional measures of HAZ and BMIZ at age 8 y, as described elsewhere (38). These conditional measures represent the change in each of these measures that is not explained by the prior measure. A positive value represents a child who is growing faster than expected and a negative value characterizes a child growing slower than expected from their measure at age 5 y and the mean pattern in the sample. The results of the multinomial regression analyses are presented as relative risk ratios (RRRs), which are interpretable as ORs. To facilitate more direct comparisons with the existing literature, we created an indicator to represent the timing of pubertal development by comparing the children’s level of development in early adolescence to that of their peers. SMS scores at age 12 y were used, because the full cohort began providing pubertal data at this wave. Children who were at the median score at 12 y of age for pubic hair, breast, and genital development were considered to be the reference category. Those with a lower score were classified as slower developers, and those with a higher score were considered to be more rapid developers. With these 3 categories, we used multinomial logistic regression to assess the association between HAZ and BMIZ at ages 5 and 8 y with stage of pubertal development at age 12 y. We adjusted all models for ethnicity (Black, White, Indian, and Coloured) and quintiles of income. We used Mplus 7.3 (Muthen & Muthen) to perform the LGCA and to handle the missing data, and STATA 13.0 for all other analyses. P < 0.05 was considered to be statistically significant.

We examined associations between sugar-sweetened beverage (SSB) intake - a chronic disease risk factor - and characteristics of 75,029 adults (≥18 y) in 9 states by using 2016 Behavioral Risk Factor Surveillance System (BRFSS) data. We used multinomial logistic regression to estimate adjusted odds ratios for SSB intake categorized as none (reference), fewer than 1 time per day, and 1 or more times per day, by sociodemographic and behavioral characteristics. Overall, 32.1% of respondents drank SSBs 1 or more times per day. We found higher odds for 1 or more times per day among younger respondents, men, Hispanic and non-Hispanic black respondents, current smokers, respondents residing in nonmetropolitan counties, employed respondents, and those with less than high school education, obesity, and no physical activity. Our findings can inform the targeting of efforts to reduce SSB consumption.

BACKGROUND: The role that childhood stunting plays in the development of overweight and obesity later in life is not well understood, particularly in adolescence and young adulthood, because most studies have only followed up through midchildhood. OBJECTIVE: The objective of this study was to examine the relation between stunting and age-specific patterns of overweight and obesity incidence from early childhood to young adulthood in the context of a country in the process of the nutrition transition while these children were growing up. METHODS: We analyzed data from 895 participants in the Birth-to-Twenty Plus Cohort (Bt20(+)), an urban South African birth cohort initiated in 1990. Anthropometric data were collected at multiple ages and participants were included if they provided height at age 24 mo and ≥1 measure of body mass index [BMI; weight (kg)/height (m)(2)] in each of the following time periods: 4–8 y, 11–12 y, 13–15 y, 16–18 y, and 22–24 y. We defined stunting at age 24 mo as height-for-age z score 1 in childhood (4–8 y) and adolescence (11–12 y, 13–15 y, and 16–18 y) and BMI ≥25 in young adulthood (22–24 y). We compared BMI, BMIZ, and the prevalence of overweight by stunting status, stratified by sex. RESULTS: Our sample was 93% black and 51% female. The prevalence of stunting at 24 mo was 26% in males and 19% in females. In young adulthood, the prevalence of overweight and obesity was 15.5% (men) and 47.5% (women). Among both males and females, neither mean BMI nor a combined measure of overweight and obesity in any subsequent period differed by stunting status at 24 mo (P ≥ 0.05). CONCLUSION: Stunting at 24 mo was not related to the risk of overweight or obesity in this cohort. Stunting may not be an important contributor to the increasing obesity rates in urban South Africa.

The Centers for Disease Control and Prevention recommends that schools adopt 10 safeguards before launching a body mass index (BMI) screening program; however, little is known about schools’ safeguard adoption. Authors identified questions from the 2014 School Health Policies and Practices Study that aligned with 4 of the 10 safeguards to estimate safeguard prevalence among schools that screened students for BMI (40.7%, N = 223). Among these schools, 3.1% had all four safeguards and 56.5% had none or one. The most prevalent safeguard was having reliable and accurate equipment (54.1%, 95% confidence interval [CI] = [46.1, 62.1]). Providing staff with appropriate expertise and training was the least prevalent; respondents in 26.4% (95% CI [17.1, 35.6]) of schools received recent training on weight status assessment, weight management, and eating disorder identification. School-based BMI screening is common, but adopting multiple recommended safeguards is not. Absent these safeguards, BMI screening programs may fall short of intended outcomes and potentially incur unintended consequences.

Background Prospective data spanning childhood and adolescence are needed to better understand obesity incidence among children and to identify important periods for intervention.

Introduction More than 42 million people in the United States are food insecure. Although some health care entities are addressing food insecurity among patients because of associations with disease risk and management, little is known about the components of these initiatives. Methods The Systematic Screening and Assessment Method was used to conduct a landscape assessment of US health care entity-based programs that screen patients for food insecurity and connect them with food resources. A network of food insecurity researchers, experts, and practitioners identified 57 programs, 22 of which met the inclusion criteria of being health care entities that 1) screen patients for food insecurity, 2) link patients to food resources, and 3) target patients including adults aged 50 years or older (a focus of this assessment). Data on key features of each program were abstracted from documentation and telephone interviews. Results Most programs (n = 13) focus on patients with chronic disease, and most (n = 12) partner with food banks. Common interventions include referrals to or a list of food resources (n = 19), case managers who navigate patients to resources (n = 15), assistance with federal benefit applications (n = 14), patient education and skill building (n = 13), and distribution of fruit and vegetable vouchers redeemable at farmers markets (n = 8). Most programs (n = 14) routinely screen all patients. Conclusion The programs reviewed use various strategies to screen patients, including older adults, for food insecurity and to connect them to food resources. Research is needed on program effectiveness in improving patient outcomes. Such evidence can be used to inform the investments of potential stakeholders, including health care entities, community organizations, and insurers.

Chronic disease, which is linked to unhealthy nutrition environments, is highly prevalent in Guam. The nutrition environment was assessed in 114 stores and 63 restaurants in Guam. Stores had limited availability of some healthier foods such as lean ground meat (7.5%) and 100% whole-wheat bread (11.4%), while fruits (81.0%) and vegetables (94.8%) were more commonly available; 43.7% of restaurants offered a healthy entrée or main dish salad, 4.1% provided calorie information, and 15.7% denoted healthier choices on menus. Improving the nutrition environment could help customers make healthier choices.

Purpose: This study examined associations between knowledge of sugar-sweetened beverage (SSB)-related health conditions and SSB intake among US adults. Design: Quantitative, cross-sectional study. Subject: The 2014 SummerStyles survey data for 4163 US adults (≥18 years) were used. Measures: The outcome measure was frequency of SSB intake (regular soda, fruit drinks, sports or energy drinks, sweetened coffee/tea drinks). Exposure measures were knowledge of 6 SSB-related health conditions: weight gain, diabetes, cavities, high cholesterol, heart disease, and hypertension. Analysis: Six logistic regression models were used to estimate adjusted odds ratios (ORs) for consuming SSBs ≥2 times/d according to knowledge of SSB-related health conditions. Results: Overall, 37.8% of adults reported consuming SSBs ≥2 times/d. Although most adults identified that weight gain (80.2%), diabetes (73.6%), and cavities (71.8%) are related to drinking SSBs, fewer adults identified high cholesterol (24.1%), heart disease (31.5%), and hypertension (33.0%) as being related to drinking SSBs. Crude analyses indicated that lower SSB intake was significantly associated with knowledge of the associations between SSBs and weight gain, diabetes, cavities, and heart disease. However, after adjustment for covariates, only lack of knowledge of the association between heart disease and SSBs was significantly associated with consuming SSBs ≥2 times/d (OR = 1.29) than non-SSB consumers. Conclusions: The finding that knowledge of SSB-related health conditions, in general, was not associated with high SSB intake suggests that knowledge on SSB-related health conditions alone may not be sufficient for adult behavior change.

OBJECTIVES: Frequent sugar-sweetened beverage (SSB) intake is associated with weight gain and type 2 diabetes. There are limited data describing SSB intake among women of reproductive age (WRA). We described SSB intake patterns among pregnant and non-pregnant WRA, and identified characteristics associated with daily SSB intake. METHODS: Using 2017 Behavioral Risk Factor Surveillance System data, we analyzed self-reported SSB intake for 392 pregnant and 11,321 non-pregnant WRA (18–49 years). SSB intake (regular soda, fruit drinks, sweet tea, and sports/energy drinks) was measured in an optional module used by the District of Columbia and 12 states (AK, AZ, AR, DE, HI, IA, NY, NC, OH, VT, WV, WI). Multivariable logistic regression models were used to estimate adjusted odds ratios (aOR) for daily (≥1 time/day) SSB intake (ref