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Long-term symptoms often associated with COVID-19 (post-COVID conditions or long COVID) are an emerging public health concern that is not well understood. Prevalence of post-COVID conditions has been reported among persons who have had COVID-19 (range = 5%-80%), with differences possibly related to different study populations, case definitions, and data sources (1). Few studies of post-COVID conditions have comparisons with the general population of adults with negative test results for SARS-CoV-2, the virus that causes COVID-19, limiting ability to assess background symptom prevalence (1). CDC used a nonprobability-based Internet panel established by Porter Novelli Public Services* to administer a survey to a nationwide sample of U.S. adults aged ≥18 years to compare the prevalence of long-term symptoms (those lasting >4 weeks since onset) among persons who self-reported ever receiving a positive SARS-CoV-2 test result with the prevalence of similar symptoms among persons who reported always receiving a negative test result. The weighted prevalence of ever testing positive for SARS-CoV-2 was 22.2% (95% confidence interval [CI] = 20.6%-23.8%). Approximately two thirds of respondents who had received a positive test result experienced long-term symptoms often associated with SARS-CoV-2 infection. Compared with respondents who received a negative test result, those who received a positive test result reported a significantly higher prevalence of any long-term symptom (65.9% versus 42.9%), fatigue (22.5% versus 12.0%), change in sense of smell or taste (17.3% versus 1.7%), shortness of breath (15.5% versus 5.2%), cough (14.5% versus 4.9%), headache (13.8% versus 9.9%), and persistence (>4 weeks) of at least one initially occurring symptom (76.2% versus 69.6%). Compared with respondents who received a negative test result, a larger proportion of those who received a positive test result reported believing that receiving a COVID-19 vaccine made their long-term symptoms better (28.7% versus 15.7%). Efforts to address post-COVID conditions should include helping health care professionals recognize the most common post-COVID conditions and optimize care for patients with persisting symptoms, including messaging on potential benefits of COVID-19 vaccination.

Background Late sequelae of COVID-19 have been reported; however, few studies have investigated the time course or incidence of late new COVID-19–related health conditions (post-COVID conditions) after COVID-19 diagnosis. Studies distinguishing post-COVID conditions from late conditions caused by other etiologies are lacking. Using data from a large administrative all-payer database, we assessed type, association, and timing of post-COVID conditions following COVID-19 diagnosis. Methods Using the Premier Healthcare Database Special COVID-19 Release (release date, 20 October 2020) data, during March–June 2020, 27 589 inpatients and 46 857 outpatients diagnosed with COVID-19 (case-patients) were 1:1 matched with patients without COVID-19 through the 4-month follow-up period (control-patients) by using propensity score matching. In this matched-cohort study, adjusted ORs were calculated to assess for late conditions that were more common in case-patients than control-patients. Incidence proportion was calculated for conditions that were more common in case-patients than control-patients during 31–120 days following a COVID-19 encounter. Results During 31–120 days after an initial COVID-19 inpatient hospitalization, 7.0% of adults experienced ≥1 of 5 post-COVID conditions. Among adult outpatients with COVID-19, 7.7% experienced ≥1 of 10 post-COVID conditions. During 31–60 days after an initial outpatient encounter, adults with COVID-19 were 2.8 times as likely to experience acute pulmonary embolism as outpatient control-patients and also more likely to experience a range of conditions affecting multiple body systems (eg, nonspecific chest pain, fatigue, headache, and respiratory, nervous, circulatory, and gastrointestinal symptoms) than outpatient control-patients. Conclusions These findings add to the evidence of late health conditions possibly related to COVID-19 in adults following COVID-19 diagnosis and can inform healthcare practice and resource planning for follow-up COVID-19 care.

OBJECTIVE We previously reported a high (˜30%) but stable prevalence of diabetic ketoacidosis (DKA) at youth-onset diagnosis of type 1 diabetes (2002 and 2010). Given the changing demographics of youth-onset type 1 diabetes, we sought to evaluate temporal trends in the prevalence of DKA at diagnosis of type 1 diabetes from 2010 to 2016 among youth RESEARCH DESIGN AND METHODS We calculated prevalence of DKA within 1 month of type 1 diabetes diagnosis by year and evaluated trends over time (2010–2016) (n = 7,612 incident diabetes cases; mean [SD] age 10.1 [4.5] at diagnosis). To assess whether trends observed were attributable to the changing distribution of sociodemographic factors among youth with incident type 1 diabetes, we estimated an adjusted relative risk (RR) of DKA in relation to calendar year, adjusting for age, sex, race/ethnicity, income, education, health insurance status, language, season of diagnosis, and SEARCH for Diabetes in Youth Study site. RESULTS DKA prevalence increased from 35.3% (95% CI 32.2, 38.4) in 2010 to 40.6% (95% CI 37.8, 43.4) in 2016 (Ptrend = 0.01). Adjustment for sociodemographic factors did not substantively change the observed trends. We observed a 2% annual increase in prevalence of DKA at or near diagnosis of type 1 diabetes (crude RR 1.02 [95% CI 1.01, 1.04] and adjusted RR 1.02 [95% CI 1.01, 1.04]; P = 0.01 for both). CONCLUSIONS Prevalence of DKA at or near type 1 diabetes diagnosis has increased from 2010 to 2016, following the high but stable prevalence observed from 2002 to 2010. This increase does not seem to be attributable to the changes in distribution of sociodemographic factors over time.

Background Although surveillance for diabetes in youth relies on provider-assigned diabetes type from medical records, its accuracy compared to an etiologic definition is unknown. Methods Using the SEARCH for Diabetes in Youth Registry, we evaluated the validity and accuracy of provider-assigned diabetes type abstracted from medical records against etiologic criteria that included the presence of diabetes autoantibodies (DAA) and insulin sensitivity. Youth who were incident for diabetes in 2002-06, 2008, or 2012 and had complete data on key analysis variables were included (n=4,001, 85% provider diagnosed type 1). The etiologic definition for type 1 diabetes was ≥1 positive DAA titer(s) or negative DAA titers in the presence of insulin sensitivity and for type 2 diabetes was negative DAA titers in the presence of insulin resistance. Results Provider diagnosed diabetes type correctly agreed with the etiologic definition of type for 89.9% of cases. Provider diagnosed type 1 diabetes was 96.9% sensitive, 82.8% specific, had a positive predictive value (PPV) of 97.0% and a negative predictive value (NPV) of 82.7%. Provider diagnosed type 2 diabetes was 82.8% sensitive, 96.9% specific, had a PPV and NPV of 82.7% and 97.0%, respectively. Conclusion Provider diagnosis of diabetes type agreed with etiologic criteria for 90% of the cases. While the sensitivity and PPV were high for youth with type 1 diabetes, the lower sensitivity and PPV for type 2 diabetes highlights the value of DAA testing and assessment of insulin sensitivity status to ensure estimates are not biased by misclassification. This article is protected by copyright. All rights reserved.

Rationale & Objective Persons with chronic kidney disease (CKD) are often unaware of their disease status. Efforts to improve CKD awareness may be most effective if focused on persons at highest risk for progression to kidney failure. Study Design Serial cross-sectional surveys. Setting & Participants Nonpregnant adults (aged ≥20 years) with CKD glomerular filtration rate categories 3-4 (G3-G4) who participated in the National Health and Nutrition Examination Survey from 1999 to 2016 (n = 3,713). Predictor 5-year kidney failure risk, estimated using the Kidney Failure Risk Equation. Predicted risk was categorized as minimal ( Outcome CKD awareness, defined by answering “yes” to the question “Have you ever been told by a doctor or other health professional that you had weak or failing kidneys?” Analytical Approach Prevalence of CKD awareness was estimated within each risk group using complex sample survey methods. Associations between Kidney Failure Risk Equation risk and CKD awareness were assessed using multivariable logistic regression. CKD awareness was compared with awareness of hypertension and diabetes during the same period. Results In 2011 to 2016, unadjusted CKD awareness was 9.6%, 22.6%, 44.7%, and 49.0% in the minimal-, low-, intermediate-, and high-risk groups, respectively. In adjusted analyses, these proportions did not change over time. Awareness of CKD, including among the highest risk group, remains consistently below that of hypertension and diabetes and awareness of these conditions increased over time. Limitations Imperfect sensitivity of the “weak or failing kidneys” question for ascertaining CKD awareness. Conclusions Among adults with CKD G3-G4 who have 5-year estimated risks for kidney failure of 5%

Objectives This study sought to: (a) assess the prevalence of diabetes complications and comorbidities screening as recommended by the American Diabetes Association (ADA) for youth and young adults (YYAs) with type 1 diabetes (T1D), (b) examine the association of previously measured metabolic status related to diabetes complications with receipt of recommended clinical screening, and (c) examine the association of satisfaction with diabetes care with receipt of recommended clinical screening. Methods The study included 2172 SEARCH for Diabetes in Youth participants with T1D (>10 years old, diabetes duration >5 years). Mean participant age was 17.7 ± 4.3 years with a diabetes duration of 8.1 ± 1.9 years. Linear and multinomial regression models were used to evaluate associations. Results Sixty percent of participants reported having three or more hemoglobin A1c (HbA1c) measurements in the past year. In terms of diabetes complications screening, 93% reported having blood pressure measured, 81% having an eye examination, 71% having lipid levels checked, 64% having a foot exam, and 63% completing albuminuria screening in accordance with ADA recommendations. Youth known to have worse glycemic control in the past had higher odds of not meeting HbA1c screening criteria (OR 1.11, 95% CI = 1.05, 1.17); however, after adjusting for race/ethnicity, this was no longer statistically significant. Greater satisfaction with diabetes care was associated with increased odds of meeting screening criteria for most of the ADA-recommended measures. Conclusions Efforts should be made to improve diabetes complications screening efforts for YYAs with T1D, particularly for those at higher risk for diabetes complications.

Background Disease surveillance of diabetes among youth has relied mainly upon manual chart review. However, increasingly available structured electronic health record (EHR) data have been shown to yield accurate determinations of diabetes status and type. Validated algorithms to determine date of diabetes diagnosis are lacking. The objective of this work is to validate two EHR-based algorithms to determine date of diagnosis of diabetes. Methods A rule-based ICD-10 algorithm identified youth with diabetes from structured EHR data over the period of 2009 through 2017 within three children’s hospitals that participate in the SEARCH for Diabetes in Youth Study: Cincinnati Children’s Hospital, Cincinnati, OH, Seattle Children’s Hospital, Seattle, WA, and Children’s Hospital Colorado, Denver, CO. Previous research and a multidisciplinary team informed the creation of two algorithms based upon structured EHR data to determine date of diagnosis among diabetes cases. An ICD-code algorithm was defined by the year of occurrence of a second ICD-9 or ICD-10 diabetes code. A multiple-criteria algorithm consisted of the year of first occurrence of any of the following: diabetes-related ICD code, elevated glucose, elevated HbA1c, or diabetes medication. We assessed algorithm performance by percent agreement with a gold standard date of diagnosis determined by chart review. Results Among 3777 cases, both algorithms demonstrated high agreement with true diagnosis year and differed in classification (p = 0.006): 86.5% agreement for the ICD code algorithm and 85.9% agreement for the multiple-criteria algorithm. Agreement was high for both type 1 and type 2 cases for the ICD code algorithm. Performance improved over time. Conclusions Year of occurrence of the second ICD diabetes-related code in the EHR yields an accurate diagnosis date within these pediatric hospital systems. This may lead to increased efficiency and sustainability of surveillance methods for incidence of diabetes among youth.

AimsTo compare left ventricular structure (LV) and diastolic function in young adults with youth- onset diabetes by type, determine the prevalence of abnormal diastolic function by diabetes type using published values from age similar healthy controls, and examine the risk factors associated with diastolic function.MethodsIn a cross sectional analysis we compared LV structure and diastolic function from two dimensional echocardiogram in participants with type 1 (T1D) and type 2 diabetes (T2D) who participated in the SEARCH for Diabetes in Youth Study. Linear models were used to examine the risk factors associated with worse diastolic function.ResultsOf 479 participants studied, 258 had T1D (mean age 21.2 ± 5.2 years, 60.5% non-Hispanic white, 53.9% female) and 221 had T2D (mean age 24.8 ± 4.3 years, 24.4% non-Hispanic white, 73.8% female). Median diabetes duration was 11.6 years. Participants with T2D had greater LV mass index and worse diastolic function that persisted after adjustment for differences in risk factors compared with participants with T1D (all p ConclusionsLV structure and diastolic function is worse in individuals with T2D compared to T1D. However, abnormal diastolic function in seen in both groups compared to published values from age similar healthy controls.

Hemoglobin A1c (HbA1c) monitoring over time is thought to facilitate care and support diabetes management goals, but only half of youth and young adults with diabetes report ≥3 HbA1c tests/year despite poor glycemic control. We explored disparities in HbA1c testing over time and association with HbA1c levels and microvascular complications (retinopathy, neuropathy, or nephropathy) in the SEARCH for Diabetes in Youth study. Individuals diagnosed with diabetes at In conclusion, we report that mainly healthcare-related factors are associated with disparities in HbA1c testing frequency over time in youth-onset type 1 and type 2 diabetes, and these disparities are associated with HbA1c levels in type 1 diabetes. Disclosure K. A. Sauder: None. R. Dagostino: Consultant; Self; AstraZeneca, Biogen, Daiichi Sankyo. D. Dabelea: None. J. M. Stafford: None. S. R. Ehrlich: None. A. D. Liese: None. S. M. Marcovina: None. A. K. Mottl: Advisory Panel; Self; Bayer U. S. C. Pihoker: None. S. Saydah: None. A. S. Shah: None. Funding National Institutes of Health; Centers for Disease Control and Prevention

The objective was to assess changes in diabetes autoantibodies (DA) in children and adolescents with diabetes and explore whether observed changes were associated with participants’ characteristics, clinical parameters and diabetes complications. Participants with diabetes had DA (GAD, IA2 and ZnT8) measured at baseline (within 10.3± 7.1 months of diagnosis) and at 1, 2 and/or ≥ 5 years after baseline. At the > 5 year follow-up, the following measures were obtained: HbA1c, insulin dose, c-peptide, frequency of diabetes ketoacidosis (DKA) and severe hypoglycemia in the past 6 months, presence of microalbuminuria, hypertension, arterial stiffness, retinopathy and neuropathy. In those with positive baseline DAs, we examined the association between the change in the number of positive DA and change in each DA status with participants’ characteristics and clinical parameters. At baseline, 3,183 participants had positive DAs. At a mean diabetes duration of 7.3±2.3 years, 1,749 participants had longitudinal DA data, with 83.4% remaining positive (35.0% had 1 DA, 48.4% had 2 or more DAs). There was less DA persistence associated with longer diabetes duration (p=0.003 for those with baseline 1 DA and p Conclusions: DA status remained relatively stable at 7 years after diabetes diagnosis in those with baseline positive DA. Younger age at diagnosis and longer duration were associated with less DA persistence. Measuring DAs after initial presentation may aid in diabetes classification but not in prediction of clinical course. Disclosure L. Merjaneh: None. D. Dabelea: None. C. Pihoker: None. L. M. Dolan: None. C. Suerken: None. R. Dagostino: Consultant; Self; AstraZeneca, Biogen, Daiichi Sankyo. G. Imperatore: None. S. Saydah: None. A. J. Roberts: None. S. M. Marcovina: None. E. J. Mayer-davis: None. Funding Centers for Disease Control and Prevention (00097, DP-05-069, DP-10-001); National Institute of Diabetes and Digestive and Kidney Diseases (1UC4DK108173, 1U18DP006131, U18DP006133, U18DP006134, U18DP006136, U18DP006138, U18DP006139); Kaiser Permanente Southern California (U18DP006133, U48/CCU919219, U01DP000246, U18DP002714); University of Colorado Denver (U18DP006139, U48/CCU819241-3, U01DP000247, U18DP000247-06A1); Cincinnati’s Children’s Hospital Medical Center (U18DP006134, U48/CCU519239, U01DP000248, 1U18DP002709); University of North Carolina at Chapel Hill (U18DP006138, U48/CCU419249, U01DP000254, U18DP002708); Seattle Children’s Hospital (U18DP006136, U58/CCU019235-4, U01DP000244, U18DP002710-01); Wake Forest University School of Medicine (U18DP006131, U48/CCU919219, U01DP000250, 200-2010-35171)

OBJECTIVE While diabetic ketoacidosis (DKA) is common in youth at the onset of the diabetes, the excess costs associated with DKA are unknown. We aimed to quantify the health care services use and medical care costs related to the presence of DKA at diagnosis of diabetes. RESEARCH DESIGN AND METHODS We analyzed data from the U.S. MarketScan claims database for 4,988 enrollees aged 3–19 years insured in private fee-for-service plans and newly diagnosed with diabetes during 2010–2016. Youth with and without DKA at diabetes diagnosis were compared for mean health care service use (outpatient, office, emergency room, and inpatient visits) and medical costs (outpatient, inpatient, prescription drugs, and total) for 60 days prior to and 60 days after diabetes diagnosis. A two-part model using generalized linear regression and logistic regression was used to estimate medical costs, controlling for age, sex, rurality, health plan, year, presence of hypoglycemia, and chronic pulmonary condition. All costs were adjusted to 2016 dollars. RESULTS At diabetes diagnosis, 42% of youth had DKA. In the 60 days prior to diabetes diagnosis, youth with DKA at diagnosis had less health services usage (e.g., number of outpatient visits: −1.17; P < 0.001) and lower total medical costs (−$635; P < 0.001) compared with youth without DKA at diagnosis. In the 60 days after diagnosis, youth with DKA had significantly greater health care services use and health care costs ($6,522) compared with those without DKA. CONCLUSIONS Among youth with newly diagnosed diabetes, DKA at diagnosis is associated with significantly higher use of health care services and medical costs.

Introduction Limited information is available on the health burden of diabetes at the state level. This study estimated state-specific attributable fractions and the number of cases attributable to diabetes for diabetes-related complications. Methods For each state, diabetes-attributable fractions for nine diabetes complications were estimated: three self-reported complications from the 2013 Behavioral Risk Factor Surveillance System, hospitalizations with three complications from 2011 to 2014 State Inpatient Databases, and three complications from 2013 Medicare data. Attributable fractions were calculated using RR and diabetes prevalence and the total number of cases using attributable fractions and total number of complications. Adjusted RR of each complication for people with and without diabetes by age and sex was estimated using a generalized linear model. Analyses were conducted in 2015–2016. Results Median state-level diabetes-attributable fractions for self-reported complications were 0.14 (range, 0.10–0.19) for mobility limitations; 0.13 (range, 0.04–0.21) for limitations in instrumental activities of daily living; and 0.12 (range, 0.06–0.20) for severe visual impairment or blindness. Median state-level diabetes-attributable fractions for diabetes-associated hospitalizations were 0.19 (range, 0.08–0.24) for congestive heart failure; 0.08 (range, 0.02–0.16) for myocardial infarction; and 0.62 (range, 0.46–0.73) for lower extremity amputations. Median state-level diabetes-attributable fractions for complications among Medicare beneficiaries were 0.17 (range, 0.14–0.23) for coronary heart disease; 0.28 (range, 0.24–0.33) for chronic kidney disease; and 0.22 (range, 0.08–0.32) for peripheral vascular disease. Conclusions Diabetes carries a significant health burden, and results vary across states. Efforts to prevent or delay diabetes or to improve diabetes management could reduce the health burden because of diabetes.

Summary Background One in three adolescents and young adults with type 1 diabetes have at least one early diabetes-related complication or comorbidity. We aimed to examine the prevalence and pattern of co-occurring complications in this population, as well as the related risk factors. Methods This observational cohort study includes data from individuals diagnosed with type 1 diabetes before age 20 years who participated in the SEARCH for Diabetes in Youth Study across five sites in the USA. We assessed sociodemographic and metabolic risk factors at baseline and at follow-up, and diabetes complications at follow-up. A frequency analysis was done to examine the difference in observed versus expected prevalence (calculated using a contingency table assuming independence across cells) of co-occurring complications or comorbidities. A cluster analysis was done to identify unique clusters of participants based on demographic characteristics and metabolic risk factors. Findings 1327 participants who completed the follow-up visit were included in the frequency analysis. The mean age was 10·1 (SD 3·9) years at the time of type 1 diabetes diagnosis and 18·0 (4·1) years at follow-up. At a mean diabetes duration of 7·8 [SD 1·9] years, co-occurrence of any two or more complications was observed in 78 (5·9%) participants, more frequently than expected by chance alone (58 [4·4%], p=0·015). Specifically, the complications that co-occurred more frequently than expected were retinopathy and diabetic kidney disease (11 [0·8%] vs three [0·2%]; p=0·0007), retinopathy and arterial stiffness (13 [1·0%] vs four [0·3%]; p=0·0016), and arterial stiffness and cardiovascular autonomic neuropathy (24 [1·8%] vs 13 [1·0%]; p=0·015). We identified four unique clusters characterised by progressively worsening metabolic risk factor profiles (longer duration of diabetes and higher glycated haemoglobin, non-HDL cholesterol, and waist-to-height ratio). The prevalence of at least two complications increased across the clusters (six [2·3%] of 261 in the low-risk cluster, 32 [6·3%] of 509 in the moderate-risk cluster, 28 [8%] of 348 in the high-risk cluster, and five [20·8%] of 24 in the highest-risk cluster). Compared with the low-risk and moderate-risk clusters, the high-risk and highest-risk clusters were characterised by a lower proportion of participants who were non-Hispanic white, and a higher proportion of participants who had a household income below US$50 000 and did not have private health insurance. Interpretation Early complications co-occur in adolescents and young adults with type 1 diabetes more frequently than expected. Identification of individuals with adverse risk factors could enable targeted behavioural or medical interventions that reduce the likelihood of early development of lifelong diabetes-related morbidity. Funding US Centers for Disease Control and Prevention, US National Institutes of Health.

BACKGROUND The diagnosis of type 1 diabetes (T1D) in youth is often associated with diabetic ketoacidosis (DKA). We aimed to evaluate if the presence of DKA at diagnosis of T1D is associated with less favorable hemoglobin A1c (HbA1c) trajectories over time. METHODS The SEARCH for Diabetes in Youth study of 1396 youth aged

As of April 19, 2021, 21.6 million COVID-19 cases had been reported among U.S. adults, most of whom had mild or moderate disease that did not require hospitalization (1). Health care needs in the months after COVID-19 diagnosis among nonhospitalized adults have not been well studied. To better understand longer-term health care utilization and clinical characteristics of nonhospitalized adults after COVID-19 diagnosis, CDC and Kaiser Permanente Georgia (KPGA) analyzed electronic health record (EHR) data from health care visits in the 28-180 days after a diagnosis of COVID-19 at an integrated health care system. Among 3,171 nonhospitalized adults who had COVID-19, 69% had one or more outpatient visits during the follow-up period of 28-180-days. Compared with patients without an outpatient visit, a higher percentage of those who did have an outpatient visit were aged ≥50 years, were women, were non-Hispanic Black, and had underlying health conditions. Among adults with outpatient visits, 68% had a visit for a new primary diagnosis, and 38% had a new specialist visit. Active COVID-19 diagnoses* (10%) and symptoms potentially related to COVID-19 (3%-7%) were among the top 20 new visit diagnoses; rates of visits for these diagnoses declined from 2-24 visits per 10,000 person-days 28-59 days after COVID-19 diagnosis to 1-4 visits per 10,000 person-days 120-180 days after diagnosis. The presence of diagnoses of COVID-19 and related symptoms in the 28-180 days following acute illness suggests that some nonhospitalized adults, including those with asymptomatic or mild acute illness, likely have continued health care needs months after diagnosis. Clinicians and health systems should be aware of post-COVID conditions among patients who are not initially hospitalized for acute COVID-19 disease.

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

OBJECTIVE Diabetes surveillance often requires manual medical chart reviews to confirm status and type. This project aimed to create an electronic health record (EHR)-based procedure for improving surveillance efficiency through automation of case identification. RESEARCH DESIGN AND METHODS Youth ( RESULTS The sample included 5,308 true cases (89.2% type 1 diabetes). The rule-based algorithm outperformed regression for overall accuracy (0.955 vs. 0.936). Type 1 diabetes was classified well by both methods: sensitivity (Se) (>0.95), specificity (Sp) (>0.96), and positive predictive value (PPV) (>0.97). In contrast, the PPVs for type 2 diabetes were 0.642 and 0.778 for the rule-based algorithm and the multinomial regression, respectively. Combination of the rule-based method with chart reviews (n = 695, 7.9%) of persons predicted to have non–type 1 diabetes resulted in perfect PPV for the cases reviewed while increasing overall accuracy (0.983). The Se, Sp, and PPV for type 2 diabetes using the combined method were ≥0.91. CONCLUSIONS An ICD-10 algorithm combined with targeted chart reviews accurately identified diabetes status/type and could be an attractive option for diabetes surveillance in youth.

Surveillance of DM status, type, and date of diagnosis requires chart review. We previously showed that ≥ 2 international classification of diseases (ICD) DM codes predicts DM status well. This project aimed to derive an EHR-based algorithm to predict diagnosis date. Youth (< 20 yrs) with potential DM evidence in 2017 (ICD DM code, elevated glucose or HbA1c, or DM medication) were identified from the inpatient and outpatient EHR data of 3 Children’s Hospitals participating in the SEARCH for Diabetes in Youth Study. Potential cases were chart reviewed to determine true DM status, DM type, and diagnosis date. Cases were restricted to those with diagnosis date and data post-2008 due to EHR limitations. We compared 2 algorithms for predicting diagnosis date: (1) first occurrence of an ICD DM code, and (2) first occurrence of any of the following: ICD DM code, elevated glucose or HbA1c, or DM medication. Among cases identified by the ICD status algorithm (n=3,678), the ICD code and multi-criteria algorithms classified diagnosis year correctly 88.9% and 88.4% of the time, respectively. Classification accuracy improved over time (Figure). Performance was poorer for type 2 than type 1. An ICD code model to predict date of diagnosis can accurately identify diagnosis date within these pediatric hospital systems. Improvement over time is likely due to increases in the amount of EHR data. Manual review of type 2 cases may be necessary. Disclosure K.M. Lenoir: None. L.E. Wagenknecht: None. J. Divers: None. R. Casanova: None. J.M. Lawrence: None. D. Dabelea: None. C. Pihoker: None. S. Saydah: None. A.D. Liese: None. D. Standiford: None. B.J. Wells: None. Funding Centers for Disease Control and Prevention (5U18DP006131-05-00)

Hypoglycemia is a common acute complication in diabetes that is often treated with carbohydrate-rich snacks or drinks. For individuals with diabetes and household food insecurity (HFI), uncertain availability of food may increase fear of hypoglycemia (FOH). FOH may prevent individuals from optimal diabetes management. We analyzed data from 1,603 adult (≥18 years, mean 23.7 years, 80% type 1 diabetes [T1D], 20% type 2 diabetes [T2D]) and 592 youth (10.0-17.9 years, mean 15.2 years, 100% T1D) participants in the SEARCH for Diabetes in Youth Study. Adult participants and parents of youth participants completed the U.S. Household Food Security Survey, where ≥3 food insecure conditions or behaviors indicate HFI. Adults, youth, and parents of youth completed the Hypoglycemia Fear Survey that provides a behavior subscale (scores 0-60), worry subscale (scores 0-72), and total score (sum of subscales). We examined the association of HFI with FOH with general linear models, adjusting for socioeconomic, demographic, and clinical covariates. Approximately 20% of adults and 17% of youth had HFI. Adults with T1D reporting HFI (vs. no HFI) had increased FOH (+3 units for behavior, +11 units for worry, +14 units for total), after adjustment for covariates (all p This research suggests that among adults with diabetes and parents of youth with T1D, those with HFI are more likely to experience FOH than those without HFI. Implementation of common approaches to ameliorate risk of hypoglycemia such as carrying snacks is problematic for people who are food insecure. Providers could consider HFI when discussing hypoglycemia and fear thereof with their patients. Disclosure L.A. Reid: None. S. Zheng: None. J.A. Mendoza: None. B.A. Reboussin: None. K.A. Sauder: None. S. Saydah: None. J.M. Lawrence: None. E.T. Jensen: None. L. Henkin: None. K. Flory: None. L.M. Knight: None. C. Pihoker: None. L.M. Dolan: None. A.D. Liese: None. Funding National Institutes of Health (1UC4DK108173, 1R01DK117461-01)

About 20 million U.S. adults with diabetes (DM) used glucose-lowering medications in 2016. We examined insurance coverage for prescription medications and its association with glycemic control in this population, overall and by age group. We used data from 3,311 adults aged ≥18y with self-reported diagnosed DM and treated with oral hypoglycemic agents or insulin in the 2005-2016 National Health and Nutrition Examination Survey. Participants reported on having health insurance that covered any part of prescription costs. Poor glycemic control (poor A1C) was defined as A1C >9%. During 2005-2016, 15.3% (95%CI 13.7-17.1) of treated U.S. adults with DM lacked prescription coverage, including 8.9% (7.9-10.1) who were uninsured and 6.4% (5.2-7.8) who were insured without any prescription coverage. Compared to treated adults ≥65y, the percentage without prescription coverage was 55% higher for treated adults 18-44y (20.3%, 16.3-25.0 vs. 13.1%, 10.7-16.0) and similar for those 45-64y (15.9%, 13.7-18.4). Overall, 21.4% (17.6-25.7) of treated adults without prescription coverage and 13.5% (11.9-15.3) with prescription coverage had poor A1C (prevalence ratio 1.58 [1.25-2.00]). By age group, % poor A1C differed by coverage status for treated adults 18-44y (covered: 24.9% [18.8-32.1]; not covered: 38.9% [28.2-50.8], p=0.034) and those 45-64y (covered: 15.2% [12.8-18.1]; not covered: 25.5% [19.8-32.3], p=0.005) but not for those ≥65y (covered: 7.5% [6.1-9.1]; not covered: 6.4% [4.0-9.9], p=0.29). For those insulin-treated, % poor A1C differed by coverage status only among adults 18-44y (covered: 31.9% [23.4-41.8]; not covered: 70.0% [53.1-82.8], p Disclosure K.M. Bullard: None. S.R. Benoit: None. S. Saydah: None. C. Mercado: None. G. Imperatore: None.

Individuals with diabetes are at high risk for heart failure with preserved ejection fraction due to diabetic cardiomyopathy (DCM), defined as abnormal myocardial structure and function in the absence of overt coronary artery disease. DCM leads to myocardial fibrosis and remodeling with one of the first signs being left ventricular (LV) diastolic dysfunction. The objective was to compare the prevalence of diastolic dysfunction among young adults with type 1 diabetes (T1D) vs. type 2 diabetes (T2D) with diabetes diagnosed in childhood. Participants had T1D or T2D and were enrolled in the SEARCH for Diabetes in Youth Study. Cardiovascular risk factors and diastolic function via echocardiography were measured at an in-person visit, after an average disease duration of 10.9 years (both groups). Unadjusted means were compared between those with T1D and T2D, p Disclosure A.S. Shah: None. D. Dabelea: None. L.M. Dolan: None. S. Isom: None. R. Dagostino: Consultant; Self; Amgen, AstraZeneca, Celgene, Daiichi Sankyo. L.E. Wagenknecht: None. G. Imperatore: None. S. Saydah: None. A.D. Liese: None. J.M. Lawrence: None. C. Pihoker: None. E.M. Urbina: None. Funding Centers for Disease Control and Prevention (1U18DP006131, U18DP006133, U18DP006134, U18DP006136, U18DP006138, U18DP006139); National Institutes of Health (1UC4DK108173)

Objectives: To examine, among youth and young adults (YYAs) with type 1 diabetes (T1D), the independent association of household food insecurity (HFI) with: 1) glycemic control and 2) episodes of diabetic ketoacidosis (DKA) and severe hypoglycemia. Methods: This cross-sectional study included SEARCH for Diabetes in Youth participants with T1D examined between 2016-2019. HFI was assessed using the U.S. Household Food Security Survey Module. HbA1c was measured from blood drawn at a research visit; episodes of DKA and severe hypoglycemia were collected by survey. Linear and logistic regression models adjusted for age, sex, race, income, health insurance, diabetes duration, and SEARCH site. Results: Of 1834 participants (mean age 20.8±5.0 years, 69.9% non-Hispanic white, diabetes duration 11.1±3.2 years), HbA1c was collected for 1029 individuals (mean HbA1c 9.2%±2.0%). The prevalence of HFI was 16.4%. In this sample, 18.7% and 9.9% reported an episode of DKA or severe hypoglycemia in the past 12 months, respectively. Compared to participants who were food secure, HFI was associated with a 0.43% [CI: 0.02%-0.68%] higher HbA1c. In two separate adjusted models, those with HFI had 1.86 [CI: 1.03, 2.34] times the odds of an episode of DKA and 1.64 [CI: 1.03, 2.34] times the odds of an episode of severe hypoglycemia as those without HFI. Conclusions: HFI is independently associated with poor glycemic control and increased odds of diabetes-related complications in YYAs with T1D. Interventions to reduce HFI and studies to longitudinally examine its impact on glycemic control and complications are warranted. Disclosure F. Malik: None. A.D. Liese: None. A.M. Ellyson: None. L.A. Reid: None. K.A. Sauder: None. E.A. Frongillo: None. C. Pihoker: None. D. Dabelea: None. J.M. Lawrence: None. E.T. Jensen: None. S. Saydah: None. S.M. Marcovina: None. D. Bowlby: None. J.A. Mendoza: None. Funding Centers for Disease Control and Prevention (00097, DP-05-069, DP-10-001); National Institute of Diabetes and Digestive and Kidney Diseases (1UC4DK108173)

Objective: To estimate change in prevalence of type 1 (T1D) and type 2 diabetes (T2D) among U.S. youth ( Methods: Diabetes cases prevalent in 2001, 2009 and 2017 were identified in geographic areas (Colorado, Ohio, South Carolina, Washington) and among managed health care members (California). Similar methods were used in each year. Prevalence (cases/1,000 youth) of physician-diagnosed T1D (0-19 years) and T2D (10-19 years) were calculated for each year. Population denominators were obtained from U.S. census data and membership counts. Race/ethnicity was categorized as non-Hispanic white (NHW), Hispanic (HSP), African American (AA), Asian/Pacific Islander (API) and American Indian (AI). Differences in proportions and annual percent change (APC) with 95% confidence intervals are reported. Results: From 2001 to 2017, prevalence increased from 1.5 to 2.2/1,000 for T1D and from 0.3 to 0.7/1,000 for T2D (Table). Significant increases (p Conclusions: While the prevalence of T1D is higher than youth-onset T2D, the APC is almost double for T2D. Efforts to identify and mitigate risk factors for onset of T1D and T2D remain vital public health priorities. Disclosure J.M. Lawrence: None. A.D. Liese: None. S. Saydah: None. G. Imperatore: None. E.T. Jensen: None. E.J. Mayer-Davis: None. D. Dabelea: None. D.J. Pettitt: None. S.M. Marcovina: None. C. Pihoker: None. L.M. Dolan: None. S. Isom: None. J. Divers: None. Funding Centers for Disease Control and Prevention; National Institute of Diabetes and Digestive and Kidney Diseases (1U18DP006131, U18DP006133, U18DP006134, U18DP006136, U18DP006138, U18DP006139)

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).

Diabetes is one of the most common chronic diseases among persons aged

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

AIMS: To estimate short-term mortality rates for individuals with type 1 or type 2 diabetes diagnosed before age 20 years from the SEARCH for Diabetes in Youth study. METHODS: We included 8358 individuals newly-diagnosed with type 1 (n=6840) or type 2 (n= 1518) diabetes from 1/1/2002–12/31/2008. We searched the National Death Index through 12/31/2010. We calculated standardized mortality ratios (SMRs) as observed divided by expected numbers of deaths based on age, sex, and race for the comparable US population in the geographic areas of the SEARCH study. RESULTS: Mean age at diabetes diagnosis was 10.7 years. During 44,893 person-years (PY) of observation (median follow-up=5.3 years), 41 individuals died (91.3 deaths/100,000 PY); 26 with type 1 (70.6 deaths/100,000 PY) and 15 with type 2 (185.6 deaths/100,000 PY) diabetes. The expected mortality rate was 70.9 deaths/100,000 PY. The overall SMR (95% CI) was 1.3 (1.0, 1.8) and was high among individuals with type 2 diabetes 2.4 (1.3, 3.9), females 2.2 (1.3, 3.3), 15–19 year olds 2.7 (1.7–4.0), and non-Hispanic blacks 2.1 (1.2, 3.4). CONCLUSIONS: Compared to the state populations of similar age, sex, and race, our results show excess mortality in individuals with type 2 diabetes, females, older youth, and non-Hispanic blacks. We did not observe excess short-term mortality in individuals with type 1 diabetes. Studies of longer-term mortality in individuals with youth-onset diabetes are warranted.

Summary Background There is controversy over the usefulness of prediabetes as a diagnostic label. Using data from US National Health and Nutrition Examination Surveys (NHANES) between 1988 and 2014, we examined the cardiovascular and renal burdens in adults with prediabetes over time and compared patterns with other glycaemic status groups. Methods We analysed cross-sectional survey data from non-pregnant adults aged 20 years and older from the NHANES survey periods 1988–94, 1999–2004, 2005–10, and 2011–14. We defined diagnosed diabetes as patients' self-report that they had been previously diagnosed by a physician or health professional; among those with no self-reported diabetes, prediabetes was defined as a fasting plasma glucose (FPG) concentration of 100–125 mg/dL (5·6–6·9 mmol/L) or an HbA 1c of 5·7–6·4% (39–47 mmol/mol); undiagnosed diabetes as an FPG of 126 mg/dL (7·0 mmol/L) or higher or an HbA 1c of 6·5% (48 mmol/mol) or higher; and normal glycaemic status as an FPG of less than 100 mg/dL (5·6 mmol/L) and an HbA 1c of less than 5·7% (39 mmol/mol). We repeated the analyses using varying definitions of prediabetes (FPG 110–125 mg/dL [6·1–6·9 mmol/L] or HbA 1c 5·7–6·4% [39–47 mmol/mol], FPG 110–125 mg/dL [6·1–6·9 mmol/L] or HbA 1c 6·0–6·4% [42–47 mmol/mol], and FPG 100–125 mg/dL [5·6–6·9 mmol/L] and HbA 1c 5·7–6·4% [39–47 mmol/mol]). For each group over time, we estimated the prevalences of hypertension and dyslipidaemia; and among individuals with those conditions, we estimated the proportions who had been treated and who were achieving care goals. By glycaemic group, we estimated those who were current, former, and never smokers; mean 10-year risk of cardiovascular disease (using estimators from the Framingham Heart Study, the United Kingdom Prospective Diabetes Study (UKPDS), and the ACC/AHA ASCVD guidelines); albuminuria (median and albumin-to-creatinine ratio ≥30 mg/g), estimated glomerular filtration rate (eGFR; mean and 2 ); and prevalence of myocardial infarction and stroke. For all estimates, we calculated predicted changes between 1988–94 and 2011–14 using logistic regression models adjusted for age, sex, and race or ethnic group. Findings We obtained data for 27 971 eligible individuals. In 2011–14, in the population of adults with prediabetes, 36·6% (95% CI 32·8–40·5) had hypertension, 51·2% (47·0–55·3) had dyslipidaemia, 24·3% (21·7–27·3) smoked; 7·7% (6·8–8·8) had albuminuria; 4·6% (3·7–5·9) had reduced eGFR; and 10-year cardiovascular event risk ranged from 5% to 7%. From 1988–94 to 2011–14, adults with prediabetes showed significant increases in hypertension (+9·7 percentage points [95% CI 5·4–14·0]); no change in dyslipidaemia; decreases in smoking (−6·4 percentage points [–10·7 to −2·1]); increased use of treatment to lower blood pressure (54·2% [49·0–59·3] to 81·4% [76·7–85·3], +27·2 percentage points [20·5–33·8] p Interpretation Over 25 years, cardiovascular and renal risks and disease have become highly prevalent in adults with prediabetes, irrespective of the definitions used. Identification of people with prediabetes might increase the opportunity for cardiovascular and renal risk reduction. Funding None.

Determine the mortality experience among adults with diabetes in meeting and not meeting less intense control for glycated hemoglobin (HbA1c), blood pressure (BP), and cholesterol.National Health and Nutrition Examination Survey 1999-2010 participants with self-report of diagnosed diabetes (N=3335), measured HbA1c, BP and non-HDL cholesterol were linked to the National Death Index through December 31, 2011. Proportional hazards models were used to estimate hazard ratios (HR) of meeting HbA1c9% and BP160/110, and non-HDL cholesterol190mg/dL. Models used age as the time scale and adjusted for demographics (sex, race/ethnicity, education), diabetes duration, history of cardiovascular and chronic kidney disease, and treatments for elevated glucose, BP, and cholesterol.Over a mean 5.4 person-years of follow-up, participants meeting all less intense control had a 37% lower mortality (HR=0.63, 95% CI 0.54, 0.74) relative to those who did not meet the goals. Of approximately 306,000 deaths per year that occur among Americans with diabetes, we estimate 39,400 might have been averted by improving the care of those who have not met these less intense control goals.Meeting the less intense control goals is associated with 37% reduction in mortality and could lead to 39,400 fewer deaths per year.

OBJECTIVE We assessed the prevalence of and risk factors for diabetic peripheral neuropathy (DPN) in youth with type 1 diabetes (T1D) and type 2 diabetes (T2D) enrolled in the SEARCH for Diabetes in Youth (SEARCH) study. RESEARCH DESIGN AND METHODS The Michigan Neuropathy Screening Instrument (MNSI) was used to assess DPN in 1,734 youth with T1D (mean ± SD age 18 ± 4 years, T1D duration 7.2 ± 1.2 years, and HbA1c 9.1 ± 1.9%) and 258 youth with T2D (age 22 ± 3.5 years, T2D duration 7.9 ± 2 years, and HbA1c 9.4 ± 2.3%) who were enrolled in the SEARCH study and had ≥5 years of diabetes duration. DPN was defined as an MNSI exam score of >2. Glycemic control over time was estimated as area under the curve for HbA1c. RESULTS The prevalence of DPN was 7% in youth with T1D and 22% in youth with T2D. Risk factors for DPN in youth with T1D were older age, longer diabetes duration, smoking, increased diastolic blood pressure, obesity, increased LDL cholesterol and triglycerides, and lower HDL cholesterol (HDL-c). In youth with T2D, risk factors were older age, male sex, longer diabetes duration, smoking, and lower HDL-c. Glycemic control over time was worse among those with DPN compared with those without for youth with T1D (odds ratio 1.53 [95% CI 1.24; 1.88]) but not for youth with T2D (1.05 [0.7; 1.56]). CONCLUSIONS The high rates of DPN among youth with diabetes are a cause of concern and suggest a need for early screening and better risk factor management. Interventions in youth that address poor glycemic control and dyslipidemia may prevent or delay debilitating neuropathic complications.

Objective This study was conducted to identify factors contributing to health satisfaction outcomes among emerging adults (ages 18-25) with diabetes. Method SEARCH for Diabetes in Youth study participants whose diabetes was prevalent in 2001 and were ≥18 years of age when they completed a 2008-2009 follow-up survey (n = 340) were included. We developed a health resilience model (HRM) to identify potentially modifiable factors (anticipatory guidance, social support, physical activity, quality of life) associated with health outcomes (satisfaction with health care, overall personal health, self-report of hemoglobin A1c [HbA1c]). Proportional odds logistic regression models were used to examine relationships between modifiable factors and health outcomes while adjusting for predetermined characteristics; linear regression models were used for reported HbA1c. Comparison of participants who reported HbA1c versus nonreporters was conducted. Results A majority (70%) of participants rated satisfaction with health care ≥7 out of 10, and most (71%) rated overall health as good, very good, or excellent. Satisfaction with health care was independent of change in provider between the ages of 17 and 24. After controlling for predetermined characteristics, we found that the odds of increased satisfaction with health care and overall personal health were 1.3 and 1.2 greater, respectively, when participants discussed additional diabetes-related anticipatory guidance topics with a provider and 1.3 and 2.1, respectively, for each 10-unit change in quality of life score. Relationship between HbA1c level and modifiable factors was not significant; however, participants reporting HbA1c had higher ratings of health care and overall health versus nonreporters. Conclusions HRM describes relationships between modifiable factors and patient-reported outcomes of satisfaction with health care and overall personal health among a cohort of ethnically and racially diverse emerging adults with longstanding diabetes, over half of whom experienced a change in provider. HRM may discern candidate areas for intervention, such as effective anticipatory guidance that can impact satisfaction with health care and overall health, to promote positive health outcomes. (PsycINFO Database Record

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

Intensive insulin therapy may have the unintended consequence of increased adiposity, contributing to the high prevalence of overweight and obesity in people with T1D. We examined the longitudinal association of insulin intensification with estimated body fat percentage (eBFP) among 746 individuals with T1D (age 10-21 years at initial visit) in the SEARCH for Diabetes in Youth study. eBFP was calculated using validated, published sex and race/ethnic specific formulae incorporating age and anthropometrics. Insulin intensification was defined as more intense, less intense or no change, in which “more intense” was an increased number of injections or addition of long-acting insulin, or a change from injections to an insulin pump. Sex-specific, linear mixed effects models used continuous eBFP at each available visit (baseline, and 1-4 follow-up visits; median T1D duration at the last visit, 8 years). Interaction terms between time and insulin intensification relative to change in eBFP tested the study hypothesis. Mean diagnosis age was 12.7 years ± 2.5, 47% were female, 80% non-Hispanic white. At baseline, mean BMIz score was 0.51 ± 0.93, with 32% overweight or with obesity. Although BMIz and weight status did not differ by sex, eBFP was significantly higher in females compared to males (32.4% ± 5.3 and 25.3% ± 5.9 respectively; p Disclosure E. Mayer-Davis: None. B.A. Reboussin: None. C. Pihoker: None. A.R. Kahkoska: None. D. Dabelea: None. L.M. Dolan: None. E.T. Jensen: None. D. Igudesman: None. J.M. Lawrence: None. S. Saydah: None. R. Wadwa: Advisory Panel; Self; Eli Lilly and Company. Research Support; Self; Beta Bionics, MannKind Corporation, Novo Nordisk Inc., Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Other Relationship; Self; Dexcom, Inc. Funding Centers for Disease Control and Prevention; National Institutes of Health

Accurate classification of diabetes type guides correct treatment. We recently showed a type 1 diabetes (T1D) genetic risk score (GRS-1), combined with clinical features and biomarkers discriminates between T1D and type 2 (T2D) and MODY in European adults. We aimed to test the ability of an improved 67 SNP T1D score (GRS-2) to discriminate T1D in an ethnically diverse U.S. pediatric population. We included 1818 SEARCH Study participants who had a C-peptide at a median (IQR) of 8 (6-9) years post diagnosis. T1D was defined based on severe insulin deficiency (fasting C-peptide The T1D GRS-2 was discriminative of T1D across all three ethnic groups AUC (95% CI) for GRS-1 vs. GRS-2 was 0.88 (0.84, 0.93) vs. 0.88 (0.83, 0.92) in white, 0.82 (0.77, 0.87) vs. 0.87 (0.83, 0.92) in blacks, and 0.87 (0.82, 0.92) vs. 0.9 (0.86, 0.95) in Hispanics). Combined risk scores were most accurate at classifying T1D. AUC for different scores were: 0.93 (0.91, 0.95) for type assigned by provider at diagnosis, 0.95 (0.93, 0.96) for age and BMI, 0.95 (0.94, 0.97) for GAD/IA-2/ZNT8 autoantibodies, and 0.99 (0.99, 1.00) for a combined approach using autoantibodies, GRS-2 and clinical features. These results were similar across all race/ethnic groups. A new T1D GRS-2 is discriminative of T1D in a racial/ethnically diverse pediatric population. Using GRS2 in a combined model of clinical features, autoantibodies and genetics offers near perfect classification of T1D and could be used to accurately classify diabetes type. Disclosure R.A. Oram: Other Relationship; Self; Randox Laboratories Ltd. S.A. Sharp: None. C. Pihoker: None. L.A. Ferrat: None. G. Imperatore: None. S. Saydah: None. A.H. Williams: None. L.E. Wagenknecht: None. J.M. Lawrence: None. M.N. Weedon: None. R. Dagostino: Consultant; Self; Acelity, Amgen Inc. W. Hagopian: Research Support; Self; Novo Nordisk A/S. J. Divers: None. D. Dabelea: None. Funding National Institutes of Health (UC4DK108173)

Little is known about the prevalence of diabetes autoantibodies (DAA) among young adults newly diagnosed with diabetes (DM). Our objective was to identify patient characteristics associated with presence of DAA among 20-45-year-olds with new-onset DM from 2.3 million Kaiser Permanente California members. Patients underwent research testing for GADA, ZnT8A, IA2A and IAA. IAA results were not counted for patients with prior insulin use. Demographic and clinical data were from electronic health records. Models predicting any DAA positivity were trained on 70% of cases and validated on the remaining 30%. The best predictive logistic regression model was based on area under the receiver operating characteristic curve (AUC), parsimony and interpretability. Of 7,862 patients with incident DM (mean age 36.8±6.0 years, BMI 37.1 ±8.7), 1,669 (mean age 36.9 ±6.0 years, BMI 37.2 ±9.2) were tested for DAA ∼9.5 (±3.6) months post-diagnosis; 130 (7.8%) were positive (5.4% single DAA, 2.4% multiple DAA). Prevalence of GADA was 6.1%, IA2A 2.0%, IAA 1.9%, and ZnT8A 1.7%. The best model (Table) yielded AUCs of 0.74 and 0.68 in training and validation datasets, respectively. Association with DAA was positive for younger age, lower BMI, and glucagon prescription and negative for Asian race. These findings provide insights into patient characteristics associated with antibody positivity in young adults. Disclosure J.M. Lawrence: None. J. Slezak: None. X. Li: None. L. Yu: None. M. Rewers: None. H.S. Takhar: None. S.B. Sridhar: None. L.I. Vega-Daily: None. J.G. Alexander: None. J. Ritchie: None. S. Saydah: None. G. Imperatore: None. A. Ferrara: None. Funding Centers for Disease Control and Prevention (1U18DP006289); JDRF (2-SRA-2018-533-S-B)

Clinical guidelines and national programs recommend control of ABCS risk factors [glycohemoglobin (A1c), Blood pressure, Cholesterol, and Smoking] to reduce diabetes-related comorbidities and mortality. We investigated changes in meeting the ABCS goals (A1c Disclosure C. Mercado: None. K.M. Bullard: None. M.K. Ali: None. G. Imperatore: None. S. Saydah: None. E. Gregg: None.

Diabetes surveillance often requires manual chart review to confirm status and type. This project aimed to derive and validate an EMR-based algorithm using structured data in the era of ICD-10 codes for improving screening efficiency. Youth ( Disclosure B.J. Wells: None. K.M. Lenoir: None. L.E. Wagenknecht: None. E. Mayer-Davis: None. D. Dabelea: None. J.M. Lawrence: None. C. Pihoker: None. S. Saydah: None. G. Imperatore: None. R. Casanova: None. C.B. Turley: Stock/Shareholder; Self; Abbott Laboratories, AbbVie Inc., CVS/Caremark, Edwards Lifesciences Corporation, Johnson & Johnson, Novartis AG, Pfizer Inc. A.D. Liese: None. D. Standiford: None. R.F. Hamman: None. M.G. Kahn: None. J. Divers: None. Funding Centers for Disease Control and Prevention

Type 1 (T1D) and type 2 (T2D) diabetes are distinct diseases, but most federal surveys used to monitor diabetes prevalence have not included questions on diabetes type. To design and validate survey questions that will distinguish between T1D and T2D in adults, we developed a survey sample in whom we first determined a "gold standard" for diabetes type from review of electronic health record (EHR) data. From >50,000 adult patients in the UNC Health Care System with diabetes, we assembled a sample of 2479 patients using a "strawman" definition based on diabetes-related diagnostic codes, laboratory results, and drugs. We stratified the sample using age, sex, race/ethnicity and crude diabetes type, oversampling T1D. For visits between 10/2014 and 9/2017, we collected clinical and demographic data including body mass index, diagnosis codes, medications, family history, and laboratory tests. We also abstracted chart notes, using trained personnel, to find additional information including diabetes duration and historical treatments. We created two models to determine diabetes type. The first was a decision tree, in which higher nodes sorted straightforward cases; lower nodes included more specific factors for those not categorized in higher nodes. The second was a weighted model, in which factors drawn from clinical or epidemiologic literature weighed weakly, moderately, or strongly in favor of or against each type of diabetes. The two models were 89% congruent for T1D and 96% congruent for T2D. An endocrinologist reviewed and determined type for the 10% of patients for which results of the two models were not congruent or for which neither was able to determine type. Overall, 70% of our sample has T2D, 27% has T1D, and 3% has indeterminate or other diabetes. We will use this sample to validate survey questions to determine type of diabetes. Such "gold standard" determinations may support validation of new and improved methods for measuring diabetes type among the adult population. Disclosure M. Kirkman: Research Support; Self; Bayer AG, Novo Nordisk A/S, Theracos, Inc. J.G. Nooney: None. S.R. Benoit: None. K.A. Bergamo: None. K.M. Bullard: None. J.R. Campione: None. R. Mardon: None. E. Pfaff: None. D.B. Rolka: None. S. Saydah: None. Funding Centers for Disease Control and Prevention

Acid retention associated with reduced glomerular filtration rate (GFR) exacerbates nephropathy progression in partial nephrectomy models of chronic kidney disease (CKD) and might be reflected in patients with CKD with reduced estimated GFR (eGFR) by increased anion gap (AG). We explored the presence of AG and its association with CKD in 14,924 adults aged ≥20 yr with eGFR ≥ 15 ml·min−1·1.73 m−2 enrolled in the National Health and Nutrition Examination Survey III, 1988–1994, using multivariable regression analysis. The model was adjusted for sociodemographic characteristics, diabetes, and hypertension. We further examined the association between AG and incident end-stage renal disease (ESRD) using frailty models, adjusting for demographics, clinical factors, body mass index, serum albumin, bicarbonate, eGFR, and urinary albumin-to-creatinine ratio by following 558 adults with moderate CKD for 12 yr via the United States Renal Data System. Laboratory measures determined AG using the traditional, albumin-corrected, and full AG definitions. Individuals with moderate CKD (eGFR: 30–59 ml·min−1·1.73 m−2) had a greater AG than those with eGFR ≥ 60 ml·min−1·1.73 m−2 in multivariable regression analysis with adjustment for covariates. We found a graded relationship between the adjusted mean for all three definitions of AG and eGFR categories ( P trend < 0.0001). During followup, 9.2% of adults with moderate CKD developed ESRD. Those with AG in the highest tertile had a higher risk of ESRD after adjusting for covariates in a frailty model [relative hazard (95% confidence interval) for traditional AG: 1.76 (1.16–2.32)] compared with those in the middle tertile. The data suggest that high AG, even after adjusting for serum bicarbonate, is a contributing acid-base mechanism to CKD progression in adults with moderate chronic kidney disease.

Summary Objective Although severe hypoglycaemia (SH) can lead to adverse health outcomes, little is known about its occurrence and re‐occurrence among youth with type 1 or type 2 diabetes. Methods This study included 2740 participants aged

ObjectivesBy race/ethnicity and socioeconomic position (SEP), to estimate and examine changes over time in (1) mortality rate, (2) mortality disparities and (3) excess mortality risk attributed to diagnosed diabetes (DM).DesignPopulation-based cohort study using National Health Interview Survey data linked to mortality status from the National Death Index from survey year up to 31 December 2015 with 5 years person-time.ParticipantsUS adults aged ≥25 years with (31 586) and without (332 451) DM.Primary outcomeAge-adjusted all-cause mortality rate for US adults with DM in each subgroup of SEP (education attainment and income-to-poverty ratio (IPR)) and time (1997–2001, 2002–2006 and 2007–2011).ResultsAmong adults with DM, mortality rates fell from 23.5/1000 person-years (p-y) in 1997–2001 to 18.1/1000 p-y in 2007–2011 with changes of −5.2/1000 p-y for non-Hispanic whites; −5.2/1000 p-y for non-Hispanic blacks; and −5.4/1000 p-y for Hispanics. Rates significantly declined within SEP groups, measured as education attainment (high school=−4.8/1000 p-y) and IPR group (poor=−7.9/1000 p-y; middle income=−4.7/1000 p-y; and high income=−6.2/1000 p-y; but not for near poor). For adults with DM, statistically significant all-cause mortality disparity showed greater mortality rates for the lowest than the highest SEP level (education attainment and IPR) in each time period. However, patterns in mortality trends and disparity varied by race/ethnicity. The excess mortality risk attributed to DM significantly decreased from 1997–2001 to 2007–2011, within SEP levels, and among Hispanics and non-Hispanic whites; but no statistically significant changes among non-Hispanic blacks.ConclusionsThere were substantial improvements in all-cause mortality among US adults. However, we observed SEP disparities in mortality across race/ethnic groups or for adults with and without DM despite targeted efforts to improve access and quality of care among disproportionately affected populations.

Aims To estimate incidence of type 1 diabetes (T1D) and to develop a T1D prediction model among young adults. Methods Adults 20–45 years newly-diagnosed with diabetes in 2017 were identified within Kaiser Permanente’s healthcare systems in California and invited for diabetes autoantibody (DAA) testing. Multiple imputation was conducted to assign missing DAA status. The primary outcome for incidence rates (IR) and the prediction model was T1D defined by ≥1 positive DAA. Results Among 2,347,989 persons at risk, 7862 developed diabetes, 2063 had DAA measured, and 166 (8.0%) had ≥1 positive DAA. T1D IR (95% CI) per 100,000 person-years was 15.2 (10.2–20.1) for ages 20–29 and 38.2 (28.6–47.8) for ages 30–44 years. The age-standardized IRs were 32.5 (22.2–42.8) for men and 27.2 (21.0–34.5) for women. The age/sex-standardized IRs were 30.1 (23.5–36.8) overall; 41.4 (25.3–57.5) for Hispanics, 37.0 (11.6–62.4) for Blacks, 21.4 (14.3–28.6) for non-Hispanic Whites, and 19.4 (8.5–30.2) for Asians. Predictors of T1D among cases included female sex, younger age, lower BMI, insulin use and having T1D based on diagnostic codes. Conclusions T1D may account for up to 8% of incident diabetes cases among young adults. Follow-up is needed to establish the clinical course of patients with one DAA at diagnosis.

IntroductionDefining type of diabetes using survey data is challenging, although important, for determining national estimates of diabetes. The purpose of this study was to compare the percentage and characteristics of US adults classified as having type 1 diabetes as defined by several algorithms.Research design and methodsThis study included 6331 respondents aged ≥18 years who reported a physician diagnosis of diabetes in the 2016–2017 National Health Interview Survey. Seven algorithms classified type 1 diabetes using various combinations of self-reported diabetes type, age of diagnosis, current and continuous insulin use, and use of oral hypoglycemics.ResultsThe percentage of type 1 diabetes among those with diabetes ranged from 3.4% for those defined by age of diagnosis 2, respectively) and percentage using oral hypoglycemics (16.1%, 11.1%, and 19.0%, respectively) were lower than for all other algorithms assessed. Among those defined by continuous insulin use alone (algorithm 1), the estimates for mean age and age of diagnosis (54.3 and 30.9 years, respectively) and BMI (30.9 kg/m2) were higher than for other algorithms.ConclusionsEstimates of type 1 diabetes using commonly used algorithms in survey data result in varying degrees of prevalence, characteristic distributions, and potential misclassification.

AIMS: To determine among adolescents and young adults with youth-onset type 1 diabetes and type 2 diabetes the rates and risk factors for albuminuria regression and progression. The research hypothesis was that youth with type 2 diabetes would be more likely to show progression of albuminuria. METHODS: Data from SEARCH, a longitudinal observational study of youth-onset type 1 diabetes (N=1316) and type 2 diabetes (N=143) were analyzed. Urine albumin:creatinine ratio (UACR) was measured from random urine specimens at baseline and follow-up visits (mean 7 years later). Albuminuria regression was defined as halving of baseline UACR when baseline UACR was ≥30μg/mg; progression was defined as doubling of baseline UACR when follow-up UACR was ≥30μg/mg, respectively. Multivariable regression assessed risk factors associated with low-risk albuminuria category (combined persistently-low albuminuria and regression) versus moderate-risk albuminuria category (combined persistently-high albuminuria and progression). RESULTS: Albuminuria progression was more common in type 2 diabetes versus type 1 diabetes (15.4% versus 6.0%, p

Background: Most people with chronic kidney disease (CKD) are not aware of their condition. Objectives: To assess screening criteria in identifying a population with or at high risk for CKD and to determine their level of control of CKD risk factors. Method: CKD Health Evaluation Risk Information Sharing (CHERISH), a demonstration project of the Centers for Disease Control and Prevention, hosted screenings at 2 community locations in each of 4 states. People with diabetes, hypertension, or aged ≥50 years were eligible to participate. In addition to CKD, screening included testing and measures of hemoglobin A1C, blood pressure, and lipids. ­Results: In this targeted population, among 894 people screened, CKD prevalence was 34%. Of participants with diabetes, 61% had A1C < 7%; of those with hypertension, 23% had blood pressure < 130/80 mm Hg; and of those with high cholesterol, 22% had low-density lipoprotein < 100 mg/dL. Conclusions: Using targeted selection criteria and simple clinical measures, CHERISH successfully identified a population with a high CKD prevalence and with poor control of CKD risk factors. CHERISH may prove helpful to state and local programs in implementing CKD detection programs in their communities.

Surveillance of type 1 diabetes provides an opportunity to address public health needs, inform etiological research, and plan health care services. We present issues in type 1 diabetes surveillance, review previous and current methods, and present new initiatives. Few diabetes surveillance systems distinguish between type 1 and type 2 diabetes. Most worldwide efforts have focused on registries and ages

Urinary (U) neutrophil gelatinase-associated lipocalin (NGAL) and kidney injury molecule-1 (KIM-1) are markers of renal tubular injury. Whether NGAL and KIM-1 are elevated in early diabetic kidney disease (DKD) is not known. We performed a pilot study to compare the U concentrations (C) of NGAL and KIM-1 in YA with T1D or T2D from the SEARCH registry and healthy controls (n 45 in each group). The YA with diabetes were selected based on age at dx > 10 years and duration >8 years and were randomly selected across the quartiles of eGFR with similar age, gender and ethnicity. T1D (age 21 ± 2 years; duration 9.6 ± 1.0 years; HbA1c 9.3 ± 2.1% eGFR 126 ± 19 mL/min/1.73m2, UACR 11 ± 16 µg/mg), T2D (age 24 ± 3 years, duration 9.8 ± 1.2 years, HbA1c 8.5 ± 3.0%, eGFR 128 ± 21 mL/min/1.73m2, UACR 38 ± 71 µg/mg), controls (age 23 ± 3 years). In each group 50% were female, 41% white, 43% AA, and 16% Hisp. NGAL and KIM-1 were significantly elevated in T1D and T2D compared with controls (p < 0.001, p< 0.007). Although there was a trend for a greater UC of NGAL in T2D than T1D and a greater UC of KIM-1 in T1D than T2D, these differences were not statistically significant. Elevated UC of NGAL and KIM-1 are present in YA with T1D and T2D suggesting the presence of tubular damage early in the course of diabetes. Further investigation will determine if these markers can be used to assess the natural history of tubular injury or monitor therapeutic intervention in DKD. Disclosure N.M. Sheanon: None. A.K. Mottl: None. R. Dagostino: Consultant; Self; Teva Pharmaceutical Industries Ltd., Acelity, RedHill Biopharma, Edwards Lifesciences. C. Suerken: None. M. Afkarian: None. D. Dabelea: None. G. Imperatore: None. S.M. Marcovina: None. D.J. Pettitt: None. S. Saydah: None. L.M. Dolan: None.

The Dietary Approaches to Stop Hypertension (DASH) diet lowers blood pressure, an important risk factor for chronic kidney disease (CKD) and end-stage renal disease (ESRD). However, it is unclear whether adherence to a DASH diet confers protection against future ESRD, especially among those with pre-existing CKD and hypertension. We examined whether a DASH diet is associated with lower risk of ESRD among 1,110 adults aged ≥ 20 years with hypertension and CKD (estimated glomerular filtration rate, eGFR 30-59 ml/min/1.73 m2) enrolled in the National Health and Nutrition Examination Survey (1988-1994). Baseline DASH diet accordance score was assessed using a 24-hour dietary recall questionnaire. ESRD was ascertained by linkage to the U.S. Renal Data System registry. We used the Fine-Gray competing risks method to estimate the relative hazard (RH) for ESRD after adjusting for sociodemographics, clinical and nutritional factors, eGFR, and albuminuria. Over a median follow-up of 7.8 years, 18.4% of subjects developed ESRD. Compared to the highest quintile of DASH diet accordance, there was a greater risk of ESRD among subjects in quintiles 1 (RH=1.7; 95% CI 1.1-2.7) and 2 (RH 2.2; 95% CI 1.1-4.1). Significant interactions were observed with diabetes status and race/ethnicity, with the strongest association between DASH diet adherence and ESRD risk observed in individuals with diabetes and in non-Hispanic blacks. Low accordance to a DASH diet is associated with greater risk of ESRD in adults with moderate CKD and hypertension, particularly in non-Hispanic blacks and persons with diabetes.

Albuminuria and low estimated glomerular filtration rate (eGFR) define chronic kidney disease in adults and youth. Different from adults, the burden of abnormal kidney markers among youth in the general United States population is largely unknown.Serial cross-sectional national surveys.Adolescents aged 12 to 18 years participating in the National Health and Nutrition Examination Surveys 1988 to 2014. Surveys were grouped into three 6-year periods.Demographic and clinical determinants of kidney markers.Prevalence and trends in persistent albuminuria, low (60mL/min/1.73mOutcomes defined as persistent albumin-creatinine ratio ≥ 30mg/g (persistent albuminuria), eGFR 90mL/min/1.73mPrevalences of persistent albuminuria were 3.64% (95% CI, 1.82%-5.46%) in 1988-1994 and 3.29% (95% CI, 1.94%-4.63%) in 2009-2014 (adjusted prevalence ratio, 0.93; 95% CI, 0.53-1.62; P=0.8 for trend). Prevalences of reduced eGFR were 31.46% (95% CI, 28.42%-34.67%) and 34.58% (95% CI, 32.07%-37.18%), respectively (adjusted prevalence ratio, 1.21; 95% CI, 1.00-1.46; P 0.001 for trend). Prevalences of low eGFR were 0.32% (95% CI, 0.12%-0.84%) in 1988-1994 and 0.91% (95% CI, 0.58%-1.42%) in 2009-2014 (adjusted prevalence ratio, 3.10; 95% CI, 1.10-9.01; P = 0.09 for trend). Prevalences of albuminuria and/or low eGFR remained at 4.0% in 1988-1994 and 2009-2014 (adjusted prevalence ratio, 1.06; 95% CI, 0.64-1.77; P = 0.8 for trend).Persistent albuminuria data were based on imputed values (for second assessment of albuminuria) in 91% of participants; lack of second eGFR assessment to confirm sustained reduction in kidney function.Albuminuria prevalence has not changed significantly in the US adolescent population between 1988 and 2014. Prevalences of both reduced and low eGFRs were higher in the most recent study period; however, 1% of adolescents had low eGFRs.