Heart Disease and Stroke Statistics-2022 Update: A Report From the American Heart Association.

BACKGROUND: The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS: The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2022 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population and an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, and the global burden of cardiovascular disease and healthy life expectancy. RESULTS: Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS: The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Heart Disease and Stroke Statistics-2021 Update: A Report From the American Heart Association.

BACKGROUND: The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS: The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2021 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population, an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, the global burden of cardiovascular disease, and further evidence-based approaches to changing behaviors related to cardiovascular disease. RESULTS: Each of the 27 chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS: The Statistical Update represents a critical resource for the lay public, policy makers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Heart Disease and Stroke Statistics-2020 Update: A Report From the American Heart Association.

BACKGROUND: The American Heart Association, in conjunction with the National Institutes of Health, annually reports on the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS: The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2020 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population, metrics to assess and monitor healthy diets, an enhanced focus on social determinants of health, a focus on the global burden of cardiovascular disease, and further evidence-based approaches to changing behaviors, implementation strategies, and implications of the American Heart Association's 2020 Impact Goals. RESULTS: Each of the 26 chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS: The Statistical Update represents a critical resource for the lay public, policy makers, media professionals, clinicians, healthcare administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Identifying Relative Changes in Social Risk Factors: An Analytic Approach.

BACKGROUND: Individuals often report concurrent social risk factors such as food insecurity, unstable housing, and transportation barriers. Comparing relative changes between pairs of social risk factors may identify those that are more resistant to change. OBJECTIVE: The objective of this study was to develop a method to describe relative changes in pairs of social risk factors. RESEARCH DESIGN: This was a prospective cohort study. SUBJECTS: Participants in a randomized controlled trial of hypertension care in an Urban Indian Health Organization. MEASURES: We measured 7 social risk factors (housing, transportation, food, clothing, health care, utilities, and debts) at enrollment, 6, and 12 months among 295 participants in the trial. We hypothesized that pairwise comparisons could identify social risk factors that were less likely to change over time. We used conditional odds ratios (ORs) with 95% confidence intervals (CIs) to rank each pair. RESULTS: Food, clothing, health care, utilities, and debts had more changes between 0 and 6 months relative to housing (OR=2.3, 3.4, 4.7, 3.5, and 3.4, respectively; all 95% CI excluded 1.0). These same social risk factors also had more changes between baseline and 6 months relative to transportation (OR=2.8, 3.4, 4.9, 4.7, and 4.1, respectively; all 95% CI excluded 1.0). Changes in housing and transportation risk factors were comparable (OR=0.7, 95% CI: 0.4-1.4). Relative changes between 6 and 12 months were similar. CONCLUSIONS: Housing and transportation exhibited fewer relative changes than other social risk factors and might be more resistant to change. Awareness of the relationships between social risk factors can help define priorities for intervention.

Predictors of Hyperkalemia and Hypokalemia in Individuals with Diabetes: a Classification and Regression Tree Analysis.

BACKGROUND: Both hyperkalemia and hypokalemia can lead to cardiac arrhythmias and are associated with increased mortality. Information on the predictors of potassium in individuals with diabetes in routine clinical practice is lacking. OBJECTIVE: To identify predictors of hyperkalemia and hypokalemia in adults with diabetes. DESIGN: Retrospective cohort study, with classification and regression tree (CART) analysis. PARTICIPANTS: 321,856 individuals with diabetes enrolled in four large integrated health care systems from 2012 to 2013. MAIN MEASURES: We used a single serum potassium result collected in 2012 or 2013. Hyperkalemia was defined as a serum potassium ≥ 5.5 mEq/L and hypokalemia as < 3.5 mEq/L. Predictors included demographic factors, laboratory measurements, comorbidities, medication use, and health care utilization. KEY RESULTS: There were 2556 hypokalemia events (0.8%) and 1517 hyperkalemia events (0.5%). In univariate analyses, we identified concordant predictors (associated with increased probability of both hyperkalemia and hypokalemia), discordant predictors, and predictors of only hyperkalemia or hypokalemia. In CART models, the hyperkalemia "tree" had 5 nodes and a c-statistic of 0.76. The nodes were defined by prior potassium results and eGFRs, and the 5 terminal "leaves" had hyperkalemia probabilities of 0.2 to 7.2%. The hypokalemia tree had 4 nodes and a c-statistic of 0.76. The hypokalemia tree included nodes defined by prior potassium results, and the 4 terminal leaves had hypokalemia probabilities of 0.3 to 17.6%. Individuals with a recent potassium between 4.0 and 5.0 mEq/L, eGFR ≥ 45 mL/min/1.73m2, and no hypokalemia in the previous year had a < 1% rate of either hypokalemia or hyperkalemia. CONCLUSIONS: The yield of routine serum potassium testing may be low in individuals with a recent serum potassium between 4.0 and 5.0 mEq/L, eGFR ≥ 45 mL/min/1.73m2, and no recent history of hypokalemia. We did not examine the effect of recent changes in clinical condition or medications on acute potassium changes.

Microvascular Outcomes in Patients With Diabetes After Bariatric Surgery Versus Usual Care: A Matched Cohort Study.

Background: Bariatric surgery improves glycemic control in patients with type 2 diabetes mellitus (T2DM), but less is known about microvascular outcomes. Objective: To investigate the relationship between bariatric surgery and incident microvascular complications of T2DM. Design: Retrospective matched cohort study from 2005 to 2011 with follow-up through September 2015. Setting: 4 integrated health systems in the United States. Participants: Patients aged 19 to 79 years with T2DM who had bariatric surgery (n = 4024) were matched on age, sex, body mass index, hemoglobin A1c level, insulin use, diabetes duration, and intensity of health care use up to 3 nonsurgical participants (n = 11 059). Intervention: Bariatric procedures (76% gastric bypass, 17% sleeve gastrectomy, and 7% adjustable gastric banding) compared with usual care. Measurements: Adjusted Cox regression analysis investigated time to incident microvascular disease, defined as first occurrence of diabetic retinopathy, neuropathy, or nephropathy. Results: Median follow-up was 4.3 years for both surgical and nonsurgical patients. Bariatric surgery was associated with significantly lower risk for incident microvascular disease at 5 years (16.9% for surgical vs. 34.7% for nonsurgical patients; adjusted hazard ratio [HR], 0.41 [95% CI, 0.34 to 0.48]). Bariatric surgery was associated with lower cumulative incidence at 5 years of diabetic neuropathy (7.2% for surgical vs. 21.4% for nonsurgical patients; HR, 0.37 [CI, 0.30 to 0.47]), nephropathy (4.9% for surgical vs. 10.0% for nonsurgical patients; HR, 0.41 [CI, 0.29 to 0.58]), and retinopathy (7.2% for surgical vs. 11.2% for nonsurgical patients; HR, 0.55 [CI, 0.42 to 0.73]). Limitation: Electronic health record databases could misclassify microvascular disease status for some patients. Conclusion: In this large, multicenter study of adults with T2DM, bariatric surgery was associated with lower overall incidence of microvascular disease (including lower risk for neuropathy, nephropathy, and retinopathy) than usual care. Primary Funding Source: National Institute of Diabetes and Digestive and Kidney Diseases.

Validation of an algorithm for identifying type 1 diabetes in adults based on electronic health record data.

PURPOSE: Algorithms using information from electronic health records to identify adults with type 1 diabetes have not been well studied. Such algorithms would have applications in pharmacoepidemiology, drug safety research, clinical trials, surveillance, and quality improvement. Our main objectives were to determine the positive predictive value for identifying type 1 diabetes in adults using a published algorithm (developed by Klompas et al) and to compare it to a simple requirement that the majority of diabetes diagnosis codes be type 1. METHODS: We applied the Klompas algorithm and the diagnosis code criterion to a cohort of 66 690 adult Kaiser Permanente Colorado members with diabetes. We reviewed 220 charts of those identified as having type 1 diabetes and calculated positive predictive values. RESULTS: The Klompas algorithm identified 3286 (4.9% of 66 690) adults with diabetes as having type 1 diabetes. Based on chart reviews, the overall positive predictive value was 94.5%. The requirement that the majority of diabetes diagnosis codes be type 1 identified 3000 (4.5%) as having type 1 diabetes and had a positive predictive value of 96.4%. However, the algorithm criterion involving dispensing of urine acetone test strips performed poorly, with a positive predictive value of 20.0%. CONCLUSIONS: Data from electronic health records can be used to accurately identify adults with type 1 diabetes. When identifying adults with type 1 diabetes, we recommend either a modified version of the Klompas algorithm without the urine acetone test strips criterion or the requirement that the majority of diabetes diagnosis codes be type 1 codes.

Association Between Bariatric Surgery and Macrovascular Disease Outcomes in Patients With Type 2 Diabetes and Severe Obesity.

Importance: Macrovascular disease is a leading cause of morbidity and mortality for patients with type 2 diabetes, and medical management, including lifestyle changes, may not be successful at lowering risk. Objective: To investigate the relationship between bariatric surgery and incident macrovascular (coronary artery disease and cerebrovascular diseases) events in patients with severe obesity and type 2 diabetes. Design, Setting, and Participants: In this retrospective, matched cohort study, patients with severe obesity (body mass index ≥35) aged 19 to 79 years with diabetes who underwent bariatric surgery from 2005 to 2011 in 4 integrated health systems in the United States (n = 5301) were matched to 14 934 control patients on site, age, sex, body mass index, hemoglobin A1c, insulin use, observed diabetes duration, and prior health care utilization, with follow-up through September 2015. Exposures: Bariatric procedures (76% Roux-en-Y gastric bypass, 17% sleeve gastrectomy, and 7% adjustable gastric banding) were compared with usual care for diabetes. Main Outcomes and Measures: Multivariable-adjusted Cox regression analysis investigated time to incident macrovascular disease (defined as first occurrence of coronary artery disease [acute myocardial infarction, unstable angina, percutaneous coronary intervention, or coronary artery bypass grafting] or cerebrovascular events [ischemic stroke, hemorrhagic stroke, carotid stenting, or carotid endarterectomy]). Secondary outcomes included coronary artery disease and cerebrovascular outcomes separately. Results: Among a combined 20 235 surgical and nonsurgical patients, the mean (SD) age was 50 (10) years; 76% of the surgical and 75% of the nonsurgical patients were female; and the baseline mean (SD) body mass index was 44.7 (6.9) and 43.8 (6.7) in the surgical and nonsurgical groups, respectively. At the end of the study period, there were 106 macrovascular events in surgical patients (including 37 cerebrovascular and 78 coronary artery events over a median of 4.7 years; interquartile range, 3.2-6.2 years) and 596 events in the matched control patients (including 227 cerebrovascular and 398 coronary artery events over a median of 4.6 years; interquartile range, 3.1-6.1 years). Bariatric surgery was associated with a lower composite incidence of macrovascular events at 5 years (2.1% in the surgical group vs 4.3% in the nonsurgical group; hazard ratio, 0.60 [95% CI, 0.42-0.86]), as well as a lower incidence of coronary artery disease (1.6% in the surgical group vs 2.8% in the nonsurgical group; hazard ratio, 0.64 [95% CI, 0.42-0.99]). The incidence of cerebrovascular disease was not significantly different between groups at 5 years (0.7% in the surgical group vs 1.7% in the nonsurgical group; hazard ratio, 0.69 [95% CI, 0.38-1.25]). Conclusions and Relevance: In this observational study of patients with type 2 diabetes and severe obesity who underwent surgery, compared with those who did not undergo surgery, bariatric surgery was associated with a lower risk of macrovascular outcomes. The findings require confirmation in randomized clinical trials. Health care professionals should engage patients with severe obesity and type 2 diabetes in a shared decision making conversation about the potential role of bariatric surgery in the prevention of macrovascular events.

Trends in diabetes incidence among 7 million insured adults, 2006-2011: the SUPREME-DM project.

An observational cohort analysis was conducted within the Surveillance, Prevention, and Management of Diabetes Mellitus (SUPREME-DM) DataLink, a consortium of 11 integrated health-care delivery systems with electronic health records in 10 US states. Among nearly 7 million adults aged 20 years or older, we estimated annual diabetes incidence per 1,000 persons overall and by age, sex, race/ethnicity, and body mass index. We identified 289,050 incident cases of diabetes. Age- and sex-adjusted population incidence was stable between 2006 and 2010, ranging from 10.3 per 1,000 adults (95% confidence interval (CI): 9.8, 10.7) to 11.3 per 1,000 adults (95% CI: 11.0, 11.7). Adjusted incidence was significantly higher in 2011 (11.5, 95% CI: 10.9, 12.0) than in the 2 years with the lowest incidence. A similar pattern was observed in most prespecified subgroups, but only the differences for persons who were not white were significant. In 2006, 56% of incident cases had a glycated hemoglobin (hemoglobin A1c) test as one of the pair of events identifying diabetes. By 2011, that number was 74%. In conclusion, overall diabetes incidence in this population did not significantly increase between 2006 and 2010, but increases in hemoglobin A1c testing may have contributed to rising diabetes incidence among nonwhites in 2011.

Intensification of antihyperglycemic therapy among patients with incident diabetes: a Surveillance Prevention and Management of Diabetes Mellitus (SUPREME-DM) study.

PURPOSE: Antihyperglycemic medication intensification practices among patients with incident diabetes are incompletely understood. We characterized the first intensification the year after oral antihyperglycemic medication initiation among incident diabetes patients. METHODS: This retrospective cohort study across 11 US health systems included adults identified with incident diabetes between 2005 and 2009 who started oral antihyperglycemic monotherapy or combination therapy within 6 months after diabetes identification. We determined intensification, defined as increased index medication dosage, addition of another oral medication, or switch to/addition of insulin 31-365 days after initial antihyperglycemic dispensing. Cox regression was used to assess intensification for patient, temporal, and system covariates, adjusting for glycosylated hemoglobin (HbA1c) as a time-dependent variable. RESULTS: Among 41,233 patients, 33.5% and 45.3% had treatment intensified within 6 and 12 months, respectively. This first intensification was most often with increased index medication dosage (78%), least often with insulin (<1%). HbA1c% was strongly associated with intensification (adjusted hazard ratios [HR] 1.59, 3.62, 4.44, and 5.52 for HbA1c 6.5% to <7%, 7% to <7.5%, 7.5 to <8%, and ≥8%, respectively, all P < 0.001, compared with HbA1c < 6.5%). In patients initially on monotherapy, age modified the HbA1c effect: at HbA1c < 7%, the HR differed little between middle-aged and older patients; at HbA1c ≥ 7%, the HR decreased with older age (e.g., age 40-49 years and HbA1c ≥ 8%: HR 8.14; age ≥ 80 years and HbA1c ≥ 8%: HR 4.44; compared with age ≥ 80 years and HbA1c < 6.5%). Within 1 year, 84.3% achieved HbA1c < 8%; 65.1% achieved HbA1c < 7%. CONCLUSIONS: Clinicians appear to be applying treatment intensification guidelines and individualizing therapy by considering patient age, achieving glycemic control among most incident diabetes patients.

Prescription medication burden in patients with newly diagnosed diabetes: a SUrveillance, PREvention, and ManagEment of Diabetes Mellitus (SUPREME-DM) study.

OBJECTIVE: To understand the burden of medication use for patients with newly diagnosed diabetes both before and after diabetes diagnosis and to identify subpopulations of patients with newly diagnosed diabetes who face a relatively high drug burden. DESIGN: Retrospective cohort study. SETTING: 11 integrated health systems in the United States. PARTICIPANTS: 196,654 insured adults 20 years of age or older newly diagnosed with type 1 or type 2 diabetes from January 2005 through December 2009. MAIN OUTCOME MEASURES: Number of unique therapeutic classes of drugs dispensed in the 12 months before and 12 months after diagnosis of diabetes in five categories: overall, antihypertensive agents, antihyperlipidemic agents, mental health agents, and antihyperglycemic agents (in the postdiagnosis period only). RESULTS: The mean number of drug classes used by newly diagnosed patients with diabetes is high before diagnosis (5.0) and increases significantly afterward (6.6). Of this increase, 81% is due to antihyperglycemic initiation and increased use of medications to control hypertension and lipid levels. Multivariate analyses showed that overall drug burden after diabetes diagnosis was higher in women, older, white, and obese patients, as well as among those with higher glycosylated hemoglobin concentrations and comorbidity levels (significant for all comparisons). The overall number of drug classes used by newly diagnosed patients with diabetes after diagnosis decreased slightly but significantly between 2005 and 2009. CONCLUSION: Patients newly diagnosed with diabetes face a substantially increased burden of medications used to control diabetes and other comorbidities. This study shows an increased focus on cardiovascular disease risk factor control after diagnosis of diabetes. However, total drug burden may be slightly decreasing over time.

Initial antihyperglycemic drug therapy among 241 327 adults with newly identified diabetes from 2005 through 2010: a surveillance, prevention, and management of diabetes mellitus (SUPREME-DM) study.

BACKGROUND: Among adults with incident diabetes, data are lacking about first antihyperglycemic initiation and whether medication choice aligns with recommendations. OBJECTIVE: To identify predictors of initiating any antihyperglycemic, and specifically sulfonylurea versus metformin. METHODS: This retrospective cohort study included 241 327 patients from 11 US health systems, 2005 through 2010. Assessments included antihyperglycemic initiation within 6 months of diabetes identification, first medication initiated, and initiation predictors. RESULTS: Only 40.3% (n = 97 350) started any antihyperglycemic; 75.2% (n = 73 221) started metformin. Glycosylated hemoglobin (HbA1c) predicted initiating any antihyperglycemic (HbA1c >9%, relative risk [RR] = 3.94, 95% CI = 3.82, 4.07, vs HbA1c >6.5%-7%). Age modified the HbA1c effect: at higher HbA1c, likelihood of starting antihyperglycemics differed little across ages; at lower HbA1c, older patients were less likely to start antihyperglycemics (P < .001). Individuals with elevated serum creatinine (SCr) were more likely to started on sulfonylurea (SCr = 1.4-2, RR = 2.21 [2.05, 2.39]; SCr >2, RR = 2.75 [2.30, 3.29] vs normal SCr), particularly as HbA1c increased: patients with HbA1c 8%-9% and SCr >2 were 5.59 times (2.94, 10.65) more likely to start sulfonylurea versus those with HbA1c >6.5%-7% and normal SCr. Age predicted sulfonylurea initiation (20-39 years, RR = 0.87 [0.79, 0.95]; ≥ 80 years, RR = 2.41 [2.20, 2.65] vs 50-59 years). CONCLUSIONS: Among adults with incident diabetes, metformin was generally the first antihyperglycemic initiated. However, 59.7% did not start any antihyperglycemic at diabetes identification. HbA1c and age predict antihyperglycemic initiation; SCr and age predict sulfonylurea initiation.

Racial and Ethnic Equity in Care for Hypertension and Diabetes in an Urban Indian Health Organization.

Approximately 70% of American Indian/Alaska Native (AI/AN) individuals reside in urban areas. Urban Indian Health Organizations (UIHOs) provide culturally engaged primary care for AI/AN patients and members of other racial and ethnic groups who have experienced disparities in diabetes and hypertension care, and are commonly affected by social and economic barriers to care. We assessed whether disparities were present between the racial and ethnic groups served by the largest UIHO in the USA. We developed retrospective cohorts of patients with hypertension or diabetes receiving primary care from this UIHO, measuring differences between AI/AN, Spanish-preferring Latinx, English-preferring Latinx, Black, and White patients in mean systolic blood pressure (SBP) and mean hemoglobin A1c (A1c) as primary outcomes. To assess processes of care, we also compared visit intensity, missed visits, and medication treatment intensity in regression models adjusted for sociodemographic and clinical characteristics. For hypertension (n = 2148), adjusted mean SBP ranged from 135.8 mm Hg among Whites to 141.3 mm Hg among Blacks (p = 0.06). For diabetes (n = 1211), adjusted A1c ranged from 7.7% among English-preferring Latinx to 8.7% among Blacks (p = 0.38). Care processes for both hypertension and diabetes varied across groups. No group consistently received lower-quality care. This UIHO provided care of comparable quality for hypertension and diabetes among urban-dwelling AI/ANs and members of other racial, ethnic, and language preference groups. Systematic assessments of care quality in UIHOs may help demonstrate the importance of their role in providing care and improve the quality of care.

Effects of metformin and intensive lifestyle interventions on the incidence of kidney disease in adults in the DPP/DPPOS.

AIMS: We analyzed the incidence of kidney disease in the Diabetes Prevention Program Outcomes Study (DPPOS) by originally randomized treatment group assignment: Intensive Lifestyle (ILS), Metformin (MET) or Placebo (PLB). METHODS: The current analyses used a time-to-event approach in which the primary outcome was kidney disease, ascertained as urine albumin-to-creatinine ratio (ACR) ≥ 3.39 mg/mmol (30 mg/g) or eGFR <45 mL/min/1.73m2, with confirmation required at the next visit, or adjudicated end-stage kidney disease (ESKD). RESULTS: At a median of 21 years following randomization in DPP, diabetes development was reduced in both the ILS (HR 0.73 [95%CI = 0.62, 0.85]) and MET groups (HR 0.85 [0.73, 0.99]) compared to the PLB group. Although risk for developing the primary kidney disease outcome was higher among those with incident diabetes compared to those without (HR 1.81 [1.43, 2.30]), it did not differ by intervention groups (ILS vs. PLB 1.02 (0.81, 1.29); MET vs. PLB 1.08 (0.86, 1.35). There was a non-significant metformin by age interaction (p = 0.057), with metformin being beneficial for kidney disease in the younger but potentially harmful in the older participants. CONCLUSIONS: Development of kidney disease was increased in participants who developed diabetes but did not differ by original treatment group assignment. CLINICAL TRIAL REGISTRATIONS: Diabetes Prevention Program (DPP) Clinical trial reg. no. NCT00004992 DPP Outcomes Study (DPPOS) Clinical trial reg. no. NCT0038727.

Characteristics of patients with primary non-adherence to medications for hypertension, diabetes, and lipid disorders.

BACKGROUND: Information comparing characteristics of patients who do and do not pick up their prescriptions is sparse, in part because adherence measured using pharmacy claims databases does not include information on patients who never pick up their first prescription, that is, patients with primary non-adherence. Electronic health record medication order entry enhances the potential to identify patients with primary non-adherence, and in organizations with medication order entry and pharmacy information systems, orders can be linked to dispensings to identify primarily non-adherent patients. OBJECTIVE: This study aims to use database information from an integrated system to compare patient, prescriber, and payment characteristics of patients with primary non-adherence and patients with ongoing dispensings of newly initiated medications for hypertension, diabetes, and/or hyperlipidemia. DESIGN: This is a retrospective observational cohort study. PARTICIPANTS (OR PATIENTS OR SUBJECTS): Participants of this study include patients with a newly initiated order for an antihypertensive, antidiabetic, and/or antihyperlipidemic within an 18-month period. MAIN MEASURES: Proportion of patients with primary non-adherence overall and by therapeutic class subgroup. Multivariable logistic regression modeling was used to investigate characteristics associated with primary non-adherence relative to ongoing dispensings. KEY RESULTS: The proportion of primarily non-adherent patients varied by therapeutic class, including 7% of patients ordered an antihypertensive, 11% ordered an antidiabetic, 13% ordered an antihyperlipidemic, and 5% ordered medications from more than one of these therapeutic classes within the study period. Characteristics of patients with primary non-adherence varied across therapeutic classes, but these characteristics had poor ability to explain or predict primary non-adherence (models c-statistics = 0.61-0.63). CONCLUSIONS: Primary non-adherence varies by therapeutic class. Healthcare delivery systems should pursue linking medication orders with dispensings to identify primarily non-adherent patients. We encourage conduct of research to determine interventions successful at decreasing primary non-adherence, as characteristics available from databases provide little assistance in predicting primary non-adherence.

Risk Factors for the Development of Retinopathy in Prediabetes and Type 2 Diabetes: The Diabetes Prevention Program Experience.

OBJECTIVE: To determine glycemic and nonglycemic risk factors that contribute to the presence of diabetic retinopathy (DR) before and after the onset of type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS: During the Diabetes Prevention Program (DPP) and DPP Outcome Study (DPPOS), we performed fundus photography over time in adults at high risk for developing T2D, including after they developed diabetes. Fundus photographs were graded using the Early Treatment Diabetic Retinopathy Study (ETDRS) grading system, with DR defined as typical lesions of DR (microaneurysms, exudates, hemorrhage, or worse) in either eye. RESULTS: By DPPOS year 16 (∼20 years after random assignment into DPP), 24% of 1,614 participants who had developed T2D and 14% of 885 who remained without diabetes had DR. In univariate analyses, using results from across the entire duration of follow-up, American Indian race was associated with less frequent DR compared with non-Hispanic White (NHW) race, and higher HbA1c, fasting and 2-h plasma glucose levels during an oral glucose tolerance test, weight, and history of hypertension, dyslipidemia, and smoking, but not treatment group assignment, were associated with more frequent DR. On multivariate analysis, American Indian race was associated with less DR compared with NHW (odds ratio [OR] 0.36, 95% CI 0.20-0.66), and average HbA1c was associated with more DR (OR 1.92, 95% CI 1.46-1.74 per SD [0.7%] increase in HbA1c). CONCLUSIONS: DR may occur in adults with prediabetes and early in the course of T2D. HbA1c was an important risk factor for the development of DR across the entire glycemic range from prediabetes to T2D.

Importance of including early nonadherence in estimations of medication adherence.

BACKGROUND: Many medication adherence metrics are based on refill rates determined from pharmacy claims databases. However, these methods do not incorporate assessment of nonadherence to new prescriptions when those prescriptions are never dispensed (primary nonadherence), or dispensed only once (early nonpersistence). As a result, published studies may overestimate adherence, but the extent of overestimation posed by not considering patients with primary nonadherence and early nonpersistence has not been assessed. OBJECTIVE: To estimate the magnitude of misestimation in adherence estimates that results from not including patients with primary nonadherence and early nonpersistence. METHODS: We conducted a retrospective cohort study of 15,417 patients enrolled in an integrated health care delivery system who were newly prescribed an antihypertensive, antidiabetic, or antihyperlipidemic medication. We linked prescription orders to medication dispensings. Based on dispensing and refill rates, we stratified patients into primary nonadherent, early nonpersistent, and ongoing dispensings groups. Adherence was estimated using the proportion of days covered (PDC). Standardized observation periods were applied across all groups. RESULTS: A total of 1142 (7.4%) patients were primarily nonadherent, 3356 (21.8%) demonstrated early nonpersistence, and 10,919 (70.8%) patients received ongoing dispensings, with a mean PDC of 84%. Not including primarily nonadherent and early nonpersistent patients in calculations resulted in adherence estimates overestimated by 9-18%. CONCLUSIONS: When medication adherence is estimated from pharmacy claims databases, adherence estimates are substantially inflated because primarily nonadherent and early nonpersistent patients are not included in the estimations. An implication of this incorrect estimation is potential distortion of the true relationship between medication adherence and clinical outcomes.