Predictors of mortality over 8 years in type 2 diabetic patients: Translating Research Into Action for Diabetes (TRIAD)

OBJECTIVE To examine demographic, socioeconomic, and biological risk factors for all-cause, cardiovascular, and noncardiovascular mortality in patients with type 2 diabetes over 8 years and to construct mortality prediction equations. RESEARCH DESIGN AND METHODS Beginning in 2000, survey and medical record information was obtained from 8,334 participants in Translating Research Into Action for Diabetes (TRIAD), a multicenter prospective observational study of diabetes care in managed care. The National Death Index was searched annually to obtain data on deaths over an 8-year follow-up period (2000–2007). Predictors examined included age, sex, race, education, income, smoking, age at diagnosis of diabetes, duration and treatment of diabetes, BMI, complications, comorbidities, and medication use. RESULTS There were 1,616 (19%) deaths over the 8-year period. In the most parsimonious equation, the predictors of all-cause mortality included older age, male sex, white race, lower income, smoking, insulin treatment, nephropathy, history of dyslipidemia, higher LDL cholesterol, angina/myocardial infarction/other coronary disease/coronary angioplasty/bypass, congestive heart failure, aspirin, β-blocker, and diuretic use, and higher Charlson Index. CONCLUSIONS Risk of death can be predicted in people with type 2 diabetes using simple demographic, socioeconomic, and biological risk factors with fair reliability. Such prediction equations are essential for computer simulation models of diabetes progression and may, with further validation, be useful for patient management

Predictors and Impact of Intensification of Antihyperglycemic Therapy in Type 2 Diabetes: Translating Research into Action for Diabetes (TRIAD)

ObjectiveThe purpose of this study was to examine the predictors of intensification of antihyperglycemic therapy in patients with type 2 diabetes; its impact on A1C, body weight, symptoms of anxiety/depression, and health status; and patient characteristics associated with improvement in A1C.Research design and methodsWe analyzed survey, medical record, and health plan administrative data collected in Translating Research into Action for Diabetes (TRIAD). We examined patients who were using diet/exercise or oral antihyperglycemic medications at baseline, had A1C >7.2%, and stayed with the same therapy or intensified therapy (initiated or increased the number of classes of oral antihyperglycemic medications or began insulin) over 18 months.ResultsOf 1,093 patients, 520 intensified therapy with oral medications or insulin. Patients intensifying therapy were aged 58 +/- 12 years, had diabetes duration of 11 +/- 9 years, and had A1C of 9.1 +/- 1.5%. Younger age and higher A1C were associated with therapy intensification. Compared with patients who did not intensify therapy, those who intensified therapy experienced a 0.49% reduction in A1C (P < 0.0001), a 3-pound increase in weight (P = 0.003), and no change in anxiety/depression (P = 0.5) or health status (P = 0.2). Among those who intensified therapy, improvement in A1C was associated with higher baseline A1C, older age, black race/ethnicity, lower income, and more physician visits.ConclusionsTreatment intensification improved glycemic control with no worsening of anxiety/depression or health status, especially in elderly, lower-income, and minority patients with type 2 diabetes. Interventions are needed to overcome clinical inertia when patients might benefit from treatment intensification and improved glycemic control

Predicting suicide attempts and suicide deaths among adolescents following outpatient visits

BACKGROUND: Few studies report on machine learning models for suicide risk prediction in adolescents and their utility in identifying those in need of further evaluation. This study examined whether a model trained and validated using data from all age groups works as well for adolescents or whether it could be improved. METHODS: We used healthcare data for 1.4 million specialty mental health and primary care outpatient visits among 256,823 adolescents across 7 health systems. The prediction target was 90-day risk of suicide attempt following a visit. We used logistic regression with least absolute shrinkage and selection operator (LASSO) and generalized estimating equations (GEE) to predict risk. We compared performance of three models: an existing model, a recalibrated version of that model, and a newly-learned model. Models were compared using area under the receiver operating curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value. RESULTS: The AUC produced by the existing model for specialty mental health visits estimated in adolescents alone (0.796; [0.789, 0.802]) was not significantly different than the AUC of the recalibrated existing model (0.794; [0.787, 0.80]) or the newly-learned model (0.795; [0.789, 0.801]). Predicted risk following primary care visits was also similar: existing (0.855; [0.844, 0.866]), recalibrated (0.85 [0.839, 0.862]), newly-learned (0.842, [0.829, 0.854]). LIMITATIONS: The models did not incorporate non-healthcare risk factors. The models relied on ICD9-CM codes for diagnoses and outcome measurement. CONCLUSIONS: Prediction models already in operational use by health systems can be reliably employed for identifying adolescents in need of further evaluation

Correlates of depression among people with diabetes: The Translating Research Into Action for Diabetes (TRIAD) study

Aim The broad objective of this study was to examine multiple dimensions of depression in a large, diverse population of adults with diabetes. Specific aims were to measure the association of depression with: (1) patient characteristics(2) outcomesand (3) diabetes-related quality of care. Methods Cross-sectional analyses were performed using survey and chart data from the Translating Research Into Action for Diabetes (TRIAD) study, including 8790 adults with diabetes, enrolled in 10 managed care health plans in 7 states. Depression was measured using the Patient Health Questionnaire (PHQ-8). Patient characteristics, outcomes and quality of care were measured using validated survey items and chart data. Results Nearly 18% of patients had major depression, with prevalence 2-3 times higher among patients with low socioeconomic status. Pain and limited mobility were strongly associated with depression, controlling for other patient characteristics. Depression was associated with slightly worse glycemic control, but not other intermediate clinical outcomes. Depressed patients received slightly fewer recommended diabetes-related processes of care. Conclusions In a large, diverse cohort of patients with diabetes, depression was most prevalent among patients with low socioeconomic status and those with pain, and was associated with slightly worse glycemic control and quality of care

Substance use disorders and risk of suicide in a general US population: a case control study

BACKGROUND: Prior research suggests that substance use disorders (SUDs) are associated with risk of suicide mortality, but most previous work has been conducted among Veterans Health Administration patients. Few studies have examined the relationship between SUDs and suicide mortality in general populations. Our study estimates the association of SUDs with suicide mortality in a general US population of men and women who receive care across eight integrated health systems. METHODS: We conducted a case-control study using electronic health records and claims data from eight integrated health systems of the Mental Health Research Network. Participants were 2674 men and women who died by suicide between 2000-2013 and 267,400 matched controls. The main outcome was suicide mortality, assessed using data from the health systems and confirmed by state death data systems. Demographic and diagnostic data on substance use disorders and other health conditions were obtained from each health system. First, we compared descriptive statistics for cases and controls, including age, gender, income, and education. Next, we compared the rate of each substance use disorder category for cases and controls. Finally, we used conditional logistic regression models to estimate unadjusted and adjusted odds of suicide associated with each substance use disorder category. RESULTS: All categories of substance use disorders were associated with increased risk of suicide mortality. Adjusted odds ratios ranged from 2.0 (CI 1.7, 2.3) for patients with tobacco use disorder only to 11.2 (CI 8.0, 15.6) for patients with multiple alcohol, drug, and tobacco use disorders. Substance use disorders were associated with increased relative risk of suicide for both women and men across all categories, but the relative risk was more pronounced in women. CONCLUSIONS: Substance use disorders are associated with significant risk of suicide mortality, especially for women, even after controlling for other important risk factors. Experiencing multiple substance use disorders is particularly risky. These findings suggest increased suicide risk screening and prevention efforts for individuals with substance use disorders are needed

Educational Disparities in Rates of Smoking Among Diabetic Adults: The Translating Research Into Action for Diabetes Study

Objectives. We assessed educational disparities in smoking rates among adults with diabetes in managed care settings. Methods. We used a cross-sectional, survey-based (2002–2003) observational study among 6538 diabetic patients older than 25 years across multiple managed care health plans and states. For smoking at each level of self-reported educational attainment, predicted probabilities were estimated by means of hierarchical logistic regression models with random intercepts for health plan, adjusted for potential confounders. Results. Overall, 15% the participants reported current smoking. An educational gradient in smoking was observed that varied significantly (P<.003) across age groups, with the educational gradient being strong in those aged 25 to 44 years, modest in those aged 45 to 64 years, and nonexistent in those aged 65 years or older. Of particular note, the prevalence of smoking observed in adults aged 25–44 years with less than a high school education was 50% (95% confidence interval: 36% to 63%). Conclusions. Approximately half of poorly educated young adults with diabetes smoke, magnifying the health risk associated with early-onset diabetes. Targeted public health interventions for smoking prevention and cessation among young, poorly educated people with diabetes are needed

Weighing the Association Between BMI Change and Suicide Mortality

OBJECTIVE: Suicide rates continue to rise, necessitating the identification of risk factors. Obesity and suicide mortality rates have been examined, but associations among weight change, death by suicide, and depression among adults in the United States remain unclear. METHODS: Data from 387 people who died by suicide in 2000-2015 with a recorded body mass index (BMI) in the first and second 6 months preceding their death ( index date ) were extracted from the Mental Health Research Network. Each person was matched with five people in a control group (comprising individuals who did not die by suicide) by age, sex, index year, and health care site (N=1,935). RESULTS: People who died by suicide were predominantly male (71%), White (69%), and middle aged (mean age=57 years) and had a depression diagnosis (55%) and chronic health issues (57%) (corresponding results for the control group: 71% male, 66% White, 14% with depression diagnosis, and 43% with chronic health issues; mean age=56 years). Change in BMI within the year before the index date statistically significantly differed between those who died by suicide (mean change=-0.72±2.42 kg/m(2)) and the control group (mean change=0.06±4.99 kg/m(2)) (p\u3c0.001, Cohen\u27s d=0.17). A one-unit BMI decrease was associated with increased risk for suicide after adjustment for demographic characteristics, mental disorders, and Charlson comorbidity score (adjusted odds ratio=1.11, 95% confidence interval=1.05-1.18, p\u3c0.001). For those without depression, a BMI change was significantly associated with suicide (p\u3c0.001). CONCLUSIONS: An increased suicide mortality rate was associated with weight loss in the year before a suicide after analyses accounted for general and mental health indicators