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

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

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

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

Changes in antidepressant use by young people and suicidal behavior after FDA warnings and media coverage: quasi-experimental study

Objective To investigate if the widely publicized warnings in 2003 from the US Food and Drug Administration about a possible increased risk of suicidality with antidepressant use in young people were associated with changes in antidepressant use, suicide attempts, and completed suicides among young people. Design Quasi-experimental study assessing changes in outcomes after the warnings, controlling for pre-existing trends. Setting Automated healthcare claims data (2000-10) derived from the virtual data warehouse of 11 health plans in the US Mental Health Research Network. Participants Study cohorts included adolescents (around 1.1 million), young adults (around 1.4 million), and adults (around 5 million). Main outcome measures Rates of antidepressant dispensings, psychotropic drug poisonings (a validated proxy for suicide attempts), and completed suicides. Results Trends in antidepressant use and poisonings changed abruptly after the warnings. In the second year after the warnings, relative changes in antidepressant use were −31.0% (95% confidence interval −33.0% to −29.0%) among adolescents, −24.3% (−25.4% to −23.2%) among young adults, and −14.5% (−16.0% to −12.9%) among adults. These reflected absolute reductions of 696, 1216, and 1621 dispensings per 100 000 people among adolescents, young adults, and adults, respectively. Simultaneously, there were significant, relative increases in psychotropic drug poisonings in adolescents (21.7%, 95% confidence interval 4.9% to 38.5%) and young adults (33.7%, 26.9% to 40.4%) but not among adults (5.2%, −6.5% to 16.9%). These reflected absolute increases of 2 and 4 poisonings per 100 000 people among adolescents and young adults, respectively (approximately 77 additional poisonings in our cohort of 2.5 million young people). Completed suicides did not change for any age group. Conclusions Safety warnings about antidepressants and widespread media coverage decreased antidepressant use, and there were simultaneous increases in suicide attempts among young people. It is essential to monitor and reduce possible unintended consequences of FDA warnings and media reporting

Locus of control, quality and outcomes of care among managed care patients with diabetes in Hawaiʻi

Thesis (Ph. D.)--University of Hawaii at Manoa, 2004.Includes bibliographical references (leaves 172-201).Also available by subscription via World Wide Webxii, 201 leaves, bound ill. 29 c

Effect of generic-only drug benefits on seniors' medication use and financial burden.

BackgroundGeneric-only drug benefits are a way to provide some coverage, as opposed to no coverage.ObjectiveTo examine how switching from brand name to generic-only drug coverage affected seniors' medication use and financial burden.Study designData are from a 2002 cross-sectional survey conducted for a separate study on benefit caps. Participants belonged to a Medicare managed care plan in one state and had $2000 capped brand name benefits in 2001 but generic-only benefits in 2002.MethodsParticipants reported their cost-cutting strategies before and after the change to generic-only coverage, the medications affected, and their financial burden. We conducted bivariate and multivariate analyses of cost-cutting strategies and financial burden before and after implementation of generic-only benefits.ResultsAmong 611 participants (63% response), rates of switching medications increased after discontinuation of brand name coverage (27% vs 8%, P < .001). Switches were from brand name drugs to generic equivalents (14%) (eg, Prozac to fluoxetine hydrochloride), to nonequivalent generics (26%) (eg, Paxil to fluoxetine), and to different brand name drugs (45%) (eg, Paxil to Zoloft). Ninety percent of brand name switches remained in the same therapeutic class (eg, selective serotonin reuptake inhibitors). After discontinuation of brand name coverage, participants reported higher rates of decreased medication use (28% vs 17%) and greater difficulty paying for prescriptions (65% vs 37%) (P < .001 for both).ConclusionsChanging from brand name benefits to generic-only drug coverage led many participants to switch to less expensive medications but also decreased medication use and increased financial burden. Insurers need to actively help patients adjust to a discontinuation of brand name coverage

Effect of generic-only drug benefits on seniors' medication use and financial burden.

BACKGROUND: Generic-only drug benefits are a way to provide some coverage, as opposed to no coverage. OBJECTIVE: To examine how switching from brand name to generic-only drug coverage affected seniors' medication use and financial burden. STUDY DESIGN: Data are from a 2002 cross-sectional survey conducted for a separate study on benefit caps. Participants belonged to a Medicare managed care plan in one state and had $2000 capped brand name benefits in 2001 but generic-only benefits in 2002. METHODS: Participants reported their cost-cutting strategies before and after the change to generic-only coverage, the medications affected, and their financial burden. We conducted bivariate and multivariate analyses of cost-cutting strategies and financial burden before and after implementation of generic-only benefits. RESULTS: Among 611 participants (63% response), rates of switching medications increased after discontinuation of brand name coverage (27% vs 8%, P < .001). Switches were from brand name drugs to generic equivalents (14%) (eg, Prozac to fluoxetine hydrochloride), to nonequivalent generics (26%) (eg, Paxil to fluoxetine), and to different brand name drugs (45%) (eg, Paxil to Zoloft). Ninety percent of brand name switches remained in the same therapeutic class (eg, selective serotonin reuptake inhibitors). After discontinuation of brand name coverage, participants reported higher rates of decreased medication use (28% vs 17%) and greater difficulty paying for prescriptions (65% vs 37%) (P < .001 for both). CONCLUSIONS: Changing from brand name benefits to generic-only drug coverage led many participants to switch to less expensive medications but also decreased medication use and increased financial burden. Insurers need to actively help patients adjust to a discontinuation of brand name coverage

Adherence to common cardiovascular medications in patients with schizophrenia vs. patients without psychiatric illness

OBJECTIVE: The purpose of the study was to examine whether individuals with diagnoses of schizophrenia were differentially adherent to their statin or angiotensin-converting enzyme inhibitor/angiotensin receptor blocker (ACEI/ARB) medications compared to individuals without psychiatric illness. METHOD: Using electronic medical record data across 13 Mental Health Research Network sites, individuals with diagnoses of schizophrenia or schizoaffective disorder receiving two or more medication dispensings of a statin or an ACEI/ARB in 2011 (N=710) were identified and matched on age, sex and Medicare status to controls with no documented mental illness and two or more medication dispensings of a statin in 2011 (N=710). Medication adherence, and sociodemographic and clinical characteristics of the study population were assessed. RESULTS: Multivariable models indicated that having a schizophrenia diagnosis was associated with increased odds of statin medication adherence; the odds ratio suggested a small effect. After adjustment for medication regimen, schizophrenia no longer showed an association with statin adherence. Having a schizophrenia diagnosis was not associated with ACEI/ARB medication adherence. CONCLUSIONS: Compared to patients without any psychiatric illness, individuals with schizophrenia were marginally more likely to be adherent to their statin medications. Given that patterns of adherence to cardioprotective medications may be different from patterns of adherence to antipsychotic medications, improving adherence to the former may require unique intervention strategies