Bostonia: The Boston University Alumni Magazine. Volume 10

Founded in 1900, Bostonia magazine is Boston University's main alumni publication, which covers alumni and student life, as well as university activities, events, and programs

Mental health in UK Biobank: development, implementation and results from an online questionnaire completed by 157 366 participants

Background UK Biobank is a well-characterised cohort of over 500 000 participants that offers unique opportunities to investigate multiple diseases and risk factors. Aims An online mental health questionnaire completed by UK Biobank participants was expected to expand the potential for research into mental disorders. Method An expert working group designed the questionnaire, using established measures where possible, and consulting with a patient group regarding acceptability. Case definitions were defined using operational criteria for lifetime depression, mania, anxiety disorder, psychotic-like experiences and self-harm, as well as current post-traumatic stress and alcohol use disorders. Results 157 366 completed online questionnaires were available by August 2017. Comparison of self-reported diagnosed mental disorder with a contemporary study shows a similar prevalence, despite respondents being of higher average socioeconomic status than the general population across a range of indicators. Thirty-five per cent (55 750) of participants had at least one defined syndrome, of which lifetime depression was the most common at 24% (37 434). There was extensive comorbidity among the syndromes. Mental disorders were associated with high neuroticism score, adverse life events and long-term illness; addiction and bipolar affective disorder in particular were associated with measures of deprivation. Conclusions The questionnaire represents a very large mental health survey in itself, and the results presented here show high face validity, although caution is needed owing to selection bias. Built into UK Biobank, these data intersect with other health data to offer unparalleled potential for crosscutting biomedical research involving mental health

Corn, 2005

William J. Wiebold is a Professor of Plant Sciences and State Extension Specialist; Howard L. Mason is a Senior Research Specialist; Delbert Knerr, Richard W. Hasty, Eddie G. Adams, David M. Schwab, and Scotty L. Smothers are Research Specialists; Travis Belt is a Research Associate in Plant Sciences and Bruce Burdick is the Superintendent of the Hundley-Whaley Research Center.Compares hybrids and includes experimental procedures, seed corn characteristics and seed corn company addresses

A Pilot Study Identifying Statin Nonadherence With Visit-to-Visit Variability of Low-Density Lipoprotein Cholesterol

Nonadherence to cardiovascular medications such as statins is a common, important problem. Clinicians currently rely on intuition to identify medication nonadherence. The visit-to-visit variability (VVV) of low-density lipoprotein (LDL) cholesterol might represent an opportunity to identify statin nonadherence with greater accuracy. We examined the clinical and pharmacy data from 782 members of the Boston Medical Center Health Plan, seen at either the Boston Medical Center or its affiliated community health centers, who were taking statins and had ≥3 LDL cholesterol measurements from 2008 to 2011. The LDL cholesterol VVV (defined by the within-patient SD) was categorized into quintiles. Multivariate logistic regression models were generated with statin nonadherence (defined by the standard 80% pharmacy refill-based medication possession ratio threshold) as the dependent variable. The proportion of statin nonadherence increased across the quintiles of LDL cholesterol VVV (64.3%, 71.2%, 89.2%, 92.3%, 91.7%). Higher quintiles of LDL cholesterol VVV had a strong positive association with statin nonadherence, with an adjusted odds ratio of 3.4 (95% confidence interval 1.7 to 7.1) in the highest versus lowest quintile of LDL cholesterol VVV. The age- and gender-adjusted model had poor discrimination (C-statistic 0.62, 95% confidence interval 0.57 to 0.67), but the final adjusted model (age, gender, race, mean LDL cholesterol) demonstrated good discrimination (C-statistic 0.75, 95% confidence interval 0.71 to 0.79) between the adherent and nonadherent patients. In conclusion, the VVV of LDL cholesterol demonstrated a strong association with statin nonadherence in a clinic setting. Furthermore, a VVV of LDL cholesterol-based model had good discrimination characteristics for statin nonadherence. Research is needed to validate and generalize these findings to other populations and biomarkers