Metabolic Syndrome and Early-Onset Coronary Artery Disease Is the Whole Greater Than Its Parts?

ObjectivesWe sought to examine the association between the metabolic syndrome (MetS) (defined both by the 2001 National Cholesterol Educational Program Adult Treatment Panel III [ATP-III] definition and the American Heart Association/National Heart, Lung and Blood Institute [AHA/NHLBI] revision incorporating the lower threshold for impaired fasting glucose [IFG]) and early-onset coronary artery disease (CAD).BackgroundThe impact of MetS on premature CAD has not been studied extensively. Lowering the threshold to define the IFG component (from 110 to 100 mg/dl) and the value of the syndrome as a whole versus its individual components are subjects of intense debate.MethodsWe performed a case-control study with 393 early-onset CAD subjects (acute myocardial infarction, angina with ≥50% stenosis, or coronary revascularization) in men under age 46 years or women under age 56 years and 393 control subjects individually matched for gender, age, and race/ethnicity.ResultsBy conditional logistic regression, presence of ATP-III MetS without diabetes (adjusted odds ratio [adj-OR] 4.9; 95% confidence interval [CI] 3.4 to 8.0) and with diabetes (adj-OR 8.0, 95% CI 4.39 to 14.6) was a strong independent determinant of early-onset CAD. Using the AHA/NHLBI revision, these ORs became slightly stronger. However, neither definition of MetS remained significantly associated with early-onset CAD in multivariate models adjusting for individual components.ConclusionsThe presence of MetS imparts a high risk of early-onset clinical CAD, but the prognostic information associated with the syndrome is not greater than the sum of its parts

Implementation of adolescent family-based substance use prevention programs in health care settings: Comparisons across conditions and programs

The majority of knowledge related to implementation of family-based substance use prevention programs is based on programs delivered in school and community settings. The aim of this study is to examine procedures related to implementation effectiveness and quality of two family-based universal substance use prevention programs delivered in health care settings, the Strengthening Families Program: For Parents and Youth 10–14 (SFP) and Family Matters (FM). These evidence-based programs were delivered as part of a larger random control intervention study designed to assess the influence of program choice vs. assignment on study participation and adolescent substance use outcomes. We also assess the effects of program choice (vs. assignment to program) on program delivery

Code-based Diagnostic Algorithms for Idiopathic Pulmonary Fibrosis. Case Validation and Improvement

RationalePopulation-based studies of idiopathic pulmonary fibrosis (IPF) in the United States have been limited by reliance on diagnostic code-based algorithms that lack clinical validation.ObjectivesTo validate a well-accepted International Classification of Diseases, Ninth Revision, code-based algorithm for IPF using patient-level information and to develop a modified algorithm for IPF with enhanced predictive value.MethodsThe traditional IPF algorithm was used to identify potential cases of IPF in the Kaiser Permanente Northern California adult population from 2000 to 2014. Incidence and prevalence were determined overall and by age, sex, and race/ethnicity. A validation subset of cases (n = 150) underwent expert medical record and chest computed tomography review. A modified IPF algorithm was then derived and validated to optimize positive predictive value.ResultsFrom 2000 to 2014, the traditional IPF algorithm identified 2,608 cases among 5,389,627 at-risk adults in the Kaiser Permanente Northern California population. Annual incidence was 6.8/100,000 person-years (95% confidence interval [CI], 6.1-7.7) and was higher in patients with older age, male sex, and white race. The positive predictive value of the IPF algorithm was only 42.2% (95% CI, 30.6 to 54.6%); sensitivity was 55.6% (95% CI, 21.2 to 86.3%). The corrected incidence was estimated at 5.6/100,000 person-years (95% CI, 2.6-10.3). A modified IPF algorithm had improved positive predictive value but reduced sensitivity compared with the traditional algorithm.ConclusionsA well-accepted International Classification of Diseases, Ninth Revision, code-based IPF algorithm performs poorly, falsely classifying many non-IPF cases as IPF and missing a substantial proportion of IPF cases. A modification of the IPF algorithm may be useful for future population-based studies of IPF

Global demand for natural resources eliminated more than 100,000 Bornean orangutans

Unsustainable exploitation of natural resources is increasingly affecting the highly biodiverse tropics. Although rapid developments in remote sensing technology have permitted more precise estimates of land-cover change over large spatial scales, our knowledge about the effects of these changes on wildlife is much more sparse. Here we use field survey data, predictive density distribution modeling, and remote sensing to investigate the impact of resource use and land-use changes on the density distribution of Bornean orangutans (Pongo pygmaeus). Our models indicate that between 1999 and 2015, half of the orangutan population was affected by logging, deforestation, or industrialized plantations. Although land clearance caused the most dramatic rates of decline, it accounted for only a small proportion of the total loss. A much larger number of orangutans were lost in selectively logged and primary forests, where rates of decline were less precipitous, but where far more orangutans are found. This suggests that further drivers, independent of land-use change, contribute to orangutan loss. This finding is consistent with studies reporting hunting as a major cause in orangutan decline. Our predictions of orangutan abundance loss across Borneo suggest that the population decreased by more than 100,000 individuals, corroborating recent estimates of decline. Practical solutions to prevent future orangutan decline can only be realized by addressing its complex causes in a holistic manner across political and societal sectors, such as in land-use planning, resource exploitation, infrastructure development, and education, and by increasing long-term sustainability