Associations between Major Health Behaviors and Sleep Problems: Results from the 2015, 2016, 2017 Canadian Community Health Survey.

BACKGROUND AND OBJECTIVES: Key health behaviors including cigarette smoking, alcohol consumption, fruit and vegetable (FV) consumption, and physical activity have been associated with sleep-related problems. This cross-sectional study describes sleep quality and duration by gender in a large adult population and examines whether health behavioral factors are associated with short/long sleep duration and sleep problems (difficulty initiating/maintaining sleep [DIMS], daytime sleepiness, and finding sleep refreshing). METHODS: Using Canadian Community Health Survey data from cycles 2015, 2016, and 2017, binary and multinomial logistic regression models were computed. RESULTS: Of the 44,911 respondents included, only half of respondents met the recommended sleep duration. Fifty-five percent of females and forty-one percent of males reported DIMS. Binge drinking was associated with increased DIMS, with the strongest relationship being among females reporting weekly binge drinking (odds ratio (OR) 2.03 [1.59,2.60]). Binge drinking was also associated with decreased odds of finding sleep refreshing among females only (OR 0.73 [0.56,0.96] in weekly binge drinkers). Compared to respondents who had never smoked, daily smokers had higher odds of short sleep (OR 1.50 [1.30,1.74] and OR 1.39 [1.21,1.60]; females and males, respectively). Similarly, former smokers had higher odds of DIMS (OR 1.18 [1.06,1.31]) and not finding sleep refreshing (OR 0.85 [0.77,0.95]), among females; similar OR among males. Increased FV consumption was associated with increased odds of finding sleep refreshing (OR 1.05 [1.03,1.07] and OR 1.06 [1.04,1.08] in females and males, respectively). CONCLUSION: There is a high prevalence of sleep problems among Canadians. Potential gender differences in the relationships between health behaviors and sleep problems warrant further research.

Generative modeling of biological shapes and images using a probabilistic α-shape sampler.

Understanding morphological variation is an important task in many areas of computational biology. Recent studies have focused on developing computational tools for the task of sub-image selection which aims at identifying structural features that best describe the variation between classes of shapes. A major part in assessing the utility of these approaches is to demonstrate their performance on both simulated and real datasets. However, when creating a model for shape statistics, real data can be difficult to access and the sample sizes for these data are often small due to them being expensive to collect. Meanwhile, the current landscape of generative models for shapes has been mostly limited to approaches that use black-box inference-making it difficult to systematically assess the power and calibration of sub-image models. In this paper, we introduce the α-shape sampler: a probabilistic framework for generating realistic 2D and 3D shapes based on probability distributions which can be learned from real data. We demonstrate our framework using proof-of-concept examples and in two real applications in biology where we generate (i) 2D images of healthy and septic neutrophils and (ii) 3D computed tomography (CT) scans of primate mandibular molars. The α-shape sampler R package is open-source and can be downloaded at https://github.com/lcrawlab/ashapesampler.

Statistical Approaches for Establishing Appropriate Immunogenicity Assay Cut Points: Impact of Sample Distribution, Sample Size, and Outlier Removal.

The statistical assessments needed to establish anti-drug antibody (ADA) assay cut points (CPs) can be challenging for bioanalytical scientists. Poorly established CPs that are too high could potentially miss treatment emergent ADA or, when set too low, result in detection of responses that may have no clinical relevance. We evaluated 16 validation CP datasets generated with ADA assays at Regeneron's bioanalytical laboratory and compared results obtained from different CP calculation tools. We systematically evaluated the impact of various factors on CP determination including biological and analytical variability, number of samples for capturing biological variability, outlier removal methods, and the use of parametric vs. non-parametric CP determination. In every study, biological factors were the major component of assay response variability, far outweighing the contribution from analytical variability. Non-parametric CP estimations resulted in screening positivity in drug-naïve samples closer to the targeted rate (5%) and were less impacted by skewness. Outlier removal using the boxplot method with an interquartile range (IQR) factor of 3.0 resulted in screening positivity close to the 5% targeted rate when applied to entire drug-naïve dataset. In silico analysis of CPs calculated using different sample sizes showed that using larger numbers of individuals resulted in CP estimates closer to the CP of the entire population, indicating a larger sample size (~ 150) for CP determination better represents the diversity of the study population. Finally, simpler CP calculations, such as the boxplot method performed in Excel, resulted in CPs similar to those determined using complex methods, such as random-effects ANOVA.

Neglected Tropical Diseases in the Context of Climate Change in East Africa: A Systematic Scoping Review.

East Africa is highly affected by neglected tropical diseases (NTDs), which are projected to be exacerbated by climate change. Consequently, understanding what research has been conducted and what knowledge gaps remain regarding NTDs and climate change is crucial to informing public health interventions and climate change adaptation. We conducted a systematic scoping review to describe the extent, range, and nature of publications examining relationships between NTDs and climatic factors in East Africa. We collated all relevant English and French publications indexed in PubMed®, Web of Science™ Core Collection, and CAB Direct© databases published prior to 2019. Ninety-six publications were included for review. Kenya, Tanzania, and Ethiopia had high rates of publication, whereas countries in the Western Indian Ocean region were underrepresented. Most publications focused on schistosomiasis (n = 28, 29.2%), soil-transmitted helminthiases (n = 16, 16.7%), or human African trypanosomiasis (n = 14, 14.6%). Precipitation (n = 91, 94.8%) and temperature (n = 54, 56.3%) were frequently investigated climatic factors, whereas consideration of droughts (n = 10, 10.4%) and floods (n = 4, 4.2%) was not prominent. Publications reporting on associations between NTDs and changing climate were increasing over time. There was a decrease in the reporting of Indigenous identity and age factors over time. Overall, there were substantial knowledge gaps for several countries and for many NTDs. To better understand NTDs in the context of a changing climate, it would be helpful to increase research on underrepresented diseases and regions, consider demographic and social factors in research, and characterize how these factors modify the effects of climatic variables on NTDs in East Africa.