The Hydrography of Barnstable Harbor, Massachusetts

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109846/1/lno1959440448.pd

Geology of the Great Lakes

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109981/1/lno1959430356.pd

New Approaches to St Stephen's Chapel, Palace of Westminster

Introduction to a group of articles in this edition, on 'One Object', St Stephen's Chape

[Introduction to] The Edge of the South: Life in Nineteenth-Century Virginia

The chapters in this volume explore diverse scenes of nineteenth-century Virginia: the big house and the slave quarters, small farms and battlefields, freed slaves in the country and freed slaves in the city, dark coal mines and brightly illuminated caverns, raucous political rallies and genteel meetings of the United Daughters of the Confederacy. Each essay offers a new perspective on a past which refuses to fit familiar ways of thinking about the nation and the South.https://scholarship.richmond.edu/bookshelf/1251/thumbnail.jp

A Portable Photocell Fluorometer For Dilution Measurements In Natural Waters

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109897/1/lno1961640457.pd

Water Transport Studies In The Straits Of Mackinac Region Of Lake Huron

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109896/1/lno1960510081.pd

Assessing the Online Social Environment for Surveillance of Obesity Prevalence

Background: Understanding the social environmental around obesity has been limited by available data. One promising approach used to bridge similar gaps elsewhere is to use passively generated digital data. Purpose This article explores the relationship between online social environment via web-based social networks and population obesity prevalence. Methods: We performed a cross-sectional study using linear regression and cross validation to measure the relationship and predictive performance of user interests on the online social network Facebook to obesity prevalence in metros across the United States of America (USA) and neighborhoods within New York City (NYC). The outcomes, proportion of obese and/or overweight population in USA metros and NYC neighborhoods, were obtained via the Centers for Disease Control and Prevention Behavioral Risk Factor Surveillance and NYC EpiQuery systems. Predictors were geographically specific proportion of users with activity-related and sedentary-related interests on Facebook. Results: Higher proportion of the population with activity-related interests on Facebook was associated with a significant 12.0% (95% Confidence Interval (CI) 11.9 to 12.1) lower predicted prevalence of obese and/or overweight people across USA metros and 7.2% (95% CI: 6.8 to 7.7) across NYC neighborhoods. Conversely, greater proportion of the population with interest in television was associated with higher prevalence of obese and/or overweight people of 3.9% (95% CI: 3.7 to 4.0) (USA) and 27.5% (95% CI: 27.1 to 27.9, significant) (NYC). For activity-interests and national obesity outcomes, the average root mean square prediction error from 10-fold cross validation was comparable to the average root mean square error of a model developed using the entire data set. Conclusions: Activity-related interests across the USA and sedentary-related interests across NYC were significantly associated with obesity prevalence. Further research is needed to understand how the online social environment relates to health outcomes and how it can be used to identify or target interventions