Results of studies comparing overall obesity and abdominal adiposity or body fat distribution with risk of mortality have varied considerably. We compared the relative importance and joint association of overall obesity and body fat distribution in predicting risk of mortality. Participants included 5,799 men and 6,429 women aged 30–102 years enrolled in the third National Health and Nutrition Examination Survey who completed a baseline health examination during 1988–1994. During a 12‐year follow‐up (102,172 person‐years), 1,188 men and 925 women died. In multivariable‐adjusted analyses, waist‐to‐thigh ratio (WTR) in both sexes (Ptrend <0.01 for both) and waist‐to‐hip ratio (WHR) in women (Ptrend 0.001) were positively associated with mortality in middle‐aged adults (30–64 years), while BMI and waist circumference (WC) exhibited U‐ or J‐shaped associations. Risk of mortality increased with a higher WHR and WTR among normal weight (BMI 18.5–24.9 kg/m2) and obese (BMI ≥30.0 kg/m2) adults. In older adults (65–102 years), a higher BMI in both sexes (Ptrend <0.05) and WC in men (Ptrend 0.001) were associated with increased survival, while remaining measures of body fat distribution exhibited either no association or an inverse relation with mortality. In conclusion, ratio measures of body fat distribution are strongly and positively associated with mortality and offer additional prognostic information beyond BMI and WC in middle‐aged adults. A higher BMI in both sexes and WC in men were associated with increased survival in older adults, while a higher WHR or WTR either decreased or did not influence risk of death.

Hookah use is becoming a commonly acceptable behavior among adolescents, and risk perception is a significant factor. Presence of hookah lounges are associated with increased hookah use among high school students and should be a target of further regulation.

Periodontitis is a progressive disease of the periodontium with a complex, polymicrobial etiology. Recent Next-Generation Sequencing (NGS) studies of the microbial diversity associated with periodontitis have revealed strong, community-level differences in bacterial assemblages associated with healthy or diseased periodontal sites. In this study, we used NGS approaches to characterize changes in periodontal pocket bacterial diversity after standard periodontal treatment. Despite consistent changes in the abundance of certain taxa in individuals whose condition improved with treatment, post-treatment samples retained the highest similarity to pre-treatment samples from the same individual. Deeper phylogenetic analysis of periodontal pathogen-containing genera Prevotella and Fusobacterium found both unexpected diversity and differential treatment response among species. Our results highlight how understanding interpersonal variability among microbiomes is necessary for determining how polymicrobial diseases respond to treatment and disturbance.

Objective Examine the relationship of family meals to children’s consumption of fruit and vegetables as well as soda and chips. Additionally, to assess the relationship between viewing TV during family meals and children’s diet. Design Cross-sectional study that used a questionnaire completed by parents. Setting Thirteen schools in San Diego, CA. Participants Seven hundred ninety-four children and their parents. Analysis Ordinal regression assessed associations between children’s intake of fruit, vegetables, soda, and chips with family meal frequency and TV viewing during family meals. Results Children who consumed breakfast, lunch, or dinner with their family at least 4 days per week ate fruit and vegetables 5 or more times a week 84%, 85%, and 80%, respectively. Of those children who ate breakfast, lunch, or dinner with their family at least 4 days per week, 40%, 44%, and 43%consumed soda and chips 5 or more times a week, respectively. Children who ate breakfast with their families at least 4 times a week were more likely to consume fruit and vegetables, and children whose TV was never or rarely on during family meals were less likely to consume soda and chips (P 0.04 and P < 0.001, respectively). Conclusions Interventions geared at increasing the frequency of eating breakfast as a family and decreasing the amount of TV watched during family meals are needed, especially among acculturating Latino families.

BackgroundExisting influenza surveillance in the United States is focused on the collection of data from sentinel physicians and hospitals; however, the compilation and distribution of reports are usually delayed by up to 2 weeks. With the popularity of social media growing, the Internet is a source for syndromic surveillance due to the availability of large amounts of data. In this study, tweets, or posts of 140 characters or less, from the website Twitter were collected and analyzed for their potential as surveillance for seasonal influenza.ObjectiveThere were three aims: (1) to improve the correlation of tweets to sentinel-provided influenza-like illness (ILI) rates by city through filtering and a machine-learning classifier, (2) to observe correlations of tweets for emergency department ILI rates by city, and (3) to explore correlations for tweets to laboratory-confirmed influenza cases in San Diego.MethodsTweets containing the keyword “flu” were collected within a 17-mile radius from 11 US cities selected for population and availability of ILI data. At the end of the collection period, 159,802 tweets were used for correlation analyses with sentinel-provided ILI and emergency department ILI rates as reported by the corresponding city or county health department. Two separate methods were used to observe correlations between tweets and ILI rates: filtering the tweets by type (non-retweets, retweets, tweets with a URL, tweets without a URL), and the use of a machine-learning classifier that determined whether a tweet was “valid”, or from a user who was likely ill with the flu.ResultsCorrelations varied by city but general trends were observed. Non-retweets and tweets without a URL had higher and more significant (P<.05) correlations than retweets and tweets with a URL. Correlations of tweets to emergency department ILI rates were higher than the correlations observed for sentinel-provided ILI for most of the cities. The machine-learning classifier yielded the highest correlations for many of the cities when using the sentinel-provided or emergency department ILI as well as the number of laboratory-confirmed influenza cases in San Diego. High correlation values (r=.93) with significance at P<.001 were observed for laboratory-confirmed influenza cases for most categories and tweets determined to be valid by the classifier.ConclusionsCompared to tweet analyses in the previous influenza season, this study demonstrated increased accuracy in using Twitter as a supplementary surveillance tool for influenza as better filtering and classification methods yielded higher correlations for the 2013-2014 influenza season than those found for tweets in the previous influenza season, where emergency department ILI rates were better correlated to tweets than sentinel-provided ILI rates. Further investigations in the field would require expansion with regard to the location that the tweets are collected from, as well as the availability of more ILI data.