IntroductionOral health is an integral component of overall health and well-being. Very little Rhode Island state-level information exists on the determinants of tooth loss. The objective of this study was to systematically identify sociodemographic characteristics, health behaviors, health conditions and disabilities, and dental insurance coverage associated with tooth loss among noninstitutionalized adults in Rhode Island.MethodsWe analyzed Rhode Island’s 2008 and 2010 Behavioral Risk Factor Surveillance System survey data in 2011. The survey had 4 response categories for tooth loss: none, 1 to 5, 6 or more but not all, and all. We used multinomial logistic regression models to assess the relationship between 4 risk factor domains and tooth loss.ResultsAn estimated 57.6% of Rhode Island adults had all their teeth, 28.9% had 1 to 5 missing teeth, 8.9% had 6 to 31 missing teeth, and 4.6% were edentulous. Respondents who had low income, low education, unhealthy behaviors (ie, were former or current smokers and did not engage in physical activity), chronic conditions (ie, diabetes and obesity) or disabilities, and no dental insurance coverage were more likely to have fewer teeth compared with their referent groups. However, the association of these variables with tooth loss was not uniform by age group.ConclusionAdults who report risky health behaviors or impaired health may be considered target subpopulations for prevention of tooth loss and promotion of good oral health.

Objectives To evaluate dose-response associations between misperceived weight and 32 health risk behaviors in a nationally representative sample of US adolescents. Methods Participants included 13,864 US high school students in the 2011 Youth Risk Behavior Survey. Comparing the degree of agreement between perceived and reported actual weight, weight misperception was determined as 5 categories. Multivariable-adjusted logistic regression analyses evaluated associations of weight misperception with 32 health risk behaviors. Results Both underestimated and overestimated weight were statistically significantly associated with all 32 health risk behaviors in a dose-response manner after adjustment for age, sex and race/ethnicity, where greater weight misperception was associated with higher engagement in health risk behaviors. Conclusions Understanding potential impacts of weight misperception on health risk behaviors could improve interventions that encourage healthy weight perception and attainment for adolescents.

A single model characterized and quantified 4 patterns of suicide risk among adolescents and identified predictors for 3 at-risk classes. Interventions for high-risk youths may help prevent adolescent suicides.