The National Immunization Survey-Child (NIS-Child) provides annual vaccination coverage estimates in the United States for children aged 19 through 35 months, nationally, for each state, and for select local areas and territories. There is a need for vaccination coverage estimates for smaller geographic areas to support local authority planning and identify counties with potentially low vaccination coverage for possible further intervention. We describe small area estimation methods using 2008-2018 NIS-Child data to generate county-level estimates for children up to two years of age born 2007-2011 and 2012-2016. We applied an empirical best linear unbiased prediction method to combine direct estimates of vaccination coverage with model-based prediction using county-level predictors regarding health and demographic characteristics. We review the predictors commonly selected for the small area models and note multiple predictors related to barriers to vaccination.

This study examined the effectiveness of LET's CONNECT (LC), a community mentorship program based on the positive youth development model. Participants were 218 youth (66.5% girls), ages 12 to 15, who reported peer victimization, bullying perpetration, and/or low social connectedness. These youth were randomized to LC or the control group (community resource information). The LC program linked youth to community mentors who connected with youth and facilitated their involvement in social growth activities across a 16-month period. Outcomes were assessed at 6 and 16 months with self-report measures of social and community connectedness, thwarted belongingness, depression, self-esteem, and suicidal ideation and behavior. In intent-to-treat analyses, LC was associated with modest positive effects for social connectedness, self-esteem, and depression. It had no effects on suicidal ideation or behavior. Results suggest that LC has the potential to positively impact the developmental trajectories of youth dealing with the interpersonal challenges of victimization, bullying perpetration, or low social connectedness. LC implementation challenges and directions for further research are also discussed.

Human mobility is a primary driver of infectious disease spread. However, existing data is limited in availability, coverage, granularity, and timeliness. Data-driven forecasts of disease dynamics are crucial for decision-making by health officials and private citizens alike. In this work, we focus on a machine-learned anonymized mobility map (hereon referred to as AMM) aggregated over hundreds of millions of smartphones and evaluate its utility in forecasting epidemics. We factor AMM into a metapopulation model to retrospectively forecast influenza in the USA and Australia. We show that the AMM model performs on-par with those based on commuter surveys, which are sparsely available and expensive. We also compare it with gravity and radiation based models of mobility, and find that the radiation model's performance is quite similar to AMM and commuter flows. Additionally, we demonstrate our model's ability to predict disease spread even across state boundaries. Our work contributes towards developing timely infectious disease forecasting at a global scale using human mobility datasets expanding their applications in the area of infectious disease epidemiology.

This study examined the effectiveness of LET's CONNECT (LC), a community mentorship program for youths who report peer social problems, which is based on a positive youth development framework. Participants were 218 youths (66.5% girls), aged 12 to 15 years, who were recruited from an urban medical emergency department and screened positive for bullying victimization, bullying perpetration, and/or low social connectedness. Youths were randomized to LC (n = 106) or the control condition (n = 112). Six-month outcomes were assessed with self-report measures of youth social connectedness, community connectedness, thwarted belongingness, depression, self-esteem, and suicidal ideation. LC was associated with a significant increase in only one of these outcomes, social connectedness (effect size = 0.4). It was associated consistently with trend-level positive changes for thwarted belongingness (decreased), depression (decreased), community connectedness, and self-esteem (effect sizes = 0.2). There was no effect on suicidal ideation (effect size = 0.0), and although not a primary outcome, eight youths in the LC condition and seven youths in the control condition engaged in suicidal behavior between baseline and follow-up. Although LC effect sizes are consistent with those from previous studies of community mentorship, there were multiple challenges to LC implementation that affected dosage and intervention fidelity, and that may account for the lack of stronger positive effects.

Youth who feel connected to people and institutions in their communities may be buffered from other risk factors in their lives. As a result, increasing connectedness has been recommended as a prevention strategy. In this study, we examined connectedness among 224 youth (ages 12–15), recruited from an urban medical emergency department, who were at elevated risk due to bullying perpetration or victimization, or low social connectedness. Regression analyses examined multiple domains of connectedness (family, school, peer, community) in relation to adjustment. Youth who felt more connected to parents reported lower levels of depressive symptoms, suicidal ideation, non-suicidal self-injury, and conduct problems, higher self-esteem and more adaptive use of free time. Youth who felt more connected to their school reported lower levels of depressive symptoms, suicidal ideation, social anxiety, and sexual activity, as well as higher levels of self-esteem and more adaptive use of free time. Community connectedness was associated with less social anxiety but more sexual activity, and peer connectedness was not related to youth adjustment in this unique sample. Findings suggest that family and school connectedness may buffer youth on a trajectory of risk, and may therefore be important potential targets for early intervention services.

Resilience is an understudied intrapersonal factor that may reduce HIV risk among men who have sex with men (MSM). Multivariable Poisson regression models were used to estimate the prevalence ratio (PR) of sexual risk behaviors, HIV prevalence, and history of sexually transmitted infections (STIs) with resilience scores in a population-based study among 364 black MSM in the Deep South. Participants with higher resilience scores had a lower prevalence of condomless anal sex with casual sexual partners in past 12 months (PR = 0.80, p value = 0.001) and during their last sexual encounter (PR = 0.81; p value = 0.009). Resilience was inversely associated with a lower prevalence of condomless anal sex with main sexual partners, participating in a sex party/orgy and having a STI in the past 12 months. Resilience may have a protective effect on HIV among black MSM, especially in the Deep South, and should be further explored in studies with prospective designs.