Implementation and Evaluation of a School-Based Human Papillomavirus Vaccination Program in Rural Kentucky.

INTRODUCTION: Human papillomavirus (HPV) vaccination rates remain marginal across the U.S., including Kentucky, a state recognized for increased HPV-related cancer burden. School-based HPV immunization programs may be a viable approach to improving vaccination initiation and completion rates among youth. Therefore, the purpose of this study was to design, implement, and evaluate a school-based HPV vaccination program conducted in rural south-central Kentucky. METHODS: Guided by evidence-based approaches to increasing immunization rates, the practical expertise of school nursing staff, and a detailed study protocol, academic and health department-based investigators implemented an HPV vaccination project in two high schools during the 2012-2013 academic year; data were analyzed in 2013-2014. Rates of returned parental consent forms, parental consent/declination, and HPV vaccination rates were documented. RESULTS: At the beginning of the school year, all 935 students at the two schools were given HPV vaccination parental consent forms. Five hundred eleven students returned consent forms (55% return rate), and 447 of these students were HPV vaccine naïve (87%). Of these students, 315 (70%) initiated the vaccine series, with 276 (62%) completing the entire three-dose series, so that 88% of students initiating the vaccine series successfully completed the series. In estimating rates for the entire school body, 45% of students had received all three doses by the end of the project. CONCLUSIONS: Despite study design limitations, results of this project provide further evidence about school-based immunization programs as an effective strategy for improving HPV vaccination rates among Kentucky and U.S. adolescents.

Tolerating the community detection resolution limit with edge weighting.

Communities of vertices within a giant network such as the World Wide Web are likely to be vastly smaller than the network itself. However, Fortunato and Barthélemy have proved that modularity maximization algorithms for community detection may fail to resolve communities with fewer than √L/2 edges, where L is the number of edges in the entire network. This resolution limit leads modularity maximization algorithms to have notoriously poor accuracy on many real networks. Fortunato and Barthélemy's argument can be extended to networks with weighted edges as well, and we derive this corollary argument. We conclude that weighted modularity algorithms may fail to resolve communities with less than √Wε/2 total edge weight, where W is the total edge weight in the network and ε is the maximum weight of an intercommunity edge. If ε is small, then small communities can be resolved. Given a weighted or unweighted network, we describe how to derive new edge weights in order to achieve a low ε, we modify the Clauset, Newman, and Moore (CNM) community detection algorithm to maximize weighted modularity, and we show that the resulting algorithm has greatly improved accuracy. In experiments with an emerging community standard benchmark, we find that our simple CNM variant is competitive with the most accurate community detection methods yet proposed.