This study characterizes the epidemiology of nonfatal pediatric firework-related injuries in the United States among children and adolescents by analyzing data from the National Electronic Injury Surveillance System from 1990 through 2014. During this 25-year period, an estimated 136 991 (95% CI = 113 574-160 408) children <20 years old were treated in US emergency departments for firework-related injuries. The annual injury rate decreased significantly by 30.4% during this period. Most of those injured were male (75.7%), mean patient age was 10.6 years, and 7.6% required hospital admission. The hands (30.0%) were the most commonly injured body region, followed by head and neck (22.2%), and eyes (21.5%). Sixty percent of injuries were burns. Injuries were most commonly associated with firecrackers (26.2%), aerial devices (16.3%), and sparklers (14.3%). Consumer fireworks pose a serious injury risk to pediatric users and bystanders, and families should be encouraged to attend public firework displays rather than use consumer fireworks.

The US Food and Drug Administration recommends against feeding infants human milk from unscreened donors, but sharing milk via the Internet is growing in popularity. Recipient infants risk the possibility of consuming contaminated or adulterated milk. Our objective was to test milk advertised for sale online as human milk to verify its human origin and to rule out contamination with cow's milk.

Background: Prediction of HIV transmission cluster growth may help guide public health action. We developed a predictive model for cluster growth in North Carolina (NC) using routine HIV surveillance data. Methods:We identified putative transmission clusters with ≥2 members via pairwise genetic distances ≤1.5% from HIV-1 pol sequences sampled 11/2010-12/2017 in NC. Clusters established by a baseline of 01/2015 with any sequences sampled within two years before baseline were assessed for growth (new diagnoses) over 18 months. We developed a predictive model for cluster growth incorporating demographic, clinical, temporal, and contact tracing characteristics of baseline cluster members. We internally and temporally externally validated the final model in the periods 01/2015-06/2016 and 07/2016-12/2017. Results:Cluster growth was predicted by larger baseline cluster size, shorter time between diagnosis and HIV care entry, younger age, shorter time since the most recent HIV diagnosis, higher proportion with no named contacts, and higher proportion with HIV viremia. The model showed areas under the receiver operating characteristic curves of 0.82 and 0.83 in the internal and temporal external validation samples. Conclusion:The predictive model developed and validated here is a novel means of identifying HIV transmission clusters that may benefit from targeted HIV control resources.

This report describes COVID-19 mortality in 2020 among U.S. residents in 46 states and New York City by usual occupation and industry.