The Complexity of the Resurgence of Childhood Vaccine-Preventable Diseases in the United States

AdaptSPEC-X: Covariate-Dependent Spectral Modeling of Multiple Nonstationary Time Series

We present the AdaptSPEC-X method for the joint analysis of a panel of possibly nonstationary time series. The approach is Bayesian and uses a covariate-dependent infinite mixture model to incorporate multiple time series, with mixture components parameterized by a time-varying mean and log spectrum. The mixture components are based on AdaptSPEC, a nonparametric model which adaptively divides the time series into an unknown number of segments and estimates the local log spectra by smoothing splines. AdaptSPEC-X extends AdaptSPEC in three ways. First, through the infinite mixture, it applies to multiple time series linked by covariates. Second, it can handle missing values, a common feature of time series which can cause difficulties for nonparametric spectral methods. Third, it allows for a time-varying mean. Through these extensions, AdaptSPEC-X can estimate time-varying means and spectra at observed and unobserved covariate values, allowing for predictive inference. Estimation is performed by Markov chain Monte Carlo (MCMC) methods, combining data augmentation, reversible jump, and Riemann manifold Hamiltonian Monte Carlo techniques. We evaluate the methodology using simulated data, and describe applications to Australian rainfall data and measles incidence in the US. Software implementing the method proposed in this paper is available in the R package BayesSpec.

Pediatric Tetanus in Central Pennsylvania

Approximately 20% of the nationally reported tetanus infections in children aged 0 to 14 years that occurred in the United States between 2005 and 2015 were treated at Penn State Children's Hospital. With an electronic medical record search, we identified 5 cases of pediatric tetanus; 100% of these cases occurred in unimmunized children. Their median length of stay was 10 days, and the costs were significant.

Increasing Immunization Compliance by Reducing Provisional Admittance

Students in Vermont with incomplete or undocumented immunization status are provisionally admitted to schools and historically had a calendar year to resolve their immunization status. The process of resolving these students’ immunization status was challenging for school nurses. We conducted a school-based quality improvement effort to increase student compliance with Vermont immunization regulations using a collaborative learning approach with public health school liaisons and school nurses from public schools to reduce provisional admittance in 2011–2012. Strategies included using a tracking system, accessing the immunization registry, promoting immunization importance, tracking immunization plans, and working with medical homes to update records. Participating school nurses observed decreases in the number of provisionally admitted students, although this reduction was not significantly different than matched comparison schools. We also found the number of provisionally admitted students fluctuated throughout the year and resolving the immunization status of New Americans and exchange students required special attention. Our approach supports the coordinated school health model and demonstrates the critical role school nurses play in improving population health outcomes.