Background. This study introduces a framework for analyzing and visualizing health care utilization for millions of children, with a focus on pediatric asthma, one of the major chronic respiratory conditions. Methods. The data source is the 2005 to 2012 Medicaid Analytic Extract claims for 10 Southeast states. The study population consists of Medicaid-enrolled children with persistent asthma. We translate multiyear, individual-level medical claims into sequences of discrete utilization events, which are modeled using Markov renewal processes and model-based clustering. Network analysis is used to visualize utilization profiles. The method is general, allowing the study of other chronic conditions. Results. The study population consists of 1.5 million children with persistent asthma. All states have profiles with high probability of asthma controller medication, as large as 60.6% to 90.2% of the state study population. The probability of consecutive asthma controller prescriptions ranges between 0.75 and 0.95. All states have utilization profiles with uncontrolled asthma with 4.5% to 22.9% of the state study population. The probability for controller medication is larger than for short-term medication after a physician visit but not after an emergency department (ED) visit or hospitalization. Transitions from ED or hospitalization generally have a lower probability into physician office (between 0.11 and 0.38) than into ED or hospitalization (between 0.20 and 0.59). Conclusions. In most profiles, children who take asthma controller medication do so regularly. Follow-up physician office visits after an ED encounter or hospitalization are observed at a low rate across all states. Finally, all states have a proportion of children who have uncontrolled asthma, meaning they do not take controller medication while they have severe outcomes.

Mammography is routinely used to screen for breast cancer. However, the radiological interpretation of mammogram images is complicated by the heterogeneous nature of normal breast tissue and the fact that cancers are often of the same radiographic density as normal tissue. In this work, we use wavelets to quantify spectral slopes of breast cancer cases and controls and demonstrate their value in classifying images. In addition, we propose asymmetry statistics to be used in forming features, which improve the classification result. For the best classification procedure, we achieve approximately 77% accuracy (sensitivity=73%, specificity=84%) in classifying mammograms with and without cancer.