Ecological momentary assessment of pelvic pain and urinary urgency variability in urologic chronic pelvic pain syndrome and their association with illness impact and quality of life: Findings from the multidisciplinary approach to the study of chronic pelvic pain symptom patterns study.

PURPOSE: This study tested the hypothesis that ecological momentary assessment (EMA) of pelvic pain (PP) and urinary urgency (UU) would reveal unique Urologic Chronic Pelvic Pain Syndrome (UCPPS) phenotypes that would be associated with disease specific quality of life (QOL) and illness impact metrics (IIM). MATERIALS AND METHODS: A previously validated smart phone app (M-app) was provided to willing Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) participants. M-app notifications were sent 4-times daily for 14 days inquiring about PP and UU severity. A clustering algorithm that accounted for variance placed participants into PP and UU variability? clusters. Associations between clusters and QOL and IIM were then determined. RESULTS: A total of 204 participants enrolled in the M-app study (64% female). M-app compliance was high (median 63% of surveys). Cluster analysis revealed k = 3 (high, low, none) PP clusters and k = 2 (high, low) UU clusters. When adjusting for baseline pain severity, high PP variability, but not UU variability, was strongly associated with QOL and IIM; specifically worse mood, worse sleep and higher anxiety. UU and PP clusters were associated with each other (p < 0.0001), but a large percentage (33%) of patients with high PP variability had low UU variability. CONCLUSIONS: PP variability is an independent predictor of worse QOL and more severe IIM in UCPPS participants after controlling for baseline pain severity and UU. These findings suggest alternative pain indices, such as pain variability and unpredictability, may be useful adjuncts to traditional measures of worst and average pain when assessing UCPPS treatment responses.

Government Responses Matter: Predicting Covid-19 cases in US under an empirical Bayesian time series framework

Since the Covid-19 outbreak, researchers have been predicting how the epidemic will evolve, especially the number in each country, through using parametric extrapolations based on the history. In reality, the epidemic progressing in a particular country depends largely on its policy responses and interventions. Since the outbreaks in some countries are earlier than United States, the prediction of US cases can benefit from incorporating the similarity in their trajectories. We propose an empirical Bayesian time series framework to predict US cases using different countries as prior reference. The resultant forecast is based on observed US data and prior information from the reference country while accounting for different population sizes. When Italy is used as prior in the prediction, which the US data resemble the most, the cases in the US will exceed 300,000 by the beginning of April unless strong measures are adopted.