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Background In the United States, human papillomavirus (HPV) vaccination has been recommended since 2011 for boys aged 11–12 years, with catch-up vaccination recommended through age 26 years for previously unvaccinated men who have sex with men (MSM). Methods During 2016–2018, a cross-sectional study enrolled MSM and transgender women aged 18–26 years in Seattle, Washington. Participants submitted self-collected penile swab specimens for HPV genotyping. HPV vaccination history was self-reported. We compared HPV prevalence among vaccinated participants with that in participants with no or unknown vaccination history, using log-binomial regression to estimate adjusted prevalence ratios and confidence intervals. Results Among 687 participants, 348 (50.7%) self-reported ever receiving ≥1 HPV vaccine dose; the median age at first HPV vaccination was 21 years, and the median age at first sex, 17 years. Overall, the prevalence of penile quadrivalent HPV vaccine (4vHPV)–type HPV was similar in vaccinated participants (12.1%) and participants with no or unknown vaccination (15.6%) (adjusted prevalence ratio, 0.69 [95% confidence interval, .47–1.01]). However, the prevalence was significantly lower in participants vaccinated at age ≤18 years than in those with no of unknown vaccination (0.15 [.04–.62]), corresponding to a vaccine effectiveness of 85% against 4vHPV-type HPV. Conclusions Results suggest that HPV vaccination is effective in preventing penile HPV infections in young MSM when administered at age ≤18 years.

Background Following the introduction of rotavirus immunization in 2006 in the United States, there were substantial declines in the domestic rotavirus disease burden. In this study, we assess the value for money achieved by the program in the decade following vaccine introduction. Methods We applied an age-specific, static, multicohort compartmental model to examine the impact and cost-effectiveness of the US rotavirus immunization program in children Results Declines in healthcare use associated with the rotavirus and acute gastroenteritis occurred from 2006 and continued to grow before stabilizing from 2010 through 2011. From 2011 to 2015, an estimated annual average of approximately 118 000 hospitalizations, 86 000 emergency department presentations, and 460 000 outpatient and physician office visits were prevented. From a societal perspective during this same period, the program was estimated to be cost saving in the base case model and in >90% of probabilistic sensitivity analysis simulations and from a healthcare system perspective >98% of simulations found an ICER below $100 000 per QALY gained. Conclusions After the program stabilized, we found the rotavirus immunization in the United States was likely to have been cost saving to society. The greater than expected healthcare and productivity savings reflect the success of the rotavirus immunization program in the United States.

This study uses an empirical analysis to quantify the downstream analysis effects of data pre-processing choices. Bootstrap data simulation is used to measure the bias-variance decomposition of an empirical risk function, mean square error (MSE). Results of the risk function decomposition are used to measure the effects of model development choices on model bias, variance, and irreducible error. Measurements of bias and variance are then applied as diagnostic procedures for model pre-processing and development. Best performing model-normalization-data structure combinations were found to illustrate the downstream analysis effects of these model development choices. In additions, results found from simulations were verified and expanded to include additional data characteristics (imbalanced, sparse) by testing on benchmark datasets available from the UCI Machine Learning Library. Normalization results on benchmark data were consistent with those found using simulations, while also illustrating that more complex and/or non-linear models provide better performance on datasets with additional complexities. Finally, applying the findings from simulation experiments to previously tested applications led to equivalent or improved results with less model development overhead and processing time.

Background Up-to-date estimates of the burden of norovirus, a leading cause of acute gastroenteritis (AGE) in the United States, are needed to assess the potential value of norovirus vaccines in development. We aimed to estimate the rates, annual counts, and healthcare charges of norovirus-associated ambulatory clinic encounters, emergency department (ED) visits, hospitalizations, and deaths in the United States. Methods We analyzed administrative data on AGE outcomes from 1 July 2001 through 30 June 2015. Data were sourced from IBM MarketScan Commercial and Medicare Supplemental Databases (ambulatory clinic and ED visits), the Healthcare Utilization Project National Inpatient Sample (hospitalizations), and the National Center for Health Statistics multiple-cause-of-mortality data (deaths). Outcome data (ambulatory clinic and ED visits, hospitalizations, or deaths) were summarized by month, age group, and setting. Healthcare charges were estimated based on insurance claims. Monthly counts of cause-unspecified gastroenteritis-associated outcomes were modeled as functions of cause-specified outcomes, and model residuals were analyzed to estimate norovirus-associated outcomes. Healthcare charges were estimated by applying average charges per cause-unspecified gastroenteritis encounter to the estimated number of norovirus encounters. Results We estimate 900 deaths (95% confidence interval [CI], 650–1100), 109 000 hospitalizations (95% CI, 80 000–145 000), 465 000 ED visits (95% CI, 348 000–610 000), and 2.3 million ambulatory clinic encounters (95% CI, 1.7–2.9 million) annually due to norovirus, with an associated $430–$740 million in healthcare charges. Conclusions Norovirus causes a substantial health burden in the United States each year, and an effective vaccine could have important public health impact.

Disease classification is a crucial element of biomedical research. Recent studies have demonstrated that machine learning techniques, such as Support Vector Machine (SVM) modeling, produce similar or improved predictive capabilities in comparison to the traditional method of Logistic Regression. In addition, it has been found that social network metrics can provide useful predictive information for disease modeling. In this study, we combine simulated social network metrics with SVM to predict diabetes in a sample of data from the Behavioral Risk Factor Surveillance System. In this dataset, Logistic Regression outperformed SVM with ROC index of 81.8 and 81.7 for models with and without graph metrics, respectively. SVM with a polynomial kernel had ROC index of 72.9 and 75.6 for models with and without graph metrics, respectively. Although this did not perform as well as Logistic Regression, the results are consistent with previous studies utilizing SVM to classify diabetes., 6 pages, 9 figures, 3 tables

Value-based healthcare in the US is a payment structure that ties reimbursement to quality rather than volume alone. One model of value-based care is the Tennessee Division of TennCare’s Episodes of Care program, which groups common health conditions into episodes using specified time windows, medical code sets and quality metrics as defined in each episode’s Detailed Business Requirements [1] , [2] . Tennessee’s program assigns responsibility for an episode to a managing physician, presenting a unique opportunity to study physician variability in cost and quality within these structured episodes. This paper proposes a pipeline for analysis demonstrated using a cohort of 599 Outpatient and Non-Acute Inpatient Cholecystectomy episodes managed by BlueCross BlueShield of Tennessee in 2016. We sorted episode claims by date of service, then calculated the pairwise Levenshtein distance between all episodes. Next, we adjusted the resulting matrix by cost dissimilarity and performed agglomerative clustering. We then examined the lowest and highest average episode cost clusters for patterns in cost and quality. Our results indicate that the facility type where the surgery takes place is important: outpatient ambulatory care center for the lowest cost cluster, and hospital operating room for the highest cost cluster. Average patient risk scores were higher in the highest cost cluster than the lowest cost cluster. Readmission rate (a quality metric tied to managing physician performance) was low for the whole cohort. Lastly, we explain how our analytical pipeline can be generalized and extended to domains beyond Episodes of Care.

Middle East respiratory syndrome (MERS) cases continue to be reported from the Middle East. Evaluation and testing of patients under investigation (PUIs) for MERS are recommended. In 2013-2014, two imported cases were detected among 490 US PUIs. Continued awareness is needed for early case detection and implementation of infection control measures.

Our traveler-based estimate was 2.3-fold higher than the number of laboratory-confirmed cases recorded., Using data from travelers to 4 countries in the Middle East, we estimated 3,250 (95% CI 1,300–6,600) severe cases of Middle East respiratory syndrome occurred in this region during September 2012–January 2016. This number is 2.3-fold higher than the number of laboratory-confirmed cases recorded in these countries.

The Impella® (Abiomed, Massachusetts) device has been increasingly used for mechanical circulatory support (MCS) in patients with acute severe cardiogenic shock (CS). Initial and continued determination of the appropriate degree of MCS is challenging. This study evaluates predictors of mortality in