In an effort to increase transparency and accountability in the US health care system, the Obama administration mandated the Centers for Medicare & Medicaid Services (CMS) to make available data for use by researchers and interested parties from the general public. Among the more well-known uses of this data are analyses published by the Wall Street Journal showing that a large, and in some cases, shocking discrepancy between what hospitals potentially charge the uninsured and what they are paid by Medicare for the same procedure. Analyses such as these highlight both potential inequities in the US health care system and, more importantly, potential opportunities for its reform. However, while capturing the public imagination, analyses such as these are but one means to capitalize on the remarkable wealth of information this data provides. Specifically, data from the public distribution CMS data can help both researchers and the public better understand the burden specific conditions and medical treatments place on the US health care system. It was this simple, but important objective that motivated the present study. Our specific analyses focus on two of what we believe to be important questions. First, using the total number of hospital discharges as a proxy for incidence of a condition or treatment, which have the highest incidence rates nationally? Does their incidence remain stable, or is it increasing/decreasing? And, is there variability in these incidence rates across states? Second, as psychologists, we are necessarily interested in understanding the state of mental health care. To date, and to the best of our knowledge, there has been no study utilizing the public inpatient Medicare provider utilization and payment data set to explore the utilization of mental illness services funded by Medicare.

We set out to model two of the leading causes of fatal automobile accidents in America - drunk driving and speeding. We created a decision tree for each of those causes that uses situational conditions such as time of day and type of road to classify historical accidents. Using this model a law enforcement or town official can decide if a speed trap or DUI checkpoint can be the most effective in a particular situation. This proof of concept can easily be applied to specific jurisdictions for customized solutions.

Introduction: Cycling is on the rise in many urban areas across the United States. The number of cyclist fatalities is also increasing, by 19% in the last 3 years. With the broad-ranging personal and public health benefits of cycling, it is important to understand factors that are associated with these traffic-related deaths. There are more distracted drivers on the road than ever before, but the question remains of the extent that these drivers are affecting cycling fatality rates. Methods: This paper uses the Fatality Analysis Reporting System (FARS) data to examine factors related to cyclist death when the drivers are distracted. We use a novel machine learning approach, adaptive LASSO, to determine the relevant features and estimate their effect. Results: If a cyclist makes an improper action at or just before the time of the crash, the likelihood of the driver of the vehicle being distracted decreases. At the same time, if the driver is speeding or has failed to obey a traffic sign and fatally hits a cyclist, the likelihood of them also being distracted increases. Being distracted is related to other risky driving practices when cyclists are fatally injured. Environmental factors such as weather and road condition did not impact the likelihood that a driver was distracted when a cyclist fatality occurred.

Injury severity describes the severity of the injury to the person involved in the crash. Understanding the factors that influence injury severity can be helpful in designing mechanisms to reduce accident fatalities. In this research, we model and analyze the data as a hierarchy with three levels to answer the question what road, vehicle and driver-related factors influence injury severity. In this study, we used hierarchical linear modeling (HLM) for analyzing nested data from Fatality Analysis Reporting System (FARS). The results show that driver-related factors are directly related to injury severity. On the other hand, road conditions and vehicle characteristics have significant moderation impact on injury severity. We believe that our study has important policy implications for designing customized mechanisms specific to each hierarchical level to reduce the occurrence of fatal accidents.

Recent years have seen the birth of a powerful tool for companies and scientists: the Google Ngram data set, built from millions of digitized books. It can be, and has been, used to learn about past and present trends in the use of words over the years. This is an invaluable asset from a business perspective, mostly because of its potential application in marketing. The choice of words has a major impact on the success of a marketing campaign and an analysis of the Google Ngram data set can validate or even suggest the choice of certain words. It can also be used to predict the next buzzwords in order to improve marketing on social media or to help measure the success of previous campaigns. The Google Ngram data set is a gift for scientists and companies, but it has to be used with a lot of care. False conclusions can easily be drawn from straightforward analysis of the data. It contains only a limited number of variables, which makes it difficult to extract valuable information from it. Through a detailed example, this paper shows that it is essential to account for the disparity in the genre of the books used to construct the data set. This paper argues that for the years after 1950, the data set has been constructed using a much higher proportion of scientific books than for the years before. An ingenious method is developed to approximate, for each year, this unknown proportion of books coming from the scientific literature. A statistical model accounting for that change in proportion is then presented. This model is used to analyze the trend in the use of common words of the scientific literature in the 20th century. Results suggest that a naive analysis of the trends in the data can be misleading.

Road safety is a major concern for all United States of America citizens. According to the National Highway Traffic Safety Administration, 30,000 deaths are caused by automobile accidents annually. Oftentimes fatalities occur due to a number of factors such as driver carelessness, speed of operation, impairment due to alcohol or drugs, and road environment. Some studies suggest that car crashes are solely due to driver factors, while other studies suggest car crashes are due to a combination of roadway and driver factors. However, other factors not mentioned in previous studies may be contributing to automobile accident fatalities. The objective of this project was to identify the significant factors that lead to multiple fatalities in the event of a car crash.

Donorschoose.org is a nonprofit organization that allows individuals to donate directly to public school classroom projects. Teachers from public schools post a request for funding a project with a short essay describing it. Donors all around the world can look at these projects when they log in to Donorschoose.org and donate to projects of their choice. The idea is to have a personalized recommendation webpage for all the donors, which will show them the projects, which they prefer, like and love to donate. Implementing a recommender system for the DonorsChoose.org website will improve user experience and help more projects meet their funding goals. It also will help us in understanding the donors' preferences and delivering to them what they want or value. One type of recommendation system can be designed by predicting projects that will be less likely to meet funding goals, segmenting and profiling the donors and using that information for recommending right projects when the donors log in to DonorsChoose.org.

All public schools in the United States require health and safety education for their students. Furthermore, almost all states require driver education before minors can obtain a driver's license. Through extensive analysis of the Fatality Analysis Reporting System data, we have concluded that from 2011-2013 an average of 12.1% of all individuals killed in a motor vehicle accident in the United States, District of Columbia, and Puerto Rico were minors (18 years or younger). Our goal is to offer insight within our analysis in order to better road safety education to prevent future premature deaths involving motor vehicles.