Objective To assess the public’s preferences regarding potential privacy threats from devices or services storing health-related personal data.Materials and Methods A pan-European survey based on a stated-preference experiment for assessing preferences for electronic health data storage, access, and sharing.Results We obtained 20 882 survey responses (94 606 preferences) from 27 EU member countries. Respondents recognized the benefits of storing electronic health information, with 75.5%, 63.9%, and 58.9% agreeing that storage was important for improving treatment quality, preventing epidemics, and reducing delays, respectively. Concerns about different levels of access by third parties were expressed by 48.9% to 60.6% of respondents.On average, compared to devices or systems that only store basic health status information, respondents preferred devices that also store identification data (coefficient/relative preference 95% CI = 0.04 [0.00-0.08], P = 0.034) and information on lifelong health conditions (coefficient = 0.13 [0.08 to 0.18], P < 0.001), but there was no evidence of this for devices with information on sensitive health conditions such as mental and sexual health and addictions (coefficient = −0.03 [−0.09 to 0.02], P = 0.24). Respondents were averse to their immediate family (coefficient = −0.05 [−0.05 to −0.01], P = 0.011) and home care nurses (coefficient = −0.06 [−0.11 to −0.02], P = 0.004) viewing this data, and strongly averse to health insurance companies (coefficient = −0.43 [−0.52 to 0.34], P < 0.001), private sector pharmaceutical companies (coefficient = −0.82 [−0.99 to −0.64], P < 0.001), and academic researchers (coefficient = −0.53 [−0.66 to −0.40], P < 0.001) viewing the data.Conclusions Storing more detailed electronic health data was generally preferred, but respondents were averse to wider access to and sharing of this information. When developing frameworks for the use of electronic health data, policy makers should consider approaches that both highlight the benefits to the individual and minimize the perception of privacy risks.

62and motorcyclist fatalities increased by 31% (from 407 to 530) during the same period of time.Motorcyclists represent the most vulnerable of road users and are particularly susceptible to being seriously injured if involved in a crash. From 2003 through 2008, motorcycle injuries increased from 6,061 to 9,708 (60%). Motorcyclists made up 4% of all traffic-related crash injuries in 2008, doubling from 2% in 2003.Multiple factors contribute to motorcyclists' crash and injury severity including speeding, alcohol use, lack of helmets, and risky and negligent behaviors by motorcyclists and other road users. Risk seekers are naturally attracted to the sport of motorcycling and their attitudes may contribute to crash risk and injury severity. Mannering and Grodsky found that risk seekers knowingly engage in risky behavior in spite of understanding that the behavior may increase their crash risk (1). They also significantly underestimate the consequences of a crash (1).When compared with national averages, rates of Texas riders are higher for improper licensure, lack of helmet use, and alcohol consumption. Data from NHTSA's Fatality Analysis Reporting System show that in Texas in 2008• The proportion of fatally injured motorcycle riders who were not properly licensed was 31% compared with 26% for the whole country,• The proportion of fatally injured motorcycle riders who were impaired (with a blood alcohol concentration greater than or equal to 0.08 g/dL) was 39% compared with 28% nationwide, and • The proportion of fatally injured motorcycle riders who were not wearing a helmet was 57% compared with 41% nationwide.Because motorcyclists make up an increasing proportion of total traffic fatalities in Texas, the development of effective methods to reverse these trends and mitigate motorcycle crash and injury severity becomes increasingly important. Knowledge of the associated causes of motorcycle crashes and the factors that contribute to the severity of injuries to crash-involved motorcyclists is useful in suggesting approaches for reducing the frequency and severity of such crashes.In this study, crash data from police-reported motorcycle crashes in Texas were used to estimate multinomial logit (MNL) models for identifying differences in factors likely to affect the severity of motorcyclists' crash injuries. Because previous studies have documented important differences in crash severity between urban and rural roadways (2, 3), probabilistic models of motorcyclists' injury severity in urban and rural crashes were estimated. PREVIOUS STUDIESIncreases in motorcycle crashes and resulting deaths and injuries shed light on the need to more fully understand factors that contribute to motorcycle crash involvement and how differences in factors likelyMotorcyclists accounted for 15% of all traffic-related deaths in Texas in 2008. This proportion increased threefold during the past decade. Knowledge of the associated causes of motorcycle crashes and the factors that contributed to the severity of injuries to motorcyclists involve...

Trips longer than 50 mi account for less than one-fortieth of all trips but nearly one-third of all distance traveled within Great Britain. Because of the small proportion of all travel that they form, long-distance trips may not be adequately represented in national databases and models. However, because they account for a substantial proportion of total distance traveled, particularly on motorways and rail, these trips are important for transport policy and have a substantial impact on congestion. Moreover, study of existing data indicates that travelers’ behavior in longdistance journeys differs substantially from that in routine journeys. Not only is the set of available modes different, but the profile of travelers is also substantially different, with income playing an important role in both travel frequency and mode choice. In addition, model responsiveness and values of time vary significantly with journey length. For these reasons, treatment of the specific properties of long-distance travel is essential for appraising the impact of transport policy aimed at this market, such as high-speed rail, highway construction and management policies, and policies directed toward domestic air travel. This paper describes the development of a model to address these policy issues. The specific aim of the modeling work is to provide empirical evidence on the relative importance of mode, destination, and frequency responses for long-distance travel models. The models that have been developed form the basis for a forecasting model that can be used for the appraisal of a wide range of transport policy aimed at long-distance journeys.

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