Rationale and design of NUDGE-FLU-CHRONIC and NUDGE-FLU-2: Two nationwide randomized trials of electronic nudges to increase influenza vaccination among patients with chronic diseases and older adults during the 2023/2024 influenza season.

BACKGROUND: Yearly influenza vaccination is strongly recommended for older adults and patients with chronic diseases including cardiovascular disease (CVD); however, vaccination rates remain suboptimal, particularly among younger patients. Electronic letters incorporating behavioral nudges are highly scalable public health interventions which can potentially increase vaccination, but further research is needed to determine the most effective strategies and to assess effectiveness across different populations. The purpose of NUDGE-FLU-CHRONIC and NUDGE-FLU-2 are to evaluate the effectiveness of electronic nudges delivered via the Danish governmental electronic letter system in increasing influenza vaccination among patients with chronic diseases and older adults, respectively. METHODS: Both trials are designed as pragmatic randomized implementation trials enrolling all Danish citizens in their respective target groups and conducted during the 2023/2024 influenza season. NUDGE-FLU-CHRONIC enrolls patients aged 18-64 years with chronic diseases. NUDGE-FLU-2 builds upon the NUDGE-FLU trial conducted in 2022/2023 and aims to expand the evidence by testing both previously successful and new nudges among adults ≥65 years during a subsequent influenza season. Persons with exemptions from the electronic letter system are excluded from both trials. In both trials, participants are randomized in a 2.45:1:1:1:1:1:1 ratio to either receive no electronic letter (usual care) or to receive one of 6 different behaviorally informed electronic letters. NUDGE-FLU-CHRONIC has randomized 299,881 participants with intervention letters delivered on September 24, 2023, while NUDGE-FLU-2 has randomized 881,373 participants and delivered intervention letters on September 13, 2023. Follow-up is currently ongoing. In both trials, the primary endpoint is receipt of influenza vaccination on or before January 1, 2024, and the secondary endpoint is time to vaccination. Clinical outcomes including respiratory and cardiovascular hospitalizations, all-cause hospitalization, and mortality are included as prespecified exploratory endpoints. Prespecified individual-level pooled analyses will be conducted across NUDGE-FLU, NUDGE-FLU-CHRONIC, and NUDGE-FLU-2. DISCUSSION: NUDGE-FLU-CHRONIC is the first nationwide randomized trial of electronic nudges to increase influenza vaccination conducted among 18-64-year-old high-risk patients with chronic diseases. NUDGE-FLU-2 will provide further evidence on the effectiveness of electronic nudges among older adults ≥65 years. Collectively, the NUDGE-FLU trials will provide an extensive evidence base for future public health communications. TRIAL REGISTRATION: NUDGE-FLU-CHRONIC: Clinicaltrials.gov: NCT06030739, registered September 11, 2023, https://clinicaltrials.gov/study/NCT06030739. NUDGE-FLU-2: Clinicaltrials.gov: NCT06030726, registered September 11, 2023, https://clinicaltrials.gov/study/NCT06030726.

Computer vision-based helmet use registration for e-scooter riders - The impact of the mandatory helmet law in Copenhagen.

PROBLEM: E-scooters are a new form of mobility used more frequently in urban environments worldwide. As there is evidence of an increased risk of head injuries, helmets are recommended and (less frequently) legislated. Denmark has enacted mandatory e-scooter helmet use legislation from January 1, 2022. So far, it is unclear how this newly implemented law influenced helmet use of e-scooter riders in Denmark immediately after its implementation. METHOD: In this observational study, we register and compare e-scooter helmet use before the mandatory helmet use legislation (December 2021) and after (February 2022). As observational survey data collection in the field can be highly time-consuming, we conducted a video-based observation survey. We trained and applied a computer vision algorithm to automatically register e-scooter helmet use in the video data. RESULTS: The trained algorithm produces accurate helmet use data, which does not differ significantly from human-registered helmet use. In applying the algorithm to video data collected in December 2021 and February 2022, we register an overall e-scooter helmet use of 4.4% in n = 1054 riders. Splitting the observation between the time before and after the implementation of the helmet use law reveals a significant increase in helmet use from 1.80% to 5.56%. DISCUSSION: In this study, we successfully train and apply an object detection algorithm to register accurate helmet use data in videos collected in Copenhagen, Denmark. Using this algorithm, we find a significant impact of a new mandatory e-scooter helmet use law on e-scooter riders' helmet use behavior. Limitations of the study as well as future research needs, are discussed. PRACTICAL APPLICATIONS: Computer vision algorithms can be used for accurate e-scooter helmet assessments. Implementing a mandatory helmet use law can increase helmet use of e-scooters at specific observation sites.

Improving knowledge of cyclist crashes based on hospital data including crash descriptions from open text fields.

INTRODUCTION: In this study we explore the added value of bicycle crash descriptions from open text fields in hospital records from the Aarhus municipality in Denmark. We also explore how bicycle crash data from the hospital complements crash data registered by the police in the same area and time period. METHOD: The study includes 5,313 Danish bicycle crashes, of which 4,205 were registered at the hospital and 1,078 by the police. All crashes occurred from 2010 to 2015. We performed an in-depth analysis of the open text fields on hospital records to identify factors associated with each crash using four categories: bicyclist, road, bicycle, and the other party. We employed the chi-squared test to compare the distribution of variables between crashes registered at the hospital and by the police. A binary logit model was used to estimate the probability that a crash factor is identified, and that each crash factor is associated with a single-bicycle crash. RESULTS: The open-ended text fields in hospital records provide detailed information about crash factors not available in police records, including riding speed, inattention, clothing, specific road conditions, and bicycle defects. The factors alcohol and curb had the highest odds of being identified in relation to a single-bicycle crash. Crash data registered at the hospital included a larger number of bicycle crashes, particularly single-bicycle crashes and crashes with slight injuries only. CONCLUSION: Crash information registered at the hospital in Aarhus Municipality contributes to a better understanding of bicycle crashes due to detailed information about crash-associated factors as well as information about a larger number of bicycle crashes, particularly single-bicycle crashes. Practical implication: Efforts to improve access to detailed information about bicycle crashes are needed to provide a better basis for bicycle crash prevention.

Factors influencing the injury severity of single-bicycle crashes.

The majority of research on bicyclist injury severity relates to bicycle-motor vehicle crashes, even though single-bicycle crashes make up more than half of bicycle crashes. This study explores the factors related to the injury severity outcome of single-bicycle crashes. We use single-bicycle crash data obtained from medical records collected in the period 2010-2015 combined with road maintenance data. The data includes three injury severity categories: 'severe injury', 'slight injury', 'no injury'. The relation between the factors surrounding single-bicycle crashes and the resulting injury severity is estimated using a latent class ordered probit model. The model estimation identifies three latent classes where the likelihood of cyclist membership depends on the bicyclist's age and gender. Furthermore, several factors appear to affect the likelihood of injuries in single-bicycle crashes. These are the road geometry (i.e. if the crash occurred on a bicycle lane or a road section), maintenance level, and the interaction between road geometry and maintenance level. The findings suggest that single-bicycle crashes on road sections result in more severe injuries than single-bicycle crashes on bicycle lanes. The largest effect is seen when a single-bicycle crash occurs on a road section with a poorly maintained bicycle lane being available. Crashes on low volume roads with few bicyclists are also related to an increased probability of severe injury as well as crashes occurring after dark.