Evaluating a smartphone application to increase the quantity and improve the quality of supervised practice driving.

BACKGROUND: The learner stage of graduated driver licensing (GDL), when teenagers are supervised by an adult driver, represents an opportunity to develop skills that could confer a safety benefit during their years of independent driving. This paper describes the design of a teenage driving study, which aims to evaluate the impact of a smartphone application, the 'DrivingApp,' to increase the quantity and improve the quality of supervised practice driving. METHODS: This longitudinal intervention study of teenage drivers and a parent/guardian spans the final 6 months of the learner licence and the first year of independent driving. Participants will be assigned to experimental or control groups using block allocation. Parent-teenage dyads assigned to the intervention arm will receive information about their practice driving via a smartphone application, including miles driven and total drive time. Baseline and monthly surveys will be administered to both experimental and control participants to measure the outcome measures during the learner stage: (1) practice driving amount, (2) consistency and (3) variety. Outcomes during independent driving are (1) self-reported number of attempts at the driving test and (2) number of crashes during the first year of independent driving. DISCUSSION: Improving the quality of teenagers' supervised practice driving is an unmet research need. This study will contribute to the evidence about what can be done during the learner period of GDL to maximise teenage drivers' safety during the first years of independent driving, when crash risk is highest.

Modeling hospital energy and economic costs for COVID-19 infection control interventions.

The study objective assessed the energy demand and economic cost of two hospital-based COVID-19 infection control interventions: negative pressure (NP) treatment rooms and xenon pulsed ultraviolet (XP-UV) equipment. After projecting COVID-19 hospitalizations, a Hospital Energy Model and Infection De-escalation Models quantified increases in energy demand and reductions in infections. The NP intervention was applied to 11, 22, and 44 rooms for small, medium, and large hospitals, while the XP-UV equipment was used eight, nine, and ten hours a day. For small, medium, and large hospitals, the annum kWh for NP rooms were 116,700 kWh, 332,530 kWh, 795,675 kWh, which correspond to annum energy costs of $11,845 ($1,077/room), $33,752 ($1,534/room), and $80,761 ($1,836/room). For XP-UV, the annum-kilowatt-hours (and costs) were 438 ($45), 493 ($50), and 548 ($56) for small, medium, and large hospitals. While energy efficiencies may be expected for the large hospital, the hospital contained more energy-intensive use rooms (ICUs) which resulted in higher operational and energy costs. XP-UV had a greater reduction in secondary COVID-19 infections in large and medium hospitals. NP rooms had a greater reduction in secondary SARS-CoV-2 transmission in small hospitals. Early implementation of interventions can result in realized cost savings through reduced hospital-acquired infections.

Public health principles to inform testing and build trust in automated vehicles.

Highly publicised crashes involving self-driving or autonomous vehicles (AVs) have raised questions about safety and eroded public trust in the technology. In this State of the Art Review, we draw on previous successes in injury prevention and public health to focus attention on three strategies to reduce risk and build public confidence as AVs are being tested on public roads. Data pooling, a graduated approach to risk exposure, and harm reduction principles each offer practical lessons for AV testing. The review points out how the eventual deployment of AV technology could have a substantial impact on public health. In this regard, inclusive testing, public education and smart policy could extend the social value of AVs by improving access to mobility and by directing deployments towards scenarios with the greatest population health impact. The application of these strategies does not imply slowing down progress; rather, their implementation could accelerate adoption and result in realising the benefits of AVs more quickly and comprehensively while minimising risks.

Autoantibodies and scleroderma phenotype define subgroups at high-risk and low-risk for cancer.

OBJECTIVES: Recent studies demonstrate autoantibodies are powerful tools to interrogate molecular events linking cancer and the development of autoimmunity in scleroderma. Investigating cancer risk in these biologically relevant subsets may provide an opportunity to develop personalised cancer screening guidelines. In this study, we examined cancer risk in distinct serologic and phenotypic scleroderma subsets and compared estimates with the general population. METHODS: Patients in the Johns Hopkins Scleroderma Center observational cohort were studied. Overall and site-specific cancer incidence was calculated in distinct autoantibody and scleroderma phenotypic subsets, and compared with the Surveillance, Epidemiology and End Results registry, a representative sample of the US population. RESULTS: 2383 patients with scleroderma contributing 37 686 person-years were studied. 205 patients (8.6%) had a diagnosis of cancer. Within 3 years of scleroderma onset, cancer risk was increased in patients with RNA polymerase III autoantibodies (antipol; standardised incidence ratio (SIR) 2.84, 95% CI 1.89 to 4.10) and those lacking centromere, topoisomerase-1 and pol antibodies (SIR 1.83, 95% CI 1.10 to 2.86). Among antipol-positive patients, cancer-specific risk may vary by scleroderma subtype; those with diffuse scleroderma had an increased breast cancer risk, whereas those with limited scleroderma had high lung cancer risk. In contrast, patients with anticentromere antibodies had a lower risk of cancer during follow-up (SIR 0.59, 95% CI 0.44 to 0.76). CONCLUSIONS: Autoantibody specificity and disease subtype are biologically meaningful filters that may inform cancer risk stratification in patients with scleroderma. Future research testing the value of targeted cancer screening strategies in patients with scleroderma is needed.

A predictive model of rat calorie intake as a function of diet energy density.

Easy access to high-energy food has been linked to high rates of obesity in the world. Understanding the way that access to palatable (high fat or high calorie) food can lead to overconsumption is essential for both preventing and treating obesity. Although the body of studies focused on the effects of high-energy diets is growing, our understanding of how different factors contribute to food choices is not complete. In this study, we present a mathematical model that can predict rat calorie intake to a high-energy diet based on their ingestive behavior to a standard chow diet. Specifically, we propose an equation that describes the relation between the body weight ( W), energy density ( E), time elapsed from the start of diet ( T), and daily calorie intake ( C). We tested our model on two independent data sets. Our results show that the suggested model can predict the calorie intake patterns with high accuracy. Additionally, the only free parameter of our proposed equation (ρ), which is unique to each animal, has a strong correlation with their calorie intake.

Simulated Models Suggest That Price per Calorie Is the Dominant Price Metric That Low-Income Individuals Use for Food Decision Making.

BACKGROUND: The price of food has long been considered one of the major factors that affects food choices. However, the price metric (e.g., the price of food per calorie or the price of food per gram) that individuals predominantly use when making food choices is unclear. Understanding which price metric is used is especially important for studying individuals with severe budget constraints because food price then becomes even more important in food choice. OBJECTIVE: We assessed which price metric is used by low-income individuals in deciding what to eat. METHODS: With the use of data from NHANES and the USDA Food and Nutrient Database for Dietary Studies, we created an agent-based model that simulated an environment representing the US population, wherein individuals were modeled as agents with a specific weight, age, and income. In our model, agents made dietary food choices while meeting their budget limits with the use of 1 of 3 different metrics for decision making: energy cost (price per calorie), unit price (price per gram), and serving price (price per serving). The food consumption patterns generated by our model were compared to 3 independent data sets. RESULTS: The food choice behaviors observed in 2 of the data sets were found to be closest to the simulated dietary patterns generated by the price per calorie metric. The behaviors observed in the third data set were equidistant from the patterns generated by price per calorie and price per serving metrics, whereas results generated by the price per gram metric were further away. CONCLUSIONS: Our simulations suggest that dietary food choice based on price per calorie best matches actual consumption patterns and may therefore be the most salient price metric for low-income populations.

Modeling the Impact of School-Based Universal Depression Screening on Additional Service Capacity Needs: A System Dynamics Approach.

Although it is widely known that the occurrence of depression increases over the course of adolescence, symptoms of mood disorders frequently go undetected. While schools are viable settings for conducting universal screening to systematically identify students in need of services for common health conditions, particularly those that adversely affect school performance, few school districts routinely screen their students for depression. Among the most commonly referenced barriers are concerns that the number of students identified may exceed schools' service delivery capacities, but few studies have evaluated this concern systematically. System dynamics (SD) modeling may prove a useful approach for answering questions of this sort. The goal of the current paper is therefore to demonstrate how SD modeling can be applied to inform implementation decisions in communities. In our demonstration, we used SD modeling to estimate the additional service demand generated by universal depression screening in a typical high school. We then simulated the effects of implementing "compensatory approaches" designed to address anticipated increases in service need through (1) the allocation of additional staff time and (2) improvements in the effectiveness of mental health interventions. Results support the ability of screening to facilitate more rapid entry into services and suggest that improving the effectiveness of mental health services for students with depression via the implementation of an evidence-based treatment protocol may have a limited impact on overall recovery rates and service availability. In our example, the SD approach proved useful in informing systems' decision-making about the adoption of a new school mental health service.

Examining social norm impacts on obesity and eating behaviors among US school children based on agent-based model.

BACKGROUND: Although the importance of social norms in affecting health behaviors is widely recognized, the current understanding of the social norm effects on obesity is limited due to data and methodology limitations. This study aims to use nontraditional innovative systems methods to examine: a) the effects of social norms on school children's BMI growth and fruit and vegetable (FV) consumption, and b) the effects of misperceptions of social norms on US children's BMI growth. METHODS: We built an agent-based model (ABM) in a utility maximization framework and parameterized the model based on empirical longitudinal data collected in a US nationally representative study, the Early Childhood Longitudinal Study - Kindergarten Cohort (ECLS-K), to test potential mechanisms of social norm affecting children's BMI growth and FV consumption. RESULTS: Intraclass correlation coefficients (ICC) for BMI were 0.064-0.065, suggesting that children's BMI were similar within each school. The correlation between observed and ABM-predicted BMI was 0.87, indicating the validity of our ABM. Our simulations suggested the follow-the-average social norm acts as an endogenous stabilizer, which automatically adjusts positive and negative deviance of an individual's BMI from the group mean of a social network. One unit of BMI below the social average may lead to 0.025 unit increase in BMI per year for each child; asymmetrically, one unit of BMI above the social average, may only cause 0.015 unit of BMI reduction. Gender difference was apparent. Social norms have less impact on weight reduction among girls, and a greater impact promoting weight increase among boys. Our simulation also showed misperception of the social norm would push up the mean BMI and cause the distribution to be more skewed to the left. Our simulation results did not provide strong support for the role of social norms on FV consumption. CONCLUSIONS: Social norm influences US children's BMI growth. High obesity prevalence will lead to a continuous increase in children's BMI due to increased socially acceptable mean BMI. Interventions promoting healthy body image and desirable socially acceptable BMI should be implemented to control childhood obesity epidemic.

Prospective policy analysis-a critical interpretive synthesis review.

Most policy analysis methods and approaches are applied retrospectively. As a result, there have been calls for more documentation of the political-economy factors central to health care reforms in real-time. We sought to highlight the methods and previous applications of prospective policy analysis (PPA) in the literature to document purposeful use of PPA and reflect on opportunities and drawbacks. We used a critical interpretive synthesis (CIS) approach as our initial scoping revealed that PPA is inconsistently defined in the literature. While we found several examples of PPA, all were researcher-led, most were published recently and few described mechanisms for engagement in the policy process. In addition, methods used were often summarily described and reported on relatively short prospective time horizons. Most of the studies stemmed from high-income countries and, across our sample, did not always clearly outline the rationale for a PPA and how this analysis was conceptualized. That only about one-fifth of the articles explicitly defined PPA underscores the fact that researchers and practitioners conducting PPA should better document their intent and reflect on key elements essential for PPA. Despite a wide recognition that policy processes are dynamic and ideally require multifaceted and longitudinal examination, the PPA approach is not currently frequently documented in the literature. However, the few articles reported in this paper might overestimate gaps in PPA applications. More likely, researchers are embedded in policy processes prospectively but do not necessarily write their articles from that perspective, and analyses led by non-academics might not make their way into the published literature. Future research should feature examples of testing and refining the proposed framework, as well as designing and reporting on PPA. Even when policy-maker engagement might not be feasible, real-time policy monitoring might have value in and of itself.

Design of a Mobile App Interface That Engages Community Members in a Food System Pilot Study.

Supermarkets are scarce in many under-resourced urban communities, and small independently owned retail stores often carry few fresh or healthy items. The Baltimore Urban food Distribution (BUD) mobile application (app) was previously developed to address supply-side challenges in moving healthy foods from local suppliers to retailers. In-app opportunities for consumers to indicate demand for these foods are crucial, but remain absent. We sought to understand community members' perspectives on the overall role, function and features of a proposed consumer-engagement module (BUDConnect) to expand the BUD app. A series of initial high-fidelity wireframe mockups were developed based on formative research. In-depth interviews (n = 20) were conducted and thematically analyzed using ATLAS.ti Web. Participants revealed a desire for real-time crowd-sourced information to navigate their food environments safely and effectively, functionality to help build community and social networks among store owners and their customers, opportunities to share positive reviews and ratings of store quality and offerings, and interoperability with existing apps. Rewards and referral systems resulting in the discounted purchasing of promoted healthy items were suggested to increase adoption and sustained app use. Wireframe mockups were further refined for future development and integration into the BUD app, the program and policy implications of which are discussed.

Advancing Transportation Equity and Safety Through Autonomous Vehicles.

Motor vehicle crashes are a leading cause of death in the United States, and disproportionately impact communities of color. Replacing human control with automated vehicles (AVs) holds the potential to reduce crashes and save lives. The benefits of AVs, including automated shuttles, buses, or cars could extend beyond safety to include improvements in congestion, reductions in emissions, and increased access to mobility, particularly for vulnerable populations. However, AVs have not attained the level of public trust that has been expected, given their potential to save lives and increase access to mobility. Public opinion surveys have highlighted safety and security concerns as reasons for this lack of confidence. In this study, we present the findings of an experiment we conducted to actively shift mindsets on AVs toward advancing health equity. We demonstrate through a nationally representative sample of 2265 U.S. adults that the public support for AVs can be improved by expanding their scope of application to include advancing social benefit. The survey began with questions on respondent's support for AVs based on a priori knowledge and beliefs. Consistent with prior surveys, baseline support (strong support and some degree of support) was low at 26.4% (95% confidence interval 24.0-29.0). After introducing information about how AVs could be used to provide mobility for older adults, those with limited income, or the vision-impaired, respondents were asked to reassess their support for AVs. Support significantly increased to include the majority of respondents. By prioritizing the deployment of AVs to serve individuals and communities in greatest need of mobility, AVs would not only demonstrate compelling social value by reducing disparities but would also gain widespread public support among the U.S. public.

Speeding behavior among teenage drivers during the learner and early independent driving stage: A case study approach.

INTRODUCTION: Speed is a primary contributing factor in teenage driver crashes. Yet, there are significant methodological challenges in measuring real-world speeding behavior. METHOD: This case study approach analyzed naturalistic driving data for six teenage drivers in a longitudinal study that spanned the learner and early independent driving stages of licensure in Maryland, United States. Trip duration, travel speed and length were recorded using global position system (GPS) data. These were merged with maps of the Maryland road system, which included posted speed limit (PSL) to determine speeding events in each recorded trip. Speeding was defined as driving at the speed of 10 mph higher than the posted speed limit and lasting longer than 6 s. Using these data, two different speeding measures were developed: (1) Trips with Speeding Episodes, and (2) Verified Speeding Time. Conclusions & Practical Applications: Across both measures, speeding behavior during independent licensure was greater than during the learner period. These measures improved on previous methodologies by using PSL information and eliminating the need for mapping software. This approach can be scaled for use in larger samples and has the potential to advance understanding about the trajectory of speeding behaviors among novice teenage drivers.

Design of an innovative digital application to facilitate access to healthy foods in low-income urban settings.

Background: Under-resourced urban minority communities in the United States are characterized by food environments with low access to healthy foods, high food insecurity, and high rates of diet-related chronic disease. In Baltimore, Maryland, low access to healthy food largely results from a distribution gap between small food sources (retailers) and their suppliers. Digital interventions have the potential to address this gap, while keeping costs low. Methods: In this paper, we describe the technical (I) front-end design and (II) back-end development process of the Baltimore Urban food Distribution (BUD) application (app). We identify and detail four main phases of the process: (I) information architecture; (II) low and high-fidelity wireframes; (III) prototype; and (IV) back-end components, while considering formative research and a pre-pilot test of a preliminary version of the BUD app. Results: Our lessons learned provide valuable insight into developing a stable app with a user-friendly experience and interface, and accessible cloud computing services for advanced technical features. Conclusions: Next steps will involve a pilot trial of the app in Baltimore, and eventually, other urban and rural settings nationwide. Once iterative feedback is incorporated into the app, all code will be made publicly available via an open source repository to encourage adaptation for desired communities. Trial Registration: ClinicalTrials.gov NCT05010018.

Protocol for the Support Application for Food PAntrieS trial: design, implementation, and evaluation plan for a digital application to promote healthy food access and support food pantry operations.

Introduction: Food-insecure households commonly rely on food pantries to supplement their nutritional needs, a challenge that was underscored during the COVID-19 pandemic. Food pantries, and the food banks that supply them, face common challenges in meeting variable client volume and dietary needs under normal and emergency (e.g., pandemic, natural disaster) conditions. A scalable digital strategy that has the capacity to streamline the emergency food distribution system, while promoting healthy food options, managing volunteer recruitment and training, and connecting to emergency management systems in times of need, is urgently required. To address this gap, we are developing a working mobile application (app) called the Support Application for Food PAntrieS (SAFPAS) and will evaluate its feasibility and impact on food pantry staff preparedness, stocking, and client uptake of healthful foods and beverages in two urban United States settings. Methods: This paper describes the protocol for a randomized controlled trial of the SAFPAS mobile application. We will conduct formative research in Baltimore, Maryland and Detroit, Michigan to develop and refine the SAFPAS app and increase scalability potential to other urban settings. Then we will test the app in 20 food pantries in Baltimore randomized to intervention or comparison. The impact of the app will be evaluated at several levels of the emergency food system, including food pantry clients (n = 360), food pantry staff and volunteers (n = 100), food pantry stock, and city agencies such as the local food bank and Office of Emergency Management. The primary outcome of the SAFPAS trial is to improve the healthfulness of the foods received by food pantry clients, measured using the Food Assessment Scoring Tool (FAST). Post-trial, we will conduct additional formative research in Detroit to prepare the app for scale-up. Discussion: We anticipate that SAFPAS will improve alignment in the supply and demand for healthy foods among food pantry clients, food pantries, and city agencies which supply food in Baltimore. Real-time, bidirectional communication between entities across the system allows for increased situational awareness at all levels during normal and emergency operations. By conducting formative research in Detroit, we hope to increase the scalability of the SAFPAS app to additional settings nationwide. Clinical trial registration: NCT87654321. https://classic.clinicaltrials.gov/ct2/show/NCT05880004.

A Systems Approach to Identify Factors Influencing Participation in Two Tribally-Administered WIC Programs.

Native American populations experience highly disproportionate rates of poor maternal-child health outcomes. The WIC program aims to safeguard health by providing greater access to nutritious foods, but for reasons not well understood, participation in many tribally-administered WIC programs has declined to a greater extent compared to the national average decline in participation over the last decade. This study aims to examine influences on WIC participation from a systems perspective in two tribally-administered WIC programs. In-depth interviews were conducted with WIC-eligible individuals, WIC staff, tribal administrators, and store owners. Interview transcripts underwent qualitative coding, followed by identifying causal relationships between codes and iterative refining of relationships using Kumu. Two community-specific causal loop diagrams (CLDs) were developed and compared. Findings from interviews in the Midwest yielded a total of 22 factors connected through 5 feedback loops, and in the Southwest a total of 26 factors connected through 7 feedback loops, resulting in three overlapping themes: Reservation and Food Store Infrastructure, WIC Staff Interactions and Integration with the Community, and State-level Administration and Bureaucracy. This study demonstrates the value of a systems approach to explore interconnected barriers and facilitators that can inform future strategies and mitigate declines in WIC participation.

Investigating the effects of absolute humidity and movement on COVID-19 seasonality in the United States.

Mounting evidence suggests the primary mode of SARS-CoV-2 transmission is aerosolized transmission from close contact with infected individuals. While transmission is a direct result of human encounters, falling humidity may enhance aerosolized transmission risks similar to other respiratory viruses (e.g., influenza). Using Google COVID-19 Community Mobility Reports, we assessed the relative effects of absolute humidity and changes in individual movement patterns on daily cases while accounting for regional differences in climatological regimes. Our results indicate that increasing humidity was associated with declining cases in the spring and summer of 2020, while decreasing humidity and increase in residential mobility during winter months likely caused increases in COVID-19 cases. The effects of humidity were generally greater in regions with lower humidity levels. Given the possibility that COVID-19 will be endemic, understanding the behavioral and environmental drivers of COVID-19 seasonality in the United States will be paramount as policymakers, healthcare systems, and researchers forecast and plan accordingly.

The Baltimore Urban Food Distribution (BUD) App: Study Protocol to Assess the Feasibility of a Food Systems Intervention.

Low-income urban communities in the United States commonly lack ready access to healthy foods. This is due in part to a food distribution system that favors the provision of high-fat, high-sugar, high-sodium processed foods to small retail food stores, and impedes their healthier alternatives, such as fresh produce. The Baltimore Urban food Distribution (BUD) study is a multilevel, multicomponent systems intervention that aims to improve healthy food access in low-income neighborhoods of Baltimore, Maryland. The primary intervention is the BUD application (app), which uses the power of collective purchasing and delivery to affordably move foods from local producers and wholesalers to the city's many corner stores. We will implement the BUD app in a sample of 38 corner stores, randomized to intervention and comparison. Extensive evaluation will be conducted at each level of the intervention to assess overall feasibility and effectiveness via mixed methods, including app usage data, and process and impact measures on suppliers, corner stores, and consumers. BUD represents one of the first attempts to implement an intervention that engages multiple levels of a local food system. We anticipate that the app will provide a financially viable alternative for Baltimore corner stores to increase their stocking and sales of healthier foods, subsequently increasing healthy food access and improving diet-related health outcomes for under-resourced consumers. The design of the intervention and the evaluation plan of the BUD project are documented here, including future steps for scale-up. Trial registration #: NCT05010018.

Development of a System Dynamics Model to Guide Retail Food Store Policies in Baltimore City.

Policy interventions to improve food access and address the obesity epidemic among disadvantaged populations are becoming more common throughout the United States. In Baltimore MD, corner stores are a frequently used source of food for low-income populations, but these stores often do not provide a range of affordable healthy foods. This research study aimed to assist city policy makers as they considered implementing a Staple Food Ordinance (SFO) that would require small stores to provide a range and depth of stock of healthy foods. A System Dynamics (SD) model was built to simulate the complex Baltimore food environment and produce optimal values for key decision variables in SFO planning. A web-based application was created for users to access this model to optimize future SFOs, and to test out different options. Four versions of potential SFOs were simulated using this application and the advantages and drawbacks of each SFO are discussed based on the simulation results. These simulations show that a well-designed SFO has the potential to reduce staple food costs, increase corner store profits, reduce food waste, and expand the market for heathy staple foods.

Optimal Design of Paired Built Environment Interventions for Control of MDROs in Acute Care and Community Hospitals.

OBJECTIVES: Our goal was to optimize infection control of paired environmental control interventions within hospitals to reduce methicillin-resistant Staphylococcus aureus (MRSA), carbapenem-resistant Enterobacteriaceae (CRE), and vancomycin-resistant Enterococci (VRE). BACKGROUND: The most widely used infection control interventions are deployment of handwashing (HW) stations, control of relative humidity (RH), and negative pressure (NP) treatment rooms. Direct costs of multidrug-resistant organism (MDRO) infections are typically not included in the design of such interventions. METHODS: We examined the effectiveness of pairing HW with RH and HW with NP. We used the following three data sets: A meta-analysis of progression rates from uncolonized to colonized to infected, 6 years of MDRO treatment costs from 400 hospitals, and 8 years of MDRO incidence rates at nine army hospitals. We used these data as inputs into an Infection De-Escalation Model with varying budgets to obtain optimal intervention designs. We then computed the infection and prevention rates and cost savings resulting from these designs. RESULTS: The average direct cost of an MDRO infection was $3,289, $1,535, and $1,067 for MRSA, CRE, and VRE. The mean annual incidence rates per facility were 0.39%, 0.034%, and 0.011% for MRSA, CRE, and VRE. After applying the cost-minimizing intervention pair to each scenario, the percentage reductions in infections (and annual direct cost savings) in large, community, and small acute care hospitals were 69% ($1.5 million), 73% ($631K), 60% ($118K) for MRSA, 52% ($460.5K), 58% ($203K), 50% ($37K) for CRE, and 0%, 0%, and 50% ($12.8K) for VRE. CONCLUSION: The application of this Infection De-Escalation Model can guide cost-effective decision making in hospital built environment design to improve control of MDRO infections.

Examining association between cohesion and diversity in collaboration networks of pharmaceutical clinical trials with drug approvals.

OBJECTIVE: Clinical trials ensure that pharmaceutical treatments are safe, efficacious, and effective for public consumption, but are extremely complex, taking up to 10 years and $2.6 billion to complete. One main source of complexity arises from the collaboration between actors, and network science methodologies can be leveraged to explore that complexity. We aim to characterize collaborations between actors in the clinical trials context and investigate trends of successful actors. MATERIALS AND METHODS: We constructed a temporal network of clinical trial collaborations between large and small-size pharmaceutical companies, academic institutions, nonprofit organizations, hospital systems, and government agencies from public and proprietary data and introduced metrics to quantify actors' collaboration network structure, organizational behavior, and partnership characteristics. A multivariable regression analysis was conducted to determine the metrics' relationship with success. RESULTS: We found a positive correlation between the number of successful approved trials and interdisciplinary collaborations measured by a collaboration diversity metric (P < .01). Our results also showed a negative effect of the local clustering coefficient (P < .01) on the success of clinical trials. Large pharmaceutical companies have the lowest local clustering coefficient and more diversity in partnerships across biomedical specializations. CONCLUSIONS: Large pharmaceutical companies are more likely to collaborate with a wider range of actors from other specialties, especially smaller industry actors who are newcomers in clinical research, resulting in exclusive access to smaller actors. Future investigations are needed to show how concentrations of influence and resources might result in diminished gains in treatment development.