A latent scale model to minimize subjectivity in the analysis of visual rating data for the National Turfgrass Evaluation Program

Introduction

Traditional evaluation procedure in National Turfgrass Evaluation Program (NTEP) relies on visually assessing replicated turf plots at multiple testing locations. This process yields ordinal data; however, statistical models that falsely assume these to be interval or ratio data have almost exclusively been applied in the subsequent analysis. This practice raises concerns about procedural subjectivity, preventing objective comparisons of cultivars across different test locations. It may also lead to serious errors, such as increased false alarms, failures to detect effects, and even inversions of differences among groups.

Methods

We reviewed this problem, identified sources of subjectivity, and presented a model-based approach to minimize subjectivity, allowing objective comparisons of cultivars across different locations and better monitoring of the evaluation procedure. We demonstrate how to fit the described model in a Bayesian framework with Stan, using datasets on overall turf quality ratings from the 2017 NTEP Kentucky bluegrass trials at seven testing locations.

Results

Compared with the existing method, ours allows the estimation of additional parameters, i.e., category thresholds, rating severity, and within-field spatial variations, and provides better separation of cultivar means and more realistic standard deviations.

Discussion

To implement the proposed model, additional information on rater identification, trial layout, rating date is needed. Given the model assumptions, we recommend small trials to reduce rater fatigue. For large trials, ratings can be conducted for each replication on multiple occasions instead of all at once. To minimize subjectivity, multiple raters are required. We also proposed new ideas on temporal analysis, incorporating existing knowledge of turfgrass.

A novel method to map community- and neighborhood-level access to rural physical activity built environments in the United States

Physical activity (PA) built environments may support PA among rural youth and families. In the United States (U.S.), differences between rural and urban PA built environments are assessed using coarse scale, county-level methods. However, this method insufficiently examines environmental differences within rural counties. The present study uses rural-specific geospatial mapping techniques and a fine scale, within-rural grouping strategy to identify differing levels of access to the PA built environment among a rural sample. First, PA infrastructure variables (parks, sidewalks) within a rural region of the Midwest U.S. were mapped. Then, households (N = 112) of participants in the NU-HOME study, a childhood obesity prevention trial, were categorized to community-level and neighborhood-level PA built environment groups using two access indicators; Rural-Urban Commuting Area (RUCA) codes and Walk Scores®, respectively. Finally, households were categorized to new groups that combined community-level RUCA codes and neighborhood-level Walk Scores® to indicate the diverse ways in which rural families might access PA built environments, including by vehicle travel and pedestrian commuting. Household access to PA infrastructure (per geospatial proximity and density analyses), parent perceptions of the PA environment, and child PA were examined across the new combined access groups. All measures of household access to PA infrastructure significantly differed by group (p <.0001). Several parent PA perceptions differed by group; child PA did not. The present study provides future researchers with innovative strategies to map and examine how access to the PA built environment differs within a rural area. Due to the public availability of the access indicators used (RUCA codes, Walk Scores®), study methods can be replicated.

The LGBTQ Supportive Environments Inventory: Methods for Quantifying Supportive Environments for LGBTQ Youth

The social environment in which lesbian, gay, bisexual, transgender, and queer (LGBTQ) youth live influences health and wellbeing. We describe the development of the LGBTQ Supportive Environments Inventory (LGBTQ SEI), designed to quantify the LGBTQ-inclusiveness of social environments in the US and Canada. We quantify aspects of the social environment: 1) Presence/quality of LGBTQ youth-serving organizations; 2) LGBTQ-inclusive Community Resources; 3) Socioeconomic and Political environment. Using GIS tools, we aggregated data to buffers around 397 schools in 3 regions. The LGBTQ SEI can be used to assess the role of the social environment in reducing health disparities for LGBTQ youth.

Use of a Geographic Information System to create treatment groups for group-randomized community trials: The Minnesota Heart Health Program

Background

Group-randomized trials of communities often rely on the convenience of pre-existing administrative divisions, such as school district boundaries or census entities, to divide the study area into intervention and control sites. However, these boundaries may include substantial heterogeneity between regions, introducing unmeasured confounding variables. This challenge can be addressed by the creation of exchangeable intervention and control territories that are equally weighted by pertinent socio-demographic characteristics. The present study used territory design software as a novel approach to partitioning study areas for The Minnesota Heart Health Program’s “Ask about Aspirin” Initiative.

Methods

Twenty-four territories were created to be similar in terms of age, sex, and educational attainment, as factors known to modify aspirin use. To promote ease of intervention administration, the shape and spread of the territories were controlled. Means of the variables used in balancing the territories were assessed as well as other factors that were not used in the balancing process.

Results

The analysis demonstrated that demographic characteristics did not differ significantly between the intervention and control territories created by the territory design software.

Conclusions

The creation of exchangeable territories diminishes geographically based impact on outcomes following community interventions in group-randomized trials. The method used to identify comparable geographical units may be applied to a wide range of population-based health intervention trials.

Trial registration

National Institutes of Health (Clinical Trials.gov), Identifier: NCT02607917. Registered on 16 November 2015.

VASA: Interactive Computational Steering of Large Asynchronous Simulation Pipelines for Societal Infrastructure

We present VASA, a visual analytics platform consisting of a desktop application, a component model, and a suite of distributed simulation components for modeling the impact of societal threats such as weather, food contamination, and traffic on critical infrastructure such as supply chains, road networks, and power grids. Each component encapsulates a high-fidelity simulation model that together form an asynchronous simulation pipeline: a system of systems of individual simulations with a common data and parameter exchange format. At the heart of VASA is the Workbench, a visual analytics application providing three distinct features: (1) low-fidelity approximations of the distributed simulation components using local simulation proxies to enable analysts to interactively configure a simulation run; (2) computational steering mechanisms to manage the execution of individual simulation components; and (3) spatiotemporal and interactive methods to explore the combined results of a simulation run. We showcase the utility of the platform using examples involving supply chains during a hurricane as well as food contamination in a fast food restaurant chain.