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Relative risk maps are widely used across various industries such as healthcare, transportation, and environmental area because they allow for the identification of risk region. In this study, a Bayesian spatial hierarchical model based on a Poisson distribution was used to estimate relative risk by incorporating spatial data from within the study area. However, while risk areas were identified based on the estimated relative risk that was the final model outcome, it is important to note that the existence of spatial correlation in certain areas within the study area may result in significantly different estimated relative risk values despite their close geographic proximity. This presents a limitation in accurately defining the areas of highest risk within the study area. The aim of this study was to estimate the risk boundary within the study area, which was defined as the boundary between areas with different estimated relative risk values based on observed variables in various traffic environments. To achieve this goal, the study proposed a method for determining the risk boundary on the relative risk map. The results of the analysis indicated that the installation ratio of bus-only lanes and the number of intersections had a statistically significant impact on determining the risk boundary.

No abstract

Purpose: Traditional summary metrics provided by accelerometer device manufacturers, known as counts, are proprietary and manufacturer specific, making it difficult to compare studies using different devices. Alternative summary metrics based on raw accelerometry data have been introduced in recent years. However, they were often not calibrated on ground truth measures of activity-related energy expenditure for direct translation into continuous activity intensity levels. Our purpose is to calibrate, derive, and validate thresholds among women 60 years and older based on a recently proposed transparent raw data-based accelerometer activity index (AAI) and to demonstrate its application in association with cardiometabolic risk factors. Methods: We first built calibration equations for estimating metabolic equivalents continuously using AAI and personal characteristics using internal calibration data (N = 199). We then derived AAI cutpoints to classify epochs into sedentary behavior and physical activity intensity categories. The AAI cutpoints were applied to 4,655 data units in the main study. We then utilized linear models to investigate associations of AAI sedentary behavior and physical activity intensity with cardiometabolic risk factors. Results: We found that AAI demonstrated great predictive accuracy for estimating metabolic equivalents (R2 = .74). AAI-Based physical activity measures were associated in the expected directions with body mass index, blood glucose, and high-density lipoprotein cholesterol. Conclusion: The calibration framework for AAI and the cutpoints derived for women older than 60 years can be applied to ongoing epidemiologic studies to more accurately define sedentary behavior and physical activity intensity exposures, which could improve accuracy of estimated associations with health outcomes.