Developing meaningful cohorts for human exposure models

Abstract

This paper summarizes numerous statistical analyses focused on the US Environmental Protection Agency's Consolidated Human Activity Database (CHAD), used by many exposure modelers as the basis for data on what people do and where they spend their time. In doing so, modelers tend to divide the total population being analyzed into ''cohorts'', to reduce extraneous interindividual variability by focusing on people with common characteristics. Age and gender are typically used as the primary cohort-defining attributes, but more complex exposure models also use weather, day-of-the-week, and employment attributes for this purpose. We analyzed all of these attributes and others to determine if statistically significant differences exist among them to warrant their being used to define distinct cohort groups. We focused our attention mostly on the relationship between cohort attributes and the time spent outdoors, indoors, and in motor vehicles. Our results indicate that besides age and gender, other important attributes for defining cohorts are the physical activity level of individuals, weather factors such as daily maximum temperature in combination with months of the year, and combined weekday/weekend with employment status. Less important are precipitation and ethnic data. While statistically significant, the collective set of attributes does not explain a large amount of variance in outdoor, indoor, or in-vehicle locational decisions. Based on other research, parameters such as lifestyle and life stages that are absent from CHAD might have reduced the amount of unexplained variance. At this time, we recommend that exposure modelers use age and gender as ''first-order'' attributes to define cohorts followed by physical activity level, daily maximum temperature or other suitable weather parameters, and day type possibly beyond a simple weekday/weekend classification.

Determinants of residential indoor and transportation activity times in Korea

“…Previous studies have reported age and gender as primary attributes that influence time-activity patterns in the general population. Graham and McCurdy (2004), using CHAD, identified age and gender as first-order attributes in defining statistically significant differences in time-activity patterns. These were followed by physical activity level, weather parameters, and weekend/ weekday classification.…”

Using human activity data in exposure models: Analysis of discriminating factors

“…Analyses of the entire CHAD database provide even more support for this conclusion (Graham and McCurdy, 2003). Exposure modelers should consider these factors, particularly age, gender, season of the year, temperature, precipitation, and day-type (or at least a weekday/weekend breakdown) when choosing activity patterns for modeling purposes.…”

“…Our literature review indicates that this has never been done before. A second paper in this series (Graham and McCurdy, 2003) expands the goal of evaluating the activity typology to the entire CHAD database, covering all ages and both genders.…”

A new method of longitudinal diary assembly for human exposure modeling

“…However, recording human activity patterns is an expensive and fairly intrusive process, so such data rarely exceed 10 consecutive days per individual (Graham and McCurdy, 2004). EPA's Consolidated Human Activity Database (CHAD; McCurdy et al, 2000) contains just 1 diary day from most of the individuals surveyed.…”