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Related Experiment Videos

Developing meaningful cohorts for human exposure models.

Stephen E Graham1, Thomas McCurdy

  • 1Exposure Modeling Research Branch, Human Exposure and Atmospheric Sciences Division, National Exposure Research Laboratory, US Environmental Protection Agency, RTP, North Carolina 27711, USA. graham.stephen@epa.gov

Journal of Exposure Analysis and Environmental Epidemiology
|January 17, 2004
PubMed
Summary
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Exposure modelers can improve cohort analysis by using age and gender, alongside physical activity and weather data. These factors help define distinct groups for better understanding human activity patterns and environmental exposure.

Area of Science:

  • Environmental Health
  • Exposure Science
  • Statistical Modeling

Background:

  • The Consolidated Human Activity Database (CHAD) is crucial for exposure modeling, providing data on human behavior and time-activity patterns.
  • Exposure modelers often stratify populations into cohorts based on characteristics like age and gender to reduce variability.
  • Existing models utilize attributes such as weather and employment status for more complex cohort definitions.

Purpose of the Study:

  • To statistically analyze attributes within the CHAD database to identify significant factors for defining distinct population cohorts.
  • To investigate the relationship between various cohort attributes and time spent outdoors, indoors, and in motor vehicles.

Main Methods:

  • Statistical analysis of numerous attributes within the US Environmental Protection Agency's Consolidated Human Activity Database (CHAD).

Related Experiment Videos

  • Focus on the correlation between cohort-defining attributes (age, gender, physical activity, weather, employment, etc.) and locational decisions.
  • Evaluation of the statistical significance and explanatory power of different attributes in predicting time spent in various environments.

    • Main Results:

    • Age and gender are significant cohort attributes, with physical activity level and weather factors (e.g., daily maximum temperature, month) also proving important.
    • Combined weekday/weekend and employment status significantly influence cohort characteristics.
    • Precipitation and ethnic data showed less importance, and overall, the analyzed attributes explained limited variance in locational decisions.

    Conclusions:

    • Recommend using age and gender as primary (first-order) cohort attributes for exposure modeling.
    • Suggest incorporating physical activity level, weather parameters (e.g., daily maximum temperature), and refined day type classifications.
    • Acknowledge that lifestyle and life stage data, if available, could further reduce unexplained variance in human activity patterns.