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  • Original Article
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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.

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Notes

  1. These models include, among others, pNEM (Johnson, 1995; McCurdy, 1995); HAPEM (Glen and Shadwick, 1998); SHEDS (Burke et al., 2001; Zartarian et al., 2002); and TRIM.Expo (APEX 3.0) (Richmond et al., 2002).

  2. The act of simulating individuals by assembling diaries based on selected attributes is a similar methodology to creating/extracting cohorts since one uses typical “cohort-type” characteristics (such as age, gender, etc.). “Cohort” in this paper refers to the result of the grouping process.

  3. Six studies have more than 1 day of activity pattern data per person. Rather than to continually use the phrase “person-days” of data when discussing specific attributes, we use days, people, or subjects. The basis for all of the data is a person-day, and about 23% of the data available for an attribute may include more than 1 day of data from a single individual.

  4. The most recent internet version of CHAD has 1.1% fewer days (n=22,716) due to the removal of diaries that either had missing age/gender information or had “excessive” missing location and activity data. However, the downloadable Microsoft® Access version of CHAD, which many exposure models use as an input file, still contains the original CHAD data set. The small reduction in person-days of information that will be present in CHAD/Access after 2004 will not materially affect any of the results discussed here.

  5. None of the national studies included non-English-speaking citizens or residents of Alaska and Hawaii, or Americans living abroad.

  6. Standard Industrial Classification (SIC) codes are used to categorize all businesses and industries into classes. The two-digit codes are general in nature, such as “mining/forestry/fishing” and “construction”. The system was developed by the US Bureau of the Budget and is used by all branches of the US government for job and economic classification purposes.

  7. Only the NHAPS studies in CHAD comes close to being able to provide inferential activity pattern information for the US population as a whole; however, they do not apply to Alaska and Hawaii residents or people who cannot speak English; see Klepeis et al. (2001).

  8. The Census Bureau now allows people to say they have more than one race, an option not allowed by studies comprising CHAD. Hispanics and Latinos may or may not be coded in the US Census as one race; so its data are not comparable with CHAD’s, which makes CHAD’s “Other” classification problematic from a comparative viewpoint also.

  9. Using the actual age boundaries used in Table 4 made for awkward-looking ranges when typed in text. Therefore, all upper age ranges were rounded off to the nearest age included in the range. For example, for purposes of this subsection, 16–17 means 16 to <18-year olds, and 1-year olds are 1 to <2 years.

  10. And all older age cohorts except for 21–44 years, which probably is a statistical artifact.

  11. But not the 65–100 years cohort!; another probable artifact.

  12. It is interesting that human activity pattern analysts emphasize location and not levels, while the opposite is true for exercise epidemiologists and physiologists. The latter disciplines do state that most active play occurs outdoors in both genders (e.g., Sallis et al., 1990; Baranowski et al., 1993) and that ♂ children 3 years or older (and adolescents) are more active than ♀ children and adolescents of the same age (e.g., Armstrong et al., 1990, 1991; Sallis et al., 1996; Beunen and Thomis, 1999; Bradley et al., 2000). In general, they also find ethnic and socioeconomic “effects” in active physical activity (e.g., Sallis et al., 1996), similar to our attributes classes.

  13. Metabolic equivalents of work sometimes called metabolic equivalents of tasks. It is a unitless ratio of the energy expended (EE) performing an individual task to the person's basal metabolic rate (BMR). This metric better accounts for differences seen in EE by age and gender, since both parameters go into the BMR denominator.

References

  • Altergott K., and McCreedy C . Gender and family status across the life course: constraints on five types of leisure. Soc Leisure 1993: 16: 151–180.

    Google Scholar 

  • Armstrong N., Balding J., Gentle P., Williams J., and Kirby B . Peak oxygen uptake and physical activity in 11-to-16-year olds. Pediatr Exer Sci 1990: 2: 349–358.

    Article  Google Scholar 

  • Armstrong N., Williams J., Balding J., Gentle P., and Kirby B . Cardiopulmonary fitness, physical activity patterns, and selected coronary risk factor variables in 11- to 16-year olds. Pediatr Exer Sci 1991: 3: 219–228.

    Article  Google Scholar 

  • Baranowski T., Thompson W.O., DuRant R.H., Baranowski J., and Puhl J . Observations on physical activity in physical locations: age, gender, ethnicity, and month effects. Res Q Exer Sports 1993: 64: 127–133.

    Article  CAS  Google Scholar 

  • Beunen G., and Thomis M . Genetic determinants of sports participation and daily physical activity. Int J Obesity 1999: 23(S3): 55–63.

    Article  Google Scholar 

  • Bradley C.B., McMurray R.G., Harrell J.S., and Deng S . Changes in common activities of 3rd through 10th graders: the CHIC Study. Med Sci Sports Exer 2000: 32: 2071–2078.

    Article  CAS  Google Scholar 

  • Burke J.M., Zufall M.J., and Özkaynak H . A population exposure model for particulate matter: case study results for PM2.5 in Philadelphia, PA. J Expos Anal Environ Epidemiol 2001: 11: 470–489.

    Article  CAS  Google Scholar 

  • Chapin Jr, F.S. Human Activity Patterns in the City. John Wiley & Sons, New York, 1974.

    Google Scholar 

  • Cody R.P., and Smith J.K. Applied Statistics and the SAS Programming Language. Prentice Hall, Inc., Upper Saddle River, NJ, 1997.

    Google Scholar 

  • Glen G., and Shadwick D. Final Technical Report on the Analysis of Carbon Monoxide Exposures for Fourteen Cities Using HAPEM-MS3. ManTech Environmental Technology, Research Triangle Park, NC, 1998.

    Google Scholar 

  • Henderson K.A., Bialeschki M.D., Shaw S.M., and Freysinger V.J . Both Gains and Gaps: Feminist Perspectives on Women's Leisure. Venture Publishing, State College, PA, 1996.

    Google Scholar 

  • Johnson T . A methodology for estimating carbon monoxide and resulting carboxyhemoglobin levels in Denver, Colorado. In: Starks T.H. (Ed.). Proceedings of the Research Planning Conference on Human Activity Patterns. US Environmental Protection Agency, Las Vegas, 1989, pp. 17-1–11-22 (EPA-600/4-89-004).

    Google Scholar 

  • Johnson T . Recent advances in the estimation of population exposure to mobile source pollutants. J Expos Anal Environ Epidemiol 1995: 5: 551–571.

    CAS  Google Scholar 

  • Johnson T, Capel J., and McCoy M . An Analysis of Ten Time/Activity Databases: The Effects of Selected Factors on the Apportionment of Time Among Microenvironments and Breathing Rate Categories. IT Corporation, Durham, NC, 1995.

    Google Scholar 

  • Johnson T., McCoy M., Capel J., Wijnberg L., and Ollison W . A comparison of ten time/activity databases: effects of geographic location, temperature, demographics group, and diary recall method. Paper presented at AWMA International Conference on Tropospheric Ozone Nonattainment and Design Value Issues, October 1992.

  • Johnson T., Weaver M., and Capel J . Development and Demonstration of the Probabilistic NAAOS Exposure Model for Application to Hazardous Air Pollutants (pNEM/HAP). IT Corporation, Cary, NC, 1997.

    Google Scholar 

  • Klepeis N.E., Nelson W.C., Ott W.R., Robinson J.P., Tsang A.M., Switzer P., Behar J.V., Hern S.C., and Engelmann W.H . The National Human Activity Pattern Survey (NHAPS): a resource for assessing exposure to environmental pollutants. J Expos Anal Environ Epidemiol 2001: 11: 231–252.

    Article  CAS  Google Scholar 

  • Klepeis N., Tsang A., and Behar J.V. Analysis of the National Human Activity Pattern Survey (NHAPS) Respondents from a Standpoint of Exposure Assessment. National Exposure Research Laboratory, US Environmental Protection Agency Las Vegas, NV, 1996 (EPA-600-R-96/074).

  • Lurmann F.W., Winer A.M., and Colomé S.D . Development and application of a new regional human exposure (REHEX) model. In: Total Exposure Assessment Methodology. Air & Waste Management Association, Pittsburgh, 1990, pp. 478–498.

    Google Scholar 

  • McCurdy T . Estimating human exposure to selected motor vehicle pollutants using the NEM series of models: lessons to be learned. J Expos Anal Environ Epidemiol 1995: 5: 533–550.

    CAS  Google Scholar 

  • McCurdy T . Conceptual basis for multi-route intake dose modeling using an energy expenditure approach. J Expos Anal Environ Epidem 2000: 10: 86–97.

    Article  CAS  Google Scholar 

  • McCurdy T., Glen G., Smith L., and Lakkadi Y . The National Exposure Research Laboratory's Consolidated Human Activity Database. J Expos Anal Environ Epidemiol 2000: 10: 566–578.

    Article  CAS  Google Scholar 

  • McCurdy T., and Graham S.E . Using human activity data in exposure models: analysis of discriminating factors. J Expos Anal Environ Epidemiol 2003 (accepted).

  • Richmond H.M., Palma T., Langstaff J., McCurdy T., Glenn G., and Smith L . Further refinements and testing of APEX (3.0): EPA's population exposure model for criteria and air toxic inhalation exposures. Joint meeting of the Society of Exposure Analysis and International Society of Environmental Epidemiology, Vancouver, Canada, 11–15 August, 2000.

  • Risk Forum. Draft Guidance on Selecting Appropriate Age Groups for Assessing Childhood Exposures to Environmental Contaminants. US Environmental Protection Agency, Washington, DC, 2000.

  • Sallis J.F., Hovell M.F., Hofstetter C.R., Elder J.P., Hackley M., Caspersen C.J., and Powell K.E . Distance between homes and exercise facilities related to frequency of exercise among San Diego residents. Public Health Rep 1990: 105: 179–185.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Sallis J.F., Zakarian J.M., Hovell M.F., and Hofstetter C.R . Ethnic, socioeconomic and sex differences in physical activity among adolescents. J Clin Epidemiol 1996: 49: 125–134.

    Article  CAS  Google Scholar 

  • SAS Institute. SAS/STAT Software (Version 8.02). SAS Institute, Cary, NC, 2002.

  • Schwab M., Colome´ S.D., Spengler J.D., Ryan P.B., and Billick I.H . Activity patterns applied to pollutant exposure assessment: data from a personal monitoring study in Los Angeles. Tox Ind Health 1990: 6: 517–532.

    CAS  Google Scholar 

  • Schwab M., McDermott A., and Spengler J.D . Using longitudinal data to understand children's activity patterns in an exposure context: Data from the Kanawha County health study. Environ Inter 1992: 18: 173–189.

    Article  CAS  Google Scholar 

  • Tsang A.M., and Klepeis N.E . Descriptive Statistics Tables from a Detailed Analysis of the National Human Activity Pattern Survey (NHAPS) Data. US Environmental Protection Agency, Las Vegas, 1996, (EPA/600/R-96/148).

    Google Scholar 

  • Tsang A.M., and Klepeis N.E . Three Telephone Surveys of Human Activities in California: The 1992–94 National Human Activity Pattern Survey; The 1987–88 California Activity Pattern Survey of Adults and Teenagers; and the 1989–90 California Activity Pattern Survey of Children. US Environmental Protection Agency, Las Vegas, NV, 1997.

    Google Scholar 

  • US Census. “Quick Tables” from http://factfinder.census.gov., 2002 (obtained in December 2002).

  • Wiley J.A., Robinson J.P., Cheng Y.-T., Plazza T., Stork L., and Pladsen K . Study of Children's Activity Patterns. Berkeley CA: Survey Research Center, University of California. 1991.

  • Xue J., McCurdy T., Spengler J., and Özkaynak H . Intra- and inter-individual activity considerations in human exposure modeling. J Expos Anal Environ Epidemiol 2003 (submitted).

  • Zartarian V.G., Xue J., Özkaynak H., Glen G., Stallings C., Smith L., Dang W., Cook N., Aviado D., Mostaghimi S., and Chen J . Technical manual: using SHEDS-Wood for the assessment of children's exposure and dose from treated wood preservatives on playsets and residential decks. Prepared for August 30, 2002 EPA/OPP FIFRA SAP meeting.

  • Zemel B.S., Ulijaszek S.J., and Leonard W. Energetics, lifestyles, and nutritional adaptation: an introduction. Am J Hum Biol 1996: 8: 141–142.

    Article  Google Scholar 

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Acknowledgements

We thank the anonymous reviewers and two of our EPA colleagues, Drs. Valerie Zartarian and Harvey Richmond, for helpful suggestions regarding the paper's content and format. Responding to all of the issues and comments that arose hopefully improved the clarity, presentation, and applicability of this article.

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Correspondence to Stephen E Graham.

Appendix

Appendix

A complete summary of the number of person-days of data available in CHAD for all relevant attributes, characteristics, and factors central to the activity typology in addition to other personal indices is provided in Table 1, A2, A3 and A4.

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Graham, S., McCurdy, T. Developing meaningful cohorts for human exposure models. J Expo Sci Environ Epidemiol 14, 23–43 (2004). https://doi.org/10.1038/sj.jea.7500293

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