This web page allows you to apply for access of the Nine City Validation Study (Kioumourtzoglou et al, 2014) relating personal daily exposure to total Particulate Matter 2.5 (PM2.5) and PM2.5 of ambient origin to their counterparts based on nearest EPA monitor and spatio-temporal smoothed exposure estimates (Yanofsky et al, 2015). With these data and the statistical methods for exposure measurement error available here, you will be able to adjust estimates of the effects of chronic exposure to total PM2.5 and PM2.5 of ambient origin for exposure measurement error in cohort studies of outcomes such as all-cause mortality, cardiovascular disease incidence and lung cancer incidence.
This dataset is to be used with the %rrc macro to correct for PM2.5 exposure measurement error, using personal PM2.5 exposures, in health studies of long-term PM2.5 exposures. The dataset can be used when surrogate exposures estimated using ambient PM2.5 concentrations measured at a nearby monitor are used in the health study.
The dataset includes a unique identifier for each subject, repeated measures of monthly averages of personal PM2.5 exposures, personal PM2.5 exposures of ambient origin, ambient PM2.5 concentrations measured at the EPA monitor nearest to each subject’s residence, year and month of the monthly averages, as well as the sex and gender of each subject and the US Census region of residence. This data set includes personal exposure data for adults only.
The data set has been constructed specifically to allow the correction of estimates of relative risk of ambient air pollution for bias due to exposure measurement error. Please read the manual carefully in order to understand how these data can be used for this purpose.
To report a problem, please email firstname.lastname@example.org.
For PM2.5 data from RIOPA Part 2, please refer to this website.
For more information on the validation dataset and error correction in health studies of long-term PM2.5 exposures please read:
- Liao X, Zucker DM, Li Y, Spiegelman D. Survival Analysis with Error-Prone Time-Varying Covariates: A Risk Set Calibration Approach. Biometrics. 2011;67:50-8. ⇨ (full text available)
- Kioumourtzoglou M.A., Spiegelman D., Szpiro A.A., Sheppard L., Kaufman J.D., Yanosky J.D., Williams R., Laden F., Hong B., Suh H.H., “Exposure measurement error in PM2.5 health effects studies: A pooled analysis of eight personal exposure validation studies”, Environmental Health, 13:2, January 2014 ⇨ (full text available)
- Yanosky JD, Paciorek CJ, Laden F, Hart JE, Puett RC, Liao D et al.. Spatio-temporal modeling of particulate air pollution in the conterminous United States using geographic and meteorological predictors. Environ Health. 2014; 13(1):63.⇨ (full text available)
- “Survival analysis with functions of mis-measured covariate histories: the case of chronic air pollution exposure in relation to mortality in the Nurses’ Health Study” by Xiaomei Liao, Molin Wang, Jaime E. Hart, Francine Laden and Donna Spiegelman. (July 2015). Harvard University Biostatistics Working Paper Series. Working Paper 198. http://biostats.bepress.com/harvardbiostat/paper198.
We’d like to thank the Health Effects Institute and the following individuals for sharing their data with us- the principal investigators of the RIOPA study (Drs. Barbara Turpin, Clifford Weisel, and Jim Zhang); Mr. Ron Williams– US Environmental Protection Agency- Research Triangle Park, NC; Dr. Helen Suh, Northeastern University, Boston, MA; and Dr. Joel Kaufman, University of Washington, Seattle, Washington.
PM: Particulate matter; particles found in the air, including dust, dirt, soot, smoke, and liquid droplets.
PM2.5: Particles less than 2.5 micrometers in diameter are referred to as “fine” particles and are believed to pose the greatest health risks. Because of their small size (approximately 1/30th the average width of a human hair), fine particles can lodge deeply into the lungs.
PM2.5 of ambient origin: Particles that originate from outdoor sources such as a factory, incinerator, automobile and airplane emissions.