Background: Although ambient concentrations of particulate matter ≤10 μm (PM10) are often used as proxies for total personal exposure, correlation (r) between ambient and personal PM10 concentrations varies. Factors underlying this variation and its effect on health outcome–PM exposure relationships remain poorly understood.
Methods: We conducted a random-effects meta-analysis to estimate effects of study, participant, and environmental factors on r; used the estimates to impute personal exposure from ambient PM10 concentrations among 4,012 nonsmoking, participants with diabetes in the Women’s Health Initiative clinical trial; and then estimated the associations of ambient and imputed personal PM10 concentrations with electrocardiographic measures, such as heart rate variability.
Results: We identified 15 studies (in years 1990–2009) of 342 participants in five countries. The median r was 0.46 (range = 0.13 to 0.72). There was little evidence of funnel plot asymmetry but substantial heterogeneity of r, which increased 0.05 (95% confidence interval = 0.01 to 0.09) per 10 µg/m3 increase in mean ambient PM10 concentration. Substituting imputed personal exposure for ambient PM10 concentrations shifted mean percent changes in electrocardiographic measures per 10 µg/m3 increase in exposure away from the null and decreased their precision, for example, −2.0% (−4.6% to 0.7%) versus −7.9% (−15.9% to 0.9%), for the standard deviation of normal-to-normal RR interval duration.
Conclusions: Analogous distributions and heterogeneity of r in extant meta-analyses of ambient and personal PM2.5 concentrations suggest that observed shifts in mean percent change and decreases in precision may be generalizable across particle size.
From the aDepartment of Epidemiology, University of North Carolina, Chapel Hill, NC; bHealth Sciences Library, University of North Carolina, Chapel Hill, NC; cUnited States Environmental Protection Agency, Research Triangle Park, Durham, NC; dDepartment of Public Health Sciences, Pennsylvania State University, Hershey, PA; eStatistical and Mathematical Sciences Institute, Research Triangle Park, Durham, NC; fDepartment of Statistics and Operations Research, University of North Carolina, Chapel Hill, NC; and gDepartment of Medicine, University of North Carolina, Chapel Hill, NC.
This work was supported by the National Institute of Environmental Health Sciences (grant R01-ES012238) and a National Research Service Award from the National Heart, Lung and Blood Institute (NHLBI), US Department of Health and Human Services (DHHS; grant T32-HL007055 to K.M.H.). The NHLBI/DHHS also funded the Women’s Health Initiative program (contracts HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, and HHSN268201100004C). R.L.S. received funding from a SAMSI grant, NSF-DMS 0635449.
The authors declare no conflicts of interest.
Although this work was reviewed by EPA and approved for publication, it may not necessarily reflect official Agency policy.
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Correspondence: Katelyn M. Holliday, Cardiovascular Disease Program, Department of Epidemiology, UNC Gillings School of Global Public Health, 137 E. Franklin St., Suite 306, Chapel Hill, NC 27514. E-mail: email@example.com.