Estimating personal exposures from ambient air pollution measures: using meta-analysis to assess measurement error.
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/m increase in mean ambient PM10 concentration. Substituting imputed personal exposure for ambient PM10 concentrations shifted mean percent changes in electrocardiographic measures per 10 µg/m 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.
Katelyn M
KM
Holliday
Christy L
CL
Avery
Charles
C
Poole
Kathleen
K
McGraw
Ronald
R
Williams
Duanping
D
Liao
Richard L
RL
Smith
Eric A
EA
Whitsel
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000393:Air Pollutants / Q000032:analysis; D000397:Air Pollution / Q000032:analysis* / Q000706:statistics & numerical data; D002648:Child; D004781:Environmental Exposure / Q000032:analysis* / Q000706:statistics & numerical data; D004784:Environmental Monitoring / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010316:Particle Size; D052638:Particulate Matter / Q000032:analysis*; D055815:Young Adult
D000393:Air Pollutants; D052638:Particulate Matter