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Estimating Error in Using Ambient PM2.5 Concentrations as Proxies for Personal Exposures: A Review

Avery, Christy L.a; Mills, Katherine T.a; Williams, Ronaldb; McGraw, Kathleen A.c; Poole, Charlesa; Smith, Richard L.d; Whitsel, Eric A.a,e

doi: 10.1097/EDE.0b013e3181cb41f7
Air Pollution: Review Article
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Background: Several methods have been used to account for measurement error inherent in using ambient concentration of particulate matter <2.5 μm/m3 (PM2.5) as a proxy for personal exposure. Such methods commonly rely on the estimated correlation between ambient and personal PM2.5 concentrations (r). However, studies of r have not been systematically and quantitatively assessed for publication bias or heterogeneity.

Methods: We searched 7 electronic reference databases for studies of the within-participant correlation between ambient and personal PM2.5.

Results: We identified 567 candidate studies, 18 (3%) of which met inclusion criteria and were abstracted. The studies were published between 1999 and 2008, representing 619 nonsmoking participants aged 6–93 years in 17 European and North American cities. Correlation coefficients (median 0.54; range 0.09–0.83) were based on a median of 8 ambient-personal PM2.5 pairs per participant (range 5–20) collected over 27–547 days. Overall, there was little evidence for publication bias (funnel plot symmetry tests: Begg's log-rank test, P = 0.9; Egger's regression asymmetry test, P = 0.2). However, strong evidence for heterogeneity was noted (Cochran's Q test for heterogeneity, P < 0.001). European locales, eastern longitudes in North America, higher ambient PM2.5 concentrations, higher relative humidity, and lower between-participant variation in r were associated with increased r.

Conclusions: Characteristics of participants, studies, and the environments in which they are conducted may affect the accuracy of ambient PM2.5 as a proxy for personal exposure.

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From the aDepartment of Epidemiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC; bUS Environmental Protection Agency, National Exposure Research Laboratory, Research Triangle Park, NC; cHealth Sciences Library, The University of North Carolina at Chapel Hill, Chapel Hill, NC; Departments of dStatistics and Operations Research, and eMedicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC.

Submitted 9 February 2009; accepted 10 July 2009; posted 19 January 2010.

Supported by National Institutes for Environmental Health Sciences (R01-ES012238, P30-ES10126) and National Heart, Lung, and Blood (T32-HL007055) funding.

Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www.epidem.com).

Correspondence: Christy Avery, Department of Epidemiology, University of North Carolina Chapel Hill, Bank of America Center, 137 E. Franklin St., Suite 306, Chapel Hill, NC 27514. E-mail: christy_avery@unc.edu.

Several studies have examined methods of accounting for the effects of error associated with the use of ambient particulate matter (PM) concentrations as proxies for personal PM exposure.1–3 Investigators from the National Morbidity and Mortality and Air Pollution Study,4 for example, compared regression calibration and multistage Poisson regression. Although such strategies are potentially useful, the comparison relied on estimates of the cross-sectional association between personal and ambient PM10 concentrations in a convenience sample of 5 panel studies representing 292 participants from 4 geographic locations. A 5-study convenience sample is potentially problematic, as nonrandom study selection may provide biased inferences.5 Also, cross-sectional PM correlations may be weaker than longitudinal, within-person PM correlations, due to interindividual variation in behaviors influencing exposure.6–8

There has not been a systematic and quantitative review of studies of the ambient-personal PM2.5 correlation and, perhaps more importantly, the modifying effects of participant, study and environment characteristics. Thus, there are no summary estimates of the correlation that could be used to adjust for the error inherent in using ambient PM2.5 concentration as proxies for personal PM exposure. To address this gap, we systematically and quantitatively reviewed the literature estimating within-participant, ambient-personal PM2.5 correlations and determined the extent and sources of measurement error inherent in using ambient PM2.5 as a surrogate for personal exposure. These results will facilitate quantification of bias resulting from the use of ambient PM2.5 as a proxy for personal exposure in a Women's Health Initiative (WHI) ancillary study, the Environmental Epidemiology of Arrhythmogenesis in the WHI.

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METHODS

Systematic Review Strategy

We searched for studies of the within-participant, ambient-personal or outdoor-personal PM2.5 correlation considering all document types, languages, and publication dates. On 12 November 2007 we searched PubMed (1950 to date), ISI Web of Science (1955 to date), ISI BIOSIS Previews (1969 to date), CSA Environmental Sciences and Pollution Management (1967 to date), Toxline (1965 to date), and Proquest Dissertations & Theses (1861 to date). STN EMBASE (1974 to date) was searched on 14 December 2007.

We used the following strategy to search PubMed: (PM 2.5 OR PM2.5 OR PM25 OR PM 25 OR fine particle*) AND (ambient OR outdoor OR outdoors OR outside OR exterior OR external OR background OR fixed site*) AND (individual OR personal) AND (correlat* OR associat* OR relat* OR compar* OR pearson OR spearman). The same 4 sets of keywords were adapted for input into Web of Science, BIOSIS, Environmental Sciences, Toxline, and EMBASE. The Dissertations & Theses search required only the first 3 sets of keywords to create a result set small enough for review.

Citations were downloaded to an electronic reference manager (EndNote X1, Thomson Reuters), duplicates were removed, and secondary references were added. The citations were independently reviewed with respect to 3 inclusion criteria: measurement of ambient PM2.5, measurement of personal PM2.5, and estimation of the within-participant, ambient-personal PM2.5 correlation. We extracted study, participant and environment characteristics from all articles meeting inclusion criteria. Study characteristics included journal of publication, publication date, setting, study dates, sample size, duration, timing (consecutive, nonconsecutive), lower limit of PM2.5 detection, number (minimum, mean) of paired PM2.5 measures, and correlation metric (Pearson, Spearman). Participant characteristics included age (mean, minimum, maximum), percent female, and the presence of comorbidities (pulmonary, cardiovascular, multiple, none). Environmental characteristics included the mean, median and standard deviation of PM2.5 concentrations (ambient, personal); the within-participant, ambient-personal PM2.5 correlation coefficients and corresponding number of paired measurements; season; average distance to ambient monitor; monitor type; air exchange rate; percent of time using air conditioning; and percent of time with windows open. Discrepant exclusions and extractions were adjudicated by consensus. We requested supplemental data from authors by electronic mail as needed. City-specific longitudes and latitudes were obtained from the GEOnet Names Server (http://earth-info.nga.mil/gns/html/whatsnew.htm#C3). Meteorologic data were obtained from the National Climatic Data Center (http://www.ncdc.noaa.gov/oa/climateresearch.html).

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Statistical Analysis

Uniform measures of association for the jth study were estimated from the personal-ambient PM2.5 correlations measured within each of the ith participants. We converted each within-participant correlation coefficient (ri) to its variance-stabilizing, Fisher's z-transform

Estimates of the within-participant variance

and between-participant variance

for the jth study were estimated from the number of paired personal-ambient PM2.5 measurements for each participant (ni), the number of participants per study (kj), the weighted sum of squared errors (Qj) = ∑i=1k(ni − 3)(ZriZri)2, and a constant

. The transformed effect size for the jth study is given by

with weights (wi)

, standard errors (Sj)

and study-specific weights

.10 Negative τ2 estimates were set to 0. Fixed-effects summary estimates were approximated using the median correlation coefficient and the average number of paired measurements for 2 studies11,12 that did not provide participant-specific correlation coefficients.

Funnel plot asymmetry was assessed using a plot of Wj versus j, the adjusted rank correlation and regression asymmetry tests,13,14 and a nonparametric “trim and fill” method that imputes hypothetically missing results due to publication bias.15 In the absence of publication bias, plots of Wj versus j usually resemble a symmetrical funnel with the more precise estimates forming the spout and the less precise estimates forming the cone, while low P values associated with the former tests (PBegg; PEgger) give evidence of asymmetry.

Interstudy heterogeneity was evaluated using a plot16 of

versus

and Cochran's Q test.17 The plot and test are related, in that the position of the jth study along the vertical axis illustrates its contribution to the Q test statistic. In the absence of heterogeneity, all studies fall within the 95% confidence limits and for the Cochran Q test PCochran > 0.1.

Variation in the strength and precision of j across levels of the study, environment, and participant characteristics was first assessed by estimating a summary random-effects estimate of within each study, environment and participant category.18 We also constructed a series of univariable random-effects meta-regression models to relate each study, environment, and participant characteristic to differences in . Lastly, a multivariable random-effects meta-regression model and a backwards-elimination strategy were used to evaluate 10 characteristics of study, participant, and environment routinely available in epidemiologic studies of PM2.5 health effects: latitude, longitude, presence of comorbidities, mean age, percent female, mean ambient PM2.5, relative humidity, sea level pressure, and mean temperature. Interval-scale characteristics were analyzed before and after dichotomization at their medians unless noted otherwise. All analyses were performed using STATA (College Station, TX). To facilitate interpretation, estimates of were back-transformed to their original metric after data analysis.

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RESULTS

Our systematic review identified 567 candidate studies for screening. Of these studies, 18 (3%) met criteria for critical appraisal and were abstracted. Abstracted studies were published between 1999 and 2008 (Table 1). The studies they described were set in 17 North American and European cities, 10 states or provinces and 4 countries, with 68% performed in the U.S. (eFigure, http://links.lww.com/EDE/A363). The studies were conducted between 1995 and 2002. The mean study duration was 2.0 months (range 0.9 to 18.2), a period in which 79% of the studies collected PM2.5 data over consecutive days. During data collection, the studies recorded an average of 8 (range 5 to 20) ambient and personal PM2.5 concentration pairs per participant on which their Pearson (37%) and Spearman (63%) correlation coefficients were based (Table 1).

TABLE 1

TABLE 1

The studies represented 619 nonsmoking participants aged 6–93 (median = 70) years, 60% of whom were female and 41% of whom did not report chronic pulmonary or cardiovascular disease (Table 2). Ambient PM2.5 concentrations (range 8.3 to 25.2 μg/m3) were lower than personal PM2.5 concentrations (range 9.3 to 28.6 μg/m3) overall, with a median personal-ambient PM2.5 difference of 0 (range −9.0 to 16.3) (Table 3). The estimated (median 0.54; range 0.09 to 0.83) and its standard deviation (median 0.12; range 0.04 to 0.31) varied widely (Table 3, Fig. 1), the latter reflecting variability in sample weights (median 82.7; range 10.3 to 552.0). Estimates of ri were similarly variable among studies (median interquartile range (IQR) 0.38; range 0.22 to 1.04), as were temperature (range −6.0 to 24.6°C) and relative humidity (range 44% to 87%), especially when comparing medians from single-season studies (44%).

TABLE 2

TABLE 2

TABLE 3

TABLE 3

FIGURE 1.

FIGURE 1.

Figure 2 provides a funnel plot of j, which suggests little evidence of asymmetry. This result is consistent with PBegg = 0.9 and PEgger = 0.2, but the “trim and fill” method imputed 4 hypothetically missing studies with r <0.15. Figure 3, a Galbraith plot in which 12 correlation coefficients (44%) fell outside the 95% confidence bounds, provided strong evidence of heterogeneity. This evidence was consistent with PCochran < 0.001.

FIGURE 2.

FIGURE 2.

FIGURE 3.

FIGURE 3.

The interquartile range (IQR) of ri (range dichotomized as ≥0.41 vs. < 0.41) was the characteristic associated with the greatest difference in : −0.37 (95% CI = −0.53 to −0.20) (Fig. 4). Other factors associated with increased were studies conducted in Europe, studies with eastern longitudes in North America, higher mean ambient PM2.5 concentrations, and higher relative humidity, although imprecision was noted. After restricting to North American studies, given the considerable heterogeneity by study locale and small number of European studies (n = 2), higher mean ambient PM2.5 concentrations and higher relative humidity were the only characteristics predictive of in multivariable meta-regression models (P < 0.05).

FIGURE 4.

FIGURE 4.

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DISCUSSION

Surveys of human activity patterns suggest that people spend more than 85% of their time indoors,19 where they are exposed to numerous sources of PM2.5, with physical and chemical properties and toxicities that often differ from those of ambient PM2.5.20,21 Thus, estimates of personal PM2.5 exposure based on ambient concentrations are associated with some degree of uncertainty. This has led to the suggestion that epidemiologic studies should use ambient PM as a surrogate only for outdoor PM exposure, not total exposure.22–26 Nonetheless, certain studies are often cited to justify using ambient PM2.5 concentrations as proxies for total personal PM2.5 exposures.8,27–29 These studies report strong within-participant ambient-personal PM2.5 correlations. Other studies, which report ambient-personal PM2.5 correlations as low as 0.1030 are rarely cited.

Motivated by this apparent pattern of citations, we reviewed studies of the within-participant correlation between ambient and personal PM2.5, examining them for publication bias and heterogeneity. We found low potential for publication bias, although the trim and fill analysis imputed 4 hypothetically missing studies. These hypothetically missing studies most likely represent unpublished findings because they differed considerably from the majority of the published literature, they could not be used to justify reliance on ambient PM2.5 as a proxy for personal PM2.5 exposure, or they were considered implausible. An alternative explanation is that they represent studies this meta-analysis did not identify, although this is less likely since we reviewed 7 electronic reference databases, evaluated secondary sources, and did not apply any document type, language, or publication starting-date limitations. Indeed, this systematic review had all the features of a meta-analysis deemed necessary to ensure its sensitivity.31–33

Although there was little evidence for pronounced publication bias, we found strong evidence for heterogeneity in —evidence that contraindicated the estimation of a single summary measure to represent the entire literature. The direct associations between European locales and eastern longitudes in North America with may reflect regional factors including higher urban PM2.5 concentrations34 or closer proximities to regulatory monitors. Furthermore, the direct associations of ambient PM2.5 concentrations and relative humidity with suggest an increased contribution of ambient PM2.5 to personal exposures through activity patterns or increased air exchange. Regional differences in geographic, household, and personal factors may explain the indirect association between variation in ri and , but further investigation was limited because these factors were uncommon, uncollected, or inconsistently reported. Similarly, we were unable to determine whether small ranges in personal or ambient PM2.5 concentrations were associated with , as few studies reported participant-specific concentrations.

We did not find a strong association between temperature and , but the investigation included several multiseason studies. On the other hand, the scope of our investigation was limited by exclusion of 12 studies of the cross-sectional ambient-personal PM2.5 correlation. Cross-sectional correlations are thought to be weaker than longitudinal, within-person correlations due to interindividual variation in activities affecting exposure (eg, spending time near smokers, cooking, or cleaning).6–8 A series of studies conducted in the Netherlands also found that ambient-personal PM correlations were stronger when analyses were conducted longitudinally.35 Because studies of within- versus between-participant correlations address systematically different questions—the recognition of which precludes simultaneous evaluation36—the a priori exclusion of cross-sectional correlations was appropriate.

We were unable to determine whether associations based on summary data were good proxies for associations estimated using individual participant data.37 One method to assess the validity of our conclusions and eliminate the potential for ecologic bias38 would have been to evaluate individual participant data. A meta-analysis based on individual participant data also would allow for increased flexibility in analyses of heterogeneity and greater consistency of reporting.39 Although such data were unavailable, the findings reported here were based on a large number of studies and have been interpreted cautiously.

The present meta-analysis focused on PM2.5 although several European countries also regulate PM10. It remains unclear whether findings for PM2.5 extend to PM10. The current meta-analysis also did not evaluate the association between ambient and personal concentrations of sulfate or elemental carbon, although these combustion products may better represent the influence of outdoor particles because their indoor sources are uncommon.40–42 Nonetheless, the results presented here have potentially important implications for studies examining the health effects of PM2.5 because methods for modifying regression equations to account for normally distributed measurement error are well established. Although the uniformity with which these results can be applied across study designs deserves additional consideration,3,6–8,43 Crooks et al44 recently described a Bayesian method for incorporating log-normal measurement error in a cross-sectional study of PM health effects. Log-normal distributions are believed more appropriate for PM, but the application of these methods requires knowledge of the conditional distribution of personal exposure given ambient exposure (specifically, the mean and standard deviation of the personal exposure distribution) as well as the ambient-personal PM2.5 correlations described here.

Limitations notwithstanding, the present report reinforces the view that characteristics of participants, studies and the environments in which the studies are conducted affect the accuracy of ambient PM2.5 as a proxy for personal exposure. The wide range in estimated correlations between personal and ambient PM2.5, as well as the associations with participant, study, and environment characteristics, suggest that the potential for exposure misclassification can be substantial. Thus, these factors warrant greater scrutiny in studies utilizing ambient PM2.5 as a proxy for personal exposure.

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ACKNOWLEDGMENTS

We thank Carry Croghan (US EPA) for assisting the authors in providing additional data analyses used in this report. Although this work has been reviewed by EPA and approved for publication, it may not necessarily reflect official Agency policy.

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