Exposure Error in Epidemiologic Studies

For the purposes of this ISA, the effects of exposure error on epidemiologic study results refers to changes in the point estimate and in the standard error of the calculated health effect estimate, β, that result from using the concentration of an air pollutant as an exposure indicator rather than using the actual personal exposure to the causal factor in the epidemiologic statistical analysis. There are many

assumptions made in going from the available experimental measurement of a pollution indicator, to an estimate of the personal exposure, to the causal factor. The importance of these assumptions and their effect on β depend on the type of epidemiologic study. A more detailed discussion of these issues is provided in Annex section AX6.1.

2.5.8.1. Community Time-Series Studies

This section applies primarily to studies of the association between short-term NO2 concentrations and short-term measures of mortality or morbidity. With NO2 time-series epidemiologic analysis, the following three exposure issues are of primary concern: (1) the relationship of the experimental

measurement of NO2 to the true concentration of NO2; (2) the relationship of day-to-day variations of the concentration of the indicator, as measured at a central monitoring site, with the corresponding variations in the avg concentration of the indicator over the geographic area from which the health measurements are drawn; and (3) the relationship of the community avg concentration of NO2 to the avg personal exposure to ambient NO2. These three issues are described below.

Since there is always some instrumental error in the experimental measurement of NO2

concentration, the correlation of the measured NO2 with the true NO2, on either a 24-h or 1-h basis, will be less than 1. Averaging across multiple unbiased ambient monitors in a region should reduce the instrument measurement error (Sheppard, 2005; Wilson and Brauer, 2006; Zeger et al., 2000). This error component is not expected to have a major effect on personal exposure estimation. It may tend to attenuate the estimate of α (Sheppard, 2005) and is unlikely to greatly affect β, particularly if the

instrument error is of the Berkson type. Zeger et al. (2000) showed that instrument error has both Berkson and non-Berkson error components.

The concentration of NO2, measured at any given monitoring site, may not be highly correlated with the avg community concentration. Large spatial variations (expressed as coefficient of divergence (COD) have been observed in some urban areas, as shown in Table 2.5-1. Site-to-site correlations of NO

local sources, monitor siting, meteorology, and topography. Low correlations between the ambient concentration and the community avg concentration quantitatively reduce β if the single pollutant model is the true model. Similarly, β will be reduced if there are subareas of the community where the

correlation of the subarea avg concentrations with the concentrations measured at the ambient monitoring site is <1. Therefore, if a local source affected a sizable portion of the population, that community might not be suitable for time-series epidemiologic analyses.

Zeger et al. (2000) made a major contribution to the understanding of exposure error by pointing out that for community time-series epidemiology, which analyzes the association between health effects and potential causal factors at the community scale rather than the individual scale, it is the correlation of the daily community avg personal exposure to the ambient concentration, Xt

A_{, with daily community avg} concentration, Ct, that is important, not the correlation of each individual’s exposure XitA with Ct. Thus, the low correlation of XitA with Ct, as frequently found in pooled panel exposure studies, is not relevant to error in community time-series epidemiologic analysis. Unfortunately, few experimental studies provide adequate information to calculate the community avg exposure. Most exposure panel studies measure one or a few subjects on 1 day, and another one or a few subjects on the next day, etc. (i.e., a pooled study design). A few studies have measured one subject for several days and another subject for a different set of several days (i.e., a longitudinal study design). This requires measurement of the personal exposure of every subject on every day along with sufficient information to separate the ambient component of exposure from the measured total personal exposure. Such information was available from one study of combined PM10 and showed that the correlation of Xt

A _{with C}

t was much greater than the correlation of

Xit

A _{with C}

t (U.S. Environmental Protection Agency, 2004). The Research Triangle Park PM Panel Study found similar effects in the relationship of outdoor and personal PM2.5 concentrations (Williams et al., 2003). Ott et al. (2000) provided a statistical argument that such an increase in the correlation of the daily avg over the individual values should be expected.

Inter-individual daily variation in αit around the daily community avg αt tends to produce Berkson error, which will not change the point estimate of β, although it may increase the standard error (Zeger et al., 2000). Overestimation of exposure by substitution of the ambient concentration for the ambient exposure leads to underestimation of the effect estimate proportional to α, or bias toward the null (Sheppard, 2005).

Panel epidemiology refers to time-series studies that follow a relatively small number of subjects for a relatively short time, usually tens of subjects for 5 to 20 days a subject. Thus, neither the averaging of exposure over millions of people, as in community time-series studies, or the averaging of exposure over time periods of years and hundreds or thousands of subjects, as in chronic cohort studies was available. Therefore, exposure errors may be more important than in other types of epidemiology. Panel studies typically examine the association between symptoms or health outcomes and either ambient concentrations or personal exposures. Most panel epidemiology studies of NO2 used ambient concentrations rather than personal exposures. Similar types of exposure error, as discussed for community time series studies apply to panel studies, with some differences depending on whether ambient concentrations or personal exposures are used.

2.5.8.2. Long-Term Exposure Studies

For long-term exposure epidemiologic studies, concentrations are integrated over time periods of a year or more, and usually for spatial areas the size of a city, county, or MSA, although integration over smaller areas may be feasible. Health effects are then regressed, in a statistical model, against the avg concentrations in the series of cities (or other areas). In time-series studies, a constant difference between the measured and the true concentration (instrument offset) will not affect β, nor will variations in the daily average α or the daily average nonambient exposure, unless the variations are correlated with the

for different cities (or other areas used in the analysis), the city-to-city long-term ambient NO2

concentrations will not be perfectly correlated with the long-term average exposure to either ambient or total NO2. This lack of correlation would be expected to lead to a lowering of the point estimate of β.

2.5.9. Summary of Issues in Assessing Exposures to NO

In summary, NO2 is monitored at far fewer sites than either O3 or PM. Large spatial variations in ambient NO2 concentrations were observed in urban areas. Measurements of NO2 are subject to artifacts both at the ambient level and at the personal level. Personal exposure to ambient and outdoor NO2 is determined by many factors as listed in Sections 2.5.1 and 2.5.2. These factors all influence the

contribution of ambient NO2 to personal exposures. Personal activities determine when, where, and how people are exposed to NO2. The variations of these physical and exposure factors determine the strength of the association between personal exposure and ambient concentrations in both longitudinal and pooled studies. In Section 2.5.6.1, three types of correlation coefficients were presented. The observed strength of the association between personal exposures and ambient concentrations are not only affected by the variation in physical parameters (e.g., P, k, a and indoor sources) but also affected by data quality and study design. The association between the ambient component of personal exposures and ambient

concentrations is more relevant to the interpretation of epidemiologic evidence but this type of correlation coefficient is not generally reported. The weak association between personal total exposures and ambient concentrations in some longitudinal studies might not reflect the true association between the ambient component of personal exposures and ambient concentrations. In the absence of indoor and local sources, personal exposures to NO2 are between the ambient level and the indoor level. However, personal

exposures could be much higher than either indoor or outdoor concentrations in the presence of these sources. A number of studies found that (community average) personal NO2 was associated with ambient NO2, but the strength of the association ranged from poor to good.

The evidence relating ambient levels to personal exposures is inconsistent. Some of the longitudinal studies examined found that ambient levels of NO2 were reliable proxies of personal exposures to NO2. However, a number of studies did not find significant associations between ambient and personal levels of NO2. The differences in results were related in large measure to differences in study design and in

exposure determinants. Measurement artifacts and differences in analytical measurement capabilities could also have contributed to the inconsistent results. Indeed, in a number of the studies examined, the majority of measurements of personal NO2 concentrations were beneath detection limits, and in all studies some personal measurements were beneath detection limits.

Some researchers concluded that ambient NO2 may be a reasonable proxy for personal exposures, while others noted that caution must be exercised if ambient NO2 is used as a surrogate for personal exposure. Reasons for the differences in study results are not clear, but are related in large measure to differences in study design, to the spatial heterogeneity of NO2 in study areas, to control of indoor

sources, to the seasonal and geographic variability in the infiltration of ambient NO2, and to differences in the time spent in different microenvironments. Measurement artifacts at the ambient and personal levels and differences in analytical measurement capabilities among different groups could also have

contributed to the mixed results. The collective variability in all of the above parameters, in general, contributes to exposure errors in air pollution-health outcome studies. The errors and uncertainties associated with the use of ambient NO2 concentrations as a surrogate for personal exposure to ambient NO2 generally tend to reduce rather than increase β, and therefore are not expected to change the principal conclusions from NO2 epidemiologic studies.