Department of Clinical Epidemiology and Public Health Hospital de la Santa Creu i Sant Pau E-08041 Barcelona, Spain
Research Group on Statistics Applied Economics and Health (GRECS) Department of Economics University of Girona E-17001 Girona, Spain
![]() |
INTRODUCTION |
---|
![]() ![]() ![]() ![]() ![]() |
---|
When information from different sources is combined, assessing the heterogeneity of effects can be as important as reporting results. Finding systematic variation in results and identifying factors that may account for such variation aid in the interpretation of data and planning future studies (2). Hence, in multicenter studies of the short-term effects of air pollution on health, different variables to explain heterogeneity have been examined without a definitive conclusion up to now. For example, the APHEA (Air Pollution and Health: a European Approach) Project found that the effects of daily variation in SO2 levels on total mortality were significantly stronger in Western Europe than in Eastern Europe (3
), even though other variables, such as age-standardized mortality and the proportion of people older than 65 years, could also explain the differences (4
). Bobak and Roberts (5
) found evidence that differences between cities may be due to the role of temperature. Recently, Levy et al. (6
) suggested that the ratio of particulate matter <2.5 µm in aerodynamic diameter (PM2.5) to particulate matter <10 µm in aerodynamic diameter (PM10) seems to be a relevant effect modifier for the effect of PM10.
We believe that the authors (1) did not consider this problem in depth in their analysis. It seems, however, that in another paper by the same authors (7
) they tried to assess the factors that could be confounding the relation between air pollution and mortality, although without a definitive conclusion. As we have mentioned above, the between-location variability should not only be taken into account when developing hierarchic models but also must be exhaustively analyzed when looking for possible explanatory factors. In fact, as the authors point out, "Numerous studies have shown a positive association between daily mortality and...air pollution" (1
, p. 397), but very little effort has gone into examining why results vary across locations. Thus, we urge the authors to study such heterogeneity in depth because they have information available on the 20 largest US cities.
![]() |
REFERENCES |
---|
![]() ![]() ![]() ![]() ![]() |
---|
Department of Statistics Iowa State University Ames, IA 50011
Department of Biostatistics School of Hygiene and Public Health Johns Hopkins University Baltimore, MD 21205-2179
Department of Epidemiology School of Hygiene and Public Health Johns Hopkins University Baltimore, MD 21205-2179
![]() |
INTRODUCTION |
---|
![]() ![]() ![]() ![]() ![]() |
---|
We have now extended the work initiated by Daniels et al. by developing methods for characterizing the heterogeneity of dose-response relations across regions. Dominici et al. (5) describe a two-stage linear regression model for combining evidence on air pollution and mortality from multiple locations that has been applied to a database of mortality, weather, and air pollution data for the 20 largest metropolitan areas in the United States. The second stage of the model describes between-city variation in the true relative rates as a function of three city-specific covariates: the percentage of people in poverty, the percentage of people older than 65 years, and the average of the daily values of particulate matter <10 µm in aerodynamic diameter (PM10). The same database was further analyzed by Samet et al. (6
) to estimate a pooled effect for other pollutants and to further investigate sources of heterogeneity. We found that differences among cities in the relative rates did not depend on the city-specific demographic characteristics considered.
More recently, the NMMAPS database and statistical analyses have been expanded to include the 90 largest US cities. The more recent work by Dominici et al. (7) (http://biosun01.biostat.jhsph.edu/fdominic/research.html) combines and extends the work of these earlier papers to investigate sources of heterogeneity among cities and to estimate pooled dose-response curves. In this paper, three-stage hierarchic models are built, with stages for city, region, and nation. Various predictors are examined for their ability to explain heterogeneity across cities and regions, including those measuring demographic characteristics, copollutant levels, precision of the air pollution measurements, and particulate size distribution (as, for example, the PM2.5/PM10 ratio).
In summary, much of our work now focuses on quantifying and explaining the heterogeneity of the air pollution effects and of the shape of the dose-response curves across cities. The paper in this Journal by Daniels et al. (2) addresses estimation of the shape of the dose-response curve, while taking into account, but not trying to explain, potential sources of heterogeneity.
![]() |
REFERENCES |
---|
![]() ![]() ![]() ![]() ![]() |
---|