Hwang and Chan Respond to "Air Pollution and Health" by Dominici

Jing-Shiang Hwang1 and Chang-Chuan Chan2

1 Institute of Statistical Science, Academia Sinica, Taipei 115, Taiwan.
2 Institute of Occupational Medicine and Industrial Hygiene, College of Public Health, National Taiwan University, Taipei 100, Taiwan.

Abbreviations: PM10, particulate matter <= 10 µm in diameter


    INTRODUCTION
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 INTRODUCTION
 REFERENCES
 
We appreciate Dr. Dominici's extensive commentary (1Go) on our paper (2Go). It appropriately elaborated on our findings regarding the use of Bayesian hierarchical models to assess effects of air pollution on daily clinic visits for lower respiratory tract illness. As Dominici pointed out, our study not only provided a consistent overall estimation of air pollution effects but also exposed interesting and important modification factors of such effects among 50 sites in Taiwan. Along this line, we would like to add more comments and explain the implications of our study results.

Dominici cited the findings of Katsouyanni et al. (3Go) and used the example of nitrogen dioxide, a traffic-derived pollutant, to explain how long-term pollution modifies the acute effects of particulate matter <= 10 µm in diameter (PM10) on mortality. As is shown in table 2 of our paper (2, p. 6), we found that the acute effect of pollution on PM10 was positively modified marginally significantly by carbon monoxide (coefficient = 0.23) but not significantly by nitrogen dioxide (coefficient = 0.03). Since our study sites included urban, rural, and industrial communities, we could better differentiate pollution sources in our ambient air monitoring data. The nitrogen dioxide concentrations in urban and industrial communities were both high, but the carbon monoxide concentrations were high in urban communities only. This indicated that the choice of suitable proxy indicators as effect modifiers was critical to fitting models and explaining results.

The findings shown in figures 2 and 3 of our paper (2, pp. 5 and 7) indicated that the two-stage modeling decreased the heterogeneity problem to some extent. We agree that heterogeneity is an important statistical and public health issue that deserves further study. As computerized data on community-related information become available, we will be able to use hierarchical models to model the origins of heterogeneity in environmental epidemiologic studies. Once the study sites become small enough, as in our design, a systematic way of collecting individual-related information may become feasible. Then we can apply either the subject-domain approach of Hwang et al. (4Go) or the case-crossover design of Maclure (5Go) and Navidi (6Go) to detect an association between exposure and response with time trends by adjusting for individual-related heterogeneity.

We think that studies on nonfatal health effects of air pollution are worthwhile and should be encouraged, even though major ongoing epidemiologic studies on air pollution are analyzing mortality. From a scientific viewpoint, studies of morbid effects can strengthen the consistency of findings regarding the biologic plausibility of mortality effects of air pollution. From a public health viewpoint, a minor health effect such as lower respiratory tract illness is an important issue, because it impacts many people in the population and can lead to the death of susceptible individuals if they are not appropriately treated.

Because more evidence of an association between low-level air pollution and various health effects has accumulated during the past two decades, we think the challenge to future epidemiologic studies on air pollution will be dealing with the following issues: Why does the exposure-response slope for individual air pollutants vary significantly among different study sites? Do pollution effects come from a single air pollutant or from mixtures of air pollutants? What is the relation between chronic exposure effects and acute exposure effects? Some forms of joint temporal-spatial models with attributes of hierarchical and small-area design may provide better approaches with which to address these challenges.


    NOTES
 
Reprint requests to Dr. Chang-Chuan Chan, Institute of Occupational Medicine and Industrial Hygiene, College of Public Health, National Taiwan University, Room 1447, Jen-ai Road, Taipei 100, Taiwan (e-mail: ccchan{at}ha.mc.ntu.edu.tw).


    REFERENCES
 TOP
 INTRODUCTION
 REFERENCES
 

  1. Dominici F. Invited commentary: air pollution and health—what can we learn from a hierarchical approach? Am J Epidemiol 2002;155:11–15.[Free Full Text]
  2. Hwang JS, Chan CC. Effects of air pollution on daily clinic visits for lower respiratory tract illness. Am J Epidemiol 2002;155:1–10.[Abstract/Free Full Text]
  3. Katsouyanni K, Touloumi G, Samoli E, et al. Confounding and effect modification in the short-term effects of ambient particles on total mortality: results from 29 European cities within the APHEA2 project. Epidemiology 2001;12:521–31.[ISI][Medline]
  4. Hwang JS, Chen YJ, Wang JD, et al. Subject-domain approach to the study of air pollution effects on schoolchildren's absenteeism. Am J Epidemiol 2000;152:67–74.[Abstract/Free Full Text]
  5. Maclure M. The case-crossover design: a method for studying transient effects on the risk of acute events. Am J Epidemiol 1991;133:144–53.[Abstract]
  6. Navidi W. Bidirectional case-crossover designs for exposures with time-trends. Biometrics 1998;54:596–605.[ISI][Medline]
Received for publication August 24, 2001. Accepted for publication September 21, 2001.


Related articles in Am. J. Epidemiol.:

Effects of Air Pollution on Daily Clinic Visits for Lower Respiratory Tract Illness
Jing-Shiang Hwang and Chang-Chuan Chan
Am. J. Epidemiol. 2002 155: 1-10. [Abstract] [FREE Full Text]  

Invited Commentary: Air Pollution and Health—What Can We Learn from a Hierarchical Approach?
Francesca Dominici
Am. J. Epidemiol. 2002 155: 11-15. [Extract] [FREE Full Text]  




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