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Regression models for air pollution and daily mortality: analysis of data from Birmingham, Alabama
Author(s) -
Smith Richard L,
Davis Jerry M,
Sacks Jerome,
Speckman Paul,
Styer Patricia
Publication year - 2000
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/1099-095x(200011/12)11:6<719::aid-env438>3.0.co;2-u
Subject(s) - particulates , air pollution , environmental science , national ambient air quality standards , regression analysis , air quality index , pollution , econometrics , particulate pollution , statistics , aerodynamic diameter , meteorology , geography , mathematics , ecology , biology
In recent years, a very large literature has built up on the human health effects of air pollution. Many studies have been based on time series analyses in which daily mortality counts, or some other measure such as hospital admissions, have been decomposed through regression analysis into contributions based on long‐term trend and seasonality, meteorological effects, and air pollution. There has been a particular focus on particulate air pollution represented by PM 10 (particulate matter of aerodynamic diameter 10 µm or less), though in recent years more attention has been given to very small particles of diameter 2.5 µm or less. Most of the existing data studies, however, are based on PM 10 because of the wide availability of monitoring data for this variable. The persistence of the resulting effects across many different studies is widely cited as evidence that this is not mere statistical association, but indeed establishes a causal relationship. These studies have been cited by the United States Environmental Protection Agency (USEPA) as justification for a tightening on particulate matter standards in the 1997 revision of the National Ambient Air Quality Standard (NAAQS), which is the basis for air pollution regulation in the United States. The purpose of the present paper is to propose a systematic approach to the regression analyses that are central to this kind of research. We argue that the results may depend on a number of ad hoc features of the analysis, including which meteorological variables to adjust for, and the manner in which different lagged values of particulate matter are combined into a single ‘exposure measure’. We also examine the question of whether the effects are linear or nonlinear, with particular attention to the possibility of a ‘threshold effect’, i.e. that significant effects occur only above some threshold. These points are illustrated with a data set from Birmingham, Alabama, first cited by Schwartz (1993, American Journal of Epidemiology 137 : 1136 – 1147) and since extensively re‐analyzed. For this data set, we find that the results are sensitive to whether humidity is included along with temperature as a meteorological variable, and to the definition of the exposure measure. We also find evidence of a threshold effect, with the greatest increase in mortality occurring above 50 µg/m 3 , which is the long‐term average level permitted by the current NAAQS. Thus, on the basis of this data set, the need for a tighter NAAQS is not established. Although this particular analysis is focussed just on one data set, the issues it raises are typical in this area of research. We do not dispute that there is a reasonable level of evidence linking atmospheric particulate matter with adverse health outcomes even within the levels permitted by current regulations. However, the impression has been created by some of the published literature that such associations are overwhelmingly supported by epidemiological research. Our viewpoint is that the statistical analyses allow different interpretations, and that the case for tighter regulations cannot be based solely on studies of this nature. Copyright © 2000 John Wiley & Sons, Ltd.