Cross-sectional mortality studies and air pollution risk assessment

Environmentlnternational, Vol. 10, pp. 55-83, 1984

0160-4120/84 $3.00 + .00 Copyright©1984Pergamon Press Ltd.

Printed in the USA. All rights reserved.

CROSS-SECTIONAL MORTALITY STUDIES AND AIR POLLUTION RISK ASSESSMENT J. S. Evans, T. Tosteson, and P. L. Kinney Harvard University, Energy and Environmental Policy Center, Cambridge, Massachusetts 02138, USA (Received 8 January 1984; Accepted 27 April 1984)

The use of coefficients,derived from cross-sectionalmortality studies, for air pollution risk assessmentis quite controversial. In this study, the major limitations of cross-sectional studies are reviewed. The consistency of results from the major recent cross-sectionalstudies is examined, and the sensitivityof results to model specificationis analyzed. Finally, the implications for risk assessmentof our inquiriesare discussed.

Introduction

physical-chemical specificity of monitoring networks. Misclassification may be either random or systematic. Random misclassification acts to increase the estimated standard e r r o r s - m a k i n g it more difficult to detect an effect. In contrast, systematic misclassification can indicate an effect where there is none. Therefore, random misclassification is o f less concern in the interpretation of positive studies (those which find effects) than is systematic misclassification. The second concern is confounding. In a confounded study an observed effect may be attributed to a noncausal agent (e.g., air pollution). In order for a factor to confound a cross-sectional mortality study it must: (1) be a cause of death; (2) be excluded from the model (or measured much less precisely than air pollution exposures); and (3) be statistically correlated with air pollution exposures (Monson, 1980). Plausible confounding factors must share the geographic patterns o f air pollution exposures. In addition to evaluating the internal validity of the results, the epidemiologist considers external validity. Hill (1965) listed several points to be considered in this phase o f the evaluation of epidemiological studies. These included:

Over the past three decades, a great deal o f effort has been devoted to studying the relationship between geographic differences in air pollution levels and community mortality rates. The coefficients from crosssectional mortality studies are often used to estimate the risk o f mortality associated with exposure to particulate air pollution. The numerical results from cross-sectional mortality studies pose a dilemma for the risk analyst. If taken at face value, several o f the studies imply that a large number of persons die prematurely due to relatively low levels o f ambient air pollution. The apparent risk is large enough to warrant analysis o f the feasibility and practicality (cost-effectiveness or cost-benefit) o f air pollution control strategies. However, as we stress in the analysis which follows, the uncertainty surrounding these risk estimates is l a r g e - s o large that the true mortality risk might in fact be zero. Since studies o f this nature are fraught with difficulties o f methodology and interpretation, we believe that an introductory discussion o f some o f these important problems is necessary. Although these studies differ in many ways from traditional epidemiological investigations (e.g., observational data on individuals), the principles routinely followed by epidemiologists should guide the interpretation o f results. Two issues that are given detailed attention in the analysis o f internal validity o f epidemiologic studies are misclassification and confounding. In cross-sectional studies it is possible that exposures could be misclassifted, due to migration from one city to another and due to the limited spatial density, temporal history, and

• • • • • 55

strength of association; consistency o f results; specificity of results; temporal gradient (cause precedes effect); biological gradient (variation in response with dose); and

56

• biological plausibility (consistency of theory, experimentation, and epidemiology) The disagreement concerning the interpretation of cross-sectional mortality studies is extreme. Ware et al. (1981) concluded that cross-sectional mortality studies "provide no reliable evidence for assessing the health effects of sulfur dioxide and particulates." In contrast, Hamilton and Manne (1978) and others have used the coefficients from these studies as a basis for risk assessment. The analysis that follows considers the evidence on these issues in an attempt to clarify the utility of parameter estimates from cross-sectional mortality studies for risk analysis. The investigation was conducted in three phases: • literature search and critical review; • quantitative summarization and analysis of the coefficients from these studies; and • statistical analysis of one of the central cross-sectional data bases. Our paper is divided into four sections. The first three sections present the results of our investigation. The final section of the paper discusses the implications of our findings for policy-oriented risk assessment.

Critical Review of the Literature The literature related to our investigation is rich. The earliest studies focused on the differences between urban and rural mortality rates. These were followed by studies which examined the association between air pollution and mortality rates using cross-tabulation, simple correlation, and analysis of variance. (See, for example, Mills, 1943; Stocks, 1947; Fairbairn and Reid, 1958; Zeidberg et aL, 1967; and Winkelstein et aL, 1967.) Although many of these studies found statistically significant associations between air pollution and mortality, they did not provide the quantitative information needed for risk assessment. More recent cross-sectional mortality studies, using regression-based methods, in theory are capable of providing quantitative risk estimates. Our review and analysis is limited to regressionbased studies. The literature search indicated that at least a dozen research groups have conducted regression-based crosssectional mortality analyses and that many have contributed critical reviews of this work (see Table 1). The single most important set of studies of this nature is the work of Lave and Seskin. Starting with a relatively simple model, Lave and Seskin have now published over 300 equations relating air pollution levels to community mortality rates. Their first equation, published in 1970, involved analysis of a linear relationship between crude mortality rates in 114 Standard Metropolitan Statistical

J.S. Evans, T. Tosteson, and P. L. Kinney Table 1. Cross-sectional mortality studies and critiques. Major Recent Studies Crocker et al. (1979), Gerking and Schulze (1981) Gregor (1976) Hickey et al. (1977) Koshal and Koshal (1973, 1974) Lave and Seskin (1970, 1973, 1977, 1979); Chappie and Lave (1981) Lipfert (1977a, 1977b, 1978a, 1978b) Liu and Yu (1976, 1977) Mendelsohn and Orcutt (1979) Schwing and McDonald (1976) Selvin et aL (1981) Seneca et al. (1979) Smith (1976a, 1976h) Thomas (1973) Critiques and Ancillary Works Berrettoni (1978) Bozzo et al. (1977) Christansen and Degan (1980) Cooper and Hamilton (1978) Finch and Morris (1977) Gibbons and McDonald (1973, 1980a, 1980b, 1980c, 1981, 1982); McDonald and Schwing (1973) Hamilton (1979) Holland et aL (1979) Kitagawa and Hauser (1973) Lipfert (1980a, 1980b) Olsen (1978) Page and Fellner (1978); Page and Fabian (1978) Pickles (1982) Pocock et al. (1981) Ricci and Wyzga (1980) Saner (1979) Thibodeau et aL (1980) Viren (1978) Ware et al. (1981) Wilson et al. (1980)

Areas (SMSAs) and five explanatory variables: a measure of Total Suspended Particulate matter (TSP) and indicators of racial mix, age structure, income, and population density. Virtually all of the work which has been done in this field since 1970 has been intended to address the limitations of this first equation. Table 2 summarizes the major issues which have been raised in criticism of this work. For ease of discussion the criticisms have been grouped into two major classifications-those directed at the data base and those focused on methodology. The criticisms of the data base are further broken down according to the variable groups they address: mortality, air pollution, or other control variables. Our literature review examines the assertions that have been made concerning the importance of each of these criticisms. Arguments are often cast in terms of the effect of various procedures on the magnitude of estimated coefficients, with little attention to their statistical significance. This approach, while somewhat controversial, is rooted in the notion of exploratory data analysis as distinguished from hypothesis testing. Here the

Cross-seetional mortality studies Table 2. Limitations and criticisms o f cross-seetional mortality studies. Inadequacies o f Data Bases Air Pollution • single central city monitor • single year's data • selection o f T S P and sulfates as exposure metrics • o t h e r - s p e c i f i c to certain studies Mortality • disease-age-sex-specific better • use o f °70 _> 65 as age control Potential C o n f o u n d i n g Variables • smoking • occupational exposures • medical care • diet and drinking water • differential migration • h o m e heating and indoor air quality Methodological Issues Influence o f Outliers Variable Selection Procedures Violations o f OLS A s s u m p t i o n s • collinearity • a m o n g pollution variables • between pollution variables • included variables (instability o f estimates) • excluded variables (confounding) • heteroscedasticity Linearity The Ecological Fallacy

studies are viewed as a group with parameter estimates based upon partially overlapping data sets. In this complex environment, the validity of conventional procedures for evaluation of the role of chance is far from clear. Criticisms o f Data Bases--Pollution Variables The use of a single central-city monitor to represent the exposure of the entire population of a SMSA has received quite a bit of attention. Although to some the issue is seen in terms of the plausibility of results based upon "inadequate" characterization of exposure, to others the issue is more appropriately cast in terms of the influence of "measurement error" in the independent variables upon the parameter estimates given by ordinary least-squares regression. Smith (1976b), Lipfert (1978b) and Lave and Seskin have all discussed this, and Moran (1971) has reviewed the theoretical implications, but not in the present context. Frederick Lipfert (1978b) explored this issue empirically. Reasoning that the representativeness of the central-city monitor should be less of a problem in data from cities than in data from SMSAs, Lipfert estimated model parameters for both SMSAs and the cities within those SMSAs. The estimated TSP coefficient for cities was about 20% higher than the coefficient estimated for SMSAs. In a pair of regressions using SO~- as the sole pollution measure, the estimated coefficient was almost 50°70 higher for cities than for SMSAs.

57

Others, including Gregor (1976), Selvin et al. (1981), and Mendelsohn and Orcutt (1978), have averaged the values from several monitors to obtain representative pollution data. However, none of them have reported comparative analyses of results based upon a single station. Comparison of their results with those of Lave and Seskin is complicated by other differences in methodology. Gregor analyzed differences in mortality rates between various areas of Allegheny County, PA. Mendelsohn and Orcutt, as well as Selvin et al., used data on counties and county groups rather than SMSAs or cities. A second frequent criticism of the air pollution exposure characterization involves use of a single year's data to characterize the entire exposure history. Table 3 gives the age distribution and death rates in each age cohort of the 1975 U.S. population. This table demonstrates that to capture the chronic influence of air pollution on chronic disease mortality, a cross-sectional study using 1980 crude mortality data might need to include air pollution information for years as early as 1900. This is because persons over 55 are responsible for 80°7o of crude mortality, and because the exposure histories of older individuals are likely to be inadequately characterized by pollution data for recent years. In addition, since some of the relevant exposure may have occurred in cities other than the place of death, it would be desirable to account for the mobility of the population. Although theory may give some indication of the influence of this inadequacy on the parameter estimates for certain circumstances, it does not provide reliable guidance in realistically complex circumstances. The empirical evidence on this issue is quite limited. Koshal and Koshal (1973) fit models in which the pollution variables were lagged as much as seven years. Increasing the averaging period tended to yield more stable response coefficients. However, the lags were not sufficiently long to fully characterize the population exposure history. Lipfert (1978b), in one series of regressions, considered the relative influence of 1957-1961 and 1969-1971 pollution data on 1969 mortality rates. In all regressions involving total mortality rates, using a stepwise variable selection procedure, the 10-yr-lagged TSP data were selected in lieu of the same-year TSP data. Unfortunately, Lipfert did not report the results of a comparable regression on a similar data base using only same-year TSP data. Lipfert, as well as Lave and Seskin, has noted the correlation among pollution measures over time. For example, in Lipfert's data base the correlation between the 1957-1961 TSP and 1969-1971 TSP values was r = + 0.72, and the correlation between the SO,2- values over this same period was r = +0.78. In the face of such autocorrelation, current pollution data may to some extent represent past pollution exposures. A third criticism of the air pollution data has involved the choice of TSP and sulfates as measures of exposure.

58

J.S. Evans, T. Tosteson, and P. L. Kinney Table 3. Age distribution and age-specific death rates in United States (1975). (Source: National Center for Health Statistics, 1975.) Age Group (yr) Fraction of population Age-specific death rate (deaths/yr per 100,000 persons) Fraction of total deaths

<5

5-14 15-24 25-34 35-44 45-54 55-64 65-74 75-84 85+

0.075 0.177 0.188 0.144 0.107 0 . I l l 0.093 0.065 0.031 0.009

375

36

199

143

267

650

1496

3189

7359 15188

0.031 0.007 0.025 0.023 0.032 0.082 0.156 0.234 0.259 0.151

Physiologists have demonstrated that only a fraction of TSP is inhalable (IP). Subsequently, it has been demonstrated that the fraction of TSP which is inhalable is not constant, but varies from place to place and time to time. Further, atmospheric chemists have stressed the variability in the chemical composition of the aerosol. Despite these problems, TSP has proved to be a useful index of air pollution in many epidemiological studies since the 1950's. There are also questions about the plausibility of SO~- as a cause of ill health. Recently, it has been noted that, in many areas, much of the atmospheric sulfate may be ammonium sulfate, a relatively innocuous species (U.S. EPA, 1982). Experimental work with both guinea pigs and normal and asthmatic humans has shown that the acid components of the sulfate aerosol, sulfuric acid and ammonium bisulfate, are more pernicious (Amdur et al., 1952; Amdur, 1958; Koenig et al., 1983; Utell et al., 1982). It is not clear that total sulfate levels serve as an adequate surrogate for acid sulfate levels. However, since in most places sulfate is concentrated in the fine fraction, total sulfate levels may serve as an index of fine particle exposures. Furthermore, the relevance of TSP and sulfates for chronic health effects is unclear. Certainly those interested in cancer mortality would want to see pollution measures which reflected the concentrations of ambient organic compounds included in the analyses. Unfortunately, for the period of interest (1960-1975), the only widely available comparative data on airborne particulate matter was from the National Air Surveillance Network (NASN). This network has been described by Olsen (1978); the following discussion is from Olsen's study. NASN was established in 1953 with monitoring sites in 17 communities across the nation. The urban sampling locations were usually located in a central business district and not more than 50 ft above ground level. Although the samplers were operated by local agencies, materials and equipment were provided by the U.S. Public Health Service. Twenty-four-hour High Volume (Hi-Vol) air samples were collected on glass filters at each site once in each 2-week period, yielding 26 samples per year. The exact sampling period within each 2-week interval was chosen randomly, to avoid day-of-week sampling bias. Procedures were specified for analyzing each filter for: TSP, beta radiation,

benzene soluble organics, sulfate, nitrate, and certain metals (iron, manganese, lead, vanadium, nickel, copper, and cadmium). Between 1954 and 1956, the network was expanded to include 66 stations. By 1961 it had expanded to 250 sites (213 urban and 37 non-urban). In the 1960's, 24-h bubblers were added to the network, permitting sampling for SO2 and NO2. In January 1971, the sampling schedule was altered. Since then a sample has been collected each sixth day. Therefore, many investigators of cross-sectional data bases limited their analyses to a few pollutants, typically TSP and SOl-. Some investigators have worked with larger sets of pollutants. However, they have generally needed to sacrifice large numbers of cities from their analyses, due to the incomplete pollution records. For example, in Lipfert's data set (1977b) there were 136 cities for which TSP, SO~-, Fe, Mn, and benzo-a-pyrene [B(a)P] data were available for 1969. However, Lipfert found acceptable data on cadmium, copper, lead, nickel, vanadium, and nitrate for only 60 of these. In addition, investigators who have used larger sets of pollution variables have found significant collinearity among the pollution variables, making interpretation of individual coefficients questionable. Table 4 presents two such correlation matrices. Hickey et aL (1977) used perhaps the largest set of air pollution variables, including SO2, SO~-, NO2, Cd, Cr, Cu, Fe, Pb, Mn, Ni, Sn, Ti, Zn, and As. His analysis suffered from both problems mentioned a b o v e - c o m plete pollution data were available for only 38 SMSAs, and the high correlation among the pollution variables necessitated the use of an "optimal" regression procedure for variable selection. In addition, pollution data for 1957-1964 were used to explain mortality rates for 1959-1961. Thus, the interpretation of coefficients is quite difficult. However, it should be noted that Hickey viewed the results as preliminary and exploratory in nature. He was interested in generating hypotheses relating chemical pollutants and chronic disease, rather than in developing quantitative coefficients for use in risk assessment. Even for those investigators who have limited their analyses to TSP and SOl-, collinearity has posed a problem. This has two causes. First, the gravimetric TSP measurement includes the mass of SO,2-. Second, mete-

Cross-sectional mo rtality studies

59

Table 4. Pearson product-moment correlation coefficients (r) a m o n g several measures o f pollution. Lipfert: 1969-1971 Data Set SO4 TSP a Mn

SO4

1.00

TSP Mn Fe BaP BSO SO2

0.44 0.25 0.40 0.35 0.32 0.67

. 1.00 0.30 0.73 0.41 0.65 0.23

.

Fe .

. 1.00 0.45 0.34 0.25 0.05

BaP b .

BSO c

.

.

.

.

. 1.00 0.41 0.37 0.20

SO2

. .

. .

. -1.00 0.22

1.00 0.62 0.30

1.00

Crocker et al.: Selected Variables from 1966-1972 Data Set NH4 NO3 SO4 NOa TSP SO2 Pb

CO

NH, NO3 SO4 NO2 TSP SO2 Pb CO

1.00

1.00 -0.16 0.78 0.28 0.51 0.52 0.04 0.14

. 1.00 0.07 0.36 0.24 0.04 0.51 0.19

.

.

. 1.00 0.34 0.68 0.72 0.07 0.28

. .

. .

. 1.00 0.30 0.35 0.40 0.21

. .

.

. . 1.00 0.50 0.19 0.36

. .

. .

.

. .

.

1.00 0.10 0.50

1.00 0.20

aTotal suspended particles. bBenzo(a) pyrene. CBenzene soluble organics.

orological differences and differences in source emissions rates tend to cause both the SOl- and the non-SO~components of TSP to vary together geographically. Lipfert explored the impact of the first difficulty by creating a "net TSP" variable [i.e., TSP - (SO~- + NO3 + Fe + Mn + B(a)P)], after appropriate corrections for molecular weight. The "net TSP" coefficient dropped by 10070in comparison with the TSP coefficient in a similar regression. The SOl- coefficient, which had been negative, became 15070 smaller in absolute value. The coefficients of the other pollution variables were unchanged. While this adjustment is advantageous, it does not eliminate the correlation between "net TSP" and the other pollution variable, SOl-. Besides collinearity among various pollutants, there is collinearity among various measures of the same pollutant. Table 5 demonstrates the correlation among the Table 5. Pearso n pro duct-moment correlations a m o n g minimum, mean, an d m a x i m u m TSP and SO,. (Source: Lave and Seskin, 1977.) a Lave and S e s k i n - 1960 Data Set Min Mean SO, SO, Min SO4 Mean SO4 Max SO4 Min TSP Mean TSP Max TSP

1.00 0.60 0.39 0.32 0.27 0.07

. 1.00 0.85 0.59 0.59 0.40

Max SO4 .

Min TSP .

. 1.00 0.42 0.54 0.53

Mean TSP

. .

. .

-1.00 0.75 0.39

Max TSP

. 1.00 0.79

1.00

aLave and Seskin found that a single principal component could explain 75°70 of the variance in these data, and that two principal components could explain 96070 of the variance.

three sulfates and three particulate measures in the 1969 Lave and Seskin data set. It would be desirable to know which parameter (i.e., minimum, mean, or maximum) of the distribution was important for mortality effects. However, it seems that the available data are not rich enough to permit realistic investigation of this issue. The accuracy and precision of the pollution measurements has been questioned. The precision of individual Hi-Vol measurements has been estimated to be between 4.5070 and 7.5070 of the measured value. The greatest source of measurement error is uncertainty in determination of flow rate, and, therefore, errors are proportional to measured concentrations. At a level of 100 /~g/m 3 this would lead to a measurement error of 7.5 /zg/m 3 for an individual measurement. A mean value of 100 #g/m 3 based upon 26 samples would have a standard error of 1.0/~g/m 3. Clearly these random measurement errors are trivial in contrast to the uncertainties introduced by estimating population exposure on the basis of the values from a single central city monitor. Olsen has discussed several other problems with the Hi-Vol method, such as the deposition of particles on the filter during nonsampling periods, the dependence of the samplers inlet characteristics upon wind speed and direction, and the possibility that under extremely high wind speeds particles could be blown off the filter. These would also seem to be second-order considerations. One additional problem has been raised concerning the Hi-Vol sulfate and nitrate data. This is the potential for formation of sulfate and/or nitrate artifact on the glass fiber filters. Coutant (1977) has developed an empirical formula for estimating sulfate artifact formation. The estimated sulfate artifact is a function of temperature, relative humidity, SO2 concentration, the flow rate of the sampler, and the ratio of sulfates to metal ions in the blank filter medium. The sulfate artifact may be as high as 5/,g/m 3. It is difficult to predict whether the artifact would tend to be correlated with the true sulfate level, and if so whether the correlation would be positive or negative. There is a similar potential for nitrate artifact formation. In a personal communication, Lipfert noted that his attempts to correct the data for sulfate artifact, by subtracting some fraction of the ambient SO2, had no effect upon his conclusions. Because the early work of Lave and Seskin plays such a prominent role, many criticisms were quite specifically directed at their 1960 data base. For example, Thibodeau et al. (1980) noted coding errors in the sulfate values for Atlanta, GA, and Bridgeport, CT, used by Lave and Seskin. These had trivial influence on the parameter estimates. More significantly, Lipfert as well as Olsen and Thibodeau et al. have correctly asserted that the 1960 sulfate data base used by Lave and Seskin consisted of an odd mixture of data. For 58 SMSAs the data were based upon 26 samples, one taken every other week

60

throughout the year. For the other 59 SMSAs the data were based upon four observations, one from each calendar quarter. More troubling is the fact that four of the latter group were based upon quarterly "heavy" samples. Heavy samples represent sulfate analysis of the filter having the highest TSP mass for the quarter. This insight certainly raises questions concerning the validity of the coefficients of the minimum and maximum SO~variables from the 1960 Lave and Seskin data set. Thibodeau et aL also called attention to the fact that much of the sulfate data was for years other than 1960 (e.g., 1958-55070, 1957-14°/0, 1959-28°/0). The Lave and Seskin data set for 1969 is not known to suffer from these defects. Criticisms o f Data B a s e s - Mortality Variables

A primary concern has been the use of crude mortality rate as the dependent variable. One issue has been the adequacy of the variable "percentage over 65" as a means of adjusting crude mortality rates to reflect the influence of differences in population age structure among SMSAs. Lipfert considered using "median age" as well as "percentage over 65" and concluded that "median age" added little explanatory power to his models. In the recent study by Selvin et al. (1981), four principal components of the age structure were used to adjust the crude mortality data. However, Selvin et aL did not report comparative analyses demonstrating the superiority of this procedure. Using data for 114 SMSAs from Lave and Seskin's 1960 data set, Gibbons and McDonald (1980c) tested the sensitivity of Lave and Seskin's basic seven- and eleven-variable regression results to the use of four mortality indices which differed in the degree and type of age, sex, and race adjustment. Except for obvious changes in the R 2 and in the significance of the one-age variable when going from unadjusted to adjusted mortality rates, no important changes were detected. In particular, the elasticities of the combined air pollution variables in the four mortality specifications all fell within 10% of one another. A second limitation associated with use of crude mortality rates is the failure to exploit any information which might be available in the disease- or age-specific results. For example, analysis of disease-specific rates might be expected to provide evidence on the biological plausibility of the crude associations, and analysis of age-specific rates might be expected to yield information useful for determination of the impact of air pollution on life expectancy. In response to these opportunities, many investigators have chosen to examine the diseasespecific and age-specific results. Table 6 summarizes the disease-specific findings of Lave and Seskin. Note the statistically significant effect of SO~- on total cardiovascular deaths and upon all of the subdivisions of this category. In contrast, while there is a statistically significant association between total cancers and SO,2-,

J.S. Evans, T. Tosteson, and P. L. Kinney Table 6. Disease-specific results of Lave and Seskin mortality, 1960.

Disease Category

Statistically Significanta SO4 Effect (t > 2.0)

Disease-Specificb Mortality Rate (Deaths/yr/100,000)

Total Respiratory Disease Tuberculosis Asthma Influenza Pneumonia Bronchitis

no no no no no no

45 5 3 3 31 2

Total Cardiovascular Disease Diseases of heart Hypertensive heart disease Endocarditis-nonrheumatic

yes yes yes yes

482 349 35 28

Total Cancers Respiratory Digestive system Breast Buccal Cavity-Pharynx

yes no yes no no

143 22 47 13 3

aSource: Lave and Seskin, 1977, Table 4.4. bSource: Lave and Seskin, 1977, Table D.3.

the associations between SO~- and specific cancers are not, in general, statistically significant. The exception is cancer of the digestive system. Similarly, counterintuitive results are found for both total respiratory disease and the components of this disease category. Although at first somewhat disturbing, the nonsignificant associations between sulfates and respiratory diseases may be due in part to their relatively low rates of occurrence as a cause of death in the U.S. population. In addition, it should be noted that many of the nominally cardiovascular deaths may indeed be secondary to respiratory problems. Sauer (1979) is one of the critics who has called attention to the errors which may be introduced by uncritical interpretation of "disease-specific" rates, noting that certain disease classes serve as catch-all categories for uncertain diagnoses. Using pollution data from Hickey et al. (1977) and mortality data from Duffy and Carroll (1967), Page and Fellner (1978) examined the relationships between pollution and disease-specific mortality in a group of 38 SMSAs. The data in Page and Fellner's analysis were from the period of 1959-1961. Simple Pearson productmoment correlations between the pollutants and the disease-specific mortality rates are shown in Table 7. The notable features are the positive correlations between SOl- and gastrointestinal cancers, arteriosclerotic heart disease, and hypertensive heart disease, as well as the negative correlations between SO42- and both emphysema and asthma. In addition to examining these raw correlations, Page and Fellner looked for underlying disease and pollution vectors using principal components and canonical cor-

Cross-sectional mortality studies

61 Table 8. Population trends in 20 major cities. (Adapted from Lipfert, 1978.)

Table 7. Disease-group- and pollutant-specific results of Page and Fellner [Pearson product-moment correlations (r)]. (Source: Page and Fellner, 1978, Table V.)a Pollutant Measure Disease Group Cancer of esophagus, stomach, small intestine, large intestine, rectum, liver, biliary passages pancreas Breast cancer Asthma Arteriosclerotic heart disease Chronic endocarditis, other myocardian degeneration, other heart disease Hypertensive heart disease Emphysema

City

SO,

SO2

NO2

0.56 0.33 -0.39 0.53

0.59 0.32 0.54

0.51 -

0.31 0.55 -0.42

0.30 -0.36

-

aOnly those correlations significant at less t h a n p = 0.05 are reported. Others are indicated by a dash.

relations. Using age-sex-race adjusted mortality rates, the analysis of principal components identified five disease factors with eigenvalues greater than 1. These were as follows: FI: emphysema, asthma, and bronchiectasis; F2: cancer of kidney, bladder, urinary tract, thyroid, pleura, lip, tongue, buccal cavity a-ld pharynx; F3: acute and chronic bronchitis; F4: chronic endocarditis, other myocardial degeneration, hypertensive heart disease, and other heart disease; and F5: cancer of larynx, trachea, bronchus, and lung. In the canonical correlation analysis, a single canonical pollution variable, dominated by SO~- and SO2, was found to be strongly correlated (r _> 0.88) with a canonical disease variable dominated by gastrointestinal cancers, arteriosclerotic heart disease, and cancer of the larynx, bronchus, and lung. Another difficulty with the mortality data used in these studies is that deaths are classified by county of "usual residence" at the time of death. For cities which have experienced high immigration rates, especially of older persons, this may result in a certain amount of misclassification of chronic air pollution exposure. Table 8, from Lipfert (1978), shows the relative population stability of 20 major cities. Misclassification of chronic exposures may also be troublesome in cities with large populations in institutions, such as hospitals, prisons, and schools. Although this might influence the estimation of chronic effects, it would not cause substantial problems for measurement of acute effects. A second-order effect, which Lipfert noted, was the inconsistency in calculating death rates from city to city. Empirical evidence indicated an unusual ratio of federal to state 1970 mortality rates for Wilmington, DE, and

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20.

Wilmington, DE Providence, RI Boston, MA Pittsburgh, PA Trenton, NJ Philadelphia, PA Hartford, CT Minneapolis, MN New York, NY San Francisco, CA Detroit, MI Salt Lake City, UT Birmingham, AL Washington, DC Seattle, WA Denver, CO Miami, FL Atlanta, GA Los Angeles, CA Houston, TX

Year Attained 75% of 1970 Population

Corresponding Age Cohort

1890 1892 1894 1904 1905 1906 1915 1916 1923 1923 1924 1926 1927 1935 1944 1948 1949 1953 1953 1959

75-84 75-84 75-84 65-74 65-74 65-74 55-64 55-64 45-54 45-54 45-54 45-54 45-54 35-44 25-34 25-34 25-34 15-24 15-24 15-24

South Charleston, WV. These were thought to be due in part to methods of handling out-of-state deaths.

Criticisms of Data Bases- Potential Confounding Variables Among the leading alternative explanations of the observed association between air pollution exposure and mortality rates are those involving confounding. A factor may confound a study if it is a cause of increased mortality rates, is statistically associated with air pollution exposures, and is unmeasured, poorly measured, and/or not included in the set of variables under consideration. Factors which have been frequently cited as confounding cross-sectional mortality studies include: smoking habits, occupational exposures, dietary factors (including drinking water supply), availability and quality of medical care, and differential migration. One of the most intuitively plausible confounding variables is smoking. According to the 1981 Statistical Abstract of the United States (U.S. Bureau of the Census, 1981), approximately one-third of the U.S. population smokes cigarettes. The mortality rates of smokers are about one and one-half times those of nonsmokers (Hammond, 1966). On this basis it can be estimated that cigarette smoking accounts for more than one-tenth of all deaths in the United States. Further, from data published by the Tobacco Tax Council, Inc. (1977), it is evident that per capita cigarette sales have varied substantially over time and space in the United States. Nationally, per capita annual sales have increased from 0.4 cigarettes in 1870 to 2534 cigarettes in 1970. In 1965, per capita annual sales varied from 1300 cigarettes in Utah to 4930 cigarettes in New Hampshire, with a mean

62

of 2702. Therefore, if geographic variations in cigarette consumption are correlated with variations in air pollution exposure, they could confound cross-sectional mortality studies. This potential has been realized since the first crosssectional mortality studies were undertaken. However, most investigators have chosen not to control for smoking due to: (1) the lack of direct data on smoking habits by SMSA, and (2) belief that smoking habits are not strongly correlated with community air pollution levels. Three investigations--one by Lipfert (1978b), one by Schwing and McDonald (1976), and one by Chappie and Lave (1981)-have explored this issue empirically. In each analysis an index of smoking habits was included as an independent variable. Schwing and McDonald's smoking index, which was based directly upon state per capita sales, accounted for only 3070 of mortality. This is much lower than would be expected-and may indicate inadequate control for smoking. In any event, they did not report pollution coefficients from analyses without control for smoking. Therefore, their analysis does not clarify the confounding issue. Geographic differences in per capita sales are not perfect indicators of differences in per capita consumption. This is due to wide variations in state tax rates (and, therefore, prices) in adjacent states. For example, in 1957 the average price of a cigarette in Massachusetts was 2.87 cents, while in neighboring New Hampshire it was only 2.20 cents (24070 lower). Lipfert's smoking variable is nominally preferable to Schwing and McDonald's because it includes adjustments for tax and price differentials among adjacent states. Although its impact varied greatly throughout his many regressions. Lipfert's variable commonly accounted for at least 10070 of total mortality. Of more importance is that the introduction of the smoking variable tended to influence only slightly the TSP and sulfate coefficients. For example, Table V-4 in Lipfert's dissertation (1978b) indicates that introduction of the smoking variable changed the sulfate coefficient by about one-half of a standard error and its effect on the TSP coefficient was less than 10070 of a standard error. A recent reanalysis by Chappie and Lave (1981) uses estimated per capita expenditures on tobacco products by SMSA, based upon the 1967 Census of Business Retail Merchandise Line Sales, to control for smoking. Chappie and Lave demonstrate that the variable is not highly correlated with any of the six air pollution variables in their analysis. (The largest of the six sample correlations was r = +0.17, N = 102.) The variable accounts for about 6°70 of total mortality at its mean. Its introduction has only trivial influence on the air pollution coefficients, inducing slight increases in them. Reflecting upon these results, Chappie and Lave found the cigarette smoking variable to be unimportant, as had Schwing and McDonald. Unfortunately, neither data source on smoking habits is perfect. Use of state sales tax data

J.S. Evans, T. Tosteson, and P. L. Kinney

may lead to underestimation of smoking in urban areas, and the census of retail sales is thought to suffer from variable and incomplete reporting and does not account for differences in cigarette price. A second, frequently mentioned potential confounding factor is occupational exposure to toxic chemicals and/or physical agents. The argument advanced rests upon idealized contrasts between the "heavily industrialized and heavily polluted" Northeast and the "pristine agricultural" Midwest and West. For occupational exposures to confound cross-sectional studies, (a) occupational exposures would have to account for an appreciable fraction of total SMSA mortality, and (b) the employment in industries responsible for pernicious occupational exposures would have to statistically correlate with the levels of air pollution variables. It seems unlikely that both of these conditions are met. First, only 97 x 106 persons (roughly 40070 of the population) were employed in 1980. Of these only a small fraction were employed in capacities which would be thought to involve appreciable occupational exposures. Table 9 summarizes data from the 1980's which are relevant to this point. For this relatively small fraction of potentially exposed workers to contribute significantly to SMSA mortality rates, each exposed worker would have to carry a large burden of job-related risk. This is particularly true for studies that exclude accidental deaths from the mortality rates under consideration. Second, the geographic distribution of the industries most commonly associated with significant mortality

Table 9. Summary data on occupation of U.S. population, 1980. (Source: Statistical Abstract of the United States (1980) Table 675.)

Job Category Professional, technical, and kindred Managers, administrators Sales workers Clerical and kindred Transport equipment operatives Operatives, except transport mine workers, 0.2 painters, mfg. goods, 0.2 textile operatives, 0.3 welders, flame cutters, 0.7 Craft and kindred workers print craftworkers, 0.2 metal craft workers, 0.6 machinists, 0.7 crane-derrick-hoist operatives, 0.1 Laborers, except farm Farmers, farm managers, farm laborers and farm supervisors Service workers, except private household firefighters, 0.2 Private household workers Total

Employment (in millions)

% of All Employed

15.6 10.9 6.2 18.1 3.5 10.3

16.0 11.2 6.4 18.6 3.6 10.6

12.5

12.8

4.4

4.5

2.7 11.9

2.8 12.2

1.0 97.3

1.0 100.0

Cross-sectional mortality studies

risks due to occupational disease is not clearly related to levels of sulfates and particulate matter (see Stone et al., 1978). Nonetheless, Lave and Seskin attempted to control for differences in the occupational mix of the population by including nine variables reflecting the percentage of SMSA employment in each of nine broad industrial classes (agriculture, construction, manufacturing, etc.), one variable reflecting the job classification of the population (percentage white collar) and one variable indicating the percentage of the SMSA potential workforce categorized as unemployed. Augmentation of the basic seven-variable data set with these 13 additional variables caused the coefficient of minimum SO,~- to drop by 63070 and the coefficient of mean TSP to drop by 29°7o. (Their introduction also caused the coefficient of population density to change sign and the coefficient of percentage poor to change by two orders of magnitude. The other socioeconomic variables were not strongly influenced.) The two "occupational variables" which were nost strongly correlated with mortality rates were "percentage unemployed" and "percentage white collar," both of which could be socioeconomic variables, indicative of differences in lifestyle rather than occupational exposures. Gibbons and McDonald's reanalysis (1980a) of the Lave and Seskin 60-variable data set indicated that "percentage white collar" was a powerful variable. It entered the stepwise regressions at the fourth step, and was highly statistically significant in all four of the "best regressions" (7, 11, 18, and 22 variables). The vague "socioeconomic" nature of these variables is highlighted by the recent analysis of 1974 mortality and pollution data by Chappie and Lave. A "percentage college graduates" variable is shown to fluctuate from being quite statistically significant (t = - 4 . 6 to insignificant (t ___ -0.1) as the set of industry mix variables are introduced. Similarly, the "percentage professional" variable is shown to shift from significance (t = -2.5) to insignificance (t = 0.5) as the "percentage college graduates" variable is introduced. In one of the first systematic national studies of socioeconomic differentials in mortality experience, Kitagawa and Hauser (1973) demonstrated the strong relationship between education and mortality experience. Moriyama and Guralnick (1956) noted the strong influence of social class, as indicated by "occupational level" on male mortality rates. They also noted that broad occupational variables control primarily for socioeconomic status. For example, clergymen, teachers, and bankers experience mortality rates similar to their spouses. Thus, it is unclear what these "occupational" variables are measuring, and it is highly likely that they are not measuring the influence of occupational exposures to toxic physical, chemical, or biological agents. Other evidence bears indirectly on the possibility of either occupational exposures or smoking habits acting

63

as confounding variables. Several authors have analyzed mortality data for men and women separately. Gregor (1976), for example, found a smaller influence of air pollution on women than men in all groups. Similarly, Finch and Morris' analysis (1977) indicated smaller effects on mortality rates for women. Mendelsohn and Orcutt's investigation (1978) yielded similar results in all adult age groups. There are several plausible interpretations of these findings: (1) Women are genetically less susceptible to the effects of air pollution. (2) Women are less susceptible to the effects of air pollution because they smoke less than men and/or have less severe occupational exposures. (3) The apparent effects of air pollution are in fact due to confounding by occupation and/or smoking. (4) Air pollution acts to multiply baseline risks (i.e., a relative risk model is correct) and baseline risks are lower for women than for men. The vague nature of the "occupational" variables, the many possible explanations for the smaller risks in women, the limited employment in heavy industry, and the lack of a clear geographic correlation between heavy industry and relevant pollution measures all weaken the argument for confounding by occupational exposures. Another confounding variable which is sometimes mentioned is medical care. A difficulty in analyzing the influence of medical care on mortality rates is correctly specifying the simultaneous equation framework for parameter estimation. The number of doctors per capita has been used as an indicator of the quality of medical care (see, for example, Crocker et aL, 1979). However, the number of doctors per capita is determined by both demand and supply. The demand for physicians is determined in part by the level of illness and disease in the population, and in part by socioeconomic factors. Factors influencing supply include the quantity and quality of major medical facilities and schools, the climate (both meteorological and intellectual) and other determinants of quality of life. In principal, the supply of physicians could influence the health of the population. As Chappie and Lave (1981) note, a simultaneous equation approach can provide more consistent estimates than a single-equation model, when an explanatory variable and the dependent variable depend simultaneously on another variable in the model. Crocker et al. specified a two-equation system in which the number of doctors was a function of five socioeconomic variables, three dietary variables, one climatic variable, and a cigarette consumption index. The community mortality rates were then estimated as a function of the number of doctors, three socioeconomic variables, three dietary variables, one climatic variable, one cigarette consumption index, and three pollution

64

variables. Using a two-stage estimation procedure, the influence of particulate air pollution on total mortality was found to be relatively low (i.e., 0.11 deaths/100,000/yr per ttg TSP/m3). In a recent analysis of 1974 data, Chappie and Lave investigated the influence of "doctors per capita" using both ordinary least squares (OLS) and simultaneous equations frameworks. They criticized Crocker et al.'s specification as inadequate and offered a four-equation system. Equations for physician demand, physician supply, and mortality rates were estimated subject to a constraint equation requiring that supply equal demand. The comparative analysis of OLS and two-stage least squares (2SLS) indicated that the estimated sulfate impact decreased somewhat, but that the particulate coefficients changed very little. Regardless of the estimation methodology, the inclusion of the "doctors per capita" variable had only minor impacts on the estimated impact of air pollution. Dietary factors are known to influence mortality rates and have been considered by some as potential confounding variables. Consumption of saturated fats is a risk factor in heart disease. Recent reports have implicated consumption of saturated and unsaturated fats, preserved meats, and alcohol as factors increasing cancer risks. Consumption of broccoli, carrots, and certain grains (bran) appear to be related to reduced cancer risks. The concentrations of certain organic compounds in drinking water supplies are thought to carry increased cancer risks, and water hardness is known to be strongly related to heart disease mortality r a t e s - t h e harder the water, the lower the mortality from heart disease. Crocker et al. introduced three dietary variables into their regressions to account for these influences. The measures were crude indicators of protein, carbohydrate, and saturated fat consumption. The variables were constructed from USDA data on food consumption patterns in each of eight income brackets and four geographic regions of the United States. These USDA estimates were based upon a sample of 3000 urban households across the United States. Crocker et al.'s regressions involving total mortality rates indicated that increased protein and saturated fat consumption were associated with increased mortality rates, and that increased carbohydrate consumption was related to decreased mortality rates. Crocker et aL did not indicate the effect of the introduction of these variables on the coefficients of the pollution variables. However, the simple pairwise correlations between these dietary variables and the three pollution variables were all below 0.60 in absolute value. Therefore, although confounding would not seem to be particularly severe, due to the level of aggregation of the original dietary data, the evidence for this assertion is weak. Systematic misclassification can mimic confounding. Differential migration, the movement of diseased people from highly polluted to less polluted cities, could bias the pollution coefficients. In the extreme it could

J.S. Evans, T. Tosteson, and P. L. Kinney

cause the investigator to assign an incorrect sign to the effect. There are no data on the migration patterns of chronically ill persons which would permit assessment of this impact. However, by separately analyzing data for cities with high and low net immigration rates, some insight may be gained. Typically, the variable percentage increase in population is used as a surrogate for immigration. Bozzo et aL (1977) noted that in 1970 areas with high immigration tended to be characterized by low pollution and high income. Data from the census indicate that migrants tend to be young. Lave and Seskin split their 1960 data set into two groups on the basis of immigration and replicated their seven-variable analysis on the two subsets of data. The coefficients of minimum sulfate were approximately equal in the two subsets, and both were only 50% as large as the coefficient from the full data set. The coefficient of mean TSP for SMSAs with high immigration was about two-thirds as large as the coefficient from the full data set. For SMSAs with low immigration the mean TSP coefficient was indistinguishable from zero. In unreported analyses Lave and Seskin explored these results in more detail. They concluded that differences in the socioeconomic characteristics of SMSAs with low and high immigration rates were pronounced, and that these differences complicated interpretation of the results. As part of a preliminary investigation of this issue, Lipfert noted that as of 1970 over 70°7o of the population of 10 states (Alabama, Kentucky, South Carolina, Mississippi, Louisiana, Maine, Pennsylvania, Wisconsin, West Virginia, and North Carolina) has lived in the same state since birth. In contrast, more than 50°70 of the population of 10 other states (Delaware, Maryland, Wyoming, Colorado, Arizona, Nevada, Washington, Oregon, California, and Alaska), plus the District of Columbia, has lived elsewhere. He also gave data on the percentage of the population over 65 years old which had lived in the same census region for the last five years (see Table 10). These data indicated that, with the ex-

Table 10. Stability of region of residence of the elderly. (Adapted from Lipfert, 1978.)

Region

Fraction of Elderlya Population Which Lived in Same Region 5 Yr Ago

Fraction of Elderly Population Which Was Born in Region

Northeast Mid-Atlantic South Atlantic Eastern North Central Eastern South Central Western North Central Western South Central Mountain Pacific

94 95 88 95 95 95 90 88 91

58 59 62 64 86 75 72 32 22

aThose over 65 years old in 1970.

Cross-sectional mortality studies

ception of the Mountain and Pacific states, the elderly population was relatively stable. The analysis of Mendelsohn and Orcutt (1978) would be expected to be less susceptible to confounding or systematic misclassification than many of the crosssectional studies. Working with mortality data for individuals from over 400 county groups, Mendelsohn and Orcutt investigated models in which regional effects were removed, effectively limiting the analysis to within regional effects. While the sulfate and sulfur dioxide coefficients were somewhat lower than those obtained when regional effects were allowed to remain, the coefficients were positive and significant. This suggests that the air pollution effects observed in other studies may be real. Criticisms o f Methodology The major methodological issues involve the influence of outlying points, failure to meet OLS assumptions, multicollinearity and linearity in the exposure response function. The influence of outliers is discussed first. Thibodeau et al. (1980) used two methods to detect outlying points in the 1960 l l7-SMSA l 1-explanatory variable data set of Lave and Seskin. The basic data set included mean TSP, minimum SO,2-, percentage nonwhite, percentage over 65, population density, percentage poor, and log SMSA population. To these were added minimum and maximum TSP and mean and maximum SOl-. First, Thibodeau et aL treated the 11 explanatory variables as a vector of observations on each of the 117 SMSAs. After suitable transformations, a measure of distance of each SMSA's explanatory vector from the median explanatory vector was calculated. Using this procedure, six outliers (Charleston, WV; Fresno, CA; Jersey City, N J; Las Vegas, NV; Macon, GA; and; Phoenix, AZ) were identified. When the regressions involving minimum SO42- and mean TSP were repeated without these observations, the SO,2- coefficient dropped 807o and the TSP coefficient increased 1007o. In the regressions involving all six pollution variables, the parameter estimates were less stable. Second, Thibodeau et al. examined residual plots from the Lave and Seskin basic seven- and eleven-variable regressions based upon this same data set. Three residuals (Scranton, PA; Wilkes-Barre, PA; and Tampa, FL) were large. These three points were omitted, in addition to the six previously identified. The parameter estimate for minimum SO42- was 28070 lower than that given by the full l l7-SMSA data set. The parameter estimate for mean TSP was 16070 lower than in the original equation. Gibbons and McDonald (1980b) considered the influence of outliers on the parameters estimated using the full 60-variable Lave and Seskin data set for 117 SMSAs in 1960. This data set includes variables on air pollution, socioeconomic status, occupational mix, climate, and

65

home-heating characteristics. After correcting the sulfate data for Bridgeport, CT, outliers were detected and deleted. A SMSA was considered an outlier if it exceeded the next largest observation by more than 50°70 on any of sixty explanatory variables. Eight SMSAs (Charleston, WV; Jersey City, N J; New York, NY; Fresno, CA; Miami, FL; Duluth, MN; Wilkes-Barre, PA; and Scranton, PA) were deleted on this basis. One SMSA, Providence, RI, was deleted on the basis of being an "inlier." Its value on one variable was almost an order of magnitude smaller than the next smallest value. The result of deleting these nine SMSAs from the basic seven-variable Lave and Seskin was to reduce the coefficient of minimum SO~- by 15070and to reduce the coefficient of mean TSP by 3107o. Gibbons and McDonald also identified outliers on the basis of the "hat matrix," a technique described by Hoaglin and Welsch (1978) to determine which SMSAs actually influenced the coefficients most substantially. By using this and more ordinary methods, a subset of 21 outliers and influential points were identified. It would seem prudent to ensure that the data for these SMSAs was accurate, and to be aware of the sensitivity of the pollution coefficients to these values. As Fig. 1 demonstrates, certain SMSAs may strongly influence the coefficient estimates. However, the Gibbons and McDonald analysis was based upon an underlying regression on all 60 variables in the 1960 Lave and Seskin data set. The coefficients in such a regression would be expected to be quite unstable due to multicollinearity.

60

40E

% 200

=

O'

n

~ -20 ID

-4o

~ -60

-80 Tampa, FL -I00

o

zo

12o Observation

Fig. 1. Sensitivity of TSP coefficient to deletion of SMSAs from Data Base. (From Gibbons and McDonald, 1980b.)

66

J.s. Evans,T. Tosteson,and P. L. Kinney

More recently, Gibbons and McDonald have extended their sensitivity analysis to include an assessment of the combined impact of influential and/or outlying observations by means of two weighted regression techniques. The "bounded influence" procedure constructs weights which limit the impact of the more influential points (in the sense of Hoaglin and Welsch). The Huber robust regression procedure downweights observations having large-scaled residuals. Consistent with the results obtained by excluding one SMSA at a time from the regression, these procedures resulted in point estimates of pollution regression coefficients and their elasticities which varied considerably from those obtained with OLS regression. Although it is clearly important to detect outlying data points and to estimate their impact upon results, it is not at all clear that outlying points should be automatically deleted. In their book, Lave and Seskin call attention to the large residuals of Tampa, FL, Wilkes-Barre, PA, and Scranton, PA, from the basic I 1-variable regression on the 117 SMSAs for 1960. They did not choose to delete the outliers from the data base. Lave and Seskin approached the problem of evaluating the effect of "extreme" observations using the jackknife analysis. Several separate analyses were conducted: (1) Subsets of nine sequential observations were deleted after sorting the entire data set by (a) mean TSP level, (b) percentage poor, and (c) mortality rate; and (2) Each thirteenth observation was deleted without sorting. Lave and Seskin report that the mean estimates averaged over all subsets of observations were quite similar to those for the full data set, and that confidence intervals generated in this way were smaller than those calculated using the standard errors for the coefficient estimates given by OLS regression. For example, in the jackknife analysis sorted by mean TSP, the coefficient of minimum SO,2- in the 1960 seven-variable regression dropped by 2% and the mean TSP coefficient dropped only 0.2% in comparison with the coefficients from the full data set. Another criticism of the methodology used by Lave and Seskin has as its basis arguments concerning departures from, or failures to meet, the assumptions of ordinary least-squares regressions. Smith (1976a) was one of the first to criticize the work on this basis. Using a data base covering 50 SMSAs for the years 1968 and 1969, Smith applied several tests suggested by Ramsey (1969) for detection of model specification errors. Thirty-six alternative models relating air pollution exposure to community mortality rates were investigated. Three questions addressed by the Ramsey tests are: (1) Are the residuals normally distributed?, (2) Are the residuals homoscedastic?, and (3) Do the residuals have null expectation?

Non-null expectation of the residuals may be indicative of omitted independent variables, incorrect functional form of the independent variables, or failure to correctly specify simultaneous equations. Most of the models' residuals appeared to have null expectation. However, many of them seemed to have heteroscedastic residuals, and most of them failed the test for normality of residuals. These limitations primarily influence the interpretation of the estimated standard errors associated with the coefficient estimates. Smith demonstrated that in the presence of heteroscedasticity, not only are OLS parameter estimates inefficient, but also that OLS tends to underestimate the standard errors associated with the parameters. A common approach to heteroscedasticity of residuals is weighted regression. Lipfert performed some weighted regressions using number of deaths as weights. He concluded that heteroscedasticity was not a serious problem. However, a direct comparison of weighted and OLS results was not presented in his published works. Mendelsohn and Orcutt (1978) also used weighted regression using binomial weights (population), but apparently did not perform unweighted regressions. In their recent reanalysis, Chappie and Lave (1981) compared OLS results with generalized least-squares (GLS) results, weighted by the square root of population. The weighting had only trivial effects on the sulfate coefficients, but caused the three particulate coefficients to fluctuate substantially (e.g., the coefficient of minimum TSP dropped by over 99.9%0), the coefficient of mean TSP increased by 33%0, and the coefficient of maximum TSP dropped by 18%. Recently, Pocock et aL (1981) critically reviewed the issue of choice of weights for cross-sectional mortality studies and suggested the use of iterative maximum likelihood methods. This approach has not been used heretofore in air pollution mortality studies. Another methodological problem encountered is multicollinearity. Least-squares estimates are unbiased in the face of multicollinearity. However, the variability of the estimates about the true parameters can become quite large. In the limit, if two or more of the explanatory variables are perfectly correlated, the estimation attempt will fail due to inability to invert the variance-covariance matrix. In cases of near-perfect collinearity, the coefficients which are estimated will be quite unstable. As has been mentioned before, there are significant correlations: (1) among the six pollutant variables used by Lave and Seskin; (2) among the 54 nonpollutant variables; and (3) between the set of pollutant variables and the set of nonpollutant variables. As Lipfert notes, collinearity among the pollutant variables severely hampers our ability to assign effects to a specific pollutant in cross-sectional studies. In addition, it is always a possibility that a variable which has not been included in the data set, either because it is unmeasured or because it is not thought to be relevant,

Cross-sectional mortality studies

is correlated highly with one or more of the explanatory variables under consideration. The implications of this last possibility were discussed in the section on confounding. Thomas (1973), as well as Schwing and McDonald (1976), used ridge regression to counter the instability induced by collinearity. Using a mortality data set from 46 SMSAs in 1960 and pollution data for 1965, Schwing and McDonald estimated the coefficients of linear models involving seven air pollution variables, an index of cigarette smoking, a measure of background radiation levels and 11 socioeconomic control variables. First, the parameters for total white male mortality rates were estimated using OLS, and then ridge regression, with the ridge parameter k set equal to 0.2. The coefficient of mean SO~- (which was negative) increased from - 0 . 7 2 5 to -0.020 and the coefficient of minimum SO,2dropped 72°/0 from their OLS values. Problems in the interpretation of the coefficients from ridge regression include: (1) the coefficients are known to be biased by an unknown amount, and (2) the ordinary t statistics are not justified in theory. As an alternative to ridge regression, Schwing and McDonald used constrained least squares with the constraint that the pollution coefficients be non-negative. Again the coefficient estimates were reduced. Gibbons and McDonald (1982) have recently reestimated Lave and Seskin's eleven-variable model using ridge regression on the full 117 SMSAs for 1960. Here, k was set equal to 0.018 based upon the results of three ridge algorithms shown previously to perform well in simulations. While the individual coefficient estimates for the six pollution measures varied somewhat from those obtained using OLS regression, the combined elasticity of the pollution variables was within 3 °70 of the OLS elasticity. Two other approaches to the collinearity problem must be mentioned. Crocker et al. (1979) examined the correlations among potential explanatory variables as an aid to variable selection. For example, after noting the correlation among twelve potential dietary variables, Crocker et al. selected only three for inclusion in their final data set. Although this produced a data set with less collinearity, it did not clarify substantially the interpretion of the resulting regression coefficients. In a 1970 paper, Lave and Seskin employed the technique of two-stage regression. The first stage consisted of a regression of mortality rates on the set of all nonpollutant explanatory variables. In the second stage, the residuals from the first stage were regressed on the pollution variables. The resulting two-stage coefficients are known to underestimate the true coefficients. Using the basic 1960 data set for 117 SMSAs, Lave and Seskin replicated the seven-variable model using two-stage regression. The coefficient of minimum SO~- dropped by 20°70 and the coefficient of mean TSP dropped by 10°70. An issue important to the interpretation of cross-

67

sectional mortality studies is the form of the relationship between air pollution exposure and community mortality rates. The most abundant model in the literature is the linear or proportional model. However, many investigators have explored various nonlinear specifications. For example, using the 1960 data base and the basic five-variable socioeconomic control group (population density, percentage over 65, percentage nonwhite, percentage poor, and logarithm of population), Lave and Seskin compared several specifications. The pollution variables under consideration were mean TSP and minimum SO,2-. The models considered were linear, linear-quadratic with interaction term, log-log, step function ("dummy variable"), and linear splines. An F-test led Lave and Seskin to conclude that the addition of the three quadratic terms added to the explanatory power of the model. However, the parameter estimates were unstable and the model was difficult to interpret. The model indicated that: (1) the influence of minimum SO,2- was sublinear; (2) the influence of mean TSP was superlinear; and (3) there was a positive interaction term. The log-log model had lower explanatory power than the linear model. The dummy variable specification had slightly greater explanatory power than the simple linear relationship. The linear spline model did not demonstrate a statistically significant increase in explanatory power in comparison to the linear model. The relationships between pollution and mortality predicted by these three forms (linear, step, linear-spline) were essentially equivalent (see Figs. 2 and 3). Lave and Seskin replicated these analyses with their 1969 data base. The log-log model had a slightly higher explanatory power than the linear model. The quadratic terms failed to add significantly to the explanatory power of the linear model. Neither the dummy variable nor the linear-spline specification demonstrated significantly greater explanatory power than the linear model. Again, the predicted in-

IO00

(3 C) 950'

/ __~___~~

Q.

Lineorform--

. . . . . - ~ - ~ - - - - - - Ou~y v o r ~

Lineorspline

900

850 "8

I-.

Fig. 2. Linear and non-linear relationships between sulfates and crude mortality rates, 1960. (From Lave and Seskin, 1977.)

68

J.S. Evans, T. Tosteson,and P. L. Kinney 1050

Oll~D o o" o

Dummyvarifies

o- 950

Lineor spline

..... ~ n e Q r

form

900 E "~ 850#-

800

.

.

2'0

.

.

.

8b

.

.

.

.

I

3

180 200, °PC g m I

Fig. 3. Linear and non-linear relationships between TSP and crude mortality rates, 1960. (From Laveand Seskin, 1977.)

cients to departures from these assumptions deserves further study. Although Lipfert, as well as Mendelsohn and Orcutt, have explored the sensitivity of results to various levels of spatial aggregation, neither group had individual data on both pollution and mortality. Therefore, neither study resolves the aggregation problem or ecologic fallacy. In summary, while various weaknesses of the statistical methodology have been demonstrated, the problems do not appear to vitiate the results. The air pollution coefficients appear to be moderately sensitive to the presence of outlying SMSAs. At the same time, they appear to be relatively insensitive to heteroscedasticity. For any single investigation, multicollinearity is a formidable problem. However, it would seem that as the results of large numbers of investigations become available, the instabilities of individual coefficients will be of less concern. Meta-Analysis

fluence of pollution on mortality was relatively insensitive to the choice between linear, dummy variable, and linear-spline models. Liu and Yu (1976) at Midwest Research Institute explored a nonlinear model. However, as Olsen (1978) has independently noted, fatal methodological flaws vitiate their analysis. Koshal and Koshal (1973) worked with log-log models. However, they did not report comparative results based upon linear models. Hickey et al. (1977) worked with log-linear models, but did not report comparative results based upon linear models. Smith (1976a) considered both linear and semilogarithmic models and concluded, on the basis of tests for specification errors, that the linear models were in general no worse than the semilogarithmic models. In conclusion, it would seem that the current data bases are inadequate to resolve the various functional forms of potential interest. Recently, Selvin et al. (1981) have raised as an issue in the interpretation of these studies the "ecologic fallacy." In 1950, Robinson illustrated the ecologic fallacy: that correlation coefficients derived from aggregated data were not necessarily equal to correlation coefficients for individuals. Selvin et al. carried the argument forward demonstrating that only in certain limited circumstances are regression coefficients derived from aggregated data equal to regression coefficients derived from data on individuals. Econometricians have been aware of the aggregation problem for some time. Theil (1971) notes that in linear models aggregation bias vanishes if the regressions for all individuals are identical. In terms of linear relationships between air pollution exposures and mortality, it can be demonstrated that there is no bias if (1) all individuals are equally sensitive to the effects of air pollution, or (2) if the average sensitivity to air pollution is identical for all cities. The sensitivity of coeffi-

In the second phase of our research, we attempted to summarize quantitatively the results of the crosssectional studies. The fact that numerous studies have been performed suggests that a combination of results might lead to greater precision. This is only true insofar as each result can be considered an independent piece of information. Because this is not the case, we do not claim that our approach has improved the state of knowledge concerning true damage coefficients. However, we do feel that the analysis that follows provides a useful means for exploring the differences between studies, and perhaps between conflicting results presented within the individual studies. Virtually all the cross-sectional studies were considered for inclusion. The minimum screening requirements were that linear exposure-response coefficients be provided and that enough information existed to construct standard errors for the coefficients. The second requirement was considered particularly important for risk assessment purposes, and it greatly reduced the total number of studies. For example, coefficients excluded in the process of optimal or stepwise regression (see, for example, Lipfert; Cooper and Hamilton, 1978) could not be included on this basis. We recognize that our approach is somewhat arbitrary, as some might argue that equations that do not include a pollutant due to the lack of statistical significance should contribute a zero coefficient. The final data set contained information for six studies from five author groups (Lave and Seskin, 1977; Chappie and Lave, 1981; Lipfert; Mendelsohn and Orcutt, 1978; Gregor, 1976; and Crocker et al., 1979). For each regression equation, the estimated coefficient and standard error for all pollutant variables were recorded, along with other classifying data. The completed file ineluded data from more than 500 equations. The over-

Cross-sectional mortality studies

69

whelming majority o f these were taken from the studies by Lave and Lipfert. To illustrate the degree of variability observed between coefficients, Fig. 4 provides percentage rankings for all TSP and sulfate coefficients from regressions using total mortality as the outcome variable and the annual mean or median as the exposure measure. The bimodal nature o f the distributions reflects the generally smaller TSP coefficients and generally larger SOl- coefficients reported by Chappie and Lave. It is also interesting that Chappie and Lave's SOl- coefficients tended to be among the largest reported. Table 11 shows the arithmetic means of the coefficients of five types of particulate matter from regressions based upon total mortality rates, either crude or age adjusted, and upon age-specific mortality rates. The standard errors shown here are arithmetic means of the reported standard errors. The hyphen entries indicate that no coefficients were in the data base. It should be noted that the results in this table are difficult to interpret physically, since they involve averaging over various measures o f pollution exposure (i.e., minimum, mean, maximum), and the coefficients can in no sense be considered independent. Nonetheless, it is interesting

Chappie and Lave Lave and Seskin Lipfert Crocker and Schulze

/x 0 o

80

•

/o

TSP

100

o,/

40

U

Y

oO

f

6O

n~

2

O0

20

0

~

-1.8

-3.0

0.6

-0.6

Sulfate

I00

18

Table 11. Average linear regression coefficients and standard e r r o r s - t o t a l mortality.

P ol l ut a nt TSP Sulfates BaP Iron Manganese

Age Adjustment

Coefficient a

Standard Error a

N umb er o f Coefficients

no yes no yes no yes no yes no yes

0.33 0.45 2.58 2.75 4244.4 -16.42 -190.8 --

0.57 0.27 3.47 2.47 2311.3 -8.04 -77.2 -

152 77 137 71 9 0 10 0 30 0

aUnits = number o f deaths x 10 -s person yr (#g/m3) -'.

that the arithmetic mean coefficients of TSP and SOlfrom the regressions on crude mortality are similar to those from the first published equation of Lave and Seskin, despite the addition of the many subsequent estimates they and others have contributed. Also, the order of magnitude o f the mean coefficients seems to be related approximately inversely to the mean concentration of each of the pollutants, perhaps suggesting the presence of surrogate relationships. Because the age-specific analyses varied as to precisely what age intervals were chosen, the two averages shown in Table 12 represent for all age intervals entirely less than or entirely greater than 45. Note that for TSP, SO~-, and B(a)P, the mean coefficients are higher for the older age group and the standard errors are also considerably larger. The results for iron are difficult to interpret. The sign of the mean coefficient for one age group is negative. The initial analyses of factors which influenced the estimated coefficients indicated that the coefficients tended to be larger for studies based upon higher mortality rates, and that the coefficients tended to decrease as more explanatory variables were added to the models. In order to investigate further which factors

~k Table 12. Average linear regression coefficients and standard e r r o r s - a g e - s p e c i f i c mortality.

80 Pollutant

60 o

2

40

o

c a)

U

Y

[]

20

9

3°

Or21

-25

Coefficienta

Standard Error a

N umb er of Coefficients

-> 45 < 45 > 45 <45 _>45 <45 _ 45 < 45 _>45 <45

3.27 0.65 36.5 1.76 5632.1 3466.7 34.7 - 7.46 316.0 454.0

1.92 0.95 19.7 7.10 18675.6 10215.3 48.4 13.4 791.7 171.3

48 111 46 107 14 3 17 7 40 5

0 &

/ D

Ages

TSP Sulfates BaP Iron

0

-I0 5 20 Deaths per 105 person years x (/zg/m 3)

Fig. 4. Percentage ranks of TSP and sulfate coefficients.

Manganese

aUnits = number of deaths x 10 -s person yr (/zg/m3)-'.

70

J.S. Evans, T. Tosteson, and P. L. Kinney

may influence the average coefficient estimate, we performed a series o f tabulations on the unadjusted (crude) total mortality TSP and sulfate coefficients. Table 13 shows the average coefficient and average standard error by author, by year of study, and by category of control variables (i.e., with and without control for smoking or diet). Interpretation of the results is not trivial. For example, the only 1974 results are those o f Chappie and Lave. There do not appear to be strong author effects on the coefficients, with the exception o f the negative SO4~- coefficients of Lipfert. It would appear that any differences among authors are not entirely due to differences in year of study. The table indicates that the primary effect of including diet and smoking as variables is to increase the standard error of the sulfate and TSP coefficients. A similar effect is seen for including age. In another analysis, the coefficients were grouped according to the measure o f exposure used. However, the analysis appeared to be dominated by an author effect, limiting its usefulness. To explore possible surrogate relationships, the TSP and SOl- coefficients and squared standard errors were regressed on variables indicating the inclusion or exclusion o f each air pollution variable. (For the purpose o f this analysis, coefficients for the minima and maxima were included after appropriate conversions.) The coef-

ficients were weighted by the inverse of the squared standard error, and the squared standard errors were weighted by the inverse of their square (i.e., the standard error to the fourth power). The results are summarized in Table 14. Student t statistics are given, as well as estimated impacts on the coefficients and the variances. It would appear that inclusion of additional TSP statistics significantly reduces TSP coefficients. The same is true for the SO~- coefficients. This is most likely due to the strong positive correlations among minimum, mean, and maximum values for these pollutants. We can also see that inclusion of iron, manganese, or TSP tends to reduce the estimated SO~- coefficient. Further, we can see that while inclusion of sulfate, SO2, or NO2 tends to reduce the estimated TSP coefficient, inclusion o f manganese or iron tends to increase the estimate. In summary, this phase of our work emphasized the variability of coefficient estimates in the literature and gave us insight into the factors with strong influences on the estimates. Although we encourage the use o f these results in summary and analysis, we recognize the deficiencies o f the method for direct use in risk assessment. In particular, the relationship of the pooled coefficients to coefficients from a pooled data set is not clear. In addition, we were unable to estimate the covariances be-

Table 13. Average linear regression coefficients and standard e r r o r s - t o t a l mortality by author, by control variables, and by year of mortality data. TSP

Sulfates

Std. a

Number o f

Coefficienta

Error

Coefficients

Coefficient

Std. Error

Number of Coefficients

Author(s): Chappie and Lave Crocker and Schulze Lave and Seskin Lipfert

0.16 0.11 0.33 0.84

1.03 0.55 0.25 0.33

60 1 69 22

3.75

4.37

60

2.51 -2.49

2.61 3.93

63 14

Control Variables: S m o k i n g - without - with Diet - without - with

0.32 0.35 0.36 0.22

0.30 0.83 0.47 1.01

75 77 124 28

2.74 2.43 2.34 3.62

2.64 4.35 3.34 4.02

70 67 110 27

Years of Mortality: 1959, 1969 1960 1960, 1969 1961 1962-1968 1969 1969, 1971b 1970 1974

1.02 0.34 0.30 0.52 0.37 0.37 0.82 0.11 0.16

0.40 0.23 0.095 0.18 0.15 0.36 0.31 0.55 1.03

3 17 12 1 12 31 15 1 60

-7.35 3.77 1.68 4.56 2.40 0.880 1.14

4.75 2.56 0.916 2.44 1.34 3.72 3.84

2 17 12 1 9 28 8

aUuits = number of deaths × 10-5 person yr (~g/m~)-'. bRepresents a three year average.

--

--

3.75

--

--

4.37

0

0

60

C r o s s - s e c t i o n a l m o r t a l i t y studies

71

T a b l e 14. E s t i m a t e d effects o f i n c l u d i n g o t h e r p o l l u t i o n m e a s u r e s o n s u l f a t e a n d T S P c o e f f i c i e n t s a n d o n s q u a r e d s t a n d a r d e r r o r s o f coefficient e s t i m a t e s , a TSP

Additional Pollution Variable(s) I n c l u d e d T S P , or a d d i t i o n a l TSP measures Sulfate, or additional sulfate measures BaP SO2 NO2 Iron Manganese

Effect on Coefficient Est.

Sulfate

t

Effect on Coefficient Est.

t

Effect on (Std. E r r o r ) 2

-0.0050

- 1.9

-0.72

-0.7

-0.69

- 1.7

-0.0070 -0.018 -0.0041 -0.0028 0.42 0.068

- 1.6 0.4 -0.7 - 1.2 1.2 3.2

- 1.42 -2.04 2.68 2.53 -22.6 - 3.52

- 3.2 -0.3 0.9 0.9 - 1.7 - 1.3

0.08 -0.32 - 3.38 - 3.40 38.6 7.91

1.0 0.0 - 1.5 - 1.5 1.1 2.5

tb

Effect on (Std. E r r o r ) 2

-0.33

-7.3

-0.10 -0.11 -0.10 -0.17 1.27 0.21

- 1.5 -0.5 -1.2 - 1.0 1.5 1.7

aUnits = n u m b e r o f d e a t h s x 10 -~ p e r s o n yr (p,g/m~) -'. bt statistics h a v e been r o u n d e d t o the nearest t e n t h .

tween coefficients for different pollutants, important statistics for assessing "uncertainty" when evaluating multiple pollution measures.

Data Analysis and Coefficient Estimation The difficulties in evaluating the consistency of results and in ascertaining the effects o f the many methods and data bases reported in the literature lead us to the third phase of our work. This research was directed at exploring the sensitivity o f results to choice of model/data base. Although many coefficient estimates were generated, it was not our intent that these coefficients be used in risk assessment, The basic 1960 117-SMSA data set o f Lave and Seskin (see Lave and Seskin, 1977), obtained from Diane Gibbons at the General Motors Research Labs, served as the basis for our investigation. The data included total mortality rate, the 60 explanatory variables of Lave and Seskin, and median age. This basic data set had been corrected to reflect the coding errors noted by Thibodeau et al. (1980). To this data set six variables, taken from Lipfert's dissertation (1978b), were added. Three of these were additional pollution measures and three were socioeconomic controls. The SMSAs and variables included in the data set are listed in the Appendix Tables A-1 and A-2. The means and standard deviations for all variables are shown in the Appendix Table A-3. The three different columns refer to three subsets o f SMSAs used during our analysis• It must be noted that the variables from Lipfert's data set were for cities, rather than SMSAs. Although mixing data from cities and SMSAs is not ideal, it is our belief that it does not significantly affect conclusions concerning sensitivity of pollution coefficients to choice of model. A preliminary analysis was conducted to gain familiarity with the data base. The first step in preliminary analysis was to examine the full correlation

matrix and to prepare scatterplots o f total mortality rate versus annual mean TSP concentration and annual mean sulfate concentration. Figure 5 shows the scatterplots. Table 15 is a portion of the full correlation matrix• It includes the correlations of both total and age-sex-race adjusted mortality rates with each o f the

1400-

o ~ ~ lZ00~: i~ o ~ 1000:; g ~ soo:~ ~_ ~g 6oov

•• o• •

°

•

°

•• °o

• • °°

°

••

°°.

°°

•

•

•

••

°

:

°-o

• o ° . . . o. °.; o°o

°

oo• °°

°

60 Mean Sulfo~'e Concentrotion (~g/m3) -1960 14OOo

~o m 12oo~ , a: -- ~ t000"~ ~ _o g ~ 800:~

= ° • g

°: =

__-'-". ,.o •

..

•

*= • :....

• o

600-

4'o

26o

2,;o

Meon TSP Concentro~ion(p.g/m 3 )-1960 Fig. 5. S c a t t e r p l o t s o f c o m m u n i t y m o r t a l i t y rates a n d t w o indices o f air p o l l u t i o n e x p o s u r e .

72

J.S. Evans, T. Tosteson, and P. L. Kinney Table 15. Pearson product-moment correlation coefficients (r) among certain key variables. (N = 117 unless otherwise indicated.)

MIN

S

MEAN

S

MAX

S

MIN.___P MEAN

P

MAX

P

IRON MANG BAP SMOKING GE65 MED_.._AGE MIGRATE POP LOGDEN STM

HHE

HAF....._HHE FLOR_._HHE FLUE_.__HHE WOFLUHHE COAL OIL

HHF HHF

BGAS....._HHF GAS

HHF

ELEC...._HHF OTH_.__HHF COAL OIL BGAS GAS ELEC OTH

WHF WHF WHF WHF WHF WHF

TMR

ASR

0.44 0.0001 0.40 0.0001 0.28 0.0019 0.16 0.0849 0.06 0.5188 -0.08 0.4207 0.04 0.7103 0.13 0.2257 0.07 0.5619 - 0.03 0.7984 0.85 0.0001 0.86 0.0001 -0.54 0.0001 0.11 0.2521 0.24 0.0107 0.61 0.0001 -0.07 0.4446 -0.48 0.0001 0.04 0.6557 -0.33 0.0003 0.50 0.0001 0.34 0.0002 -0.24 0.0102 -0.47 0.0001 -0.07 0.4303 -0.16 0.0949 0.44 0.0001 0.47 0.0001 0.07 0.4749 -0.38 0.0001 -0.02 0.8597 0.22 0.0159

0.41 0.0001 0.39 0.0001 0.30 0.0009 0.30 0.0011 0.29 0.0018 0.17 0.0646 0.19 0.0753 0.14 0.1763 0.11 0.3611 - 0.01 0.9414 0.08 0.3791 0.30 0.0012 -0.42 0.0001 0.06 0.5435 0.27 0.0028 0.43 0.0001 -0.16 0.0831 -0.29 0.0015 0.11 0.2389 -0.15 0.0998 0.52 0.0001 0.14 0.1416 -0.36 0.0001 -0.29 0.0013 -0.06 0.5157 -0.01 0.9086 0.50 0.0001 0.23 0.0114 -0.19 0.0378 -0.25 0.0056 -0.07 0.4728 0.03 0.7674

MEAN...._S MEAN 0.62 0.0001 1.00 0.0000 0.85 0.0001 0.56 0.0001 0.56 0.0001 0.38 0.0001 0.43 0.0001 0.35 0.0005 0.12 0.3103 0.20 0.0453 0.29 0.0016 0.45 0.0001 -0.33 0.0002 0.36 0.0001 0.50 0.0001 0.50 0.0001 0.15 0.1123 -0.44 0.0001 -0.19 0.0458 -0.33 0.0003 0.25 0.0077 0.24 0.0098 -0.40 0.0001 -0.24 0.0107 -0.04 0.6420 -0.35 0.0001 0.13 0.1489 0.42 0.0001 -0.14 0.1424 -0.09 0.3540 -0.16 0.0913 -0.07 0.4631

0.25 0.0056 0.56 0.0001 0.55 0.0001 0.75 0.0001 1.00 0.0000 0.79 0.0001 0.71 0.0001 0.37 0.0003 0.08 0.5250 0.08 0.4460 -0.04 0.6466 0.08 0.4125 -0.04 0.6359 0.24 0.0089 0.20 0.0301 0.12 0.2055 0.23 0.0128 -0.13 0.1717 -0.16 0.0784 -0.22 0.0150 0.29 0.0014 -0.18 0.0479 -0.32 0.0004 0.08 0.3899 0.08 0.4196 -0.31 0.0006 0.19 0.0355 - 0.07 0.4702 -0.29 0.0013 0.16 0.0759 -0.16 0.0908 -0.20 0.0316

P NW POOR UNEMP COLLEGE WriT

COL

AGRIC CONST MFG___D MFG

ND

TRANSP TRADE FINANCE EDUCAT PUB.... ADM HOUSING MIN

T

MINTLE32 MINTLE0 DEG_.__DAYS MAX..._.T MAXTGE90 MAXTLE32 PRECIP RAIN SNOW FOG AM...._HUM PM

HUM

WIND TRAN___USE MALE WO_..._AC

TMR

ASR

-0.27 0.0033 -0.06 0.5175 0.33 0.0003 -0.52 0.0001 -0.35 0.0001 -0.18 0.0502 -0.45 0.0001 0.09 0.3181 0.27 0.0031 0.11 0.2204 -0.18 0.0480 -0.15 0.1074 -0.41 0.0001 -0.24 0.0089 0.65 0.0001 -0.30 0.0008 0.33 0.0003 0.05 0.5926 0.35 0.0001 -0.42 0.0001 -0.51 0.0001 0.22 0.0148 0.26 0.0042 0.38 0.0001 0.33 0.0003 0.11 0.2309 0.16 0.0781 0.11 0.0452 0.05 0.5992 0.28 0.0021 -0.03 0.7489 0.39 0.0001

0.11 0.2339 0.09 0.3602 0.36 0.0001 -0.48 0.0001 -0.43 0.0001 -0.39 0.0001 -0.38 0.0001 0.05 0.5596 0.38 0.0001 0.12 0.1959 -0.27 0.0028 -0.25 0.0069 -0.40 0.0001 -0.08 0.3679 0.43 0.0001 -0.12 0.2079 0.18 0.0499 -0.20 0.0287 0.14 0.1456 -0.17 0.0713 -0.26 0.0045 0.03 0.7591 0.19 0.0405 0.28 0.0020 0.14 0.1271 -0.03 0.7633 0.03 0.7693 - 0.06 0.4924 -0.08 0.3741 0.33 0.0003 -0.02 0.8662 0.23 0.0132

MEAN -0.20 0.314 -0.34 0.0002 0.I0 0.2665 -0.46 0.0001 -0.13 0.1660 -0.37 0.0001 -0.44 0.0001 0.31 0.0007 0.18 0.0537 - 0.04 0.6498 -0.32 0.0004 -0.10 0.3005 -0.31 0.0007 -0.23 0.0145 0.51 0.0001 -0.28 0.0019 0.34 0.0002 -0.11 0.2240 0.32 0.0005 -0.38 0.0001 -0.48 0.0001 0.19 0.0359 0.08 0.4023 0.31 0.0008 0.40 0.0001 0.06 0.5308 0.01 0.9410 0.05 0.6273 -0.03 0.7585 0.48 0.0001 0.26 0.0055 0.31 0.0008

S MEAN -0.19 0.413 -0.19 0.0399 0.15 0.1005 -0.19 0.0675 -0.04 0.6370 -0.15 0.1048 -0.19 0.0370 0.17 0.0612 0.05 0.4929 - 0.02 0.8049 -0.15 0.1081 -0.09 0.3347 -0.22 0.0160 -0.08 0.4157 0.05 0.301 -0.18 0.0531 0.22 0.0181 -0.09 0.3503 0.17 0.0740 -0.13 0.1731 -0.08 0.3952 0.15 0.1043 -0.28 0.0024 -0.01 0.8793 0.23 0.0130 -0.12 0.2068 -0.38 0.0001 - 0.26 0.0044 0.09 0.3370 0.22 0.0192 0.33 0.0003 0.11 0.2427

P

Cross-sectionalmortality studies explanatory variables, and the correlations of both mean sulfate and mean TSP with each o f the other explanatory variables. The scatterplots reveal that: (1) total mortality rates are better correlated with sulfates than with TSP; (2) neither sulfate nor TSP is a strong determinant o f mortality; and (3) there is no strong evidence o f nonlinearity in either of the associations over the limited range of observed values• The correlation matrix indicates that the two variables most highly correlated with total mortality rate are median age and percentage o f population over 65, r = +0.86. Mean sulfate has the fifteenth highest pairwise correlation, r = + 0.40. The pairwise correlation of mean TSP with total mortality rate was only r = 0.06. Manganese and B(a)P both have higher correlations than mean TSP. The correlations among the explanatory variables are of as much importance as the correlations between the explanatory and the dependent variables. Of particular interest are variables having high correlations with the primary pollution indices, mean sulfate and mean TSP. Note, for example, the correlations of mean sulfate with median age, logarithm of population density, housing quality, employment in construction, use of steam and floor heating equipment, use of bottled gas homeheating fuel and oil water-heating fuel, frequency of maximum temperatures over 90 °F, and use of public transportation by the work force. All these are above Jrl > 0.40. In contrast, the five highest correlations of mean TSP with the nonpollution variables are: air humidity (r = - 0 . 3 8 ) , percentage o f work force which is male (r = +0.32), bottled gas home-heating fuel ( r - - - 0 . 3 2 ) , bottled gas water-heating fuel (r = - 0 . 2 9 ) , and coal home-heating fuel (r = + 0.29). Correlations that are especially important to the proper interpretation o f the pollution coefficients in regressions are those among the pollution variables. Note, for example, that mean sulfate and maximum sulfate are highly correlated (r = + 0.85). Mean sulfate is also moderately correlated with minimum sulfate (r = + 0.62), mean TSP (r = +0.56), iron (r = +0.43), and manganese (r = +0.35), is highly correlated with minimum TSP, maximum TSP and iron (i.e., Irl > +0.70). It is moderately correlated with manganese (r = +0.38). After completing these preliminary analyses, the parameters of a model with eight independent variables were estimated. This basic model was to be used simply as a foundation for the sensitivity analyses. The dependent variable was total mortality rate. The independent variables were chosen on the basis both o f physical plausibility and explanatory power in previous analyses. This set of variables is quite similar to those commonly used in the regressions discussed in the first section of this paper. Two measures o f population age structure were used because age is such an important determinant of crude mortality rates. Four socioeconomic variables

73 reflecting race, income, education, and crowding were used, since these factors are all known to influence mortality experience. An index of smoking was included to minimize the potential for confounding. The annual mean sulfate level was chosen as an index of pollution which might best reflect exposure to fine particles. This choice was not made with the intent of singling out sulfates as the harmful component of ambient aerosols. The explanatory variables included: • • • • •

percentage of the population over 65 years old; median age of the population; percentage of the population nonwhite; decimal logarithm of the population density; percentage of the population with four or more years of college; • smoking index; • percentage of the population poor; and • annual mean sulfate concentration. The model was linear in these variables. In our data set, values of all eight variables were present for only 98 of the 117 SMSAs. The sulfate coefficient from this regression was 2.63 deaths/100,000 per year per/~g/m 3. The estimated standard error of the regression coefficient was 1.40 deaths/ 100,000 per year per #g/m 3. We plotted residuals versus predicted values and mapped residuals. The plot and map are shown as Figs. 6 and 7. The map did not indicate strong geographic behavior of residuals. The plot suggests heteroscedasticity. To examine the correlation structure of the matrix of eight basic explanatory variables more closely, we performed a factor analysis. The results showed that three underlying factors accounted for 75G70o f the variance in the original eight variables. The first factor exhibited a strong, positive correlation with the two age variables and a moderate negative correlation with percentage

180 -

• Scranton • Terre Haute

0 ~D

• Savannah

;~

• Wilkes Borre

90-

• ..

Q.

c-'% o

:

•

•

• New Orleans

° . •

--

e

.; :="

"

...'..

I

. .

~ g -90g Tampa

-180-

•

.Miami

Predicted

Total Mortality

Rote-1960

(deaths/yr

per lOr~ersons)

Fig. 6. Scatterplot of residuals vs. predicted values from basic model.

74

J.S. Evans,T. Tosteson,and P. L. Kinney

RESIDUALS F / / / / / / / / I u NDER- 3 7 = OBSERVED-PREDICTED (deoths/yr per IO0,O(X~) [ x . ~ x l 15 TO 28

I/////I

- 3 z TO - 3

I-3 To 15

~x.~xql OVER 28

Fig. 7. Averageresidualurban mortalityrate by state from basic model.

nonwhite. The second factor was loaded strongly on percentage poor and moderately on percentage nonwhite. The third factor was most strongly correlated with sulfate concentration and log population density. The three factors were then entered simultaneously as explanatory variables in a multiple regression on crude mortality rate. The regression coefficients which resulted were all positive and significant at the level p < 0.01. Factors one through three explained 67°70, 5°70, and 3~70 of the variance in mortality, respectively. Once the preliminary analysis was complete, several variations on this basic model were estimated, in the spirit of a sensitivity analysis. The first eight model variations examined the influence of collinearity with (confounding by) nonpollution variables. Various groups of potential confounding variables (e.g., oc o cupational mix, home heating, climate) were included or excluded from the model in this exercise. In the tenth regression, the effect of using age-sex-race adjusted mortality was explored. The following three regressions allowed estimation of the effects of deleting influence points and/or points with large residuals. The last five sets of models examined the effects of: (1) jointly or separately estimating the coefficients of several mea-

sures of particulate air pollution; (2) correcting the TSP measure for its sulfate component; and (3) substituting a linear-threshold exposure-response for a strictly proportional one. The results are summarized in Table 16. In these 21 regressions, the coefficient varied from 1.22 to 3.72. The standard error varied from 1.02 to 2.41. Using the full data set, the variations in model which most strongly reduced the sulfate coefficient were: inclusion of the nine occupational variables; removal of the ten SMSAs with extremely high or low migration rates; removal of five outlying SMSAs; inclusion of the housing quality variable; inclusion of the home-heating variables; and inclusion of the climate variables. Examination of the correlations between the occupational variables and several other variable classes (including meteorology, migration, and especially sulfate) suggest a spurious effect of the occupational variables on the estimated impact of sulfates. The influence of three of the five outlying SMSAs can be explained by unusually high occupation-related mortality rates or unusual population age structures. In any case, the largest standardized residuals from the 98 observations were - 2.5 and + 2.5, neither "extreme." Interpretation of the regressions involving home-

Cross-sectional mortality studies

75 Table 16. Sensitivity Analysis-Sulfate Coefficient.

Regression 1. Basic model 2. Naive model--only 3 variables: GE65, MEDAGE, MEAN...._S 3. Basic/occupational variables: F = 3.82, p = 0.0005 4. Basic/unemployment and white collar variable 5. Basic/home heating variables: F = 3.21, p = 0.0002 6. Basic/climate variables: F = 1.77, p = 0.06 7. Basic model/with MEDAGE variable deleted 8. Basic model/with HOUSING variable added 9. Basic/5 highest and 5 lowest immigration SMSAs deleted 10. Basic model/age-sex-race adjusted mortality substituted for TMR and GE65, MEDAGE and NW deleted 11. Basic/5 outliers removed 12, Basic/5 influential points removed 13, Basic/5 influential points and 3 outliers removed 14. Basic model/various threshold for MEAN___S (a) 2p,g / m 3 (b) 4 ttg/m 3 (c) 6/~g/m ~ (d) 8 ~g/m ~ 15. Basic/only SMSAs with all 5 pollution measures 16. Basic/add mean TSP, same subset 17. Basic/all 5 pollutants, same subset 18. Basic model/subset with values for all 5 pollutants, both MEAN S and NETTSP in equation (NETTSP = MEAN P - 1.37 x MEAN___S)

Sulfate Coefficient

Std. Error of Coefficient

R2

na

pa

2.63

1.40

0.863

98

9

2.68

i.50

0.788

98

4

1.22

1.28

0.904

98

18

2.37

1.35

0.875

98

11

1.64

1.29

0.927

98

28

1.72

1.55

0.897

98

23

3.31

1.58

0.820

98

8

1.46

1.38

0.877

98

10

1.35

1.02

0.897

88

9

3.54 1.41

1.58 1.04

0.327 0.909

98 93

6 9

3.59

1.54

0.880

93

9

2.60

1.29

0.910

90

9

2.63 2.59 2.58 2.39

1.40 1.41 1.47 1.59

0.863 0.863 0.862 0.861

98 98 98 98

9 9 9 9

3.72

1.90

0.851

66

9

2.77

2.27

0.853

66

10

3.00

2.41

0.854

66

13

3.02

2.12

0.853

66

10

an = number of observations, p = number of variables (including the intercept).

heating fuels and equipment is complex. The reductions in deaths related to electric or gas water-heating fuel might be due to their less polluting nature, or may simply be an artifact of some unidentified, or poorly measured, factor that varied geographically with the same pattern as water-heating fuel. In the case of the climate variables, the pairwise correlation between total mortality rate and maximum temperature is - 0 . 4 2 , indicating that cities in colder areas tend to have high mortality

rates. While it has been demonstrated that short-term temperature fluctuations are strongly associated with acute mortality, it is not at all clear that this same effect is operating in cross-sectional analyses. Rather, it seems likely that the climate variables may be acting to some extent as surrogates for geographical variations in mortality rates. Since the choice of sulfates as an index of exposure is somewhat arbitrary, several regressions were performed

76

J . S . Evans, T. Tosteson, and P. L. Kinney Table 17. Estimated pollution coefficients, variances, and covariances from regressions with multiple pollutants (n = 66). Note that all coefficients are in units of deaths/yr/100,000 persons per/~g/m 3. Variances and covariances are in consistent units. (1) MEAN S and MEAN P /~s = 2.77 S Var~

s

I- 519

P

L-0.293

/3,o = 0.181 P

] 0.w56

(2) All five pollutants /3s = 3.00 S

/3p = 0.118 P

81 = 1.91 I

~ g = --8.90 M

fib = --421 B

P -0.382 0.121 Var~ = I 1.81 -1.33 28.6 M 17.8 1.38 -40.6 905.0 B 78.3 - 50.4 471.0 - 3240.0 654000 The diagonal elements in these matrices are variances of coefficient estimates and the off-diagonal elements are the associated covarancies. S = SO4, P = TSP, I = Iron, M = Manganese, B = B(a)P

in which the coefficients of several measures of particulate air pollution were simultaneously estimated. For comparison, the coefficients of these same measures were estimated separately. The results are presented in Tables 17 and 18. Note that the separately estimated coefficients and sulfates and TSP are 3.72 ( ± 1.90) and 0.338 ( ± 0.198), respectively. However, when jointly estimated, they are 2.77 and 0.181. The joint estimation procedure highlights the interdependence of the coefficient estimates. From the covariance matrix it may be seen that the correlation of the sulfate and TSP coefficients is - 0 .5 6 . Similar results are found when the coefficients of sulfate, TSP, iron, manganese, and B(a)P are estimated separately and jointly. If risk assessments are to be based upon exposure estimates for more than one measure of airborne particles (e.g., sulfate and TSP), risk coefficients must be taken from equations in which the coefficients were simultaneously estimated. The estimates of uncertainty associated with the risk coefficients should reflect not only the variance of each coefficient but also its covariance. However, as discussed previously, measures of many pollutants are not available for all SMSAs. Therefore, there is an unavoidable trade-off between size of data base and number of pollutants for which coefficients may be jointly estimated. In summary, using several plausible models on a single data set, we have produced coefficient estimates which vary by a factor of about 3, and estimates of the

Table 18. Estimated pollution coefficients and standard errors from regressions with a single pollutant (n = 66). /38 = 3.72 /~,, = 0.338

S~ = 1.90 S~ = 0.198

L

s~ =

= 4.80

/~,~ = 10.3 /~B = 218

3.36

S~ = 27.6 Sb = 790

standard errors of these coefficients which vary by a factor of nearly 2½. Although quite variable, the coefficients are not as sensitive to reasonable differences in model choice as the work of Thibodeau et al. or that of Gibbons and McDonald might suggest. In addition, although many of the estimates are small in comparison with their standard errors, all of the sulfate coefficients are positive. The variability of coefficients produced in the analysis illustrates the need for external evidence as a basis for differentiating among alternative models. Without a clear understanding of the determinants of community mortality rates, it is impossible to choose objectively among these models. Conclusions

The first phase of our work indicated that many of the putative criticisms of cross-sectional data bases and methodologies are not as compelling as they might seem at first. However, the review clearly demonstrated that many interesting and important questions related to the interpretation of cross-sectional studies remain unanswered. For example: it is unclear which specific pollutants(s) is (are) responsible for any observed effect; the shape of the exposure-response relationship is unresolved; and it is possible that the observed effects are due, in part, to confounding or systematic misclassification. The second section of the paper illustrated the diversity of the results reported in the cross-sectional literature. To some extent, these apparent inconsistencies can be explained by the use of different population subsets, the treatment of multiple pollutants, and the selection of controlling variables. However, the analysis clearly indicates a residual of uncertainty confronting the risk analyst relying on cross-sectional studies. The third and final phase of the w o r k - a reanalysis of

Cross-sectional mortality studies

one of the data bases central to the literature-was useful in two ways. First, it indicated that results are not as sensitive to reasonable variations in choices of model and/or data set as previous studies seemed to suggest. Second, it illustrated the differences in results obtained by joint and separate estimation of the coefficients of several measures of particulate pollution. As a whole, the analysis emphasized that crosssectional mortality studies provide no clear answers to the questions central to risk assessment: 1. Which coefficient(s) should be used in risk assessment? 2. Which standard errors or confidence intervals should accompany these coefficients? Nonetheless, unless decisions are to be postponed, both questions must be answered now. We see three basic approaches to these questions, each with strengths and weaknesses. 1. Use the coefficient and standard error from the model and data set in the literature which one feels is best. Although this approach might be practical from the point of view of a solitary decision maker in private enterprise, it has many limitations for public policy. First, it is unlikely that any group will be able to agree on one model and data set. Second, the judgment as to which model is "best" unavoidably involves subjective elements. Third, the estimated standard error does not reflect all of the important sources of uncertainty (e.g., variable selection, functional form, estimation technique, influence of outliers). 2. Use an average of all coefficients reported in the open literature, perhaps weighted by a reasonable measure of reliability. Although at first this may seem objective, use of a simple average gives greatest emphasis to investigators who have published many regressions. In addition, in practice there are many unanticipated difficulties in converting the results for comparison. These were discussed in an earlier section of the report. Some have suggested that qualitative weights, based upon one's regard for the studies, be applied. Although appealing, choice of the weights introduces all of the subjective elements discussed above. To complicate matters further, the distribution of the coefficient estimates is not necessarily normal. Therefore, it may be necessary to base confidence intervals upon distribution-free methods. 3. Use coefficients drawn from a subjective probability density function.

77

One may take an explicitly subjective approach, such as that outlined by Morgan et al. (1978). The strength of this approach is that it allows incor]aoration of nonsampling errors, which are believed to be at least as large as sampling errors. However, there exists substantial controversy over the use of subjective probability estimates (see, for example, Gnedenko, 1968). An alternative to the purely subjective approach would be to modify a subjective prior distribution with observed data as is commonly done in Bayesian methods for statistical inference. Of at least equal importance as the procedures for deriving quantitative risk estimates from these studies is an appreciation of the limitations of the estimates so derived. Our literature review, meta-analysis, and sensitivity analysis imply that: 1. To some extent all of the particulate pollution variables must be viewed as surrogates for whatever are the truly pernicious components of airborne particulate matter. Therefore, risk assessments based upon these numbers will be most valid for emissions similar to those represented by the "typical" ambient aerosol of the 1960's. Certainly they are of limited value for distinguishing among technologies with similar emission rates but different particle compositions. This is a potentially severe limitation for selecting optimal control strategies. 2. The studies have been unable to differentiate among several alternative plausible forms of exposure-response function. Therefore, while the coefficients may be used with some confidence to predict the impact of small changes in particulate concentrations in areas with exposures near those typical of the SMSAs involved in these studies, they must be used with caution to predict the impacts of: (a) large increments in exposure, (b) incremental exposures on low (or high) baseline exposures, and (c) intermittent emissions patterns which would differentially influence the parameters of the distribution of daily exposures. Further, as we have pointed out in a related paper, it is unclear whether the cross-sectional studies measure the sum of acute impacts on mortality from chronic disease or the impact of chronic exposure on development of chronic disease (see Evans et al., 1984). Thus, the projected mortality impacts might occur in the same year as the projected increase in emissions, or the mortality impacts might be spread over the 20 to 30 years following the increase in emissions. Beyond these caveats related to "extrapolation" of the results, there are more serious philosophical issues. Paramount among them are the related issues of confounding and causality. The issues raised by Hill (1965) provide a framework for the evaluation of causality. However, the interpretation of each issue involves

78 u n a v o i d a b l y subjective elements. T h e r e f o r e , it is to be e x p e c t e d t h a t large v a r i a t i o n s in c o n c l u s i o n s m a y rema i n. In his c o n c l u d i n g r e m a r k s o n a s s o c ia ti o n an d c a u s a t i o n , Hill stated: • . . In asking for very strong evidence I would, however, repeat emphatically that this does not imply crossing every "t", and swords with every critic, before we act. All scientific work is incomplete-whether it be observational or experimental. All scientific work is liable to be upset or modified by advancing knowledge. That does not confer on us a freedom to ignore the knowledge we already have, or to postpone the action that it appears to demand at a given time. W e are o f the o p i n i o n that the cross-sectional studies reflect a causal r e l a t i o n s h i p b e t w e e n e x p o s u r e to airb o r n e particles a n d p r e m a t u r e m o r t a l i t y . F r o m o u r p o i n t o f view it is as likely that p a r a m e t e r s h a v e been u n d e r e s t i m a t e d (due to inclusion o f n o n c a u s a l variables in the m o d e l s wh i ch are positively c o r r e l a t e d with air p o l l u t i o n ) as t h a t they are o v e r e s t i m a t e d due to conf o u n d i n g . W e g r a n t that the a p p a r e n t a s s o c ia ti o n is w e a k to m o d e r a t e in strength, that the specific type o f particle responsible has n o t been identified, a n d that cross-sectional studies p r o v i d e no i n f o r m a t i o n on the t e m p o r a l n a t u r e o f response. But, we believe that the r e p e a t e d a n d relatively consistent findings in c o n j u n c t i o n with the evidence f r o m o t h e r sources [e.g., timeseries m o r t a l i t y studies, t o x i c o l o g y , a n d clinical studies (see, f o r e x a m p l e , O z k a y n a k et al., 1982)] s u p p o r t a causal i n t e r p r e t a t i o n . H o w e v e r , we are in the m i n o r i t y in t a k i n g this view. This a m b i g u i t y poses a d i l e m m a f o r the risk analyst a n d p o i n t s to the need f o r f u r t h e r research utilizing alternative approaches.

Acknowledgements- We gratefully acknowledge the assistance of our many colleagues, especially those who have reviewed previous drafts and/or contributed to the development of the work including: Professors Douglas Cooper, Lestor Lave, Guy Orcutt, John Spengler, James Ware and Richard Wilson; Drs. Frederick Lipfert and Haluk Ozkaynak; and Diane Gibbons, Neil Numark, Debra Swanson, and C. J. Hicks• The work was supported by the Health and Environmental Risk Analysis Program of the U.S. Department of Energy, Agreement No. DE-AC02-81EVI0731.

J.S. Evans, T. Tosteson, and P. L. Kinney

Appendix Table A-1. SMSAs in data base. City Birmingham Mobile Montgomery Phoenix Little Rock Fresno Los Angeles Sacramento San Diego San Francisco San Jose Denver Bridgeport Hartford New Haven Wilmington Washington Jacksonville Miami Orlando Tampa Atlanta Augusta Columbus Macon Savannah Chicago Rockford Gary Indianapolis South Bend Terre Haute Des Moines Topeka Wichita Baton Rouge New Orleans Shreveport Portland Baltimore Boston Brockton Fall River Springfield Worcester Detroit Flint Jackson Lansing Saginaw Duluth Minneapolis Jackson Kansas City St. Louis Omaha Las Vegas Manchester

State AL AL AL AZ AR CA CA CA CA CA CA CO CTa CTa CTa DE DC FL FL FL FL GA GA GA GA GA IL IL IN IN IN IN IA KS KS LA LA LA MEa MD MAa MAa MAa MAa MAa MI MI MI MI MI MN MN MS MO MO NE NV NH

aState Economic Area (SEA).

City Atlantic City Jersey City Newark Albuquerque New York Charlotte Greensboro Raleigh Canton Cincinnati Cleveland Columbus Dayton Hamilton Lorain Springfield Toledo Youngstown Oklahoma City Portland Allentown Harrisburg Johnstown Philadelphia Pittsburgh Reading Scranton Wilkes Barre York Providence Charleston Columbia Greenville Sioux Falls Chattanooga Knoxville Memphis Nashville Austin Beaumont Dallas El Paso Fort Worth Galveston Houston San Antonio Waco Salt Lake City Norfolk Richmond Roanoke Seattle Tacoma Charleston Huntington Wheeling Madison Milwaukee Winston-Salem

State NJ NJ NJ NM NY NC NC NC OH OH OH OH OH OH OH OH OH OH OK OR PA PA PA PA PA PA PA PA PA RI SC SC SC SD TN TN TN TN TX TX TX TX TX TX TX TX TX UT VA VA VA WA WA WV WV WV WI WI NC

Cross-sectional mortality studies

79 Table A-2. Variables in the Data Set.

Mortality TMR Unadjusted total mortality rate (deaths/yr/100,000) ASR Age-sex-race adjusted mortality rate (deaths/yr/100,000) Air Pollution MIN S Smallest biweekly sulfate concentration (0. l/~g/m 3) MEAN_.._S Arithmetic mean of biweekly sulfate concentrations (0.1 ~g/m 3) MAX_._S Largest biweekly sulfate concentration (0.1/zg/m 3) MIN P Smallest biweekly TSP concentration (#g/m 3) MEAN_.._P Arithmetic mean of biweekly TSP concentrations ~ g / m 3) MAX_.._P Largest biweekly TSP concentration ~ g / m 3) Socioeconomic GE65 Percent of SMSA population at least 65 years old (0.1070) MED_.._AGE Median age of SMSA population (0.1 yr) MIGRATE Percent increase in SMSA population, 1950 to 1960 (0.1070) NW: Percent of nonwhites in SMSA population (0.1 070) POOR Percent of SMSA families with income below the poverty level (0.1 070) POP SMSA population (persons) POPDEN SMSA population density (persons/mi 2) LOGDEN Decimal logarithm of population density Occupation Mix (0.1 070 of civilian labor force) UNEMP Unemployed Male MALE AGRIC Agriculture CONST Construction MFG_._D Durable goods manufacturing MFG__ ND Nondurable goods manufacturing TRANSP Transportation, communication, and other public utilities TRADE Wholesale and retail trade FINANCE Finance, insurance and real estate EDUC Educational services PUB___ADM Public administration WHT COL White collar occupations TRAN___USE Persons using public transportation to/from work Climate MIN___T Average daily minimum temperature (0.1 °F) MINTLE32 Days with minimum temperature 32 °F and below (days/yr) MINTLE0 Days with minimum temperature 0 °F and below (days/yr) MAX...__T Average daily maximum temperature (0.1 °F) MAXTGE90 Days with maximum temperature 90 °F and above (days/yr) MAXTLE32 Days with maximum temperature 32 °F and below (days/yr) DEG___DAYS Annual heating demand (OF day) PRECIP Annual precipitation (0.01 in.) RAIN Days with at least 0.01 in. precipitation (days/yr) SNOW Days with at least 1.0 in. snow or sleet (days/yr) FOG Days with heavy fog (days/yr) AM___HUM Average daily 1 a.m. relative humidity (%) Average daily 1 p.m. relative humidity (070) PM__.._HUM WIND Average hourly wind speed (0.1 mi/h) Heating and Cooling Equipment (0.01 070 of homes, unless specified) STM___HHE Steam or hot water Warm air furance WAF_._HHF FLOR___HHE Floor, well, or pipeless furnace ELEC HHE Built-in electric units FLUE_._HHE Other system with flue Other system without flue WOFLU___HHE NONE_.._HHE Without heating equipment WO___AC Without air conditioning (0.1070 of homes) Heating Fuel (0.01% of homes) COAL__ HHF Coal or coke Fuel oil, kerosine, etc. OIL___HHF BGAS___HHF Bottled, tank, or LP gas GAS___HHF Utility gas ELEC___HHF Electricity OTHER___HHF Other

80

J . S . Evans, T. Tosteson, and P. L. Kinney

NONE_..._HHF Without heating fuel Water Heating Fuel (0.0107o of homes) COAL WHF Coal or coke OIL WHF Fuel oil, kerosine, etc. BGAS WHF Bottled, tank, or LP gas GAS WHF Utility gas ELEC___WHF Electricity OTH_._WHF Other NONE WHF Without water heating fuel LIPFERT VARIABLES IRON MANG BAP SMOKING COLLEGE HOUSING

Arithmetic mean annual iron c o n c e n t r a t i o n - 1962 ( # g / m ~) Arithmetic mean annual manganese concentration - 1962 ( # g / m 3) Arithmetic mean annual benzo(alpha)pyrene c o n c e n t r a t i o n - 1959 Ozg/m 3) Estimated cigarette usage ( p a c k s / p e r s o n / y r ) Percent of population with 4 or more years of college Percent of substandard housing units (0.107o)

Table A-3. Summary statistics for three alternative data sets, U.S. SMSAs, 1960. n = 117 Variablesa TM R ASR MIN_.._S MEAN S MAX.__P MIN_._P MEAN___P MAX___P IRON MANG BAP GE65 MED_._AGE MIGRATE SMOKING NW POOR UNEMP COLLEGE c WHT_._COL POP POPDEN LOGDEN HOUSING c

Units

Mean

deaths/yr/100,000 deaths/yr/100,000 0.1 p.g/m 3 0.1/zg/m 3 0.1 # g / m 3 ttg/m 3 /~g/m3 #g/m ~ #g/m 3 /zg/m ~ 0.001 /~g/m 3 0.1 °70 0.1 yr 0.1070 packs/person/yr 0.107o 0.1 070 0.1% 070 0.1070 persons persons/mi2 1070

912 1020 47.7 101 228 45.5 118 268 . . . 83.9 290 308 125 181 50.1 435.2 .811M 700 2.63 -

Occupational Mix Variables Pr opor tio n of the civilian labor force 0.107o employed in each of following AGR1C CONST MFD___D MFD ND TRANSP TRADE FINANCE EDUC

n = 98

s.d.

max

153 78 32.2 53.8 124 18.6 40.9 137 . . .

. . .

21.1 31.1 260 104 65.2 15.4 -53.2 1.39M 1350 0.394 -

1400 1360 189 283 940 99 247 958 . . . 171 366 1630 400 327 104 -588 10.7M 13,600 4.13 -

Mean 908 1020 103 121 -

n = 66 s.d.

150 78.2 53 40.3 -

. . . 83.5 291 312 173 120 175 8.21 439 2.65 78

20.4 30.4 261 27.4 103 62.4 3.09 53.6 0.4 13.6

223 579 1430 1160 698 1800 436

204 150 980 644 218 275 139

1610 1014 4440 3300 1380 2280 933

210 570 1460 1150 708 1800 441

206 147 983 636 220 286 142

492

164

1260

484

148

Mean 915

s.d. 146

b

110

50.5

127

43.8

3.03 0.163 11.8 84.8 294

2.36 0.326 11.2 20.1 28.2

173 117 166

29.9 99.9 62.4

2.72

0.493

R

R

m

Cross-sectional mortality studies

81 Table A-3. (Continued) n = 117

Variablesa

Units

Mean

Home Heating and Cooling Characteristics Equipment Proportion of 0.01% homes with STM_.._HHE 1930 WAF___HHE 3560 FLOR_.__HHE 1260 FLUE HHE 1780 WOFLUHHE 1190 NONE HHE WO_. AC 843 Fuel Proportion of occupied homes with the following COAL HHF OIL.___HHF BGAS HHF GAS HHF ELEC HHF OTHER_._HHF NONE___HHF Water Fuel Proportion of occupied homes with the following COAL_.._WHF OIL___WHF BGAS___WHF GAS___WHF ELEC_.._WHF OTH___WHF

s.d.

n = 98 max

Mean

n = 66 s.d.

2180 2230 1250 1200 1820

8900 8320 5070 5560 6290

1970 3700 1270 1740 1120

2110 2200 1320 1160 1650

115

987

846

116

1110 3150 315 4920 237 213

1500 2910 326 3300 736 279

8980 8230 2490 9440 4820 1420

1160 3120 292 4900 267 196

1590 2870 320 3330 800 256

281 901 427 5350 2200 47.8

863 1590 243 2800 2180 76.4

7210 7170 1300 9440 8270 699

312 852 418 5430 2200 48.9

937 1450 239 2790 2210 81.7

460 94.3 3.5 655 38.2 27.2 4680 3710 110 8.21 27.1 76.8 57 91.7

75.7 49.6 7.54 79.8 39.2 29 1970 1310 26.7 6.62 19 8.11 7.39 19

679 188 48 841 156 113 9740 7030 166 29 121 88 76 133

454 96.3 3.88 650 37.0 28.9 4800 3620 109 8.52 26 76.3 56.8 91.6

74.3 49.7 8.04 79.5 38.6 30.1 1970 1340 28.4 6.68 17.8 8.57 7.75 18

657 108

30.1 71.5

656 109

29.1 63.4

Mean

s.d.

m

0.1%

R

R

m

0.01%

Climate Variables MIN___T MINT LE32 MINT LEO MAX T MAXT GE90 MAXT LE32 DEG_.._DAYS PRECIP RAIN SNOW FOG AM_.._HUM PM_.._HUM WIND

0.1 °F day/yr day/yr 0.1 °F day/yr day/yr °F day 0.01 in. day/yr day/yr day/yr 070 070 0.1 mi/h

Other Variables MALE TRANS USE

0.1% 0.107o

737 514

aDefinitions of variables are given in Table Ao2. bData set did not include variable. CThese variables from Lipfert's data set are strictly applicable to the city corresponding to the SMSA of interest.

m

m

R

m

m

R

m

m

w

m

i

82

References Amdur, M. O., Silverman, L., and Drinker, P. (1952) Inhalation of sulfuric acid mist by human subjects, Arch. Ind. Hyg. 6, 305-313. Amdur, M. O. (1958) The respiratory response of guinea pigs to sulfuric acid mist, Arch. Ind. Hyg. 18, 407. Berretoni, J. N. (1978) A critique on the use of multiple linear regression in concluding whether air pollution is associated with mortality rates in the s t u d y - A i r Pollution and Human Health by Lester B. Lave and Eugene P. Seskin. Unpublished paper, Case Western Reserve University. Bozzo, S. R., Novak, K. M., Galdos, F., Hakoopian, R., and Hamilton, L. D. (1977) Mortality, migration, income and air pollution: a comparative study. Biomedical and Environmental Assessment Division, Brookhaven National Lab., Upton, NY. Chappie, M. and Lave, L. (1981) The health effects of air pollution: a reanalysis, J. o f Urban Economics, 12, 346-376, 1982. Christiansen, G. B. and Degen, C. G. (1980) Air pollution and mortality rates: a note on Lave and Seskin's pooling of cross-section and time-series data, J. Environ. Economics and Management, 7, 149-155. Cooper, D. E. and Hamilton, W. C. (1978) Atmospheric sulfates and mortality--a statistical confection. Research and Development Department, Continental Oil Company, PoncaCity, OK. Coutant, R. W. (1977) Effect of environmental variables on the collection of atmospheric sulfate, Environ. Sci. TechnoL 4, 873-878. Crocker, T. D., Schulze, W., Ben-David, S., and Kneese, A. (1979) Methods development for assessing air pollution control benefits. Vol. I, Economics of air pollution epidemiology. EPA-600/5-79001a, U.S. Environmental Protection Agency, Washington, DC. Duffy, E. A. and Carroll, R. C. (1967) U.S. Metropolitan Mortality, 1954--61. Public Health Service Publication 999-AP-39, Department of Health, Education and Welfare, Washington, DC. Evans, J. S., Kinney, P. L., Koehler, J. L., and Cooper D. W. (1984) The relationship between time-series and cross-sectional mortality studies, J. Air Pollut. Control Assoc., 34, 551-553. Fairbairn, A. S. and Reid, D. D. (1958) Air pollution and other local factors in respiratory disease, Brit. J. Prevent. Soc. Medicine, 12, 94. Finch, S. J. and Morris, S. C. (1977) Consistency of reported health effects of air pollution. BNL 21808-R, Brookhaven National Laboratory, Biomedical and Environmental Assessment Division, Upton, NY. Gerking, S. and Schulze, W. (1981) What do we know about the benefits of reduced mortality from air pollution control?, Amer. Econ. Rev. 71,228-234. Gibbons, D. I. and McDonald, G. C. (1980a) Examining regression relationships between air pollution and mortality. GMR-3278, General Motors Research Laboratories, Warren, MI. Gibbons, D. I. and McDonald, G. C. (1980b) Identification of influential geographic regions in an air pollution and mortality analysis. GMR-3455, General Motors Research Laboratories, Warren, MI. Gibbons, D. I. and McDonald, G. C. (1980c) Sensitivity of an air pollution and health study to the choice of a mortality index. GMR-3256, General Motors Research Laboratories, Warren MI. Gibbons, D. I. and McDonald, G. C. (1981) Influential data diagnostics and model building techniques applied to an air pollution mortality analysis. Presented at the Sixth Annual SAS Users Group International Conference, Orlando, FL. Gibbons, D. I. and McDonald, G. C. (1982) Regression diagnostics applied to an air pollution and mortality model. GMR-3655R, General Motors Research Laboratories, Warren, MI. Gnedenko, B. V. (1968) The Theory o f Probability. (English translation of augmented 2nd ed. of Russian text). Chelsea, New York, NY Gregor, J. J. (1976) Mortality and air quality, the 1968-1972 Allegheny County Experience. Center for the Study of Environmental Policy, Pennsylvania State University, University Park, PA. Hamilton, J. D. (1979) Memorandum on the validity of Lave and Seskin's regression estimates in light of new research funded by the Environmental Protection Agency. Public Interest Economics West, San Francisco, CA. Hamilton, J. D. and Manne, A. S. (1978) Health and economic costs of alternative energy sources, IAEA Bull. 20, 44-57.

J.S. Evans, T. Tosteson, and P. L. Kinney Hammond, E. C. (1966) Smoking in relationship to the death rates of one million men and women, Epidemiologic Approaches to the Study o f Cancer and Other Chronic Diseases edited by W. Haenzel. National Cancer Institute Monograph 19, U.S. Public Health Service, Bethesda, MD. Hickey, R. J., Boyce, D. E., Clelland, R. C., Bowers, E. J., and Slater, P. B. (1977) Demographic and chemical variables related to chronic disease mortality in man. Technical Report No. 15, Department of Statistics, University of Pennsylvania, Philadelphia, PA. Hill, A. B. (1965) The environment and diseases: association and causation, Proc. R. Soc. Medicine Occup. Medicine, 58, 295-300. Hoaglin, D. C. and Welsch, R. E. (1978) The hat matrix in regression and ANOVA, Amer. Statist. 32, 17-22. Holland, W. W., Bennett, A. E., Cameron, I. R., Florey, C. duV., Leeder, S. R., Schilling, R. S. F., Swan, A. V., and Waller, R. E. (1979) Health effects of particulate pollution: reappraising the evidence, Amer. J. EpidemioL 110, 525-659. Kitngawa, E. M. and Hauser, P. M. (1973) Differential Mortality in the United States. Harvard University Press, Cambridge, MA. Koenig, J. Q., Pierson, W. E., and Horike, M. (1983) The effects of inhaled sulfuric acid on pulmonary function in adolescent asthmatics, Amer. Rev. Respir. Dis. 128, 221-225. Koshal, R. K. and Koshal, M. (1973) Environments and urban mortality-an econometric approach, Environ. Pollut. 4, 247-259. Koshal, R. K. and Koshal, M. (1974) Air pollution and the respiratory disease mortality in the United States-a quantitative study, Soc. Indicators Res. 1, 263-278. Lave, L. B. and Seskin, E. P. (1970) Air pollution and human health, Science 169, 723-733. Lave, L. B. and Seskin, E. P. (1973) An analysis of the association between U.S. mortality and air pollution, J. Amer. Statist. Assoc. 68, 284-290. Lave, L. B. and Seskin, E. P. (1977) Air Pollution and Human Health. Johns Hopkins University Press, Baltimore, MD. Lave, L. B. and Seskin, E. P. (1979) Epidemiology, causality and public policy, Amer. Scientist 67, 178-186. Lipfert, F. W. (1977a) The association of air pollution with human mortality: multiple regression results for 136 U.S. cities, 1969. Presented at the 70th Annual Meeting of the Air Pollution Control Association, June 20-24, Toronto, Canada. Lipfert, F. W. (1977b) The association of human mortality with air pollution: a review of previous studies. Presented at the 70th Annual Meeting of the Air Pollution Control Association, June 20-24, Toronto, Canada. Lipfert, F. W. (1978a) Statistical studies of geographical factors in cancer mortality: association with community particulate air pollution. Presented at the 71st Annual Meeting of the Air Pollution Control Association, June 25-30, Houston, TX. Lipfert, F. W. (1978b) The association of human mortality with air pollution: statistical analyses by region, by age, and by cause of death. Ph.D. dissertation, Union Graduate School, Cincinnati, OH. Lipfert, F. W. (1980a) Sulfur oxides, particulates, and human mortality: a synopsis of statistical correlations, J. Air Poilut. Control Assoc. 30, 336-371. Lipfert, F. W. (1980b)Differential mortality and the environment: the challenge of multicollinearity in cross-sectional studies, Energy Syst. Policy 3, 367-400. Liu, B. C. and Yu, E. S. H. (1976) Physical and economic damage functions for air pollutants by receptor. EPA-600/5-76-011, U.S. Environmental Protection Agency, Corvallis Environmental Research Laboratory, Corvallis, OR. Liu, B. C. and Yu, E. S. H. (1977) Mortality and air pollution revisited, J. Air Pollut. Control Assoc. 26, 968-971. McDonald, G. C. and Schwing, R. C. (1973) Instabilities of regression estimates relating air pollution to mortality, Technometrics, 15, 463 -481. Mendelsohn, R. and Orcutt, G. (1979) An empirical analysis of air pollution dose response curves, J. Environ. Econ. Management 6, 85-106. Mills, C. A. (1943) Urban air pollution and respiratory diseases, Amer. J. Hygiene 37, 131. Moran, P. A. P. (1971) Estimating structural and functional relationships, J. Multivar. Anal. 5, 232-255.

Cross-sectional mortality studies Morgan, M. G., Morris, S. C., Meior, A. K., and Shenk, D. L. (1978) A probabilistic methodology for estimating air pollution health effects from coal-fired power plants, Energy Syst. Policy 2, 287-310. Monson, R. R. (1980) Occupational Epidemiology, CRC Press, Boca Raton, FL. Moriyama, I. M. and Guralnick, L. (1956) Occupational and social class differences in mortality, in Trends and differences in mortality, by Milbank Memorial Fund. Presented at the 1955 Conference of the Milbank Memorial Fund, NY. National Center for Health Statistics (1975) Vital statistics of the United States, Vol II, Part A. The Center, Washington, DC. Olsen, A. R. (1978) A review of cross-sectional studies of the relationship of air pollution to human mortality. Draft, final report, EPRI, Palo Alto, CA. Orcutt, G., Franklin, S., Mendelsohn, R. and Smith, J. (1977) Does your probability of death depend on your environment: a microanalytic study, Amer. Econ. Rev. 67, 260-264. Ozkaynak, H., Warren, A. J., Tosteson, T. D., Evans, J. S., Thurston, G. D., and Colome, S. D. (1982) Analysis of health effects of population exposures to ambient particulate matter. Health and Environmental Effects Document, prepared for U.S. Dept. of Energy, Health and Environmental Risk Analysis Program, Harvard University, Cambridge, MA. Page, W. P. and Fabian, R. G. (1978) Factor analysis: an exploratory methodology and management technique for the economics air pollution, J. Environ. Management, 6, 185-192. Page, W. P. and Fellner, W. (1978) Exploratory techniques for the determination of potential dose-response relationships between human health and air pollution, J. Environ. Econ. Management 5, 376-389. Pocock, S. J., Cook, D. G., and Beresford, S. A. A. (1981) Regression of area mortality rates on explanatory variables: What weighting is appropriate?, Appl. Statist. 30, 286-295. Ramsey, J. B. (1969) Tests for specification errors in classical linear least squares regression analysis, J. R. Statist. Soc. Ser. B, 31, 350-371. Robinson, W. S. (1950) Ecological correlation and the behavior of individuals, Amer. Sociol. Rev. 15, 351-357. Sauer, H. I. (1979) The relationship of components of air pollution to mortality: Methods of study. Presented at the 72nd Annual Meeting of the Air Pollution Control Association, Cincinnati, OH. Schwing, R. C. and McDonald, G. C. (1976) Measures of association of some air pollutants, natural ionizing radiation, and cigarette smoking with mortality rates, Science, Total Environ. 5, 139-169. Seivin, S., Merrill, D., Kwok, L., and Sacks, S. (1981) Ecologic regression analysis and the study of the influence of air quality on mortality. Preprint LBL-12217, Lawrence Berkeley Laboratory, Berkeley, CA. Seneca, J. J., Asch, P., and Brennan, K. (1979) Mortality and air

83 pollution in New Jersey, in 12th Annual Report--Economic Policy Council and Office of Economic Policy. State of New Jersey, Department of the Treasury, Trenton, NJ. Smith, V. K. (1976a) The measurement of mortality-air pollution relationships, Environ. Planning 8, 149-162. Smith, V. K. (1976b) The Economic Consequences of Air Pollution. Ballinger Publishing Co., Cambridge, MA. Stocks, P. (1959) Cancer and bronchitis mortality in relation to atmospheric deposit and smoke, Brit. Medical J. 1, 74. Stone, B. J., Blot, W. J., and Fraumeni, J. F. (1978) Geographic patterns of industry in the United States-an aid to the study of occupationai disease, J. Occup. Medicine 20, 472-477. Theft, H (1971) Principles of Econometrics. John Wiley and Sons, Inc., Santa Barbara, CA. Thibodeau, L. A., Reed, R. B., Bishop, Y. M. M., and Kammerman, L. (1980) Air pollution and human health: A review and reanalysis, Environ. Health Perspect. 34, 165-183. Thomas, T. J. (1973) An investigation into excess mortality and morbidity due to air pollution. Ph.D. dissertation, Purdue University, W. Lafayette, IN. Tobacco Tax Council, Inc. (1977) The tax burden on tobacco, V. 12, The Council, Richmond, VA. U.S. Bureau of the Census (1981) Statistical abstract of the United States: 1981, 102nd ed. The Bureau, Washington, DC. U.S. Environmental Protection Agency (1982) Air quality criteria for particulate matter and sulfur oxides, Vol. II. EPA-660/8-82-029b, EPA, Research Triangle Park, NC. Utell, M. J., Morrow, P. E., Hyde, R. W. (1982) Comparison of normal and asthmatic subjects' responses to sulfuric pollutant aerosols, Ann. Occup. Hygiene 26, 691-697. Viren, J. R. (1978) Cross-sectional estimates of mortality due to fossil fuel pollutants: a case for spurious association. Report for Division of Policy Analysis, Department of Energy, Washington, DC. Winkelstein, W., Jr., Kantor, S., Davis, E. W., Maneri, C. S., and Mosher, W. E. (1967) The relationship of air pollution and economic status to total mortality and selected respiratory system mortality in man, Part I: suspended particulates, Arch. Environ. Health 14, 162. Ware, J. H., Thibodeau, L.A., Speizer, F. E., Colome, S., and Ferris, B. G., Jr. (1981) Assessment of the health effects of atmospheric sulfur oxides and particular matter: Evidence from observational studies. Environ. Hlth. Persp. 41, 255-276. Wilson, R., Coiome, S., Spengler, J. D., and Wilson, D. G. (1980) Health Effects of Fossil Fuel Burning: Assessment and Mitigation. Ballinger Publishing Co., Cambridge, MA. Zeidberg, L. D., Horton, R. J. M., and Landau, E. (1967) The Nashville air pollution study, Part V: mortality from diseases of the respiratory system in relation to air pollution, Arch. Environ. Health 15, 214.