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Total Suspended

  1. 1. I 1- I Total Suspended Particulate Matter and Daily Mortality in Cincinnati, Ohio Joel Schwartz Environmental Epidemiology Program, Harvard School of Public Health, Boston, MA 02115 USA nia, cardiovascular disease, and the elderly The London smog disaster of December 1952 established that high levels of air- can be replicated. In addition, this study used diagnostic plots and sensitivity analy- borne particles and sulfur dioxide pro- duced large increases in daily death ratesI. ses that have been used in few if any prior studies to ensure that the association is not Since the London episode, time-series confounded by inadequate control for analyses have associated daily mortality weather. with daily particle concentrations in Daily deaths of residents of Hamilton London (2,3), New York (4,5), Santa Clara, California (6), Detroit (A), Steuben- County, Ohio, the county containing Cincinnati (population 873,224 in 1980) ville, Ohio (8), Philadelphia (9), St. Louis were extracted from the detail mortality and eastern Tennessee (10), Utah Valley tapes of the National Center for Health (11), Minneapolis (12), and Birmingham, Statistics for the calendar years 1977-82. AL (13). Both the air pollution concentra- Deaths from accidental causes (ICD 9 tions and mortality increases were lower >800) and deaths occurring outside the than seen in London in 1952. county were excluded from the analysis. In These studies have reported that the addition to daily deaths, deaths of persons primary association was with particles and aged 65 or older and daily deaths from car- not sulfur dioxide, and none have found diovascular disease (ICD 390-448) and any evidence for a threshold. In Santa from pneumonia (ICD 480-486) were Clara (6) and the Utah Valley (11), coex- tabulated. These daily counts were match- posure with sulfur dioxide was essentially absent, avoiding any possible confounding ed to the 24-hr average (midnight to mid- night) of total suspended particulates with that pollutant. Clearly, airborne parti- (TSP) from all the population-oriented cles are associated with daily mortality independent of sulfur dioxide exposure. monitors within the city. Pollution data were retrieved from the Environmental The regression coefficients reported in ....~~~~~~~~~~. Protection Agency's aerometric data bank. Santa Clara and Utah were similar to the For each day, TSP from all available moni- and monthly variations, including the pos- regression coefficients in the other studies. This suggests that the other associations sibility that seasonal peaks may vary in tors were averaged to produce an area wide were due to the particle and not the sulfur mean. Daily monitoring for TSP began in date and magnitude from year to year. dioxide exposure. In the other cities, the Multiple categories of temperature and 1977. Only one monitoring station was regression coefficients of particles in mod- dew point temperature allow for a U- running every day in the city of Cincinnati els containing sulfur dioxide were little that year. In 1979-81, two additional shaped relationship with daily deaths, or changed from their values without simulta- other nonlinearities, if present. A linear monitors were operated on a daily sched- neous consideration of sulfur dioxide; they ule. Several additional monitoring stations and quadratic time term were also includ- also remained statistically significant. In were operational on a one-day-in six-basis, ed to capture any continuous temporal contrast, the coefficients of sulfur dioxide and were included in the average when trend in mortality over the period. Several alternative approaches were were generally much lower in models that available. Data on daily mean temperature and daily mean humidity were obtained also examined to see how sensitive the air controlled for particle exposure and were from the weather station at the Cincinnati not statistically significant. This indicates pollution results were to the method used to control for these potential confounders. that variations in particle concentrations airport. independent of sulfur dioxide were associ- Counts of rare events, such as dying on The second approach replaced the dummy a particular day, are not normally distrib- variables with natural splines. This mod- ated with variations in daily mortality. eled the dependence of mortality on time Variations in sulfur dioxide concentrations uted, and were modeled in Poisson regres- that were independent of particle concen- sions. Substantial seasonal and other long- and temperature using cubic polynomials fit to intervals of time (or temperature). term temporal patterns are often found in trations did not appear to be associated The cubic polynomials were subject to daily mortality data. While air pollution with variations in daily mortality in these continuity constraints at the boundaries of may be responsible for part of that pattern, studies. the intervals. The number of intervals each other factors clearly play the dominant The study in Philadelphia provided variable was divided into was chosen by some more details on the specific causes of role. Temperature and humidity may be analysis of deviance. First an initial num- death that were elevated on high air pollu- related to mortality in a nonlinear way. The analytic method was chosen to deal tion days. The relative risks were highest for pneumonia and cardiovascular disease. with these issues. Address correspondence to J. Schwartz, En- vironmental Epidemiology Program, Harvard The basic approach taken in this analy- They were also highest for the elderly. The School of Public Health, 665 Huntington Avenue, sis was to use dummy variables for each of present analysis sought to examine the rela- Boston MA 02115 USA. the 72 months in the study period and for tionship between airborne particles and Joel Schwartz is supported in part by a John D. 8 categories of temperature and dew point daily mortality in Cincinnati, Ohio. In and Catherine T. MacArthur Fellowship. temperature. Dummy variables for each particular, it sought to determine if the Received 21 September 1993; accepted 28 Dec- month in the study controlled for seasonal finding of higher relative risks for pneumo- ember 1993. Environmental Health Perspectives 186
  2. 2. .9e - * 99. . The final approach used nonparametric old model for air pollution. In addition to ber of intevals was chosen. For example, regression. The generalized additive model the statistical test, the smoothed covariate the initial number of intervals chosen for (14) allows a Poisson regression to be fit as adjusted plot of daily deaths versus air pol- time was 24, which means each interval lution allows a direct graphical examina- a sum of nonparametric smooth functions was 6 months long, and was fit with a sep- tion of the shape of the relationship of each of the predictor variables. A non- arate cubic polynomial. Then the number parametric smoother is a tool for summa- between exposure and outcome. of intervals was increased (decreasing the Time series data are often serially corre- rizing the trend of a response measure- number of months per interval in our ment, Y, as a function of one or more pre- lated. Although this may be due to the example) until the improvement in model weather, there may be serial correlation dictor measurements, Xi. . . XP . Most fit for the increased degrees of freedom remaining in the residuals of the regression smoothers are generalizations of weighted used was not statistically significant. The models. Ignoring such serial correlation spline approach has the capability of fitting moving averages. These predict the expect- ed value of Yat AX as the weighted mean of can lead to biased hypothesis tests. All dependencies that may be highly nonlinear regression models were tested for serial cor- the actual values of the Y's corresponding and is therefore useful in assuring that any relation in the residuals, and if any was association with TSP is not confounded by to all the X's in a symmetric neighborhood found, autoregressive Poisson models were around A. The weights decline with dis- inadequate control for weather or season. estimated (18). tance from the center of the neighborhood. Another approach to ensuring that any Table 1 shows the distribution of air The properties of such smoothers have observed association with TSP is not due pollution, temperature, dew point temper- been extensively discussed (15,16). Loess to a coincidence of high TSP days with (17) was used in this analysis, which fit the ature, and daily mortality in Cincinnati extreme weather conditions is to repeat the during the years 1977-82. The distribu- counts of daily deaths to smooth functions analysis excluding such days. The basic tion of counts of daily deaths is illustrated of temperature, dew point temperature, analysis was repeated excluding days with 24-hr mean temperature below 100F or day of study, and TSP. Loess uses a run- ning regression rather than a running mov- above 80'F. These represent the coldest ing average, with weights that decline as and warmest 2.5% of the days, respective- the cube of the distance from the center of ly. In addition, the analysis was repeated the neighborhood. The generalized addi- using the minimum daily temperature EE tive model allows for a test of the improve- instead of the average daily temperature as ment in fit compared to a linear no-thresh- the control variable. Table 1. Distribution of variables in the analysis: daily mortality and total suspended particulates (TSP) in I Cincinnati, 1977-1982 a SD 75th Percentile Mean Variable 25th Percentile Median 4.92 21 Daily deaths 24 17 20 TSP (pg/m3) 76 30.8 53 71 93 Temperature, 0F 19.6 52 37 70 55 43 19 Dew point temperature, OF 60 28 44 a 2M 30 10 Daily Deaths 4.1 15 17 12 14 Daily deaths, >age 65 Figure 1. Histogram of daily deaths in Cincinnati, 0.8 0.6 Pneumonia deaths 1 0 0 Cardiovascular deaths Ohio, during the study period, 1977-1982. 3.5 13 11 9 11 2 a 30 -0 U 0 a lt a t *1. a II Week of Study Week of Study Figure 2. (A) Plot of daily deaths in Cincinnati, Ohio, versus time during the course of the study. Each point represents 1 of the 2191 days during the study period. The line shown is a nonparametric smoothed curve fit to the data and illustrates the seasonal variability in the data. (B) Plot of the residuals of daily mortality (from the basic regression model without total suspended particulates) versus time. There is no long-term pattern remaining in the data, as indicated by the non- parametric smooth. 187 Volume 102, Number 2, February 1994
  3. 3. ------- a a I 2 = IUl S 6 'i w I k t 0 U Temperature OF Temperature OF Figure 3. (A) Plot of the residuals of daily mortality (from the basic regression model without total suspended particulates) versus temperature. There is no inter- val of temperature where the model net over- or underestimates daily mortality, as indicated by the nonparametric smoothed curve. (B) Plot of the residuals of daily mortality (from the basic regression model without total suspended particulates) versus dew point temperature. There is no interval of dew point tempera- ture where the model net over- or underestimates daily mortality, as indicated by the nonparametric smoothed curve. in Figure 1, which shows the slight skew- basic model produced no significant im- showing systematic over- or underestima- ness expected. An average of 21 persons tion of mortality. provement in fit (p = 0.98). Excluding the Table 2 shows the relative risk of (RR) died each day in the metropolitan area, warmest and coldest days also had little death for a 100 Pg/mi3 increase in TSP producing an annual death rate of 8.8 per effect on the association between TSP and from the basic model and the various sensi- daily deaths. The relative risk for TSP in 1000. The basic regression model did a good tivity analyses. TSP was a significant risk that model was also similar to the basic job of controlling for the seasonal variation factor for mortality in this analysis (RR = model. The test for nonlinearity in the 1.06, 95% CI = 1.03-1.10). The use of TSP association was not significant (p = in mortality. For example, Figure 2a shows a plot of daily deaths by week of study. 0.20). Figure 4 shows the nonparametric natural splines resulted in a more parsimo- nious model, with 34 intervals of time and The line through the points is a nonpara- smoothed plot of daily deaths versus TSP metric smooth. The strong seasonal varia- 2 intervals each of temperature and dew in Cincinnati, after controlling for covari- point temperature being fit with cubic tion is obvious. Figure 2b shows the ates. deviance residuals from the basic regression Table 3 shows the relative risk of death polynomials within each interval. The dif- for a 100 pg/m3 increase in TSP concen- ference in explanatory power of the model without air pollution. The seasonal pattern has been removed, as illustrated by dummy variable model was not significant trations for the age and cause specific death the nonparametric smooth. (p = 0.99), despite its using 39 more rates examined. The same pattern seen in Philadelphia (9) was evident in Cincinnati. degrees of freedom. The association of Figure 3a shows the deviance residuals mortality with TSP was unchanged in the The relative risk was higher for the elderly (again from the basic model without air pollution) plotted against temperature, and spline model (Table 2). Similarly, the gen- Figure 3b shows the deviance residuals eralized additive model fit the daily mortal- plotted against dew point temperature. ity data more parsimoniously than the Again, a good fit seems to have been dummy variable model. Again, the use of achieved, with no temperature intervals 42 additional degrees of freedom in the 3 21.! Table 2. Relative risk of death and 95% confidence intervals for a 100 pg/mr3 increase in total suspended a particulates under different modeling alternatives a a! 201 95% Confidence interval Model Relative risk a Basic modela 1.03-1.10 1.06 Natural spline modelb 1.03-1.10 1.07 No extreme days 1.02-1.10 1.06 Generalized additive models 1.04-1.13 1.08 Minimum temperatures 1.03-1.10 1.07 ZU 4I HMa ISl W TV a72 dummy variables for each month of study, 8 categories each of temperature and dew point tempera- Total Suspended Particulates ture, and linear and quadratic time trend variables. bSeparate cubic polynomials fit for 34 intervals of time, and 2 intervals each of temperature and dew point Figure 4. Nonparametric smooth curve of daily temperature. mortality versus total suspended particulate, after CModel a, excluding days with temperature below 100F or above 80IF. controlling for temperature, dew point tempera- dNonparametric smooth functions of time, temperature, and dew point temperature. ture, and time in a generalized additive model. No eModel 1, but with daily minimum temperature instead of daily average temperature. evidence for a threshold is seen in the relationship. Environmental Health Perspectives 188
  4. 4. ae= * eeM - a respirable particles. Moreover, the London Table 3. Relative risk of death and 95% confidence intervals for a 100 pg/m3 increase in total suspended episode of 1952 provides ample demon- particulates for all-cause mortality, and for selected subgroups by age and cause of death stration of biological plausibility: it is clear that respirable particles increased mortality Outcome Relative risk 95% Confidence interval in that episode, although no mechanism All deaths 1.06 1.03-1.10 was determined for that case, either. 1.09 Deaths, age >65 1.05-1.14 1.16 0.95-1.42 Pneumonia deaths 1.03-1.14 Cardiovascular deaths 1.08 REFERENCES All models are after adjustment for covariates. 1. HMSO. Mortality and morbidity during the London fog of December 1952. Report no. the model in removing seasonal variation and for deaths from pneumonia and car- 95. on Public Health and Medical Subjects, from the data. The results are not con- diovascular disease. Her Majesty's Public Health Service. London: founded by extreme weather conditions The association between mortality and Her Majesty's Stationary Office, 1954. because the association is little changed TSP seen in Cincinnati was almost identi- 2. Mazumdar S, Schimmel H, Higgins ITT. when those days are excluded. In addition, cal to that seen in Philadelphia. Moreover, Relation of daily mortality to air pollution: an analysis of 14 London winters, 1958/59- the residual plots indicate that the basic the relationships between TSP and cause- 1971/72. Arch Environ Health 37:213-220 model did not under- or overestimate the and age-specific daily deaths in the two (1982). cities were remarkably similar. For compar- dependence on weather conditions in any 3. Schwartz J, Marcus A. Mortality and air pol- ison, in Philadelphia, the relative risk for a temperature range. The sensitivity of the lution in London: a time series analysis. Am J 100 pg/m3 increase in TSP was 1.07 (95% results to alternative ways of controlling for Epidemiol 131:185-194(1990). CI =1.04-1.10) for all-cause mortality, weather was slight. In the absence of a 4. Glaser M, Greenburg L. Air pollution, mor- tality, and weather: New York City, 1960- plausible candidate for a major con- 1.11 (95% CI = 0.97-1.27) for pneumonia 1964. Arch Environ Health 22:334-343 founder, the conclusion that the observed deaths, 1.10 (95% CI = 1.06-1.14) for car- (1971). association is causal seems reasonable. diovascular deaths, and 1.10 (95% CI = 5. Schimmel H, Murawski TJ. The relation of 1.06-1.13) for deaths in the elderly. The One way to indirectly assess the possi- air pollution to mortality. J Occup Med finding of such similar associations in dif- bility of confounding by omitted factors is 18:316-333(1976). ferent locations makes it less likely that the to examine the relationship in different 6. Fairley D. The relationship of daily mortality to suspended particulates in Santa Clara associations could be due to a confounder. locations, as the correlation between those County, 1980-1986. Environ Health Per- Smoking habits, occupational expo- factors and air pollution should differ spect 89:159-168(1990). among cities. If the association is due to an sure, dietary patterns, and socioeconomic 7. Schwartz J. Particulate air pollution and daily factors are not potential confounders of omitted or imperfectly controlled con- mortality in Detroit. Environ Res 56:204- this association because their day-to-day founder, the induced regression coefficient 213(1991). for particulate air pollution will depend on variation is small and not correlated with 8. Schwartz J, Dockery DW. Particulate air pol- lution and daily mortality in Steubenville, the regression coefficient of the con- air pollution. More likely, potential con- Ohio. Am J Epidemiol 135:12-20(1992). founder with daily mortality and the founders are epidemics, other factors that 9. Schwartz J, Dockery DW. Increased mortality produce the seasonal variation in mortality, regression coefficient of the confounder in Philadelphia associated with daily air pollu- and weather. In Cincinnati, daily mortality with particulate air pollution. If the latter tion concentrations. Am Rev Respir Dis and respiratory epidemics both peaked in varies widely but the coefficient of partic- 145:600-604(1992). the winter. In contrast, winter was the sea- ulate air pollution with mortality does not 10. Dockery DW, Schwartz J, Spengler JD. Air pollution and daily mortality: associations vary widely, confounding by that factor is son with the lowest TSP concentrations. with particulates and acid aerosols. Environ Hence, these factors are unlikely sources of unlikely. The recent studies of particulate Res 59:362-373(1992). upward bias in the association. Further- air pollution and daily mortality have all 11. Pope CA, Schwartz J, Ransom M. Daily mor- more, Figure 2 demonstrates the success of reported relatively similar effect sizes. This tality and PM10 pollution in Utah Valley. lends credence to their findings, since if Arch Environ Health 42(3):211-217(1992) the associations were due to confounding, 12. Schwartz J. Particulate air pollution and daily mortality: a synthesis. Public Health Rev 1 we would expect a greater variability in the Cincinnati Philadelphia 19:39-60(1992). effect size estimates. Figure 5 shows the rel- Minneapolis 13. Schwartz J. Air pollution and daily mortality Steubenville ative risk of death for a 100 pg/m3 increase in Birmingham, AL. Am J Epidemiol 137: Tennessee_ * in TSP concentration from the recent St. Louis _ 1136-47(1993). 0 Birmingham g studies of air pollution and daily mortality. Utah | _ _ _ 14. Hardle W. Smoothing techniques with imple- 0 n0 The consistency is remarkable. mentation in S. New York. New York: Springer-Verlag, 1991. Although the mechanism by which air- 15. Gasser T, Muller HG, Mammitsch V. Ker- borne particles exacerbate illnesses and nals for nonparametric curve estimation. J R fd *_ 0.01 '.9--I----w 1 increase their mortality rates is not under- Stat Soc B 47:238-52(1985). stood, neither is the mechanism by which i~~~~ct 16. Cleveland WS, Devlin SJ. Robust locally- 00Pm3inrease in ail toa suspne par- tobacco smoking increase the risk of death weighted regression and smoothing scatter- from myocardial infarctions. This fact has plots. J Am Stat Assoc 74:829-36(1988). 17. Hastie T, Tibshirani R. Generalized additive not prevented a conclusion being drawn City models. London:Chapman and Hall, 1990. from the strong epidemiologic information Figure 5. Relative risk of all-cause mortality from a 18. Liang KY, Zeger SL. Longitudinal data analy- in the case of smoking and it should not 100 pg/rn3 increase in daily total suspended par- sis using generalized linear models. Bio- impede a similar conclusion in the case of ticulates from studies in different cities. metrika 73:13-22(1986). 189 Volume 102, Number 2, February 1994