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Total Suspended Particulate Matter and DailyMortality in Cincinnati, OhioJoel SchwartzEnvironmental Epidemiology Program, Harvard School of Public Health, Boston, MA02115 USA

The London smog disaster of December1952 established that high levels of air-borne particles and sulfur dioxide pro-duced large increases in daily death ratesI.Since the London episode, time-seriesanalyses have associated daily mortalitywith daily particle concentrations inLondon (2,3), New York (4,5), SantaClara, California (6), Detroit (A), Steuben-ville, Ohio (8), Philadelphia (9), St. Louisand eastern Tennessee (10), Utah Valley(11), Minneapolis (12), and Birmingham,AL (13). Both the air pollution concentra-tions and mortality increases were lowerthan seen in London in 1952.

These studies have reported that theprimary association was with particles andnot sulfur dioxide, and none have foundany evidence for a threshold. In SantaClara (6) and the Utah Valley (11), coex-posure with sulfur dioxide was essentiallyabsent, avoiding any possible confoundingwith that pollutant. Clearly, airborne parti-cles are associated with daily mortalityindependent of sulfur dioxide exposure.The regression coefficients reported inSanta Clara and Utah were similar to theregression coefficients in the other studies.This suggests that the other associationswere due to the particle and not the sulfurdioxide exposure. In the other cities, theregression coefficients of particles in mod-els containing sulfur dioxide were littlechanged from their values without simulta-neous consideration of sulfur dioxide; theyalso remained statistically significant. Incontrast, the coefficients of sulfur dioxidewere generally much lower in models thatcontrolled for particle exposure and werenot statistically significant. This indicatesthat variations in particle concentrationsindependent of sulfur dioxide were associ-ated with variations in daily mortality.Variations in sulfur dioxide concentrationsthat were independent of particle concen-trations did not appear to be associatedwith variations in daily mortality in thesestudies.

The study in Philadelphia providedsome more details on the specific causes ofdeath that were elevated on high air pollu-tion days. The relative risks were highestfor pneumonia and cardiovascular disease.They were also highest for the elderly. Thepresent analysis sought to examine the rela-tionship between airborne particles anddaily mortality in Cincinnati, Ohio. Inparticular, it sought to determine if thefinding of higher relative risks for pneumo-

nia, cardiovascular disease, and the elderlycan be replicated. In addition, this studyused diagnostic plots and sensitivity analy-ses that have been used in few if any priorstudies to ensure that the association is notconfounded by inadequate control forweather.

Daily deaths of residents of HamiltonCounty, Ohio, the county containingCincinnati (population 873,224 in 1980)were extracted from the detail mortalitytapes of the National Center for HealthStatistics for the calendar years 1977-82.Deaths from accidental causes (ICD 9>800) and deaths occurring outside thecounty were excluded from the analysis. Inaddition to daily deaths, deaths of personsaged 65 or older and daily deaths from car-diovascular disease (ICD 390-448) andfrom pneumonia (ICD 480-486) weretabulated. These daily counts were match-ed to the 24-hr average (midnight to mid-night) of total suspended particulates(TSP) from all the population-orientedmonitors within the city. Pollution datawere retrieved from the EnvironmentalProtection Agency's aerometric data bank.For each day, TSP from all available moni-tors were averaged to produce an area widemean. Daily monitoring for TSP began in1977. Only one monitoring station wasrunning every day in the city of Cincinnatithat year. In 1979-81, two additionalmonitors were operated on a daily sched-ule. Several additional monitoring stationswere operational on a one-day-in six-basis,and were included in the average whenavailable. Data on daily mean temperatureand daily mean humidity were obtainedfrom the weather station at the Cincinnatiairport.

Counts of rare events, such as dying ona particular day, are not normally distrib-uted, and were modeled in Poisson regres-sions. Substantial seasonal and other long-term temporal patterns are often found indaily mortality data. While air pollutionmay be responsible for part of that pattern,other factors clearly play the dominantrole. Temperature and humidity may berelated to mortality in a nonlinear way.The analytic method was chosen to dealwith these issues.

The basic approach taken in this analy-sis was to use dummy variables for each ofthe 72 months in the study period and for8 categories of temperature and dew pointtemperature. Dummy variables for eachmonth in the study controlled for seasonal

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and monthly variations, including the pos-sibility that seasonal peaks may vary indate and magnitude from year to year.Multiple categories of temperature anddew point temperature allow for a U-shaped relationship with daily deaths, orother nonlinearities, if present. A linearand quadratic time term were also includ-ed to capture any continuous temporaltrend in mortality over the period.

Several alternative approaches werealso examined to see how sensitive the airpollution results were to the method usedto control for these potential confounders.The second approach replaced the dummyvariables with natural splines. This mod-eled the dependence of mortality on timeand temperature using cubic polynomialsfit to intervals of time (or temperature).The cubic polynomials were subject tocontinuity constraints at the boundaries ofthe intervals. The number of intervals eachvariable was divided into was chosen byanalysis of deviance. First an initial num-

Address correspondence to J. Schwartz, En-vironmental Epidemiology Program, HarvardSchool of Public Health, 665 Huntington Avenue,Boston MA 02115 USA.Joel Schwartz is supported in part by a John D.and Catherine T. MacArthur Fellowship.Received 21 September 1993; accepted 28 Dec-ember 1993.

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ber of intevals was chosen. For example,the initial number of intervals chosen fortime was 24, which means each intervalwas 6 months long, and was fit with a sep-arate cubic polynomial. Then the numberof intervals was increased (decreasing thenumber of months per interval in ourexample) until the improvement in modelfit for the increased degrees of freedomused was not statistically significant. Thespline approach has the capability of fittingdependencies that may be highly nonlinearand is therefore useful in assuring that anyassociation with TSP is not confounded byinadequate control for weather or season.

Another approach to ensuring that anyobserved association with TSP is not dueto a coincidence of high TSP days withextreme weather conditions is to repeat theanalysis excluding such days. The basicanalysis was repeated excluding days with24-hr mean temperature below 100F orabove 80'F. These represent the coldestand warmest 2.5% of the days, respective-ly. In addition, the analysis was repeatedusing the minimum daily temperatureinstead of the average daily temperature asthe control variable.

30

The final approach used nonparametricregression. The generalized additive model(14) allows a Poisson regression to be fit asa sum of nonparametric smooth functionsof each of the predictor variables. A non-parametric smoother is a tool for summa-rizing the trend of a response measure-ment, Y, as a function of one or more pre-dictor measurements, Xi. . . X . Most

Psmoothers are generalizations of weightedmoving averages. These predict the expect-ed value of Yat AX as the weighted mean ofthe actual values of the Y's correspondingto all the X's in a symmetric neighborhoodaround A. The weights decline with dis-tance from the center of the neighborhood.The properties of such smoothers havebeen extensively discussed (15,16). Loess(17) was used in this analysis, which fit thecounts of daily deaths to smooth functionsof temperature, dew point temperature,day of study, and TSP. Loess uses a run-ning regression rather than a running mov-ing average, with weights that decline asthe cube of the distance from the center ofthe neighborhood. The generalized addi-tive model allows for a test of the improve-ment in fit compared to a linear no-thresh-

Table 1. Distribution of variables in the analysis: daily mortality and total suspended particulates (TSP) inCincinnati, 1977-1982

Variable 25th Percentile Median 75th Percentile Mean SD

Daily deaths 17 20 24 21 4.92TSP (pg/m3) 53 71 93 76 30.8Temperature, 0F 37 55 70 52 19.6Dew point temperature, OF 28 44 60 43 19Daily deaths, >age 65 12 14 17 15 4.1Pneumonia deaths 0 0 1 0.6 0.8Cardiovascular deaths 9 11 13 11 3.5

old model for air pollution. In addition tothe statistical test, the smoothed covariateadjusted plot of daily deaths versus air pol-lution allows a direct graphical examina-tion of the shape of the relationshipbetween exposure and outcome.

Time series data are often serially corre-lated. Although this may be due to theweather, there may be serial correlationremaining in the residuals of the regressionmodels. Ignoring such serial correlationcan lead to biased hypothesis tests. Allregression models were tested for serial cor-relation in the residuals, and if any wasfound, autoregressive Poisson models wereestimated (18).

Table 1 shows the distribution of airpollution, temperature, dew point temper-ature, and daily mortality in Cincinnatiduring the years 1977-82. The distribu-tion of counts of daily deaths is illustrated

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Figure 1. Histogram of daily deaths in Cincinnati,Ohio, during the study period, 1977-1982.

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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.

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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 ofdaily 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-ness expected. An average of 21 personsdied each day in the metropolitan area,producing an annual death rate of 8.8 per1000.

The basic regression model did a goodjob of controlling for the seasonal variationin mortality. For example, Figure 2a showsa plot of daily deaths by week of study.The line through the points is a nonpara-metric smooth. The strong seasonal varia-tion is obvious. Figure 2b shows thedeviance residuals from the basic regressionmodel without air pollution. The seasonalpattern has been removed, as illustrated bythe nonparametric smooth.

Figure 3a shows the deviance residuals(again from the basic model without airpollution) plotted against temperature, andFigure 3b shows the deviance residualsplotted against dew point temperature.Again, a good fit seems to have beenachieved, with no temperature intervals

showing systematic over- or underestima-tion of mortality.

Table 2 shows the relative risk of (RR)death for a 100 Pg/mi3 increase in TSPfrom the basic model and the various sensi-tivity analyses. TSP was a significant riskfactor for mortality in this analysis (RR =1.06, 95% CI = 1.03-1.10). The use ofnatural splines resulted in a more parsimo-nious model, with 34 intervals of time and2 intervals each of temperature and dewpoint temperature being fit with cubicpolynomials within each interval. The dif-ference in explanatory power of thedummy variable model was not significant(p = 0.99), despite its using 39 moredegrees of freedom. The association ofmortality with TSP was unchanged in thespline model (Table 2). Similarly, the gen-eralized additive model fit the daily mortal-ity data more parsimoniously than thedummy variable model. Again, the use of42 additional degrees of freedom in the

basic model produced no significant im-provement in fit (p = 0.98). Excluding thewarmest and coldest days also had littleeffect on the association between TSP anddaily deaths. The relative risk for TSP inthat model was also similar to the basicmodel. The test for nonlinearity in theTSP association was not significant (p =0.20). Figure 4 shows the nonparametricsmoothed plot of daily deaths versus TSPin Cincinnati, after controlling for covari-ates.

Table 3 shows the relative risk of deathfor a 100 pg/m3 increase in TSP concen-trations for the age and cause specific deathrates examined. The same pattern seen inPhiladelphia (9) was evident in Cincinnati.The relative risk was higher for the elderly

Table 2. Relative risk of death and 95% confidence intervals for a 100 pg/mr3 increase in total suspendedparticulates under different modeling alternatives

Model Relative risk 95% Confidence intervalBasic modela 1.06 1.03-1.10Natural spline modelb 1.07 1.03-1.10No extreme days 1.06 1.02-1.10Generalized additive models 1.08 1.04-1.13Minimum temperatures 1.07 1.03-1.10

a72 dummy variables for each month of study, 8 categories each of temperature and dew point tempera-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 pointtemperature.CModel a, excluding days with temperature below 100F or above 80IF.dNonparametric smooth functions of time, temperature, and dew point temperature.eModel 1, but with daily minimum temperature instead of daily average temperature.

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Total Suspended Particulates

Figure 4. Nonparametric smooth curve of dailymortality versus total suspended particulate, aftercontrolling for temperature, dew point tempera-ture, and time in a generalized additive model. Noevidence for a threshold is seen in the relationship.

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Table 3. Relative risk of death and 95% confidence intervals for a 100 pg/m3 increase in total suspendedparticulates for all-cause mortality, and for selected subgroups by age and cause of death

Outcome Relative risk 95% Confidence intervalAll deaths 1.06 1.03-1.10Deaths, age >65 1.09 1.05-1.14Pneumonia deaths 1.16 0.95-1.42Cardiovascular deaths 1.08 1.03-1.14

All models are after adjustment for covariates.

and for deaths from pneumonia and car-diovascular disease.

The association between mortality andTSP seen in Cincinnati was almost identi-cal to that seen in Philadelphia. Moreover,the relationships between TSP and cause-and age-specific daily deaths in the twocities were remarkably similar. For compar-ison, in Philadelphia, the relative risk for a100 pg/m3 increase in TSP was 1.07 (95%CI =1.04-1.10) for all-cause mortality,1.11 (95% CI = 0.97-1.27) for pneumoniadeaths, 1.10 (95% CI = 1.06-1.14) for car-diovascular deaths, and 1.10 (95% CI =1.06-1.13) for deaths in the elderly. Thefinding of such similar associations in dif-ferent locations makes it less likely that theassociations could be due to a confounder.

Smoking habits, occupational expo-sure, dietary patterns, and socioeconomicfactors are not potential confounders ofthis association because their day-to-dayvariation is small and not correlated withair pollution. More likely, potential con-founders are epidemics, other factors thatproduce the seasonal variation in mortality,and weather. In Cincinnati, daily mortalityand respiratory epidemics both peaked inthe winter. In contrast, winter was the sea-son with the lowest TSP concentrations.Hence, these factors are unlikely sources ofupward bias in the association. Further-more, Figure 2 demonstrates the success of

CincinnatiPhiladelphiaMinneapolisSteubenville

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the model in removing seasonal variationfrom the data. The results are not con-founded by extreme weather conditionsbecause the association is little changedwhen those days are excluded. In addition,the residual plots indicate that the basicmodel did not under- or overestimate thedependence on weather conditions in anytemperature range. The sensitivity of theresults to alternative ways of controlling forweather was slight. In the absence of aplausible candidate for a major con-founder, the conclusion that the observedassociation is causal seems reasonable.

One way to indirectly assess the possi-bility of confounding by omitted factors isto examine the relationship in differentlocations, as the correlation between thosefactors and air pollution should differamong cities. If the association is due to anomitted or imperfectly controlled con-founder, the induced regression coefficientfor particulate air pollution will depend onthe regression coefficient of the con-founder with daily mortality and theregression coefficient of the confounderwith particulate air pollution. If the lattervaries widely but the coefficient of partic-ulate air pollution with mortality does notvary widely, confounding by that factor isunlikely. The recent studies of particulateair pollution and daily mortality have allreported relatively similar effect sizes. Thislends credence to their findings, since ifthe associations were due to confounding,we would expect a greater variability in theeffect size estimates. Figure 5 shows the rel-ative risk of death for a 100 pg/m3 increasein TSP concentration from the recentstudies of air pollution and daily mortality.The consistency is remarkable.

Although the mechanism by which air-borne particles exacerbate illnesses andincrease their mortality rates is not under-stood, neither is the mechanism by whichtobacco smoking increase the risk of deathfrom myocardial infarctions. This fact hasnot prevented a conclusion being drawnfrom the strong epidemiologic informationin the case of smoking and it should notimpede a similar conclusion in the case of

respirable particles. Moreover, the Londonepisode of 1952 provides ample demon-stration of biological plausibility: it is clearthat respirable particles increased mortalityin that episode, although no mechanismwas determined for that case, either.

REFERENCES

1. HMSO. Mortality and morbidity during theLondon fog of December 1952. Report no.95. on Public Health and Medical Subjects,Her Majesty's Public Health Service. London:Her Majesty's Stationary Office, 1954.

2. Mazumdar S, Schimmel H, Higgins ITT.Relation of daily mortality to air pollution: ananalysis of 14 London winters, 1958/59-1971/72. Arch Environ Health 37:213-220(1982).

3. Schwartz J, Marcus A. Mortality and air pol-lution in London: a time series analysis. Am JEpidemiol 131:185-194(1990).

4. Glaser M, Greenburg L. Air pollution, mor-tality, and weather: New York City, 1960-1964. Arch Environ Health 22:334-343(1971).

5. Schimmel H, Murawski TJ. The relation ofair pollution to mortality. J Occup Med18:316-333(1976).

6. Fairley D. The relationship of daily mortalityto suspended particulates in Santa ClaraCounty, 1980-1986. Environ Health Per-spect 89:159-168(1990).

7. Schwartz J. Particulate air pollution and dailymortality in Detroit. Environ Res 56:204-213(1991).

8. Schwartz J, Dockery DW. Particulate air pol-lution and daily mortality in Steubenville,Ohio. Am J Epidemiol 135:12-20(1992).

9. Schwartz J, Dockery DW. Increased mortalityin Philadelphia associated with daily air pollu-tion concentrations. Am Rev Respir Dis145:600-604(1992).

10. Dockery DW, Schwartz J, Spengler JD. Airpollution and daily mortality: associationswith particulates and acid aerosols. EnvironRes 59:362-373(1992).

11. Pope CA, Schwartz J, Ransom M. Daily mor-tality and PM10 pollution in Utah Valley.Arch Environ Health 42(3):211-217(1992)

12. Schwartz J. Particulate air pollution and dailymortality: a synthesis. Public Health Rev 119:39-60(1992).

13. Schwartz J. Air pollution and daily mortalityin Birmingham, AL. Am J Epidemiol 137:1136-47(1993).

14. Hardle W. Smoothing techniques with imple-mentation in S. New York. New York:Springer-Verlag, 1991.

15. Gasser T, Muller HG, Mammitsch V. Ker-nals for nonparametric curve estimation. J RStat Soc B 47:238-52(1985).

16. Cleveland WS, Devlin SJ. Robust locally-weighted regression and smoothing scatter-plots. J Am Stat Assoc 74:829-36(1988).

17. Hastie T, Tibshirani R. Generalized additivemodels. London:Chapman and Hall, 1990.

18. Liang KY, Zeger SL. Longitudinal data analy-sis using generalized linear models. Bio-metrika 73:13-22(1986).

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