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Atmospheric Environment 43 (2009) 481–485

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Atmospheric Environment

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Absence of trends in relative risk estimates for the association between BlackSmoke and daily mortality over a 34 years period in The Netherlands

Paul Fischer*, Caroline Ameling, Marten Marra, Flemming R. CasseeNational Institute of Public Health and the Environment (RIVM), Centre for Environmental Health Research, P.O. Box 1, 3720 BA Bilthoven, The Netherlands

a r t i c l e i n f o

Article history:Received 13 March 2008Received in revised form9 October 2008Accepted 13 October 2008

Keywords:Black SmokeMortalityTime trendCausality

* Corresponding author. Tel.: þ31 30 274 3315; faxE-mail address: p.fischer@rivm.nl (P. Fischer).

1352-2310/$ – see front matter � 2008 Elsevier Ltd.doi:10.1016/j.atmosenv.2008.10.036

a b s t r a c t

A major issue in air pollution epidemiology is whether the associations that are found in the statisticalanalyses on the health effects of air pollution reflect real causal associations of single components ormixtures thereof, or just reflect statistical associations that are mainly the result of the high correlationbetween the separate components, one of them being the true causal factor.In a previous analysis on the relationship between daily SO2 levels and daily mortality in The Netherlands[Buringh, E., Fischer, P., Hoek, G., 2000. Is SO2 a causative factor for the PM-associated mortality risks inThe Netherlands? Inhal. Toxicol. 12 (Suppl. 1), 55–60.], it was shown that the statistical significantassociation between daily variation in SO2 and daily mortality did not reflect a causal relation. BlackSmoke levels in The Netherlands have decreased 4-fold during the 34 years in the period 1972–2006(annual average from 27 mg m�3 to 6 mg m�3). This large decrease in concentrations enabled us to use thesame approach for this component as was done earlier for SO2 to assess whether a decreasing trend inBlack Smoke levels in The Netherlands is associated with an increasing trend in mortality relative risks ornot.We used daily averaged Black Smoke (BS) data from 1972 to 2006. In the first two decades (1970–1990)only sparse data were available. Based on the availability of the data, we selected data from 1972 to 1974and from 1982 to 1984 because during these two periods continuous daily measurement series wereavailable. For the later years (1989–2006) data covering the whole of The Netherlands were available,giving a total of 24 years of daily data. Data on daily total mortality counts (excluding external causes),cardiovascular mortality and respiratory mortality for the whole population of The Netherlands wereanalyzed with regard to daily Black Smoke levels using generalized additive Poisson regression models(GAM). Period specific relative risk estimates were compared and differences in estimates betweenperiods were evaluated.We found no consistent increase in relative risks for daily total and cause-specific mortality over time,despite the decreasing trend in the Black Smoke levels in The Netherlands. Average relative risks for totalmortality varied over the different periods from 0.997 per 10 mg m�3 daily Black Smoke to 1.010 per10 mg m�3. Average relative risks for cardiovascular mortality varied from 0.988 per 10 mg m�3 to 1.010per 10 mg m�3 and for respiratory mortality from 1.000 to 1.010 per 10 mg m�3. For weekly averagedconcentrations the average relative risks for total mortality varied over the different periods from 1.004per 10 mg m�3 Black Smoke to 1.018 per 10 mg m�3. Average relative risks for cardiovascular mortalityvaried from 1.003 per 10 mg m�3 to 1.016 per 10 mg m�3 and for respiratory mortality from 1.000 to 1.050per 10 mg m�3.The result of our analyses suggests that Black Smoke cannot be excluded as a potential causal agentbecause relative risks over time show no increasing trend despite the decreasing trend in Black Smokeconcentrations.

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1. Introduction

In the recent years over 100 studies have been published onthe relationship between daily variations in air pollution anddaily variations in mortality counts. Most of the studies focusedon particulate matter related air pollution, although a large

P. Fischer et al. / Atmospheric Environment 43 (2009) 481–485482

number of studies also assessed the relationship betweengaseous components and mortality. The overall picture that ari-ses from the studies is that both particulate related air pollutionand gaseous air pollution are statistically significant associatedwith daily mortality. Due to the relative high correlation betweendaily levels of air pollution components, it is difficult to disen-tangle the influence of the separate components in statisticalmodels and different statistical approaches have been suggestedpreviously (Kim et al., 2007). In several studies however, this hasbeen analyzed, showing that risk estimates for one pollutant maybe confounded by other pollutants (Burnett et al., 2004; Kwonet al., 2001), while others show no confounding by otherpollutants (Fairley, 2003).

A major issue in the scientific literature is whether the associ-ations that are found in the statistical analyses reflect real causalassociations of single components or mixtures thereof or justreflect statistical associations that are mainly the result of the highcorrelation between the separate components, one of them beingthe true causal factor.

One useful design to study causal relationships is the informa-tion collected from so-called intervention studies. In an interven-tion study the health benefits of improvement of the air quality arestudied. Intervention studies have been reported recently for thecities of Atlanta (Friedman et al., 2001), Dublin (Clancy et al., 2002),and Hong Kong (Hedley et al., 2002). The intervention studiessuggest that improvements in the air pollution mixture can resultin health benefits and can be helpful in identifying causal agents.Although intervention studies are strong in design, they are noteasy to execute because they depend on policy measures to betaken during a short time period aimed solely at air quality inter-vention. Unfortunately these preconditions are very rare andtherefore other approaches to disclose causality should be lookedfor. Assessing the stability of effect measures over time is one ofsuch approaches.

In previous analysis of the relationship between daily SO2 levelsand daily mortality in The Netherlands, it was shown that despitethe strong decrease in SO2 levels over time, the relative risks formortality increased over time (Buringh et al., 2000). The interpre-tation of this result was that the statistical significant associationbetween daily variation in SO2 and daily mortality did not reflecta causal relation but that this was just the result of the correlationbetween the daily variations in levels of SO2 with (an) so farunknown causal agent(s) in the air pollution mixture. It wasconcluded that although SO2 remains associated with health effectsin The Netherlands, it seems highly implausible that SO2 is thecausal factor for the health effects.

In general, air pollution levels have decreased in The Nether-lands in the past decades, not only for SO2 but for other compo-nents as well, among which Black Smoke. Black Smoke is a measureof the blackness of a particle sample, sampled on a white filterpaper, transformed to a mass value (mg m�3) for the particle sampleby means of a standard curve (ISO 9835, 1993). It gives a relativevalue for the soot content of the sample. The size of the particlessampled with a Black Smoke sampler is below 5 mm (Chow, 1995).Black Smoke measurements belong to the earliest monitoringactivities in many countries in Europe. It is related to thecombustion of organic fuel and an indicator of road transport (51%)and industrial and domestic oil combustion (34%) (Schaap et al.,2004). Due to decreases in Black Smoke emissions by transition ofcoal burning to gas burning, and by technical improvements in theGerman Ruhr area and the former German Democratic Republic,Black Smoke levels in The Netherlands have decreased 4-foldduring the last 36 years (1972–2006; annual average from27 mg m�3 to 6 mg m�3). This large decrease in concentrationsenabled us to use the same approach for this component as wasdone earlier for SO2 to assess whether a decreasing trend in Black

Smoke levels in The Netherlands is associated with an increasingtrend in mortality relative risks or not.

2. Data and methods

Sampling of Black Smoke, reflectance measurements andcalculation to mass concentrations were done according to OECDrequirements (OECD, 1964). We used daily averaged Black Smoke(BS) data from 1972 to 2006. In the first two decades only sparsedata were available. Based on the availability of the data weselected data from 1972 to 1974 and from 1982 to 1984 becauseduring these two periods continuous daily measurement serieswere available. Data were collected by the Dutch Institute forApplied Sciences (TNO/MEP) at a measurement site near the city ofDelft (western part of The Netherlands). Approximately 5% of theBlack Smoke data at the Delft station in the periods 1972–1974 and1982–1984 were missing. These missings were replaced using datafrom 3 other stations measuring Black Smoke less frequently. Datawere imputed by linear imputation based on station averages anddaily average of available stations. For the later years (1989–2006)data covering the whole of The Netherlands were available fromthe National Air Pollution Monitoring Network operated by theNational Institute of Public Health and the Environment RIVM(Elskamp, 1989), giving a total of 24 years of daily data. Black Smokemeasurements procedures did not change during the study periodand were the same for both institutes. For the period 1989–2006we calculated the overall 24 h average Black Smoke concentrationsfrom the data of 14 monitoring stations throughout The Nether-lands. Missing data at each individual station (3–15% missing days)were imputed by the same method as described above before thecountry average concentration was calculated. We divided theperiod 1989–2006 into six consecutive periods of three years each.

Data on daily total mortality (excluding external causes, Inter-national Classification of Diseases ICD-10 A00-R99) were providedby Statistics Netherlands (CBS). We used daily mortality counts forthe whole population of The Netherlands. In addition to total dailymortality we analyzed cause-specific mortality data for cardiovas-cular mortality (ICD-10 I00-I79) and total respiratory mortality(ICD-10 J00-J99).

Meteorological parameters (temperature and relative humidity)were obtained from the Royal Dutch Meteorological Institute(KNMI). Influenza prevalence, based on general practitionersreports was provided by The Netherlands Institute for HealthServices Research (NIVEL).

3. Statistical analyses

We analyzed the association between Black Smoke andmortality in the eight 3-years periods separately. We used gener-alized additive Poisson regression models (GAM) to estimate rela-tive risks, adjusted for potential confounding due to long-termtrends, seasonal trends, influenza epidemics, ambient temperature,ambient air pressure, ambient relative humidity, day of the weekand holidays. The MGCV package in R was applied to build themodels using thin plate penalized splines to model time-varyingpotential confounders. No splines were used for the day of the weekand holidays. For the potential confounders ambient temperature,ambient air pressure and ambient relative humidity we used thevalue on the day preceding the mortality day. For influenza prev-alence we calculated the one-week running average for the weekpreceding the mortality day. Daily averaged Black Smoke data onthe same day (lag 0), the previous day (lag 1), the day before that(lag 2), and the weekly average (lags 0–6) were analyzed in asso-ciation with daily mortality counts. The association between dailymortality and BS was expressed as relative risk estimates per10 mg m�3 increase in daily or weekly Black Smoke concentration.

Table 2Daily mortality counts by cause of death by period.

P. Fischer et al. / Atmospheric Environment 43 (2009) 481–485 483

Period specific relative risk estimates were compared and differ-ences in estimates between periods were evaluated.

Period Total mortality Cardiovascularmortality

Respiratorymortality

mean (range) mean (range) mean (range)

1972–1974 285 (210–463) 136 (94–238) 20 (5–92)1982–1984 307 (225–416) 144 (95–220) 22 (8–53)1989–1991 339 (256–489) 141 (92–211) 29 (11–89)1992–1994 351 (270–529) 143 (96–215) 31 (8–96)1995–1997 359 (270–518) 139 (86–217) 35 (12–90)1998–2000 368 (277–521) 135 (88–205) 39 (16–103)2001–2003 373 (280–505) 130 (85–184) 38 (12–97)2004–2006 358 (261–495) 118 (72–176) 37 (13–94)

4. Results

In Tables 1 and 2 the Black Smoke levels (Table 1) and dailymortality (Table 2) per three years period are presented. Dailyaveraged Black Smoke levels decreased over time, while daily totalmortality increased over time due to the increasing older pop-ulation. The decrease in Black Smoke concentrations over thewhole study period was about 4-fold. While total and respiratorymortality increased over time, cardiovascular mortality showeda decrease. Table 3 shows the relative risk estimates per 10 mg m�3

Black Smoke for the three causes of death (the relative risks belongto the 1 day lagged concentrations; results for the other daily lagswere essentially the same). Results of the analyses with weeklyaveraged concentrations are presented in Table 4 (relative riskestimates per 10 mg m�3 increase). The results for total mortality aregraphically presented in Fig. 1. No clear trend in relative risks overtime was observed for the different causes of death. Relative riskswere fluctuating over time with lowest and statistically insignifi-cant relative risks in the last period for the 1 day lagged concen-trations, but with lowest relative risks in the first period for theweekly averaged concentrations. The statistical non-significantrisks for the last periods can probably be attributed to the smallrange in measured Black Smoke concentrations, resulting in lessstatistical power.

Table 3Relative risk estimates (RR)a and 95% confidence intervals (CI) per 10 mg m�3 BlackSmoke as daily average (lag 1) by period.

Period Total mortality Cardiovascularmortality

Respiratory mortality

5. Discussion

We found no consistent increase in relative risks for daily totaland cause-specific mortality over time, despite the decreasingtrend in the Black Smoke levels in The Netherlands. These resultssuggest that Black Smoke cannot be excluded as a potential causalagent.

In a previous study, in which we analyzed the trend in relativerisks for mortality and SO2, we found increasing relative risksassociated with decreasing SO2 levels. Our interpretation of thatfinding was that the statistical significant association between dailyvariation in SO2 and daily mortality did not reflect a causal relationbut that it was just the result of the correlation between the dailyvariations in levels of SO2 with an unknown correlated factor. Thus,contrary to what we found in our previous SO2 analyses, noconsistent increase in relative risk estimates over time wasobserved. This result suggests that Black Smoke cannot be dis-carded as a causal factor in the air pollution mixture with respect todaily mortality. Associations between Black Smoke and healtheffects have been found in several epidemiological studies. In theEuropean APHEA-study, Katsouyanni et al. (1997) and Le Tertreet al. (2002) have reported associations between daily variation inBlack Smoke concentrations and daily mortality. In a previousDutch time-series analyses, we also found statistical significantassociations between daily Black Smoke levels and mortality (Hoeket al., 2000). Studies on the health effects of long-term exposure to

Table 1Measured Black Smoke concentrations (mg m�3) by period.

Period mean min p5 p10 p50 p90 p95 p99 max

1972–1974 27 0 5 7 22 56 72 96 1191982–1984 13 0 2 3 10 28 35 58 961989–1991 14 1 3 4 10 31 40 71 1031992–1994 12 1 2 3 8 23 32 50 1271995–1997 12 1 2 3 9 27 33 50 701998–2000 8 1 2 2 7 16 21 30 582001–2003 8 0 1 2 6 16 21 34 652004–2006 6 0 1 1 5 13 16 26 35

traffic-related air pollution suggest that Black Smoke is associatedwith lung function decreases in children (Brunekreef et al., 1997)and increased respiratory symptoms (Janssen et al., 2003).

Only a few studies have addressed the association of changes inlevels of air pollution over time and changes in health effect esti-mates over time. Dominici et al. (2007) analyzed in the NMMAPSdata set whether the short-term effects of PM10 on mortalitychanged during 1987–2000 when several air quality regulationswere implemented in the US Relative risk rate estimates for 1987–1994 were not significantly different from the relative risk rateestimates for 1995–2000, although there was weak evidence thatshort-term effects declined. Laden et al. (2006) and Janes et al.(2007) showed that in US. Cities with the largest reduction inparticulate matter air pollution the largest drop in mortality ratesoccurred.

In contrast with SO2, which is a gas with fixed toxicity, thetoxicity of Black Smoke could have changed over time due tochanges in the composition of Black Smoke. While in the sixtiesBlack Smoke levels in The Netherlands were dominated by coalburning, this was less the case from the seventies onward, becausecoal burning was abandoned almost completely at that time andreplaced by gas burning. Therefore, our Black Smoke data aremainly (diesel)-traffic combustion related throughout the wholestudy period. However, no information is available about a changein toxicity of diesel emissions over the last 3 decades and we cannotexclude that this could have influenced our results. An increase intoxicity would have led to consistent higher relative risks, a patternwe do not see in our data. However, this is only true when othercircumstances did not change over time, which is not the case. TheDutch population age-distribution has changed during the lastforty years, with an increase in the elderly. As elderly might bea sensitive sub-group of the population with respect to air pollutioneffects, this could have influenced our results. Based on an ageing

mean (range) mean (range) mean (range)

1972–1974 1.006 (1.003–1.008) 1.004 (1.001–1.007) 1.009 (1.001–1.017)1982–1984 1.010 (1.007–1.014) 1.010 (1.005–1.014) 1.017 (1.005–1.030)1989–1991 1.007 (1.004–1.010) 1.006 (1.001–1.010) 1.015 (1.005–1.025)1992–1994 1.004 (1.001–1.008) 1.002 (0.997–1.008) 1.012 (1.001–1.023)1995–1997 1.006 (1.003–1.010) 1.007 (1.001–1.013) 1.000 (0.989–1.012)1998–2000 1.008 (1.002–1.013) 1.001 (0.992–1.010) 1.013 (0.996–1.029)2001–2003 1.004 (0.999–1.008) 0.997 (0.989–1.005) 1.014 (1.000–1.029)2004–2006 0.997 (0.990–1.004) 0.988 (0.976–1.000) 1.003 (0.981–1.024)

a Generalized additive Poisson regression models (GAM) adjusted for potentialconfounding due to long-term trends, seasonal trends, influenza epidemics, ambienttemperature, ambient air pressure, ambient relative humidity, day of the week andholidays.

Table 4Relative risk estimates (RR)a and 95% confidence intervals (CI) per 10 mg m�3 BlackSmoke as weekly average by period.

Period Total mortality Cardiovascularmortality

Respiratory mortality

mean (range) mean (range) mean (range)

1972–1974 1.004 (1.001–1.007) 1.003 (0.998–1.008) 1.000 (0.988–1.012)1982–1984 1.013 (1.008–1.019) 1.013 (1.006–1.020) 1.042 (1.022–1.062)1989–1991 1.016 (1.011–1.021) 1.016 (1.009–1.023) 1.043 (1.027–1.059)1992–1994 1.012 (1.008–1.017) 1.012 (1.005–1.020) 1.025 (1.009–1.041)1995–1997 1.012 (1.006–1.018) 1.012 (1.003–1.022) 1.009 (0.991–1.027)1998–2000 1.018 (1.008–1.027) 1.010 (0.995–1.025) 1.027 (0.998–1.056)2001–2003 1.015 (1.008–1.022) 1.011 (0.999–1.022) 1.050 (1.028–1.073)2004–2006 1.010 (0.998–1.022) 1.009 (0.989–1.030) 1.038 (1.001–1.076)

a Generalized additive Poisson regression models (GAM) to estimate relative risks,adjusted for potential confounding due to long-term trends, seasonal trends,influenza epidemics, ambient temperature, ambient air pressure, ambient relativehumidity, day of the week and holidays.

P. Fischer et al. / Atmospheric Environment 43 (2009) 481–485484

population we might expect some increase in the relative risks overtime. However, we did not find such an increase in our data whichmay have different reasons, such as a limited increase of thesensibility of the population or a decreasing toxicity of Black Smokein combination with an older population.

We used data from a measurement station in the western part ofThe Netherlands for the first 2 decades and the national dailyaveraged concentration from 14 measurement sites for the laterperiods. The underlying assumption was that the measurementstation in the western part of The Netherlands was representativefor the entire population. Unfortunately we could not comparethe Black Smoke data series directly, because no overlappingmeasurement periods were available. But we could compare thedata in an indirect way by comparing the Delft data with a station inthe western part with overlapping data (Naaldwijk), and comparingthe averaged daily values of the 1989–2006 with the same station(Naaldwijk) for the period with overlapping data. Between Delft

0.98

1.00

1.02

1972-1974 1982-1984 1989-1991 1992-199

1972-1974 1982-1984 1989-1991 1992-1990.98

1.00

1.02

1.04

RR

per

10

ug/m

3R

R p

er 1

0 ug

/m3

Fig. 1. Relative risk estimates for total mortali

and Naaldwijk the correlation was 0.89, with a regression coeffi-cient (Delft on Naaldwijk) of 1.02 and an intercept of 1.14 mg m�3.Between the averaged data from 14 measurements sites andNaaldwijk we found a correlation of 0.95 and a regression coeffi-cient (Netherlands on Naaldwijk) of 0.74 with an intercept of1.94 mg m�3. Assuming that the average of the 14 measurementsites is the best representative exposure measure for the entirepopulation, these results suggest that the relative risk estimatesbased on the Delft station tend to underestimate the true relativerisks. Our research question was to assess whether a decreasingtrend in Black Smoke levels in The Netherlands is associated withan increasing trend in mortality relative risks or not. If the relativerisks during the first two periods are underestimated, this resultwould not change our conclusions.

We used all available Black Smoke data for the period 1989–2006 and applied this to the daily mortality counts of the Dutchpopulation. Data were measured in all parts of The Netherlands andtherefore we assume this average is a good measure of exposure forthe whole population. We may have introduced exposuremisclassification because there is some spatial variability in BlackSmoke levels in The Netherlands, but apart from heavy busy roadsspatial differences are small. Locally, e.g., in busy streets, BlackSmoke levels still can be high, annual average about 15 mg m�3

(Hoek et al., 2002). Over the period 1992–2002 the average dailymean tenth- and ninetieth-percentile in the four major cities of TheNetherlands (Amsterdam, Rotterdam, The Hague and Utrecht) was3 mg m�3 and 23 mg m�3 respectively and in The Netherlandsexcluding the four major cities 2 mg m�3 and 22 mg m�3 respec-tively, showing that no large differences in ranges between citystations and regional stations exist (Fischer et al., 2005). This mightof course be different for very busy street locations, but relativelyfew people (5%) live at these locations (Hoek et al., 2002).

We still cannot exclude that Black Smoke levels show the sametime trend as the ‘real’ causal factor that correlates highly withBlack Smoke. But, in contrast to our analyses for SO2, there is noevidence arising from this analysis to suggest that Black Smoke can

4 1995-1997 1998-2000 2001-2003 2004-2006

4 1995-1997 1998-2000 2001-2003 2004-2006

daily average, lag1

weekly average

ty per 10 mg m�3 Black Smoke over time.

P. Fischer et al. / Atmospheric Environment 43 (2009) 481–485 485

be excluded as a causal factor with respect to premature mortality.As in many countries Black Smoke levels have decreased during thelast decades, our approach is probably feasible in several otherBlack Smoke data sets. We encourage other investigators to repeatour analyses to improve our knowledge on the causal agent(s) inthe air pollution mixture that bring(s) about the health effectsobserved in epidemiological studies.

Acknowledgements

Black Smoke data over the period 1972–1974 and 1982–1984were kindly provided by the Dutch Institute for Applied Sciences(TNO/MEP).

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