Implications and Use of Time Series Mortality Studies for Health Impact Assessment
The Measurement and Economic Valuation of the Health Effects of Air Pollution
London, England; February 19, 2001
United Nations Economic Commission for Europe (IN/ECE)
Convention on Long-Range Transboundary Air Pollution
Network of Experts on Benefits and Economic Instruments (NEBEI)
London, England
February 19, 2001
Bart Ostro, Ph.D., Chief
Air Pollution Epidemiology Unit
California EPA
Introduction
Over the past two decades, several dozen time-series studies spanning five
different continents have demonstrated associations between daily counts of mortality
and daily or multi-day changes in air pollution. Among these pollutants, particulate
matter – measured as either PM10 (those less than 10 microns), PM2.5 (less than 2.5),
black smoke, or sulfates -- appears to show the most consistent association with
mortality, with some associations also reported for nitrogen dioxide and other gases.
With increasing statistical sophistication, these studies have shown that either one-day or
multi-day averages are associated with both total and cardiopulmonary mortality. These
studies have been utilized, along with those demonstrating effects of chronic exposure to
air pollution, to provide aggregate estimates of the health and economic effects of air
pollution. For example, the U.S. Environmental Protection Agency recently calculated
the effects of Clean Air Act (U.S. EPA, 1999). Ostro and Chestnut (1998) estimated the
benefits of controlling particulate matter air pollution in the U.S. Estimates of the health
benefits of controlling particulate matter and ozone were also generated for Southern
California (Hall et al. 1992).
Despite their widespread use, there is still question about the appropriate use of
these studies. For example, McMichael et al (1998) suggest that these provide little
relevant information to policy makers since they cannot indicate the length of life lost.
Ostro and Chestnut (1999), in a response, claim that these studies provide important
information on the number of cases of premature mortality that can be expected to result
from changes in air pollution. In addition, superior databases and statistical methodology
over the past several years have enabled epidemiologists to provide additional
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information that may be relevant to the economic valuation of the effects. This paper
summarizes some of the recent findings that may have implications for subsequent
economic valuation.
Effects From Chronic Versus Acute Exposure
For quantitative health impact assessment, the prospective cohort mortality
studies (e.g., Dockery et al., 1993; Pope et al, 1995) generate a more complete assessment
of the impacts of air pollution, relative to the time-series studies, since they can be used
to provide estimates of the number of lives saved per year and the amount of life-years
lost. These studies reflect mortality effects resulting from long-term exposure and
include some from short-term exposure, as well. However, time-series studies are
important since they provide estimates of the cases of premature mortality per year due to
relatively acute exposures. This is different from the number of deaths per year, which
may be less than the cases of prematurity since some deaths may be shifted by a few days
or weeks within the same year as a result of pollution exposure. Information on both the
number of people affected each year (provided by the time-series studies) and the number
of life-years lost (provided by the prospective cohort studies) is useful input for decisionmakers and the public.
Besides providing an indication of the number of cases of premature mortality
that may be expected from alternative air pollution concentrations, there are several other
useful roles for time-series studies in health impact assessment. First, these studies help
answer the question, particularly important in countries where developing a
comprehensive environmental policy has been problematic, about whether there is any
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health impact from air pollution. The time-series studies are well documented, more
transparent and more easily verifiable in other countries where available data are limited.
Second, since time-series studies have been undertaken around the world -- including
cities like Madrid, Rome, Milan, Rotterdam, Brisbane, Delhi, Bangkok, Santiago (Chile),
Seoul, Mexico City -- it attests to the transferability of the mortality effect and provides
important evidence for policy makers about the potential impact of air pollution in their
own country. Finding risks from air pollution using local data can be a powerful
motivator. Third, these studies contribute to a “weight of evidence” argument so that
quantitative findings are built on a full complement of available information. In addition,
these findings provide evidence about the coherence of air pollution-related health
effects. Fourth, they provide direct evidence on the impact of acute exposure, which may
be useful for standard setting and intervention strategies. In addition, they may measure
some mortality effects, such as those resulting from displacement, that are not
incorporated in the prospective cohort studies. Fifth, the time-series studies can provide
additional information on subgroups such as children, and can generate important
information by conducting analysis by gender, SES, prior health and hospitalization
status, age, season, etc.
Finally, the time-series studies provide a clear lower bound on mortality effects,
especially if there are questions about the findings or applicability of the prospective
cohort studies. In their re-examination of the American Cancer Society (ACS) data set
originally analyzed by Pope et al. (1995), Krewski et al. (2000) conducted an exhaustive
set of sensitivity analyses. They considered a wide range of alternative specifications,
ecological variables, corrections for spatial autocorrelation, interactions, adjustment for
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time-varying parameters, and measures of occupational exposure, smoking, and physical
activity. Their findings are generally supportive of those of the original study. There
were several factors, however, that served to either reduce the size of the estimate or add
uncertainty to how the findings should be generalized outside of the United States. For
example, for a 24.5 g/m3 change in fine particles in the ACS data set, the relative risk
(RR) for all-cause mortality is 1.18 (95% CI = 1.10 – 1.27). If the city mean, rather than
the median, concentration of fine particles is used in the proportional hazard model, the
RR decreases by a third. Likewise, use of time-specific exposures (rather than the mean
or median over the available data) reduces the RR.
It is also of note that the RR estimates from the prospective cohort studies vary
significantly when the model is stratified by educational attainment. For those with a less
than high school education, RR = 1.35 (1.17 – 1.56), while for those with more than a
high school education, RR = 1.06 (0.95- 1.17). This decrease in the estimated effect and
statistical significance was also observed in the education-stratified re-analysis of the
Dockery et al. (1993) study. The lack of an association among the higher education
individuals may indicate that nutrition and availability and use of health care (as
correlated with education) may be important co-factors in the air pollution-associated
mortality. Among those individuals with lower educational attainment, poverty, poor
nutrition, lack of health insurance and insufficient use of medical resources are more
common. Thus, extrapolating the results of the prospective cohort studies to countries
with high educational attainment, universal health care and/or low prevalence of poverty
may involve greater uncertainty. In the extreme case, the effects of acute exposure
indicated by the time-series studies may dominant the mortality effect. On the other
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hand, extrapolation of the central estimates from Pope et al. (1995) and Krewski et al.
(2000) may result in an underestimated effect in countries with widespread poverty or
lack of quality medical care. With additional studies of long-term exposure in the U.S.
and other countries, we will gain a better understanding of how the U.S. findings should
be applied.
Magnitude of the Effect
Associations between short-term (i.e., daily) exposure to particulate matter and
mortality have been reported in several dozen studies. Earlier meta-analyses of these
studies suggest that, after converting the alternative measures of particulate matter used
in the original studies to an equivalent concentration of PM10, the effects on mortality
are fairly consistent (Ostro, 1993; Dockery and Pope, 1994; Schwartz, 1994).
Specifically, the mean effect of a 10 g/m3 change in PM10 implied by these studies is
approximately 0.8 percent, with a range of effects of 0.5 percent to 1.6 percent. Since
these meta-analyses, more recent findings tend to support this general range. This
includes not only studies from the U.S., but those from a diverse group of cities such as
Santiago, Chile (Ostro et al., 1996), Mexico City (Castillejos, 2000), Sao Paulo, Brazil
(Saldiva et al., 1995), Amsterdam (Verhoff, 1996), Bangkok (Ostro et al., 1999) and
Sydney (Morgan et al., 1998), as well. Such cities span a wide range of activity patterns,
temperature – air pollution covariations, housing characteristics, and baseline health
conditions.
Samet et al. (2000a) applied a wide range of statistical tools and sensitivity
analyses to a database consisting of the 20 largest cities in the United States. The
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combined effect indicated an association generally similar but near the lower end of the
range to those reported by earlier researchers. Samet et al. claim that the estimates may
be at the lower end of the range since the database include a wide range of cities and
incorporate findings in some cities where no effects were observed. However, only lags
of 1 or 2 days were considered, although other studies have reported greater effects with
longer or cumulative lags. In a companion study of the 90 largest U.S. cities (Samet et
al., 2000b) the most significant regional effects were found for the Northeast and
Southern California, with regional heterogeneity in response. However, the use of a
similar statistical model for all cities may also have resulting in diminished and different
effects. Apparently similar smoothers of time and temperature where used throughout
the country although seasonality and population growth are fairly diverse. Besides the
modeling, the regional heterogeneity may also be a result of differences in either: (1) the
particle composition and size: (2) the underlying susceptibility among the local
population; (3) the local behavior, activity patterns and exposures; or (4) the density of
monitors and relative exposure measurement error.
Recent analysis has demonstrated that the effect estimate increases when a longerterm average of exposure is used. For example, Schwartz (2000a) examined mortality for
those above age 65 in 10 U.S. cities. A regression model that allowed for an air pollution
effect to persist over several days using a distributed lag was incorporated, resulting in a
doubling of the relative risk. In addition, in studies where out of hospital (versus in
hospital) deaths were examined, the effect size increased two- to four-fold (Schwartz
2001; Schwartz 2000c). This finding indicates that air pollution has a much larger impact
among those not currently in the hospital. This suggests that sudden death may be an
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important factor in the pollution-related mortality and that the loss of life-years is large
since these are not people who are currently in the hospital with an end-stage disease.
Additional support for pollution-related mortality occurring outside of the hospital and
for the likelihood of significant shortening of life span is provided by recent studies
reporting associations between particulate matter and heart rate, heart rate variability, and
arrhythmia (Liao et al., 1999; Pope et al., 1999; Peters et al. 2000; Gold et al., 2000). All
of these outcomes are significant predictors of sudden mortality or of mortality from
heart failure. Direct evidence for a non-trivial reduction in life span is provided by
studies that statistically net out the phenomenon of mortality displacement; i.e., that
mortality from air pollution is only pushing the time of death forward by a few days. If
all pollution-related mortality were associated with displacement, the total life shortening
would be very small -- only a few days. However, both Schwartz (2000b) and Zeger et
al. 1999 have shown, using both frequency and time domain methods, that most of the air
pollution mortality is not due to this displacement effect. For cardiovascular mortality,
displacement does not appear to be a major factor, as the average life shortening appears
to be greater than two or three months. Finally, a significant loss in life years from air
pollution is provided by the studies of infants and children. For example, several crosssectional studies have reported an association between particulate matter and neo-natal or
infant mortality in Rio de Janeiro (Penna and Duchiage, 1991), the Czech Republic
(Bobak and Leon, 1992), San Paulo (Pereira et al. 1998) and the United States (Woodruff
et al., 1997. Daily time-series studies have reported associations between changes in
particulate matter and infant or child mortality in Mexico City (Loomis et al., 1999) and
Bangkok (Ostro et al., 1999). Finally, Ritz et al (2000) report associations be particulate
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matter and both lower birth weight and prematurity. While these effects may not have a
large impact on the community loss in life years, since they constitute a small cohort
relative to the age group 30 and above for which the effects of long-term exposures have
been reported), they obviously represent a significant number of life years lost on an
individual basis.
Summary
This brief review of the recent evidence suggests that the effects of short-term air
pollution exposures appear greater than that indicated from earlier studies. Specifically,
use of cumulative averages of a few days rather than single-day exposures, of distributed
lag functions, and of longer-term (i.e., 30 to 60 days) windows of exposure generate
considerably larger effects than previous thought. In addition, the evidence suggests that
there may be significant reductions in life years from these short-term exposures. Sudden
death in adults may be part of the process, based on evidence from epidemiologic studies
examining the association between air pollution and electrical functioning of the heart. In
addition, there is now considerable evidence of effects on infants and children.
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