A major challenge for investigators in environmental epidemiology is to correctly
identify populations at risk from exposure to environmental contaminants. To
date, three methods have been used to identify the populations at risk from
point sources of air pollution: physical monitoring, environmental monitoring,
or mathematical modeling (Williams and Ogston 2002). This article is a discussion
of the utilization of a computerized air pollution model, normally used by
the environmental protection authorities for assessing pollution values (immissions),
and the putative offense of legally set thresholds of emission. To test the
model for its appropriateness as an improved tool for assessment of exposure,
an actual case was used of known dioxin air pollution in an urban area.
In the town of Kolding in the southern part of the Jutland peninsula, Denmark,
three outlets of dioxin were identified. All three emitted dioxin into the
air through their chimneys. The dioxin consisted mainly of 2,3,7,8-tetrachlorodibenzo-p-dioxin
(TCDD, or dioxin). One outlet in particular, an aluminum recycling plant, was
found on two occasions--early November and early December 2000--to have emitted
large quantities of dioxins, up to 180 ng/m3/hr. All three plants
had been operating for years. The main culprit was the aluminum recycling plant,
which had been in continuous operation since 1970 with an almost unchanged
method of production and output.
Our plan was to layer the computer-simulated exposure model in a geographic
information system (GIS) and use the simulated immission concentrations to
more accurately demarcate the exposed population. The information on addresses,
vital statistics, migration, and cancer of the population of Denmark or any
subset was available on individuals and on the delineated population in this
study. This information was layered into the same GIS environment, enabling
a more exact identification of the exposed population in both space and time.
All malignant cancers were used as the health indicators of the exposed population
to assess eventual negative health outcomes caused by the dioxin pollution.
TCDD is a major environmental carcinogen causing various types of cancers (IARC
1997).
Materials and Methods
The air pollution simulation model used in Denmark to assess hourly immissions
of airborne pollutants is a Gaussian air dispersion model based on emission
data of the actual pollutant(s) and time series of meteorological data such
as wind speed, wind direction, wind temperature, rain, snow, number of stacks,
their heights, surrounding buildings, and surrounding terrain. The model [OML,
Operationel Meteorologiske Luftkvalitetsmodeller (Danish)] has been widely
validated both in Europe and North America and is reliable in predicting hourly
immissions of one or more airborne pollutants (DMU 2001). Only two measurements
of dioxin were available as input source, and both were obtained in November
and December 2000. The GIS used was the software package ArcGis version 8.12
(ESRI, Atlanta, GA, USA).
Figure 1. Map of Kolding Town with dioxin source in red and
address points in green .
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All current and past addresses in Denmark since 1999 were geocoded with Universal
Transverse Mercator coordinates with a precision of a few meters and subsequently
layered into the GIS. The Address Project is described elsewhere (Briggs et
al. 2002). By linkage of all individuals to these addresses using the unique
Central Population Register (CPR) number (10-digit number in the Civil Registration
System), the GIS eventually contained all the following information in addition
to the addresses of each individual: date of birth, sex, migration (into, out
of, and around the study area), and date of death (Figure 1). Each green spot
represents an address and a table of the mentioned attributes.
The CPR contains data on more than 7 million people who are or have been
residents in Denmark since 1968. The key to the register is the personal identification
number, the CPR number, which is a unique 10-digit number that all residents
in Denmark are assigned at birth or when immigrating. In addition to the present
address of all residents, the CPR also contains historical addresses and the
dates on which the individual moved to and from that address. If a person dies,
disappears, or takes up residence abroad, this is also recorded as having moved
away from the address. The CPR, its structure, updating, and other details
are described elsewhere (Briggs et al. 2002).
All health registers in Denmark use the CPR number as entry key, which makes
it easy to merge health data into a GIS where CPR has been incorporated. In
this study all malignant cancer (except skin cancer) was used as the health
outcome indicator. By merging the Danish Cancer Register with CPR data, the
necessary cancer incidence information was retrieved. Details on Danish health
registers have been described elsewhere (Briggs et al. 2002), particularly
in the Danish Cancer Register (Storm 1991).
In this study, 1986-1998 was the time span chosen for analyses. The
first year, 1986, was chosen as a compromise between the arduous work of geocoding
historical addresses and the cost of this operation versus having a sufficient
number of years with cancer data for analyses. The main dioxin producer, the
aluminum recycling plant, became operational in 1970, which was sufficient
time from start of production (and pollution) to account for the induction
and latency time of developing (eventual) cancer in the surrounding population.
The end of study year, 1998, was chosen because that was the last year with
obtainable cancer data at the time a request was sent to the Danish Cancer
Register.
In this study the exposure simulation model, OML, was used to demarcate three
zones relevant for studying cancer development related to the dioxin exposure:
- Zone 1 encompassed the whole residential area identified to be
exposed to dioxin.
- Zone 2 included the area identified to be exposed to 3.5 ng dioxin/m3/hr
or higher. Zone 2 is part of zone 1.
- Zone 3 included the inner and highest exposed area with estimated
dioxin immissions of 4.5 ng dioxin/m3/hr or higher. Zone 3 is
part of zones 2 and 3.
Figure 2. Computer-simulated exposure of dioxin from three
sources (red) are layered onto the electronic map (GIS) and seen as
different colored bands, with highest dioxin immissions in bright red
and lowest in faint green. The immission concentration band borders
(blue) are used to demarcate the three zones used for analyses of cancer
development.
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Figure 3. Demarcation of zone 3 and the addresses
(and individuals) in blue within the polygon. Cancers diagnosed among
the individuals in
zone 3 during 1986–1998 are marked in yellow (overlayed on the
blue dots). |
Figure 4. Migration of residents living in the study area in
January 1986 from the area from 1986 to the end of 1998. |
Table 1
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These zones and the selection are illustrated in Figure 2, where the immission
concentration band borders are used to demarcate the three zones. In Figure
3 the malignant cancers have been linked and overlayered, appearing as yellow
dots. For confidentiality reasons, any single, outstanding cancer case has
been obscured so that only clusters of cancers (aggregated over 13 years) are
visible.
The following criteria were used to select the study population:
- Individuals were included if they lived in the area between 1986
and 1998. Individuals were included in a calendar year if they moved into
the area
on or after 1 January in the same year.
- Cancer cases were included if the year of diagnosis was in or later
than the year the individual moved into the area (i.e., cancer cases with
year of diagnosis before migration into the area were excluded).
- Skin cancer diagnoses (ICD-7 code 191; National Board of Health
2003) were excluded because they comprise a considerable number of rather
harmless
cancers.
The reference population chosen was the Danish population in the same period,
1986-1998. It was desirable but not financially possible to obtain a reference
area similar to the study area.
Study Population
At the start of follow-up, 1 January 1986, 15,404 individuals resided in
the study area. During the next 13 years, between 2,069 and 3,470 individuals
moved into the area each year; a total of 46,392 different individuals resided
in the area during the 13-year follow-up period. During the study period the
population gradually increased to 20,217 individuals by the end of 1998. Among
the 46,392 persons who lived in the study area from 1986 to 1998, 3,205 individuals
were newborns who had their first-ever address in the area.
Among the 15,404 individuals residing in the study area 1 January 1986, 7,758
(50.4%) were still living in the study area at the end of 1998, and 4,799 (31.2%)
had not changed their address. Figure 4 illustrates this development. Among
those who were < 10 years of age on 1 January 1986, 57% were still residing
in the area 13 years later, whereas only 30% of those 10-20 years of age
still lived in the area at the end of 1998. This figure was 43% for the group
21-30 years of age; 65% for the group 31-40 years of age, and increased
to 71% for the group 41-50 years of age. The same figure gradually decreased
for the group 51-60 years of age to 65% for those who remained in the
area until the end of the study period, and rapidly decreased for older age
groups.
The 46,392 individuals who lived in the study area had a total of 75,437
periods of residence. Among the same 46,392 individuals, 61.5% had one address,
21.5% had two different addresses, and 17% had three or more addresses within
the study area during 1986-1998.
On 1 January 1986, 50.3% of the females and 55.8% of the male residents in
the study area were < 40 years of age. The corresponding figures for the
whole population of Denmark in 1986 were 54.0% for male residents and 58.3%
for females.
Statistical Methods
For each year from 1986 through 1998, information on the number of eligible
residents and cancers from the study area (zones 1-3) were retrieved through
the GIS model and cumulated into nine 10-year age bands (0-9, 10-19,
20-29 . . . 
80)
stratified by sex.
The Danish population during the same period was used as the reference population.
Number of cancers was derived from the Danish Cancer Register and population
data were from the Bureau of Statistics (Danmarks Statistik). These data were
similarly grouped into cumulated 10-year age bands stratified by sex. Years
of risk were calculated using the number of residents in each calendar year
in the study area ("living in and not moved out") and in the general population,
respectively. Each resident in a given year was counted as 1 year of exposure.
The number of expected cases of cancer was calculated based on the total number
of person-years for each 10-year age category multiplied by the cancer rate
of the Danish men and women, respectively, during the same period. The standardized
incidence ratio (SIR)--the ratio between observed and expected numbers--was
calculated with 95% confidence limits (95% CL) assuming a Poisson distribution
of the cancer cases. Normal distribution for observed cancers was assumed when
figures were above 100.
Results
The method of using an air pollution simulation model to identify exposure
and exposed population was operational, and the subsequent incorporation into
a GIS environment integrating individual statistics of address, vital statistics,
and cancer created no severe technical problems.
Results of the statistical analyses are presented in Table 1. Only a single
age band in zone 1 had confidence limits above 1.0. No excess of cancer in
the study area during 1986-1998 could be demonstrated. The study population
was anticipated to be geographically stable, but this appeared not to be true,
with only one-third of the original residents still living in the area at the
end of the study period.
Discussion
The OML, a commercial product (Danish Environmental Protection Agency 1997),
is used widely by environmental regulatory bodies in Denmark to assess immission
values of airborne pollutants. This product proved useful to visualize exposure
in a GIS milieu to outline the research area. The incorporation of the model
in GIS presented no serious technical problems.
However, the OML output, like with most models, is no better than the quality
of the input data, and in this case only two dioxin measurements from the chimney
smoke were available. In 2000, when the environmental authorities discovered
grossly excessive emissions (180 ng dioxin/m3/hr) with a legal threshold
of 1 ng dioxin/m3/hr, the aluminum recycling plant immediately started
injecting active carbon and chalk into the smoke-cooling process, hence reducing
the content of dioxin to far below thresholds. The emission of dioxin has been
reduced further since then. The official threshold was lowered in 2001 to 0.1
ng dioxin/m3/hr, following EU regulations. So the first and only
data available were two measurements in autumn 2000, which do not allow for
extensive conclusions on the amount of airborne dioxin dispersed to the adjacent
surroundings.
Airborne dioxin alone is adsorbed onto plants, trees, vegetables, and soil
but is easily washed away by rain. A soil examination in the exposed area in
the summer 2001 produced no evidence of a major contamination of the area (Vejle
Amt 2001).
A major study on environmental and hereditably caused cancers (Lichtenstein
et al. 2000) concluded that genetic factors make only a minor contribution
to development of sporadic cancer, with environmental factors being the major
contributor.
Airborne dioxin is presumably absorbed in the lungs, making up 75% of the
total content. European average dioxin concentrations range between 0.01 and
0.4 pg/m3, which translated into a Danish situation for an average
adult is an intake via the lungs of 0.2-8 pg dioxin/day. Dioxin via the
airways is not the only entrance into the body; intake via food is assumed
to constitute as much as 15 pg a day (2.44 pg/kg body weight (bw)/day; 70 kg)
(Danish Environmental Protection Agency 1997).
If people in Kolding have had concentrations of airborne dioxin in their
ambient environment for many years in the range of the measured values of 100-200
ng dioxin/m3/hr that produce inhalation concentrations in the range
of 1-6 pg dioxin/m3/hr, then using the above estimate would
have caused a daily extra intake of 20 pg dioxin in the least polluted area
and up to 120 pg in the highest polluted area (zone 3).
Tolerable daily intake is 5 pg/kg bw in Denmark (Danish Environmental Protection
Agency 1997). In the Netherlands authorities recommend figures be lowered to
1 pg/kg bw (Health Council of the Netherlands 1996). An extra intake of up
to 120 pg dioxin/day for an adult would entail a substantial extra burden for
the body of a well-known carcinogen.
In the Kolding case no one knows whether actual emissions over the years
have been even higher (or lower) than the measured values, meaning that the
measured values could just as well have been in the lower range of the actual
pollution of dioxin. However, the information above on dioxin in soil in the
exposure area (Vejle Amt 2001), together with the fact that no excess cancers
were detected in any of the years under study, in any of the zones, in any
age group or any sex group, indicate that no major pollution of the study area
with airborne dioxin has taken place over the years. As the peak dioxin values
were detected in late 2000, any later consequence on cancer development will
not be detectable until later. The relevant authorities have decided to continuously
scrutinize the cancer data of the area in years to come.
Four years of latency has been chosen as a very conservative restriction
to allow for any early effect. Most likely the latency, at least for adults,
is much longer.
A planned follow-up of the present study is a search in the Danish Cancer
Register for cancers diagnosed outside the study area among previous residents
of the study area.
The tool we have developed has its limitations. Most environmental exposures
in a modern industrial society stem from food or are widely present in the
environment, for example, exhaust from vehicles. Fewer are present in well-defined
geographical areas, and few are strong enough to have any significant impact
on health. These factors limit the opportunity to investigate environmental
health relationships using spatial analytical methods, and inhibit the types
of problems that can be addressed.
The Address Project offers new and unique possibilities for performing studies
of relationships between environmental exposures and health of the population
in Denmark. These studies might be based on a range of different study designs
(Aylin et al. 1999; Elliott et al. 1992). Because of the ability to track individuals
over time, retrospective, space-time studies are possible. In each case,
the detailed address-based data now available and the ability to link data
files are likely to enhance these studies.
In this study we decided to use the knowledge of the migration of the population
to apply two restrictions: to include only individuals who had actually stayed
in the area and to include only the cancer cases that were diagnosed after
the individual had moved into (or after their birth in) the area.
Further restrictions could have been implemented, but this would have implicated
the choice of a reference area with a population where the same restrictions
could be made. The actual restrictions that were applied based on the individual
migration data available are an improvement in dealing with this misclassification
problem in epidemiologic studies and indicate the vast opportunities in the
system.
There are, however, several important limitations on what can be expected
even from a fully developed system, and several issues concerning choice of
study design need to be carefully considered.
First, it is important to base such studies on defined and plausible hypotheses
about the relationships being examined. Possible exposure pathways also need
to be identified. Without these preconditions, results are likely to be difficult
to interpret, at best, or are even misleading.
Second, by considering environment, one is concerned with more than just
the soil on which we walk, the water we drink, and the air we inhale. Environment
is also what we eat and wear, what we smoke, where we work and relax, and from
a biological point of view, it is more likely that the causes of death and
diseases may be found here rather than geographically varying pollution in
the ambient environment.
In addition to these theoretical considerations, a number of other limitations
must be recognized. For example, most geographically based studies assume that
populations are static and that exposures occur in fixed locations (usually
the place of residence). Obviously, this is not true. People are highly mobile,
both in terms of short-term activities (e.g., daily travel to work) and long-term
migration. Rates of migration in a population may be high. Therefore, knowledge
about where people work or have worked is essential if all misclassification
is to be ruled out.
The analysis of the stability of the population in the study area disclosed
a high mobility. Less than one-third lived at the same address after 13 years
of observation, and only half were still residents in the study area. A high
degree of mobility within the study area was also found. The chosen study area
is an ordinary mixed residential and industrial suburb, and the observed mobility
of the population is likely to be representative of similar areas in Denmark.
In ecologic studies, information on exposure and the exposed individuals is
of vital importance. Such studies will therefore be highly susceptible to the
fact that only a relatively small proportion of the study population remains
in the area during a prolonged exposure in the local environment. A further
improvement in exposure assessment would be to measure actual at-risk time
for each individual. This was not done in this study although it is possible
within the model and with the Danish data sets. We hope to perform such a study
in the future.
In an extensive analysis of geographic exposure modeling and its usefulness
in environmental epidemiology (Beyea and Hatch 1999), the authors emphasized
the importance of considering all uncertainty aspects when making the models:
type and quantity of pollutants, their pathways into surroundings, exposed
population, and time of pollution.
The tested GIS with linkage of addresses and individual health information
gives new opportunities for high-quality, small-area health studies in a wide
range of situations. When fully developed and covering the whole of Denmark,
it will create a useful tool both for administrators, planners, and public
health offices as well as researchers. As with all such systems, however, it
is crucial to recognize the limitations of the system and to apply it only
where appropriate. The geographical stability of the study population is especially
crucial to address, describe, and include in the exposure assessment. Otherwise,
this misclassification may totally distort the true picture.
