Association between Residential Proximity to PERC Dry Cleaning Establishments and Kidney Cancer in New York City

Abstract

Perchloroethylene (PERC) is commonly used as a dry cleaning solvent and is believed to be a human carcinogen, with occupational exposure resulting in elevated rates of kidney cancer. Living near a dry cleaning facility using PERC has been demonstrated to increase the risk of PERC exposure throughout the building where the dry cleaning is conducted, and in nearby buildings. We designed this study to test the hypothesis that living in an area where there are many PERC dry cleaners increases PERC exposure and the risk of kidney cancer. We matched the diagnosis of kidney cancer from hospitalization discharge data in New York City for the years 1994-2004 by zip code of patient residence to the zip code density of dry cleaners using PERC, as a surrogate for residential exposure. We controlled for age, race, gender, and median household income. We found a significant association between the density of PERC dry cleaning establishments and the rate of hospital discharges that include a diagnosis of kidney cancer among persons 45 years of age and older living in New York City. The rate ratio increased by 10 to 27% for the populations in zip codes with higher density of PERC dry cleaners. Because our exposure assessment is inexact, we are likely underestimating the real association between exposure to PERC and rates of kidney cancer. Our results support the hypothesis that living near a dry cleaning facility using PERC increases the risk of PERC exposure and of developing kidney cancer. To our knowledge, this study is the first to demonstrate an association between residential PERC exposure and cancer risk.

Full text

Hindawi Publishing Corporation
Journal of Environmental and Public Health
Volume 2009, Article ID 183920, 7pages
doi:10.1155/2009/183920
Research Article
Association between Residential Proximity to PERC Dry Cleaning
Establishments and Kidney Cancer in New York City
Jing Ma,1Lawrence Lessner,1, 2 Judith Schreiber,3and David O. Carpenter2
1Department of Epidemiology and Biostatistics, University at Albany School of Public Health, Rensselaer, NY 12144, USA
2Institute for Health and the Environment, University at Albany, Rensselaer, NY 12144, USA
3New York State Office of the Attorney General, The Capitol, Albany, NY 12224-0341, USA
Correspondence should be addressed to David O. Carpenter, carpent@uamail.albany.edu
Received 23 June 2009; Accepted 4 November 2009
Recommended by Suminori Akiba
Perchloroethylene (PERC) is commonly used as a dry cleaning solvent and is believed to be a human carcinogen, with occupational
exposure resulting in elevated rates of kidney cancer. Living near a dry cleaning facility using PERC has been demonstrated to
increase the risk of PERC exposure throughout the building where the dry cleaning is conducted, and in nearby buildings. We
designed this study to test the hypothesis that living in an area where there are many PERC dry cleaners increases PERC exposure
and the risk of kidney cancer. We matched the diagnosis of kidney cancer from hospitalization discharge data in New York City for
the years 1994–2004 by zip code of patient residence to the zip code density of dry cleaners using PERC, as a surrogate for residential
exposure. We controlled for age, race, gender, and median household income. We found a significant association between the
density of PERC dry cleaning establishments and the rate of hospital discharges that include a diagnosis of kidney cancer among
persons 45 years of age and older living in New York City. The rate ratio increased by 10 to 27% for the populations in zip codes with
higher density of PERC dry cleaners. Because our exposure assessment is inexact, we are likely underestimating the real association
between exposure to PERC and rates of kidney cancer. Our results support the hypothesis that living near a dry cleaning facility
using PERC increases the risk of PERC exposure and of developing kidney cancer. To our knowledge, this study is the first to
demonstrate an association between residential PERC exposure and cancer risk.
Copyright © 2009 Jing Ma et al. This is an open access article distributed under the Creative Commons Attribution License, which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
1. Introduction
Perchloroethylene (PERC), also known as tetrachloroethy-
lene or tetrachloroethene, is a volatile, nonflammable liquid
with a sweet odor. It has been used as the primary solvent
in the dry cleaning industry since the 1930s. Because
of its volatility, PERC is released into the environment
from processes used in dry cleaning establishments, by
volatilization from dry cleaned clothing, from spills, and
from wastes containing PERC such as still bottoms, waste
water, used filters and spent carbon. The United States
Environmental Protection Agency (EPA) estimates that there
are about 34,000 dry cleaner facilities nationwide and
approximately 82% of them use PERC as their primary
solvent [1]. The Agency for Toxic Substances and Disease
Registry [2] estimates that more than 650,000 workers might
be regularly exposed to PERC [2]. Studies have shown that
people exposed to PERC either by occupation or residential
proximity to PERC dry cleaners have elevated levels of
PERC in blood, exhaled breath, urine, and in breastmilk (in
lactating women) [3–6]. The absorption and distribution of
PERC to body tissues and fluids is not disputed, although
adverse health effects of exposure are still being evaluated.
Residential populations living close to dry cleaners
are often exposed to levels of PERC considerably above
background levels. Schreiber et al. [7] reported substantially
elevated levels of PERC in apartments located above dry
cleaning establishments (some as high as 55,000 micrograms
per cubic meter, µg/m3), as well as elevated levels of PERC
in outdoor air near dry cleaners. These elevations were up to
two orders of magnitude above those at distant sites, based
on repeated 12-hour monitoring periods, both when the dry
cleaners were operating and when they were not. In addition,
next door neighbors’ indoor air also shows elevated PERC

2 Journal of Environmental and Public Health
Exposure level (density of dry cleaners) in New York City
Density of DC in New York City
1 (0–0.47 per sq km)
2 (0.47–0.9 per sq km)
3 (0.9–1.5 per sq km)
4 (1.5–2.7 per sq km)
5 (2.7–16.43 per sq km)
Figure 1: Map of dry cleaner density in New York City by zip code. Blue refers to excluded zip code.
levels, ranging from 11 (near background concentration) to
636 µg/m3[8].
PERC is classified as a hazardous air pollutant by the
USEPA in the National Emission Standards for Hazardous
Air Pollutants [1], and is considered “reasonably anticipated
to be a human carcinogen” by the National Toxicology
Program [9]. The International Agency for Research on
Cancer classifies PERC as Group 2A, “probably carcinogenic
to humans” [10]. PERC exposure has been related to
development of kidney, bladder, liver, and esophageal cancer
[9].
Occupational studies have demonstrated an association
between PERC exposure and kidney cancer. Mandel et al.
[11] investigated the relationship between occupational
PERC exposure and kidney cancer in a study of 1732
cases and 2309 controls from Australia, Denmark, Germany,
Sweden, and the United States, and found that exposure
to dry cleaning solvents significantly increased relative risk
(RR =1.4; 95% CI, 1.1–1.7). McCredie and Stewart [12]
analyzed risk of kidney cancer in New South Wales. They
interviewed 489 cases of renal cell cancer, 147 cases of renal
pelvic cancer, and 523 controls based on their employment in
certain industries. Employment in the dry cleaning industry
was strongly associated with both renal pelvic (RR =4.68;
95% CI, 1.32–16.56) and renal cell (RR =2.49; 95% CI
0.97–6.35) cancer. Katz and Jowett [13] evaluated at the
mortality pattern of 671 female laundry and dry cleaning
workers for the period of 1963–1977, using Wisconsin death
certificate data. They found the standardized mortality odds
ratio for developing kidney cancer was 2.57 (95% CI, 1.04–
5.34). A similar study conducted by Duh and Asal [14]
analyzed 330 laundry and dry cleaning worker for the
period 1975–1981 using Oklahoma death certificate data.
The standardized mortality odds ratio was calculated as 3.8
(95% CI, 1.48–7.59). Ulm et al. [15]conductedameta-
analysis of occupational studies of the association of kidney
cancer and exposure to PERC, and reported a summary odds
ratio of 1.49 (95% CI, 1.24–1.8).
We designed this study to test the hypothesis that living
in an area where there are many PERC dry cleaners increases
PERC exposure and the risk of kidney cancer. We used data
available from the New York State Department of Health
to assess by zip code the number of people hospitalized
for treatment of kidney cancer (see Figure 1). We used the
“density of dry cleaners that use PERC” by zip code in New
York City as a surrogate for PERC exposure. We recognize
that other solvents are also used in some of these dry cleaners,
but the data from the registry documents that they use PERC,
which was the criterion for inclusion.
Residential populations living close to PERC dry cleaning
facilities are often exposed to levels of PERC considerably
above PERC levels in buildings away from PERC dry cleaning
facilities where background mean is 3 µg/m3[5]. Schreiber
et al. [7] found that PERC levels were up to more than
three orders of magnitude higher in residences located above
PERC dry cleaning facilities than those at distant sites.

Journal of Environmental and Public Health 3
Whileneurologicaleffects (abnormalities in visual contrast
sensitivity) can be detected in people following residential
exposure to PERC [6,16], there have been no investigations
of cancer risks resulting from nonoccupational exposure to
PERC. There is, however, reason to suspect that because
of high indoor air PERC concentrations living very near a
PERC dry cleaning facility might increase risk of cancer and
other adverse effects of exposure. The National Emissions
Standards for Hazardous Air Pollutants (NESHAP) EPA
proposedrulereportedcancerriskestimatesashighas3in
100 (30,000 in one million) for residents exposed to PERC
in indoor air based on exposure to a concentration of 5,000
µg/m3. As a result of the high theoretical risks, the final
NESHAP for Dry Cleaners [1] prohibits future colocation of
dry cleaners in apartment buildings.
There are about 900 small dry cleaning facilities using
PERC in New York City, with about half located in buildings
with residential tenants [17]. Based on the street address of
PERC dry cleaning facilities in New York City, we determined
that a large population is potentially exposed to PERC. We
estimate that 105,250 persons live in buildings with PERC
dry cleaning operations in New York City, including 10,500
children. Many more people live near these facilities, with
approximately 2,269,000 people living within 200 meters
of these facilities [18]. Others are exposed in buildings
where PERC dry cleaners are co-located with offices, schools,
medical facilities and other business, including strip malls
[8].
2. Materials and Methods
The study population was all residents of New York City from
1993 to 2004 who were admitted as inpatients to a state-
regulated hospital. Hospital discharge data were obtained
from the New York Statewide Planning and Research
Cooperative System (SPARCS) for the years 1993–2004.
SPARCS requires all state-regulated hospitals to report to
the NYSDOH the principal diagnosis and up to 14 other
diagnoses for each inpatient upon discharge. Diagnoses are
made according to the International Classification of Disease,
Ninth Revision (ICD-9). The SPARCS data provides age,
race, gender, and zip code of residence for each patient.
In this study, we selected hospital discharge data with a
diagnosis of kidney and/or renal cancer (ICD-9 189.0 and
189.1).
The publicly available data includes only zip code of
residence of the patient, not the hospital in which treatment
was provided or information about possible occupational
exposure. Since we do not have personal identifiers for
SPARCS discharges, we are not able to distinguish multiple
hospital discharges by a single individual from hospital
discharges of distinct individuals. The outcome variable in
this study is thus the frequency of disease diagnosis at
hospital discharge by zip code of patient residence, not
disease incidence. Incidence of kidney or renal cancer is
not available from this dataset, which is neither a death
nor a cancer registry. However in zip codes with increases
in the rate of kidney cancer, assuming that that the access
and willingness to use inpatient care are equal, we expect
to see an increase in the rate of hospital discharges that
include kidney cancer in the diagnostic codes. Furthermore
the rate of diagnoses at discharge, although a novel measure,
is an adequate and interpretable measure of the presence of
disease, and may allow us to more easily detect a rare disease
like kidney cancer.
Given the unknown latency between exposure and
disease, it is possible that exposures prior to 1993 (the
first year of data used) contribute to the cancers, but it
is unlikely that the distribution of PERC dry cleaners is
markedly different before 1993. New York State residents
who seek out-of-state healthcare are not included in this
dataset, nor are patients in federal hospitals, such as those
operated by the Veterans Administration. In general the
hospitalized population consists of individuals with relatively
severe illness, such as the kidney and renal cancer studied
here, and not individuals receiving outpatient or emergency
room care. For New York City, the dataset contains about
800,000 discharges per year for twelve years. With this dataset
we can track residence near PERC facilities, but not the
duration of residential or occupational exposure to PERC.
We studied only those zip codes where the median
household income fell in the range from $17,864 to $142,926.
Zip codes outside of this range were excluded. This restric-
tion criterion was selected based on evidence [19–21] that
rates and causes of hospitalization for individuals at both
extremes of income are quite different from those in the
group selected. Of the total of 181 zip codes in New York
City, six zip codes were not considered because of having no
population or income information (these are post office box
zip codes), and ten zip codes were not considered because
they did not meet the inclusion criteria (1 zip code had
median household income greater than $142,926 and 9 zip
codes below $17,864). The analysis was then based on the
remaining 164 zip codes.
Zip code-level population data was derived from US
Census data, obtained from Claritas, Inc. (http://www.claritis
.com/eReports/default.jsp) which provides population totals
for each zip code stratified by age, race, and gender. We
selected our study population of persons at least 45 years old,
because kidney and renal cancer are rare in younger persons,
and to account for the expected latency period between
exposure and disease as well as the general decrease in use of
PERC over time. Age was further divided into four groups:
45 to 54, 55 to 64, 65 to 74, and, 75 years and above, and
restricted to Caucasian (white) and African American (black)
populations due to the large number of individuals in these
groups. We used Claritas zip code-level median household
income information to control for socioeconomic status. Zip
code level median household income is divided into three
approximately equal groups: $17,864 to $33,353; $33,354 to
$48,996; $48,997 to $142,926. Population density was also
derived from Claritas data and was defined as the number of
persons per square km, and was similarly divided into three
approximately equal groups: 480 to 6,671, 6,672 to 17,509,
and 17,510 to 60,102 persons per square km.
By federal law and New York State regulation, each dry
cleaner is required to report its usage of PERC. We used

4 Journal of Environmental and Public Health
Tab le 1: Summary of exposure levels based on density of dry
cleaners.
Exposure level Density of dry cleaners
(dry cleaners per sq km)
Exposure level 1 0 to 0.47
Exposure level 2 0.47 to 0.90
Exposure level 3 0.90 to 1.50
Exposure level 4 1.50 to 2.70
Exposure level 5 2.70 to 16.43
the list of dry cleaners using PERC which is maintained
by the New York State Department of Environmental
Conservation to determine the density of dry cleaners that
use PERC in each zip code in New York City (the number
of dry cleaners using PERC in a zip code divided by area
of the zip code). In the absence of measurements of PERC
concentrations at all sites, we use the density of dry cleaners
using PERC in each zip code as a surrogate measure of PERC
exposure. We did not incorporate information on the volume
of PERC used, as this varies year by year and in general has
declined over time because of increasing regulatory standards
after 1996. The density of dry cleaners was divided into five
equal groups by zip code based on the assumption that a
higher dry cleaner density in a zip code leads to greater PERC
exposure for persons living in that zip code. The highest
density of dry cleaners was in Manhattan and some areas of
Queens. See Table 1 for the distribution of density of PERC
dry cleaners’ exposure levels used in our analysis.
The unit of analysis is the population living in a zip code.
These subpopulations were defined by the strata formed
from the variables age, race, gender, population density,
and median household income within each zip code. The
outcome measured was the number of hospital discharges for
each zip code population with a principal or other diagnosis
of kidney cancer. We used a log linear model for hospital
discharge rate, regressed on exposure and other covariates as
follows:
Log (expected number of kidney cancer discharges) =
log {(person time) + Intercept + b1 ∗Exposure 2 + b2 ∗
Exposure 3 + b3 ∗Exposure 4 + b4 ∗Exposure 5 + b6 ∗
middle population density + b7 ∗High population density
+b8∗middle MHI + b9 ∗High MHI + b10 ∗Age 55–64 +
b11 ∗Age 65–74 + b12 ∗Age 75 and above + b13 ∗Black +
b14 ∗Female+Interactions}.
Initially we used a Poisson distributed log linear model,
which resulted in a deviance/degree of freedom of 2.0,
indicating over-dispersion and a poor fit. We therefore
applied a negative binomial model which resulted in a
deviance/degree of freedom of 1.25, indicating an adequate
fit.
Because the distribution of the covariates of the popula-
tions living in different zip codes is not the same, the model
was examined for effect modifiers in the exposure versus
outcome association. In particular, all interactions at each
exposure level with age, gender, race, population density and
median household income were considered. The deviance/dF
for the model remained unchanged, indicating adequate fit.
A residual analysis was conducted on the final model in order
to determine whether any one or more zip codes exerted
excessive leverage. We excluded each zip code, one at a time,
and checked if the parameter estimates and the estimated
rate ratios changed significantly. Three observations were
readily identified as extreme values in Exposure levels 1
and 2 (the lowest and second lowest exposure levels),
and when they were included in the analysis we found
differences of as much as 20% in the estimated regression
coefficients. In each of these observations the observed
number of hospital discharges was much smaller than the
expected number. This extraordinary dependence on a few
observations is considered undesirable. Consequently these
three observations were dropped from the analysis. The
resulting analysis fit well, residuals looked good and there
were no other observations with extraordinary leverage on
the estimated parameters.
All statistical analysis was performed with SAS software,
Version 9.1 (SAS Institute Inc.).
3. Results
Table 2 presents information on all cancer discharges,
including or excluding skin cancers, and all kidney and
renal cancers by exposure category. Our analysis found no
significant relationship between the density of PERC dry
cleaners and “all cancer,” but, more importantly, did identify
a significant association between the density of PERC dry
cleaning establishments and the rate of hospital discharges
that included a diagnosis of kidney cancer (ICD9 189.0 and
189.1). Table 3 presents results that employ a main-effects-
only model and presents rate ratios and comparisons of the
rate of kidney discharges of a given strata with the baseline
strata for exposure as well as the other risk factors. Exposure
levels 2, 4 and 5 are all positive, statistically significant,
and quite similar, with rate ratios (RR) of 1.14, 1.17, and
1.15, respectively, and with P-values of .01, .006, and .03,
respectively. Exposure level 3 is marginally significant with
aP-value of .15. The estimated rate ratios for age are
positive, statistically significant, and increase monotonically.
The discharge diagnosis rate is higher for males than females.
This is consistent with the evidence that age and gender are
important risk factors for kidney cancer. The discharge rate
is larger for Caucasians than for African-Americans. This
pattern is consistent with the crude hospital discharge rate
for NYC from 1993 to 2004, which shows white males with
the highest discharge rate, followed by black males, white
females, and black females (see Table 3 ).
Table 4 presents the estimated rate ratios and their
confidence intervals from the main effects and interactions
model, with information on effect modifiers particular to
each exposure category. Rate ratios compare the effect in
Exposure level 1 with the effect in Exposure levels 2, 3,
and 4, and, are summary estimates obtained from weighted
averages that will be discussed later. For example, the rate
ratio for the entire population in Exposure level 4 was 1.27,
while for the white population it was 1.35. This means that
the rate ratio for kidney cancer in whites, living in Exposure

Journal of Environmental and Public Health 5
Tab le 2: Total cancers by exposure levels.
Exposure All cancer All cancer Kidney/renal
(exc. skin cancer) (Inc. skin cancer) cancer
1 90,908 91,510 1,458
2 131,165 132,014 2,289
3 113,629 114,396 1,838
4 162,945 164,093 2,766
5 171,170 172,506 2,565
Total 669,817 674,519 10,916
Tab le 3: Rate ratios for exposure levels and covariates from the
main effects only model.
Variable Rate ratio 95% CI
By Exposure level
Exposure level 1 1.00
Exposure level 2 1.14 1.03 1.27
Exposure level 3 1.09 0.97 1.21
Exposure level 4 1.17 1.05 1.32
Exposure level 5 1.15 1.01 1.30
By population density
Low 1.00
Middle 0.84 0.76 0.93
High 0.66 0.59 0.74
By median household income (MHI)
Low 1.00
Middle 0.96 0.89 1.04
High 1.04 0.95 1.14
By age group
Age 45–54 1.00
Age 55–64 2.22 2.02 2.44
Age 65–74 3.77 3.44 4.14
Age 75+ 4.22 3.85 4.64
By race
Caucasian (white) 1.00
African American (black) 0.89 0.83 0.95
By gender
Male 1.00
Female 0.44 0.41 0.47
level 4 versus Exposure level 1 increased by 35% (statistically
significant). For the same comparison, the rate ratio for
kidney cancer in blacks was 1.08, indicating an increase of 8%
(not statistically significant). This difference with race might
reflect genetic sensitivity as well as frequency of use of dry
cleaners, although race was not found to be an effect modifier
in the other exposure levels.
We found that for each exposure level, the effect modi-
fiers were different. We found no effect modifier for Exposure
level 2. But for Exposure level 3, population density was an
effect modifier, and the middle and high population density
levels were statistically significant interactions. Race was an
effect modifier only in Exposure level 4. Exposure level 5
had two effect modifiers, median household income and
age. Within the low and medium household income group,
there was an increase in the rate ratio with age. In the high
income group there was a substantial increase in the rate
ratio beginning at an earlier age, which indicates greater
risk. Increases in rates of disease with advancing age are to
be expected, but here the interaction of median household
income with age is a more complex effect modification
than that of age alone. We speculate that the higher income
implies a greater use of dry cleaners.
Inordertoobtainasummaryrateratiofortheeffects
of exposure at each exposure level, we exponentiated the
weighted average of the beta coefficients. For example, in
Exposure level 3, population density is the effect modifier.
The beta coefficient for low population density is −0.12,
while for both middle and high population density it was
0.15. Using a standard population composition in NYC (low
density, 19.6%; middle density, 43%; high density, 37.4%),
we obtained a weighted average, −.12(.196) +.15(.43)
+.15(.374) =.098 for a log-linear estimate. The summary
rate ratio is exp (.098) =1.102. The summary rate ratios
calculated this way are comparable across different exposure
levels. The rate ratios for Exposure levels 2 to 5 are all
positive, ranging from 1.10 to 1.27. Thus, we see an increase
from 10 to 27% in the rate of discharges for Exposure levels
2to5comparedtoExposurelevel1.
In order to validate our choice of exposure classification,
we conducted a permutation test. The 164 zip codes in
the study were randomly assigned into five exposure levels
and the same main effect model was applied. Then we
examined the sign of the exposure coefficients and the
significance of those coefficients. In a sample of 5000
iterations, the probability of getting an analysis with positive
and statistically significant (P-value <.05) coefficients for
Exposure levels 2, 4, and 5 was very low, 1.42%. Because each
random assignment or iteration is equivalent to a different
exposure measurement, this permutation test indicated that
using density of PERC dry cleaners as a measure of exposure
was critical to finding a significant association with the rate of
hospital discharge that included a diagnosis of kidney cancer.
4. Discussion
We found a significant association between the density of
dry cleaning establishments using PERC and the rate of
hospital discharges that include a diagnosis of kidney cancer
among persons 45 years of age and older living in New
York City. The rate ratio increased by 10 to 27% for the
populations living in zip codes with higher density of PERC
dry cleaners (i.e., Exposure levels 2, 3, 4, and 5 compared
to Exposure level 1). These observations are consistent with
the hypotheses that living near to a PERC dry cleaning
establishment increases the risk of exposure to PERC,
and that increased exposure to PERC increases the risk
of developing kidney cancer. These results are compelling
because of their strength of association and consistency with
animal and occupational studies, particularly in light of
the limitations in exposure assessment in this study. Other
studies of residential populations have demonstrated that

6 Journal of Environmental and Public Health
Tab le 4: Rate ratios for exposure levels and interactions of effect modifiers.
Rate ratio all
population
Effect
modifier
Effect modifier
level
Rate ratio (RR) for
certain level effect
modifier
95% confidence interval for RR
Exposure level 1 1None
Exposure level 2 1.15∗None
(95% CI 1.04, 1.27)
Exposure level 3
1.102 Population
density
Low 0.90 0.74 1.08
(95% CI 1.00, 1.24) Middle 1.16 1.01 1.33
High 1.19 1.01 1.41
Exposure level 4 1.27∗
Race White 1.35 1.19 1.53
(95% CI 1.13, 1.42) Black 1.08 0.83 1.40
Exposure level 5
Low/mid MHI
Age =45–54 0.87 0.70 1.07
Age =55–64 1.20 0.94 1.53
Age =65–74 1.21 1.00 1.46
1.16∗MHI and Age Age =75 + 1.24 1.04 1.49
(95% CI 1.02, 1.33) High MHI
Age =45–54 1.14 0.87 1.48
Age =55–64 1.57 1.18 2.09
Age =65–74 1.59 1.24 2.03
Age =75 + 1.63 1.28 2.07
∗Statistically significant at P<.05.
people living in such buildings are exposed to PERC and are
at risk of neurological effects [3,6,16].
Effect modifiers, age, and MHI, in Exposure level 5,
reflect the general trend toward greater exposure and risk
for males (predominately white) with an increased risk as
income and age increase, possibly because of greater use of
dry cleaning, greater likelihood of living in or near a building
with a dry cleaner, or living in an area of higher population
density which implies more exposure to PERC per person.
The elevated risk found at younger ages in the high income
zip codes is particularly striking.
5. Limitations
There are several significant limitations in our study. Popu-
lation density is based on the subpopulation of a zip code
and can vary over time. Dry cleaner density per zip code
is a crude measure of PERC exposure. A more satisfactory
approach would include the individual level of exposure
and the distance from a dry cleaning establishment to the
residence, but information on the residential address of
patients was not available.
Dry cleaning facilities use differing amounts of PERC
and have differences in emission controls, and operate
in buildings with differing structures, building integrity,
ventilation, and other variables which are not incorporated
in our model. Air currents may influence the concentration
of PERC in a zip code as well. Using density of PERC
dry cleaners as a measure of exposure does not capture
all aspects of residential exposure. For example, one would
expect exposure to be greater in apartments co-located with
PERC dry cleaners, and to decrease in buildings nearby with
increasing distance.
Zip codes in New York City, in general, are smaller in
area than in other regions having lower population density.
Thusexposuremaybemoreuniforminasmallarea.Weused
all zip codes that met our inclusion criteria, including those
in Staten Island which cover large areas and have relatively
low population density leading to a large variation in PERC
exposure within that zip code. We have no information on
population mobility, and only know current zip code of
residence. We have no direct measurements of PERC levels in
the residences of patients or controls. In addition we have no
information concerning occupational exposures. All of these
limitations serve to attenuate the measure of effect, the rate
ratio.
In spite of these limitations our study has significant
strengths. The SPARCS data is comprehensive for more
than 10 years, and the large number of hospitalizations
that include kidney cancer as well as our outcome variable,
rate of discharges, allows detection of patterns of disease
distribution that would otherwise not be discernable. We
have detailed information on the location and use of PERC
by dry cleaning establishments. We also have demonstrated
that the results cannot be explained by random zip code
assignments.
6. Conclusions
Given the significant limitations in our study, particularly
in exposure assessment, the relationship between residential

Journal of Environmental and Public Health 7
exposure to PERC and kidney cancer may be stronger than
what we report in this study. This suggestion is consistent
with the cancer risk estimate made previously by EPA (3 in
100 residents in buildings containing a PERC dry cleaning
establishment). Clearly further study of the relationship
between residential exposure to PERC and kidney cancer
is needed, with more rigorous exposure and cancer risk
assessment. Ideally a study comparing residents living in
buildings with PERC dry cleaning establishments to residents
farfromPERCdrycleanersshouldbeconducted.
The analysis presented here supports the hypothesis that
residential proximity to PERC dry cleaning facilities in New
York City increases the risk of kidney cancer. Because a
large residential population is potentially exposed to PERC
from these facilities, more evaluation is needed to determine
whether such a relationship would be found in a cross-
sectional or case-control study.
Acknowledgments
This work was supported by the Institute for Health and
the Environment of the University at Albany. The authors
appreciate the assistance provided by the New York State
Department of Environmental Conservation and the New
York State Department of Health who provided access to
data that enabled us to conduct this study. Dr. Schreiber,
Chief Scientist of the New York State Office of the Attorney
General’s Environmental Protection Bureau, contributed
scientific and technical analysis to this publication. This
paper presents information for scientific purposes only and
is not intended to represent the views and policies of the
New York State Office of Attorney General. The use of
data from the New York Statewide Planning and Research
Cooperative System (SPARCS) has been declared “exempt”
by the University at Albany Institutional Review Board on the
basis of the fact that it uses unidentifiable, nonconfidential
data and is secondary analysis of existing data.
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