EPA/600/8-91/049A
Journal Supplement
UNITED STATES ENVIRONMENTAL PROTECTION AGENCY
Environmental Criteria and Assessment Office (MD-52)
Research Triangle Park, North Carolina 27711
• . • March 1992 • . , •
Dear Requestor:
The comment period for public review of the External Review Draft of
Air Quality Criteria for Oxides of Nitrogen has been extended. The revised
deadline for submitting comments on the document is March 30, 1992. Because
of your specified interest in the document, we have enclosed a copy of the
paper, Synthesis of Environmental Evidence: Nitrogen Dioxide Epidemiology
Studies, authored by Vic Hasselblad, Dennis J. Kotchmar, and David M. Eddy.
The paper presents the information and analysis contained in Chapter 14 of
the document, Epidemiology Studies of Oxides of Nitrogen. The paper has been
accepted for publication by the Journal of the Air and Waste Management
Association.
Sincerely yours,
Lester D. Grant, Ph.D.
Director, Environmental Criteria
and Assessment Office
Enclosure
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EPA/600/8-91/049A
Journal Supplement
Synthesis of Environmental Evidence:
Nitrogen Dioxide Epidemiology Studies
Vic Hasselblad
Dennis J. Kotchmar
David M. Eddy
Vic Hasselblad, Ph.D., Research Associate Professor, Center for Health Policy
Research and Education, 125 Old Chemistry Building, Duke University, Durham, NC
27706.
Dennis J. Kotchmar, M.D., Medical Research Officer, Environmental Criteria and
Assessment Office, U.S. Environmental Protection Agency, Research Triangle Park,
NC 27711.
David M. Eddy, M.D., Ph.D., Professor, Center for Health Policy Research and
Education, Duke University, Durham, NC 27706.
Printed on Recycled Paper
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Disclaimer
The views expressed in this paper are those of the authors and do not necessarily reflect
the views or policies of the U.S. Environmental Protection Agency. The U.S. Government
has the right to retain a nonexclusive royalty-free license in and to any copyright covering
this article.
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Acknowledgment
The authors thank the members of the Oxides of Nitrogen Workshop Review Group for
their Suggestions that lead to this paper. The authors also thank James Ware, Lucas Neas,
and Jonathan Samet for their advice. Finally, the authors thank the journal's reviewers
whose suggestions added greatly to the paper.
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Practical Implications—
"Synthesis of Environmental Evidence: Nitrogen Dioxide Epidemiology Studies"
The assessment methodology discussed in the paper, meta-analysis, provides an
alternative approach for assessing environmental data bases. Meta-analysis has the potential
to increase the ability to estimate a small but meaningful change in the risk of a health
outcome measure by analyzing the total evidence from all studies simultaneously. The
specific example of the relationship of lower respiratory illness and nitrogen dioxide (NO2)
exposure is a case in point. The evidence of the individual studies was inconclusive. When
taken as a whole, the results of the meta-analysis suggest an increase of at least 20% in the
odds of respiratory illness in children exposed to an increase of 30 jug/m3 NO2. This analysis
can be considered along with other evidence in assessing health effects of exposure to NO2.
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Abstract
The use of meta-analysis is becoming more common in the medical literature, but it is
not common in the environmental literature. Although meta-analysis cannot combine a group
of poorly executed, conflicting studies to get an unequivocal answer, there are certain
situations where it can be helpful. The inability of studies to produce similar results may be
a function of the power of the studies rather than a reflection of their quality. The literature
on the effects of nitrogen dioxide on the odds of respiratory illness in chiildren is such an
example. Three quantitative methods for the synthesis of this evidence are presented.
Although the methods produce slighlty different results, the conclusion from all three methods
is that the increase in the odds of respiratory illness in children exposed to a long-term
i*
increase of 30 jttg/nr (comparable to the increase resulting from exposure to a gas stove) is
about 20%. This estimated increase is not sensitive to the method of analysis.
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Introduction
The United States Environmental Protection Agency (U.S. EPA) is directed by the
Clean Air Act1 to promulgate standards that protect the public health from air pollutants and
are based on air quality criteria. Such criteria are to reflect the latest scientific information
useful in indicating the kind and extent of all identifiable public health effects that may be
expected from the presence of ambient air pollutants. Air Quality Criteria Documents
(AQCDs) for these pollutants attempt to integrate and synthesize key information from
several disciplines to provide a coherent framework from which interpretation and judgments
can be made concerning the risk to human health. Reducing the uncertainties inherent in
such information strengthens the conclusions that can be drawn.
Over the past decade, quantitative approaches have been developed to synthesize
evidence from multiple studies. Making use of such approaches in evaluating and
synthesizing epidemiologic evidence as part of AQCi) preparation requires that the methods
be able to handle the results of a variety of analyses, including multiple logistic regression
analyses, and provide combined estimates of the probability of a given type of health effect
occurring at a specified exposure level. This eliminated many meta-analysis methods often
used with clinical trials, as well as the method of effect sizes. In preparing a revised AQCD
for nitrogen oxides, three methods were found to be useful. These methods are described
below, and comparisons are then made between the results of the three methods as applied to
the evaluation and synthesis of epidemiologic evidence concerning the effects of nitrogen
dioxide on respiratory disease symptoms in children.
The purpose of this paper is to (1) demonstrate that there are situations where the
synthesis of environmental evidence is feasible, (2) describe some of the models used for this
synthesis, and (3) apply these models to a specific data set of interest.
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Quantitative Methods for Synthesizing Evidence
The three quantitative approaches employed in evaluating the nitrogen dioxide
health effects evidence are (1) the variance-weighted method, (2) random-effects models as
described by DerSimonian and Laird,2 and (3) the Confidence Profile Method as described by
Eddy3 and Eddy et al.4'5
Variance-Weighted Method
One of the oldest methods for combining estimates of a parameter is the variance-
weighted method, which is described by Hald.6 Assume that there are n studies, each giving
estimates, 6t, of a parameter 6, where i = 1,2,...,«. The method assumes that each study
is independent of the other studies and that each study is estimating exactly the same
parameter. We shall refer to this as the fixed-effects model. The minimum variance estimate
for any estimate, which is a linear combination of the 0/s, is:
n
n
(1)
where wt = 1 / variance (^. The statistic for testing the null hypothesis that all 0/s are
estimating the same parameter is
(2)
which is approximately distributed as a chi-square distribution with (n - 1) degrees of
freedom.
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Combining Using Random-Effects Models
Random-effects models have been used for many years, although they are sometimes
called two-stage or hierarchical models. Random-effects models arise when the parameter or
parameters of "mother nature" do not remain constant from study to study. Instead, they
vary randomly and are in fact assumed to be random variables from some distribution. The
problem then becomes to estimate (or derive a posterior distribution for) some function of
these parameters.
One such random-effects model is the so-called "normal-normal" model. In this model
we assume that each estimate, 0,-, of the parameter 6 is sampled from a normal distribution
2 2
with mean ^ and variance a.. For now, we assume that a. is known and that ^ is a random
value from another normal distribution with mean 0 and variance r2. The likelihood for the
estimates from n different studies is
L « exp
E
z=
(3)
r\
The parameter 0 can be estimated, or a posterior for 6 and t can be calculated if prior
distributions (priors) are specified for 6 and r2 A flat prior [p(T2) = 1] will work for r2
but the natural noninformative prior 1/v, leads to problems. The integrals do not converge
for r2 near zero, and the intergrals do not converge as T2 approaches infinity unless n is at
least 4. A safer prior for r2 is
Usually, the actual variances of the estimates, oi , are not known and are replaced by their
sample values.
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Often the study parameter that varies is an effect measure, such as an odds ratio. This
parameter then has two sources of variation: (1) the variation resulting from -"mother nature"
choosing different parameter values for each study and (2) the sampling variation from the
study itself given a particular value from "mother nature". DerSimoniati and Laird2 give
formulas for partitioning the variation in a random-effects model without making any
particular distributional assumptions about the parameters. Let &t be the estimate of the
parameter, as before, and let wt be the inverse of the sampling variance of dt. Define $w
as
n
n
(5)
Estimate r2 by
= max "
0, [*w- (n - I)]/
n
(6)
Now define v,- as l/(l/w,. + t2). The estimate of 6 is
n . n
i'
(7)
and the variance of 8 is approximately
n
£
/=!
1 / v,.
(8)
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Confidence Profile Method
The Confidence Profile Method is a very general method for combining virtually any
Mnd of evidence about various parameters, as long as those parameters can be described in a
model. The Confidence Profile Method can be used in either a Bayesian mode, to estimate a
joint posterior distribution for the parameters of interest, or in a non-Bayesian mode, to
estimate a joint likelihood function for the parameters of interest. The method will be
applied to the special case where each study is estimating the same endpoint. The Confidence
Profile Method uses a model that consists of three elements: (1) basic parameters,
(2) functional parameters, and (3) likelihood functions relating evidence to basic or functional
parameters. The description of these elements follows,
Basic Parameters. Basic parameters are those parameters that appear in the model that
i
are not functions of any other parameters. For example, the respiratory disease rate in
children living in homes with electric stoves in a particular study could be a basic parameter.
For convenience, we will denote the basic parameters as 6 1,^2^ --^k' ^ a Bayesian analysis
is to be used, then all basic parameters must have prior distributions, Noninformative priors
for these parameters can be derived in a variety of ways, One standard method is to use a
Jeffreys' prior.7
Functional Parameters. Each functional parameter, 6-, is defined as a function of the
basic parameters 0j,02 ..... dk> and ^ previously defined functional parameters Ok+ii«»Qj-i:
Although the functions can be any mathematical expression, certain functions are very
common. For example, the multiple logistic model is often used:
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= 1 /
1 + exp
k
-£''*
(10)
where the 0y s are logistic regression coefficients, the Xy& are known constants and
81 represents the probability that the jth response is positive.
Likelihood Functions. Likelihood functions connect observed evidence to basic and
functional parameters. For example, the likelihood function for a multiple logistic regression
problem could be:
n
(11)
where 6t is defined in equation (10) and where yt equals 1 if the response; is positive and
equals zero otherwise. For a more complete discussion of likelihood functions, see Barnard
and Sprout.8
The Model. Once the basic parameters, functional parameters, and likelihoods are
defined, the model has been formulated. The general log-likelihood for the model (assuming
independence of experiments) is:
(12)
£ ln
s=\ i=l
where / indexes the observations of an individual study, s indexes the S different experiments,
ns is the number of observations in experiments S, and Yt is the observed data for the
/ study. If priors have been defined, then the m dimensional posterior is; defined as:
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S ns
H T
k
n
M
(13)
Methods of Model Solution. There are five different methods for solving the model
once it has been defined: (1) maximum likelihood estimation, (2) maximum likelihood
methods for determining the posterior model, (3) exact solutions uncertain special cases,
(4) approximate solutions using moments, and (5) Monte Carlo simulation. The choice
depends on the complexity of the model, the accuracy required, and the amount of
computational power and time available. For additional discussion of these, see Eddy et al/
The Confidence Profile Method can be used for both fixed and random effects models.
An Application to Studies of Nitrogen Dioxide Effects on Respiratory Illness Symptoms
Lower respiratory tract illness (LRI) is one of the major causes of childhood morbidity
in the United States,9 This is of public health importance because childhood respiratory
illness is extremely common and the potential for exposure to NO? is great.10 Lower
respiratory illness takes on added importance since recurrent childhood respiratory illness may
be a risk factor for later susceptibility to lung damage.10"1'3 Various studies of LRI have
reported rates ranging from about 20 to 30 illnesses per 100 children in the first year of
life.9'14'15 The rate of LRI in children is affected by several factors that include age,
immunologic status, prior viral infections, level of health, socioeconomic status, day care
attendance,1^1 environmental tobacco smoke (ETS), and exposure to NO2 and other pollutants.
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Bacteria are not thought to be common causes of LRI in nonhospitalized infants in the
United States.15 Seventy-five percent of the isolated microbes were one of four types:
respiratory syncytial virus, parainfluenza virus Types 1 and 3, and M. pneumonia.9'14 Early
insult from virus infection in the lower respiratory tract is an essential element of the
development of chronic and persistent impairment.11"13 It is now recognized that infections,
reactive airways, ETS, and other inhaled pollutants, are the most important risk factors in the
development of chronic lung disease.17 Thus, factors such as NO2, which increase the risk
for LRI, are important because of the associated public health concern and the potential for
increase in the development of chronic lung disease.
Epidemiological studies of the relationship between NO2 exposure and a health outcome
such as LRI in children provide the majority of the evidence for examination of such
relationships. Several factors arise in the interpretation of epidemiological studies of the
health effects of NO2: (1) measurement error in exposure, (2) misclassification of the health
outcome, (3) selection bias, (4) adjustment for covariates, (5) publication bias, (6) internal
consistency, and (7) plausibility of the effect based on other evidence.
The effect of measurement error on estimation has been studied by several authors,
including Shy et al.,18 Gladen and Rogan,19 StephansM and Carroll,20 Fuller,21 Schafer,22
and Whittemore and Keller.23 In general, measurement error that is independent of the health
outcome will result in estimated effects biased towards the null. Whittemore and Keller23
specifically consider the data of Melia et al.29 as described by Florey et al.34 and show that a
20% inisclassificatkMi, rate of the exposure category will result in an underestimate of the
logistic regression coefficient by as much as 50%. StefansM and Carroll20 have shown that,
even without the independence of error related to outcome, the bias is towards the null in
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Situations where the risks are not extremely clpse tP 0 or 1. The use of the presence of a gas
stove as a surrogate for actual NQ2 exposure introduces misclassificatipn.
Most studies pf respiratory disease and NQ^ exposures measured the important
covariates pf age, gender, soeio^econpmic level pf the parents, and parental smoking habits.
The estimated effect (regression coefficient of disease on NO^ exposure) will be an
overestimate when a missing soyariate is either positively or negatively correlated with both
the exposure variable and the health putepme. The estimated effect will be an underestimate
When a missing epvariate is positively correlated with the exposure variable and negatively
correlated with the health outcome, °r vice versa, Ware et al.24 found that parents with
spme college education were more likely to report respiratory symptoms and were less likely
to use a gas stpve, leading tP an underestimate of the health effect if education were left put
Of the analysis.
Studies that exajnine NO2 relationships to respiratory illness, when reviewed
independently, produce somewhat mixed resylts.2^ The use of quantitative rnethods of
Synthesizing evidence presents the opportunity to examine the consistency between these
studies arid the strength of the total data ba^e, Selected studies are discussed, followed by a
combined analysis.
British Studies
Results of British studies have, been reported by Melia et al.,26"31 Goldstein et al.,32'33
and Florey et al.34'3^ The initial study, reported by Melia et al,,2^ was based on a survey of
5,658 children (excludes asthmatics, thus 100 less than the number reported), aged 6 to
11 years, with sufficient information in 28 randomly selected areas of England and Scotland.
The Study included a self-administered, parent-completed questionnaire that obtained
14 :
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information on the presence of morning cough, day or night cough, colds going to chest,
chest sounds of wheezing or whistling, and attacks of bronchitis. The questionnaire was
distributed in 1973 and asked about symptoms during the previous 12 months. Colds going
to chest accounted for the majority of the symptoms reported. Information about cooking
fuel (gas or electric), age, gender, and social class (manual or nonmanual labor) was
obtained, but information on parental smoking was not. No measurements of NO2, either
indoors or outdoors, were given. The authors presented their results in the form of a
contingency table with complete covariate information for nonasthmatic children under age
eight. The authors indicated that there was a trend for increased symptoms in homes with
gas stoves, but that the increase was only significant for girls in urban areas; however, they
did not report odds ratios or other measures of increased risk.
Our reanalysis of the authors' data was performed using a multiple-logistic model.
Because it had been suggested that gender had an effect on the relationship with "gas
cooker," interaction terms for gender were included in the original model. None of these
proved to be significant, and they were subsequently dropped from the model. When
separate terms for each gender were used for the effect of "gas cooker," an estimated odds
ratio of 1.25 was obtained for boys and an odds ratio of 1.39 was obtained for girls, but the
odds ratios were not significantly different. The combined odds ratio for both genders was
1.31 (95% confidence limits of 1.16 to 1.48) and was statistically significant from 1.00
(p < .0001). The other main effects of gender, socio-economic status, and age were all
statistically significant.
Melia et al.28 report further results of the national survey covering two groups:
(1) a new cohort of 4,827 boys and girls, aged 5 to 10 years, from 27 randomly selected
areas who were examined in 1977 and (2) 2,408 children first examined in 1973 who were
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followed-up for at least one year and whose parents reported the use of the same cooking fuel
for each year the child was studied. The 1977 study collected information on the number of
smokers in the homes. In the 1977 cross-sectional study, only the prevalence of day or night
cough in boys (p « 0.02) and colds going to chest in girls (p < 0.05) were found to be
significantly higher in children from homes where gas was used for cooking compared with
children from homes where electricity was used. Grouping responses according to the six
respiratory questions into one or more symptoms or diseases, or none, yielded a prevalence
higher in children from homes where gas was used for cooking than in those from homes
where electricity was used (p » 0.01 in boys, p = 0.07 in girls). The authors examined the
effect of gender, social class, use of pilot lights, and number of smokers in the homes.
Our reanalysis of the authors' data was performed applying a multiple-logistic model.
This model contained the same terms that were included in our analysis of Melia et al.26 As
in the previous analysis, none of the interaction terms proved to be significant, and they were
subsequently dropped from the model. The maximum likelihood estimate of the odds ratio
was 1.24 (95% confidence limits of 1.09 to 1.42. This effect was statistically significant
(p < .0001). The other main effects of gender, socio-economic status, and age were all
statistically significant.
This study was followed by a study in 1978 of 808 schoolchildren,29 aged six to seven
years, in Mddlesborough, an urban area in northern England. Respiratory illness was
defined in the same manner as in the previous study. Indoor NO2 measurements were
collected from 66% of the homes, with the remaining 34% refusing to participate. Nitrogen
dioxide was measured by Palmes tubes36 attached to walls in the kitchen areas and in
children's bedrooms. In homes with gas stoves, levels of NO2 in kitchens ranged from 10 to
596 fig/m3 (0.005 to 0.317 ppm [1 jug/m3 = 0.00053 ppm at 25 °C, 760 mmHg]), with a
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mean of 211 jig/m3; and levels in bedrooms ranged from 8 to 318 jug/m3,, with a mean of 56
<•»
/*g/m . In homes with electric stoves, levels of NO2 in kitchens ranged from 11 to
"2 ^
353 jtg/m , with a mean of 34 jttg/m , and in bedrooms NO2 levels ranged from 6 to
*2 ** '•
70 /Kg/m , with a mean of 26 /wg/m3. Outdoor levels of NO2 were determined using
diffusion tubes systematically located throughout the area, and the weekly average ranged
from 26 to 45 jwg/m3.
One analysis by the authors29 was restricted to those 103 children in homes where gas
stoves were present and where bedroom NO2 exposure was measured. A multiple logistic
regression model was fitted to the presence or absence of respiratory illness. Measured NO2
exposure was found to be associated with respiratory illness, independent of social class, age,
gender, or the presence of a smoker in the house (p = 0.06). However, when social class
was excluded from the regression, the association was weaker (p = 0.11). For the six- to
seven-year-old children living in gas stove homes, there appeared to be an increase of
respiratory illness with increasing levels of NO2 in their bedrooms (p = 0.10), but no
significant relationship was found between respiratory symptoms in those children'or their
siblings or parents and levels of NO2 in kitchens.
Since no exposure-response estimates were given by the authors, a multiple-logistic
model was fitted to the data with a linear slope for NO2 and separate intercepts for boys and
girls. Nitrogen dioxide levels for the groups were estimated by fitting a lognormal
distribution to the NO2 data, which was reported by intervals, and the average exposures
within each interval were estimated.37 The estimated logistic regression coefficient for NO2
(in /*g/m3) was 0.015 with a standard error of 0.007. This result is not directly comparable
with the previous two analyses since it gives the increase in the logarithm of the odds of
respiratory illness per unit increase in NO2 exposure. Since most studies of gas stove
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exposure (both in the United States and the United Kingdom) show an approximate increase
of 30 jtg/m3 in (he NO2 levels, the slope was multiplied by 30 to get the increase due to gas
StOY<5 exposure, and then converted to an odds ratio by exponentiation. All of this assumed
that the logarithm of the odds ratio was linear in N02 exposure. The result was an odds ratio
of 1.53, with 95% confidence limits of 1,04 to 2,24.
The study was repeated January through March of 1980 by Melia et al.30 This time,
five- and, six-year-old children were sampled from the same neighborhood as the previous
study, but only families with gas stoves were recruited. Environmental measurements were
madi? and covariate data were collected in a manner similar to the previous study.
Measurement^ of NO2 were available from 54% of the homes. The unadjusted rates of one
or more symptoms by gender and exppsure level were analyzed by the authors, and they
concluded that;".,, no relation was found between the prevalence of respiratory illness and
levels of NQ2," A reanalysis of the data was made using a multiple-logistic model similar to
the one used for the previous study. The model included a linear slope for NO2 and separate
intercepts for boys and girls. Nitrogen dioxide levels for the groups were estimated by fitting
a lognprmal distribution to the grouped bedroom NO2 data. The estimated logistic regression
coefficient for NO2 (in jig/m3) was 0.004 with a standard error of 0.005. As for Melia
et al.,29 the regression coefficient was converted, to an odds ratio for an increase of 30 /ig/m3
in NO2 assuming that the logarithm of the odds ratio was linear in NO2 exposure. This gave
an odds ratio of 1.11, with. 95% confidence limits of .83 and 1.49.
Melia et al,31 investigated the association between gas cooking in the home and
respiratory illness in a study of 390 infants bom between 1975 and 1978, When a child
reached one year of age, the child's mother was interviewed by a trained field worker who
completed a questionnaire. The mother was asked whether the child usually experienced
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morning cough, day or night cough, wheeze, or colds going to chest, and whether the child
had experienced bronchitis, asthma, or pneumonia during the past 12 months. No relation
was found between the type of fuel used for cooking in the home and the prevalence of
respiratory symptoms and diseases recalled by the mother after allowing for the effects of
gender, social class, and parental smoking. The authors gave prevalence rates of children
having at least one symptom by gas stove use and gender. The combined odds ratio for
presence of symptoms by gas stove use was 0.63, with 95% confidence limits of 0.36 to
1.10.
United States Six-Cities Studies
Several authors24'35"46 have reported on a series of studies conducted in six U.S. cities.
The six cities were selected to represent a range of air quality based on their historic levels of
outdoor pollution and included Watertown, MA; Kingston and Harriman, TN; southeast
St. Louis, MO; Steubenville, OH; Portage, WI; and Topeka, KS. Approximately
1,500 grade-school children were enrolled in each community and were followed for several
years. Families reported the number of persons living in their homes and their smoking
habits, parental occupations and educational backgrounds, and fuels used for cooking and
heating. Outdoor pollution was measured at fixed sites in the communities and at selected
households. Indoor pollution, including NO2, was measured in several rooms of selected
households. Results of monitoring in Portage, WI, verify that the presence of a gas stove
contributes dramatically to indoor NO2 levels. The results clearly show the effect of a gas
stove on not only the indoor concentrations but also on the personal exposure of the
individual. The study42 was conducted very carefully with excellent quality control. It gave
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an average estimate of 29 jig/in3 increase in exposure resulting from the use of gas stoves in
cities studied in the United States.
Ware et al.24 reported results from the six-cities studies based on 8,120 children, aged
6 to 10 years, who were followed from 1974 to 1979. An initial report on a subset of the
data was given by Speizer et al.39 Health endpoints were measured by a standard respiratory
questionnaire that was completed by parents of the children. The authors used log-linear
models to estimate the effect of gas stoves versus electric stoves on the rates of serious
respiratory illness before age two. Directly standardized rates of reported illnesses and
symptoms did not show any consistent pattern of increased risk for children from homes with
gas stoves. Logistic-regression analyses controlling for age, gender, citys and maternal
smoMng level gave estimated odds ratios fof the effect of gas stoves ranging from 0.93 to
1.07 for bronchitis, cough> wheeze, LRI index, and illness for the past year. The index for
LRI was defined as the presence of either bronchitis, respiratory illness that kept the child
home 3 days or more, or persistent cough for 3 months of the past year. None of these odds
ratios were statistically different from 1. Only two odds ratios approached statistical
significance: (1) history of bronchitis (odds ratio = 0.86, 95% confidence interval 0.74 to
1.00) and (2) respiratory illness before age two (odds ratio = 1.13, 95% confidence
interval 0.99 to 1.28). When the odds ratio for respiratory illness before age two was
adjusted for parental education, the odds ratio was 1.11, with 95% confidence limits of
0.97 to 1.27 (p = 0.14). Thus, the study suggests an increase in respiratory illness of about
11%, although the increase was not statistically significant at the 0.05 level. The endpoint in
the Ware et al.24 study most similar to that of the Melia studies was the LRI index. The
authors gave the unadjusted rates, and from those an estimated odds ratio of 1.08, with 95%
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confidence limits of 0.97 to 1.19, were calculated. Although this rate was not adjusted for
other covariates, the effect of those adjustments on other endpoints was minimal.
Neas et al.45'46 studied a cohort of 6,273 children from the same six cities. This cohort
included children that were part of the Dockery et al.44 analysis but was restricted to white
children 7 to 11 years of age with complete covariate information and at least one valid
indoor measurement of both NO2 and respirable particles. This resulted in 1,286 children
being included in the analysis. Methods for measuring indoor pollutants were described by
Spengler et al.38 Indoor pollutants were measured in each child's home for two weeks
during the heating season and two weeks during the cooling season. Nitrogen dioxide was
measured by Palmes tubes at three locations in each home.
The analysis of the Neas et al.45'46 study was based on the third symptom questionnaire
that was completed by parents following the indoor measurements. The questionnaire
reported symptoms during the previous year, including shortness of breath, chronic wheeze,
chronic cough, chronic phlegm, and bronchitis. The authors used a multiple-logistic model,
which had separate-city intercepts, indicator variables for gender and age, parental history of
chronic obstructive pulmonary disease and asthma, parental education, and single-parent
family status. The sampling strategy minimized the association between NO2 and passive-
smoking exposure. The increases in symptoms were estimated for an additional 31 jtg/m3
NO2 exposure. This corresponded to the average difference in NO2 concentrations monitored
in homes with a gas stove with a pilot light, based on exposure information from the study.
Table I shows the odds ratios for the five separate symptoms associated with the increase in
NO2 exposure.
All of these odds ratios are consistent with the size of effect seen in the other analyses
of the six-city data and the analyses of the British studies. The authors defined a combined
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Table I. Odds ratios and 95% confidence intervals for the effect of an additional 31 jig/m3
nitrogen dioxide on the symptom prevalence.
Symptom
Shortness of breath
Chronic wheeze
Chronic cough
Chronic phlegm
Bronchitis
Combined symptoms score
Odds Ratio
1.27
1.19
1.21
1.29
1.05
1.47
95% Confidence Interval
0.92 to 1.73
0.87 to 1.61
0.86 to 1.71
0.93 to 1..79
0.71 to L56
1.17 to L86
Source: Neas et al.45
symptom, which was the presence of one or more of the symptoms just reported, and an
analysis of this combined indicator of respiratory symptoms gave an estimated odds ratio of
1.47, with 95% a confidence interval of 1.17 to 1.86. When split by gender, the odds ratio
was higher in girls, and when split by smoking and nonsmoking homes, it was higher in
smoking homes.
Tayside Study
Ogston et al.47 studied infant mortality and morbidity in the Tayside region of northern
Scotland. The subjects were 1,565 infants born to mothers who were living in Tayside in
1980. Episodes of respiratory illness were recorded during the first year of life. The
information was supplemented by observations made by a health visitor and scrutinized by a
pediatrician who checked diagnostic criteria and validity. One health endpoint assessed was
defined as the presence of any respiratory illness during the year. This endpoint was
analyzed by the authors using a multiple-logistic regression model that included terms for
parental smoking, age of mother, and presence of a gas stove. The estimated odds ratio for
22
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the presence of a gas stove was 1.14, with 95% confidence limits of 0.86 to 1.50. Only the
coefficient for parental smoking was statistically significant (p < 0.01).
Iowa Study
Ekwo et al.48 surveyed 1,355 children 6 to 12 years of age for respiratory symptoms
and lung function in the Iowa City School District. Parents of the school children completed
a questionnaire that was a modification of the questionnaire developed by the American
Thoracic Society.49 Eight different measures of respiratory ilhiess were reported by the
authors, but only the endpoint of chest congestion and phlegm with colds was similar to the
endpoints used in the British studies and the six-city studies. Information on parental
smoking was obtained and used as a covariate in the analysis. The result of the analysis,
which was based on 1,138 children, was an odds ratio of 1.10 for gas stove use. The 95%
confidence limits of 0.79 and 1.53 were derived from the authors' data. No NO2
concentrations, either inside or outside the homes, were reported.
Dutch Studies
In the Netherlands, Houthuijs et al.,50 Brunekreef et al.,51 and Dijkstra et al.52 studied
the effects of indoor factors on respiratory health in children. The population consisted of
6- to 9-year-old children from 10 primary schools in five nonindustrial communities in the
southeast region of the Netherlands. Personal exposure to NO2 and homes concentrations
were measured. An important NO2 emission and exposure source in these homes are
geysers, which are unvented gas-fired hot water sources at the water tap. Exposure to
tobacco smoke was assessed with a questionnaire that also reported symptom information.
Pulmonary function was measured at school. The study used Palmes tubes to measure a
23
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single weekly average personal NO2 exposure. Potential high peak exposures from the
geysers may not be well characterized by the weekly average personal exposure
measurements. In January and February of 1985, the homes of 593 children who had not
moved in the last four years were measured for one week for NO2. Personal exposure was
also estimated from time budgets and room monitoring.
Three measures of health (cough, wheeze, and asthma) were obtained from the
questionnaire, which was a modified form of the World Health Organization questionnaire.53
Asthma was defined as attacks of shortness of breath with wheezing in the last year. The
presence of any of the three symptoms was used as a combination variable, and a logistic-
regression model was used to fit the combination variable. Exposure was estimated by fitting
a lognorm'al distribution to the exposure data, which was reported in intervals; and the mean
exposure values for each group were estimated by a maximum likelihood technique.37 The
estimated logistic-regression coefficient was —0.002, corresponding to an odds ratio of .94
for an increase of 30 /tg/m3 in NO2, with 95% confidence limits of 0.66 to 1.33. This
assumed a linear relationship between the logarithm of the odds ratio and the NO2 exposure.
The rates were not adjusted for covariates such as parental smoking and age of the child.
Ohio Study
Keller et al.54 and Mitchell et al.55 originally conducted a 12-month study of respiratory
illness and pulmonary function in families in Columbus, OH, prior to 1978. The study
measured NO2 exposure by both the Jacobs-Hochheiser and continuous-chemiluminescence
methods. The electric stove users averaged 38 jug/m3 NO2 exposure, whereas the gas stove
users averaged 94 jtg/m3. Thus, the estimated average difference between gas and electric
stove use was 58 /zg/m3. The paper did not report which rooms were measured in order to
24
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get these averages. In a second related study,56 580 persons drawn from households that
participated in the earlier study were examined to confirm the reports and to determine the
frequency distribution of reported symptoms among parents and children in gas or electric
stove homes. A nurse-epidemiologist examined selected persons who were reported ill.
Unfortunately, these rates were not adjusted for other covariates. The percentage of children
having lower respiratory symptoms in homes with a gas stove was 53.2% (n = 267) and
50.7% (n = 286) in homes with electric stoves. Although the difference is not statistically
significant, these rates give an estimated odds ratio of 1.10, with 95% confidence limits of
0.74 to 1.54.
Synthesis of the Evidence
In order to combine the studies just described, several assumptions were necessary.
First, although each study used a slightly different health outcome as an endpoint, we
assumed that the endpoints are similar enough to warrant their combination. Second, the
exposure levels were different in each study. An increase of 30 pg/m3 was used as a
standard increase, and all studies were used to estimate the effect of an increase of 30 /ig/m3,
even if they had a different exposure range. Third we assumed that each study controlled for
key covariates, or that those covariates were properly adjusted for or are of minimal
significance. The omission of covariates such as parental education almost certainly biases
the results towards the null, and for this reason we retained some studies, which arguably
could have been excluded.
The studies described used different indicators to study health endpoints. The symptoms
describing LRI evaluated in the studies varied but are, in general, reasonable indicators of
LRI. They include colds going to chest, chronic wheeze and cough, bronchitis, chest cough
25
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with phlegm, episodes of respiratory illness, and various respiratory indexes, which are
combinations of more than one of these symptoms. These symptpms are comparable to
indicators of LRI in children that were used in other studies. In order to compare these
studies on respiratory effects of NO2, a cqmmqn endpqint was defined, and then each study
was compared with this standard endpoint. The endpoint was the presence of reported LRI
symptoms in children age 12 or younger.
An attempt was made to include as many studies as possible. The requirements for
inclusion were (1) the health endpoint measured must be reasonably close to the standard
endpoint; (2) exposure differences must, exist, and some estimate of exposure (either direct or
indirect) must be available; and (3) an odds ratio for a specified exposure must have been
calculated, or data presented so that it can be calculated. These studies are summarized in
Table H.
The approximate likelihoods for each study are shown in Figure 1. Each curve can be
treated as a likelihood function or pQsteriQr-'probability distribution. If treated as a likelihood
function, then 95% confidence limits for the odds ratio can be calculated as those two points
on the horizontal axis between which 95% of the area under the curve is contained. If
treated as a posterior-probability distribution, then the area under the curve between any two
points is the probability that the odds ratio lies, between those two points. Note that all
11 likelihoods show some overlap. A ehi-square goodness-of-fit test of the homogeneity of
the 11 studies gives a chi-square of 18,75 with 10 degrees of freedom (p — 0.0436),
suggesting some lack of homogeneity in the 11 studies.
The studies were combined using four methods: (1) the variance-weighted method,
assuming a fixed-effects model; (2) the Confidence Profile Method, assuming a fixed-effects,
2:6
-------
I
.g
S
o
i1
I
s
i
.3
,8
-------
Figure 1. Meta-analysis of epiderniologic studies of 30 jug/nr nitrogen dioxide exposure
increase on respiratory illness in children $12 years old.
model; (3) the DerSimonian and Laird method, assuming a random-effects model; and (4) the
Confidence Profile Method, assuming a random-effects model. Results of the use of these
models in synthesizing the NO2 evidence are presented in Table III for four subsets of the
studies. The first includes all 11 studies; the second excludes the two studies on children less
than one year of age; the third excludes the younger children and those studies that did not
measure NO2 directly; and the fourth excludes the younger children and those studies that
measured NO2 directly.
28 ;
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Table m. Summary of synthesis of studies on respiratory illness effects of nitrogen dioxide.
Model-Method
Fixed
Random
Studies
Variance- Confidence
Weighted Profile
Method6 Method5
DerSimonian Confidence
and Profile
Laird2
Method'
All 11 studies
Children aged
5-12 years,
9 studies
1.18 1.18
(1.11, 1.25)a (1.11, 1.25)
1.19 1.19
(1.12, 1.27) (1.12, 1.27)
1.18 1.18
(1.08, 1.30) (1.08, 1.29)
1.19. 1.20
(1.09, 1.30) (1.10, 1.31)
Measured NO2,
children aged
5-12 years,
4 studies
Surrogate NO2
estimate based
on presence of
gas stove,
children aged
5-12 years,
5 studies
1.27 1.27
(1.09, 1.47) (1.09, 1.47)
1.18 1.18
(1.10, 1.26) (1.10, 1.26)
1.27 1.25
(1.02, 1.58) (0.99, 1.58)
1.18 1.18
(1.07, 1.29) (1.09, 1.28)
°95 % confidence limits given in parentheses.
The results from all analyses are reasonably similar. The variance-weighted method and
the Confidence Profile Method have identical answers because the log normal approximation
for the likelihood function was used in the calculation of the solution by the Confidence
Profile Method. In general, the results should be nearly identical for reasonable sample
sizes. The DerSimonian and Laird2 method and Confidence Profile Method5 for the analysis
of a random-effects model gave similar but not identical results.
29
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The analysis of the nine studies with children 5 to 12 years old was done separately
because the other two studies were of infants. The exclusion of these two studies made little
difference in the results.
All studies that used the presence of a gas stove as a surrogate for NO2 exposure
obviously suffer from measurement error. In general, measurement error will decrease the
estimated effect. When the four studies of children over age 5 years with measured NO2
levels were combined, the estimated o
-------
may be greatly improved by analyzing the total evidence from all studies; simultaneously, that
is, by conducting a meta-analysis. A potential limitation of a meta-analysis is that the studies
available for use may represent a spectrum of quality. Well executed studies may be mixed
with studies containing flaws-studies with missing data or confused definitions and outcome
measures. On the other hand, such problems may be slight compared to problems with
alternative traditional reviews. Meta-analysis fills a need by assisting in the reconciliation of
conflicting research results. While some physical sciences may allow the; identical replication
of experiments, many fields such as environmental science allow only the repetition of studies
that introduce variation and produce uncertainty. Meta-analysis is one way of dealing with
uncertainty.
All meta-analysis methods previously discussed assume that each piece of evidence
(study) is independent of the others. Under the fixed-effects model, the evidence is assumed
to pertain to a common parameter. Under the random-effects model, this assumption is
relaxed to allow for a distribution for the parameter of interest. The computations for both
models are relatively straightforward and can be made on a personal computer. The method'
of calculation has less impact on the conclusion than does the choice of model.
In Air Quality Criteria for Oxides of Nitrogen, prepared 10 years ago by U.S. EPA,58
a group of studies examining the relationship between respiratory illness and exposure in the
home to gas combustion products from cooking fuel were evaluated. At that time, those
studies inferred the presence of NO2 by the presence of gas combustion emission sources.
The evidence from individual studies of the effect of NO2 on respiratory illness was
somewhat mixed. Since then, new studies have been conducted, and earlier ones updated,
that provide data on NO2 concentrations and estimates of exposure.
31
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The studies of respiratory illness in children exposed to increased levels of NQ2
provides an excellent example of the application of meta-analysis. Taken by themselves,
t
most of the 11 studies were reported as not being statistically significant at the 0.05 level
based on analyses performed by the original authors. The studies differed in design and
sample size, and this likely contributed to the lack of significance of some of the studies.
However, use of the meta-analysis methods described above indicates that, taken as a whole,
the collective evidence from the evaluated studies strongly suggests an increase of at least
/j
20% in the odds of respiratory illness in children exposed to an increase of 30 jug/nr NO2
for extended periods of time.
The choices of model (fixed or random) and computational method make little
difference in the estimates in this particular example. In particular, the estimates do not
depend strongly on the assumption that each study is estimating the same parameter. Thus,
any lack of homogeneity is not a major concern. The choice of the computational method
(e.g., DerSimonian and Laird2 versus the Confidence Profile Method5) also makes little
difference in the estimates when restricted to the particular problem described in this paper.
The Confidence Profile Method can be applied to a much broader class of problems,
however.
There is always the concern that the studies described are not the complete list of
studies, but contain primarily the positive studies, since these are the studies most likely to be
published. This is referred to as "publication bias". There are two reasons not to be
concerned with publication bias in this particular situation. First, prospective
epidemiological studies are very expensive and require the work of many individuals. The
studies are usually described to the scientific community before the results are even known.
Second, most of the studies cited were reported as negative studies by the authors themselves,
32
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indicating that there was no difficulty in publishing negative results. In spite of this, it is of
interest to contemplate an undiscovered study with results so negative that, when combined
with the other studies, produces a confidence interval for the odds ratio Ihat includes the
value 1. If we assume that the hypothetical study is the size of the Ware et al.24 study, then
its odds ratio for increased respiratory symptoms as the result of a 30 jug/m3 exposure would
have to be 0.766.
Although there may be reasons to weight certain studies or groups of studies more
heavily than others, the final conclusion has to be that there is an increase in the odds of
respiratory illness of children, especially those of elementary-school age. The estimates are
generally centered about an odds ratio of 1.2, with 95% confidence limits of 1.1 to 1.3,
although the studies using measured NO2 give a slightly higher estimate of the odds ratio.
This kind of synthesis may be possible for other areas of environmental aissessment where
multiple studies of a given health endpoint are available.
33
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