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BiometricsDOI: 10.1111/j.1541-0420.2007.01039.x

Bayesian Distributed Lag Models: Estimating Effects of Particulate

Matter Air Pollution on Daily Mortality

L. J. Welty,1,∗R. D. Peng,2S. L. Zeger,2and F. Dominici2

1Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine,

680 North Lake Shore Drive, Suite 1102, Chicago, Illinois 60611, U.S.A.

2Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health,

615 North Wolfe Street, Baltimore, Maryland 21205, U.S.A.

∗email: lwelty@northwestern.edu

Summary.

ables as covariates; its corresponding distributed lag (DL) function describes the relationship between the

lag and the coefficient of the lagged exposure variable. DLagMs have recently been used in environmental

epidemiology for quantifying the cumulative effects of weather and air pollution on mortality and morbid-

ity. Standard methods for formulating DLagMs include unconstrained, polynomial, and penalized spline

DLagMs. These methods may fail to take full advantage of prior information about the shape of the DL

function for environmental exposures, or for any other exposure with effects that are believed to smoothly

approach zero as lag increases, and are therefore at risk of producing suboptimal estimates. In this article,

we propose a Bayesian DLagM (BDLagM) that incorporates prior knowledge about the shape of the DL

function and also allows the degree of smoothness of the DL function to be estimated from the data. We

apply our BDLagM to its motivating data from the National Morbidity, Mortality, and Air Pollution Study

to estimate the short-term health effects of particulate matter air pollution on mortality from 1987 to 2000

for Chicago, Illinois. In a simulation study, we compare our Bayesian approach with alternative methods

that use unconstrained, polynomial, and penalized spline DLagMs. We also illustrate the connection be-

tween BDLagMs and penalized spline DLagMs. Software for fitting BDLagM models and the data used in

this article are available online.

A distributed lag model (DLagM) is a regression model that includes lagged exposure vari-

Key words: Air pollution; Bayes; Distributed lag; Mortality; NMMAPS; Penalized splines; Smoothing;

Time series.

1. Introduction

Distributed lag models (DLagMs; Almon, 1965) are regression

models that include lagged exposure variables, or distributed

lags (DLs), as covariates. They have recently been employed

in environmental epidemiology for estimating short-term cu-

mulative effects of environmental exposures on daily mortal-

ity or morbidity (e.g., Pope et al., 1991; Pope and Schwartz,

1996; Braga et al., 2001; Zanobetti et al., 2002; Kim, Kim,

and Hong, 2003; Bell McDermott, Zeger, Samet, and Do-

minici, 2004; Goodman, Dockery, and Clancy, 2004; Welty

and Zeger, 2005). DLagMs are specialized types of varying-

coefficient models (Hastie and Tibshirani, 1993) and dynamic

linear models (Ravines, Schmidt, and Migon, 2006).

For Poisson log-linear DLagMs that estimate the effects

of lagged air pollution levels on daily mortality counts, the

sum of the DL coefficients is interpreted as the percentage

increase in daily mortality associated with a one unit in-

crease in air pollution on each of the previous days. Because

the time from exposure to event will almost certainly vary in

a population, this sum is a more appropriate measure of the

effect of short-term exposure than a single day’s coefficient.

Results from previous time series studies suggest that com-

pared to DLagMs, models with single day pollution exposures

might underestimate the risk of mortality associated with air

pollution (Schwartz, 2000; Zanobetti et al., 2003; Goodman

et al., 2004; Roberts, 2005).

Exposure variables, such as ambient air pollution levels,

may be highly correlated over time, making DL coefficients

difficult to estimate. A general solution is to constrain the co-

efficients as a function of lag. Common constraints include a

polynomial (Almon, 1965) or a spline (Corradi, 1977). Esti-

mating DLagMs as varying-coefficient models constrains the

coefficients to follow a natural cubic spline (Hastie and Tib-

shirani, 1993). The DL function for air pollution and mor-

tality has been estimated with polynomial constraints (e.g.,

Schwartz, 2000, Braga et al., 2001; Kim et al., 2003; Bell,

Samet, and Dominici, 2004; Goodman et al., 2004), spline

constraints (Zanobetti et al., 2000), and without constraints

(Zanobetti et al., 2003).

Each type of constraint on the DL coefficients is an appli-

cation of prior knowledge to model specification. In the con-

text of air pollution and mortality, prior knowledge suggests

that short-term risk of mortality varies smoothly as a func-

tion of lag and decreases to zero. Prior knowledge about the

effects of air pollution on mortality at early lags is limited.

There may be short delays in health effects after exposure,

C ? 2008, The International Biometric Society

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as suggested by studies of single day pollution exposures that

find the largest effect on mortality at lag day 1 (Zmirou et al.,

1988; Katsouyanni et al., 2001; Dominici et al., 2003). In the

scenario of mortality displacement (Schimmel and Murawsky,

1978), in which high air pollution levels may advance by sev-

eral days the deaths of frail individuals, the DL function may

be zero or positive at early lags, then decrease and become

negative (Zanobetti et al., 2000, 2002). If there were both a

delay in health effect and mortality displacement, hypotheses

concerning the sign or smoothness of the DL function at early

lags would be tenuous at best.

For more appropriate model specification and improved es-

timation, it may be advisable to formulate DLagMs so that

(i) coefficients are constrained to approach zero smoothly

with increasing lag and (ii) early coefficients are relatively

unconstrained. Neither polynomial nor spline constraints, the

most common methods for specifying DLagMs, include this

prior information in estimation. In this article, we develop

Bayesian DLagMs (BDLagMs) that incorporate our under-

standing of the relationship between short-term fluctuations

of particulate matter (PM) air pollution and daily fluctuations

in mortality counts. Our prior distribution specifies that as

lag increases, the DL function will have increasing smooth-

ness and approach zero. An advantage of our approach is

that the degree of smoothness of the DL function is estimated

from the data. We note that BDLagMs have been explored in

economics (e.g., Leamer, 1972; Schiller, 1973; Ravines et al.,

2006), and autoregressive priors have been used generally to

smooth time-dependent coefficients in generalized linear mod-

els (e.g., Fahrmeir and Knorr-Held, 1997; Manda and Meyer,

2005). However, our prior is quite different from those using

a constant degree of smoothness (Schiller, 1973), a particu-

lar parametric form (Leamer, 1972; Ravines et al., 2006), or

an autoregressive structure (e.g., Fahrmeir and Knorr-Held,

1997; Manda and Meyer, 2005).

We apply our BDLagM to data from the National Mor-

bidity, Mortality, and Air Pollution Study (NMMAPS) to es-

timate the shape of the DL function between daily PM and

daily deaths for Chicago, Illinois from 1987 to 2000. We exam-

ine the sensitivity of the estimated DL function to the speci-

fication of the BDLagM prior. We compare the air pollution

effect estimated with the BDLagM to that estimated using

unconstrained maximum likelihood (ML). We also compare

air pollution effects estimated under the full formulation of

the BDLagM, computed using a Gibbs sampler, to those es-

timated under an approximate formulation, computed using

a closed form expression.

We also conduct a simulation study comparing BDLagMs

to unconstrained, polynomial, and penalized spline DLagMs.

For penalized spline DLagMs, we compare estimates obtained

using generalized cross validation (GCV) and restricted maxi-

mum likelihood estimation (REML; Ruppert, Wand, and Car-

roll, 2003). We include DLagMs that are consistent with bi-

ological knowledge along with DLagMs for which our BD-

LagMs may be misspecified.

Because constraining DL coefficients is a way of smooth-

ing, we consider how our Bayesian approach relates to pe-

nalized spline DLagMs. We demonstrate that BDLagMs are

analogous to penalized spline DLagMs with a specific penalty

matrix derived from the BDLagM prior.

Though our BDLagM formulation was motivated by a de-

sire to model flexibly the DL function between lagged PM

levels and daily mortality counts, it is relevant to situations

in which the lagged effects of an exposure on an outcome

are unknown for the first few lags but are believed to dissi-

pate with lag. Using BDLagMs with repeated measures data

would require extensions to our approach. For documenta-

tion and to encourage implementation, our BDLagM soft-

ware is available online at http://www.ihapss.jhsph.edu/

software/BayesDLM/.

2. Bayesian DLagMs

Let ytand xtbe the outcome and exposure time series. We

consider a generalized linear DLagM g(E[yt|x1,...,xt]) =

?L

is the vector of the DL coefficients to be estimated. Initially

we will consider the normal linear model E[yt|x1,...,xt] =

?θ?xt−?, with Yt independent normal with constant vari-

ance.

The goal is to specify a prior on θ = (θ0, θ1,...,θL)?that

is uninformative on the DL coefficients for small ? but that

constrains the coefficients with larger ? to be smoother and ap-

proach zero. We assume θ ∼ N(0, Ω), where Ω is constructed

so that for increasing lag the diagonal elements decrease to

zero (Var(θ?) → 0) and the off–diagonal elements in its corre-

lation matrix increase to one (Cor(θ?−1, θ?) → 1). Care must

be taken to construct Ω so that it remains positive definite.

A natural approach is to define Ω = ABA, where AATis the

diagonal matrix of the individual variances of the θ?s, and B is

the correlation matrix for θ. Specifying an appropriate Ω may

then be achieved by setting A equal to the Cholesky decom-

position of a diagonal matrix with the desired prior variances

and setting B equal to the correlation matrix for increasingly

correlated normal random variables.

To define A, let the parameter σ2be the prior variance of

θ0, and set Var(θ1) = v1σ2,...,Var(θL) = vLσ2where the v?s

are a decreasing sequence of weights such that 1 ≥ v1≥ ··· ≥

vL> 0. We parameterize them by v?(η1) = exp(η1?), η1≤ 0,

so that the hyperparameter η1governs how quickly the prior

variances of the θ?s approach zero. Choosing the exponential

function is convenient but not required. Let V(η1) be the

diagonal matrix with entries 1, v1(η1)1/2,...,vL(η1)1/2. We

set A = σV(η1).

To specify the correlation matrix B, we similarly define

w?(η2) = exp(η2?), η2 ≤ 0, to be a decreasing sequence of

weights, and M(η2) to be the (L + 1) × (L + 1) diago-

nal matrix with entries 1, w1(η2),...,wL(η2). We let B =

W(η2), where W(η2) is the correlation matrix derived from

the covariance matrix M(η2)M(η2)?+ {IL+1− M(η2)}1L+1×

1?L+1{IL+1− M(η2)}?, where by 1L+1we mean a (L + 1) × 1

vector of ones and by IL+1we mean the (L + 1) × (L + 1)

identity matrix. Then W(η2) is the correlation matrix for

the mixture of normal random variables M(η2)X1+ {IL+1−

M(η2)}1L+1X2 where X1 ∼ N(0, IL+1) and X2 ∼ N(0, 1).

The first few elements of the independent X1 are weighted

more heavily than the corresponding first few elements of the

dependent 1L+1X2, and the latter elements of the dependent

1L+1X2are weighted more heavily than the latter elements of

the independent X1. The parameter η2controls how quickly

the mixture moves from independent to dependent. The final

?=0θ?xt−?where L is the maximum lag and θ = (θ0,...,θL)?

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form for the prior on θ is then N(0, σ2Ω(η)), where Ω(η) =

V(η1)W(η2)V(η1) and η = (η1, η2)?.

Letˆθ be the ML estimate of the unconstrained DL co-

efficients and let Σ be the sample covariance matrix. For a

normal linear DLagM,ˆθ is N(θ, Σ), so the posterior for θ

conditional on η and σ is

θ |ˆθ,η,σ2∼ N

??1/σ2Ω(η)−1+ Σ−1?−1Σ−1ˆθ,

?1/σ2Ω(η)−1+ Σ−1?−1?

.

(1)

For a general linear DLagM, the posterior distribution for θ

may not be available in closed form, but it may be computed

through Gibbs sampling or other Markov chain Monte Carlo

methods (e.g., Carlin and Louis, 2000). We discuss such an

approach for our PM air pollution and mortality example, in

which the Ytare Poisson distributed daily mortality counts,

log(E[yt|x1,...,xt]) =?L

is Poisson.

The influence of the prior distribution in estimating θ

depends on the values of hyperparameters σ2and η =

(η1, η2)?. The hyperparameter σ2, the prior variance of θ0,

can be viewed as a tuning parameter determining the starting

point of the DL function. In practice there is little informa-

tion in the data to jointly estimate σ2and η. We therefore

assume σ2is ten times the estimated statistical variance of θ0

so that even for relatively large values of η, the prior has little

to no influence on the first few DL coefficients. We examine

sensitivity of BDLagM estimates to choice of σ in Section 5.

Rather than setting values for η = (η1, η2)?and directly de-

termining the influence of the prior, we let η = (η1, η2)?have

a discrete uniform prior on N1× N2, where N1and N2are

finite sets of possible values for η1 and η2. Then the poste-

rior distribution for θ can be defined as the weighted sum

p(θ |ˆθ) =?

probability density. Under the assumption thatˆθ ∼ N(θ,Σ),

the marginal posterior density of the hyperparameter η is

available in closed form. For a given η∗:

?=0θ?xt−?, and the likelihood forˆθ

ηp(θ |ˆθ,η)p(η |ˆθ), where p denotes a general

p(η∗|ˆθ) =

|σ2Ω(η∗)Σ−1+ I|−1/2exp

?

−1

?

2

ˆθ

??

Σ−1− Σ−1?

??

Σ−1+

1

σ2Ω(η∗)−1?−1

1

σ2Ω(η)−1?−1

Σ−1

?

?

ˆθ

?

?

η

|σ2Ω(η)Σ−1+ I|−1/2exp

−1

2

ˆθ

Σ−1− Σ−1?

Σ−1+

Σ−1

ˆθ

?. (2)

Sufficiently large ranges for N1 and N2 insure that the

data drive the strength or weakness of the prior distribution

and therefore the eventual smoothness of the estimated DL

function.

3. Bayesian DLagMs and Penalized Splines

Following the well-established connection between nonpara-

metric smoothing and Bayesian modeling (e.g., Silverman,

1985), we illustrate the relationship between normal linear

BDLagMs and p-spline DLagMs. We show that estimating

the normal linear DL function under model (1) is analogous

to fitting a p-spline to DL coefficients with penalty derived

from our prior. An advantage of this connection is that our

method of putting a prior directly on the coefficients may be

viewed as a transparent means for eliciting p-spline penalties,

which are otherwise difficult to relate to biological or other

prior knowledge.

Let θ = U γ, where U is a spline basis matrix and γ

is a vector of spline coefficients. Letˆθ be the ML esti-

mate of θ, and assume thatˆθ = U γ + ν, ν ∼ N(0,Σ), where

Σ is the estimated covariance matrix forˆθ. Under a p-

spline approach, we estimate γ by minimizing the criterion

(ˆθ − U γ)?Σ−1(ˆθ − U γ) + λγTDγ, where λ is a penalty pa-

rameter and D a positive semidefinite matrix (Eilers and

Marx, 1996; Ruppert et al., 2003).

To show the connection between minimizing this criterion

and estimating the BDLagM, (1), we reformulate the p-spline

in its Bayesian formˆθ |γ ∼ N(U γ,Σ) and γ ∼ N(0, Γ),

where Γ is the prior covariance matrix of γ. Because θ =

U γ, the prior on γ translates to prior θ ∼ N(0, U ΓU?). In

(1) we assume θ ∼ N(0, σ2Ω(η)), so we need Γ such that

U ΓU?= σ2Ω(η), or Γ(η) = R−1Q?σ2Ω(η)QR?−1where QR

is U’s qr-decomposition.

Underthis formulation

is, uptoa constant,

1

2γ?U?(U Γ(η)W?)−1U γ, and maximizing the log poste-

rior for γ is equivalent to minimizing the above criterion with

λ = 1 and D = U?(U Γ(η) W?)−1U (Silverman, 1985; Green

and Silverman, 1994). For a given value of the hyperparame-

ter η, the estimated DL coefficients are given by the posterior

mean U (U?Σ−1U + U?(U Γ(η)U?)−1U−1)−1U?Σ−1ˆθ, and the

equivalent degrees of freedom equal the trace of the smoother

matrix

X(XTΣ−1X

+

XT(XΓ(η)XT)−1X−1)XTΣ−1

(Ruppert et al., 2003).

Though a prior on DL coefficients may be translated to

a specific p-spline penalty, the spline approach requires that

the DL function follow a specific form, θ = U γ. For our air

pollution mortality example, we found that using a b-spline

basis with L + 1 degrees of freedom produced estimates of θ

identical to those from the BDLagM. In the following simula-

tion study, we compare BDLagMs to p-splines with penalties

unrelated to the prior.

the

−1

logposterior for

γ

2(ˆθ − U γ)?Σ−1(ˆθ − U γ) −

4. Simulation Study

We conducted a simulation study to compare BDLagMs with

four methods for estimating DL functions—unconstrained,

polynomial, p-splines with penalty parameter chosen by GCV,

and p-splines estimated with REML. We generated data un-

der 25 different sets of true DL coefficients, including examples

for which coefficients do not decrease to zero and smoothness

does not increase with lag. We categorize the DL functions

by four characteristics: (1) shape—decaying exponential (E),

step function (St), or gamma distribution (G); (2) latency—

0 or 2, the number of initial coefficients equal to zero; (3)

oscillation—as described by (−1)?mod 2, to mimic mortality

displacement; and (4) maximum nonzero lag−7 or 14, the lag

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by which the coefficients are less than 0.01. We also considered

a null DL function with all zero coefficients. All DL functions

included current day (? = 0). We set L = 14 as in the sub-

sequent air pollution mortality example. Except for the null

model, all the DL functions were normalized so the sum of

squares of the DL coefficients is 1. We refer to the nonnull

functions by [Shape]o([latency], [max lag]), where the super-

script indicates oscillation.

Under each of the 25 scenarios, we generated 500 outcome

series yt from the model yt= δ?14

i.i.d. N(0,1), and δ is a constant to balance signal and noise.

For the exposure series xt we used mean centered PM10 for

1996 from Chicago, Illinois because there were no missing ob-

servations and the autocorrelation is similar to what we ex-

perience when estimating the association between PM10and

mortality for Chicago for 1987–2000. For simplicity we take

the ?tto be independent N(0, 1), noting that our simulations

still apply to situations in which the ?tare autocorrelated be-

cause application of an appropriate linear filter will result in

a new DLagM with independent normal errors. We set δ =

0.25 to generate moderate evidence for a total effect,?θ?,

in nonnull models (we empirically determined that δ = 0.25

generates ytsuch that the t-statistic for the ML estimate for

?

generate strong evidence for total effect (we empirically de-

termined that δ = 0.475 generates ytsuch that the t-statistic

for the ML estimate for?

each simulated data set we compared the DL functions un-

der five methods: (1) unconstrained ML; (2) the proposed

Bayes’ method (Bayes) using the normal posterior as in (1);

(3) ML with a polynomial of degree four (Poly); (4) a pe-

nalized spline with penalty chosen by GCV (GCV); and (5)

a penalized spline estimated with REML (REML). We also

considered estimating the DL function using an AR-1 model.

With the exception of the null model and St0(2, 14), the AR-1

model was not competitive, and was substantially worse when

the DL function oscillates then goes to zero.

Figure 1 shows the estimated DL functions (white) av-

eraged across the 500 simulations with the 95% confidence

bands (gray) for 24 of the true DL functions (black) (results

not pictured for null model). Results are reported for δ =

0.25. Visual inspection of this figure indicates that the BD-

LagM performs consistently well and estimates the true DL

function with narrower confidence bands than other methods.

To quantify the comparison, we summarize the mean

squared errors of the estimated total effect (?θ?) and DL

coefficients at lags 0, 7, and 14 under the five estimation meth-

ods and for the 25 scenarios. Table 1 summarizes the results

for δ = 0.25. Results for δ = 0.475 are available in Web Ta-

ble 1. Mean squared errors are expressed as percentages of

the mean squared error of the corresponding unconstrained

ML estimates. Values smaller than 100 favor the proposed

estimation methods with respect to unconstrained ML.

When the DL function decreases to zero, BDLagM is 10 to

15% better at estimating the total effect than ML, whereas

Poly, GCV, and REML perform comparably to ML. Results

are similar for δ = 0.25 and δ = 0.475. The better performance

of the Bayesian method with respect its competitors is mainly

due to its greater flexibility in estimating the DL coefficients

at the longer lags. Bayes is consistently 20–30% better than

ML for lag 0; GCV and REML may be substantially better or

?=0θ?xt−?+ ?t where ?t ∼

?θ? is approximately two). Similarly we set δ = 0.475 to

?θ? is approximately four). For

substantially worse. However, Bayes consistently outperforms

the others in estimating the lag 7 and the lag 14 coefficients

for scenarios in which the coefficients go to zero by lag 7 or 14.

When the BDLagM is misspecified and the DL coefficients do

not decrease smoothly to zero, performance of the BDLagM is

less predictable. Bayes may estimate the total effect only 5%

worse than ML (and Poly and REML), or nearly 15% better

(superior to Poly, GCV, REML).

Mortality counts are often modeled with Poisson log-linear

regression, so we also examine how our results extend to

the Poisson case. We simulated data from Yt∼ Poisson(µt),

log(µt) = log(100) + Σ?=14

by 100 were determined empirically to approximate Chicago

mortality levels in 1996. For each set of DL coefficients, we

generated 1000 mortality series. We estimated the posterior

distribution for θ two ways—using (1) (approximatingˆθ as

normal) or a Gibbs sampler. Web Table 2 compares the mean

squared errors of the total effects. The errors are comparable,

suggesting that the simulation results for normal outcomes

are not necessarily misleading for Poisson outcomes.

?=0xt−?θ?/100. The offset and division

5. Application to Particulate Matter Air Pollution

and Mortality

In this section, we apply BDLagMs to daily time series of

PM with aerodynamic diameter less than 10 microns (PM10)

and nonaccidental deaths for Chicago, Illinois for the period

1987–2000. The data were collected from publicly available

sources as part of the NMMAPS. NMMAPS contains daily

time series of age classified mortality, temperature, dew point,

and PM10 for 109 U.S. cities from 1987 to 2000. We ana-

lyzed the time series for Chicago because it is the largest U.S.

city in NMMAPS with few missing PM10values. Additional

details regarding NMMAPS data assembly are available at

http://www.ihapss.jhsph.edu/ and are discussed in previ-

ous NMMAPS analyses (Samet, Zeger, Dominici, Curriero,

Dockery, Schwartz, and Zanobetti, 2000; Samet, Zeger, Do-

minici, Schwartz, and Dockery, 2000; Dominici et al., 2003).

Poisson log-linear regression is frequently used to estimate

the association between day-to-day variations in mortality

counts and day-to-day variations in ambient air pollution lev-

els. We accordingly assume that the mortality in Chicago on

day t, t = 1,...,5114, is a Poisson random variable Ytwith

expectation E[Yt] = µt. As above, we let θ = (θ0,...,θL)?

be the unknown DL coefficients we wish to estimate. We let

xtdenote the PM10time series and for t > L we let xtde-

note the length L + 1 vector of lagged PM10values (xt,...,

xt−L)?.

Multisite time series studies of single day exposure PM10

and mortality have found strong evidence of an association

between PM10 at lags l = 0, 1, and 2 and daily mortality

(e.g., Zmirou et al., 1988; Burnett, Cakmak, and Brook, 1998;

Katsouyanni et al., 2001; Dominici et al., 2003); single city

studies with DLagMs have similarly found the largest effects

in the first seven lags (e.g., Schwartz, 2000; Zanobetti et al.,

2003; Goodman et al., 2004). Though lags beyond two weeks

may have some influence on daily mortality (e.g., mortality

displacement), it is unlikely that lags beyond 2 weeks have

substantial influence on mortality compared to lags less than

2 weeks (Zanobetti et al., 2003). Models containing lags be-

yond 2 weeks are additionally difficult to estimate because

long-term averages of PM10 have strong seasonal variation.

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Bayesian Distributed Lag Models

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Figure 1.

unconstrained ML, the proposed Bayesian method (Bayes), ML with a polynomial of degree four (Poly), a penalized spline

with penalty chosen by GCV (GCV), and a penalized spline estimated with REML (REML). Outcome series were simulated

under moderately strong evidence for the sum of the DL coefficients (δ = 0.25).

Mean estimated DL functions (white) and 95% posterior bands (gray) under five estimation methods—

We set L = 14 to capture the majority of short-term effects

of PM10on mortality without confounding estimation of DL

coefficients with seasonal trends in mortality.

When estimating air pollution health effects from time se-

ries studies it is important to account for potential time-

varying confounders such as weather, seasonality, and in-

fluenza epidemics (e.g., Schwartz, 1993; Samet et al., 1998;

Braga, Zanobetti, and Schwartz, 2000; Samoli et al., 2001;

Bell, Samet, and Dominici, 2004; Dominici, McDermott, and

Hastie, 2004; Peng, Dominici, and Louis, 2005; Welty and

Zeger, 2005). We let ztdenote the vector of time-varying co-

variates to include in the model, and we specify ztas in pre-

vious NMMAPS analyses (Dominici et al., 2003). The exact

specification is documented in the associated R code, availa-

ble at http://www.ihapss.jhsph.edu/software/BayesDLM/.

Our goal is to estimate the DL coefficients θ as part of the

generalized linear model

log(µt) = x?

tθ + z?

tβ. (3)

The estimate for 1000 × θ?corresponds to the percentage

increase in daily mortality associated with a 10µg/m3increase

in PM10at lag ?, and 1000 ×?14

increase in PM10at lags ? = 0,...,14.

Bayesian estimation of the generalized linear model in (3)

with our proposed prior for the DL coefficients θ requires two

?=0θ?corresponds to the per-

centage increase in daily mortality associated with a 10µg/m3