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Heat Effects on Mortality in 15 European Cities

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Epidemiologic studies show that high temperatures are related to mortality, but little is known about the exposure-response function and the lagged effect of heat. We report the associations between daily maximum apparent temperature and daily deaths during the warm season in 15 European cities. The city-specific analyses were based on generalized estimating equations and the city-specific results were combined in a Bayesian random effects meta-analysis. We specified distributed lag models in studying the delayed effect of exposure. Time-varying coefficient models were used to check the assumption of a constant heat effect over the warm season. The city-specific exposure-response functions have a V shape, with a change-point that varied among cities. The meta-analytic estimate of the threshold was 29.4 degrees C for Mediterranean cities and 23.3 degrees C for north-continental cities. The estimated overall change in all natural mortality associated with a 1 degrees C increase in maximum apparent temperature above the city-specific threshold was 3.12% (95% credibility interval = 0.60% to 5.72%) in the Mediterranean region and 1.84% (0.06% to 3.64%) in the north-continental region. Stronger associations were found between heat and mortality from respiratory diseases, and with mortality in the elderly. There is an important mortality effect of heat across Europe. The effect is evident from June through August; it is limited to the first week following temperature excess, with evidence of mortality displacement. There is some suggestion of a higher effect of early season exposures. Acclimatization and individual susceptibility need further investigation as possible explanations for the observed heterogeneity among cities.
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ORIGINAL ARTICLE
Heat Effects on Mortality in 15 European Cities
Michela Baccini,
a,b
Annibale Biggeri,
a,b
Gabriele Accetta,
b
Tom Kosatsky,
c
Klea Katsouyanni,
d
Antonis Analitis,
d
H. Ross Anderson,
e
Luigi Bisanti,
f
Daniela D’Ippoliti,
g
Jana Danova,
i
Bertil Forsberg,
i
Sylvia Medina,
j
Anna Paldy,
k
Daniel Rabczenko,
l
Christian Schindler,
m
and Paola Michelozzi
g
Background: Epidemiologic studies show that high temperatures
are related to mortality, but little is known about the exposure-
response function and the lagged effect of heat. We report the
associations between daily maximum apparent temperature and
daily deaths during the warm season in 15 European cities.
Methods: The city-specific analyses were based on generalized
estimating equations and the city-specific results were combined in
a Bayesian random effects meta-analysis. We specified distributed
lag models in studying the delayed effect of exposure. Time-varying
coefficient models were used to check the assumption of a constant
heat effect over the warm season.
Results: The city-specific exposure-response functions have a V shape,
with a change-point that varied among cities. The meta-analytic esti-
mate of the threshold was 29.4°C for Mediterranean cities and 23.3°C
for north-continental cities. The estimated overall change in all natural
mortality associated with a 1°C increase in maximum apparent temper-
ature above the city-specific threshold was 3.12% (95% credibility
interval 0.60% to 5.72%) in the Mediterranean region and 1.84%
(0.06% to 3.64%) in the north-continental region. Stronger associations
were found between heat and mortality from respiratory diseases, and
with mortality in the elderly.
Conclusions: There is an important mortality effect of heat across
Europe. The effect is evident from June through August; it is limited
to the first week following temperature excess, with evidence of
mortality displacement. There is some suggestion of a higher effect
of early season exposures. Acclimatization and individual suscepti-
bility need further investigation as possible explanations for the
observed heterogeneity among cities.
(Epidemiology 2008;19: 711–719)
The impact of weather on human health is a matter of
increasing concern, especially in light of climate change
and the 2003 heat wave in Western Europe.
1,2
Climatologists
forecast that temperature across Europe will rise over coming
decades and the frequency of periods characterized by ex-
tremely high temperatures will double.
3
Optimal health pro-
tection requires an understanding of the nature of the effect of
weather conditions on health.
High temperatures have been examined in relation to
natural (nonaccidental) mortality, to certain specific causes of
death, and to other health effects such as hospitalizations.
1,4
Time-series studies of the effects of heat on mortality have
been conducted for several cities in the United States and in
various European areas.
5
The association between tempera-
ture and mortality has often been shown as a J- or V-shaped
function, with the lowest mortality rates at moderate temper-
atures and rising progressively as temperatures increase or
decrease.
6
Variations in the temperature at which minimum
mortality occurs may reflect population differences in accli-
matization.
6,7
In epidemiologic studies of the effects of heat, temper-
ature has been generally used as the exposure of interest,
sometimes with adjustment for humidity. More recently,
several studies have considered the effect on mortality of
apparent temperature, which is a thermal discomfort index
that combines air temperature and humidity.
7–9
The present work was developed within the PHEWE
project (Assessment and Prevention of acute Health Effects
of Weather conditions in Europe).
10
In this project, separate
analyses have been performed on the health effects of mete-
orologic conditions during warm and cold seasons. In the
present paper, we report findings concerning the association
between maximum apparent temperature and mortality dur-
ing the warm season, defined as the period from 1 April to 30
September. The choice of a 6-month season was made with
Submitted 26 July 2007; accepted 18 March 2008; posted 27 May 2008.
From the
a
Department of Statistics, University of Florence, Italy;
b
Biostatistic
Unit, CSPO-Scientific Institute of Tuscany, Florence, Italy;
c
WHO,
Regional Office for Europe, Rome, Italy;
d
Department of Hygiene and
Epidemiology, University of Athens Medical School, Greece;
e
Division
of Community Health Sciences, St. George’s, University of London, UK;
f
Epidemiology Unit, ASL “Citta di Milano,” Milan, Italy;
g
Department of
Epidemiology, Local Health Authority Roma E, Rome, Italy;
h
Depart-
ment of Epidemiology, 3rd Medical School, Charles University, Prague,
Czech Republic;
i
Occupational and Environmental Unit, Umea Univer-
sity, Umea Sweden;
j
Departement Sante-Environnement, Institut de
Veille Sanitaire, Bordeaux, France;
k
Department of Biology, Jozsef
Fodor National Center of Public Health, Budapest, Hungary;
l
Department
of Medical Statistics, National Institute of Hygiene, Warsaw, Poland; and
m
Institute of Social and Preventive Medicine of the University of Basel,
Switzerland.
Supported by EU contract QLK4-CT2001-00152 from the European Union
Fifth Framework Program, Quality of Life and management of living
resources, key action 4, Environment and Health.
Correspondence: Michela Baccini, Department of Statistics, University of
Florence, Italy, Viale Morgagni 59-50134 Florence, Italy. E-mail:
baccini@ds.unifi.it.
Copyright © 2008 by Lippincott Williams & Wilkins
ISSN: 1044-3983/08/1905-0711
DOI: 10.1097/EDE.0b013e318176bfcd
Epidemiology Volume 19, Number 5, September 2008 711
the aim of being able to detect the effect of early and late
warm-season exposures. Our primary purpose was to inves-
tigate the relationship between heat and mortality across
Europe, with particular attention to a comparison among
cities. We also explored the delayed effect of heat and the
assumption of a constant heat effect over the summer period.
METHODS
The effect of weather on mortality during the warm
season was explored in 15 cities: Athens, Barcelona, Budap-
est, Dublin, Helsinki, Ljubljana, London, Milan, Paris, Pra-
gue, Rome, Stockholm, Turin, Valencia, and Zurich. Data
were collected for the period 1990 –2000. Population size,
percentage of elderly (75 years) and study period are
reported in Table 1. The total population studied was about
30 million. The number of study years ranged from 5 in
Athens to 11 in Dublin, Helsinki, Milan, and Stockholm.
Mortality Data
All cities provided daily counts of deaths for all causes
excluding external causes (natural mortality, ICD-9: 1–799),
cardiovascular diseases (ICD-9: 390 459) and respiratory
diseases (ICD-9: 460 –519). Mortality counts were subdi-
vided by age groups (15– 64 yrs, 65–74 yrs, 75yrs). The
daily average number of deaths for all natural causes during
the warm season (April-September) ranges from 6.3 in
Ljubljana (263,290 inhabitants) to 149 in London (6,796,900
inhabitants). Daily averages range from 2.6 to 61.0 per day
for deaths from cardiovascular causes and from 0.4 to 23.7
for mortality due to respiratory diseases (Table 1).
Meteorologic and Air Pollution Data
All enrolled cities provided 3-hour meteorologic data
(air temperature and dew point temperature, wind speed, and
barometric pressure at sea level). We retrieved this informa-
tion from the nearest airport weather station.
We focused on the effect of apparent temperature on
mortality. Apparent temperature is a measure of relative
discomfort due to combined heat and high humidity. It was
developed by Steadman
11
on the basis of physiologic studies
on evaporative skin cooling. It can be calculated as a com-
bination of air temperature (temp) and dew point (dew),
according to the following formula
12
:
AT –2.653 0.994 temp 0.0153 (dew)2.
Daily values of apparent temperature, barometric pres-
sure, and wind speed were computed for each city from
3-hour measurements. For all cities except Barcelona, we
used daily maximum apparent temperature defined as the
maximum of 3-hour values. On Barcelona, we calculated the
daily average apparent temperature from the daily mean
values of temperature and dew point, given the lack of 3-hour
data. Sensitivity analyses using daily minimum apparent
temperature as exposure variable were performed.
A large variability in climate characteristics was ob-
served among cities. Daily average of maximum apparent
temperature during April-September ranged from 14.3°C in
Helsinki to 27.9°C and 29.5°C in Athens and Valencia,
respectively (Table 1).
TABLE 1. Population Size, Study Period and Number of Valid Days in the Period; Daily Average of Deaths for All Natural,
Cardiovascular, and Respiratory Causes; Descriptive Statistics (Mean, Minimum, and Maximum) of Daily Maximum Apparent
Temperature During the Warm Season (April–September)
City Population Study Period
Valid
Days
Average Daily Deaths by Cause During
the Warm Season
Maximum Apparent
Temperature; °C
Natural Cardiovascular Respiratory Mean Min Max
Athens 3,188,305 1992–1996 914 67.5 32.6 4.2 27.9 7.9 41.6
Barcelona 1,512,971 1992–2000 1182 35.9 13.0 3.1 23.3 6.5 36.9
Budapest 1,797,222 1992–2001 1822 71.0 34.9 2.2 21.9 0.2 38.8
Dublin 481,854 1990–2000 2013 11.4 4.9 1.4 14.7 1.5 28.5
Helsinki 955,143 1990–2000 2013 17.1 8.2 1.4 14.3 3.7 32.8
Ljubljana 263,290 1992–1999 1302 6.3 2.6 0.4 20.1 1.7 35.4
London 6,796,900 1992–2000 1485 149.0 61.0 23.7 18.1 1.5 35.2
Milan 1,304,942 1990–2000 1769 26.3 9.9 1.7 25.4 2.7 40.8
Paris 6,161,393 1991–1998 1458 115.7 34.8 7.7 19.7 1.5 39.4
Prague 1,183,900 1992–2000 1628 34.9 20.3 1.1 17.8 3.3 36.3
Rome 2,812,573 1992–2000 1638 52.8 20.9 2.6 26.1 5.9 40.5
Stockholm 1,173,183 1990–2000 1997 27.9 13.5 2.2 15.4 2.1 34.0
Turin 901,010 1991–1999 1519 19.1 7.9 1.0 23.4 4.2 45.8
Valencia 739,004 1995–2000 1094 14.6 5.3 1.4 29.5 10.6 44.9
Zurich 990,000 1990–1996 1272 11.6 5.2 0.6 19.0 0.7 35.2
PHEWE Study, 1990 –2000.
Baccini et al Epidemiology Volume 19, Number 5, September 2008
© 2008 Lippincott Williams & Wilkins712
Air pollution data coming from the urban monitoring
network were collected for each city. We used the maxi-
mum hourly value of nitrogen dioxide (NO
2
) as an indi-
cator of the overall daily air pollution level for all the
cities, except in Dublin, where we considered the daily
average level of black smoke.
A negligible percentage of isolated missing values (less
than 1% for most cities, with a maximum of 4% for Turin)
characterized meteorologic and air pollution data. We im-
puted these through the average of values observed in the
same month. For a few cities (Barcelona, Turin, and London),
meteorologic information was lacking for series of consecu-
tive days (1 or even 2 months). In this case, the missing
periods were not included in the analysis (Table 1). Sensitiv-
ity analyses excluding years with higher number of missing
measurements produced similar results (results not reported).
Rationale for the City-Specific Modeling
Approach
In the larger project, we have investigated the effect of
cold and heat separately. Restriction to 6-month seasons permit-
ted us to reduce the complexity of controlling for temporal
confounding and enabled us to use different model specifications
by season. A common a priori definition of warm season, from
1 April to 30 September, was adopted for all the cities.
As data from each city were represented by several
disjoint 6-month daily time series (clusters), we used a
generalized estimating equations (GEE) approach, which ex-
tends generalized linear models to the analysis of longitudinal
data, when the observations on different clusters can be
assumed to be independent and the observations within clus-
ters are correlated.
13
For each city, the data consisted of an
outcome variable (number of deaths) and several covari-
ates (confounders and apparent temperature), observed day
by day for each available summer. We assumed that obser-
vations during a single summer were correlated, while obser-
vations from different summer periods were independent. A
similar approach has been suggested by Schwartz and Dock-
ery
14
in the context of the analysis of short-term effects of air
pollution on health and recently by Hajat et al
15
in studying
the health effect of heat waves in 3 major European cities.
Other examples are in the papers by Fouillet et al
16
and by
Gorjanc et al.
17
Since the number of clusters is mostly lower than the
number of observations within clusters, specifying the appro-
priate autocorrelation structure is a relevant point to assure
consistency of the standard error estimates.
18
We established
the correct specification of the error structure taking advan-
tage of an exploratory analysis based on dynamic models.
19
The dynamic models are linear regression models with lagged
regressors and correlated errors. To highlight a common
autocorrelation structure, we defined dynamic models with
different correlations between errors and, for each season and
for each city, we employed an automatic method for the best
model selection. The results suggested that we should use a
first order autocorrelation structure. We then used the model-
based variance estimator, as recommended in the presence of
few large clusters.
13
City-Specific Model Specification
For each city, we specified a GEE model, assuming the
dependent variable was Poisson distributed. Confounders
included in the models were dummy variables for holidays,
day of the week and calendar month; linear and quadratic
terms to pick up long-term time trend; linear terms for
barometric pressure (lag 0 –1) and wind speed. Air pollution
was found to confound the effect of high temperature.
20,21
We adjusted for maximum hourly NO
2
concentrations (lag
0 –1). The choice of NO
2
as the air pollution indicator was
based on comparability, availability, and completeness of
daily measurements among cities.
Exposure-Response Relationship
We studied the effect of lag 0 –3 exposure (the average
of the current and the previous 3 days maximum apparent
temperature).
First, we used a flexible parametric approach to de-
scribe the exposure-response curve, including in the model a
cubic regression spline for apparent temperature with 1 knot
every 8°C.
22
Second, to summarize and to address heteroge-
neity, we further described the relationship between apparent
temperature and mortality during the warm season by 2 linear
terms constrained to join at a common point, which we call
the threshold. The threshold is the value of apparent temper-
ature, which corresponds to a change in the effect estimate.
For a V-shaped curve, this is the value of apparent tempera-
ture associated with the minimum mortality rate. The city-
specific thresholds were obtained by the maximum likelihood
approach proposed by Muggeo.
23
The slopes above the
breakpoint were used as effect estimates.
A drawback of this method is that threshold estimation
can be strongly dependent on the algorithm starting point.
23
We used the same starting point for all the cities to avoid post
hoc data dredging, but a sensitivity analysis showed that in
our data the threshold estimate is substantially independent of
the starting point.
Delayed Effect of Exposure
The lagged effect of daily maximum apparent temper-
atures above the city-specific threshold was investigated up to
40 days using a constrained distributed-lag model.
24
We
assumed that all coefficients from lag 0 to lag 40 fell on a
curve of a 5th-order polynomial, to obtain a reasonably
flexible distribution of lags and parsimony in the number of
parameters estimated. We considered alternative constraint
definitions and performed analyses leaving the current day
effect unconstrained to check the robustness of estimates to
Epidemiology Volume 19, Number 5, September 2008 Heat-Related Mortality in Europe
© 2008 Lippincott Williams & Wilkins 713
outlying immediate effects. The various approaches produced
negligible differences (results not shown).
Time-Varying Effect of Maximum Apparent
Temperature
We relaxed the season definition extending our GEE
model to allow a time-varying effect of lag 0 –3 maximum
apparent temperature during the warm season. We assumed
that the effect of exposure above the threshold was smooth over
time. We used sine and cosine terms to describe this pattern
25
:
t
0
k0
1
1ksin
2
t
183 2k
k0
1
2kcos
2
t
183 2k
where tindexes time, from 1 April to 30 September (t1,
2, . . . , 183),
t
is the effect of maximum apparent temper-
ature and (
0
,
10
,
11
,
20
,
21
) are unknown coefficients to
be estimated. This analysis provided a way for checking
whether the slope above the threshold is constant over the
warm season. At the same time, it gave information about the
effective period when the effect is significant.
All city-specific analyses were conducted using
STATA/SE 9.0 (StataCorp, College Station, TX) and R
Software 1.2.1 (Team R Development Core).
Combined Analysis
City-specific results were pooled into 2 groups defined
on the basis of meteorologic and geographic criteria
10
:
1. “Mediterranean” cities: Athens, Rome, Barcelona, Valen-
cia, Turin, Milan, and Ljubljana.
2. “north-continental” cities: Prague, Budapest, Zurich,
Paris, Helsinki, Stockholm, London, and Dublin.
The aim was to control part of the heterogeneity among
cities and to compare the 2 regions. The definition of the
geographic subgroups was done a priori.
FIGURE 1. Regression splines (pointwise 95% confidence bands) describing, on log scale, the adjusted relationship between daily
maximum apparent temperature (lag 0–3) and natural mortality in 15 European cities: Athens, Barcelona (mean apparent
temperature), Budapest, Dublin, Helsinki, Ljubljana, London, Milan, Paris, Prague, Rome, Stockholm, Turin, Valencia and Zurich.
Baccini et al Epidemiology Volume 19, Number 5, September 2008
© 2008 Lippincott Williams & Wilkins714
We obtained overall exposure-response curves fitting
GEE regression models with a cubic regression spline for
maximum apparent temperature (with one knot every 8°C) on
the pooled data sets.
22
We did the analysis separately for
Mediterranean and north-continental cities. Fixed city-spe-
cific intercepts and interaction terms between confounders
and city indicators were introduced in the model. We as-
sumed independence among years and first-order autocorre-
lation structure within season.
To produce regional averages of city-specific estimates
of the effect parameters, we specified Bayesian hierarchical
models.
26
Let
ˆ
1
, ... ,
ˆ
c
, ... ,
ˆ
C
be the vectors of the
city-specific estimates of interest and V
1
,...,V
c
,...,V
C
the
corresponding variance-covariance matrix estimates. In com-
bining results from the distributed lag models and from the
time-varying models,
ˆ
c
is a vector of model parameters. In
pooling estimates of threshold or slope,
ˆ
c
is a scalar. A multi-
variate Gaussian distribution with unknown mean vector
c
and
variance-covariance matrix V
c
was assumed for each
ˆ
c
:
ˆc
c
ind N
c,Vc
and a multivariate normal distribution with common mean
vector and unknown variance-covariance matrix was as-
sumed on
c
:
c
,
ind N
c,
models the intercity variability. Vague proper priors were
elicited on hyper-parameters. Inference was based on an
algorithm proposed by Everson and Morris, implemented in
the TLNise library of R software or on MCMC methods with
WinBUGS 14.
27
RESULTS
The relationship between maximum apparent tempera-
ture (lag 0 –3) and log mortality rates were Vor Jshaped for
most cities. This indicated an excess of risk for exposures to
apparent temperature above a threshold that varies among
cities (Fig. 1).
We combined the city-specific curves except Barcelona,
where the exposure was defined in terms of daily mean apparent
temperature. The curves in Figure 2 represent the overall esti-
mate of the exposure-response relationship, separately for Med-
iterranean and north-continental cities. The pooled curves had a
minimum around higher apparent temperatures and the effect of
heat appeared slightly stronger in Mediterranean cities than in
north-continental cities. When we used minimum apparent tem-
perature as the exposure indicator, the combined curve was
similar in shape, but, as expected, with a minimum around lower
apparent temperature values (Fig. 2).
We report the heat effect as percent change in mortality
associated with a 1°C increase in maximum apparent tempera-
ture above the city-specific threshold. City-specific and overall
meta-analytic estimates of thresholds and percent change are
reported in Table 2. For Ljubljana, Stockholm, and Zurich
estimated thresholds were just below 22°C whereas for Athens,
Milan, and Rome estimates were over 30°C. The overall meta-
analytic value of the threshold was 29.4°C (95% credibility
interval CrI兴⫽25.7 to 32.4) for Mediterranean cities (exclud-
ing Barcelona) and about 6 degrees lower for North-continental
FIGURE 2. Fixed effects meta-ana-
lytic curves (pointwise 95% confi-
dence bands) describing, on log
scale, the adjusted effect of daily
maximum (top) and daily mini-
mum (bottom) apparent tempera-
ture at lag 0–3 on natural mortality.
The left panel illustrates meta-ana-
lytic curves for Mediterranean cities
(excluding Barcelona). The right
panel shows the same curves for
north-continental cities.
Epidemiology Volume 19, Number 5, September 2008 Heat-Related Mortality in Europe
© 2008 Lippincott Williams & Wilkins 715
cities (23.3°C; 22.5 to 24.0). The posterior distributions in
Figure 3A indicate larger heterogeneity in thresholds among
Mediterranean cities than among north-continental cities. Heter-
ogeneity among Mediterranean cities remained high even if
Ljubljana was removed, with the city-specific threshold esti-
mates ranging in this case between 27.0°C (Turin) and 32.7°C
(Athens). Overall meta-analytic percent change per degree of
above-threshold apparent temperature equaled 3.1 (0.6 to 5.7)
and 1.8 (0.1 to 3.6) for Mediterranean and north-continental
cities, respectively. Heterogeneity was larger among Mediterra-
nean cities (Fig. 3B).
Meta-analytic estimates of percent change in mortality
per degree of above-threshold apparent temperature by cause
and age class are reported in Table 3. We estimated different
city-specific thresholds for each cause of death, which were
close to those obtained for all natural mortality (not reported).
The overall meta-analytic estimates of percent change in
cardiovascular mortality per degree of above-threshold tem-
perature were 3.7 (0.4 to 7.0) for Mediterranean cities and 2.4
(0.1 to 5.3) for north-continental cities. Higher associations
were found between heat and mortality due to respiratory
diseases, with estimated percent changes equal to 6.7 (2.4 to
TABLE 2. Regional Meta-Analytic Estimates and City-Specific Estimates of Threshold and
Percent Change in Natural Mortality Associated With a 1°C Increase in Maximum
Apparent Temperature Above the City-Specific Threshold
Threshold (°C) (95% CrI/CI)
a
% Change (95% CrI/CI)
a
Region
North-continental 23.3 (22.5 to 24.0) 1.84 (0.06 to 3.64)
Mediterranean 29.4
b
(25.7 to 32.4) 3.12 (0.60 to 5.72)
City
Athens 32.7 (32.1 to 33.3) 5.54 (4.30 to 6.80)
Barcelona 22.4
c
(20.7 to 24.2) 1.56 (1.04 to 2.08)
Budapest 22.8 (21.9 to 23.7) 1.74 (1.47 to 2.02)
Dublin 23.9 (20.7 to 27.1) 0.02 (5.38 to 5.65)
Helsinki 23.6 (21.7 to 25.5) 3.72 (1.68 to 5.81)
Ljubljana 21.5 (15.0 to 28.0) 1.34 (0.32 to 2.37)
London 23.9 (22.6 to 25.1) 1.54 (1.01 to 2.08)
Milan 31.8 (30.8 to 32.8) 4.29 (3.35 to 5.24)
Paris 24.1 (23.4 to 24.8) 2.44 (2.08 to 2.80)
Praha 22.0 (20.4 to 23.6) 1.91 (1.39 to 2.44)
Rome 30.3 (29.8 to 30.8) 5.25 (4.57 to 5.93)
Stockholm 21.7 (18.2 to 25.3) 1.17 (0.41 to 1.94)
Turin 27.0 (25.2 to 28.9) 3.32 (2.53 to 4.13)
Valencia 28.2 (23.7 to 32.7) 0.56 (0.35 to 1.47)
Zurich 21.8 (16.5 to 27.0) 1.37 (0.49 to 2.25)
a
95% credibility interval for regional meta-analytic estimates and 95% confidence interval for city-specific estimates.
b
Excluding Barcelona.
c
Mean apparent temperature.
0 1020304050
0.0 0.2 0.4 0.6 0.8 1.0 1.2
Threshold Heterogeneity
Mediterranean Cities
North−Continental Cities
0 10203040
0.00 0.05 0.10 0.15 0.20
FIGURE 3. Posterior distribution of the het-
erogeneity variance for (A) threshold and
(B) slope above the threshold among
Mediterranean cities (excluding Barce-
lona) and north-continental cities. Values
of the posterior density function on y-axis.
Baccini et al Epidemiology Volume 19, Number 5, September 2008
© 2008 Lippincott Williams & Wilkins716
11.3) and 6.1 (2.6 to 11.1) for Mediterranean and north-
continental cities, respectively.
The effect of heat was particularly large in the elderly.
For people aged 75 and older, we estimated that a 1°C
increase in maximum apparent temperature above the thresh-
old was associated with an increase in mortality for all natural
causes of 4.2% for the Mediterranean region and of 2.1% for
the north-continental region. The same effect estimates were
8.1% and 6.6%, respectively, when only deaths for respira-
tory causes were considered.
Figure 4 shows the meta-analytic distributed lag curves
by geographic region. A strong effect of high apparent tem-
peratures was evident within the first week. The excess
mortality declined in subsequent days, became negative, then
returned to the level of baseline mortality-more slowly in the
Mediterranean cities, faster in the north-continental cities.
Table 4 summarizes the posterior distributions of the cu-
mulative effects calculated at various lags from unre-
stricted models. Cumulative effects were larger for the
Mediterranean cities than for the north-continental cities.
They declined after the first 5 days from the day of
above-threshold apparent temperature, becoming immedi-
TABLE 3. Overall Meta-Analytic Percent Changes (95%
Credibility Intervals) in Mortality for All Natural,
Cardiovascular, and Respiratory Causes, in All Ages and by
Age Group, Associated With a 1°C Increase in Maximum
Apparent Temperature Above the City-Specific Threshold
Mediterranean Cities North-Continental Cities
Age; yrs % Change (95% CrI) % Change (95% CrI)
Natural mortality
All 3.12 (0.60 to 5.73) 1.84 (0.06 to 3.64)
15–64 0.92 (1.29 to 3.13) 1.31 (0.94 to 3.72)
65–74 2.13 (0.42 to 4.74) 1.65 (0.51 to 3.87)
754.22 (1.33 to 7.20) 2.07 (0.24 to 3.89)
Cardiovascular mortality
All 3.70 (0.36 to 7.04) 2.44 (0.09 to 5.32)
15–64 0.57 (2.47 to 3.83) 1.04 (2.20 to 4.92)
65–74 1.92 (1.49 to 5.35) 1.50 (1.12 to 4.62)
754.66 (1.13 to 8.18) 2.55 (0.24 to 5.51)
Respiratory mortality
All 6.71 (2.43 to 11.26) 6.10 (2.46 to 11.08)
15–64 1.54 (3.68 to 7.22) 3.02 (1.55 to 7.42)
65–74 3.37 (1.46 to 8.22) 3.90 (0.16 to 8.92)
758.10 (3.24 to 13.37) 6.62 (3.04 to 11.42)
TABLE 4. Meta-Analytic Cumulative Percent Changes (95%
Credibility Intervals) in Natural Mortality Associated With a
1°C Increase in Maximum Apparent Temperature Above the
City-Specific Threshold
Mediterranean Cities North-Continental Cities
Lag Days % Change (95% CrI) % Change (95% CrI)
0 2.25 (0.07 to 4.49) 1.25 (0.50 to 2.98)
3 3.12 (0.60 to 5.72) 1.84 (0.06 to 3.64)
5 3.00 (0.35 to 5.75) 1.28 (0.71 to 3.17)
10 2.57 (0.12 to 5.10) 0.94 (0.94 to 2.75)
15 1.88 (0.36 to 4.16) 0.79 (1.14 to 2.64)
20 1.38 (0.72 to 3.51) 0.71 (1.21 to 2.58)
25 1.01 (1.06 to 3.09) 0.65 (1.22 to 2.51)
30 1.26 (0.78 to 3.36) 0.51 (1.36 to 2.36)
0 10203040
−0.005 0.000 0.005 0.010 0.015 0.020
0 10203040
−0.005 0.000 0.005 0.010 0.015 0.020
FIGURE 4. Meta-analytic curves (pointwise 95% credibility
bands) describing the delayed effects of maximum apparent
temperature above the threshold up to a lag of 40 days for
Mediterranean and north-continental cities.
0510
1 30 61 91 122 153 183
0510
1 30 61 91 122 153 183
FIGURE 5. Mediterranean and north-continental meta-analytic
time-varying effects (pointwise 95% credibility bands) of max-
imum apparent temperature above the threshold on natural
mortality (lag 0–3).
Epidemiology Volume 19, Number 5, September 2008 Heat-Related Mortality in Europe
© 2008 Lippincott Williams & Wilkins 717
ately not significant for north-continental cities and after
15 days for Mediterranean cities.
The results of the time-varying coefficients analysis
indicated an effect of apparent temperature exposure above
the threshold between June and August. Evidence of a stron-
ger association between early above-threshold exposure and
total mortality was found, although there were a small num-
ber of days during which maximum apparent temperature
exceeded the threshold in April and May (Fig. 5).
DISCUSSION
Study Design and Modeling Issues
The exposure-response curves using daily maximum ver-
sus minimum apparent temperature were substantially similar in
shape although (of course) shifted. The minimum apparent
temperature curve was less precise and had a reduced slope
above the threshold. This result could indicate a misclassifica-
tion bias with the minimum apparent temperature: perhaps
minimum (usually overnight) temperature is a less sensitive
indicator of risk than the maximum apparent temperature.
We based our analysis on a GEE approach, assuming
independence of the observations from different summer
periods. This assumption implies the absence of long-term
harvesting-like phenomena; ie, we are excluding the possi-
bility that high mortality levels during one summer induce
low mortality levels in the next summer, and vice versa, thus
focusing on the effect of heat only within season.
For each city, the effect of heat was summarized by a
minimum risk threshold and a linear term describing the
effect of exposures exceeding the threshold. In principle, the
algorithm for segmented regression would allow more than
one threshold,
23
but the introduction of 2 cut-points would
have made comparison among cities more difficult (despite a
possible gain in terms of fitting).
The Muggeo algorithm for threshold estimation can
be unstable, especially in small sample or when cut-points
are not well defined.
23
In some studies, threshold estima-
tion has been avoided and the effect of heat calculated by
comparing the mortality rate at 2 different points over a
flexible curve that describes the mortality-temperature as-
sociation.
9
This approach provides effect estimates inde-
pendent from the threshold, but dependent on the 2 points
selected for comparison. This arbitrariness can be a draw-
back in comparing results from cities characterized by
different ranges of exposure.
We did not make the strong assumption of a common
threshold for all the cities, but instead obtained city-specific
thresholds. In the same way, we estimated different thresh-
olds for each cause of death, because in principle the mini-
mum of the exposure-response curves can be different for
different specific causes of death. In either situation, an
incorrect assumption of common threshold could produce
biased estimates of the slope. The same reasoning is in
principle valid also for the age-specific analyses, but in this
case estimating different thresholds for each age group is
problematic due to the small number of daily events among
younger people. This would worsen the performance of the
estimation algorithm.
Epidemiologic Issues
We investigated the effect of heat on a large scale
across a variety of climate conditions and socioeconomic
and demographic characteristics. We applied a standard-
ized methodology to data from 15 European cities, with a
gain of power and assessment of variability across city-
specific effect estimates.
We did not focus on heat wave episodes. Our results
provide evidence of an effect of heat on daily mortality.
Many investigators have reported V- or J-shaped associations
between temperature and mortality.
6,7,9,15
We confirmed
these findings.
Previous studies have shown that the temperature level
corresponding to the minimum mortality rate (threshold)
varies from city to city, and across different latitudes accord-
ing to the local climate.
6,7,28
We found that the apparent
temperature threshold in the Mediterranean cities was higher
than in the north-continental cities, indicating that residents
of north-continental cities are susceptible at lower values of
apparent temperature.
As far as the effect of exposures that exceed the
threshold is concerned, stronger percent changes in all natural
mortality and in respiratory and cardiovascular mortality
were observed in the Mediterranean region. Steeper slopes
were found for mortality for respiratory causes, consistent
with results of previous studies.
29
The associations with mortality were stronger after 74
years of age than earlier in life, suggesting that the elderly are
particularly vulnerable.
9,29,30
Knowledge of the lag time between exposure to ex-
treme weather conditions and negative health outcomes is
important for public health authorities and health care pro-
viders in developing prevention plans. The effect of high
apparent temperature is immediate both in Mediterranean and
north-continental cities. This result is consistent with findings
of previous studies indicating that the impact of heat on
mortality reaches its maximum in less than a week.
31,32
There is contradictory evidence as to whether the increase
of mortality is followed by a deficit that partly compensates for
the negative effect (harvesting). In some cases, a harvesting
effect is found to fully balance the observed excess, whereas in
others only part of the excess is compensated.
32,33
We found
evidence of harvesting in both the Mediterranean and north-
continental regions. The mortality displacement partially com-
pensates for the effect of heat observed during the first week
after exposure. In the Mediterranean cities, the harvesting phe-
nomenon is more prolonged, but in both regions the cumulative
effect at lag 25 is around 30% of the cumulative effect at lag 3.
Baccini et al Epidemiology Volume 19, Number 5, September 2008
© 2008 Lippincott Williams & Wilkins718
The evidence of harvesting suggests the presence of subgroups
of susceptible individuals for which heat precipitates deaths by a
few days to weeks.
An overall heat effect was clearly present from June to
August. However, the time-varying coefficients models sug-
gested a greater effect of earlier exposures. This evidence was
based on few observations (there were few days during the
first months of the warm season with a maximum apparent
temperature above the threshold), but was largely consistent
among cities. The stronger effect of early exposure could
have various explanations. From a physiologic point of view,
the human organism may react better to later heat exposure
due to acclimatization,
34
although this finding could also be
explained by a changing composition of the population at risk
over time, due to a harvesting-like phenomenon.
We adjusted for the confounding effect of air pollution.
The discrepancy between adjusted and unadjusted effect
estimates varied among cities. For example, in Athens, ad-
justment changed the percent increase in total mortality with
1°C rise in maximum apparent temperature from 6.2 to 5.5,
while in Stockholm, adjustment had a negligible effect.
Future studies should investigate possible effect modifiers
that could explain heterogeneity in threshold and slope esti-
mates, both between and within regions. Varying acclimatiza-
tion to weather changes could explain the observed heterogene-
ity.
35
However the observed differences among cities and
between regions could also reflect demographic, cultural, socio-
economic, and technological circumstances that produce differ-
ent proportions of susceptible persons in the enrolled cities.
21
In
this sense, the estimated exposure-response curves can be
considered a mixture of curves, each of which describes
the relationship between apparent temperature and mortal-
ity in a subgroup of the population. Also meteorologic and
geographic characteristics should be studied as possible
contributors to heterogeneity among cities, insofar as they
can modify mechanisms leading to acclimatization or in-
teract with temperature, modifying the effect of heat.
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Epidemiology Volume 19, Number 5, September 2008 Heat-Related Mortality in Europe
© 2008 Lippincott Williams & Wilkins 719
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