Content uploaded by Thomas Frei
Author content
All content in this area was uploaded by Thomas Frei on Mar 19, 2015
Content may be subject to copyright.
nature publishing group ORIGINAL CONTRIBUTIONS
INFLAMMATORY BOWEL DISEASE
1
© 2013 by the American College of Gastroenterology The American Journal of GASTROENTEROLOGY
see related editorial on page x
INTRODUCTION
Instrumental observations of global temperature evolution reveal
a pronounced warming during the past 150 years. One expres-
sion of this warming is the observed increase in the occurrence of
heat waves as it has been reported for the record-breaking central
European summer temperatures in 2003 ( 1 ).
Climate change a ects human health through multiple path-
ways, including direct e ects and indirect e ects that oper-
ate through changes in the range of disease vectors, water
quality, or air quality (increase of airborne pollen or new allergenic
pollen in certain regions) ( 2 ). e health impact of heat waves has
been reported several times during the past decades, not only in
European countries but all around the world. In 1999, ~ 500 deaths
were attributed to a heat wave in Chicago ( 3,4 ), and up to 30,000
to 70,000 additional deaths occurred during the European 2003
heat wave, particularly in France and Germany ( 5 – 7 ). An esti-
mated 7 % increase in all-cause mortality occurred in Switzer-
land during the months June to August 2003 ( 5 ). e increase in
mortality during heat waves is known to be higher in urban areas.
is might be caused by a more distinctive urban – rural contrast
with regard to heat stress related to a higher population density or
a nightly pronounced urban heat island e ect ( 8 ).
Although there have been several reports on increase in mortal-
ity, only scarce information exists on the impact of temperature
Heat Waves, Incidence of Infectious Gastroenteritis,
and Relapse Rates of Infl ammatory Bowel Disease:
A Retrospective Controlled Observational Study
Christine N. Manser , MD
1
, Michaela Paul , PhD
2
, Gerhard Rogler , MD, PhD
1
, Leonhard Held , PhD
2
,
4
and Thomas Frei , PhD
3
,
4
OBJECTIVES:
The objective of this study was to evaluate the effect of heat waves on fl ares of infl ammatory bowel
disease (IBD) and infectious gastroenteritis (IG).
METHODS: In this retrospective controlled observational study, data from 738 IBD and 786 IG patients
admitted to the University Hospital of Zurich in the years 2001 – 2005, as well as from 506 other
noninfectious chronic intestinal infl ammations, which were used as control, were collected.
Climate data were obtained from the Swiss Federal Offi ce for Meteorology and Climatology.
RESULTS: The presence of a heat wave increased the risk of IBD fl ares by 4.6 % (95 % confi dence interval
(CI): 1.6 – 7.4 % , P = 0.0035) and of IG fl ares by 4.7 % (95 % CI: 1.8 – 7.4 % , P = 0.0020) for every
additional day within a heat wave period. In the control group there was no signifi cant effect (95 %
CI: − 6.2 – 2.9 % , P = 0.53). Screening of alternative forms for the effect of heat waves suggested that
for IG the effect is strongest when lagged by 7 days (risk increase per day: 7.2 % , 95 % CI: 4.6 – 9.7 % ,
P < 0.0001), whereas for IBD no such transformation was required. Other formulations with additive
effects, interactions between heat waves and time of the year, and additional adjustments for daily
average temperature did not show any improvement in model fi t.
CONCLUSIONS: In this retrospective controlled observational study, we found a substantial increase in hospital
admissions because of fl ares of IBD and IG during heat wave periods. Whereas the effect on IG
is strongest with a delay of 7 days, the effect on IBD fl ares is immediate, suggesting different
mechanisms.
SUPPLEMENTARY MATERIAL is linked to the online version of the paper at http://www.nature.com/ajg
Am J Gastroenterol advance online publication, 13 August 2013; doi: 10.1038/ajg.2013.186
1
Division of Gastroenterology and Hepatology, Department of Internal Medicine, University Hospital , Zurich , Switzerland ;
2
Division of Biostatistics, Institute for
Social and Preventive Medicine, University of Zurich , Zurich , Switzerland ;
3
Federal Department of Home Affairs, Federal Offi ce of Meteorology and Climatology ,
Zurich , Switzerland ;
4
These authors contributed equally to this work . Correspondence: Thomas Frei, PhD , Federal Department of Home Affairs, Federal Offi ce of
Meteorology and Climatology , Kr ä hb ü hlstr. 58 , CH-8044 Zurich , Switzerland . E-mail: thomas.frei1@bluewin.ch
Received 9 October 2012; accepted 7 May 2013
The American Journal of GASTROENTEROLOGY VOLUME 104
|
XXX 2012 www.amjgastro.com
2
INFLAMMATORY BOWEL DISEASE
Manser et al.
increase on morbidity. In one French report, medical records of
726 patients aged >65 years who had been admitted to the emer-
gency department during August 2003 were reviewed. In 42
patients, heat-related illnesses were retrospectively diagnosed, of
which not even one had been diagnosed in the primary evalua-
tion ( 9 ). Another study identi ed cardiac troponin I as an inde-
pendent risk factor for severe myocardial damage among patients
with heat-related illnesses, associated with signi cantly decreased
1-year survival ( 10 ). A French nested cross-sectional study reported
an 8.8 % increase of self-reported health deterioration and a 7.8 %
increase of objective morbidity among the elderly a er the August
2003 heat wave ( 11 ).
However, information on the impact of heat waves on young
patients and typical young patients ’ illnesses, such as in ammatory
bowel disease (IBD), is rare. IBD has two major forms: ulcerative
colitis (UC) and Crohn ’ s disease (CD). ey are characterized by
relapsing in ammation of the gastrointestinal tract with potential
extraintestinal manifestations and are de ned by various clinical,
pathological, endoscopic, and radiologic features ( 12 ). A genetic
predisposition has been demonstrated to have an important e ect
on the development of IBD, especially CD ( 13 ). Although genetic
factors are believed to account for ~ 50 % of new cases of IBD, envi-
ronmental factors are also considered important for the etiology of
CD and UC ( 13 ).
Several studies have identi ed seasonal variation in the clinical
course of IBD patients. Some studies report an increase in relapses
during autumn and winter in CD patients ( 14 ), whereas other stud-
ies report a contrary result, suggesting an increase during spring
and summer ( 15 ). In UC studies claiming a seasonal impact, there
are con icting results as well, reporting both an increase during
spring and summer ( 16 ) and during winter ( 17 ).
In this retrospective study we evaluate the in uence of heat
waves on incidence of hospital admissions due to IBD. Within the
investigated time period, the accumulation of heat waves during
the 2003 summer gave us the opportunity to record several heat
waves to be compared with time periods without heat waves. As
there have been several reports on an in uence of the development
of IBD by enteric pathogens ( 18 ), and as there have additionally
been reports on the in uence of enteric microbial infections like
Salmonellosis or Campylobacteriosis by climate changes, we also
assessed the incidence of infectious gastroenteritis (IG). Other
noninfectious chronic intestinal in ammations (NIIs) have been
used as control group.
METHODS
Study population
All patients who were admitted to the University Hospital of
Zurich with symptoms leading to the nal diagnosis of IBD, IG,
and NII from 1 January 2001 through 31 December 2005 were eli-
gible for the study. Patients were selected from an administrative
database providing information about age, date of hospital entry,
and discharge. Furthermore, the database provided the Interna-
tional Classi cation of Diseases version 10 (ICD-10) ( 19 ) codes
for nal hospital diagnoses. Patients with CD (ICD-10: K50.0-9),
UC (ICD-10: K51.0-9), and NII (ICD-10: K52.0-9) as well as IG
(ICD-10: A00.0-A09.0) were enrolled. To validate the data, we
reviewed a random sample of 228 out of all 2,030 patients. Of this
sample, two ICD-10 codes were incorrectly encoded. As Zurich
and its environment are frequently visited by tourists, we also
reviewed the sample for hospital admissions of tourists. Only 1
out of the 228 reviewed patients was a tourist.
Climate data
Meteorological data were collected at the meteorological sta-
tion Z ü rich-Fluntern, 556 m above sea level. e air temperature
is measured 2 m above ground level according to the recom-
mendations of the World Meteorological Organization. e
meteorological data were collected with an automatic weather
station and aggregated on a daily basis for the study period
2001 – 2005.
Heat wave defi nitions
For this study we used the de nition of heat wave recommended
by the World Meteorological Organization, classifying any period
of 6 days with maximum temperature >5 ° C (9 ° F) above the daily
average maximum temperature to be a heat wave ( 20 ). To assess
a possible cumulative e ect of heat waves on hospital admissions,
we used the day within a heat wave period as a possible predictor
for disease incidence. Alternatively, a simple additive e ect of a
heat wave has also been investigated.
Statistical analysis
We employed Poisson regression ( 21 ) to compute relative risk
estimates for the e ect of heat waves on daily incidence of IBD,
IG, and NII, respectively. e 95 % pro le likelihood con dence
intervals (CIs) for the relative risk and associated P values based
on the likelihood ratio test statistic have been computed. We
adjusted our risk estimates for day-of-the-week e ects (with
7 categories, treating public holidays as Sundays), long-term
time trends (assumed to be linear), and yearly seasonal patterns
(assumed to follow a sine – cosine form) ( 22 ). e analysis is
automatically controlled for age, sex, and other patient-speci c
characteristics, similar to the self-controlled case series method
( 23,24 ), because the time-dependent exposure variables are com-
mon to all patients. With regard to the lagged e ect of heat waves,
we analyzed models considering a lagged e ect of a heat wave
on daily incidence of up to 14 days. To investigate whether the
heat wave e ect interacts with the time of the year, we included
an interaction between the heat wave predictor and the seasonal
pattern. Similarly, interactions between heat wave and sex and
between heat wave and age group ( < 25, 25 – 65, and > 65 years)
have been considered. Screening of alternative formulations
such as lagged e ects of heat waves and additional adjustments
for daily average temperature has been done using the Bayesian
information criterion. e Bayesian information criterion, a con-
sistent model selection criterion that penalizes model complexity
stronger than the Akaike information criterion, has been used to
avoid over tting ( 25 ). Statistical analyses were performed using
the R so ware ( 26 ).
© 2013 by the American College of Gastroenterology The American Journal of GASTROENTEROLOGY
3
INFLAMMATORY BOWEL DISEASE
Heat Waves ’ Impact on Infectious Gastroenteritis and IBD
RESULTS
During the study period 2001 – 2005, a total of 738 IBD patients
aged 15 – 94 years, 786 IG patients aged 0 – 96 years, as well as 506
patients aged 0 – 94 years su ering from NIIs were admitted to the
University Hospital of Zurich. A total of 17 heat waves, accord-
ing to the World Meteorological Organization de nition, were
identi ed in that period, with length of up to 19 days (30 May to
17 June 2003).
Results from several Poisson regression models are shown in
Table 1 . For each disease group (IBD, IG, NII), the estimated
e ect of heat waves adjusted for day-of-the-week, long-term time
trends, and seasonal pattern is given. ere is evidence for an
increase of IBD hospital admissions by 4.6 % (95 % CI: 1.6 – 7.4 % ,
P = 0.0035) for each additional day within a heat wave period.
Presence of a heat wave was estimated to increase the risk of IG
by 4.7 % for every additional day within a heat wave period (95 %
CI: 1.8 – 7.4 % , P = 0.002). In the control group there was no evi-
dence for a heat wave e ect (risk increase per day: − 1.4 % , 95 %
CI: − 6.2 to 2.9 % , P = 0.53). e alternative additive formulation
showed an increase of 34.4 % (95 % CI: 6.6 – 67.4 % ) and 34.8 %
(95 % CI: 7.3 – 67.3 % ) for IBD and IG, respectively, during heat
wave periods.
Table 2 shows that this additive formulation provided a slightly
inferior t according to the Bayesian information criterion.
Lagging the cumulative e ect of heat waves revealed that a 7-day
lag considerably improved the t for IG but not for IBD or NII
(see Supplementary Material online). For this lag, the risk increase
of IG hospital admissions was estimated to be 7.2 % (95 % CI:
4.6 – 9.7 % , P < 0.0001) for each additional day within a heat wave
period (compare Table 1) .
Formulations with additional adjustments for daily average
temperature did not show any improvement in model t (com-
pare Table 2 and Supplementary Material) online. Including
an interaction between the heat wave predictors and the sea-
sonal pattern did not change the results signi cantly ( P = 0.56
for IBD, P = 0.64 for IG, and P = 0.35 for NII; see also Table 2
and Supple mentary Material online). We also included an inter-
action between heat wave predictors and sex as well as heat wave
predictors and age groups. Regarding IBD ares, there was some
weak evidence for an interaction between heat wave predictors
and sex ( P = 0.029), with an increase to 7.5 % (95 % CI: 3.6 – 11.1 % )
for females (see Supplementary Material online). In this for-
mulation, the e ect for females decreases to 0.9 % (95 % CI: − 4.1
to 5.4 % ). ere was, however, no evidence for this interaction
Table 1 . Increase in hospital admissions during heat waves for
infl ammatory bowel disease (IBD), infectious gastroenteritis (IG),
and other noninfectious intestinal infl ammations (NIIs)
RR 95 % CI P value
IBD
Increase per day 1.046 1.016 – 1.074 0.0035
Increase (additive) 1.344 1.066 – 1.674 0.013
Increase per day (7-day lag) 1.016 0.983 – 1.047 0.34
IG
Increase per day 1.047 1.018 – 1.074 0.002
Increase (additive) 1.348 1.073 – 1.673 0.011
Increase per day (7-day lag) 1.072 1.046 – 1.097 < 0.0001
NII
Increase per day 0.986 0.938 – 1.029 0.53
Increase (additive) 1.085 0.794 – 1.448 0.60
Increase per day (7-day lag) 1.002 0.958 – 1.041 0.94
CI, profi le likelihood confi dence interval; RR, relative risk.
Table 2 . BIC for various models
Exposure IBD IG NIIs
BIC Δ BIC BIC Δ BIC BIC Δ BIC
– 2,929.5 1.0 3,167.2 19.6 2,390.7 0
Heat wave 2,928.5 0 3,165.1 17.5 2,397.8 7.1
Heat wave (additive) 2,930.9 2.4 3,168.2 20.6 2,397.9 7.2
Heat wave (7-day lag) 2,936.1 7.6 3,147.6 0 2,398.2 7.5
Average temperature 2,934.2 5.7 3,174.7 27.1 2,397.5 6.9
Heat wave + average temperature 2,935.7 7.2 3,170.5 22.9 2,404.0 13.3
Heat wave *season 2,942.4 13.8 3,179.2 31.6 2,410.7 20.0
Heat wave (additive) *season 2,945.2 16.6 3,183.0 35.4 2,409.3 18.7
Heat wave (7-day lag) *season 2,950.0 21.5 3,151.5 3.9 2,411.8
21.1
BIC, Bayesian information criterion; IBD, infl ammatory bowel disease; IG, infectious gastroenteritis; NII, noninfectious intestinal infl ammation.
Models with interaction terms between the heat wave variable and the seasonal pattern are indicated by the asterisk. All models include a day-of-the-week effect
(including holidays), a linear trend, and a seasonal component. The difference in BIC ( Δ BIC) is given in comparison with the best fi tting model.
The American Journal of GASTROENTEROLOGY VOLUME 104
|
XXX 2012 www.amjgastro.com
4
INFLAMMATORY BOWEL DISEASE
Manser et al.
for IG ( P = 0.52) or NII ( P = 0.75) (see Supplementary Material
online). Including an interaction between heat wave e ect and
age group also did not change the results signi cantly ( P = 0.92
for IBD, P = 0.65 for IG, and P = 0.58 for NII; see also Supple-
mentary Material online).
Admittance because of ares of IBD showed substantial day-of-
the-week variation with higher incidence on week days (compare
Figure 1) . A similar, but slightly less pronounced, day-of-the-week
pattern can be seen for IG and NII. Hospital admissions increased
by 14 % (95 % CI: 8 – 20 % , P < 0.0001) and 20 % (95 % CI: 13 – 28 % ,
P < 0.0001) in each year for IBD and NNI, respectively, but no such
evidence for a trend was seen for IG (95 % CI: − 7 to 3 % , P = 0.38).
ere was weak evidence for a yearly seasonal pattern peaking
in winter for IG ( P = 0.052), but not for IBD ( P = 0.70) and NNI
( P = 0.17).
DISCUSSION
Climate change a ects the health of populations in many
ways. Some impacts will become evident before others. Some
will occur via direct pathways (heat waves and death), and
others will occur via indirect pathways entailing distur bances
of natural ecological systems (e.g., mosquito population
range and activity or new aeroallergens). Some other impacts
might occur via a combination of both as suggested by this
study.
In this first study on the impact of climate change —
represented by an increasing number of heat waves — on
diarrheal diseases in industrialized countries, we identified
a cumulative effect of heat waves on hospital admissions
because of IBD and IG flares. After adjustment for tempo-
ral and seasonal trends, hospital admissions are estimated
to increase by 3.7 % and 5.0 % for IBD and IG, respectively,
for each additional day of a heat wave period. For IG admis-
sions, an even more pronounced increase of 7.7 % can be seen
if a 7-day delayed effect of heat waves is considered. This indi-
cates that the effect of heat waves on IG flares occurs with a
1-week delay, whereas it is immediate with respect to flares
of IBD.
What factor(s) could relate heat waves to ares of IBD and IG
and explain our ndings? On one hand, it is obvious that envi-
ronmental bacterial growth conditions are dependent on air, soil,
and water temperature. During a heat wave, changes in bacterial
composition of food, skin, soil, and water may occur. is has
never been investigated in much detail. However, recent research
suggests that temperature plays a crucial role for the expansion
of enterohemorrhagic Escherichia coli and other pathogenic
bacteria. For our ndings on infectious gastroenteritis, a sim-
ple explanation would be that heat waves favor the expansion of
potential pathogenic bacteria and / or viruses. is is supported
by the nding that the e ect of a heat wave was maximized by
a 7-day lag, suggesting infectious gastroenteritis requiring some
lag time to develop. A similar e ect was described in a study by
Zhang et al. ( 27 ) in 2008. ey reported on the e ect of weather
on the transmission of bacterial dysentery in China. Maximum
temperature 1 month before was one major risk factor for an
increase in the incidence of dysentery. However, a clear limita-
tion of our study preventing similar conclusions is the lack of
data to di erentiate between viral and bacterial intestinal infec-
tions for IG patients.
There are a number of further limitations of our study. Even
though we controlled for potential confounders as age and gen-
der, we were not able to control for all potential confounders,
e.g., smoking status. As smoking habits might change during
heat waves, and as these changed smoking habits might espe-
cially influence flares in IBD patients, this might be a poten-
tial confounder. Moreover, we were not able to adjust for other
time-dependent exposure variables, e.g., humidity. We also had
no detailed data on the patient-level exposure to heat, which
might run the risk of an ecological fallacy. Additional multi-
regional studies will be required to underline our findings.
Another limitation of the study is a potential risk of misclas-
sification of cases because of retrieving data from a database.
However, to quantify this risk, we reviewed ~ 10 % of the cases
and only in a very small portion a misclassification was found.
Nevertheless, generalizability of the results of this single-
center study to other regions still needs to be investigated.
Finally, there are potential limitations of our control group
6
5
4
3
2
1
Mon Tue Wed Thu
P value < 0.0001
Fri Sat Sun
6
5
4
3
2
1
Mon Tue Wed Thu
P value < 0.0001
Fri Sat Sun
6
5
4
3
2
1
Mon Tue Wed Thu
P value < 0.0001
Fri Sat Sun
IBD IG NII
Figure 1 . Day-of-the-week effects in hospital admissions because of fl ares of infl ammatory bowel disease (IBD), infectious gastroenteritis (IG), and other
noninfectious intestinal infl ammations (NIIs). Shown are relative risks (RR) with 95 % profi le likelihood confi dence intervals and overall P values. Sundays
are taken as reference category.
© 2013 by the American College of Gastroenterology The American Journal of GASTROENTEROLOGY
5
INFLAMMATORY BOWEL DISEASE
Heat Waves ’ Impact on Infectious Gastroenteritis and IBD
Study Highlights
WHAT IS CURRENT KNOWLEDGE
3 Heat waves can affect mortality, especially among the
elderly.
3 There are seasonal variations in the clinical course of
infl ammatory bowel disease (IBD).
3 Enteric microbial infections might additionally be
infl uenced by climate changes.
WHAT IS NEW HERE
3 Heat waves signifi cantly increase the risk of IBD fl ares
compared with controls.
3 Heat waves signifi cantly increase the risk of infectious
gastroenteritis compared with controls.
3 The impact of heat waves on infectious gastroenteritis is
strongest for a 7-day time lag.
REFERENCES
1 . S c h a r C , V i d a l e P L , L ü t h i D et al. e role of increasing temperature
variability in European summer heatwaves . Nature 2004 ; 427 : 332 – 6 .
2 . Frei T , Gassner E . Climate change and its impact on birch pollen quantities
and the start of the pollen season an example from Switzerland for the
period 1969 – 2006 . Int J Biometeorol 2008 ; 52 : 667 – 74 .
3 . Hartz DA , Golden JS , Sister C et al. Climate and heat-related emergencies
in Chicago, Illinois (2003 – 2006) . Int J Biometeorol 2012 ; 56 : 71 – 83 .
4 . N a u g h t o n M P , H e n d e r s o n A , M i r a b e l l i M C et al. Heat-related mortality
during a 1999 heat wave in Chicago . Am J Prev Med 2002 ; 22 : 221 – 7 .
5 . Grize L , Huss A , o m m e n O et al. Heat wave 2003 and mortality in
Switzerland . Swiss Med Wkly 2005 ; 135 : 200 – 5 .
6 . R o b i n e J M , C h e u n g S L , L e R o y S et al. Death toll exceeded 70,000 in
Europe during the summer of 2003 . C R Biol 2008 ; 331 : 171 – 8 .
7 . United Nations Environment Programme . Impact of Summer 2003 Heat
Wave in Europe . Environmental Alert Bulletin , 2004 .
8 . F i s c h e r E M , O l e s o n K W , L a w r e n c e D M . C o n t r a s t i n g u r b a n a n d r u r a l h e a t
stress responses to climate change . Geophys Res Lett 2012 ; 39 : 3 .
9 . Oberlin M , Tubery M , Cances-Lauwers V et al. Heat-related illnesses
during the 2003 heat wave in an emergency service . Emerg Med J 2010 ; 27 :
297 – 9 .
1 0 . H a u s f a t e r P , D o u m e n c B , C h o p i n S et al. Elevation of cardiac troponin I
during non-exertional heat-related illnesses in the context of a heatwave .
Crit Care 2010 ; 14 : R99 .
1 1 . L a r r i e u S , C a r c a i l l o n L , L e f r a n c A et al. F a c t o r s a s s o c i a t e d w i t h
morbidity during the 2003 heat wave in two population-based cohorts
of elderly subjects: PAQUID and ree City . Eur J Epidemiol 2008 ; 23 :
295 – 302 .
12 . Xavier RJ , Podolsky DK . Unravelling the pathogenesis of in ammatory
bowel disease . Nature 2007 ; 448 : 427 – 34 .
1 3 . R o g l e r G . U p d a t e i n i n ammatory bowel disease pathogenesis . Curr Opin
Gastroenterol 2004 ; 20 : 311 – 7 .
14 . Zeng L , Anderson FH . Seasonal change in the exacerbations of Crohn’s
disease . Scand J Gastroenterol 1996 ; 31 : 79 – 82 .
1 5 . A r a t a r i A , P a p i C , G a l l e t t i B et al. Seasonal variations in onset of symptoms
in Crohn’s disease . Dig Liver Dis 2006 ; 38 : 319 – 23 .
1 6 . B a i A , G u o Y , S h e n Y et al. S e a s o n a l i t y i n ares and months of births
of patients with ulcerative colitis in a Chinese population . Dig Dis Sci
2009 ; 54 : 1094 – 8 .
1 7 . M o u m B , A a d l a n d E , E k b o m A et al. Seasonal variations in the onset of
ulcerative colitis . Gut 1996 ; 38 : 376 – 8 .
18 . Sonnenberg A . Seasonal variation of enteric infections and in ammatory
bowel disease . In amm Bowel Dis 2008 ; 14 : 955 – 9 .
19 . World Health Organization . ICD-10: International Statistical Classi cation
of Diseases and Related Health Problems , Vol. 10th revision. ed. Geneva ,
1992 .
20 . Frich P , Alexander LV , Della-Marta P et al. Observed coherent changes in
climatic extremes during the second half of the twentieth century . Clim Res
2002 ; 19 : 193 – 212 .
as some noninfectious inflammations of the intestine might
be the result of misclassification and might correctly need
a classification as IBD or IG. However, we could not find evi-
dence for this during very detailed chart review of ~ 25 % of the
cases included in our analysis.
Altered environmental bacterial growth conditions might also
contribute to the observed increase in the number of IBD ares.
Recent research suggests that genetic susceptibility factors for the
pathogenesis of IBD are mainly located in genes / proteins of the
innate immune response dealing with our intestinal microbiota
( 28 ). Genetic variants of bacterial receptors or pattern recogni-
tion receptors such as NOD2 or TLR5 or genes involved in the
intracellular processing of invading bacteria such as ATG16L1 or
IRGM have been shown to be important risk factors for IBD ( 28 ).
is indicates that a change in bacterial compositions challenging
this innate immune response could well play a role for disease
ares and symptoms. However, in contrast to IG there was no
delay between the onset of heat waves and the increased risk of
IBD ares.
Another explanation would t better with the immediate
e ect of heat waves on IBD ares: heat waves are known to
cause physical stress to humans as evident from increased fre-
quencies of other stress-dependent health events such as heart
attacks ( 10 ). Physical as well as mental stress have been shown
to cause ares of IBD ( 29 ) and may explain the increase in IBD
hospital admissions during heat waves. However, besides the
possibility of heat waves triggering ares of IBD, it might also
only worsen a clinically not apparent underlying are. With our
observational study design, it is not possible to discriminate
between the two e ects, and additional studies on that hypo-
thesis are required .
Keeping in mind that the rate of heat waves is likely to increase
in Europe, the possible impact on health becomes more important.
is is underlined by the already mentioned new global climate
model considering the major urban impact ( 8 ), as almost half of
the world ’ s population is living in urban areas ( 30 ). Finally, this
also indicates that the impact of climate change on health has a
relevant economic aspect. erefore, mitigation and adaptation
strategies are needed to reduce current vulnerability to climate
change and to address the health risks projected to occur over the
coming decades.
CONFLICT OF INTEREST
Guarantor of the article: Christine N. Manser, MD.
Speci c author contributions : Christine N. Manser: data collec-
tion, data interpretation, literature search, and writing; Michaela
Paul: data analysis, data interpretation, gures, and writing;
Gerhard Rogler: study design, data interpretation, and writing;
omas Frei: study design, data interpretation, and writing;
Leonhard Held: study design, data analysis, data interpretation,
gures, and writing.
Financial support : is study was supported by a research grant
from the Swiss National Science Foundation (3347CO-108792)
to G.R.
Potential competing interests: N o n e .
The American Journal of GASTROENTEROLOGY VOLUME 104
|
XXX 2012 www.amjgastro.com
6
INFLAMMATORY BOWEL DISEASE
Manser et al.
2 1 . K u h n L , D a v i d s o n L L , D u r k i n M S . U s e o f P o i s s o n r e g r e s s i o n a n d t i m e
series analysis for detecting changes over time in rates of child injury fol-
lowing a prevention program . Am J Epidemiol 1994 ; 140 : 943 – 55 .
22 . Diggle PJ . Time Series: A Biostatistical Introduction . Oxford University
Press: Oxford , 1990 .
2 3 . W h i t a k e r H J , F a r r i n g t o n C P , S p i e s s e n s B et al. Tutorial in biostatistics: the
self-controlled case series method . Stat Med 2006 ; 25 : 1768 – 97 .
24 . Whitaker HJ , Hocine MN , Farrington CP . On case-crossover methods for
environmental time series data . Environmetrics 2007 ; 18 : 157 – 71 .
25 . Claeskens G , Hjort NL . Model Selection and Model Averaging . Cambridge
University Press: Cambridge , 2008 .
26 . R Development Core Team . R: A Language and Environment for Statistical
Computing . R Foundation for Statistical Computing: Vienna, Austria , 2010 .
27 . Zhang Y , Bi P , Hiller JE . Weather and the transmission of bacillary dysen-
tery in Jinan, northern China: a time-series analysis . Public Health Rep
2008 ; 123 : 61 – 6 .
28 . Scharl M , Rogler G . Microbial sensing by the intestinal epithelium in the
pathogenesis of in ammatory bowel disease . Int J In am 2010 ; 2010 : 671258 .
29 . Mawdsley JE , Rampton DS . Psychological stress in IBD: new insights into
pathogenic and therapeutic implications . Gut 2005 ; 54 : 1481 – 91 .
30 . Martine G , Marshall A . State of world population 2007; Unleasing the
potential of urban growth, report . U.N. Popul. Fund: New York , 2007 .
