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Environmental Influences on Daily Emergency Admissions in
Sickle-Cell Disease Patients
Armand Mekontso Dessap, MD, PhD, Damien Contou, MD, Claire Dandine-Roulland, MSc,
Franc¸ois Hemery, MD, Anoosha Habibi, MD, Anaı
¨
s Charles-Nelson, MSc, Frederic Galacteros,
MD, Christian Brun-Buisson, MD, Bernard Maitre, MD, PhD, and Sandrine Katsahian, MD, PhD
Abstract: Previous reports have suggested a rolefor weather conditions
and air pollution on the variability of sickle cell disease (SCD) severity,
but large-scale comprehensive epidemiological studies are lacking.
In order to evaluate the influence of air pollution and climatic factors
on emergency hospital admissions (EHA) in SCD patients, we conducted
an 8-year observational retrospective study in 22 French university
hospitals in Paris conurbation, using distributed lag non-linear models,
a methodology able to flexibly describe simultaneously non-linear and
delayed associations, with a multivariable approach.
During the 2922 days of the study, there were 17,710 EHA, with a
mean daily number of 6.1 2.8. Most environmental factors were
significantly correlated to each other. The risk of EHA was significantly
associated with higher values of nitrogen dioxide, atmospheric particu-
late matters, and daily mean wind speed; and with lower values of carbon
monoxide, ozone, sulfur dioxide, daily temperature (minimal, maximal,
mean, and range), day-to-day mean temperature change, daily bright
sunshine, and occurrence of storm. There was a lag effect for 12 of 15
environmental factors influencing hospitalization rate. Multivariate
analysis identified carbon monoxide, day-to-day temperature change,
and mean wind speed, along with calendar factors (weekend, summer
season, and year) as independent factors associated with EHA.
In conclusion, most weather conditions and air pollutants assessed
were correlated to each other and influenced the rate of EHA in SCD
patients. In multivariate analysis, lower carbon monoxide concentrations,
day-to-day mean temperature drop and higher wind speed were associ-
ated with increased risk of EHA.
(Medicine 93(29):e280)
Abbreviations: ACS = acute chest syndrome, EHA = emergency
hospital admissions, SCD = sickle cell disease, VOC = vaso-
occlusive painful crises.
INTRODUCTION
Sickle cell disease (SCD) is one of the most common severe
inherited disorders in the world. It is characterised by
recurrent vaso-occlusive painful crises (VOCs), which are
the most common reasons for patient’s emergency admissions.
Although its pathophysiology is still unclear, many factors are
known or suspected to precipitate VOC, including dehydration,
hypoxemia, pregnancy, infections, and surgery. VOCs may be
complicated by respiratory symptoms defining the acute chest
syndrome (ACS). The pathophysiology of ACS is also complex,
and may include pulmonary fat embolism
1
secondary to bone
marrow necrosis during VOC, pulmonary artery thrombosis
2
and/or in situ lung capillary vaso-occlusion.
Erythrocyte sickling is enhanced by lower temperatures
and physiological studies have demonstrated a link between
skin cooling and vaso-occlusion.
3–5
Previous epidemiological
studies exploring the influence of weather conditions and air
pollution on the variability of SCD severity yielded mixed
results.
6–11
However, all these studies used a univariate meth-
odology. Because meteorological factors and pollution factors
frequently display between-group and within-group inter-
relation, the use of a multivariable approach may be crucial
in this setting. In addition, environmental stressors may have
non-linear effects and their impact may appear with some
latency, and persist for some time after exposure (lag
effect).
12,13
None of the previous studies assessed the time
structure of the effects analyzed.
Our objective was to evaluate the influence of air quality
and weather on the incidence of emergency department admis-
sions for VOC and chest disease in patients with homozygous
SCD in an urban environment (Paris conurbation). We used
distributed lag non-linear models (dlnm), a methodology able to
flexibly describe simultaneously non-linear and delayed associ-
ations, with a multivariable approach.
METHODS
Patients
The study was retrospectively performed using data col-
lected during an 8-year period (2922 days) from January 1, 2004
to December 31, 2011 in 22 hospitals from the Assistance
Publique-Ho
ˆpitaux de Paris (the public hospital network of
Paris conurbation) (Figure 1). Using billing record discharge
summaries, we included all emergency department visits for
VOCs or chest disease in SCD patients (SS, SC, or S-thalasse-
mia genotype) aged from 2 to 70 years. Chest disease was
Editor: Alexandros Makis.
Received: July 27, 2014; revised and accepted: October 24, 2014.
From the AP-HP, Hoˆpital H. Mondor – A. Chenevier, Service de
Re´animation Me´dicale, CARMAS research group (AMD, DC, CB-B);
Universite´ Paris Est, Faculte´deme´decine (AMD, DC, FG, CB-B, BM);
Inserm, U955, Equipe 8 (AMD, BM); AP-HP, Hoˆpital H. Mondor – A.
Chenevier, Unite´ de Recherche Clinique (CD-R, AC-N, SK); AP-HP,
Hoˆpital H. Mondor – A. Chenevier, Service d’Information Me´ dicale (FH);
AP-HP, Hoˆpital H. Mondor – A. Chenevier, Unite´ des Maladies du Globule
Rouge (AH, FG); and AP-HP, Hoˆpital H. Mondor – A. Chenevier, Unite´de
Pneumologie, Cre´teil 94000, France (BM); AP-HP, Hoˆpital Europe´en
Georges Pompidou (SK); Inserm UMRS1138, Centre de Recherche des
Cordeliers, Equipe 22, Universite´ Paris Descartes (SK).
Correspondence: Armand Mekontso Dessap, Re´animation Me´dicale,
Hoˆpital Henri Mondor, 51 Avenue du Mare´chal de Lattre de Tassigny,
Cre´teil 94010, France (e-mail: armand.dessap@hmn.aphp.fr).
Damien Contou and Claire Dandine-Roulland contributed equally to this
work.
The authors have no funding and conflicts of interest to disclose.
Copyright #2014 Wolters Kluwer Health, Inc. All rights reserved.
This is an open access article distributed under the terms of the Creative
Commons Attribution-NonCommercial-ShareAlike 4.0 License, which
allows others to remix, tweak, and build upon the work non-commercially,
as long as the author is credited and the new creations are licensed under
the identical terms.
ISSN: 0025-7974
DOI: 10.1097/MD.0000000000000280
Medicine Volume 93, Number 29, December 2014 www.md-journal.com |1
defined as any new-onset lower acute respiratory tract disease
that was compatible with ACS
14
with the exclusion of other
formally defined diagnoses like trauma, cardiogenic pulmonary
oedema, or pneumothorax. We used the chest disease terminol-
ogy instead of ACS because the latest diagnosis is not formally
defined in the International Classification of Diseases 10 and
was not available in billing record discharge summaries. Ethical
approval was not required as per French legislation on observa-
tional retrospective studies on already collected data.
Meteorological and Air Quality Data
Meteorological and air quality data were obtained for the
same period from the French meteorology agency (Meteo
France, https://public.meteofrance.com/public/accueil) and the
Paris conurbation air quality agency (AirParif, http://www.air-
parif. asso.fr/telechargement/telechargement-station). We aver-
aged hourly recorded data from 7 synoptic meteorological
stations within the Paris conurbation to compute the following
variables: daily minimal temperature (8C), daily maximal
temperature (8C),daily mean temperature (8C), daily temperature
range (8C), day-to-day mean temperature change (8C, calculated
as the difference between mean temperature of the day and mean
temperature of the previous day), daily rainfall (mm), daily
relative humidity (%), daily bright sunshine (%), daily mean
wind speed (m/s), daily maximal wind speed (m/s) and occur-
rence of a storm (yes or no). We also averaged hourly recorded
data from 13 to 50 synoptic air pollution stations within the Paris
conurbation to compute the daily mean concentrations (mg/m
3
)of
the following compounds: carbon monoxide (CO), nitrogen
dioxide (NO
2
), ozone (O
3
), sulfur dioxide (SO
2
), and atmospheric
particulate matters with aerodynamic diameter smaller than
10 mm(PM
10
)or2.5mm(PM
2.5
).
Statistical Analysis
The data were analyzed using SPSS Base 13 (SPSS Inc,
Chicago, IL) and R 2.15.2 (The R Foundation for Statistical
Computing, Vienna, Austria). Categorical variables were
expressed as percentages and continuous data were expressed
as mean standard deviation. We used the chi-square or Fisher
exact test to compare categorical variables between groups and
the Student Ttest to compare continuous variables. Correlations
were tested using the Spearman’s method.
To assess the effects of daily meteorological and air quality
measurements on daily countsof hospital emergency admissions,
we used the dlnm package implemented within the statistical
software R.
15
This procedure can simultaneously represent non-
FIGURE 1. Paris conurbation map with the public hospital net-
work (H) and monitoring stations for meteorological (black circles)
and air quality (white circles) data.
TABLE 1. Descriptive Statistics of Environmental Factors
Variable Mean Standard Deviation Minimum
Percentile
Maximum
25 50 75
Air pollutants
CO (mg/m
3
) 744.6 295.5 185.2 515.1 696.9 929.7 2716.2
NO
2
(mg/m
3
) 40.7 13.6 10.6 30.5 39.5 49.9 130.6
O
3
(mg/m
3
) 43.7 20.9 1.1 28.3 44.1 57.6 134.5
SO
2
(mg/m
3
) 3.8 3.5 0.0 1.31 2.8 5.2 30.6
PM
10
(mg/m
3
) 27.7 12.5 6.5 19.5 24.8 32.6 138.1
PM
2.5
(mg/m
3
) 19.3 11.2 4.3 11.9 16.4 23.1 130.1
Meteorological variables
Daily rainfall (mm) 1.7 3.4 0.0 0.0 0.1 1.7 35.5
Daily minimal temperature (8C) 7.9 5.9 11.8 3.5 8.3 12.7 21.9
Daily maximal temperature (8C) 15.9 7.8 4.3 10.0 16.4 21.9 36.8
Daily mean temperature (8C) 11.7 6.7 6.1 6.6 12.1 17.0 28.1
Daily temperature range (8C) 8.0 3.7 0.7 5.2 7.6 10.7 19.7
Day-to-day mean temperature change (8C) 0.0 2.1 8.8 1.3 0.1 1.4 8.6
Daily mean wind speed (m/s) 3.5 1.4 0.7 2.5 3.2 4.2 10.1
Daily maximal wind speed (m/s) 10.9 3.6 3.6 8.3 10.3 12.8 31.9
Daily relative humidity (%) 75.8 11.6 38.9 67.6 77.4 84.9 97.9
Daily bright sunshine (%) 36.5 31.2 0.0 6.7 30.2 63.8 96.5
CO ¼carbon monoxide, NO
2
¼nitrogen dioxide, O
3
¼ozone, PM
10
¼atmospheric particulate matters with aerodyamic diameter smaller than
10 mm, PM
2.5
¼atmospheric particulate matters with aerodyamic diameter smaller than 2.5 mm, SO
2
¼sulfur dioxide.
Dessap et al Medicine Volume 93, Number 29, December 2014
2|www.md-journal.com Copyright #2014 Wolters Kluwer Health, Inc. All rights reserved.
TABLE 2. Matrix of Correlation Coefficients Between Environmental Factors
CO
NO
2
0.41
O
3
0.26
0.43
SO
2
0.64
0.58
0.38
PM
10
0.25
0.49
0.17
0.23
PM
25
0.29
0.51
0.26
0.27
0.96
Rain 0.11
0.08
0.07 0.11
0.22
0.18
Temp
min
0.23
0.41
0.55
0.46
0.26
0.34
0.12
Temp
max
0.16
0.32
0.62
0.41
0.08 0.20
0.02
0.89
Temp
mean
0.19
0.37
0.61
0.44
0.16
0.27
0.05
0.95
0.98
Temp
range
0.03 0.01
y
0.42
0.11
0.24
0.12
0.15
0.27
0.68
0.55
Wind
mean
0.33
0.24
0.08
0.10
0.40
0.39
0.22
0.02 0.17
0.11
0.32
Wind
max
0.35
0.26
0.19
0.19
0.41
0.43
0.34
0.10
0.01 0.03
y
0.19
0.88
Hum 0.20
0.18
0.59
0.18
0.09
0.05 0.26
0.36
0.61
0.53
0.71
0.07
0.01
y
Sun 0.04 0.02 0.27
0.04 0.28
0.18
0.29
0.07
0.40
0.27
0.73
0.24
0.21
0.69
Temp
change
0.07
0.12
0.13
0.12
0.08
0.06
0.06
0.02
y
0.18
0.15
0.35
0.04
0.06
0.13
0.08
Storm 0.12 0.24 0.25
y
0.19
0.12
0.14
0.03 0.31
0.30
0.31
0.13
0.01 0.05 0.15 0.03 0.04
CO NO
2
O
3
SO
2
PM
10
PM
25
Rain Temp
min
Temp
max
Temp
mean
Temp
range
Wind
mean
Wind
max
Hum Sun Temp
change
Storm
Correlation is significant at the 0.01 level.
y
Correlation is significant at the 0.05 level.
CO ¼carbon monoxide, Hum ¼daily relative humidity, NO
2
¼nitrogen dioxide, O
3
¼ozone, PM
10
¼atmospheric particulate matters with aerodyamic diameter smaller than 10 mm, PM
2.5
¼atmo-
atmospheric particulate matters with aerodyamic diameter smaller than 2.5 mm, Rain ¼daily rainfall, SO
2
¼sulfur dioxide, Storm¼occurrence of a storm during the 7 preceding days, Sun ¼daily bright
sunshine, Temp
max
¼daily maximal temperature, Temp
mean
¼daily mean temperature, Temp
min
¼daily minimal temperature, Temp
range
¼daily temperature range, Wind
max
¼daily maximal wind
speed, Wind
mean
¼daily mean wind speed.
Medicine Volume 93, Number 29, December 2014 Environment and Sickle Cell Disease
Copyright #2014 Wolters Kluwer Health, Inc. All rights reserved. www.md-journal.com |3
linear exposure–response dependencies and delayed effects.
16
The relationship with environmental factors was modeled
through a generalized linear model with Poisson family, a natural
cubic spline and boundary knots located at the range of the
observed values.
We estimated associations between environmental variables
and emergency hospital admissions (EHAs) for various single-
day lags. For example, a lag of 3 days corresponds to the
association between environmental variables in a given day
and the risk of hospital admission 3 days later. The lagged effect
was specified from lag1 to lag7. Lag0 (unlagged, which refers to
the association between environmental variables in a given day
and hospital admission in the same day), was excluded in order to
avoid the bias of analyzing environmental data recorded during
hours following the index hospitalization. Mean values of
environmental factors were used as reference values to calculate
the relative risks. The specification for thedegrees of freedom (df)
in each dimension was chosen so as to minimize the quasi-Akaike
Information Criterion. In order to be able to capture non-linear
effects and their time structure while keeping the model on the
ground of parcimony,we testeddf 1 to 2 in space dimension and in
time dimension. The influence of year on emergency admissions
was also assessed using dlnm. Wilcoxon rank sum test with
continuity correction was used to assess the effects of weekend,
summer season (from July 1 to August 31), and occurrence of
storm during the preceding week on emergency admissions.
To evaluate independent factors associated with emer-
gency admissions, significant univariate risk factors were
examined using stepwise multivariate analysis. Among signifi-
cant univariate factors that were closely related with a corre-
lation coefficient >0.80 (minimal temperature, maximal
temperature, and mean temperature for temperatures; PM
10
and PM
2.5
for particulate matters), only the most clinically
pertinent and straightforwardly interpretable for decision mak-
ing purposes (daily mean temperature and PM
10
) were entered
into the multivariate model in order to minimize the effect of
colinearity. Thus, the 15 variables entered into the multivariable
analysis were: daily mean temperature, daily temperature range,
day-to-day mean temperature change, daily relative humidity,
daily mean wind speed, daily bright sunshine, occurrence of a
storm, daily mean concentrations of CO, NO
2
,O
3
,SO
2
, and
PM
10
, weekend, holiday, and year. Two-sided Pvalues <0.05
were considered signicant. Univariate analyses were repeated in
the 2 subgroups defined by age up to 18 or 18 years and over.
RESULTS
Study Population and Environmental Factors
During the 2922 days of the study, there were 17,710
emergency admissions for VOC or chest disease, involving a
total of 4426 patients. The mean daily number of emergency
admissions was 6.1 2.8 (from 0 to 19), mean hospital length of
stay was 4.8 4.8 days, and mean patient age was 19.3 11.3
years. Table 1 shows the descriptive statistics for weather
conditions and air quality. There were 265 (9.1%) days with
storm occurrence during the study period. Table 2 shows the
matrix of correlation coefficients between environmental fac-
tors. Almost all meteorological variables and air pollutants
correlated closely to each other.
Determinants of EHAs
Table 3 shows the dlnm univariate analysis of the relation
between environmental factors and EHAs. Higher values of
TABLE 3. Univariate Analysis of the Relation Between Environmental and Hospital Admissions
Variable dfvar Knot
Estimate (PValue) of dlnm
Space Dimension Time Dimension
Air polluants
CO (mg/m
3
) 2 696.8 0.35 (<10
15
); 0.29 (<10
4
) 0.34 (<10
5
); 0.26 (0.02)
NO
2
(mg/m
3
) 2 39.4 0.06 (0.02); 0.02 (0.71) 0.18 (<10
3
); 0.08 (0.44)
O
3
(mg/m
3
)1–0.04 (<10
4
)–
SO
2
(mg/m
3
) 2 2.8 0.19 (<10
14
); 0.05 (0.48) 0.07 (0.29); 0.15 (0.21)
PM
10
(mg/m
3
) 2 24.8 0.19 (<10
10
); 0.08 (0.27) 0.20 (<10
3
); 0.25 (0.02)
PM
2.5
(mg/m
3
) 2 16.3 0.15 (<10
5
); 0.11 (0.18) 0.07 (0.20); 0.26 (0.03)
Meteorological factors
Daily rainfall (mm) 2 1.6 0.03 (0.65) 0.14 (0.07)
Daily minimal temperature (8C) 2 7.9 0.01 (0.84); 0.09 (<10
10
) 0.15 (0.12); 0.07 (0.13)
Daily maximal temperature (8C) 2 16.4 0.04 (0.11); 0.11 (<10
10
) 0.20 (0.02); 0.17 (<10
3
)
Daily mean temperature (8C) 2 12.1 0.03 (0.30); 0.10 (<10
12
) 0.21 (0.01); 0.16 (<10
3
)
Daily temperature range (8C) 2 7.6 0.10 (<10
3
); 0.03 (0.29) 0.02 (0.68); 0.12 (<0.01)
Day-to-day mean temperature change (8C) 2 0.1 0.46 (<10
4
); 0.18 (<0.01) 0.07 (0.53); 0.12 (0.02)
Daily relative humidity (%) 2 77.4 0.08 (0.049); 0.05 (<10
4
)0.30 (<10
4
); 0.02 (0.54)
Daily bright sunshine (%) 2 30.2 0.06 (<0.01); 0.01 (0.56) 0.03 (0.23); 0.04 (0.04)
Daily mean wind speed (m/s) 2 3.24 0.01 (0.69); 0.06 (0.01) –
Daily maximal wind speed (m/s) 1 – 0.04 (0.25) 0.11 (0.04)
dlnm ¼distributed lag non-linear models; the degree of freedom in space dimension (dfvar) and in time dimension (dflag) were chosen as to
minimize the quasi-Akaike Information Criterion; in space dimension, there is one estimate if dfvar¼1 (from minimal value to maximal value) and 2
estimates if dfvar ¼2 (the first from minimal value to central knot and the second from central knot to maximal value); in time dimension, there is no
estimate if dflag ¼1 and 1 estimate if dflag ¼2, CO ¼carbon monoxide, Hum ¼daily relative humidity, NO
2
¼nitrogen dioxide, O
3
¼ozone,
PM
10
¼atmospheric particulate matters with aerodynamic diameter smaller than 10 mm, PM
2.5
¼atmospheric particulate matters with aerodynamic
diameter smaller than 2.5 mm, Rain ¼daily rainfall, SO
2
¼sulfur dioxide, Sun ¼daily bright sunshine, Temp
max
¼daily maximal temperature,
Temp
mean
¼daily mean temperature, Temp
min
¼daily minimal temperature, Temp
range
¼daily temperature range, Wind
max
¼daily maximal wind
speed, Wind
mean
¼daily mean wind speed.
Dessap et al Medicine Volume 93, Number 29, December 2014
4|www.md-journal.com Copyright #2014 Wolters Kluwer Health, Inc. All rights reserved.
NO
2
,PM
2.5
,PM
10
, and daily mean wind speed; and lower
values of CO, O
3
,SO
2
, daily minimal temperature, daily
maximal temperature, daily mean temperature, daily tempera-
ture range, day-to-day mean temperature change, daily bright
sunshine and occurrence of storm were significantly related
with the risk of EHAs while the association with daily relative
humidity was U shaped (Figures 2 and 3). Dlnm evidenced a lag
effect for 12 of 15 significant environmental factors, with a
short-term effect (before lag3) for CO (lower values), SO
2
,
minimal, maximal, and mean temperatures; and a delayed effect
(after lag3) for CO (higher values), NO
2
,PM
10
,PM
2.5
, daily
temperature range day-to-day mean temperature change,
humidity, and sunshine (Table 3, see Supplemental Digital
Content Figure SDC1 to Figure SDC16, http://links.lww.-
com/MD/A95). The number of EHAs increased with year of
admission (dlnm estimate of 0.07, P<10
15
) and was signifi-
cantly lower during the summer season as compared the rest of
the year and during weekends as compared to weekdays
(P<10
10
for both comparisons, Figure 4). Multivariate
analysis identified lower values of CO (dlnm estimate of
0.18, P<10
3
), day-to-day temperature drops (dlnm estimate
of 0.30, P<0.01), higher values of mean wind speed (dlnm
estimate of 0.05, P¼0.03), weekend (dlnm estimate of 0.13,
P<10
11
), summer season (dlnm estimate of 0.15, P<10
8
),
and increasing year (dlnm estimate of 0.05, P<10
12
)as
independent factors associated with EHAs (Table 4); all these
factors were also significantly related with the risk of EHAs in
the subgroup of 1953 children (<18 years old, 8054 admissions)
and in the subgroup of 2473 adults (9656 admissions) except for
mean wind speed in children.
DISCUSSION
We found that the majority of air pollutants and environ-
mental factors were correlated to each other and influenced
EHAs in SCD patients, over a lag period of 1 week. Multivariate
analysis identified day-to-day temperature drop, increased
mean wind speed and decreased CO concentration as indepen-
dent factors associated with a higher risk of EHAs, while
controlling for calendar factors.
Meteorological Factors
In the present study, a decrease in all daily temperatures
(minimal, maximal, mean and range) and a drop in day-to-day
mean temperature were associated with a higher risk of hospi-
talization. These results are in accordance with previous studies
reporting that seasonally colder temperatures may exacerbate
sickle cell-related pain.
17– 21
Patients with SCD exhibit hyper-
sensitivity to thermal stimuli
22
and often report cooler weather
or exposure to cold as the most important precipitating factor for
VOC.
6,7,23
This effect is not likely to be mediated by direct
sickling, because lowering of the temperature reduces HbS
polymerization in vitro.
24,25
In addition, VOC and ACS are
poorly related to indices of chronic hemolysis.
26
The reflex
constriction of superficial blood vessels in response to skin
cooling is enhanced in SCD as compared to normal individuals.
5
This vasoconstrictor reflex is even stronger in SCD patients who
are more prone to painful crises.
3
Serjeant and Chalmers
27
hypothesized that this vasoconstriction may be associated with
diversion of blood (‘‘vascular steal’’) away from active bone
marrow and may cause avascular necrosis and precipitate VOC.
This hypothesis is corroborated by radioisotope scanning evi-
dence of impaired blood flow in the bone marrow during a
painful crisis and biopsies of sites of maximal tenderness
RR
Daily mean temperature
RR
Daily temperature range
CD
−10 0 10 20 30 0 5 15 2010
0.8 0.9 1.0 1.1 1.2 1.3
0.7 0.8 0.9 1.0
GH
05 1510
020406080100
RR
Sunshine
RR
Mean wind speed
12345
1.00 1.05 1.10 1.15 1.20 1.25
E
RR
−15 −10 0 5 10 15−5
Day to day temperature change
0246810
F
30 40 50 60 70 80 90 100
RR
Relative humidity
1.0 1.2 1.4 1.6
IJ
0 5 15 20 25 3010 010203040
RR
0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3
0.90 0.95 1.00 1.05 1.10 1.15 1.20
Maximal wind speed
RR
Rainfall
RR
Minimal temperature
RR
Maximal temperatureAB
010203040
10 0 10 20
0.6 0.7 0.8 0.9 1.0
0.6 0.7 0.8 0.9 1.0
−
FIGURE 2. Overall effect with relative risk (red line) and 95%
confidence interval (grey area) for associations between
the number of daily emergency admissions in sickle cell
disease patients and daily minimal temperature (panel A),
daily maximal temperature (panel B), daily mean temperature
(panel C), daily temperature range (panel D), day-to-day
mean temperature change (panel E), daily relative humidity
(panel F), daily bright sunshine (panel G), daily mean wind speed
(panel H), daily maximal wind speed (panel I), and daily rainfall
(panel J).
Medicine Volume 93, Number 29, December 2014 Environment and Sickle Cell Disease
Copyright #2014 Wolters Kluwer Health, Inc. All rights reserved. www.md-journal.com |5
yielding necrotic marrow.
28
Increased wind speed and low
humidity are both likely to accelerate skin cooling. Convection
and sweat evaporation are 2 main mechanisms of human body
heat loss. The rate of convective cooling increases with higher
wind speed and low atmospheric humidity accelerates evapora-
tive cooling. In our study, highwind speed and low humidity were
associated with increased admissions of SCD patients, as pre-
viously reported in other areas of temperate climate.
29,30
How-
ever, in our study, the relation linking relative humidity to
admission risk was significant only by univariate analysis and
displayed a U shape, with an increase in hospital admission with
higher humidity after a knot around 70% of relative humidity. A
similar positive association between relative humidity and hos-
pitalization rate in SCD patients was previously reported.
17,21
Air Pollutants
The association between daily variations in the levels of
urban air pollution and adverse healtheffects has been established
in the general population.
31
Most patients with SCD in developed
countries live in urban areas with variable and often poor air
quality. In our study, an increased admission risk was associated
with higher levels of NO
2
,PM
10
, and PM
2.5
and lower levels of
CO, O
3
, and NO
2
. These findings differ from those of a previous
smaller report
32
except for the protective association of CO. CO
was the only air pollutant (negatively) associated with hospital
admission by multivariate analysis in our cohort. CO binds to
hemoglobin with an affinity over 200 times that of O
2
to form
carboxyhaemoglobin (HbCO), which increases the affinity of
other binding sites for O
2
and shifts the oxygen dissociation curve
to the left. This reduces the level of deoxyHbS and the tendency
for HbS to polymerise.
33
CO also inhibits vasoconstriction and
platelet aggregation.
34
Inhaled CO reduces inflammation, leuco-
cytosis,
35
and vasoocclusion
36
in murine models of SCD. CO
administrationto SCD patients induced a significant prolongation
of red cell survival.
37
Altogether, these findings suggest CO may
be beneficial to patients having SCD.
Calendar Factors
dlnm showed a progressive yearly increase in admission
rate during the study period, which persisted in multivariate
analysis. This increase may be explained by the progressive
increase in cohorts of patients treated for SCD in Paris con-
urbation during the same period. Weekend and summer season
were associated with lower admission rate by multivariate
analysis. The difference in hospitalization rate between week-
end and weekdays has been reported in other settings like
myocardial infarction.
38
This difference may be attributable
to a lifestyle change between weekdays and weekends and/or to
an attempt by some patients to delay hospital admission until
Monday because of the leisurely pace of life on weekends. The
decreased incidence during summer season is likely related to
higher temperatures and/or traveling outside the Paris conurba-
tion during that holiday period.
Clinical Implications
Education of both SCD patients and their families about
how to avoid crises may lead to a decrease in their number and
severity.
39
Our findings may help health care providers and
RR
CO concentration
RR
NO2 concentration
RR
O3 concentration
ABC
DEF
RR
PM10 concentration
RR
PM25 concentration
RR
SO2 concentration
010203040
020406080100
120 140
20
500 1000 1500 2000 2500 40 60 80 100
0.4 0.6 0.8 1.0 1.2 1.4 1.6
0.80 0.85 0.90 0.95 1.00 1.05 1.10
0.2 0.4 0.6 0.8 1.0 1.2 1.4
0.4 0.6 0.8 1.0 1.2
0.40.2 0.6 0.8 1.0
1.51.0 2.0 2.5 3.0
1.2
120 140
050100150
0 50 100 150
FIGURE 3. Overall effect with relative risk (red line) and 95% confidence interval (grey area) for associations between the number of daily
emergency admissions in sickle cell disease patients and daily mean concentrations of carbon monoxide (panel A), nitrogen dioxide (panel
B), ozone (panel C), atmospheric particulate matters with aerodynamic diameter smaller than 10 mm (panel D) or 2.5 mm (panel E), and
sulfur dioxide (panel F). CO ¼carbon monoxide, NO
2
¼nitrogen dioxide, O
3
¼ozone, PM
10
¼atmospheric particulate matters with
aerodyamic diameter smaller than 10 mm, PM
2.5
¼atmospheric particulate matters with aerodyamic diameter smaller than 2.5 mm,
SO
2
¼sulfur dioxide.
Dessap et al Medicine Volume 93, Number 29, December 2014
6|www.md-journal.com Copyright #2014 Wolters Kluwer Health, Inc. All rights reserved.
patients to adopt preventive measures to avoid hospitalizations.
Our report reinforces general recommendation provided to SCD
patients, such as to avoid cold, to wear warm clothes outside in
cold weather and inside of air-conditioned rooms, and not to
swim in cold water.
40
Increased wind speed and day-to-day
temperature drop (but not mean daily temperature) were the 2
meteorological factors selected by our multivariable model as
modifiers of the risk of emergency hospitalization. Permanently
low temperatures may dictate clothing choices and time spent
outdoors whereas day-to-day unanticipated falls in temperature
may specifically expose SCD patients to outdoor cooling.
Patients with SCD should be particularly careful in case of
anticipated temperature drop or windy weather. Concerning air
pollution, further clinical studies are needed to explore the
potential for inhaled CO to alleviate VOC and/or ACS.
Study Strengths and Limitations
Our study is the first multicentric study in SCD patients
with a very large sample size spanning several years, with
concurrent daily environmental and clinical data, adjusted to
calendar data, and using an analytic approach able to capture
time structure and non-linear effects in a multivariable analysis.
The multivariable approach was necessary given that almost all
oNseY
051015
Weekend
seYoN
051015
Summer season
2004 2005 2006 2007 2008 2009 2010 2011
051015
Year
Daily hospitalisation number Daily hospitalisation number
Daily hospitalisation number
FIGURE 4. Box and Whisker plots of the number of daily emergency admissions in sickle cell disease patients according to weekend (panel
A), summer season (panel B), and year of the study (panel C).
Medicine Volume 93, Number 29, December 2014 Environment and Sickle Cell Disease
Copyright #2014 Wolters Kluwer Health, Inc. All rights reserved. www.md-journal.com |7
environmental factors variables were observed to correlate. In
addition, a lag effect is virtually inevitable in SCD patients, who
usually attempt to manage pain at home prior to seeking medical
care. The association with the risk of hospital admission by
dlnm was delayed (starting after lag3 to lag4) for CO (higher
values), NO
2
,PM
10
,PM
2.5
, daily temperature range, day-to-day
temperature change, daily relative humidity, and daily bright
sunshine.
Our study has several limitations. First, it was performed in
an urban environment with a temperate climate and our findings
may not be extrapolated to other climates. We could not
evaluate the extent to which patients might have mitigated
environmental factors (eg, by using warm clothing, indoor
air conditioning and/or heating), which may lessen the strength
of associations between some climate factors and hospitaliz-
ations. On the same line, we did not have information about
smoking habits, which is a major determinant of HbCO levels
and could influence the association between atmospheric CO
and hospital admissions. Second, we only studied patients
presenting to the emergency department, and some painful
crises may have been managed at home, inducing a selection
bias. In addition, the chest disease terminology used in the
present report may not perfectly overlap ACS because the latest
diagnosis is not formally defined in the International Classifi-
cation of Diseases 10. Third, we did not analyze barometric
pressures, but Paris conurbation is a relatively flat region and
none of the sites had unusually high elevation that would be
associated with consistently low barometric pressures. Sim-
ilarly, we could not compute perceived temperature because
wind measurements were made at a standard height of 33 feet,
which do not correspond with the wind experienced by patients,
as friction attenuates wind speed closer to the ground. Last,
we could not explore the role of several patient characteristics
on the influence of environmental factors on hospital admis-
sions.
In conclusion, the majority of weather conditions and air
pollutants assessed were correlated to each other and influenced
the rate of EHA in SCD patients. In multivariate analysis,
weekdays, non-summer seasons, lower CO concentrations,
day-to-day mean temperature drop and higher wind speed were
associated with an increased risk of EHA.
ACKNOWLEDGMENTS
We are very grateful to Me
´te
´o France and Air Parif for
providing meteorological and air pollution data.
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TABLE 4. Multivariate Analysis of the Relation Between Environmental and Calendar Factors and Hospital Admissions
Variable
Estimate (PValue) of the dlnm
Space Dimension Time Dimension
Air polluants
Carbon monoxide (mg/m
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Meteorological factors
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