I
S
T
M
124
Predictive Factors of Imported Malaria in 272 Febrile Returning
Travelers Seen as Outpatients
Séverine Ansart, MD,∗† Lucia Perez, MD,∗‡ Marc Thellier, PhD,∗ Martin Danis, MD,∗
François Bricaire, MD,∗ and Eric Caumes, MD∗
∗ Service
des Maladies Infectieuses et Tropicales, Hôpital de la Pitié-Salpêtrière, Paris, France; † Service de Maladies Infectieuses,
Centre Hospitalier Universitaire Cavale Blanche, Brest, France; ‡ Service de Médecine Polyvalente U53, Centre Hospitalier du
Mans, Le Mans, France
DOI: 10.1111/j.1708-8305.2009.00382.x
Background. We conducted a prospective study to evaluate the aetiologies of fever in returning travelers and to identify the
clinical and laboratory factors predictive of malaria in travelers returning from tropical areas with fever.
Methods. We included those consulting for fever appearing less than 3 months after return. Destinations were classified according
to the visited continent (America including Caribbean, Asia, Africa, Oceania). We prospectively included all returning travelers
consulting our department between November 2002 and May 2003 for health problems and investigated those presenting fever
within 3 months after return from a tropical country. We then conducted a case control study to identify factors predictive of
malaria. Control group was defined as febrile travelers without malaria.
Results. A total of 272 febrile travelers were included. They were 152 tourists (55.9%), 58 immigrants (21.3%), 33 expatriates
(12.1%), and 29 business travelers (10.7%). Besides malaria (54 cases), the main diagnosis in the 218 controls were bacterial
enteritis, bacterial pneumonia, infectious cellulitis, pyelonephritis, prostatis, dengue fever, primary viral infection (HIV, EBV,
CMV, parvovirus B19), and tuberculosis. Multivariate regression analysis showed correlations between malaria and travel to Africa
(OR = 11.9), abdominal pain (OR = 14.1), vomiting (OR = 19.4), myalgia (OR = 6.3), inadequate prophylaxis (OR = 10.1), and
platelets <150,000/μL (OR = 25.2).
Conclusions. Our results suggest that no single clinical or biological feature had both good sensitivity and specificity to predict
malaria in febrile travelers seen as outpatients within 3 months after returning from the tropics.
F
ever is one of the main causes of consultation in persons returning from the tropics. Of the 50 million
persons traveling in developing countries,1 8% to 19%
need medical support after return and 3% to 11% are
febrile.2 – 5
Malaria is one of the leading causes of fever in
returning travelers, with gastrointestinal, respiratory
tract, and skin infections.6 – 8 Indeed, of 24,920
febrile returning travelers seen from March 1997 to
March 2006 in Geosentinel clinics around the world,
malaria accounted for 21% of the causes of fever.9
Similarly, malaria accounted for 11.8% to 42% of
the causes of hospital’s admissions in febrile travelers
Corresponding Author: Séverine Ansart, MD, Service des
Maladies Infectieuses, Centre Hospitalier Universitaire Cavale
Blanche, Bld Tanguy Prigent, F-29609 Brest Cedex, France.
E-mail: [email protected]
© 2010 International Society of Travel Medicine, 1195-1982
Journal of Travel Medicine 2010; Volume 17 (Issue 2): 124–129
in various countries.5,7,10 – 12 Besides its frequency,
malaria remains the first diagnosis to suspect in febrileexposed travelers, because of its potential rapid fatal
outcome.5,13 The lethality of imported malaria has
been estimated about 0.3% in Canada14 and 0.44%
in France.15
Prior predictive factors for malaria have been
identified in particular populations such as hospitalized
children10,11 or adults in endemic areas14 or in returning
travelers selected by the demand of blood smear.13,16,17
To the best of our knowledge, no study focused on
febrile outpatients.
We investigated the patients consulting our tropical
disease unit for fever after returning from a tropical
country and analyzed the reasons why they consulted
our unit. We then evaluated the epidemiological,
clinical, and biological variables predictive of imported
malaria.
125
Imported Malaria in Travelers
Patients and Methods
We prospectively investigated all consecutive patients
(age >18 years) consulting our department between 1
November 2002 and 31 May 2003 for fever (≥38◦ C)
appearing less than 3 months after return from a
tropical country. Tropical countries were defined as
countries with tropical or subtropical environment in
the Americas (south and central continental), Caribbean
islands, Asia, Africa, and Oceania. We analyzed the
causes of fever and conducted a case control study to
identify factors predictive of malaria.
Cases were defined as adults diagnosed with imported
malaria (blood smears positive for Plasmodium).
Controls were febrile patients diagnosed with diseases
other than malaria. In these controls, diagnoses relied on
the detection of bacterial agents in blood samples, stools
or urine-analysis, or by sero-conversion for infectious
agent compatible with clinical findings. All patients were
diagnosed by two physicians (SA, EC) and were followed
up during the study period.
Patients consulting without fever, patients who never
traveled, or patients under 18 years old were excluded.
For all patients, we collected the following epidemiological data: demographic findings (age, sex, country of
birth, country of residence), travel category (immigrants
visiting friends and relatives ie, VFRs, tourists, expatriates, business), travel history (destination and duration),
health advice prior exposure (including malaria prophylaxis), and aim of the travel. Travel destination was
classified according to the region visited (America,
Caribbean, Asia, Africa, Oceania). Immigrants were
defined as persons born in tropical areas, but living
in France and returning to their country of origin for
visiting friends and relatives (ie, VFRs). Tourists were
defined as persons traveling for holidays. Expatriates
were defined as persons born in France and living in
tropical areas for more than 6 months. Business travelers
were defined as persons born in France and visiting
tropical areas for short periods, less than 6 months.
We assessed the following symptoms: temperature,
chills, headache, myalgia, malaise, abdominal pain,
cough, dyspnea, diarrhea, vomiting. We recorded the
following biological data: creatinine, liver function tests,
blood cell count including hemoglobin concentration,
platelets count.
We conducted a case control study with two controls for one case. The size of the sample was estimated
according to the frequency of exposure in controls,
to detect odds ratio ≥2. For this purpose, we took into
account the results of two others studies in which factors
predictive of imported malaria were evaluated in hospitalized travelers undergoing blood smears.13,16 As the
main factor predictive of malaria in these studies was the
migrant status with an odds ratio between 2 and 2.5, we
estimated the frequency of exposure at 30% in the control population. To detect such difference, with alpha
risk of 5% and beta risk of 20% (power of the study =
80%), we needed to include 47 cases and 94 controls.
All variables were collected on Microsoft Excel. The
relative frequency of biological and clinical findings in
cases and controls were analyzed with Epi-Info 6.04
and stata statistical software (Version 6.0, College
Station, TX). The statistical significance of differences
in dichotomous variables was determined by using χ2
tests with Fischer’s two-tailed exact test, and by using
t-test or U-test of Mann–Whitney for quantitative
variables. All variables correlated in univariate analysis
with imported malaria were included in a stepwise
backward regression model (significance level for
exclusion of p ≥ 0.25) to identify predictors of the
disease. Logistic regression analysis was performed by
stata statistical software (Version 6.0). The sensitivity,
specificity, positive predictive value (PPV) and negative
predictive value (NPV) were determined.
Results
A total of 272 travelers, 54 malaria cases and 218
controls, were included. The M/F ratio was 1.34 (116
F and 156 M), and the mean age 37.4 (±11.9) years.
They consisted of 152 tourists (55.9%), 58 immigrants
(21.3%), 33 expatriates (12.1%), and 29 business
travelers (10.7%). The following regions were visited:
Africa (n = 169; 62.1%), Asia (n = 47; 17.3%), America
(n = 14; 5.1%), and Caribbean (n = 12; 4.4%).
The median duration of travel was 15 days (1–1095
days). Forty-seven patients (17.3%) stayed in the tropics
for more than 3 months. The median interval between
return and presentation was 6 days (1–151 days). The
median lag time between the onset of the symptoms
and presentation was 7.5 days (1–90 days). Symptoms
started during travel in 38% of our patients.
Seventy-three percent of the patients had taken
medical advice before travel (general practitioner 7%;
specialist in tropical disease 61.8%; travel agency 3.3%;
telephonic center 1.5%). The chemoprophylaxis was
inadequate in 170 cases (62.5%), regarding the choice
of drug (n = 44) or adherence to prophylaxis (n = 156).
The characteristics of patients are listed in Table 1.
Of the 272 febrile patients, 54 (19.8%) were
diagnosed with imported malaria (= case). Of these
54 cases of malaria, 36 were because of Plamodium
falciparum (67%), 14 cases to P vivax (26%), and 4 to
P ovale (7%) (none for P malariae and P knowlesi) whereas
45 cases were acquired in sub-Saharan Africa (83%).
The main diagnosis in the 218 controls were as
follows: bacterial enteritis (n = 50), bacterial pneumonia
(n = 20), infectious cellulitis (n = 20), pyelonephritis
(n = 13), prostatis (n = 9), dengue fever (n = 16),
viral (non HIV) primary infection (EBV, CMV,
parvovirus B19) (n = 11), tuberculosis (n = 12), invasive
schistosomiasis (n = 4), rickettsiosis (n = 3), brucellosis
(n = 2), and primary HIV infection (n = 2). No
diagnosis was made in 15 cases (5.5%) (Table 2). Overall
an imported disease was diagnosed in 30.5% of these
febrile patients.
J Travel Med 2010; 17: 124–129
126
Ansart et al.
Table 1 Clinical and biological variables in 272 returning
travelers with fever
Variables
Temperature (mean)
Temperature >38◦ 5C
Chills
Malaise
Headaches
Myalgia
Vomiting
Diarrhea
Abdominal pain
Cough
Dyspnea
Platelets (mean)
Platelets <150,000
White cells count G/L (mean)
Hemoglobin g/dL (mean)
Creatinine μmol/L (mean)
Table 3 Socio-demographical characteristics associated
with imported malaria (univariate analysis∗ )
Results
38.4 (38–40.6)
136 (50%)
94 (34.2%)
46 (16.9%)
71 (26.1%)
87 (32%)
28 (10.3%)
66 (24.3%)
46 (16.9%)
56 (20.6%)
17 (6.3%)
216 (34–712)
91 (33.5%)
10.3 (0.3–20.8)
12.2 (7.2–15.6)
83.5 (48–556)
Malaria Controls
(n = 54) (n = 218)
Sex ratio
(male/female)
Expatriate
Business traveler
Tourist
Immigrant
Inadequate
chemoprophylaxy
Medical advice
before travel
Vaccination
Travel’s duration
>3 months
Travel in Africa
p
OR (IC95%)
31/23
125/93
0.99
1.00 (0.53–1.91)
14
6
12
22
47
19
23
140
36
123
0.001
0.905
<0.001
<0.001
<0.001
3.67 (1.59–8.45)
1.06 (0.36–2.94)
0.16 (0.07–0.34)
3.48 (1.73–7)
5.19 (2.19–14.15)
52
147
<0.001 12.56 (3.13–108.79)
29
19
101
28
0.331 1.34 (0.71–2.55)
<0.001 3.68 (1.76–7.72)
45
124
<0.001 3.79 (1.68–8.79)
∗ The
statistical significance of differences in dichotomous variables was
determined by using χ2 tests with the Fischer’s two-tailed exact test.
Table 2 Etiologies of fever in cases other than malaria
(controls, n = 218)
Diagnosis
GI diseases
Bacterial enteritis
Hepatitis A
Amebiasis
Respiratory tract infection
URTI
Pneumonia
Flu-like syndrome
Eosinophilic pneumonia
Dermatosis
Cellulitis
Pyoderma
Herpes, zoster
Urinary tract infection
Dengue fever
Viral diseases (EBV, CMV, parvovirus B19, HIV)
Tuberculosis
Invasive schistosomiasis
Rickettsiosis and brucellosis
Fever unknown origin
Total
Total
59 (21.7%)
50
5
4
41 (15.1%)
6
20
10
5
31 (11.4%)
20
8
3
22 (8.1%)
16 (5.9%)
13 (4.7%)
12 (4.4%)
4 (1.5%)
5 (1.8%)
15 (5.5%)
218
In univariate analysis, the following variables were
associated with malaria (Tables 3–5): immigrants,
expatriates, travel to Africa, inadequate prophylaxis,
medical advice taken before travel, duration of travel
>3 months, temperature above 38◦ 5C, chills, headache,
myalgia, abdominal pain, and thrombocytopenia. The
duration of travel and the lag time between return
and presentation to our unit were significantly more
prolonged in cases than in controls (22 days vs 6 days,
p = 0.001 and 40 vs 14 days, p < 0.001 respectively).
Of the 54 patients with malaria, 35 (64.8%) were
receiving chemoprophylaxis that was considered to
J Travel Med 2010; 17: 124–129
Table 4 Symptoms associated with imported malaria
(univariate analysis∗ )
Fever > 38◦ 5
Chills
Malaise
Headaches
Myalgia
Abdominal pain
Vomiting
Diarrhea
Cough
Dyspnea
Malaria
(n = 54)
Controls
(n = 218)
p
OR (IC95%)
38
33
9
33
41
20
8
9
0
0
98
61
37
38
46
26
20
57
56
17
0.001
<0.001
0.957
<0.001
<0.001
<0.001
0.22
0.14
<0.001
0.029
2.91 (1.47–5.82)
4.04 (2.08–7.90)
0.98 (0.41–2.30)
7.44 (3.71–15.04)
11.79 (5.55–25.42)
4.34 (2.07–9.14)
1.72 (0.65–4.45)
0.56 (0.24–1.29)
0 (0–0.21)
0 (0–0.94)
∗ The
statistical significance of differences in dichotomous variables was
determined by using χ2 tests with the Fischer’s two-tailed exact test.
be inadequate (regarding observance during travel,
duration of chemoprophylaxis after return and choice
of medication) in 74.3% of cases.
Multivariate regression analysis showed correlations
between malaria and travel to Africa, abdominal pain,
vomiting, myalgia, inadequate prophylaxis, and platelets
<150.103 /μL (Table 6). Sensitivity, specificity, PPV,
and NPV of variables appear in Table 7.
Discussion
We evaluated the predictive factors of imported malaria
in returning travelers seen as outpatients and prospectively included on the existence of fever. We showed
that the following variables are independent predictive factors of malaria: travel in Africa, abdominal pain,
vomiting, myalgia, inadequate chemoprophylaxis, and
platelets <150.103 /μL.
127
Imported Malaria in Travelers
Table 5 Biological and clinical findings associated with
imported malaria (univariate analysis∗ )
Malaria Controls
(n = 54) (n = 218)
Platelets
Creatinine
Hemoglobin
White cells G/L
(mean)
Temperature
Age
Interval between
consultation
and symptoms
Interval between
consultation
and return
Duration of
travel
p
OR (IC95%)
<0.001
0.566
0.122
0.065
0.970 (0.962–0.979)
0.999 (0.994–1.005)
1.083 (0.982–1.195)
0.942 (0.889–0.997)
93.5
86
12.5
9.25
234.5
82.5
11.9
11.2
38.6
34
5
38.4
35
6
40
14
<0.001 1.038 (1.021–1.056)
22
6
0.001 3.193 (1.594–6.394)
0.001 3.138 (1.722–5.718)
0.260 0.979 (0.953–1.005)
0.006 0.953 (0.907–1.000)
∗ The
statistical significance of differences in quantitative variables (median) was
determined by using t-test or U-test of Mann-Whitney.
Table 6 Factors predictive of imported malaria (final
model of logistic regression in multivariate analysis∗ )
Variables
Africa
Abdominal pain
Vomiting
Myalgia
Inadequate chemoprophylaxis
Platelet <150,000
p
OR (IC95%)
<0.001
0.03
0.019
0.005
0.002
<0.001
11.97 (3.26–43.94)
14.17 (1.29–155.10)
19.48 (1.64–231.73)
6.3 (1.73–22.73)
10.17 (2.36–43.83)
25.27 (15.6–75.7)
∗ All variables correlated in univariate analysis with imported malaria were included
in a stepwise backward regression model (significance level for exclusion of
p ≥ 0.25) to identify predictors of malaria.
Table 7 Sensitivity, specificity, PPV, and NPV of variables
included in the final model
Variables
Africa
Abdominal pain
Vomiting
Myalgia
Inadequate chemoprophylaxis
Platelets <150,000/μL
Sensitivity Specificity
83.3%
37%
14.8%
75.9%
87%
98.1%
43.1%
88.1%
90.8%
78.9%
43.6%
82.6%
PPV
NPV
26.6%
43.5%
28.6%
47.1%
27.6%
58.2%
91.3%
84.9%
81.1%
93.0%
93.1%
99.4%
In endemic areas, predictors of malaria have been
assessed in populations at risk such as children
or hospitalized adults.18,19 Nonetheless, these results
cannot apply to non-immune populations such as
travelers in whom the prescription of a presumptive
antimalarial treatment, in response to the results of
blood smears (if they are not routinely available) is a
cause of concern.
Three studies previously evaluated factors associated
with imported malaria in non-immune travelers
returning from the tropics, but the criteria of inclusion
was the prescription of a blood smear.13,16,17 In a cohort
of 336 Swiss travelers (97 cases and 239 controls),16
variables included in the final model of logistic
regression were inadequate chemoprophylaxis, sudden
onset, lack of abdominal pain, temperature >39◦ C,
alteration of general status, splenomegaly, hemoglobin
<12 g/dL, white cells count <10.103 /μL, platelets
<150.103 /μL and eosinophilia <5%. In another
study, performed in 783 French patients admitted in
the emergency department of a Parisian hospital,13
factors associated with malaria were travel in subSaharan Africa, temperature >38◦ 5C, chills, platelets
<130,000/μL, bilirubin >18 μmol/L. In a more recent
Danish study, some laboratory variables predictive
of malaria were compared in 66 febrile returning
travelers with negative blood smears and 40 travelers
with malaria (P falciparum : n = 28; P vivax/P ovale:
n = 12).17 Platelet and leukocyte counts and coagulation
factors II–VII and X were significantly lower in the
malaria group. Similarly C-reactive protein, lactate
dehydrogenase, and bilirubin levels were significantly
higher in this group, particularly in P falciparum group.
Although the inclusion criteria was the presence
of fever, our study has some limits. At first, patients
consulted our unit as it was one of the two centers
specialized in tropical diseases in Paris and 21.3% were
immigrant VFRs. In addition, the study was performed
from November 2002 to May 2003, a period marked by
the emergence of the severe acute respiratory syndrome
(SARS). This fact could explain why 62.1% of our
febrile travelers returned from Africa, regarding that
WHO recommended avoiding Asian destinations at that
time.20 As a result, only 11.8% of our patients traveled
to Southeast Asia. The choice of destinations could
explain some of our results regarding febrile diseases
other than malaria as previously discussed.21
We evaluated the predictive factors of imported
malaria in febrile travelers whatever was the visited
country within a continent. However, the risk of
malaria varies across continent and moreover, across
countries, not every country being at similar risk for
malaria. This point is a source of heterogenity in
this study. Nonetheless, the aim of our study was to
provide practitioners not fully aware of the geographic
distribution of malaria with easy to determine predictive
factors of malaria. Malaria cases were not divided into
subspecies, which is of importance when evaluating
predictive factors. Indeed, we were unable to establish
predictive factor of malaria regarding plasmodium
species because of the small number of cases in most
groups. However, it is noteworthy that most of our
malaria cases (67%) due to P falciparum, and occurred
in VFRs (55%) and in travelers returning from Africa.
This is concordant with national records of imported
malaria in France. Of 8,056 imported malaria cases seen
in France in 2000, 83% were attributed to P falciparum
and 63% occurred in VFRs from African origin.22 In
our study, none of the 54 malaria cases were observed
in travelers returning from India which is concordant
J Travel Med 2010; 17: 124–129
128
with recent data showing that the incidence of malaria in
travelers to India decreased from 93/100 to 19 cases/100
travelers between 1992 in 2005.23
In this study, we compare cases versus non cases. Our
controls (non cases) were febrile returning travelers
with fever due to illness other than malaria. We
previously compared the characteristics of our travelers
with those presenting in our unit for pretravel advice.
Our ill travelers were representative of our ‘‘pretravel
population’’(data not shown).
Our patients were indifferently examined by the two
investigators. Recording of data was performed before
the final diagnosis was made. We only assessed variables
easy to collect in any febrile patient. In the Swiss study,
some clinical factors were difficult to use routinely such
as splenomegaly, which is not easily reproducible by
physicians.16 Similarly we recorded biological criteria
available only routinely. This is the reason why we did
not look at hypercholesterolemia, a factor strongly associated with malaria (OR = 75.22) in a previous study.24
Surprisingly, we found an association between inadequate chemoprophylaxis and medical advice taken
before travel. This discrepancy may be explained in
different ways. At first, medical advice may have been
motivated by vaccinations rather than chemoprophylaxis. Indeed, as most of our travelers were coming
back from sub-Saharan Africa, they may have needed
vaccination against yellow fever before their departure.
In addition, we defined ‘‘inadequate malaria chemoprophylaxis’’ as the occurrence of at least one missing
tablet; such definition could explain the high percentage of inadequate prophylaxis. Then, chemoprophylaxis
was considered inadequate in 62.5% of cases, including
interruption of treatment after return (25.9%). Last,
the high cost of chemoprophylaxis may have impaired
the adherence to the prophylactic regimen prescribed
during the pretravel consultation.
Of the biological factors assessed in our study,
only thrombocytopenia <150.103 /μL was associated
in multivariate analysis with malaria, a result which
was also found in others studies.13,16,17 Contrary to
other studies,17 neither leukopenia nor increased WBC
count was associated with malaria. This difference may
be because of the association between malaria and
thrombocytopenia, which is so strong that it does not
permit the appearance of other associations between
biological variables and cases.
Not a single clinical or biological criteria had both
a good sensitivity and specificity. The most sensitive
criteria was thrombocytopenia <150,000 (98.1%), as
previously observed in a French study (sensitivity =
75.22%).24 Although the predictive positive value of the
final model was 11.3% in the presence of two criteria
(carrying risk of omitting two malaria cases in this study,
unacceptable when due to P falciparum), it increased to
100% when five or six parameters were recorded (data
not shown).
In conclusion, our results suggested that no single
clinical or biological feature had both good sensitivity
J Travel Med 2010; 17: 124–129
Ansart et al.
and specificity to predict malaria in febrile travelers.
Therefore, blood smear for malaria must be prescribed
systematically in any febrile traveler returning from
endemic areas, whatever may be the associated clinical
or biological signs.
Declaration of Interests
The authors state that they have no conflict of interest.
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