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  • Research article
  • Open Access
  • Open Peer Review

Global prevalence and distribution of coinfection of malaria, dengue and chikungunya: a systematic review

BMC Public Health201818:710

https://doi.org/10.1186/s12889-018-5626-z

  • Received: 19 February 2018
  • Accepted: 29 May 2018
  • Published:
Open Peer Review reports

Abstract

Background

Malaria, Dengue and Chikungunya are vector borne diseases with shared endemic profiles and symptoms. Coinfections with any of these diseases could have fatal outcomes if left undiagnosed. Understanding the prevalence and distribution of coinfections is necessary to improve diagnosis and designing therapeutic interventions.

Methods

We have carried out a systematic search of the published literature based on PRISMA guidelines to identify cases of Malaria, Dengue and Chikungunya coinfections. We systematically reviewed the literature to identify eligible studies and extracted data regarding cases of coinfection from cross sectional studies, case reports, retrospective studies, prospective observational studies and surveillance reports.

Results

Care full screening resulted in 104 publications that met the eligibility criteria and reported Malaria/Dengue, Dengue/Chikungunya, Malaria/Chikungunya and Malaria/Dengue/Chikungunya coinfections. These coinfections were spread over six geographical locations and 42 different countries and are reported more frequently in the last 15 years possibly due to expanding epidemiology of Dengue and Chikungunya. Few of these reports have also analysed distinguishing features of coinfections. Malaria/Dengue coinfections were the most common coinfection followed by Dengue/Chikungunya, Malaria/Chikungunya and Malaria/Dengue/Chikungunya coinfections. P. falciparum and P. vivax were the commonest species found in cases of malaria coinfections and Dengue serotype-4 commonest serotype in cases of dengue coinfections. Most studies were reported from India. Nigeria and India were the only two countries from where all possible combinations of coinfections were reported.

Conclusion

We have comprehensively reviewed the literature associated with cases of coinfections of three important vector borne diseases to present a clear picture of their prevalence and distribution across the globe. The frequency of coinfections presented in the study suggests proper diagnosis, surveillance and management of cases of coinfection to avoid poor prognosis of the underlying etiology.

Background

In recent years the spread of vector borne diseases has gained concern worldwide, especially in tropical and subtropical regions because of their recurring outbreaks [1]. Some of these diseases have become endemic in many areas causing millions of cases every year [2]. The most common of these diseases includes Malaria, Dengue and Chikungunya spread by mosquito bites. Malaria has been long recognized as a significant public health threat with around 212 million cases reported in 2015 alone [3]. Malaria is caused by five different species of Protozoal parasite, Plasmodium. These include P. falciparum, P. ovale, P. malariae, P. vivax and P. knowlesi that are carried and spread by Anopheles mosquito [4, 5]. Dengue and Chikungunya are caused by viruses named Dengue virus (DENV) and Chikungunya virus (CHIKV) respectively. Both are spread by common mosquito vectors Aedes s p. Dengue viruses have four serotypes DENV-1, 2,3 and 4 [6]. As many as 400 million people are affected with Dengue every year [7]. Chikungunya follows somewhat unique pattern of spread across the world, it has the potential to emerge and re-emerge, drastically affecting a population and then remaining undetected for years [8]. In recent years many tropical countries have seen an unexpected rise and spread in cases of Dengue and Chikungunya [9].

These three vector borne diseases share an overlapping epidemic pattern with most cases reported from tropical regions of the world. Several studies have been published reporting co-circulation of Malaria, Dengue and Chikungunya [10, 11]. Apart from shared endemicity, the three diseases also share similar clinical presentation with febrility as the most common symptom. There are several distinguishing features also, like periodic increase and decrease of fever in Malaria, hemorrhagic conditions and depletion of platelet count in Dengue and severe arthralgia in case of Chikungunya infection [12, 13]. The cumulative burden of these infections has increased in recent times with frequent outbreak of Dengue and Chikungunya being reported from several parts of the world. Global travel and rapid urbanisation are important factors that have contributed in expansion of disease endemicity by introducing the vector population to exotic surroundings [14].

Simultaneous infections with more than one infectious agent complicate the diagnosis and course of treatment available. Due to the similar nature of initial symptoms for Malaria, Dengue and Chikungunya and overlapping endemicity, misdiagnosis of dual infection as monoinfection is a real possibility. Indeed several reports have been published reporting such scenarios. These arthropod borne diseases affect some of the poorest countries and in resource poor settings; clinician might rely on symptoms and endemicity for diagnosis, which might lead to underdiagnosis of cocirculating pathogens [15]. Despite similar clinical presentation the course of treatment is entirely different for all three diseases. Malaria is treated using antimalarial drugs. In case of Dengue and Chikungunya no vaccine or drug is available and clinicians rely on supportive therapy [13, 16]. Any delay in either diagnosis or start of therapy for any of these infections could have fatal outcomes. Also, there is lack of sufficient information on how concurrent infections affect disease severity and outcome. Several studies have been published that report cases of concurrent infection with two of these pathogens and in rare instances concurrent infection with all three vector borne infections. Such reports have the potential to inform public health officials and clinicians about the prevalence, disease severity and treatment options available for concurrent infections. The purpose of the present review is to assess the prevalence of such infections by thorough search and analysis of published literature.

Methodology

Search strategy

We did a review based on PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to identify all relevant publications pertaining to the prevalence of Malaria, Dengue and Chikungunya coinfection. We systematically searched PubMed and Web of Knowledge from inception up to April 2018, using the following search terms anywhere in the articles: Malaria AND Dengue or Malaria AND Chikungunya or Dengue AND Chikungunya. We searched without any bar on language, publication or nature of studies. To identify additional studies, reference list of publications were carefully screened.

Eligibility criteria

Initial assessment was based on review of title and abstract of all studies. Full texts of potentially relevant studies were further analysed for coinfection prevalence data. Cross-sectional studies, retrospective analysis and case reports with full text availability and reporting data about any/all of the coinfections were included in the study. We excluded studies carried out in animals, reviews, letters, opinion pieces, grey literature, dissertations and conference abstracts.

Data extraction

The data extracted from the selected publications included first author, date of survey, place where the study was carried out, sample size and age, type of diagnostic testing performed, study design and prevalence of coinfection. All the data was entered in an excel file and double-checked.

Prevalence mapping

The extracted data was used to create a map of prevalence of coinfection cases. All the cases reported were from seven geographical locations, South Asia, Africa, Southeast Asia, South America, North America, Caribbean and the Middle East. A total of 19 countries reported cases of Malaria/Dengue coinfection; while 24 countries reported coinfection cases of Dengue/Chikungunya. Malaria/Chikungunya cases were reported from 6 countries. Malaria/Dengue/Chikungunya coinfections were reported from only 3 countries. The maps were created using openly available maps (https://www.freeworldmaps.net).

Results

We were able to identify 109 publications that reported the data for any coinfections (Fig. 1, Additional file 1: Table S1). The full text of 104 publications were available out of which 48 were cross sectional studies, 37 were case reports, 13 were retrospective analysis, 5 were prospective studies and 1 surveillance report [17120]. 49 studies reported only Malaria/Dengue coinfections (Table 1) while 44 studies reported only Dengue/Chikungunya coinfections (Table 2). 1 study reported only Malaria/Chikungunya infection. 3 studies reported both Malaria/Dengue and Malaria/Chikungunya coinfections (Table 3) and 1 study reported Malaria/Dengue, Dengue/Chikungunya and Malaria/Chikungunya coinfections. Malaria/Dengue/Chikungunya coinfections were reported by 4 separate studies (Table 4). 2 studies reported Malaria/Dengue, Dengue/Chikungunya, Malaria/Chikungunya and Malaria/Dengue/Chikungunya coinfections. All of the studies, except two, were published after year 2005. Cases of coinfections were reported from all age groups and two studies from India and Burma reported data from only pregnant females. Blood smear was the most prevalent method for detection of Malaria parasite, while NS1 (Non-structural protein-1) and immunoglobulin ELISA were the most common methods for the detection of Dengue. IgM ELISA was the predominant method for the detection of most cases of Chikungunya. In 14 studies P. falciparum was the cause of Malaria while another 13 reported P. vivax as the infecting species alongside coinfecting arbovirus. 12 studies reported both P. falciparum and P. vivax with Dengue virus in the same population. Another 5 studies reported P. falciparum, P. vivax and Dengue virus in the same individuals. P. knowlesi was reported by two studies and P. ovale was reported by one study.
Fig. 1
Fig. 1

Schematic representation of the study selection process

Table 1

Coinfection cases of Malaria and Dengue

S.No.

Citation

Place

Year

Study design

N

Positive for coinfection

Coinfection (%)

Age

Diagnostic test ML/DN

Remarks

South Asia

1

Abbasi [17]

Karachi,

Pakistan

Sept.2007-Jan. 2008

Cross sectional

112

26

23

13–70

Blood smear / IgM and IgG ELISA

P. vivax- 25,  P. falciparum- 1

2

Ahmad [18]

Uttarakhand, India

Dec 2012-Dec2013

Retrospective observational studies

233

9

3.8

38.6 ± 16

Blood smear/ IgM ELISA

NM

3

Alam [19]

Patna,

India

2013

Case report

1

1

NA

42

Blood smear /NS1, IgM and IgG ELISA

P. falciparum

4

Ali [20]

Rawalpindi,

Pakistan

Nov. 2003-Oct. 2004

Cross sectional

800

9

1

17–50 years

Blood smear /IgM ELISA

P. vivax-8, P. falciparum-1

5

Arya [21]

Delhi,

India

2003

Case report

2

2

NA

35 and 63 years

Blood smear /IgM ELISA

P. vivax

6

Assir [22]

Lahore,

Pakistan

Aug- Nov 2012

Cross sectional

856

17

2

12–32

Blood smear /PCR, NS1 and IgM ELISA

P. vivax - 14, P. falciparum-3

7

Barua [23]

Mumbai,

India

June-Nov. 2014, June -Nov. 2015

Retrospective analysis

573

44

8

NM

Blood smear / NS1 and IgM ELISA

NM

8

Bhagat [24]

Mumbai,

India

2014

Case report

3

3

NA

8 months −12 year

Blood smear, RDT/NS1, IgM and IgG ELISA

P. vivax

9

Bhalla [25]

Delhi,

India

2006

Case report

1

1

NA

21

Blood smear /IgM ELISA

P. falciparum

10

Chander [26]

Chandigarh,

India

2009

Case report

1

1

NA

28

Blood smear /IgM ELISA

P. falciparum

11

Deresinski [27]

USA, infected in India

2003, Dec

Case report

1

1

NA

27

Blood smear/IgM and IgG ELISA

P. vivax

12

Faruque [28]

Chittagong,

Bangladesh

Dec. 2008-Nov. 2009

Cross sectional

720

1

0.1

All ages

RDT/IgM ELISA

P. vivax

13

Hati [29]

Kolkata,

India

Aug 2005-Dec 2010

Cross sectional

2971

46

1.5

NM

Blood smear /IgM and IgG ELISA

P. vivax-28, P. falciparum-18

14

Kaushik [30]

Dehradun,

India

2006

Case report

1

1

NA

26

Blood smear/ IgM and IgG ELISA

P. vivax + P. falciparum

15

Malhotra [31]

Patiala,

India

2012

Case report

1

1

NA

27

Blood smear /NS1 and IgM ELISA

P. vivax

16

Mittal [32]

Dehradun, India

Dec 2012- Nov 2013

Retrospective observational study

2547

8

0.3

Above 18

Blood film, RDT/IgM, NS1 ELISA

NM

17

Mohapatra [33]

Odisha,

India

June-Sep 2011

Prospective observational study

469

27

6

NM

Blood smear /IgM and NS1 ELISA

P. falciparum-24, P. vivax – 2, P. falciparum + P. vivax - 1

18

Mørch [34]

Assam,  Bihar, Chhattisgarh, Maharashtra, Anantpur Tamilnadu

India

April 2011–November 2012

Cross sectional

1564

58

3.7

34 mean age

Blood smear/IgM, NS1 ELISA/

NM

19

Mushtaq [35]

Srinagar, infected in Delhi,

India

Oct - 2012

Case report

1

1

NA

25

Blood smear, RDT/ IgM ELISA

P. falciparum + P. vivax

20

Pande [36]

Meerut,

India

2013

Case report

1

1

NA

25

Blood smear /NS1 and IgM ELISA

P. falciparum, P. vivax

21

Raja [37]

Chennai,

India

May 2013- Jan 2014

Cross sectional

100

3

3

NM

Blood smear/ELISA

NM

22

Rani [38]

Hyderabad, India

2015

Case report

1

1

NA

30s

Blood smear/IgM ELISA

NM

23

Rao [39]

Odisha (Angul), India

Jan-Dec 2013

Cross sectional

1980

22

1

All ages

Blood smear, RDT/ IgM and NS1 ELISA, PCR

P. falciparum- 12, P. vivax- 10

24

Singh [40]

Dehradun, India

July-Nov 2013

Retrospective

1141

9

0.8

12–80

Blood smear/IgM, NS1 ELISA

NM

25

Saksena [41]

Delhi,

India

2017

Case report

1

1

NA

17 male

RMAT, PCR/IgM ELISA

P. vivax, P. falciparum

26

Singla [42]

Chandigarh, India

Jan 2011-Dec 2012

Cross sectional

300

1

0.3

NM

NM/NS1 and IgM ELISA

P. vivax

27

Shah [43]

Ahmedabad, India

June 2013-Nov 2014

Retrospective

8364

27

0.3

NM

Blood smear/NS1, IgM ELISA

P. vivax + DENV-17, P. falciparum + DENV-9,

P. falciparum +  P. vivax + DENV-1

28

Thangaratham [44]

Alappuzha,

Kerala

2006

Case report

1

1

NM

22

Blood smear /IgM ELISA

P. vivax, DENV2

29

Yasir [45]

Karachi,

Pakistan

April 2013-Jan 2014

Cross sectional

159

5

3

15–53 years

Blood smear /IgM ELISA

NM

Africa

30

Ayorinde [46]

Ogun, Nigeria

April-May 2014

Cross sectional

60

1

2

All ages

Blood smear, RDT, PCR/NS1, IgM and IgG ELISA

P. falciparum

31

Baba [47]

Nigeria

July-Dec. 2008

Cross sectional

310

18

6

All ages

Blood smear /PRNT

P. falciparum

32

Charrel [48]

France, infected in Guinea, Senegal and Sierra Leone

2004, march

Case report

1

1

NA

37

Blood smear /IgM and IgG ELISA

P. falciparum, DENV3

33

Chipwaza [49]

Morogoro, Tanzania

March–May and Aug-Oct. 2013

Cross sectional

364

31

9

2–13

Blood smear /IgM and IgG ELISA, PCR

NM

34

Dariano [50]

Bo, Sierra Leone

2012–2013

Cross sectional

1260

3

0.2

All ages

RDTs/IgM, IgG, NS1 ELISA

NM

35

Kolawole [51]

Ilorin,

Nigeria

2016

Cross sectional

176

5

3

All ages

RDT/IgM ELISA, PCR

DENV2, DENV3, DENV4

36

Oyeoro [52]

Ibadan, Nigeria

Jan-April 2013

Cross sectional

188

19

10

All ages

NM/IgG, IgM, NS1 ELISA

NM

37

Sow [53]

Kedougou, Senegal

July 2009–March 2013

Cross sectional

13,845

1

0.01

All ages

Blood smear, RDT/ IgM ELISA, PCR

P. falciparum

38

Stolar [54]

Ghana

2011–2014

Retrospective analysis

218

7

3

2–14 years

RDT/IgM and IgG, ELISA, PCR

P. falciparum

39

Vu [55]

Kenya

2016

Cross sectional

579

33

6

1–17 years

Blood smear /PCR

NM

Caribbean

40

Serre [56]

Spain,

Infected in Haiti

2011

Case report

1

1

NA

27

Blood smear, PCR/IgM, IgG and NS1 ELISA, PCR

P. falciparum, DENV4

Southeast Asia

41

Che rahim [57]

Kelantan, Malaysia

2017

Case report

1

1

NA

59

Blood smear, PCR/NS1 ELISA

P. knowlesi

42

Chong [58]

Malaysia

2017

Case report

1

1

NA

59

Blood smear/NS1 and IgM ELISA

P. knowlesi

43

Issaranggoon [59]

Thailand

2014

Case report

1

1

NA

11

Blood smear/ NS1, IgM ELISA

P. falciparum

44

McGready [60]

Thai-Burmese border

Jan 2004-May 2006

Cross sectional

209

1

0.5

Pregnant women

Blood smear/IgM ELISA, NS1 ELISA

P. falciparum, P. vivax

45

Mueller [61]

(Oun Kouma, Ou Chra, Snoul)

Rural Cambodia

Jan 2008- Dec 2010

Prospective observational study

1193

30

2.5

7–49 years

RDT/PCR

P. falciparum, P. vivax

46

Thaha [62]

Surabaya, Indonesia

Nov 2008

Case report

1

1

NA

NM

Blood smear/IgM, IgG ELISA

NM

47

Ward [63]

East Timor

2006

Case report

1

1

NA

7

Blood smear /IgM ELISA

P. falciparum

48

Yong [64]

Riau Island Indonesia

2012

Case report

1

1

NA

49

Blood smear/IgM, NS1 ELISA

P. falciparum

South America

49

Carme [65]

French Guiana

July 2004-June 2005

Retrospective analysis

1723

17

1

NM

Blood smear/PCR, IgM ELISA, virus isolation

P. vivax − 14, P. falciparum- 3, DENV3–5, DENV1–1, NM-11

50

Epelboin [66]

French Guiana

2004–2010

Retrospective matched pair study

NM

104

NA

All ages

Blood smear/PCR, NS1, IgM, IgA ELISA

P. vivax – 80, P. falciparum – 21, P. vivax + P. falciparum – 3, DENV1–3, DENV2–2, DENV3–5, NM-94

51

Lupi [67]

Rio de Janeiro, Brazil

Apr 2013

Case report

1

1

NA

52

Blood smear, RDT, PCR/ IgM and NS1 ELISA, PCR

P. ovale wallikeri

52

Magalhaes [68]

Brazilian Amazon

Manaus

Brazil

March 2009 to April 2010

Retrospective study

132

11

8

Mean age, 42.7 yrs

Blood smear, PCR/NS1 ELISA, PCR

P. vivax DENV2, DENV3, DENV4

53

Magalhaes [69]

Brazilian Amazon

Manaus

Brazil

2009–2011

Cross-sectional

1578

44

3

All ages

Blood smear, PCR/ NS1 ELISA, PCR

P. vivax

54

Mendonca [70]

Brazilian Amazon

Manaus

Brazil

2009–2013

Prospective observational study

All febrile patients

30

NA

31.11 median age

Blood smear, PCR/ IgM and NS1 ELISA

P. vivax, DENV4–8, DENV3–1, DENV2–18, DENV1–3

55

Santana [71]

Novo Repartimento (Pará), Brazil

May 2003 to August 2005

Cross sectional

111

2

2

>  18 years

Blood smear/PCR

P. vivax, DENV2

N – sample size, ML/DN - Malaria/Dengue coinfection, ELISA - Enzyme linked immunosorbent assay, NS1 - Dengue non-structural protein − 1, PCR - Polymerase Chain reaction, RDT - rapid diagnostic test, PRNT - Plaque reduction neutralisation test, RMAT - Rapaid malaria antigen test, NM - not mentioned, NA - not applicable

Table 2

Coinfection cases of Dengue and Chikungunya

S.No.

Citations

Place

Year

Study design

N

Positive for coinfection

Coinfection (%)

Age

Diagnostic test DN/CK

Remarks

South Asia

1.

Afreen [72]

Delhi,

India

2014

Cross sectional

87

9

10

All ages

NS1, IgM, IgG ELISA, PCR/ IgM ELISA, PCR

DENV2 + CHIKV-5, DENV3 + CHIKV -2, DENV1 + CHIKV-1, DENV1 + DENV2+ CHIKV-1

2.

Carey [73]

Vellore,

India

1964

Cross sectional

477

8

2

All ages

Virus isolation

Serological

Complement fixation and Hemagglutination inhibition assay for both infection

NM

3.

Chahar [74]

Delhi,

India

2006

Cross sectional

69

6

9

All ages

PCR/PCR

DENV1, DENV3, DENV4

4.

Galate [75]

Mumbai,  Maharashtra

April 2012-Oct. 2013

Cross sectional

200

19

10

13–60

IgM ELISA/IgM ELISA

NM

5.

Hapuarachchi [76]

Sri Lanka

2006

Case report

1

1

NA

70

PCR/PCR

NM

6.

Kalawat [77]

Tirupati,

India

2011

Retrospective analysis

72

2

3

All ages

IgM ELISA / IgM ELISA

NM

7.

Kaur [78]

Delhi,

India

Aug-Dec. 2016

Cross sectional

600

152

25

11–68

IgM ELISA, NS1 ELISA, PCR/IgM ELISA, PCR

NM

8.

Londhey [79]

Mumbai,

India

June 2010–April 2015

Prospective observational study

300

30

10

All ages

IgM ELISA, PCR/ IgM ELISA, PCR

NM

9.

Mørch [34]

Assam, Bihar, Chhattisgarh, Maharashtra, Anantpur, Tamilnadu

India

April 2011–November 2012

Cross sectional

1564

25

1.6

34 mean age

IgM, NS1 ELISA/IgM ELISA

NM

10.

Mukherjee [80]

Kolkata,

India

July 2014-Oct. 2015

Cross sectional

326

53

16

All ages

IgM and NS1 ELISA, PCR/IgM ELISA, PCR

DENV2, DENV4

11.

Neeraja [81]

Hyderabad, Telangana

2007

Cross sectional

713

8

1

NM

IgG, IgM, PCR/PCR

NM

12.

Paulo [82]

Potugal,

Infected in India

2016

Case report

1

1

NA

65

PCR/IgM ELISA

DENV3

13.

Rahim [83]

Dhaka, Bangladesh

2017

Case report

1

1

NA

23 female

NS1 ELISA/IgM ELISA

NM

14.

Saswat [84]

Khurda, Odisha

Aurangabad,

Maharashtra India

July-Dec. 2013

Cross sectional

222

43

19

All ages

NS1, IgM, IgG ELISA, PCR/IgM ELISA, PCR

DENV2

15.

Shaikh [85]

Karnataka,

India

July 2010–June 2013

Cross sectional

6554

532

8

NM

IgM ELISA/IgM ELISA

NM

16.

Schilling [86]

Chennai,

India

September 2008

Case report

1

1

NA

25

NS1, IgM ELISA and IFA/IgM IFA

NM

17.

Taraphdar [87]

West Bengal, India

2010

Cross sectional

550

68

12

All ages

IgM ELISA, PCR / IgM ELISA, PCR

DENV2, DENV3

18.

Kularatne [88]

Peradeniya, Srilanka

Dec. 2006-March 2007

Cross sectional

54

3

5

15–74

IgM ELISA, Hemagglutination inhibition/ IgM ELISA, Hemagglutination inhibition

NM

Africa

19.

Baba [47]

Nigeria

July-Dec. 2008

Cross sectional

310

63

20

All ages

PRNT/PRNT

NM

20.

Caron [89]

Gabon

Sep 2007-Aug 2010

Cross sectional

4287

37

1

All ages

PCR of partial E gene/ PCR of partial E1 gene

DENV2

21.

Dariano [50]

Bo, Sierra Leone

2012–2013

Cross sectional

1260

13

1

All ages

IgM, IgG, NS1 ELISA/IgM ELISA

NM

22.

Leroy [90]

Gabon

March–July 2007

Cross sectional

773

8

1

NM

PCR/ PCR

DENV2

23.

Nkoghe [91]

Franceville, Gabon

Feb-July 2010

Cross sectional

433

20

4.6

1–77

PCR/PCR

NM

24.

Parreira [92]

Portugal, infected in Luanda, Angola

January 2014

Case report

1

1

NA

Early 50s

NS1 IgM, IgG ELISA, PCR/IgM ELISA, PCR

DENV4

25.

Ratsitorahina [93]

Tomasina,

Madagascar

Jan-March 2006

Cross sectional

55

10

18

NM

IgM ELISA, PCR/IgM ELISA, PCR

DENV1

Caribbean

26.

Edwards [94]

Guatemala

June 2015

Surveillance report

144

46

32

All ages

PCR/ PCR

DENV1–4, DENV2–40, DENV4–2

27.

Omarjee [95]

Island of Saint Martin

Dec. 2013-

Jan 2014

Cross sectional

1502

16

1

All ages

IgM, IgG ELISA and PCR / IgM, IgG ELISA and PCR

DENV1–10, DENV2–2, DENV4–4

Southeast Asia

28.

Cha [96]

Osong korea Infected (2 in Philllipine, 1 Vietnam, 1 Indonesia, 1 East Timor)

2009–2010

Cross sectional

486

5

1

11–70

IgM ELISA, PCR/ IgM ELISA, PCR

NM

29.

Chang [97]

Taipei China, infected in Singapore

2009 April

Case report

1

1

NA

12

IgM and IgG ELISA, PCR/ IgM and IgG ELISA, PCR

DENV2

30.

Khai Ming [98]

Rangoon, Burma

July 1970-Dec. 1972

Cross sectional

2060

55

2.6

0–11

HI, CF/HI, CF

NM

31.

Laoprasopwattana [99]

Southern Thailand

April–July 2009

Prospective Cohort study

50

1

2

≤15

IgM ELISA and Hemagglutination inhibition/IgM IFA, PCR

NM

32.

Nayar [100]

Kinta,

Malaysia

2006

Case report

2

2

NA

22 and 28

NS1, IgM ELISA, PCR/PCR

DENV1

33.

Ooi [101]

Selangor, Malaysia,

2009

Case report

1

1

NA

NM

NM/Complete Genome sequencing of CHIKV

DENV2

34.

Phommanivong [102]

Champasak Laos

July-Aug 2013

Cross sectional

40

5

12.5

5–65

PCR/PCR

DENV2–3,

DENV3–2

35.

Tun [103]

Mandalay,

Myanmar

July–October 2010

Cross sectional

116

7

6

≤12

IgM ELISA, PCR/IgM ELISA, PCR

NM

North America

36.

Kariyawasam [104]

Toronto, Canada

May 2006-April 2007 and Feb 2013-March 2014

Retrospective analysis

1304

1

0.07

0–91

PCR/PCR

DENV-1

37.

Lindholm [105]

Maryland,

USA

Dec 2013-May 2015

Cross sectional

267

2

0.7

25–60

IgM, IgG ELISA, PCR, PRNT/ IgM, IgG ELISA, PCR, PRNT

NM

South America

38.

Bocanegra [106]

Barcelona Spain Infected in south America

April 2014–2015

Retrospective

42

5

12

34.6 mean age

IgM ELISA/IgM ELISA, PCR

NM

39.

Brooks [107]

Santos,

Brazil

2017

Case report

1

1

NA

27

IgM ELISA/IgM ELISA

NM

40.

Calvo [108]

Girardot,

Colombia

Feb 2015

Cross sectional

8

4

50

0–10

IgM ELISA, PCR/PCR

NM

41.

Carrillo-Hernández [109]

Norte de Santander, Colombia

August 2015 – April 2016

Cross sectional

157

12

7.6

26.81

PCR/PCR

NM

42.

Farrell [110]

Machala, Ecuador

2015

Case report

1

1

NA

35

IgM, IgG ELISA/PCR

NM

43.

Gomez-Govea [111]

Nuevo leon,

Mexico

Jan-Oct 2015

Cross sectional

101

5

5

31 median age

IgM ELISA/IgM ELISA, PCR

NM

44.

Mercado [112]

Bogota, Colombia

Sept 2014-Oct 2015

Retrospective analysis

58

7

12

NM

IgM ELISA, PCR/PCR

NM

45.

Rosso [113]

Cali,

Colombia

2015

Case report

1

1

NA

72

PCR/ PCR

DENV3

Middle East

46.

Malik [114]

Al-Hudaydah, Yemen

Oct 2010-March 2011

Cross sectional

136

1

0.7

NM

IgM ELISA, PCR/IgM ELISA

NM

47.

Rezza [115]

Al-Hudaydah

Yemen

2012

Cross sectional

400

14

3.5

All ages

IgM, IgG ELISA and PCR/ IgM, IgG ELISA and PCR

DENV2 Predominantly

N – sample size, DN/CK – Dengue/Chikungunya coinfection, ELISA – Enzyme linked immunosorbent assay, NS1 - Dengue non-structural protein −1, PCR – Polymerase Chain reaction, IFA – immunofluorescence assay, PRNT – Plaque reduction neutralisation test, NM – not mentioned, NA – not applicable

Table 3

Coinfection cases of Malaria and Chikungunya

S.No.

Citations

Place

Year

Study design

N

Positive for coinfection

Coinfection(%)

Age

Diagnostic test ML/CK

Remarks

South Asia

1.

Mørch [34]

Assam, Bihar, Chhattisgarh, Maharashtra, Anantpur, Tamilnadu

April 2011–Nov 2012

Cross sectional

1564

20

1.3

34 mean age

IgM, NS1 ELISA/IgM ELISA

NM

Africa

2.

Ayorinde [46]

Ogun, Nigeria

April-May 2014

Cross sectional

60

9

15

All ages

Blood smear, RDT, PCR/IgM ELISA

P. falciparum

3.

Baba [47]

Nigeria

July-Dec. 2008

Cross sectional

310

21

6.7

All ages

Blood smear /PRNT

P. falciparum

4.

Chipwaza [49]

Morogoro, Tanzania

March–May and Aug-Oct. 2013

Cross sectional

364

2

0.6

2–13 years

Blood smear / IgM and IgG ELISA,

NM

5.

Dariano [50]

Bo, Sierra Leone

2012–2013

Cross sectional

1260

118

9

All ages

RDTs/IgM ELISA

NM

6.

Mugabe [116]

Quelimane Mozambique

Feb-June 2016

Cross Sectional

163

2

1.2

28 median age

RDT /IgM ELISA, PCR

NM

7.

Sow [53]

Kedougou, Senegal

July 2009–March 2013

Cross sectional

13,845

3

0.02

All ages

Blood smear, RDT/ IgM ELISA, PCR

P. falciparum

N – sample size, ML/CK- Malaria/Chikungunya coinfection, ELISA – Enzyme linked immunosorbent assay, NS1 - Dengue non-structural protein −1, PCR – Polymerase Chain reaction, RDT – rapid diagnostic test, PRNT – Plaque reduction neutralisation test, NM – not mentioned

Table 4

Coinfection cases of Malaria, Dengue and Chikungunya

S.No.

Citations

Place

Year

Study design

N

Positive for coinfection

Coinfection (%)

Age

Diagnostic test ML/DN/CK

Remarks

South Asia

1.

Abdullah [117]

Delhi,

India

2016

Case report

1

1

NA

21

Blood smear, RDT/PCR/IgM ELISA, PCR

P. vivax, DENV3

2.

Gupta [118]

Delhi,

India

2017

Case report

1

1

NA

55

RDT/NS1, IgM ELISA/PCR

P. falciparum

3.

Mørch [34]

Assam, Bihar, Chhattisgarh, Maharashtra, Anantpur, Tamilnadu India

April 2011–Nove 2012

Cross sectional

1564

2

0.1

34 mean age

Blood smear/IgM, NS1 ELISA/IgM ELISA

NM

4.

Tazeen [119]

Delhi,

India

2016

Case report

1

1

NA

3

Blood smear /PCR/PCR

P. vivax

Africa

5.

Dariano [50]

Bo, Sierra Leone

2012–2013

Cross sectional

1260

4

0.3

All ages

RDTs/IgM, IgG, NS1 ELISA/IgM ELISA

NM

6.

Raut [120]

India

Infected in Nigeria

2014

Case report

1

1

NA

21

Blood smear / NS1 ELISA, PCR/PCR

P. falciparum

N – sample size, ML/DN/CK – Malaria/Dengue/Chikungunya coinfection, ELISA – Enzyme linked immunosorbent assay, NS1 - Dengue non-structural protein −1, PCR – Polymerase Chain reaction, RDT – rapid diagnostic test, NA – not applicable, NM-not mentioned

Out of the 55 reports about Malaria/Dengue coinfections, only ten have reported the serotype of the Dengue virus. Out of the 47 reports about Dengue/Chikungunya coinfections 20 reports have mentioned the serotype of Dengue virus. Earliest report of Malaria/Dengue coinfection came in 2003 from Brazil, while earliest reported case of Dengue/Chikungunya coinfection came in 1964 from India. Malaria/Chikungunya cases were reported as late as 2008 from Nigeria. A retrospective matched pair study from French Guiana reported most cases (104) of Malaria/Dengue coinfections. Maximum cases of Dengue/Chikungunya coinfections (532) were reported from Karnataka in India and most cases of Malaria/Chikungunya coinfections (118) were reported from Bo, Sierra Leone.

Most cases of coinfections were reported from South Asia (52), primarily from India, followed by Africa (25), South-east Asia (16), South America (15), Caribbean (3) and Middle East (2). Two studies from North America reported coinfections of Dengue/Chikungunya in returning travellers without identifying the location where coinfections occurred. Malaria/Dengue coinfections were reported from 44 unique locations spread across 20 different countries (Fig. 2). Dengue/Chikungunya coinfections were reported from 48 unique locations spread across 26 countries (Fig. 3). 5 countries from African continent and India reported cases of Malaria/Chikungunya coinfections (Fig. 4). Cases of Malaria/Dengue/Chikungunya coinfections were reported from India, Sierra Leone and Nigeria (Fig. 5). Seven countries reported infection in returning travellers(Fig. 6). Based upon cross sectional studies Malaria/ Dengue prevalence varied widely, ranging between 0.1–23% from south Asia, 0.01–9% from Africa, 0.5–2.5% from Southeast Asia and 1–3% from South America. The frequency of Dengue/Chikungunya coinfections ranged from 1 to 25% from South Asia, 1–20% from Africa, 1–32% from Caribbean, 1–12.5% from Southeast Asia, 0.07–0.7% from North America, 5–50% from South America and 0.7–3.5% from Middle east. Malaria/Chikungunya coinfections frequency ranged from 0.02–15% from Africa and a single study reported from India reported 1.3% patients coinfected with both pathogens. Malaria/Dengue/Chikungunya coinfection frequency was reported by two cross sectional studies, one from India with 0.1% prevalence and another from Sierra Leone with 0.3% prevalence.
Fig. 2
Fig. 2

Global distribution of Malaria/Dengue coinfections

Fig. 3
Fig. 3

Global distribution of Dengue/Chikungunya coinfections

Fig. 4
Fig. 4

Global distribution of Malaria/Chikungunya coinfections

Fig. 5
Fig. 5

Global distribution of Malaria/Dengue/Chikungunya coinfections

Fig. 6
Fig. 6

Countries from where coinfection cases were reported in returning travellers

Discussion

Malaria, Dengue and Chikungunya are arthropod borne diseases that have shared endemic profiles. These diseases are spread by mosquito vector, which are found in abundance in tropical regions of the world. Anopheles mosquito, which transmits Malaria parasite, is a night biting mosquito and breed in stagnant water [121]. Aedes that spreads Dengue and Chikungunya, on the other hand bites in daylight and breeds in stored clean water [122]. Expansion of the Aedes vector has lead to introduction of Dengue and Chikungunya to newer locations. Rapid urbanisation without the development of civic infrastructure, constant movement of population for livelihood, monsoon dependent breeding patterns and overlapping habitats have lead to co-circulation and coinfection of these pathogens in the same population [123]. Diagnosis of cases of coinfection is compounded by the fact that initial symptoms of all three diseases are very similar that include febrility as the common factor. Several reports have been published that does not identify the coinfecting pathogen due to lack of distinguishing symptoms at the time, but retrospective analysis later revealed otherwise. In resource poor settings and during outbreaks clinicians might not have the resources or time to rely on detailed investigations.

We have attempted to identify regions of the world from where cases of mixed infection with Malaria, Dengue and Chikungunya have been reported. We searched the databases to identify published reports about any of these coinfections. Most reports of Malaria/Dengue and Dengue/Chikungunya coinfections were reported from India. In recent years there have been many outbreaks of Dengue and Chikungunya in India, not to mention that the first published report of Dengue/Chikungunya coinfection was reported from India in 1967 [72]. However the overall percentage of Malaria/Dengue coinfections was low which, can be explained by different vector species for Malaria verses Dengue and Chikungunya. The highest frequency of Malaria/Dengue coinfections was reported from Pakistan that is endemic for both Malaria and Dengue. Lowest frequency was reported form Senegal with only 0.01%. 41 reports clearly identified the parasite species for Malaria infection but only 10 reported the serotype of Dengue virus. All four serotypes were found to exist with Malaria parasite. Coinfection cases were found in all age groups and gender. Nearly 85% of the reports for Malaria/Dengue coinfections have used microscopic confirmation of the Malaria parasite identifying the parasite load and species. Dengue infections were primarily detected by a combination of immunoglobulin ELISA, NS1 ELISA and PCR.

Dengue/Chikungunya coinfections were reported by 47 studies and an overall higher percentage as compared to Malaria/Dengue coinfection possibly because of similar vector species. The Highest frequency of Dengue/Chikungunya coinfections was reported from Colombia and lowest from Canada in returning travellers. Dengue virus serotype-4 was the predominant serotype found in cases of coinfections. Malaria/Chikungunya coinfections were rare with only 7 published reports. All of them were reported from Africa and India. 6 studies reported Malaria/Dengue/Chikungunya coinfections, four of them were case reports and two cross sectional studies. Three of the case reports were infected in Delhi while another one could have been infected in Nigeria or India. Delhi has become a hub of Industrial and social activities with a burgeoning population. Almost every year during monsoon season the city witnesses Dengue outbreaks with thousands of people getting infected. Due to the lack of distinguishing clinical features, laboratory diagnosis based on endemic patterns and outbreak reports are the only way for adequate clinical management of double or triple coinfections. At least 12 studies reported coinfections in returning travellers underlining the role of travel-based spread of the diseases. This phenomenon has been observed for SARS, MERS-CoV and Dengue [124126]. Exposing a naïve population to new pathogens might lead to disease outbreak, not to mention viral mutations to adapt its human or mosquito host resulting in more pathogenic strain. Travel advisories and routine surveillance of returning travelers to endemic regions should be implemented stringently to control spread of infections.

Interaction of multiple pathogens within a host may potentially result in several different outcomes. Firstly, if the coinfecting organisms are dependent on similar tissues, the host may have to deal with multiple pathogens at the same time and place. Such interactions are likely to be detrimental to the host as happens in the case of coinfection with Hepatitis B, C and Delta virus coinfections. Hepatitis B, C and Delta virus coinfections results in severe chronic disease that responds poorely to the interferon alpha treatment [127] as compared to single infections. Secondly, the immune effector mechanisms triggered by one pathogen may weaken or divert the host immunity leading to severe outcomes or increased resistance to therapy as exemplified in the case of infection with Mycobacterium tuberculosis and parasite coinfections [128]. Thirdly, the coinfection may not have any serious effect on the prognosis of disease. However, even in such cases the misdiagnosis and mistreatment that may result, can be detrimental to the host. And finally, a coinfection may infact lead to better prognosis. For instance, it has been observed in the decreased mortality rate among the HIV patients coinfected with hepatitis G virus as compared to patients infected with HIV [129]. Plasmodium, Dengue virus and Chikungunya virus all infect different cell types in humans and might influence immune effector mechanism by downregulationg proinflammaotry cytokines like IL-12 and IFN-γ [11, 130]. A proper clinical analysis of Malaria, Dengue and Chikungunya coinfection is necessary to form an informed opinion on following a treatment regimen that best supports the patient and leads to an early resolution of the infection. Out of 104 reports, there are very few reports that have actually looked at the disease severity by establishing proper controls and comparing it with cases of monoinfections systematically. For Malaria/Dengue coinfections, prolonged fever, thrombocytopenia, anemia, renal failure and Jaundice were more pronounced as compared to monoinfections. Dengue/Chikungunya coinfections can result in diarrahea, deep bleeding, hepatomegaly and overall increase in disease severity. High grade fever was the only distinguishing feature of Malaria/Chikungunya coinfection. More such studies are required to create a consensus about disease outcome in cases of coinfections. Animal models that can replicate the coinfection scenario would be very helpful in identifying severity patterns for these diseases.

The distribution of Aedes vector has been reported from Southeast Asia, South Asia, East, Central and West Africa, Caribbean and South America. Aedes aegypti and Aedes albopictus are responsible for the spread of Dengue, Chikungunya, West Nile, Yellow fever and Zika virus [131]. It is difficult to distinguish whether cases of coinfection are due to separate mosquito bites delivering the viruses or single bite by mosquito harboring both viruses. The incubation period of both viruses is nearly same so both diseases are manifested around the same time. Anopheles has also been reported from the above-mentioned regions and also from East and central Asia, Europe and North America [132]. Most cases of Malaria/Dengue and Malaria/Chikungunya coinfections were found from the regions where both vector species are present. In many instances a seasonal pattern of infections is observed with most cases being reported during monsoon season, which coincides with the breeding season of Mosquito vector. Climatic, sociodemographic and environmental factor play a crucial role in survivability and distribution of the mosquito vector thereby influencing cases of coinfections [133]. Vector control continues to be an integral part of reducing disease burden but very few studies reported about the vectors responsible for cases of coinfection. Routine collection of vector surveillance data and thorough analysis of the role of vectors in coinfection cases need to be assessed.

Data collection is prone to bias, to this affect we have made every effort to search and analyze the current literature with broad search queries, nonetheless many relevant studies were unavailable due to lack of full text availability. Also the review relied completely on published literature where grey literature and studies with minimal or negative results may not have been included resulting in publication bias. Furthermore, studies obtained were of variable quality and many did not reported data on disease severity and outcomes in cases of coinfections. Despite these lacunas, the present study attempts to clearly identify regions of the world from where cases of coinfections were reported by thorough search and analysis of published reports. Our analysis indicates that coinfections with Malaria, Dengue and Chikungunya or in rare instances all three is a possibility. Our analysis also indicates that there are higher percentages of people with febrile symptoms, which might have Dengue/Chikungunya coinfections as compared to Malaria/Dengue or Malaria/Chikungunya coinfections. Shared epidemiology, vector distribution and co-circulation of pathogens are some of the reasons for coinfections. We have georeferenced cases of coinfections and identified affected countries of the worlds, establishing co-endemicity of these infections, which might help in proper and complete diagnosis of cases of coinfections with similar initial symptoms.

Conclusion

This systematic review has found evidence of Malaria, Dengue and Chikungunya coinfections in 42 Countries spread across several geographical locations. Malaria/Dengue was the most prevalent coinfection followed by Dengue/Chikungunya. These infections often affect same populations due to share endemicity and can be present simultaneously in the same individual. Similar initial symptoms make it harder for clinicians to identify cases of coinfections. Most coinfections were found from South Asia and Africa. P. falciparum and P. vivax were the most common malaria species found with coinfecting arbovirus and DENV-4 was the most common serotype found in cases of Dengue coinfections. Prolonged and high grade fever, thrombocytopenia, diarrhea, Jaundice and hepatomegaly were some of the signs and symptoms associated with cases of coinfection. We also found evidence of coinfections in returning travellers, which have the potential to introduce the pathogen into new locations with established vector populations. Our study highlights the global prevalence of cases of coinfection and their geographical distribution, which could help in systematic planning, surveillance, diagnosis and health care delivery to the affected population.

Abbreviations

CHIKV: 

Chikungunya Virus

DENV: 

Dengue Virus

ELISA: 

Enzyme linked immunosorbent assay

MERS-CoV: 

Middle East respiratory syndrome corona virus

PCR: 

Polymerase chain reaction

SARS: 

Severe Acute Respiratory Syndrome

Declarations

Availability of data and materials

The datasets analysed during the current study is available from the corresponding author on reasonable request.

Authors’ contributions

NS, SM, AH, extracted the data, AAC, FD and SP cross checked and tabulated the data, NS wrote the manuscript. All the authors read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
College of Medicine, Al-Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
(2)
Department of Parasitology, College of Medicine, Umm Al-Qura University, Mecca, Saudi Arabia
(3)
Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, 110025, India

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