Malaria Prevalence in Endemic Districts of Bangladesh
Ubydul Haque1, Syed Masud Ahmed2, Shahed Hossain1, Mamun Huda1, Awlad Hossain2, Mohammad
Shafiul Alam1, Dinesh Mondal1, Wasif Ali Khan1, Mohammod Khalequzzaman1, Rashidul Haque1*
1 International Center for Diarrhoeal Disease Research Bangladesh, Mohakhali, Dhaka, Bangladesh, 2 BRAC, BRAC Centre, Dhaka, Bangladesh

Abstract
Background: Following the 1971 ban of DDT in Bangladesh, malaria cases have increased steadily. Malaria persists as amajor health problem in the thirteen south-eastern and north-eastern districts of Bangladesh. At present the nationalmalaria control program, largely supported by the Global Fund for AIDS, Tuberculosis and Malaria (GFATM), provides
interventions including advocacy at community level, Insecticide Treated Net (ITN) distribution, introduction of Rapid
Diagnostic Tests (RDT) and combination therapy with Coartem. It is imperative, therefore, that baseline data on malaria
prevalence and other malaria indicators are collected to assess the effectiveness of the interventions and rationalize theprevention and control efforts. The objective of this study was to obtain this baseline on the prevalence of malaria and bed
net use in the thirteen malaria endemic districts of Bangladesh.
Methods and Principal Findings: In 2007, BRAC and ICDDR,B carried out a malaria prevalence survey in thirteen malaria
endemic districts of Bangladesh. A multi-stage cluster sampling technique was used and 9750 blood samples werecollected. Rapid Diagnostic Tests (RDT) were used for the diagnosis of malaria. The weighted average malaria prevalence inthe thirteen endemic districts was 3.97%. In five south-eastern districts weighted average malaria prevalence rate was 6.00%
and in the eight north-eastern districts weighted average malaria prevalence rate was (0.40%). The highest malaria
prevalence was observed in Khagrachari district. The majority of the cases (90.18%) were P. falciparum infections. Malaria
morbidity rates in five south-eastern districts was 2.94%. In eight north-eastern districts, morbidity was 0.07%.
Conclusion and Significance: Bangladesh has hypoendemic malaria with P. falciparum the dominant parasite species. The
malaria situation in the five north-eastern districts of Bangladesh in particular warrants urgent attention. Detailed maps ofthe baseline malaria prevalence and summaries of the data collected are provided along with the survey results in full, in a
supplemental information
Citation: Haque U, Ahmed SM, Hossain S, Huda M, Hossain A, et al. (2009) Malaria Prevalence in Endemic Districts of Bangladesh. PLoS ONE 4(8): e6737.
doi:10.1371/journal.pone.0006737
Editor: Simon I. Hay, University of Oxford, United Kingdom
Received May 6, 2009; Accepted June 19, 2009; Published August 25, 2009
Copyright: ß 2009 Haque et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This study was implemented with Global Fund for AIDS TB and Malaria (GFATM) support. The project was jointly implemented by BRAC (BRAC is abrand name and it can’t be expanded) and ICDDR,B (International center for diarrhoeal disease research Bangladesh). The funders had no role in study design,
data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: rhaque@icddrb.org

the 1990s [3,4]. In the late 1990s, more than 500 deaths werereported with 70,000 laboratory-confirmed cases and 900,000
clinical cases of malaria in Bangladesh [5].
The number of malaria cases in Bangladesh fluctuates
seasonally. The majority of these cases occur in the thirteen
districts close to and/or bordering India and Myanmar. These
thirteen districts, out of the 64 administrative districts of
Bangladesh, are recognized as malaria endemic. Ninety eightpercent of the malaria case reports come from these thirteen
districts. Three out of these thirteen districts, Bandarban,
Khagrachari and Rangamati, collectively known as the Chittagong Hill Tracts (CHT) districts, report the highest incidence ofmalaria within the country. These thirteen districts are difficult toreach due to the hilly terrain and therefore have inadequatepassive surveillance and information systems resulting in poor
reporting of malaria cases by the Ministry of Health, Governmentof Bangladesh [6,7].
In 2006, the Global Fund for AIDS, TB and Malaria(GFATM), awarded Bangladesh USD 39.6 million to support
the national malaria control program. These funds were used byIntroduction
Malaria is estimated to be directly responsible for around one
million deaths annually worldwide [1]. The morbidity andmortality burden caused by malaria are responsible for nearly
3% of the world’s DALYs [2]. Even though Africa accounts for
90% of the mortality burden for malaria, South-east Asia still
suffers considerable mortality and morbidity. Malaria is a major
public health problem in Bangladesh. Of the 11 countries of theWorld Health Organization South East Asian Regional Office, ten
countries including Bangladesh are malaria endemic.
Due to the frequent use of DDT by the Malaria Eradication
program of the then Government of East Pakistan, malaria wasmostly under control before 1971 [3]. After the independence of
Bangladesh from Pakistan, DDT was banned in 1985 and thenumber of malaria cases began to increase. Since the incidence ofmalaria in the eastern regions was low and there was a lack ofadequate funds and programs, no control efforts maintained in themalaria endemic areas of Bangladesh. Without these control
efforts, malaria cases started to increase and became epidemic in
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the national malaria control program to start advocacy atcommunity level, ITN distribution, introduction of rapid diagnosis
test (RDT) and to introduce combined therapy with coartem. In
the absence of baseline information, these resources could not be
equitably distributed among the population. Furthermore, thesedata would be needed for future comparisons to assess theeffectiveness of these programs. Therefore in 2007, BRAC andICDDR,B carried out this first ever malaria prevalence survey ofBangladesh, the results of which are presented here.

Methods
Objectives
Our primary objective was to assess the prevalence of malaria inthirteen malaria endemic districts of Bangladesh. In addition to
the prevalence survey, we investigated the use of bednets inendemic regions.

Study Area
The research was carried out in seventy Thanas (sub districts) ofthirteen malaria endemic districts of Bangladesh. According to the
Ministry of Health, Government of Bangladesh, 98% of the cases
are reported from these Thanas of Bangladesh [8]. These thirteen
districts are divided into two parts. Eight north-eastern districts
share a common border with India. Five south-eastern districts
situated in the south-eastern part of Bangladesh. Three of these
districts share the common border with India and Myanmar andare known as Chittagong Hill Tracts (CHT) Districts. These three
districts area is mostly hilly, forested and thus relatively
inaccessible.

Figure 1. Shows study procedure and sampling.
doi:10.1371/journal.pone.0006737.g001

Ethical considerations
The protocol was reviewed and approved by the researchreview committee and the ethical review committee of ICDDR,B.
The probability and magnitude of harm or discomfort anticipatedin the proposed research are not greater in and of themselves thanthose ordinarily encountered in daily life or during theperformance of routine physical, psychological examinations or
tests. The study is therefore expected to be minimal risk. Writtenconsent was obtained from the participants.

Sample size calculation
We anticipated the malaria prevalence in our eight northeastern endemic districts to be around 2%, while in the five southeastern endemic districts it would be more than 3%. Given thedesign of the study as a cluster survey, the sample size had toincorporate these expectations [10]. The sample size wascalculated using web-based software C-Survey 2.0 based on theconservative estimates of malaria prevalence and the design effect.
Sample size was estimated assuming the lowest estimate of malaria
prevalence at 2% with a precision of 1.5%, at 95% confidence
interval with a design effect of two. Thus, 750 individuals from 750
households were taken in each district for this study. Totally 9,750
individual samples from thirteen malaria endemic districts werecollected for this study.

Recruitment, training and deployment of theinterviewers
The survey team comprised of experienced interviewers andtheir supervisors. A five-day intensive training was organized forthe interviewers consisted of didactic lectures, mock interviews,
role play and field practice at community level. Each teamtypically consisted of four members including one person trainedin malaria microscopy. The training was organized by the experts
from BRAC and ICDDR,B prior to the start of the survey.

Field operation
The malaria prevalence survey was conducted during September, October and November 2007. In each of the selected mauza(‘‘clusters’’), the study team drew a map. The field team selectedhouseholds systematically, as they moved from the center or
periphery, following a designated path determined by the ‘‘spin
the bottle’’ methodology [11].
All members of the household were listed. Only one individualfrom a household was enrolled into this study using a simple
randomisation procedure. Informed consent was obtained before
proceeding with the survey activities. Selected individuals were
tested for malaria and information collected for any febrile illness
in the past 15 days. One team member drew four drops of finger
prick blood from each participant. Rapid Diagnostic test (RDT)
for malaria was performed on the spot. We also kept one note inour record whether he/she suffered with fever within last 15 days
or not. If he/she suffered with fever, we recorded that morbidity.
Patients diagnosed as having malaria were provided Coartem fortreatment. A socioeconomic survey related to malarial knowledge
and relevant health-seeking behavior was also conducted. A semi-

Sampling
A three stage cluster sampling technique was employed using
population figures from the 2001 census. City Corporations andtowns were excluded from this survey because they are urban andof extremely low malaria risk. For each of the thirteen districts, all
Mauzas (Mauzas are the lowest administrative unit of Bangladesh,bigger than village and have a polygon boundary) were listed
alphabetically and 30 Mauzas were selected using a probability
proportional to size (PPS) sampling procedure [11]. These Mauzas
were the primary sampling unit of the survey. Twenty-fivehouseholds were selected using systematic randomization fromeach Mauzas/cluster (Figure 1). All population in a cluster was
eligible to participate in the survey. Information about bed net use
was obtained from the head or spouse (or a knowledgeable
member in their absence) of the selected household. For
prevalence (parasitological) survey, one household member fromeach household was randomly chosen.
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Figure 2. Shows the survey cluster locations.
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MPEDBstructured questionnaire was developed and use for this purpose,
the results of which will be presented elsewhere.

the clustered nature of the data, the svy command in STATA wasused with the cluster as the primary sampling unit (psu) stratified byDistrict. All results were weighted (weight = 1/probability of
selection) to account for unequal probabilities of selection ofclusters across District. To test for differences in proportion ofparticipant livelihood (categorized as south-eastern and northeastern district), bed-net using status in their house (bed-net related
variable is listed as a binary variable with their mean as the cut-off
point) between malaria positive and malaria negative household aPearson chi-square test according to survey design (cluster andstratification) was used and the test statistic converted to an Fstatistic using the second-order Rao and Sott correction yielding
wider confidence intervals and conservative P-value compared tothe uncorrected Chi-square test. To find the independent risk
factors for malaria positive households, a logistic regression
analysis were performed. Variables with a p value , = 0.05 levelin the bivariate analysis (converted F-statistic) were included in a
stepwise logistic regression procedure.

Diagnosis of Malaria
Malaria was diagnosed by Rapid Diagnostic Tests (RDT) that
detects both P. falciparum-specific antigen and Plasmodium vivaxspecific antigen. The trade name of this RDT is ‘‘FalciVax’’ and is
produced by Zephyr Biomedicals, India (www.tulipgroup.com).
Each FalciVax is rapid self-performing, qualitative, two site
sandwich immunoassay utilizing whole blood for the detection of
P. falciparum specific histidine rich protein-2 (Pf, HRP-2) and P.
vivax specific pLDH. The test can be used for specific detection anddifferentiation of P. falciparum and P. vivax malaria. The
standardization of this test has already been done by the Zephyr
Biomedicals. Sensitivity of the RDT is similar to that commonly
achieved by good field microscopy. Sensitivity and specificity of
the RDT used for the detection of P. falciparum and P. vivax is morethan 95% and now been recommended for use in the malaria
control program by the World Health Organization [12,13,14].

Results

GPS data collections

In thirteen malaria endemic districts, the overall weighted
malaria prevalence rate was 3.97% (Table 1). The survey clusters(see supplemental information file) and malaria prevalence maps
were prepared (figure 2, 3 and 4 respectively). Figure two shows
the survey cluster locations. The distribution of P. falciparum ispresented in figure three. Plasmodium falciparum was found in
eleven districts of the thirteen surveyed districts. The prevalence
rate varied from 0.13% to 15.07%. Figure four, shows that P. vivax
was found in ten districts; in which the prevalence rate varied
from 0.13 to 1.2%. The weighted prevalence of P. falciparum was
3.58% and the P. vivax 0.21% and mixed infection with P.
falciparum and P. vivax was 0.18%. The proportion of P. falciparumwas 90.18% while P. vivax and mixed infection with these two
species were 5.29 and 4.53% respectively in these thirteen
districts. Prevalence of P. falciparum in males and females was
3.96% and 3.98% respectively. The prevalence of malaria wassignificantly higher in children. The prevalence of falciparum
malaria in children 0–4 years of age was 8.5% and 5–14 years ofage 6.6%. P. vivax prevalence among male are 0.15%, and female

The coordinates (longitude and latitude) of all selected Mauzas(n = 390) were recorded on-site using eTrex Venture Garmin
single handheld GPS receivers. GPS points were uploaded to aFox Pro database system and checked for accuracy at the fieldlevel. GPS points were superimposed on the polygon boundaryand verified accuracy. Arc GIS 9.2 software was used fordeveloping map. Figure 2 shows the exact cluster locations from
where blood samples were collected.

Data preparation and statistical analysis
An anonymous database was created and double checked. Fox
Pro was used for data entry and storage. Data was cleaned andchecked for duplicates. District, Thana, Union, Mauza, household
and person identifier was given and later converted into a unique
identifier. Of the 9750 questionnaire obtained, one questionnaire
about bed nets was lost and 9749 questionnaires were used foranalysis. Descriptive summaries of infection prevalence weregenerated using STATA 10 and MS Excel 2007. To account for

Table 1. Overall and age, sex and area wise prevalence of different Malaria parasite infection (Weighted).

PrevalenceOver allAny (pf or pv) infection

Pf infection

Pv infection

Mixed (pf and pv) infection% [95% CI]% [95% CI]% [95% CI]% [95% CI]

3.97 [3.11–4.83]

3.58 [2.75–4.42]

0.21 [0.08–0.34]

0.18 [0.01–0.35]

By sexMale

3.96 [2.96–4.97]

3.60 [2.67–4.54]

0.15 [0.01–0.29]

0.21 [21.1–0.53]

Female3.98 [2.89–5.08]

3.57 [2.50–4.64]

0.27 [0.06–0.48]

0.15 [0.03–0.27]

0.40 [20.26–1.05]

By age group
0–4 yrs

11.34 [6.83–15.9]

10.32 [5.98–14.7]

0.63 [20.03–1.28]

5–14 yrs

8.69 [6.06–11.32]

8.35 [5.75–10.94]

0.21 [20.04–0.46]

0.13 [20.02–0.28]

15–49 yrs

2.74 [2.06–3.42]

2.42 [1.77–3.07]

0.19 [0.02–0.37]

0.13 [0.01–0.25]. = 50 yrs

1.84 [0.89–2.80]

1.30 [0.58–2.03]

0.17 [20.07–0.43]

0.36 [20.24–0.96]

South-eastern

6.00 [4.67–7.34]

5.49 [4.18–6.79]

0.31 [0.11–0.49]

0.21 [20.05–0.50]

North-eastern

0.40 [0.19–0.63]

0.25 [0.01–0.41]

0.04 [20.04–0.12]

0.11 [0.02–0.21]

By area/districts

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Figure 3. Distribution of Plasmodium falciparum in endemic areas of Bangladesh. The distribution of P. falciparum is presented in Figure 3.
Plasmodium falciparum was found in eleven districts of the thirteen surveyed districts. The prevalence rate varied from 0.13% to 15.07%.
doi:10.1371/journal.pone.0006737.g003

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Figure 4. Distribution of Plasmodium vivax in endemic areas of Bangladesh. Figure four, shows that P. vivax was found in ten districts; inwhich the prevalence rate varied from 0.13 to 1.2%.
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Table 2. Bed net use pattern as well as participants districts by malaria infected and non-infected households (HH).

Malaria infected HH

Malaria non-infected HH

N = 300

P-value

N = 9449%(95% CI)n%(95%CI)N

Having bed net

91.1(86.6–94.2)

267

93.7

(92.6–94.6)

8799

Having , = 2 bed nets

57

(49.1–64.6)

198

46.4

(44.4–48.3)

5461

0.006

, = 5 family member use bed net regularly

57.6(50.0–65.0)

214

55.2

(53.3–57.2)

6487

0.529

Bed-net treated with medicine within last 6 month

01.7(0.64–5.50)

05

02(1.3–2.8)

163

0.885

All family member

80.3(73.2–86.0)

239

83.8

(82.0–85.4)

7917

0.274

, = 3 adults members

68.1(61.6–73.9)

232

61.4

(59.2–63.5)

6653

0.044

, = 2 child members

57.4(48.9–65.5)

206

62.2

(59.7–64.6)

6690

0.255

, = 2 male members

53.3(46.7–60.0)

191

46.8

(45.3–48.4)

5424

0.054

, = 2 female members

50.5(43.9–57.2)

189

51.8

(50.1–53.6)

5853

0.885

South-eastern

96.3(93.5–98.0)

271

62.4

(61.7–63.0)3480,0.0001

North-eastern

03.7(02.1–06.5)

29

37.7

(37.0–38.3)

5969

Bed net status in participant house
0.118

Last night bed-net use by

Participants districts

doi:10.1371/journal.pone.0006737.t002

are 0.27%. Mixed infection prevalence among male and female
were 0.21% and 0.15% respectively. In five south-eastern districts
the average weighted prevalence rate was 6.00% while in the eight
north -eastern districts average weighted prevalence rate was
0.40%. The overall malaria prevalence in Chittagong Hill Tracts(CHT) districts was 11.7%. The prevalence rate was 15.25%,
10.97% and 7.42% in Khagrachari, Bandarban and Rangamati
districts respectively.
When we conducted the survey, we randomly identified one
person and performed a RDT. We recorded whether thisindividual reported fever within last fifteen days. If he/she hadsuffered with fever within last fifteen days and was RDT positive,
we considered that is morbid event. Malaria morbidity was foundto be higher in the five south-eastern districts when compared tothe eight north-eastern districts.
A total of 9750 participants in the study area, 300 were RDT
positive and 9450 were negative. Please see the supplemental
information (Table S1). Households of RDT positive participants
were considered as malaria infected households and RDT negative
participant’s households are malaria non-infected households.

Bednet use information of 9749 participants was used for statisticalanalysis and only one participant’s information was missing. About
91.1% (n = 267) and 93.7% (8799) participant’s households had a
bed net among the malaria positive and negative households
respectively (p = 0.118, not significant). We have also observed thatthe bed net use pattern in the infected and non-infectedhouseholds and found that malaria infected households having
significantly less number of bednets (, = 2) compared to malaria
non-infected households (Table 2). It was also found thatsignificantly more malaria infected households are in the fivesouth-eastern districts compared malaria non-infected households(Table 2). In the five south-eastern districts the percentage of bed
nets treated with medicine within last six months were similar forboth types of households. Table 3 shows the adjusted odds ratios(OR) for the variables using the logistic regression model.
Independent risk factors for malaria infection found significant
in multivariate analysis for the entire surveyed area when less thanor equal to two bednets in household (OR = 1.87, 95% CI = 1.30–
2.71) and households in south-eastern districts (OR = 17.31, 95%
CI = 9.60–31.30).

Table 3. Significant independent risk factors associated with malaria infection: adjusted OR with 95% CI as estimated by logistic
regression.

Malaria infected HH (N = 300)

Malaria non-infected HH (N = 9449)

Bed net in housennAdjusted OR(95% CI)P- value, = 2 bed-net

198.2 bed-net

202

5461

1.87

(1.30–2.71)

0.001

4288

1.00

(9.60–31.30),0.0001(1.30–2.71)

0.001

Participants districts
South-eastern district

271

3480

17.31

North-eastern district

29

5969

1.00

Bed net in house

198

5461

1.87

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MPEDBstudy have been found to be consistent with similar studies
conducted in Somalia [20]. This study also confirms the results
obtained in Vietnam that the households having two or more than
two bednets are more protective [21]. The use of two or more than
two bednets in a household is an effective malaria control
intervention in Bangladesh. Efforts should be focused to increase
the supply of at least two bednets in the malaria endemic areas of
Bangladesh, especially in the south-eastern districts of Bangladesh.
Total population in endemic areas are 26.9 million, and the risk
exposure is as high as in many African countries [22,23].
Longitudinal studies are needed to assess the variation of
asymptomatic parasite carriage over time, and its exact contribution to transmission. Population-based prevalence studies on aregular basis are required to understand the burden of disease.
More studies should be conducted in the future to map thechanging malaria epidemiology in Bangladesh as control activities
are scaled up.

Discussion
In order to implement an effective malaria control program in
Bangladesh, accurate information on the incidence and prevalenceof malaria is required. In this study, the first malaria prevalence
survey was conducted to provide the baseline parasitologicalinformation for population living in the malaria endemic districtsof Bangladesh. This cross-sectional survey provides point prevalence data on malaria in these thirteen malaria endemic districts.
These data will also be a massive help of global initiatives ofmalaria mapping [15].
We have found a much higher prevalence of malaria thanwould be expected by investigating the national passive surveillance information [16]. This indicates that by the passive
surveillance conducted by the national malaria control programsignificantly underestimates the burden of malaria in these 13
malaria endemic districts, especially in the five south-eastern
districts.
Given that no significant difference was found between maleand female malaria prevalence rates, it can be concluded that thegender difference will not be an issue for malaria control.
However, there were significantly higher rates of infection amongchildren, thus indentifying a need for targeted interventions foryoung populations.
Malaria is not equally distributed in all malaria endemic districtsof Bangladesh. Prevalence of malaria in five south-eastern districts
is significantly higher than the eight north-eastern districts.
Chittagong Hill Tracts (CHT) districts have the highest prevalence
than the other endemic districts. The reason might be that CHT
districts are hilly and covered with forest and lakes, provide an
excellent habitat for malaria vectors [17]. Resources should be
differentially targeted to this area given its larger malaria burden.
The combined use of GIS (geographic information system) andremote sensing provides a significant tool to control malaria [18].
We hope GIS and high resolution satellite images can be used todetect malaria hot spots and vectors habitat sites, particularly in
CHT districts, as part of our ongoing work.
From this study it is also clear that P. falciparum is the dominant
species in the malaria endemic districts of Bangladesh, with thehighest prevalence occurring in the CHT districts. This is of highconcern since P. falciparum is known to be the most deadly anddrug resistance for treatment of P. falciparum is a worldwide
problem [19]. Risk factors regarding the use of bed nets in thisLimitations
The main limitation of the survey was we failed to start thesurvey in thirteen districts synchronously. The first the survey
started in south eastern part and as soon as we finished, we moved
to north eastern part. So, work in the north eastern part was notdone during malaria peak season. Any future work mapping the
distribution of prevalence in Bangladesh will have to consider thisfact in the modelling.

Supporting Information
Table S1Found at: doi:10.1371/journal.pone.0006737.s001 (0.13 MB
XLS)

Acknowledgments
We are grateful for all field staff for collecting data.

Author Contributions
Conceived and designed the experiments: UH SMA SH AH MSA DM
WAK MK RH. Analyzed the data: UH MH. Contributed reagents/
materials/analysis tools: UH RH. Wrote the paper: UH. Edited the initial
versions of manuscript: SH DM RH. Principal investigator of the project:
RH.

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August 2009 | Volume 4 | Issue 8 | e6737