(2021) 20:117
Ngatu et al. Malar J
https://doi.org/10.1186/s12936-021-03647-9

Malaria Journal
Open Access

RESEARCH

Malaria rapid diagnostic test (HRP2/
pLDH) positivity, incidence, care accessibility
and impact of community WASH Action
programme in DR Congo: mixed method study
involving 625 households
Nlandu Roger Ngatu1* , Basilua Andre Muzembo2, Nattadech Choomplang3, Sakiko Kanbara4,
Roger Wumba5, Mitsunori Ikeda4, Etongola Papy Mbelambela6, Sifa Marie‑Joelle Muchanga7, Tomoko Suzuki3,
Koji Wada3, Hasan Al Mahfuz1, Tomohiko Sugishita8, Hiroyuki Miyazaki9, Shunya Ikeda3 and Tomohiro Hirao1

Abstract
Background: Malaria is one of the most prevalent and deadliest illnesses in sub-Saharan Africa. Despite recent gains
made towards its control, many African countries still have endemic malaria transmission. This study aimed to assessmalaria burden at household level in Kongo central province, Democratic Republic of Congo (DRC), and the impact ofcommunity participatory Water, Sanitation and Hygiene (WASH) Action programme.
Methods: Mixed method research was conducted in two semi-rural towns, Mbanza-Ngungu (a WASH action site)
and Kasangulu (a WASH control site) in DRC between 1 January 2017 through March 2018, involving 625 households(3,712 household members). Baseline and post-intervention malaria surveys were conducted with the use of World
Bank/WHO Malaria Indicator Questionnaire. An action research consisting of a six-month study was carried out which
comprised two interventions: a community participatory WASH action programme aiming at eliminating mosquito
breeding areas in the residential environment and a community anti-malaria education campaign. The latter was
implemented at both study sites. In addition, baseline and post-intervention malaria rapid diagnostic test (RDT) was
performed among the respondents. Furthermore, a six-month hospital-based epidemiological study was conductedat selected referral hospitals at each site from 1 January through June 2017 to determine malaria trend.
Results: Long-lasting insecticide-treated net (LLIN) was the most commonly used preventive measure (55%); 24%of households did not use any measures. Baseline malaria survey showed that 96% of respondents (heads of house‑holds) reported at least one episode occurring in the previous six months; of them only 66.5% received malaria care
at a health setting. In the Action Research, mean incident household malaria cases decreased significantly at WASH
action site (2.3 ± 2.2 cases vs. 1.2 ± 0.7 cases, respectively; p < 0.05), whereas it remained unchanged at the Control
site. Similar findings were observed with RDT results. Data collected from referral hospitals showed high malaria
incidence rate, 67.4%. Low household income (ORa = 2.37; 95%CI: 1.05–3.12; p < 0.05), proximity to high risk area for*Correspondence: doc.rogerngatu@gmail.com
1
Department of Public Health, Kagawa University Graduate Schoolof Medicine, Miki‑cho 761‑0793, Japan
Full list of author information is available at the end of the article© The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing,
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Ngatu et al. Malar J(2021) 20:117

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malaria (ORa = 5.13; 95%CI: 2–29-8.07; p < 0.001), poor WASH (ORa = 4.10; 95%CI: 2.11–7.08; p < 0.001) were predictorsof household malaria.
Conclusion: This research showed high prevalence of positive malaria RDT among the responders and high house‑
hold malaria incidence, which were reduced by a 6-month WASH intervention. DRC government should scale up
malaria control strategy by integrating efficient indoor and outdoor preventive measures and improve malaria careaccessibility.
Keywords: Democratic republic of congo, Household malaria, Incidence, Malaria care, Rapid diagnostic test

Background
Worldwide, progress has been made towards malaria
control and elimination in the two decades. However,many countries of the sub-Saharan Africa (SSA) still
have endemic malaria transmission. Thus, the search for
improved malaria control or elimination strategies is stillin progress [1]. Malaria is transmitted by the Anopheles
mosquito, and it is one of the most prevalent and deadliest illnesses in developing countries [2, 3]. The World
Health Organization (WHO) estimates that the disease
has caused between 473,000 and 789,000 deaths in theworld in 2012, which was lower compared to 1, 000,000
deaths per year reported in the 1990s [2]. Estimates showthat 90% of malaria-related deaths in the world occur in
SSA, 40% of them occurring in the Democratic Republicof the Congo (DRC) and Nigeria. The high prevalence ofasymptomatic malaria among young women and men is
an important contributor in high transmission areas. In
addition, in SSA, households lose approximately 25% of
their total income to malaria-related expenses, suggesting that malaria is a disease that causes or deepens poverty [3–5].
Recently, the WHO reports have shown progress madeby several countries in reducing malaria burden, and estimated showed that more than 6,000,000 malaria deaths
have been averted in SSA between 2000 and 2015 [6].
Nevertheless, the emergence of drug-resistant malaria
parasites and pesticide-resistant Anopheles mosquitoes,
inaccessibility to treatment for most at risk population,
residual and outdoor transmission, the absence of rigorous evaluation of the effectiveness of malaria interventions and the absence of malaria vaccine are among
factors that hinder malaria control and elimination programmes in most endemic countries [7–9]. Despite anoticeable reduction in global malaria burden in the lasttwo decades, recent trends show stagnation of the progress made and an increase of disease burden in somecountries. Additionally, nowadays malaria experts agree
that to achieve malaria eradication, interventions should
focus not only on the disease prevention, but also on
reducing the disease transmission [10, 11].
In fact, most of malaria preventive measures implemented to scale up malaria control are those appliedindoor. However, other contributing factors to the disease transmission, such as outdoor environmental factors, which increase mosquito population in residentialarea, are often excluded when designing malaria control
interventions. In DRC, apart from challenges related tothe availability of and use of malaria preventive measures, issues related to accessibility to health care services,
including malaria care is another challenge [12]. A previous pilot study conducted in Congolese rural and urban
counties showed that poor WASH and low income wereassociated with malaria [13]. Furthermore, other previous works showed evidence on the effects of malaria on
workers’ absenteeism, productivity, medical costs [14,15], population growth, as well as children developmental retardation and premature death [16, 17].
Currently, there is a growing concern about the widespread anti-malarial drug resistance in Plasmodium
parasites, and the disease vector’s resistance to mostcommonly used insecticides in bed nets, which contribute to the failure of malaria prevention programmes inmost endemic countries such as DRC. In DRC, malaria
vector control policy is based on the use of long-lasting
insecticidal bed nets (LLINs) [18, 19]. A recent study thatassessed gene mutations involved in resistance phenomenon to pyrethroid and dichlorodiphenyl-trichloroethane(DDT)—two of most used insecticides against the disease
vector—showed that 85% of Anopheles gambiae collected
in DRC carried the kdr mutations [20]. This, suggests thenecessity to find novel approaches susceptible to help
scaling up malaria control programme in the Congo.
Moreover, despite the United Nations’ resolutions calling for accelerating progress towards equitable access tohealth services some countries of the SSA region, DRC inparticular, are still left behind in terms of ensuring primary health care access to populations. There have been
no studies conducted in central Africa region (in DRC
in particular) that explored the impact of WASH intervention coupled with anti-malarial education at community level. The present research assessed the spatial
malaria risk distribution, disease incidence and evaluatedthe impact of a community participatory WASH action
on household malaria incidence in Kongo Central province, DRC. Additionally, this study searched to determine

Ngatu et al. Malar J(2021) 20:117

malaria trend in health settings located at the study sites
using hospital-based epidemiological data.

Methods
Study design and sites

Mixed method research comprising three distinct studies was conducted in two rural towns of Kongo Central
province, DRC, from 1 January 2017 through March2018. The main study, which involved 625 households(3,712 household members), consisted of an Action
Research in which pre-(baseline) intervention and postintervention surveys were carried out using the Malaria
Indicator Questionnaire from the World Bank and WHO
Malaria Programme in Africa and Madagascar [13, 21]. It
was a 6-month prospective study comprising two interventions: community participatory WASH action andmalaria education campaign. The latter intervention was
implemented in both study sites (Fig. 1a). In addition,baseline and end-of-study malaria testing was performed
among the survey responders (household heads).
The second study was a prospective hospital-based
epidemiological study was conducted from 1 January
through June 2017; medical records of patients were collected during the 6-month period, which was conductedusing medical records of patients admitted from 1 January through 30 June 2017 at two randomly selected referral health settings.
Sampling procedure and inclusion criteria

In the main study, a two-stage cluster sampling technique was used to randomly select two study sites, at the‘health zone’ and municipality (county) levels as shown in
Fig. 1b. Loma county (WASH action site) was randomly
selected in Mbanza-Ngungu health zone in the rural
town of Mbanza-Ngungu, located at 154 km from thecapital Kinshasa, with an area of 8,460 ­km2. It has a population of 651,092. On the other hand, Quartier residentiel
county (Control site) was randomly selected in Kasangulu health zone in the town of Kasangulu. Located at
33 km from the capital Kinshasa, Kasangulu has an areaof 4,680 k­ m2 and a population of 194,190 inhabitants. In
DRC, a health zone consists of primary operational unitsof the health system and, usually, it covers a population of
100,000–150,000 inhabitants in rural areas and 200,000–
250,000 inhabitants in an urban area [22]. The following
criteria were used to select the county where the study
should be conducted: (1) having a referral health setting
under the supervision of the health zone inspector, and(2) the health setting should have a well-handled patients’records. Hospital-based epidemiological data were collected at each study site to determine malaria incidence.
In the main study, all households from the randomly
selected study sites having at least three members were

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eligible. Considering a power of 80% (β = 0.80) for α valueof 0.05, we expected to have at least 200 households participate in this research. Within each study site, blocks of
50 houses were created; thereafter, data collectors haverandomly selected every second house on each street in
the area of each block. Data collectors were public healthnurses and doctors who served under the supervision of
one of the authors (WR) who is professor at the Facultyof Medicine, University of Kinshasa in DRC.
Additionally, only households having at least threemembers were finally included in the study. In total, 625
households (3,712 individuals) were surveyed, including
316 (50.6%) from the WASH action site and 309 (49.4%)
from the Control site.
Surveys and interventions

Surveys were conducted simultaneously at both study
sites at baseline and at the end of six-month intervention period, following a schedule that was announced toresidents by local health zone staff. The French version ofmalaria indicator survey (MIS) questionnaire was usedin this study. It is a validated questionnaire used by the
National Malaria Programme of several French speaking
African countries to estimate household malaria burden.
MIS comprises an informed consent form and questions related sociodemographic and anthropometric characteristics, household characteristics, past
medical history, WASH status, malaria preventive measures, malaria status and care. All household heads participated in the baseline and post-intervention surveys.
Additionally, home visits were undertaken by local collaborative research team and Health Zone staff to evaluate WASH status at home and in the living environment,
and check indoor and outdoor preventive measures usedby household members, and ascertain consistency of
their use. A hand-held GPS GIS device was used to collect data on geospatial localization of mosquito breedingsites; that is to estimate the distance between residences
and mosquito breeding sites (grassy area, stagnant water
spot, garbage spot and river side). We assumed that whena residence was located at less than 200 m from a mosquito breeding site, household members were consideredat high risk for malaria.
Anti-malaria interventions comprised the following
actions: (1) community WASH action consisting of aweekly participatory hygiene and sanitation transformation (PHAST) was carried out only in WASH action
site in order to clean the residential environment andeliminate mosquito breeding spots; (2) community
anti-malaria education. The latter was implemented
in both study sites after the baseline survey. Education sessions were organized in communities, schools
and leaflets that display risk factors and behaviors wereNgatu et al. Malar J(2021) 20:117

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Fig. 1 Maps of DR Congo (a) showing the study area in Kongo central province and main study flow chart (b) comprising both study designsof the main study ( Source of original maps: Celine Lahaye, ADES-DyMSET (2006) and CAID – DR Congo (Cellule d’ Analyse des Indicateurs de
Developpement), 2017). RDT, rapid diagnostic test for malaria; WASH, water, sanitation and hygiene

being distributed to the participants as well as household heads in each study site. The PHAST approachis a learning methodology commonly used to preventa broad range of infectious diseases at community
level, through improvement of hygiene behaviors andsanitation, and encourages a better community management of water and sanitation services. In this study,
PHAST was extended to periodic cleansing of the living environment, by local volunteers and community
members.

Ngatu et al. Malar J(2021) 20:117

Diagnostic procedure for malaria and geospatial
categorization of high risk area

Participants underwent the rapid diagnostic test (RDT)
for malaria at baseline and at the end of the study. Blood
sample was collected via finger prick into Heparin-coatedtube. RDT is a validated diagnostic test for malaria; it has
high accuracy for malaria diagnosis; it has the advantagesof rapid-detection and it is easy to use and cost-effective.
Thus, it is useful diagnostic procedure in resource-limited and endemic areas for malaria, particularly. It can
detect malaria parasite’s specific antigens in the blood:
histidine-rich protein-2 (HRP2) and lactate dehydrogenase (LDH). The test allows to diagnose malaria caused
by Plasmodium falciparum and other Plasmodium species [23]. All participants with a positive RDT at baseline received malaria treatment and were followed for six
months.
Regarding the hospital-based epidemiological study,
only patients admitted to internal medicine and pediatricdepartments o between 1 January through 30 June 2017,
and whose records showed final diagnoses were included;
records that showed no diagnosis were excluded. For
medical records showing comorbidities, the first diagnosis was considered.
In general, measures that reduce outdoor and indoor
mosquito population are believed to play a crucial role
in reducing malaria prevalence, especially in malaria
endemic countries. Thus, during WASH intervention,
mosquito breeding sites, which are considered high risk
areas, were targeted.
Households living in proximity (distance less than
200 m) to a river, still and stagnant water/grassy spots
were considered high risk areas for malaria. On the other
hand, residences located at higher altitude or far fromriver, grassy/stagnant water spots were at lower risk formalaria.
Outcome variables and statistical analysis

Outcome variables were the following: (1) prevalence of
positive RDT among responders, (2) survey-based selfreported household incident malaria (number of doctor-diagnosed malaria cases in household members at
any health setting), and (3) hospital-based malaria incidence from data registered at selected referral hospitalat each study site. Data are presented as proportions for
categorical variables, whereas means and their standard
deviations are used for continuous variables. Comparisons within and between study groups were performed
using paired t test (for incident malaria incidence cases)
and chi-square test (for categorical variables); however,for categorical variables with repeated measures (RDT),
McNemar’s test was used. All variables that showed aPage 5 of 13

significant or marginally significant association withhousehold malaria in the bivariate logistic regression
analysis were subjected to a multivariate analysis model
to determine predictors of malaria. Analyses were performed with the use of Stata software version 15.

Results
Baseline characteristics of respondents and households

Of the 625 respondents (household heads), 70.9% werefemales; overall mean age was 33.8 ± 8.8 years. The
majority (66.2%) of the respondents were married men
and women. Regarding education level, most respondents (64.6%) had high school level, followed by those withprimary education level (13.6%), whereas 10.2% were
illiterate. Mean household size was 5.9 ± 2.8 members,
and almost 80% of them earned less than 100 US dollars
a month. About 25% of households used appropriate/
acceptable latrines. Baseline survey results also revealed
that periodic sanitation intervention was not carried out
in the residential area; this was supported by 97.6% of
the respondents; 18.6% reported that the general sanitation status in their living environment was unacceptable (11.3% at control site vs. 25.6% at WASH action site)(Table 1).
Malaria preventive measures (baseline survey) and trendof prevalence of positive RDT

Long-lasting insecticide-treated net (LLIN) was themost commonly used malaria preventive measure (55%of households), followed by mosquito repellent (15%),
whereas indoor residual spraying (IRS) and the combination of LLIN and IRS accounted for 2% each. Strikingly,
24% of households did not use any of the measures asshown in Fig. 2.
Regarding malaria testing among the respondents,
results showed that the prevalence of positive RDT
increased in the post-intervention RDT at the Control
site, but not significantly (25% in pre-test vs. 35% in posttest, p > 0.05), whereas a marked reduction was observedin the WASH-action site (38% vs. 20%, respectively;
p < 0.05) (Fig. 3).
Survey‑based self‑reported incident malaria (at least one
episode) and care accessibility

When comparing the malaria status before and after
intervention, the disease incidence decreased among
the respondents (96% vs. 52%, respectively) (not shown).
Of the 96% of respondents who reported malaria eventat baseline, only 66.5% received malaria care at a healthsetting. At household level, mean incident malaria cases
decreased markedly in the WASH action site (2.3 ± 2.2
cases in pre-test vs. 1.2 ± 0.7 in post-test; p < 0.05) following 6-month sanitation intervention; however, noNgatu et al. Malar J(2021) 20:117

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Table 1 Characteristics of the respondents
Characteristics of respondents
Control site

WASH action site

All respondents

n = 309 (%)

n = 316 (%)

N = 625 (%)

Male

71 (23)111 (35.1)

182 (29)

Female238 (77) *205 (64.9) *444 (71)

Gender (%)

Age (years; mean ± SD)
24–50

51–98
Marita status33.71 ± 8.74

60.17 ± 8.51

33.86 ± 8.48

62.73 ± 9.25

33.73 ± 8.68

61.27 ± 8.88

Married205 (66.3)

209 (66.1)

414 (66.2)

Divorced

22 (7.1)

21 (6.7)

43 (6.9)

Widower

32 (10.4)

15 (4.7)

47 (7.5)

Single with child/children

28 (9.1)

37 (11.7)

65 (10.4)

Single without child

22 (7.1)

34 (10.8)

56 (8.9)

Education
No education

19 (6.1)

45 (14.2)

64 (10.2)

Primary37 (12)48 (15.2)

85 (13.6)

High school

203 (65.7)

201 (65)404 (64.6)

College/university

40 (12.9)

14 (4.5)

54 (8.6)

Technical/professional school

10 (3.2)

8 (2.5)18 (3)
590 (94.4)

Smoking status
Never smoked

306 (99)284 (89.9)

Has quit (> 36 months)

0 (0.0)15 (4.7)

15 (2.4)

Yes

3 (1)

17 (5.4)

20 (3.2)

Alcohol consumption
Never

257 (83.1) *Has quit (over 36 months)

207 (65.5)

464 (74.2)

15 (4.7)

22 (3.5)

At most 2 glasses/day

7 (2.3)53 (16.8)

73 (11.7)

More than 2 glasses/day

20 (6.5)
25 (8.1)

41 (13)66(10.6)

Physical activity (≥ 20 min 2x/week)No

209 (67.6)

105 (33.2)

316 (50.6)

Yes

100 (32.4)

211 (66.8)

309 (49.41)

Control site
(Kasangulu)

WASH action site
(Mbanza-Ngungu)

All households

n = 309 (%)

N = 316 (%)

N = 625 (%)

Characteristics of the households
Household characteristics

Sociodemographic characteristics
Household/family size  1–5

126 (40.8)

152 (48.1)

278 (44.5)

  6–10153 (49.5)

148 (46.8)

301 (48.2)   ≥ 1130 (9.7)

16 (5.1)

46 (7.4)

  Monthly income (US$) < 100

221 (44.38)

277 (55.62)

498 (79.68)

  100 or higher

79 (62.20)

48 (37.80)

127 (20.32)

WASH status/ cleanliness of residential environment
Latrine type (n = 603)

  Appropriate with flushing system

8 (2.6)5 (1.6)13 (2.1)

  Appropriate with manual watering system

47 (15.2)

99 (31.3)

146 (23)

Ngatu et al. Malar J(2021) 20:117

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Table 1 (continued)
Characteristics of the households
Household characteristics

Control site
(Kasangulu)

WASH action site
(Mbanza-Ngungu)

All households

n = 309 (%)

N = 316 (%)

N = 625 (%)

  Inappropriate but covered

68 (22)54 (17.1)

122 (19.5)122 (19.5)

  Inappropriate and uncovered

177 (57.3)

126 (39.9)

303 (48.5)

  No latrine

5 (1.6)

14 (4.4)

19 (3)

  Other/ no answer

4 (1.3)

11 (3.5)

22 (3.5)  Yes

49 (22.69)

167 (77.31) *216 (34.56)

  No

260 (63.73)

149 (36.43)

409 (65.44)

  Proximity to river

42 (13.6)

17 (5.4)

59 (9.4)

  Proximity to/residence on mountain/hill

20 (6.5)

4 (1.3)24 (3.8)

Shared latrine? (n = 625)

Ecological feature of residence area

  Proximity to garden, grass/bush

66 (21.3)

88 (27.8)

154 (24.6)

  Proximity—stagnant water spot

54 (17.5)

92 (29.1)

146 (23.4)

  Proximity or residence in slum

20 (6.5)

15 (4.7)

35 (5.6)

  Proximity to river and mountain

38 (12.3)

4 (1.3)42 (6.7)

  Proximity—river and slum

49 (15.8)

20 (6.3)

69 (11)

  Nothing particular

11 (3.6)

64 (20.2)

75 (12)

  Other/no answer

9 (2.9)12 (3.8)

21 (3.4)

Periodic cleansing in residential areaYes

5 (11.6)

10 (3.2)

15 (2.4)No

304 (98.4)

296 (95.8)

610 (97.6)

0 (0)

3 (1)

0 (0)

Other/no answer
Cleanliness of residential area
  Good135 (43.7)

13 (4.1)

148 (23.6)

  Acceptable

139 (45)222 (70.3)

361 (57.8)

  Not acceptable

35 (11.3)

81 (25.6)

116 (18.6)

  Other/no answer

0 (0)

0 (0)

0 (0)

  High frequency

86 (27.8)

134 (42.4)*

220 (35.2)

  Moderate

184 (59.5)

153 (48.4)

337 (53.9)

  No bite or rare38 (12.3)

29 (9.2)

67 (10.7)

  Other/no answer

1 (0.3)0 (0)1 (0.2)Frequency of mosquito bites*p value less than 0.05

significant change was observed at the Control site
(1.8 ± 0.5 vs. 2.0 ± 0.7, respectively) (Fig. 4).

among households living in proximity to grassy and/or
still and stagnant water spots (Fig. 5a, b).

Survey‑based self‑reported incident household malaria
according to residential area (baseline data)

Predictors of household malaria (baseline survey data)

Each study site was divided into areas of low and highrisk for malaria as described in methods section. Results
showed that household malaria incidence rate was markedly higher (83%) among households with residences
located at high risk areas as compared to those living in
low risk areas (83 vs. 17%, respectively; p < 0.001). Moreover, household malaria incidence rate was highest (60.9%)

The multivariate logistic regression analysis (withadjustment for age, gender and occupation) showedthat household malaria was positively associatedwith household size (ORa = 1.39; 95%CI: 2.62–4.89;
p < 0.05), proximity of residence to high risk area formalaria (ORa = 5.13; 95%CI: 2.29–8.07; p < 0.001)
and the frequency of mosquito bites (2.68; 95%CI:

Ngatu et al. Malar J(2021) 20:117

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Fig. 2 Malaria preventive measures used by household members. IRS, indoor residual spraying; LLIN, long-lasting insecticide treated net. The figure
shows that LLIN was the most used anti-malaria measure (55%) in households followed by mosquito repellent (15%), whereas 24% of households
did not use any preventive measure (p < 0.05)

Fig. 3 Prevalence of positive RDT for malaria among the respondents (household heads). NS, not significant; RDT + , rapid diagnostic test formalaria; WASH, water, sanitation and hygiene. The figure shows a significant decrease of prevalence rate of positive RDT at WASH action site (38% vs.
20%; p < 0.05) as compared to baseline RDT result

2.84–4.17; p < 0.05), whereas inverse association was

found between household malaria and income status(OR = 2.37; 95%CI: 1.05–3.12; p < 0.05), also between
household malaria and general WASH status in the

Ngatu et al. Malar J(2021) 20:117

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Fig. 4 Survey-based self-reported incident household malaria before (baseline) and after interventions. SD, standard deviation; p, p-value. The
figure shows a significant decrease in incident malaria cases (mean values) among household members at WASH action site following interventions(p < 0.05)

Fig. 5 Spatial distribution of household incident malaria by risk category of residential area (a) and proximity of risk geographical high malaria risk
area (b) (GIS-based data). The figure shows that higher malaria incidence was found among households living in proximity to high malaria risk areas
such as grassy areas/stagnant water (60.9%) and residences near river (15.7%)

residential environment (ORa = 4.10; 95%CI:2.11–7.08;
p < 0.001) (Table 2).
Hospital‑based malaria incidence (epidemiological datafrom referral hospitals)

Figure 6 shows the disease distribution at the two referral hospitals located at both study sites. Overall malaria

incidence was high, 67.36% (1,108/1,645), followed byrespiratory diseases (21.22%; 349/1,645) and other conditions, including gastrointestinal disorders (11.43%;
188/1,645) (Fig. 6a). Pediatric malaria was more frequent,69.67%, whereas of the disease accounted for 30.33% of
patients admitted to Internal Medicine Departments(Fig. 6b).

Ngatu et al. Malar J(2021) 20:117

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Table 2 Predictors of household malaria (multi-variate
analysis)
Predictors

ORa (SD)

95% CI

p-valueAge (< 40 years vs. 40 or older)

1.03 (0.02) 0.98–1.07

Gender (M vs. F)

0.27 (0.21) 0.05–1.27

0.099

Household size (2–5 vs. > 5)

1.39 (0.16) 2.62–4.89< 0.05

Education level (Low vs. high)0.88 (0.27) 0.48–1.62

Living environment (high risk vs. low
risk)

5.13 (0.9)

2.29–8.07< 0.001

Income status (US$) (< 100 vs. 100 or
higher)2.37 (1.42) 1.05–3.12< 0.05General WASH status (poor vs. good)

4.1 (0.5)

2.11–7.08< 0.001

Mosquito bite (frequent vs. lessfrequent)

2.68 (0.27) 2.84–4.17< 0.05

0.184

0.701

Multiple logistic regression model was performed with adjustment for age,gender, occupation

When considering malaria status at each study site, therate was 61.35% at Loma county (WASH site) and 73.92%
in Kasangulu (Control site) (Fig. 6c). Highest malaria
incidence rates were observed between April (83.37%)
and May (85.37%); it decreased in June 61.36%, whichcorresponds to the dry (cold) season (Fig. 6d).

Discussion
This study was carried out in two Congolese towns,
Mbanza-Ngungu and Kasangulu, in Kongo central province, DRC. The trend of RDT positivity among the participants (respondents), survey-based self-reported
individual/household malaria between July 2017 and 31
March 2018 and related predictors were investigated.
On the other hand, hospital-based malaria incidence forpatients admitted at referral health settings located at
the study sites during the first semester of 2017 was also
determined. Prevalence of positive RDT among the participants and self-reported household malaria incidence
were significantly reduced by WASH action. Of the survey respondents (96%) who reported having sufferedfrom at least one malaria episode, about 33% did not haveaccess to malaria care at a health setting. Most of themrelied on self-medication, which is a common practice inpoor Congolese communities.
It is noteworthy to say that while RDT results revealed
the effect of community-led WASH action on malariarate, household malaria survey helped to identify geospatial disease clusters, with high risk areas having highdisease frequency. For example, households whose residences were located in proximity to river, grassy area or

Fig. 6 Hospital-based malaria incidence trend at referral hospitals located in study sites, Kongo Central province, DR Congo. Figure 6 shows highmalaria incidence rates in the two referral health settings that participated in this study (a), in pediatric departments (b), study sites (c). Whenconsidering hospital-based monthly incidence rate in first semester of 2017 (d), malaria was more frequent in February, April and May whichcorrespond to months with high pluviometry in western area of DR CongoNgatu et al. Malar J(2021) 20:117

still/stagnant water spots had higher malaria incidence
than those located far from such areas. This finding
provides evidence to be used in designing outdoor antimalarial interventions. As reported by Zinszer and colleagues, vegetative indices such as elevation (altitude),surface moisture, land cover are among environmental factors associated with temperature and precipitation, which influence the distribution of malaria in mostmalaria endemic countries in Africa [24].
Furthermore, malaria incidence was high in local referral hospitals, compared to other diseases, according tohospital-based epidemiological data, and highest incidence occurring in June. In DRC, as well as other countries of the Central African region, June is the colder
month of the year. Previous studies have confirmed therelationship between climatic conditions such as temperature and rainfall on the one hand, and malaria on theother hand [25, 26].
Amidst severe economic crisis in the DRC, caused inpart by the longstanding armed conflicts, and due to poverty, unemployment, lack of national health insurance
and universal health care system, accessibility to quality health care is still a mere dream for many Congolese
households [13]. Additionally, the issue related to low
household income might explain the considerable rate(24%) of household that did not use any preventive measures against malaria. In SSA region, health inequalities
have been previously reported to be influenced by socioeconomic status. For example, a study conducted in rural
Kenya [27] showed highest malaria prevalence among
poorest individuals.
In the present study, high self-reported incident
malaria cases were observed (with two cases or more
per household) in the previous 6-month period prior to
implementing interventions, according to baseline survey results. Similarly, this fact was evidenced by resultsfrom the hospital-based epidemiological study thatrevealed high malaria incidence. On the other hand, thebaseline survey also showed higher malaria incidence inhouseholds with residences in areas with highest risk formalaria transmission, whereas it significantly decreasedin the WASH action site where WASH action was implemented. This outcome implies the importance of maintaining the residential environment clean and free ofmosquito breeding sites, in order to reduce the risk of
disease transmission through mosquito bites.
This study also showed an association between
malaria and household income status, with members ofhousehold earning less than 100 $USD per month having higher malaria risk. A recent systematic review andmeta-analysis conducted by Degarege et al. [28] also
confirmed this fact. Similarly, another study by Tasting
et al. [29], which focused on paediatric malaria, showed

Page 11 of 13

that the odds of malaria infection were higher in poorest children as compared with the least poor ones, suggesting that socioeconomic development could be aneffective and sustainable intervention against malaria.
Obviously, households with low income have difficulty
to regularly afford the cost of effective anti-malaria preventive measures.
This study can be considered a valuable scientificcontribution, as it provides novel insights on malaria
risk distribution according to residential environment,
accessibility to preventive measures and malaria care,
as well as the role of community involvement in thefight against this deadly disease, particularly in malaria
endemic countries.
As limitations, low literacy rate in rural areas in DRC[13] could be an obstacle in educational interventions.
In addition, respondents with low literacy could havedifficulty to report malaria events occurring in theirhousehold in the previous six-month period during
the surveys, which might cause recall bias. Nonetheless, this study has a number of strong points. Home
visits by teams of surveyors allowed to have 100% ofparticipation rate across the study period. Furthermore, WASH intervention was implemented with theactive participation of community members and could
significantly reduce malaria rates. This could be a costeffective intervention that be used to control malaria,
in addition to commonly used antimalarial preventive
strategies.

Conclusion
This study explored malaria RDT positivity, incidence,and household malaria predictors in Kongo central province, DRC. In order to reduce the malaria burden in
DRC, there is a need to consider the role of community
involvement in the fight against malaria, particularly interms of promotion of clean-living environment throughinterventions that reduce mosquito population and therisk of disease transmission. In DRC, a malaria endemic
country, the use of indoor preventive measures such as
LLIN and IRS might not be enough to control the disease.
According to the finding, LLIN and IRS users and nonusers were equally exposed to malaria due to high diseaserisk (numerous mosquito breeding sites, high population in the living environment). There is a necessity for
DRC health policymakers to consider integrating indoor
and outdoor preventive measures, with the involvementof communities for an efficient malaria control. Furthermore, DRC government should make primary health careaccessible to its population in effort towards achieving
universal health coverage.

Ngatu et al. Malar J(2021) 20:117

Page 12 of 13

Abbreviations
HRP2: Histidine-rich protein 2; IRS: Indoor residual spraying; LLIN: Long-lasting
insecticide-treated bed net; MIS: Malaria indicator survey; PHAST: Participatory
hygiene and sanitation transformation; pLDH: Plasmodium lactate dehydroge‑nase; WASH: Water, sanitation and hygiene.

4.

Acknowledgements
The authors would like to thank the officials and community leaders of theadministrative territories of Mbanza-Ngungu and Kasangulu in Kongo Central
province, DRC, and the staff of Congo Heiwa Mura for their support during theimplementation of this study.

6.

Authors’ contributions
NRN, RW, SK, MI, SMJM and SN participated in the conception and writing of
the research protocol and data analysis. RW, NRN, TH, TS (1), EPM, TS (2), NC
and BAM participated in the implementation of the study activities and datacollection process. KW, NRN, SI, BAM, TH, AHM and MI performed the data
analysis and interpretation. All authors proofread the manuscript.

5.

7.
8.

9.

Funding
This research was supported in part by JSPS KAKENHI Grant Number
JP17H04675.

10.

Availability of data and materials
The datasets analyzed during this study are available from the corresponding
author on reasonable request. The study data are available from the cor‑
responding author.

11.

Ethics approval and consent to participate
Participation was voluntary based on the willingness of community leadersand household heads to take part in this study. Prior to implementing thisresearch, informed consent from was obtained from each household head,and ethical approval was also obtained from the School of Public Health of the
University of Lubumbashi in DRC (Approval number: UNILU/CEM/070/2016).
All medical procedures performed in this study were in accordance with theHelsinki declaration regarding the use of human subjects in research.
Consent for publication
Not applicable.
Competing interests
The authors have no conflict of interest related to this study.
Author details
1
Department of Public Health, Kagawa University Graduate Schoolof Medicine, Miki‑cho 761‑0793, Japan. 2 Graduate School of Medicine,
Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan.
3
Department of Public Health, School of Medicine, International University
of Health and Welfare, Narita, Japan. 4 Faculty of Nursing, University of Kochi,
Kochi, Japan. 5 Department of Tropical Medicine, Faculty of Medicine, Uni‑
versity of Kinshasa, Kinshasa, Democratic Republic of Congo. 6 Departmentof Environmental Medicine, Kochi University Medical School, Nankoku, Japan.
7
National Center for Global Health and Medicine, Tokyo, Japan. 8 Depart‑
ment of International Affairs and Tropical Medicine, Tokyo Women’s Medical
University, Tokyo, Japan. 9 Center for Spatial Information Science, Universityof Tokyo, Tokyo, Japan.
Received: 5 May 2020 Accepted: 12 February 2021

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15.
16.

17.
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19.

20.

21.
22.
23.

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