Profiling malaria infection among under-five children in the Democratic Republic of Congo.

INTRODUCTION: In 2018, Malaria accounted for 38% of the overall morbidity and 36% of the overall mortality in the Democratic Republic of Congo (DRC). This study aimed to identify malaria socioeconomic predictors among children aged 6-59 months in DRC and to describe a socioeconomic profile of the most-at-risk children aged 6-59 months for malaria infection.
MATERIALS AND METHODS: This study used data from the 2013 DRC Demographic and Health Survey. The sample included 8,547 children aged 6-59 months who were tested for malaria by microscopy. Malaria infection status, the dependent variable, is a dummy variable characterized as a positive or negative test. The independent variables were child's sex, age, and living arrangement; mother's education; household's socioeconomic variables; province of residence; and type of place of residence. Statistical analyses used the chi-square automatic interaction detector (CHAID) model and logistic regression.
RESULTS: Of the 8,547 children included in the sample, 25% had malaria infection. Four variables-child's age, mother's education, province, and wealth index-were statistically associated with the prevalence of malaria infection in bivariate analysis and multivariate analysis (CHAID and logistic regression). The prevalence of malaria infection increases with child's age and decreases significantly with mother's education and the household wealth index. These findings suggest that the prevalence of malaria infection is driven by interactions among environmental factors, socioeconomic characteristics, and probably differences in the implementation of malaria programs across the country. The effect of mother's education on malaria infection was only significant among under-five children living in Ituri, Kasaï-Central, Haut-Uele, Lomami, Nord-Ubangi, and Maniema provinces, and the effect of wealth index was significant in Mai-Ndombe, Tshopo, and Haut-Katanga provinces.
CONCLUSION: Findings from this study could be used for targeting malaria interventions in DRC. Although malaria infection is common across the country, the prevalence of children at high risk for malaria infection varies by province and other background characteristics, including age, mother's education, wealth index, and place of residence. In light of these findings, designing provincial and multisectoral interventions could be an effective strategy to achieve zero malaria infection in DRC.

Background

Malaria remains a threat to gains in health and development [1-5], even though the number of deaths due to this disease has been halved since 2000 [3, 4, 6]. One component of the Global Technical Strategy for Malaria 2016–2030 in the African region is to accelerate efforts toward elimination of malaria and attainment of malaria-free status by 2030 [7].

Malaria is a major public health concern in the Democratic Republic of Congo (DRC) [8], which accounts for 11% of the global malaria burden. In 1998, DRC created a national malarial control program (NMCP). The main objective of this program was to reduce malaria mortality by 50% and malaria morbidity by 25% by 2015. NMCP activities consist of the following interventions: distribution of insecticide-treated nets (ITNs), promotion of indoor residual spraying, promotion and implementation of intermittent preventive treatment in pregnancy, promotion of rapid diagnostic tests, and implementation of community and mother case management with artemisinin-based combination therapies [9, 10].

Over the past 15 years, the NMCP has intensified implementation of malaria control strategies [9]. The number of health zones covered by the NMCP has increased, from 271 out of 516 health zones in 2009 to all health zones (516) in 2016 [9]. In addition, the NMCP established a network of 11 sentinel sites for integrated surveillance of priority diseases, including malaria in 2003, with an expansion to 26 new provinces in 2016 [9].

Despite this progress, malaria accounted for 38% of overall morbidity and 36% of overall mortality in 2018 [4, 8–10] in the country. In 2018, the country reported 15 million malaria cases and about 27,458 malaria deaths [8]. A majority of malaria cases and rapid progression to death occur in young children [8-10]. Therefore, the gap between the current prevalence of malaria and the World Health Organization (WHO) goal of zero malaria case by 2030 remains important for DRC.

During the past three decades, interest in understanding factors associated with malaria among under-five children has increased in sub-Saharan Africa, but less is known about the socioeconomic profile of under-five children at higher risk of malaria infection in DRC. Published studies on malaria infection among under-five children can be divided into three groups. Some studies have analyzed trends in the prevalence of malaria infection among under-five children at the national and sub-national (province, district, and health zone) levels [11, 12]. Others have described socioeconomic factors associated with malaria morbidity and mortality among patients attending health facilities [13-15]. Some other studies have analyzed individual and community socioeconomic factors associated with malaria infection [16-18].

This situation is due to data limitations. The 2013 DRC Demographic and Health Survey (DHS) is the first national survey that collected data on malaria status, individual characteristics, maternal variables, and household characteristics. Using chi-square and regression models, previous studies did not automatically explore interactions between variables. The most-at-risk groups for malaria infection could be a result of different interactions between malaria risk factors. For instance, existing literature revealed that children living in poorest households, children of less-educated mothers, children living in rural areas, and children aged above 23 months had greater risk of malaria infection [13-18]. However, a child could belong to all these risk categories, or could belong to the least risk groups and high risk groups at the same time [8].

Against this backdrop, this study aims to identify socioeconomic predictors of malaria and describe a socioeconomic profile of malaria prevalence among under-five children in DRC, who are considered to be one of most-at-risk groups affected by malaria, using chi-square, logistic regression, and chi-square automatic interaction detector (CHAID). Findings from this study will allow for the design of targeted interventions and evidence-based prevention programs as well as optimize coverage, reduce costs, and lower the number of new infections, given the high cost of interventions (194 million USD in 2013) [9, 10].

Malaria risk factors and those most-at-risk for malaria

People are at risk of acquiring malaria infection due to factors related to environment, demographics, socioeconomic status, and exposure to prevention interventions [6, 11–18].

Fig 1 presents selected risk factors and categories under higher and lower risk of malaria using the WHO classification of malaria epidemiology [19].

Fig 1

Predictors of malaria among under five children and risk/vulnerable groups.

Source: Authors based on malaria literature.

Environmental and ecological factors, including distance from a household to the nearest body of water, altitude, temperature, and rainfall, determine malaria transmission zone: high transmission zone (mesoendemic, hyperendemic, and holoendemic) or low transmission zone (hypoendemic) [19, 20]. In this study, province of residence has been used as a proxy of geographical location.

Considering socioeconomic factors, children living in poor households, children whose mothers are less educated, and children living in rural areas are more likely to suffer from malaria [13-26]. Individuals belong to several categories, which in turn might belong to different malaria clusters (high risk or low risk).

Data and methods

Variables

The dependent variable for this analysis is the malaria infection status defined as a positive or negative malaria test. The independent variables include 10 variables grouped into 3 major types: (1) child variables (sex, age, living arrangement, whether slept under an ITN the night preceding data collection); (2) mother’s education and household’s characteristics (sex of the head of household, age of the head of household, wealth index); and (3) contextual factors, including province and place of residence. The choice of these variables is guided by the literature on factors associated with malaria infection [6, 13–26].

Data sources

This study used data from the 2013 DRC DHS. The survey used a two-stage stratified-cluster sampling design based on the sampling frame of the 1984 Population and Housing Census, which was partially updated several times by administrative censuses and in the context of the presidential and legislative elections of 2011. The final survey unit chosen was the cluster (district or village), and, in total, 540 clusters were drawn. The first stage of sampling involved the selection of clusters known as primary sampling units. The second stage of sampling involved the selection of households from each cluster. Stratification in the first stage was achieved by grouping the 11 provinces into urban and rural areas. Primary sampling units in provinces with a very small population were selected with equal size allocation.

The DHS data offer a unique opportunity to profile malaria infection in the country due to the paucity of routine data, which are associated with unknown denominator and selection bias because all malaria cases are not reported in health facilities. In addition, those data do not include individual and household characteristics considered as predictors of the epidemy. The DHS incorporated five biomarker tests, including malaria testing. Malaria testing was carried out among children aged 6–59 months in half of the 18,360 selected households using microscopy. Using a finger (or heel) prick, a drop of blood was collected on a slide to prepare a thick film. All health technicians were trained to perform finger (or heel) pricks in the field according to the manufacturer’s instructions. A total of 8,547 children aged 6–59 months were tested for malaria. The survey report provides more details on the sampling and microscopy process [26].

Statistical analyses

Statistical analyses relied on Pearson’s χ2, using the CHAID decision-tree algorithm implemented in SPSS V.21, and logistic regression. Pearson’s χ2 was performed to identify associations between the malaria infection (positive or negative) and independent variables, including socioeconomic and demographic characteristics. The study applied the nominal CHAID model to identify the most significant determinants of malaria infection among under-five children and to describe the characteristics of the most-at-risk children for malaria infection considering interactions between predictors [27-31]. The model operates sequentially by recursively splitting under-five children into separate and distinct segments called nodes. The variation of the prevalence of malaria infection is minimized within each node and is maximized between nodes. After the initial splitting of the population (under-five children who received a malaria test) into different nodes based on the most significant predictor, the model repeats the process on each of the nodes until no significant predictors remain or until the number of observations in the node does not allow further partitions. Ideally the minimum number of cases is estimated at 50 cases for child nodes, although the minimum number of cases can be lowered [27-30].

CHAID displays outcomes in hierarchical tree-structured form, in which the root is the population, which in this case is under-five children who received a malaria test. The root node, ‘Node 0’ or ‘initial segment,’ is the outcome variable, and subsequent levels include parent node and child node. Parent node is the upper node compared with nodes on the subsequent (lower) level, whereas any sub-node of a given node is called a child node. Sibling nodes are nodes on the same hierarchical level under the same parent node. Ancestor nodes comprise all nodes higher than a given node in the same lineage, and all nodes below the given node are called descendants. The terminal nodes are any node that does not have child nodes. They are the last categories of the CHAID tree. Findings include a table with five major columns describing each terminal node regarding content, population size, number with malaria infection, and the prevalence of malaria infection [27-31]. The analysis is focused on column 4, prevalence of malaria infection in each terminal node (category).

The study also employed logistic regression to identify predictors of malaria infection among under-five children. This consists of comparing the proportions using the logarithms of the odds ratio (log-odds). For each selected category, the model estimates the parameter ß (the ratio between the logit of a selected group and that of the reference group) and calculates the odds ratios while specifying their significance level (95% in this case) [31, 32]. If the odds ratio is equal to one, there is no difference between the considered group and the reference group regarding the risk of malaria infection. If the odds ratio is less than unity, children in the considered group are less likely to suffer from malaria infection, compared to children in the reference group. By contrast, if the odds ratio is greater than one, children in the selected group are more likely to suffer from malaria infection than children in the reference group [30-32]. However, the logistic regression model fails to incorporate non-monotonic relationships. Furthermore, it does not automatically detect interactions between segments or categories of independent variables. Significant differences have been established at p<0.05.

Data analysis strategies

We weighted data (CHAID in SPSS) and applied the SVY (logistic regression in STATA 15) to account for the complex design of the household survey. Missing values were treated as a separate category. For instance, the “Do not know” category for mother’s education included children whose mother’s education was missing.

Ethical considerations

The DHS questionnaire, procedures, and testing protocol underwent a host country ethical review (by the DRC School of Public Health Ethical Review Committee) and were reviewed by ICF institutional review board. Participation in the individual survey and in malaria testing was voluntary, and parents signed the consent form before the interview and before their child’s blood collection.

Interviews and biomarker testing were performed as privately as possible. Results of interviews and biomarker testing were strictly confidential. Only the DHS research team (interviewers, health specialists, editors, and supervisors) were allowed to access the data, essentially for communications. Each respondent’s interview and biomarker data files were identified only by a series of numbers. The questionnaire cover sheets containing identifier numbers were destroyed after data processing.

Results

Participants

Table 1 shows the distribution of the study population by selected background characteristics. Of the total 8,547 children aged 6–59 months who were tested for malaria, 50% were female.

Table 1

Descriptive characteristics of children aged 6–59 months who had a malaria test, Democratic Republic of Congo Demographic and Health Survey, 2013.

Background variables	%	N	Background variables	%	N
Sex of child			Province
Male	49.8	4,258	Kinshasa	5.3	453
Female	50.2	4,289	Kwango	4.4	379
Child’s age group (months)			Kwilu	5.0	424
6–11	11.0	943	Mai-Ndombe	3.9	331
12–23	22.0	1,882	Kongo Central	4.5	386
24–35	22.2	1,894	Equateur	2.9	245
36–47	22.7	1,943	Mongala	3.5	299
48–59	22.1	1,885	Nord-Ubangi	3.0	255
Living arrangement			Sud-Ubangi	3.7	314
Not living with mother	9.0	770	Tshuapa	2.8	239
Living with mother only	26.5	2,266	Kasaï	4.4	374
Living with both parents	64.5	5,511	Kasaï-Central	4.3	368
Mother’s education			Kasaï-Oriental	3.6	310
None	19.8	1,689	Lomami	4.7	405
Primary	40.8	3,490	Sankuru	3.5	299
Secondary and above	30.4	2,597	Haut-Katanga	3.5	295
Don’t know	9.0	771	Haut-Lomami	3.5	295
Wealth index			Lualaba	2.5	209
Poorest	27.0	2,305	Tanganyka	3.3	279
Poorer	22.4	1,915	Maniema	4.9	415
Middle	20.2	1,730	Nord-Kivu	5.7	483
Richer	17.6	1,503	Bas-Uele	2.6	225
Richest	12.8	1,094	Haut-Uele	2.6	223
Sex of the head of household			Ituri	3.4	291
Male	77.3	6,605	Tshopo	3.1	266
Female	22.7	1,942	Sud-Kivu	5.7	485
Age of the head of household (years)			Place of residence
<25	5.8	498	Capital and large cities	12.8	1,094
25–34	34.6	4,321	Small cities and towns	16.5	1,409
35–44	29.8	2,042	Countryside	70.7	6,044
45–64	25.6	948	Used ITN last night
65+	4.2	738	No	47.1	4,022
			Yes	52.9	4,525
Total	100.0	8,547

The distribution of the sample by age shows that 11% of the population was aged 6–11 months. The average age of the sample was estimated at 32.4 months (standard deviation 15.7). Children living with both parents constituted about 64% of the sample. A majority of participants lived in rural areas (71%) and in households headed by males (77%). Half of the participants (50%) were living in households headed by people aged 25–39 years, and 4% were living in households headed by people aged 65 years or above. By province, the sample size varied, from almost 3% (Lualaba) to almost 6% (Sud-Kivu). About half of children tested (53%) slept under an ITN the night preceding the survey. Regarding the household wealth index, 27% of children were living in the poorest households, and 13% were living the richest households.

Factors associated with malaria prevalence: Findings from the bivariate analysis

Overall, out of 8,547 children considered, 25% (95% confidence interval [CI]: 24.3%-26.2%) had malaria infection. Table 2 reports the prevalence of malaria infection among under-five children in DRC by selected background characteristics. Of the 10 independent variables included in the study, 6 were statistically significantly associated with malaria infection status. Child’s sex and sex and age of the head of household were not statistically associated with the likelihood of malaria infection.

Table 2

Malaria prevalence by selected socioeconomic characteristics.

Variables	%	N	Chi-sq	P-value	Variables	%	N	Chi-sq	P-value
Sex of child					Place of residence
Male	25.7	4,258	1.085	0.298	Capital and large cities	17.6	1,094
Female	24.7	4,289			Small cities and towns	24.6	1,409	41.177	<0.001
Child’s age group (months)					Rural	26.8	6,044
6–11	14.5	943			Province
12–23	19.1	1,882	159.688	<0.001	Kinshasa	16.6	453
24–35	25.2	1,894			Kwango	9.0	379
36–47	30.4	1,943			Kwilu	8.0	424
48–59	31.4	1,885			Mai-Ndombe	29.3	331
Living arrangement					Kongo Central	26.4	386
Not living with mother	30.8	770			Equateur	17.6	245
Living with mother only	23.6	2,266	15.927	<0.001	Mongala	15.4	299
Living with both parents	25.1	5,511			Nord-Ubangi	36.9	255
Mother’s education					Sud-Ubangi	20.4	314
None	29.0	1,689			Tshuapa	15.9	239
Primary	28.3	3,490	135.171	<0.001	Kasaï	30.0	374
Secondary and above	17.0	2,597			Kasaï-Central	38.0	368	611.068	<0.001
Don’t know	30.7	771			Kasaï-Oriental	27.7	310
Sex of the head of household					Lomami	38.3	405
Male	25.6	6,605	1.848	0.174	Sankuru	19.7	299
Female	24.1	1,942			Haut-Katanga	26.1	295
Age of the head of household (years)					Haut-Lomami	23.4	295
<25	24.5	498			Lualaba	41.6	209
25–34	24.9	4,321			Tanganyka	50.2	279
35–44	23.8	2,042	8.880	0.064	Maniema	35.7	415
45–64	27.5	948			Nord-Kivu	8.1	483
65+	25.1	738			Bas-Uele	43.6	225
Wealth index					Haut-Uele	35.0	223
Poorest	26.9	2,305			Ituri	37.1	291
Poorer	29.7	1,915			Tshopo	26.7	266
Middle	27.2	1,730	132.722	<0.001	Sud-Kivu	12.8	485
Richer	24.4	1,503			Used ITN last night
Richest	11.8	1,094			No	28.7	4,022	49.108	<0.001
Total	25.2	8,547			Yes	22.1	4,525

The prevalence of malaria infection regularly increased with age. The percentage of children with malaria was estimated at 14% among children aged 6–11 months and 31% among those aged 48–59 months. The prevalence of malaria infection was low among children living with their mothers alone (23%) or living with both parents (25%), compared to children living with others (31%). Table 2 also shows a significant negative association between mother’s education and malaria infection among under-five children: Malaria prevalence was higher among children whose mother did not attend school (29%) and lower among children whose mother had a secondary or higher education (17%).

The prevalence of malaria infection was higher in rural areas (27%) and small cities and towns (25%) than in large cities, including Kinshasa, the capital city (17%). The proportion of children with malaria was lower in the richest households (12%), compared to those living in all other households (from poorest to richer). Findings also show low prevalence of malaria infection (22%) among children who slept under an ITN the previous night, compared to those who did not sleep under an ITN (29%). The malaria endemicity shows regional heterogeneity, with a higher prevalence (50%) observed in Tanganyika province. Children living in Kwango (9%), Kwilu (8%), and Nord-Kivu (8%) had the lowest prevalence of malaria infection.

Socioeconomic predictors of malaria: Findings from the CHAID model

Table 3 shows summary information on the specifications used to build the final CHAID model. Ten independent variables were examined, and five of those were statistically significant in the final model.

Table 3

Malaria prevalence among under-five children: Summary of CHAID model.

Model components	Model specification	Results
Dependent variable	Parasitemia (via microscopy) in children aged 6–59 months	25%
Independent variables	Child’s sex, child’s age, child’s living arrangement, ITN used the night before the survey, place of residence, mother’s education, age of the head of household, sex of the head of household, province, wealth index	Province, child’s age, place of residence, mother’s education, wealth index
Maximum tree depth	3	3
Minimum number of children in parent node	100	100
Minimum number of children in child node	50	50
Number of nodes	Na	42
Number of terminal nodes	Na	26

The CHAID tree diagram depicted in Fig 2A shows that the province of residence (χ2 = 603.06, p<0.001) is the best predictor of malaria infection. Fig 2A–2E and Table 4 report predictors of malaria infection among under-five children in DRC by province.

Fig 2

(A-E) Findings from CHAID model (End).

Table 4

Socioeconomic predictors of malaria infection among under-five children by province, Democratic Republic of Congo Demographic and Health Survey, 2013.

Province	First predictor	Second predictor
Equateur, Kinshasa, Mongala, Sud-Kivu, Tshuapa	Child’s age (χ2 = 31.82, p<0.001)	Place of residence (χ2 depends on child’s age and province)
Kwango, Kwilu, Nord-Kivu	Place of residence (χ2 = 14.62, p<0.001)	Child’s age (χ2 depends on place of residence and province)
Haut-Uele, Ituri, Kasaï-Central, Lomami, Maniema, Nord-Ubangi	Child’s age (χ2 = 53.15, p<0.001)	Mother’s education (χ2 depends on mother’s education category and province)
Tanganyika	-	-
Haut-Katanga, Kongo Central, Kasaï, Kasaï-Oriental Mai-Ndombe, Tshopo,	Wealth index (χ2 = 111.16, p<0.001)	Child’s age (χ2 depends on wealth index and province)
Haut-Lomami, Sankuru, Sud-Ubangi	Child’s age (χ2 = 19.61, p<0.001)	-
Bas-Uele, Lualaba	Place of residence (χ2 = 12.57, p<0.001)	-

Depending on province, the main predictors include child’s age, wealth index, place of residence, and mother’s education. No subsequent malaria infection predictor was identified in Tanganyika. In Haut-Lomami, Sankuru, and Sud-Ubangi, child’s age is the only significant predictor of malaria prevalence (χ2 = 19.61, p<0.001).

In Bas-Uele and Lualaba, place of residence (χ2 = 12.57, p<0.001) is the only significant predictor of malaria infection among under-five children.

Malaria infection among under-five children: Risk groups

The CHAID model splits participants into 26 homogeneous sub-groups, or terminal nodes, regarding the prevalence of malaria infection. Fig 2A–2E depict the process of creating the homogeneous groups, including the variables comprising each category. Table 5 describes these groups by their size (columns A and B), number of children with malaria infection (column C), the share in children with malaria infection (column E), and the proportionality of the share in malaria epidemic compared to the demographic weight (column F). The 26 homogenous sub-groups could be grouped into 4 major clusters (the third cluster includes two sub-clusters), consistent with WHO classification of malaria epidemiology [19]. Table 5 reports the characteristics of each group.

Table 5

Chi-square automatic interaction detector risk groups.

Node description	Node size		Tested +		Prevalence		Index[f]
	N[a]	%[b]	N[c]	%[d]		(%)[e]
Cluster 1	493	5.8	263	12.2		53.3		211.5
Poorer in Haut-Katanga and Kasaï-Oriental	73	0.9	47	2.2		64.4		255.2
Living in rural area in Lualaba	141	1.6	76	3.5		53.9		213.7
Living in Tanganyika	279	3.3	140	6.5		50.2		198.9
Cluster 2	6,186	72.4	1,761	81.7		28.5		112.9
Age 36–59 months; living in Ituri, Kasaï-Central, Haut-Uele, Lomami, Nord-Ubangi, Maniema	908	10.6	407	18.9		44.8		177.7
Living in rural area in Bas-Uele	183	2.1	78	3.6		42.6		169.0
Age 36–59 months; in poorest/middle/richer quintiles; living in Mai-Ndombe, Tshopo, Haut-Katanga, Kongo Central, Kasaï, Kasaï-Oriental	532	6.2	201	9.3		37.8		149.8
Age 12–35 months; mother with primary education; living in Ituri, Kasaï-Central, Haut-Uele, Lomami, Nord-Ubangi, Maniema	436	5.1	159	7.4		36.5		144.6
In poorer quintile; living in Mai-Ndombe, Tshopo, Kongo Central, Kasaï	313	3.7	111	5.1		35.5		140.6
Age 36–59 months; living in small cities/towns in Tshuapa, Equateur, Sud-Kivu, Mongala	73	0.9	25	1.2		34.2		135.8
Age 6–11 months; living in Ituri, Kasaï-Central, Haut-Uele, Lomami, Nord-Ubangi, Maniema; mother with none or primary education	129	1.5	40	1.9		31.0		122.9
Age 24–35 months; in poorest/middle/richer quintiles; living in Mai-Ndombe, Tshopo, Haut-Katanga, Kongo Central, Kasaï, Kasaï-Oriental	252	2.9	73	3.4		29.0		114.8
Living in small cities/towns (Bas-Uele, Lualaba)	110	1.3	31	1.4		28.2		111.7
Age 12–35 months; mother with none or secondary and above education; living in Ituri, Kasaï-Central, Haut-Uele, Lomami, Nord-Ubangi, Maniema	394	4.6	109	5.1		27.7		109.7
Aged 24–59 months; living in Haut-Lomami, Sankuru, Sud-Ubangi	607	7.1	154	7.1		25.4		100.6
Aged 24–35 months; living in Kinshasa, Equateur	156	1.8	34	1.6		21.8		86.4
Age 6–23 months; in poorest/middle/richer quintiles; living in Mai-Ndombe, Tshopo, Haut-Katanga, Kongo Central, Kasaï, Kasaï-Oriental	427	5.0	86	4.0		20.1		79.8
Age 36–59 months; living in capital/large cities or rural areas in Tshuapa, Equateur, Kinshasa, Sud-Kivu, Mongala	681	8.0	128	5.9		18.8		74.5
In richest quintile; living in Tshopo, Kasaï, Kasaï-Oriental	128	1.5	19	0.9		14.8		58.8
Living in large cities/small cities and towns in Kwilu, Nord-Kivu, Kwango	320	3.7	43	2.0		13.4		53.3
Age 6–23 months; living in Haut-Lomami, Sankuru, Sud-Ubangi	301	3.5	38	1.8		12.6		50.0
Age 24–35 months; living in Tshuapa, Sud-Kivu, Mongala	236	2.8	25	1.2		10.6		42.0
Cluster 3	1,868	21.9	132	6.1		7.1		28.0
Age 6–23 months; living in Tshuapa, Equateur, Kinshasa, Sud-Kivu, Mongala	575	6.7	52	2.4		9.0		35.9
Age 6–11 months; living in Ituri, Kasaï-Central, Haut-Uele, Lomami, Nord-Ubangi, Maniema; mother with secondary and above education, mother’s education unknown	90	1.1	8	0.4		8.9		35.2
Age 24–59 months; living in rural areas in Kwilu, Nord-Kivu, Kwango	658	7.7	58	2.7		8.8		34.9
In richest quintile; living in Mai-Ndombe, Haut-Katanga, Kongo Central	237	2.8	8	0.4		3.4		13.4
Age 6–23 months; living in rural areas in Kwilu, Nord-Kivu, Kwango	308	3.6	6	0.3		1.9		7.7
Overall	8547	100.0	2156	100.0		25.2		100.0

Notes: N = number of children.

[a] Number of children who received malaria test.

[b] Demographic size in percentage = ([a] /Σ[a]) × 100.

[c] Number of children tested positive.

[d] Demographic size in percentage among children tested positive = ([c] /Σ[c]) × 100.

[e] Prevalence of malaria in each group = ([c] /Σ[a]) × 100.

[f] Node index (proportionality index) = [([c] /Σ[c]) /([a] /Σ[a])] × 100.

Cluster 1—Children living in poor households in Haut-Katanga and Kasaï-Oriental

Children in this cluster represent 1% of participants and 2% of children who tested positive for malaria infection, yielding an index of 255%. Malaria prevalence was estimated at 64% in this cluster.

Cluster 2—Children living in Tanganyika and rural Lualaba

The prevalence of malaria infection was estimated at 51.4% among children living in Tanganyika province and the rural area of Lualaba. This cluster accounts for 4% of participants and 10% of children with malaria infection, yielding an index of 204%. Like Cluster 1, it is located in the southern belt of DRC.

Cluster 3—Mixed socioeconomic categories and different provinces

Cluster 3 includes the larger group of children (72.4% of children who received a malaria test). Malaria prevalence was estimated at 28.5%, ranging from 10.6% (children aged 24–35 months living in Tshuapa, Sud-Kivu, and Mongala) to 45% (children aged 36–59 months living in Ituri, Kasaï-Central, Haut-Uele, Lomami, Nord-Ubangi, and Maniema). Children in this cluster represent 82% of children with malaria infection. They live in 25 out of 26 provinces. This cluster includes children belonging to all age groups and socioeconomic characteristics (poorest-richest, living in rural and urban areas, children whose mothers never attended school, and children whose mothers had primary to secondary education).

Cluster 4—Young children or living in high socioeconomic strata in 20 provinces

This cluster includes 22% of children who received a malaria test. The prevalence of malaria was estimated at 7% and accounts for 6% of all children with malaria. This cluster includes five subgroups: (1) children aged 6–23 months and living in Tshuapa, Equateur, Kinshasa, Sud-Kivu, and Mongala; (2) children aged 6–11 months and living Ituri, Kasaï-Central, Haut-Uele, Lomami, Nord-Ubangi, and Maniema, whose mothers have secondary education and above or whose mother’s education is unknown; (3) children aged 24–59 months and living in rural areas in Kwilu, Nord-Kivu, and Kwango; (4) children living in the richest quintile in Mai-Ndombe, Haut-Katanga, and Kongo Central; and (5) children aged 6–23 months and living in rural areas in Kwilu, Nord-Kivu, and Kwango.

Socioeconomic predictors of malaria: Findings from the logistic regression model

Of the nine variables included in the logistic regression model (Table 6), five are statistically associated with the prevalence of malaria infection among under-five children: child’s age, sleeping under an ITN, mother’s education, household wealth index, and province of residence. The risk of malaria infection increases significantly with child’s age. Compared to children aged 6–11 months, those aged 12–23 months have 1.7 times more risk of malaria (95% CI = 1.24–2.36). This risk is estimated at 2.6 (95% CI = 2.02–3.54) for children aged 24–35 months, 3.3 (95% CI = 2.53–4.46) for children aged 36–47 months, and 3.4 (95% CI = 2.53–4.62) for children aged 48–59 months.

Table 6

Factors associated with malaria infection among under-five children in the Democratic Republic of Congo: Findings from logistic regression.

	Odds ratio	P-value	95% confidence interval
Sex of child
Male	Reference
Female	0.962	0.578	0.841	1.101
Child’s age group (months)
6–11	Reference
12–23	1.713	<0.001	1.245	2.357
24–35	2.675	<0.001	2.020	3.543
36–47	3.356	<0.001	2.526	4.459
48–59	3.417	<0.001	2.527	4.622
Mother’s education
No education	Reference
Primary	0.965	0.773	0.754	1.233
Secondary and above	0.669	0.003	0.513	0.874
Don’t know	0.892	0.447	0.665	1.198
Used ITN last night
No	Reference
Yes	0.857	0.044	0.738	0.996
Sex of the head of household
Male	Reference
Female	0.841	0.083	0.692	1.023
Age of the head of household (years)
<25	Reference
25–34	0.878	0.394	0.652	1.184
35–44	0.871	0.392	0.633	1.196
45–64	0.980	0.897	0.719	1.335
65+	0.779	0.248	0.510	1.190
Household wealth index
Poorest	Reference
Poorer	1.201	0.120	0.953	1.512
Middle	1.000	0.997	0.765	1.309
Richer	0.692	0.028	0.498	0.962
Richest	0.187	<0.001	0.095	0.369
Type of place of residence
Capital, large city	Reference
Small cities and towns	1.084	0.810	0.562	2.090
Rural	0.808	0.510	0.429	1.524
Province of residence
Kinshasa	Reference
Kwango	0.106	<0.001	0.035	0.318
Kwilu	0.096	<0.001	0.035	0.268
Mai-Ndombe	0.399	0.081	0.142	1.120
Kongo Central	0.447	0.084	0.179	1.114
Equateur	0.189	0.039	0.039	0.923
Mongala	0.256	0.015	0.085	0.769
Nord-Ubangi	0.464	0.119	0.176	1.218
Sud-Ubangi	0.211	<0.001	0.082	0.542
Tshuapa	0.190	<0.001	0.069	0.524
Kasaï	0.339	0.080	0.101	1.137
Kasaï-Central	0.634	0.319	0.259	1.555
Kasaï-Oriental	0.493	0.069	0.230	1.056
Lomami	0.614	0.299	0.245	1.543
Sankuru	0.175	0.002	0.057	0.532
Haut-Katanga	0.633	0.319	0.257	1.558
Haut-Lomami	0.394	0.094	0.132	1.174
Lualaba	0.837	0.764	0.261	2.681
Tanganyka	0.915	0.865	0.329	2.543
Maniema	0.441	0.087	0.173	1.128
Nord-Kivu	0.053	<0.001	0.018	0.157
Bas-Uele	1.028	0.953	0.407	2.598
Haut-Uele	0.760	0.635	0.244	2.369
Ituri	0.512	0.199	0.184	1.423
Tshopo	0.344	0.029	0.132	0.897
Sud-Kivu	0.092	<0.001	0.033	0.258
_cons	0.68647	0.377	0.2973776	1.584656

The likelihood of malaria infection is low among under-five children living in the least poor households. The risk of malaria infection is 31% lower (odds ratio = 0.61; 95% CI = 0.50–0.97) among children living in richer households, and about 81% lower (odds ratio = 0.19; 95% CI = 0.09–0.37) among children living in the richest households, compared to children living in the poorest households. Considering mother’s education, children whose mothers have secondary education have about 33% lower risk (odds ratio = 0.67; 95% CI = 0.51–0.87) of malaria infection, compared to those whose mothers did not attend school. There is no significant difference in the prevalence of malaria infection between children whose mothers attended only primary school and those whose mothers did not attend school. Children who slept under an ITN have 14% lower risk (odds ratio = 0.86; 95% CI = 0.74–0.99) of malaria infection, compared to children who did not sleep under an ITN.

After controlling for other variables, the risk of malaria infection among under-five children is lower in all provinces compared to Kinshasa. However, the difference is statistically significant in the following 10 provinces only: Kwango, Kwilu, Equateur, Mongala, Sud-Ubangi, Tshuapa, Sankuru, Tshopo, and Nord-Kivu (p-value: <0.05).

Discussion

This study aimed to identify predictors of malaria infection among under-five children in DRC and describe the socioeconomic profile of children with malaria infection. The discussion is organized around three points: complexity of findings, complementarity between methodological approaches, and policy implications. Table 7 summarizes key findings and reports those that are consistent with the literature.

Table 7

Summary of key findings.

Variables	Chi-square (bivariate)	Logistic regression	CHAID	Consistency with literature
Child’s sex	Not significant	Not significant	Not significant	Yes
Child’s age	Significant	Significant	Significant	Yes
ITN used the night before the survey	Significant	Significant	Not significant	Depends on method
Place of residence	Significant	Not significant	Significant	Depends on method
Mother’s education	Significant	Significant	Significant	Yes
Province	Significant	Significant	Significant	Yes
Wealth index	Significant	Significant	Significant	Yes

Of the 10 variables analyzed, 4 were statistically associated with the prevalence of malaria infection in bivariate analysis and multivariate analysis (CHAID and logistic regression): child’s age, mother’s education, province, and wealth index. These findings are consistent with previous studies [8, 9, 13–25]. The risk of malaria infection among under-five children increases with child’s age. Two hypotheses, which we were not able to test in this study, may explain this finding. First, younger children may be protected from malaria because of the antibodies they acquire from their mother during pregnancy and during breastfeeding [33]. Second, younger children in some countries in sub-Saharan Africa, including DRC, share a bed with their mother and are more likely to be covered properly with a blanket or an ITN than older children [34-36]. Fig 3 shows the proportion of under-five children who slept under an ITN the night preceding the study by age in DRC.

Fig 3

Proportion of children who slept under a mosquito net the night preceding the study in DRC.

Findings also show that the higher the level of a mother’s education, the lower the prevalence of malaria among under-five children. Previous studies reported that mothers with higher levels of education were more knowledgeable about malaria prevention and signs and were therefore more proactive and reactive regarding prevention than mothers with lower levels of education [34, 37–40]. In 2018, the proportion of under-five children who slept under an ITN was higher (more than 60%) among children of the most educated mothers (secondary education or higher), compared to children whose mothers did not reach that level of education (36% for children whose mothers did not attend school and 46% for children whose mothers attended only primary school) [39].

The results of this study also show that malaria cases were less prevalent among children from the richest households, compared to children from the poorest households. People in a higher wealth quintile are more likely to live in improved houses and more likely to be educated and have better access to knowledge about the steps to prevent malaria infection. They are also more likely to be able to afford ITNs and to use them correctly, as well as to be able to afford insecticides used for indoor spraying [5, 20, 38–40]. Data from the DRC 2018 Multiple Indicator Cluster Survey showed that the proportion of under-five children who slept under an ITN varied, from 35% in the poorest households to 69% in the richest households [39].

Provincial differences in malaria prevalence among under-five children could be explained by environmental and ecological factors, including distance from a household to the nearest body of water, altitude, temperature, and rainfall [6, 8, 9, 19, 20]. Data from the 2013 DHS as well as the 2018 MICS report variations in the use of ITNs among under-five children by province in DRC [26, 39].

Surprisingly, although bivariate analysis and logistic regression models report low prevalence of malaria infection among children who slept under an ITN (odds ratio  =  0.86; 95% CI = 0.74–0.99; p-value = 0.04), compared to those who were not protected, the variable “slept under mosquito net” is not statistically associated with the prevalence of malaria if one considers the findings from the CHAID model. It is likely that this effect has been captured by other variables, such child’s age, mother’s education, household’s wealth index, place of residence, and province of residence, which are associated with the use of ITNs by under-five children and with the prevalence of malaria among under-five children. In a previous study, Ferrari [16] found that the effect of mosquito nets was not significant in the lower transmission strata in DRC. That study reported that children aged less than two years were more likely to sleep under a mosquito net, compared to older children [16].

This study also shows that the effect of place of residence was not statistically significant in the logistic regression model. The CHAID model reported significant differences between children living in urban areas and those living in rural contexts, particularly among children living in: (1) Kwango, Nord-Kivu, Kwango, Lualaba, and Bas-Uele; and (2) Equateur, Mongala, Tshuapa, Sud-Kivu, and Kinshasa and aged 36–59 months. This difference could be explained by the fact that logistic regression does not automatically detect interactions between independent variables or the segments in which the model is statistically significant.

Comparison of findings by method of analysis also shows that the province of residence (spatial location) is the most important predictor of malaria prevalence among under-five children in DRC [8, 9, 13–20, 37]. The effect of other variables, such as child’s age, place of residence, mother’s education, or wealth index, depends on the province. For instance, mother’s education is only significant for children aged 6–11 months and 12–35 months living in Ituri, Kasaï-Central, Haut-Uele, Lomami, Nord-Ubangi, and Maniema provinces. The findings also show that children belonging to the same socioeconomic category (e.g., age, place of residence, wealth index) might belong to different risk groups (high or less), depending on their region (province) of residence. These findings support results from Ferrari [16], which revealed that predictors of child malaria varied by strata (malaria high transmission zone versus malaria low transmission zone). Furthermore, these findings suggest that the prevalence of malaria infection is driven by interaction among environmental factors, socioeconomic characteristics, and probably differences in the implementation of malaria programs across the country. Table 8 summarizes key findings and recommendations.

Table 8

Summary of key findings and recommendations.

Key findings	Recommendations
Prevalence of malaria infection is driven by interactions between environmental factors and socioeconomic characteristics.	Rename the malaria program as the “National Multisectoral Malaria Program” involving the Ministries of Health, Agriculture, Education, Urbanization and Habitat, Rural Development, Social and Humanitarian Affairs, Interior Affairs, Gender and Family, and Environment (Fig 4).
High-risk groups for malaria exist in the majority of provinces.	Implement universal coverage of ITNs and house improvement in all provinces because they are the most cost-efficient intervention to reduce both burden and transmission, irrespective of the ecology within a setting [9, 18, 20].Include malaria education in school curriculum as part of the stand-alone “Family Life Course” implemented in DRC schools because malaria is a public health problem with a prevalence of 25% among under-five children [41].Integrate malaria prevention and care into workplace policies and use campaigns to raise awareness among employees [41].
Spatial variation of malaria: malaria prevalence is high in some provinces.	Promote province-based implementation studies on malaria as well as malaria interventions. In high endemic clusters (prevalence above 40%), ITNs could be associated with seasonal malaria chemoprevention or indoor residual spraying [43–45].
There is a high prevalence of malaria among children aged 24 months and above.	Increase vitamin A and zinc supplementation among under-five children as part of immunization [37].
There is a high prevalence of malaria among children of mothers with low education and/or living in the poorest households and/or living in rural areas.	Promote the community engagement strategy for malaria prevention and treatment and share malaria prevention information on social media [41].

Transforming the current malaria program to a “National Multisectoral Malaria Program” involving the Ministries of Health, Agriculture, Education, Urbanization and Habitat, Rural Development, Social and Humanitarian Affairs, Interior Affairs, Gender and Family, and Environment (Fig 4) will strengthen the fight again malaria. This institution should also involve key stakeholders working on malaria and other related programs, including members of civil society organizations, nongovernmental organizations, and academia.

Fig 4

Multisectoral malaria program.

The current national program should play the role of Technical Secretariat. Such an institution will be consistent with the United Nations Development Programme (UNDP) and Malaria Roll Back (MRB) multisectoral framework for malaria [5]. It will design the national malaria policy and advise the government.

Study limitations

This study has two methodological limitations, which do not affect its quality. First, the CHAID model does not consider the hierarchical structure of the DHS data, which might influence the overall prevalence of malaria infection. However, CHAID does allow automatic detection of segments in which the prevalence of malaria infection is similar and addresses the failure to incorporate non-monotonic relationships in logistic regression. Furthermore, CHAID is a diagnostic technique for partitioning the data set into several segments [31]. Because of the heterogeneity of the data, segment-wise prediction models (CHAID) are more advantageous than the logistic global model.

Second, the study did not control for the seasonality of malaria transmission. DRC is crossed by the equator, with rainy and dry seasons varying across the country by province and by district and health zone within the same province. DHS data did not collect information about the season.

Missing values were treated as a separate category. For instance, the “Do not know” category for mother’s education included children whose mother’s education was missing.

Conclusion

In summary, findings from this study could be used for designing malaria target interventions in DRC. They show heterogeneity in malaria burden among under-five children in DRC. Consistent with findings from previous studies, four of the nine variables included in multivariate models (logistic regression and CHAID) were statistically associated with the prevalence of malaria infection: child’s age, mother’s education, province, and wealth index. Furthermore, findings from the CHAID model reveal that predictors of malaria infection vary by province. In each province, child’s age is the common predictor of malaria infection. Other predictors include place of residence, wealth index, and mother’s education. These findings also suggest that the prevalence of malaria infection is driven by interactions among environmental factors, socioeconomic characteristics, and probably differences in the implementation of malaria programs across the country. The most-at-risk groups for malaria in one province might not be the ones at greater risk in other provinces. Therefore, designing provincial and multisectoral interventions could be the most effective strategies to achieve zero malaria infection in DRC. Some of the key interventions are outlined in the World Malaria Report 2019 [42] and include investments in malaria programs and research, malaria prevention, diagnostic testing and treatment, and malaria surveillance systems.

Due to the multiplicity of factors that are linked to malaria transmission, it is important that various actions that directly and indirectly affect malaria prevention policy are designed and operate in synergy, through a multi-sectorial malaria prevention policy and national program.

4 Aug 2020

PONE-D-20-10152

Profiling malaria infection among under-five children in the Democratic Republic of Congo

PLOS ONE

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Reviewer #1: The study by Jacques and others, entitled “Profiling malaria infection among under-five children in the Democratic Republic of Congo”, aimed at identifying the malaria socioeconomic association and predictors among children aged 6-59 months and to provide socioeconomic data on the malaria most-at-risk children using Pearson’s chai square and Chi square Automatic Interaction Detector statistical model.

While the study is not novel in term of using this type of analysis, it is considered new for the malaria profile in these parts of Congo.

The authors need to go through the comments bellow:

1. Table 1 (descriptive characteristics):

• Age of child (months): to be change into Age group (months).

• Place of residence is categorized in the tables as 1) Capital & large cities and 2) Small cities & countryside, while it is Urban and Rural in text and figures.

Preferably change into urban and rural for consistency.

2. Table 2 (malaria prevalence by selected socioeconomic characteristics):

• Table is better to be presented in 2X2 format.

• Age group (months) as in table 1.

• Mother’s education:

change Secondary &+ into Secondary and above as in table 1 for consistency.

change DKN into Don’t know as in table 1 for consistency.

3. Table 3 (socioeconomic predictors):

• Chi square value in Node 1 province (Tshuapa, Equateur, Kinshasa, Sud Kivu, Mongala) for the child’s age was 14.62 while it was 31.82 in the analysis (figure 2B).

Please correct it.

4. Although the CHAID is a good analysis to find out the variable interaction associations, it seems not able to determine which group among the variable is responsible for the significant association (causal association between dependent and independent variable).

From my own opinion, it will be clear if the author can include a table of multivariate logistic regression for the significant variables as comparison.

Reviewer #2: Comments

This is an interesting paper looking at malaria predictors in DRC. The use of DHS data provides an opportunity to have a nation-wide data set and look at risk factors at national level.

However, the overall language of the manuscript needs improvement. Some of the recommendations and conclusions are not supported by the results presented. References need attention. There is a lot of unnecessary citations and important statements without references. The choice of words is not always standard. There are several statement that are either not accurate or not appropriate to malaria situation in DRC.

Specific comments

- Abstract: The abstract on the manuscript summary (editorial manager page 1) is different from the actual abstract on the manuscript document (manuscript page 1)

- Include results of measure of association in the abstract to support results

- I would remove the first sentence of the abstract.

- Line 9: increased from 271 in 2009 to 516 in 2016: out of how many? Could also specified covered with what? Malaria Interventions?

- Line 10: malaria accounted for 38% of global morbidity: the word global here is a bit confusing. Is it in DRC or Globally?

- Line 10: Remove advanced

- Line 17: The sample includes: included

- Line 17: malaria test: specify which one/ones?

- Line 19: positive or negative parasitemia test: I’d remove parasitemia

- Line 21: (2) mother’s and household’ socio-economic variables: education household’s characteristics (sex of the head of household, age of the head of household, wealth index) : which one is for mother?, check punctuation, maybe a comma missing after education

- Line 23: (3) contextual factors (province of residence and place of residence): what is meant by place of residence (is it rural vs urban?, if yes please specify)

- Line 26: (95% confidence interval 24.3%-26.2%): 95%CI and remove % inside brackets

- Lines 28-30: While predictors…..all provinces: check punctuation

- Line 31: effect of … on malaria infection, not prevalence

- Line 44: DRC is not a malaria eliminating country

- Background and methods sections: don’t have line numbers, making it difficult to reference comments

- Background: need some rewriting, some of the points are not relevant. Some unnecessary referencing, and references missing at important points

- Paragraph 1, line 3: Malaria epidemic…..: Not sure the author refer to an actual malaria epidemic or to the overall global malaria situation. I would delete “epidemic”

- Paragraph 1, line 4: Accounting for 11%...: reference

- Paragraph 1, line 6: An estimated 97%...: reference

- Paragraph 1, line 8: xxxx reported malaria deaths: delete reported

- Paragraph 2: first sentence: not sure about its relevance. WHO ….have developed”: I would replace develop by recommend.

- Despite this progress, malaria accounted for 38% of global morbidity…: is it at global level or national level? Seems like national level. Use of Global is a bit confusing. Maybe use overall

- Given the high cost of interventions, 194 million USD in 2013: reference

- Despite the growing literature…… especially in DRC: would be good to mention what has been done previously and the findings. I know for sure that some work has been done in DRC on malaria infection predictors.

- Against this backdrop, ….describe socioeconomic profile of malaria prevalence…by malaria: Profile of malaria infection

- Figure 1 presents …….endemicity class: check punctuation and grammar

- Figure 1: not clear if this is made by the author or just a general figure on malaria predictors. If the former, please make it clear and explain how these were selected. If the latter, please provide a reference. The reference provided is a very old reference on malaria stratification, this figure is not from there. As it is, the figure is hard to understand and is misleading. The color code is hard to understand. A given factor can be a predictor of malaria in different level of malaria endemicity.

- Hypoendemic malaria if the prevalence varies between 0 and 10%, Mesoendemic malaria if the prevalence comprises between 10 and 50%, Hyperendemic malaria if the prevalence varies between 50 and 75%, Holoendemic malaria if the malaria prevalence is estimated above 75%: I don’t think this is needed in the paper, as long as there’s a reference

- Individuals belong to several categories, which in their turn might belong…: remove “their”, not sure what the authors mean by categories. Do they mean risk groups?

- Some individuals might belong to categories included in the low malaria risk clusters: same as above about categories

- Last two paragraphs of “Malaria risk factors and vulnerable children for malaria”: there is a lot of unnecessary referencing. Additionally, I don’t see the relevance of mentioning all possible malaria interventions.

- Exposure to these interventions might likely explain socioeconomic differences in malaria prevalence among children aged less than five years [9, 18-48].:

o Related to the previous comment on unnecessary referencing, there are more than 30 references for this sentence only.

o I am not sure about this statement: The difference in socioeconomic status is more likely to explain difference in access to interventions.

- The dependent variable for this analysis is the malaria infection status characterized as positive or negative parasitemia test.: replace “characterized” by “defined” and “parasitemia” by “malaria”. The primary outcome is malaria infection. Authors should be consistent when referring to it, not change it to malaria prevalence or malaria epidemy.

- The data offer a unique …..the epidemy:

o The footnote can be integrated in the main text.

o Epidemy: as said above, be consistent when referring to your primary outcome

- Malaria testing was carried out among children aged 6-59 months in half of selected households. In the DHS, malaria test relied on microscopy (reading of thick-smear slide): Suggestion: Malaria testing using microscopy was carried out among children aged 6-59 months in half of selected households.

- The survey report provides more details on the sampling and microscopy process: the paper should present at least a summary of the sampling and selection of participants.

- Table 1 shows the distribution of the study population by selected background characteristics: All these results (participants characteristics) should go under results.

- Missing values were treated as a separate category. For instance, “Do not know” category for maternal education included children whose mother’s education was missing: This should go under analyses

- Statistical analyses:

o Too much details on CHAID model. Should summarize and provide references for more details

o Provide more information about how the CHAID model was constructed in this specific study. How were the explanatory variables selected and included? What is the level of significance?

o How was wealth index estimated?

o DHS is a cluster survey: was clustering taken into account? Please specify

o Was any survey weight applied?

o Consider carefully the choice of words (malaria structure?): do the authors mean malaria status?

- Study limitations:

o should be discussed in the discussion section.

o Please explain how the hierarchical nature of DHS data would affect the overall prevalence

- Results

o Participants characteristics (table 1) presented in “data sources” should be briefly presented here

o Line 3: the 95% CI is very narrow, suggesting that clustering was not taken into account

o Line 12: (23%-25%): what is this range for?

o Line 38: Box 1 and CHAID outputs show 5 significant predictors not 6 as mention here.

o Line 48: Missing residence

o Line 49: In Tanganyika, there is no predictor of malaria since the prevalence is very high (50%) for all children. Suggestion: No subsequent malaria infection predictor was identified in Tanganyika.

o Line 58: there’s no table 4

o Line 63: WHO classification: please specify classification of what?

o Clusters: Since these clusters are built by authors using the 26 final nodes of CHAID, I would expect malaria prevalence in each cluster to be presented as a range

o If the main purpose of creating the clusters is to summarize the population at risk (and protected) based on identified risk factors ( or protective factors), then cluster 3 is not informative as it includes the majority of the sample and all explanatory variables.

- Discussion

o Overall, the discussion is very limited and brief, to say the least. There are several points that could be discussed, but the authors shortly discussed 1 or 2 points only.

o The recommendations provided are not supported by the findings of this paper

o Line 97: demographic as well

o Line 103 and 104: predictor of malaria infection

o Line 104: These are not “the main predictor” but identified predictors of malaria infection

o Line 105: The sentence starting with

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25 Feb 2021

RESPONSES TO COMMENTS

1. In Tables 2 and 3, please report more specific p-values.

Answer: P-values have been inserted in Tables 2 and 3. Chi-square’ p-values are reported per variable, not per category of each variable.

3. In your ethics statement in the Methods section and in the online submission form, please provide additional information about the data used in your retrospective study. Specifically, please ensure that you have discussed whether all data were fully anonymized before you accessed them and/or whether the IRB or ethics committee waived the requirement for informed consent. Please see https://journals.plos.org/plosone/s/submission-guidelines#loc-personal-data-from-third-party-sources for our submission guidelines on this topic.

Answer: We have inserted the ethical considerations in the Data and Methods section by stating that The DHS questionnaire and the testing protocol undergo a host country ethical review as well as an ethical review at ICF Macro. Furthermore, participation in individual survey and in malaria testing is voluntary. Parents should sign the consent form before interview and before children’ blood collection.

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Answer: The ORCID iD has been provided

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Answer: Thank you for the comment which has been implemented.

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Answer: Thank you for the comment which has been implemented.

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Answer: We have included the ethical considerations in the Data and methods section.

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Answer: Thank you for the comment which has been implemented.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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Reviewer #1: Yes

Reviewer #2: Partly

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Reviewer #1: Yes

Reviewer #2: I Don't Know

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #2: No

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Reviewer #1: The study by Jacques and others, entitled “Profiling malaria infection among under-five children in the Democratic Republic of Congo”, aimed at identifying the malaria socioeconomic association and predictors among children aged 6-59 months and to provide socioeconomic data on the malaria most-at-risk children using Pearson’s chai square and Chi square Automatic Interaction Detector statistical model.

While the study is not novel in term of using this type of analysis, it is considered new for the malaria profile in these parts of Congo.

The authors need to go through the comments bellow:

1. Table 1 (descriptive characteristics):

• Age of child (months): to be change into Age group (months).

Answer: Thank you for the comment which has been implemented.

• Place of residence is categorized in the tables as 1) Capital & large cities and 2) Small cities & countryside, while it is Urban and Rural in text and figures.

Preferably change into urban and rural for consistency.

Answer: Thank you for the comment. We prefer to have three categories because small cities and town might be different to large cities and capital regarding socioeconomic infrastructures. However, sometime children belonging to the two sub-groups had similarities. In that case we can combine into the one group (urban)..

2. Table 2 (malaria prevalence by selected socioeconomic characteristics):

• Table is better to be presented in 2X2 format.

Answer: Thank you for the comment. We think that the current format is readable and avoid confusion.

• Age group (months) as in table 1.

Answer: Thank you for the comment which has been implemented.

• Mother’s education:

change Secondary &+ into Secondary and above as in table 1 for consistency.

change DKN into Don’t know as in table 1 for consistency.

Answer: Thank you for the comment which has been implemented.

3. Table 3 (socioeconomic predictors):

• Chi square value in Node 1 province (Tshuapa, Equateur, Kinshasa, Sud Kivu, Mongala) for the child’s age was 14.62 while it was 31.82 in the analysis (figure 2B).

Please correct it.

Answer: Thank you for the comment which has been implemented.

4. Although the CHAID is a good analysis to find out the variable interaction associations, it seems not able to determine which group among the variable is responsible for the significant association (causal association between dependent and independent variable).

Answer: Box 1 reports the CHAID model (variables included in the model and variables significantly associated with malaria infection among children aged less than five years. Significant differences have been established at p<0.05.

From my own opinion, it will be clear if the author can include a table of multivariate logistic regression for the significant variables as comparison.

Answer: Thank you for your comment. We have included the logistic regression model.

Reviewer #2: Comments

This is an interesting paper looking at malaria predictors in DRC. The use of DHS data provides an opportunity to have a nation-wide data set and look at risk factors at national level.

However, the overall language of the manuscript needs improvement. Some of the recommendations and conclusions are not supported by the results presented. References need attention. There is a lot of unnecessary citations and important statements without references. The choice of words is not always standard. There are several statement that are either not accurate or not appropriate to malaria situation in DRC.

Specific comments

- Abstract: The abstract on the manuscript summary (editorial manager page 1) is different from the actual abstract on the manuscript document (manuscript page 1)

Answer: Thank you for your comment which we have implemented.

- Include results of measure of association in the abstract to support results

Answer: Thank you for your comment. We have included the summary of findings from different methods.

- I would remove the first sentence of the abstract.

Answer: Thank you for this comment which has been implemented.

“Though the number of health zones covered by the national malaria control program (PNLP) increased from 271 out 516 in 2009 to 516 in 2016, malaria accounted for 38% of global morbidity and 36% of overall mortality”.

- Line 9: increased from 271 in 2009 to 516 in 2016: out of how many? Could also specified covered with what? Malaria Interventions?

Answer: Thank you for this comment which has been implemented.

- Line 10: malaria accounted for 38% of global morbidity: the word global here is a bit confusing. Is it in DRC or Globally?

Answer: Thank you for this comment. We specified that:

Though the number of health zones covered by the national malaria control program (PNLP) increased from 271 out 516 in 2009 to 516 in 2016, malaria accounted for 38% of overall morbidity and 36% of overall mortality in DRC.

- Line 10: Remove advanced

Answer: Thank you for this comment which has been implemented.

- Line 17: The sample includes: included

Answer: Thank you for this comment which has been implemented.

- Line 17: malaria test: specify which one/ones?

Answer: Thank you for this comment. We have specified the malaria test performed:

The sample included 8,547 children aged 6-59 months who got a malaria microscopy test (reading of thick-smear slide) among which half were female and 71% were living in rural areas.

- Line 19: positive or negative parasitemia test: I’d remove parasitemia

Answer: Thank you for this comment which has been implemented.

- Line 21: (2) mother’s and household’ socio-economic variables: education household’s characteristics (sex of the head of household, age of the head of household, wealth index) : which one is for mother?, check punctuation, maybe a comma missing after education.

Answer: Thank you for this comment. We have revised the sentence:

“mother’s education and household’ socio-economic variables (sex of the head of household, age of the head of household, wealth index).

- Line 23: (3) contextual factors (province of residence and place of residence): what is meant by place of residence (is it rural vs urban?, if yes please specify)

Answer: Thank you for this comment which has been implemented as below:

“contextual factors (province of residence and type of place of residence (rural or urban))”.

- Line 26: (95% confidence interval 24.3%-26.2%): 95%CI and remove % inside brackets

Answer: Thank you for this comment which has been implemented.

- Lines 28-30: While predictors…..all provinces: check punctuation

Answer: Thank you for this comment. We have revised the sentence as follow:

“While predictors of malaria infection (type of place of residence, mothers’ education and wealth index) varied by province of residence, the effect of child’s age on malaria infection was significant in all provinces”.

- Line 31: effect of … on malaria infection, not prevalence

Answer: Thank you for this comment which has been implemented.

- Line 44: DRC is not a malaria eliminating country

Answer: We have changed the sentence.

- Background and methods sections: don’t have line numbers, making it difficult to reference comments

Answer: Thank you for this comment. We have added line numbers in these sections.

- Background: need some rewriting, some of the points are not relevant. Some unnecessary referencing, and references missing at important points

- Paragraph 1, line 3: Malaria epidemic…..: Not sure the author refer to an actual malaria epidemic or to the overall global malaria situation. I would delete “epidemic”

Answer: Thank you for this comment which has been implemented.

- Paragraph 1, line 4: Accounting for 11%...: reference

Answer: Thank you for this comment. We have inserted the reference.

- Paragraph 1, line 6: An estimated 97%...: reference

Answer: Thank you for this comment. We have inserted the reference.

- Paragraph 1, line 8: xxxx reported malaria deaths: delete reported

Answer: Thank you for this comment which has been implemented.

- Paragraph 2: first sentence: not sure about its relevance. WHO ….have developed”: I would replace develop by recommend.

Answer: Thank you for this comment which has been implemented.

- Despite this progress, malaria accounted for 38% of global morbidity…: is it at global level or national level? Seems like national level. Use of Global is a bit confusing. Maybe use overall

Answer: Thank you for this comment. We have revised this sentence.

“Despite this progress, malaria accounted for 38% of overall morbidity and 36% of overall mortality [4,10] in DRC”.

- Given the high cost of interventions, 194 million USD in 2013: reference

Answer: Thank you for this comment. We have inserted the reference.

- Despite the growing literature…… especially in DRC: would be good to mention what has been done previously and the findings. I know for sure that some work has been done in DRC on malaria infection predictors.

- Against this backdrop, ….describe socioeconomic profile of malaria prevalence…by malaria: Profile of malaria infection

Answer: We have mentioned some existing publications on malaria in DRC.

- Figure 1 presents …….endemicity class: check punctuation and grammar

Answer: Thank you for this comment. We have revised the sentence

- Figure 1: not clear if this is made by the author or just a general figure on malaria predictors. If the former, please make it clear and explain how these were selected. If the latter, please provide a reference. The reference provided is a very old reference on malaria stratification, this figure is not from there. As it is, the figure is hard to understand and is misleading. The color code is hard to understand. A given factor can be a predictor of malaria in different level of malaria endemicity.

Answer: Thank you for this comment. We have inserted source of Figure 1: Developed by Authors based on malaria literature.

- Hypoendemic malaria if the prevalence varies between 0 and 10%, Mesoendemic malaria if the prevalence comprises between 10 and 50%, Hyperendemic malaria if the prevalence varies between 50 and 75%, Holoendemic malaria if the malaria prevalence is estimated above 75%: I don’t think this is needed in the paper, as long as there’s a reference.

Answer: We have deleted the categories and just left the reference.

- Individuals belong to several categories, which in their turn might belong…: remove “their”, not sure what the authors mean by categories. Do they mean risk groups?

Answer: Thank you for this comment which has been implemented.

- Some individuals might belong to categories included in the low malaria risk clusters: same as above about categories.

Answer: We have removed this sentence since it was tautological.

- Last two paragraphs of “Malaria risk factors and vulnerable children for malaria”: there is a lot of unnecessary referencing. Additionally, I don’t see the relevance of mentioning all possible malaria interventions.

Answer: We have revised the section.

- Exposure to these interventions might likely explain socioeconomic differences in malaria prevalence among children aged less than five years [9, 18-48].:

Answer: We have dropped the paragraph on interventions.

o Related to the previous comment on unnecessary referencing, there are more than 30 references for this sentence only.

Answer: We have reduced number of references.

o I am not sure about this statement: The difference in socioeconomic status is more likely to explain difference in access to interventions.

Answer: We have revised the sentence.

- The dependent variable for this analysis is the malaria infection status characterized as positive or negative parasitemia test.: replace “characterized” by “defined” and “parasitemia” by “malaria”. The primary outcome is malaria infection. Authors should be consistent when referring to it, not change it to malaria prevalence or malaria epidemy.

Answer: We have edited accordingly based on the suggestions above.

- The data offer a unique …..the epidemy:

o The footnote can be integrated in the main text.

o Epidemy: as said above, be consistent when referring to your primary outcome

- Malaria testing was carried out among children aged 6-59 months in half of selected households. In the DHS, malaria test relied on microscopy (reading of thick-smear slide): Suggestion: Malaria testing using microscopy was carried out among children aged 6-59 months in half of selected households.

Answer: We have edited based on the suggestions above.

- The survey report provides more details on the sampling and microscopy process: the paper should present at least a summary of the sampling and selection of participants.

Answer: Thank you for this comment. We have inserted a paragraph summarizing the sampling process.

Malaria testing was carried out among children aged 6-59 months in half of the 18,360 selected households. Malaria testing using microscopy was carried out among children aged 6-59 months in half of selected households. Using a finger (or heel) prick, a drop of blood was collected on a slide to prepare a thick drop. After drying, the slides were stored in special boxes with desiccants and humidity controllers. These dishes were transferred regularly to the NRL for the detection of hematozoa by microscopy, which was carried out regularly. All health technicians were trained to perform finger (or heel) prick in the field according to the manufacturers’ instructions. A total of 8,547 children aged 6-59 months got malaria. The survey report provides more details on the sampling and microscopy process

- Table 1 shows the distribution of the study population by selected background characteristics: All these results (participants characteristics) should go under results.

Answer: Thank you for this comment which has been implemented.

- Missing values were treated as a separate category. For instance, “Do not know” category for maternal education included children whose mother’s education was missing: This should go under analyses

Answer: Thank you for this comment which has been implemented.

- Statistical analyses:

o Too much details on CHAID model. Should summarize and provide references for more details

Answer: We have summarized the description of CHAID model.

o Provide more information about how the CHAID model was constructed in this specific study. How were the explanatory variables selected and included? What is the level of significance?

Answer: Thank you for the comment. The Malaria risk factors and the most-at-risk for malaria section guides the selection of variables included in the study.

o How was wealth index estimated?

Answer: DHS datasets provide variable wealth index (hv270).

o DHS is a cluster survey: was clustering taken into account? Please specify

Answer: We weighted analyses to consider clustering.

o Was any survey weight applied?

Answer: The survey weight was applied. Proportions (%) are from the weighted sample while numbers are unweighted.

o Consider carefully the choice of words (malaria structure?): do the authors mean malaria status?

- Study limitations:

o should be discussed in the discussion section.

Answer: Thank you for the comment. We have displaced the study limitations into the discussion section.

o Please explain how the hierarchical nature of DHS data would affect the overall prevalence

- Results

o Participants characteristics (table 1) presented in “data sources” should be briefly presented here

Answer: Thank you for your comment. We have displaced participants’ description to this section.

o Line 3: the 95% CI is very narrow, suggesting that clustering was not taken into account

Answer: We weighted data to account for the complex design, including clustering of the household survey.

o Line 12: (23%-25%): what is this range for?

Answer: The prevalence of malaria infection was low among children living with their mothers alone (23%) or with the two parents (25%) compared to those others (31%).

o Line 38: Box 1 and CHAID outputs show 5 significant predictors not 6 as mention here.

Answer: Thank you for your comment which has been implemented.

o Line 48: Missing residence

Answer: Thank you for your comment which has been implemented.

o Line 49: In Tanganyika, there is no predictor of malaria since the prevalence is very high (50%) for all children. Suggestion: No subsequent malaria infection predictor was identified in Tanganyika.

Answer: Thank you for the suggestion which has been implemented.

o Line 58: there’s no table 4

Answer: Thank you for the comment. Table 4 has been inserted.

o Line 63: WHO classification: please specify classification of what?

Answer: Thank you for the comment. We have specified “WHO classification of malaria epidemiology”.

o Clusters: Since these clusters are built by authors using the 26 final nodes of CHAID, I would expect malaria prevalence in each cluster to be presented as a range

Answer: We calculated the average for each cluster. Indicator within each cluster are from the 26 nodes.

o If the main purpose of creating the clusters is to summarize the population at risk (and protected) based on identified risk factors ( or protective factors), then cluster 3 is not informative as it includes the majority of the sample and all explanatory variables.

Answer: Thank you for your comment. We understand the problem you raised. These findings show the cluster where the majority of children belong and the complexity of their profile.

- Discussion

o Overall, the discussion is very limited and brief, to say the least. There are several points that could be discussed, but the authors shortly discussed 1 or 2 points only.

Answer: We have provided more discussion points.

o The recommendations provided are not supported by the findings of this paper

Answer: Recommendations are suggested by key findings.

o Line 97: demographic as well

Answer: We revised the sentence

o Line 103 and 104: predictor of malaria infection

Answer: We revised the sentence

o Line 104: These are not “the main predictor” but identified predictors of malaria infection

Answer: We revised the sentence

o Line 105: The sentence starting with

Answer: We revised the sentence

Submitted filename: 2 COMMENTS_JE2021.02.15.docx

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23 Mar 2021

PONE-D-20-10152R1

Profiling malaria infection among under-five children in the Democratic Republic of Congo

PLOS ONE

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Submitted filename: 4 Profiling_Malaria_EminaR1_editor.docx

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7 Apr 2021

Dear Editor

We have edited the manuscript to meet Plos One standard. The ethics statement appears only in the Methods section.

Submitted filename: 2 COMMENTS_JE2021.02.15.docx

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12 Apr 2021

Profiling malaria infection among under-five children in the Democratic Republic of Congo

PONE-D-20-10152R2

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Reviewers' comments:

21 Apr 2021

PONE-D-20-10152R2

Profiling malaria infection among under-five children in the Democratic Republic of Congo

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