Factors Influencing Malaria Infection in Rwanda 2010: A Cross-Sectional Survey Study Using Generalized Structural Equation Modeling

Research Article

J Fam Med. 2022; 9(5): 1303.

Factors Influencing Malaria Infection in Rwanda 2010: A Cross-Sectional Survey Study Using Generalized Structural Equation Modeling

Muhammad Abu Bakar¹*, Rahma Fiaz² and Eustasius Musenge³

1Department of Cancer Registry & Clinical Data Management, Shaukat Khanum Memorial Cancer Hospital and Research Centre, Lahore, Pakistan

2Department of Internal Medicine, Evercare Hospital, Lahore, Pakistan

3The Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa

*Corresponding author: Muhammad Abu Bakar, Department of Cancer Registry & Clinical Data Management, Shaukat Khanum Memorial Cancer Hospital and Research Centre, (SKMCH & RC), 7-A Block R-3, Johar Town, Lahore, Pakistan

Received: May 17, 2022; Accepted: June 20, 2022; Published: June 27, 2022


Background: Malaria is one of the world's primary public health concerns. In Rwanda, malaria prevention has become a significant problem against the double-barreled burden of an overstretched health system and strained financial resources.

Methods: A cross-sectional survey study design was done with a primary outcome variable was an ordinal variable with three categories; no malaria, probable malaria, and confirmed malaria cases. Statistical analysis was done using survey ordinal logistic regression modeling adjusting for random effects for direct effects.

Results: The 11,865 participants had a mean age of 22 years, and twothirds of the participants were females (67%). Household related variables (socioeconomic status, health insurance, age in years) showed a significant total effect on malaria infection. Socio-economic status had the most significant total effect, which was a sum of the direct and indirect effects influenced indirectly by education, health insurance and the number of rooms for sleeping in isolation.

Conclusion: Poverty is still the core issue to the morbidity patterns driving the malaria. Access to health facility and health insurance has a high positive impact on decreasing disease. A better understanding of the drivers of morbidity directly and/or indirectly can better target interventions to be more efficient in those affected areas.

Keywords: Generalized Structural Equation Modeling (G-SEM); Malaria Morbidity; Malaria Indicator Survey (MIS); Demographic and Health Survey (DHS)


G-SEM: Generalized Structural Equation Modeling; MIS: Malaria Morbidity, Malaria Indicator Survey; DHS: Demographic and Health Survey; SES: Socioeconomic Status; ALMA: Infectious Disease, African Leaders Malaria Alliance; LLIN: Long-Lasting Insecticidal Nets; ACT: Artemisia Combination Therapy; NISR: National Indicator Survey of Rwanda; WHO: World Health Organization; FA: Factor Analysis; GDP: Gross Domestic Product; UN: United Nation.


Malaria is a significantpublic health issue in the world [1]. Globally, it was estimated that there were 660,000 deaths due to malaria, of which a large proportion over 86% were in children (less than five years age) and older people (above sixty) [2]. It is a preventable and treatable infectious disease transmitted by mosquitoes, yet it kills more than one million people every year in sub-Saharan Africa.In sub- Saharan Africa, malaria is the leading cause of death in children less than five years of age and accounts for about 91% of deaths occurring in children (less than five years of age) and older people (over sixty) [1]. The African Leaders Malaria Alliance (ALMA), in collaboration with the nine countries (Angola, Cameroon, Chad, Congo, Gabon, Equatorial Guinea, Central African Republic, DR Congo, and Sao Tomé-et-Principe) developed an African Roadmap to eliminate malaria by 2030 [1]. According to the World Health Organization (WHO) country-specific statistics, 2.2 million inhabitants out of 11.5 million people are at risk of malaria in Rwanda [3,4].

Malaria is mesoendemic (area in which a disease incidence is sufficiently high) in the low-lands and hypoendemic (area in which a disease incidence is sufficiently low) in the highlands of Rwanda [5]. Globally, in endemic areas where transmission occurred in long regular seasons, infection rates were highest among children less than five years of age who had not yet established immunity to the disease, contrary to epidemic areas where malaria transmission took place in short seasons; malaria infections in all age categories were high [6]. Furthermore, a study conducted in Rwanda's Ruhuha region concluded that there was also an increased risk of malaria in older age groups during long regular malaria season [7]. The reason may be that older people did not sleep under treated bed nets than young ones. Another reason might be that older people stayed out longer than younger ones, making them more likely to be bitten by mosquitoes [8,9].

Historically, it is believed that the most deadly malaria species, Plasmodium falciparum is prevalent in sub-Saharan Africa [10]. Consequently, malaria is hard to control in Africa due to vector species' efficacy and the predominance of the most severe species Plasmodium falciparum [11]. The risk of malaria infection is high in developing countries' rural areas and can be attributed to poverty and low lifestyle [12]. Factors that play the significant role in disease risk include proximity to the vector breeding sites, age, socio-economic status, altitude, moderate use of control measures, low income, limited access to the health facility, illiteracy, land use near pools, and open houses [13-16].

A recent global report shows that due to good political commitment and better utilization of funding, 54% of the above reduction has been experienced in African countries (WHO regions) [17]. As is the case in other sub-Saharan countries, in Rwanda, the use of Long-Lasting Insecticidal Nets (LLIN), Indoor Residual Spraying (IRS), and treating malaria cases with Artemesinin-based combination therapy (ACT) had reduced malaria infection up to 50% [18,19]. Therefore, it has a significant influence on the health and socio-economic well-being of people. Therefore, this work focuses on determining the direct and indirect determinants of malaria morbidity in poorer/financially challenged households and at an individual level in Rwanda in 2010 using malaria indicator survey (MIS) data accessible from the Demographic and Health Survey (DHS) website.


This study was conducted as a cross-sectional survey design. The dataset used in this work is known as the Malaria Indicator Survey (MIS) dataset of Rwanda, part of the Demographic and Health Surveys (DHS) 2010. The previous study collected the data elements on basic demographic and health indicators, malaria prevention, treatment, and morbidity. The study includes both males and females tested for malaria diagnosis (rapid malaria test and confirmatory blood smear test), either positive or negative results. This work was conducted in Rwanda,a Central African country located South of the equator between latitude 1°4' and 2°51' South and longitude 28°63' and 30°54' East. It has a surface area of 26,338 square kilometers and is bordered by Uganda to the North, Tanzania to the East, the Democratic Republic of the Congo to the West, and Burundi to the South. Landlocked, Rwanda lies 1,200 kilometers from the Indian Ocean and 2,000 kilometers from the Atlantic Ocean [20]. Rwanda is divided into five geographically-based provinces–North, South, East, West, and the City of Kigali, with the provinces, further subdivided into 30 districts, 416 sectors, 2,148 cells, and 14,837 villages [20].

Sampling in the previous study was done in two stages. In the first stage, 492 villages formed the clusters were selected with probability proportional to the village size. The village population size also indicates the number of households in the village. The mapping and listing of all households in the selected villages was done. The resulting list served as the sampling frame for the second stage of sample selection. All of the 492 clusters were set for the modeling as surveyed for the 2010 RDHS. The selected data contained 11,865 households who consented to participate in the study and completed the individual’s questionnaires. Data for children less than five years of age were collected from their mothers' parents or legal guardians [20,21].

According to the Center for Disease Control (CDC) and World Health Organization case definition criteria, the malaria outcome variable for this study was defined. The CDC and WHO definition indicates three possible states of malaria infection [22,23].

1. No malaria

2. Probable or (symptomatic or asymptomatic) malaria infection

3. Confirmed malaria infection

Two types of tests were conducted on surveyed population (rapid malaria test and confirmatory blood smear laboratory test). Participants who showed a negative result in both the tests (rapid malaria test and confirmatory blood smear laboratory test) were put in the category of "No Malaria Cases."The participants who were not tested for confirmatory blood smear laboratory or either show negative test but showed positive in the rapid malaria test were considered as "Probable or symptomatic or asymptomatic Malaria Cases".

Those who have positive confirmatory blood smear laboratory tests, regardless of their results from rapid malaria test, either positive or negative, were considered as confirmed malaria cases. The independent variables were split into four categories: individuallevel variables (education gain in years, age in years, social-economic status, health insurance, household-related variable (no of rooms for sleeping), ecological variables (cluster altitude in meters and region) and behavioral variables (has clean water facility for drinking, and sleep under bed nets). Factor analysis (FA) was used to derive socioeconomic status (SES) using indicators data from the 2010 Rwanda Demographic Health Survey (RDHS). Factor analysis is a useful tool for investigating variables relationships for complex concepts such as socio-economic status [24]. It allows researchers to explore that are not easily measured directly by collapsing many variables into a few interpretable underlying factors. The process to create socioeconomic status is shown in (Figure SI and SII).

Reiter P. in 2008 studied the effects of temperature on Malaria transmission. His study proposed that temperature, rainfall, and humidity cannot be considered in exclusion without considering humans' behavior, and humidity cannot be considered in exclusion without considering humans' behavior. Additional factors that influence the malaria infection directly and/or indirectly; household and individual level related variables (SES, age, education, and health insurance), behavioral variables (Clean water facility for drinking and sleep under bed net), ecological variables [Regions (north, east, west and south)] and cluster altitude in meters).This study considers all the direct and indirect factors with the inclusion of human behavior as suggested by Reiter P. in 2008 [25]. This work considers human behavior and related direct and indirect factors (mentioned above), which have not been considered in previous studies.

Conceptual modeling technique and variables are divided into three main categories; ecological, household and/or individual level, and behavioral variables in the analysis with the purpose that these determinative factors run through the standard set of either ‘proximate direct or indirect variables that have an influence on malaria morbidity [26,27]. Variables are either endogenous (dependent variable) or exogenous (explanatory variable) or both, which can be modeled by using generalized structural equation models as shown in (Figure SI and SII). Details of these models provided in (Figure III and Figure IV).

Statistical analysis

Power computation was done using a STATA ado-file [28]. A sample prevalence of 2.1% comprised of 1.4% children and 0.7% adults adopted from the Rwanda Malaria Indicator Survey [3] and a 3% assumed population prevalence at an alpha level of 0.05. A design effect of 10 was used and intercluster correlation (ICC) of 0.071 with 492 clusters and average households per cluster of 115. Power was obtained at 89%.

Descriptive analysis was conducted using a survey chi-square test (Rao-Scott adjustment) adjusted for cluster effect. For categorical variables weighted percentages (proportion) with adjusted F-statistic are reported in (Table I). For continuous variables, mean and confidence interval are reported as shown in (Table I). Bivariate analysis was done using a survey chi-square test adjusting for cluster effect to establish the relationship between two categorical variables (such as malaria morbidity and gender). For the continuous explanatory variables such as age, survey ordinal univariate analysis adjusted for cluster effect was done. For bivariate analysis, odds ratio with p-value is reported in (Table II).