RESEARCH ARTICLE Year : 2023  |  Volume : 60  |  Issue : 1  |  Page : 38-48

Socioeconomic determinants of malaria prevalence among under five children in Uganda: Evidence from 2018-19 Uganda Malaria Indicator Survey

Ronald Kooko1, Solomon T Wafula2, Phillip Orishaba3
1 Department of Epidemiology and Public Health, Central University of Tamil Nadu, Neelakudi, Thiruvarur, India
2 Department of Disease Control and Environmental Health, School of Public Health, College of Health Sciences, Makerere University, Kampala, Uganda
3 Ministry of Health, Uganda

Date of Submission21-Jul-2021Date of Acceptance05-May-2022Date of Web Publication5-Apr-2023

Correspondence Address:
Ronald Kooko
Department of Epidemiology and Public Health, Central University of Tamil Nadu, Neelakudi, Thiruvarur 610005
India

Source of Support: None, Conflict of Interest: None

Check

DOI: 10.4103/0972-9062.353251

Background & objectives: Malaria remains a significant public health problem in sub-Saharan Africa, affecting mainly children and pregnant women. In Uganda, little is known about the underlying socioeconomic correlates of malaria prevalence in children under five years of age. This study investigated the link between malaria infection among children under five and the socio-economic factors in Uganda.
Methods: We estimated the prevalence of malaria among under-five children using secondary data from the 2019 Uganda Malaria Indicator Survey. Malaria infection status was ascertained using rapid diagnostic tests (RDTs). Multivariable logistic regression was employed to explore the socioeconomic correlates of malaria prevalence. Svyset command in STATA 16.0 was used to control for survey design.
Results: Overall, 6503 children were enrolled in the study. Of these, 1516 children tested positive for malaria, leading to an observed malaria prevalence of 23.3%. Older children (OR 1.01, 95%CI 1.01–1.01), and those from rural areas (OR 1.8, 95%CI 1.09–2.84) had higher odds of malaria infection. Children belonging to the highest wealth quintile had lower odds of malaria (OR 0.2, 95%CI 0.08–0.44). Indoor residual spray (OR 0.2, 95%CI 0.10–0.51) and use of treated bed nets (OR 0.8, 95%CI 0.69–0.99) were associated with reduced odds of malaria in children
Interpretation & conclusion: Despite the significant increase in malaria preventive interventions in the last two decades, malaria remains highly prevalent in Ugandan under-five children. Indoor residual spraying and treated bed nets need to be promoted countrywide to reach malaria control targets. It is also imperative that appropriate education on proper and consistent use of mosquito bed-nets should be emphasized alongside embracing living habits that reduce the chances of mosquito bites like staying indoors.

Keywords: Malaria; children; prevalence; determinants; Uganda

How to cite this article:
Kooko R, Wafula ST, Orishaba P. Socioeconomic determinants of malaria prevalence among under five children in Uganda: Evidence from 2018-19 Uganda Malaria Indicator Survey. J Vector Borne Dis 2023;60:38-48
How to cite this URL:
Kooko R, Wafula ST, Orishaba P. Socioeconomic determinants of malaria prevalence among under five children in Uganda: Evidence from 2018-19 Uganda Malaria Indicator Survey. J Vector Borne Dis [serial online] 2023 [cited 2023 Apr 6];60:38-48. Available from: http://www.jvbd.org//text.asp?2023/60/1/38/353251   Introduction 

Malaria is a vector-borne disease usually referred to as the ‘disease of the poor’ transmitted from one infected individual to another by an adult female Anopheles mosquito with roughly 229 million cases and 409,000 deaths globally in the year 2019[1],[2]. A notable proportion of these deaths happened in Africa, where many countries are categorised as malaria endemic and Uganda falls under this category with approximately ninety percent of the country’s population affected[3]. In spite of the global decline in malaria prevalence from 80 cases per 1000 population at risk in 2000 to 57 cases in 2019, malaria undoubtedly remains a global public health challenge[4]. Moreover, 95% of the global malaria burden is contributed by less than 30 countries with 94% of these cases occurring in the WHO African Region[5].

Globally, malaria-related deaths amongst children under-five have significantly declined over the last decade and malaria is no longer considered the leading cause of death in children <5 years. However, it remains the most common cause of morbidity in African children with 10% deaths among children under-five[5]. In sub-Saharan Africa (SSA), approximately two-thirds of all malaria related deaths occur in children under-five[6]. However, due to the unending fight against malaria through intervention programs, malaria parasitemia and clinical incidence has significantly decreased by 50% and 40%, between 2000 and 2015, respectively[7].

Despite the remarkable decline in the prevalence of malaria in children under five in SSA, prevalence remains high in many countries. For instance, the malaria prevalence based on the last two recent malaria indicator survey studies from Tanzania was 14% in 2016 and 7% in 2017; Burundi 22% in 2013 and 27% in 2016–2017; Kenya 5% in 2015; Rwanda 2% in 2015 and 7% in 2017; Uganda 30% in 2015 and 30.3% in 2016; South Sudan 32% in 2017[8],[9].

Uganda ranked sixth on the global perspective and third in Africa only behind Nigeria and Democratic Republic of Congo in mortality due to malaria with 16 million cases and over 10,000 deaths reported per annum[10]; this could be because the country experiences weather conditions that often allow transmission throughout the year with a few places experiencing unstable and low transmission[11].

Malaria stands out as the disease with the highest distribution inequality compared to other disease of public health importance since almost 58% of malaria deaths happen among the 20% of the world's poorest population[12]. Malaria endemicity leads to tremendous annual economic losses and a domino effect of the vicious cycle of poverty in under developed countries[13],[14]. To eliminate malaria, the global control strategy has strongly stressed the idea of malaria control by insecticide-treated bed nets (ITNs), increased case detection and subsequent treatment, indoor residual spraying (IRS) plus other vector control measures, however less attention has been given to the significance of socio-economic factors towards malaria[15]. Hence, in addition to healthcare system strengthening, the success of malaria control largely depends upon the socio-economic status and knowledge of the affected populations since malaria is usually labelled as a ‘disease of the poor’[16].

Therefore, to counterbalance the burden of malaria, there is a need to continuously understand the risk factors associated and epidemiology of malaria[17]. Even though numerous studies have been done worldwide to identify a wide variety of socioeconomic risk factors associated with malaria, there is need for the same to be done in the local context to enable formulation of feasible national malaria control programmes[10],[18]. The recent studies on malaria in Uganda were mostly hospital-based, investigating clinical malaria in children and pregnant women[19]. However, little is known about the socioeconomic determinants of malaria prevalence amongst children aged below five years, in Uganda. Consequently, this study aims at analysing data from 2019 Uganda Malaria Indicator Survey (UMIS) to examine the relationship between malaria status amongst children under-five in Uganda and socioeconomic risk factors/determinants of malaria.

  MATERIAL & METHODS 

Study area and sample design

This was a nationwide study conducted in Uganda, a landlocked country located on the equator in East Africa. It borders Rwanda and Tanzania from the south, the Democratic Republic of Congo from the west, Kenya from the east, and South Sudan from the north. Uganda is estimated to occupy an area of 241,550 km2 with about 41,743.2 km2 being swamps and open water, notably Lakes Victoria, Albert, Edward and River Nile and these water bodies provide an ambient breeding sites for Anopheles mosquito, the vector responsible for transmitting malaria in humans[20].

Uganda experiences tropical climate favourable for both mosquitoes and man with relatively two rainy seasons annually, heavy rains between March and May and moderate rains from September to December. The pinnacle of clinical malaria incidence usually comes after the peak of the rains with approximately 4–6 weeks of delay, implying that most cases are registered between May and July with another episode between December and February.

The 2018–19 UMIS was conducted between December 2018 and January 2019 to align with peak malaria transmission. This was done to provide current estimates of demographic and health indicators related to malaria to enable policy makers to design feasible strategies in an attempt to improve the health of the population. To achieve this, 2018–19 UMIS collected a broad spectrum of information ranging from vector control interventions like IRS and mosquito nets to malaria knowledge, practices and behaviour.

The sample was stratified into fifteen different survey regions of the country as shown in [Figure 1]. A stratified two-stage sample design was used to select the sample during the survey. In the first sampling stage, a total of 320 clusters were chosen based on probability proportional to size from the Enumeration areas (EAs) covered in the 2014 National Population and Housing Census (NPHC). From these clusters, 236 were in rural areas and 84 in urban areas while 22 clusters were selected proportionate to size from the EAs covered in the refugee frame. The second sampling stage involved selecting 28 households from each cluster by equal probability systematic sampling.

Uganda Malaria Indicator Survey (UMIS) data

The trained personnel visited the selected households to interview and capture required information using different questionnaires. Three questionnaires were used namely; Household Questionnaire; Biomarker Questionnaire; and Woman Questionnaire. The household Questionnaire captured basic information such as each member biograph, characteristics of the household’s dwelling unit like water, toilet facilities, nature of house floor, wall and roof, ownership of assets, and ownership and use of mosquito nets. The Woman’s Questionnaire collected an array of information from all eligible women in the sample. The Biomarker Questionnaires were used to record the results of malaria and anaemia testing of children under five years after consent from a parent or guardian.

The prevalence of malaria in the children under-five was determined using two test procedures; microscopy and rapid diagnostic test (RDT). The SD Bioline Antigen rapid diagnostic test was used test for the parasite P. falciparum using a drop of blood based on the principle of antigen-antibody reaction and the results of the test were readily available within 15 minutes. The second test procedure involved use of microscope as the gold standard for malaria testing using two blood smears; thin and thick. Using a microscope, thick smears were first examined to determine Plasmodium infection and the corresponding thin smears of positive thick smears were examined to determine the species of Plasmodium parasite.

Response variable/Dependent variable

RDTs are cheap, more readily available and easy to use needing no technicians with specified skills unlike the microscopy tests. In spite of that, the RDT does not detect the P. falciparum parasite but rather its specific protein which usually remains in the blood for several days after treatment. Thus, this test is less sensitive leading to increased false positives which literally results in overestimation of rates of malaria. For study purposes, the prevalence of malaria in children under-five was determined based on RDT test results. Therefore, the response variable was binary stating whether a test was negative or positive for malaria.

Independent variables

In this study, a number of socio-economic factors were considered as independent variables and their relationship with malaria outcome was explored. Such variables included age of the child, housing, education level of mothers, area of residence, electricity, and IRS application.

Statistical model

All statistical analyses were performed after accounting for clustering by residence and the sampling weights using STATA version 16.0. Descriptive and the binary logistic analyses were performed. All variables with P-values <0.05 at bivariable models were selected for the multivariable analysis. All variables that remained significant in the multivariable model were thus significantly associated with malaria among children aged under 5 years.

Data availability

All datasets used in the analysis of the current study are available in the public domain from the Measure DHS website; https://dhsprogram.com/data/dataset_admin/index.cfm

Ethical statement

The 2019 UMIS received ethical clearance from ICF International’s institutional review board, Uganda National Council of Science and Technology (UNCST), Makerere University School of Biomedical Sciences Higher Degrees Research and Ethics Committee (SBS-HDREC). Confidentiality was ensured while collecting data and other related information. The benefits and risks of participation in the survey were clearly explained to participants and informed consent was sought from all the participants before the commencement of the interview or blood collection.

  Results 

Out of the 6503 children who participated in the study, 1516 (23.3%) tested positive for malaria using RDT with the minor difference in the observed prevalence between females and males of 22.7% and 24.0% respectively suggesting that gender is not a significant risk factor of malaria. This was a remarkable reduction in malaria prevalence as compared to 43.3% in the MIS of 2009. This substantial reduction can be attributed to the strategic control measures by the national malaria control program through massive distribution of free insecticide treated mosquito nets, indoor residual spraying and intensive awareness programs on national broadcasting television channels sensitizing people about malaria prevention and control.

In this study, children whose mothers had the highestlevel of education showed the least prevalence of malaria at 8.2% and were 90% less susceptible to malaria (OR=0.1, 95%CI=0.05-0.41) compared to 37.6% in children whose mothers had the lowest level of education. Generally, the observed trend showed that an increase in the levels of education was associated with a decrease in the prevalence of malaria in children [Table 1].

Table 1: Bivariate analysis of mother and child characteristics associated with malaria (logistic regression)

Click here to view

It was observed that rural areas had high malaria prevalence among children at 25.1% compared to 7.5% in urban settings. This observation aligns with the similar previous study in Uganda by Roberts and Mathews[10]. Children from rural setting were more than 4 times (OR=4.1, 95%CI=2.12-7.95) likely to have malaria infection than their counterparts in urban areas [Table 1]. This could be because people living in rural settings do not have sufficient access to healthcare facilities coupled with poor housing facilities that expose children to malaria bites. Another argument is based on the fact that children of school-going age from rural settings get infected while travelling to and from school since most of them pass through agricultural plantations and forests which are the breeding sites of mosquitoes, exposing children to mosquito bites compared to their counterparts in the urban settings.

Insecticide-treated mosquito bed nets use was significantly associated with malaria. Malaria prevalence decreased with an increase in use of insecticide-treated mosquito bed nets with an observed 28.4% malaria prevalence among children who didn’t use mosquito nets compared to 10% among those who slept under mosquito nets. Children who slept under the mosquito nets were 0.3 times less likely to get malaria infection (OR=0.3, 95%CI=0.10-0.76, p-value=0.013) as opposed to their counterparts who did not sleep under the mosquito net [Table 1].

Based on religious affiliation, the highest percentage of malaria cases (28.4%) was reported amongst the children whose mothers were catholic whereas children whose mothers were anglican had the least proportion of malaria at 16.7%. Children from the catholic mothers were 2 times (OR=2, 95%CI=1.31-2.99, p-value 0.001) more likely to have malaria than those whose mothers were anglicans.

There was a significantly high prevalence of malaria cases among children from household without electricity at 28.1% compared to 10% among children from households with electricity [Table 2]. Children from households with electricity were half times less likely to have malaria infection as compared to children from households without it (OR 0.5, 95%CI 0.28-2.21, p-value 0.011). From [Table 2], there was a substantially high proportion of malaria cases 30.7% among children from households with earth/sand as the main material of the floor compared to 11% for children residing in households with the cemented floors. Children from households with cemented floor were at less risk of malaria infection compared to those from households with only earth/sand as the final surface of the floor (OR 0.3, 95%CI 0.15-0.50). Compared to children from households having poles with mud, burnt bricks with cement as the wall material, children from unburnt bricks with mud as the main wall material have relatively high proportion of malaria cases at 36.5%. These children were more than twice likely to get malaria infection (OR=2.1, 95%CI 1.00-4.45 p-value=0.05) compared to their counterparts in households having poles with mud as the main wall material. Children from households having burnt bricks with cement as the main wall material had lowest odds of malaria as opposed to those from households with unburnt bricks as the main wall material (OR 0.6, 95 % CI 0.24-1.74). From [Table 2], it was observed that there was an insignificant difference between the unspecified (others) and unburnt bricks as the wall material (p-value = 0.620).

Table 2: Bivariate analysis of household characteristics associated with malaria (logistic regression)

Click here to view

The age of the child was significantly associated with malaria parasitemia in children under five. The results showed that there was a 1.0% increase in the prevalence of malaria among children for every unit increase in age in

There was a significant association between the number of household members and malaria among children under-five. It was observed that for a unit increase in number of household members, the prevalence of malaria increased by 10.0% (OR: 1.10, 95%CI: 1.08-1.18) [Table 3]. The roofing material of a house was significantly associated with the risk of malaria among children under-five. Children living in houses roofed with iron sheets were 0.2 times (OR: 02, 95%CI: 0.12-0.45) less likely to have malaria as opposed to their counterparts living in thatched houses [Table 3]. Children from homes roofed with other materials (besides palm leaf, iron sheets and tarpaulin) were 0.3 times (OR: 0.3, 95%CL: 0.19-0.59) less likely to have malaria compared to those from houses with palm leaf as the roofing material [Table 3].

The finishing material of the floor of a house was significantly associated with malaria parasitemia among children. Children from households with dung as the finishing material of the floor were 0.6 times (OR:0.6, 95%CI: 0.42-0.84) less likely to have malaria compared to their counterparts from households with earth/sand as the finishing material [Table 3]. It was observed that IRS was significantly associated with the risk factors of malaria. Children in households with IRS in the 12 months before the survey were 0.2 times less likely to have malaria infections than their counterparts from households without IRS in the same time frame (OR: 0.2, 95%CI: 0.10-0.51) [Table 3]. There was no significant difference among those who did not know if the area was sprayed or not and those whose households were sprayed (p-value=0.711) [Table 3]. Results from this study showed that malaria parasitemia increased with decrease in the wealth status of the given household. Children from the households with the poorest economic status recorded the highest parasitemia prevalence of 38.2%, whereas those from the richest households recorded the least proportion at only 3.6%. Children from the richest households were 0.1 times less susceptible to malaria infection (OR: 0.1, 95%CI 0.02-0.16) than children from the poorest households. There was a significant association between malaria parasitemia among children and the wealth status of their households at 95% confidence interval with a p-value <0.001.

  Discussion 

In this study, we assessed the relationship between socioeconomic factors and malaria infection amongst children under-five using the secondary data from nationally representative 2019 UMIS. In this study, it was observed that the prevalence of malaria in children below five was 23.3% and the prevalence increased with an increase in the age of the child. This was a remarkable reduction in malaria prevalence as compared to 43.3% in the 2009 UMIS. This is partly attributed to the roll out of malaria control programs in 2014 such as free distribution of ITNs, IRS and IPTp. Similar studies by Roberts and Matthews[10] in Uganda also showed that malaria prevalence increased with an increase in age of children under-five. This can be linked to the fact that infants possess passive immunity acquired from mothers through exclusive breastfeeding. However, this immunity wanes with time as the children grow, increasing their risk of infection before the development of their own active immunity derived from repetitive infections[21]. This can also be argued that since the use of mosquito bed nets in most of sub-Saharan African countries generally decreases with an increase in age among children, then it is obvious that malaria prevalence is high amongst older children due to lack of protection and more exposure to mosquito bites[22].

Findings also indicated that malaria parasitemia was more prevalent among children from households belonging to lower wealth quintiles in comparison to those from rich households. This outcome was in consistence with findings from other similar studies conducted in Ghana by Yanksonet al.,[23], and in Gambia by Sonko et al.,[24] which have shown that malaria parasitemia is highly correlated with low income (poverty). All these studies reported that children from the poorest quintiles were significantly more likely to have malaria compared to children from the richest, fourth, third and second quintiles. This can however be explained from different angles. It is suggested that households within the highest wealth quintile can afford malaria preventive measures, such as insecticide-treated bed nets, proper housing facilities, quick diagnosis and timely medication in case of an infection without necessarily depending on public facilities which are always overcrowded and understocked.

Children whose caregivers had a low level of education had high malaria parasitemia than those whose mothers had high educational attainment. This finding aligns with findings from similar studies by Mahfouz et al.,[25], Snymanet al.,[26] whose observations showed that, the lower the level of mother’s education, the higher the child morbidity and mortality rates. In all these studies, the high-level of mother’s education acted as a shielding factor against malaria infection. Based on assumption that mothers with higher education are more likely to be employed, thus, having a better financial power enabling to afford healthcare and other preventive measures for malaria and may make better decisions on their children’s health. Children whose parents had higher education were significantly less likely to have malaria infections compared to their counterparts from mothers with least level of education.

The use of insecticide-treated mosquito bed-nets among children in Uganda provided a shielding factor against malaria infection. Children who slept under the mosquito nets were significantly less likely to have malaria infection than those who did not. Similar studies in Ghana and Tanzania by Nyarko and Cobblah[27] and Somiet al.,[28] respectively exhibited a similar trend where children from households using mosquito nets reported less malaria cases among children compared to those from households without mosquito nets. Findings from this study therefore affirm that insecticide-treated mosquito bed net ownership and use was an important protective factor against malaria infection in children. The study showed a significant association between children whose mothers were catholic, and malaria parasitaemia. Religious bodies influence exposure to and treatment of malaria since the community listens and believes religious leaders more than other groups, however, the allegiance differs from religion to religion as some religions are stricter than others. There is a need for research to be done to investigate the religious affiliation factor in predicting prevention and treatment of malaria among children because there are no studies in a local context outlining reasons why there is varying degree of prevalence among different religious domains.

This study showed a significantly high prevalence of malaria among children from rural areas than those from urban settings. In 2020, a similar study by Habyarimana and Ramroop[22] in Rwanda reported more malaria cases in children from rural areas than urban areas which is consistent with the present study. This is because people living in rural settings may not have sufficient access to healthcare facilities coupled with higher exposure to breeding sites and poor housing facilities that expose children to malaria bites. Another argument is based on the fact that children of school-going age from rural settings get infected while travelling to and from school since most of them pass through agricultural plantations and forests, the breeding sites of mosquitoes exposing children to mosquito bites compared to their counterparts in the urban settings.

Indoor Residual Spray (IRS) use was significantly associated with reduced malaria risk. IRS had a strong protective effect against malaria prevalence. A study by Ssempiira et al.,[29] revealed that IRS was associated with a significant reduction in children’s malaria risk. IRS is thought to be more effective in killing adult mosquitos as they rest on walls after a blood meal, this shortens the life span of the mosquito while disrupting the lifecycle of Plasmodium parasite hence reducing vector density leading to reduced malaria transmission rate. However, a similar study in Ghana by Yanksonet al.,[23] cited an insignificant association between IRS use and malaria prevalence among children below five years. This non-significant association between malaria prevalence and IRS use could be attributed to low coverage of IRS.

Chances of malaria infection increased with an increase in the number of household members. A study by Ayeleet al.,[30] revealed that malaria prevalence correlated with the size of the household. This is because, if one member of the family is infected with malaria, he or she is most likely to act as the reservoir leading to rapid spread of malaria among other family member(s).

Housing type is also an important predictor of malaria. In this study, a significant association was observed between the type of house roof materials and malaria prevalence. Children living in poorly constructed houses had higher chances of contracting malaria. Poorly constructed houses may provide the entry points for mosquitoes into the house since the house. This finding was consistent with finding in a similar study in Ethiopia by Ayeleet al.,[30], this further indicated that effective malaria control, prevention and eradication should not only be limited to malaria control programs but in unison with measures towards improving economic status of the households which in turn leads to improved standard of living through proper housing.

Households without electricity reported significantly more malaria cases at 28.1% compared to 15% reported by households with electricity. Children from households with electricity were significantly less likely to contract malaria compared to their counterparts in households without electricity. Whereas electricity in a household may be related to individual’s socio-economic status, it may as well improve individual’s way of life, where individuals from households with no electricity are often required to go outside and thus becoming more prone to mosquito bites increasing chances of malaria infection.

  Conclusion 

Child’s age, type of residence, wealth index, housing type, mother’s education level, mosquito net-use the night before the survey, number of household members and IRS in last 12 months were the most important risk factors of malaria parasitemia in children under-five in Uganda based on analysis of 2019-UMIS data used in this study. In spite of the tremendous upscaled efforts in malaria control, Uganda still remains miles away from arriving at its targeted levels of morbidity and mortality owing to malaria in 2030. Despite the significant increase of mosquito bed-nets use, other control measure such as IRS should be incorporated in full scale to sufficiently and effectively tackle this malaria plague.

Appropriate education on proper and consistent use of mosquito bed-nets should be emphasized alongside embracing living habits that reduce the chances of mosquito bites like staying indoor during peak biting or most active hours of the mosquito. This will undoubtedly reduce malaria burden in Uganda. Uganda being a resource limited country, these limited resources should be consolidated towards spatial modelling of malaria risk maps to have targeted malaria control intervention to effectively reduce the burden of malaria in regions where malaria has been a great challenge and, by doing so, the burden of malaria especially in children below five will be reduced if not eradicated.

  Abbreviations 

MOH: Ministry of Health, Uganda

OR: Odds ratio

UBOS: Uganda Bureau of Statistics

WHO: World Health Organization

IRS: Indoor Residual Spraying

IPTp: Intermittent Preventive Treat ment in Pregnancy

ITNs: Insecticide Treated Nets

UMIS: Uganda Malaria Indicator Survey

DHS: Demographics Health Survey

Conflict of interest: None

  Acknowledgements 

Special thanks to Uganda Malaria Indicator survey team for granting me permission to use secondary data.

 

  References 
1.World Health Organization. World Malaria Report 2020: 20 years of global progress and challenges. WHO Geneva, 2020.  
    2.Gunathilaka N, Abeyewickreme W, Hapugoda M, Wickremasinghe R. Determination of demographic, epidemiological, and socio-economic determinants and their potential impact on malaria transmission in Mannar and Trincomalee districts of Sri Lanka. Malaria Journal 2016; 15(1):1–1.  
    3.Uganda Ministry of Health Integrated Management of Malaria Training, A Practical Guide for Health Workers. National Malaria Control Programme (NMCP), June 2015.  
    4.Sharma RK, Rajvanshi H, Bharti PK, Nisar S, Jayswar H, Mishra AK, et al. Socio-economic determinants of malaria in tribal dominated Mandla district enrolled in Malaria Elimination Demonstration Project in Madhya Pradesh. Malaria Journal 2021; 20(1):1–3.  
    5.World Health Organization. World malaria report 2015. World Health Organization; 2016 Jan 30.  
    6.World Health Organization. World malaria report 2018. Geneva: World health organization; 2018. Fecha de consulta2019; 23: 238.  
    7.Bhatt S, Weiss DJ, Cameron E, Bisanzio D, Mappin B, Dalrymple U, et al.The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015. Nature 2015; 526(7572): 207–11.  
    8.Uganda Bureau of Statistics; ICF. Uganda Demographic and Health Survey 2016: Key Indicators Report; UBOS: Kampala, Uganda; ICF: Rockville, MD, USA, 2017.  
    9.National Institute of Statistics of Rwanda. Rwanda Demographic and Health Survey 2014 2015; National Institute of Statistics of Rwanda: Kigali, Rwanda, 2015  
    10.Roberts D, Matthews G. Risk factors of malaria in children under the age of five years old in Uganda. Malaria Journal 2016; 15(1):1–1.  
    11.Uganda Bureau of Statistics (UBOS) and ICF International. Uganda malaria indicator survey report; 2014–15. Kampala and Rockville: UBOS and ICF International  
    12.Yadav K, Dhiman S, Rabha B, Saikia PK, Veer V. Socio-economic determinants for malaria transmission risk in an endemic primary health centre in Assam, India. Infectious diseases of poverty 2014; 3(1): 1–8.  
    13.Dhiman SK. Malaria control: Behavioural and social aspects. DRDO Sci Spec 2009; 183: 186.  
    14.Nonvignon J, Aryeetey GC, Malm KL, Agyemang SA, Aubyn VN, Peprah NY, et al. Economic burden of malaria on businesses in Ghana: a case for private sector investment in malaria control. Malaria Journal 2016; 15(1): 1–0.  
    15.Heggenhougen HK, Hackethal V, Vivek P. The behavioural and social aspects of malaria and its control: an introduction and annotated biliography. Geneva:: World Health Organization; 2003.  
    16.Enayati A, Hemingway J. Malaria management: past, present, and future. Annual review of entomology 2010; 55: 569–91.  
    17.PullanRL, Bukirwa H, Staedke SG, Snow RW, Brooker S. Plasmodium infection and its risk factors in eastern Uganda. Malaria Journal 2010; 9(1):1–1.  
    18.Nahum A, Erhart A, Mayé A, Ahounou D, Van Overmeir C, Menten J, et al. Malaria incidence and prevalence among children living in a peri-urban area on the coast of Benin, West Africa: a longitudinal study. The American journal of tropical medicine and hygiene 2010; 83(3): 465–73.  
    19.Okethwangu D, Opigo J, Atugonza S, Kizza CT, Nabatanzi M, Biribawa C, et al. Factors associated with uptake of optimal doses of intermittent preventive treatment for malaria among pregnant women in Uganda: analysis of data from the Uganda Demographic and Health Survey, 2016. Malaria journal 2019; 18(1): 1–8.  
    20.Republic of Uganda. The Uganda Malaria Reduction Strategic Plan 2014–2020.  
    21.McLean AR, Stanisic D, McGready R, Chotivanich K, Clapham C, Baiwog F, et al. Falciparum infection and maternofetal antibody transfer in malaria-endemic settings of varying transmission. PloS one 2017; 12(10): e0186577.  
    22.Habyarimana F, Ramroop S. Prevalence and Risk Factors Associated with Malaria among Children Aged Six Months to 14 Years Old in Rwanda: Evidence from 2017 Rwanda Malaria Indicator Survey. International Journal of Environmental Research and Public Health 2020; 17(21): 7975.  
    23.Yankson R, Anto EA, Chipeta MG. Geostatistical analysis and mapping of malaria risk in children under 5 using point-referenced prevalence data in Ghana. Malaria Journal 2019; 18(1):1–2.  
    24.Sonko ST, Jaiteh M, Jafali J, Jarju LB, D’Alessandro U, Camara A, et al. Does socio-economic status explain the differentials in malaria parasite prevalence? Evidence from The Gambia. Malaria Journal 2014; 13(1):1–2.  
    25.Mahfouz MS, Surur AA, Ajak DA, Eldawi EA. Level and determinants of infant and child mortality in Malakal Town-southern Sudan. Sudanese journal of public health 2009; 4(2): 250-5.  
    26.SnymanK, Mwangwa F, Bigira V, Kapisi J, Clark TD, Osterbauer B, et al. Poor housing construction associated with increased malaria incidence in a cohort of young Ugandan children. The American Journal of Tropical Medicine and Hygiene 2015; 92(6):1207.  
    27.Nyarko SH, Cobblah A. Sociodemographic determinants of malaria among under-five children in Ghana. Malaria research and treatment 2014; 2014.  
    28.Somi MF, Butler JR, Vahid F, Njau J, Kachur SP, Abdulla S. Is there evidence for dual causation between malaria and socio-economic status? Findings from rural Tanzania. The American Journal of Tropical Medicine and Hygiene 2007; 77(6): 1020–7.  
    29.Ssempiira J, Nambuusi B, Kissa J, Agaba B, Makumbi F, Kasasa S, et al. Geostatistical modelling of malaria indicator survey data to assess the effects of interventions on the geographical distribution of malaria prevalence in children less than 5 years in Uganda. PLoS One 2017; 12(4): e0174948.  
    30.Ayele DG, Zewotir TT, Mwambi HG. Prevalence and risk factors of malaria in Ethiopia. Malaria Journal 2012; 11(1): 1–9.  
    
  [Figure 1]
 
 
  [Table 1], [Table 2], [Table 3]