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Determinants of Malaria Morbidity Among School-Aged Children Living in East Hararghe Zone, Oromia, Ethiopia: A Community-Based Case–Control Study

Authors Abdishu M, Gobena T , Damena M, Abdi H , Birhanu A 

Received 6 November 2021

Accepted for publication 28 April 2022

Published 18 May 2022 Volume 2022:13 Pages 183—193

DOI https://doi.org/10.2147/PHMT.S347621

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Professor Roosy Aulakh



Mohammedawel Abdishu,1 Tesfaye Gobena,2 Melake Damena,2 Hassen Abdi,2 Abdi Birhanu3

1Public Health Emergency Management Officer at Gursum District Health Office, Gursum, Oromia Region, Ethiopia; 2Haramaya University, College of Health and Medical Sciences, School of Public Health, Harar, Ethiopia; 3Haramaya University, College of Health and Medical Sciences, School of Medicine, Harar, Ethiopia

Correspondence: Abdi Birhanu, Email [email protected]

Background: Understanding the determinants of malaria morbidity offers helpful insights toward the changing malaria situation, which might lead to the adjustment of malaria program activities. Even though the determinants of malaria morbidity remain unknown, school-aged children were the highest malaria morbidity contributors in the East Hararghe Zone. Therefore, this study aimed to assess the determinants of malaria morbidity among school-aged children in the study area from February 1 to May 31, 2020.
Methods: A case-control study was conducted among school-aged children living in ten randomly selected low, moderate, and high malaria transmission kebeles. Cases were confirmed as positive for malaria, while controls were confirmed as negative for malaria among randomly selected school-aged children. Rapid diagnostic testing (RDT) and blood film (BF) malaria testing methods were used. Multivariable logistic regression was used to identify association between malaria and its determinants.
Results: The determinants of malaria infection were having no formal education (adjusted odds ratio (AOR)=4.91, 95% CI: 1.20– 20.17), low family wealth index (AOR=2.50, 95% CI: 1.22– 5.12), being from rural residence (AOR=2.34, 95% CI: 1.87– 4.12), living near to stagnant water (AOR=2.01, 95% CI: 1.14– 3.54), having a maximum of three family members (AOR=0.37, 95% CI: 0.18– 0.78), using indoor residual spraying (IRS) (AOR=0.15, 95% CI: 0.08– 0.29) and long-lasting insecticide-treated net (LLITN) over the last night (AOR=0.19, 95% CI: 0.10– 0.35), and living in the house surrounded by cultivated land(AOR=0.24, 95%CI: 0.10-0.60) compared with their counterparts.
Conclusion: This study revealed that residence, family size, education, wealth index, stagnant water existence, and using LLITN and IRS had significant association with malaria morbidity. Thus, all concerned bodies, including the community should strengthen working on stagnant water elimination around their house to cut the breeding site of the malaria vector mosquito. Moreover, the findings have an important implication for improving interventions targeting the economic status and literacy of the society that may help in the reduction of the risk of malaria in the school-aged children.

Keywords: determinants, malaria, school-aged children, Ethiopia

Introduction

Malaria is one of the most devastating infectious diseases costing many lives each year in the world. In most of the malaria-endemic countries, the infection is one of the public health problems, causing 445,000 global deaths of which 91% occurred in sub-Saharan Africa in 2016. Each year, over 90% of malaria cases are reported in Africa, reflecting a disproportionately higher disease burden on the continent compared to the rest of the world.1–3 Especially, the malaria burden among school-aged children (5–14 years) is more prevalent than in other pediatric age groups.4

Africa has made great progress toward reducing transmission through the use of vector control tools such as bed nets and indoor residual spraying (IRS).5–8 However, a recent study showed that malaria infection is still the most prevalent cause of death in Africa. The study also revealed that P. faclciparum cospecialize with Anopheles gambiae mostly spreading in the continent.2 In 2020, Africa contributed 95% of malaria cases and 96% of malaria deaths occurring around the globe. These deaths were majorly recorded among children of the continent.9

Although Ethiopia has brought changes in malaria diagnosis, treatment, vector-borne disease control, and health services coverage, there is still a gap in malaria burden reducing interventions.10 Studies showed that in Ethiopia, malaria cases increased from 621,345 to three million and deaths related to malaria raised from 1561 to 4782 from 2015 to 2016, over one year.11,12 Regarding the dominant malaria transmitting species in Ethiopia, between June 2016 and July 2017, of the total confirmed malaria cases, 70% were caused by P. falciparum, while 30% of them were by P. vivax.13 The species dominance and the number of malaria morbidities vary due to different factors such as previous weather conditions or intervention measures.14

Studies exposed that factors associated with malaria infection included geographical locations, season, age, socioeconomic status, environmental factors, changing malaria situation, and sex.15–17 Particularly, school-aged children lead to an increase in the incidence of clinical attacks of malaria, including severe attacks, even though it substantially declined in the disease burden.11 Several studies presented that the malaria morbidity peak age was school-aged children.18–21 Among these school-aged children, malaria status was determined by the level of transmission, level of acquired immunity, intervention programs, level of transmission, and age.13,16,22 In spite of the fact that school-aged children are the main source of malarial transmission between human and mosquito, Cohee et al reported that the impact of malaria infection among school-aged children of sub-Saharan Africa is still not appreciated very well.23 Similarly, the determinants of malaria infection are not explored well among school-aged children in Ethiopia. Therefore, this study aimed to assess the determinants of malaria morbidity among school-aged children in East Hararghe Zone, Oromia, Ethiopia.

Methods and Materials

Study Setting, Design, and Period

A community-based case–control study was conducted in East Hararghe Zone, Oromia, Eastern Ethiopia between February 1 and May 31, 2020. East Hararghe Zone administrative center is located in Harar, 525 km away from Addis Ababa due east. The zone is one of the twenty administrative zones in the Oromia Regional State, bordered by West Hararghe due West and Somali Regional State due east. The area of the zone is 24,247.66 sq. km. East Hararghe Zone consists of twenty administrative districts and four administrative towns, which further comprise a total of about 548 kebeles (sub-districts). It is the most populated zone in Oromia regional state with an estimated total population of 3.7 million.24 According to the current malaria stratification in the East Hararghe Zone, about 282 (52%) kebeles were endemic malaria areas by the level of malarial endemicity, while the rest 20%, 30%, and 50% were categorized in high, moderate, and low, respectively25 (Figure 1).

Figure 1 Map of East Hararghe Zone, Oromia Regional State, Eastern Ethiopia, 2020

Notes: Chineksen, Jarso, Kombolcha, Gursum, Kersa, Meta, Dedar, Haramaya, Kurfa Chale, Babile, Badano, Gurewa, Fadis, Malka Balo, Mayu Muluke, Qumbii, Gola Oda, Qumbi.

Study Population and Eligibility

All households which had school-aged children were included in the study. An active case detection (ACD) was done through weekly household visits to identify and register malaria and non-malaria cases. Children who were confirmed to be positive for the malaria test were categorized as a case group for malaria morbidity, while the children who tested negative for malaria were categorized as a control group. For the households that had more than one school-aged child one of them was selected randomly. The selected children were included in the study regardless of malaria symptom manifestation. A household that had no school-aged children was excluded. In addition, malaria positive case that had no neighboring school-aged malaria negative child was excluded from the cases group. Each strata of low, moderate, and high endemic malaria transmission had relatively similar transmission across rural and urban kebeles. Moreover, children who were treated for malaria two weeks before the data collection period were excluded from the study.

Sample Size Determination and Sampling Procedure

A total of 504 samples (168 cases and 336 controls) were determined using OpenEpi Version 3, open-source calculator SSCC (Sample Size Case Control).26 The sample size determination was done using the two population proportions with assumptions such as a power of 80%, proportion of malaria (p=56%) among school-aged children who did not use LLITN in the previous night,27 5% of level of significance, anticipated odds ratio (OR=1.75) among cases, and a case to control ratio=1:2. Cases and controls were automatically selected based on the RDT/microscopy results. The sample size was determined using the following formula26 :

, where,

r(p1–p2)2

n2=rn1, n1=number of cases, n2=number of controls, Zα/2=standard normal deviate for two-tailed test based on alpha level (relates to the confidence interval level), Zβ=standard normal deviate for two-tailed test based on beta level (relates to the power level), r=ratio of controls to cases, p1=proportion of cases with exposure and q1=1–p1, p2=proportion of controls with exposure and q2=1–p2, p¯=p1±rp2 ∕ r+1, and q¯=1–p¯.

Kebeles were randomly selected using a stratified sampling technique based on considering Epidemiological Strata such as the National Malaria Strategic Plan (NMSP) of malaria status low, moderate, or high transmission. According to Ethiopian Federal Ministry of Health (FMOH) malaria stratification,16 East Hararghe Zone has 282 (52%) malaria-endemic kebeles which were high endemic 56 (20%), moderate endemic kebeles 85 (30%), and low endemic kebeles 141 (50%).25 Therefore, ten kebeles were selected proportionally based on the level of malaria endemicity using a simple random sampling (SRS) technique. Then, strata were created for all selected kebeles based on their category of level of malaria endemicity. Finally, 168 cases and 336 controls were identified from each strata after examining the children for malaria. The only difference of the case and control was the malarial status, while other characteristics were assumed to be similar for both groups. To ensure the assumption of their similarity, we randomly selected the control from the neighbor of the case that detected positive for malaria. In addition, the same technique, equipments (microscopy and RDT), and examiners were strictly used to get the malaria result.

Procedures for Laboratory Examinations

Rapid Diagnostic Testing of Malaria in Human Blood

The CareStartTM malaria HRP2 test kit (Access Bio Inc., NJ, USA) is used for the rapid qualitative detection of Malaria Histidine-rich Protein 2 (HRP2) in human blood as an aid in the diagnosis of malaria infection. Using this kit, 5 µL of whole blood is introduced into the sample with the aid of a pipette after finger pricking. Two drops of assay buffer are added to the well. The result was read within 20 min.28

Malaria Blood Films for Microscopy

Thick blood films were prepared on a glass slide using 10 µL of blood, evenly spread to cover an area of 15×15 mm2 of the slide. The smear was stained with 10% Giemsa for 10 minutes and then examine under oil immersion with a light microscope (magnification ×100). The slides were double-read by a trained laboratory technician. Asexual parasite densities were estimated by counting the number of parasites per 200 white blood cells (WBCs) in the thick film. Parasite counts were converted to parasites per microliter (µL) using relative WBC of 8000 leukocytes per µL of blood. Similarly, gametocyte rate and density were determined by counting against 500 leukocytes and converted to parasites per microliter as for asexual parasites.28 A sample was considered negative if no parasite was counted after 200 high power fields have been read. In the case of discrepancy occurrence in the findings in a slide between the two initial technicians (positive or negative or a 50% or more difference in parasite density), a third, more senior laboratory technician reading was deemed necessary and then adopted. All of the positive blood films including a 10% random sample of negative blood slides were examined for quality control.

Data Collection Tools and Methods

Data were collected by nineteen trained laboratory technicians and supervised by experienced public health professional (one) and microbiologists (two). The training was provided for two days for data collectors and supervisors on the objectives and laboratory techniques used for testing malaria. All data collectors were familiar with the study area and spoke the local language (Afaan Oromo). The questionnaire was a modified version of the Malaria Indicator Survey (MIS) questionnaire that was used by Ethiopia FMOH in 2015.29 The questionnaire had an observation part for screening malaria suspects and blood tests for the determination of malaria infection using microscopy/RDTs. Parents/guardians’ children were asked a simple set of structured questions on socioeconomic variables, environment-related characteristics, malaria prevention methods such as using insecticide-treated bednets and indoor residual spraying in the interior wall of their home. The tool was translated into a local language (Afaan Oromo) and pretested before the actual data collection period.

Data Processing and Analysis

Once the accuracy, completeness, and consistency of the data were manually checked, the data were entered into Epidata version 3.1 and exported to SPSS version 22 for analysis. A binary logistic regression model was used to assess the association between the determinants and malaria morbidity among school-aged children. Variables that had a p-value <0.25 in bivariable logistic regression were transformed to the final model. The association between explanatory variables and malaria morbidity was determined by a multivariable logistic regression model with an adjusted odds ratio. Finally, the significance level of the association between the independent variables and malaria morbidity was declared at a p-value of <0.05 at 95% of confidence intervals. The Hosmer–Lemeshow (P>0.05) test was used to check the goodness-of-fit of the logistic regression model assumptions.

Ethical Consideration

Ethical clearance was obtained from the Haramaya University College of Health and Medical Sciences Institutional Health Research Ethics Review Committee (IHRERC) after reviewing the study protocol. The letter of permission was obtained from the college and sent to the East Hararghe Zonal health office. The zonal health office further sent a letter of cooperation down to the district health office and then to kebeles in the selected districts. This study was conducted in agreement with the Declaration of Helsinki. Data collection was started after getting ethical approval from the institutional ethics committee. The purpose of the study was briefly explained to the study participants. A written informed consent was obtained from parents of all selected children. Blood samples were collected by trained health workers/laboratory technicians who had experience in the blood sample collection and examination. A sterilized blood lancet was used to avoid infection. Individuals who tested positive for malaria were treated according to the Ethiopia standard treatment guideline.30 The data that were collected from the participants were kept confidential. Voluntary participation was guaranteed and participants were free to opt out.

Results

A total of 168 cases and 336 controls participated in the study. One-third, 56 (33.3%) of the cases and nearly a quarter, 77 (22.9%) of the controls were children aged between 10–14 years. The median age and interquartile range (IQR) of cases and controls were 11 years with IQR of 8 (7–15) and 12 years with IQR 12 (8–16), respectively. The majority, 141 (83.9%) of cases and 244 (72.6%) of controls were rural residents. Around two-thirds, 113 (67.3%) of the cases and more than one-third, 141 (42.0%) of the controls lived in a family whose household head had no formal education. A quarter, 42 (25%) of cases and 18 (5.4%) of controls were living in a household with an overcrowded family (Table 1).

Table 1 Socio-Demographic Characteristics of Study Participants, East Hararghe Zone, Oromia, Ethiopia, 2020

Environmental Characteristics and Malaria Prevention Measures Used

Nearly half, 76 (45.2%) of cases and more than a quarter, 90 (26.8%) of the controls lived near the stagnant water. The majority, 143 (85.1%) of cases and more than two-thirds, 214 (67.1%) of controls observed with liquid waste in or around their living compound. One hundred and twenty-two (72.6%) of cases and 147 (43.8%) of controls had no IRS sprayed for their dwelling homes during the last six months before the survey. Few 11 (6.6%) cases and only four (1.2%) controls had a previous history of treatment for malaria. All 168 (100%) cases and almost all 334 (99.8%) controls either lived in a malaria area or had a travel history to malarious areas in the last two weeks (Table 2).

Table 2 Environmental Charactersitics and Malaria Prevention Measures Used Among Study Participants in East Hararghe Zone, Oromia, Ethiopia, 2020

Determinants of Malaria Morbidity

In multivariable logistic regression, determinants of malaria infection including residence area, family educational status, family size, family wealth index, nearness of stagnant water around the living house, LLITN utilization, living around cultivated land, and spraying IRS chemicals in-dwelling home had a statistically significant association with malaria morbidity at p-value of less than 0.05.

This study showed that children who lived in a rural area had 2.34 times higher odds of malaria than those who lived in an urban area (AOR=2.34, 95% CI: 1.87–4.12). Odds of having malaria were 4.91 times more likely higher among children whose family had no formal education compared to those whose family had a higher educational level (AOR=4.91, 95% CI: 1.20–20.17). Moreover, this study revealed that children born from a family with a lower household wealth index had malaria 2.50 times more likely than those who were born from a rich family (AOR=2.50, 95% CI: 1.22–5.12). Children who lived around stagnant water had malaria 2.01 times more likely than those who lived beyond 2 km away from stagnant water (AOR=2.01, 95% CI: 1.14–3.54). Children whose home was surrounded by cultivated land had malaria 76% less likely than those whose home was surrounded by shrub type of land (AOR=0.24, 95% CI: 0.10–0.60). Children whose family had less family size had odds of malaria 63% less likely than the family who had larger family size (AOR=0.37, 95% CI: 0.18–0.78). Children whose households used the LLITN in last night had reduced malaria morbidity by 81% compared to their counterparts (AOR=0.19, 95% CI: 0.10–0.35). Moreover, children who lived in insecticide chemical sprayed interior wall of the home had malaria 85% less likely compared with their counterparts (AOR=0.15, 95% CI: 0.08–0.29) (Table 3).

Table 3 Determinants of Malaria Morbidity in School-Aged Children in East Hararghe Zone, Oromia, Ethiopia, 2020

Discussion

In Ethiopia, malaria fighting programs have been maximized through LLITN and IRS provisions, improving healthcare services, and improving malaria diagnosis and treatment. However, at country level, the interventions are primarily targeted to areas with greater than 2500 meter elevation. Thus, malaria is still one of the health problems that is affecting many lives.10 The school-aged children are the highest malaria morbidity contributors.21 The data from the study zonal health department (2019) also revealed that malaria morbidity declined substantially but continued to be increased among school-aged children.25 However, little is known about the individual and household risk factors. The current study identified that residence, family educational status, family economic status, size of family members, using LLITN, using IRS, and living around stagnant water were associated with malaria morbidity among school-aged children.

Based on the results of this study, it was found that rural resident children had greater odds of malaria morbidity when compared to urban resident children. This finding was similar to other studies conducted in Ethiopia27 and Malawi.31 Reasons could be due to rural residents could have low socioeconomic status, made their house from thatched roofing, use agriculture as their main source of family income, and have no access the health services easily.32 Residence place of the child could also contribute to the individual’s way of life, housing condition, and vectors breeding site; rural resident children could move about freely and may go outside more often and therefore may be more susceptible to mosquito bites, thereby getting malaria infection.

The children whose families had low wealth index had a higher odds of malaria morbidity than those whose families had higher wealth index level. This study finding was in line with studies from Tanzania33 and South Africa,34 Madhya Pradesh India,35 Republic of Tanzania,36 and Myanmar.37 The parents who have a lower wealth index could not afford to protect the child from getting malaria morbidity. One possible reason could be that household economic status plays an important role in the management of illnesses, for the household with low wealth index could suffer from transportation, treatment costs, and diagnosis costs.38,39

This study revealed that numbers of household members had a significant association with the odds of having malaria. Families who had many household members had increased odds of malaria morbidity compared to those who had less household members. This study showed a similar result to the studies done in India,35,40 Ethiopia,41 and Vietnam.42 The possible reason could be that family size plays an important role in the household socioeconomic status due to lower health services access distribution among the members. In addition, this can be due to the higher probability of easy familial malaria transmission among larger family members in the household because of nearness of infected and susceptible humans if there is an infected person in the family.43

Furthermore, children whose family had no formal education had a higher odds of malaria morbidity compared to children whose family had formal education. This result agreed with the results from Ethiopia,27 Mali,44 and India.35 It is the fact that uneducated people can not read and understand the messages written as teaching aids such as leaflets, brochures, and banners that focus on the prevention and control methods of malaria. A study reported that the health needs of school-aged children have been largely ignored.45 In addition, if uneducated people get sick from malaria, they could delay in seeking treatment that results in the further transmission of malaria from infected individuals to healthy people.

Children whose family used LLITN had odds of contracting malaria less likely compared to children whose family did not use LLITN. The result was in agreement with studies conducted in different parts of Ethiopia.27,46–48 In fact, using LLITN is one of the major malarial disease transmission prevention. The children whom their house wall was sprayed with chemicals (IRS) during the past six months were less likely to have malaria morbidity than those whom their house wall was not sprayed. This aligned with the studies conducted in south Ethiopia49 and Equatorial Guinea.50 However, this finding was contrary to the finding from Tanzania51 that showed that using IRS did not reduce the risk of malaria infection. This can be due to the resistance of mosquito species to the sprayed chemicals despite that the family trust the protective effect of the chemical.52

The current study revealed that the proximity of water body within less than 2 km to home increased odds of malaria morbidity than children with their home located beyond 2 km from the water body. This study was similar with the findings from southwest Ethiopia,15 Uganda,53 and Cameroon.54 It is the fact that the open water sources such as rivers, swamps, and stagnant water may ultimately become the ideal mosquito breeding habitats. Thus, the mosquito can easily move within 2 km radius of areas from their breeding sites that enable the mosquito to bite any person within this areas.

This study result showed that the shrub land cover type around home increased odds of malaria morbidity than the home surrounded by bare land. This study was in line with the studies conducted in Ethiopia27 and Kenya.55 This might be due to the fact that the shrub areas may favour the breeding of mosquitos. The reproduction of the vector is one of the malaria transmission cycle.

Strengths and Limitations of the Study

This study could generate a highly needed information as a malaria among school-aged children is not well-characterized in the previous studies particularly in Ethiopia. In addition, the malaria testing validity was mantained by reexamining all positive and 10% of randomly selected negative tested samples using microscopy examination as the quality check of the RDT test.The limitation was the inclusion of only one season (February to May), when the high malaria morbidity is expected.

Conclusions and Recommendations

This study revealed that being a rural resident, having a larger family size, lower economic status, and living in the home proximal to the stagnant water were found to be determinants of malaria morbidity among school-aged children in the East Hararghe Zone. The evidence of this study shows that interventions such as using LLITN and IRS in the home focused on malaria illness of school-aged children may potentially benefit to prevent malaria. In addition, all concerned bodies including the community should work on the elimination of stagnant water surrounding the living house to cut the breeding sites of mosquitoes. Moreover, the findings have an important implication for improving interventions such as economic growth and literacy of the society that may help in reducing the risk of malaria morbidity in school-aged children.

Abbreviations

AOR, adjusted odds ratio; CI, confidence interval; COR, crude odds ratio; CSA, Central Statistics Agency; IRS, insecticide residual spray; LLITN, long lasting insecticide treated net; RDT, rapid diagnostic test; NMSP, National Malaria Strategic Plan.

Data Sharing Statement

All the data used in this study can be available from MA upon request.

Acknowledgments

We thank all the data collectors, supervisors, and study participants for their valuable contribution to the success of this study.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

The authors declare that they have no conflicts of interest.

References

1. World Health Organization. The “World Malaria Report 2019” at a Glance. Geneva, Switzerland: World Health Organization; 2019.

2. Mbacham WF, Ayong L, Guewo-Fokeng M, Makoge V. Current situation of malaria in Africa. Methods Mol Biol. 2019;40:29–44.

3. World Health Organization. World Malaria Report in 2017. Geneva; 2017.

4. Makenga G, Menon S, Baraka V, et al. Prevalence of malaria parasitaemia in school-aged children and pregnant women in endemic settings of Sub-Saharan Africa: a systematic review and meta-analysis. Parasite Epidemiol Control. 2020;11:e00188. doi:10.1016/j.parepi.2020.e00188

5. Padonou GG, Gbedjissi G, Yadouleton A, et al. Decreased proportions of indoor feeding and endophily in anopheles gambiae s.l. populations following the indoor residual spraying and insecticide-treated net interventions in Benin (West Africa). Parasit Vectors. 2012;5(1):262. doi:10.1186/1756-3305-5-262

6. Okumu FO, Moore SJ. Combining indoor residual spraying and insecticide-treated nets for malaria control in Africa: a review of possible outcomes and an outline of suggestions for the future. Malar J. 2011;10(1):208. doi:10.1186/1475-2875-10-208

7. Rehman AM, Coleman M, Schwabe C, et al. How much does malaria vector control quality matter: the epidemiological impact of holed nets and inadequate indoor residual spraying. PLoS One. 2011;6(4):e19205. doi:10.1371/journal.pone.0019205

8. Okumu FO, Kiware SS, Moore SJ, Killeen GF. Mathematical evaluation of community level impact of combining bed nets and indoor residual spraying upon malaria transmission in areas where the main vectors are anopheles arabiensis mosquitoes. Parasit Vectors. 2013;6(1):1–13.

9. World Malaria report. 2021. https://apps.who.int/iris/bitstream/handle/10665/350147/9789240040496-eng.pdf?sequence=1

10. Taffese HS, Hemming-Schroeder E, Koepfli C, et al. Malaria epidemiology and interventions in Ethiopia from 2001 to 2016. Infect Dis Poverty. 2018;7(1):103. doi:10.1186/s40249-018-0487-3

11. World malaria report 2018. 2018. https://books.google.com/books hl=en&lr=&id=sXeyDwAAQBAJ&oi=fnd&pg=PR3&dq=World+Health+Organization+,+2018.+World+Malaria+Report+2018.+Geneva,+Switzerland:&ots=I50vxQmJCb&sig=pLq7J3n_Gd6lh7JIc6xvAMVuc8s

12. Girum T, Shumbej T, Shewangizaw M. Burden of malaria in Ethiopia, 2000–2016: findings from the Global Health Estimates 2016. Trop Dis Travel Med Vaccines. 2019;5(1):11. doi:10.1186/s40794-019-0090-z

13. PMI. President’s malaria initiative Ethiopia malaria operational plan; 2019.

14. Deribew A, Dejene T, Kebede B, et al. Incidence, prevalence and mortality rates of malaria in Ethiopia from 1990 to 2015: analysis of the global burden of diseases 2015. Malar J. 2017;16(1):1–7. doi:10.1186/s12936-017-1919-4

15. Dadi LS. Analysis of spatiotemporal dynamics and associated factors of malaria: a comparative study around gilgel-gibe hydroelectric dam and control villages, Jimma zone, Southwest Ethiopia [dissertation]. Addis Ababa University; 2015.

16. MoH FDE. National malaria strategic plan 2014–2020. Addis Ababa: Minister of health; 2014.

17. Houngbedji CA, N’Dri PB, Hürlimann E, et al. Disparities of plasmodium falciparum infection, malaria-related morbidity and access to malaria prevention and treatment among school-aged children: a national cross-sectional survey in côte d’Ivoire. Malar J. 2015;14(1):7. doi:10.1186/1475-2875-14-7

18. Chacky F, Runge M, Rumisha SF, et al. Nationwide school malaria parasitaemia survey in public primary schools, the United Republic of Tanzania. Malar J. 2018;17(1):1–16. doi:10.1186/s12936-018-2601-1

19. Kapesa A, Kweka EJ, Atieli H, et al. The current malaria morbidity and mortality in different transmission settings in Western Kenya. PLoS One. 2018;13(8):e0202031. doi:10.1371/journal.pone.0202031

20. Ashton RA, Kefyalew T, Batisso E, et al. The usefulness of school-based syndromic surveillance for detecting malaria epidemics: experiences from a pilot project in Ethiopia. BMC Public Health. 2015;16(1):1–13. doi:10.1186/s12889-015-2680-7

21. Tadesse F, Hailemeskel E, Kefyalew S, et al. Asymptomatic P. falciparum and P. vivax infections: epidemiology and infectivity to mosquitoes. Gates Open Res. 2019;3.

22. Abeku TA, Helinski MEH, Kirby MJ, et al. Monitoring changes in malaria epidemiology and effectiveness of interventions in Ethiopia and Uganda: beyond Garki project baseline survey. Malar J. 2015;14(1):337. doi:10.1186/s12936-015-0852-7

23. Cohee LM, Opondo C, Clarke SE, et al. Preventive malaria treatment among school-aged children in sub-Saharan Africa: a systematic review and meta-analyses. Lancet Globl Health. 2020;8(12):e1499–e1511. doi:10.1016/S2214-109X(20)30325-9

24. Ethiopia C. Summary and statistical report of the 2007 population and housing census. Addis Ababa, Ethiopia: Federal democratic republic of Ethiopia population census commission; 2008: 1.

25. Office EHZH. East Hararghe Zone health office annual report; 2019.

26. Kelsey JL, Whittemore AS, Evans AS, Thompson WD. Methods in Observational Epidemiology. Vol. 10. Monographs in Epidemiology and; 1996.

27. Ashton RA Measuring low and unstable malaria transmission in Ethiopia: strategies for malaria surveillance and epidemic detection [dissertation]. London School of Hygiene & Tropical Medicine; 2014.

28. FMOH. National malaria guidelines fourth edition. EPHI report; 2018.

29. EPHI. Ethiopia malaria indicator survey summary report; 2015.

30. Contents DAaCAoE. Standard treatment guideline for primary hospitals; 2010.

31. Chitunhu S, Musenge E. Direct and indirect determinants of childhood malaria morbidity in Malawi: a survey cross-sectional analysis based on malaria indicator survey data for 2012. Malar J. 2015;14(1):265. doi:10.1186/s12936-015-0777-1

32. Iloh GUP, Chuku A, Amadi AN, Ofoedu JN. Proximate family biosocial variables associated with severe malaria disease among under-five children in resource-poor setting of a rural hospital in Eastern Nigeria. J Family Med Prim Care. 2013;2(3):256–262. doi:10.4103/2249-4863.120739

33. Edwin P. Prevalence and socio-demographic factors associated with malaria infection among children under five years in Tanzania. JPHE. 2018;10(11):387–394. doi:10.5897/JPHE2018.1055

34. Coleman M, Coleman M, Mabaso MLH, et al. Household and microeconomic factors associated with malaria in Mpumalanga, South Africa. Trans R Soc Trop Med Hyg. 2010;104(2):143–147. doi:10.1016/j.trstmh.2009.07.010

35. Sharma RK, Singh MP, Saha KB, et al. Socio-economic & household risk factors of malaria in tribal areas of Madhya Pradesh, Central India. Indian J Med Res. 2015;141(5):567–575. doi:10.4103/0971-5916.159515

36. de Castro MC, Fisher MG. Is malaria illness among young children a cause or a consequence of low socioeconomic status? Evidence from the united Republic of Tanzania. Malar J. 2012;11(1):161. doi:10.1186/1475-2875-11-161

37. Liu H, Xu J-W, Guo X-R, et al. Coverage, use and maintenance of bed nets and related influence factors in Kachin Special Region II, northeastern Myanmar. Malar J. 2015;14(1):212. doi:10.1186/s12936-015-0727-y

38. Terefa DR, Sinkie SO, Daka DW. Economic burden of malaria and associated factors among rural households in Chewaka District, Western Ethiopia. CEOR. 2020;12:141–152. doi:10.2147/CEOR.S241590

39. Alelign A, Dejene TM. Current status of Malaria in Ethiopia: evaluation of the burden, factors for transmission and prevention methods. APG. 2016;7(1):1–6.

40. Salunkhe L, Gupta A, Hameed S. A household survey to assess prevalence of malaria and risk factors under urban field practice area, Dakshin Kannada. Int J Community Med Public Health. 2019;6(1):223. doi:10.18203/2394-6040.ijcmph20185247

41. Ayele DG, Zewotir TT, Mwambi HG. Prevalence and risk factors of malaria in Ethiopia. Malar J. 2012;11(1):195. doi:10.1186/1475-2875-11-195

42. Abe T, Honda S, Nakazawa S, et al. Risk factors for malaria infection among ethnic minorities in Binh Phuoc, Vietnam. Southeast Asian J Trop Med Public Health. 2009;40(1):18.

43. Ahmed A, Mulatu K, Elfu B. Prevalence of malaria and associated factors among under-five children in sherkole refugee camp, Benishangul-Gumuz region, Ethiopia. A cross-sectional study. PLoS One. 2021;16(2):e0246895–e0246895. doi:10.1371/journal.pone.0246895

44. Safeukui-Noubissi I, Ranque S, Poudiougou B, et al. Risk factors for severe malaria in Bamako, Mali: a matched case-control study. Microbes Infect. 2004;6(6):572–578. doi:10.1016/j.micinf.2004.02.007

45. EMOE. National school health and nutrition strategy manual. Ethiopia Ministry of Education; 2012.

46. Deressa W, Yihdego YY, Kebede Z, Batisso E, Tekalegne A, Dagne GA. Effect of combining mosquito repellent and insecticide treated net on malaria prevalence in Southern Ethiopia: a cluster-randomised trial. Parasit Vectors. 2014;7(1):1–10. doi:10.1186/1756-3305-7-132

47. Bekele D, Belyhun Y, Petros B, Deressa W. Assessment of the effect of insecticide-treated nets and indoor residual spraying for malaria control in three rural kebeles of Adami Tulu District, South Central Ethiopia. Malar J. 2012;11(1):127. doi:10.1186/1475-2875-11-127

48. Alemu A, Tsegaye W, Golassa L, Abebe G. Urban malaria and associated risk factors in Jimma town, South-West Ethiopia. Malar J. 2011;10(1):173. doi:10.1186/1475-2875-10-173

49. Loha E, Lindtjørn B. Predictors of plasmodium falciparum malaria incidence in Chano Mille, South Ethiopia: a longitudinal study. Am J Trop Med Hyg. 2012;87(3):450–459. doi:10.4269/ajtmh.2012.12-0155

50. Kleinschmidt I, Torrez M, Schwabe C, et al. Factors influencing the effectiveness of malaria control in Bioko Island, Equatorial Guinea. Am J Trop Med Hyg. 2007;76(6):1027–1032. doi:10.4269/ajtmh.2007.76.1027

51. West PA, Protopopoff N, Rowland M, et al. Malaria risk factors in North West Tanzania: the effect of spraying, nets and wealth. PLoS One. 2013;8(6):e65787–e65787. doi:10.1371/journal.pone.0065787

52. Okia M, Hoel DF, Kirunda J, et al. Insecticide resistance status of the malaria mosquitoes: anopheles gambiae and anopheles funestus in eastern and northern Uganda. Malar J. 2018;17(1):157. doi:10.1186/s12936-018-2293-6

53. Staedke SG, Nottingham EW, Cox J, Kamya MR, Rosenthal PJ, Dorsey G. Proximity to mosquito breeding sites as a risk factor for clinical malaria episodes in an urban cohort of Ugandan children. Am J Trop Med Hyg. 2003;69(3):244–246. doi:10.4269/ajtmh.2003.69.244

54. Kimbi HK, Nana Y, Sumbele IN, et al. Environmental factors and preventive methods against malaria parasite prevalence in rural Bomaka and Urban Molyko, Southwest Cameroon. J Bacteriol Parasitol. 2013;4(162):4172.

55. Kweka EJ, Munga S, Himeidan Y, Githeko AK, Yan G. Assessment of mosquito larval productivity among different land use types for targeted malaria vector control in the western Kenya Highlands. Parasit Vectors. 2015;8(1):356. doi:10.1186/s13071-015-0968-1

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