Prevalence of Malaria and Associated Risk Factors among Febrile Under-five Refugee Children Attending Panyadoli Health Centre III, Kiryandongo District, Mid-western Uganda

doi:10.21203/rs.3.rs-5030231/v1

Download PDF

Research Article

Prevalence of Malaria and Associated Risk Factors among Febrile Under-five Refugee Children Attending Panyadoli Health Centre III, Kiryandongo District, Mid-western Uganda

https://doi.org/10.21203/rs.3.rs-5030231/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background: Malaria presents a big threat to the health of refugees, internally displaced persons, returnees and other such persons affected by humanitarian emergencies, with almost two thirds of these groups of persons living in malaria endemic regions. The aim of this study was to determine the prevalence of malaria and associated risk factors among refugee children <5 years attending a Health Centre in Panyadoli Refugee Camp, Kiryandongo District.

Methods: A cross sectional survey was done between February to April 2022, targeting refugee children < 5 years old seeking health attention at Panyadoli Health Centre III in Kiryandongo District. Simple random sampling was employed to select 380 participants who were then tested for the presence of malaria parasites using Malaria Rapid Diagnostic Tests (RDTS) and Microscopy techniques. Data on risk factors of malaria was collected using a pre-tested and standardized semi-structured questionnaire. Bivariate and multivariate logistic regression analyses was used to identify the risk factors, at 95% confidence interval and p < 0.05.

Results:Malaria prevalence among the refugee children < 5 years attending Panyadoli HCIII was 12.6% [95% CI: 8.7–18.0]. The associated risk factors for malaria infection included: application of Indoor Residual Spraying (IRS) in last 12 months [AOR = 4.323; 95% CI: 1.231–7.212], history of malaria in children (AOR = 5.861; 95% CI: 1.562 - 8.433], and sleeping under insecticide treated nets (AOR = 3.141; 95% CI: 0.865 - 5.221).

Conclusion: Malaria remains a threat to refugee children <5 years old at Panyadoli Refugee Settlement Camp. Health education should aim to sensitize and encourage the community to embrace malaria prevention measures, majorly IPT for pregnant mothers, use of ITNs, and IRS of households.

Malaria

refugees

Panyadoli

risk factors

febrile

under-five children

Malaria is a major public health problem-affecting humans, particularly in the Sub-Saharan Africa [1]. It is caused by protozoan parasites of the genus plasmodium, with Plasmodium falciparum accounting for the most hospital admissions [2]. Globally, there were 249 million estimated malaria cases in 85 malaria endemic countries in 2022, a figure that represents an increase of 5 million cases compared with 2021 [3]. Notably, over 95% of these cases occurred in Sub-Saharan Africa (SSA), where the economic toll of malaria amounts to approximately $12 billion annually [4]. Therefore, malaria is a major cause of morbidity in the continent with 10% of all deaths occurring in children under the age of five [4].

Uganda, unfortunately, is not exempt from the burden of the disease as it continues to grapple with malaria as a leading cause of death, especially among children. In 2020, Uganda ranked as the third-highest contributor to global malaria cases and fatalities (5.4%) and had the fifth-highest proportion of malaria cases in East and Southern Africa (23.2%) [5]. Malaria is endemic in 95% of the country, affecting over 90% of the population and accounting for 20% of all hospital deaths [6]. According to the country’s health ministry, a significant percentage of unreported deaths due to malaria occurs at home and 27.2% of inpatient deaths among children <5 years are due to malaria [7].

Refugees are particularly vulnerable to malaria infections as a consequence of poor housing, under nutrition, unclean water, overcrowding and limited access to health care among others [8]. Uganda is a top refugee hosting country in Africa, with over 1.36 million refugees [9]. Panyadoli Refugee Settlement Camp in Kiryandongo district is one of the largest, hosting refugees from South Sudan, Democratic Republic of Congo (DRC) and Burundi [10]. While studies have been conducted in other refugee camps, there is limited data on prevalence and risk factors associated with malaria in Panyadoli Refugee Camp, a place with limited health services and high mobility due to rate of turnover of refugees. With the apparent influx of refugees from South Sudan, and increasing cases of malaria, there’s need for heightened surveillance and intervention because human mobility is one of the factors that influence the transmission dynamics of many infectious diseases, by either introducing pathogens into susceptible populations or changing the frequency of contacts between infected and susceptible individuals or both [11]. The aim of this study was to determine the prevalence and risk factors associated with malaria among refugee children under 5 years old attending Panyadoli Health Center III in Kiryandongo district.

Study area

The study was conducted in Panyadoli Refugee Settlement Camp (Coordinates: 1.93998, 1.93998) located in Panyadoli Village, Bweyale subcounty Kiryandongo District (Figure 1). Panyadoli is one of the largest camps harboring refugees and asylum seekers from South Sudan, DRC, Burundi and Bududa survivors among others [12]. In 1990, the Ugandan government gazetted the virtually uninhabited land around Panyadoli in Kiryandongo for refugee resettlement. The Settlement currently provides shelter, land, and support for more than 100,000 people. The major economic activities carried out in the settlement are agricultural. The majority of the refugee farmers depend on growing cassava, sweet potatoes, maize, beans, rice, vegetables and groundnuts, mainly for domestic consumption. Additional activities in the Settlement include livestock rearing, fishing, and beekeeping. Panyadoli Health Centre III is the main facility at the Camp, providing healthcare services to over 100,000 refugees and persons in the community.

Figure 1: Map of Uganda showing location of Kiryandongo District and the resettlement camp.

Study design and sample size

This was a descriptive cross sectional study conducted between February to April 2022 in Kiryandongo district. Data collection involved both qualitative and quantitative tools. Microscopic and Rapid Diagnostic examinations were employed for diagnosis of malaria. Additionally, questionnaires were used to collect data on socio-demographic characteristics and malaria associated risk factors. The sample size of participants was calculated using a single population proportion formula, the 95% confidence limits (Zα/2 = 1:96), and a 5% margin of error (d) with a maximum proportion of 50% as follows:

Sample size = Z(α/2)² P (1- P)/ d², (1.96)² *0.221 (1–0.221)/ (0.05)² = 384.

However, a total of 380 children <5 years old who presented at Panyadoli Health Centre III Heath Centre with signs and symptoms of malaria was enrolled. Simple random sampling was conducted to select the participants using the random number generator approach. Briefly, random numbers were generated from a sampling frame of 700 obtained from the outpatients malaria register. These numbers were aligned against the patients’ identification number in the register. Any patient who was assigned a random number was included in the study. Parents/guardians of the selected febrile patients were triaged and recorded in the register using the patients` identification number. After a complete physical examination of the patients by the physician and referral to the laboratory for testing, the parent/guardian of the participants were interviewed to determine the predisposing factors to malaria infections.

Inclusion criteria.

Participants were eligible for the study if they were refugee children ≤ 5 years of age, had suspected symptoms of malaria such as anorexia, vomiting, or abdominal discomfort with or without diarrhea (based on the standard MOH definition of suspected malaria). In addition, the patient with a body (axillary) temperature > 37.4°C or history of fever in the 24–48 hours were included.

Exclusion criteria

Patients who came for confirmation or retesting for malaria following treatment were excluded from the study.

Data collection

Well-designed structured questionnaires were used to collect the qualitative data on risk factors for malaria infection. The questionnaire was initially developed in English and translated into local languages for data collection. Trained Research Assistants then administered the questionnaires during face-to-face interviews with parents/guardians of the under-five refugee children. The questionnaires were initially pretested and validated at Kiryandongo Hospital to ensure its reliability and validity.

Specimen Collection and Laboratory procedures

Blood specimens were obtained from the thumb using sterile blood lancet and cleaned with 70% ethanol or an alcohol swab and left to air dry to prepare thick and thin blood film smears. Blood specimen collection and processing was done by trained and competent laboratory technicians who had records of certification by WHO in malaria diagnosis and were under the technical supervision of the researcher, a senior Medical Laboratory Technologist. Malaria diagnosis was conducted using two approaches; the malaria Rapid Diagnostic Test (mRDT) and microscopic examination.

Malaria RDTs (Carestart, Lot no. 05CDH019A/05CDH030A) with a pre-determined sensitivity and specificity of 97.89%-100% and 99.50%-100% respectively as provided by its manufacturer were employed. Briefly, the test device and the sample pipette were removed from the foil pouch. The test device was labeled with the patient identification number and then placed on a flat surface. Using the provided loop or micropipette, 5µ whole blood sample was obtained from a finger prick and added to the sample well of the test card. Two drops (60µl) of assay diluent were added into the diluent well and allowed to flow by capillary action. The test result was read within 15-20 minutes and recorded as Positive or Negative depending on the test outcome.

For microscopic examination, a clean glass slide was labeled with the patient’s assigned unique number and accession in the register. The blood specimens were collected from the child upon consent/assent from their parents/guardians. Finger prick blood samples were then spotted onto carefully labeled slides to make thick and thin blood films, followed by air-drying. Field’s stains A and B were used for processing the blood slides. The thin film was stained with Field stain A for 5 seconds, fixed in methanol for 1 minute and then allowed to air dry. It was then gently dipped in Field stain B for 5 seconds and later washed in clean water. The slide was then dipped in Field stain A for 15-30 seconds and washed in clean water. The blood smears were examined under 100x microscopes for the presence of malaria parasites.

The thick smear was used to determine whether the malaria parasites were present or absent and the thin smear was used to identify the type of Plasmodium species. A positive result was defined as the presence of one or more asexual stages (trophozoite, ring stage, merozoite, or gametocyte) of plasmodium. A slide was regarded as negative after 200 fields had been examined without finding of Plasmodium parasite by two laboratory technologists. To assure quality of the microscopic examinations, all the positive and 10% of the negative slides were reexamined by a third reader to remove discrepant result.

Examination for the film was done for at least 10 minutes (approximately 200 oil immersion fields), before declaring the slide negative. Quality Control was performed on the stains to be used for sample processing by filtering each working day, and pre-tested known malaria negative and positive samples were analyzed in the same manner as the routine patient samples before analyzing the study samples, to ensure quality of results.

Data analyses

Data was entered into a Microsoft Excel Sheet, coded checked for completeness, and exported to SPSS version-20.0 (SPSS Inc., Chicago, IL, USA, 2011) for statistical analyses. Chi-square (χ2) tests were used to determine the association between the independent variables (risk factors) and the dependent variable (prevalence of malaria). Variables that were found significant were selected for inclusion in the final multiple logistic regression to determine the predictors of malaria infection. Confidence interval was set at 95% and a P<0.05.

General characteristics of study participants

A total of 380 febrile refugee children <5 years old took part in the study, with a 100% response rate (Table 1). As expected, majority of the refugee children were of South Sudan origin (366; 96.3%). Other nationalities included Kenya (03; 0.8%), Rwanda (05; 1.3%) and DRC (12; 2.3%). Refugee children who were below the age of 12 months formed the majority of the study population (111; 29.2%), followed by age groups 12-23 months (68; 17.9%), 36-47 months (; 65%) and 24-35 months (57; 15.0%). Most of the children came from households with parents/guardians who were catholic 259(68.2%) and had no formal education (163; 42.9%) or primary level (166; 43.7%). Moreover, most households where the children hailed from had 6 to 9 members (197; 51.8%). Majority of the houses where the children came from were made of mud and iron sheets as construction materials (223; 58.7%), and at least having a window(s) (307; 80.1%). In terms of malaria control measures, a good number of households (220; 57.9%) where the children came from had their houses sprayed in the last 12 months under the Indoor Residual Spray (IRS) programme of the Ministry of Health and their partners, while 75.8% (288/380) of the children where reported to be sleeping under insecticide treated mosquito nets (Table 1)

Fit the table if the target is BMC malaria journal (Tables are acceptable in the manuscript body for BMC malaria journal except if its beyond one page)

Table 1: General characteristics of the respondents surveyed. Significant p values indicated in bold

Variable	n	Percentage	95% CI	Upper	χ²	p
Variable	n	Percentage	Lower	Upper	χ²	p
Sex
Male	168	44.2	38.6	48.3	0.771	0.074
Female	212	55.8	49.1	59.2
Religion
Catholic	156	41.1	37.4	47.6	0.642	0.401
Protestant	98	25.8	21.2	29.3
Muslim	23	6.1	3.2	8.3
Born again	68	17.9	13.1	21.3
Others	35	2.4	0.8	4.6
Age of child (in months)
<12 12-23	81 93	21.3 24.5	18.8 21.1	25.1 26.2	12.232	0.001
24-35	87	22.9	17.5	26.3
36-47	56	14.7	10.3	17.4
48-59	63	16.6	11.2	19.6
Mothers/caretakers age group
15-24	138	36.3	31.4	42.1	0.305	0.814
25-34	162	42.6	36.6	47.2
35-49	80	21.1	18.4	25.6
IRS service in last 12 months
Sprayed	220	57.9	50.5	63.3	3.720	0.022
Not sprayed	160	42.0	37.7	47.1
Lighting source
Present	24	6.4	3.6	8.2	0.482	0.378
Absent	356	93.6	81.2	98.4
Sleep under ITN
Yes	288	75.8	63.4	81.2	7.841	<0.001
No	92	24.2	19.6	26.1
Construction material for house
Bricks and iron sheets Mud and iron sheets Cement and iron sheets Mud only Wooden house	33 223 12 109 3	8.7 58.7 3.2 28.7 0.8	5.1 52.4 1.2 24.2 0.3	11.3 63.6 5.4 33.5 1.2	0.331	0.548
House screen
Windows present Window absent	30773	80.1 19.2	71.0 15.4	85.1 23.2	0.768	0.381
Education level of the mother or child caretaker
No formal education	163	42.9	36.6	45.5	0.152	0.859
Primary	166	43.7	38.2	47.6
Secondary and above	51	13.4	8.2	15.3
Number of household member/family size
Less than 5	138	36.3	31.5	39.8	7.673	0.004
6 to 9	197	51.8	47.6	54.7
10 to 15	45	11.8	8.8	15.0
Taking long outdoor at night
Yes	187	49.2	46.8	52.1	0.559	0.439
No	193	50.8	48.1	53.4
Use mosquito repellents at night
Yes	12	3.2	5.2	12.1	0.152	0.697
No	268	96.8	81.0	102.5
Wears long clothing at night
Yes	87	22.9	16.4	26.7	0.392	0.344
No	293	77.1	63.3	84.6
Presence of stagnant water near dwelling place
Yes	197	51.8	47.4	55.3	1.411	0.235
No	183	48.2	44.7	54.6
Dwelling place surrounded by vegetation
Yes	195	51.3	47.3	54.6	0.930	0.351
No	185	48.7	45.4	52.4
Treatment
Always self medication	176	46.3	41.6	50.2	5.771	0.008
From health facility	204	53.4	48.8	57.4
Mother received all 3 episodes of IPT
Yes	274	72.1	65.4	76.7	9.771	0.002
No	106	27.9	23.3	34.6
Health education on malaria in last 6 months
Yes	142	37.4	33.2	43.5	0.350	0.721
No	238	62.6	56.5	66.8
History of malaria in child
Yes	258	67.9	63.8	71.4	8.362	<0.001
No	122	32.1	27.6	36.2
Country of origin
South Sudan	360	94.7	86.2	99.1		0.541
Rwanda	5	1.3	0.5	3.8
Kenya	3	0.8	0.2	1.6
DRC	12	3.2	0.7	5.3

Prevalence of malaria and risk factors among refugee children <5 years in Panaydoli Refugee Settlement Camp.

The overall prevalence of malaria in children <5 years at Panyadoli was found to be 12.6% (95% CI: 8.7–18.0) by microscopy and 13.2% (95% CI: 8.2–17.3). Majority (41/48; 85.42%) of the parasites were microscopically determined as Plasmodium falciparum malaria, while a few (7/48; 14.58%) were P. vivax. We did not detect any cases of mixed infections.

Bivariate analyses showed that age of child (in months) (p=0.001), indoor residual spraying (IRS) in last 12 months (p=0.022), history of malaria in child (p<0.001), treatment behavior (self-medication vs health facility (p=0.008), family size (p=0.004), and sleeping under insecticide treated nets (p=0.001) had significant associations with malaria (Table 1).

Multivariable logistic regression analysis confirmed that IRS in last 12 months (AOR = 4.323; 95% CI: 1.231–7.212), history of malaria in child (AOR = 5.861; 95% CI: 1.562 - 8.433), and sleeping under insecticide treated nets (AOR = 3.141; 95% CI: 0.865 - 5.221) were the independent predictors of falciparum malaria among under-five children in Panyadoli Refugee Settlement Camp (Table 2).

Table 2: Multivariate logistic regression analyses of the risk factors associated with malaria prevalence among refugee children < 5 years in Panyadoli Health Centre III, Kiryandongo District.

Variable	n	COR, P-values, 95% CI	AOR, 95% CI	P-value
Variable	n	COR, P-values, 95% CI	AOR, 95% CI	P-value
Age of child (in months)
<12	81	0.987 (0.384-2.541)	1.028 (0.550-3.221)	NS
12-23	93	0.525 (0.123-3.731)	0.663 (0.213-3.134)
24-35	87	0.228 (0.038 - 2.466)	0.528 (0.083 - 2.767)
36-47	56	1.248 (0.539-4.503)	1.314 (0.932-6.305)
48-59	63	1
IRS service in last 12 months
Sprayed	220	1
Not sprayed	160	4.020 (0.342-7.246)	4.323 (1.231-7.212)	<0.001*
Sleep under ITN
Yes	288	1
No	92	3.072 (1.524-7.038)	3.141 (0.865 - 5.221)	0.003*
Number of household member/family size
Less than 5	138	1		NS
6 to 9	197	0.825 (0.297-3.133)	1.181 (0.743-5.123)
10 to 15	45	0.625 (0.151-2.660)	0.547 (0.182-3.011)
Treatment
Always self medication	176	0.726 (0.281-1.873)	0.811 (0.124-2.376)	NS
From health facility	204	1
Mother received all 3 episodes of IPT
Yes	274	1
No	106	0.793 (0.445-2.971)	1.920 (0.386 - 6.756)	0.072
History of malaria in child
Yes	258	5.019 (0.703-9.630)	5.861 (1.562 - 8.433)	<0.001*
No	122	1

Note: * indicates P<0.05 statistically significant, NS = Not significant.

This study was carried out to assess the prevalence of malaria and associated risk factors in children aged 5 years and below who sought health management at Panyadoli Health Centre III in Kiryandongo District. These children were refugees and as such generally present a highly vulnerable group to malaria due to the factors such as overcrowded living conditions, inadequate access to healthcare, and disruption of malaria control programs. Moreover, refugee settlements often face challenges like limited infrastructure and resources, which exacerbate the spread and impact of malaria [13]. As a result, malaria can severely affect the health of refugees, particularly children and pregnant women, leading to higher morbidity and mortality rates and straining already limited health services [3]. Understanding the prevalence and risk factors allows for targeted interventions and resource allocation, helping to mitigate the disease burden and improve health outcomes in these vulnerable communities [14].

The overall infection rate was about 12.6% by microscopy. Compared to previous studies, this prevalence is somewhat moderate or slightly lower. For example, a study that utilized data from the Uganda’s Malaria Indicator Survey 2018-2019 in Uganda's refugee camps reported a malaria rate of 36.6% [15]. Regionally, East African countries like Kenya and Tanzania report variable prevalence rates, often between 5% and 30%, influenced by factors such as seasonal changes and control measures [16]. Globally, refugee camps frequently exhibit higher malaria prevalence compared to host populations due to overcrowded conditions and inadequate health infrastructure [17]. The moderate prevalence in Panyadoli may reflect successful control measures but also indicates ongoing challenges. The survey showed that a substantial number of households from where the children hail use mosquito nets while a good number of also embraced other mosquito control initiatives like IRS which is carried out by different stakeholders under the auspices of the Ugandan Ministry of Health.

As a factor, households that had implemented IRS in the last 12 months before the study was done were four times less likely to suffer from malaria compared to those that did not. This is consistent with previous observations indicating the effectiveness of IRS in reducing malaria incidence. Numerous studies in similar contexts have demonstrated that IRS can significantly lower malaria prevalence by targeting the vector population indoors, where mosquitoes typically rest after feeding [4]. For instance, a study in Uganda [18], Ethiopia [19] and Benin [20] found a comparable reduction in malaria cases following IRS implementation. The success of IRS in Panyadoli also reflects the importance of sustained vector control interventions in refugee settings, where the risk of malaria is heightened due to factors like overcrowding and limited access to healthcare [3]. In Panyadoli, IRS activities are typically carried out by the Uganda Ministry of Health supported by the United States Agency for International Development (USAID)/President’s Malaria Initiative (PMI), with spraying typically occurring on an annual basis or as determined by epidemiological needs. The apparent effectiveness observed in this study highlights the critical role of consistent and well-managed vector control strategies in combating malaria in refugee camps.

This study also found that children who had history of malaria were more likely to suffer malaria than those who did not have a history of malaria infection. The increased likelihood of recurrent malaria in children with a history of the disease can be attributed to a combination of factors including incomplete immunity, ongoing parasite exposure, environmental conditions or even improper treatment [21]. It was also found that infection with P. falciparum malaria in the early stages of life (usually first 6 months) was associated with higher incidence of both malaria infection and clinical malaria in early childhood. This is mainly attributed to the decreasing levels of maternally derived malaria specific immunoglobulin (IgG) as the child grows older [22, 23]. On the other hand, the presence of antibodies 19-kDa C-terminal fragment of Msp-1₁₉ at birthdoes provide resistance to clinical malaria at a later stage of life, meaning the absence of this antibody markers would definitely provide no resistance to malaria for such children [22]. Moreover, the socioeconomic conditions and environmental factors might also play a role; children in refugee settlements like Panyadoli with poor access to healthcare, inadequate malaria control measures, and high vector density may be at greater risk of both initial and recurrent malaria infections.

Another significant predictor for malaria infection in the children was whether or not they slept under insecticide treated mosquito nets. Numerous studies have confirmed that ITNs are highly effective in reducing malaria incidence [24–27]. This protective effect is attributed to the reduction in mosquito bites and the insecticide's ability to kill mosquitoes. Other studies report similar findings that ITNs are one of the most cost-effective malaria control interventions, significantly reducing the incidence of malaria among children and improving overall health outcomes [25]. ITNs reduce the number of mosquito bites by providing a physical barrier between mosquitoes and humans. The nets are treated with insecticides such as pyrethroids, which kill mosquitoes on contact. This direct effect reduces the number of mosquitoes that can transmit malaria parasites. Moreover long-term use of ITNs can also lead to changes in mosquito behavior, such as reduced feeding or seeking alternative blood sources, further contributing to decreased malaria transmission [5]. In the case of Panyadoli, an encouraging statistics was that over 75% of the respondents reported that their children slept under ITNs, which were either mostly distributed by government or privately procured by the household heads. The mosquito net distribution program in Kiryandongo Refugee Camp is part of Uganda's broader malaria control strategy. The Ugandan government, in collaboration with international organizations such as the World Health Organization (WHO), UNICEF, and various non-governmental organizations (NGOs), distributes ITNs to refugees and local communities, with the distribution generally conducted through mass campaigns, with nets provided to households at regular intervals.

The malaria prevalence in children <5 years old at Panyadoli Refugee Settlement Camp remains moderately high.. Health education should aim to sensitize and encourage the community to embrace malaria prevention measures, majorly IPT for pregnant mothers, use of ITNs, and IRS of households.

Study limitations

The study was cross sectional in design and took place between the months of February to April 2022 only. And yetmalaria is influenced by environmental factors, that can exert an effect on its seasonality, distribution, and transmission intensity [28]. This may influence the number of children being treated as outpatients in hospitals, and hence the estimates of the actual true burden in our study.

Additionally, we used only two techniques (RDT and microscopy) to detect as well as speciate the malaria parasites in blood. More sensitive techniques like PCR could have provided better resolution and/or confirmation of the species of malarial parasites.

AOR: Adjusted Odds Ratio

CI: Confidence Interval

SPSS: Statistical Package for the Social Sciences

DRC: Democratic Republic of Congo

IPT: Intermittent Prophylactic Treatment

IRS: Indoor Residual Spraying

ITNs: Insecticide Treated Nets

MOH: Ministry of Health

SSA: Sub Saharan Africa

UBOS: Uganda Bureau of Statistics

UNHCR: United Nations High Commission for Refugees

WHO: World Health Organization

Ethical approval and consent to participate

Ethical approval to conduct the study was obtained from Gulu University Research Ethics Committee (Approval number GUREC-2021-131). Permission to conduct the study were sought from Kiryandongo District Health Officer, Chief Administrative Officer, District Laboratory Focal Person, the UNHCR District Field Coordinator and Panyadoli HCIII Authority to conduct the study at the above facility. All parents/guardians assented to and signed informed consent forms before enrolling their children for the study.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Competing interests

We do not have any competing interests to declare.

Funding

The research work did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors.

Authors' contributions

AD, ASP and RO conceived and designed the study, collected data, performed initial analyses and wrote initial draft of manuscript. BM and BBA critically revised the manuscript. All authors read and approved the final version of the manuscript.

Acknowledgements

We appreciate the support of the In-charge of Panyadoli Health Centre III, the Local Council I Chairperson of Panyadoli, and all the parents/guardians who consented to the study.

Authors’ information

Not applicable

Geneva: IGO WHO: World malaria report 2020: 20 years of global progress and challenges. 2020, Licence: C.
Gilles JRL, Schetelig MF, Scolari F, Marec F, Capurro ML, Franz G, Bourtzis K: Towards mosquito sterile insect technique programmes: exploring genetic, molecular, mechanical and behavioural methods of sex separation in mosquitoes. Acta Trop 2014, 132:S178–S187.
Organisation WH: World Malaria Report 2023. Geneva 2023, icence: CC.
World Health Organisation G: World Malaria Report 2022. 2022, Licence: C.
World Health Organisation G: World Malaria Report 2021. 2021, CC BY-NC-S.
Zalwango MG, Bulage L, Zalwango JF, Migisha R, Agaba BB, Kadobera D, Kwesiga B, Opigo J, Ario AR: Trends and Distribution of Severe Malaria Cases, Uganda, 2017–2021: Analysis of Health Management Information System Data. .
Uganda R of: The Uganda Malaria Reduction Strategic Plan 2014–2020. 2015.
Anderson J, Doocy S, Haskew C, Spiegel P, Moss WJ: The burden of malaria in post-emergency refugee sites: a retrospective study. Confl Health 2011, 5:1–8.
Ahimbisibwe F, Ingelaere B, Vancluysen S: Rwandan refugees and the cessation clause: the possibilities for local integration in Uganda. Afrika Stud Stud Dokumentatiecentrum [Brussel]; Cent d’étude Doc africaines [Bruxelles]-Brussel, 1993, currens 2019:411–433.
Oboth P, Gavamukulya Y, Barugahare BJ: Prevalence and clinical outcomes of Plasmodium falciparum and intestinal parasitic infections among children in Kiryandongo refugee camp, mid-Western Uganda: a cross sectional study. BMC Infect Dis 2019, 19:1–8.
Wesolowski A, Buckee CO, Engø-Monsen K, Metcalf CJE: Connecting mobility to infectious diseases: the promise and limits of mobile phone data. J Infect Dis 2016, 214(suppl_4):S414–S420.
Statistics. UB of: National Population and Housing Census 2014, Enumerators’ Manual. Kampala: UBOS. 2014.
Aylett-Bullock J, Gilman RT, Hall I, Kennedy D, Evers ES, Katta A, Ahmed H, Fong K, Adib K, Al Ariqi L: Epidemiological modelling in refugee and internally displaced people settlements: challenges and ways forward. BMJ Glob Heal 2022, 7:e007822.
Rowland M, Nosten F: Malaria epidemiology and control in refugee camps and complex emergencies. Ann Trop Med Parasitol 2001, 95:741–754.
Semakula HM, Liang S, Mukwaya PI, Mugagga F, Swahn M, Nseka D, Wasswa H, Kayima P: Determinants of malaria infections among children in refugee settlements in Uganda during 2018–2019. Infect Dis Poverty 2023, 12:31.
Leal Filho W, May J, May M, Nagy GJ: Climate change and malaria: some recent trends of malaria incidence rates and average annual temperature in selected sub-Saharan African countries from 2000 to 2018. Malar J 2023, 22:248.
Hershey CL, Doocy S, Anderson J, Haskew C, Spiegel P, Moss WJ: Incidence and risk factors for malaria, pneumonia and diarrhea in children under 5 in UNHCR refugee camps: a retrospective study. Confl Health 2011, 5:1–11.
Tukei BB, Beke A, Lamadrid-Figueroa H: Assessing the effect of indoor residual spraying (IRS) on malaria morbidity in Northern Uganda: a before and after study. Malar J 2017, 16:1–9.
Hamusse S, Balcha T, Belachew T: The impact of indoor residual spraying on malaria incidence in East Shoa Zone, Ethiopia. Glob Health Action 2012, 5:11619.
Akogbeto M, Padonou GG, Bankole HS, Gazard DK, Gbedjissi GL: Dramatic decrease in malaria transmission after large-scale indoor residual spraying with bendiocarb in Benin, an area of high resistance of Anopheles gambiae to pyrethroids. Am J Trop Med Hyg 2011, 85:586.
Andronescu LR, Buchwald AG, Sharma A, Bauleni A, Mawindo P, Liang Y, Gutman JR, Mathanga DP, Chinkhumba J, Laufer MK: Plasmodium falciparum infection and disease in infancy associated with increased risk of malaria and anaemia in childhood. Malar J 2023, 22:217.
Branch OH, Udhayakumar V, Hightower AW, Oloo AJ, Hawley WA, Nahlen BL, Bloland PB, Kaslow DC, Lal AA: A longitudinal investigation of IgG and IgM antibody responses to the merozoite surface protein-1 19-kiloDalton domain of Plasmodium falciparum in pregnant women and infants: associations with febrile illness, parasitemia, and anemia. Am J Trop Med Hyg 1998, 58:211–219.
Sehgal VM, Siddiqui WA, Alpers MP: A seroepidemiological study to evaluate the role of passive maternal immunity to malaria in infants. Trans R Soc Trop Med Hyg 1989, 83:105–106.
Yang G, Kim D, Pham A, Paul CJ: A meta-regression analysis of the effectiveness of mosquito nets for malaria control: the value of long-lasting insecticide nets. Int J Environ Res Public Health 2018, 15:546.
Lengeler C: Insecticide‐treated bed nets and curtains for preventing malaria. Cochrane database Syst Rev 2004.
Choi HW, Breman JG, Teutsch SM, Liu S, Hightower AW, Sexton JD: The effectiveness of insecticide-impregnated bed nets in reducing cases of malaria infection: a meta-analysis of published results. Am J Trop Med Hyg 1995, 52:377–382.
Hawley WA, Phillips-Howard PA, Kuile FO ter, Terlouw DJ, Vulule JM, Ombok M, Nahlen BL, Gimnig JE, Kariuki SK, Kolczak MS: Community-wide effects of permethrin-treated bed nets on child mortality and malaria morbidity in western Kenya. Am J Trop Med Hyg 2003, 68:121–127.
Yusuf SK: Spatial temporal impacts of climate variability on malaria distribution in Gulu and Mpigi districts in Uganda using GIS. 2013.

No competing interests reported.

Download PDF

Reviews received at journal
06 Nov, 2024
Reviewers agreed at journal
28 Oct, 2024
Reviewers invited by journal
28 Oct, 2024
Editor assigned by journal
04 Sep, 2024
Submission checks completed at journal
04 Sep, 2024
First submitted to journal
04 Sep, 2024

You are reading this latest preprint version

Prevalence of Malaria and Associated Risk Factors among Febrile Under-five Refugee Children Attending Panyadoli Health Centre III, Kiryandongo District, Mid-western Uganda

Status:

Version 1

Abstract

Figures

Introduction

Materials And Methods

Sample size = Z(α/2)² P (1- P)/ d², (1.96)² *0.221 (1–0.221)/ (0.05)² = 384.

Inclusion criteria.

Exclusion criteria

Data collection

Specimen Collection and Laboratory procedures

Results

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1

Prevalence of Malaria and Associated Risk Factors among Febrile Under-five Refugee Children Attending Panyadoli Health Centre III, Kiryandongo District, Mid-western Uganda

Status:

Version 1

Abstract

Figures

Introduction

Materials And Methods

Sample size = Z(α/2)2 P (1- P)/ d2, (1.96)2 *0.221 (1–0.221)/ (0.05)2 = 384.

Inclusion criteria.

Exclusion criteria

Data collection

Specimen Collection and Laboratory procedures

Results

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1

Sample size = Z(α/2)² P (1- P)/ d², (1.96)² *0.221 (1–0.221)/ (0.05)² = 384.