Prevalence and associated risk factors of malaria among febrile patients at Kidist Hana Health Center Fogera District, Amhara Regional State, Northwest Ethiopia

Abstract

Background

Malaria remains a major public health problem in Ethiopia. Monitoring local prevalence and associated risk factors is essential for effective control and elimination strategies. This study aimed to determine the prevalence of malaria and identify associated risk factors among patients attending Kidist Hana Health Center.

Methods

A facility-based cross-sectional study was conducted. Blood samples were collected from febrile patients and examined using both microscopy and First response malaria Ag P.f and P.v rapid diagnostic tests (RDTs). A structured questionnaire was used to gather socio-demographic and environmental data. Data were analyzed using SPSS version 26. Bivariable and multivariable logistic regression analysis was carried out to measure the strength of association. An Adjusted Odds Ratio (AOR) with 95% Confidence Interval (CI) was used. Moreover, variables with p-value ≤0.05 were considered as statistically significant with the outcome variable.

Results

The overall prevalence of malaria by microscopy was 40.5%, with Plasmodium falciparum (78.4%) being the most predominant species. The prevalence varied significantly across different kebeles. Multivariate analysis revealed that malaria infection was significantly associated with marital status (widowed), age below five years, low household income, lack of insecticide-treated net (ITN) ownership, presence of stagnant water near the residence, and outdoor sleeping habits. Microscopy demonstrated a higher detection rate compared to First response malaria Ag P.f and P.v RDTs (40.5% vs. 38.9%). The sensitivity of RDT test was 95.91%. The specificity of RDT was 91.24%. The Positive Predictive Value was 88.16%. The Negative Predictive Value was 97.04%.

Conclusion

The study reveals a high malaria burden in the study area, driven by a combination of socio-demographic, environmental, and behavioural factors. To reduce transmission, intensified public health interventions are recommended, including ensuring universal ITN coverage and use, implementing community-based environmental management to eliminate mosquito breeding sites, and targeting high-risk groups with focused health education.

1. Introduction

1.1. Background of the study

Malaria is an acute febrile illness caused by Plasmodium parasites, which are transmitted to people through the bites of infected female Anopheles mosquitoes (Daily and Parikh, 2025). Human malaria is caused by five common Plasmodium species (P. falciparum, P. vivax, P. malariae, P. ovale, and P. knowlesi), of which P. vivax and P. falciparum present the most risk. The most prevalent and lethal malaria parasite on the continent of Africa is P. falciparum, while in most countries outside sub-Saharan Africa, P. vivax is the prevalent malaria parasite (Li et al., 2024). It poses a significant global health challenge, particularly in tropical and subtropical regions. Malaria affects millions of people every year, leading to a significant number of deaths, especially among children under the age of five (Adam et al., 2025).

Globally, malaria is distributed mainly in three WHO regions: the African Region, Southeast Asian Region, and Eastern Mediterranean Region (World Health Organization, 2023). The WHO African Region still bears the largest burden of malaria morbidity and mortality every year (Minwuyelet and Aschale, 2025). In 2018, an estimated 228 million malaria cases and 405,000 deaths were recorded worldwide, of which 93% were in the WHO African Region, followed by the WHO Southeast Asian Region (3.4%) and the Eastern Mediterranean Region (2.1%) (World Health Organization, 2019).

According to WHO, the estimated global malaria cases in 2020 were 241 million, with 627,000 deaths; the African continent accounted for 95% of malaria cases and 96% of malaria deaths (Kolawole et al., 2022). The prevalence of malaria is highest in sub-Saharan Africa (SSA) for various reasons, including favorable environmental conditions for vector reproduction, ongoing poverty, and incomplete understanding of the disease's epidemiology (Oladipo et al., 2022).

Malaria in Ethiopia causes serious complications including severe anemia, cerebral malaria, acute renal failure, and hypoglycemia (Kendie, 2021). In Ethiopia, malaria transmission is seasonal, unstable, and epidemic-prone, showing strong variation by altitude and rainfall. Areas below approximately 1500–2000 m above sea level—especially lowland and western regions—face more intense and constant transmission. About 68–70% of Ethiopia's population lives in areas at risk of malaria infection, with risk zones tied to climatic factors such as rainfall and temperature (Nigussie et al., 2022). In most parts of Ethiopia, high malaria transmission occurs between September and December, while low transmission occurs between January and July (President's Malaria Initiative, 2019). The Southern Nations, Oromia, and Amhara Regional States are among the most affected (Adugna et al., 2022a). Several risk factors can increase malaria likelihood, including lack of protective measures, travel to endemic areas, and certain demographic and environmental conditions (Mesele et al., 2025). It is essential to take appropriate precautions, such as using insecticide-treated nets, taking prophylactic medication, and wearing protective clothing, to prevent malaria infection (Taffese et al., 2023). Therefore, malaria remains a major public health problem in many parts of the country, including the present study area. Kidist Hana is one of the rural clusters kebeles of Fogera District, South Gondar Zone, Amhara Region, where malaria remains a public health concern. Local evidence on the magnitude of malaria and its associated risk factors is limited, yet such information is crucial for guiding targeted prevention and control strategies. The aim of this study was to determine the prevalence of malaria and associated risk factors among febrile patients.

2. Materials and methods

2.1. Description of study area

The study was conducted in Kidist Hana Health Center (KHHC), which is located in the rural area of Fogera District, South Gondar Zone, and Amhara Regional state. It serves the communities in Kidist Hana, Wagetera, Nabega, and Shina kebeles (administrative subdivisions) of Fogera district. Fogera District itself is predominantly rural, characterized by agricultural activities and a relatively lower population density compared to urban areas. The study area is located 11°84’North latitude and 37° 52′East longitude. Its elevation is 1810 m above sea level. Based on ten years' average meteorological data, the mean annual rainfall is 1300 mm. The temperature in the Fogera district, including Kidist Hana, varies depending on the season. However, the region generally experiences a tropical climate, with average temperatures ranging from 20 to 30 degrees Celsius. The majority of the population is made up of farmers who work in settled mixed agriculture producing Rice (Oryza sativa), onions (Allium sativium), maize (Zea mays), Teff (Eragrostis teff), and Wheat (Triticum aestivum), which are the principal food crops produced in the area using both irrigational practice and summer season.

2.2. Study design and period

A facility based cross-sectional study was conducted from December 2022 to March 2023. to determine the prevalence of malaria and evaluate risk factors associated with it among febrile patients who were visiting Kidist Hana Health Center.

2.3. Source and study population

The source population of the study was all individuals who visited Kidist Hana health center seeking treatment during the study period and fulfilled the selection criteria of the study.

2.4. Eligibility criteria

2.4.1. Inclusion

The study recruited only voluntary individuals who visited KHHC during the study period and in case of children whose parents or guardians signed a written informed consent form and those were febrile patients willing to provide blood samples.

2.4.2. Exclusion criteria

Participants that unable to provide informed consent, those with incomplete data and subjects who might have obtained antimalarial drug treatment from health extension workers or self-medication 42 days prior to data collection and those who were unable to properly communicate were excluded.

2.5. Study variables

2.5.1. Independent variables

The study's independent variables were age, sex, residence, educational status, monthly income, occupation, ITN availability, presence of stagnant water around home, presence of dense mat of water hyacinth around residence, house sprayed with insecticide, history of anti-malarial treatment, outdoor stay at night, presence of hole on the wall of house, health education on malaria in the last 6 months, knowledge of participants about malaria (malaria is curable, malaria is transmittable, method of transmission, breeding site of mosquitoes, malaria can be prevented, method of malaria prevention, who are more affected by malaria, treatment seeking within 24 h).

2.5.2. Dependent variable

The prevalence of malaria among febrile patients attending the KHHC, which was determined using microscopic examination and RDT diagnosis of the blood samples of the study participants.

2.5.3. Sample size

The required sample size was determined by applying the single population proportion formula [Charan and Biswas, 2013]. The calculation assumed a 95% confidence level and a proportion (p) of 0.5. This value was selected because no previous studies reporting malaria prevalence were available for the Kidist Hana Health Center catchment area:

$$\mathrm{n}=\frac{{\mathrm{z}}^2\mathrm{p}\left(1-\mathrm{p}\right)}{{\mathrm{d}}^2}$$

After adding 10% for possible non-responsive, the total sample size became 384 + 384*(0.1) =422.

2.6. Sampling techniques

A systematic random sampling method was employed to select participants. The sampling frame was defined as the average monthly number of patients diagnosed with malaria at Kidist Hana Health Center (KHHC) in 2022, which was 1022 (total annual patients: 12,264). This figure was used as a proxy for the expected population size during the study period. The sampling interval (k) was determined by dividing this population (1022) by the required sample size (422), resulting in an interval of 2. The selection process began with a randomly chosen patient from the initial sampling frame, followed by the systematic selection of every second patient thereafter.

2.7. Data collection methods

Data were collected over a three-month period from December 2022 to March 2023. The study employed a dual-method approach, utilizing both semi-structured questionnaires and laboratory-based blood smear tests.

2.7.1. Questionnaire survey

The data was collected using structured questionnaires that included both socio-demographic characteristics and potential risk factors. Structured questionnaire was originally prepared in English, and then the questionnaire was translated into the local language, Amharic, in order to collect the required data. Finally, it was translated back to English again for maintaining consistency. The questionnaire contains socio-demographic information such as sex, age, location of residence, level of education, occupation, family income; and ITN utilization status; IRS use; outdoor sleeping or activities, and knowledge of malaria infection.

2.7.2. Laboratory parasitological examinations

Laboratory detection of malaria was performed using both Rapid Diagnostic Tests (RDTs) and microscopic examination of blood smears for all study participants.

2.7.3. Rapid diagnostic tests (RDTs-kit)

Rapid Diagnostic Tests (RDTs) are cassette-based tests that detect malaria antigens, delivering results within 15–20 min. The test procedure involves applying a blood sample and buffer to the designated pad. The buffer lyses the red blood cells and carries the lysate across the strip. The principle is based on a sandwich immunoassay: if malaria antigens are present, they form a complex with conjugated antibodies, which are then captured at specific test lines, generating visible bands. The pattern of these bands identifies the infecting species (e.g., P. falciparum or P. vivax). A separate control line confirms the test has functioned correctly. First response malaria Ag P.f and P.v RDT used in this study was manufactured by Premier Medical Corporation Private Limited, a company that operates in the medical industry (A1–302, GIDC, Sarigam 396,155.Dist. Valsad, Gujarat, INDIA).

2.7.4. Microscopy examinations

Capillary blood was collected via finger-prick from each participant after cleansing the fingertip with 70% alcohol. Approximately 50 μL of blood was used to prepare both thick and thin blood films on a labeled microscope slide according to standard protocols. The thin film was fixed with pure methanol, and both films were air-dried. All slides were stained with 5% Giemsa solution and transported to the KHHC parasitology laboratory for examination.

Microscopic examination was performed by trained laboratory technologists. Thick films were used to detect the presence of malaria parasites, while thin films were used for species identification. To ensure quality control, all slides were re-examined by a second, independent technologist.

2.7.5. Methods of data analysis

Data were entered, cleaned, and analyzed using the Statistical Package for the Social Sciences (SPSS), version 26. Descriptive statistics, including proportions and means, were used to summarize the study population's characteristics. Logistic regression was employed to identify the association between potential predictors of malaria infection. To control confounding effects of variables, all Variables with p value less than 0.25 in bivariate analysis were selected and entered for multivariate logistic regression analysis (Bursac et al., 2008). P value less than 0.05 was considered to indicate statistically significance.

3. Results

3.1. Socio-demographic characteristics of the study participants

This study included 422 febrile patients, with a mean age of 22 years, ranging from 1 to 70, randomly recruited from Kidist Hana Health Center. Data on sociodemographic factors and blood samples were collected via semi-structured questionnaires. Out of the 422 febrile patients, the majority (44.8%) were residents of Wagetera kebele. The overall distribution of males and females was 47.4, and 52.6, respectively. And also, the majority of the studies participants were farming 63.7%). In terms of socioeconomic status, the majority reported farming as their occupation (63.7%) and fell into a low-income category (58.3%). (Table 1). (See Fig. 1, Fig. 2, Fig. 3.)

Table 1.

Socio-Demographic Characteristics of the study participants of Kidist Hana Health Center; Fogera District (N = 422).

Characteristics	Categories	Frequency (%)
Kebeles	Wagetera Nabega Kidist Hana Shina	189(44.8) 50(11.8) 152(36.0) 31(7.3)
Sex	Male Female	200(47.4) 222(52.6)
Age	≤5 6–15 16–25 26 = 35≥  36	37(8.8) 131(31.0) 91(21.6) 65(15.4) 98(23.2)
Residence	Rural Urban	422(100.0) 0(0.0)
Marital status	Single Married Widowed Divorced	170(40.3) 207(49.1) 30(7.1) 15(3.6)
Educational Status	Illiterate Literate Primary Secondary and above	232(55.0) 156(37.0) 16(3.8) 18(4.3)
Occupational Status	Farmer Daily laborer Merchant Student Gov't Employee	269(63.7) 42(10.0) 34(8.1) 39(9.3) 38(9.0)
Number of individuals in the family	≥ 5 <5	169(40.0) 253(60.0)
Family income per month	Low Medium High	246(58.3) 103(24.4) 73(17.3)

(Low (≤2280 ETB/month), Medium (2280–9080 ETB/month), High (>9080 ETB/month) in Ethiopian Birr).

Fig. 1.

Map of study area.

Fig. 2.

Dense mat of water hyacinth in Wagetera (A) and Nabega (B).

Fig. 3.

Rapid diagnostic test-positive result band.

3.2. Prevalence of malaria among study participants using microscopy and RDT diagnostic methods

Out of 422 blood samples examined with microscopy, the overall prevalence of malaria, as determined by microscopy (the gold standard), was 40.5% (171/422). In comparison, Rapid Diagnostic Tests (RDTs) detected malaria antigens in 38.9% (164/422) of the samples. The concordance between the two methods was 164 cases. The sensitivity of RDT test was 95.91% (95% CI: 91.75% to 98.34%). The specificity of RDT was 91.24% (95% CI: 87.03% to 94.43%). The Positive Predictive Value was 88.16% (95% CI: 83.31% to 91.75%). The Negative Predictive Value was 97.04% (95% CI: 94.06% to 98.54%). The accuracy was 93.13% (95% CI: 90.28% to 95.35%) (Table 2).

Table 2.

Diagnostic performance of first response RDT kit compared to Microscopy.

		Microscopy
	Test	Positive	Negative
First response RDT	Positive	True Positive (164)	False Positive (22)
	Negative	False Negative (7)	True Negative (229)

3.3. Prevalence of malaria with respect to Plasmodium species

Among the 171 confirmed malaria cases, Plasmodium falciparum was the most prevalent species, accounting for 78.4% (134/171) of infections. Plasmodium vivax and mixed (P. falciparum/P. vivax) infections constituted 12.3% (21/171) and 9.4% (16/171) of cases, respectively. The prevalence rates relative to the total study population (n = 422) were 31.7% for P. falciparum, 5.0% for P. vivax, and 3.8% for mixed infections (Table 3).

Table 3.

The Prevalence of Plasmodium Species with respect to Age and Gender.

Variables	Categories	P. falciparum n (%)	P. vivax n (%)	Mixed n (%)	Total N (%)	χ2	P-value
	Total	134 (31.7%)	21(5%)	16 (3.8%)	171(40.5%)
Gender	Male Female	73(36.5%) 61(27.4%)	8 (4.0%) 13 (5.85%)	6 (3.0%) 10 (4.5%)	87(43.5%) 84(37.8%)	2.07	0.20
Age	≤5 6–15 16–25 26–35≥  36	13 (35.1%) 57(43.5%) 20 (21.9%) 15 (23%) 29 (29.5%)	3 (8.11%) 9 (6.8%) 3 (3.2%) 4 (6.1%) 2 (2%)	2 (5.4%) 2 (1.5%) 8 (8.7%) 2 (3%) 2 (2%)	18(48.6%) 68 (51.9%) 31 (34.3%) 21 (32.0%) 33 (33.6%)	16.3	0.03*

n = Number or Frequency, % = Prevalence in Percent, χ² = Chi-square value.

3.4. The prevalence of Plasmodium species with respect to age and gender

The distribution of Plasmodium species varied by gender and age. While P. falciparum infections were more common in males (54.4%, 73/134), females showed higher rates of P. vivax (61.9%, 13/21) and mixed infections (62.5%, 10/16). However, these gender-based differences were not statistically significant (p = 0.20). In contrast, a significant association was found between age group and parasite species (p = 0.03). The highest rate of P. falciparum infection (43.5%) was observed in the 6–15 years age group, while the highest rate of mixed infections (8.7%) was found in the 16–25 years age group (Table 3).

3.5. Prevalence of malaria in relation to socio-demographic characteristics

The prevalence of malaria was higher among male participants (43.5%, 87/200) than females (37.8%, 84/222). Age-specific analysis revealed the highest infection rate in children aged 6–15 years (51.9%, 68/131), followed by those under five years (48.6%, 18/37) (Table 2). By occupation, daily laborers had the highest prevalence (45.2%), followed by farmers (41.2%). Participants who were illiterate also showed a high infection rate (42.2%) compared to other educational groups (Table 4).

Table 4.

Prevalence of malaria across socio-demographic characteristics of study participants in Kidist Hana Health center, Fogera district, Northwest Ethiopia, 2021/2023.

Variables	Categories	No Cases examined	No of Positives	No of Negative	Prevalence (%)	χ2	P-value
Kebeles	Wagetera Nabega Kidist Hana Shina	189 50 152 31	79 20 60 12	110 30 92 29	41.7 40.0 39.4 38.7	0.24	0.97
Age	≤5 6–15 16–25 26–35≥  36	37 131 91 65 98	18 68 31 21 33	19 63 60 44 65	48.6 51.9 34.0 32.0 33.6	13.36	0.01*
Gender	Male Female	200 222	87 84	113 138	43.5 37.8	1.4	0.23
Marital Status	Single Married Widowed Divorced	170 207 30 15	80 72 9 10	90 135 21 5	47.0 34.7 30.0 66.6	11.47	0.009*
Educational Status	Illiterate Literate Primary Secondary and above	232 156 16 15	98 60 6 7	134 96 10 11	42.2 38.4 37.5 46.6	0.64	0.88
Occupational Status	Farmer Daily laborer Merchant Student Gov't employee	269 42 34 39 38	111 19 14 13 14	158 23 20 26 24	41.2 45.2 41.1 33.3 36.8	1.5	0.82
Number of family per household	≥ 5 <5	169 253	70 101	99 152	41.4 39.9	0.09	0.75
Family income per month	Low Medium High	246 103 73	111 37 23	135 66 50	45.1 35.9 31.5	6.01	0.04*

(Low (≤2280 ETB/month), Medium (2280–9080 ETB/month), High (>9080 ETB/month) in Ethiopian Birr).

3.6. Potential risk factors associated with malaria

This study assessed several potential risk factors for malaria infection. A high proportion of respondents lived near mosquito breeding sites (69.2%, 292/422) and reported the presence of stagnant or swampy water (69.0%, 291/422), which are primary larval habitats. Furthermore, 68.5% (289/422) of respondents reported a habit of sleeping outdoors.

Regarding protective measures, insecticide-treated net (ITN) availability and use were low. Nearly half (45.9%, 194/422) of participants stated that ITNs were not available in their locality, and 60.0% of households had no ITNs at all. Among those with access, 29.4% (124/422) did not use them regularly. Additionally, 30.6% (129/422) reported that their farms had not been sprayed with insecticides.

Other significant factors included limited access to healthcare, with 80.6% (340/422) of respondents living over an hour's journey from the health center, and travel exposure, with 70.1% (296/422) reporting a previous visit to a malaria-endemic area. Knowledge was also a concern, as 36.3% (153/422) of respondents lacked knowledge about malaria control and prevention. Most participants (67.7%) were aware that water hyacinth mats can serve as mosquito breeding sites.

Indoor residual spraying was strongly associated with reduced odds of malaria infection (OR = 0.32; 95% CI: 0.21–0.48; p < 0.001). Ownership of at least one insecticide-treated net was also protective (OR = 0.58; 95% CI: 0.39–0.88; p = 0.013). Other factors, including sleeping outdoors, distance from health facilities, and knowledge of malaria prevention, were not significantly associated with malaria infection. (Table 5).

Table 5.

Factors associated with malaria infection related to prevention practices among febrile patients at Kidist Hana Health Center, Fogera district, Northwest Ethiopia, 2022/2023 (N = 422).

Variables	Frequency (%)	Positive (171)	Negative (251)	Negative (251)	OR (95% CI)	p-value
Sleep outdoor during the night time	289	116	173	173	0.95 (0.63–1.44)	0.9
	133	55	78	78
Mosquito breeding site around your residence	292	111	181	181	0.72 (0.47–1.09)	0.14
	130	60	70	70
Stagnant water around your residence	291	121	170	170	1.15 (0.76–1.76)	0.58
	131	50	81	81
The distance of residence from the health Center	340	137	203	193	0.95 (0.58–1.56)	0.95
	82	34	48	58
Availability of (ITN) in your locality	228	97	131	132	1.41 (0.97–2.04)	0.07
	194	67	127	119
Spray your house with (IRS)	293	94	199	209	0.32 (0.21–0.48)	<0.001
	129	77	52	42
Wear long sleeved cloth during night	195	78	117	137	0.96 (0.64–1.44)	0.83
	227	93	134	114
Use ITN regularly	298	122	176	206	0.74 (0.50–1.08)	0.12
	124	59	65	45
Number of ITN you own per household	254	84	170	150	0.58 (0.39–0.88)	0.013
	157	72	85	95
	11	8	3	6
Have ever-visited malaria endemic places before	296	118	178	198	0.91 (0.60–1.39)	0.75
	126	53	73	53
Water hyacinth around your residence	252	101	151	141	0.96 (0.64–1.42)	0.9
	170	70	100	110
Knowledge on malaria control and prevention?	289	116	173	173	0.95 (0.63–1.44)	0.9
	133	55	78	78

3.7. Multivariate logistic regression analysis of selected variables

All variables with a p-value <0.25 in the bivariate analysis were included in an initial multivariate logistic regression model to control for potential confounders. The initial model included thirteen variables: age, marital status, family income, outdoor sleeping, presence of mosquito breeding sites, stagnant water, ITN availability, indoor residual spraying, regular ITN use, number of ITNs per household, travel to malaria-endemic areas, presence of water hyacinth, and knowledge of malaria prevention.

Following a backward stepwise selection process, only five independent predictors remained significantly associated with malaria infection in the final model (p < 0.05): age group, marital status, monthly household income, availability of ITNs, and number of ITNs per household.

Multivariate analysis showed that Children aged ≤5 years and 6–15 years had significantly higher odds of malaria compared to adults ≥36 years with AOR = 1.30 (95% CI: 1.05–1.64), p = 0.01 and AOR = 1.45 (95% CI: 1.09–1.93), p = 0.01. Low-income households had a significantly higher risk of malaria than high-income households AOR = 1.39 (95% CI: 1.05–1.83), p = 0.01.

In terms of marital status, Widowed individuals had 5.6 times higher odds of malaria infection compared to individuals of other marital statuses (AOR = 5.58; 95% CI: 1.39, 22.30).

ITN availability was a strong protective factor. Individuals with no ITNs had 7.2 times higher odds of infection (AOR = 7.20; 95% CI: 1.84, 28.86). Similarly, likelihood of malaria among individuals who owned only one ITN was nearly 5 folds (4.83) higher as compared to those with own two ITNs per household (AOR = 4.83; 95%CI = 1.63–19.76) (Table 6).

Table 6.

Multivariate Logistic Regression Analysis of Selected Variables Associated Factors for Malaria, Kidist Hana health center, Fogera district; Northwest Ethiopia, 2022/2023.

Variables	Categories	Malaria		Bivariate Analysis			Multivariate Analysis
		Pos	Neg	COR	95%Cl	P-value	AOR	95%	P-value
Age (year)	≤5	18	19	2.23	(1.05–4.73)	0.01⁎	1.30	(1.05 - 1.64)	0.01⁎
	6-15	68	63	2.67	(1.51–4.72)	0.01⁎	1.45	(1.09 - 1.93)	0.01⁎
	16-25	31	60	1.27	(0.67–2.41)	0.47	1.10	(0.92–1.31)	0.29
	26–35	21	44	1.17	(0.58–2.34)	0.67	1.05	(0.87–1.26)	0.62
	≥ 36 (Ref)	33	65	1.00			1.00
Marital status	Single Married Widowed Divorced	80 72 9 10	90 135 9 10	2.47 3.91 5.50 1	(0.81–7.54) (1.28–11.89) (1.43–21.09)	0.01⁎ 0.11 0.01⁎ 0.01⁎	4.19 4.46 4.68 1	(1.19–14.71) (1.42–13.99) (1.19–18.41)	0.02⁎ 0.01⁎ 0.02⁎
Monthly Household Income	Low	107	139	1.39	(1.06–1.81)	0.01⁎	1.39	(1.05 - 1.83)	0.01⁎
	Medium	36	67	0.93	(0.64–1.36)	0.72	0.93	(0.64–1.36)	0.72
	High (Ref)	21	52	1.00			1.00
Availability of (ITN) in your locality	Yes No	97 67	131 127	1 0.71	(0.48–1.05)	0.09	1 0.62	(0.41–0.96)	0.03⁎
Number of ITN per household	0 1 2	8 72 84	3 85 170	0.54 …… 1	(0.37–0.77)	0.01⁎	7.21 4.83 1	(1.84–28.86) (1.63–19.76)	0.005 0.03⁎

^⁎P value less than 0.05: Pos: Positive, 1 = Indicate Reference Group, n = number, Neg: Negative, COR: Crude Odds Ratio, AOR: Adjusted Odds Ratio, CI: Confidence Interval, Low ((<2280 ETB/month), Medium (2280–9080 ETB/month, High (>9080 ETB/month in Ethiopian Birr).

4. Discussion

Malaria remains a significant public health challenge, particularly in tropical and subtropical regions of the world (Demsash et al., 2024; Weiss et al., 2023). In Ethiopia, despite ongoing control efforts, malaria continues to be a major public health concern, causing substantial health and socioeconomic impacts, especially during the planting and harvesting seasons when transmission peaks (Federal Ministry of Health, 2021). To effectively combat malaria, monitoring prevalence and identifying associated risk factors is crucial (Woldesenbet et al., 2025).

In the present study, the overall prevalence of malaria infection at Kidist Hana Health Center was 40.5%. This finding was comparable to previous studies in Ethiopia: Woreta Health Center, Fogera District (32.6%) (Alelign et al., 2018), Dansha Town (35%) (Kidanemariam, 2021), Kola Diba (39.6%) (Alemu et al., 2012), and Adi Arkay (36.1%) (Tesfa et al., 2018). The result is also consistent with a cross-sectional study in Eastern Uganda, which reported 38.5% prevalence (Asante et al., 2011).

The observed prevalence was lower than in Tanzania (64.5%) (Yimer et al., 2015), Nigeria (55%) (Okocha et al., 2005), Lake Tana and surrounding areas (51.1%) (Adugna et al., 2022b), and Afar Region (64%) (Woday et al., 2019). Conversely, it was higher than in Dembiya District (22.4%) (Tarekegn et al., 2021), Mizan-Aman (21.1%) (Duguma et al., 2022), Arba Minch (22.1%) (Abossie et al., 2020), East Shewa Zone (25%) (Tadesse et al., 2018), Bibugn District (5.6%) (Assemie and Atenafu, 2020), University of Gondar Hospital (7.7%) (Mulugeta et al., 2022), and Mojo Town (4.2%) (Abate et al., 2022). The higher prevalence may be attributed to seasonal temperature fluctuations (Dawit et al., 2012), inadequate bed net use (Killeen et al., 2007), and variability in malaria intervention programs. Environmental and topographic factors such as low altitude and stagnant water also promote vector breeding (Woyessa et al., 2012). Behavioural factors, including reluctance to use insecticide-treated nets due to discomfort, high temperatures, or perceived low mosquito density (Pulford et al., 2011; Kudom and Mensah, 2010), can increase exposure. Temperature and humidity may further enhance mosquito survival, contributing to high prevalence (O'Meara et al., 2008).

In the study area, malaria prevalence was highest in Wagetera kebele (41.3%) compared to others. The variation may relate to differences in awareness, geography, altitude, and environmental conditions (Adugna et al., 2022b).

P. falciparum was the predominant species (78.4%), followed by P. vivax (12.3%) and mixed infections (9.4%), aligning with findings from other Ethiopian regions (Alelign et al., 2018; Tesfa et al., 2018; Yibeltal et al., 2018; Bogale et al., 2021). However, this is higher than national estimates of 60% P. falciparum and 40% P. vivax (President's Malaria Initiative, 2019), possibly due to local variations in parasite distribution (Tesfaye et al., 2011). Microscopy showed 40.5% positivity, while RDT showed 38.9%, indicating slightly lower RDT sensitivity, consistent with other studies (Oyeniran et al., 2017; Prasad et al., 2019). Microscopy remains the gold standard for malaria diagnosis.

Risk factor analysis indicated associations with age, income, knowledge, outdoor activity, stagnant water, ITN use, and ITN number similar to findings in Debre Elias District (Abebaw et al., 2019). Males had 1.2 times higher odds, possibly due to greater outdoor activity, consistent with behavioural findings (Alelign et al., 2018). ITN ownership was a key protective factor; households without nets had 7.2 times higher infection odds, consistent with Debre Elias (Abebaw et al., 2019) and South Gondar (Workineh et al., 2021).

Children under five had 1.2 times higher infection odds, aligning with WHO reports (World Health Organization, 2022) and Merawi Town findings (Aynalem and Nibret, 2017), likely due to weaker immunity (Centers for Disease Control and Prevention, 2021). Similar patterns were seen elsewhere in Ethiopia (Abebe et al., 2008). However, Alelign et al. (Alelign et al., 2018) found older individuals more affected, showing regional variation. Marital status was significantly linked, with widowed individuals showing 4.46 times higher odds, consistent with Afar region findings (Woday et al., 2019). Low household income also increased malaria risk, consistent with studies in Debre Elias (Abebaw et al., 2019) and South Gondar (Workineh et al., 2021).

Rural livelihoods in Fogera District are predominantly based on subsistence agriculture, which often requires extended outdoor activities during evening and night-time hours, increasing exposure to mosquito bites. Housing conditions are frequently substandard, with limited physical barriers against vectors, and access to preventive tools such as insecticide-treated nets and indoor residual spraying may be inconsistent or insufficient. In addition, poverty, limited health literacy, and long distances to health facilities can delay care-seeking behaviour, contributing to continued transmission and adverse outcomes. Seasonal population movements related to agricultural labour further increase the risk of malaria exposure, particularly in endemic lowland areas. These findings were supported by a scoping review study that reported other malaria prevention strategies beyond LLINs and IRS have increasingly become important in LMICs (Nalinya et al., 2022).

These findings have important implications for malaria elimination strategies. Standard interventions alone may be insufficient unless they are adapted to local socio-economic and behavioural contexts. Elimination efforts should prioritise targeted vector control in high-risk households, improve consistent access to and proper use of insecticide-treated nets, and strengthen community-based health education that addresses local practices such as outdoor sleeping. Moreover, improving access to timely diagnosis and treatment through strengthened primary health care and community health services is critical. Integrating malaria control with broader rural development initiatives, including housing improvement and livelihood-sensitive interventions, may further enhance the effectiveness and sustainability of malaria elimination efforts in similar rural Ethiopian settings.

4.1. Limitations of the study

This study has some limitations that should be considered when interpreting the findings. First, the cross-sectional study design limits the ability to establish causal relationships between malaria infection and the identified risk factors; associations observed may not reflect temporal or causal pathways. Second, the study was conducted at a single health center and included only febrile patients seeking care, which may limit the generalisability of the findings to the wider community, including asymptomatic individuals who may contribute to ongoing transmission.

Third, behavioural and socio-economic variables, such as insecticide-treated net use, outdoor sleeping habits, income, and knowledge of malaria prevention, were self-reported and may therefore be subject to recall bias and social desirability bias. Despite these limitations, the study provides valuable local evidence on malaria prevalence and associated risk factors in a rural Ethiopian setting and offers important insights to inform targeted malaria control and elimination strategies.

5. Conclusion

The overall malaria prevalence in the study area was 40.5% (171/422), which showed that malaria remains a major health problem in the area with Plasmodium falciparum as the predominant species in the study area. Most of the malaria cases in the study area were due to P. falciparum followed by P. vivax and their mixed infections. The high transmission observed reflects a complex interplay of behavioural and environmental factors, including outdoor sleeping and the presence of stagnant water near residences. Critically, the study underscores the profound protective effect of insecticide-treated nets (ITNs), with their absence emerging as a major risk factor. These findings underscore the need for targeted interventions, including universal ITN coverage, environmental management, and focused education for high-risk groups, to effectively reduce the malaria burden in the area.

Availability of data and material

All the generated data in this article are included in the manuscript. The original data can be obtained from the corresponding author upon request Alemayehu Abate alexu2love@gmail.com

CRediT authorship contribution statement

Solomon Alebele: Writing – original draft, Investigation, Formal analysis, Data curation, Conceptualization. Alemayehu Abate: Writing – review & editing, Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Miseganaw Sisay: Methodology, Investigation, Formal analysis. Tesfalem Asnake: Writing – original draft, Investigation, Formal analysis. Mastewal Alehegn: Methodology, Formal analysis, Conceptualization. Desalegn Adisu: Methodology, Investigation, Formal analysis. Abaineh Munshea: Writing – review & editing, Visualization, Supervision, Methodology, Investigation, Conceptualization.

Ethical approval and consent to participate

Ethical approval was obtained from the Ethical Review Board of Bahir Dar University's Science College. Permission letters were obtained from the health office at the zonal and district levels. Written informed consent was obtained from the patients and parents/guardians of the febrile patients after the study's purpose and procedures were thoroughly explained.

Funding

No funding was given to support this study.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.