Prevalence of malaria and associated factors among febrile patients visiting Gissa Health Center, Awi Zone, Northwest Ethiopia

Abstract

Background

The public health and socio-economic burden of malaria has increased in Ethiopia in recent years. The prevalence of malaria varies considerably across districts and kebeles (smallest administrative units) in the country. Up-to-date information at the lower administration level is essential for policymakers and health authorities. Therefore, this study aimed to determine the prevalence of malaria and its associated risk factors among febrile patients attending Gissa Health Center in Awi Zone, North West Ethiopia.

Methods

The study was conducted from February to April 2024 among 424 febrile patients attending Gissa Health Center. Socio-demographic characteristics and potential malaria associated factors were collected using semi-structured questionnaire. A purposive sampling technique was employed to select study participants based on clinical examination. Blood samples were obtained from selected participants through finger-prick technique. Thin and thick blood samples were prepared and examined microscopically for the detection of malaria parasite. Blood collection and processing were conducted by trained laboratory technicians. All data were cleaned and analyzed using SPSS version 26.0. Logistic regression analysis was employed to assess the association between dependent and independent variables.

Results

Of the total of 424 selected participants, 411 individuals took part in the study, yielding a response rate of 97%. Overall, 140 (34.1%) tested positive for malaria parasites. The prevalence of Plasmodium falciparum, Plasmodium vivax and mixed infections were 20.9%, 9.7% and 2.4%, respectively. Multivariable logistic regression analysis revealed that age below 5 years (AOR = 2.77; 95% CI 1.09–7.00), age between 6 and 15 years (AOR = 3.85; 95% CI 1.49–9.92), being illiterate (AOR = 2.83; 95% CI 1.13–7.08), lack of regular insecticide-treated net (ITN) use (AOR = 2.08; 95% CI 1.04–4.17), and absence of indoor residual spraying (IRS) (AOR = 1.54; 95% CI 1.29–3.24) were independent predictors of malaria infection in the study areas.

Conclusion

This study revealed that the prevalence of malaria in the study area remains moderate Therefore, strengthening integrated malaria prevention and control strategies including consistent ITN use and regular IRS, is essential to reduce the burden of malaria in the study area.

Introduction

Background

In recent years, Ethiopia experienced a substantial increase in malaria cases and deaths. The annual malaria cases rose from about 2.6 million cases in 2019 to 12.4 million malaria cases in 2024, representing more than a fourfold increase [1]. Furthermore, malaria cases increased by nearly 27% between 2023 and 2024; rising from 9.56 million to 12.4 million cases. This unprecedented increase may be associated with several factors, including ongoing conflict in the Oromia and Amhara region and the spread of invasive mosquito species Anopheles stephensi in the country. The primary malaria vector in Ethiopia is Anopheles arabiensis while A. pharonsis, A. funestus and A. nili are considered as secondary vectors [2]. Anopheles stephensi, an urban malaria vector, has recently spread rapidly in many parts of the country [3], which may contribute to the increased number of malaria cases. In addition, approximately 75% of the country landmass is malaria-endemic, and about 69% of the populations lives in areas at risk of malaria transmission [4].

The Amhara Region is the third most malarious region in the country, following to the former Southern Nations, and Nationalities, and peoples’ region (SNNRS) and Oromia [5]. Approximately 80% of the region's landmass is considered malarious, and nearly 68% of the population is at risk of malaria infection [6]. Recent report indicate that about 1.88 million cases have been recorded in the region [7]. In recent years, malaria hotspot areas have expanded in several zones of the Amhara region, including West Gojjam, North Gojjam, Awi, South and North Gondar Zones. The Awi zone includes several districts including Dangila district, which contains several kebeles. A study conducted in Dangila health center in Dangila town reported that 16.6% of suspected individuals tested positive for malaria [8]. However, there is limited information regarding the prevalence of malaria in rural kebeles located far from Dangila town. Therefore, this study aimed to assess the prevalence of malaria and associated risk factors among febrile patients visiting Gissa Health Center, Awi Zone, Northwest Ethiopia.

Material and methods

Description of the study area

The study was conducted at Gissa Health Center, which is located in the rural area of Dangila District, Awi Zone, Amhara Regional state, Ethiopia. The health center provides health services to four rural kebeles; Gissa, Sehara, Dibaka, Windi, and Zurzur. Gissa is a rural kebele located about 22 km northwest of Dangila town, 99 km from Bahir Dar city, and 504 km from Addis Ababa. Gissa health center is situated at 11º18' N and 36º67'E, at an altitude of 2,137 m above sea level. The four kebeles included in this study were selected from the twenty seven rural kebeles of Dangila district.

The majority of the populations living in the selected kebeles are farmers who cultivate crops using rain-fed agriculture during summer and irrigation during the winter period. The area is characterized by the presence of two long rivers, Awisi and Gizani, as well as several small streams, which contribute to the humid and swampy environment. These environmental conditions favor mosquitoes breeding, making the area a potential hotspot area form malaria transmission. The average annual rainfall in the area is between 800 and 1200 mm, while the annual average temperature falls within 15 °C and 20ºC. According to the 2007 national census, the total population of Dangila district was 158,688 [9]. However, the population is now estimated to be between 204,000 and 219,000.

Study design and period

A health institution based cross-sectional study was conducted from February to April 2024 to determine the prevalence of malaria and associated risk factors among febrile patients visiting Gissa Health Center.

Study population

The study population consisted of patients suspected of having malaria visited Gissa Health Center during the study period and fulfilled the inclusion criteria. The suspected patients commonly presented clinical symptoms such as fever, chills, and headache.

Eligibility criteria

Inclusion

Patients suspected of malaria who visited Gissa Health Center during the study period and provided written informed consent were included in the study. In case of children less than 18 years of age, assent was obtained from the children, and written informed consent was obtained from their parents or guardians.

Exclusion criteria

Participants who had taken antimalarial drug within one month prior to data collection and those who were seriously sick were excluded from the study.

Study variables

Independent variables include age, sex, residence, educational status, marital status, family monthly income, occupation, family size, kebele, ITN availability, presence of mosquito breeding site, regular use of ITN, number of ITN per house, presence of water hyacinth near the residence, use of indoor residual spray (IRS), history of visiting malarial epidemic areas, outdoor activities at night, wearing long-sleeved clothing at night, and basic awareness about malaria prevention and control. The dependent variable was infection status with Plasmodium species (malaria positivity).

Sample size

The sample size was calculated using a single population proportion formula with the following assumptions; 95% confidence interval, prevalence is considered as 16.6% in Dangila district [8], and 5% marginal error. Based on those considerations, the sample size was calculated using the following formula as described in by Naing et al. 2006 [10], $n=\frac{z^{2}p(1-p)}{d^2}$. Based on this calculation, the estimated sample size was 212 study participants. To improve the precision of the estimate, a design effect of two was applied, resulting in a final sample size of 424 participants.

Sampling techniques

Study participants were selected using a non-probabilistic sampling technique. All febrile patients suspected for malaria was sequential recruited based on their willingness to provide blood samples until the required sample size was achieved. During recruitment, socio-demographic information and potential risk factors were collected from the study participants.

Data collection methods

Questionnaire survey

Information related to socio-demographic and potential risk factors was collected using pre-tested semi-structured questionnaires. The questionnaire was initially prepared in English, and then translated into the local languages Amharic and Agewgna. It was subsequently translated back into English to ensure consistency and accuracy.

Blood sample collection and processing

Blood microscopy remains the gold standard methods for laboratory confirmation of malaria parasite [11, 12]. The fingertips of each study participant were cleaned with cotton soaked in 70% alcohol. Blood samples were collected from fingertips using finger-prick methods by trained laboratory technicians. Duplicate thin and thick blood films were prepared on frosted microscopic slide. The thin blood film were fixed with 100% methanol and allowed to air dry. Both thin and thick blood film were stained with 10% Gimsa stain for 30 min. The stained smear rinses with buffered water and placed in upright position to air dry. A thick blood smear was first examined under 10 × magnifications, followed by 100 × oil immersion objective to detect the presence of malaria parasites. If the thick smear is positive for malaria parasite, the thin blood smear was carefully examined under the same magnification power to identify the species of Plasmodium. All positive samples were re-examined by a second microscopist who blinded to the prior results.

Quality assurance

Data collection was conducted by trained laboratory technician and nurses. The questionnaire was prepared in English and then translated into Amharic and Agewgna for clarity. Prior to the study, the questionnaire was pretested in a nearby health facility on 10% of the sample size. All laboratory reagents were checked for its expiration dates, and standard operating procedures were strictly followed during specimen collection and processing. As part of the quality control process, all malaria positive samples were rechecked by senior laboratory technologist for confirmation.

Data analysis

Data was entered, cleaned and analyzed using Statistical Package for Social Science (SPSS) software version 26.0. Descriptive statistics were used to determine the proportion and means. Logistic regression analysis was employed to assess the association between malaria prevalence and potential factors. To control confounding effects, variables with p-value less than 0.25 in the bivariable analysis were included the multivariable logistic regression model [13]. The strength of association between the dependent and independent variables were determined using odds ratio with 95% confidence interval (CI). Multicollinearity among variables was performed using variance inflation factor (VIF) while goodness-of-fit of the logistic regression model was assessed using the Hosmer–Lemeshow test. P-value less than 0.05 was considered as statistically significant.

Results

Socio-demographic characteristics of study participants

Out of 424 participants recruited for this study, 411individual participated in the study, yielding a response rate of 96.9%, by providing complete information and blood samples. Out of those study participants, the majority (33.4%) were residents of Gissa kebele. The age of the study participants were ranged from less than 1 year to 70 years, with a mean age of 27 ± 4.52 years. The majority of the study participants were female (53.5%), farmers by occupations (35.8%), illiterate (36.5%), single in marital status (39.9%). In addition, 32.4% of the study participants were in the age group of 5–15 years, and 46% of them had low monthly income (Table 1).

Table 1.

Prevalence of malaria across socio-demographic characteristics of the study participants of Gissa Health Center; Awi Zone, Northwest Ethiopia, 2023/2024 (n = 411)

Variables	Categories	Malaria status
		Positives No (%)	Negative No (%)	Total No (%)
Sex	Male	62 (15.1)	129 (31.4)	191 (46.5)
	Female	78 (19)	142 (34.5)	220 (53.5)
Kebeles	Gissa	57 (13.9)	84 (20.4)	141 (34.3)
	Sehara	44 (10.7)	71 (17.3)	115 (28.0)
	Dibaka	13 (3.2)	52 (12.7)	65 (15.8)
	Windi	16 (3.9)	32 (7.8)	48 (11.7)
	Zurzur	10 (2.4)	32 (7.8)	42 (10.2)
Age	<  = 5	32 (7.8)	46 (11.2)	78 (19.0)
	6–15	43 (10.5)	90 (21.9)	133 (32.4)
	16–25	34 (8.3)	60 (14.6)	94 (22.9)
	26–35	22 (5.4)	36 (8.8)	58 (14.1)
	>  = 36	9 (2.2)	39 (9.5)	48 (11.7)
Marital Status	Single	58 (14.1)	106 (25.8)	164 (39.9)
	Married	39 (9.5)	88 (21.4)	127 (30.9)
	Widowed	21 (5.1)	43 (10.5)	64 (15.6)
	Divorced	22 (5.4)	34 (8.3)	56 (13.6)
Educational Status	Illiterate	44 (10.7)	106 (25.8)	150 (36.5)
	Only bread & Write	43 (10.5)	61 (14.8)	108 (26.3)
	Primary school	28 (6.8)	61 (14.8)	89 (21.7)
	Secondary school & above	25 (6.1)	39 (9.5)	64 (15.6)
Occupational Status	Farmer	35 (8.5)	112 (27.3)	147 (35.8)
	Daily laborer	47 (11.4)	48 (11.7)	95 (23.1)
	Merchant	25 (6.1)	40 (9.7)	65 (15.8)
	Student	20 (4.9)	48 (11.7)	68 (16.5)
	Gov’t employee	13 (3.2)	23 (5.6)	36 (8.8)
Family size	≤ 5	73 (17.8)	133 (32.4)	206 (50.1)
	> 5	67 (16.3)	138 (33.6)	205 (49.9)
Family Monthly income	< 2000 Birr	63 (15.3)	126 (30.7)	189 (46.0)
	2000–5000 Birr	43 (10.5)	94 (22.9)	137 (33.3)
	> 5000 Birr	34 (8.3)	51 (12.4)	85 (20.7)
Total		140 (34.1)	271 (65.9)	411 (100)

Prevalence of malaria parasite among the study participants

Among the 411 febrile study participants involved in the study, 140 (34.1%) of them were microscopically confirmed for malaria infection. The prevalence of malaria was slightly higher among residents of Gissa kebele compared with other kebeles. Similarly, a higher malaria infection was observed among study participants aged between 5 and 15 years, those with single marital status, and daily laborers, compared with their respective counterparts (Table 1).

Among the confirmed malaria cases, P. falciparum, P. vivax and mixed infections accounted for 86(20.9%), 40(9.7%) and 14(2.4%) of the malaria infection in the study area, respectively (Table 2). Plasmodium falciparum was the dominant causes of malaria, accounting for 61.8% of the total positive cases, while P. vivax accounts for 28.6% (Fig. 1).

Table 2.

Prevalence of Plasmodium species among the study subjects at Gissa Health Center, Awi Zone, Northwest Ethiopia, 2023/2024 (n = 411)

Plasmodium Species	Cases	Prevalence (%)
P. falciparum	86	20.9%
P. vivax	40	9.7%
Mixed	14	2.4%
Total	140	34.1%

Fig. 1.

Plasmodium species and their contribution from the total confirmed malaria cases (n = 140)

Potential risk factors associated with prevalence of malaria in the study area

Binary logistic regression analysis was employed to identify independent explanatory factors associated with malaria infection among the study participants. Among the socio-demographic variables, age of the study participants showed a strong association with malaria prevalence, while kebele of residence showed a marginal association with Plasmodium infection.

The bivariable logistic regression analysis showed that age, kebeles of residence, educational status, occupation, and monthly income were eligible for multivariable logistic regression analysis (p < 0.25). Among these eligible potential associated factors, age of the study participants was identified as important explanatory factors for malaria infection. Participants age ≤ 5 years (AOR = 2.77; 95% CI 1.09–7.00, p = 0.031), 6–15 years (AOR = 3.84; 95% CI 1.49–9.93, p = 0.005), and age ranges from 25 to 34 years (AOR = 3.91; 95% CI 1.32–11.61, p = 0.014) were at a significantly higher risk of malaria infection than their counterparts. In addition, illiterate study participants were nearly threefold more likely to be infected with malaria (AOR = 2.83; 95% CI 1.13–7.09, p = 0.026) compared with participants with higher educational levels (Table 3).

Table 3.

Logistic regression analysis of socio-demographic factors associated with malaria among febrile patients at Gissa Health Center, Awi Zone, Northwest Ethiopia 2023/2024 (n = 411)

Characteristics	Categories	Malaria infection status		COR	95%CI	P-value	AOR	95%CI	P-value
		Positive	Negative
Sex	Female	78	142	0.875	0.581–1.318	0.523	-
	Male	62	129	1
Age (year)	<  = 5	32	46	0.332	0.141–0.779	0.011	2.77	1.097–7.00	0.031*
	6–14	43	90	0.483	0.215–1.087	0.079	3.858	1.499–9.929	0.005*
	15–24	34	60	0.407	0.178–0.941	0.036	1.874	0.750–4.681	0.179
	25–34	22	36	0.378	0.154–0.927	0.034	3.913	1.320–11.606	0.014”
	>  = 35	9	39	1			1
Kebeles	Gissa	57	84	0.461	0.210–1.010	0.053	0.463	0.180–1.188	0.109
	Sehara	44	71	0.504	0.226–1.126	0.095	0.469	0.184–1.193	0.112
	Dibaka	13	52	1.25	0.491–3.183	0.640	1.586	0.538–4.675	0.403
	Windi	16	32	0.625	0.247–1.584	0.322	0.654	0.225–1.903	0.436
	Zurzur	10	32	1
Marital Status	Single	58	106	1.183	0.633–2.208	0.599
	Married	39	88	1.460	0.758–2.812	0.258
	Widowed	21	43	1.325	0.627–2.800	0.461
	Divorced	22	34	1
Educational status	Illiterate	44	106	1.544	0.837–2.851	0.165	2.833	1.132–7.089	0.026*
	Literate	43	65	0.969	0.515–1.825	0.922	0.792	0.352–1.780	0.572
	Primary	28	61	1.397	0.713–2.736	0.330	0.854	0.337–2.168	0.740
	Secondary school & above	25	39	1			1
	Farmer	35	112	1.809	0.830–3.941	0.136	1.931	0.836–4.458	0.123
	Daily laborer	25	40	0.904	0.389–2.103	0.815	0.948	0.365–2.460	0.912
	Merchant	47	48	0.577	0.262–1.272	0.173	0.705	0.297–1.673	0.428
	Student	20	48	1.357	0.576–3.196	0.486	0.854	0.337–2.168	0.740
	Gov’t employee	13	23	1			1
Family size	< 5	73	133	1
	> 5	67	138	1.131	0.752–1.701	0.556
Family income per month	< 2000 birr	63	126	1.333	0.786–2.263	0.286	0.865	0.458–1.633	0.655
	2000–5000 birr	43	94	1.457	0.829–2.563	0.191	0.995	0.503–1.965	0.988
	> 5000 birr	34	51	1			1

*: p-value less than 0.05

The association between other potential factors and malaria infection among febrile patients was also assessed using logistic regression model. Of the nine potential risk factors examined in the bivariable logistic regression model, use of ITN, number of ITN per household, and IRS were identified as significant explanatory factors for malaria infection in the study. Absences of ITN in their house were nearly twice more likely to be infected with malaria (AOR = 1.92; 95% CI 0.903–4.061, p = 0.004) compared with households who had one or more ITN. Similarly, participants who did not regularly use ITN were about two times more likely to be infected with malaria (AOR = 2.08; 95% CI 1.038–4.179, p = 0.039) compared with those who regularly use ITNs. Furthermore, households that did not practice indoor residual spraying (IRS) were approximately 1.5 times more likely to have malaria infection (AOR = 1.54; 95% CI 1.29–3.242, p = 0.05) compared with households that regularly applied IRS in their home (Table 4).

Table 4.

Logistic regression analysis of potential risk factors associated with malaria among febrile patients at Gissa Health Center, Northwest Ethiopia 2023/2024 (n = 411)

Characteristics	Categories	Malaria infection status		COR	95% CI	P-value	AOR	95% CI	P-value
		Positive	Negative
Sleeping outdoor at night	Yes	107	154	1.463	1.558–3.896	0.000	0.634	0.341–1.177	0.149
	No	33	117	1			1
Availability of mosquito breeding site in their home	Yes	117	204	1.671	0.988–2.825	0.056	0.901	0.472–1.720	0.753
	No	23	67	1			1
Availability of ITN	Yes	47	125	1.694	1.108–2.590	0.015	0.673	0.370–1.223	0.193
	No	93	146	1			1
Regular use of ITN	No	105	134	3.067	1.955–4.813	0.000	2.083	1.038–4.179	0.039*
	Yes	35	137	1			1
Number of ITN per household	No	18	87	3.394	1.868–6.166	0.000	1.915	0.903–4.061	0.004*
	1	56	90	1.128	0.713–1.785	0.606	0.525	0.247–1.115	0.094
	2	66	94	1
Spray house with IRS	No	105	142	2.725	1.736–4.277	0.000	1.54	1.29–3.242	0.05*
	Yes	35	129	1			1
Wear long sleeved cloth at night	No	116	200	1.716	1.024–2.875	0.040	0.921	0.486–1.747	0.801
	Yes	24	71	1			1
Availability of water hyacinth around residence	Yes	95	154	1.604	1.045–2.462	0.031	0.836	0.513–1.362	0.472
	No	45	117	1			1
Knowledge on malaria control and prevention	No	96	164	0.702	0.456–1.082	0.109	0.843	0.507–1.400	0.509
	Yes	44	107	1			1

*: p-value less than 0.05

Discussion

The prevalence of malaria has increased in sub-Saharan Africa, including Ethiopia, following the COVID-19 pandemic. According to WHO recent estimate, the annual number of malaria cases in Ethiopia increased from 2.6 million in 2019 to 12.4 million in 2024 [1]. Assessing the prevalence of malaria and its potential risk factors is essential at all levels of national administration, starting from the lower administration unit (kebeles). In line with this objective, this study was conducted in four rural kebeles around Gissa Health Center in Dangila district, Awi Zone, to determine the prevalence of malaria and its associated factors. By reporting findings from these rural kebeles, this study contributes to the local, regional, and national malaria surveillance data.

The finding of this study showed that 34.1% of the eligible study participants were positive for malaria parasites. This finding is comparable with study reported from various part of Ethiopia, including 33.8% in Abeshge [14], 31.1% in Ziquala District [15], 32.6% in Woreta Health Center [16], and 38.2% in Yeki District [17]. However, the prevalence of malaria observed in this study exceeds malaria prevalence reported in other areas [18–24]. In contrast, higher malaria prevalence rate have been reported from elsewhere in Ethiopia [25, 26], and Nigeria [27].

The variations in malaria prevalence observed across studies might be attributed to several factors, including environmental, seasonal variations, diagnostic approaches, characteristics of the study population, study design and sampling techniques, and other vector-related factors. The recent increase in malaria cases may also be associated with the expansion of An. stephensi in urban areas of Ethiopia. Anopheles stephensi has recently emerged as invasive malaria vector in the country and is now widely distributed in the several districts, which pose challenges for malaria control efforts. Furthermore, the majority of malaria cases in Ethiopia are caused by P. falciparum, which represent significant challenges for diagnosis and the emergence, and rapid spread of drug-resistant strains [28]. These factors may contribute to the increasing malaria burden in the country. In addition, the ongoing conflict in the Amhara region has further weakened malaria control and prevention strategies by disrupting health services, vector control program and access to preventive measures. These factors may contribute to the increasing malaria burden in the country.

The current study showed that P. falciparum is responsible for 61.4% while P. vivax accounts for 28.6% of the total malaria cases. This finding is consistent with several studies conducted in Ethiopia [15, 16, 25, 29, 30]. Plasmodium falciparum is the dominant causes of malaria at the national level followed by P. vivax; P. falciparum accounts for 60–70% while P. vivax responsible for 30–40% of the total malaria cases [31, 32]. In some areas of the country, even higher prevalence of P. falciparum has been reported in Burji [22, 33]. The high prevalence of P. falciparum may be attributed to the parasite ability to develop resistance to commonly used antimalarial drugs.

In contrast to our finding, Plasmodium vivax has been reported to be more prevalent than P. falciparum in certain parts of Ethiopia, including East shoe Zone [34], Butajra [35], and areas around Akaki [36]. This variation may be explained by climatic and ecological factors; P. vivax is generally more common in higher altitude and cooler areas, whereas P. falciparum tends to predominant in warm climate and lowland areas [37]. This difference may also be related to vector preference and the developmental requirements of Plasmodium species within the mosquito vectors. Specifically, P. vivax requires lower temperature for sporogonic development in the mosquito gut compared with P. falciparum [38].

Socio-demographic and other potential risk factors associated with malaria were assessed to identify most important explanatory factors in the study. The result indicated that age of the study participants was a significant predictor of malaria infection. Children aged five years or younger were particularly vulnerable to malaria infection, which is consistent with findings from previous studies [30, 39]. This age related susceptibility may be explained by the low level of herd immunity among under-five children. In addition, individuals in the age groups of 6–14 years and 25–34 years were found to have higher likelihood of malaria infection, which is consistent with reports from other studies [16, 20, 33]. This might be related to increased exposure to mosquitoes bite during nighttime activities, such as playing outdoors at night, involvement in farming activities, and frequent visits to swampy areas for irrigation activities. It is well established that mosquitoes biting activity is more frequent at night than during the day time.

Educational status was also identified as an important risk factors associated with malaria infection. Study participants without formal education were about threefold higher chance of being infected with malaria than those participant who had some level of education, which is consistent with studies conducted elsewhere [25, 27, 33]. Education play pivotal role in promoting health-seeking behavior, timely diagnosis and treatment, and effective utilization of malaria prevention and control strategies. Lack of proper education may therefore increase vulnerability to malaria infection in endemic areas.

This study also revealed that participants who did not regularly use ITN were twofold higher chance of being infected with malaria compared with their counterparts. This finding is consistent with studies conducted in several parts of Ethiopia and Nigeria [19, 20, 27, 29, 30, 40]. It is widely recognized that the availability and regular use of ITN is one of the most effective personal protective measures against mosquitoes bite in malaria endemic areas. In the study area, many households had large family size, which may limit the availability of sufficient ITNs for all members and thereby increase the risk of malaria infection.

Lastly, the study indicated that indoor residual spraying (IRS) was significantly associated with malaria infection. Participant whose houses were not regularly sprayed with residual insecticides were more likely to be infected with malaria compared with those who practiced regular IRS, which is consistent with other studies conducted elsewhere [19, 22, 24, 40]. Spraying of houses with insecticide is an important malaria prevention and control strategy because it target endophilic mosquitoes resting on door surface such as roofs, walls, and household furniture, thereby reducing the risk of malaria transmission [41].

The results of this study showed that malaria is a serious public health problem in the study area. This study identified important predictors of malaria, including age, educational status, the number of ITN and their regular use, as well as IRS in the study area. Therefore, the local health extension officers and the kebele health offices should focus on improving awareness of the use of ITN and IRS, as well as integrating different malaria control strategies in the study area.

Limitation of the study

This study provides important information on malaria infection in rural kebeles of Dangila district, particularly in areas served by Gissa Health Centers. However, several limitations should be considered when interpreting the findings. The cross-sectional study design did not allow the assessment of seasonal variation in malaria transmission. Second, the study was conducted in only one health center, which may limit the generalizability of the finding to the entire district. Furthermore, the study conducted only on febrile patients seeking care at the health facility, and therefore the results may not fully represent the malaria burden in the general population, including asymptomatic infections. Finally, the study was conducted from February to April, a period characterized by a short spring rains that favor mosquitoes breeding. This period may differ from the peak malaria transmission season in Ethiopia, which occurs immediately after the main rain season from September to December. Consequently, the observed prevalence may not fully reflect the peak malaria transmission period in the study area. Finally, the use of non-probabilistic sampling approach may another limitation to consider.

Abbreviations

AOR

Adjusted odds ratio

COR

Crude odds ratio

PRCSVD

Postgraduate research and community service vice dean

SPSS

Statistical package for social sciences

WHO

World Health Organization

Author contributions

BA was involved in the conception of the idea, data collection, processing, interpretation, drafting the manuscript. TH was involved in the design, processing, interpretation of the findings, and reviewing and editing the manuscript.

Funding

No fund was received for this research.

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participant

Ethical clearance was obtained from the Ethical Review Committee of Science College, Bahir Dar University (Ref. No. PRCSVD/139/2016 E.C.). After explaining the purpose of the study, an official permission letter was secured from the Medical Director of Gissa Health Center to conduct the study. The purpose of the study was clearly explained to all febrile patients, and their voluntary participation was requested. Written informed consent was collected from each study participants, or parents/guardians for participants under the age of 18 years of age. Participants were informed that they had the right to withdraw from the study at any without losing their benefit from the health center. In addition, all malaria positive subjects were advised to receive appropriate treatment at the health center using their family health insurance. Every response of the study participant was handled confidentially and anonymously.

Consent for publication

All authors have approved the submission of the final copy for publication.

Competing interests

The authors declare no competing interests.