Prevalence and associated factors of microscopy-confirmed malaria among the general population in Ethiopia: a systematic review and meta-analysis

Abstract

Background

Malaria remains a major public health problem in Ethiopia despite ongoing elimination efforts targeting interruption of local transmission by 2030. Reliable and up-to-date estimates of malaria burden in the general population are essential for guiding control strategies. However, there has been no comprehensive synthesis of microscopy-confirmed malaria prevalence and its determinants among the general population since 2020. This review aimed to estimate the pooled prevalence of microscopy-confirmed malaria and identify its determinants in Ethiopia.

Methods

We conducted a systematic review and meta-analysis following PRISMA 2020 guidelines, with the protocol registered in PROSPERO (CRD420251026861). Databases (PubMed, Scopus, ScienceDirect, HINARI, and AJOL) and gray literature sources were retrieved. Heterogeneity among studies was assessed using Higgins’s I² statistic and visualized with forest plots. When substantial heterogeneity was detected, subgroup and sensitivity analyses were performed. A random-effects model with Knapp–Hartung adjustment was used to estimate pooled effect sizes. Publication bias was evaluated using funnel plots, Egger’s test, and trim-and-fill methods.

Results

Fourteen studies involving 8,762 participants were included. The pooled prevalence of microscopy-confirmed malaria was 7.1% (95% CI: 4.9–10.2%; I² = 95.7%). Substantial regional variation was observed, with the highest prevalence in Amhara (10.7%) and the southwest (7.3%). Significant determinants included presence of stagnant water (OR = 6.71; 95% CI: 4.93–9.14), absence of indoor residual spraying (OR: 5.90, 95% CI: 3.40, 10.4), presence of mosquito breeding sites (OR: 6.13; 95% CI: 3.06–12.3), travel history to endemic areas (OR: 5.86; CI: 2.47–13.8), living in houses with eaves (OR: 4.5, 95% CI: 2.17–9.50), history of malaria infection (OR: 3.54; 95% CI: 1.79–6.98), and ITN non-use (OR: 4.8, 95% CI: 3.41–6.78). Trim-and-fill analysis for potential publication bias revealed an adjusted pooled prevalence of 8.2% (95% CI: 5.6%–11.7%).

Conclusion

The pooled prevalence of malaria remains notable among the general population in Ethiopia. Strengthening community awareness on the regular use of insecticide-treated bed nets and better environmental management, such as getting rid of standing water, could help reduce the spread of malaria.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12936-026-05938-5.

Introduction

Malaria has continued to pose a significant morbidity and mortality globally; the African region bears a high proportion of the disease burden [1]. In 2024, there were an estimated 282 million cases and 610,000 malaria deaths globally. The burden of malaria in sub-Saharan Africa is disproportionately high, and the distribution of malaria transmission is concentrated in only a few countries with a high malaria burden. Approximately half of the global malaria burden is concentrated in five countries: Nigeria (24.3%), the Democratic Republic of the Congo (12.5%), Uganda (4.7%), Ethiopia (4.4%), and Mozambique (3.6%) [2, 3].

Ethiopia remains one of the countries with a high malaria burden and varying levels of malaria transmission intensity. According to national surveillance reports, malaria remains a problem in major areas of the country with varying levels of malaria burden and risk. In fact, there is evidence of increasing malaria cases in different regions in Ethiopia from 2019 to 2023, within the context of climate change, displacement of populations, and weakening health facility functionality and vector control activities [2, 4]. In Ethiopia, the Federal Ministry of Health has adopted the "National Malaria Elimination Program" to aim for interrupting local transmission by 2030. This initiative has involved integrated vector control activities, early detection and treatment of cases, and improved surveillance efforts [5]. However, there have been disparities in the program’s progression, and ensuring rational resource allocation has come to involve community-specific epidemiological information [1, 6].

The estimation of malaria prevalence depends strongly on the diagnostic method used. A light microscopy of stained blood smears is still the gold standard in most endemic settings, but sensitivity decreases substantially when parasite density is low[7, 8]. Several studies show that conventional microscopy usually detects infections only when parasite density surpasses 50–100 parasites/µL[9, 10], while RDTs usually detect parasitemia at about 100–200 parasites/µL, depending on antigen target and test brand [11, 12]. In a community study from Ghana, RDT sensitivity relative to qPCR was about 55.7%, compared to 39.3% for microscopy, with both methods missing over 40% of PCR-detected infections [13]. In contrast, molecular methods like PCR detect parasite densities as low as 0.1–1 parasite/µL, thus uncovering a large reservoir of low-density and asymptomatic infections missed by both microscopy and RDTs [14]. Consequently, prevalence estimates based on routine diagnostic tools are likely to systematically underestimate the true infection burden, particularly in low-transmission and pre-elimination settings such as parts of Ethiopia [15].

Recent primary studies from Ethiopia demonstrate substantial geographic variation in malaria prevalence, with estimates ranging from approximately 3% to over 20%, reflecting differences in ecological conditions, intervention coverage, population mobility, and diagnostic approaches.[16–19]. Although several systematic reviews have examined malaria prevalence in specific subpopulations such as children, pregnant women, and other vulnerable groups [20, 21], there remains a lack of an up-to-date, comprehensive synthesis focused on microscopy-confirmed malaria in the general Ethiopian population. This gap limits a clear national-level understanding of the current burden and hinders evidence-based prioritization of malaria control and elimination strategies. Therefore, this systematic review and meta-analysis aimed to estimate the pooled prevalence of microscopy-confirmed malaria and identify associated risk factors among the general population in Ethiopia and provide rigorous, policy-relevant evidence to support the country's 2030 malaria elimination agenda.

Methods

This review follows the standards from the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) [22]. The study has been registered in the International Prospective Register of Systematic Reviews (PROSPERO) database with protocol number CRD420251026861.

Search strategy

An extensive search was conducted on PubMed, Scopus, ScienceDirect, HINARI, the Cochrane Library, African Journals Online, and Google Scholar. In addition, the reference lists of eligible articles and previous systematic reviews were screened for other relevant studies. The search was restricted to studies published between January 2020 and January 2025 and to English-language articles. The search strategy was constructed using both MeSH terms and free-text keywords to ensure comprehensive coverage of relevant literature. Free-text keywords included variations and synonyms of the main concepts, such as “malaria,” “Plasmodium infection,” “prevalence,” “epidemiology,” “microscopy,” and “Ethiopia”. Boolean operators were applied to structure the search. The “OR” operator was used to combine synonymous terms within each concept, while the “AND” operator was used to combine different concepts to ensure that retrieved studies addressed malaria, outcome measures, diagnostic methods, and the Ethiopian setting (S1 File).

Eligibility criteria

We applied the PICOS (Population, Intervention/Exposure, Comparison, Outcome, and Study design) framework to identify eligible studies. (i) Population (P): studies conducted among the general population in Ethiopia without restriction on age, sex, or clinical status. (ii) Intervention/Exposure (I): not applicable, as observational studies were included; however, studies reporting exposure variables (e.g., insecticide-treated net use, indoor residual spraying, and other relevant factors) were considered where available. (iii) Comparison (C): comparisons within studies included individuals who tested negative for malaria infection or relevant exposure categories such as ITN users versus non-users, IRS versus no IRS, and other subgroup comparisons reported in the primary studies. (iv) Outcome (O): malaria diagnosis confirmed by light microscopy of stained blood smears. (v) Study design (S): observational studies, including cross-sectional and cohort studies, as well as baseline data from interventional studies that reported prevalence estimates. Studies were excluded if they: (i) focused exclusively on specific subgroups such as pregnant women or children under five, (ii) did not use microscopy for malaria confirmation (e.g., rapid diagnostic tests only), (iii) reported only species-specific prevalence without overall malaria prevalence, (iv) were non-primary studies (e.g., reviews, editorials, commentaries, case reports, or case series), or (v) were duplicate publications, in which case the most complete or recent dataset was included.

Study selection

The EndNote 21 version of the program was used to remove duplicates. Titles and abstracts of the research were utilized as selection criteria and were separately reviewed for the possibility of eligibility by Tekele Tora and Desalegn Dawit. Studies were removed throughout the article screening step if their abstracts and titles did not meet the eligibility criteria. Eligible studies had gone through full-text screening. TT and DD separately conducted full-text screening throughout the article eligibility phase. Any disagreements amongst data extractors were settled through discussion at each stage of the study selection process. Ultimately, a study that met the criteria of inclusion was included for review.

Data extraction procedure

A standardized data extraction form was used to collect the following information from each included study: first author, year of publication, study region, study design, sample size, diagnostic method (microscopy), number of positive cases, prevalence, and reported associated risk factors. Data extraction was conducted independently by two reviewers, and inconsistencies were resolved by consensus. For studies reporting seasonal data, prevalence estimates were combined to generate a single study-level estimate to avoid double-counting.

Outcome measures

The primary outcome of this review was the prevalence of malaria, and this was defined as the number of persons with microscopy-confirmed malaria parasitemia divided by the total number of persons tested for malaria. Only studies that reported results from light microscopy of stained blood smears were considered for inclusion in the review.

Associated factors were defined as individual-level, environmental, and housing variables reported to have a statistical association with malaria parasitemia in the primary studies. These were also collected with their original definitions. These include socio-demographic factors such as age and sex. Behavioral factors such as the use of insecticide-treated bed nets were self-reported. Environmental and housing factors such as stagnant water, housing types, and the presence of screens on windows were assessed through observations. Data on associated factors were extracted as reported in the original studies, including odds ratios and corresponding confidence intervals. Covariates included in the analysis were specified a priori based on epidemiological relevance and data availability across studies. Only factors reported in at least two studies with comparable definitions were included in the quantitative synthesis; others were summarized narratively.

Data synthesis and statistical analysis

Pooled prevalence estimates were estimated using the random-effects meta-analysis model to account for expected heterogeneity. To stabilize variances and enhance statistical performance for proportion data, prevalence estimates were logit-transformed before pooling. This transformation is recommended over other transformations due to better statistical properties and reduced bias, especially with extreme values and high heterogeneity. Pooled estimates were subsequently back-transformed to the original scale for interpretation. Between-studies variance (τ²) was estimated using the restricted maximum likelihood method. To obtain more reliable estimates, especially with limited study numbers and high heterogeneity, the Hartung and Knapp adjustment was used to derive the confidence intervals. A 95% prediction interval was also computed to assess the expected range of effects in future studies.

Heterogeneity was assessed using the I² statistic and Cochran’s Q test. Heterogeneity was classified according to defined cutoff values: low heterogeneity, < 25%; moderate, 25–75%; and high, > 75% [23]. Subgroup analyses were conducted to identify a possible source of heterogeneity between studies. Additionally, meta-regression analyses were carried out to evaluate the effect of certain covariates on the results. The covariates selected for analysis were predetermined depending on their epidemiological significance. The presence of publication bias was determined using a funnel plot and statistically using Egger’s regression test. To further assess and adjust for the potential impact of publication bias, the trim-and-fill method (Duval and Tweedie) was applied [24]. A leave-one-out sensitivity analysis was done to evaluate the effect of each study on the results. All analyses were carried out using Stata version 17 (StataCorp LLC, USA), and a p-value < 0.05 was considered statistically significant.

Risk of bias assessment

The quality of included studies was assessed for methodological quality using a modified Newcastle–Ottawa Scale for observational studies. The modified scale was applied to three domains, including selection of participants in studies, comparability of study groups, and assessment of outcomes [25]. Based on the scores obtained in the three domains, the studies were classified into those of high risk, moderate, and low risk of bias. Two independent reviewers assessed the quality of the studies, and discrepancies were resolved through discussion or consultation with a third reviewer.

Results

Study selection

A total of 512 records were retrieved through the search of the various electronic databases: PubMed (n = 92), Scopus (n = 169), HINARI(n = 74), ScienceDirect (n = 65), Google Scholar (n = 109), and AJOL or other sources (n = 3). After the removal of 139 duplicated records, 373 records were screened by titles and abstract, resulting in the exclusion of 268 records that did not fulfill the inclusion criteria. Of the 105 full-text articles considered for inclusion, 91 articles were excluded due to various reasons (Fig. 1). Finally, 14 articles were selected for the systematic review and meta-analysis.

Fig. 1.

PRISMA 2020 flow diagram for new systematic reviews which included searches of databases, registers and other sources

Characteristics of the included studies

A total of 8,762 participants were included from 14 studies. Four studies conducted in the Amhara region were Debash et al. (2023), Cherkos et al. (2024), Abebaw et al. (2022), and Minwuyelet et al. (2020), and had a total of 1,681 participants, while three studies were conducted in the Southwest, for example, Demelash et al. (2025), Duguma et al. (2022), Duguma et al. (2023), and had 1331 participants. Three studies were published in 2022, for example, Yhdego et al. (2022), Duguma et al. (2022), and Abebaw et al. (2022). The minimum and maximum prevalence of malaria varied from 2.3% to 21.2% across studies. Based on the modified Newcastle–Ottawa Scale (NOS) quality assessment, all included studies met the stated quality criteria with a score ≥ 7 (S2 File) (Table 1).

Table 1.

Characteristics of the included studies among the General population in Ethiopia

Author (year)	Region	Setting	Clinical status	Sample size	Diagnostic Method (RDT, Microscopy)	microscopy- confirmed malaria (%)
Mulachew et al., (2025) [26]	Central	Urban	Asymptomatic	422	Both	4.5
Demelash et al., (2025) [27]	Southwest	Urban	Asymptomatic	353	Both	2.3
Yutura et al. (2024) [28]	South	Rural	Asymptomatic	1674	Microscopy	4.5
Cherkos et al. (2024) [29]	Amhara	Rural	Asymptomatic	357	Microscopy	9.2
Duguma et al., (2023)[30]	Southwest	Rural	Asymptomatic	566	Both	6.7
Debash et al., (2023) [31]	Amhara	Rural	Both	633	Both	21.2
Dabaro et al., (2023)[32]	Sidama	Both	Asymptomatic	573	Both	4.0
Yhdego et al., (2022)[33]	Tigray	Urban	Asymptomatic	403	Microscopy	4.7
Mengesha et al., (2022) [34]	Multiple¥	Both	Asymptomatic	1336	Both	8.1
Duguma et al., (2022) [35]	Southwest	Urban	Asymptomatic	412	Microscopy	21.1
Abebaw et al., (2022) [36]	Amhara	Rural	Both	440	Both	5
Hailemeskel et al., (2021) [4]	Multiple€	Both	Asymptomatic	860	Both	12.2
Minwuyelet et al. (2020) [37]	Amhara	Rural	Asymptomatic	251	Microscopy	12
Dufera et al., (2020) [38]	Oromia	Rural	Asymptomatic	452	Microscopy	3.1

Multiple region¥ (Afar, Gambell, and Southern Nations, Nationalities, and Peoples regional states); € (Amhara, SNNPR, and Benishangul)

Pooled prevalence of malaria in Ethiopia

The pooled prevalence of malaria was 7.1% (95% CI: 4.7–10.5%) using a random effects model with the Knapp-Hartung adjustment. There was significant heterogeneity among included studies (I² = 95.70%; τ² = 0.5135; p < 0.001). The 95% prediction interval ranged from 1.5% to 27.9% (Fig. 2).

Fig. 2.

Random effect analysis for the pooled prevalence of malaria in Ethiopia

Sub‑group analysis

Subgroup analysis showed variation in malaria prevalence across study characteristics. Regional estimates were comparable, ranging from 7.3% to 10.7%, indicating persistent geographic heterogeneity. Stratification by clinical status indicated comparable prevalence between asymptomatic cases (7.0%) and mixed populations (7.8%). Differences by setting were minimal, with similar estimates in urban (6%), rural (7.5%), and mixed settings (7.6%), suggesting widespread transmission. High heterogeneity across subgroups (I² > 89%) reflects substantial variability in effect sizes across studies (Table 2).

Table 2.

Random effect analysis for subgroup analysis of malaria in Ethiopia

Variables	Category	Number of studies	Pooled prevalence of malaria (%)	Heterogeneity within the studies
				I2 (%)	p-value**
Region	Southwest	3	7.3 (4–61.9)	97.6	< 0.001
	Amhara Region	4	10.7 (3.6–26.1)	94.1	< 0.001
	Multiple Region	2	10 (6–66)	89.9	< 0.001
Clinical status	Asymptomatic	12	7 (4.4–11)	96.4	< 0.001
	Both	2	7.8 (0–97.2)	90.4	< 0.001
Setting	Urban	4	6.0 (1.2–25.1)	95.7	< 0.001
	Rural	6	7.5 (4–13.7)	95.1	< 0.001
	Both	3	7.6 (1.8–26.6)	95.4	< 0.001
Sample size	< 500	8	6.3 (3.3–11.7)	93.4	< 0.001
	> 500	6	8.2 (4.1–15.6)	96.9	< 0.001

Meta-regression analysis

We conducted a meta-regression analysis to assess temporal trends in malaria infection over the past five years. The results demonstrated that the overall prevalence of malaria infection did not exhibit a statistically significant increase between 2017 and 2023 (coefficient = 0.133, p = 0.21), indicating no significant upward trend during the study period (Fig S1).

Pooled odds ratio estimate

Twelve studies were included to assess factors associated with malaria infection in the general population. Presence of stagnant water (OR: 6.71, 95% CI: 4.93–9.14), absence of indoor residual spraying (OR: 5.90, 95% CI: 3.40, 10.4), living in a house with eaves (OR: 4.5, 95% CI: 2.17–9.50), ITN non-use (OR: 4.8, 95% CI: 3.41–6.78), travel history of malaria endemic area (OR: 5.86; 95%; CI: 2.47–12.8), and presence of mosquito breeding sites (OR: 6.1, 95% CI; 3.06–12.3) were risk for malaria (Table 3).

Table 3.

Pooled risk factors associated with malaria among the general population in Ethiopia

Variables	Number of studies	Pooled odds ratio	% I2
Presence of stagnant water	4 (26, 31, 34, 35)	6.71 (4.93–9.14)	–
Absence of indoor residual spraying	4 (27, 30, 34, 38)	5.9 (3.40–10.4)	–
Living in a house with eaves	4 (26, 30, 32, 36)	4.5 (2.17–9.50)	–
Non-use of insecticide-treated nets	11 (26–32, 35–38)	4.8 (3.41–6.78)	47.4
Travel history of malaria endemic area	2 (26, 32)	5.86 (2.47–13.8)	–
Presence of mosquito breeding sites	2 (27, 28)	6.1 (3.06–12.3)	–
Outdoor stay at night	2 (29, 31)	3.73 (0.85–16.3)	85.5
Previous malaria history	3 (29, 36, 37)	3.54 (1.79–6.98)	–

Publication bias

Publication bias was evaluated using the visualization of funnel plots, Egger’s regression test, Begg’s correlation test, and trim-fill analysis. Visual inspection of the funnel plot indicated clear asymmetry. Egger’s regression test (p = 0.023) and Begg’s correlation test (p = 0.025) both demonstrated statistically significant small-study effects, suggesting potential publication bias (Fig S2). To further evaluate the robustness of the findings, a trim-and-fill analysis was performed, which imputed two potentially missing studies on the right side of the funnel plot. After adjustment, the pooled prevalence was 8.2% (95% CI: 5.6%–11.7%), indicating that the potential publication bias had a limited impact on the overall estimate (Fig S3). Publication bias for the pooled odds ratio (OR) estimates was assessed using funnel plot visualization, Egger’s regression test, and Begg’s correlation test. A visual inspection of the funnel plot indicated asymmetry around the pooled effect size. Even though the funnel plot appears asymmetrical, neither Egger's regression test (p = 0.889) nor Begg's test (p = 0.136) showed statistically significant small-study effects. This suggests that the asymmetry may be due to heterogeneity between studies rather than publication bias (Fig. S4).

Sensitivity analysis

A leave-one-out sensitivity analysis was conducted to evaluate the robustness of the pooled estimate. A leave-one-out sensitivity analysis demonstrated that the pooled prevalence remained stable (approximately 6.5%–7.8%) with overlapping confidence intervals across all iterations, indicating that no single study materially influenced the magnitude or direction of the overall estimate and confirming the robustness of the findings despite substantial between-study heterogeneity (Fig. 3).

Fig. 3.

Leave-one-out sensitivity analysis of pooled malaria prevalence

Discussion

Despite the commitment to the elimination of malaria by 2030, it remains a major public health problem in Ethiopia. According to recent reports from the World Health Organization, there has been an increase in malaria transmission from 2023 to 2024, similar to the global increase in malaria cases due to challenges in malaria control, including funding constraints, preventive measures, service delivery, vector resistance, and climate change [1, 2]. In this context, the present systematic review and meta-analysis estimated a pooled malaria prevalence of 7.1%, indicating ongoing transmission and the need for intensified control efforts to achieve elimination targets [39].

The pooled estimate is consistent with previous meta-analyses conducted in Ethiopia among specific populations, including pregnant women (7.20%) [21], children (9.07%) [40], vulnerable populations (11.1%) [20], and adults (13.61%) [41]. The slightly lower prevalence rate found in this review might be due to the inclusion of the general population, which comprises different exposure and protection levels due to ongoing control measures. In contrast, higher prevalence estimates were reported in studies from Mali (13%) [42], Mauritania (14.9%) [43], under-five children in Ethiopia (22.03%) [44], pregnant women in Sub-Saharan Africa (26.1%) [45], and refugee camps in Africa (35.93%) [46], may be explained by differences in transmission intensity, population vulnerability, and contextual factors such as overcrowding, limited access to preventive measures, and ecological conditions.

There was a considerable heterogeneity among included studies (I² = 95.7%). The subgroup analysis showed that malaria prevalence varied across regions, with Amhara showing higher estimates of 10.7% and Southwest Ethiopia showing lower estimates of 7.3%. This difference may be attributed to the uneven number of studies in each region; the largest proportion of studies came from Amhara (28.57%) and Southwest Ethiopia (21.43%), while other regions were represented by few studies, which limits the reliability of region-specific comparisons. Meta-regression analysis showed no significant association between study year and malaria prevalence, suggesting that the observed heterogeneity is more likely driven by location-specific factors, including environmental conditions, vector ecology, health system performance, and contextual factors such as conflict and population movement [47].

This review also demonstrates the significant relationship between both environmental and behavioral determinants and malaria infection. Among the environmental determinants, the presence of stagnant water and mosquito breeding sites showed the strongest associations. These findings are consistent with studies conducted on vulnerable groups in Ethiopia [20] and Ethiopian studies examining asymptomatic malaria infections [48]. This indicates the significant contribution of these determinants in the maintenance of the vector population. Additionally, the absence of indoor residual spraying was associated with a significantly increased risk of malaria infection.

Behavioral factors also played a significant role. The non-use of insecticide-treated bed nets was linked with nearly fivefold odds of malaria infection, consistent with previous evidence [48, 49]. However, ITN utilization in Ethiopia remains below national targets, reflecting persistent gaps in coverage and use [50].The non-use of ITNs is partly due to misconceptions, discomfort, perceived mosquito density, and limited community engagement, all of which contribute to reduced effectiveness [51, 52].

Housing conditions were also identified as an important factor, with houses having open eaves being associated with increased malaria risk, possibly because of the ease of entry for the mosquitoes. Prior malaria infection was also significantly associated with the presence of the disease in the present. Moreover, travel to malaria-endemic regions was significantly related to increased odds of infection, possibly because of increased exposure to infectious mosquito bites in high-transmission regions. This risk could be further increased by inadequate use of protective measures and a lack of awareness about the risks of transmission during travel.

Limitations

This review has several limitations that should be considered when interpreting the findings. The cross-sectional design of the included studies constrains causal inference and may introduce residual confounding. Relying on microscopy to diagnose malaria may also miss some cases because of its lower sensitivity to detect low-density infections. There were also substantial differences between the included studies, which might have been caused by their region-specific features but could not be explained. The uneven distribution of studies across regions, with greater representation from Amhara and Southwest Ethiopia, may compromise the reliability of regional comparisons and the overall representativeness of the findings. Finally, the possibility of publication bias and the exclusion of high-risk populations may limit the generalizability of the findings. Therefore, readers and relevant stakeholders should take these contextual constraints into account when interpreting the findings. Despite these limitations, this review provides useful evidence to inform malaria control and prevention strategies at different levels of the healthcare system in Ethiopia.

Conclusion

This systematic review and meta-analysis demonstrate that malaria continues to be a significant public health issue in Ethiopia, with a pooled microscopy-confirmed prevalence of 7.1% and considerable regional variation. The risk of malaria infection is associated with environmental, behavioral, and housing-related factors, including stagnant water, mosquito breeding sites, lack of indoor residual spraying, ITN non-use, travel to endemic areas, and housing characteristics such as open eaves. The presence of significant heterogeneity and indications of possible publication bias imply that the actual burden may be slightly higher than the reported estimate. These findings highlight the necessity for enhanced integrated malaria control strategies emphasizing vector control, environmental management, improved housing conditions, and enduring community-level prevention practices to facilitate malaria eradication initiatives in Ethiopia.

Abbreviations

CI

Confidence interval

IRS

Indoor residual spraying

LLIN

Combined with Long-lasting insecticidal net: Simple random sampling;

OR

Odds ratio

NOS

Newcastle–Ottawa Scale

PRISMA

Preferred Reporting Items for Systematic Review and Meta-Analysis

Author contributions

TTF and DDA developed the protocol, design selection, and TTF, DDA, and MM were involved in data extraction and quality assessment. TT participated in dispute management during extraction and assessment, and DDA, MM, and TT prepared and edited the final draft of the manuscript. All authors read and approved the manuscript.

Funding

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Data availability

The authors confirm that all data generated and analyzed during this study are included in this published article.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.