Background

Plasmodium falciparum presents a significant public health challenge in sub-Saharan Africa (SSA), constituting the majority of reported malaria cases. In 2022, out of the 249 million malaria cases recorded globally, 233 million occurred in SSA, contributing to an estimated 580,000 out of the 608,000 malaria-related deaths worldwide [1]. While the development of a robust immune response is necessary for controlling Plasmodium falciparum infection [2], timely diagnosis and the administration of effective treatments [3] are required to control symptomatic infection and reduce transmission.

The control of Plasmodium falciparum is hindered by the high propensity for genetic diversity of parasites infecting individuals and the frequency of multiplicity of infection (MOI) within individual infections. These factors favour immune evasion, may contribute to malaria pathology, and could promote the emergence of variants resistant to anti-malarial drugs [4]. Moreover, genetic diversity, particularly involving protein-coding genes targeted by diagnostic tests such as histidine-rich protein 2/3 (HRP2/3) [5], which have become important tools for malaria diagnosis and surveillance, could have significant implications for malaria surveillance and control.

Genetic diversity and MOI are emerging as relevant biomarkers of Plasmodium falciparum transmission. Plasmodium falciparum genetic diversity arises from genetic recombination during the parasite lifecycle in the mosquito [6], while MOI results from infection by multiple distinct parasite genotypes [7]. Infection by distinct parasite genotypes occurs either when an individual is bitten by different mosquitoes carrying unique parasite strains (superinfection) or when bitten by a single mosquito carrying multiple distinct genotypes (co-transmission) [8, 9].

The genetic diversity and MOI of Plasmodium falciparum may be assessed by targeted genotyping of markers such as msp-1, msp-2, and glurp, which are coding and therefore targets for immune evasion [10] or microsatellite markers, which are not targets for immune evasion [11]. High-throughput methods, including molecular (DNA) barcodes, targeted deep sequencing, and genome-wide variation analysis, have also been utilized [12, 13], but these are expensive. Although more labour-intensive and subject to some biases, such as amplification efficiency bias due to size differences between msp-1, msp-2, and glurp alleles [14, 15], genotyping of these markers is cheaper and more readily available in resource-limited settings in SSA [12].

The mean values of parasite genetic diversity and MOI are higher in areas with high malaria transmission intensity [16, 17] and lower in those with low transmission intensity [18]. Additionally, mean Plasmodium falciparum genetic diversity and MOI apparently decreased following the suppression of Plasmodium falciparum transmission intensity in areas of Ethiopia [19] and Senegal [20]. In other studies, mean values of Plasmodium falciparum genetic diversity were higher among individuals with symptomatic infections [21, 22] and lower in those with asymptomatic infections [23], and were inversely correlated with parasite density and patient age [24].

Data on Plasmodium falciparum genetic diversity and MOI are relatively sparse, making it difficult to easily identify relevant patterns in SSA. Some studies have focused solely on MOI but not genetic diversity [25, 26], while others have been conducted within a single country [27], or utilized a single genetic marker [28]. This study collated published data on Plasmodium falciparum genetic diversity and MOI in SSA and summarized this data for symptomatic and asymptomatic individuals using a few genetic markers that are widely utilised for parasite genotyping in SSA. The aim of the study was to generate a systematic summary that can inform public health initiatives for malaria control in different regions of SSA.

Methods

Study design and protocol registration

The systematic review was conducted using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines [29]. The review protocol is registered in PROSPERO (#CRD42021267661).

Review question

The study reviewed data on Plasmodium falciparum genetic diversity and MOI in SSA based on msp-1, msp-2, glurp, and microsatellite genetic markers from articles published from January 2000 through May 2023. This period was chosen because access to malaria genomic technologies was reasonably high, yielding representative data in the regions sampled [30]. Also, the period coincides with rapid decline in malaria incidence in SSA [31]. The objectives of the review were to: a) characterize the geographical distribution of Plasmodium falciparum genetic diversity in SSA; b) determine the prevalence of Plasmodium falciparum polyclonal infections in SSA; and c) identify factors associated with Plasmodium falciparum genetic diversity and MOI in SSA.

Search strategy and information sources

A systematic search, conducted by an experienced librarian (AAK), utilized PubMed, EMBASE, EBSCOhost, Web of Science, and the first 50 pages of Google Scholar after searching several pages and found no more relevant studies Additionally, citation lists of the identified articles were searched for additional relevant articles [32,33,34,35,36,37]. The search terms included keywords such as 'Plasmodium falciparum,' 'P. falciparum genetic diversity,' 'P. falciparum multiplicity of infection,' and 'sub-Saharan Africa' (Additional file 1).

Eligibility

Inclusion criteria

The review considered:

  • Articles published in English,

  • Study design, i.e., observational (cross-sectional/survey, case control, and cohort) or randomized clinical trials (RCTs),

  • Minimum required data elements: country, sample size, calendar year(s) when the study was conducted, and detailed laboratory methods used to genotype markers for genetic diversity or MOI,

  • Detailed methods for determining Plasmodium falciparum genetic diversity and MOI, including mean expected heterozygosity, allele frequencies, and mean MOI or percentage of multiple infections.

Exclusion criteria

  • Absence of key terms ‘Plasmodium falciparum genetic diversity and or MOI’ in the title and or abstract,

  • Studies using experimental animals,

  • Review articles, case reports, case series, or editorials

  • Use of inappropriate laboratory molecular methods (DNA extraction, PCR and then fragment analysis).

Article screening and data extraction

The articles were deduplicated using Endnote software version X9. Subsequently, the unique articles underwent screening by two independent reviewers (AM and RWN), who also performed data abstraction using predetermined review criteria (Additional file 2). Abstracted data were compared, and any disagreements were resolved through discussion.

Harmonized extracted data included:

  • Study characteristics (author name, article title, publication year, country, malaria transmission setting, and study design); participant characteristics (sample size, age group, and malaria clinical category),

  • Malaria diagnosis and genotyping (malaria diagnosis method, name of genotyped markers, and PCR fragment analysis method),

  • Outcome results on Plasmodium falciparum genetic diversity and MOI based on mean expected heterozygosity, allele frequencies of selected genotyped genetic markers, and the prevalence of polyclonal infections or mean MOI, respectively. Data on factors associated with Plasmodium falciparum genetic diversity and MOI were also extracted.

Data analysis

Meta-analysis was performed using the random effects model (DerSimonian and Laird approach) in STATA (version 17, Stata Corporation, College Station, TX). Forest plots included only studies that reported measures of dispersion such as SD or CI for the respective effect sizes (Mean MOI and or Mean He) to enable computation of standard error for use in meta-analysis using STATA. Pooled estimates for Plasmodium falciparum genetic diversity and MOI were generated, sorted by region. Additionally, patterns of Plasmodium falciparum genetic diversity and MOI were assessed according to malaria clinical categories (asymptomatic and symptomatic malaria infection) [38] and specific genetic marker(s) used to evaluate Plasmodium falciparum genetic diversity and MOI [12].

Heterogeneity analysis

Heterogeneity across the studies was assessed using the chi-squared test and Cochran’s Q statistic, with a 5% level of statistical significance [39], and the I-squared (I2) statistic [40]. An I2 statistic of 25% indicates low heterogeneity, 50% indicates moderate heterogeneity, and > 75% indicates high heterogeneity [41].

Risk of bias and quality of evidence assessment

Risk of bias (RoB) in the selected articles was independently evaluated by two reviewers (AM, RWN) using an RoB assessment tool adapted from Joanna Briggs Institute’s (JBI) critical appraisal tools [42]. The quality of evidence was determined by two independent reviewers using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) guidelines [43]. RoB assessment covered five domains: study design and limitations, inconsistency in selected articles, indirectness of the evidence, imprecision, and publication bias. Studies scoring 0 to 1, 2 to 3, 4 to 5, and at least 6 were judged to be very low, low, moderate, and high-quality studies, respectively.

Publication bias

Publication bias was assessed by visualizing the asymmetry of the funnel plot and examining the presence and distribution of dots in the plot [44]. Egger’s statistical test was performed to assess the asymmetry of the funnel plot. A statistically significant result (p < 0.05) in Egger’s test indicates that the funnel plot asymmetry is due to small-study effects [45]. All data analysis was conducted using STATA version 17 software package (Stata Corporation, College Station, TX).

Missing data

Variables that were missing from included articles were recorded as not reported (NR). Authors of articles with missing data were contacted for additional information, but only a small number (5/12: 41.67%) responded.

Ethics considerations

The study used already published literature with no direct human subject contact and posed no risk to the participants who participated in the primary studies as determined by the Makerere University School of Medicine Institutional Review Board (# Mak-SOMREC-2021-152) and Uganda National Council for Science and Technology (# HS2744ES) (Table 1).

Table 1 The PECOST framework
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Results

A total of 1,718 articles were retrieved from the literature search, and an additional 6 articles were found through a search of the bibliographies of the identified articles. Of these, 52 articles met the inclusion criteria and were included in the review analysis (Fig. 1). The articles were from 23 of the 54 countries in SSA, covering a total of 11,640 genotyped parasite isolates from 9,062 symptomatic and 2,578 asymptomatic Plasmodium falciparum infections. Among the 52 articles, 39 (75%) employed cross-sectional study design while 13 (25%) utilized RCT/cohort study designs. A total of 23 studies enrolled both children and adults, while 22 studies enrolled only children, 2 studies enrolled only adults, and 5 studies did not specify the age group of their study population. The predominant genetic markers used to genotype parasites were the antigen-coding loci, especially msp-1 and/or msp-2, in 76.9% (40/52) of the studies, followed by microsatellites markers only in 19.2% (10/52). In one study (1.92%; 1/52), both microsatellites and msp-1 and/or msp-2) were used, while in another one study (1.92%; 1/52), genotyping of Plasmodium falciparum parasites involved the use of both msp-1, msp-2, and single nucleotide polymorphisms (SNPs) (Table 2).

Fig. 1
figure 1

PRISMA. Flow diagram for identification of articles included in the review

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Table 2 Summary of P. falciparum genetic diversity and MOI
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Plasmodium falciparum genetic diversity in SSA

Across studies, P. falciparum genetic diversity, primarily assessed using antigen-coding loci (msp-1, msp-2, and glurp), and microsatellites, was reported using either allele frequency, mean expected heterozygosity, or both. The frequencies of msp-1 alleles (K1, MAD20, and RO33) were 20.8%, 4.2%, and 4.2%, respectively, in Ethiopia, a country with moderate malaria transmission in East Africa [79]. In high malaria transmission areas of Equatorial Guinea in West Africa, these same alleles had substantially higher frequencies of 96.07%, 96.09%, and 70.78%, respectively [22]. The frequency of the msp-2 gene I/C 3D7 allele ranged from 15.9% to 98.3% in Ethiopia [19, 79], while the FC27 allele frequency ranged from 10.3% in Ethiopia [84] to 98.9% in high malaria transmission areas in Benin [36]. Meanwhile, the frequency of glurp ranged from 39.53% among symptomatic individuals in a high malaria transmission setting in Nigeria [68] to 97.6% among severe malaria cases living in malaria-moderate areas in Uganda [74] (Table 2).

The overall pooled allele frequencies of msp-1 alleles K1, MAD20, and RO33 were 61%, 44%, and 40%, respectively, while the overall polled allele frequencies of msp-2 I/C 3D7 and FC27 alleles were 61% and 55%, respectively, across reviewed studies. Across regions, the pooled allele frequencies of msp-1 alleles K1, MAD20, and RO33 were 46%, 42%, and 31%, respectively, in East Africa; 74%, 51%, and 48%, respectively, in Central Africa; and 67%, 43%, and 44%, respectively, in West Africa. In comparison, the pooled allele frequencies of the msp-2 I/C 3D7 and FC27 alleles were 60% and 55%, respectively, in East Africa, 67% in Central Africa, and 62% and 50%, respectively, in West Africa. For glurp, the overall pooled allele frequency was 66%, with a pooled frequency of 90% and 70% in East and West African regions, respectively. The only two reviewed studies from Southern Africa [58, 65] studied parasite genetic diversity and MOI using only microsatellites and not msp-1, msp-2, or glurp (Table 3).

Table 3 Pooled proportions of K1, MAD20, RO33, IC/3D7, FC27 and glurp alleles across studies
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Based on expected heterozygosity, the mean expected heterozygosity was 0.09 in Ethiopia [19], a country with moderate malaria transmission in East Africa, and 0.93 in the DRC [80], a country with high malaria transmission in Central Africa (Table 2). The overall pooled mean expected heterozygosity across all studies was 0.65 (95% CI: 0.51–0.78). Across regions, the pooled mean expected heterozygosity was 0.58 (95% CI: 0.29–0.86), 0.84 (95% CI: 0.81–0.86), 0.74 (95% CI: 0.73–0.75), and 0.69 (95% CI: 0.62–0.75) in East, Central, Southern, and West African regions, respectively (Fig. 2).

Fig. 2
figure 2

Forest plot representing the pooled mean expected heterozygosity of P. falciparum infection across 17 studies that reported measures of dispersion (CI and SD) for mean expected heterozygosity in malaria-affected countries in SSA, sorted by region

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Each blue square bar indicates the estimated mean expected heterozygosity in one study, and the lines through the square represent the confidence interval around the estimate. The red diamond symbols represent the pooled mean expected heterozygosity in each region, while the green diamond symbol represents the overall pooled mean expected heterozygosity across all regions. The x-axis represents the scale for mean expected heterozygosity which ranges between 0 to 1.

Plasmodium falciparum MOI across malaria-affected countries in SSA

Plasmodium falciparum MOI, defined as the number of distinct parasite genotypes co-existing within a given infection. Plasmodium falciparum MOI was reported using mean MOI and or prevalence of polyclonal infection. The mean MOI ranged from 1.09 in Ethiopia in East Africa [19] to 5.51 in Equatorial Guinea in West Africa [22] (Table 2). The overall pooled mean MOI across studies was 2.09 (95% CI: 1.88–2.30). Across regions, the pooled mean MOI was 2.05 (95% CI: 1.83–2.26), 2.37 (95% CI: 1.28–3.46), 2.16 (95% CI: 2.09–2.23), and 1.96 (95% CI: 1.53–2.39) in East, Central, Southern, and West African regions, respectively (Fig. 3).

Fig. 3
figure 3

Forest plot representing the pooled mean MOI of P. falciparum infection across 32 studies that reported measures of dispersion (CI and SD) for mean MOI in malaria-affected countries in SSA, sorted by region. Each blue square bar indicates the estimated mean P. falciparum MOI in one study, and the lines through the square represent the confidence interval around the estimate. The red diamond symbols represent the pooled mean P. falciparum MOI in each region, while the green diamond symbol represents the overall pooled P. falciparum mean MOI across all regions. The x-axis represents the scale for mean MOI

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The prevalence of polyclonal infections ranged from 16.3% in Ethiopia in East Africa [19] to 98% in Equatorial Guinea in Central Africa [22] to 98% in Equatorial Guinea in Central Africa [27] (Table 2). The overall pooled prevalence of Plasmodium falciparum polyclonal infections was 63% (95% CI 56–70) across all studies. Across the regions, the pooled prevalence of polyclonal infections was 62% (95% CI: 53–71), 61% (95% CI: 51–71), 65% (95% CI: 43–88), and 71% (95% CI: 63–79) in East, West, Central, and Southern Africa regions, respectively (Fig. 4).

Fig. 4
figure 4

Forest plot representing the pooled prevalence P. falciparum polyclonal infections reported by 48 studies from malaria-affected countries in SSA, sorted by region. Each gray square bar with a black dot indicates the estimated prevalence of P. falciparum polyclonal infections in one study, and the lines through the square represent the confidence interval around the estimate. The diamond symbol represents the pooled prevalence of P. falciparum polyclonal infections

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Factors associated with Plasmodium falciparum genetic diversity and MOI across SSA

In three studies [24, 72, 88], a positive association between patient age and parasite density with Plasmodium falciparum genetic diversity and MOI was observed; however, this association was not consistent across other studies [36, 69, 79]. Some studies indicated an association between Plasmodium falciparum genetic diversity and MOI with the use of chemotherapy to suppress malaria infections. For instance, in a study by Huang, B et al. [66], a decrease in genetic diversity was found over a 10-year period following the introduction of artemisinin-based combination therapy (ACT) in an island population, with a 28% decrease for msp-1 (from 32 to 23) and msp-2 (from 29 to 21). MOI declined from 3.11 to 1.63 for msp-1 and from 2.75 to 1.35 for msp-2. The prevalence of polyclonal infection for msp-1 declined from 76.7% to 29.1% (P < 0.01), and for msp-2, it declined from 62.4% to 28.3% (P < 0.01).

Similarly, a study by Tadele et al. [19] reported a decline in Plasmodium falciparum genetic diversity and MOI. Variations in Plasmodium falciparum genetic diversity and MOI were found in both rural and urban settings. MOI was higher in rural than in urban settings; for instance, the mean MOI for rural versus urban areas was 1.88 versus 1.55, while the prevalence of polyclonal infection was 42.2% versus 57.7% (p = 0.04) [71]. However, in a study conducted in an urban setting in Uganda [74], the mean MOI values were even higher (3.0 to 3.7 for severe and mild malaria cases, respectively p = 0.002) than those observed in rural areas elsewhere. High Plasmodium falciparum genetic diversity and MOI were also reported among both symptomatic [72, 74, 89] and asymptomatic malaria cases [16, 90]. Furthermore, a positive association between the genetic diversity and MOI of Plasmodium falciparum with malaria transmission settings, showing higher values in areas with high malaria transmission and lower values in those with low malaria transmission (2.13 in high and 1.29 in low malaria transmission; p < 0.0001) has been reported [70]. Meanwhile, the expected heterozygosity was high (0.49 to 0.62) and low (0.26 to 0.28) in high and low malaria transmission settings, respectively. However, this relationship was not observed elsewhere reported [37].

Subgroup analysis of Plasmodium falciparum genetic diversity and MOI based on malaria clinical category and the genotyped markers

Subgroup analysis of genetic diversity and MOI was conducted using mean expected heterozygosity and mean MOI, respectively. Considering patient phenotype, the pooled mean expected heterozygosity was 0.64 (95% CI 0.515–0.78) in studies that enrolled only individuals with asymptomatic infection and 0.63 (0.42–0.83) in those that enrolled only individuals with symptomatic infection. However, the pooled mean expected heterozygosity was 0.77 (0.75–0.79) in studies that enrolled individuals with either asymptomatic or symptomatic Plasmodium falciparum infections.

Based on antigen-coding loci, msp-1, and/or msp-2 genotypes, the pooled Plasmodium falciparum mean expected heterozygosity was 0.49 (95% CI 0.24–0.74). In comparison, it was 0.76 (95% CI 0.72–0.79) based on microsatellite markers. The pooled mean MOI was 1.90 (95% CI 1.50–2.30) in studies enrolling asymptomatic individuals, 2.16 (95% CI 1.92–2.41) in studies enrolling symptomatic individuals, and 1.85 (95% CI 1.59–2.11) in studies enrolling both asymptomatic and symptomatic cases. In studies using antigen-coding loci msp-1 and/or msp-2 only, the pooled mean MOI was 2.14 (95% CI 1.91–2.38), while in studies using microsatellites, it was 1.63 (95% CI 1.11–2.15).

Plasmodium falciparum heterogeneity in the included studies

Studies were combined and assessed for heterogeneity. Based on mean expected heterozygosity, there was high heterogeneity among studies enrolling asymptomatic individuals (I2 = 99.61%, P < 0.001) and among studies enrolling individuals with symptomatic malaria (I2 = 99.94%, P < 0.001). Similarly, a high level of heterogeneity was observed among studies using antigen-coding loci, namely msp-1 and msp-2 (I2 = 99.87%, P < 0.001), as well as microsatellites (I2 = 98.28%, P < 0.001). Regarding mean MOI, heterogeneity was high in studies enrolling individuals with asymptomatic malaria (I2 = 98.64%, P < 0.001), symptomatic malaria (I2 = 99.60%, P < 0.001), and both asymptomatic and symptomatic cases (I2 = 96.20%, P < 0.001). A high level of heterogeneity was also observed across the genotyped markers, including antigen coding loci, msp-1, and msp-2 (I2 = 99.619%, P < 0.001), and microsatellites (I2 = 96.52%, P < 0.001) (Additional file 3).

Risk of bias in the included studies

Methodological quality and reporting bias were identified as high in 28.8% (15/52) of the studies. There was a potential for selection bias, as successful genotyping was reported to be < 90% in 11.5% (6/52) of the reviewed articles. Additionally, detection bias related to the assessment of confounding factors was noted in 36.5% (19/52) of the included articles (Additional file 4).

Publication bias assessment

Visual inspection of the funnel plots obtained using mean expected heterozygosity and mean MOI revealed an asymmetrical distribution of estimates from the middle line (Additional file 5). Egger’s statistical test showed a coefficient of 2.16, z = 0.54, and P = 0.59 for mean expected heterozygosity, and a coefficient of 1.65, z = 1.28, and P = 0.2 for mean MOI.

Discussion

The systematic review covered studies investigating Plasmodium falciparum genetic diversity and MOI in malaria-affected countries in SSA. Study findings indicate substantial genetic diversity and MOI among parasites circulating in SSA. The substantial regional variation in parasite genetic diversity and MOI identified in this current study likely reflects differences in regional malaria transmission intensity. This suggests that these markers may be useful in evaluating malaria transmission patterns and the effectiveness of control interventions.

Parasite genetic diversity exhibited variations across regions, with msp-1 (K1, MAD20, and RO33) and msp-2 (I/C3D7 and FC27) alleles showing different frequencies in different regions. The finding that high Plasmodium falciparum genetic diversity was reported in both high [51] and low [65] malaria transmission areas in SSA is interesting. Parasite genetic diversity results from genetic recombination in the mosquito [6, 91], and is more likely in areas with high local malaria transmission intensity. The high parasite genetic diversity in some areas is therefore a cause for concern because it may indicate ongoing transmission despite the intensification of malaria control measures [53]. Nonetheless, there are areas with low Plasmodium falciparum genetic diversity, indicating that malaria control and surveillance efforts should be tailored accordingly.

Plasmodium falciparum mean MOI also exhibited wide variations across regions, ranging from 1.09 to 5.51, with an overall pooled mean MOI of 2.09. Meanwhile, the prevalence of polyclonal infection also varied significantly, ranging from 16.3% to 98%, with an overall pooled prevalence of 63% across studies. Previous reports have documented wide variations in Plasmodium falciparum mean MOI (ranging from 1 to 6.1) and the percentage prevalence of polyclonal infections (ranging from 0 to 96%) [25]. High mean MOI and the presence of polyclonal infections serve as key indicators of high malaria transmission intensity [16, 70]. These factors are influenced by increased vector populations, promoting either superinfection or the concurrent transmission of unrelated parasite genotypes [92]. The variations across regions suggest differences in malaria transmission patterns across SSA, emphasizing the need for modifications in malaria vector control and the implementation of customized regional malaria control measures.

The review identified several factors associated with Plasmodium falciparum genetic diversity and MOI, including parasite density, the clinical category of malaria infection, patient age, malaria control interventions, and malaria transmission intensity [16, 19, 24, 48, 70, 72, 89]. These findings extend those from previous studies that reported a positive correlation between parasite density and parasite genetic diversity/MOI [70, 75]. Higher parasite density increases the likelihood of carrying distinct parasite genotypes [70], while an increase in age enhances immunity to malaria [93] Additionally, Plasmodium falciparum genetic diversity and MOI were found to be higher in rural settings [71], although other reports indicated higher genetic diversity in urban settings [74]. Low parasite genetic diversity and MOI have been reported in areas of Ethiopia, suggesting the effectiveness of malaria control interventions [19].

High Plasmodium falciparum genetic diversity and MOI were reported among both symptomatic [72, 74, 89] and asymptomatic malaria cases [16, 48, 90]. The occurrence of multiple Plasmodium falciparum infections could pose a challenge to parasite elimination efforts [16] due to its positive association with antimalarial drug failure [94]. Asymptomatic infection is typically characterized by low parasitaemia [95] and high MOI [38]. Asymptomatic individuals with low parasitaemia often remain undetected, thus forming a reservoir for malaria transmission and its spread [96]. The variability in parasite genetic diversity infection profiles has implications for treatment strategies, as well as the efficacy of antimalarial drugs.

msp-1 and msp-2 are commonly used genetic markers for assessing Plasmodium falciparum genetic diversity and MOI. Studies exclusively employing antigen-coding loci (msp-1 and/or msp-2) reported a higher pooled mean MOI (2.14), while those utilizing microsatellites showed a lower pooled mean MOI (1.63). The abundance and high polymorphism of microsatellites make them more suitable for estimating MOI compared to msp-1, msp-2, and glurp, which are relatively fewer and exhibit lower levels of polymorphism [13]. Moreover, msp-1, msp-2, and glurp are susceptible to immune selection [97]. Another limitation arises from significant size variations among msp-1, msp-2, and glurp alleles, potentially introducing bias in amplification efficiency. In cases of multiclonal infections, this bias may result in the preferential amplification of shorter fragments, leading to the loss of longer alleles [14, 15]. This emphasizes the importance of employing advanced tools such as microsatellite analysis and whole-genome sequencing for accurately assessing Plasmodium falciparum genetic diversity.

Implications for future research and policy

The substantial variations in Plasmodium falciparum genetic diversity and MOI in SSA necessitates continuous genomic surveillance in different malaria transmission settings. Current research predominantly focuses on symptomatic malaria infections in children utilising msp-1 and msp-2 genetic markers for assessing genetic diversity and MOI. Future studies should broaden their focus to include both adults and children across different malaria transmission contexts. Incorporation of advanced tools like microsatellite and whole-genome sequencing, is crucial for accurate assessments of parasite genetic diversity.

Strengths of the study

The review focused on peer-reviewed articles published over an extended period of time to adequately appreciate the genetic diversity and MOI of Plasmodium falciparum parasites circulation in SSA, an area which contributes over 95% of global malaria cases. The review focused on genetic markers (msp-1, msp-2, glurp, and microsatellites) that are more common and readily available in resource-limited settings in SSA. The review was conducted following standard PRISMA-P review guidelines to enhance the reliability of the findings.

Limitations of the study

The present study has several limitations. Firstly, reliance on peer-reviewed published articles may have introduced potential publication bias. Secondly, studies that did not explicitly mention Plasmodium falciparum genetic diversity and/or MOI in the title may have been missed. Additionally, the geographical coverage of articles was not comprehensive, as they did not encompass all countries in SSA, thereby impacting the generalizability of findings within the region. For instance, only two of the reviewed studies originated from Southern Africa [58, 65], focusing solely on microsatellites rather than msp-1, msp-2, and glurp, which limits inferences about marker distribution in this region. Furthermore, the exclusion of studies lacking measures of dispersion (CI and SD) affected the meta-analysis of mean expected heterozygosity and mean MOI.

Conclusion

This systematic review reveals considerable variations in Plasmodium falciparum genetic diversity and MOI across malaria-affected countries in SSA. Despite control efforts, the high observed parasite genetic diversity and MOI emphasize the necessity for customized, region-specific malaria control strategies, and continuous surveillance.