Prevalence of Plasmodium falciparum coinfection with Schistosoma haematobium and Schistosoma mansoni among children in sub-Saharan Africa: a systematic review and meta-analysis

Abstract

Background

Malaria and schistosomiasis are major public health challenges in sub-Saharan Africa (SSA), with overlapping geographical distributions leading to high rates of coinfection among children. Plasmodium falciparum (P. falciparum) causes most severe malaria cases, while Schistosoma haematobium (S. haematobium) and Schistosoma mansoni (S. mansoni) are the predominant schistosome species. Coinfections result from shared environmental factors, but interactions between these parasites remain unclear due to inconsistent study findings. This systematic review and meta-analysis aim to provide pooled prevalence estimates of P. falciparum–Schistosoma coinfection in children aged 0–17 years in SSA.

Method

This systematic review and meta-analysis followed the PRISMA guidelines. We searched PubMed, the Cochrane Library, Web of Science, EBSCOhost, Google Scholar, ProQuest, and publisher platforms (Wiley Online Library, SpringerLink) and screened the reference lists for observational studies reporting coinfection with P. falciparum and S. haematobium and/or S. mansoni among children in SSA. Data from 21 eligible studies were extracted using Microsoft Excel and analysed using Stata version 17. Study quality was assessed using the Joanna Briggs Institute critical appraisal tool. Proportions were stabilized using the Freeman–Tukey double arcsine transformation, and pooled prevalence estimates with 95% confidence intervals (CIs) were calculated using a random-effects model. Heterogeneity was assessed using I², and publication bias was evaluated using funnel plots, Egger’s test, and Begg’s test.

Result

Twenty-one studies were included in this analysis. The aggregated prevalence of P. falciparum–S. haematobium coinfection was 10.0% (95% CI 6.0–14.0%), with substantial heterogeneity (I² = 98.0%, p < 0.001). The highest prevalence was recorded in West Africa (13.0%, 95% CI 6.0–22.0%), followed by Central Africa (9.0%, 95% CI 7.0–10.0%) and East Africa (3.0%, 95% CI 0.0–11.0%); however, the regional differences were not significant (p = 0.150). No significant temporal variation was detected (p = 0.185), with prevalence estimates of 22.0% (2011–2015), 5.0% (2016–2020), and 7.0% (2021–2025). The diagnostic methods yielded significantly different estimates (p < 0.001), ranging from 13.0% (PCR + microscopy) and 11.0% (microscopy) to 3.0% (RDT + microscopy) and 1.0% (PCR + PCR). The pooled prevalence of P. falciparum–S. mansoni coinfection was 13.0% (95% CI 6.0–24.0%), with very high heterogeneity (I² = 99.28%, p < 0.001). Significant regional variation was identified (p < 0.001), with East Africa exhibiting the highest prevalence (25.0%, 95% CI 13.0–39.0%) compared to Central Africa (3.0%, 95% CI 2.0–5.0%) and West Africa (2.0%, 95% CI 1.0–3.0%). Temporal patterns did not show significant differences (p = 0.683), with prevalence estimates of 13.0% (2010–2014), 18.0% (2015–2019), and 14.0% (2020–2025). Diagnostic approaches also varied significantly (p < 0.001), with prevalence ranging from 12.0% (microscopy + microscopy) to 6.0% (PCR + PCR) and 2.0% (PCR + microscopy).

Conclusion

Coinfections of P. falciparum and Schistosoma species remain a major health challenge in SSA, particularly affecting children owing to systemic healthcare gaps, poverty, and inadequate water and sanitation. Geographic and temporal patterns reflect persistent transmission hotspots, with diagnostic limitations underestimating the true prevalence. Effective control requires integrated public health strategies prioritizing children, combining medical treatment, improved sanitation, and affordable, sensitive diagnostics to stop transmission.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12936-026-05902-3.

Background

Malaria remains a significant global health challenge, with SSA bearing the greatest burden. The World Malaria Report 2024 documented an estimated 263 million cases worldwide in 2023, of which the WHO African Region accounted for 94% of cases and 95% of deaths. The disease claimed approximately 597,000 lives, with children under 5 years of age representing the most vulnerable population, comprising 76% of all malaria-related deaths in the WHO African Region [1]. Although five Plasmodium species infect humans, P. falciparum accounts for the majority of severe malaria-related illnesses and deaths worldwide [1, 2]. Clinical manifestations, such as fever, nausea, and vomiting, primarily occur during the parasite’s asexual replication phase within red blood cells [2, 3]. While individuals with substantial acquired immunity may remain asymptomatic, untreated infections in non-immune hosts can rapidly progress to severe malaria. This severe form is characterized by life-threatening complications, including cerebral malaria, severe anaemia, acute respiratory distress, and acute renal failure, particularly in P. falciparum infections [2, 4].

Schistosomiasis poses a major public health threat in many of the same geographical areas as malaria. SSA bears a disproportionate burden, accounting for approximately 94% of all individuals requiring preventive chemotherapy globally [5]. In 2023, the WHO African Region delivered approximately 96.5% of global preventive chemotherapy campaigns for schistosomiasis, treating over 100 million school-aged children; however, substantial gaps remain, as 11 countries needing intervention have implemented no mass drug administration rounds [6]. Although six species of the genus Schistosoma infect humans, S. haematobium and S. mansoni are the predominant pathogens in SSA [7]. S. mansoni, which causes intestinal schistosomiasis, and S. haematobium, which causes urogenital schistosomiasis, are the main forms of human schistosomiasis in this region [8]. Infections caused by either species manifest as a wide range of signs and illnesses, including anaemia, nutritional deficiencies, dysuria, and developmental retardation, which is particularly frequent in school-aged children [9].

Coinfection arises from overlapping life cycles and environmental factors that facilitate the transmission of both malaria and helminths. Communities near water bodies serve as breeding grounds for Anopheles mosquitoes, increasing malaria risk. These same water sources, often used for domestic purposes, may also harbour helminth eggs and larvae [10], creating conditions conducive to simultaneous exposure and infection by both pathogens.

The geographical distributions of Plasmodium and Schistosoma species largely coincide across SSA, resulting in elevated rates of coinfection [11]. Nevertheless, research examining the interactions between these parasites has yielded inconsistent results. Regarding S. haematobium, some studies have documented a reduced incidence and density of P. falciparum in coinfected children [12, 13]. Conversely, the relationship between S. mansoni and malaria appears antagonistic, although this interaction is influenced by the host’s age and the intensity of egg deposition [14]. Additionally, other evidence suggests a strong correlation between high-intensity schistosomiasis and severe Plasmodium parasitaemia [15].

Despite the significant morbidity and socioeconomic burdens imposed by these coinfections, particularly on children in endemic regions, comprehensive epidemiological data remain insufficient. Current studies present a fragmented picture, with variations in study design, age groups, and outcomes leading to divergent estimates that critically hinder efforts to accurately assess the true burden of coinfection. This inconsistency impedes policymakers from effectively evaluating risks and formulating integrated, evidence-based control strategies.

To address this gap, this systematic review and meta-analysis consolidate available studies to derive pooled prevalence estimates of P. falciparum–Schistosoma coinfection among children aged 0–17 years in SSA.

Method

Study protocol and search strategy

This meta-analysis and systematic review examined the prevalence of coinfections involving P. falciparum, S. haematobium, and S. mansoni in children aged 0–17 years in SSA. A comprehensive literature search was conducted across multiple electronic databases, including PubMed, the Cochrane Library, Web of Science, EBSCOhost, Google Scholar, and ProQuest. To ensure extensive coverage, additional searches were performed on publisher platforms such as Wiley Online Library and Springer Link. A tailored search strategy was developed for each database, using combinations of Medical Subject Headings (MeSH) and free-text terms linked with Boolean operators (“AND” and “OR”); in the Cochrane Library, truncation and proximity operators (e.g., NEAR/n) were also applied to enhance precision. The core search terms included: (“Plasmodium falciparum” OR “P. falciparum” OR “malaria”) AND (“Schistosoma haematobium” OR “S. haematobium” OR “urogenital schistosomiasis” OR “Schistosoma mansoni” OR “S. mansoni” OR “intestinal schistosomiasis” OR “schistosomiasis” OR “bilharziasis”) AND (“coinfection” OR “co-infection” OR “concomitant”) AND (“prevalence” OR “epidemiology”) AND (“child” OR “children” OR “paediatric” OR “adolescent” OR “school-age”) AND (“sub-Saharan Africa” OR “Africa” OR “SSA”). Database-specific adaptations were applied where necessary. The reference lists of included studies and relevant systematic reviews were also screened manually to identify additional eligible publications. The protocol for this meta-analysis and systematic review adhered to the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) 2015 statement [16] (Supplementary File S1) and PRISMA 2020 guidelines [17] (Supplementary File S2). This review was not prospectively registered.

Study selection

Based on the inclusion and exclusion criteria, all retrieved articles were imported into Covidence to remove any duplicate records. Following deduplication, two reviewers independently screened the titles and abstracts to identify potentially eligible studies for inclusion. Articles that could not be excluded based on the title and abstract alone were subsequently examined in full text for eligibility. The entire study selection process is documented and visualized using a PRISMA flow diagram, which was generated using the PRISMA2020 Shiny application [18].

Inclusion criteria

All observational studies (cross-sectional and cohort) that reported the baseline prevalence of P. falciparum coinfection with S. haematobium or S. mansoni among children aged 0–17 years in SSA and were published in peer-reviewed journals were included in this review. The search was restricted to studies published between 2010 and 2025, and only full-text articles in English were considered. All participants within the specified age range, regardless of sex, were eligible for inclusion.

Exclusion criteria

Studies were excluded if they contained duplicate data, lacked sufficient information for the extraction of outcome variables, or failed to report the specific prevalence of P. falciparum coinfection with S. haematobium or S. mansoni. Non-original research, such as systematic reviews, editorials, letters to the editor, and qualitative studies were also excluded. Additionally, studies focusing on coinfections with Schistosoma species other than the two targeted forms were deemed ineligible for this review.

Quality appraisal

The methodological quality of the included studies was assessed using the Joanna Briggs Institute (JBI) critical appraisal checklist for studies reporting prevalence data [19]. The full quality assessment scores for all included studies are provided in (Supplementary File S3). The tool consists of nine items that assess internal validity and bias. In this review, each “yes” response was scored as 1, yielding a total score of 0–9. Studies with scores of 0–4, 5–7, and 8–9 were classified as low, medium, and high quality, respectively. Only studies of medium or high quality (score ≥ 5) were included in the final synthesis of the results.

Outcome measures

The primary outcome was the baseline prevalence of P. falciparum coinfection with either S. mansoni or S. haematobium among children in SSA. Eligible studies explicitly identified Schistosoma species co-infecting with P. falciparum. The prevalence was calculated as the number of children infected with both P. falciparum and the respective Schistosoma species divided by the total number of children in the study, multiplied by 100.

Data extraction

Two authors (UH and AS) independently assessed the retrieved articles according to predefined inclusion criteria. Data extraction was subsequently conducted using a standardized Excel template, which systematically recorded study identifiers, first author, year of publication, geographic location, total sample size, quality assessments, age groups, and diagnostic methodologies for P. falciparum, S. haematobium, and S. mansoni, and the overall study design. Any discrepancies encountered during the screening or extraction process were resolved through discussion and consensus.

Statistical analysis

Data synthesis and statistical analyses were performed using Stata 17. The pooled prevalence of P. falciparum coinfection with S. haematobium and S. mansoni among children aged 0–17 years in SSA was estimated using a random-effects model, accounting for inter-study variability [20]. Prior to pooling, individual study prevalence estimates were stabilized using the Freeman–Tukey double arcsine transformation [21]. Heterogeneity across the included studies was assessed using Cochran’s Q test and quantified with the I² statistic; I² values of 25%, 50%, and 75% were considered to indicate low, moderate, and high heterogeneity, respectively [22, 23]. A leave-one-out sensitivity analysis was performed to assess the robustness of the pooled estimates and identify influential studies [24].

Visual inspection of funnel plots was conducted to assess potential publication bias, where symmetry indicates a low probability of systematic bias or small-study effects. For the meta-analysis of P. falciparum–S. haematobium coinfection, where at least 10 studies were available, funnel plot asymmetry was formally assessed using Egger’s regression test [25, 26]. Egger’s test was not applied to the P. falciparum –S. mansoni analysis because fewer than 10 studies were available, which would provide insufficient statistical power and yield an unstable test [26].

Subgroup analyses were performed to evaluate heterogeneity in the pooled coinfection prevalence across geographic regions in SSA, publication years, and diagnostic methods. Additionally, a leave-one-out sensitivity analysis using a random-effects model was conducted to assess the influence of individual studies on the overall prevalence estimates. The statistical significance for all tests was defined as p < 0.05.

Results

Characteristics of the included studies

The initial database search resulted in the identification of 1431 records. Subsequently, 260 duplicate records were eliminated; of these, 215 were identified using automated tools, and 45 were manually removed. After deduplication, 1171 records remained for title and abstract screening, of which 1000 were deemed irrelevant and excluded. A full-text assessment of the remaining 171 articles led to the exclusion of 150 based on the prespecified inclusion and exclusion criteria, resulting in 21 unique studies being included in the meta-analysis (Fig. 1). Of these, 15 examined P. falciparum–S. haematobium coinfection, 9 examined P. falciparum–S. mansoni coinfection, and three contributed data to both coinfection types [27–29].

Fig. 1.

Flow chart of study selection for the systematic review and meta-analysis of P. falciparum–S. haematobium and P. falciparum–S. mansoni coinfections among children in SSA

The included studies were conducted in multiple countries in SSA, including West, East, and Central Africa. Research on P. falciparum–S. haematobium coinfection was concentrated in West Africa, whereas studies on P. falciparum–S. mansoni coinfection were more broadly distributed across West, East, and Central Africa. The main characteristics of the included studies are summarized in Table 1.

Table 1.

Descriptive summary of studies included in the systematic review and meta-analysis

Authors, publication year	Country	Study design	Age Range (years)	Sample size	Pf + Sh Prevalence (%)	Pf + Sm Prevalence (%)	Diagnostic method	JBI quality score
Adedoja et al., [30]	Nigeria	CS	4–15	309	20.1	–	Pf: PCR and microscopy; Sh: Urine centrifugation	8
Mazigo et al., [31]	Tanzania	CS	8–16	400	–	10.5	Pf: Microscopy; Sm: Kato–Katz	8
Mboera et al., [32]	Tanzania	CS	11.1 (mean)	578	10.9	–	Pf: Microscopy; Sh: Urine filtration	8
Nkemngo et al., [33]	Cameroon	CBCS	3–15	495	–	2.2	Pf: RDT, microscopy, and PCR; Sm: Kato–Katz	9
Adedoja et al., [34]	Nigeria	CS-I	1–15	103	54.4	–	Pf: Microscopy; Sh: Urine sedimentation	8
Adukpo et al., [35]	Nigeria	CS	5–15	450	11.6	–	Pf: Microscopy and nested PCR; Sh: Urine filtration	8
Kabatereine et al., [36]	Uganda	CS	10–14	3569	–	23.5	Pf: Microscopy; Sm: Kato–Katz	8
Kinung’hi et al., [27]	Tanzania	CS	3–13	1546	3.0	8.1	Pf: Microscopy; Sh: Urine filtration (Nucleopore); Sm: Kato–Katz	8
Nkengazong et al., [37]	Cameroon	CS	3–15	254	–	6.3	Pf: Microscopy; Sm: Kato–Katz	9
Afolabi et al., [29]	Senegal	PPB	1–14	910	1.1	0.7	Pf: Microscopy and PCR; Sh: Urine filtration, POC-CCA, PCR; Sm: Kato–Katz, PCR,	9
Orish et al. [38]	Ghana	CS	6–14	550	8.4	–	Pf: RDT and microscopy; Sh: Urine sedimentation; Sm: Wet mount microscopy	8
Dejon-Agobé et al., [39]	Gabon	PL	6–16	739	9.1	–	Pf: Microscopy (Lambaréné method); Sh: Urine filtration	9
Amoo et al., [40]	Nigeria	CBCS	4–15	1103	8.1	–	Pf: RDT and microscopy; Sh: Urine sedimentation; Sm: Kato–Katz	8
Dassah et al., [41]	Ghana	CS	5–17	326	–	7.7	Pf: Microscopy, Sm: Kato–Katz	8
Sumbele et al., [42]	Cameroon	CS	4–14	628	8.8	–	Pf: Microscopy, Sh: Urine filtration	9
Kinung’hi et al., [28]	Tanzania	CS	6–15	928	0.0	22.4	Pf: Microscopy, Sh: Urine filtration Sm: Kato–Katz	9
Nyarko et al., [43]	Ghana	CS	9–14	404	1.0	–	Pf: RDT, Sh: Urine filtration (Nuclepore)	9
Ola et al., [44]	Nigeria	CS	2–16	185	9.2	–	Pf: RDT, Sh: Urine microscopy	8
Morenikeji et al. [45]	Nigeria	CS	11.5 (mean)	167	34.13	–	Pf: Microscopy, Sh: Microscopy	7
Esum et al., [46]	Cameroon	CS	5–15	397	7.81	–	Pf: Microscopy, nPCR, Sh: Microscopy	8
Namulondo et al., [47]	Uganda	CS	10–15	210	–	71.4	Pf: qPCR Sm: CAA, qPCR	7

CS, cross-sectional; CBCS, community-based cross-sectional; CS‑I, cross-sectional with interventional component; PPB, prospective population-based; PL, prospective longitudinal; Pf, Plasmodium falciparum; Sh, Schistosoma haematobium; Sm, Schistosoma mansoni; RDT, rapid diagnostic test; PCR, polymerase chain reaction; POC-CCA, point-of-care circulating cathodic antigen; Nucleopore membrane filtration

Pooled prevalence of P. falciparum and S. haematobium coinfection among children in SSA

Fifteen studies were included to estimate the pooled prevalence of P. falciparum and S. haematobium coinfection among children in SSA. The pooled prevalence was 10.0% (95% CI 6.0–14.0%), as shown in the forest plot (Fig. 2). Individual study estimates vary widely, ranging from 0.0% (Kinung’hi et al., [28]) to 54.0% (Adedoja et al., [34]). The study weights were relatively uniform, ranging from 6.17% to 6.83%, indicating that the random-effects model balanced the contribution of each study despite the high heterogeneity. Significant heterogeneity was observed across studies (I² = 98.00%, p < 0.001), reflecting substantial variability in coinfection prevalence across different epidemiological settings.

Fig. 2.

Forest plot showing the pooled prevalence of P. falciparum and S. haematobium coinfection among children in SSA

Pooled prevalence of P. falciparum and S. mansoni coinfection among children in SSA

Nine studies were included to estimate the pooled prevalence of P. falciparum and S. mansoni coinfection among children in SSA. The overall pooled prevalence was 13.0% (95% CI 6.0–24.0%), as shown in the forest plot (Fig. 3). Individual study estimates exhibit substantial variability, ranging from 1.0% Afolabi et al., [29] to 71.0% Namulondo et al., [47]. Statistical analysis indicated significant heterogeneity across the studies (I² = 99.28%, p < 0.001). Despite the wide range of prevalence estimates, the weights assigned to individual studies were relatively consistent (10.94–11.24%), suggesting that the influence of the random-effects model was distributed evenly across studies.

Fig. 3.

Forest plot showing the pooled prevalence of P. falciparum and S. mansoni coinfection among children in SSA

Subgroup analysis

Geographic subgroup analysis identified distinct regional patterns for both types of coinfections. For P. falciparum–S. haematobium coinfection (Fig. 4A), the highest prevalence was observed in West Africa at 13.0% (95% CI 6.0–22.0%), followed by Central Africa at 9.0% (95% CI 7.0–10.0%) and East Africa at 3.0% (95% CI 0.0–11.0%). However, the differences among these regions were not statistically significant (p = 0.150), and the overall heterogeneity for this group was I² = 98.00%. In contrast, P. falciparum–S. mansoni coinfection (Fig. 4B) demonstrated significant regional heterogeneity (p < 0.001), with East Africa exhibiting the highest prevalence at 25.0% (95% CI 13.0–39.0%), which was substantially higher than the rates in Central Africa at 3.0% (95% CI 2.0–5.0%) and West Africa at 2.0% (95% CI 1.0–3.0%). The overall heterogeneity for this coinfection was (I² = 99.28%), reflecting the substantial diversity in transmission dynamics across the different study sites.

Fig. 4.

Forest plots showing the pooled prevalence of P. falciparum–S. haematobium (A) and P. falciparum–S. mansoni (B) coinfection among children in SSA, stratified by geographic region

Temporal stratification revealed distinct patterns across publication periods for both types of coinfections. For P. falciparum–S. haematobium coinfections (Fig. 5A), the pooled prevalence remained relatively stable over time (p = 0.185), with estimates of 22.0% (95% CI 6.0–44.0%) for 2011–2015, 5.0% (95% CI 1.0–15.0%) for 2016–2020, and 7.0% (95% CI 4.0–12.0%) for 2021–2025. Similarly, P. falciparum–S. mansoni coinfections (Fig. 5B) exhibited no significant temporal variation (p = 0.683), with prevalences of 13.0% (95% CI 4.0–26.0%) in 2010–2014, 18.0% (95% CI 16.0–21.0%) in 2015–2019, and 14.0% (95% CI 0.0–43.0%) in 2020–2025. Notably, heterogeneity remained high across most subgroups; the I² statistic frequently exceeded 95%, indicating substantial between-study variability that should be considered when interpreting these pooled estimates.

Fig. 5.

Forest plots showing the pooled prevalence of P. falciparum–S. haematobium (A) and P. falciparum–S. mansoni (B) coinfection among children in SSA, stratified by publication period

The diagnostic methodology played a significant role in contributing to the heterogeneity observed in coinfections estimates (p < 0.001) (Fig. 6). In the case of P. falciparum–S. haematobium coinfection (Fig. 6a), the highest pooled prevalence was recorded at 13.0% (95% CI 7.0–20.0%) when PCR was combined with microscopy, based on three studies. This was followed by microscopy alone, which yielded a prevalence of 11.0% (95% CI 6.0–19.0%), derived from nine studies. Lower estimates were observed for RDT combined with microscopy, with a prevalence of 3.0% (95% CI 1.0–4.0%) based on two studies, whereas the lowest prevalence was reported for PCR combined with PCR at 1.0% (95% CI 1.0–2.0%) based on a single study. Similarly, for P. falciparum–S. mansoni coinfections (Fig. 6b), the highest prevalence was obtained using microscopy combined with microscopy at 12.0% (95% CI 7.0–20.0%) across six studies. Diagnostic approaches incorporating molecular detection yielded lower estimates; the estimate for PCR combined with PCR was 6.0% (95% CI 5.0–8.0%) based on two studies, and PCR combined with microscopy was 2.0% (95% CI 1.0–4.0%) based on only one study. The overall heterogeneity remained high for both coinfection types (I² = 98.00% and 99.28%, respectively), indicating substantial between-study variability that persists despite accounting for diagnostic differences.

Fig. 6.

Forest plots showing the pooled prevalence of P. falciparum–S. haematobium (A) and P. falciparum–S. mansoni (B) coinfections among children in SSA, stratified by diagnostic method

Publication bias

Publication bias was evaluated for the overall pooled prevalence through visual inspection of funnel plots (Fig. 7) and quantitative statistical tests, as applicable. In the analysis of P. falciparum–S. haematobium coinfection (Fig. 7A), the funnel plot exhibited clear asymmetry, indicating the presence of small-study effects. This observation was corroborated by Egger’s linear regression test (coefficient = 16.68; 95% CI 9.70–23.66; p < 0.001) and Begg’s rank correlation test (p = 0.0075), both of which demonstrated significant publication bias across the 15 included studies (Table 2). For the P. falciparum–S. mansoni coinfection analysis (Fig. 7B), the interpretation was based on qualitative inspection of the funnel plot. The plot revealed a highly dispersed and asymmetrical pattern, consistent with the extreme overall heterogeneity (I² = 99.28%) observed in the primary analysis. A notable absence in the lower-left quadrant suggests that small studies reporting low or null prevalence for the P. falciparum–S. mansoni pairing may be underrepresented in the published literature.

Fig. 7.

A Funnel plot of P. falciparum and S. haematobium coinfection among children in SSA. B Funnel plot of P. falciparum and S. mansoni coinfection among children in SSA

Table 2.

Egger’s and Begg’s tests for publication bias in studies of P. falciparum–S. haematobium coinfection among children in sub-Saharan Africa

Test	Parameter	Estimatea,	Std. Erra/SDb	Statistic	P-value	95% Conf. interval
Egger’s test	Bias Coefficient (ftse)	16.68 (bias)	3.23a	5.16 (t)	0.001	[9.70, 23.66]
Begg’s Test (continuity corrected)	Score ($\text{P - Q}$)	0.52 ${\mathrm{\tau }}_b$	20.21b	55 (S)	0.0075	–

ft_se: bias coefficient; t: Student’s t test statistic; S: Kendall’s score; ${\mathrm{\tau }}_b$: Kendall’s rank correlation coefficient; CI confidence interval.

Sensitivity analysis

The leave-one-out sensitivity analyses for both P. falciparum with S. haematobium (Fig. 8A) and P. falciparum with S. mansoni (Fig. 8B) demonstrate strong stability and robustness of the pooled effect estimates. In each case, the pooled effect size remains well within the overall 95% confidence interval boundaries throughout the omission of individual studies, indicating that no single study disproportionately influences the meta-analytic results. Specifically, no outliers are identified in the first analysis, including Adedoja et al., [34], while in the second, although Namulondo et al., [47], show a somewhat lower effect size, its omission does not meaningfully alter the pooled estimate beyond the confidence limits. These findings confirm the reliability and robustness of the meta-analytic conclusions for both coinfection pairs.

Fig. 8.

A Sensitivity analysis of P. falciparum and S. haematobium coinfection among children in SSA. B Sensitivity analysis of P. falciparum and S. mansoni coinfection among children in SSA

Discussion

Coinfections involving P. falciparum and endemic helminths, particularly S. haematobium and S. mansoni, continue to impose a significant burden across tropical regions in Africa and East Asia [48]. In SSA, the extensive geographical overlap of these parasites habitats naturally results in a high incidence of coinfection [11]. Within these overlapping transmission zones, children disproportionately bear the burden of these neglected diseases [49]. Their increased vulnerability is not solely a biological issue; it arises from a complex interplay of environmental factors, daily behaviours, and systemic health disparities.

Many endemic communities are located adjacent to freshwater bodies [50], leading to continuous exposure of children to contaminated environments. These children frequently swim, bathe, and play in lakes and rivers, often without adult supervision or awareness of the associated transmission risks [51]. This routine behavioural exposure is exacerbated by a chronic lack of access to safe drinking water and basic sanitation facilities. When considering the broader structural context, the challenges become even more pronounced. Widespread poverty, coupled with overburdened and under-resourced local clinics, severely limits the capacity for early detection and timely treatment of these infections [10].

The situation is particularly dire in regions that are overlooked by mass drug administration (MDA) programs, where life-saving antimalarials and praziquantel are either distributed irregularly or are entirely absent [52]. Ultimately, this interwoven network of environmental exposure, structural neglect, and natural childhood behaviours ensnares many children in SSA in a relentless cycle of Plasmodium–Schistosoma coinfection.

In this meta-analysis, the pooled prevalence of P. falciparum and S. haematobium coinfection was estimated to be 10.0% (95% CI 6.0–14.0%). This finding is broadly comparable to the 13.36% (95% CI 6.16–20.56%) reported in a recent systematic review conducted in sub-Saharan Africa by Abebe and Geto [52]. This general alignment suggests a notable regional burden of this coinfection, which is further supported by an individual study from Tanzania (10.9%) [32] and a country-specific meta-analysis from Nigeria (15.0%) [53]. Similarly, the pooled prevalence of P. falciparum and S. mansoni coinfection among children in SSA was estimated to be 13.0% (95% CI 6.0–24.0%). This estimate appears consistent with the 17.39% (95% CI 5.94–28.84%) reported by Wagwa Abebe et al. [54] across all age groups [54], and the 10.50% (95% CI 6.13–14.86%) documented by Setegn et al. among the general population in Ethiopia [55], suggesting a relatively consistent burden of P. falciparum–S. mansoni coinfection across diverse populations and settings within the region. However, these pooled estimates should be interpreted with caution, given the substantial heterogeneity and potential publication bias identified in this review. Rather than definitive point estimates, these figures may serve as approximate indicators of the regional coinfection burden.

Although these findings appear broadly consistent with previous research, there was considerable variation in the pooled prevalence estimates for both types of coinfections. Specifically, the combined prevalence of P. falciparum and S. haematobium coinfection showed high heterogeneity (I² = 98.00%, p < 0.001) (Fig. 2), as did the prevalence of P. falciparum and S. mansoni coinfection (I² = 99.28%, p < 0.001) (Fig. 3). This degree of variability, while not uncommon in prevalence meta-analyses involving diverse African settings, limits the precision of the pooled estimates and warrants careful interpretation. Differences in study locations, diagnostic techniques, sample sizes, age groups, endemicity levels, and the inconsistent application of MDA programs across studies may explain much of this variation. To further explore the potential sources of this heterogeneity, subgroup analyses were conducted.

Subgroup analysis based on geographic region revealed distinct spatial patterns for each coinfection type across SSA. For P. falciparum and S. haematobium, the highest pooled prevalence was recorded in West Africa (13.0%), although considerable within-region variability was observed. Individual study results ranged from 1.0% in Ghana [43] to 6.3% in Mali [56], with intermediate rates of 3.3% [57] and 3.4% [53] reported in Nigeria. Central Africa exhibited a moderate pooled prevalence of 9.0%, which was supported by relatively consistent primary data. Studies in Cameroon documented rates of 6.6% [58] and 7.8% [59], with the latter notably persisting despite more than 8 years of sustained control measures. Gabon similarly reported a 9.0% prevalence [39]. Conversely, East Africa yielded the lowest overall pooled estimate (3.0%) for this coinfection, yet contained the most extreme between-study variability. Although a prevalence of 2.8% was reported in Ethiopia [60], considerably higher rates were observed in Tanzania (10.9%) [32]. This wide disparity within East Africa may reflect differences in local ecological conditions, proximity to freshwater bodies [61], and varying levels of schistosomiasis control program implementation.

In contrast to this pattern, the geographic distribution of P. falciparum and S. mansoni coinfection appeared to follow an inverse spatial trend. The burden of this coinfection was predominantly concentrated in East Africa, with a pooled prevalence of 25.0%. However, this estimate was heavily influenced by considerable between-study variability. Reported prevalence ranged from 7.6% [62] and 22.6% [31] in Tanzania, to 10.5% in Ethiopia [55], and 13.1% in Kenya [63]. Notably, a particularly high coinfection rate of 71.0% was reported from selected sites along the Albert Nile in northwest Uganda [47]. This elevated burden in East Africa may be partly attributable to the region’s topography, as the extensive African Great Lakes network potentially provides favourable breeding habitats for the Biomphalaria snail intermediate host. Meanwhile, pooled prevalences in Central and West Africa were comparatively low, at 3.0% and 2.0%, respectively. These modest figures appear consistent with individual study estimates showing rates of 2.2% in Cameroon [58], 1.5% in the DR Congo [64], 1.4% in Ghana [65], and 0.7% in Senegal [29]. These contrasting regional patterns may reflect the distinct ecological preferences of schistosome intermediate hosts, with Bulinus snails potentially supporting S. haematobium transmission predominantly in West African settings and Biomphalaria snails facilitating S. mansoni transmission in the freshwater basins of East Africa.

Subgroup analysis based on publication year was performed to evaluate potential temporal trends in the prevalence of coinfection. For P. falciparum and S. haematobium, pooled prevalences exhibited numerical variation across different time periods (22.0% in 2011–2015, 5.0% in 2016–2020, and 7.0% in 2021–2025); however, this temporal variation did not reach statistical significance (Between-group p = 0.185). Similarly, the prevalence of P. falciparum and S. mansoni coinfection appeared to remain statistically stable over time, with estimates of 13.0% (2010–2014), 18.0% (2015–2019), and 14.0% (2020–2025) showing no significant difference across periods (between-group p = 0.683). However, these temporal observations should be interpreted with caution. The apparent stability might be partially influenced by the uneven distribution of published literature, including missing studies from certain high-burden regions during specific time intervals, and the potential publication bias noted earlier.

Subgroup analyses by geographical region and year of publication revealed notable variations in the coinfection prevalence. Subsequent subgroup analysis based on diagnostic methodology showed significant differences in the reported prevalence of both P. falciparum–S. haematobium and P. falciparum–S. mansoni coinfections. For P. falciparum–S. haematobium, the highest pooled prevalence was observed in studies utilizing PCR combined with microscopy (13.0%) and dual microscopy (11.0%), while other diagnostic approaches, such as RDT plus microscopy (3.0%) and dual PCR (1.0%), yielded lower estimates. Similarly, for P. falciparum–S. mansoni, dual microscopy produced the highest pooled prevalence (12.0%), followed by dual PCR (6.0%) and PCR plus microscopy (2.0%). These diagnostic-related differences were statistically significant (between-group p < 0.001), with very high between-study heterogeneity (I² > 98%, p < 0.001), indicating substantial variability beyond chance. However, the uneven distribution of primary studies across diagnostic subgroups, some represented by only one or two studies, likely influenced these patterns by limiting statistical precision and increasing susceptibility to site-specific effects. Therefore, the observed variation across diagnostic categories may not solely reflect true differences in diagnostic sensitivity or specificity but may also partly arise from the limited and unbalanced representation of studies within certain subgroups.

If these estimates accurately reflect regional trends, the observed stability suggests that current public health interventions, including mass drug administration, vector control, and improvements in water, sanitation, and hygiene, have not achieved the coverage or effectiveness necessary to significantly reduce population-level coinfection rates [66]. Evidence also shows that even well-implemented multi-year MDA programs often encounter ‘persistent hotspots’ where schistosomiasis prevalence and intensity fail to decline as expected [67]. The high heterogeneity within subgroups across all study periods indicates that variability is driven more by site-specific epidemiological factors, such as local endemicity, demographic characteristics, and proximity to freshwater transmission sites, rather than temporal shifts in disease burden [68].

In SSA, diagnostic strategies for coinfections of P. falciparum and Schistosoma species in children rely heavily on traditional methods, such as microscopy for malaria and Kato-Katz or urine filtration for schistosomiasis. These methods remain central because of their cost-effectiveness, minimal infrastructure requirements, and suitability for point-of-care use [69–71]. However, their limited sensitivity, particularly in detecting low-intensity infections common after mass drug administration campaigns, affects prevalence estimates and may lead to underreporting in meta-analyses [70, 71].

The reliance on traditional diagnostic methods has significant implications for meta-analyses concerning the prevalence of coinfections. As most field studies continue to utilize these reference standard methods, the pooled estimates predominantly reflect their detection thresholds, potentially leading to an underestimation of infections occurring at low intensities. Several systematic reviews have highlighted that microscopy and the Kato–Katz technique remain widely employed as operational gold standards despite their reduced sensitivity in low-endemicity settings where light-intensity infections are prevalent [70, 71].

Molecular techniques, such as PCR, offer greater sensitivity for detecting submicroscopic P. falciparum parasitaemia and low-intensity Schistosoma infections. For example, real-time PCR identified S. mansoni in Ghanaian preschool children at rates substantially higher than those detected by the Kato-Katz or urine-CCA assays [72, 73]. Similarly, PCR-based methods have revealed hidden malaria reservoirs in asymptomatic children in The Gambia, where traditional diagnostics often miss low-density infections [74]. Despite these advantages, the widespread use of molecular diagnostics is limited by the requirements for stable electricity, trained personnel, and costly reagents, which are often unavailable at primary healthcare facilities in SSA [70, 75, 76].

A further challenge in malaria diagnosis is the emergence of P. falciparum strains with deletions in the pfhrp2/3 genes, causing false-negative results with the widely used HRP2-based rapid diagnostic tests (RDTs). This genetic adaptation undermines the reliability of key diagnostic tools in many SSA settings [77, 78].

Future strategies to enhance control efforts must prioritize the accurate assessment of malaria, schistosomiasis, and their coinfections. This can be achieved by expanding the use of highly sensitive diagnostic tools, establishing regional PCR laboratories in high-risk zones, and training healthcare workers to promptly detect and manage cases. Vector control remains pivotal, with the sustained distribution of insecticide-treated nets, indoor residual spraying, and ongoing malaria vaccination campaigns. Strengthening community clinics to provide reliable early diagnosis and treatment services is essential.

Given the significant infection burden among children, including those of preschool age, integrating school-based health education programs is crucial. Empowering parents and teachers through targeted community initiatives will foster behaviours that reduce exposure to vectors and the transmission of S. haematobium, S. mansoni, and malaria in the long term. Additionally, regular MDA, improved sanitation infrastructure, increased access to clean water, and active community involvement in hygiene promotion are vital for interrupting the transmission cycles of both schistosomiasis and malaria.

Sustained government commitment is fundamental to support these interventions, enhance surveillance systems, and ensure durable progress in controlling malaria and schistosomiasis at the population level.

Conclusion

Coinfections of P. falciparum and Schistosoma species remain a major public health issue in SSA, disproportionately affecting vulnerable populations, particularly children. Their prevalence stems from systemic healthcare gaps, poverty, and inadequate water and sanitation facilities. Routine childhood exposure to contaminated water and under-resourced clinics, alongside inconsistent mass drug administration, sustains overlapping infections.

These coinfections vary according to geography, time, and diagnostic methods. S. haematobium predominates in West and Central Africa, while S. mansoni is common in East Africa’s freshwater basins. The prevalence has remained stable, indicating persistent transmission hotspots. Reliance on traditional diagnostics underestimates the true burden of disease, as low-intensity infections are often missed. Although molecular diagnostics are more accurate, they face infrastructural and financial barriers. Addressing these challenges requires integrated and multisectoral public health strategies.

Future efforts should focus on child-cantered programs through schools and communities, combining treatment with investments in water, sanitation, and hygiene (WASH) infrastructure. Affordable, high-sensitivity diagnostic tools that are suitable for low-resource settings are essential. Empowering parents and teachers to promote protective behaviours can help interrupt transmission. Consistent mass drug administration, improved sanitation, access to clean water, and active community hygiene participation are critical. Strengthening malaria control via insecticide-treated nets, timely diagnosis, and vaccine uptake is important. Integrating enhanced diagnostics, environmental improvements, and comprehensive paediatric care will effectively disrupt neglected tropical diseases.

Study limitations

This review had several limitations. The quality of reporting across the included studies varied considerably, and some investigations were based on small sample sizes, reducing the statistical power. Restricting the search to English publications may have led to the omission of relevant studies published in other languages. Substantial heterogeneity in study design, diagnostic approaches, sampling strategies, and study periods further constrains the comparability of the findings and the generalizability of the pooled estimates. Visual inspection of the funnel plots revealed clear asymmetry, and both Egger’s and Begg’s tests supported this pattern, indicating the presence of small-study effects or a potential publication bias.

Author contributions

Author contributions Conceptualization: JZ, UH, YZ. Data curation: UH, AS. Formal analysis: UH. Methodology: UH, YZ, JZ. Validation: UH, AS. Writing—original draft: UH. Writing—review & editing: UH, YZ, JZ.

Funding

This study was supported by grants from the Academic Aid Program for Top-notch Talents in Provincial Universities (gxbjZD2020071), and the Key Project in Natural Science Research in Higher Education Institutions of Anhui Province (2022AH051236). We are grateful for the financial support provided by the above departments.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.