Characterisation of malaria and glucose-6-phosphate dehydrogenase deficiency in conflict-affected zones of southern and eastern Sudan

Abstract

Background

Malaria causes high morbidity and mortality in Sudan. Malaria control efforts have been disrupted by conflict and displacement, which affected the whole health system. This study aimed to characterize malaria and glucose-6-phosphate dehydrogenase deficiency in conflict-affected zones of southern and eastern Sudan.

Methods

This cross-sectional study was conducted between 2023 and 2024, enrolling 717 patients with clinical symptoms suggestive of malaria in Kosti (southern Sudan, n = 252) and Kassala (Eastern Sudan, n = 465). Malaria infection was confirmed by light microscopy as a standard test, and qPCR was used as a reference method. Haematological indices were analysed on automated analysers, and PCR-RFLP was used to determine G6PD genotypes.

Results

Malaria prevalence was 62.6% (291/465; 95% CI 58.2–67.0%) in Kassala and 52% (133/252; 95% CI 46.6–59.0%) in Kosti using PCR. In Kassala, 157 cases (54%; 95% CI: 48.2–59.7%) were Plasmodium vivax (P.v), 99 (34%; 95% CI: 28.6–39.8%) were Plasmodium falciparum (P. f), and 35 (12%; 95% CI: 8.6–16.2%) were P. f/P. v infections. In Kosti, P. f was detected in 130 (97.7%) subjects, and P. v was detected in 3 (2.7%; all were negative by microscopy). There were 37 (8.7%) subjects not detected by microscopy but positive by PCR (submicroscopic), 20 (15%) in Kosti and 17 (5.8%) in Kassala. The G6PD B variant predominated in Kassala (438/94.2%) and Kosti (200/79.4%). The African A− variant was detected in 9 (3.5%) individuals in Kosti (7 males, 2 females). In Kosti females, BA, BA−, AA, and AA− were observed in 11 (7%), 4 (2.5%), 4 (2.5%), and 9 (5.7%), respectively, compared to 10 (4.1%), 4 (1.6%), and 7 (3%) BA, BA−, and AA cases in Kassala females. No significant association was observed between G6PD genotype and parasite density. Malaria prevalence did not differ significantly between Internally Displaced Persons (IDPs) and residents.

Conclusions

Regional variation was observed in malaria parasite species, including submicroscopic infections, as well as in G6PD genotypes. The study identified the G6PD A⁻ variant in Sudan. Ongoing conflict in Sudan has similarly impacted the health of both resident and displaced populations.

Clinical trial number

Not applicable.

Supplementary information

The online version contains supplementary material available at 10.1186/s12879-026-13335-1.

Background

Malaria is one of the top ten leading causes of mortality and morbidity in the world, particularly in sub-Saharan Africa [1]. The 2023 global malaria estimates reported 263 million malaria cases worldwide, with 597,000 deaths, the vast majority in Africa, where Plasmodium falciparum is the major parasite species. The 2023 global malaria incidence rate was 60.4 cases per 1,000 people at risk, up from 58.6 in 2022. Rates in Africa also increased, from 58.8 to 61.1 cases per 1,000 people at risk [2].

In Sudan, malaria is among the most critical public health problems. The country accounts for nearly half of all malaria cases and 70% of malaria-related deaths in the WHO Eastern Mediterranean Region [3]. Data from the Sudan National Malaria Control Program’s (NMCP) national surveillance system recorded 3.7 million malaria cases and 1,760 deaths across Sudan in 2021, accounting for 17.0% of outpatient visits and 14.7% of hospital admissions. These statistics were obtained nationally and not from the specific study regions. Plasmodium falciparum was responsible for the majority of infections (87.6%), followed by P. vivax (8.1%) [4].

Malaria transmission in Sudan is hypoendemic in the northern regions, mesoendemic in the central and part of eastern regions, and hyperendemic in the southern regions [3]. Historically, P. falciparum has been the dominant species; however, recent surveillance data suggest a high prevalence of P. vivax across different parts of Sudan [5–8]. Possible drivers of this change in prevalence include population migration, climate variation, and the potential adaptation of emerging parasites to Duffy-negative hosts, a subject of current investigation [9]. Internally displaced persons (IDPs) are particularly at risk of vector-borne diseases due to a lack of preventive measures and available health services [10, 11].

Diagnosis of malaria in Sudan relies on microscopy and rapid diagnostic tests (RDTs) as per the NMCP case management protocol, in line with the WHO “test and treat” strategy [12]. However, the accuracy of diagnosis is hampered by variable laboratory infrastructure, shortage of trained microscopists, and inconsistent RDT performance [13, 14]. Molecular diagnostics can provide superior sensitivity and specificity; however, they are limited to reference and research laboratories due to cost and logistical constraints [15]. Submicroscopic malaria infections, which cannot be detected by light microscopy but can be detected by molecular methods, make up a significant portion of the infectious reservoir. PCR has been shown to detect many more Plasmodium infections than microscopy, particularly P. vivax infections. These infections can lead to continued transmission and underreporting of parasite prevalence in surveillance [16]. Emerging automated digital microscopes and digital molecular tools show promise as near point-of-care applications but have not yet been widely validated [17, 18].

Management of P. vivax malaria is complicated by the presence of Glucose-6-phosphate dehydrogenase deficiency (G6PDd) [19, 20]. It is an X-linked inherited disorder caused by reduced enzyme activity. It has a worldwide distribution with high frequency in malaria-endemic countries [20]. Some G6PD variants have been associated with partial protection against P. f infection, but they pose a major challenge for P. v treatment [21]. Globally, the G6PD B allele (202 G/376A) is the most widespread genotype, while the A and A⁻ variants are characteristic of African populations. In sub-Saharan Africa, A variant present at a low frequency and deficient haplotype (202A/376 G) is largely absent [22]. In Sudan, national guidelines recommend G6PD testing before administration of primaquine for P. vivax [23]. However, because reliable G6PD testing is often unavailable, patients may be at risk of severe hemolysis.

The armed conflict that began on 15 April 2023 severely disrupted the health system, displaced millions, and may have impacted the malaria burden through population movement, poor sanitation, and loss of vector control measures [24]. Evidence from other conflict-affected zones in the region has reported malaria prevalence as high as 60% among displaced populations, with the majority of infections caused by P. falciparum [25, 26]. This study aimed to characterize malaria and glucose-6-phosphate dehydrogenase deficiency in conflict-affected zones of southern and eastern Sudan. The rationale for this combination is to develop a point-of-care molecular testing that combines malaria species identification and G6PD genotype screening in an area co-endemic of P. falciparum and P. vivax. This study provides the background characterization of malaria species and G6PD epidemiology in two distinct areas affected by conflict in Sudan.

Methods

Study area and setting

As shown in Figure S1, Kosti (13.1626° N, 32.6635° E) and Kassala (15.4500° N, 36.4000° E) were selected in southern Sudan and Eastern Sudan, respectively. Kosti is located about 300 km south of Khartoum, on the White Nile, and Kassala is 410 km east, on the Sudan/Eritrea/Ethiopia tri-border area. Transmission at both sites is seasonal and unstable, with a peak in and just after the rainy season (June–October). Prior to the conflict, P. falciparum predominated in Kosti (>85%). In contrast, Kassala reported a higher proportion of P. vivax, consistent with earlier estimates of a national P. vivax prevalence of 26.6% and marked regional variation linked to population movements and proximity to neighbouring endemic countries [6, 27]. Transmission is also affected by ecological factors. Kassala is semi-arid, with an average annual rainfall of about 251 mm. Seasonal flooding of the Gash River creates temporary mosquito breeding sites. The landscape of Kosti is savannah, with an average annual rainfall of about 351 mm [28]. Perennial water bodies in Kosti provide a more stable habitat for Anopheles mosquitoes. Anopheles arabiensis is the principal malaria vector at both sites [29]. Participants were recruited from 14 health facilities: 3 in Kosti and 11 in Kassala.

Study design

A cross-sectional descriptive clinical study was conducted between 2023 and 2024 to explore the epidemiology, clinical, and haematological aspects of malaria and the distribution of G6PD in conflict-affected zones that host IDPs in Sudan. The study areas were purposively selected for intense malaria transmission, high IDP concentration, and disruption of public health interventions due to ongoing conflict [30].

Study subjects and inclusion criteria

Patients were enrolled consecutively from the outpatient clinics of the study sites’ health facilities during the transmission peak to ensure adequate representation of cases. Consequently, prevalence estimates reflect clinic-based cases rather than the community-level malaria burden. Patients aged ≥ 5 years presented with symptoms suggestive of malaria were enrolled after obtaining written informed consent; for children aged 5–11 years, guardian consent was obtained, for adolescents aged 12 to 17 years, written assent and guardian consent were obtained. Children aged < 5 years and pregnant women were excluded due to distinct clinical and haematological profiles. Participants were grouped into three age categories (<10 years, 10–18 years, and >18 years) according to the WHO malaria surveillance age-stratification recommendations (children, adolescents, and adults), to reflect differences in malaria exposure and acquired immunity [31]. Additionally, following WHO guidelines, exclusion criteria included severe malaria and recent use of antimalarial therapy (within the previous 4 weeks) [32].

Sample size

A total of 717 subjects (252 in Kosti and 465 in Kassala) were enrolled. Sample size was calculated using Epitools (AusVet, Australia) based on a comparison of two independent proportions [6]. Literature indicated an expected P. vivax prevalence of approximately 26.6% in some Sudanese states and 8% in others [5, 25]. Using these as the assumed proportions (p₁ = 0.266, p₂ = 0.08), with 80% power and a 5% two-sided significance level, the required minimum sample size was 61 participants per site (122 total). Our achieved sample of 717, therefore, exceeded the minimum requirement and allowed site-specific estimation of Plasmodium species prevalence and G6PD variant frequencies with ≥95% confidence and ±5% precision.

Sample collection and processing

Demographic, clinical, and laboratory data were obtained using standardized case report forms. Vital signs (temperature, pulse, respiratory rate, and blood pressure) and malaria-related symptoms (fever, headache, chills, sweats, nausea, etc.) were documented by qualified clinicians on enrolment. Fever was defined as a temperature ≥ 37.5 °C.

Approximately 2.5 mL of venous blood was collected in EDTA tubes by qualified laboratory personnel. Fifteen µL of collected blood was used to prepare two microscope slides with thin and thick films immediately [33]. Subsequently, the blood sample was processed for Complete Blood Count (CBC) using haematology analysers. Four 100 µL aliquots were spotted onto Whatman® 3 MM Protein Saver filter paper, air-dried horizontally at approximately 22–25 °C under clean, dust-free conditions. Each DBS card was individually placed in a sealed zip-lock bag containing silica gel desiccant and stored at room temperature. Desiccant was periodically monitored until DNA extraction was performed.

Light microscopy

Light microscopy was used as a standard test for malaria diagnosis. Parasite density was quantified using patient-specific total leukocyte count rather than assuming a fixed value of 8,000/µL. All readings were blindly verified by a Level-1 WHO-certified expert microscopist. A second expert re-examined a randomly selected subset of approximately one-third of the slides for quality control, with no discrepancies observed [33].

DNA extraction and qPCR

DNA was extracted from the DBS samples using the QIAamp DNA Mini Kit (QIAGEN #51306, Netherlands) according to the manufacturer’s instructions. Three pieces of 3 mm discs were punched per sample. DNA was stored at −20 °C until used. Multiplex real-time PCR (qPCR) was used as a reference test to detect Plasmodium species by amplifying the 18S rRNA gene using LightCycler® 96 Instrument (Roche Diagnostics, Germany) and Quant Studio™ 7 Real-Time PCR System (Applied Biosystems, USA), which were used interchangeably according to availability, under identical assay conditions and quality control procedures. Previously published species-specific primers and probes for malaria diagnosis were used [34]. A triplex assay was employed to detect P. falciparum, P. ovale, and P. malariae, while P. Vivax was detected using a singleplex reaction.

Each 10 μL reaction contained 5 μL GoTaq® qPCR Master Mix (Promega, USA), 1 μL primer mix, and nuclease-free water. Thermal cycling conditions were: 95 °C for 2 min; 40 cycles of 95 °C for 15 sec and 60 °C for 60 sec. DNA samples were run in duplicate. Primer specificity was validated using a synthetic DNA positive control. In each run, a positive and a no-template control were included.

G6PD genotyping

Genotyping was performed on the DBS DNA-extracted samples using a validated PCR-RFLP method [35]. G6PD A376G Mediterranean polymorphism was identified by PCR amplification followed by FokI restriction digestion under manufacturer-recommended conditions. Genotypes were assigned based on the presence of characteristic restriction patterns corresponding to wild-type, mutant, or heterozygous alleles.

Samples harbouring the A376G substitution were further genotyped for the G202A mutation, defining the sub-Saharan African A- variant, using similar RFLP methodology with NlaIII digestion. PCR products were separated on 3% (w/v) agarose gel containing 0.5 µg/ml ethidium bromide and visualized under UV transillumination.

Statistical analysis

Data were entered into REDCap and analysed using STATA 17.0 (StataCorp, USA), MedCalc 22.015 (MedCalc Software Ltd., Belgium), and RStudio 4.4.1 (R Foundation for Statistical Computing, Vienna, Austria). Statistical significance was defined as p ≤ <0.05 (two-sided).

Categorical variables were presented as frequencies and percentages; continuous variables were presented as means ± SD or medians (IQR), depending on the distribution. Group comparisons were performed using χ² or Fisher’s exact tests for categorical data and t-tests or non-parametric equivalents for continuous variables. Likelihood ratios (LR⁺ and LR⁻) with 95% confidence intervals (CIs) were calculated. LR⁺ > 5 or LR⁻ < 0.2 was considered strong diagnostic evidence, while values close to 1 indicated little or no discriminatory ability [36].

Normality of haematological parameters was assessed using the Shapiro-Wilk test and visual inspection. Normally distributed variables were analysed by ANOVA with Bonferroni post-hoc testing; non-normal data were analysed using the Kruskal-Wallis test with Dunn’s post-hoc comparisons. Spatial mapping of administrative regions and study sites was performed using GIS tools in R.

Ethical consideration

The study was approved by the Ethics Committee of the Medical and Health Sciences, University of Khartoum, Sudan, on September 4, 2023. All participants or guardians of minor patients signed written informed consent (or assent, when applicable). According to Sudan’s National Guidelines, all malaria-positive patients received antimalarial treatment [37].

Results

Characteristics of study participants

A total of 717 patients with signs and symptoms suggestive of malaria were recruited: 465 (65%) in Kassala and 252 (35%) in Kosti (Table 1). Adults (age > 18 years) accounted for 56% and 42.1% of the study population in Kassala and Kosti, respectively, while adolescents (10–18 years) accounted for 44% in Kosti and 26.2% in Kassala. Females were more represented in Kosti (62.3%) than in Kassala (51.8%). Internally displaced persons accounted for 45% of the total study population: 60% in Kosti and 37.2% in Kassala. Almost all patients reported a history of malaria in the last year (100% in Kosti, 97.9% in Kassala). Fever history was higher in Kassala (76.8%) than in Kosti (33.7%), while current fever was similarly high in both (74.8% and 68%, respectively) (Table 1). The presenting symptoms of the patients were comparable between the two study populations (Table S1). The three most common symptoms in the study populations (headache, vomiting, and fatigue) had no discriminatory value for malaria infection (LR⁺ = 0.98, 1.04, and 1.06, respectively; LR⁻ = 1 for all). Other reported symptoms (abdominal pain, nausea, cough, chills, and diarrhoea) and the rare symptoms (stomachache, dizziness, sweats, and dysuria) similarly demonstrated no association with malaria status, with LR⁺ values close to 1 (0.91–1.10) and LR⁻ values ranging from 0.96 to 1.16.

Table 1.

Demographic and clinical characteristics of study subjects

	Description	Study Site			p-value
		Kassala (n = 465)	Kosti (n = 252)	Total (n = 717)
		n (%)	n (%)	n (%)
Age category (Years)	<10	83 (17.8)	35 (13.9)	118 (16)
	Oct-18	122 (26.2)	111 (44)	233 (32)	<0.001*
	>18	260 (56)	106 (42.1)	366 (52)
Sex	Female	241 (51.8)	157 (62.3)	398 (56)	0.013*
	Male	224 (48.2)	95 (37.7)	319 (44)
Residence status	IDP	173 (37.2)	151 (60)	324 (45)	<0.001*
	Resident	292 (62.8)	101 (40)	393 (55)
Diagnosed with malaria in the past	No	10 (2.1)	0 (0)	10 (1.4)	0.36†
	Yes	455 (97.9)	252 (100)	707 (98.6)
History of fever in last 4 Weeks	No	357 (76.8)	85 (33.7)	442 (62)	<0.001*
	Yes	108 (23.2)	167 (66.3)	275 (38)
Presenting with feverΨ	<37.5	117 (25.2)	80 (31.7)	197 (27)	0.092*
	≥37.5	348 (74.8)	172 (68.3)	520 (73)
Have a co-morbid Disease	No	427 (91.8)	234 (92.9)	661 (92)	0.62*
	Yes	38 (8.2)	18 (7.1)	56 (7.8)

*=Chi-square, †=Fisher’s test, Ψ = Fever defined as forehead temperature ≥ 37.5 °C

Self-reported comorbidities were present in 56 patients (Table S2). Hypertension was the most frequently reported condition, occurring in 19 (34%) of patients, followed by diabetes (15, 27%) and asthma (7, 13%).

Regional variation in malaria prevalence and parasite species distribution

Malaria prevalence differed significantly between sites. The reported prevalence is based on data from outpatient symptomatic cases. Microscopy identified parasites in 274 (59%) of the patients in Kassala and 113 (44.9%) in Kosti. In Kassala, P. vivax was the predominant species (55.8%), followed by P. falciparum (41.6%) (Table 2). In Kosti, only P. falciparum was detected. Mean P. falciparum density in Kassala was 8,597, and in Kosti was 11,021. P. vivax density was 7,688 in Kassala (Table 2).

Table 2.

Parasitological characteristics of study subjects, and G6PD variants distribution by study site

	Kassala (n = 465)	Kosti (n = 252)	p-value
Microscopy Results and species
Negative	191 (41)	139 (55.1)	<0.001*
Positive	274 (59)	113 (44.9)
P. falciparum	114 (41.6)	113 (44.9)
P. vivax	153 (55.8)	0	<0.001†
P. falciparum/P. vivax	7 (2.6)	0
P. malariae	0	0
P. ovale	0	0
Parasitemia (parasites/μl)
P. falciparum			0.57*
<1000	19 (16.6)	20 (17.7)
1000–10000	43 (37.7)	35 (31)
>10000	52 (45.7)	58 (51.3)
P. vivax
<1000	22 (14.4)	0
1000–10000	77 (50.3)	0
>10000	54 (35.3)	0
P. falciparum/P. vivax
<1000	22 (14.4)	0
1000–10000	77 (50.3)	0
>10000	54 (35.3)	0
Parasite count (Geometric mean ± SD)
P. falciparum	8597 ± 1909.5	11020.9 ± 3326.9	0.53‡
P. vivax	7687.6 ± 1267.6	N. A
P. falciparum and P. vivax	_	N. A
qPCR results and species
Negative	174 (37.4)	121 (48)	<0.001*
Positive	291 (62.6)	133 (52)
P. falciparum	99 (34)	130 (97.7)	<0.001*
P. vivax	157 (54)	3 (2.3)	<0.001†
P. falciparum/P. vivax	35 (12)	0	<0.001†
P. malariae	0	0
P. ovale	0	0
G6PD genotype
Males			<0.001*
B	218 (97.3)	73 (76.8)
A	6 (2.7)	15 (15.8)
A-	0	7 (7.4)
Females			<0.001*
B	220 (91.3)	127 (80.9)
BA	10 (4.1)	11 (7)
BA-	4 (1.6)	4 (2.5)
A	7 (3)	4 (2.5)
AA-	0	9 (5.7)
A-	0	2 (1.4)

± Standard Deviation (SD). *=Chi-square, †=Fisher’s, ‡=t-test (log), N. A = Not Applicable

PCR detected 290 (62.6%) malaria-positive cases in Kassala and 133 (52%) in Kosti. P. falciparum accounted for 134 (46.2%) in Kassala and 130 (97.7%) in Kosti. P. vivax was detected in 192 (66.2%) of the samples from Kassala, and 35 (12.4) Pf/Pv, only 3 (2.7% samples with P.vivax were detected in Kosti (Table 2). P. malariae and P. ovale were not detected in both study sites. Thirty-seven (8.7%) of the PCR-positive subjects were not detected by microscopy at both sites.

PCR positivity was significantly associated with study site, gender, age, and history of fever (p < 0.05) (Table 3). Malaria infection was significantly more common among participants aged < 18 years compared with adults (99/264 [37.5%] and 50/195 [25.6%] vs. 115/264 [43.6%] for P. f; 87/195 [44.6%] vs. 108/195 [55.4%] for P. v; age distribution p = 0.006). Furthermore, P. f and mixed (P. f/P. v) infections were significantly more frequent among males than females (136/264 [51.5%] vs. 128/264 [48.5%] for P. falciparum; 19/35 [54.3%] vs. 16/35 [45.7%] for mixed infections; p = 0.007). A strong, significant association was noted between fever history and PCR positivity (p < 0.001), whereas current fever and residence status (IDP vs. resident) were not significant. P. vivax infection was more common among participants without a reported fever in the last month (Table 3).

Table 3.

Association of demographic and clinical variables according to parasite species**

Variable	Category	Negative n (%)	Parasite species			p-value
			P. f n (%)	P. v n (%)	P. f/P. v n (%)
Study site	Kassala	174 (59)	134 (50.2)	192 (98.4)	35 (100)	<0.001†
	Kosti	121 (41)	133 (49.8)	3 (1.6)	0 (0.0)
Age category	<10	38 (12.8)	50 (18.9)	37 (19)	6 (17.1)	0.006*
	10–18	94 (31.8)	99 (37.5)	50 (25.6)	9 (25.7)
	>18	163 (55.4)	115 (43.6)	108 (55.4)	20 (57.2)
Sex	Female	183 (62)	128 (48.5)	103 (52.8)	16 (45.7)	<0.007*
	Male	112 (38)	136 (51.5)	92 (47.2)	19 (54.3)
Residence	IDP	137 (46.5)	118 (44.7)	80 (41)	11 (31.4)	0.14*
	Resident	158 (53.5)	146 (55.3)	115 (59)	24 (68.6)
History of fever in last 4 Weeks	No	174 (59)	154 (58.4)	144 (73.8)	30 (46.2)	<0.001*
	Yes	121 (41)	110 (41.6)	51 (26.2)	5 (7.7)
Presenting with feverΨ	No	86 (29.2)	79 (30)	42 (21.5)	10 (28.5)	0.3*
	Yes	209 (70.8)	185 (70)	153 (78.5)	25 (71.5)

* = Chi-square, †=Fisher’s, P. f = Plasmodium falciparum, P. v = Plasmodium vivax, Ψ = Fever defined as forehead temperature ≥ 37.5 °C, **qPCR used as confirmatory test

Percentages within each species column are column-based (i.e., show the proportion of that species occurring in each category) and do not represent within-site prevalence

G6PD variants and their association with parasite density

Restriction fragment patterns of the G6PD A376G and G202A are shown in Fig. 2. G6PD B variant was the most frequent, with 438 (94.2%) in Kassala and 200 (79.4%) in Kosti. The African A- variant was detected in 9 (3.5%) subjects in Kosti (7 males and 2 females) (Table 2). In females, BA, BA-, AA, and AA- were detected in 11 (7%), 4 (2.5%), 4 (2.5%), and 9 (5.7%), respectively, in Kosti. Females in Kassala showed BA, BA-, and AA in 10 (4.1%), 4 (1.6%), and 7 (3%), respectively. Table S3 shows the distribution of parasite density by G6PD genotype, stratified by sex and study site. No significant difference in parasite density across genotypes was observed (all adjusted p > 0.05) (Fig. 1).

Fig. 1.

Distribution of parasite density according to G6PD genotype, stratified by sex and study site (Kassala and Kosti). Individual points represent subjects (males: circles; females: triangles). Boxes indicate the interquartile range (IQR), and horizontal lines represent the median. Parasite density is displayed on a log₁₀ scale (parasites/µL). Pairwise comparisons between genotypes within each sex and site were performed using Wilcoxon rank-sum tests with Benjamini–Hochberg correction for multiple testing

Fig. 2.

Restriction fragment length polymorphism (RFLP) analysis of the G6PD A376G, G202A mutations. (A) RFLP analysis of the G6PD A376G mutation following digestion with FokI. PCR-amplified fragments were digested with FokI and resolved on a 3% agarose gel. A 50 bp DNA ladder (L) was used as a molecular size marker. L = 50 bp DNA ladder; lanes 1 and 5 = G6PD A variant (376 G mutant allele); lanes 2 and 4 = B variant (376 wild-type allele); lane 3 = BA heterozygous variant. (B) RFLP analysis of the G6PD G202A mutation following digestion with NlaIII. PCR amplified fragments were digested with NlaIII restriction enzyme and resolved on 3% agarose gel. A 50 bp DNA ladder (L) was used as a molecular size marker. L = 50 bp DNA ladder; lane 8 = G6PD A variant (202A mutant allele); lanes 1–3 = AA- variant (202 G wild-type allele); lanes 4–7 = A- variant (202A mutant allele)

Haematological profile of the study subjects

The mean haemoglobin concentration of study subjects is shown in Table S4. There was no significant difference between the haemoglobin concentration of P. falciparum and/or P. vivax and noninfected subjects. Mean platelet counts and lymphocytes were lower across all parasite species compared with the uninfected group (p < 0.001) (Table S4).

Discussion

Humanitarian crises—such as the ongoing armed conflict in Sudan—create conditions that exacerbate malaria transmission and impede control efforts, driven by fragile health systems, large-scale population displacement, and related destabilizing factors.

This facility-based study reported heterogeneity in malaria species distribution between the two study areas. Kassala, eastern Sudan, was more affected by Plasmodium vivax; on the other hand, Kosti in southern Sudan was mainly affected by P. falciparum malaria. The parasite endemicity in both areas resembles that of neighbouring countries, Eritrea and South Sudan. Cross-border movement between Eritrea and Kassala would have influenced the spread of vivax malaria into eastern Sudan, while malaria patterns in Kosti and South Sudan are dominated by P. falciparum [6, 38]. The reported high prevalence of malaria at both sites confirms the continuing burden of malaria in Sudan. Kosti follows the hyperendemic pattern, predominantly caused by P. falciparum [27]. While Kassala has a more complex mesoendemic pattern, with co-endemicity of P. vivax and P. falciparum, and a substantial proportion of mixed infection between the two species.

The high P. vivax prevalence observed in eastern Sudan could be attributed to local parasite adaptation and population movement across the border with Eritrea [5]. Although the P. vivax infection seems to be geographically restricted to the eastern part of Sudan, it is important to keep an eye on a possible wider distribution, as cases in Kosti in this study and elsewhere have been reported, pointing to a gradual geographic expansion of the parasite [5, 7].

The presence of submicroscopic infections has important implications for malaria control and elimination strategies, as these low-density infections may contribute to ongoing transmission. Molecular surveillance may therefore be essential for accurately estimating malaria burden in low-transmission or pre-elimination settings. [39].

This study demonstrated regional heterogeneity in G6PD variants between the two sites, pointing to a heterogeneous distribution of allelic variants across Sudan and a likely influence of differences in ethnic background, selective forces such as malaria endemicity, and historical gene flow [38]. Most subjects in this study carry the normal B variant, while a number of patients, especially at the Kosti site, carry the African defective variants A and A-, with females showing a homogeneous A-A- variant. [9] These findings confirm site-specific differences in G6PD genotype distribution within Sudan and extend the limited molecular data available on the African A⁻ haplotype in the country. The presence of the A⁻ variant could indicate stronger historical malaria-driven selection pressure for protection against severe P. falciparum infection, as G6PD deficiency is known to confer partial resistance to this parasite [40]. In this study, no significant association was observed between G6PD genotype and parasite density, suggesting that these variants do not play a role in the amelioration of malaria [41].

In contrast to previous reports, which found a higher malaria rate among IDPs [10, 42], this study found no significant difference in malaria prevalence between IDPs and residents. This could be explained by the fact that the health system has been disrupted similarly for hosting communities and IDPs. It’s worth noting that there was no difference between the two groups in hemoglobin concentration, which was below the reference interval, suggesting a possible role of the conflict in the health of these communities.

This study has some limitations, including that it is a health facility-based study of symptomatic subjects, which may not reflect the real prevalence of malaria in the studied communities. Another limitation is that G6PD was studied based on common genotypic variants, without phenotypic testing of enzyme activity, limiting its use for patient management.

Conclusions

This study demonstrated regional differences in the distribution of malaria parasite species and in G6PD genotype frequencies. Molecular testing identified submicroscopic infections, suggesting a role for point-of-care molecular testing in malaria control and elimination.

The African G6PD A⁻ variant was detected for the first time in Sudan, requiring further investigation to confirm its role in malaria presentation. This study showed that malaria affected both resident and displaced populations similarly, highlighting the effects of conflict on health system disruption and, in turn, on disease burden.

Electronic supplementary material

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Abbreviations

CBC

Complete Blood Count

CI

Confidence Interval

DBS

Dried Blood spot

EDTA

Ethylenediaminetetraacetic Acid

G6PDd

Glucose-6-Phosphate Dehydrogenase deficiency

GIS

Geographic Information System

IDP

Internally Displaced Person

IQR

Interquartile Range

LR⁺

Positive Likelihood Ratio

LR⁻

Negative Likelihood Ratio

NMCP

National Malaria Control Program

PCR

Polymerase Chain Reaction

PCR-RFLP

Polymerase Chain Reaction-Restriction Fragment Length Polymorphism

qPCR

Quantitative Polymerase Chain Reaction

RDT

Rapid Diagnostic Test

REDcap

Research Electronic Data Capture

RNA

Ribonucleic Acid

SD

Standard deviation

UV

Ultraviolet

WBC

White Blood Cell Count

WHO

World Health Organization

Author contributions

WMAJ, MMAH, and AOM contributed equally to the work. WMAJ, MMAH, and AOM: study design and implementation. WMAJ: Data collection, data entry. WMAJ and EAB: PCR-RFLP test performance. WMAJ, MC: qPCR test performance. SAM: Expert microscopy examination. WMAJ: data curation, analysis, and interpretation. WMAJ, MMAH, AOM: Draft preparation and revision of the final manuscript. WMAJ, MMAH, AOM, MC, JRM, and AJC: critical revision of the manuscript. The manuscript was written through the contributions of all authors. All authors have approved the final version of the manuscript.

Funding

This research was funded by the NIHR [NIHR134694] using UK international development funding from the UK Government to support global health research. The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR or the UK government.

Data availability

All data supporting the findings of this study are available in the article. Any Additional data requested can be directed to the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

The study received ethical approval from the Medical and Health Sciences Ethical Review Board, University of Khartoum, Sudan, on September 4, 2023. Additionally, the study was also supported by the prospective state ministries of health in Sudan. Informed written consent was obtained from all subjects and/or their legal guardians prior to participation. Study participation was voluntary. To ensure respondent privacy, all datasets were fully de-identified; specifically, all direct identifiers, including names and residential addresses, were removed prior to analysis

Consent for publication

Not applicable

Competing interests

The authors declare no competing interests.

Consortium members

NIHR Global Health Research Group on Digital Diagnostics for African Health Systems

Waleed M. A. Jebreel ¹, Muzamil M. Abdel Hamid ¹, Abdelrahim O. Mohamed ², Fahad A. Elnour ³, Martin Chamai ^4,22, Kenny Malpartida-Cardenas ^5,6, Jesus Rodriguez-Manzano ^5,6,7, Aubrey J. Cunnington ^8,9, François Kiemde ¹⁰, Lindsay B. Stewart ¹¹, Pantelis Georgiou ^5,12, Halidou Tinto ¹⁰, Annette Erhart ¹³, Ivana Pennisi ^5,6,14, Umberto D’Alessandro ¹³, Dimbintsoa Rakotomalala Robinson ^13,14, Linda E. Amoah ¹⁵, Mamadu Baldeh ¹³, Azumah Abdul-Tawab ¹⁶, Jamal-Deen Abdulai ¹⁷, Mohammed Abumanga ³, Jane Achan ¹⁸, Darlington A. Akogo ¹⁹, Angelina A. Amengu ²⁰, Tochukwu D. Anyaduba ⁶, Emilia A. Udofia ²¹, Gordon A. Awandare ^4,22, Frances B. da-Costa Vroom ²³, Leonard Baatiema ²⁴, Kevin Baker ^6,18,25, Fatou Baldeh ^13,26, Julie Balen ^13,26, Jake Baum ²⁷, Weston Baxter ²⁰, Craig Bonnington ¹⁸, Salome A. Bukachi ^28,29, Céire E. Costelloe ^30,31, Luc P. de Witte ³², Samuel Duodu ^4,22, Francis Dzabeng ^4,17, Joe Fitchett ³³, Sahar Gamil ², Sebastian S. Hachizovu ³⁴, Prudence Hamade ¹⁸, Jethro A. Herberg ^8,9, Jean-Bertin Kabuya ³⁴, Flavia K. Bawa ^4,22, Myrsini Kaforou ^8,9, Dennis O. Laryea ¹⁶, Michael Levin ^8,9, Christine Manyando ³⁴, Shola K. Molemodile Dele-Olowu ²⁴, Nicolas Moser ^5,6,12, Sydney Mwanza ³⁵, Erick Odoyo ³⁶, Patience Ofosuhemaa ³⁷, Lucy C. Okell ³⁸, Abubakr Omer ², Francesca M. Piffer ⁸, Talya Porat ²⁰, Anthony W. Sifuna ³⁹, Maria Suau Sans ¹⁸, Faiza U. Bawah ^17,40, Effua Usuf ¹³, Alfred E. Yawson ⁴¹, Shunmay Yeung ⁴², & Asadu Sserwanga ¹⁸.

¹ Institute of Endemic Diseases, University of Khartoum, Khartoum, Sudan. ² Department of Biochemistry, Faculty of Medicine, University of Khartoum, Khartoum, Sudan. ³ Patients Helping Fund, Khartoum, Sudan. ⁴ West African Centre for Cell Biology of Infectious Pathogens, University of Ghana, Accra, Ghana. ⁵ Department of Infectious Disease, Imperial College London, London, UK. ⁶ ProtonDx, Translation & Innovation Hub, Imperial College London, London, UK. ⁷ The Fleming Initiative, Imperial College London and Imperial College Healthcare NHS Trust, London, UK. ⁸ Section of Paediatric Infectious Disease, Department of Infectious Disease, Imperial College London, London, UK. ⁹ Centre for Paediatrics and Child Health, Imperial College London, London, UK. ¹⁰ Institut de Recherche en Sciences de la Santé, Clinical Research Unit of Nanoro, Nanoro, Burkina Faso. ¹¹ Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK. ¹² Department of Electrical and Electronic Engineering, Imperial College London, London, UK. ¹³ MRC Unit The Gambia at the London School of Hygiene and Tropical Medicine, Fajara, The Gambia. ¹⁴ Clinical Research Department, Faculty of Infections and Tropical Disease, London School of Hygiene and Tropical Medicine, London, UK. ¹⁵ Immunology Department, Noguchi Memorial Institute for Medical Research, University of Ghana, Accra, Ghana. ¹⁶ Disease Surveillance Department, Ghana Health Service, Accra, Ghana. ¹⁷ Department of Computer Science, University of Ghana, Accra, Ghana. ¹⁸ Malaria Consortium, London, UK. ¹⁹ minoHealth AI Labs, Accra, Ghana. ²⁰ Dyson School of Design Engineering, Imperial College London, London, UK. ²¹ Department of Community Health, University of Ghana Medical School, College of Health Sciences, University of Ghana, Accra, Ghana. ²² Department of Biochemistry, Cell and Molecular Biology, University of Ghana, Accra, Ghana. ²³ Department of Biostatistics, School of Public Health, College of Health Sciences, University of Ghana, Accra, Ghana. ²⁴ Department of Health Policy, Planning and Management, School of Public Health, University of Ghana, Accra, Ghana. ²⁵ Department of Global Public Health, Karolinska Institute, Stockholm, Sweden. ²⁶ School of Nursing, Midwifery, Allied and Public Health, Canterbury Christ Church University, Canterbury, UK. ²⁷ The University of New South Wales, Sydney, Australia. ²⁸ Institute of Anthropology, Gender and African Studies, University of Nairobi, Nairobi, Kenya. ²⁹ Department of Anthropology, Durham University, Durham, UK. ³⁰ Health Informatics, Division of Clinical Studies, Institute of Cancer Research, London, UK. ³¹Department of Primary Care and Public Health, School of Public Health, Imperial College London, London, UK. ³² The Hague University of Applied Sciences, The Hague, Netherlands. ³³ Institut Pasteur de Dakar, Dakar, Sénégal. ³⁴ Department of Public Health, National Health Research and Training Institute, Ndola, Zambia. ³⁵ Department of Biomedical Sciences, National Health Research and Training Institute, Ndola, Zambia. ³⁶ Department of Biological Sciences, Masinde Muliro University of Science and Technology, Kakamega, Kenya. ³⁷ Health Promotion Division, Ghana Health Service, Accra, Ghana. ³⁸ MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK. ³⁹ Department of Medical Biochemistry, Masinde Muliro University of Science and Technology, Kakamega, Kenya. ⁴⁰ Department of Computer Science and Informatics, University of Energy and Natural Resources, Sunyani, Ghana. ⁴¹ Office of the Provost, College of Health Sciences, University of Ghana, Accra, Ghana. ⁴² Clinical Research Department, Faculty of Infections and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK.