Submicroscopic Malaria Parasite Carriage and Hemoglobin Levels among Individuals with Asymptomatic Malaria Infections

Submicroscopic Malaria Parasite Carriage and Hemoglobin Levels among Individuals with Asymptomatic Malaria Infections | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Submicroscopic Malaria Parasite Carriage and Hemoglobin Levels among Individuals with Asymptomatic Malaria Infections Benjamin Tetteh Mensah, Lucas Amenga-Tego, Hannah Otu, Dorinda Naa Okailey Armah, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9086362/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Malaria remains a major public health challenge in sub-Saharan Africa, where asymptomatic infections continue to hinder elimination efforts. Although the effects of microscopic infections are well documented, the health implications of submicroscopic Plasmodium falciparum infections remain poorly understood, particularly in Ghana. Beyond serving as reservoirs for sustained transmission, emerging evidence suggests these infections may contribute to adverse health outcomes. This study assessed the burden of submicroscopic P. falciparum infections and their association with anemia and antimalarial drug resistance markers among asymptomatic individuals attending Korle Bu Teaching Hospital. Methods A cross-sectional study involving 345 participants was conducted. Malaria infection was assessed using mRDT, blood smear microscopy, and LAMP-PCR for parasite detection and species identification. Hemoglobin levels and red blood cell indices (MCV, RDW, RBC count, MCHC, and HCT) were measured using a hematology analyzer. Antimalarial drug resistance markers were analyzed by multiplex PCR and Oxford Nanopore sequencing. Statistical analysis was performed using STATA version 14, applying Pearson’s chi-square test and logistic regression. Results The overall prevalence of asymptomatic P. falciparum infection was 35.0% (117/334), with 18.9% microscopic and 16.1% submicroscopic infections. Both infection types were significantly associated with lower hemoglobin levels after adjusting for age and anemia severity (p = 0.024). Low MCHC emerged as the strongest predictor of submicroscopic infection (p = 0.04). The wild-type pfcrt K76 allele, associated with chloroquine susceptibility, was highly prevalent (90.1%). Accra Central and surrounding areas were identified as transmission hotspots. Conclusion Asymptomatic P. falciparum infections are common in this population, with a substantial proportion occurring at submicroscopic levels. Their association with reduced hemoglobin suggests a potential contribution to anemia despite the absence of symptoms. The high prevalence of chloroquine-susceptible parasites and identified transmission hotspots underscore the need for sensitive diagnostics and targeted interventions to support malaria elimination efforts in Ghana. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction Malaria remains a major global health burden, particularly in sub-Saharan Africa, despite sustained control efforts, with asymptomatic and submicroscopic infections continuing to drive transmission [ 1 , 2 ]. In highly endemic settings, over 90% of infections may be asymptomatic, allowing undetected carriers to sustain parasite transmission [ 3 , 4 ]. Traditionally defined as microscopy-detected parasitemia in the absence of symptoms [ 5 ], asymptomatic malaria also includes low-density infections detectable only by PCR, with evidence showing that up to two-thirds of microscopy-negative individuals harbor submicroscopic infections [ 6 ]. Beyond their epidemiological importance, submicroscopic infections are associated with chronic mild anemia and alterations in red blood cell (RBC) indices such as MCV, MCH, and RDW, reflecting ongoing erythrocyte destruction [ 7 , 8 ]. Persistent low-density parasitemia may also promote antimalarial drug resistance under sub-therapeutic drug pressure [ 9 ]. Key P. falciparum resistance genes: pfcrt, pfdhfr, pfdhps, pfmdr1 , and pfkelch13 mediate resistance to major antimalarials [ 10 , 11 ], yet their prevalence in asymptomatic infections in Ghana remains poorly defined. Asymptomatic malaria has additionally been linked to anemia and impaired cognitive performance [ 5 , 7 , 8 ], and recent studies in Ghana report increasing rates of asymptomatic and submicroscopic infections across transmission zones [ 12 , 13 ]. Given these challenges, a comprehensive understanding of the epidemiology of submicroscopic infections, their hematological consequences, and associated resistance profiles is urgently needed. This study therefore aims to determine the prevalence of submicroscopic P. falciparum infections among asymptomatic individuals at Korle Bu Teaching Hospital, assess associations with anemia and RBC indices, and identify molecular markers of antimalarial drug resistance to inform malaria elimination strategies in Ghana. METHODOLOGY 3.1 Study Setting The study will be carried out at the Central Laboratory and Child Health Laboratory of the Korle-Bu Teaching hospital located in the Ablekuma south municipal assembly, a sub-district in the Greater Accra Region. 3.2 Study Design This study was a cross-sectional study. 3.3 Study Population Patients from the Out-Patient Department (OPD) visiting the Central laboratory and the Child health laboratory were our study population. 3.3.1 Inclusion criteria Afebrile patients above 2 years old who had no signs and symptoms of malaria and had not taken any antimalarial drugs (ACTs) within the past 2 weeks were included in this study after informed consent had been obtained. Also, participants who tested negative for malaria will be included in the study as a control group. 3.3.2 Exclusion criteria Patients with signs and symptoms of malaria (defined as axillary temperature above 37.5℃) at the time of recruitment. Patients who had taken antimalarial drugs (ACTs) within the past two weeks and Patients who are severely ill, pregnant women and patients with hemoglobinopathies. 3.4 Sampling method Patients from the Out-Patient Department (OPD) of the Central Laboratory and the Child Health Laboratory of the Korle Bu Teaching Hospital were recruited into the study using purposive sampling technique. 3.5 Study procedure Enrolment and clinical groups Data was collected between April and July 2025. A total of 345 participants were enrolled after passing the inclusion criteria. Participants were then interviewed using a structured questionnaire with information on age, sex, location, number of household members, occupation, educational level, history of fever, number of previous episodes of malaria/fever, last date of malaria diagnosis and result of diagnosis and antimalarial drugs used in malaria treatment. Participants were then grouped into three distinct age categories, namely, less than 18 years, 18 to 35yrs and 36 and above, classified as children, young adult and older adult populations. During the study, four samples were lost due to logistical mishaps, and seven samples were excluded from the study after full blood count reports showed the presence of hemoglobinopathies (such as very high WBC counts and very low Hb counts), resulting in 334 samples remaining for analysis. Also, anemia was defined as hemoglobin levels below 11g/dl and classified into mild if Hb was in the range, 10–10.9g/dl, moderate if Hb was 7–9.9g/ dl, and severe if Hb was < 7 g/dl and normal, when hemoglobin levels were 11g/dl and above, using WHO’s standard criteria. Definition of clinical groups Asymptomatic malaria infections were defined and categorized into three clinical groups according to the microscopy and PCR results. Samples which tested positive for microscopic asymptomatic infections, were defined as positive microscopy infections. Also, submicroscopic infections were defined as negative microscopy but positive LAMP PCR results, with uninfected, defined as negative for both microscopy and LAMP PCR. Sample collection and other laboratory procedures About 3 mL of whole blood was drawn by an experienced phlebotomist by venipuncture into a 5 mL ethylenediaminetetraacetic acid (EDTA) tube using a sterile disposable syringe and thoroughly mixed to prevent it from clotting. The participants’ unique identification number on the consent form was written on each tube to ensure confidentiality. 200uL of the whole blood was placed on 3mm Whatman No. 1 filter paper for parasite DNA extraction for molecular work. Also, 6µL and 2µL were placed on a labelled glass slide, with thick and thin blood films prepared and stained with Giemsa stain according to standard operating procedures. Also, about 5 µL of blood was used to test for and differentiate between P. falciparum and other malaria species using the OnSite malaria rapid diagnostic test kit (mRDT). The remaining blood was then used for Full blood count analysis, using Mindray BC 6800 hematological analyzer (Mindray Biomedical Co. Ltd, China). Hematological parameters including hemoglobin levels (Hb), mean cell volume (MCV), red cell distribution width (RDW), red blood cell count (RBC count), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC) and hematocrit (HCT) were measured according to standard operating procedures and recorded. The dried blood spots (DBS) samples were preserved in zip-lock plastic bags with desiccants and transported to the Malaria Laboratory of the West African Centre for Cell Biology of Infectious Pathogens (WACCBIP) for molecular analysis. Malaria diagnosis by microscopy Thick and thin peripheral blood films were prepared for each participant by pipetting 6µL and 2µL of whole blood onto a clean, well-labelled glass slide respectively, as previously described [14]. The thin smears were fixed in absolute methanol, after which the slides were allowed to dry and stained using 10% Giemsa working solution and subsequently observed using the x100 objective. The thick blood film was used for malaria parasite identification and quantification, while the thin film was used for malaria parasite speciation. Malaria parasite detection was done by examining at least 100 high-power fields. Asexual parasites were counted per 200 white blood cells and parasite quantification obtained by multiplying the asexual parasite count per 200 white blood cells by a white cell count of 8000/µL as previously described [14]. Number of Parasites counted × 8000 = Number of parasites /µL of blood Number of White blood cells counted Microscopy results were read by two independent, highly experienced microscopists blinded to both RDT and each other’s results, with discrepant results observed by a third highly experienced microscopist. The average of the two closest counts was then taken as the parasite count. Also, a slide was declared negative, if no malaria parasite was seen after observing 100 high-power fields, and a slide was declared positive if at least one malaria parasite was observed after examining 100 high-power fields, with an additional 100 fields observed to detect mixed infections as previously described [14]. Malaria diagnosis by RDT The OnSite malaria rapid diagnostic test kit (mRDT) (CTK Biotech Inc., USA), which has a Combo design based on Pf- Histidine Rich Protein-II (HRP II) antigen and pan-Plasmodium aldolase antigen ( Pf/Pan) for simultaneous detection and differentiation of P. falciparum (Pf), P. vivax (Pv), P. ovale (Po) or P. malariae (Pm) antigen in participant whole blood, was used as a screening test, to screen participants for malaria. Briefly, the RDT was labelled with a unique participant code and date, after which 5µL of whole blood was pipetted into the sample well. Afterwards, two drops of the buffer solution provided by the manufacturer were added to the buffer well on the cassette and the results, read after twenty minutes according to the manufacturer’s instructions. Malaria diagnosis by PCR Extraction and quantification of genomic DNA Genomic DNA (gDNA) was extracted from both P. falciparum positive and negative DBS samples using the QIAamp ® DNA Blood Mini Kit (Qiagen, Germany), following the manufacturer’s protocol, but with some modifications to maximize yield. Briefly, 2-3mm punches were obtained from each DBS sample and placed in a sterile 1.5mL microcentrifuge tube. 180µL of buffer ATL and 20µL of Proteinase K were then added to each sample, the mixture was vortexed briefly and incubated overnight at 56℃ in a thermomixer at 900 rpm to ensure complete lysis. After lysis, 200µL of buffer AL was added, vortexed and incubated at 70℃ for 10 minutes. After that, 200µL of 100% ethanol was added to the lysate, mixed thoroughly and then transferred to a QIAamp spin column. The spin column was centrifuged at 8000 rpm for 1minute, after which the flow through was discarded. The column was then washed with 500µL of buffer AW1 and centrifuged at 8000 rpm for 1 minute, followed by a second wash with buffer AW2, centrifuged at 14,000 rpm for 3 minutes, followed by an optional dry spin to remove residual ethanol. The DNA was then eluted by adding 50µL of pre-warmed buffer AE, incubated at room temperature for 30 minutes and then centrifuged at 14,000 rpm for 1 minute. The extracted DNA was then stored at -20℃ until further molecular analysis. 1μl of eluted gDNA was used to quantify the extracted DNA using Invitrogen Qubit TM 1× dsDNA High Sensitivity (HS) assay kit with Qubit Fluorometer (Thermo Fisher Scientific, USA), after which the samples were then stored at -20°C until they were ready for use. Detection of submicroscopic malaria infections using Real Time-LAMP PCR assay All the microscopy malaria-negative gDNA samples were subjected to Loop-Isothermal Amplification assay (LAMP assay) to determine the presence of submicroscopic malaria infections of P. falciparum, P. ovale and P. malariae . Briefly, the reaction for the assay was carried out in a total volume of 20µL comprising of the isothermal buffer (20mM Tris-HCl, 10mM Ammonium Sulphate ((NH 4 ) 2 SO 4 ), 50mM KCl, 2mM MgSO 4 , 0.1% Tween 20, pH 8.8 at 25℃), magnesium chloride (MgCl 2 ), 0.8 M betaine, 4 mM deoxyuridine triphosphate (dUTPs), 25 mM deoxynucleotides triphosphate (dNTPs), 1.5 µM forward and backwards inner primer (FIP and BIP), 0.2 µM forward and backward outer primer (F3 and B3) and 0.4 µM of forward and backward loop primers (FLP and BLP), 30.0 ng/µL of purified Bst-LF polymerase, 2.0 µM SYTO-9 dye and 3 µL of purified DNA template. The assays were performed at 65℃ for 30 minutes using QuantStudio5 (Applied Biosystems, Waltham, MA), and the results, analyzed using a melt curve analysis approach. Insert Figure 1 here Figure 1: Amplification plots after LAMP PCR assay analysis Insert Figure 2 here Figure 2: Melting curve plots depicting true positives and false negatives after LAMP PCR assay analysis Detection of drug-resistant molecular markers Multiplex PCR (panel amplification) and gel electrophoresis All malaria-positive cases, that is, both microscopic and submicroscopic malaria cases, were subjected to antimalarial drug resistance analysis to identify molecular markers of drug-resistant parasite strains. Briefly, using Multiplex PCR, amplicons of pfcrt , pfdhfr , pfdhps , pfmdr1 , and pfkelch13 were generated as previously described [15]. Forty-seven (47) µL of prepared PCR master mix was added to 2μL of high-quality extracted gDNA (~5 ng/µL) in 96-well PCR plates and mixed thoroughly. Nuclease-free water was used as a negative control, while gDNA of P. falciparum KH02 isolate was used as a positive control. The plates were firmly sealed and briefly centrifuged before running on the PCRmax™ Alpha Cycler 4 (Thermo Fisher Scientific, USA) under already set thermocycling conditions (Appendix C). Also, 2% agarose gel was used to confirm the presence of the expected PCR product fragments and visualized on the Amersham Imager 600 (Cytiva, Japan). Insert Figure 3 here Figure 3: Gel electrophoresis showing Multiplex PCR amplification of P. falciparum drug resistance markers. Amplicon purification and library preparation PCR amplicons were purified using the QIAamp ® DNA MiniElute Kit (Qiagen, Germany) according to the manufacturer’s instructions. Eluted gDNA amplicons were then quantified as described above and stored at 4°C. Afterwards, three sequencing libraries were prepared for the purified PCR amplicons (22 clinical isolates, KH02 positive control and negative control per batch) using the SQK-NBD112.24 native barcoding kit (Oxford Nanopore Technologies, UK). The manufacturer’s protocol was followed for the library preparation. Briefly, 12.5 μl of each purified DNA amplicon (~200 fmol) was end-prepped using 1x Ultra II End-prep enzyme mix, incubated for 5 min at 20 °C and 5 min at 65 °C. End-prepped PCR amplicons were then purified with 1x AMPure XP Beads and eluted in 10 μl nuclease-free water. The purified end-prepped gDNA was then barcoded with 24 unique native barcodes using 1x Blunt/TA Ligase for 20 min at room temperature (RT). After barcoding, all the 24 samples were pooled and purified with 1x AMPure XP. After, the barcoded gDNA amplicons were eluted in 35 μL of nuclease-free water. Afterwards, 30 µL of the pooled barcoded gDNA was then ligated to the Adapter Mix II H (AMII H) using the Quick T4 Ligase for 20 min at room temperature. Purification was then performed with 1x AMPure XP using the Short Fragment Buffer (SFB), with adapter-ligated amplicons eluted in 15 μL of Elution Buffer. Sequencing, base-calling, alignment and SNP detection The prepared DNA libraries to be loaded for sequencing were prepared by thoroughly mixing 12 μL of prepared DNA library (~20 fmol), 37.5 μL of Sequencing Buffer II (SBII) and 25.5 μL of Loading Beads II (LBII). After that, 75 μL of the mixture was gently administered to the flow cells (Version FLO-MIN107) in the MinION Mk1b sequencer. Sequencing was performed between 6-8 hours with real-time high-accuracy guppy base calling using the MinKNOW software. The resulting fastq files were processed through a custom Nextflow pipeline: nano-rave (Nanopore Rapid Analysis and Variant Explorer) [15]. After quality control (QC) checks, sequence reads were mapped to 3D7 reference sequences for each of the amplicon target genes using minimap2 . Amplicon coverage data were then generated using BEDTools . Also, Medaka haploid was used for variant calling to generate Variant Call Format (VCF) file outputs for each amplicon for each sample (ONT barcode), and the VCF files were processed using custom R scripts to calculate SNP frequencies at the five drug resistance loci. A cut-off of >10x coverage was applied for an amplicon to be included in the analysis. Determining the hotspots of malaria transmission using spatio-temporal analysis In this study, spatio-temporal analysis was employed to identify malaria transmission hotspots within the Greater Accra Region. Although the region is generally classified as a low-transmission zone, with an estimated malaria prevalence of 2% [16], localized pockets of moderate to high transmission are suspected. Participants’ home locations were recorded, and GPS coordinates were generated where available. Cases identified with submicroscopic malaria infections following laboratory analysis were selected for spatial mapping and hotspot assessment. To examine the spatial distribution of infections, the dataset was reshaped into a long format with infection type as a single variable. Data were grouped by location, region, and infection subset, and the number of submicroscopic cases was calculated for each group. These counts were converted to percentages relative to the total number of positive cases across all regions, ensuring that regional prevalence summed to 100%. Bar plots were generated to visualize the proportion of positive cases per region and infection category, with each bar representing the contribution of a specific location to the total infection burden. The regional plots were then integrated using the patchwork package in R, enabling clear visual comparison of spatial variations and regional contributions to overall malaria prevalence patterns. Data Analysis Baseline characteristics of demographics, hematological Parameters and asymptomatic malaria infection outcomes of participants were analyzed using STATA Version 14.1 software. Descriptive statistics, chi-square test, student t-test, ANOVA, linear and logistic regression analysis were used to test for association between sociodemographic characteristics, hematological parameters and infection outcomes. Also, statistical significance was determined at 95% confidence interval, with a P-value of <0.05 considered statistically significant. RESULTS Demographics and clinical characteristics of asymptomatic study participants A total of 345 participants meeting the inclusion criteria were initially enrolled. Thick and thin blood films, malaria rapid diagnostic tests (RDTs), dried blood spots for molecular analysis, and full blood count (FBC) assessments were performed for all participants. Seven samples were excluded due to abnormally high white blood cell counts and very low hemoglobin levels, suggestive of hematological malignancies, and four were lost during processing, leaving 334 participants for analysis. Participants were categorized into three groups: children (<18 years), young adults (18–35 years), and older adults (≥36 years), as summarized in Table 1. Of the 334 participants, 196 (58.68%) were female and 138 (41.32%) were male, with a median age of 26 years (IQR: 2–79 years). RDTs detected 62 malaria-positive cases, while microscopy identified 63, with five discrepant results—two RDT-positive/microscopy-negative and three microscopy-positive/RDT-negative. All microscopy-negative samples were subsequently analyzed using loop-mediated isothermal amplification (LAMP) PCR to detect submicroscopic Plasmodium falciparum infections and other Plasmodium species, including P. ovale and P. malariae . Insert Figure 4 here Figure 4: A flow diagram of the study participants Table 1 : Showing the demographic and clinical characteristics of study participants Prevalence of asymptomatic malaria infections, including submicroscopic infections All baseline infections were asymptomatic, with no participants exhibiting fever or malaria-like symptoms. The overall prevalence of asymptomatic P. falciparum infection (microscopic and submicroscopic combined) was 35.0% (117/334) . Microscopy detected 18.9% (63/334) of participants as positive for asymptomatic P. falciparum , while LAMP PCR identified 17.6% (54/334) additional submicroscopic infections among the microscopy-negative cases (figure 5A). Parasite speciation using LAMP PCR revealed an overall 32.9% (110/334) prevalence of submicroscopic Plasmodium spp. infections, comprising P. falciparum ( 51.8%, 57/110 ), P. ovale ( 35.5%, 39/110; 11.7% overall ) and P. malariae ( 12.7%, 14/110; 4.2% overall ) (figure 5B). Age-stratified analysis showed that microscopic P. falciparum infection was highest among young adults ( 27.7%, 28/101 ) compared with children ( 16.9%, 20/118 ) and older adults ( 13.0%, 15/115 ), with a significant association between age and infection status (χ² (2, N=334) =8.00, p =0.018) (figure 5C). Submicroscopic infections were more common among older adults ( 20.9%, 24/115 ) and young adults ( 17.8%, 18/101 ) than children ( 12.7%, 15/118 ). Submicroscopic P. ovale infection predominated in children ( 16.1%, 19/118 ), while P. malariae infection was more frequent among older adults ( 6.1%, 7/115 ) (figure 5D). Co-infections were detected in 9 participants with P. falciparum and P. ovale , and in 2 participants each with P. malariae plus either P. ovale or P. falciparum . No triple-species co-infections were observed. Sex distribution showed nearly equal prevalence of microscopic infections in males ( 49.2%, 31/63 ) and females ( 50.8%, 32/63 ), while submicroscopic infections were slightly higher in females ( 57.4%, 31/54 ) than in males ( 48.1%, 26/54 ) Insert Figure 5 here Figure 5: A is bar graph showing the prevalence of microscopic asymptomatic infections by age group . B shows the prevalence of submicroscopic asymptomatic infections by age group. C shows the distribution of the prevalence of submicroscopic infections by age groups in males and females and D shows the distribution of the prevalence of microscopic infections by age groups in males and females. Association between submicroscopic P. falciparum carriage, haemoglobin levels, and other RBC indices in asymptomatic individuals. Overall, low hemoglobin levels were observed among participants, with anemia prevalence varying significantly by age group (χ²(2, N=334)=27.83, p <0.001). Children were most affected ( 54.5% , 95% CI [43–61]), followed by young adults ( 24.0% , 95% CI [19–36]) and older adults ( 21.4% , 95% CI [14–29]). The mean hemoglobin concentration for all participants was 11.5 g/dL (SD=1.9, 95% CI [4.9–16.4]), with mean levels of 11.3 g/dL in microscopic and 11.7 g/dL in submicroscopic infections. Although microscopic infection was not significantly associated with anemia (χ²(2, N=334)=2.09, p =0.14), regression analysis adjusted for age revealed that microscopic infection correlated with lower hemoglobin levels (β = −0.50, t (330)= −1.83, p =0.012). ANOVA confirmed significant differences in mean hemoglobin by age group ( F (2,331)=3.71, p =0.003), with children showing lower levels ( M=10.2 g/dL , 95% CI [9.1–11.3]) than older participants ( M=11.7 g/dL , 95% CI [11.1–12.3]). When anemia severity was compared across infection types and age groups, a significant association was observed (χ²(2, N=334)=27.6, p =0.023). A higher proportion of anemic children with submicroscopic infection ( 7.9% ) was noted compared with young adults ( 1.36% ) and older adults ( 5% ), though not statistically significant (χ²(2, N=334)=0.02, p =0.87). Among children, no significant difference in hemoglobin was found between microscopic-positive ( M=10.53 g/dL , SD=1.8) and submicroscopic ( M=10.2 g/dL , SD=2.2) infections ( t (29)=0.47, p =0.32), indicating similar levels of anemia. Among submicroscopic infections, normal hemoglobin levels predominated ( 74.1% , 40/54), followed by moderate ( 18.5% ) and mild ( 7.4% ) anemia, with no severe anemia observed. No significant associations were found between hemoglobin levels and P. ovale (χ²(2, N=334)=0.02, p =0.87) or P. malariae (χ²(2, N=334)=2.06, p =0.15) infections, even after adjusting for age and anemia severity. Insert Figure 6 here Figure 6 : A is a stack bar plot showing the association between anemia severity and infection status after adjusting for age group . B shows anemia severity among microscopic and submicroscopic infections and C shows anemia status grouped as normal, moderate, mild and severe by age group. Table 2 : Anemia severity in submicroscopic infections among participants Variable Level Overall (n = 55) None Mild Moderate Severe Age group [0–17] (Children) 13 (23.64%) 6 (46.15%) 2 (15.38%) 5 (38.46%) 0 (0%) [18–35] (Young Adults) 18 (32.73%) 17 (94.44%) 1 (5.56%) 0 (0%) 0 (0%) [36+] (Older Adults) 24 (43.64%) 19 (79.17%) 0 (0%) 5 (20.83%) 0 (0%) Sex FEMALE 31 (56.36%) 25 (80.65%) 2 (6.45%) 4 (12.9%) 0 (0%) MALE 24 (43.64%) 17 (70.83%) 1 (4.17%) 6 (25%) 0 (0%) Education BASIC 9 (16.36%) 7 (77.78%) 1 (11.11%) 1 (11.11%) 0 (0%) N/A 6 (10.91%) 2 (33.33%) 1 (16.67%) 3 (50%) 0 (0%) SECONDARY 24 (43.64%) 20 (83.33%) 0 (0%) 4 (16.67%) 0 (0%) TERTIARY 16 (29.09%) 13 (81.25%) 1 (6.25%) 2 (12.5%) 0 (0%) Occupation N/A 7 (12.73%) 3 (42.86%) 1 (14.29%) 3 (42.86%) 0 (0%) PRIVATE SECTOR 27 (49.09%) 21 (77.78%) 1 (3.7%) 5 (18.52%) 0 (0%) PUBLIC SECTOR 8 (14.55%) 8 (100%) 0 (0%) 0 (0%) 0 (0%) RETIRED 4 (7.27%) 3 (75%) 0 (0%) 1 (25%) 0 (0%) STUDENT 9 (16.36%) 7 (77.78%) 1 (11.11%) 1 (11.11%) 0 (0%) Table 3 : Anemia severity in microscopic infections among participants Variable Level Overall (n = 63) None Mild Moderate Severe Age group [0–17] (Children) 20 (31.75%) 9 (45%) 1 (5%) 7 (35%) 3 (15%) [18–35] (Young Adults) 28 (44.44%) 17 (60.71%) 5 (17.86%) 6 (21.43%) 0 (0%) [36+] (Older Adults) 15 (23.81%) 11 (73.33%) 3 (20%) 1 (6.67%) 0 (0%) Sex FEMALE 32 (50.79%) 17 (53.12%) 7 (21.88%) 7 (21.88%) 1 (3.12%) MALE 31 (49.21%) 20 (64.52%) 2 (6.45%) 7 (22.58%) 2 (6.45%) Education BASIC 11 (17.46%) 6 (54.55%) 1 (9.09%) 2 (18.18%) 2 (18.18%) N/A 6 (9.52%) 1 (16.67%) 0 (0%) 4 (66.67%) 1 (16.67%) SECONDARY 30 (47.62%) 18 (60%) 5 (16.67%) 7 (23.33%) 0 (0%) TERTIARY 16 (25.4%) 12 (75%) 3 (18.75%) 1 (6.25%) 0 (0%) Association of Submicroscopic and Microscopic Infections with RBC Indices A multiple linear regression analysis was conducted to assess the relationship between malaria infection status (submicroscopic and microscopic) and red blood cell (RBC) indices—HGB, HCT, MCV, MCH, MCHC, and RDW—after adjusting for age and sex. Submicroscopic infections showed slightly higher mean HGB (B = 0.08, SE = 0.28, p = 0.76), HCT (B = 0.40, SE = 0.87, p = 0.64), and MCH (B = 1.05, SE = 0.67, p = 0.12) compared with uninfected individuals, though these differences were not statistically significant. Nevertheless, the overall regression models for HGB and HCT were significant (F = 7.57, p < 0.001, R² = 0.08), indicating modest explanatory power. In contrast, submicroscopic infections were significantly associated with lower MCHC (B = –0.77, SE = 0.37, p = 0.04, 95% CI [–1.50, –0.03]), with the model explaining 6% of variance (R² = 0.06). This suggests MCHC may serve as a sensitive marker of submicroscopic infection. Although MCV and RDW were slightly elevated among infected individuals, these differences were not significant (p > 0.05), despite overall significant model fits (MCV: F = 14.66, p < 0.001, R² = 0.16; RDW: F = 3.26, p = 0.012, R² = 0.04). For microscopic asymptomatic infections, similar but more pronounced trends were observed. Lower mean values of MCH (B = –0.53, p = 0.54), MCHC (B = –0.80, p = 0.037), RDW (B = –1.54, p = 0.002), HCT (B = –0.53, p = 0.43), and HGB (B = 0.33, p = 0.25) were recorded compared with uninfected participants, while MCV was slightly higher (B = 1.71, p = 0.14). Among these , low MCHC (p = 0.037) and low RDW (p = 0.002) were the strongest predictors of microscopic asymptomatic infections, explaining 5.4% and 4% of the variance, respectively. Overall, these findings highlight subtle hematological alterations linked to submicroscopic and microscopic infections, with MCHC and RDW emerging as potential early indicators of subclinical malaria-related anemia. Insert Figure 7 here Figure 7 : A shows malaria infection status and association with mean RBC indices and B shows malaria infection status (submicroscopic and uninfected group) and association with RBC indices Insert Figure 8 here Figure 8: Distribution of infection status (submicroscopic and uninfected group) and its association with mean RBC indices. The prevalence of known antimalarial drug resistance genes circulating in the study population Mutations in pfcrt, pfmdr1, pfdhfr, pfdhps, and pfk13 genes associated with resistance to chloroquine, amodiaquine, lumefantrine, pyrimethamine, sulfadoxine, and artemisinin were analyzed in 63 microscopy-positive and 53 submicroscopic P. falciparum samples (n = 334) using Oxford Nanopore sequencing. The prevalence of resistance-associated alleles was pfcrt K76T (9.5%, 95% CI: 5.1–16.9), pfmdr1 (40%, 95% CI: 31.2–50.2), pfdhfr (27.1%, 95% CI: 19.3–36.7), and pfk13 (11.4%, 95% CI: 9.8–13.3). Complete pfdhps resistance to sulfadoxine was observed in all samples, although no SP-IPTp–associated mutations were detected; however, SP combination resistance occurred in 21.5% (95% CI: 14.5–30.7). Chloroquine resistance declined markedly, with 90.5% harboring the sensitive pfcrt K76 allele; the K76T mutant was predominantly found in submicroscopic infections (88.9%), females (70%), and children (50%). The wild-type pfmdr1 N86Y haplotype was absent, while the Y184F mutant (51%), associated with reduced lumefantrine susceptibility, was common, especially among microscopy-positive (52.9%) and female (46.3%) participants. Multiple pfdhps haplotypes were identified, led by A437G (79%), S436A (12%), A581G (9%), and A613S (5%), with no K540E, A613T, or S436F detected. For pfdhfr , N51I (24%), C59R (27%), and S108N (26%) were observed, with the triple mutant IRNI (35%) predominating, followed by wild-type NCSI (12%) and double mutant NRNI (4.3%). Predominant pfdhps genotypes included SGKAA (85.4%), SGKGA (33.1%), and AGKAS (14%), with common dhfr–dhps combinations being IRNI + SGKAA (21%), NCSI + SGKAA (10%), and IRNI + AGKAA (4.3%). Notably, pfk13 propeller mutations linked to partial artemisinin resistance, particularly C580Y (12%), were detected exclusively in submicroscopic infections, while other variants (R539T, T573T, G453S, I601I, R575T) were not associated with clinical resistance. Insert Figure 9 here Figure 9: A shows the frequency distribution of CRT and MDR1 alleles in microscopic and submicroscopic infection status. B shows Plasmodium falciparum dhfr-dhps combined haplotypes by microscopic (micro) and sub-microscopic (sub) infection and C shows the distribution of resistance and sensitive phenotype by drug and intervention type. CQ: Chloroquine; PYR: Pyrimethamine resistance; SP.IPT p : Sulfadoxine-Pyrimethamine for Intermittent prevention of malaria in pregnancy; SP.RX: Sulfadoxine-Pyrimethamine resistance; SX: Sulfadoxine The hotspots of malaria transmission using spatio-temporal analysis To identify hotspots of malaria transmission due to submicroscopic malaria infections in the study area, spatio-temporal analysis was conducted on submicroscopic positive cases using the R statistic package. Data collected over the study period (May to July), were linked to household GPS coordinates to identify hotspots of malaria transmission within the study area. Three main regional hotspots were identified, mainly, Greater Accra region (88.2%), Central region (10%) and Eastern region (1.8%), with the primary hotspots predominantly located in the Greater Accra region. Insert Figure 10 here Figure 10: Map of Ghana highlighting the regional hotspots of cases The Accra Central sub-district, mainly made up of communities such as, Jamestown, Ussher town, Tudu and Accra business district, showed the highest prevalence of P. falciparum , P. ovale and P. malariae submicroscopic malaria infections. Also, secondary hotspots were detected in the Kasoa, Dansoman, Darkuman, Achimota, Bubiashie, Odorkor and Korle Gonno communities, with P. ovale submicroscopic infection being predominant in Dansoman and Korle-Gonno communities. Although P. falciparum was the most common parasite species in these community hotspots, almost all primary and secondary hotspots of malaria transmission had multiple coinfections with the three the Plasmodium species , that is, P. falciparum , P. ovale and P. malariae coinfections. Also, in the Central region, the predominant hotspots were detected in the Kasoa, Cape Coast and Ankafo prisons, with the highest hotspot found in the Kasoa communities. Also, from the Eastern region, the Nsawam district was the district with the highest hotspot for submicroscopic malaria, with the predominant parasite species being P. falciparum . Insert Figure 11 here Figure 11: Location-specific distribution of malaria positive cases. Discussion This study revealed a surprisingly high prevalence of asymptomatic P. falciparum infections (35%) among participants, a figure much higher than the reported 2% malaria prevalence for the Greater Accra Region [ 16 ]. This finding aligns with earlier reports showing that asymptomatic infections frequently exceed symptomatic ones in endemic regions [ 17 ]. Notably, submicroscopic infections (32.9%) were more common than microscopic infections (18.9%), confirming that microscopy underestimates total parasite carriage and misses low-level parasitemia [ 18 ]. The high prevalence of submicroscopic infections in this low-transmission setting supports previous assertions that such infections often persist where malaria control measures have reduced overall transmission [ 19 , 13 ]. Similar patterns have been observed in Senegal and Tanzania [ 20 , 21 ], suggesting that localized hotspots and partial host immunity may sustain these reservoirs [ 22 , 23 ]. Age-related patterns showed that submicroscopic infections were more common among adults, consistent with evidence that partial immunity acquired over years of exposure allows adults to harbor low-level infections without symptoms [ 24 , 12 ]. Conversely, children with less-developed immunity, were more prone to microscopic infections and anemia. Interestingly, a notable proportion of submicroscopic P. ovale infections (11.5%) occurred predominantly in children, echoing findings from Ghana and other regions [ 25 , 26 ]. The detection of multiple species coinfections (24.1%) underscores the complexity of malaria epidemiology in low-transmission settings and the need to broaden control strategies to include non- falciparum infections. In assessing diagnostic tools, the rapid diagnostic test (RDT) demonstrated high sensitivity relative to microscopy, with few discrepancies. Cases that were RDT-negative but microscopy-positive were confirmed as P. falciparum and P. ovale infections by LAMP PCR, suggesting possible pfhrp2 gene deletions—an emerging threat to HRP2-based RDT reliability [ 27 ]. This underscores the importance of continuous surveillance of pfhrp2/3 deletions to ensure diagnostic accuracy. Conversely, RDT-positive but microscopy-negative cases likely represented submicroscopic infections, reaffirming the limited sensitivity of microscopy at low parasitemia. Anemia was common across participants, varying significantly by age and infection type. Microscopic infections in children were associated with greater anemia severity, consistent with previous studies linking P. falciparum density to hemoglobin reduction [ 28 , 29 , 30 ]. Submicroscopic infections in adults were associated mainly with mild to moderate anemia, reflecting their chronic, low-grade nature. Although this study could not fully account for confounding causes of anemia—such as nutritional deficiencies, hemoglobinopathies, or co-infections—the associations observed remain meaningful. The cross-sectional design was another limitation, restricting causal inference and temporal assessment. Longitudinal studies would better clarify how asymptomatic infections contribute to chronic anemia and transmission dynamics. Hematological parameters showed potential for predicting asymptomatic infections. Low mean corpuscular hemoglobin concentration (MCHC) and high red cell distribution width (RDW) were significant predictors of microscopic infections, whereas low MCHC alone predicted submicroscopic infection. These findings partially align with previous work [ 29 , 31 ] and suggest that subtle red cell changes may serve as indicators of chronic low-grade parasitemia in resource-limited settings [ 32 , 33 ]. Although larger studies are needed, such hematological markers could support early screening and monitoring of asymptomatic carriers. Analysis of antimalarial drug resistance markers revealed encouraging trends and emerging concerns. The frequency of pfcrt K76T (9.1%) indicated a marked return of chloroquine sensitivity, likely due to reduced drug pressure following its withdrawal [ 34 , 35 ]. Similar reversions have been reported in Zambia and Malawi [ 36 ]. However, persistence of pfmdr1 Y184F (51%), associated with decreased lumefantrine sensitivity, warrants attention, though clinical ACT resistance remains unreported. High mutation frequencies in pfdhps (A437G, S436A, A581G) and pfdhfr (N51I, C59R, S108N) suggest ongoing SP pressure, though no K540E mutation linked to IPTp failure was detected. The observed triple pfdhfr haplotype (IRNI, 35%) and dominant pfdhps haplotypes (SGKAA, SGKGA) confirm widespread SP resistance, consistent with national data [ 10 ]. Importantly, no validated pfk13 mutations conferring artemisinin resistance were found, though variants like C580Y and R539T warrant monitoring, as they have been implicated in resistance in East Africa (Rwanda, Uganda and Tanzania). Continuous genomic surveillance of both microscopic and submicroscopic infections remains essential to protect ACT efficacy. Spatial analysis identified major hotspots in the Greater Accra, Central, and Eastern regions, with Accra Central showing the highest submicroscopic carriage. Environmental and socioeconomic factors such as unplanned urbanization, poor drainage, and urban poverty likely sustain breeding sites and parasite persistence [ 37 ]. The adaptability of Anopheles gambiae to urban conditions further enhances transmission potential [ 38 , 39 ]. These findings highlight the need for urban-focused interventions mass drug administration (MDA), mass screen and treat (MSaT), and enhanced vector control to reduce silent reservoirs fueling ongoing transmission. Conclusion This study demonstrated a substantial hidden burden of asymptomatic and submicroscopic P. falciparum infections in a low-transmission setting, with clear hematological consequences and diverse resistance genotypes. The observed resurgence of chloroquine susceptibility, persistence of SP resistance, and emerging pfk13 variants emphasize the need for vigilant drug-resistance monitoring. Although limited by its cross-sectional design and inability to adjust for all anemia confounders, the findings underscore the epidemiological importance of asymptomatic carriers in sustaining malaria transmission. Strengthening molecular diagnostic capacity, expanding active surveillance, and targeting identified hotspots through integrated control strategies are critical to accelerating malaria elimination efforts in Ghana and similar settings. Abbreviations AA Aplastic Anemia ATC Antihuman Thymocyte Globulin CART Chimeric antigen receptor T-cell CHIP Clonal hematopoiesis of indeterminate potential GPI Glycosylphosphatidylinositol HSC Hematopoietic Stem Cell HSPC Hematopoietic Stem and Progenitor cells IAA Idiopathic Aplastic Anemia IFNγ Interferon gamma IST Immunosuppressive Therapy LAG 3 Lymphocyte-activation gene 3 MDS Myelodysplastic syndrome NK Natural Killer Cells NSAA Non-severe aplastic Anemia SAA Severe Aplastic Anemia SLE Systemic lupus Erythematosus Th1 T-helper cell-1 TLS Tertiary lymphoid structure TNFα Tumor Necrosis Factor Alpha PD 1 Programmed Death-1 PRF Platelet-rich fibrin VSAA Very severe Aplastic Anemia WACCBIP West Africa Centre for Cell Biology of Infectious Pathogens Declarations Ethics approval and consent to participate Ethical approval for the study was obtained from the Ethical and Protocol Review Board of the School of Biomedical and Allied Health Sciences, University of Ghana (SBAHS/AA/MLAB/11366043/2024-2025), and from the Korle Bu Teaching Hospital Institutional Review Board (KBTH-STC/IRB/00028/2025). All participants were informed of the study’s purpose, potential risks, and procedures before enrollment. Written informed consent was obtained from all adult participants, while assent was obtained from minors aged 12–18 years, alongside parental or guardian consent for younger children. Participants’ comfort and safety during blood collection were ensured, with all procedures conducted under minimal risk. Individuals who tested positive for malaria were informed and referred for appropriate treatment in accordance with the national malaria case management guidelines of Ghana. Consent for publication Not applicable Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding The study was funded using the University of Ghana book and research allowances of the research team members. The University of Ghana did not play any role as far as the design of the study, collection, analysis, and interpretation of data as well as writing of the manuscript are concerned. Author’s contributions BTM was involved in Conceptualization, Supervision, Writing-original draft, Writing-review & editing. LAT – Conceptualization, Investigation, Data Curation, Writing-original draft, Writing-review & editing. HO – Conceptualization, Investigation, Data Curation, Writing-original draft, Writing-review & editing. DNOA – Data curation, Resources, Validation, Writing-review & editing. CTA – Data curation, Resources, Validation, Writing-review & editing. SA-B – Conceptualization, Supervision, Writing-original draft, Writing-review & editing. Acknowledgements We are grateful to the directors and laboratory managers of the Korle-Bu Teaching Hospital and the WACCBIP for their assistance in carrying out this study. References Abad, M. A., Reyes, R., Fernández, S., & González, A. (2022). 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Molecular surveillance of antimalarial resistance markers in Ghana post-chloroquine withdrawal. BMC Infectious Diseases, 23 (1), 425. Mwanza, S., Chaponda, M., Malunga, P., Soko, D., & Mharakurwa, S. (2016). Return of chloroquine sensitivity in Zambia after cessation of drug use. Malaria Journal, 15 (1), 584. De Silva, P. M., & Marshall, J. M. (2012). Factors contributing to urban malaria transmission in sub-Saharan Africa: A systematic review. Journal of Tropical Medicine, 2012 , 819563. https://doi.org/10.1155/2012/819563 Doumbe-Belisse, P., Ngadjeu, C. S., Sonhafouo-Chiana, N., Talipouo, A., Djamouko-Djonkam, L., Kopya, E., … & Antonio-Nkondjio, C. (2018). High malaria transmission intensity in urban areas of Douala, Cameroon. BMC Infectious Diseases, 18 , 469. Antonio-Nkondjio, C., Fossog, B. T., Ndo, C., Djantio, B. M., Togouet, S. Z., Awono-Ambene, P., … & Wondji, C. S. (2013). Anopheles gambiae distribution and insecticide resistance in urban areas of Cameroon. PLoS ONE, 8 (5), e63460. https://doi.org/10.1371/journal.pone.0063460 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 04 May, 2026 Reviews received at journal 03 May, 2026 Reviews received at journal 30 Apr, 2026 Reviewers agreed at journal 11 Apr, 2026 Reviewers agreed at journal 10 Apr, 2026 Reviewers agreed at journal 10 Apr, 2026 Reviewers invited by journal 09 Apr, 2026 Editor assigned by journal 11 Mar, 2026 Submission checks completed at journal 11 Mar, 2026 First submitted to journal 10 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9086362","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":621347780,"identity":"8854a6f4-e620-4590-b6b5-d90fb759378c","order_by":0,"name":"Benjamin Tetteh Mensah","email":"","orcid":"","institution":"University of Ghana","correspondingAuthor":false,"prefix":"","firstName":"Benjamin","middleName":"Tetteh","lastName":"Mensah","suffix":""},{"id":621347781,"identity":"b9ac62b9-8cd0-4d5b-b88a-9b6e90dcb772","order_by":1,"name":"Lucas Amenga-Tego","email":"","orcid":"","institution":"West African Centre for Cell Biology of Infectious 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curve plots depicting true positives and false negatives after LAMP PCR assay analysis\u003c/p\u003e","description":"","filename":"FIG2.png","url":"https://assets-eu.researchsquare.com/files/rs-9086362/v1/42e4b7f1e0bd74c4e49113f9.png"},{"id":107254491,"identity":"93cc913a-fb25-43d2-9c4f-b05c0318e20c","added_by":"auto","created_at":"2026-04-19 12:02:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":203505,"visible":true,"origin":"","legend":"\u003cp\u003eGel electrophoresis showing Multiplex PCR amplification of P. falciparum drug resistance markers.\u003c/p\u003e","description":"","filename":"FIG3.png","url":"https://assets-eu.researchsquare.com/files/rs-9086362/v1/ee12cf3cd98cfbaa3c15b71d.png"},{"id":107254492,"identity":"763fc5bc-39be-4a3b-9f0a-e10e143d2cf6","added_by":"auto","created_at":"2026-04-19 12:02:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":247550,"visible":true,"origin":"","legend":"\u003cp\u003eA flow diagram of the study participants\u003c/p\u003e","description":"","filename":"FIG4.png","url":"https://assets-eu.researchsquare.com/files/rs-9086362/v1/5532c7f7a3081e9556dffbbc.png"},{"id":107484417,"identity":"403800b5-c4d8-479f-918d-6dd7bb120b37","added_by":"auto","created_at":"2026-04-22 02:31:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":439323,"visible":true,"origin":"","legend":"\u003cp\u003eA is bar graph showing the prevalence of microscopic asymptomatic infections by age group. B shows the prevalence of submicroscopic asymptomatic infections by age group. C shows the distribution of the prevalence of submicroscopic infections by age groups in males and females and D shows the distribution of the prevalence of microscopic infections by age groups in males and females.\u003c/p\u003e","description":"","filename":"FIG5.png","url":"https://assets-eu.researchsquare.com/files/rs-9086362/v1/1f3967d67fb1016d6ec4a500.png"},{"id":107254495,"identity":"ae37c7c2-2de6-48f7-a4c2-94b27327d0a8","added_by":"auto","created_at":"2026-04-19 12:02:08","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":270921,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e is a stack bar plot showing the association between anemia severity and infection status after adjusting for age group. \u003cstrong\u003eB\u003c/strong\u003e shows anemia severity among microscopic and submicroscopic infections and \u003cstrong\u003eC\u003c/strong\u003e shows anemia status grouped as normal, moderate, mild and severe by age group.\u003c/p\u003e","description":"","filename":"FIG6.png","url":"https://assets-eu.researchsquare.com/files/rs-9086362/v1/7fcea6ce03794703e6eac457.png"},{"id":107484421,"identity":"cdcb2c49-27cc-4ba1-a5db-587262bb9586","added_by":"auto","created_at":"2026-04-22 02:31:57","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":195690,"visible":true,"origin":"","legend":"\u003cp\u003eA shows malaria infection status and association with mean RBC indices and B shows malaria infection status (submicroscopic and uninfected group) and association with RBC indices\u003c/p\u003e","description":"","filename":"FIG7.png","url":"https://assets-eu.researchsquare.com/files/rs-9086362/v1/d4b1d8b7150e69639b481d3b.png"},{"id":107254496,"identity":"df599336-1a6f-477f-b758-ee3b6f737534","added_by":"auto","created_at":"2026-04-19 12:02:08","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":284980,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of infection status (submicroscopic and uninfected group) and its association with mean RBC indices.\u003c/p\u003e","description":"","filename":"FIG8.png","url":"https://assets-eu.researchsquare.com/files/rs-9086362/v1/4ab00dbaa3b6039a4d30c495.png"},{"id":107485006,"identity":"7fed6338-f525-4a53-bac6-1cbaacf8f24c","added_by":"auto","created_at":"2026-04-22 02:33:27","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":235090,"visible":true,"origin":"","legend":"\u003cp\u003eA shows the frequency distribution of CRT and MDR1 alleles in microscopic and submicroscopic infection status. B shows Plasmodium falciparum dhfr-dhps combined haplotypes by microscopic (micro) and sub-microscopic (sub) infection and C shows the distribution of resistance and sensitive phenotype by drug and intervention type.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCQ: Chloroquine; PYR: Pyrimethamine resistance; SP.IPT\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e: Sulfadoxine-Pyrimethamine for Intermittent prevention of malaria in pregnancy; SP.RX: Sulfadoxine-Pyrimethamine resistance; SX: Sulfadoxine\u003c/em\u003e\u003csub\u003e\u003cem\u003e\u0026nbsp;\u0026nbsp; \u003c/em\u003e\u003c/sub\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e","description":"","filename":"FIG9.png","url":"https://assets-eu.researchsquare.com/files/rs-9086362/v1/6a80f4aaa43defe078ec8ed6.png"},{"id":107484366,"identity":"466d1f01-f058-4fb6-94d4-ecfe91d50c46","added_by":"auto","created_at":"2026-04-22 02:31:44","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":235681,"visible":true,"origin":"","legend":"\u003cp\u003eMap of Ghana highlighting the regional hotspots of cases\u003c/p\u003e","description":"","filename":"FIG10.png","url":"https://assets-eu.researchsquare.com/files/rs-9086362/v1/c41dbb6e5a848d928a74cf47.png"},{"id":107254498,"identity":"f74a9cd0-f2e3-432e-9794-fa62048089b4","added_by":"auto","created_at":"2026-04-19 12:02:08","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":425671,"visible":true,"origin":"","legend":"\u003cp\u003eLocation-specific distribution of malaria positive cases.\u003c/p\u003e","description":"","filename":"FIG11.png","url":"https://assets-eu.researchsquare.com/files/rs-9086362/v1/ed3b906ed3f74cccbac6c2f0.png"},{"id":107487876,"identity":"799a0131-b33a-4d8e-8646-537b879ad5c8","added_by":"auto","created_at":"2026-04-22 02:43:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4123358,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9086362/v1/6cbb3c28-f80f-4a6e-b48a-d8a4567c0c01.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Submicroscopic Malaria Parasite Carriage and Hemoglobin Levels among Individuals with Asymptomatic Malaria Infections","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMalaria remains a major global health burden, particularly in sub-Saharan Africa, despite sustained control efforts, with asymptomatic and submicroscopic infections continuing to drive transmission [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In highly endemic settings, over 90% of infections may be asymptomatic, allowing undetected carriers to sustain parasite transmission [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Traditionally defined as microscopy-detected parasitemia in the absence of symptoms [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], asymptomatic malaria also includes low-density infections detectable only by PCR, with evidence showing that up to two-thirds of microscopy-negative individuals harbor submicroscopic infections [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBeyond their epidemiological importance, submicroscopic infections are associated with chronic mild anemia and alterations in red blood cell (RBC) indices such as MCV, MCH, and RDW, reflecting ongoing erythrocyte destruction [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Persistent low-density parasitemia may also promote antimalarial drug resistance under sub-therapeutic drug pressure [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Key \u003cem\u003eP. falciparum\u003c/em\u003e resistance genes: \u003cem\u003epfcrt, pfdhfr, pfdhps, pfmdr1\u003c/em\u003e, and \u003cem\u003epfkelch13\u003c/em\u003e mediate resistance to major antimalarials [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], yet their prevalence in asymptomatic infections in Ghana remains poorly defined. Asymptomatic malaria has additionally been linked to anemia and impaired cognitive performance [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], and recent studies in Ghana report increasing rates of asymptomatic and submicroscopic infections across transmission zones [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven these challenges, a comprehensive understanding of the epidemiology of submicroscopic infections, their hematological consequences, and associated resistance profiles is urgently needed. This study therefore aims to determine the prevalence of submicroscopic \u003cem\u003eP. falciparum\u003c/em\u003e infections among asymptomatic individuals at Korle Bu Teaching Hospital, assess associations with anemia and RBC indices, and identify molecular markers of antimalarial drug resistance to inform malaria elimination strategies in Ghana.\u003c/p\u003e"},{"header":"METHODOLOGY","content":"\u003ch2\u003e3.1 Study\u0026nbsp;Setting\u003c/h2\u003e\n\u003cp\u003eThe study will be carried out at the Central Laboratory and Child Health Laboratory of the Korle-Bu Teaching hospital located in the Ablekuma south municipal assembly, a sub-district in the Greater Accra Region. \u0026nbsp;\u003c/p\u003e\n\u003ch2 id=\"_Toc210964455\"\u003e3.2 Study\u0026nbsp;Design\u003c/h2\u003e\n\u003cp\u003eThis study was a cross-sectional study.\u0026nbsp;\u003c/p\u003e\n\u003ch2 id=\"_Toc210964456\"\u003e3.3 Study Population\u003c/h2\u003e\n\u003cp\u003ePatients from the Out-Patient Department (OPD) visiting the Central laboratory and the Child health laboratory were our study population.\u0026nbsp;\u003c/p\u003e\n\u003cp id=\"_Toc192507034\"\u003e\u003cstrong\u003e3.3.1 Inclusion criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfebrile patients above 2 years old who had no signs and symptoms of malaria and had not taken any antimalarial drugs (ACTs) within the past 2 weeks were included in this study after informed consent had been obtained. Also, participants who tested negative for malaria will be included in the study as a control group.\u0026nbsp;\u003c/p\u003e\n\u003cp id=\"_Toc192507035\"\u003e\u003cstrong\u003e3.3.2 Exclusion\u0026nbsp;criteria\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003ePatients with signs and symptoms of malaria (defined as axillary temperature above 37.5℃) at the time of recruitment.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003ePatients who had taken antimalarial drugs (ACTs) within the past two weeks and\u003c/li\u003e\n \u003cli\u003ePatients who are severely ill, pregnant women and patients with hemoglobinopathies.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003cspan id=\"_Toc210964459\"\u003e3.4 Sampling method\u003c/span\u003e\u003c/h2\u003e\n\u003cp\u003ePatients from the Out-Patient Department (OPD) of the Central Laboratory and the Child Health Laboratory of the Korle Bu Teaching Hospital were recruited into the study using purposive sampling technique. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cspan id=\"_Toc210964460\"\u003e3.5 Study\u0026nbsp;procedure\u003c/span\u003e\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eEnrolment and clinical groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData was collected between April and July 2025. A total of 345 participants were enrolled after passing the inclusion criteria. Participants were then interviewed using a structured questionnaire with information on age, sex, location, number of household members, occupation, educational level, history of fever, number of previous episodes of malaria/fever, last date of malaria diagnosis and result of diagnosis and antimalarial drugs used in malaria treatment. Participants were then grouped into three distinct age categories, namely, less than 18 years, 18 to 35yrs and 36 and above, classified as children, young adult and older adult populations. During the study, four samples were lost due to logistical mishaps, and seven samples were excluded from the study after full blood count reports showed the presence of hemoglobinopathies (such as very high WBC counts and very low Hb counts), resulting in 334 samples remaining for analysis. Also, anemia was defined as hemoglobin levels below 11g/dl and classified into mild if Hb was in the range, 10\u0026ndash;10.9g/dl, moderate if Hb was 7\u0026ndash;9.9g/ dl, and severe if Hb was \u0026lt; 7 g/dl and normal, when hemoglobin levels were 11g/dl and above, using WHO\u0026rsquo;s standard criteria.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDefinition of clinical groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAsymptomatic malaria infections were defined and categorized into three clinical groups according to the microscopy and PCR results. Samples which tested positive for microscopic asymptomatic infections, were defined as positive microscopy infections. Also, submicroscopic infections were defined as negative microscopy but positive LAMP PCR results, with uninfected, defined as negative for both microscopy and LAMP PCR.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample collection and other laboratory procedures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbout 3 mL of whole blood was drawn by an experienced phlebotomist by venipuncture into a 5 mL ethylenediaminetetraacetic acid (EDTA) tube using a sterile disposable syringe and thoroughly mixed to prevent it from clotting. The participants\u0026rsquo; unique identification number on the consent form was written on each tube to ensure confidentiality. 200uL of the whole blood was placed on 3mm Whatman No. 1 filter paper for parasite DNA extraction for molecular work. Also, 6\u0026micro;L and 2\u0026micro;L were placed on a labelled glass slide, with thick and thin blood films prepared and stained with Giemsa stain according to standard operating procedures.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlso, about 5 \u0026micro;L of blood was used to test for and differentiate between \u003cem\u003eP. falciparum\u003c/em\u003e and other malaria species using the \u003cem\u003eOnSite\u0026nbsp;\u003c/em\u003emalaria rapid diagnostic test kit (mRDT). The remaining blood was then used for Full blood count analysis, using Mindray BC 6800 hematological analyzer (Mindray Biomedical Co. Ltd, China). Hematological parameters including hemoglobin levels (Hb), mean cell volume (MCV), red cell distribution width (RDW), red blood cell count (RBC count), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC) and hematocrit (HCT) were measured according to standard operating procedures and recorded. The dried blood spots (DBS) samples were preserved in zip-lock plastic bags with desiccants and transported to the Malaria Laboratory of the West African Centre for Cell Biology of Infectious Pathogens (WACCBIP) for molecular analysis. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMalaria diagnosis by microscopy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThick and thin peripheral blood films were prepared for each participant by pipetting 6\u0026micro;L and 2\u0026micro;L of whole blood onto a clean, well-labelled glass slide respectively, as previously described [14]. The thin smears were fixed in absolute methanol, after which the slides were allowed to dry and stained using 10% Giemsa working solution and subsequently observed using the x100 objective. The thick blood film was used for malaria parasite identification and quantification, while the thin film was used for malaria parasite speciation. Malaria parasite detection was done by examining at least 100 high-power fields. Asexual parasites were counted per 200 white blood cells and parasite quantification obtained by multiplying the asexual parasite count per 200 white blood cells by a white cell count of 8000/\u0026micro;L as previously described [14].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eNumber of Parasites counted\u003c/u\u003e \u0026times; 8000 = Number of parasites /\u0026micro;L of blood\u003c/p\u003e\n\u003cp\u003eNumber of White blood cells counted\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMicroscopy results were read by two independent, highly experienced microscopists blinded to both RDT and each other\u0026rsquo;s results, with discrepant results observed by a third highly experienced microscopist. The average of the two closest counts was then taken as the parasite count. Also, a slide was declared negative, if no malaria parasite was seen after observing 100 high-power fields, and a slide was declared positive if at least one malaria parasite was observed after examining 100 high-power fields, with an additional 100 fields observed to detect mixed infections as previously described [14].\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMalaria diagnosis by RDT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eOnSite\u0026nbsp;\u003c/em\u003emalaria rapid diagnostic test kit (mRDT) (CTK Biotech Inc., USA), which has a Combo design based on \u003cem\u003ePf-\u0026nbsp;\u003c/em\u003eHistidine Rich Protein-II (HRP II) antigen\u0026nbsp;and\u0026nbsp;pan-Plasmodium aldolase antigen (\u003cem\u003ePf/Pan)\u003c/em\u003e for simultaneous detection and differentiation of \u003cem\u003eP. falciparum (Pf), P. vivax (Pv), P. ovale (Po) or P. malariae (Pm)\u0026nbsp;\u003c/em\u003eantigen in participant whole blood, was used as a screening test, to screen participants for malaria. Briefly, the RDT was labelled with a unique participant code and date, after which 5\u0026micro;L of whole blood was pipetted into the sample well. Afterwards, two drops of the buffer solution provided by the manufacturer were added to the buffer well on the cassette and the results, read after twenty minutes according to the manufacturer\u0026rsquo;s instructions. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMalaria diagnosis by PCR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExtraction and quantification of genomic DNA\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenomic DNA (gDNA) was extracted from both \u003cem\u003eP. falciparum\u003c/em\u003e positive and negative DBS samples using the QIAamp \u0026reg; DNA Blood Mini Kit (Qiagen, Germany), following the manufacturer\u0026rsquo;s protocol, but with some modifications to maximize yield. Briefly, 2-3mm punches were obtained from each DBS sample and placed in a sterile 1.5mL microcentrifuge tube. 180\u0026micro;L of buffer ATL and 20\u0026micro;L of Proteinase K were then added to each sample, the mixture was vortexed briefly and incubated overnight at 56℃ in a thermomixer at 900 rpm to ensure complete lysis. After lysis, 200\u0026micro;L of buffer AL was added, vortexed and incubated at 70℃ for 10 minutes. After that, 200\u0026micro;L of 100% ethanol was added to the lysate, mixed thoroughly and then transferred to a QIAamp spin column. \u0026nbsp;The spin column was centrifuged at 8000 rpm for 1minute, after which the flow through was discarded. The column was then washed with 500\u0026micro;L of buffer AW1 and centrifuged at 8000 rpm for 1 minute, followed by a second wash with buffer AW2, centrifuged at 14,000 rpm for 3 minutes, followed by an optional dry spin to remove residual ethanol. The DNA was then eluted by adding 50\u0026micro;L of pre-warmed buffer AE, incubated at room temperature for 30 minutes and then centrifuged at 14,000 rpm for 1 minute. The extracted DNA was then stored at -20℃ until further molecular analysis. 1\u0026mu;l of eluted gDNA was used to quantify the extracted DNA using Invitrogen Qubit TM 1\u0026times; dsDNA High Sensitivity (HS) assay kit with Qubit Fluorometer (Thermo Fisher Scientific, USA), after which the samples were then stored at -20\u0026deg;C until they were ready for use.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDetection of submicroscopic malaria infections using Real Time-LAMP PCR assay\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the microscopy malaria-negative gDNA samples were subjected to Loop-Isothermal Amplification assay (LAMP assay) to determine the presence of submicroscopic malaria infections of \u003cem\u003eP. falciparum, P. ovale\u003c/em\u003e and \u003cem\u003eP. malariae\u003c/em\u003e. Briefly, the reaction for the assay was carried out in a total volume of 20\u0026micro;L comprising of the isothermal buffer (20mM Tris-HCl, 10mM Ammonium Sulphate ((NH\u003csub\u003e4\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e), 50mM KCl, 2mM MgSO\u003csub\u003e4\u003c/sub\u003e, 0.1% Tween 20, pH 8.8 at 25℃), magnesium chloride (MgCl\u003csub\u003e2\u003c/sub\u003e), 0.8 M betaine, 4 mM deoxyuridine triphosphate (dUTPs), 25 mM deoxynucleotides triphosphate (dNTPs), 1.5 \u0026micro;M forward and backwards inner primer (FIP and BIP), 0.2 \u0026micro;M forward and backward outer primer (F3 and B3) and 0.4 \u0026micro;M of forward and backward loop primers (FLP and BLP), 30.0 ng/\u0026micro;L of purified Bst-LF polymerase, 2.0 \u0026micro;M SYTO-9 dye and 3 \u0026micro;L of purified DNA template. The assays were performed at 65℃ for 30 minutes using QuantStudio5 (Applied Biosystems, Waltham, MA), and the results, analyzed using a melt curve analysis approach. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInsert Figure 1 here\u003cspan id=\"_Toc210963553\"\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1:\u0026nbsp;\u003c/strong\u003eAmplification plots after LAMP PCR assay analysis\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInsert Figure 2 here\u003cspan id=\"_Toc210963554\"\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2:\u0026nbsp;\u003c/strong\u003eMelting curve plots depicting true positives and false negatives after LAMP PCR assay analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDetection of drug-resistant molecular markers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultiplex PCR (panel amplification) and gel electrophoresis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll malaria-positive cases, that is, both microscopic and submicroscopic malaria cases, were subjected to antimalarial drug resistance analysis to identify molecular markers of drug-resistant parasite strains. Briefly, using Multiplex PCR, amplicons of \u003cem\u003epfcrt\u003c/em\u003e, \u003cem\u003epfdhfr\u003c/em\u003e, \u003cem\u003epfdhps\u003c/em\u003e, \u003cem\u003epfmdr1\u003c/em\u003e, and \u003cem\u003epfkelch13\u003c/em\u003e were generated as previously described [15]. Forty-seven (47) \u0026micro;L of prepared PCR master mix was added to 2\u0026mu;L of high-quality extracted gDNA (~5 ng/\u0026micro;L) in 96-well PCR plates and mixed thoroughly. Nuclease-free water was used as a negative control, while gDNA of \u003cem\u003eP. falciparum\u003c/em\u003e KH02 isolate was used as a positive control. The plates were firmly sealed and briefly centrifuged before running on the PCRmax\u0026trade; Alpha Cycler 4 (Thermo Fisher Scientific, USA) under already set thermocycling conditions (Appendix C). Also, 2% agarose gel was used to confirm the presence of the expected PCR product fragments and visualized on the Amersham Imager 600 (Cytiva, Japan).\u003c/p\u003e\n\u003cp\u003eInsert Figure 3 here\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cspan id=\"_Toc210963555\"\u003e\u003cstrong\u003eFigure 3:\u0026nbsp;\u003c/strong\u003eGel electrophoresis showing Multiplex PCR amplification of P. falciparum drug resistance markers.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/span\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAmplicon purification and library preparation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePCR amplicons were purified using the QIAamp\u003csup\u003e\u0026reg;\u003c/sup\u003e DNA MiniElute Kit (Qiagen, Germany) according to the manufacturer\u0026rsquo;s instructions. Eluted gDNA amplicons were then quantified as described above and stored at 4\u0026deg;C. Afterwards, three sequencing libraries were prepared for the purified PCR amplicons (22 clinical isolates, KH02 positive control and negative control per batch) using the SQK-NBD112.24 native barcoding kit (Oxford Nanopore Technologies, UK). The manufacturer\u0026rsquo;s protocol was followed for the library preparation. Briefly, 12.5 \u0026mu;l of each purified DNA amplicon (~200 fmol) was end-prepped using 1x Ultra II End-prep enzyme mix, incubated for 5 min at 20 \u0026deg;C and 5 min at 65 \u0026deg;C. End-prepped PCR amplicons were then purified with 1x AMPure XP Beads and eluted in 10 \u0026mu;l nuclease-free water. The purified end-prepped gDNA was then barcoded with 24 unique native barcodes using 1x Blunt/TA Ligase for 20 min at room temperature (RT). After barcoding, all the 24 samples were pooled and purified with 1x AMPure XP. After, the barcoded gDNA amplicons were eluted in 35 \u0026mu;L of nuclease-free water. Afterwards, 30 \u0026micro;L of the pooled barcoded gDNA was then ligated to the Adapter Mix II H (AMII H) using the Quick T4 Ligase for 20 min at room temperature. Purification was then performed with 1x AMPure XP using the Short Fragment Buffer (SFB), with adapter-ligated amplicons eluted in 15 \u0026mu;L of Elution Buffer.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSequencing, base-calling, alignment and SNP detection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe prepared DNA libraries to be loaded for sequencing were prepared by thoroughly mixing 12 \u0026mu;L of prepared DNA library (~20 fmol), 37.5 \u0026mu;L of Sequencing Buffer II (SBII) and 25.5 \u0026mu;L of Loading Beads II (LBII). After that, 75 \u0026mu;L of the mixture was gently administered to the flow cells (Version FLO-MIN107) in the MinION Mk1b sequencer. Sequencing was performed between 6-8 hours with real-time high-accuracy \u003cem\u003eguppy\u003c/em\u003e base calling using the MinKNOW software. The resulting fastq files were processed through a custom Nextflow pipeline: \u003cem\u003enano-rave\u003c/em\u003e (Nanopore Rapid Analysis and Variant Explorer) [15]. After quality control (QC) checks, sequence reads were mapped to 3D7 reference sequences for each of the amplicon target genes using \u003cem\u003eminimap2\u003c/em\u003e. Amplicon coverage data were then generated using \u003cem\u003eBEDTools\u003c/em\u003e. Also, \u003cem\u003eMedaka haploid\u003c/em\u003e was used for variant calling to generate Variant Call Format (VCF) file outputs for each amplicon for each sample (ONT barcode), and the VCF files were processed using custom R scripts to calculate SNP frequencies at the five drug resistance loci. A cut-off of \u0026gt;10x coverage was applied for an amplicon to be included in the analysis.\u0026nbsp;\u003c/p\u003e\n\u003ch2 id=\"_Toc210964464\"\u003eDetermining the hotspots of malaria transmission using spatio-temporal analysis\u003c/h2\u003e\n\u003cp\u003eIn this study, spatio-temporal analysis was employed to identify malaria transmission hotspots within the Greater Accra Region. Although the region is generally classified as a low-transmission zone, with an estimated malaria prevalence of 2% [16], localized pockets of moderate to high transmission are suspected. Participants\u0026rsquo; home locations were recorded, and GPS coordinates were generated where available. Cases identified with submicroscopic malaria infections following laboratory analysis were selected for spatial mapping and hotspot assessment. To examine the spatial distribution of infections, the dataset was reshaped into a long format with infection type as a single variable. Data were grouped by location, region, and infection subset, and the number of submicroscopic cases was calculated for each group. These counts were converted to percentages relative to the total number of positive cases across all regions, ensuring that regional prevalence summed to 100%. Bar plots were generated to visualize the proportion of positive cases per region and infection category, with each bar representing the contribution of a specific location to the total infection burden. The regional plots were then integrated using the \u003cem\u003epatchwork\u003c/em\u003e package in R, enabling clear visual comparison of spatial variations and regional contributions to overall malaria prevalence patterns.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eData Analysis\u003c/h2\u003e\n\u003cp\u003eBaseline characteristics of demographics, hematological Parameters and asymptomatic malaria infection outcomes of participants were analyzed using STATA Version 14.1 software. Descriptive statistics, chi-square test, student t-test, ANOVA, linear and logistic regression analysis were used to test for association between sociodemographic characteristics, hematological parameters and infection outcomes. Also, statistical significance was determined at 95% confidence interval, with a P-value of \u0026lt;0.05 considered statistically significant.\u0026nbsp;\u003c/p\u003e"},{"header":"RESULTS","content":"\u003ch2\u003eDemographics and clinical characteristics of asymptomatic study participants\u003c/h2\u003e\n\u003cp\u003eA total of 345 participants meeting the inclusion criteria were initially enrolled. Thick and thin blood films, malaria rapid diagnostic tests (RDTs), dried blood spots for molecular analysis, and full blood count (FBC) assessments were performed for all participants. Seven samples were excluded due to abnormally high white blood cell counts and very low hemoglobin levels, suggestive of hematological malignancies, and four were lost during processing, leaving 334 participants for analysis. Participants were categorized into three groups: children (\u0026lt;18 years), young adults (18\u0026ndash;35 years), and older adults (\u0026ge;36 years), as summarized in Table 1.\u003c/p\u003e\n\u003cp\u003eOf the 334 participants, 196 (58.68%) were female and 138 (41.32%) were male, with a median age of 26 years (IQR: 2\u0026ndash;79 years). RDTs detected 62 malaria-positive cases, while microscopy identified 63, with five discrepant results\u0026mdash;two RDT-positive/microscopy-negative and three microscopy-positive/RDT-negative. All microscopy-negative samples were subsequently analyzed using loop-mediated isothermal amplification (LAMP) PCR to detect submicroscopic \u003cem\u003ePlasmodium falciparum\u003c/em\u003e infections and other \u003cem\u003ePlasmodium\u003c/em\u003e species, including \u003cem\u003eP. ovale\u003c/em\u003e and \u003cem\u003eP. malariae\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eInsert Figure 4 here\u003c/p\u003e\n\u003cp id=\"_Toc210963556\"\u003e\u003cstrong\u003eFigure 4:\u0026nbsp;\u003c/strong\u003eA flow diagram of the study participants\u003cem\u003e\u003cbr clear=\"all\"\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp id=\"_Toc210963525\"\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e: Showing the demographic and clinical characteristics of study participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1776435804.png\" width=\"839\" height=\"879\"\u003e\u003c/p\u003e\n\u003ch2 id=\"_Toc210964469\"\u003ePrevalence of asymptomatic malaria infections, including submicroscopic infections\u003c/h2\u003e\n\u003cp\u003eAll baseline infections were asymptomatic, with no participants exhibiting fever or malaria-like symptoms. The overall prevalence of asymptomatic \u003cem\u003eP. falciparum\u003c/em\u003e infection (microscopic and submicroscopic combined) was \u003cstrong\u003e35.0% (117/334)\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e Microscopy detected \u003cstrong\u003e18.9% (63/334)\u003c/strong\u003e of participants as positive for asymptomatic \u003cem\u003eP. falciparum\u003c/em\u003e, while LAMP PCR identified \u003cstrong\u003e17.6% (54/334)\u003c/strong\u003e additional submicroscopic infections among the microscopy-negative cases (figure 5A). Parasite speciation using LAMP PCR revealed an overall \u003cstrong\u003e32.9% (110/334)\u003c/strong\u003e prevalence of submicroscopic \u003cem\u003ePlasmodium\u003c/em\u003e spp. infections, comprising \u003cem\u003eP. falciparum\u003c/em\u003e (\u003cstrong\u003e51.8%, 57/110\u003c/strong\u003e\u003cstrong\u003e),\u003c/strong\u003e \u003cem\u003eP. ovale\u003c/em\u003e (\u003cstrong\u003e35.5%, 39/110; 11.7% overall\u003c/strong\u003e\u003cstrong\u003e)\u0026nbsp;\u003c/strong\u003eand \u003cem\u003eP. malariae\u003c/em\u003e (\u003cstrong\u003e12.7%, 14/110; 4.2% overall\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e (figure 5B).\u003c/p\u003e\n\u003cp\u003eAge-stratified analysis showed that microscopic \u003cem\u003eP. falciparum\u003c/em\u003e infection was highest among young adults (\u003cstrong\u003e27.7%, 28/101\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e compared with children (\u003cstrong\u003e16.9%, 20/118\u003c/strong\u003e) and older adults (\u003cstrong\u003e13.0%, 15/115\u003c/strong\u003e), with a significant association between age and infection status (\u0026chi;\u0026sup2; (2, N=334) =8.00, \u003cem\u003ep\u003c/em\u003e=0.018) (figure 5C).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSubmicroscopic infections were more common among older adults (\u003cstrong\u003e20.9%, 24/115\u003c/strong\u003e) and young adults (\u003cstrong\u003e17.8%, 18/101\u003c/strong\u003e) than children (\u003cstrong\u003e12.7%, 15/118\u003c/strong\u003e). Submicroscopic \u003cem\u003eP. ovale\u003c/em\u003e infection predominated in children (\u003cstrong\u003e16.1%, 19/118\u003c/strong\u003e), while \u003cem\u003eP. malariae\u003c/em\u003e infection was more frequent among older adults (\u003cstrong\u003e6.1%, 7/115\u003c/strong\u003e) (figure 5D).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCo-infections were detected in 9 participants with \u003cem\u003eP. falciparum\u003c/em\u003e and \u003cem\u003eP. ovale\u003c/em\u003e, and in 2 participants each with \u003cem\u003eP. malariae\u003c/em\u003e plus either \u003cem\u003eP. ovale\u003c/em\u003e or \u003cem\u003eP. falciparum\u003c/em\u003e. No triple-species co-infections were observed. Sex distribution showed nearly equal prevalence of microscopic infections in males (\u003cstrong\u003e49.2%, 31/63\u003c/strong\u003e) and females (\u003cstrong\u003e50.8%, 32/63\u003c/strong\u003e), while submicroscopic infections were slightly higher in females (\u003cstrong\u003e57.4%, 31/54\u003c/strong\u003e) than in males (\u003cstrong\u003e48.1%, 26/54\u003c/strong\u003e)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInsert Figure 5 here\u003c/p\u003e\n\u003cp id=\"_Toc210963557\"\u003e\u003cstrong\u003eFigure 5:\u0026nbsp;\u003c/strong\u003eA is bar graph showing the prevalence of microscopic asymptomatic infections by age group\u003cspan id=\"_Toc210963560\"\u003e. B shows the prevalence of submicroscopic asymptomatic infections by age group. C shows the distribution of the prevalence of submicroscopic infections by age groups in males and females and D shows the distribution of the prevalence of microscopic infections by age groups in males and females.\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003ch2 id=\"_Toc210964470\"\u003eAssociation between submicroscopic P. \u003cem\u003efalciparum\u003c/em\u003e carriage, haemoglobin levels, and other RBC indices in asymptomatic individuals.\u003c/h2\u003e\n\u003cp\u003eOverall, low hemoglobin levels were observed among participants, with anemia prevalence varying significantly by age group (\u0026chi;\u0026sup2;(2, N=334)=27.83, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001). Children were most affected \u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003e54.5%\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e 95% CI [43\u0026ndash;61]), followed by young adults \u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003e24.0%\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e 95% CI [19\u0026ndash;36]) and older adults \u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003e21.4%\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e 95% CI [14\u0026ndash;29]). The mean hemoglobin concentration for all participants was \u003cstrong\u003e11.5 g/dL\u003c/strong\u003e (SD=1.9, 95% CI [4.9\u0026ndash;16.4]), with mean levels of \u003cstrong\u003e11.3 g/dL\u003c/strong\u003e in microscopic and \u003cstrong\u003e11.7 g/dL\u003c/strong\u003e in submicroscopic infections.\u003c/p\u003e\n\u003cp\u003eAlthough microscopic infection was not significantly associated with anemia (\u0026chi;\u0026sup2;(2, N=334)=2.09, \u003cem\u003ep\u003c/em\u003e=0.14), regression analysis adjusted for age revealed that microscopic infection correlated with lower hemoglobin levels (\u0026beta; = \u0026minus;0.50, \u003cem\u003et\u003c/em\u003e(330)= \u0026minus;1.83, \u003cem\u003ep\u003c/em\u003e=0.012). ANOVA confirmed significant differences in mean hemoglobin by age group (\u003cem\u003eF\u003c/em\u003e (2,331)=3.71, \u003cem\u003ep\u003c/em\u003e=0.003), with children showing lower levels (\u003cstrong\u003eM=10.2 g/dL\u003c/strong\u003e, 95% CI [9.1\u0026ndash;11.3]) than older participants \u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003eM=11.7 g/dL\u003c/strong\u003e, 95% CI [11.1\u0026ndash;12.3]).\u003c/p\u003e\n\u003cp\u003eWhen anemia severity was compared across infection types and age groups, a significant association was observed (\u0026chi;\u0026sup2;(2, N=334)=27.6, \u003cem\u003ep\u003c/em\u003e=0.023). A higher proportion of anemic children with submicroscopic infection \u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003e7.9%\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e was noted compared with young adults (\u003cstrong\u003e1.36%\u003c/strong\u003e) and older adults \u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003e5%\u003c/strong\u003e\u003cstrong\u003e),\u003c/strong\u003e though not statistically significant (\u0026chi;\u0026sup2;(2, N=334)=0.02, \u003cem\u003ep\u003c/em\u003e=0.87). Among children, no significant difference in hemoglobin was found between microscopic-positive \u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003eM=10.53 g/dL\u003c/strong\u003e, SD=1.8) and submicroscopic \u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003eM=10.2 g/dL\u003c/strong\u003e, SD=2.2) infections (\u003cem\u003et\u003c/em\u003e(29)=0.47, \u003cem\u003ep\u003c/em\u003e=0.32), indicating similar levels of anemia.\u003c/p\u003e\n\u003cp\u003eAmong submicroscopic infections, normal hemoglobin levels predominated \u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003e74.1%\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e 40/54), followed by moderate \u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003e18.5%\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e and mild \u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003e7.4%\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e anemia, with no severe anemia observed. No significant associations were found between hemoglobin levels and \u003cem\u003eP. ovale\u003c/em\u003e (\u0026chi;\u0026sup2;(2, N=334)=0.02, \u003cem\u003ep\u003c/em\u003e=0.87) or \u003cem\u003eP. malariae\u003c/em\u003e (\u0026chi;\u0026sup2;(2, N=334)=2.06, \u003cem\u003ep\u003c/em\u003e=0.15) infections, even after adjusting for age and anemia severity.\u003c/p\u003e\n\u003cp\u003eInsert Figure 6 here\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 6\u003c/strong\u003e: \u003cstrong\u003eA\u003c/strong\u003e is a stack bar plot showing the association between anemia severity and infection status after adjusting for age group\u003cspan id=\"_Toc210963563\"\u003e. \u003cstrong\u003eB\u003c/strong\u003e shows anemia severity among microscopic and submicroscopic infections and \u003cstrong\u003eC\u003c/strong\u003e shows anemia status grouped as normal, moderate, mild and severe by age group.\u003c/span\u003e\u003c/p\u003e\n\u003cp id=\"_Toc210963526\"\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e: Anemia severity in submicroscopic infections among participants\u003c/strong\u003e\u003c/p\u003e\n\u003ctable style=\"float: ;width: 7.1e+2pt;border: none;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall (n = 55)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eNone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eMild\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eModerate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eSevere\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"3\"\u003e\n \u003cp\u003eAge group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e[0\u0026ndash;17] (Children)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e13 (23.64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e6 (46.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2 (15.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e5 (38.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e[18\u0026ndash;35] (Young Adults)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e18 (32.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e17 (94.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1 (5.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e[36+] (Older Adults)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e24 (43.64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e19 (79.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e5 (20.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eFEMALE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e31 (56.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e25 (80.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2 (6.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e4 (12.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eMALE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e24 (43.64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e17 (70.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1 (4.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e6 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"4\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eBASIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e9 (16.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e7 (77.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1 (11.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1 (11.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e6 (10.91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2 (33.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1 (16.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e3 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eSECONDARY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e24 (43.64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e20 (83.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e4 (16.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eTERTIARY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e16 (29.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e13 (81.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1 (6.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2 (12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"5\"\u003e\n \u003cp\u003eOccupation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e7 (12.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e3 (42.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1 (14.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e3 (42.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003ePRIVATE SECTOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e27 (49.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e21 (77.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1 (3.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e5 (18.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003ePUBLIC SECTOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e8 (14.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e8 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eRETIRED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e4 (7.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e3 (75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eSTUDENT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e9 (16.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e7 (77.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1 (11.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1 (11.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp id=\"_Toc210963527\"\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e: Anemia severity in microscopic infections among participants\u003c/strong\u003e\u003c/p\u003e\n\u003ctable style=\"width: 7.4e+2pt;border: none;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall (n = 63)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eNone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eMild\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eModerate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eSevere\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"3\"\u003e\n \u003cp\u003eAge group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e[0\u0026ndash;17] (Children)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e20 (31.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e9 (45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e7 (35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e3 (15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e[18\u0026ndash;35] (Young Adults)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e28 (44.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e17 (60.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e5 (17.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e6 (21.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e[36+] (Older Adults)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e15 (23.81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e11 (73.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e3 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1 (6.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eFEMALE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e32 (50.79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e17 (53.12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e7 (21.88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e7 (21.88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1 (3.12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eMALE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e31 (49.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e20 (64.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2 (6.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e7 (22.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2 (6.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"4\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eBASIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e11 (17.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e6 (54.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1 (9.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2 (18.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2 (18.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e6 (9.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1 (16.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e4 (66.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1 (16.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eSECONDARY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e30 (47.62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e18 (60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e5 (16.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e7 (23.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eTERTIARY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e16 (25.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e12 (75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e3 (18.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1 (6.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAssociation of Submicroscopic and Microscopic Infections with RBC Indices\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA multiple linear regression analysis was conducted to assess the relationship between malaria infection status (submicroscopic and microscopic) and red blood cell (RBC) indices\u0026mdash;HGB, HCT, MCV, MCH, MCHC, and RDW\u0026mdash;after adjusting for age and sex.\u003c/p\u003e\n\u003cp\u003eSubmicroscopic infections showed slightly higher mean HGB (B = 0.08, SE = 0.28, p = 0.76), HCT (B = 0.40, SE = 0.87, p = 0.64), and MCH (B = 1.05, SE = 0.67, p = 0.12) compared with uninfected individuals, though these differences were not statistically significant. Nevertheless, the overall regression models for HGB and HCT were significant (F = 7.57, p \u0026lt; 0.001, R\u0026sup2; = 0.08), indicating modest explanatory power.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn contrast, submicroscopic infections were significantly associated with lower MCHC (B = \u0026ndash;0.77, SE = 0.37, p = 0.04, 95% CI [\u0026ndash;1.50, \u0026ndash;0.03]), with the model explaining 6% of variance (R\u0026sup2; = 0.06). This suggests MCHC may serve as a sensitive marker of submicroscopic infection. Although MCV and RDW were slightly elevated among infected individuals, these differences were not significant (p \u0026gt; 0.05), despite overall significant model fits (MCV: F = 14.66, p \u0026lt; 0.001, R\u0026sup2; = 0.16; RDW: F = 3.26, p = 0.012, R\u0026sup2; = 0.04).\u003c/p\u003e\n\u003cp\u003eFor microscopic asymptomatic infections, similar but more pronounced trends were observed. Lower mean values of MCH (B = \u0026ndash;0.53, p = 0.54), MCHC (B = \u0026ndash;0.80, p = 0.037), RDW (B = \u0026ndash;1.54, p = 0.002), HCT (B = \u0026ndash;0.53, p = 0.43), and HGB (B = 0.33, p = 0.25) were recorded compared with uninfected participants, while MCV was slightly higher (B = 1.71, p = 0.14). Among these\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003elow MCHC (p = 0.037)\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003elow RDW (p = 0.002)\u003c/strong\u003e were the strongest predictors of microscopic asymptomatic infections, explaining 5.4% and 4% of the variance, respectively.\u003c/p\u003e\n\u003cp\u003eOverall, these findings highlight subtle hematological alterations linked to submicroscopic and microscopic infections, with MCHC and RDW emerging as potential early indicators of subclinical malaria-related anemia.\u003c/p\u003e\n\u003cp\u003eInsert Figure 7 here\u003c/p\u003e\n\u003cp id=\"_Toc210963564\"\u003e\u003cstrong\u003eFigure 7\u003c/strong\u003e: A shows malaria infection status and association with mean RBC indices\u003cspan id=\"_Toc210963565\"\u003e\u0026nbsp;and B shows malaria infection status (submicroscopic and uninfected group) and association with RBC indices\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eInsert Figure 8 here\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 8:\u0026nbsp;\u003c/strong\u003eDistribution of infection status (submicroscopic and uninfected group) and its association with mean RBC indices.\u0026nbsp;\u003c/p\u003e\n\u003ch2 id=\"_Toc210964471\"\u003eThe prevalence of known antimalarial drug resistance genes circulating in the study population\u003c/h2\u003e\n\u003cp\u003eMutations in \u003cem\u003epfcrt, pfmdr1, pfdhfr, pfdhps,\u003c/em\u003e and \u003cem\u003epfk13\u003c/em\u003e genes associated with resistance to chloroquine, amodiaquine, lumefantrine, pyrimethamine, sulfadoxine, and artemisinin were analyzed in 63 microscopy-positive and 53 submicroscopic \u003cem\u003eP. falciparum\u003c/em\u003e samples (n = 334) using Oxford Nanopore sequencing. The prevalence of resistance-associated alleles was \u003cem\u003epfcrt\u003c/em\u003e K76T (9.5%, 95% CI: 5.1\u0026ndash;16.9), \u003cem\u003epfmdr1\u003c/em\u003e (40%, 95% CI: 31.2\u0026ndash;50.2), \u003cem\u003epfdhfr\u003c/em\u003e (27.1%, 95% CI: 19.3\u0026ndash;36.7), and \u003cem\u003epfk13\u003c/em\u003e (11.4%, 95% CI: 9.8\u0026ndash;13.3). Complete \u003cem\u003epfdhps\u003c/em\u003e resistance to sulfadoxine was observed in all samples, although no SP-IPTp\u0026ndash;associated mutations were detected; however, SP combination resistance occurred in 21.5% (95% CI: 14.5\u0026ndash;30.7).\u003c/p\u003e\n\u003cp\u003eChloroquine resistance declined markedly, with 90.5% harboring the sensitive \u003cem\u003epfcrt\u003c/em\u003e K76 allele; the K76T mutant was predominantly found in submicroscopic infections (88.9%), females (70%), and children (50%). The wild-type \u003cem\u003epfmdr1\u003c/em\u003e N86Y haplotype was absent, while the Y184F mutant (51%), associated with reduced lumefantrine susceptibility, was common, especially among microscopy-positive (52.9%) and female (46.3%) participants. Multiple \u003cem\u003epfdhps\u003c/em\u003e haplotypes were identified, led by A437G (79%), S436A (12%), A581G (9%), and A613S (5%), with no K540E, A613T, or S436F detected. For \u003cem\u003epfdhfr\u003c/em\u003e, N51I (24%), C59R (27%), and S108N (26%) were observed, with the triple mutant IRNI (35%) predominating, followed by wild-type NCSI (12%) and double mutant NRNI (4.3%). Predominant \u003cem\u003epfdhps\u003c/em\u003e genotypes included SGKAA (85.4%), SGKGA (33.1%), and AGKAS (14%), with common dhfr\u0026ndash;dhps combinations being IRNI + SGKAA (21%), NCSI + SGKAA (10%), and IRNI + AGKAA (4.3%). Notably, \u003cem\u003epfk13\u003c/em\u003e propeller mutations linked to partial artemisinin resistance, particularly C580Y (12%), were detected exclusively in submicroscopic infections, while other variants (R539T, T573T, G453S, I601I, R575T) were not associated with clinical resistance.\u003c/p\u003e\n\u003cp\u003eInsert Figure 9 here\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 9:\u0026nbsp;\u003c/strong\u003eA shows the frequency distribution of CRT and MDR1 alleles in microscopic and submicroscopic infection status. B shows Plasmodium falciparum dhfr-dhps combined haplotypes by microscopic (micro) and sub-microscopic (sub) infection\u003cspan id=\"_Toc210963569\"\u003e\u0026nbsp;and C shows the distribution of resistance and sensitive phenotype by drug and intervention type.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCQ: Chloroquine; PYR: Pyrimethamine resistance; SP.IPT\u003csub\u003ep\u003c/sub\u003e: Sulfadoxine-Pyrimethamine for Intermittent prevention of malaria in pregnancy; SP.RX: Sulfadoxine-Pyrimethamine resistance; SX: Sulfadoxine\u003csub\u003e\u0026nbsp; \u0026nbsp;\u003c/sub\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003ch2 id=\"_Toc210964472\"\u003eThe hotspots of malaria transmission using spatio-temporal analysis\u003c/h2\u003e\n\u003cp\u003eTo identify hotspots of malaria transmission due to submicroscopic malaria infections in the study area, spatio-temporal analysis was conducted on submicroscopic positive cases using the R statistic package. Data collected over the study period (May to July), were linked to household GPS coordinates to identify hotspots of malaria transmission within the study area.\u003c/p\u003e\n\u003cp\u003eThree main regional hotspots were identified, mainly, Greater Accra region (88.2%), Central region (10%) and Eastern region (1.8%), with the primary hotspots predominantly located in the Greater Accra region.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInsert Figure 10 here\u003c/p\u003e\n\u003cp\u003e\u003cspan id=\"_Toc210963570\"\u003e\u003cstrong\u003eFigure 10:\u0026nbsp;\u003c/strong\u003eMap of Ghana highlighting the regional hotspots of cases\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe Accra Central sub-district, mainly made up of communities such as, Jamestown, Ussher town, Tudu and Accra business district, showed the highest prevalence of \u003cem\u003eP. falciparum\u003c/em\u003e, \u003cem\u003eP. ovale\u003c/em\u003e and \u003cem\u003eP. malariae\u003c/em\u003e submicroscopic malaria infections. Also, secondary hotspots were detected in the Kasoa, Dansoman, Darkuman, Achimota, Bubiashie, Odorkor and Korle Gonno communities, with \u003cem\u003eP. ovale\u003c/em\u003e submicroscopic infection being predominant in Dansoman and Korle-Gonno communities. Although \u003cem\u003eP. falciparum\u003c/em\u003e was the most common parasite species in these community hotspots, almost all primary and secondary hotspots of malaria transmission had multiple coinfections with the three the \u003cem\u003ePlasmodium species\u003c/em\u003e, that is, \u003cem\u003eP. falciparum\u003c/em\u003e, \u003cem\u003eP. ovale\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;P. malariae\u0026nbsp;\u003c/em\u003ecoinfections.\u003cem\u003e\u0026nbsp;\u003c/em\u003e Also, in the Central region, the predominant hotspots were detected in the Kasoa, Cape Coast and Ankafo prisons, with the highest hotspot found in the Kasoa communities. Also, from the Eastern region, the Nsawam district was the district with the highest hotspot for submicroscopic malaria, with the predominant parasite species being \u003cem\u003eP. falciparum\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eInsert Figure 11 here\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 11:\u0026nbsp;\u003c/strong\u003eLocation-specific distribution of malaria positive cases.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study revealed a surprisingly high prevalence of asymptomatic \u003cem\u003eP. falciparum\u003c/em\u003e infections (35%) among participants, a figure much higher than the reported 2% malaria prevalence for the Greater Accra Region [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This finding aligns with earlier reports showing that asymptomatic infections frequently exceed symptomatic ones in endemic regions [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Notably, submicroscopic infections (32.9%) were more common than microscopic infections (18.9%), confirming that microscopy underestimates total parasite carriage and misses low-level parasitemia [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The high prevalence of submicroscopic infections in this low-transmission setting supports previous assertions that such infections often persist where malaria control measures have reduced overall transmission [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Similar patterns have been observed in Senegal and Tanzania [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], suggesting that localized hotspots and partial host immunity may sustain these reservoirs [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAge-related patterns showed that submicroscopic infections were more common among adults, consistent with evidence that partial immunity acquired over years of exposure allows adults to harbor low-level infections without symptoms [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Conversely, children with less-developed immunity, were more prone to microscopic infections and anemia. Interestingly, a notable proportion of submicroscopic \u003cem\u003eP. ovale\u003c/em\u003e infections (11.5%) occurred predominantly in children, echoing findings from Ghana and other regions [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The detection of multiple species coinfections (24.1%) underscores the complexity of malaria epidemiology in low-transmission settings and the need to broaden control strategies to include non-\u003cem\u003efalciparum\u003c/em\u003e infections.\u003c/p\u003e \u003cp\u003eIn assessing diagnostic tools, the rapid diagnostic test (RDT) demonstrated high sensitivity relative to microscopy, with few discrepancies. Cases that were RDT-negative but microscopy-positive were confirmed as \u003cem\u003eP. falciparum\u003c/em\u003e and \u003cem\u003eP. ovale\u003c/em\u003e infections by LAMP PCR, suggesting possible \u003cem\u003epfhrp2\u003c/em\u003e gene deletions\u0026mdash;an emerging threat to HRP2-based RDT reliability [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This underscores the importance of continuous surveillance of \u003cem\u003epfhrp2/3\u003c/em\u003e deletions to ensure diagnostic accuracy. Conversely, RDT-positive but microscopy-negative cases likely represented submicroscopic infections, reaffirming the limited sensitivity of microscopy at low parasitemia.\u003c/p\u003e \u003cp\u003eAnemia was common across participants, varying significantly by age and infection type. Microscopic infections in children were associated with greater anemia severity, consistent with previous studies linking \u003cem\u003eP. falciparum\u003c/em\u003e density to hemoglobin reduction [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Submicroscopic infections in adults were associated mainly with mild to moderate anemia, reflecting their chronic, low-grade nature. Although this study could not fully account for confounding causes of anemia\u0026mdash;such as nutritional deficiencies, hemoglobinopathies, or co-infections\u0026mdash;the associations observed remain meaningful. The cross-sectional design was another limitation, restricting causal inference and temporal assessment. Longitudinal studies would better clarify how asymptomatic infections contribute to chronic anemia and transmission dynamics.\u003c/p\u003e \u003cp\u003eHematological parameters showed potential for predicting asymptomatic infections. Low mean corpuscular hemoglobin concentration (MCHC) and high red cell distribution width (RDW) were significant predictors of microscopic infections, whereas low MCHC alone predicted submicroscopic infection. These findings partially align with previous work [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and suggest that subtle red cell changes may serve as indicators of chronic low-grade parasitemia in resource-limited settings [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Although larger studies are needed, such hematological markers could support early screening and monitoring of asymptomatic carriers.\u003c/p\u003e \u003cp\u003eAnalysis of antimalarial drug resistance markers revealed encouraging trends and emerging concerns. The frequency of \u003cem\u003epfcrt\u003c/em\u003e K76T (9.1%) indicated a marked return of chloroquine sensitivity, likely due to reduced drug pressure following its withdrawal [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Similar reversions have been reported in Zambia and Malawi [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. However, persistence of \u003cem\u003epfmdr1\u003c/em\u003e Y184F (51%), associated with decreased lumefantrine sensitivity, warrants attention, though clinical ACT resistance remains unreported. High mutation frequencies in \u003cem\u003epfdhps\u003c/em\u003e (A437G, S436A, A581G) and \u003cem\u003epfdhfr\u003c/em\u003e (N51I, C59R, S108N) suggest ongoing SP pressure, though no \u003cem\u003eK540E\u003c/em\u003e mutation linked to IPTp failure was detected. The observed triple \u003cem\u003epfdhfr\u003c/em\u003e haplotype (IRNI, 35%) and dominant \u003cem\u003epfdhps\u003c/em\u003e haplotypes (SGKAA, SGKGA) confirm widespread SP resistance, consistent with national data [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Importantly, no validated \u003cem\u003epfk13\u003c/em\u003e mutations conferring artemisinin resistance were found, though variants like C580Y and R539T warrant monitoring, as they have been implicated in resistance in East Africa (Rwanda, Uganda and Tanzania). Continuous genomic surveillance of both microscopic and submicroscopic infections remains essential to protect ACT efficacy.\u003c/p\u003e \u003cp\u003eSpatial analysis identified major hotspots in the Greater Accra, Central, and Eastern regions, with Accra Central showing the highest submicroscopic carriage. Environmental and socioeconomic factors such as unplanned urbanization, poor drainage, and urban poverty likely sustain breeding sites and parasite persistence [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The adaptability of \u003cem\u003eAnopheles gambiae\u003c/em\u003e to urban conditions further enhances transmission potential [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. These findings highlight the need for urban-focused interventions mass drug administration (MDA), mass screen and treat (MSaT), and enhanced vector control to reduce silent reservoirs fueling ongoing transmission.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrated a substantial hidden burden of asymptomatic and submicroscopic \u003cem\u003eP. falciparum\u003c/em\u003e infections in a low-transmission setting, with clear hematological consequences and diverse resistance genotypes. The observed resurgence of chloroquine susceptibility, persistence of SP resistance, and emerging \u003cem\u003epfk13\u003c/em\u003e variants emphasize the need for vigilant drug-resistance monitoring. Although limited by its cross-sectional design and inability to adjust for all anemia confounders, the findings underscore the epidemiological importance of asymptomatic carriers in sustaining malaria transmission. Strengthening molecular diagnostic capacity, expanding active surveillance, and targeting identified hotspots through integrated control strategies are critical to accelerating malaria elimination efforts in Ghana and similar settings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"501\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 406px;\"\u003e\n \u003cp\u003eAplastic Anemia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eATC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 406px;\"\u003e\n \u003cp\u003eAntihuman Thymocyte Globulin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eCART\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 406px;\"\u003e\n \u003cp\u003eChimeric antigen receptor T-cell\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eCHIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 406px;\"\u003e\n \u003cp\u003eClonal hematopoiesis of indeterminate potential\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eGPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 406px;\"\u003e\n \u003cp\u003eGlycosylphosphatidylinositol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eHSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 406px;\"\u003e\n \u003cp\u003eHematopoietic Stem Cell\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eHSPC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 406px;\"\u003e\n \u003cp\u003eHematopoietic Stem and Progenitor cells\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eIAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 406px;\"\u003e\n \u003cp\u003eIdiopathic Aplastic Anemia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eIFN\u0026gamma;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 406px;\"\u003e\n \u003cp\u003eInterferon gamma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eIST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 406px;\"\u003e\n \u003cp\u003eImmunosuppressive Therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eLAG 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 406px;\"\u003e\n \u003cp\u003eLymphocyte-activation gene 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eMDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 406px;\"\u003e\n \u003cp\u003eMyelodysplastic syndrome\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eNK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 406px;\"\u003e\n \u003cp\u003eNatural Killer Cells\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eNSAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 406px;\"\u003e\n \u003cp\u003eNon-severe aplastic Anemia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eSAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 406px;\"\u003e\n \u003cp\u003eSevere Aplastic Anemia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eSLE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 406px;\"\u003e\n \u003cp\u003eSystemic lupus Erythematosus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eTh1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 406px;\"\u003e\n \u003cp\u003eT-helper cell-1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eTLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 406px;\"\u003e\n \u003cp\u003eTertiary lymphoid structure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eTNF\u0026alpha;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 406px;\"\u003e\n \u003cp\u003eTumor Necrosis Factor Alpha\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003ePD 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 406px;\"\u003e\n \u003cp\u003eProgrammed Death-1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003ePRF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 406px;\"\u003e\n \u003cp\u003ePlatelet-rich fibrin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eVSAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 406px;\"\u003e\n \u003cp\u003eVery severe Aplastic Anemia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eWACCBIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 406px;\"\u003e\n \u003cp\u003eWest Africa Centre for Cell Biology of Infectious Pathogens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u0026nbsp;Ethical approval for the study was obtained from the Ethical and Protocol Review Board of the School of Biomedical and Allied Health Sciences, University of Ghana (SBAHS/AA/MLAB/11366043/2024-2025), and from the Korle Bu Teaching Hospital Institutional Review Board (KBTH-STC/IRB/00028/2025). All participants were informed of the study\u0026rsquo;s purpose, potential risks, and procedures before enrollment. Written informed consent was obtained from all adult participants, while assent was obtained from minors aged 12\u0026ndash;18 years, alongside parental or guardian consent for younger children. Participants\u0026rsquo; comfort and safety during blood collection were ensured, with all procedures conducted under minimal risk. Individuals who tested positive for malaria were informed and referred for appropriate treatment in accordance with the national malaria case management guidelines of Ghana.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe study was funded using the University of Ghana book and research allowances of the research team members. The University of Ghana did not play any role as far as the design of the study, collection, analysis, and interpretation of data as well as writing of the manuscript are concerned.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBTM was involved in Conceptualization, Supervision, Writing-original draft, Writing-review \u0026amp; editing. LAT \u0026ndash; Conceptualization, Investigation, Data Curation, Writing-original draft, Writing-review \u0026amp; editing. HO \u0026ndash; Conceptualization, Investigation, Data Curation, Writing-original draft, Writing-review \u0026amp; editing. \u0026nbsp;DNOA \u0026ndash; Data curation, Resources, Validation, Writing-review \u0026amp; editing. CTA \u0026ndash; Data curation, Resources, Validation, Writing-review \u0026amp; editing. SA-B \u0026ndash; Conceptualization, Supervision, Writing-original draft, Writing-review \u0026amp; editing.\u0026nbsp;\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp id=\"_Toc120762\"\u003eWe are grateful to the directors and laboratory managers of the Korle-Bu Teaching Hospital and the WACCBIP for their assistance in carrying out this study.\u003c/p\u003e"},{"header":"References","content":"\u003col class=\"decimal_type\"\u003e\n\u003cli\u003eAbad, M. A., Reyes, R., Fern\u0026aacute;ndez, S., \u0026amp; Gonz\u0026aacute;lez, A. (2022). Prevalence and transmission dynamics of asymptomatic malaria infections in endemic regions. \u003cem\u003eMalaria Journal, 21\u003c/em\u003e(1), 234. https://doi.org/10.1186/s12936-022-04231-7.\u003c/li\u003e\n\u003cli\u003eDe Mast, Q., Brouwers, J., \u0026amp; van der Ven, A. (2015). 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Malaria: Even more chronic in nature than previously thought; evidence for subpatent parasitaemia detectable by PCR. \u003cem\u003eTransactions of the Royal Society of Tropical Medicine and Hygiene, 90\u003c/em\u003e(1), 15\u0026ndash;19.\u003c/li\u003e\n\u003cli\u003eLin, J. T., Saunders, D. L., \u0026amp; Meshnick, S. R. (2014). The role of submicroscopic parasitemia in malaria transmission: What is the evidence? \u003cem\u003eTrends in Parasitology, 30\u003c/em\u003e(4), 183\u0026ndash;190.\u003c/li\u003e\n\u003cli\u003eMales, S., Gaye, O., \u0026amp; Garcia, A. (2008). Long-term asymptomatic carriage of Plasmodium falciparum protects from malaria attacks: A prospective study among Senegalese children. \u003cem\u003eClinical Infectious Diseases, 46\u003c/em\u003e(4), 516\u0026ndash;522.\u003c/li\u003e\n\u003cli\u003eShekalaghe, S. A., Drakeley, C., Gosling, R., \u0026amp; Sauerwein, R. (2007). Submicroscopic Plasmodium falciparum infections in an area of low transmission in Tanzania. \u003cem\u003eMalaria Journal, 6\u003c/em\u003e, 219.\u003c/li\u003e\n\u003cli\u003eBejon, P., Williams, T. N., Nyundo, C., Hay, S. I., Benz, D., Gething, P. W., \u0026hellip; \u0026amp; Marsh, K. (2010). A micro-epidemiological analysis of febrile malaria in Coastal Kenya showing hotspots within hotspots. \u003cem\u003eeLife, 3\u003c/em\u003e, e02130. https://doi.org/10.7554/eLife.02130\u003c/li\u003e\n\u003cli\u003eBousema, T., Griffin, J. T., Sauerwein, R. W., Smith, D. L., Churcher, T. S., Takken, W., \u0026hellip; \u0026amp; Drakeley, C. (2010). Hitting hotspots: Spatial targeting of malaria for control and elimination. \u003cem\u003ePLoS Medicine, 7\u003c/em\u003e(1), e1000302.\u003c/li\u003e\n\u003cli\u003ePava, Z., Handayuni, I., Trianty, L., \u0026amp; Price, R. N. (2016). Submicroscopic and asymptomatic Plasmodium infections in low-endemic areas. \u003cem\u003eMalaria Journal, 15\u003c/em\u003e(1), 121.\u003c/li\u003e\n\u003cli\u003eMueller, I., Zimmerman, P. A., \u0026amp; Reeder, J. C. (2007). Plasmodium malariae and Plasmodium ovale\u0026mdash;the \u0026ldquo;bashful\u0026rdquo; malaria parasites. \u003cem\u003eTrends in Parasitology, 23\u003c/em\u003e(6), 278\u0026ndash;283.\u003c/li\u003e\n\u003cli\u003eAmoah, L. E., Opong, A., Abankwa, J., \u0026amp; Nwaefuna, E. K. (2019). The changing epidemiology of Plasmodium species in southern Ghana. \u003cem\u003eBMC Infectious Diseases, 19\u003c/em\u003e(1), 379. https://doi.org/10.1186/s12879-019-4012-0\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. (2023). \u003cem\u003eWorld malaria report 2023\u003c/em\u003e. Geneva: WHO.\u003c/li\u003e\n\u003cli\u003ePrice, R. N., Simpson, J. A., Nosten, F., Luxemburger, C., \u0026amp; White, N. J. (2001). 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Molecular surveillance of antimalarial resistance markers in Ghana post-chloroquine withdrawal. \u003cem\u003eBMC Infectious Diseases, 23\u003c/em\u003e(1), 425.\u003c/li\u003e\n\u003cli\u003eMwanza, S., Chaponda, M., Malunga, P., Soko, D., \u0026amp; Mharakurwa, S. (2016). Return of chloroquine sensitivity in Zambia after cessation of drug use. \u003cem\u003eMalaria Journal, 15\u003c/em\u003e(1), 584.\u003c/li\u003e\n\u003cli\u003eDe Silva, P. M., \u0026amp; Marshall, J. M. (2012). Factors contributing to urban malaria transmission in sub-Saharan Africa: A systematic review. \u003cem\u003eJournal of Tropical Medicine, 2012\u003c/em\u003e, 819563. https://doi.org/10.1155/2012/819563\u003c/li\u003e\n\u003cli\u003eDoumbe-Belisse, P., Ngadjeu, C. S., Sonhafouo-Chiana, N., Talipouo, A., Djamouko-Djonkam, L., Kopya, E., \u0026hellip; \u0026amp; Antonio-Nkondjio, C. (2018). High malaria transmission intensity in urban areas of Douala, Cameroon. \u003cem\u003eBMC Infectious Diseases, 18\u003c/em\u003e, 469.\u003c/li\u003e\n\u003cli\u003eAntonio-Nkondjio, C., Fossog, B. T., Ndo, C., Djantio, B. M., Togouet, S. Z., Awono-Ambene, P., \u0026hellip; \u0026amp; Wondji, C. S. (2013). Anopheles gambiae distribution and insecticide resistance in urban areas of Cameroon. \u003cem\u003ePLoS ONE, 8\u003c/em\u003e(5), e63460. https://doi.org/10.1371/journal.pone.0063460\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"malaria-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"malj","sideBox":"Learn more about [Malaria Journal](http://malariajournal.biomedcentral.com/)","snPcode":"12936","submissionUrl":"https://submission.nature.com/new-submission/12936/3","title":"Malaria Journal","twitterHandle":"@malariajournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9086362/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9086362/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMalaria remains a major public health challenge in sub-Saharan Africa, where asymptomatic infections continue to hinder elimination efforts. Although the effects of microscopic infections are well documented, the health implications of submicroscopic \u003cem\u003ePlasmodium falciparum\u003c/em\u003e infections remain poorly understood, particularly in Ghana. Beyond serving as reservoirs for sustained transmission, emerging evidence suggests these infections may contribute to adverse health outcomes. This study assessed the burden of submicroscopic \u003cem\u003eP. falciparum\u003c/em\u003e infections and their association with anemia and antimalarial drug resistance markers among asymptomatic individuals attending Korle Bu Teaching Hospital.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional study involving 345 participants was conducted. Malaria infection was assessed using mRDT, blood smear microscopy, and LAMP-PCR for parasite detection and species identification. Hemoglobin levels and red blood cell indices (MCV, RDW, RBC count, MCHC, and HCT) were measured using a hematology analyzer. Antimalarial drug resistance markers were analyzed by multiplex PCR and Oxford Nanopore sequencing. Statistical analysis was performed using STATA version 14, applying Pearson\u0026rsquo;s chi-square test and logistic regression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe overall prevalence of asymptomatic \u003cem\u003eP. falciparum\u003c/em\u003e infection was 35.0% (117/334), with 18.9% microscopic and 16.1% submicroscopic infections. Both infection types were significantly associated with lower hemoglobin levels after adjusting for age and anemia severity (p\u0026thinsp;=\u0026thinsp;0.024). Low MCHC emerged as the strongest predictor of submicroscopic infection (p\u0026thinsp;=\u0026thinsp;0.04). The wild-type \u003cem\u003epfcrt\u003c/em\u003e K76 allele, associated with chloroquine susceptibility, was highly prevalent (90.1%). Accra Central and surrounding areas were identified as transmission hotspots.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAsymptomatic \u003cem\u003eP. falciparum\u003c/em\u003e infections are common in this population, with a substantial proportion occurring at submicroscopic levels. Their association with reduced hemoglobin suggests a potential contribution to anemia despite the absence of symptoms. The high prevalence of chloroquine-susceptible parasites and identified transmission hotspots underscore the need for sensitive diagnostics and targeted interventions to support malaria elimination efforts in Ghana.\u003c/p\u003e","manuscriptTitle":"Submicroscopic Malaria Parasite Carriage and Hemoglobin Levels among Individuals with Asymptomatic Malaria Infections","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-19 12:02:03","doi":"10.21203/rs.3.rs-9086362/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-04T07:03:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-03T05:58:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-30T17:30:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"130163347493600330571606227595223299518","date":"2026-04-11T08:47:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"218814204328607443357966622589173299550","date":"2026-04-10T13:31:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"96850115593552297841124206946335848996","date":"2026-04-10T05:07:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-09T08:10:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-11T18:34:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-11T18:33:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Malaria Journal","date":"2026-03-10T16:24:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"malaria-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"malj","sideBox":"Learn more about [Malaria Journal](http://malariajournal.biomedcentral.com/)","snPcode":"12936","submissionUrl":"https://submission.nature.com/new-submission/12936/3","title":"Malaria Journal","twitterHandle":"@malariajournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"893bcee7-9132-41bc-a0f5-bafdbac8ace5","owner":[],"postedDate":"April 19th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-04T07:03:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-03T05:58:14+00:00","index":18,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-30T17:30:55+00:00","index":17,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T13:09:57+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-19 12:02:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9086362","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9086362","identity":"rs-9086362","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}