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Assessing the burden of submicroscopic Plasmodium infections in a pre-elimination malaria setting in sub-Saharan Africa, Guinea-Bissau

Malaria Journal volume 23, Article number: 316 (2024) Cite this article

Abstract

Background

Submicroscopic Plasmodium infections can be a source of persistent malaria transmission. The aim of this study was to assess their frequency, distribution, morbidity and associated factors in a pre-elimination malaria setting in sub-Saharan Africa, Guinea-Bissau, where the Plasmodium falciparum is the predominant Plasmodium species.

Methods

Dried fingerprick whole blood samples from 601 participants in the 2017 national, household-based, cross-sectional survey to estimate malaria prevalence were subjected to DNA extraction. The DNA was used in nested end-point PCR assays targeting genus- and species-specific regions of the Plasmodium 18S rRNA genes. Statistical analysis of socio-demographic, clinical and molecular data was carried out using the Statistical Package for the Social Sciences, version 29. Factors associated with submicroscopic P. falciparum infections and their magnitude were sought using Chi-square test and multiple logistic regression models, respectively. Statistically significant level was considered at P-value < 0.05.

Results

Nested PCR assays detected submicroscopic P. falciparum infections in 20.3% (95% CI = 16.8–23.8) of individuals microscopically negative for Plasmodium species in the general population and in 21.4% (95% CI = 9.9–36.5) of microscopically negative pregnant women. Submicroscopic Plasmodium malariae infections were also detected as co-infections in 3.0% individuals who were microscopically positive only for P. falciparum. Infections with other Plasmodium species were not detected. Submicroscopic P. falciparum infections were not associated with age, sex, or the presence of fever. A logistic regression model adjusted for ethnicity and health region showed that individuals from the Balanta and Bijagos ethnic groups, most of whom live in the low malaria-transmission areas of Quinara and Bissau, and the Bijagos archipelago, respectively, were less likely to have submicroscopic P. falciparum infections than individuals from the large Fula ethnic group, most of whom live in the high malaria-transmission area of Gabu. Submicroscopic P. falciparum infections were not associated with anaemia in children under 5 years of age.

Conclusion

The results obtained highlight the contribution of asymptomatic and submicroscopic P. falciparum infections to malaria transmission in high malaria-transmission areas and the need for molecular-based tools to detect submicroscopic Plasmodium species.

Background

Malaria remains a serious public health problem and the leading cause of morbidity and mortality in several endemic countries, despite the efforts made in recent decades to reduce its impact. Continued disruptions to national malaria control programmes due to socio-political-economic instability, the emergence of artemisinin-resistant Plasmodium falciparum parasites, histidine-rich protein 2 deleted P. falciparum parasites, pyrethroid-resistant Anopheles mosquitoes and climate change, have contributed to halting the global decline in malaria cases since 2015 [1,2,3].

To get back on track towards malaria control and elimination, in addition to overcoming the barriers mentioned above, it is essential to ensure that all cases of malaria are properly diagnosed and treated. These include asymptomatic malaria cases, which do not lead to seeking medical care, and submicroscopic Plasmodium infections, e.g., Plasmodium infections with levels of parasitaemia below the detection limit of routine diagnostic methods, such as optical microscopy or rapid diagnostic tests [4]. Without diagnosis and treatment, asymptomatic and submicroscopic Plasmodium infections are thought to sustain malaria transmission and contribute to residual malaria transmission and outbreaks [5] by acting as parasite reservoirs [4, 6] and providing female Anopheles mosquitoes with gametocyte forms of the parasite during a blood meal. Asymptomatic and submicroscopic Plasmodium infections have been reported in several malaria endemic countries [7,8,9] including Guinea-Bissau on the west coast of sub-Saharan Africa [10,11,12].

Malaria in Guinea-Bissau is mostly caused by P. falciparum, which causes life-threatening forms of the disease and remains endemic throughout the country with seasonal and regional variations [13]. The global trend in malaria incidence has also been observed in Guinea-Bissau over the past two decades. A huge decrease in estimated malaria cases from almost 500 000 to just over 100 000 was observed in the period 2000–2006, followed by a new increase in the subsequent years, culminating in 208 000 estimated malaria cases in 2011, and a new marked decrease in the subsequent period with 120 000 estimated malaria cases in 2017 [3]. A new period of progressive increase in malaria incidence has been observed since 2017, with 225 000 estimated malaria cases in 2022 [3]. Whether asymptomatic malaria cases and submicroscopic Plasmodium infections have contributed to the stagnation of malaria control and successive fluctuations in malaria incidence in Guinea-Bissau is unknown.

The aim of this study was to shed light on malaria epidemiology in Guinea-Bissau by assessing the frequency, distribution, morbidity and associated factors of submicroscopic Plasmodium infections, and characterizing the epidemiological profile of the infected individuals.

Methods

The 2017 Malaria Indicator Survey

This study was conducted using socio-demographic and clinical data and dried fingerprick whole blood samples from the Malaria Indicator Survey (MIS) carried out in Guinea-Bissau in 2017. The 2017 MIS was a household two-stage cluster survey, stratified by the 11 health regions of Guinea-Bissau, carried out towards the end of the rainy season (September to October), using the standardized Roll Back Malaria (RBM) protocol with a few local adaptations [14]. After informed consent was obtained, a structured questionnaire was applied collecting, among others, data on the occurrence of fever in the previous 14 days. Thick and thin fingerprick whole blood smears were collected from all individuals enrolled in the malaria prevalence survey and subjected to optical microscopy (OM) analysis by experienced microscopists at the laboratory of the National Institute of Health of Guinea-Bissau.

In parallel, drops of fingerprick whole blood, from each participant in the malaria prevalence survey, were collected onto filter paper and stored at room temperature with silica gel with colour indicator. Children aged from six to 59 months were screened for anaemia by measuring haemoglobin levels using the HemoCue® haemoglobinometer. Anaemia was considered in children with an haemoglobin (Hb) < 11.0 g/dL of whole blood according to WHO criteria for anaemia in children at this age range [15]. Those with history of fever on the day of visit were screened for P. falciparum parasites using rapid diagnostic tests (RDTs) based on the detection of the histidine rich protein 2 of the P. falciparum parasite. Positive cases were treated with the combination of artemether and lumefantrine (COARTEM®), the standard malaria treatment to treat P. falciparum infections adopted by the Guinea-Bissau Ministry of Health [16], or referred to a nearby health centre. Data were collected on Android devices using the Open Data Kit (ODK) data collection platform version GeoODK, with the added value of capturing and visualizing Global Positioning System (GPS) coordinates of each household (HH). The 2017 MIS protocol was approved by the National Committee for Ethics in Health of Guinea-Bissau.

The malaria prevalence survey enrolled 4011 children aged 6–59 months and 4796 individuals aged 5–99 years old. One hundred and twenty-one individuals were infected exclusively with P. falciparum parasites and four exclusively with Plasmodium malariae by OM analysis of blood smears. Malaria prevalence was estimated at 0.7% in children younger than 5, 2.4% in children aged 5–14 years, 1.9% in individuals aged 15–24 years, 0.5% in individuals aged 25–44 years and 0.3% in individuals aged 45 years or older. In a logistic regression model adjusted for health region, area of residence, age, sex and use of long-lasting insecticide-treated nets, the factors independently associated with higher malaria prevalence were the health regions of Bafata, Bijagos, Gabu and Tombali, the age group 5–14 years and the male sex. A detailed description of the 2017 MIS results was reported in the HC-NFM3 Malaria Project Document (PRODOC), which is publicly available online on the United Nations Development website’s Transparency Portal (https://open.undp.org/projects/00130819) as a downloadable Portable Document Format (PDF) file.

Setting

Guinea-Bissau is located on the west coast of sub-Saharan Africa, 11 8037° N and 15 1804° W, and is composed by a continental and an island group with more than 80 islands. It borders Senegal to the north, Guinea to the east and south and the Atlantic Ocean to the west. It is divided in 11 health regions, namely Bafata, Bijagos, Biombo, Bolama, Cacheu, Farim, Gabu, Oio, Quinara, the Autonomous Sector of Bissau (SAB, the capital of Guinea-Bissau), and Tombali. The country lies in the malaria belt and present a perennial transmission pattern with a peak in the pre- and post-rainy season (May to mid-November).

Selection of dried fingerprick whole blood samples

The number of dried fingerprick whole blood samples (DBSs) to be analysed was calculated using the formula for the finite population correction [17] [n = N*X/(X + N-1)], where X = (Zα/2)2*p*(1-p)/MOE2, Zα/2 is the critical value of the Normal distribution at α/2, MOE is the margin of error, p is the sample proportion, and N is the population size of individuals negative for Plasmodium species by OM according to the 2017 malaria prevalence survey, stratified by the age groups defined in the 2017 malaria prevalence survey data analysis. Values for α of 0.05, a sample proportion of 50.0% and an MOE of 2.0% were considered.

Dried fingerprick whole blood samples (DBSs) from individuals negative for Plasmodium species by OM (OM-negative individuals) across the country were selected to search for submicroscopic Plasmodium infections. For that, MIS identification codes present in the 2017 MIS anonymized database were sorted using the random number generator function followed by the truncate random numbers function of the Statistical Package for the Social Sciences (IBM® SPSS® Statistics) version 29. Three hundred eighty-one (381) DBSs from OM-negative individuals across the country were selected, and 122 DBSs from OM-negative individuals sharing HH or village with 98 individuals positive for P. falciparum by OM (OM-positive individuals) identified in 2017 MIS were also included. Then, a total of 503 DBSs from OM-negative individuals were selected, 11.0% of the calculated number to be selected. Plasmodium infections by different Plasmodium spp., including P. malariae, Plasmodium ovale wallikeri, P. ovale curtisi, and Plasmodium vivax were also searched among the 503 DBSs from OM-negative individuals and among the 98 DBSs from the 98 positive individuals for P. falciparum by OM. Then, a total of 601 DBSs were analysed in this study.

Laboratory investigation

DNA extraction

The 601 DBSs on filter paper were subjected to DNA extraction using the previously described saponin-Chelex® (Sigma-Aldrich®, Merck KGaA, Darmstadt, Germany) method [18, 19]. Three 6 mm-diameter circles from each DBS were used. DNA was eluted in sterile MilliQ® water, quantified by using a spectrophotometer (NanoDrop™ One–Thermo Fisher Scientific, Waltham, MA, USA) and stored at – 20 °C until utilization and at − 80 °C afterwards. All DNA samples presented A260/A280 ratio > 1.8 and a concentration of 30 ng/µL on average.

Polymerase chain reactions and primer pairs targeting 18S rRNA genes of Plasmodium parasites

Samples were screened for Plasmodium parasites by two subsequent polymerase chain reactions [nested PCR assays (nPCR)] targeting the gene encoding the 18S rRNA, according to previously described [20,21,22]. The primers used in each reaction are shown in Additional file 1: Table S1. They were synthesized by STABVIDA Lda. (Caparica, Portugal).

A strategy targeting the five variants of the P. falciparum 18S rRNA gene was adopted to test individually the 503 DBSs from OM-negative individuals for Plasmodium spp. by OM. The alignment of the five complete sequences of 18S rRNA variants found in the 3D7 P. falciparum reference strain, (PF3D7_0112300, PF3D7_0531600, PF3D7_0725600, PF3D7_1148600, PF3D7_1371000), respectively located on chromosomes (chrs) 01, 05, 07, 11 and 13, and available at GenBank https://www.ncbi.nlm.nih.gov/genbank/, using Clustal Omega (1.2.4) Multiple Sequence Alignment software, available on EBML-EBI website (https://www.ebi.ac.uk/Tools/services), is shown in the Additional file 1: Fig. S1. The hybridization loci of the primer pairs used in each reaction are shown in the same Additional file 1: Fig. S1. The 18S rRNA variants on chrs 05 and 07 share 100.0% identity, and the 18S rRNA variants on chrs 01, 11 and 13 share more than 99.0% identity. The 18S rRNA variants on chrs 05 and 07 share around 85% identity with the later three 18S rRNA variants on chrs 01, 11 and 13 [23]. Their unique feature is the stage-specific expression of the rRNAs during the parasite life cycle [24].

To target the P. falciparum 18S rRNA variants on chr 05 and 07, the primer pair rPLU1/rPLU5 [37–1675 base pair (bp)], which targets a 1.6 kilobase (kb) conservative fragment common for Plasmodium genus, was used in the first reaction, followed by the primer pair FAL1ass/FAL2ass (666–854 bp), which targets a 188 bp fragment specific for P. falciparum, in the second reaction. To target the P. falciparum 18 S rRNA variants on chr 01, 11 and 13, the primer pair rPLU6/rPLU5 (609–1675 bp), which targets a 1.1 kb conservative fragment common for Plasmodium genus, was used in the first reaction, followed by the primer pair rFAL1/rFAL2 (665–870 bp), which targets a 205 bp fragment specific for P. falciparum, in the second reaction (Additional file 1: Table S1).

The presence of Plasmodium infections with different Plasmodium spp. was also investigated. For that, three primer pairs were used in parallel in the first genus-specific reaction, the primers rPLU1/rPLU5 and rPLU6/rPLU5 mentioned above, and the primer pair rPLU6/rPLU2, which targets a shorter sequence than the others. The previously described primer pairs for P. malariae and P. vivax [20], and P. ovale wallikeri and P. ovale curtisi, the same primer is used to detect both P. ovale spp. [25] (Additional file 1: Table S1), were used in the second species-specific reaction. For the sequences amplified with the primer pair rPLU6/rPLU2, the species-specific reaction to identify P. ovale spp. was carried out with primers rPLU6/Powc1. The 503 DBSs from OM-negative individuals and 98 DBSs from 98 OM-positive individuals exclusively for P. falciparum were analysed. Samples were tested in pools of five containing 30 ng/µL of extracted DNA from each DBS. The pools with positive results were analysed individually for the identification of the positive DBS.

PCR procedures and conditions

First and second PCR assays were made in a final volume of 20µL, containing 1 × Colorless GoTaq® Flexi buffer (Promega Biotech Iberica SL, Madrid, Spain), 2 mM de MgCl2 (Promega Biotech Iberica SL, Madrid, Spain), 250 μM of each dNTP (Promega Biotech Iberica SL, Madrid, Spain), 125 nM of forward and reverse oligonucleotide primers, 0.02U/μL of GoTaq® G2 Flexi DNA polymerase (Promega Biotech Iberica SL, Madrid, Spain) and 30 ng of extracted DNA in the first reaction and 1μL of the amplified product in the second reaction. The PCR assays were run in a Biometra T1 thermocycler (Alfagene®, Thermo Fisher Scientific, Waltham, MA, USA). DNA extracted from the 3D7 P. falciparum reference strain, which is maintained in culture in the laboratory according to standardized conditions previously described [26, 27] was used as positive control and the reaction mix without DNA was used as negative control in all PCRs.

The previously described PCR cycling parameters [20,21,22] were used with slight modifications regarding the number of cycles from 30 to 35 cycles in the first and second reactions, according to previous in-house optimizations comparing the sensitivity and specificity of different PCR techniques using Plasmodium spp. DNA as template. The product of the second reaction was visualized in 2% agarose gel stained with GreenSafe ® (NZYTech Corporation, Lisbon, Portugal) under ultra-violet (UV) light.

Geo-mapping of P. falciparum infections

The GPS coordinates of each HH were plotted on a Leaflet interactive map, generated using Python version 3.10.12, in Google Colab, with the package Folium version 0.14.0. The composed image presented was produced from prints of the interactive map and edited in Photoshop CS5. The location of individuals was displayed on the map distributed in three groups: (i) positive for optical microscopy (OM+); (ii) negative for optical microscopy and positive for PCR (OM−/nPCR+); (iii) negative for both diagnostic methods (OM−/nPCR−). As the goal was geo-mapping these three types of individuals, we did not represent as an independent group the OM-positive individuals negative for nPCR (OM+/nPCR−) and then, they were included in the first group (OM+). The HHs were coded on the map according to the different types of individuals living in them. The HHs inhabited by OM-positive individuals and individuals with submicroscopic P. falciparum infections detected only by nPCR were represented by black circles (OM+ / OM−/nPCR+), the HHs inhabited by OM-positive individuals without submicroscopically-infected individuals were represented by red circles (OM+), the HHs inhabited by individuals with submicroscopic P. falciparum infections without microscopically-infected individuals were represented by green circles (OM−/nPCR+) and the HHs inhabited only by non-infected individuals were represented by orange circles (OM−/nPCR−).

Statistical analysis

Raw data from the 601 individuals included in this study were extracted from the 2017 MIS anonymized database. Statistical analysis of socio-demographic, clinical and molecular data was performed in the Statistical Package for the Social Sciences version 29 (IBM® SPSS® Statistics 29). Absolute and relative frequencies (number and percentage of cases) were calculated by each categorical variable. The distribution of values of the continuous variables age and parasitaemia levels estimated by OM was tested for normality by the Shapiro–Wilk test and the results were displayed in Additional file 1: Table S2. Then, the distribution of values of age and parasitaemia levels was described as median, interquartile range (IQR) and minimum (min) and maximum (max) values, the distribution of parasitaemia levels were displayed in box-plot graphics, and group comparisons for independent variables were performed using Mann–Whitney U-test or Kruskal–Wallis H-test, as appropriate. Agresti-Coull and Wald 95% confidence intervals were calculated to estimate the frequency of submicroscopic P. falciparum infections among OM-negative individuals. Associations between categorical variables were tested using Pearson Chi-square test (X2). In case of failure of assumptions of the Pearson Chi-square test, P values were calculated by Fisher’s exact test. To adjust for confounding, the magnitude of the associations was estimated by the odds ratios obtained by multiple logistic regression models considering the binary dependent variables: submicroscopic P. falciparum infections (yes/no) and microscopic P. falciparum infections (yes/no), using the entry method, and excluding those independent variables without significance to the model or responsible for outliers with a standardized residues > 3, until to reach the final model. The independent variables to be considered in the models were tested for absence of multicollinearity, e.g., Collinearity Coefficients Tolerance > 0.1 and Variance Inflation Factor (VIF) < 10 in a linear regression model, and the categories of the variable ethnicity with 10 or less cases (Mansonca, Nalu, Saracole, Wolof and those from unknown ethnicity, Additional file 1: Table S3) were grouped to be considered in the model with submicroscopic P. falciparum infections. The same procedure was not carried out for the model regarding microscopic P. falciparum infections to not biased the results, as most infections were related to two ethnicities Fula and Balanta. The models comprising the independent variables were compared to the model without predictors and with the model in the previous step using the Omnibus test. The power of the model, e.g., the percentage of cases that are explained by the model was verified by the Nagelkerke R-square (R2Nagelkerke =|0–1|). The Wald test was used to verify the statistically significant coefficients of each variable in the model and to show the odds that each statistically significant variable offered of having or not having submicroscopic or microscopic P. falciparum infections compared to the large and reference categories. Significance levels P < 0.05 were considered for all comparisons.

Results

Socio-demographic profile of participants

The distribution of the 601 individuals, whose DBSs were included in this analysis, according to health region, age group, and sex is shown in Table 1. The age groups defined in the data analysis of the 2017 malaria prevalence survey were maintained in this study. The distribution of individuals by age group and sex is similar in each of the 11 health regions (Chi-square test, X2 = 49.1, df = 40, P = 0.15, and X2 = 9.5, df = 10, P = 0.49, respectively). Age ranged from 6 months to 98 years old, most were children of 6 months to 4 years old (age group 0–4 years, 35.0%), 75.0% lived in the rural zones located on mainland or island regions, and most were female (60.0%). Females were older than males (females age median = 15.0 years old, interquartile range (IQR) = 3.5–27.0 years old, min = 6 months, max = 98 years old versus males age median = 7.0 years old, IQR = 2.0–23.0 years old, min = 6 months, max = 95 years old, Mann–Whitney U Test, U = 48 442.5, Z = 2.841, P = 0.004). Of 361 females included in this analysis, 144 (40.0%) aged 15–44 years old and 40 (28.0%) were pregnant.

Table 1 Distribution of the 601 DBS’s donors analysed in this study by health region, age group and sex
Full size table

More than 60.0% of the 601 participants belonged to the ethnic groups Fula (n = 224) (who live mainly in the health regions Bafata and Gabu), Balanta (n = 98) (in the health regions Quinara and the Autonomous Sector of Bissau, SAB, the capital of Guinea-Bissau) and Bijagos (n = 68) (in the health regions Bijagos and Bolama). The Fulas are present in several countries in the Sahel and West Africa and are the largest ethnic group in Senegal and Guinea, while the Balanta are the largest ethnic group in Guinea-Bissau and are also present in Senegal, Guinea, The Gambia, and Cape Verde [28, 29]. The frequency of all ethnic groups in each of the 11 health regions is shown in Additional file 1: Table S3.

Nested PCR assays identified Plasmodium infections missed by OM

The 503 DBSs from individuals negative for Plasmodium spp. by OM were individually tested for the presence of submicroscopic P. falciparum infections using the PCR strategies described in the Methods section. Of the 503 DBSs, 102 were positive for P. falciparum infections: four DBSs were positive for P. falciparum exclusively using the strategy targeting the 18S rRNA variants on chrs 05 and 07, 82 DBSs were positive for P. falciparum exclusively using the strategy targeting the 18S rRNA variants on chrs 1, 11, and 13, and 16 DBSs were positive for both strategies. The same strategies confirmed P. falciparum infections in 82 of the 98 DBSs from the 98 individuals positive for P. falciparum by OM.

Of the 601 DBSs screened for Plasmodium infections by different species using nPCR, three DBSs from the 98 individuals exclusively positive for P. falciparum by OM were also positive for P. malariae. No single or mixed infections with P. ovale wallikeri, P. ovale curtisi, or P. vivax were detected among the 601 DBSs analysed.

In summary, the nPCR assays detected 102 submicroscopic P. falciparum infections among the 503 DBSs from negative individuals for Plasmodium spp. by OM. This represents 20.3% submicroscopic P. falciparum infections among OM-negative individuals in Guinea-Bissau (Wald 95% CI = 16.8–23.8, Z = 0.100, P = 0.92 for 20.0% submicroscopic P. falciparum infections among OM-negative individuals in Guinea-Bissau). The nPCR assays also detected three co-infections with P. malariae (3.0%) that were missed by OM among the 98 individuals exclusively positive for P. falciparum by OM. These results were incorporated in the box-plot graphic with the distribution of the parasitaemia levels of the 98 OM-positive individuals in Additional file 1: Fig. S2.

Geo-mapping of P. falciparum infections

The 601 individuals whose corresponding DBSs were included in this analysis were distributed across 305 HHs, as shown on the map of Guinea-Bissau in Fig. 1. The average number of members per HH was nine (min–max: 1–39) and the most common number of members per HH was six.

Fig. 1
figure 1

Spatial distribution of the 305 HHs by GPS coordinates. Guinea-Bissau is located on the west coast of sub-Saharan Africa, as shown on the map of the African continent at the bottom right. Of the 305 HHs plotted on the map by the corresponding GPS coordinates, 20 (black circles) were inhabited by OM-positive individuals and individuals with submicroscopic P. falciparum infections, 68 (red circles) were inhabited by OM-positive individuals, 64 (green circles) were inhabited by individuals with submicroscopic P. falciparum infections, and 153 (orange circles) were inhabited by non-infected individuals. Circles are opaque to better visualize overlapping HHs. OM-positive individuals negative for nPCR were included into one of the groups with OM-positive individuals (black or red circles), accordingly. The Leaflet interactive map was generated using Python version 3.10.12, in Google Colab, with the package Folium version 0.14.0. The composed image presented was produced from prints of the interactive map and edited in Photoshop CS5

Full size image

The 20 HHs represented by the black circles had a total of 23 OM-positive individuals and 28 individuals with submicroscopic P. falciparum infections. All HHs, excluding one, were in the Fula territory, most near the north border with Senegal. The 68 HHs represented by the red circles had a total of 75 OM-positive individuals. They were spread over 10 health regions, excluding Farim, with 63.0% in the Bafata and Gabu health regions, e.g., in the Fula territory. The 64 HHs represented by the green circles had a total of 74 individuals with submicroscopic P. falciparum infections. They were spread over 10 health regions, excluding Bijagos, with 76.0% in the health regions of Biombo, Gabu, Oio, SAB and Tombali. The remaining 153 HHs represented by the orange circles had a total of 247 non-infected individuals.

Factors associated with submicroscopic P. falciparum infections

The frequencies (number and percentages of cases) of the 102 infected individuals with submicroscopic P. falciparum infections among the 503 OM-negative individuals by health region, age, sex, ethnicity, pregnancy and presence of fever are shown in Table 2. Associations between the socio-demographic and clinical data and the presence of submicroscopic P. falciparum infections were tested using Chi-square test and multiple logistic regression models. Similar data related to the 98 individuals with microscopic P. falciparum infections are shown in Additional file 1: Table S4.

Table 2 Associations of socio-demographic and clinical data with submicroscopic P. falciparum infections using Chi-square tests and multiple logistic regression models
Full size table

Health region, ethnicity, age, sex, and pregnancy

The 102 individuals with submicroscopic P. falciparum infections were identified in 10 of the 11 health regions, namely Bafata (n = 17), Biombo (n = 11), Bolama (n = 3), Cacheu (n = 5), Farim (n = 2), Gabu (n = 30), Oio (n = 10), Quinara (n = 3), SAB (n = 13), and Tombali (n = 8). They belonged to 10 of the 15 ethnicities included in this study, namely Balanta (n = 3), Balanta Mané (n = 3), Beafada (n = 8), Bijagos (n = 6), Fula (n = 48), Mancanha (n = 1), Mandinga (n = 12), Mandjaco (n = 4), Mista (n = 7), and Pepel (n = 10). They aged 6 months to 74 years old (median = 20.0 years old, IQR = 3.75—34.0 years old) and 60.0% were women. Of the 40 pregnant women included in this study, 27 were negative for Plasmodium spp. by OM in the 2017 MIS, and six of them had submicroscopic P. falciparum infections detected in this study by nPCR. Thirteen pregnant women were infected with P. falciparum by OM in the 2017 MIS (Additional file 1: Table S4). These data showed 46.1% (6/13) increase in the number of pregnant women infected with P. falciparum parasites, and a proportion of pregnant women with submicroscopic P. falciparum infections among OM-negative pregnant women of 21.4% (Agresti-Coull, 95% CI = 9.9–36.5, Z = -0.19, P = 0.83 for 25.0% submicroscopic P. falciparum infections among pregnant women with OM-negative results in Guinea-Bissau). Statistically significant associations were observed between submicroscopic P. falciparum infections and the variables health region (X2 = 34.23, df = 10, P < 0.001) and ethnicity (X2 = 45.60, df = 10, P < 0.001) (Table 2). No associations were observed regarding age, sex, and pregnancy. On the opposite, the microscopic P. falciparum infections were associated with health region (X2 = 58.76, df = 10, P < 0.001), age (X2 = 41.90, df = 4, P < 0.001), sex (X2 = 5.31, df = 1, P = 0.021), and pregnancy (X2 = 53.46, df = 14, P < 0.001) (Additional file 1: Table S4).

Fever

One hundred twenty-three individuals of the 601 analysed in this study reported fever in the 14 days prior to blood sampling and 76 of them also reported fever on the day of blood collection. Among the 123 individuals who reported fever, 26.0% were infected with submicroscopic P. falciparum parasites and among the individuals without fever, 19.0% were infected with submicroscopic P. falciparum parasites. No association was observed between fever or submicroscopic P. falciparum infections (Table 2). Regarding the individuals with microscopic P. falciparum infections, 14.0% of whom reported fever were infected versus 26.0% of whom without fever. A statistically significant association between fever and microscopic P. falciparum infection showed that most of them was asymptomatic (X2 = 9.81, df = 1, P = 0.002) (Additional file 1: Table S4).

Multiple logistic regression models

After assuring the absence of multicollinearity between the independent variables, binary logistic regression models were carried out to verify whether health region and ethnicity were predictors of submicroscopic P. falciparum infection (yes/no). The models carried out separately for each variable were statistically significant (health region, Omnibus tests, X2 = 40.97, df = 10, P < 0.001, R2Nagelkerke = 0.123; ethnicity, Omnibus tests, X2 = 52.62, df = 10, P < 0.001, R2Nagelkerke = 0.156). Hierarchic binary logistic regression models comprising both variables were built with the order of entry alternating between the two variables. The model with health region at the first step followed by ethnicity at the second step was statistically significant (Omnibus test, X2 = 64.36, df = 20, P < 0.001, R2Nagelkerke = 0.189) and the entry of the ethnicity still contributed to the model (Omnibus test, X2 = 23. 40, df = 10, P = 0.009), however both variables were not significant in the model. On the opposite, the model with ethnicity at the first step followed by health region at the second step was statistically significant (Omnibus test, X2 = 64.36, df = 20, P < 0.001, R2Nagelkerke = 0.189), but the entry of the health region did not contribute to the model (Omnibus test, X2 = 11.74, df = 10, P = 0.30) and both variables were not significant in the model. Then, the final model comprises only the ethnicity as a predictor of submicroscopic P. falciparum infections (Table 2). Individuals from the ethnicities Balanta (4.0%) and Bijagos (9.0%) were less infected with submicroscopic P. falciparum infections than individuals from the other ethnicities (Table 2) and were less likely to have submicroscopic P. falciparum infections than individuals in the large ethnic group and reference category Fula (Balanta, OR = 0.086, 95% CI = 0.03–0.29; Bijagos, OR = 0.248, 95% CI = 0.10–0.61). Balanta and Bijagos people live in the health regions Quinara, SAB, Bijagos and Bolama, where individuals were less likely to have submicroscopic P. falciparum infections than individuals living in the more numerous health region and reference category Gabu, most occupied by Fula ethnicity (Quinara, OR = 0.099, 95% CI = 0.03–0.34; SAB, OR = 0.459, 95% CI = 0.22–0.95).

A hierarchic binary logistic regression model was built comprising all variables associated with microscopic P. falciparum infections according to the Chi-square tests showed in Additional file 1: Table S4, to search for predictors of microscopic P. falciparum infections among the 601 individuals included in this study. The entry of ethnicity was not significant to the model and was removed. The final model comprising age, health region, fever and sex, in this order, was statistically significant (Omnibus test, X2 = 116.52, df = 16, P < 0.001, R2Nagelkerke = 0.301) and the four variables were statistically significant in the model (Additional file 1: Table S4). The results showed that individuals aged 5–14 years old were more likely to have microscopic P. falciparum infections than children under 5 years old (OR = 4.352, 95% CI = 2.33–8.11); individuals living in the health regions Biombo, Cacheu, Farim, Oio, Quinara and SAB were less likely to present microscopic P. falciparum infections than individuals living in Gabu and individuals with fever were more likely to have microscopic P. falciparum infections than individuals without fever (OR = 2.140, 95% CI = 1.21–3.79). The results showing that males were more likely to have microscopic P. falciparum infections than females were not considered, as the inferior limit of the 95% CI includes the value 1.00 (OR = 1.685, 95% CI = 1.00–2.82).

Haemoglobin levels in submicroscopic and microscopic P. falciparum infections

Of the 213 children aged 6–59 months included in this analysis, 178 participated in the anaemia prevalence survey, and 83 had anaemia. Of the 83 anaemic children, 68 had mild anaemia (11.0 g/dL > Hb ≥ 9.0 g/dL), 14 had moderate anaemia (9.0 g/dL > Hb ≥ 7.0 g/dL), and one child had severe anaemia (Hb < 7.0 g/dL). Data are shown in Table 3. Of the 68 children who had mild anaemia, five were positive for P. falciparum by OM (OM+), 10 were infected with submicroscopic P. falciparum infections (OM−/nPCR+) and 53 were not infected with Plasmodium parasites (OM−/nPCR−), whereas of the 14 children with moderate anaemia, three were positive for P. falciparum by OM, one had submicroscopic P. falciparum infection, and 10 were not infected with Plasmodium parasites. The single child with severe anaemia (Hb = 4.8 g/dL) was positive for P. falciparum by OM (OM+).

Table 3 Frequency of anaemic children aged 6–59 months infected with microscopic and submicroscopic P. falciparum infections
Full size table

Anaemia was not associated with submicroscopic or microscopic P. falciparum infections, the degree of anaemia was not associated with submicroscopic P. falciparum infections, but it was associated with microscopic P. falciparum infections (Table 3).

Discussion

The aim of this study was to assess the frequency, distribution, and morbidity of submicroscopic Plasmodium infections, as well the factors associated with them in a pre-elimination malaria setting, Guinea-Bissau. Guinea-Bissau is an endemic malaria country located on the west coast of sub-Saharan Africa, with seasonal and regional variations in malaria transmission, where the P. falciparum is the predominant Plasmodium species.

Submicroscopic Plasmodium infections, probably low-density infections with less than 100 parasites/µL of whole blood [30], can sustain malaria transmission in low and high malaria-transmission areas. The results obtained in this study showed a frequency of 20.0% submicroscopic P. falciparum infections in the general population and among pregnant women with a microscopically-negative diagnosis in Guinea-Bissau. They also showed that submicroscopic P. falciparum infections were widely distributed throughout the country, occurring in 10 of the 11 health regions, and in almost all ethnic groups, age groups, males, females and pregnant women. The factors associated with submicroscopic P. falciparum infections among the 503 microscopically-negative individuals were ethnicity and health region according to Chi-square tests. A logistic regression model comprising both variables showed that ethnicity was a predictor of submicroscopic P. falciparum infections, and individuals from the Balanta and Bijagos ethnic groups, most of whom live in the low malaria-transmission health regions of Quinara and SAB, and the Bijagos archipelago, respectively, were less likely to have submicroscopic P. falciparum infection than individuals from the larger Fula ethnic group, most of whom live in the high malaria-transmission health region of Gabu. This suggests that submicroscopic P. falciparum infections are mostly asymptomatic and may contribute to maintaining malaria transmission, especially in high malaria-transmission areas, acting as silent parasite reservoirs and a barrier to achieving malaria control and elimination in these areas. Further investigation using more sensitive molecular strategies that allow parasite quantification and stratification of different levels of parasitaemia may help to identify predictors of submicroscopic P. falciparum infections.

On the opposite, health region, ethnicity, age, sex, pregnancy and presence of fever were associated with microscopic P. falciparum infections using Chi-square tests. Despite the presence of fever being associated with microscopic P. falciparum infections, some infected individuals with microscopic P. falciparum infections did not report fever, even younger individuals. In addition to the development of an anti-parasitic acquired immune response, this suggests the development of immune tolerance in exposed individuals, allowing the occurrence of asymptomatic infections even at parasitaemia levels detected by OM [31].

A logistic regression model comprising age, health region, fever and sex showed that the first three factors were predictors of microscopic P. falciparum infections. Individuals aged 5–14 years old, and fever were more likely associated with microscopic P. falciparum infections than children under 5 years old, and the absence of fever. Possible reasons for the emergence of malaria in children aged 5–14 years [32,33,34,35,36] include successful intervention programmes targeted at very young children, and various social, cultural, and economic behavioural factors that increase exposure of children older than 5 to infected mosquito bites and reduce the uptake and effectiveness of interventions in this target group [37]. For example, children under 5 are commonly prioritized for long-lasting insecticide nets use, whereas children older than 5 are not [38]. Higher levels of parasitaemia were also observed in pregnant women compared with non-pregnant women of the same age range, probably explained by pregnancy-associated immunosuppression [39], highlighting the vulnerability of this group to malaria [40]. Finally, individuals living in the health regions of Biombo, Cacheu, Farim, Oio, Quinara and SAB were less likely to have microscopic P. falciparum infections than individuals living in Gabu.

The results obtained also showed a frequency of 3.0% of mixed infections with P. falciparum and P. malariae in individuals infected only with P. falciparum by OM in the 2017 national malaria prevalence survey. The identification of mixed infections by OM is not easy, even for experienced microscopists, especially when one species is numerically predominant and the others are present at low levels [22]. In addition to the experience of the microscopist, the quality of the blood smear preparation [41] and the low-incidence of non-falciparum infections in certain areas contribute to the difficulty of detecting morphological differences between different Plasmodium spp. at the ring stage [42], which may hamper the detection of mixed Plasmodium infections. Plasmodium malariae is a neglected Plasmodium parasite that has been largely observed in sub-Saharan Africa as co-infection with P. falciparum as the dominant species [43]. The results obtained showed the necessity to consider P. malariae infections in the strategies focused on malaria control and elimination in Guinea-Bissau.

Absence of amplification of P. falciparum genetic fragments was observed in 16 DBSs samples from microscopically positive individuals for P. falciparum. In addition to reflecting possible damage to the samples due to prolonged storage, which could have reduced the quality and quantity of extracted parasite DNA, this may indicate, at a lower probability, possible single nucleotide polymorphisms in the P. falciparum 18S rRNA genes that deserve further investigation, especially considering the scarcity of P. falciparum sequences from Guinea-Bissau deposited in genetic databases.

Regarding the morbidity of malaria infections, the results obtained showed that submicroscopic P. falciparum infections were not associated with anaemia or degrees of anaemia, but microscopic P. falciparum infections were associated with severe anaemia.

Conclusions

The results obtained indicate the urgent need for molecular-based strategies, including PCR-based point-of-care devices, to screen for submicroscopic P. falciparum infections and mixed Plasmodium infections; that submicroscopic P. falciparum infections should not be ignored in surveillance strategies, especially in high malaria-transmission areas, and that malaria surveillance strategies can be designed to target the predictors of microscopic P. falciparum infections to achieve malaria control and elimination.

Availability of data and materials

No datasets were generated or analysed during the current study.

Abbreviations

CHR:

Chromosome

DBS:

Dried fingerprick whole blood sample

DF:

Degrees of freedom

DNA:

Desoxyribonucleic acid

EBML-EBI:

European Molecular Biology Laboratory-European Bioinformatics Institute

GPS:

Global positioning system

Hb:

Haemoglobin

HH:

Household

IQR:

Interquartile range

MAX:

Maximum value

MIN:

Minimum value

MIS:

Malaria indicator survey

MOE:

Margin of error

nPCR:

Nested polymerase chain reaction

OM:

Optical microscopy

PDF:

Portable document format

RBM:

Roll Back Malaria

RDT:

Rapid diagnostic test

rRNA:

Ribosomal Ribonucleic acid

SAB:

Autonomous sector of Bissau, the capital of Guinea-Bissau

SPSS:

Statistical package for the social sciences

UV:

Ultra-violet

VIF:

Variance inflation factor

WHO:

World Health Organization

References

  1. WHO. World Malaria Report 2021. Geneva: World Health Organization; 2021.

  2. WHO. World Malaria Report 2022. Geneva: World Health Organization; 2022.

  3. WHO. World Malaria Report 2023. Geneva: World Health Organization; 2023.

  4. WHO. Meeting report of the WHO Evidence Review Group on Low-Density Malaria Infections. Geneva: World Health Organization; 2017.

  5. Karl S, Gurarie D, Zimmerman PA, King CH, St. Pierre TG, Davis TME. A sub-microscopic gametocyte reservoir can sustain malaria transmission. PLoS ONE. 2011;6:e20805.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Lindblade KA, Steinhardt L, Samuels A, Kachur SP, Slutsker L. The silent threat: asymptomatic parasitemia and malaria transmission. Expert Rev Anti Infect Ther. 2013;11:623–39.

    Article  PubMed  CAS  Google Scholar 

  7. Djoufounna J, Mayi MPA, Bamou R, Foyet JV, Tabue R, Lontsi-Demano M, et al. High prevalence of asymptomatic Plasmodium falciparum malaria in Makenene, a locality in the forest-savannah transition zone, Centre Region of Cameroon. Curr Res Parasitol Vector-Borne Dis. 2022;2: 100104.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. Geiger C, Agustar HK, Compaoré G, Coulibaly B, Sié A, Becher H, et al. Declining malaria parasite prevalence and trends of asymptomatic parasitaemia in a seasonal transmission setting in north-western Burkina Faso between 2000 and 2009–2012. Malar J. 2013;12:27.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Abebaw A, Aschale Y, Kebede T, Hailu A. The prevalence of symptomatic and asymptomatic malaria and its associated factors in Debre Elias district communities, Northwest Ethiopia. Malar J. 2022;21:167.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Arez AP, Pinto J, Pålsson K, Snounou G, Jaenson TGT, Do Rosário VE. Transmission of mixed Plasmodium species and Plasmodium falciparum genotypes. Am J Trop Med Hyg. 2003;68:161–8.

    Article  PubMed  Google Scholar 

  11. Satoguina J, Walther B, Drakeley C, Nwakanma D, Oriero EC, Correa S, et al. Comparison of surveillance methods applied to a situation of low malaria prevalence at rural sites in the Gambia and Guinea Bissau. Malar J. 2009;8:274.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Mcgregor D, Texeira E, Grignard L, Goncalves A, Vasileva H, Mabey D, et al. The epidemiology of Plasmodium falciparum malaria in the Bijagos Islands of Guinea-Bissau. Am J Trop Med Hyg. 2021;104:2117–22.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Hutchins H, Power G, Ant T, Teixeira E, Goncalves A, Rodrigues A, et al. A survey of knowledge, attitudes and practices regarding malaria and bed nets on Bubaque Island, Guinea-Bissau. Malar J. 2020;19:1–15.

    Article  Google Scholar 

  14. MEASURE Evaluation, MEASURE DHS, President’s Malaria Initiative, Roll Back Malaria Partnership, UNICEF, World Health Organization. Household Survey Indicators for Malaria Control. 2013.

  15. WHO. Haemoglobin concentrations for the diagnosis of anaemia and assessment of severity. Vitamin and Mineral Nutrition Information System. Geneva, World Health Organization, 2011 (WHO/NMH/NHD/MNM/11.1).

  16. Rodrigues A, Schellenberg JA, Kofoed PE, Aaby P, Greenwood B. Changing pattern of malaria in Bissau, Guinea Bissau. Trop Med Int Health. 2008;13:410–7.

    Article  PubMed  Google Scholar 

  17. Daniel WW, Cross CL. Biostatistics: a foundation for analysis in the health sciences. 7th ed. New York: John Wiley & Sons; 1999.

    Google Scholar 

  18. Strøm GEA, Tellevik MG, Hanevik K, Langeland N, Blomberg B. Comparison of four methods for extracting DNA from dried blood on filter paper for PCR targeting the mitochondrial Plasmodium genome. Trans R Soc Trop Med Hyg. 2014;108:488–94.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Teyssier NB, Chen A, Duarte EM, Sit R, Greenhouse B, Tessema SK. Optimization of whole-genome sequencing of Plasmodium falciparum from low-density dried blood spot samples. Malar J. 2021;20:116.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Snounou G, Viriyakosol S, Zhu XP, Jarra W, Pinheiro L, do Rosario VE, et al. High sensitivity of detection of human malaria parasites by the use of nested polymerase chain reaction. Mol Biochem Parasitol. 1993;61:315–20.

    Article  PubMed  CAS  Google Scholar 

  21. Snounou G, Singh B. Nested PCR analysis of Plasmodium parasites. In: Doolan DL, editor. Methods Mol Med. Humana Press; 2002. p. 189–203.

    Google Scholar 

  22. Snounou G, Viriyakosol S, Jarra W, Thaithong S, Brown KN. Identification of the four human malaria parasite species in field samples by the polymerase chain reaction and detection of a high prevalence of mixed infections. Mol Biochem Parasitol. 1993;58:283–92.

    Article  PubMed  CAS  Google Scholar 

  23. Rooney AP. Mechanisms underlying the evolution and maintenance of functionally heterogeneous 18S rRNA genes in Apicomplexans. Mol Biol Evol. 2004;21:1704–11.

    Article  PubMed  CAS  Google Scholar 

  24. Sharma S, Jarori GK. Translation and post-translational modifications in Plasmodium. Curr Sci. 2012;102:741–8.

    CAS  Google Scholar 

  25. Fuehrer HP, Stadler MT, Buczolich K, Bloeschl I, Noedl H. Two techniques for simultaneous identification of Plasmodium ovale curtisi and Plasmodium ovale wallikeri by use of the small-subunit rRNA gene. J Clin Microbiol. 2012;50:4100–2.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Morais I, Medeiros MM, Carvalho M, Morello J, Teixeira SM, Maciel S, et al. Synthetic red blood cell-specific glycolytic intermediate 2,3-diphosphoglycerate (2,3-DPG) inhibits Plasmodium falciparum development in vitro. Front Cell Infect Microbiol. 2022;12: 840968.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Trager W, Jensen JB. Human malaria parasites in continuous culture. Science. 1976;193:673–5.

    Article  PubMed  CAS  Google Scholar 

  28. Spínola C, Bruges-Armas J, Brehm A, Spínola H. HLA-A polymorphisms in four ethnic groups from Guinea-Bissau (West Africa) inferred from sequence-based typing. Tissue Antigens. 2008;72:593–8.

    Article  PubMed  Google Scholar 

  29. Vicente M, Priehodová E, Diallo I, Poloni ES, Viktor Č, Schlebusch CM. Population history and genetic adaptation of the Fulani nomads: inferences from genome-wide data and the lactase persistence trait. BMC Genomics. 2019;20:915.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Kojom Foko LP, Moun A, Singh V. Addressing low-density malaria infections in India and other endemic part of the world—the opportune time? Crit Rev Microbiol. 2024:1–17.

  31. Ademolue TW, Aniweh Y, Kusi KA, Awandare GA. Patterns of inflammatory responses and parasite tolerance vary with malaria transmission intensity. Malar J. 2017;16:145.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Ursing J, Rombo L, Rodrigues A, Aaby P, Kofoed PE. Malaria transmission in Bissau, Guinea-Bissau between 1995 and 2012: malaria resurgence did not negatively affect mortality. PLoS ONE. 2014;9: e101167.

    Article  PubMed  PubMed Central  Google Scholar 

  33. WHO. Guidelines for malaria 2023. Geneva: World Health Organization; 2023.

  34. Snow RW, Amratia P, Kabaria CW, Noor AM, Marsh K. The changing limits and incidence of malaria in Africa: 1939–2009. Adv Parasitol. 2012;78:169–262.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Habyarimana F, Ramroop S. Prevalence and risk factors associated with malaria among children aged six months to 14 years old in Rwanda: evidence from 2017 Rwanda malaria indicator survey. Int J Environ Res Public Health. 2020;17:7975.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Tairou F, Diallo A, Sy O, Kone A, Manga IA, Sylla K, et al. Malaria-associated risk factors among adolescents living in areas with persistent transmission in Senegal: a case–control study. Malar J. 2022;21:193.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Lubinda J, Haque U, Bi Y, Shad MY, Keellings D, Hamainza B, et al. Climate change and the dynamics of age-related malaria incidence in Southern Africa. Environ Res. 2021;197: 111017.

    Article  PubMed  CAS  Google Scholar 

  38. Njatosoa AF, Mattern C, Pourette D, Kesteman T, Rakotomanana E, Rahaivondrafahitra B, et al. Family, social and cultural determinants of long-lasting insecticidal net (LLIN) use in Madagascar: secondary analysis of three qualitative studies focused on children aged 5–15 years. Malar J. 2021;20:1–15.

    Article  Google Scholar 

  39. Schantz-Dunn J, Nour NM. Malaria and pregnancy: a global health perspective. Rev Obstet Gynecol. 2009;2:186–92.

    PubMed  PubMed Central  Google Scholar 

  40. Steketee RW, Nahlen BL, Parise ME, Menendez C. The burden of malaria in pregnancy in malaria-endemic areas. Am J Trop Med Hyg. 2001;64:28–35.

    Article  PubMed  CAS  Google Scholar 

  41. Fong Amaris WM, Martinez C, Cortés-Cortés LJ, Suárez DR. Image features for quality analysis of thick blood smears employed in malaria diagnosis. Malar J. 2022;21:74.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. Amoah LE, Donu D, Abuaku B, Ahorlu C, Arhinful D, Afari E, et al. Probing the composition of Plasmodium species contained in malaria infections in the Eastern region of Ghana. BMC Public Health. 2019;19:1617.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Holzschuh A, Gruenberg M, Hofmann NE, Wampfler R, Kiniboro B, Robinson LJ, et al. Co-infection of the four major Plasmodium species: effects on densities and gametocyte carriage. PLoS Negl Trop Dis. 2022;16: e0010760.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We would like to thank the Bandim Health Project for providing the DBSs for analysis and all the participants of the MIS 2017 for their valuable contribution, as well as Professor Pedro Cravo (NOVA IHMT) and Doctor Marcelo Urbano Ferreira (NOVA IHMT and University of São Paulo/Brasil) for discussion and critical reading of the manuscript.

Funding

We are grateful to the Fundação para a Ciência e a Tecnologia (FCT, Portugal) for funding under GHTM-UID/04413/2020, LA-REAL—LA/P/0117/2020 and CE3C—UIBD/00329/2020. RS is supported by an FCT PhD fellowship UI/BD/151069/2021.

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Authors and Affiliations

  1. Global Health and Tropical Medicine, GHTM, Associate Laboratory in Translation and Innovation Towards Global Health, LA-REAL, Instituto de Higiene e Medicina Tropical, IHMT, Universidade NOVA de Lisboa, UNL, Rua da Junqueira 100, 1349-008, Lisbon, Portugal

    Ronise Silva, Luis Filipe Lopes, Ana Paula Arez & Márcia M. Medeiros

  2. Bandim Health Project, Apartado 861, 1004, Bissau Codex, Bissau, Guinea-Bissau

    Ronise Silva & Amabelia Rodrigues

  3. Centre for Ecology, Evolution and Environmental Changes (cE3c), Faculdade de Ciências da Universidade de Lisboa, Campo Grande, 1749-016, Lisbon, Portugal

    Luis Filipe Lopes

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Contributions

R.S., A.P.A. and M.M.M. designed the experiments. R.S., L.F.L. and M.M.M. generated data. R.S. and M.M.M. analysed data. A.P.A. and M.M.M. supervised data analysis. R.S. and M.M.M. wrote the main manuscript text. M.M.M. prepared all tables and supplementary material. L.F.L. prepared Figure 1. All authors reviewed the manuscript.

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Correspondence to Márcia M. Medeiros.

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This study is part of the SUBFAM protocol (Submicroscopic infections of Plasmodium falciparum: an obstacle for malaria elimination) led by MMM and approved by the National Committee for Ethics in Health of Guinea-Bissau under the code 035/CNES/INASA/2019.

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Silva, R., Lopes, L.F., Rodrigues, A. et al. Assessing the burden of submicroscopic Plasmodium infections in a pre-elimination malaria setting in sub-Saharan Africa, Guinea-Bissau. Malar J 23, 316 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12936-024-05138-z

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Malaria Journal

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