In Africa, the relationship between nutritional status and malaria remains complex and difficult to interpret in children. Understanding it is important in the development of malaria control strategies. This study evaluated the effect of nutritional status on the occurrence of multiple malaria episodes in children aged 6 to 59 months between 2013 and 2017 living in the village of Dangassa, Mali.A community-based longitudinal study was conducted using cross-sectional surveys (SSCs) at the beginning (June) and end (November) of the malaria transmission season associated with passive case detection (PCD) at the Dangassa Community Health Center. Children with asymptomatic malaria infection during cross-sectional surveys were selected and their malaria episodes followed by PCD. Palustrine indicators in person-months were estimated using an ordinal-logistic model repeated on subjects during follow-up periods.The incidence rate (IR) during the period of high transmission (June to October), for 1 episode and for 2 + episodes peaked in 2013 with 65 children (IR = 95.73 per 1000 person-months) and 24 cases (IR = 35.35 per 1000 person-months), respectively. As expected, the risk of multiple episodes occurring during the period of high transmission was 3.23 compared to the period of low transmission after adjusting for other model parameters . Children with anemia were at high risk of having multiple episodes . However, the risk of having 2 + episodes for anemic children was higher during the period of low transmission  compared to the period of high transmission . The trend indicated that anemic and underweight children were significantly associated with multiple malaria episodes during the period of low transmission (p < = 0.001).Our results indicate that multiple episodes of malaria are significantly related to the nutritional status (anemia and underweight) of the child during the two transmission seasons and more pronounced during the dry season (period of low transmission). Further research including other malnutrition parameters will be needed to confirm our findings. Malaria, anemia, and malnutrition are responsible for more than 50% of child mortality in the world, particularly among children under five years of age –3. In MaBesides malaria, malnutrition remains a significant proportion of morbidity and mortality among children under 5 in Mali and is mainly due to micronutrient deficiencies with high frequencies observed during the malaria transmission season (June to October) . In MaliAs one of one of the most common public health problems in the world, and especially one of the complications of malaria infection in endemic regions, anemia plays a major role in malaria morbidity and mortality , 10. It Plasmodium infection and infectious episodes [Children’s nutritional status is a sensitive indicator of community health. The effects of malnutrition in children are long-lasting and extend decades beyond childhood . Howeverepisodes , 14.The relationship between nutritional status, anemia, and malaria episodes among children under the age of five is not well documented in Mali. Our study aimed to investigate longitudinally the influence of nutritional status on multiple episodes of clinical malaria in children aged 6–59 months asymptomatic to malaria in Dangassa, health district of Kati, from 2013 to 2017.The study was conducted in Dangassa (8° 12′ 37.253″ W and 12° 8′ 46.279″ N) which is located 75 km southwest of Bamako in the health district of Ouéléssebougou. This is a Sudan savannah eco-geographical zone with a rainy season lasting from June to October and a dry season from November to May. The dominant winds are the monsoon (rainy season) and the harmattan (dry season). Malaria transmission is seasonal in Dangassa, with 6 to 7 months of transmission per year (June to December).HemoCue®Hb 301 was used to measure hemoglobin levels. For each participant, data on individual use of LLINs and fever defined as axillary temperature ≥ 37.5°C or fever within the previous 48 hours) was collected. A blood smear was prepared for each participant during both SSCs and PCD to determine malaria parasitemia. During the SSCs, only participants with fever were tested for malaria using malaria a rapid diagnostic test RDT. Participants tested positive for malaria were provided treatment according to the policies of the Malian NMCP. We defined a malaria episode for the same participant as new when time between two subsequent infections was greater than 20 days. The number of episodes per child was classified into three categories 0,1 and 2 or more. Weight was measured using a weigh scale. ENA For Smart software was used to calculate scores (z-scores) based on the national results at the WHO reference population. Malnutrition indicators were determined using a standard deviation of −2 standard deviations or Z-score. The weight-for-age (WAZ) of children was classified in two categories, Z-score according to WHO growth standards references for children aged zero to five years 16. Score ≤ −2 standard deviation (SD) were defined as underweight, while score > −2 standard were defined as normal from the reference mean. Anemia was classified into two categories: Anemia: Hb level < 11 g/dl; No anemia: Hb level ≥ 11 g/dl 18.Data on the malaria prevalence were collected through seven cross-sectional surveys: in June (at the start of the rainy season) and in October/November (at the end of the rainy season). During each survey, demographic and clinical data were collected from each participant. Microscopy (smear) was used to assess P. falciparum infection, while Through community health based passive surveillance a physician and a biologist followed the cohort participants for malaria incidence from February 2013 to March 2017. During this follow-up, data on anemia and malnutrition were collected in the same way as during the cross-sectional surveys. Study participants with fever were invited to the health center  and tested using RDT and microscopy. All RDT positive cases received free antimalarial treatment according to national policy by the local health staffBIOLANE®) for P. falciparum infection were performed according to the manufacturer’s instructions. RDTs used were those currently recommended by the NMCP of Mali. Hemoglobin concentration was measured in venous blood using HemoCue 301. And anemia was defined as a hemoglobin level < 11 g/dl (16).Blood films were stained with 10% Giemsa and examined under a 100X oil immersion objective lens of a light microscope. Asexual parasites were estimated based on the number of parasitized red blood cells per 300 white blood cells multiplied by 25, assuming an average white blood cell count of 7500/μl. A minimum of 100 fields were examined before a thick drop was considered negative. by Two different experienced microscopists read each smear separately. RDTs  with the hybrid method (in which Fisher scoring iterations are performed before switching to the Newton-Raphson method) for the parameter’s estimations and maximum likelihood for the algorithm convergence was applied on the model. The working correlation matrix has shown an exchangeable structure. The repeated subject effect was the children study ID with the intrasubject effect as the follow-up period. The convergence criteria were satisfied, and the data fit well the model. The children exposure times within each transmission period were computed as the offset. The outcome variable for the model was the episodes numbers with three categories levels 0,1 or 2 + episodes. Five risk factors: age groups, gender, anemia status, weight for age (WAZ) and transmission period) were used. From the model, the predict Incidence rate (IR), odd ratio (OR) and/or the relative risk (RR) for having higher malaria-episodes numbers were estimated.Seven SSCs, from 2013 (February), 2014 , 2015  and 2016 , were carry-out during which 199,107,233,151,324,232 and 154 children were respectively enrolled at the beginning of each of the malaria-transmission-periods. The prevalence of clinical malaria was 15.6% in 2013, 15.0% vs 16.7%, 8.6% vs 13.6%, and 6.9% vs 13.6% respectively at the start and end of transmission in 2014, 2015, and 2016. Using the PCD data, the unadjusted comparison of no zero percentage for malaria episode numbers by each risk factor and by transmission period indicated a significant difference between sex in 2013 (February to June), in WAZ status in 2014 (June to October) and in Anemia status in 2015 (June to November) . There was no significant difference elsewhere. Table 2aThe peak of incidence risk (IR) for one  and 2+  episodes was observed in 2013 during the high transmission period (June to October). IR decreased to zero cases at the end of the study. The lowest IR for 1 episode and 2 + episodes were observed in the second half time of the study follow-up period in 2016 (June – December) with 9 cases (IR = 6.78 in 1000 person-months) and 5 cases (IR = 3.77 in 1000 persons-months), respectively. Table 2bThe univariate analysis of risk factors between age groups indicated that children 36–59 months were more likely to have higher numbers of malaria episodes than 6–35 months old. Males were more likely to experience a higher number of multiple malaria episodes as well as anemia compared to females. However, none of the differences was significant . After adjusting for all other parameters, the odds ratio (OR) for having a higher number of malaria episodes during the high transmission period was higher than in the low transmission period . The children with anemia were more likely to have higher numbers of malaria episodes  than those with normal hemoglobin levels. Table 3During Low and High transmission periods, the trends of the predicted IR in person-months among the categories of each risk factor are shown in In contrast, the Relative Risk of two or more episodes of anemia in low transmission periods  was slightly higher than in high transmission periods . Table 4In endemic countries, malaria is a major public health problem, particularly in low-income countries . DespiteP. falciparum infection during the dry season has been demonstrated [Based on our baseline descriptive data we found that symptomatic malaria cases varied between SSCs. The number of cases was higher at the end of the rainy season than at the beginning. In malaria-endemic areas, the parasite density is usually higher at the end than the start of the rainy season . During nstrated .As a baseline, asymptomatic children were selected at each SSCs as a baseline, and this allowed the number of malaria episodes were counted and categorized as 0, 1 episode and 2 + episodes for each child. The percentage of zero malaria episodes in the follow-up period from June to November 2015 indicated a significant difference according to anemia status. Anemia is a major cause of malaria in tropical areas and is also an additional indicator for monitoring the burden of malaria in the community . Indeed,The incidence peak in our study was observed in 2013 during the period of high transmission (June to October), with respectively 65 children ((IR = 95.73 in 1000 person-months) for multiple episodes and 24 cases (IR = 35.35 in 1000 person-months) for those with 1 episode. Most malaria cases detected by PCD in Dangassa are symptomatic and occur during the five- to six-month transmission season (June to November) .However, the frequency of malaria episodes decreased significantly between 2013 and 2017 in our cohort. As a result, the intensification of malaria control strategies in Dangassa, including the distribution of LLINs and the introduction of SMC, have significantly reduced the burden of malaria in Dangassa ; CommuniAccording to our results, underweight is associated with a higher number of episodes compared to normal children 10% error . Almeida M. et al in 2015 , in a ruThe adjusted model showed a significant association between multiple episodes in anemic and normal children . Thus, in high transmission areas such as Dangassa, where the disease is prevalent, many young children are anemic, and people infected with the disease may receive a single bite each day, exposing them to repeated episodes of the disease. Consequently, in these contexts, severe malarial anemia (hemoglobin < 7g/dl) , in younThe occurrence of multiple episodes was significantly associated with anemic and underweight children during the low transmission period. Anemia has a complex etiology, including single- and multifactorial causality . Thus, tWe found that the risk of having multiple malaria episodes during the high transmission period was 3.23 times higher than during the period of low transmission . Although the number of multiple malaria episodes increase following exposure to malaria transmission, the rainfall, which is low during periods of low transmission and high during periods of high transmission, is an important factor in the dynamics of malaria transmission. Toure M et al (2016) reported the same results and showed a clear seasonal pattern, with a reduced below 50% malaria incidence during the dry season.The study has the limitation that, because the data were analyzed secondary, some anthropometric parameters needed to determine children’s nutritional status were not collected. Consequently, some variables related to these indices were not able to be incorporated into our data analysis.The presence of multiple malaria episodes was significantly associated with nutritional status (anemia and low weight-for-age) in children during both high and low transmission periods. However, this relationship was more pronounced during the dry season (“low transmission” period). A further study including more parameters are necessary to support this hypothesis.


Malaria and intestinal parasite infection are common in developing countries. These Parasites causes anaemia and malnutrition mostly in children. For this reason, it is important to study these infections and their effects in order to monitor interventions to control them. This study aims to determine prevalence of malaria and intestinal parasite infections and their association with nutritional status among febrile children in Accra, Ghana.The study was conducted among febrile children aged 6 months to 5 years attending three health facilities in Accra from May to October, 2022. A total of 315 children were selected for the study. Anthropometric measurement was done for each participant. Blood and stool samples were collected for investigation. Thick and thin blood smears stained with 10% Giemsa were prepared and examined for Plasmodium parasite using microscopy. Stool samples were processed using direct wet mount and formalin-ether concentration method and examined for intestinal parasites using microscopy. Haemoglobin concentration was measured using automatic haematology analyzer.Plasmodium falciparum accounted for 77.6% (59/76) of parasitaemia, whereas Plasmodium malariae was 22.4% (17/76). Prevalence of intestinal parasite infection was 10.7% (34/315). Giardia lamblia accounted for 17/315 (5.3%) of the entire children, followed by Ascaris lumbricoides 8/315 (2.5%), Hookworm 6/315 (1.9%) and Trichuris trichiura 3/315 (0.9%). A total of 15/315 (5%) of the participants had co-infection of malaria and intestinal parasite infection. Prevalence of anaemia, malnutrition, stunting, wasting and underweight were (72%), (30.7%), (16.2%), (24.4%) and (57.1%) respectively. Malaria was significantly associated with anaemia (p = 0.000) and underweight (p = 0.013). Ascaris lumbricoides was significantly associated with wasting (p = 0.010). Giardia lamblia was significantly association with malnutrition (p = 0.000) and Stunting (p = 0.000), whereas Hookworm was found to be significantly associated with anaemia (p = 0.021).A total of 24% (76/315) were positive for malaria. Prevalence of IPI in this study was less than previously reported, most likely due to regular deworming of most of the children. However, Malaria and intestinal parasitic infection were significantly associated with anaemia and malnutrition including wasting, stunting, and underweight. Plasmodium falciparum. Plasmodium falciparum accounts for more than 90% of malaria mortality in Ghana [Malaria and intestinal parasite infection (IPI) are of great public health concern, causing death and morbidity in South-Saharan Africa , 2. Recein Ghana . High moin Ghana –13.Plasmodium falciparum infection causes loss of appetite, which might lead to underweight [Undernutrition in children is reported to be an important public health issue in Ghana . Nutritierweight .Ascaris lumbricoides has the potential to bind to the absorptive surfaces of the intestinal walls and affect intake of nutrients meant for the body of its host, leading to malnutrition [Intestinal parasite infections of public health importance in Ghana are amoebiasis, giardiasis, acariasis, hookworm, and trichuriasis , 23, 7. utrition , 25. Hooutrition . To prevutrition . Thus, tutrition , 29.In Ghana, studies have established the endemicity of malaria and intestinal parasitic infections , 30, 31.This study was a cross-sectional study carried out at the outpatient department (OPD) and inpatient department (emergency and admission wards) of three hospitals in Accra from May 2022 to October 2022. A semi-structured questionnaire was used to collect data. Anthropometry was also measured.This study was conducted at three hospital facilities in Accra: Princess Marie Louise Children Hospital, Shukura Hospital, and Mamprobi Hospital. Princess Marie Louise Children’s hospital (PML)  is located within Ashiedu Keteke sub-metropolitan district with a population of 443,768 people . The hosShukura community hospital  is one of the best medical facilities located in the Ablekuma Central Municipality in Accra. Shukura Community hospital provides medical services for people in the municipality. It is a 61-bed capacity hospital and patients are mostly self-referred. Mamprobi hospital  is located in the Ablekuma South Sub Metropolitan District in Accra. The population of Ablekuma South District is 15,051 . It has Febrile children (body temperature > 37.5°c) with diarrhea, from the age of 6 months to 5 years old who reported to the hospital were eligible for this study. Febrile children who had convulsion based on evidence from the medical history were excluded from the study.2pq/e2. Using a 95% confidence level, 0.5 standard deviations, and ± 5% precision; where n = Minimum sample size, z = Z Score , p = An estimated proportion of an infection that is present in the population (50%), e = percentage margin of error taken as 5% and q = 1-p . Thus, n = Z2pq/e2 = (1.645)2(0.5) (0.5/ (0.05)2 = 278. Therefore, the minimum sample size (n) for this study was 278.The minimum sample size for the study was determined using Cochran’s formula, n = Z Participants were selected from each study sites using a convenient sampling method. Selection of this method was based on study participants availability at the selected hospitals.In-person interviews were used to collect primary data using a semi-structured questionnaire with questions about participant’s sociodemographic characteristics such as gender and age, and medical history such as clinical signs and symptoms. Weight was measured with a calibrated Seca 750 Robust mechanical floor scale to the nearest 0.1Kg for children above 2 years at all study sites. Weight was measured with a calibrated Seca 374 scale for children below 2 years at all study sites. Height measurement was taken with Seca 213 stadiometer to the nearest 0.1cm at PML and Mamprobi Hospital, and Seca 216 stadiometer to the nearest 0.1cm at Shukura Hospital for children above 2-year-old. Length was taken to the nearest 0.1cm with Seca 416 baby infantometer at PML Hospital, and Seca 210 at Mamprobi and Shukura Hospital for children below 2 years. Mid-upper arm circumference was measured with MUAC tape to the nearest 0.1cm and recorded using World Health Organization (WHO) standard . The antBlood and stool samples were collected from the participants. About 3ml of a venous blood sample was collected into test tubes containing EDTA anticoagulant using aseptic techniques for haemoglobin (Hb) measurements and malaria testing. Parents/guardians were instructed to put a teaspoon of the participant’s stool into the clean, leak-proof wide-neck stool containers provided. Stool samples were processed within 6 hours of collection and examined microscopically within 30 minutes of preparation. Both direct wet mount and formol-ether concentration technique was used to confirm the presence of intestinal parasites in stool .Thin and thick blood smears were prepared, stained with 10% Giemsa, and observed under a light microscope using 40x and 100x objective lenses for malaria parasites. Malaria parasite density was calculated by comparing number of parasites against 200 leucocytes in thick films . HeamoglData from interviews and parasitic investigations were entered into Microsoft Excel 2016, and exported into Statistical Package for Social Sciences  for statistical analyses. Anaemia was defined as Hb < 11 g/dl; severe anaemia, Hb < 7 g/dl; moderate anaemia, Hb 7 to < 10 g/dl; and mild anaemia, Hb 10–11 g/dl . ChildreOverall, 315 febrile children were enrolled in this study. A total of 113/315 (35.9%) study participants were recruited from Princess Marie Louise Children’s hospital, 100/315 (31.7%) from Mamprobi hospital and 102/315 (32.4%) from Shukura hospital. Out of the total study participants recruited for the study, 182 (58%) were female and 133 (42%) were male. Mean (SD) of the age(s) of participants was 1.17 (± 0.691). The average mean (SD) of the weight and height of participants were 11.95 (± 3.479) kg and 86.6 (± 17.535) cm respectively. Among the clinical presentations of the participants were headache (29.5%), vomiting (34.6%) and chills (35.9%). The socio-demographic characteristics of the participants have been summarised in Plasmodium falciparum constituted 77.6% (59/76) of parasitaemia, whereas Plasmodium malariae was 22.4% (17/76)       were positive for malaria, of which  (17/76) . Malaria (17/76) . Malaria= 0.731) . Majorit= 0.319) . In thiss (5.3%) . Among ts (5.3%) . Female = 0.920) . The pre= 0.313) . About 5= 0.313) .2 = 0.164, df = 1, p = 0.003)       and wasting  respectively as shown in table The average haemoglobin (Hb) mean value (± SD) was 10.420 (± 1.165). A high prevalence of anaemia was recorded among the study participants 72%  . Anaemia= 0.003) . Childre= 0.003) . A signi= 0.000) . Out of = 0.000) . Further= 0.000) . A total= 0.000) . The pre= 0.000) . Malnutr= 0.000) . Malnutr= 0.003) . Over 30= 0.003) . Neverth= 0.000) . Prevale= 0.013) . IntestiHealthy growth, especially in children, is reflected in their nutritional status. Studies show that, there is a strong link between nutritional status and parasitic infection . A childFindings from this study indicated a high malaria prevalence (24%). However, this finding was contrary to a study by Kanwugu and colleagues in Tamale Metropolis, Ghana. They reported a lower malaria prevalence (2.6%) . These dIn this study, a lower prevalence of intestinal parasite infection was observed (10.7%). These findings differ from a study by Martinez and colleagues in overcrowded urban slums in Accra, Ghana, who reported a higher prevalence of IPI (47%) . Our preGiardia lamblia was the most prevalent intestinal parasite species. This result is consistent with a research study carried out by Anim-Baidoo et al [Giardia lamblia species (5%) of all children in their study at Princess Marie Louise Children’s Hospital in Accra, Ghana. They emphasized that, assemblage B could be the predominant genotype causing giardiasis in children. Intestinal parasite infection was common among febrile children at Shukura hospital in this study. This could be due to the poor sanitation and hygiene within the community [In this study, oo et al . They reommunity . Lack ofPlasmodium falciparum and hookworm were significantly associated with anaemia. This finding corroborates with a study by Osazuwa et al. [Findings from this study revealed that more than 70% of the children were aneamic. This result is higher than 53.8% reported in Hohoe Municipality in Ghana  and 49.2Giardia lamblia was significantly associated with malnutrition in this study. This finding agrees with what was earlier reported by Kanokwanvimol et al. [The prevalence of malnutrition in this study was 30.7%, which raises concerns for public health. Prevalence of stunting (16.2%), wasting (24.4%), and underweight (74%) among febrile children in this study emphasizes the extent of growth retardation in the studied area. l et al. .Ascaris lumbricoides infection had wasting, and a significant association was found between Ascaris lumbricoides and wasting. Giardiasis is a strong predictor of stunting [Giardia lamblia and stunting. This finding was also consistent with findings from a study by Caron and colleagues in Cambodia, which reported that giardiasis was found to be a protective factor for acute diarrhea, yet, associated with stunting [Findings of the current study revealed a significant association between malaria and underweight. This evidence is stronger than an earlier study conducted in Ethiopia by Hassan and Ali, . Their sstunting . In thisstunting . Our finstunting .In a few instances, it was not possible to process stool samples within the stipulated time due to distance of study sites from the laboratory, thus it is possible that some motile parasites were lost in the process, and this was a limitation to our study. The proportion of children with low-intensity infections may have been mistakenly categorized as uninfected since investigations for intestinal parasites only used one stool sample from each child which could have led to an underestimation of the prevalence of intestinal parasite infections.This study revealed a lower prevalence of intestinal parasites than previously reported and that, malaria and intestinal parasitic infection are significantly associated with anaemia, wasting, stunting, and underweight. These results emphasize the necessity for initiatives of intervention aimed at further lowering the burden of malaria and intestinal parasite infection to reduce their impact on nutritional status of children in the research area. Thus, malaria control initiatives by Ghana Health Service should go hand in hand with effort to improve sanitation especially in areas with poor environments.


Sickle cell trait (SCT) refers to the carriage of one abnormal copy of the β-globin gene, the HbS allele. SCT offers protection against malaria, controlling parasite density and preventing progression to symptomatic malaria. However, it remains unclear whether SCT also affects transmission stages and mosquito infection parameters. Deciphering the impact of the SCT on human to mosquito malaria transmission is key to understanding mechanisms that maintain the trait in malaria endemic areas.Plasmodium species by PCR and hemoglobin typing by RFLP. Infectiousness of gametocytes to mosquitoes was assessed by membrane feeding assays using blood from gametocyte carriers of HbAA and HbAS genotypes. A zero-inflated model was fitted to predict distribution of oocysts in mosquitoes according to hemoglobin genotype of the gametocyte source.The study was conducted from June to July 2017 among asymptomatic children living in the locality of Mfou, Cameroon. Blood samples were collected from asymptomatic children to perform malaria diagnosis by microscopy, P. falciparum gametocytes was 18.47% in HbAS individuals and 13.57% in HbAA, and the difference is significant . Multiplicity of infection was lower in HbAS gametocyte carriers . Gametocyte densities in the blood donor significantly influenced mosquito infection prevalence in both HbAS and HbAA individuals. The HbAS genotype had no significant effect on mosquito infection outcomes when using immune or naïve serum in feeding assays. In AB replacement feeding experiments, the odds ratio of mosquito infection for HbAA blood as compared to HbAS was 0.56 (95% CI 0.29–1.10), indicating a twice higher risk of infection in mosquitoes fed on gametocyte-containing blood of HbAS genotype.Among the 1557 children enrolled in the study, 314 (20.16%) were of the HbAS genotype. The prevalence of children with Plasmodium transmission stages were more prevalent in SCT individuals. This may reflect the parasite’s enhanced investment in the sexual stage to increase their survival rate when asexual replication is impeded. The public health impact of our results points the need for intensive malaria control interventions in areas with high prevalence of HbAS. The similar infection parameters in feeding experiments where mosquitoes received the original serum from the blood donor indicated that immune responses to gametocyte surface proteins occur in both HbAS and HbAA individuals. The higher risk of infection in mosquitoes fed on HbAS blood depleted of immune factors suggests that changes in the membrane properties in HbAS erythrocytes may impact on the maturation process of gametocytes within circulating red blood cells.The online version contains supplementary material available at 10.1186/s12879-023-08134-x. Plasmodium falciparum malaria have been maintained in the human population and their highest frequencies have been found in sub-Saharan Africa [Malaria parasites have exerted the strongest known selective pressure on the human genome over the last 10,000 years [n Africa . Sickle n Africa .P. falciparum infection have reported inconsistent results [SCT protects against severe and uncomplicated malaria, which is linked to diminished proliferation of the parasite asexual stage, and lower malaria parasite densities are reported in febrile cases –6. Prote results –12. SCT  results , 11 whil results , 12.P. falciparum [Asymptomatic malaria infections represent an important reservoir for parasites and contribute to onward mosquito infections . In endelciparum , 15. Prelciparum –18. Monolciparum . In polylciparum , 20. Ourlciparum , 18.Transmission from human to mosquito relies on the availability of sexual stages, the gametocytes, and multiple factors such as gametocyte density, sex ratio, maturity or other human and mosquito factors known to influence parasite transmissibility , 21. It These studies have not elucidated how this enhanced infectivity may occur and what other infection parameters may be associated with SCT and transmission. Here, we investigate the association between malaria transmission parameters and SCT among asymptomatic children in an area of high malaria transmission in Cameroon.This study was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the National Committee of Ethics Research for Human Health (CNERSH) under agreement 2017/06/922/CE/CNERSH/SP and obtained research agreement from the Ministry of Health (032/L/MINSANTE/SG/DRSPC/SSD/MFOU). The informed consent was obtained from legal parent/guardian. All participants were enrolled as volunteers after their legal parent/guardian signed the informed consent form.® Grade 17 paper were obtained from finger pricks. Genomic DNA from DBSs was extracted using the Chelex-100  method as previously described [Plasmodium spp using a multiplex PCR as described in Padley [P. falciparum gametocytes. A 5 ml heparinised tube was used for experimental infections of mosquitoes and a 5 ml EDTA tube for gametocyte purification and P. falciparum genotyping.Screening surveys were conducted in June 2017 in villages of the health district of Mfou, about 30 km from Yaoundé, Cameroon. Recruitment was performed without knowledge of the genetic background of volunteers. Malaria diagnosis was performed upon microscopic examination of Giemsa-stained blood smears. Parasite-positive volunteers received free treatment with Artesunate Amodiaquine according to national guidelines. Parasite densities were estimated per microliter of blood, by counting the number of trophozoites per 500 leucocytes and gametocytes per 1000 leukocytes, and assuming a standard number of 8000 leukocytes/µL. Dried blood spots (DBSs) on Whatmanescribed . DNA extn Padley  and for n Padley . Venous ® system  with magnetic columns using 1 ml of serum-free blood as previously described [® software . Multiplicity of infection (MOI) was defined as the maximum number of alleles at the most polymorphic locus.Gametocyte purification was performed using a MACSescribed . Genotypescribed . LabelleAn. coluzzii, as previously described [Membrane feeding assays (MFAs) were performed using the local Ngousso colony of escribed , 30. ForP. falciparum oocyst per midgut and intensity of infection as the mean number of oocysts per infected mosquito.Fully engorged female mosquitoes were kept in the insectary under standard conditions  until dissection at 8 days post-feeding. Individual midguts were examined under a light microscope at X200 magnification for oocyst counts. Prevalence of infection was defined as the proportion of mosquitoes with at least one 2 test. Trophozoites and gametocytes densities were compared using Wilcoxon sum rank test for two group comparisons on log-transformed parasite densities and generalized linear models (GLMs) were used for multiple comparisons. P-values less than 0.05 were considered statistically significant.Statistical analyses were carried out using R (version 4.0.5). For data from the screening surveys among children, the effects of hemoglobin variant, sex, age and school were tested for parasite prevalence and infection densities. Prevalences of infection were compared using χOocyst count data are overdispersed and inflated in zeros. We then applied zero-inflated negative binomial models, as they were the best fitted to our dataset. The predictor variables fitted in the models include hemoglobin status, MOI and gametocyte densities, and the blood donor was set as a random effect to control for variation between individuals. Gametocyte densities and MOI were centered and scaled prior to modeling. In the model, the zero-inflation component contains logit coefficients for predicting excess zeros and the negative binomial regression component estimates the effect of a predictive variable on the count data. Maximum likelihood estimates were obtained using the glmmTMB package in R.P = 0.0125, Table A total of 1557 asymptomatic children aged 1–16 years were enrolled in 12 villages from the Mfou health district. Hemoglobin genotyping identified 1238 (79.51%) individuals of the HbAA genotype, 314 (20.16%) of the HbAS genotype and 5 (0.32%) were of the HbSS genotype. Given the small number of HbSS subjects, they were not included in further analyses. Males were over represented in the HbAS group . P. falciparum was the most prevalent species, present in 95.3% of the infections (1016/1066), followed by P. malariae, identified in 27.8% of the infections (297/1066). P. ovale was found in only one infection .Prevalence of as 68.7% 066/1552.P. falciparum prevalence and parasite densities according to host factors  or the participant village and data are shown in Additional file We computed results of P. falciparum infection in HbAA and HbAS individuals was similar for asexual blood stage (ABS) carriage, 66.24% (820/1238) vs 62.42% (196/314), respectively , as compared to HbAA individuals, 13.57% (168/1238)  or gametocyte stages  , CI and P-values.Estimates from the zero-inflated negative binomial GLMM were computed for the three blood feeding conditions  presented  of Mfou –33. P. fP. falciparum asexual densities did not differ significantly between the HbAS and HbAA groups, which is in agreement with other previous surveys [P. falciparum gametocytes in individuals with HbAS, which corroborates a former work from Uganda [P. falciparum gametocytes in SCT carriers [Prevalence of SCT was 20.2% in our cohort, which is in agreement with the reported range in Central Africa , 35. In  surveys , 36, 37. surveys , 37, 38.carriers , 40.The lower multiplicity of infection we found in gametocyte samples of HbAS genotypes likely results from lower MOI in asexual stages. Previous studies that investigated parasite genetic diversity in SCT carriers reported contrasting results. Gong et al. reported lower MOI in blood samples from HbAS children while Ntoumi and colleagues found a higher prevalence of polyclonal infections in SCT children , 35. DifIn this study, we found that the risk of infection was doubled for mosquitoes fed on HbAS blood when compared to those fed on HbAA blood in the serum replacement feeding condition, and this result is consistent with previous studies , 24. FurThe increased risk of infection for mosquitoes fed on HbAS blood was not evidenced in membrane feedings where naturally acquired antibodies were present (OWN). This indicates that both HbAS and HbAA individuals are capable of mounting immune responses against gametocyte infected RBCs with an equivalent transmission reducing activity. Human antibody responses to surface gametocyte proteins are known to reduce infectiousness of the sexual stage in the mosquito and this was the basis for the development of transmission blocking vaccines –49. ThusA limitation of our study is the small sample size for each of the HBB genotypes, HbAA and HbAS, used for membrane feeding assays and larger assays will be necessary to accurately determine if the HBB genotype has an impact on gametocyte infectiousness. Additionally, in our membrane feeding assays, gametocyte donor children were aged over 5 years and the observed effect may vary in other age groups. Indeed, previous studies have reported that protection of HbAS against symptomatic malaria or high density parasitemia varied with age as a result of acquired immunity in endemic areas –6, 11.P. falciparum would have developed means to increase its infectiousness for the mosquito to compensate the protection against malaria conferred by the SCT in the human host. In HbAS individuals, protection against severe malaria occurs through reduced cytoadherence capacity of infected RBCs to microvascular blood vessel endothelium and increased oxidative stress in RBCs infected by asexual parasites [Our study indicated a higher risk of mosquito infection when fed on blood from HbAS individuals and this suggests higher infectiousness of gametocytes circulating in HbAS blood. Our observation may represent a mechanism underlying host–pathogen co-evolution, whereby arasites , 50, 51.Additional file 1: Table S1. Distribution of Plasmodium infections in the cohort and according to the hemoglobin type of the children. Table S2. Parasite metrics of the participants. Table S3. Characteristics of the gametocyte donors and parameters of mosquito infection according to the feeding experiments. Table S4. Estimates of the zero-inflated negative binomial generalized linear mixed model for oocyst count in the OWN membrane feeding condition assays. Table S5. Estimates of the zero-inflated negative binomial generalized linear mixed model for oocyst count in the whole blood membrane feeding assays. Table S6. Results of the zero-inflated negative binomial generalized linear mixed model for oocyst count in the AB replacement feedings. Table S7. Results of the zero-inflated negative binomial generalized linear mixed model for oocyst count in the OWN feeding assays. Table S8. Results of the zero-inflated negative binomial generalized linear mixed model for oocyst count in the whole blood feedings.


Plasmodium vivax malaria with an 8-aminoquinoline (8-AQ) drug such as primaquine (PQ) together with a partner drug such as chloroquine (CQ) is usually explained as CQ inhibiting asexual parasites in the bloodstream and PQ acting against liver stages. However, PQ’s contribution, if any, to inactivating non-circulating, extra-hepatic asexual forms, which make up the bulk of the parasite biomass in chronic P. vivax infections, remains unclear. In this opinion article, I suggest that, considering its newly described mode of action, PQ might be doing something of which we are currently unaware.Enhanced therapeutic efficacy achieved in treating  Plasmodium vivax malaria [P. vivax malaria is far from clear. The question of what effective 8-AQ dosages are safe to use is still being researched [P. vivax malarial reinfections, recrudescences and relapses [The 8-aminoquinolines (8-AQs) primaquine (PQ)  and tafe malaria ,4,5. Thi malaria  that is  malaria , and app malaria ,10,11,12 malaria ,14,15, isearched ,17,18,19relapses .P. vivax malarial recurrences are relapses. The origin of relapse in malaria is by definition hepatic hypnozoite activation [P. vivax stages are the origin of recurrences . These tivation , I use turrences , becauseP. vivax malaria are relapses, it is nevertheless unclear to me why hypnozoites should be the origin, in many human communities, of such a large number of non-reinfection recurrences. It does seem feasible, however [P. vivax [P. vivax malaria, but rather why they would not be. I see these concealed asexual parasites as a threat to achieving the goal of eliminating malaria [Despite the general acceptance of the assertion that most non-reinfection episodes of recurrent  however ,27,28,29 however ,31. The P. vivax ,35,36,37 malaria , as is n malaria ,41,42,432O2), which can be deleterious for cells at above-physiological levels [2O2 involvement in malaria aside [2O2 apparently accumulates as a result of PQ administration is in the bone marrow [P. vivax is not readily detectable and in which it thrives [In this opinion piece, I refer to known intra-erythrocytic plasmodial parasite inactivation by the two currently used 8-AQ drugs, especially PQ. I also refer, more specifically, to the consequence of exposure of these stages to hydrogen peroxide  that exposure to H2O2 caused the parasite’s adenosine triphosphate level to drop. This was accompanied by a marked disturbance of intracellular pH regulation, namely acidification of the parasite’s cytosol and alkalinisation of the digestive vacuole. In the experimental system of Wezena et al. [P. falciparum strains 3D7 and Dd2, parasite killing required high concentrations of H2O2.As regards oxidative stress in relation to the possible mechanism of action of some compounds used for treating malaria, van Schalkwyk et al.  showed fa et al. , using P2O2 in combination with some antibiotics markedly inhibited P. falciparum parasites of the K1 strain. This led the authors to comment that “by a judicious choice of drug combinations it should be possible to obtain beneficial anti-plasmodial drug partners of otherwise non-efficacious antimalarials”. The co-administration of PQ and CQ comes to mind as a possible example hereof in some instances.Utaida et al.  determin2O2 be via damage to the intra-erythrocytic parasites, or their host cells, or both [2O2 and PQ [P. falciparum (3D7 strain) was unable to penetrate into cells that had such cytoskeletal damage and increased membrane stiffness [In the bone marrow, would an inhibitory effect of H or both ? A singl2 and PQ . Moreovetiffness .P. falciparum, P. vivax and rodent malarial parasites.Many researchers have noted enhanced anti-plasmodial activity in vivo ,84 or inP. falciparum or fresh clinical isolates have repeatedly been used to see what happens when blood stages are subjected to dual PQ or TQ and partner schizontocide exposure. Depending on the drug combination and the parasite strain, interactions in vitro have frequently been synergistic and sometimes additive or antagonistic [P. falciparum in vitro [PQ has for a long time normally been regarded as a weak blood schizontocide . Nonethegonistic ,90,91,92P. vivax in patients harbouring CQ-resistant parasites. The possibility of increased blood-stage asexual parasite inhibition has also been taken into account in other P. vivax studies where PQ was co-administered, e.g., [Baird et al.  suggested, e.g., . The mecP. vivax malaria with a combination of PQ and CQ, and both metallic and non-metallic PQ-CQ hybrid compounds have proved to have significant inhibitory effects on blood-stage Plasmodium parasites [P. falciparum.Chemical research has been carried out with a view to improving the blood-stage antimalarial efficacy of PQ , with maarasites ,103. Somarasites , inhibitP. vivax malaria is PQ + CQ. However, when CQ-resistance has been suspected, an alternative companion drug to PQ has been tried, e.g., [In practice, the most common combination treatment currently given to patients with d, e.g., .P. vivax, such as in the bone marrow [P. vivax parasites, a subject that has been raised elsewhere [The effect of 8-AQ antimalarials on non-circulating asexual stages of e marrow ,107, neelsewhere ,63. It ilsewhere  also reflsewhere .


In the Greater Mekong Subregion, case–control studies and national-level analyses have shown an association between malaria transmission and forest activities. The term ‘forest malaria’ hides the diversity of ecosystems in the GMS, which likely do not share a uniform malaria risk. To reach malaria elimination goals, it is crucial to document accurately  the influence of environmental factors on malaria to improve resource allocation and policy planning within given areas. The aim of this ecological study is to characterize the association between malaria dynamics and detailed ecological environments determined at village level over a period of several years in Kayin State, Myanmar.We characterized malaria incidence profiles at village scale based on intra- and inter-annual variations in amplitude, seasonality, and trend over 4 years (2016–2020). Environment was described independently of village localization by overlaying a 2-km hexagonal grid over the region. Specifically, hierarchical classification on principal components, using remote sensing data of high spatial resolution, was used to assign a landscape and a climate type to each grid cell. We used conditional inference trees and random forests to study the association between the malaria incidence profile of each village, climate and landscape. Finally, we constructed eco-epidemiological zones to stratify and map malaria risk in the region by summarizing incidence and environment association information.n = 19) corresponding to a gradient from pristine to highly anthropogenically modified landscapes. Within this diversity of landscapes, only three were associated with malaria-affected profiles. These landscapes were composed of a mosaic of dense and sparse forest fragmented by small agricultural patches. A single climate with moderate rainfall and a temperature range suitable for mosquito presence was also associated with malaria-affected profiles. Based on these environmental associations, we identified three eco-epidemiological zones marked by later persistence of Plasmodium falciparum, high Plasmodium vivax incidence after 2018, or a seasonality pattern in the rainy season.We identified a high diversity of landscapes (The term forest malaria covers a multitude of contexts of malaria persistence, dynamics and populations at risk. Intervention planning and surveillance could benefit from consideration of the diversity of landscapes to focus on those specifically associated with malaria transmission.The online version contains supplementary material available at 10.1186/s13071-023-05915-w. Plasmodium falciparum to prevent the spread of multidrug-resistant parasite lineages [The Greater Mekong Subregion (GMS) has made progress towards malaria elimination during the past decades. Between 2000 and 2016, the number of malaria cases declined sevenfold, dropping from approximately 2,400,000 cases to 325,000 due to improvements in early access to diagnosis and treatment, and targeted interventions for the most affected populations (i.e. migrant workers and displaced persons) [In the GMS, studies based on aggregated surveillance data associated malaria transmission with forested areas , 6. IndiMalaria transmission requires the presence of specific vectors and a sufficient density of human hosts over long enough periods of time to allow the parasite to complete its life cycle. It is therefore likely that the risk of malaria is not uniform across different types of forested ecosystems in the GMS. Activities related to these ecosystems  expose individuals to diverse environments, at different times.As the incidence of malaria continues to decrease, the targeting of programmed or reactive interventions becomes increasingly relevant. According to the World Health Organization guidelines, to be most effective, interventions must be guided by malaria stratification, taking into account the intensity of transmission and the receptivity of ecosystems . MalariaWithin the GMS, Myanmar accounts for 90% of malaria cases and remains the country with the highest malaria burden. In 2016, the countrywide incidence of malaria reached 0.45 cases per 1000 individuals at risk. However, Myanmar exhibits high regional and local spatial heterogeneity, with local foci of high malaria incidence persisting in mountainous-forest borderlands, such as Karen/Kayin State . In respKaren/Kayin State is largely covered by the north–south-oriented, forested, Dawna range, which reaches an altitude of 2080 m above sea level. Settlements are located on the slopes and in the valleys  of this mountain range. The density of villages is higher on the plain to the west of the mountains .P. falciparum. We use the term ‘METF region’ to represent the area where the program was implemented, which covers four townships in Karen/Kayin State: Myawaddy, Kawkareik, Hlaingbwe and Hpapun [Plasmodium falciparum-Plasmodium vivax rapid diagnostic test (SD Bioline Ag P.f./P.v.) at the MPs, which send weekly reports specifying the number of diagnosed cases, by age group and gender, of P. falciparum and P. vivax to METF [P. falciparum was 39 cases per 1000 person-years, while the incidence of P. vivax was 63 cases per 1000 person-years [P. falciparum and P. vivax in the four townships. Specifically, P. falciparum incidence decreased by 97% (1 case per 1000 person-years), and P. vivax incidence decreased by 71% (18.5 cases per 1000 person-years) in the period from May 2019 to April 2020.The Malaria Elimination Task Force (METF) program was initiated in 2014 in this region to eliminate artemisinin-resistant d Hpapun . This pr to METF . In addi to METF , 13. Duron-years . FollowiP. falciparum and P. vivax), MDA, climate and distance between villages and landscapes at the METF region scale and at the Northern Township scale. Based on this association study, we established a map of eco-epidemiological zones in the METF region, i.e. described climate and landscapes that likely drive local heterogeneity in malaria dynamics.The first aim of this ecological study was to describe ecological landscapes and climate diversity in Karen/Kayin State. We described forest landscapes at the METF region level. We then focused on a single township  where malaria incidence was highest . Second,P. falciparum and P. vivax weekly incidence reported by MPs, villages sharing similar dynamics were grouped in P. falciparum and P. vivax ‘incidence profiles’. Incidence profiles were determined by partitioning around medoids clustering combined with dynamic time warping distance following functional time series smoothing. The methodology is described in this earlier study [This study was based on previous work that described malaria incidence heterogeneity in Karen/Kayin State from March 2016 to February 2020 for 662 villages . Based oer study .P. falciparum and 11 P. vivax incidence profiles were identified in this previous work [Plasmodium falciparum incidence profiles distinguished villages by the amplitude of the incidence  and the occurrence of seasonal sporadic annual peaks . Plasmodium vivax incidence profiles also separated villages according to incidence amplitude , but also by intra-annual patterns with one or two annual peaks, their seasonality and tendency   ‘malaria-affected incidence profiles’.Eleven ous work  and are n = 123 for P. falciparum and n = 205 for P. vivax) were grouped in the Northern Township of Karen/Kayin State, and along the Thailand-Myanmar border for P. vivax  for day and night land surface temperature  and Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data (UCSB-CHG/CHIRPS/DAILY) for daily rainfall  , 16. TheTo characterize landscapes in detail, we produced a land use land cover (LULC) map for the entire METF target region based on Sentinel-2 satellite images from 2019 to 2020 using object-based image analysis with eCognition software. These images have high spatial (10–60 m) and temporal resolutions (~ 5 days). In order to validate the accuracy of the LULC, the METF field team described LULC of 300 sites which were used as ground truth data. We completed the coverage provided by these observations with 300 photo-interpreted points from Google Earth . We extracted elevation and slope data of the METF target region from the GMTED 2010 digital elevation model  . We usedP. vivax incidence and a long-term reduction of P. falciparum incidence [P. vivax, we considered MDA campaigns undertaken during the study period (i.e. from March 2016 to February 2020). For P. falciparum, we considered MDA campaigns undertaken before the study period to distinguish villages that may have been at high risk before the study period. We did not consider the MDA during the study period for P. falciparum because it was implemented in villages with high prevalence, and thus was associated with high incidence [MDA interventions were conducted in 69 villages as part of the METF program; these were carried out in 35 villages before March 2016 and in 34 villages during the study period  that limits CART bias in covariate selection [quad test, distribution estimation by Monte Carlo method with 100,000 replications, 0.95 = 1—α as splitting criteria value of statistic test, and no minimum of node sample size. The CIT method provides an interpretable tree for covariate effects and interaction. We also used CRF to quantify covariate importance. We used the following CRF parameters: 10,000 trees, and number of covariates randomly selected for each tree equal to the minimum of the number of covariates included divided by 2 [We used conditional inference trees (CIT) and conditional random forests (CRF) to explore the association between election . We usedded by 2 , 23.P. falciparum and P. vivax.Both mosquito dispersion and human mobility occur within a limited radius around the Karen villages included in this study , 25. We P. falciparum and P. vivax incidence profiles obtained with CIT and confirmed by CRF. Specifically, we crossed CIT nodes regrouping malaria-affected incidence profiles and defined by their proximity to an environmental variable. Successive crossing tables of selected nodes defined eco-epidemiological zones as follows: 1/region vs. township scale for P. falciparum; 2/region vs. township scale for P. vivax; 3/P. falciparum vs. P. vivax.Eco-epidemiological zones were defined based on variables significantly associated with P. falciparum and 79% (161/205) by P. vivax. Midland climate presented moderate rainfall  and temperatures (24 °C on average)  and precipitation . The High mountain climate had the lowest precipitation and temperature, the West plain climate the highest precipitation, and the Foothills climate the highest temperature. Villages with malaria-affected incidence profiles predominantly belonged to the Midland climate category: 92% (113/123) of villages affected by The final LULC classification produced from Sentinel-2 images from 2019 to 2020 includes the following 10 classes: dense forest, sparse forest, plantation, cropland, grass/shrubland, bare soil, wetland, road, water, built-up. The comparison with the observed field points indicated a satisfactory classification with a Cohen's kappa coefficient of 0.73 . Confusion in the classification mainly arises between bare ground and crops, or between herbaceous areas and scattered woodland, which can be confounded depending on the date when the data were recorded. Weaker agreement with the data recorded in the field can be explained by the fact that these were often recorded at points on the side of roads, and therefore near other landscapes, and not at the centre of the landscapes they represented. In total, forested areas represent 76% of the surface area, and up to 78% if plantations and orchards are included. The Northern Township presented the most important surface area of dense forest (45%) compared to the rest of the METF region (12.5%). During the study period, no large-scale forest loss was detected in the METF region that drastically affected forest cover . CroplanWe merged water and wetland classes and excluded roads to obtain eight LULC classes. By analysing the percentage of surface covered by each class, fragmentation indices, slope and altitude, we could classify the entire region into 17 landscapes. These landscapes were diverse, with gradients of sparse and dense forest (up to 90% forest), altitude (averaging 17–1020 m above mean sea level) and with multiple combinations of cropland (0–96.5%), grassland/shrubland, or plantation  or grassland/shrubland   and the highest proportion of patches of sparse forest  interspersed with cropland and grassland/shrubland.Likewise, seven landscapes were retained for analysis in the Northern Township    and for P. vivax (P < 0.001)  Fig. . When li01) Fig. . CRF ideP. falciparum and P. vivax with a 10 km cut-off  was significantly associated with malaria-affected profiles for P. vivax in the analysis with a 15-km cut-off  with a secondary climate-related division which distinguished only 11 villages located outside, but near, the Midland climate (P < 0.001)  Fig. a, b.P. vivax, a second landscape, DSG, was also associated with malaria-affected incidence profiles   with an increasing P. vivax incidence. CRF confirmed the importance of DSC and DSG landscapes. Subdivision by DSG landscape was significant according to first-order risk of 0.05 (P = 0.048), but was not confirmed by the sensitivity analysis  produced seven eco-epidemiological zones . Plasmodium falciparum incidence decreased after 2018 and P. vivax incidence remained stable, without clear seasonality. In addition, 10 adjacent villages sharing the same landscape and the same malaria dynamics were identified as a separate zone (zone 2) because of their warmer climate (Foothills). Zones 1 and 2 also stood out due to their high proportion of malaria cases among children under 5 years of age compared to other zones   of DSC landscape defined the highest environmental risk zone zone 1) and included 129 villages of the Northern Township.  and inclPlasmodium falciparum incidence was low and P. vivax incidence exhibited a strong persistent increase in 2018. Out of the 82 villages in this zone, 32 (39%) showed Low increasing, 15 (18%) Rainy increasing, five (6%) 2018 increasing and nine (11%) Persistent  incidence profiles, geographically distributed throughout the zone.The next zone, zone 3, included 82 villages of the Northern Township located in Midland climate, away from DSC (> 2.5 km) and near DSG (< 7.5 km). P. falciparum incidence and highly seasonal P. vivax incidence during the rainy season. The stable overall mean incidence does not reflect the opposing trends of P. vivax incidence profiles included in this zone (i.e. Rainy decreasing or Persistent profile). This zone differed from the others in having the highest proportion of P. vivax cases in males over the age of 15 years (39.5%), and the lowest proportion of P. vivax cases in children (9.8%) . These 24 villages were located near SG landscape and exhibited very low The three remaining zones were only located in the South midland climate, Midland climate and/or were sufficiently far away from at-risk landscapes. They presented low, very low and zero malaria incidence, respectively Fig. .P. falciparum, up until 2018; another zone showed high P. vivax incidence after 2018; and the third displayed stable seasonal P. vivax incidence without P. falciparum.This geo-epidemiological study identified a high diversity of landscapes within the METF region corresponding to a gradient from intact (i.e. dense forest) to heavily human-modified landscapes, characterized by different types of agriculture. Within this diversity of landscapes, three landscapes  and a single climate were associated with malaria-affected profiles. Three eco-epidemiological zones with contrasting dynamics were identified by combining these environmental risk factors. One of these zones exhibited longer persistence of One major climate (Midland climate) was linked to malaria-affected profiles irrespective of species or spatial scale. This climate presented optimal conditions for mosquito and parasite development, according to the literature: moderate rainfall between April and October, an annual mean temperature between 18 and 32 °C, and limited temperature variation , 28.Residing in villages at the forest fringe or inside forested areas was identified as a risk factor for malaria infection in the GMS . Forest  Anopheles-favourable environments in nearby forest [In the Northern Township, DSC and DSG landscapes were associated with malaria-affected incidence profiles. They stood out due to their high proportion of forest (≈90%) fragmented by patches of cropland or grassland/shrubland. Another landscape  that was not associated with incidence profile, was highly fragmented but had a higher cover of cropland (32%) and a lower proportion of forest (58%), especially dense forest (20%). The other landscapes had the same proportion of forest cover, but the forest was less fragmented by agricultural patches. In the literature, the role of forest fragmentation in malaria transmission, specifically with respect to the type and proportion of forest or agriculture, remains unclear –33. Thesy forest –36.P. falciparum persistence and stable P. vivax incidence (zone 1) corresponded to cropland located in broad valley bottoms, indicative of inundated rice paddies. Shorter P. falciparum persistence and increasing P. vivax incidence (zone 3) corresponded to patches of grassland/shrubland located on slopes, a pattern matching traditional Karen taung yar agriculture, a rotational farming, 10-year cycle which is practised on slopes [In addition, we identified specific dynamics in relationships with specific agricultural types. Long n slopes . This suIn the Northern Township, the persistent malaria burden in children under the age of 5 years suggests that transmission may still take place within villages, while the lower incidence in adults may be explained by partial immunity.P. vivax cases in adult males and few P. falciparum cases. Two factors may have contributed to this unique dynamic. First, the predominance of P. vivax over P. falciparum likely resulted from longer access to early diagnosis and treatment for malaria, which depletes the P. falciparum reservoir faster than that of P. vivax [In the southeast border area of the study region, eco-epidemiological zone 4 stood out due to its proximity to the SG landscape associated with highly seasonal P. vivax , 12. SecP. vivax . The exaPlasmodium falciparum and P. vivax dynamics could also be studied in parallel or combined. This was especially relevant as METF interventions were targeted at P. falciparum and had less impact on P. vivax, leading to different persistence patterns.Using the unique dataset of weekly malaria case reports collected by the METF MP network, we were able to study the association between malaria incidence and forested environments in unprecedented detail. This dataset allowed the study of malaria incidence at village scale without requiring further spatial aggregation. Compared to the annual parasite index, incidence profiles allowed the temporal characterization of intra- and inter-annual variation in amplitude, seasonality, and tendency over a 4-year period. They enabled us to identify villages that were long-term hotspots or showed malaria persistence. In addition, malaria transmission through mosquito bites can occur within villages or in remote locations where there is human activity and the precise location of infection is usually unknown. It is therefore difficult to estimate the distance between households and transmission sites, as this depends on the particular activity carried out , geographical accessibility  and means of travel. To overcome this, we described the climate and landscape over the entire region regardless of the location of the villages. We also relied on high-resolution land use and land cover data, and used fragmentation indices, which are key determinants of landscapes at risk for malaria in the Northern Township. Fragmentation indices are important for the characterization of interfaces between types of natural environment, such as forest, and human-modified environment (e.g. cropland), and where the probability of contact between vectors and humans is higher.To study the association between malaria incidence profiles and environment, we chose to use a tree-based model and random forests to overcome intrinsic issues associated with regression models, i.e. small sample size for some incidence profiles, unknown distribution, and potential correlation or interaction between covariates (i.e. climate and landscape).P. falciparum incidence profiles [This analysis ultimately identified eco-epidemiological zones, which included villages within a similar risk area but with heterogeneous malaria incidence profiles. Beyond following World Health Organization recommendations concerning malaria risk stratification (i.e. based on transmission and receptivity), this approach is of interest in an elimination context where outbreaks are more stochastic, as illustrated by profiles . These rP. falciparum, and 69% a very low incidence profile for P. vivax. The other 10 profiles only corresponded to a small number of villages (from one to 46). The imbalance in these distributions limited the subsequent analysis of factors associated with each incidence profile and the use of regression models. The CIT analysis mostly distinguished malaria-affected from malaria-free incidence profiles, except for P. vivax incidence in the Northern Township. However, by combining environmental factors associated with both Plasmodium species, we were able to describe sub-regional eco-epidemiological zones, which corresponded to specific local environments and were characterized by specific trends and at-risk populations. These results suggested a relationship between malaria dynamics and environment at a spatial scale higher than that of the village. These findings agree with previous reports of P. falciparum prevalence hotspots clustering within a radius of 10 km in this region [The first challenge in this study was the overall low malaria incidence in the METF region over the study period. Indeed, 81% of the villages were classified as having a very low incidence profile for s region .P. vivax infections resulting from an infective bite from those due to a relapse. This could have resulted in an overestimation of P. vivax transmission, or inability to identify some seasonal patterns (e.g. zone 1). However, in the analysis of 1441 recurrent episodes pooled from two trials conducted in the same setting, 95% of P. vivax recurrences (88% relapses and 12% reinfections) were detected upon active follow-up, while only 5% were associated with sufficient symptoms to trigger an intercurrent consultation [We relied on the incidence of clinical cases of malaria diagnosed by MP as a proxy of malaria transmission. This assumption likely led to the underestimation of transmission, since individuals with sufficient immunity will not necessarily develop clinical malaria episode upon infection. On the other hand, using the incidence of clinical cases of malaria to determine transmission, we could not distinguish ultation . In thesFinally, even if the LULC classification allowed a 10-m resolution, validated with field observations, there were still some classes that were difficult to distinguish, i.e. roads from rivers, and agricultural patches from low vegetation. These elements of the environment may modulate vector presence and thus the association of environmental factors with incidence profiles. In addition, this study focused on land cover patterns, such as fragmentation and vegetation density. Identifying differences in floristic composition across forested areas was not feasible here, but could allow for further characterization of forest heterogeneity.The aims of this study of malaria dynamics at the local level were to improve our understanding of the epidemiology of this disease in an increasingly heterogeneous context and to contribute to intervention planning. The analysis identified eco-epidemiological zones with higher malaria incidence where specific interventions could be targeted . These Health systems often rely on the collection of aggregated surveillance data . This study demonstrates how rich surveillance data could inform an understanding of malaria epidemiology and intervention planning, especially in a highly heterogeneous context. Our results support the current trend of investing in state-of-the-art epidemiological information systems. However, the size of the eco-epidemiological zones reported here suggest that analyses performed at the scale of a health facility catchment area (e.g. a dozen villages) could yield relevant results for larger regions or country-wide studies.Anopheles presence and determine proxies of human exposure. Combining existing lower resolution LULC with other remotely sensed data sources  could provide community-level assessments of receptivity and human activities in or at the fringe of forested areas [Additionally, eco-epidemiological zones were characterized by differences in climate, landscape, and agricultural systems, which are biologically relevant factors that may be applicable to other forested areas. Studying structural environmental factors at a broader scale in the GMS could help identify areas favourable to ed areas .This study shows that the generic term ‘forest’ hides a substantial diversity of environments, which can differ with respect to malaria persistence, dynamics and populations at risk. This diversity highlights the fact that there is no one-size-fits-all approach for forest malaria with respect to policy planning, modelling approaches and implementation. The existence of diverse human populations within heterogeneous eco-epidemiological settings should be taken into consideration when designing malaria elimination strategies.Additional file 1: Figure S1. Maps showing incidence profiles. Mapped villages in the malaria elimination task force (METF) target region according to malaria incidence profiles. A Plasmodium falciparum incidence profiles and B Plasmodium vivax incidence profiles; inset shows a map of the Hpapun/Mutraw administrative township (Northern Township). This original figure presents raw data and results published previously [ Method S1. Landscape. Figure S2. Villages receiving mass drug administration (MDA) according to the year. MDAs were conducted in high prevalence hotspots identified by prevalence surveys [Figure S3. Climate description. Ombrothermic diagram of the five climates identified at region scale. Temperature data of June, July and August were excluded from the analysis because of too many missing data points due to cloud cover in the rainy season (dotted lines). Rainfall data of November, December, January, February and March, corresponding to the cold dry season, were also excluded because of the occurrence of rare, local and intense thunderstorms. Figure S4. Land use and land cover classification description. A Map of the land use and land cover (LULC) classification. B Distribution of the LULC class at the region scale and the Northern township scale. Figure S5. Landscape map at region scale. A Landscape map. B Altitude map. Figure S6. Landscape description at region scale. LULC description of landscapes representing more than 1% of the region surface, included in association analysis (A) or excluded (B). C Description of landscapes identified at region scale according to altitude. Figure S7. Covariates selection. Landscapes were selected according to their median distance to villages at region (A) and township (B) scale. Landscapes with a median ≤ 10 km (red dotted and solid lines) were included. Sensitive analysis was conducted with cut-offs of 5 km (grey dotted line) and 15 km (blue dotted and solid lines). Landscapes selected with the 10-km and 5-km cut-offs were similar. Figure S8. Landscapes in the Northern Township. A Description of the two landscapes specifically identified in the Northern Township according to the LULC classification. The other landscapes were also identified at region scale and described in figure S5. B Map of the most common landscapes identified at the township scale. Figure S9. Study association between malaria incidence profiles and environment in the METF region by conditional random forests (CRF). A 10-km cut-off was applied to selected landscape covariates. A CRF of Plasmodium falciparum incidence profiles depending on landscapes, climates, and MDA before March 2016. B CRF of Plasmodium vivax incidence profiles depending on landscapes, climates, and MDA after February 2016. Figure S10. Study association between malaria incidence profiles and environment in the Northern Township by CRF. A 10-km cut-off was applied to select landscape covariates. A CRF of P. falciparum incidence profiles depending on landscapes, climates, and MDA before March 2016. B CRF of P. vivax incidence profiles depending on landscapes, climates, and MDA after February 2016. Figure S11. Association study between malaria incidence profiles and environment in METF region by conditional inference trees (CIT). A 15-km cut-off was applied to select landscape covariates. A CIT of P. falciparum incidence profiles depending on landscapes, climates, and MDA before March 2016. B Location of villages depending on their nodes obtained with P. falciparum CIT. C CIT of P. vivax incidence profiles depending on landscapes, climates, and MDA after February 2016. D Location of villages depending on their nodes obtained with P. vivax CIT. In CIT, ‘node’ refers to a group of villages. Figure S12. Association study between malaria incidence profiles and environment in METF region by CRF. A 15-km cut-off was applied to select landscape covariates. A CRF of P. falciparum incidence profiles depending on landscapes, climates, and MDA before March 2016. B CRF of P. vivax incidence profiles depending on landscapes, climates, and MDA after February 2016. Figure S13. Association study between malaria incidence profiles and environment in the Northern Township by CIT. A 15-km cut-off was applied to select landscape covariates. A CIT of P. falciparum incidence profiles depending on landscapes, climates, and MDA before March 2016. B Location of villages depending on their nodes obtained with P. falciparum CIT. C CIT of P. vivax incidence profiles depending on landscapes, climates, and MDA after February 2016. D Location of villages depending on their nodes obtained with P. vivax CIT. In CIT, ‘node’ refers to a group of villages. Figure S14. Association study between malaria incidence profiles and environment in the Northern Township by CRF. A 15-km cut-off was applied to select landscape covariates. A CRF of P. falciparum incidence profiles depending on landscapes, climates, and MDA before March 2016. B CRF of P. vivax incidence profiles depending on landscapes, climates, and MDA after February 2016. Figure S15. Description of eco-epidemiological zones according to age and gender malaria cases. Proportion of reported P. falciparum (A) and P. vivax (B) cases during the study period by malaria posts depending on gender , age, and eco-epidemiological zones. The total number of malaria cases is specified in the legend. Five classes described cases by age and gender: woman and men between 0 and 5 years (W/M 0-5), women between 5 and 15 years (W 5-15), women older than 15 years (W 15-99), men between 5 and 15 years (M 5-15), men older than 15 years (M 15-99). Table S1. Eco-epidemiological zone construction. Tables present the number of villages in each node and proportions in brackets. A Plasmodium falciparum zone construction by cross analysis between CIT results at region and township scale. B Plasmodium vivax zone construction by cross analysis between CIT results at region and township scale. C Cross analysis between P. falciparum and P. vivax. DSC Dense forest, sparse forest, cropland landscape; DSG dense forest, sparse forest, grassland landscape; SG sparse forest, grassland landscape; M midland climate; SM south midland climate; N node.eviously . Method  surveys . At the  surveys . In addi


Malaria remains the leading cause of acute febrile illness (AFI) in Africa despite successful control measures and programs. Acute febrile illnesses can be misdiagnosed as malaria as a result of the overlapping spectrum of nonspecific symptoms or may not be pursued because of limited diagnostic capabilities. This study investigated potential etiologies of AFIs in Ghana and determined the relationship between coinfection between malaria and Q fever, leptospirosis, and culturable bacteria in febrile patients. Participants were enrolled between July 2015 and December 2019 from four Ghanaian military treatment facilities. Of the 399 febrile participants, 222 (55.6%) males and 177 (44.6%) females were enrolled. Malaria was diagnosed in 275 (68.9%) participants. Malaria coinfection occurred with leptospirosis, Q fever, and blood-cultured bacteria in 11/206 (5.3%), 24/206 (11.7%), and 6/164 (3.7%) participants, respectively. Among the 124 malaria-negative samples, the positivity rates were 4.1% (3/74), 8.1% (6/74), and 3.6% (2/56) for leptospirosis, Q fever, and bacterial pathogens isolated from blood culture, respectively. The majority of documented clinical signs and symptoms were not significantly associated with specific diseases. Approximately 10% of malaria-positive participants also had evidence suggesting the presence of a bacterial coinfection. Therefore, even in the case of a positive malaria test, other pathogens contributing to febrile illness should be considered. Understanding the frequency of malaria coinfection and other etiological agents responsible for AFIs will improve diagnosis and treatment and better inform public health knowledge gaps in Ghana. Malaria often presents with nonspecific symptoms and can be difficult to distinguish clinically owing to limited diagnostic capabilities and resources in low- to middle-income countries.Salmonella and Salmonella typhi.Improved control measures have significantly decreased the worldwide incidence of malaria,–Bacterial diseases such as Q fever and leptospirosis are known to cause AFI syndromes and have been reported in humans, livestock, and rodents in Ghana,This was a cross-sectional study that enrolled febrile patients from inpatient and outpatient settings in Ghana. The study was conducted from July 2015 to December 2019 at the 37 Military Hospital located in Accra and three military clinics in Sekondi-Takoradi. The clinics included the Western Naval Command Medical Center in Sekondi, as well as the 2 Medical Reception Station (2MRS Armed Forces clinic) and the Air Force Medical Center in Takoradi. These clinics in the Western Region of Ghana serve primarily the military and their families, but also civilians.Patients between 1 month and 65 years old presenting with a documented or self-reported fever (≥ 38°C or history of fever within the last 24 hours) without a localizing etiology  were eligible for enrollment. All patients with AFIs seeking medical care at the medical centers were screened for eligibility to participate in the study. The informed consent process was completed for all eligible individuals. For participants less than 18 years old, both child assent and parental permission were obtained. Questionnaires were administered to capture demographic and clinical data.Ethical approval was obtained from the institutional review boards of the Ghana Health Service (GHS-ERC 14/05/13), Ghana Armed Forces (GAF-IRB 002/13), Noguchi Memorial Institute for Medical Research (NMIMR-IRB 126/12-13), and the U.S. Naval Medical Research Center (NMRC-IRB NAMRU3.2014.0003). The study protocol was approved by the aforementioned institutional review board in compliance with all applicable federal regulations governing the protection of human subjects.g for 15 minutes to obtain sera. One to three milliliters was placed in the BACTEC bottles for bacteria culture, along with two to three drops (approximately 60 μL) of EMJH medium for leptospiral culture. At each site, all blood samples were tested using the First Response Malaria Ag PLDH/HRP2 Combo rapid test kit  to diagnose malaria. As recommended by the manufacturer, 5 μL of whole blood was transferred into the sample collection well, and about 60 μL of assay buffer was dropped into the assay buffer well. The result was read 20 minutes after the specimen and buffer were added to the test device. A sample was interpreted as positive when two color bands appeared at Control line “C” and the test line “P.f” or “PAN.” The kit has a sensitivity and specificity of 100% and 98.1%, respectively (as stated in the packet insert). Sera, whole blood and BACTEC samples were then transferred to the Naval Medical Research Unit-3 (NAMRU-3) laboratory at Noguchi Memorial Institute for Medical Research located in Accra, Ghana, for bacterial culture, serology, or polymerase chain reaction (PCR) (Blood samples were collected via needle prick (1 month to 4 years) or venipuncture (5 years and above) from all participants. The volume of blood collected was age dependent but did not exceed 10 mL. At the study sites, a specimen of 1 to 10 mL of blood was collected from study participants based on age group  and put on (PCR) . Follow-,Leptospira Antibody (IgM and IgG) ELISA kit  was used for the qualitative determination of human Leptospira antibody concentrations in serum. All reagents in the kit and frozen serum samples were brought to room temperature and concentrated wash buffer (20×) was diluted to 1×. The serum samples were first diluted with the sample diluent and absorbent as instructed by manufacturers before they were added into the microtube strips (eight strips), which were pre-coated with Leptospira antigens. Sample dilution preparation for the IgM and IgG ELISA was done by making a 40-fold dilution with the sample diluent. This was achieved by adding 10 μL of sample in 390 μL of sample diluent. For IgM, 100 μL of diluted sample was added to 40 μL of sample absorbent to complete the sample dilution process. All assays were run with positive, negative, and reference (only for IgM) sera, which were provided in the kit. The controls and each sample were read using a microplate reader  at a wavelength of 450 nm. As recommended by manufacturers, to calculate the human Leptospira antibody (IgM and IgG), the optical density (OD) value of the samples was compared with that of the controls. An OD value less than 0.2 was considered negative, whereas an OD value greater than or equal to 0.5 was considered positive. Samples were retested when their OD value was between 0.2 and 0.5. The assay has a high sensitivity and excellent specificity for the detection of human Leptospira antibodies (IgM and IgG).An ELISA was performed using commercially available kitsCoxiella burnetii (Q fever) in the serum was conducted using the Abcam  Anti–Coxiella burnetii Phase 2 IgM and IgG ELISA kit. Before the reagents and serum samples were used for the assay, they were equilibrated to room temperature. Sample preparation involved making a 1:100 dilution in provided sample dilution buffer, and concentrated wash buffer (20×) was diluted to 1×. After sample dilution was completed, 100 μL of the diluted sample was added to the microtube plate pre-coated with Anti–Coxiella burnetii Phase 2 IgM/IgG. Coxiella burnetii IgM and IgG controls  were included in duplicate for each assay performed to determine the test result. Downstream analysis was performed according to the manufacturer’s instructions. The controls and each sample were read using a microplate reader (BioTek EL 808 microplate reader) at the wavelength of 450 nm. As recommended by the manufacturer, certain criteria needed to be met for a test to be considered valid. A blank had to be < 0.100, negative control was < 0.200 and less than cutoff, cutoff was control 0.150–1.300, and positive control was greater than cutoff control. To estimate the optical density (OD) value of each sample was first calculated and used to calculate the result of each sample. To calculate the result in a standard unit, we used the following equation:The qualitative measurement of IgM and IgG antibodies against A calculated standard unit of < 9 was considered negative and > 11 was considered positive. The kit has a sensitivity and specificity of > 90.BACTEC bottles containing blood specimen were incubated overnight for 18 to 24 hours at 35–37°C. The cultured blood sample was collected aseptically by disinfecting the surface of the BACTEC bottle with 70% alcohol prep-pad and using a sterile 1-mL syringe with a thin needle and dropping it on the surface of blood, chocolate, and MacConkey agar and allowing it to dry. The plates were then streaked using a sterile 1-μL inoculating loop and incubated at 35–37°C for 18 to 24 hours. Grown bacterial isolates were identified by colony morphology, Gram staining, biochemical tests , and Analytical Profile index  based on the manufacturer’s instructions and standard procedures.The DNA was isolated from blood embedded on a Whatman protein saver card  using the  blood DNA mini kit following manufacturer’s instructions.1111a region of C. burnetii was used to detect C. burnetii in collected samples.ligA and B genes of Leptospira. Conventional PCR  was performed using the PCR core kit  and primers as described by Palaniappan et al.Real-time PCR targeting the IS2 and Fisher’s exact tests were used to determine significant associations between the presenting symptoms and laboratory test results (ELISA and culture) stratified by malaria status.All statistical analyses were conducted using R 3.3.1 software package. The χMultiple correspondence analysis (MCA) was used to identify data patterns and associations between variables to provide a graphical visualization between active variables (symptoms and sociodemographic characteristics) and supplementary variables (infection status). The infection status consisted of three levels, namely bacterial infection, malaria, and coinfection , which were used as the dependent variables. The number of dimensions maintained in the final model was determined using the scree plot.Supplemental Table 1). Less than half of the patients (48.2%) were younger than 18 years, and 29.8% were younger than 5 years . In addition to fever, other reported symptoms are listed in Supplemental Table 1, with the most common being headache , chills , joint pain , and muscle pain . The commonest clinical diagnoses were malaria  and respiratory tract infection .A total of 399 participants were enrolled; 222 (55.6%) were male and 177 (44.4%) were female (N = 399) were tested for malaria using rapid diagnostic test (RDT) and for leptospirosis and Q fever using PCR. Serological testing was done on 280 samples using serology (participants > 5 years old), and culture was performed on 220 samples (participants > 10 years old because of blood volume limitation) (All samples (itation) . Malariaitation) . All PCRS. typhi and 1 (0.5%) each for Salmonella spp., Staphylococcus aureus, and Bacillus cereus  for Q fever and leptospirosis, respectively . Of the s cereus . Malarias cereus . Among ts cereus . A totals cereus . PolymerSupplemental Table 2, Supplemental Figure 1). Variables defining the first dimension were mainly associated with or without sore throat and joint and muscle pain. The most discriminant variables for dimension 2 were age, with or without muscle and joint pain. Variables that were mainly associated with dimension 3 were age, with or without headache, retro-orbital pain, and conjunctivitis.The first three MCA dimensions explain approximately 35% of the variability among febrile patients with an infection  as compared to sore throat, muscle pain, and joint pain.The clinical presentation of fever without any other symptoms did not correlate specifically to any of the infections based upon its close proximity to the origin in the MCA analysis, suggesting it cannot be used to distinguish between malaria, bacterial infections, and coinfections , althoug,C. burnetii, and 3.6% bacteremia in patients who were malaria negative, identifying these pathogens as contributing to the burden of nonspecific febrile cases in Ghana.Although the WHO reports a global decline in malaria since 2010, it remains a significant problem in sub-Saharan Africa. Despite the positive impact of presidents malaria initiative (PMI) and other malaria initiatives, Ghana—a country with one of the highest burdens of malaria—reported an increased incidence of malaria cases in 2018.The overall seropositivity against Q fever antibody was 10.7%, similar to 9.6% IgM seropositivity reported in a febrile study in Mali.Seroconversion of the initial and follow-up serum samples was seen in both Q fever and leptospirosis infections. Nine serum samples that were initially negative for acute testing were found to be positive (IgM + IgG−) during follow-up testing for Q fever, adding up to the three serum samples that were positive in the initial testing. Likewise, for leptospirosis, seven serum samples were positive (IgM+ IgG−) for the initial testing, one serum sample seroconverted from IgM+ IgG− to IgM− IgG+, with one serum sample remaining IgM− IgG+, confirming that IgG can remain in the bloodstream for a long time. This finding confirms the need for paired sera testing because it is known to take about 7 to 15 days or more after the onset of illness for seroconversionCoxiella burnetii and Leptospira DNA were not detected by PCR; however, antibodies were detected by ELISA. Even though serology alone may not prove infection with Q fever and leptospirosis, it has been documented that as antibodies build, nucleic acid detection decreases to levels below the PCR detection limit.S. typhi and 0.5% of Salmonella spp., S. aureus, and B. cereus each were identified , unlike a study in Nigeria that reported the highest prevalence of Salmonella infections was found in young adults.Our findings suggest that patients 5 years of age and older irrespective of having muscle and joint pain correlated with malaria infection rather than malarial-bacterial coinfection. Njeru and othersOur limited findings in this austere environment demonstrate the need for more comprehensive diagnostic modalities—ones that complement the findings of the clinical exam—to better understand the multiple etiologies contributing to AFIs in tropical environments such as Ghana and West Africa. Continuous febrile illness surveillance can help inform local epidemiology and febrile illness management and will be essential for the detection of disease outbreaks.,n = 71) compared with Takoradi (n = 328).This study was subject to limitations that are associated with using commercial ELISA kits and malaria RDT kits. These are prone to cross-reactivity among pathogens. Acquiring follow-up samples for paired testing was a challenge, as mentioned in other febrile illness research.Although positive rapid diagnostic tests for malaria are common among Ghanaians from urban settings presenting with fever, about 10% of participants have evidence suggesting at least one other bacterial cause of AFI. Therefore, additional etiologies for fever should be considered when patients present with AFI, even in the setting of a positive malaria test. In general, clinical signs and symptoms were not significantly associated with the presence of coinfections, but retro-orbital pain, conjunctivitis, joint pain, and muscle pain were more likely among those who had a positive malaria rapid diagnostic test and lacked evidence of coinfections.This study highlights that bloodstream infections, leptospirosis, and Q fever could contribute to AFIs in Ghana. We report that coinfections with these pathogens and malaria could range from 3–11% among febrile patients in Ghana. We also found that fever without any other symptoms could not be used to accurately distinguish between malaria, bacterial infections, and coinfections. Symptoms associated with malaria included retro-orbital pain, conjunctivitis, and joint and muscle pain, whereas malaria coinfection with Q fever or leptospirosis was associated with headaches and sore throat. Laboratory-confirmed bacterial diagnosis of AFIs should be improved because clinical presentation alone is too similar to malaria to discriminate the presence of other coinfections.Supplemental materials