Plasmodium falciparum biology limits elimination approaches to target asymptomatic malaria. Therefore, the association of parasite factors involved in cytoadhesion, parasite multiplication and gametocyte maturation with afebrile malaria was assessed.Poor knowledge on the afebrile Plasmodium falciparum isolates were collected from febrile (axillary temperature ≥ 37.5 °C or a reported fever in the previous 24 h) and afebrile (fever neither at the visit nor in the previous 24 h) individuals residing in Southern Mozambique. var, PfSir2a and Pfs25 transcript levels were determined by reverse transcriptase quantitative PCRs (RT-qPCRs) and compared among 61 pairs of isolates matched by parasite density, age and year of sample collection.varC and PfSir2a transcripts was higher in P. falciparum isolates from afebrile individuals (P ≤ 0.006), while varB and DC8 genes (P ≤ 0.002) were higher in isolates from individuals with febrile infections. After adjusting the analysis by area of residence, doubling the relative transcript unit (RTU) of varC and PfSir2a was associated with a 29.7 (95% CI 4.6–192.3) and 8.5 (95% CI 1.9–32.2) fold increases, respectively, of the odds of being afebrile. In contrast, doubling the RTU of varB and DC8 was associated with a 0.8 (95% CI 0.05–0.6) and 0.2 (95% CI 0.04–0.6) fold changes, respectively, of the odds of being afebrile. No significant differences were found for Pfs25 transcript levels in P. falciparum isolates from afebrile and febrile individuals.The level of var and gametocyte-specific transcript patterns in febrile and afebrile infections from southern Mozambique matched by age, parasite density and recruitment period suggest similar transmissibility but differential expression of variant antigens involved in cytoadhesion and immune-evasion. Plasmodium falciparum infections debilitate the health of affected population while representing a hidden source of continuous transmission that can compromise elimination efforts [Asymptomatic  efforts . Poor knP. falciparum infections is dependent on gametocyte production and parasite virulence [Plasmodium homologue of Sir2 (PfSir2a), a transcriptional factor that regulates ribosomal DNA transcription and P. falciparum multiplication rate [PfSir2a has been implicated in the epigenetic regulation of var gene expression [P. falciparum erythrocyte membrane protein 1 (PfEMP1) that mediates cytoadhesion of infected erythrocytes to host receptors. var genes can be subdivided as per the motifs present in non-coding sequences and locus position  and on the combination of domain cassettes (DCs) [var groups A, B, as well as DC8, 11 and 13 have been associated with severe malaria [var gene expression and asymptomatic malaria [Transmissibility and clinical impact of irulence . Higher irulence , 3. The ion rate , was fouion rate . Moreovepression . This mues (DCs) . var gro malaria , 8. Conf malaria –11, probParasite survival, virulence and transmission depend on a balanced investment in within-host replication and between-host transmission . It is hP. falciparum infection by rapid diagnostic test (RDT), microscopy, quantitate real-time PCR (qPCR) and storage in RNAprotect. Plasmodium falciparum-infected individuals were defined as afebrile if their axillary temperature at the time of visit was < 37.5 °C without reporting having had fever in the preceding 24 h, and febrile if the axillary temperature was ≥ 37.5 °C at the time of visit or reported fever during the preceding 24 h. Febrile and afebrile cases were individually matched based on year of sample collection, age  and parasite density . The Hospital Clínic  ethics review committee and National Mozambican Ethical Review Committee (Mozambique) approved the study.This study took place in the district of Manhiça (southern Mozambique) where community-based age-stratified cross-sectional surveys were conducted in May of 2012 to 2015. After giving written informed consent, participants were finger-pricked and their blood was used to detect Plasmodium falciparum infections were also identified through qPCR from 25 μL of dried blood spots collected on filter papers. DNA was extracted using the QIAamp DNA Mini kit , as per the manufacturer’s instructions. Parasite DNA amplification targeting the 18S rRNA gene was performed using the ABI PRISM 7500 HT Real-Time System , following a method described elsewhere [Approximately 10 μL of finger-pricked blood were used to perform PfHRP2-based rapid diagnostic tests  by trained technicians following the manufacturer’s instructions. Thin and thick blood smears were stained with Giemsa as described previously . Two explsewhere , 15. Thelsewhere .P. falciparum infected blood (50 µL) preserved in 250 µL RNAprotect  was performed using 700 µL of Trizol  following manual extraction with phenol/chloroform phase separation to elute total RNA. Extracted total RNA was subjected to DNase treatment using DNase Max kit  at 37 °C for 30 min followed by reverse transcription of 400–800 ng of total RNA using PrimeScript™ RT Master Mix reagents . To confirm the removal of gDNA or the synthesis of cDNA, qPCR was performed using primers targeting P. falciparum ubiquitin-conjugating enzyme (PF08_0085) on reverse transcription positive and negative controls (RNA samples without reverse transcriptase enzyme).Extraction of RNA from var genes such as varA-exon2, varA-DBLα1 (varA-notDC3), varB group (varB-UTR region) and varC group (varC-UTR region) were assessed using degenerate primers and quantitative reverse transcriptase PCRs (RT-qPCRs) [DC8 (CIDRα1.1), DC11 (CIDRβ2 + DBLγ7) and DC13 (CIDRα1.4) [PfSir2a) and gametocyte mature stage (Pfs25) was also performed using previously described RT-qPCR primers [PF08_0085 gene was used as a housekeeping gene [® Green PCR Master Mix , 1 or 0.33 μM of each forward and reverse primers and 5 μL of template cDNA. The reaction volume was prepared with PCR-grade water. Amplifications were performed with a holding for 3 min at 50 °C, initial denaturation for 10 min at 95 °C, followed by 40 cycles of 95 °C for 15 s and 60 °C or 55 °C for 1 min. Samples with a Ct > 40 for any gene target were considered as not expressed. The specificity of primer pairs against human gDNA was also determined. The 7500 System SDS software v1.4 was used to analyse the collected data. A standard curve was then used to calculate the PCR efficiency of each primer from 7 log dilutions of the P. falciparum 3D7 gDNA, by using the formula (E = 10−1/m), where “m” was the slope. The formula C/ECtΔ was used to convert Ct values to copy numbers [C” was the number of copies of the gene in the P. falciparum 3D7 genome, “E” was the efficiency of the PCR [P. falciparum 3D7 reference gDNA loaded in each plate [PF08_0085 (housekeeping gene) transcript levels. Non-template controls were tested in every plate [Transcript levels of T-qPCRs) , 10, 17.3 CIDRα1. [PfSir2a primers , 19. PF0ing gene . In briech plate . The relWilcoxon matched-pairs signed rank test and McNemar’s Chi squared test were used to compare continuous and categorical variables, respectively. The association between being febrile or afebrile and log-transformed RTUs was tested by conditional logistic regression models adjusted by area of residence in order to account for differences in transmission intensities between areas. All statistical analyses were performed using R 3.3.2, and GraphPad Prism 5.0 (GraphPad Software) was used to generate graphs.P. falciparum positive by qPCR, microscopy and/or RDTs, had information on axillary temperature as well as on fever in the previous 24 h, and had available blood samples in RNAprotect  of the 71 pairs  were statistically different between afebrile and febrile infections , while varB and DC8 genes (P ≤ 0.002) were higher in isolates from individuals with febrile infections. After adjusting the analysis by area of residence, doubling the RTU of varC and PfSir2a was associated with a 29.7 (95% CI 4.6–192.3) and 8.5 (95% CI 1.9–32.2) fold increases, respectively, of the odds of being afebrile. In contrast, doubling the RTU of varB and DC8 was associated with a 0.8 (95% CI 0.05–0.6) and 0.2 (95% CI 0.04–0.6) fold changes, respectively, of the odds of being afebrile.The RT-qPCR efficiencies of targeted genes ranged between 83.0% and 97.6% Table . Among tvar group C and PfSir2a transcripts in afebrile P. falciparum infections compared to age- and parasite density-matched febrile infections, while opposite patterns were observed for var group B and DC8 transcript levels. Similar gametocyte levels were observed in both types of infections, based on the lack of differences observed for Pfs25 transcripts. Overall, these findings suggest that the expression of variant surface antigens involved in cytoadhesion and immune-evasion can determine the persistence of afebrile infections.This study identified higher abundance of var group B and DC8 genes found to be more expressed in febrile than afebrile infections have been previously linked to cytoadhesion phenotypes  associated with severe malaria [var C transcripts in afebrile infections might indicate the presence of less-pathogenic variant surface antigens compatible with the subclinical persistence of these infections [var gene repertoire, leading to the expression of var group C variants capable of evading immunity though higher switch-off rates [PfSir2a in afebrile infections might have contributed to this switch towards var C subgroups [ malaria , 22, thufections . Increasff rates , 9. The ubgroups , 23, sugPfSir2a with afebrile malaria is contradictory to a finding where PfSir2a was found associated with severe malaria [Pfs25 transcripts was strongly dependent on parasite density [This study demonstrates a few noted discrepancies with previous results as well as several limitations. To begin, the association of  malaria . The res malaria , 25. How malaria , 24–28.  malaria . This di malaria . It has  density . Lastly,var and PfSir2a gene expression between parasites collected from Mozambican individuals with febrile and afebrile outcomes when matched by host age, parasite density and time-period of sample collection. Increased expression of var C genes may increase parasite persistence through a balance between antigenic escape and cytoadhesion to avoid splenic clearance. Several epigenetic mechanisms that include PfSir2a expression might contribute to this transcriptional shift [Major differences exist in al shift . In cont


The global impact of artemisinin-based combination therapies on malaria-associated mortality and their origins in ancient Chinese medicine has heightened interest in the natural discovery of future antimalarials.Plasmodium falciparum asexual or mature sexual stage cultures and assayed for parasite viability after 72 hours.A double-blind study to identify potential ingredients with antimalarial activity from traditional remedies with reported antipyretic properties. Recipes of clear broths, passed down by tradition in families of diverse ethnic origin, were sourced by school children. Broths were then tested for their ability to arrest malaria parasite asexual growth or sexual stage development in vitro. Clear broth extract was incubated with in vitro cultures of P. falciparum asexual blood stages, with 2 having comparable inhibition to that seen with dihydroartemisinin, a leading antimalarial. Four other broths were found to have >50% transmission blocking activity, preventing male parasite sexual stage development. After unblinding, two active broths were found to be from siblings from different classes, who had brought in the same vegetarian soup, demonstrating assay robustness.Of the 56 broths tested, 5 were found to give >50% in vitro growth inhibition against in vitro growth, arising from complex vegetable and/or meat-based broths. This represented a successful child education exercise, in teaching about the interface between natural remedies, traditional medicine and evidence-based drug discovery.This screening approach succeeded in finding broths with activity against malaria parasite  Malaria is one of the leading causes of global paediatric mortality.Effective antimalarial drugs are available to treat infections, but resistance continues to emerge to each antimalarial licenced.Spread of resistance to Africa could prove catastrophic so the need for discovery efforts to find new drugs is essential to eradicate malaria.This is the first study of its kind engaging children to screen traditional soup broths for their antimalarial properties.in vitro antimalarial activity.Complex vegetable and/or meat broths have substantial Traditional medicine or remedies and evidenced-based research can be used to enhance school science education about drug discovery and action.Plasmodium are responsible for causing malaria, P. falciparum is the most deadly, causing 99% of deaths.Nearly half of the world’s population is at risk from malaria infection. Each year, this results in nearly half a million childhood deaths,Artemisia annua), giving rise to artemisinin and its derivative drugs.Recognising the impact of ACTs on malaria disease treatment, the 2016 Nobel Prize in Physiology or Medicine went to Professor Dr Tu Youyou of China who was instrumental in the isolation of the active endoperoxide from ‘Qinghao’ a herb from the Artemisia family  into 1.5 mL aliquots at school for transportation to the laboratory. Of the 60 collected broths, some did not filter (containing too much dense particulate matter) and some had a >50% level of oil that blocked the filters. Fifty-six in total were successfully collected.Soup samples were collected by children from their families who came from a variety of ethnic backgrounds. The children transported the soup to school in 15 mL plastic tubes for processing . Each sample was frozen on arrival at school, then batches were thawed, centrifuged for 5 min at 1000 P. falciparum NF54 strain  sexual and 3D7 strain  asexual stages culture was performed as previously described.3 and 10% pooled human type A+ serum) was replaced daily for 14 days without fresh erythrocyte addition. Human serum was obtained from Interstate Blood Bank, A+ serotype; no aspirin 2 hours before drawing and no antimalarials 2 weeks before drawing. On day 14 after culture induction, gametocyte production was assessed by thin smear and Giemsa staining, and exflagellation was counted in a haemocytometer.Plasmodium SYBR Green assay that measures parasite DNA2/5% O2/90% N2 gas before being frozen at −20°C before analysis. All assays were completed in triplicate with a dihydroartemisinin (DHA) positive control and water only control and were assayed as two independent biological replicates.Asexual growth was assayed via a standardised et al2/3% O2/92% N2 gas before gamete formation was triggered by addition of activation medium  and by cooling the plate at 4°C for 4 min and then a further 5 min at 28°C. Exflagellation was recorded immediately by brightfield microscopy as described.Assessment of the ability to inhibit male gamete maturation (exflagellation) , label 2A total of 56 traditional soups or broths, with a family history of reported antipyretic activity, were collected by children attending a UK state-funded primary school. Traditional soup samples were brought into school for filtration and subsequent lab testing for antimalarial properties. Filtered soup extracts were tested for their ability to inhibit parasite replication by asexual growth assaySoup broths derived from family recipes originating from across ethnic backgrounds including Europe, North Africa and the Middle East. While recipes were not directly collected, discussions about the content of each soup broth found no consistent anecdotal pattern in the inhibitory potential of samples such as a common ingredient. Many were noted as vegetarian, chicken based or beef based. Notably, there was no overlap between samples that had asexual-blocking activity versus transmission-blocking activity, although this is consistent with the very different cellular biology at work in the different lifecycle stages. One observation was that on unblinding, samples 16 and 36 were found to come from the same household, and on further investigation, samples 16 and 36 were found to be the same soup . This demonstrates the robustness of the assays in finding inhibitory activity and, given that the soup was vegetarian in nature (red cabbage based), these data clearly demonstrate that chicken is not the only antipyretic broth.To the best of our knowledge, this is the first study of its kind engaging children to screen traditional soup broths for their antimalarial properties; a study aimed at contributing both educationally and scientifically to the global goal to eradicate malaria. Our study centred around the question whether children could engage in the concept of finding new treatments for malaria by testing their inherited traditional family soup broth recipes used to treat fever  and explore whether family recipes may represent an untapped resource for finding primary ingredients that could advance to become future antimalarials. The utility of any broth found to have antimalarial activity will of course depend significantly on standardisation of soup preparation and ultimately identification of the active source ingredient, its fractionation and, towards its progression, detailed toxicology with first human cells and later preclinical models. This journey, mirroring that of artemisinin from the Qinghao herb, may as yet reveal another source of potent anti-infective treatment. More importantly, however, the exercise demonstrates the power of simple classroom-based exercises in engaging children in the normally disparate disciplines of traditional medicine or remedies and laboratory or evidenced-based research. At a time when there is a resurgent voice against evidence-based medicine, such exercises have great importance for educating the next generation about how new drugs are discovered, how they might work and how untapped resources still exist in the fight against global diseases of significance. One disappointment from the study was the lack of a reported upsurge in finishing of meals, suggesting further work may be needed in translation of discovery science into childhood meal consumption.10.1136/archdischild-2019-317590.supp1Supplementary data10.1136/archdischild-2019-317590.supp2Supplementary data


Plasmodium and Leishmania, which respectively cause malaria and leishmaniasis. These parasites are both known to cause significant disease burden in their endemic locales, and thus pose a threat to military travelers. This review introduces readers to basic life cycle and disease mechanisms for each. Local and military epidemiology are described, as are the specific actions taken by the US military for prevention and treatment purposes. Complications of such measures with regard to human health are also discussed, including possible chemical toxicities. Additionally, poor recognition of these diseases upon an individual’s return leading to complications and treatment delays in the United States are examined. Information about canine leishmaniasis, poorly studied relative to its human manifestation, but of importance due to the utilization of dogs in military endeavors is presented. Future implications for the American healthcare system regarding malaria and leishmaniasis are also presented.As the United States military engage with different countries and cultures throughout the world, personnel become exposed to new biospheres as well. There are many infectious pathogens that are not endemic to the US, but two of particular importance are  Escherichia coli (ETEC), leishmaniasis, brucellosis, and toxoplasmosis  Armed Forces, comprising of the Marine Corps, Navy, Air Force and Army, with nearly 200,000 troops currently stationed overseas . Leavinglasmosis . These plasmosis . Consequlasmosis , both wiHigh infection rates of parasitic diseases, principally malaria and leishmaniasis, are expected in these locales, and the deployed members of the United States military have not been spared , with inDespite the vast number of clinically relevant pathogens in locales where members of the US military are deployed, this review will address only two of such infections: malaria and leishmaniasis. The seriousness of these diseases and their implications for healthcare system within the homeland demands critical analysis, and form the basis for this review. Due to the chronic and sometimes relapsing nature of these diseases, infected individuals may present symptoms months/years after exposure , especiahttp://www.health.mil), in addition to websites of international organizations such as the World Health Organization (https://www.who.int/tdr/en/) and the Centers for Disease Control (http://www.cdc.gov), to retrieve articles focused on parasitic diseases in specific endemic locations. Subject directed keywords or terms utilized in our search include armed forces, deployment, malaria, leishmaniasis, epidemiology, military-related autochthonous parasitic diseases, prevention and treatment. References include articles directly relating to research data, relevant case reports or clinical presentations in the United States, Canada, or Europe from deployed service men and women. Of additional interest in our search were articles relating to zoonotic (canine) leishmaniasis among military dogs deployed overseas alongside their handlers and potential for disease transmission on return to the United States.For the purpose of this review, we divided the article into sections and disease-specific subsections, focusing on specific diseases, one at a time. To access relevant and related publications, we carried out a search on pubmed.gov for armed forces deployment information, using AND as the link word, as the case may be. For others, we searched journal specific or government websites  species: P. vivax, P. falciparum, P. ovale and P. malariae, though an animal species, P. knowlesi has been confirmed as responsible for significant human infections in parts of southeast Asia , can remain for months or even years after the initial mosquito bite, becoming symptomatic if the parasites leave the liver to invade healthy red blood cells  or picaridin to exposed skin; (3) proper wearing of military uniform; (4) use of permethrin-treated bed net, and (5) continuous chemoprophylaxis during all phases of deployment  administered by the military, also have known adverse effects, associated with use. Historically, mefloquine was developed by the US military , but is P. vivax malaria was treated with primaquine for complete pathogen eradication. It acts by targeting the dormant hypnozoites in the liver, preventing the possibility for recurrence, thereby facilitating complete recovery , insect repellants are recommended, and personnel are given pyrethrin-treated bed nets . TheoretFortunately, modern medicine has afforded CL cure rates up to 91%. The most effective treatment is sodium stibogluconate, given intravenously at doses of 20 mg per kilogram of body weight, for 20 days , with siLeishmania species  is a common veterinary problem worldwide, with recent prevalence estimates as high as 25–80% . Brazil  animals . Of dome animals , potentibilities , and Canbilities . CanL wabilities , with libilities . Without measure , though  measure , questioGlobal political alignments often shift, and the US military remains mobile in response, with a higher likelihood that a greater number of troops may be deployed to the Middle East in response to current trends. Engagement with local leishmania and malaria-endemic regions puts American servicemen and women at risk of disease contraction, thereby highlighting the disease burden in these foreign countries.P. vivax is endemic to North Korea (P. vivax malaria in the North-South demilitarized zone (Malaria is endemic to much of the world, and the United States military will likely continue to engage in endemic areas. Drug-resistant strains of malaria have been found, indicating the need for further preventative measures . Most ofth Korea , and thozed zone , includized zone .Leishmania disease burden in the Middle East is estimated at 100,000 cases annually, indicating that exposure risk remains significant (CL is found primarily in the Middle East, and the US military continues to be at risk of infection. The nificant . Iran annificant . True innificant . This fuParasitic diseases are common worldwide, and exposure is common in most deployment locations of the United States military. The majority are not yet preventable with vaccines, and the treatments and prophylaxis that are available are accompanied by many side effects. It is important to continue funding for the treatment and eradication of infectious diseases worldwide for many reasons, including the exposure danger posed to the armed forces. Significantly, the possibility that these pathogens can be imported into the United States by returning service men and women, unknowingly serving as reservoir hosts, should serve as an alarm bell for us to re-evaluate, refocus and strategize how to face the challenges of the 21st century military deployment, protecting them from local pathogenic insults that can potentially lead to epidemics in the homeland.


The Gambia has successfully reduced malaria transmission. The human reservoir of infection could further decrease if malaria-infected individuals could be identified by highly sensitive, field-based, diagnostic tools and then treated.Plasmodium falciparum infections using highly sensitive rapid diagnostic tests (HS-RDT) and PCR. The sensitivity and specificity of the HS-RDT were estimated (assuming PCR as the gold standard) across varying transmission intensities and in different age groups. A deterministic, age-structured, dynamic model of malaria transmission was used to estimate the impact of mass testing and treatment (MTAT) with HS-RDT in four different scenarios of malaria prevalence by PCR: 5, 15, 30, and 60%, and with seasonal transmission. The impact was compared both to MTAT with conventional RDT and mass drug administration (MDA).A cross-sectional survey was done at the peak of the 2017 malaria season in 47 Gambian villages. From each village, 100 residents were randomly selected for finger-prick blood samples to detect Malaria prevalence by HS-RDT was 15% . The HS-RDT sensitivity and specificity were 38.4%  and 88.5% , respectively. Sensitivity was the highest  in high prevalence villages (20–50% by PCR). The model predicted that in very low transmission areas (≤ 5%), three monthly rounds of MTAT with HS-RDT, starting towards the end of the dry season and testing 65 or 85% of the population for 2 consecutive years, would avert 62 or 78% of malaria cases (over 2 years), respectively. The effect of the intervention would be lower in a moderate transmission setting. In all settings, MDA would be superior to MTAT with HS-RDT which would be superior to MTAT with conventional RDT.The HS-RDT’s field sensitivity was modest and varied by transmission intensity. In low to very low transmission areas, three monthly rounds per year of MTAT with HS-RDT at 85% coverage for 2 consecutive years would reduce malaria prevalence to such low levels that additional strategies may achieve elimination. The model prediction would need to be confirmed by cluster-randomized trials. After a decade of considerable success in malaria control, progress has stalled, with no further reduction of malaria cases between 2015 and 2017 . HoweverStrategies such as mass drug administration (MDA) or mass testing and treatment (MTAT) target the human reservoir of infection and may decrease malaria prevalence to such low levels that other strategies may achieve elimination. While MDA aims at administering a full anti-malarial treatment to the whole population, without screening for either symptoms or infections , MTAT woLow-density infections can be identified by more sensitive molecular diagnostic methods that detect the parasites’ nucleic acid. Depending on the blood volume examined and the molecular target, molecular tests can detect infections with parasite densities as low as 0.02–0.1 parasites/µl . HoweverPlasmodium falciparum parasites, and 84% in Ugandan asymptomatic children (high transmission); its specificity was greater than 96% [P. falciparum infections detected by ultra-sensitive qPCR and 26% of P. falciparum infections detected by both standard qPCR and ultra-sensitive qPCR [The highly sensitive RDTs (HS-RDT) were developed for the field detection of low-density infections, with a detection limit of 40–125 pg/ml histidine-rich protein-2 (HRP2), tenfold lower than that of conventional SD Bioline Malaria Ag P.f RDT (800–1000 pg/ml) . When cothan 96% . In Myanthan 96% . In Papuive qPCR .It is unclear what effect MTAT with HS-RDT would have on malaria transmission as a proportion of infected people would remain undetected and untreated. The objective of this study was to determine the field sensitivity and specificity of HS-RDT  PCR. These values were used to predict the effect of MTAT campaigns with HS-RDT on malaria prevalence using a mathematical model.Malaria transmission in The Gambia is highly seasonal, with peak transmission from October to November. A cross-sectional survey was conducted over a month (7 November to 8 December 2017) in 47 villages in eastern Gambia. The selected villages with populations between 100 and 800 residents are included as part of the Health and Demographic Surveillance System (HDSS)  and/or history of fever in the previous 24 h were treated with artemether-lumefantrine, the first line treatment in The Gambia.Data was collected and managed using the REDCap (Research Electronic Data Capture) data capture tool that has an intuitive interface for validated data entry, audit trails for tracking and exporting data. Individual data were completed for demographic characteristics, history of fever and recent anti-malarial use, axillary temperature, long-lasting insecticidal net (LLIN) ownership and use, and prior indoor residual spraying (IRS).Assuming malaria prevalence of 10%, 100 individuals per village would be able to estimate malaria prevalence with a precision of ± 6%.The HS-RDT was carried out following the manufacturer’s recommendations. Briefly, a finger-prick blood sample was collected and delivered to the sample well in the test cassette. Four drops of buffer were added and the results were interpreted within 20 min by a study nurse .varATS assay was used to generate qualitative P. falciparum results. Briefly, three 3-mm dried blood spots were punched into 96-well plates and digested in 20 µl of proteinase K and 180 µl of ATL tissue lysis buffer solution. The Plasmodium DNA was extracted using the QIAamp 96 DNA QIAcube HT kit  and Qiacube HT® robot. Extracted DNA was eluted into 80 µl of elution buffer and stored at − 20 °C till further use. For the analysis, the var gene acidic terminal sequence  of P. falciparum was amplified [The mplified . All PCRmplified . The PCRhttps://CRAN.R-project.org/package=rjags). Infection status was defined by four categories: (1) malaria-infected individuals with detectable HRP2 levels: both PCR and HS-RDT positives; (2) individuals with a recently cleared infection, either naturally or by treatment, with residual HRP2: PCR negative and HS-RDT positive; (3) malaria-infected individuals without residual HPR2: PCR positive and HS-RDT negative test result; and, (4) non-infected individuals: both negative PCR and HS-RDT. The map of the study sites was generated using Quantum (Q)GIS version 7.5.0 with GRASS 7.4.4 programs. The administrative base map of The Gambia was created by downloading the “shapefiles” of The Gambia into QGIS and the administrative layers were generated. A final “shapefile” was created using the village coordinates and PCR infection prevalence and then layered onto the administrative map of The Gambia.Malaria prevalence was calculated by dividing the number of positive samples, either by HS-RDT or PCR, by the total number of tested samples. An asymptomatic infection was defined as an individual with positive HS-RDT or PCR without history of fever in the previous 24 h and axillary temperature ≤ 37.5 °C [P. falciparum infected for 310 days on average [An existing deterministic age-structured dynamic model of malaria transmission was used to estimate the impact of MTAT with HS-RDT (compared to no intervention) on malaria prevalence and the proportion of clinical malaria cases averted . The mod average . InfectiThe model was extended to include MTAT with HS-RDTs compared to either MTAT with conventional RDTs or MDA implemented as three monthly rounds per year for 2 consecutive years. The MTAT is implemented in the model whereby, at defined time points, a proportion of individuals (the intervention coverage) are tested. A proportion of infected individuals are assumed to test positive (determined by the diagnostic test’s sensitivity), treated and moved to the ‘prophylactic protection’ compartment. It is assumed that there is non-random coverage of who partakes in each intervention round: the population is split in two groups, one which partakes in all three treatment rounds, and one which has random and uncorrelated coverage over the three rounds.The HS-RDT’s specificity is accounted for by assuming that a proportion of uninfected individuals will be HS-RDT positive and will receive treatment and prophylactic protection. MDA is implemented in the model by assuming that at defined time points, a proportion  of individuals are treated (regardless of infection status) and move to the ‘prophylactic protection’ compartment. Coverage between treatment rounds is assumed to be correlated as described above.The model used the HS-RDT sensitivity and specificity values estimated by the survey. It was assumed that sensitivity of conventional RDT is 22% lower than the HS-RDT and has 95% specificity  and 30 dCommunity verbal approval was followed by individual written consent for adults who also provided consent for children below 18 years of age. Children between 12 and 17 years provided assent. Ethical approval was obtained from the Gambia Government/Medical Research Council Joint Ethics Committee.A total of 4060 participants distributed in 47 villages were screened Fig. ; the medP. falciparum infection was 15.0%  by HS-RDT and 13.1%  by PCR. HS-RDT’s sensitivity and specificity were 38.4%  and 88.5% , respectively  or history of fever, malaria prevalence by HS-RDT was 17.1%  and 14.1%  in those without fever. HS-RDT sensitivity was similar in individuals with  or without  fever or history of fever in the previous 24 h (p = 0.38).P. falciparum prevalence by PCR  or very low transmission 90.9%   villages  than in low-moderate transmission 89.3%  , with the largest proportion of ‘false negatives’ in villages with low-moderate transmission. The odds of being false negative in the low-moderate and high prevalence villages was not different than in the very low prevalence villages.  individuals were ‘false positives’, i.e. HS-RDT positive and PCR negative. This proportion increased with increasing malaria prevalence by PCR with the odds of being false positive significantly higher in the high transmission villages than in very low transmission villages  was assumed to be 10%. At very low prevalence (~ 5%), three monthly rounds of MTAT with HS-RDT per year at 85% coverage would decrease prevalence by 80%, from 5 to around 1%, up to 18 months after the final MTAT round , three monthly rounds of MTAT with HS-RDT at 85% coverage would decrease prevalence to around 7 and 5% in the first and second year, respectively. Prevalence would return to almost pre-MTAT levels 12 months after the last round if the intervention is not continued  and high  transmission areas, MTAT with HS-RDT at 85% coverage would decrease malaria prevalence by about 60% during the intervention months; however, prevalence would rapidly return to pre-MTAT levels after the last round, i.e. 6 months in moderate and 3 months in high transmission areas  , Papua NP. falciparum density was not measured, it is not possible to determine whether false negative samples had parasite densities below the HS-RDT detection threshold. Such limitation also prevents to establish whether the differential performance of the HS-RDT by age group and prevalence could be explained by variations in parasite densities. Indeed, HS-RDT’s sensitivity and specificity varied by malaria prevalence, with the highest sensitivity and the lowest specificity in high transmission villages. This may be due to the higher probability of being infected (and re-infected) at any given moment (high sensitivity), possibly with higher parasite densities in individuals repeatedly infected, and by the detection of circulating P. falciparum HRP2 antigens from past infections (low specificity). Where malaria prevalence was very low, despite missing more than half of infected individuals, HS-RDT would identify those with circulating parasite antigens who had been infected about 1 month earlier [Most discrepant results were represented by false negatives, i.e., negative by HS-RDT and positive by PCR, possibly due to the lower detection threshold of PCR compared to HS-RDT . False n earlier , 38, indIn this context, treating HS-RDT positive individuals, either with a detectable infection by PCR or with circulating HRP2 from a previous infection, could further decrease malaria prevalence, when using a treatment with a long post-treatment prophylactic period such as dihydroartemisinin–piperaquine (DHA–PQ) . When maTrials evaluating the effect of multiple MTAT using conventional RDTs found temporary reductions of malaria prevalence in communities in Burkina Faso and Zambia , 41, andNevertheless, at very low or low transmission intensities, MTAT with HS-RDT at 85% coverage would have a similar effect than MDA at 65% coverage and reduce malaria prevalence by 80%, from 5 to 1% for about 18 months after the last MTAT round. At lower coverage, MTAT with HS-RDT would have a much lower effect, like that obtained by MTAT with conventional RDT at 85% coverage. The success of MDA depends on high coverage which in turn is heavily influenced by its acceptability by the local populations, which can change and decrease over time. MTAT may be more acceptable to the local population as it identifies and treats infected individuals , 44. An The timing of MTAT implementation may also be important; in a highly seasonal setting, the three rounds should be implemented at the end the dry season, before the malaria seasonal transmission starts. This may improve the HS-RDT performance as individuals infected during the previous transmission season would have cleared any residual parasite-circulating antigen, thus reducing the percentage of false positives.The mathematical model used to estimate the effect on MTAT with HS-RDT is based on several assumptions. These predictions would need to be empirically confirmed by field studies, e.g., cluster randomized trials, which may compare MDA against MTAT or, depending on the malaria prevalence, the two interventions implemented sequentially, i.e., MDA to reduce malaria prevalence to levels at which MTAT could have an additional effect.HS-RDTs were developed with the aim of reducing the human reservoir of infection as they would identify asymptomatic, malaria-infected individuals  to be trAdditional file 1: Table S1. Comparison of HS-RDT sensitivity and specificity by intensity of transmission.Additional file 2: Table S2. Risk of being false negative: PCR positive and HS-RDT negative by malaria prevalence.Additional file 3: Table S3. Risk of false positive: PCR negative and HS-RDT positive by transmission intensity.Additional file 4: Table S4. Percentage reduction in clinical malaria cases over 2 years after simulated MTAT and MDA scenarios shown in Fig.


Malaria is the leading cause of morbidity in Ghana representing 40-60% of outpatient hospital attendance with about 10% ending up on admission. Microscopic examination of peripheral blood film remains the most preferred and reliable method for malaria diagnosis worldwide. But the level of skills required for microscopic examination of peripheral blood film is often lacking in Ghana. This study looked at determining the extent to which haematological parameters and demographic characteristics of patients could be used to predict malaria infection using logistic regression. The overall prevalence of malaria in the study area was determined to be 25.96%; nonetheless, 45.30% of children between the ages of 5 and 14 tested positive. The binary logistic model developed for this study identified age, haemoglobin, platelet, and lymphocyte as the most significant predictors. The sensitivity and specificity of the model were 77.4% and 75.7%, respectively, with a PPV and NPV of 52.72% and 90.51%, respectively. Similar to RDT this logistic model when used will reduce the waiting time and improve the diagnosis of malaria. Apart from loss of valuable life, malaria is the leading cause of morbidity in Ghana representing 40-60% of outpatient hospital attendance with about 10% ending up on admission . The burSome clinicians think missing a case of malaria is a bigger problem than it is in reality and therefore treat most people with fever for malaria . OverdiaIn malaria diagnosis, RDTs are relatively simple and quick way of detecting the presence of the parasites in human blood. However, RDTs are expensive to use compared to malaria microscopy and may not be able to detect some infections with low parasitaemia . AnotherThough expensive, most medical laboratory scientists and technicians in district hospitals in Ghana have access to haematological analysers; unlike malaria microscopy, they can easily and accurately perform full blood count to assess haematological parameters. Since haematological parameters are known to change with malaria infections, it is necessary to better understand this relationship to improve the diagnosis of this killer disease. This study therefore aims at determining the extent to which haematological parameters and demographic characteristics of patients can be used to predict malaria infection using logistic regression.The data for this study was obtained as secondary data from the laboratory department of St. Patrick's Hospital, Offinso, from January 2018 to June 2018 by reviewing the haematological records book. Full blood count and malaria test data from a total of 2076 patients with suspicion of malaria were used, comprising 1200 females and 876 males between the ages of 1 to 102 years. The diagnosis of malaria was done using Rapid Diagnostic Test (RDT) and confirmed using Giemsa staining technique by trained microscopist. Full blood count analyses were done using Mindray BC – 3000Plus auto analyser after careful calibration according to the manufacturer's standards. Haematological parameters used in this study were haemoglobin (Hb), white blood cell (WBC), platelet (Plt), lymphocyte (Lymph), mixed cell count (MXD), and neutrophil (Neut).For this study, the data obtained was coded as follows: malaria (positive = 1 and negative = 0); gender . Age and all haematological parameters used in this study were numeric variables and therefore did not need coding. This study adopts a logistic regression model to analyse haematological parameters as predictors for malaria infection.The data obtained was initially entered in Microsoft Excel (2016) and checked for errors after which it was exported to IBM SPSS Statistics 22 software for logistic regression analysis. y =α+βx, the mean of the dependent variable y in terms of an independent variable x is modelled in linear regression. However, this model is not good enough, because extreme values of x will give values of α+βx that is not within 0 and 1. The logistic regression solves this problem by transforming the odds using the natural logarithm. So, in logistic regression we replace the odds with its natural log (logit) and model it as a linear function of the explanatory variable to obtain the simple logistic model.When the probability of an event of interest occurring is p, then 1-p is probability of that event not occurring. The odds of that event are the ratio between the two probabilities: the probability that an event of interest will occur and the probability that it will not occur. ic model.logity=InThe simple logistic model could be extended to include multiple independent variables; in that case the complex logistic regression model will be constructed asi increases by 1 unit, then the odds will increase by a factor eβi. This factor (eβi) gives the relative amount by which the odds of the outcome increase or decrease when the value of the independent variable (xi) is increased by 1 unit; therefore, eβi is the odds ratio (OR) for the independent variable xi.The odds ratio (OR) compares the odds of two different events. For two different events z and x, the corresponding odds of z occurring relative to x occurring are called the odd ratio.Out of the total 2076 patients tested for malaria, 539 (25.96%) were positive. Within the various age groups, children between the ages of 5 and 14 years recorded the highest malaria prevalence of 45.30% (164 out of 362). A total of 206 (29.99%) out of 687 children less than 5 years old tested positive for the disease; adults > 24 years recorded a prevalence of 13.79% (100 out of 725) .A backward stepwise selection of predictors into the binary logistic model produced series of models. Tables β) for each predictor variable in The final model considering their respective parameters  = 52.72% and the negative predictive value = 1163/(122 + 1163) = 90.51%. This means that the final model is able to predict 52.72% of patients diagnosed with malaria and 90.51% of patients without malaria. A summary of various evaluation tests done on the model to ascertain its diagnostic capabilities is shown in From This study identified that 29.99% of children less than five years tested positive for malaria. This is slightly higher than results from a recent nationwide survey where malaria parasite prevalence among children aged 6-59 months was determined to be 27.5% and 20.4% using RDT and microscopy, respectively . This stThe main objective of this study is to predict malaria infection using gender, age, and haematological parameters. To achieve this goal, a logistic regression model was employed. The first regression step included these parameters: gender, age, haemoglobin, white blood cell, platelet, lymphocyte, mixed cell count, and neutrophil. However, after using the stepwise selection method to select predictors into the binary logistic model, only age, haemoglobin, platelet, and lymphocyte were significant with significance values less than 0.01 as shown in From In this study, the sensitivity and specificity of the logistic model to predict malaria infection were 77.4% and 75.7%, respectively. The current model with sensitivity of 77.4% is slightly higher than RDT (62.5%) and similar to presumptive diagnosis (70.83%) of malaria in Ho, Ghana . Nonethe3/μL (69%), Hb <10g/dL (60%), and significantly lower for lymphocytes counts <2000/μL (6%) [The PPV and NPV of the final model developed were 52.72% and 90.51%, respectively. Even though this model has a high sensitivity of 77.4%, only 52.72% of the positive cases truly have malaria. However, the model is very effective in identifying malaria negative cases since 90.51% of all the malaria negative cases identified by this model truly do not have the disease. A malarial study conducted in Kenya obtained high negative predictive values comparable to the current model for most haematological parameters investigated but recorded slightly higher PPV for platelet <150 x10/μL (6%) . Further/μL (6%) . The NPV/μL (6%) , 23. But/μL (6%) . This shLimitations of the Study. The haematological analyser produced 19 parameters; however, the secondary data contained only the six prominent ones which were used in the model. It is possible that some of the unused parameters, when included in the model, could produce a better model than the current one.The data used contained more females than males; if females have a different risk of malaria infection than men, then it will affect the overall estimates of malaria indices in the catchment communities and this may lead to wrong interpretation and policy initiatives.Approximately half of the patients sampled were children aged not more than 14 years and since children have more malaria cases than adults, this might affect the overall reported diseases prevalence in the population.The binary logistic model developed for this study identified age, haemoglobin, platelet, and lymphocyte as the most significant predictors. The overall prevalence of malaria in the study area was determined to be 25.96%. The final logistic model obtained for use in the diagnosis of malaria is In(odds) = In(p/(1 − p)) = 5.164 − 0.41Age − 0.183Hb − 0.011Plt − 0.031Lymph.The sensitivity and specificity of the above model were 77.4% and 75.7%, respectively, with a PPV and NPV of 52.72% and 90.51%, respectively. Similar to RDT this logistic model when used will reduce the waiting time and improve the diagnosis of malaria.


Case fatality rates in severe falciparum malaria depend on the pattern and degree of vital organ dysfunction. Recent large-scale case-control analyses of pooled severe malaria data reported that glucose-6-phosphate dehydrogenase deficiency (G6PDd) was protective against cerebral malaria but increased the risk of severe malarial anaemia. A novel formulation of the balancing selection hypothesis was proposed as an explanation for these findings, whereby the selective advantage is driven by the competing risks of death from cerebral malaria and death from severe malarial anaemia. We re-analysed these claims using causal diagrams and showed that they are subject to collider bias. A simulation based sensitivity analysis, varying the strength of the known effect of G6PDd on anaemia, showed that this bias is sufficient to explain all of the observed association. Future genetic epidemiology studies in severe malaria would benefit from the use of causal reasoning. Plasmodium falciparum in the peripheral blood film  or haemoglobinopathies.Severe falciparum malaria is defined by one or more criteria indicating vital organ dysfunction in the presence of microscopy confirmed asexual blood stages of ood film . Multiplood film . Common ood film . HoweverThere is considerable interest in understanding the mechanisms conferring protective effects against severe falciparum malaria of the genetic polymorphisms which are common in malaria endemic areas . For somP. falciparum infection  and decreases the risk of cerebral malaria (CM) . These cnfection . CollideTwo published analyses of pooled data from observational studies of patients with severe falciparum malaria used severe malarial anaemia (SMA) and cerebral malaria (CM) as the main endpoints (outcomes) of interest . Both thon score , it was on score . This paBy excluding patients with both SMA and CM (approximately 12% of those with either SMA or CM in the pooled data), the number of G6PDd patients in the CM category is artificially reduced and we would expect there to be fewer G6PDd patients than in the control group. This re-analysis of recent reports that G6PDd reduced the risk of CM directly  suggestsThe example reported here highlights a major difficulty when attempting to estimate causal contributions when factors of interest define inclusion into the clinical study and the subsequent data analysis. All prospective observational severe malaria studies suffer from two major issues. First, case definitions are subjective and change over time . Primary analyses comprised tests of association between each SNP and severe malaria phenotypes across all individuals combined as well as separately by sex (X-chromosome SNPs only) and study site: genotypic, additive, dominant, recessive and heterozygote advantage genetic models of inheritance were considered." .For reasons of sample size, we did not conduct a detailed analysis of other sub-types of severe malaria, or of those individuals who had both cerebral malaria and severe malarial anaemia’. Standard logistic regression models were also used to obtain the odds ratios: ‘In primary analyses, standard fixed effects logistic regression methods were used for tests of association with severe malaria and sub-types at each SNP under additive, dominant, recessive and heterozygous models. [..] Results were adjusted for sickle hemoglobin (HbS), gender and ethnicity." (bottom of page 18).The odds ratios for G6PDd in cases versus controls are given in Table 3 of HbS are the true confounders .In order to characterise the proportion of the reported association explained by collider bias we constructed a simple simulation study based on the analytical procedures in The hypothetical data were simulated based on the following assumptions:G6PD deficiency increases the risk of SMA in acute symptomatic malaria (the size of the effect is the only free parameter in the model and varies from 1 to 2 as defined by the odds ratio for G6PDd in SMA cases versus controls). In reality this would be expected to be a function of genotype and gene dose (i.e. hemizygotes and homozygotes would have a greater risk than heterozygotes).CM is independent of G6PD status.A population of males only  with homogeneous background frequency of G6PDd.CM and SMA occur independently.From the data in If we denote The true proportion of G6PDd in the controls is known and fixed as We then simulate cases as follows. For each value of Step 1. Simulate 1 million patients such that:Step 2. Select only the patients who have either just CM, or just SMA, filtering out those with concomitant SMA and CM.Step 3. In the remaining data compute Case definition which is a collider between  G6PD deficiency and Cerebral malaria (CM). Implementation of the simulation model in R can be found at: https://github.com/Stije/SevereMalariaAnalysis/SelectionBiasSimulation.Rmd .The causal diagram which corresponds to exclusion operating in Step two is shown in  In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been reviewed by two peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by a Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Ellie Murray (Reviewer #2).Thank you for submitting your work entitled "Causal pathways in severe falciparum malaria" for consideration by eLife.Our decision has been reached after consultation between the reviewers. Based on these discussions and the individual reviews below, we regret to inform you that your work will not be considered further for publication in This is a very interesting and thoughtful effort to bring causal reasoning to the extremely complex world of malaria clinical outcomes. Such approaches will be important to generating hypotheses for how to improve clinical care in this field, as the authors note, and I am glad to see these efforts. Toward that end, however, we have substantial concerns about the way it has been applied.However for reasons stated below, both reviewers have major concerns with the analysis. As a result, this paper cannot be published in something like its current form. The first Case Study is not appropriate because it doesn't ask well-posed causal questions, and the second Case Study is essentially a critique of another paper that I can't verify includes a correct description of what the other paper did. If it is a correct description, it is a very important point that should be made as a letter or some other way, and the simulation is superfluous except to provide an illustration to those outside the epidemiology field.eLife declines a paper the reviews are appended verbatim to the review, while only revise and resubmit decisions have a combined letter detailing required changes. In this instance, the reviewers had considerable dialogue post review and pre-decision, so we have written a combined letter that contains our key points from the original reviews, revised after discussion.Normally when Major comments:1) In Case Study I, most of the exposures studied are health states, rather than interventions. As has been discussed in the context of obesity , the causal question "what is the effect of  on (a particular outcome)" is incoherent, or at best incomplete; causal questions imply a specified  intervention that could be applied in a hypothetical randomized trial to change the health state in a particular way. If the intervention is not specified, then two hypothetical trials employing different interventions to achieve the same change in a patient's health state would potentially get different (even different signs of) results.The most important implications of a lack of well-defined interventions are (1) that confounding can be hard to define when the way in which a variable is changed is unknown; (2) the practical utility of the estimate is limited since we do not know how to act upon it; and (3) when there are methods of changing the variable that have effects in different directions we are estimating a weighted average and may make the wrong conclusion. For an example of this 3rd issue, consider the exposure weight loss. Some individuals lose weight by diet and/or exercise and this is expected to improve their health status, but other individuals lose weight by developing cancer and this is expected to worsen their health status. If we don't know the relative frequency of these interventions then we might find that losing weight is harmful to overall health even though intentional weight loss via diet and exercise would be beneficial.To take a key example for this paper, anemia on admission could be changed by (1) moving up the date of admission, presumably to an earlier time in the disease course – e.g. by active case finding and referral; (2) administering blood transfusions on admission; (3) administering drugs to stimulate red cell production on admission; (4) iron dietary supplements at the population level (pre-admission). Even if these were calibrated to achieve exactly the same mean change in hematocrit, (1) could be beneficial; (2) could be slightly harmful ; (3) and (4) might go either way, depending on whether they protect the host more or enhance parasite replication more. Similarly, almost any of the other harmful states could be changed on admission by either earlier admission or changing treatment at admission, with potentially different effects. As a result, even if all confounding pathways were blocked, the interpretation of the resulting estimate would be as a weighted average of the effects of 1-4 (and any other possible methods of changing anemia) with unknown weights, making the answer uninterpretable for clinical or public health decision-making.By this logic, the effects in Figure 4, with the exception of the treatment effect, are not the answers to well-posed causal questions and should be removed.We note that both reviewers, who are knowledgeable about causal inference, acknowledge that the view taken here – that well-defined interventions are required for a well-posed causal question – is not universal in the field. Some have defended the use, for example, of race or sex as causes of disease outcomes, without a well-defined notion of how an intervention could change these. However, we make a distinction between effectively "unalterable" states (such as sex and race), about which controversy exists as to whether they can be considered in causal analyses, and readily alterable states (such as hematocrit) in which very straightforward alterations  plausibly have opposite signs of effect. Here, using the machinery of causal inference to isolate the "causal" effect of hematocrit does not helpfully inform interventions, because the interventions might have the same or the opposite effect as suggested by the analysis. Besides this there were other concerns about the causal reasoning in Case Study I.2) It was not clear that the DAG in Figure 3 supports the authors conclusions about the identifiability of a causal effect of parasitemia as drawn – in the fifth paragraph of the subsection Case study 1: factors determining survival in "severe malaria", this is described as biased through an open path at immunity, while in the subsection "Simulation study for case study 2" as through an open path at anaemia. But, assuming that all the blue and green nodes represented measured covariates which can be adjusted for, there are no open backdoor paths from parasitemia to death through either immunity (the path becomes blocked again at anaemia) or anaemia itself (the open collider path is blocked at age). However, it is certainly reasonable that there exist other unmeasured common causes of anaemia or immunity , and if those were included  there would indeed be an open backdoor path. Most common biases in real data analyses occur because of paths that involve unmeasured variables, so including unmeasured variables such as genes, diet, or coinfections as unknowns in your figure would also be useful for didactic purposes. Furthermore, it's not clear that it would be appropriate to condition on all the blue and green variables for exactly this reason – they are likely to be colliders and therefore conditioning can induce bias; and conditioning on them would remove some of the effect of parasitemia.3) If this paper is intended to be a tutorial for readers who have not had prior exposure to causal inference or directed acyclic graphs, the description of the assumptions required for these methods and their comparison to other approaches is insufficient. A simpler example would be more useful and the paper should probably include more detail on how to read the DAG to determine potential for bias. In general, a separate DAG should be drawn for each causal question – the DAG in Figure 3 contains many more variables than are necessary to estimate an effect of parasitemia (presuming we could define one sufficiently well), but does not include sufficient variables to estimate the effect of anti-malarial drugs except from a randomized controlled trial (and in that case has too many variables). Finally, it seems likely that a number of these variables have a cyclic / feedback relationship, in which case there may be time-varying exposure-confounder feedback which could exacerbate the problems of a lack of well-defined interventions.eLife that they criticize. We can't tell on a brief look at that paper whether the comparison was between:4) For Case Study II, it is hard to understand what was done in Clarke et al., the paper in a) Severe malaria anemia (SMA)  vs. population controls, and separately CM+-SMA vs. population controls, for G6PD status (which would not suffer from the problem the authors posit);b) SMA only vs. population controls, and CM only vs. population controls ;c) SMA vs. CM among severe malaria , which seems to be what the R code posted on GitHub assumes.This needs to be clarified further before it can be evaluated.[Editors’ note: what now follows is the decision letter after the authors submitted an appeal.]I and the reviewers have read and considered your letter of appeal, which makes a number of thoughtful points. After discussion among the reviewers, our view is as follows:1) The first half of the paper could be a focused effort to assess the evidence for the effectiveness of rapid transfusion in preventing malaria mortality, with causal machinery used to improve the credibility of this inference compared to more informal methods. Such a study would avoid attributing causal effects to state variables such as hematocrit or temperature or the like, and would focus on a well-defined intervention. Without resolving the philosophical question of whether causal questions can ever be asked without a well-defined intervention, it seems that in the context of acute malaria mortality, with an explicit justification in the introduction that the authors seek to inspire clinical studies, this is a reasonable constraint. If you resubmit a version with this issue, please also attend to the other issues raised by the second reviewer.2) The second half of the paper is really quite unrelated, except by the use of causal methodology, and while it seems to make a very important point, it does so in a way that is confusing in the sense that the back-story and evidence for what was done in previous studies is obscure. In my personal opinion, this seems to be a short paper of its own, where the history of prior studies needs to be presented in such a way that the reader can clearly tell what has been done, before the authors of the present manuscript explain why that is fallacious.3) There is a third, implicit purpose of the paper, which is to introduce the malaria clinical epi community to causal reasoning. This is in our view not ideally done by the first half of the paper, which is extremely complex and raises the issue of what is a causal question as well as the rather complicated mechanics of how to control for various confounders. The second half may be better suited to this purpose as it is simpler.eLife given the publication of the prior paper in the journal); a tutorial paper that uses the second part as an example but is more generally about causal reasoning; or perhaps the full paper . This last possibility seems unwieldy for the same reasons as the original paper.We can think of a few different ways you might proceed; a focused paper on the second part  would be of greatest interest to [Editors’ note: what now follows is the decision letter after the authors submitted for further consideration.]eLife. Your article has been reviewed by two peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Neil Ferguson as the Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "Berkson's bias and the apparent protective effect of glucose-6-phosphate dehydrogenase deficiency on cerebral malaria" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.This paper attributes findings of a protective effect of G6PD deficiency against cerebral malaria to a statistical/causal artifact known as Berkson's bias (though maybe not the orthodox version thereof) or more generally selection or collider bias. It uses argumentation and simulation to suggest that the problem is:1)G6PDd is a cause of increased risk of severe malarial anemia (SMA);2) Two prior studies compared cases with various forms of severe malaria, specifically SMA and CM, to community controls for the prevalence of G6PDd, and found it was higher than controls in SMA and lower than controls for CM. They concluded a likely causal protective effect of G6PDd on CM;3) The case definitions used were CM alone  and SMA alone  Simulation shows this could account for the full effect.The reviewers and reviewing editor have discussed the manuscript and believe it can be made acceptable for publication after some crucial but relatively minor revisions:1) Clarity about what was done. Reviewers had a hard time establishing precisely what was done in the two papers, and how that relates to the simulations. In particular, please:a) State precisely the regression that was made in each paper […] However for reasons stated below, both reviewers have major concerns with the analysis. As a result, this paper cannot be published in something like its current form. The first Case Study is not appropriate because it doesn't ask well-posed causal questions, and the second Case Study is essentially a critique of another paper that I can't verify includes a correct description of what the other paper did. If it is a correct description, it is a very important point that should be made as a letter or some other way, and the simulation is superfluous except to provide an illustration to those outside the epidemiology field.The summary letter clearly demonstrates that both the editors and the reviewers have spent considerable time delving into the paper and the accompanying code. We are very grateful. The synthesized criticisms are informed and constructive and this assessment and review is much appreciated and will ultimately lead to a substantially improved manuscript.We would like to appeal against the decision to reject the paper.The major concern (point 1) is that Case Study 1 does not ask well-posed causal questions because it does not discuss interventions. However, both reviewers acknowledge that this view is not universal in the field of causal inference and that this perspective is subject to some debate. We accept that consideration and presentation of this important aspect was lacking in the submitted version of the paper. However, contextual understanding of the medical aspects of severe malaria is needed for a correct interpretation of our results. Severe malaria is a medical emergency with high mortality, and a very rapid evolution. Most deaths occur in the first 24 hours following admission to hospital resulting from sudden onset of acute complications. The pathological process and therapeutic implications are more comparable to haemorrhage. There is a single well-defined and feasible intervention: blood transfusion. Indeed, severe malaria is the major reason for blood transfusion in children in sub-Saharan Africa. Severe malaria is the consequence of the malaria parasite invading a substantial proportion of circulating red blood cells, and these invaded red cells blocking the microcirculation – a process with a time course measured in hours. Anaemia results largely from obligatory haemolysis following schizogony, and sequestration of infected and uninfected red cells. To this extent, the primary way in which the variable haematocrit changes is clear. Furthermore, the degree of anaemia (measured as haematocrit) is certainly a determinant of patient outcome and has considerable practical utility. The counterfactual outcomes of interest are those that would be observed if it were possible to change the haematocrit on admission. Transfusion on admission is the only acute intervention to treat severe malaria in this emergency situation. The other interventions that are mentioned to treat anaemia are not relevant in this context. Patients would die before any drugs acting on the blood marrow took effect; active case detection cannot be done in remote rural areas where severe malaria kills most children; iron supplements at a population level are not a reasonable intervention providing no protection against acute haemolysis causing anaemia in falciparum malaria. Thus, in the case of haematocrit, the way in which this variable changes is known; there is practical utility in its measurement; and the only relevant intervention is transfusion. For these reasons we do not think that the measured causal effect can be explained as a weighted average of multiple factors. However, we agree with the reviewers, that this might not the case for each of the other variables.The other critique is the dependence on the time of admission. Every study in severe malaria suffers from the selection bias of only considering patients who seek treatment at larger health centres. However, the problem addressed in our paper addresses the real-life situation on how to manage patients with severe falciparum malaria admitted to hospital. Whether or not to transfuse these patients on admission is a very important question and we believe causal reasoning is essential in the debate. We find that moderate anaemia is not harmful and could be even be beneficial in severe malaria. We are careful not to overstate the implications for blood transfusion due to the exploratory nature of the analysis. Our finding may explain why the very large FEAST study reported a six-fold higher mortality in patients who were above the recommended haematocrit transfusion threshold yet still received a blood transfusion. In summary, the causal question for haematocrit is well-posed and this work should help guide future analyses of transfusion-based intervention studies.Well-defined interventions are also potentially available for raised blood urea nitrogen: the intervention is here is hemofiltration or dialysis; high parasitaemia: the intervention is the anti-parasitic drug; pulmonary oedema: the intervention is oxygen and positive pressure ventilation. For seizures, anticonvulsants can be given. For acidosis there are experimental treatments. For coma itself there are no direct interventions possible and we agree to remove this from Figure 4. However, we think anaemia, pulmonary oedema, seizures and blood urea nitrogen should stay in Figure 4. None of these variables are chronic unalterable health states but the result of the acute infection.The second major concern (point 4) is that Case Study 2 is a critique of two recent major papers (notably one in eLife) and that the validity of this critique cannot be verified. Both Nature genetics and eLife only accept correspondence up to one year after publication , and therefore this channel of communication is closed. The availability of open channels for balanced critiques of published research is essential for reproducible research. We are sure that eLife promotes this healthy scientific exchange. We have asked Professor Kwiatkowski (corresponding author for both papers) which of the possible analyses were done and it was confirmed to be scenario b (from your summary letter). When we subsequently sent our concerns along with a draft manuscript , no response was given. We have the email correspondence to verify these exchanges.This highlights the problems of publishing data analytic results with no accompanying code. Even if their data were openly available, reproducing the analysis would require reverse engineering. We have gone to significant effort to support all our results with open, carefully annotated code and deidentified data.As a final point, it is mentioned that the simulation study we present in Case Study 2 is superfluous. We respectfully disagree. This simple simulation shows that reasonable values of the effect of G6PD deficiency on anaemia (taken from previous published estimates) would suffice to explain the observed negative association between cerebral malaria and severe malarial anaemia. This `sensitivity’ analysis is an important building block in the argument: if larger, unreasonable effects were necessary to explain the observed results, a residual direct effect could still be posited.Would eLife consider our appeal under the following set of conditions?· We will add to the Methods section careful consideration of the counterfactual states underlying the exposure variables.· We will remove coma as an exposure with an interpretable causal effect and add a point to this effect in the discussion.· We will address the concerns in point 2. This highlights an error in the manuscript and this will be changed accordingly.· We disagree with point 3. All variables considered are at the point of admission and therefore there are no feedback loops involved. The didactic aspect will also be addressed, but the DAG in Figure 3 is useful in that it shows the overall picture of expert knowledge in severe malaria using the key admission variables.[Editors’ note: the author responses to the re-review follow.][…] We can think of a few different ways you might proceed; a focused paper on the second part ; a tutorial paper that uses the second part as an example but is more generally about causal reasoning; or perhaps the full paper . This last possibility seems unwieldy for the same reasons as the original paper.You are free to take or leave any of these suggestions, but this is how we see the paper. If you do split it up, we would suggest that the G6PD part  would be of greatest interest to eLife given the prior publication. The mortality prediction part is a large and complex approach to a focused clinical question that might by itself be a very good paper for a more specialized journal.This is a re-submission of a previously rejected paper, entitled "Causal pathways in severe malaria". Following the recommendations of the senior editor (Prof. Neil Ferguson), after a successful appeal against the reject decision, we have made considerable changes to the manuscript. We have kept only the section which deals with Berksonian bias in two recent major publications  which report a protective effect of G6PD deficiency in severe cerebral falciparum malaria. Our re-submission focuses on highlighting how Berkson’s bias likely explains all of the observed association. This would invalidate the model of balancing selection for G6PD deficiency mutations proposed by Clarke et al.The only relevant reviewer comment concerning this re-submission is now:4) For Case Study II, it is hard to understand what was done in Clarke et al., the paper in eLife that they criticize. We can't tell on a brief look at that paper whether the comparison was between:a) Severe malaria anemia (SMA)  vs. population controls, and separately CM+-SMA vs. population controls, for G6PD status (which would not suffer from the problem the authors posit);b) SMA only vs. population controls, and CM only vs. population controls ;c) SMA vs. CM among severe malaria , which seems to be what the R code posted on GitHub assumes.This needs to be clarified further before it can be evaluated.The Materials and methods section of the paper now shows that scenario 3 is almost certainly the one used in Clarke et al.to demonstrate a protective effect of G6PD deficiency on cerebral malaria. Although it is not directly stated in the Clarke et al. Materials and methods section, we show that it can be deduced from tabular data presented in the paper.We suggest that the article type of this re-submission is modified from `Research Article’ to `Short Report’ in light of its new brevity.[Editors’ note: the author responses to the re-review follow.]The reviewers and reviewing editor have discussed the manuscript and believe it can be made acceptable for publication after some crucial but relatively minor revisions:1) Clarity about what was done. Reviewers had a hard time establishing precisely what was done in the two papers, and how that relates to the simulations. In particular, please:a) State precisely the regression that was made in each paper [which we believe was Pr(CM and not SMA) vs Pr (control), in logistic regression with G6PD genotype as predictor in a case-control format, and similarly for SMA and not CM) and quote the relevant passage in each paper that states the exclusion of dual cases.b) State that this is the same comparison  in the simulation.We agree that the original presentation lacked clarity and we have substantially rewritten the Materials and methods section in light of this comment  Address a reviewer concern that the statistical noise around the estimated OR looks large in your simulations given the use of 1 million people. This may be a false impression or may be due to the rarity of one cell in the odds ratio, but please explain.5 rather than 106 individuals. We have corrected this and the noise in the plot  is greatly reduced. We thank the reviewer for pointing this out!This was a typo in the code – the original plot was not based on 103) Address a reviewer concern that producing a close quantitative match to the biased odds ratio for CM is not easily interpretable given the simplifying assumptions notably males only – would that not change the value substantially so that the agreement becomes qualitative rather than quantitative? A simulation including females would be simple to do.In fact, we contrast our simulated results against the reported results in males only. We get an almost perfect match for the reported results in males. This has been made clearer in the Results (second paragraph). Therefore, in our opinion, there are no major simplifying assumptions that should impact this simulation/sensitivity analysis.Although it is simple to generate proportions of females and males with G6PDd under an assumption of Hardy-Weinberg equilibrium, a simulation involving females is in fact more complicated. It necessitates assumptions regarding the gene dose effect . As the simulation is currently written, there is a unique free parameter and this facilitates interpretation. The gene dose effect would require one extra parameter for which there are little data on which to calibrate it.4) Reviewers found point #1 confusing perhaps for several reasons, one of which is the use of Berkson's bias as the explanation here. Indeed, on first reading I had thought that the mistake was that the earlier papers had looked at predictors of SMA and CM among all severe malaria patients . That would be classic Berkson's bias as taught in basic epidemiology classes. The bias you have identified is closely related, is still a form of collider bias, but is not exactly the same; it is that "CM" is really "CM and not SMA" and vice versa. The wording about Berkson's bias may just mislead people – maybe you want to say collider bias, and make clearer in the DAG how this works. Removing Berkson from the title could also clarify for those who only read the title!We agree and thank you for this suggestion. The title has been changed to "collider bias" and we have changed all mentions in the main text to collider bias also.In the DAG in Figure 1, we have changed the name of the collider variable from "Included in study" to "Case definition". This shows how the case definition is simultaneously dependent on both clinical presentations.


Plasmodium falciparum prevalence in Magoda (1992–2017) and Mpapayu (1998–2017) villages of Muheza district, North-eastern Tanzania, in relation to changes in the levels of different interventions and rainfall patterns.Although the recent decline of malaria burden in some African countries has been attributed to a scale-up of interventions, such as bed nets , the contribution of other factors to these changes has not been rigorously assessed. This study assessed the trends of P. falciparum infections in relation to different malaria interventions deployed, particularly bed nets and anti-malarial drugs. Trends and patterns of rainfall in Muheza for 35 years (from 1981 to 2016) were assessed to determine changes in the amount and pattern of rainfall and their possible impacts on P. falciparum prevalence besides of those ascribed to interventions.Individuals aged 0–19 years were recruited in cross-sectional surveys to determine the prevalence of P. falciparum prevalence decreased in both villages over the years but with a peak prevalence shifting from children aged 5–9 years to those aged 10–19 years from 2008 onwards. Bed net coverage increased from < 4% in 1998 to > 98% in 2001 and was ≥ 85.0% in 2004 in both villages; followed by fluctuations with coverage ranging from 35.0 to ≤ 98% between 2008 and 2017. The 12-month weighted anomaly standardized precipitation index showed a marked rainfall deficit in 1990–1996 and 1999–2010 coinciding with declining prevalence and despite relatively high bed net coverage from 2000. From 1992, the risk of infection decreased steadily up to 2013 when the lowest risk was observed , but it was significantly higher during periods with positive rainfall anomalies . The risk was lower among individuals not owning bed nets compared to those with nets .High prevalence (84–54%) was reported between 1992 and 2000 in Magoda, and 1998 and 2000 in Mpapayu, but it declined sharply from 2001 to 2004 (from 52.0 to 25.0%), followed by a progressive decline between 2008 and 2012 (to ≤ 7% in both villages). However, the prevalence increased significantly from 2013 to 2016 reaching ≥ 20.0% in 2016 (both villages), but declined in the two villages to ≤ 13% in 2017. Overall and age specific A decline in prevalence up to 2012 and resurgence thereafter was likely associated with changes in monthly rainfall, offset against changing malaria interventions. A sustained surveillance covering multiple factors needs to be undertaken and climate must be taken into consideration when relating control interventions to malaria prevalence.The online version of this article (10.1186/s12936-018-2395-1) contains supplementary material, which is available to authorized users. Sub-Saharan Africa has witnessed an epidemiological transition in the distribution and intensity of malaria transmission since 2000, with a remarkable decline of malaria burden up to 2015 and a resurgence reported in 2016 . HoweverLongitudinal studies in Kenya , 8, Seneof Plasmodium falciparum declined from 78% in 2003 to 13% in 2008 in the lowland village and from 25 to 3% in the highland village [P. falciparum prevalence in the two villages and examine plausible factors that may have contributed to the changing transmission intensity of P. falciparum between 1992 and 2017.Despite scarcity of long-term data on malaria prevalence across Tanzania, the consensus that the burden of malaria has declined significantly since 2000 is most likely valid , 15. How village . Finally village . The preThe data used in this analysis was obtained from studies conducted in two villages of Magoda and Mpapayu in Muheza district. It is an extension of the analysis which covered studies implemented from 1992 to 2004 where different types of studies were undertaken in these villages including drug efficacy trials, bed net effectiveness trials and parasitological surveillance through cross-sectional surveys (CSS) . With drAlthough the CSS conducted before 2008 involved individuals of all age groups, for comparison reasons the current analysis focused only on those aged 0–19 years. For the CSS conducted from 2008 onwards, 250 individuals were randomly selected in each study village as previously described . From 19Blood smears were stained using 3% Giemsa solution for 45 min and examined at a magnification of 1000× to detect parasite species and to determine parasitaemia. Reporting, quantification and quality control of microscopic examination of blood smears were performed as described elsewhere , 24.Between 1992 and 2001, chloroquine was used for the treatment of uncomplicated malaria while sulfadoxine/pyrimethamine (SP) and amodiaquine (AQ) were also used before and after policy changes in 2001 and until 2006 when they were withdrawn. In November 2006, artemether–lumefantrine (AL) replaced SP and has been the first-line drug for the treatment of uncomplicated falciparum malaria without parasitological confirmation (from January 2007) or with confirmed parasitological test by malaria rapid diagnostic tests (RDTs) from 2012. A weekly mobile clinic was introduced in Magoda in 1994 and Mpapayu in 1997; whereby during each visit, febrile individuals were tested for malaria by microscopy and treated with either AQ or SP. In 2004, the mobile clinic was replaced with a dispensary which was constructed by the project . From 20In December 1998, each sleeping bed in Magoda received a permethrin insecticide-treated net (ITN), while in Mpapayu, deltamethrin-treated nets were distributed in March 2001; all ITNs were re-impregnated with the same insecticides twice a year until 2003 [Anopheles gambiae was the main malaria vector in the community, together with Anopheles funestus and a low proportion of Anopheles arabiensis . It was also shown that An. gambiae sensu stricto was the main malaria vector in Muheza and other parts of north-eastern Tanzania followed by An. funestus until midi 2000’s when An. arabiensis started to become prominent [Few studies were conducted between 1992 and 2004 to monitor mosquito vectors in the study villages and showed that rominent . Other srominent , 28. HowRainfall data for Muheza district spanning a period of 35 years (from 1981 to 2016) was obtained from the Tanzania Meteorological Agency (TMA), covering weather stations around Muheza town (situated about 10 km from Magoda and Mpapayu villages). Monthly total rainfall data was used in the analysis and for months with missing data, the gaps were filled with satellite-generated data.The studies which provided parasitological and clinical data for this paper were approved by the Medical Research Coordinating Committee (MRCC) of the National Institute for Medical Research (NIMR). Permission to conduct the study was sought from the regional and district medical officers of Tanga and Muheza, respectively; and from leaders of the two villages. Meetings were held with all community members to obtain their acceptance before the project started. Verbal and written informed consent was sought from patients or parents/guardians in case of children. Further meetings were held in each village to give feedback including results of previous surveys and discuss the study plans with community members. A written report of the previous survey was also given at each meeting.Data collected in previous studies were managed as described earlier , while tP. falciparum infections in Magoda village showed a moderate decline, except for a marked drop to 62.2% in 1996  was reported in 1995 and it declined to 48.1% in 2000. Thereafter, the prevalence among under-fives continued to decline reaching approximately 26% in 2004 and 2008, and then declined to the lowest level of 3.3% in 2012  was also observed in this age group with the lowest prevalence in 2012 (6.7% in Magoda and 3.2% in Mpapayu); but with an increase in the prevalence in both villages reaching the highest prevalence of 32.3% in 2015 in Magoda and 26.9% in Mpapayu in 2014. A decline in prevalence occurred (among children aged 5–9 years) in 2016 and 2017 in Magoda and from 2015 in Mpapayu with the lowest of approximately 15.0% in 2017 in the two villages , the opposite situation was observed whereby the highest prevalence shifted to this age group except in Mpapayu in 2017  and Mpapayu (from 1999) had the highest prevalence of  and MpapIn Magoda village, coverage (ownership) of bed nets (ITNs/LLINs) increased from less than 4% in 1998 to 92.7% in 1999 and reached the highest level of 95.2% in 2001  index (baseline = January 1981) showed a marked rainfall deficit in the periods between 1991–2000 and 2001 and 2010  and this seems to coincide with a significant decrease of malaria prevalence Fig. . When exP. falciparum infections was observed between 2000 and 2012 [P. falciparum declined, which would easily be ascribed to the interventions.Previously, in the villages of Magoda and Mpapayu, Tanga region, Tanzania, a significant decline in the prevalence of and 2012 . ParasitAfter the end of major research projects in the two villages in 2004, ITNs and LLINs were distributed to the study communities through different national initiatives including nets for pregnant women between 2006 and 2014, and mass distribution in 2011 and 2016 . Moreover, the mobile clinic operating in the villages since the mid-1990s was in 2004 replaced with a dispensary, which offered medical services to all members of the community. Thus, the observed decline in malaria prevalence may be attributed to high coverage of ITNs/LLINs and improved case management at the dispensary, including deployment of effective anti-malarials (artemisinin-based combination therapy from 2007), as also described in the previous study .A large epidemiological malaria study conducted in Kenya suggests that the main factor responsible for the decline of malaria was the considerable use of SP in the management of uncomplicated malaria, when it replaced chloroquine as standard treatment in the early 2000’s . WhetherAn. gambiae and An. funestus; from a sampling of 5382 Anopheles sp. in 2004 to merely a total of 14 mosquitoes in 2009 [P. falciparum prevalence observed between 2000 and 2012 in the present study sites was affected by lack of malaria vectors during this period is likely, and could possibly be related to the abnormalities in rainfall (amount and patterns), but unfortunately, no entomological surveys were performed during that study period.Alternatively, an entomological study performed in two villages of the same Tanga region located about 20 km from the present study observed a drastic decline in abundance of  in 2009 . The cau in 2009 . WhetherFrom 2013 onwards, there has been a sustained re-emergence of malaria in the study area with an increase in parasite prevalence reaching 25% in 2014 and remaining relatively higher up to 2016 and a remarkable decline in 2017. Re-emergence of malaria in the study area occurred during a period where ITNs/LLINs are expected to work and a lack of protective effect of ITNs/LLINs somewhat reflects similar observations from a Kenyan study, where an increase in malaria prevalence was reported despite recent mass ITNs/LLINs distribution . AlthougAnopheles mosquitoes. These changes in prevalence occurred simultaneously with the interventions  applied during the study period, indicating that multiple factors could be responsible for the observed trends. Conversely, the lack of changes in 2016 and a decline in prevalence in 2017 could possibly be attributed to the negative anomaly in 2016 and high bed net coverage (> 92%).The declining prevalence up to 2012 occurred in the period with overall negative rainfall anomaly suggesting that the decline in parasite prevalence and malaria burden reported in this area (and other parts of the country) was also possibly affected by the variability in the amount and pattern of rainfall directly affecting the P. falciparum infections (and current resurgence) is most likely due to multiple factors including anti-malarial control interventions, but the exact contribution of the interventions is uncertain and possibly varies through time [Thus, the decline in the prevalence of ugh time . Additiough time , where mP. falciparum infections observed up to 2012 was followed by a sustained resurgence of malaria, which is possibly associated with changes in the amount and pattern of rainfall in Muheza, apart from intensified malaria control. Thus, climate variability must be taken into consideration when relating control interventions to malaria prevalence. A sustained multi-factorial surveillance needs to be undertaken to monitor changes in malaria transmission and determine other factors which could be associated with continued transmission and resurgence of malaria in this and other area with a similar malaria epidemiological transition.A significant decline in the prevalence of Additional file 1. Number of individuals sampled in CSS which were conducted in Magoda and Mpapayu between 1998 and 2016.Additional file 2. Overall and age specific prevalence of Plasmodium falciparum detected by microscopy in the two villages of Magoda (1992–2017) and Mpapayu (1998 to 2017) in Muheza district north-eastern Tanzania.