Malaria Parasite burden in areas of northern Uganda where Indoor Residual spraying has been withdrawn

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Indoor residual spraying (IRS) for mosquito malaria vector control was last conducted in this region in 2014. The Government of the republic of Uganda intends to re-introduce IRS alongside RTS,S/AS01 malaria vaccine in this area as a strategy to achieve the ambitious goal of global malaria elimination by 20230. There is need to ascertain current parasite burden within selected areas of the region in order to predict the impact of future co-deployment of IRS and RTS,S/AS01. Methods : 3–4 mls of whole blood was collected in an EDTA tube from 353 participants who consented/assented to the blood draw and interviews. Participants were patients seeking for medical care at Gulu Regional Referral Hospital and were drawn from Acholi and Lango districts. Attending clinicians documented disease symptoms and severity. Blood samples were tested for malaria parasite using rapid diagnostics tests and confirmed by microscopy thick smear. Thin blood smears were made, stained, and examined to quantify the parasites. Data was entered into excel and later exported to GraphPad prism (ver8) and STATA where analysis was done. The non-parametric t test was used to compare means of parasitemia across two variables/characteristics while one way ANOVA was used to compare means of parasitemia across three groups and above. 2x2 contingency table of malaria related symptoms versus test outcome was drawn and odd ratios were calculated to identify which symptoms are most associated with malaria in northern Uganda. Results : Test positivity rate was 63.34% at enrollment with 93.21% of participants having fever as well. 258 of the 353 participants were children below 5 years while majority were females (60.3%). Vomiting, diarrhea, prostration, and seizures (indicators of severe malaria )were identified among children < 5 years only. There was no significant difference in parasite burden between male and female participants. Thick smear is an accurate measure of parasitaemia as there was a performance agreement between its quantification and that of a thin smear. There was no significant difference in the parasite count across different age categories. There is a very high association between joint pain and malaria, while fever is least associated with malaria among symptoms selected. Conclusion : This study found malaria test positivity rate to be within the normal range for endemic regions like northern Uganda indicating that the additive effect of Indoor residual spraying is temporal and insignificant in the long run if inconsistently applied. Amidst sustained interventions, malaria test positivity rate always fluctuates within a narrow range in areas of stable transmission. We are not certain if the introduction of RTS,S/AS01 or R21 matrix vaccine will come with real additional benefits. Children and infants under 5 years still carry the highest parasite and severe malaria burden. This finding nullifies the hypothesis that cases of severe malaria was shifting from younger to older ages in areas with stable transmission & vector control measures such as northern Uganda. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Uganda alongside Nigeria, the Democratic Republic of Congo and Mozambique accounted for almost half of the global malaria burden in 2022[1]. According to the Ugandan Ministry of Health Epidemiological report for the entire 2023, malaria burden (incidence, mortality, and morbidity) was highest in the Eastern, Northern, and west Nile regions of the country with average test positivity rates standing at about 56% across all the regions. In selected districts of the Acholi region where this study was conducted, malaria prevalence in 2018 and 2019 were 69.46% and 93% respectively[2]. A retrospective study done from January 2019 to December 2020 in one of the districts in northern Uganda reported that 64.8% of total Hospital admission were attributable to malaria and 93.8% of these admissions were children below 12 years. 40% of the cases met the case definition of severe malaria[3]. Over the last decade, the national malaria control program with support from donors and implementing partners adopted a number of strategies to reduce malaria disease burden [4]. This included the use of long-lasting insecticide treated bed nets, prompt diagnosis and treatment using WHO anti-malaria medicines, and intermittent preventive therapy for pregnant women. In addition to this, indoor residual spraying using bendiocarb, a carbamate was implemented in ten districts of Northern Uganda that had the highest burden from 2010 to 2014[5]. As a result, a marked reduction was observed in mid-north where the prevalence of malaria reduced from 63% in 2009 to 20% in 2014 with the districts in which IRS was implemented having the lowest prevalence of 14%. However, there was a significant increase in malaria incidence and prevalence in the region following cessation of IRS. A cross sectional survey done in 2010 in Apac district (four years after IRS was initiated in the region) amongst children under 5 years found parasite prevalence to be 55.8% (115/206) by microscopy and 71.9% (41/57) by polymerase chain reaction[6]. This was way above the national average. However, from 2014 till date, indoor residual spraying has not been done in the region and severe forms of malaria has continued to cause havoc in the region. Uganda National Institute of Public Health reported a general decrease in severe malaria cases among children below 5 years from 10% in 2017 to 5% between 2017 to 2021 across other regions of Uganda but noted an increase in mid North and Bukedi regions of the country. This trend analysis was done three years after cessation of IRS in Northern and Bukedi regions. Justin M et al,2012 reviewed 75 articles related to malaria resurgence in areas where IRS has been applied and reported that 91% of all resurgence were attributable in part to at least the weakening of malaria control program for a variety of reasons; including limited resources[7]. Due to increased insecticide resistance to pyrethroids, change in mosquito feeding behavior, emergence of new species of mosquitos such as Anopheles stephensi , a fundamental question on the effectiveness and relevancy of IRS in mosquito vector eradication visa vie its disruption of projected development of immune system and promotion of parasite resistance has popped up[8]. Before introduction of IRS in Northern Uganda, Malaria transmission & severity was relatively similar across the country; including regions not included in the pilot IRS program of 2006. Upon initiation and inconsistently applying the intervention in the districts of Northern Uganda , severe cases have significantly increased among child population in the region compared to other parts of the country with equally high transmission but IRS has not applied[9]. Kitgum district in northern Uganda experienced a malaria epidemic in the 2014 and 2015 (a year after cessation of IRS) with over 20-fold increase above the normal rate. There was also an exponential increase in the test positivity rate[10]. Similar trend was observed for Hospitalization, severe malaria, and malaria in pregnancy. Intervention such as use of IRS is reported to set up children for a catastrophic rebound the moment it is ultimately withdrawn[11] . Inconsistent application of IRS has been reported to limit exposure to the vast repertoire of Plasmodium falciparum antigens resulting into incomplete development of immunity. Right after IRS, there is always almost zero malaria transmission because of massive death of mosquito vector. This ultimately leads to immunological memory loss because of lack of antigenicity to keep agitating for immune response. Inconsistent application of IRS has also been associated with multiplicity of infections and quicker activation of different PfEMP1 var genes [12] Much as the long-term interventions disrupt malaria transmission, but they do not remove underlying risks. The catastrophic rebound in cases and disease severity following withdrawal of the interventions and control measures ends up being a death trap. Most literatures related to the subject of study did not holistically look at indoor residual spraying in relation to malaria severity and parasitemia. This inadequate information potentially affects management of malaria cases in such settings. This includes choice of medicine for effective treatment across different patient categories[7]. This lack of information and its effects on patient management could potentially mask resistance, increase cases of severity, mortality and morbidity among the vulnerable child and adult population. This study investigated malaria test positivity rate, parasitemia and severe disease across different age categories from selected communities in northern Uganda where indoor residual spraying has not been conducted for the past ten years. Selection of study participants: participants were patients seeking for medical care at Gulu Regional Referral Hospital. The Hospital is the biggest Government referral health facility in Acholi region and located right at the heart of Gulu city. The city is about 350 kms north of Kampala, the capital city of Uganda. Participants came from the districts of Adjumani(6), Amuru(43), Gulu(45), Gulu city(141), Nwoya(54), Omoro(37), Oyam(11)and Pader(16) (table 1). With permission from the Hospital administration, research assistants and health educators introduced the study to patients at the different patients point such as outpatient department (OPD), inpatients units, antenatal care section, immunization unit and accident and emergency unit. Patients who expressed willingness to participate in the study were requested to converge at another office where further details of the study were given. Consent forms were read to individual participant and upon acceptance, each signed at the end of the form as proof of consent. For illiterate participants, they were allowed to use a thump print as proof of consent. For child participants and minors, a guardian or parent was requested to offer permission to participate in the study while the child offered assent for finger prick and blood draw. Each participant was assigned a study number and Laboratory identification number. Participants’ demographics and clinical information: A total of 353 individuals participated in the study and were drawn from the districts of Gulu, Gulu city, Adjumani, Oyam, Omoro, Pader, Nwoya and Amuru. Majority of the participants were female (60.1%) (table 1), while children under 5 years had the highest number of participants at 258 representing 73%. Test positivity was determined using malaria RDT and microscopy thick smear and stood at 63.74% at enrollment (table 1). Up to 93.21% of participants were confirmed to have fever at enrollment. Those who reported fever had their status confirmed by a thermometer that was put either under the armpit, sublingually or at the rectum for children with high temperature. A participant was termed as having severe malaria when he or she has fever and either diarrhea, vomiting, prostration, seizures or +3 parasitaemia (thick smear) [13]. Out of the 63.74% who tested positive, 54 were categorized as +1, 74 as +2, 95 as +3 using a thick blood smear microscopy (table 1) [14]. Table 1: summary of participants’ demographics and clinical information Value/proportion District of origin Adjumani=6 Amuru=43 Glu = 45 Gulu City=141 Nwoya=54 Omoro=37 Oyam=11 Pader=16 Gender Female=60.3% Age distribution 15 years=20 Test positivity/Parasitaemia at enrollment 63.74 tested positive for malaria RDT History of fever 93.21% had fever at enrollment Level of parasitemia by thick smear +1=54 +2=74 +3=95 Preparation, staining and examination of thick and thin blood smear for malaria identification. Principles of the stain : Giemsa was used to stain both thin and thick smears in order to detect the parasites. It is composed of both the Acidic and Basic dyes. Azure and methylene blue are basic dyes that binds to the acid nucleus producing blue-purple color. Eosin is an acidic dye attracted to the cytoplasm and cytoplasmic granules and produces an alkaline-producing red coloration. The stain was buffered with water to pH 6.8 in order to precipitate the dyes to bind simple materials [15]. Staining procedures : Thin smear :On clean dry microscopic glass slides, thin films of the specimen (blood) were made and left to air dry. The smears were dipped (2-3 dips) into pure methanol for fixation and were left to air dry for 30 seconds. Slides were then flooded with 5% Giemsa stain solution for 20-30 minutes and were then flushed with tap water and left to dry. Thick smear : Drops of blood were added in the middle of well labelled slides and the drops spread in a circular pattern from inside out and allowed to air dry for 1 hour on a staining rack. The thick blood smears were dipped into diluted Giemsa stain (was prepared by taking 1ml of the stock solution and adding to 49ml of phosphate buffer or distilled water).The smears were then washed by dipping in buffered water or distilled water for 3-5 minutes and were left to dry. The Giemsa-stained blood film slides to be examined were then placed on the microscope stage for examination. Microscopic examination : The methods used were described by [16]; but briefly, the thick films were positioned in line with the 10x objective lens. The microscope light was switched on ,and light source adjusted optimally, and the focus was found by looking through the ocular and the 10x objective. The blood films were then scanned for parasites and blood elements. Part of the films that were well stained and had evenly distributed white blood cells were then selected. A small drop of immersion oil was added to the thick film. To avoid cross-contamination, the immersion oil applicator was not allowed to touch the slide. The 40x objective was not allowed to touch the oil as well. The 100x oil immersion objective was then switched on over the selected portion of the thick film. Fine focus adjustment was made to see the image clearly. The mechanical stage was also raised to avoid damaging the slide. Using the fine adjustment, the cell elements were focused on, and confirmation that the film was acceptable for routine examination was done: 15–20 white blood cells per thick film field was used to give a satisfactory film thickness. Films with fewer white blood cells per field were examined more extensively. Slides were examined in a systematic manner, starting at the top left of the film, and beginning from the periphery of the field, then followed by horizontal movement to the right, field by field. When the other end of the film was reached, the slides would be moved slightly downwards, then to the left, field by field, and so forth. For efficient examination, continuous focus and refocus with the fine adjustment throughout examination of each field was ensured. Quantification of Parasitaemia : For ≥ 100 parasites counts in 200 white blood cells, counting would be stopped , and results recorded as the number of parasites per 200 white blood cells. For ≤ 99 parasites in 500 white blood cells, counting would be stopped as well, and results recorded as the number of parasites per 500 white blood cells. All parasites and white blood cells in the final field were counted, even if the white cell count exceeds 200 or 500.Actual numbers of parasites and white blood cells counted was recorded on an appropriate worksheet. When counting was completed, the parasite density was calculated on the basis of the patient’s actual white cell count. Statistical Analysis : Participants’ demographic was captured by the research assistants. Clinical information such as history of diarrhea, prostration, vomiting, and seizures were filled in the case investigation form by the attending clinician. Laboratory test results on parasitaemia were filled by the Laboratory technicians at Gulu Regional Referral Hospital. Demographic information, clinical information and accompanying meta data were entered into an excel spread sheet by the data clerk. Bar graphs and column plots were done in excel spreadsheet.t test in graph pad prism ver 8 was used to derive statistical significance in box plot for two groups. One way ANOVA in graph pad prism was used to derive statistical significance in means of three groups and above. To compare accuracies of different malaria related signs and symptoms in predicting true Prescence of the etiology, a 2 by 2 contingency table was constructed. Odd ratios were generated by dividing the number of those confirmed to have the parasites and the symptom chosen by the numbers of those who have the symptoms chosen but test negative for malaria (RDT). The higher the odd ratio, the more likely the symptom is better predictor of malaria. Results Cases of severe malaria by age category: According to World Health Organization malaria treatment guidelines and Uganda Clinical guidelines, severe malaria is confirmed if a patient has parasitaemia and either diarrhea or vomiting or seizures or prostration (these are the variables included in this study) [17]. This categorization was also adopted for this study. Cases of severe malaria were identified among children below 5 years only (fig 1). Vomiting, diarrhea, seizures, and prostration were found among children under 5 years. Cases of diarrhea and vomiting were more common between the age bracket of 0 to 5 years while prostration and seizures were more pronounced among children between 0 to 2 years (fig 1). Fig 1: severe malaria by age category Parasitaemia by age category: Thick smear malaria quantification by age Parasitemia was quantified using thick smear. In Uganda and so many other African Countries, quantification of malaria parasitemia using + system is routinely done. Its utility is more pronounced in areas with limited equipment, few personnel, high patients’ volume, and other challenges. +1 equates to low parasitemia, +2 equates to moderate parasitemia and +3 equates to high parasitemia. Children under 5 years (average age of 4.52 years) had highest level of parasitemia (+3) (fig 2) followed by those between 5 to 6 years (average age of 5.82) with parasitaemia quantified as +2. Parasitaemia of different quantification was detected in children below 10 years. Fig 2: Thick smear parasite quantification by age Quantification of parasite load by gender using thin blood smear Thin smear has the advantage of allowing the exact parasite count thus giving the right quantification. There was no significant difference in the level of parasitemia between male and female (p=0.6966) (fig 3). However, female had slightly higher parasitemia (mean value=361.8) compared to male (mean value=337.7). Parasites were counted per 200 white blood cells while box and whisker plot were used to compared level of parasitaemia between the two gender (fig 3) Fig 3: Comparison of parasite load by gender Test Performance agreement between thin and thick blood smear as a method of malaria parasite quantification . This study also intended to determine performance agreement between thin and thick blood smear. The gold standard for parasite quantification is a thin blood smear, but as previously mentioned, quantification using thick smear is used across various African countries where resources are limited. There were significant differences in actual parasite counts using thin smear for the three quantification categories of thick smear (+1, +2 and +3). Those quantified as +3 by thick smear had highest number of parasites count/200 WBCs (p<0.0001) followed by those quantified as +2 (p<0.0001) and least parasite count was noted with those quantified as +1 by a thick blood smear. This shows that quantification of parasitemia using a thick blood smear is an effective method in areas with limited resources such as personels, reagents, counting chambers among others. Fig 4: Performance agreement between thin and thick blood smear as a method of malaria diagnosis Parasite load across different age categories Participants were stratified into different age categories. These were (0-5) years, (6-10)years, (11-15) years and >15 years. Categorization was based on projected immunological development and vulnerability to malaria (fig 5). One way Analysis of variance was used to compare parasitemia. The actual parasite count was done using a stained thin blood smear. There was no significant difference in parasite load among different age categories (p=0.3262) (fig 5) . However, children under 5 years of age had highest parasitaemia followed by those between (6-10) years, then those above 15 years and least parasitaemia was noted with children between (11-15 ) years (fig 5). Fig 5: Parasite load by different age categories Accuracy of different signs and symptoms in predicting malaria among patients seeking treatment at Gulu Regional Referral Hospital . The World Health Organization (WHO) and the Ugandan Ministry of Health rolled out test and treat for malaria. This meant malaria diagnosis could only be confirmed by Prescence of malaria parasites in the blood (either by Rapid diagnostics Test or Microscopy). A 2x2 contingency table was drawn for each malaria related symptom versus test outcome by Rapid diagnostics test (Table 2A, 2B, 2C, 2D & 2E).Joint pain had the highest odd ratio (5.60) meaning its presence was highly associated with malaria (Table 2D & 3). This was followed by Headache (OR=2.04) (Table 2C & 3), then followed by Diarrhea (OR=1.82) (Table 2E& 3), then followed by chills (OR=1.79) (table 2B& 3) and the least Odds ratio was noted with Fever (OR=1.72) (table 2A& 3). Given that odd ratios are above one, all symptoms/ signs are associated with malaria and can be used to clinically predict the disease in areas where laboratories are not easily accessible. Table 2: 2x2 contingency table for different malaria signs/symptoms versus test outcome for prediction of a true malaria case Table 2A): 2x2 contingency table for fever versus malaria test outcome Table 2B): 2x2 contingency table for Chills versus malaria test outcome Table 2C): 2x2 contingency table for headache versus malaria test outcome Table 2D): 2x2 contingency table for Joint pain versus malaria test outcome Table 2E): 2x2 contingency table for Diarrhea versus malaria test outcome A) Positive RDT Negative RDT Total Had Fever 205 119 324 No Fever 17 09 26 Total 222 128 350 B) Positive RDT Negative RDT Total Had Chills 206 115 321 Had no Chills 19 13 32 Total 225 128 353 C) Positive RDT Negative RDT Total Had Headache 188 92 280 Had no Headache 62 37 99 Total 250 129 379 D) Positive RDT Negative RDT Total Had joint pain 182 32 214 Had no joint pain 53 87 140 Total 235 119 354 E) Positive RDT Negative RDT Total Had Diarrhea 69 38 107 Had no Diarrhea 156 90 246 Total 225 128 353 Table 3 : odd ratios of different malaria related signs/symptoms in predicting true malaria. Disease related sign/symptom Odds of having true malaria Fever 1.72 Chills 1.79 Headache 2.04 Joint pains 5.6 Diarrhea 1.82 Discussion As the world heads towards the 2030 dateline for global malaria eradication agenda, disease endemic countries such as Uganda continues to deploy all kind of desperate interventions & measures to achieve the ambition[18]. Because countries which have heavy malaria burden also are generally poor, control interventions such as Indoor residual spraying (IRS) are either inconsistently applied or there are no scientific backed up studies to evaluate the effectiveness and the consequences of such long-term interventions on the parasite, vector and human population[19]. This study set out to determine malaria parasite burden in selected areas of northern Uganda where IRS was stopped in 2014. We found test positivity rate to be 63.74% while 93.21% of participants had fever at enrollment. BB Tukei et al;2017 reported that IRS was associated with significant reduction in northern Uganda in the first three months of the chemical application, but this effects wanes off followed by increased slide positivity rate at four months and above after cessation of spray. They noted a reduction in slide positivity rate from about 56–37% in all selected health facilities spread across the ten districts in northern Uganda within the first three months after IRS[20]. A study done in Eastern region of Uganda compared malaria prevalence from two parishes where indoor residual spraying has been sustained for a long period of time and one parish where IRS has never been applied. They found no significant difference in parasite prevalence in IRS exposed and non-exposed study sites. Moreover the incidence of malaria was higher in one parish where IRS has been consistently applied compared to the parish where application has never been done[21]. A similar study done in Eastern Uganda concluded that whilst Malaria burden significantly reduced within the five years of consistent IRS application, a significant proportion of the population remained parasitemic especially in school going children with submicroscopic parasitaemia providing a potential reservoir for malaria transmission[22]. A study done in selected rural areas of India reported that parasite prevalence among patients with acute undifferentiated fever ranged from 19–35%. The relatively low prevalence of parasitemia in patients with fever means there could be other etiologies causing fever and acute febrile illnesses in these patients and efforts must be put in place to do differential diagnosis. The study also found severe malaria among children under five years. All clinical features of severe malaria (Seizures, prostration, vomiting and diarrhea ) were noted in participants under 5 years of age only. A systematic review done using studies conducted across malaria endemic countries of sub-Saharan Africa reported a shift in burden of cerebral malaria towards younger age with increasing intensity of transmission. They concluded that although the peak age of cerebral malaria will increase as transmission intensity decreases in Africa, more than 75% of all pediatric Hospital admissions of severe malaria are likely to remain under 5 years old in most epidemiological settings[23]. Another systematic review analysis reported that Hospital admission with severe malaria were more concentrated among younger children with this effect being even more pronounced for malaria diagnosed deaths. They further reported that for all outcomes, the burden of malaria shifted towards younger ages with increasing transmission intensity[24]. Children under 5 years are more vulnerable to severe malaria because their immune system is still developing. Anti-disease immunity requires a well-developed cell mediated and humoral arm of the immune system which takes time. A child has to be exposed to multiple strains and variants of malaria parasite in order to acquire anti disease immunity. This is projected to take up to 15 years of non-interrupted exposure to malaria parasites[25]. The study found no significant difference in the level of parasitaemia between male and female (p = 0.6966) though mean level of parasitemia in female was slightly higher than males (361.8 for female compared to male = 333.7). The finding partially agrees with those from a couple of studies conducted across Africa. One study done in Uganda found incidence of malaria to be higher among females compared to male. They reported incidence of malaria per 1000 person years was 735 among females and 449 among males[26]. Among contributing factors were frequent visits to these facilities independent of malaria and a higher reported rate of seeking care at these facilities for these febrile illnesses. A cross sectional study conducted among patients presenting with fever at health facilities in Kano City of Nigeria reported malaria prevalence among female to be at 54% while among male was 46% [27]. One study conducted across selected health facilities in Kumasi, Ghana used a sequential mixed method design comprising of qualitative and quantitative method. They reported that most malaria cases were among less educated women (62%) with more external factors associated with risk of infections. Amongst factors highlighted by this group is long exposure to mosquito bites by women as they go about with their gender roles. There was a perfect agreement in performance between thick and thin smear for quantification of malaria parasites. +1 in thick smear corresponds with low parasite counts in thin smear, while + 2 corresponds average parasite counts in thin smear and + 3 smear corresponds to high parasite counts in thin smear. There were no easily available studies that directly compared performance agreement between thin and thick smear for malaria parasites quantification. However, a group of researchers compared performance agreement between thick smear and automated computational Techniques. The Techniques discriminates malaria cells from segmented cells using morphological operations and color based pixel discriminator and found this method to have a higher positive predictive rate and lower number of false positives compared to a thick smear[28]. One prospective study tested 853 blood samples using malaria RDT, thick smear and thin smear and reported that RDT had superior performance than the standard Giemsa thick blood smear. They reported that the RDT’s sensitivity for all malaria was 97% compared to 85% for thick blood smear and RDT also had a superior Negative predictive value of 99.6% with 98.2% for the tick blood smear. This study also found no significant difference in parasitaemia among different age categories much as children under 5 years had slightly higher parasite load. This finding agrees with that of a study done among communities in northern Ghana to determine seasonal profile of malaria infections. They reported malaria prevalence among adults (between 50–60 years) to be at 38% and 82% among children between 5 to 10 years. They concluded that the assumed protective mechanisms did not reduce susceptibility or blunt infections and that congenital induced resistance to malaria infections may not be realized among children and infants [29]. Another study done in Uganda reported that malaria prevalence significantly rose from children at younger ages (< 6 months) by 1.62 times among those between 7 to 12 months and four times among those between 49 to 59 months. They justified their finding of malaria parasitaemia increasing with increase in age as being development of age specific immunity due to continuous exposure to infected mosquito bites [30]. This study also found joint paint to be most associated with malaria and fever least associated. However, all selected signs and symptoms positively predicts for malaria since all their odds ratio is above one. These symptoms include chills, headache, and diarrhea. A study done among pregnant going for antenatal visits in selected Hospitals in the republic of Benin reported that headache was the only symptom associated with positive thick blood smear for routine antenatal visits while for non-scheduled visits, headache and shivering were associated with malaria infections and almost 90% of the women presented with either of the symptoms [31]. According to Center for Diseases control and prevention, signs and symptoms of uncomplicated malaria include fever, sweat, chills, muscle pain, headaches, nausea, and vomiting. Symptoms of severe malaria include confusion, coma, focal neurological signs, severe anemia, and respiratory difficulties[32]. Joint pain in malaria results from micro-vascular sequestration of parasitized red blood cells, decreasing oxygen delivery leading to obstructed blood flow and Tissue hypoxia. The skeletal muscle microvascular function and its oxygen consumption is significantly impaired in malaria infections[33]. Conclusion This study found malaria test positivity rate to be within the normal range for endemic regions like northern Uganda indicating that the additive effect of Indoor residual spraying is temporal and insignificant in the long run if inconsistently applied. Amidst sustained interventions, malaria test positivity rate is always consistent in areas of stable transmission. We are not certain if the introduction of RTS,S/AS01 or R21 matrix vaccine will come with real additional benefits. Children and infants under 5 years still carry the highest parasite and severe malaria burden. This finding nullifies the hypothesis that cases of severe malaria was shifting from younger to older ages in areas with stable transmission & vector control measures such as northern Uganda. Declarations Ethical Approval : This study was nested in a primary study. The primary study got approval from the Gulu University Research and ethics committee. It was also registered with the National council for Science and Technology. The primary study also got approval by Gulu Regional Referral Hospital Research and Ethics committee. The current study was reviewed and approved by the Makerere University school of Biomedical sciences Research and Ethics committee and registered with the National Council of Science and Technology with registration number SBS-2023-362. Participants’ consent: The primary study obtained written consent from Funding Declaration: This study was not funded by any agency, donor, or Institution. It is part of the PhD work; we have not yet been successful in obtaining financial support to do this work. Attempts are being made to secure one for the remaining objectives. For now, supervisors, Doctoral committee and the student are putting in their personal resources . Availability of Data : All relevant data will be made available upon request. Data access link will be shared with the editors and scientific editors of the journal. References Organization WH. World malaria report 2022: World Health Organization; 2022. Divas S, Opiyo EA, Austin R, Ochaya S. Assessing the Malaria burden and Community Response to the Malaria Control and Management Programs in Omoro District, Northern Uganda. 2023. Ocen E, Opito R, Tegu C, Oula A, Olupot-Olupot P. Severe malaria burden, clinical spectrum and outcomes at Apac district hospital, Uganda: a retrospective study of routine health facility-based data. Malaria Journal. 2023;22(1):325. Bryce J, Roungou J-B, Nguyen-Dinh P, Naimoli J, Breman JG. Evaluation of national malaria control programmes in Africa. Bulletin of the World Health Organization. 1994;72(3):371. Okullo AE, Matovu JK, Ario AR, Opigo J, Wanzira H, Oguttu DW, et al. Malaria incidence among children less than 5 years during and after cessation of indoor residual spraying in Northern Uganda. Malaria journal. 2017;16:1-10. Proietti C, Pettinato DD, Kanoi BN, Ntege E, Crisanti A, Riley EM, et al. Continuing intense malaria transmission in northern Uganda. The American journal of tropical medicine and hygiene. 2011;84(5):830. Cohen JM, Smith DL, Cotter C, Ward A, Yamey G, Sabot OJ, et al. Malaria resurgence: a systematic review and assessment of its causes. Malaria journal. 2012;11:1-17. Carter TE, Yared S, Gebresilassie A, Bonnell V, Damodaran L, Lopez K, et al. First detection of Anopheles stephensi Liston, 1901 (Diptera: culicidae) in Ethiopia using molecular and morphological approaches. Acta tropica. 2018;188:180-6. Kassam R, Collins JB, Liow E, Rasool N. Narrative review of current context of malaria and management strategies in Uganda (Part I). Acta tropica. 2015;152:252-68. Ogwang R, Akena G, Yeka A, Osier F, Idro R. The 2015–2016 malaria epidemic in Northern Uganda; What are the implications for malaria control interventions? Acta tropica. 2018;188:27-33. Stanisic D, Mueller I, Betuela I, Siba P, Schofield L. Robert Koch redux: malaria immunology in Papua New Guinea. Parasite immunology. 2010;32(8):623-32. Sherrard-Smith E, Griffin JT, Winskill P, Corbel V, Pennetier C, Djénontin A, et al. Systematic review of indoor residual spray efficacy and effectiveness against Plasmodium falciparum in Africa. Nature communications. 2018;9(1):4982. Trampuz A, Jereb M, Muzlovic I, Prabhu RM. Clinical review: Severe malaria. Critical care. 2003;7(4):1-9. Prudhomme O’Meara W, Remich S, Ogutu B, Lucas M, Mtalib R, Obare P, et al. Systematic comparison of two methods to measure parasite density from malaria blood smears. Parasitology research. 2006;99:500-4. Horobin R. How Romanowsky stains work and why they remain valuable—including a proposed universal Romanowsky staining mechanism and a rational troubleshooting scheme. Biotechnic & Histochemistry. 2011;86(1):36-51. Ba EH, Baird JK, Barnwell J, Bell D, Carter J, Dhorda M, et al. Microscopy for the detection, identification and quantification of malaria parasites on stained thick and thin blood films in research settings: procedure: methods manual. 2015. Organization WH. Management of severe malaria: a practical handbook: World Health Organization; 2000. Feachem RG, Chen I, Akbari O, Bertozzi-Villa A, Bhatt S, Binka F, et al. Malaria eradication within a generation: ambitious, achievable, and necessary. The Lancet. 2019;394(10203):1056-112. Ricci F. Social implications of malaria and their relationships with poverty. Mediterranean journal of hematology and infectious diseases. 2012;4(1). Tukei BB, Beke A, Lamadrid-Figueroa H. Assessing the effect of indoor residual spraying (IRS) on malaria morbidity in Northern Uganda: a before and after study. Malaria journal. 2017;16:1-9. Nankabirwa JI, Bousema T, Blanken SL, Rek J, Arinaitwe E, Greenhouse B, et al. Measures of malaria transmission, infection, and disease in an area bordering two districts with and without sustained indoor residual spraying of insecticide in Uganda. Plos one. 2022;17(12):e0279464. Nankabirwa JI, Arinaitwe E, Rek J, Kilama M, Kizza T, Staedke SG, et al. Malaria transmission, infection, and disease following sustained indoor residual spraying of insecticide in Tororo, Uganda. The American journal of tropical medicine and hygiene. 2020;103(4):1525. Roca-Feltrer A, Carneiro I, Smith L, Schellenberg JRA, Greenwood B, Schellenberg D. The age patterns of severe malaria syndromes in sub-Saharan Africa across a range of transmission intensities and seasonality settings. Malaria journal. 2010;9:1-9. Carneiro I, Roca-Feltrer A, Griffin JT, Smith L, Tanner M, Schellenberg JA, et al. Age-patterns of malaria vary with severity, transmission intensity and seasonality in sub-Saharan Africa: a systematic review and pooled analysis. PloS one. 2010;5(2):e8988. Langhorne J, Ndungu FM, Sponaas A-M, Marsh K. Immunity to malaria: more questions than answers. Nature immunology. 2008;9(7):725-32. Okiring J, Epstein A, Namuganga JF, Kamya EV, Nabende I, Nassali M, et al. Gender difference in the incidence of malaria diagnosed at public health facilities in Uganda. Malaria Journal. 2022;21(1):1-12. Nas F, Yahaya A, Ali M. Prevalence of malaria with respect to age, gender and socio-economic status of fever related patients in Kano City, Nigeria. Greener Journal of Epidemiology and Public Health. 2017;5(5):044-9. Dave IR, editor Image analysis for malaria parasite detection from microscopic images of thick blood smear. 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET); 2017: IEEE. Koram KA, Owusu‐Agyei S, Fryauff DJ, Anto F, Atuguba F, Hodgson A, et al. Seasonal profiles of malaria infection, anaemia, and bednet use among age groups and communities in northern Ghana. Tropical Medicine & International Health. 2003;8(9):793-802. Wanzira H, Katamba H, Okullo AE, Agaba B, Kasule M, Rubahika D. Factors associated with malaria parasitaemia among children under 5 years in Uganda: a secondary data analysis of the 2014 Malaria Indicator Survey dataset. Malaria journal. 2017;16(1):1-9. Huynh B-T, Fievet N, Gbaguidi G, Borgella S, Mévo BG, Massougbodji A, et al. Malaria associated symptoms in pregnant women followed-up in Benin. Malaria Journal. 2011;10(1):1-8. Bartoloni A, Zammarchi L. Clinical aspects of uncomplicated and severe malaria. Mediterranean journal of hematology and infectious diseases. 2012;4(1). Marrelli MT, Brotto M. The effect of malaria and anti-malarial drugs on skeletal and cardiac muscles. Malaria Journal. 2016;15(1):1-6. Additional Declarations No competing interests reported. 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University","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"","lastName":"Echodu","suffix":""},{"id":268060759,"identity":"8d5694ec-4f04-49fb-af6f-f6981b0203a2","order_by":9,"name":"Moses Ocan","email":"","orcid":"","institution":"Makerere University","correspondingAuthor":false,"prefix":"","firstName":"Moses","middleName":"","lastName":"Ocan","suffix":""}],"badges":[],"createdAt":"2024-01-11 19:44:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3854585/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3854585/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49963998,"identity":"bc96a1e5-b1b8-4a04-97bf-b73005413cfe","added_by":"auto","created_at":"2024-01-22 11:09:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":82955,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSevere malaria by age category\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3854585/v1/d37c05f673cc1d4d3effb182.png"},{"id":49964001,"identity":"38129c17-ded7-425e-859b-8561a3f13eda","added_by":"auto","created_at":"2024-01-22 11:09:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5142,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eThick smear parasite quantification by age\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Onlinedrawingimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3854585/v1/e9c37c620fe93f67ebe06621.png"},{"id":49963999,"identity":"02cadd67-6b83-4c24-81bc-f719930f8640","added_by":"auto","created_at":"2024-01-22 11:09:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":6150,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of parasite load by gender\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3854585/v1/79ec5faa5d39668a80c20acf.png"},{"id":49964000,"identity":"153059af-4055-428f-ba67-939094fdfeda","added_by":"auto","created_at":"2024-01-22 11:09:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3335,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePerformance agreement between thin and thick blood smear as a method of malaria diagnosis\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-3854585/v1/b8daa46a3d5fd36f703b6899.png"},{"id":49964002,"identity":"5a9ebf57-3f77-45ec-9393-631960b2d90b","added_by":"auto","created_at":"2024-01-22 11:09:45","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":4054,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eParasite load by different age categories\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-3854585/v1/7359bc17ff817b3bcbc1e035.png"},{"id":50271305,"identity":"12b2707b-580e-4d24-93f4-f7664eb87829","added_by":"auto","created_at":"2024-01-28 17:42:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":498683,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3854585/v1/bfefbc01-840b-4344-9c99-af9c1c154713.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Malaria Parasite burden in areas of northern Uganda where Indoor Residual spraying has been withdrawn","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUganda alongside Nigeria, the Democratic Republic of Congo and Mozambique accounted for almost half of the global malaria burden in 2022[1]. According to the Ugandan Ministry of Health Epidemiological report for the entire 2023, malaria burden (incidence, mortality, and morbidity) was highest in the Eastern, Northern, and west Nile regions of the country with average test positivity rates standing at about 56% across all the regions. In selected districts of the Acholi region where this study was conducted, malaria prevalence in 2018 and 2019 were 69.46% and 93% respectively[2]. A retrospective study done from January 2019 to December 2020 in one of the districts in northern Uganda reported that 64.8% of total Hospital admission were attributable to malaria and 93.8% of these admissions were children below 12 years. 40% of the cases met the case definition of severe malaria[3].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Over the last decade, the national malaria control program with support from donors and implementing partners adopted a number of strategies to reduce malaria disease burden\u0026nbsp;[4]. This included the use of long-lasting insecticide treated bed nets, prompt diagnosis and treatment using WHO anti-malaria medicines, and intermittent preventive therapy for pregnant women. In addition to this, indoor residual spraying using bendiocarb, a carbamate was implemented in ten districts of Northern Uganda that had the highest burden from 2010 to 2014[5]. As a result, a marked reduction was observed in mid-north where the prevalence of malaria reduced from 63% in 2009 to 20% in 2014 with the districts in which IRS was implemented having the lowest prevalence of 14%. However, there was a significant increase in malaria incidence and prevalence in the region following cessation of IRS. A cross sectional survey done in 2010 in Apac district (four years after IRS was initiated in the region) amongst children under 5 years found parasite prevalence to be 55.8% (115/206) by microscopy and 71.9% (41/57) by polymerase chain reaction[6]. This was way above the national average.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, from 2014 till date, indoor residual spraying has not been done in the region and severe forms of malaria has continued to cause havoc in the region. \u0026nbsp;Uganda National Institute of Public Health reported a general decrease in severe malaria cases among children below 5 years from 10% in 2017 to 5% between 2017 to 2021 across other regions of Uganda but noted an increase in mid North and Bukedi regions of the country. This trend analysis was done three years after cessation of IRS in Northern and Bukedi regions. Justin M et al,2012 reviewed 75 articles related to malaria resurgence in areas where IRS has been applied and reported that 91% of all resurgence were attributable in part to at least the weakening of malaria control program for a variety of reasons; including limited resources[7]. Due to increased insecticide resistance to pyrethroids, change in mosquito feeding behavior, emergence of new species of mosquitos such as \u003cem\u003eAnopheles stephensi\u003c/em\u003e, a fundamental question on the effectiveness and relevancy of IRS in mosquito vector eradication visa vie its disruption of projected development of immune system and promotion of parasite resistance has popped up[8]. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBefore introduction of IRS in Northern Uganda, Malaria transmission \u0026amp; severity was relatively similar \u0026nbsp;across \u0026nbsp; the country; including regions not included in the pilot IRS program of 2006. \u0026nbsp;Upon initiation and inconsistently applying the intervention in the districts of Northern Uganda , severe cases have significantly increased among child population in the region \u0026nbsp;compared to other parts of the country with equally high transmission but IRS has not applied[9]. Kitgum district in northern Uganda experienced a malaria epidemic in the 2014 and 2015 (a year after cessation of IRS) with over 20-fold increase above the normal rate. There was also an exponential increase in the test positivity rate[10]. Similar trend was observed for Hospitalization, severe malaria, and malaria in pregnancy.\u0026nbsp;Intervention such as use of IRS is reported to set up children for a catastrophic rebound the moment it is ultimately withdrawn[11]\u0026nbsp;. Inconsistent application of IRS has been reported to limit exposure to the vast repertoire of \u003cem\u003ePlasmodium falciparum\u003c/em\u003e antigens resulting into incomplete development of immunity. Right after IRS, there is always almost zero malaria transmission because of massive death of mosquito vector. This ultimately leads to immunological\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ememory loss because of lack of antigenicity to keep agitating for immune response. Inconsistent application of IRS has also been associated with multiplicity of infections and quicker activation of different PfEMP1 var genes\u0026nbsp;[12]\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Much as the long-term interventions disrupt malaria transmission, but they do not remove underlying risks. The catastrophic rebound in cases and disease severity following withdrawal of the interventions and control measures ends up \u0026nbsp;being a death trap. Most literatures related to the subject of study did not holistically look at indoor residual spraying in relation to malaria severity and parasitemia. This inadequate information potentially affects management of malaria cases in such settings. This includes \u0026nbsp;choice of \u0026nbsp; medicine for effective treatment across different patient categories[7]. This lack of information and its effects on patient management could potentially mask resistance, increase cases of severity, mortality and morbidity among the vulnerable child and adult population. This study investigated malaria test positivity rate, parasitemia and severe disease across different age categories from selected communities in northern Uganda where indoor residual spraying has not been conducted for the past ten years.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSelection of study participants:\u0026nbsp;\u003c/strong\u003eparticipants were patients seeking for medical care at Gulu Regional Referral Hospital. The Hospital is the biggest Government referral health facility in Acholi region and located right at the heart of Gulu city. The city is about 350 kms north of Kampala, the capital city of Uganda. Participants came from the districts of Adjumani(6), Amuru(43), Gulu(45), Gulu city(141), Nwoya(54), Omoro(37), Oyam(11)and Pader(16) (table 1). With permission from the Hospital administration, research assistants and health educators introduced the study to patients at the different patients point such as outpatient department (OPD), inpatients units, antenatal care section, immunization unit and accident and emergency unit. Patients who expressed willingness to participate in the study were requested to converge at another office where further details of the study were given. Consent forms were read to individual participant and upon acceptance, each signed at the end of the form as proof of consent. For illiterate participants, they were allowed to use a thump print as proof of consent. For child participants and minors, a guardian or parent was requested to offer permission to participate in the study while the child offered assent for finger prick and blood draw. Each participant was assigned a study number and Laboratory identification number.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants\u0026rsquo; demographics and clinical information:\u0026nbsp;\u003c/strong\u003eA total of 353 individuals participated in the study and were drawn from the districts of Gulu, Gulu city, Adjumani, Oyam, Omoro, Pader, Nwoya and Amuru. Majority of the participants were female (60.1%) (table 1), while children under 5 years had the highest number of participants at 258 representing 73%. Test positivity was determined using malaria RDT and microscopy thick smear and stood at 63.74% at enrollment (table 1). Up to 93.21% of participants were confirmed to have fever at enrollment. Those who reported fever had their status confirmed by a thermometer that was put either under the armpit, sublingually or at the rectum for children with high temperature. A participant was termed as having severe malaria when he or she has fever and either diarrhea, vomiting, prostration, seizures or +3 parasitaemia (thick smear)\u0026nbsp;[13]. Out of the 63.74% who tested positive, 54 were categorized as +1, 74 as +2, 95 as +3 using a thick blood smear microscopy (table 1)\u0026nbsp;[14].\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 1: summary of participants\u0026rsquo; demographics and clinical information\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.85964912280702%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.14035087719298%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eValue/proportion\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.85964912280702%\" valign=\"top\"\u003e\n \u003cp\u003eDistrict of origin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.14035087719298%\" valign=\"top\"\u003e\n \u003cp\u003eAdjumani=6\u003c/p\u003e\n \u003cp\u003eAmuru=43\u003c/p\u003e\n \u003cp\u003eGlu = 45\u003c/p\u003e\n \u003cp\u003eGulu City=141\u003c/p\u003e\n \u003cp\u003eNwoya=54\u003c/p\u003e\n \u003cp\u003eOmoro=37\u003c/p\u003e\n \u003cp\u003eOyam=11\u003c/p\u003e\n \u003cp\u003ePader=16\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.85964912280702%\" valign=\"top\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.14035087719298%\" valign=\"top\"\u003e\n \u003cp\u003eFemale=60.3%\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.85964912280702%\" valign=\"top\"\u003e\n \u003cp\u003eAge distribution\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.14035087719298%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;=5 years=258\u003c/p\u003e\n \u003cp\u003e(6-10) years=66\u003c/p\u003e\n \u003cp\u003e(11-15) years=09\u003c/p\u003e\n \u003cp\u003e\u0026gt;15 years=20\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.85964912280702%\" valign=\"top\"\u003e\n \u003cp\u003eTest positivity/Parasitaemia at enrollment\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.14035087719298%\" valign=\"top\"\u003e\n \u003cp\u003e63.74 tested positive for malaria RDT\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.85964912280702%\" valign=\"top\"\u003e\n \u003cp\u003eHistory of fever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.14035087719298%\" valign=\"top\"\u003e\n \u003cp\u003e93.21% had fever at enrollment\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.85964912280702%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eLevel of parasitemia by thick smear\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.14035087719298%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e+1=54\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e+2=74\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e+3=95\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePreparation, staining and examination of thick and thin blood smear for malaria identification.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrinciples of the stain\u003c/strong\u003e: Giemsa was used to stain both thin and thick smears in order to detect the parasites. It is composed of both the Acidic and Basic dyes. Azure and methylene blue are basic dyes that binds to the acid nucleus producing blue-purple color. Eosin is an acidic dye attracted to the cytoplasm and cytoplasmic granules and produces an alkaline-producing red coloration. The stain was buffered with water to pH 6.8 in order to precipitate the dyes to bind simple materials\u0026nbsp;[15].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStaining procedures\u003c/strong\u003e: \u003cstrong\u003eThin smear\u003c/strong\u003e :On clean dry microscopic glass slides, thin films of the specimen (blood) were made and left to air dry. The smears were dipped (2-3 dips) into pure methanol for fixation and were left to air dry for 30 seconds. Slides were then flooded \u0026nbsp;with 5% Giemsa stain solution for 20-30 minutes and were then flushed with tap water and left to dry.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThick smear\u003c/strong\u003e: Drops of blood were added in the middle of well labelled slides and the drops spread in a circular pattern from inside out and allowed to air dry for 1 hour on a staining rack. The thick blood smears were dipped \u0026nbsp;into diluted Giemsa stain (was prepared by taking 1ml of the stock solution and adding to 49ml of phosphate buffer or distilled water).The smears were then washed by dipping in buffered water or distilled water for 3-5 minutes and were left to dry. The Giemsa-stained blood film slides to be examined \u0026nbsp;were then placed on the microscope stage for examination.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMicroscopic examination\u003c/strong\u003e: \u0026nbsp;The methods used were described by\u0026nbsp;[16]; but briefly, the thick films were positioned in line with the 10x objective lens. The microscope light was switched on ,and light source adjusted \u0026nbsp; optimally, and the focus was found \u0026nbsp; by looking through the ocular and the 10x objective. The blood films were then scanned \u0026nbsp;for parasites and blood elements. Part of the films that were well stained and had evenly distributed white blood cells were then selected. A small drop of immersion oil was added to the thick film. To avoid cross-contamination, the immersion oil applicator was not \u0026nbsp;allowed to touch \u0026nbsp;the slide. The 40x objective was not allowed to touch the oil as well. The 100x oil immersion objective was then switched on over the selected portion of the thick film. Fine focus adjustment was made to see the image clearly. The mechanical stage was also raised to avoid damaging the slide. Using the fine adjustment, the cell elements were focused on, and confirmation \u0026nbsp;that the film was acceptable for routine examination was done: 15\u0026ndash;20 white blood cells per thick film field was used to give a satisfactory film thickness. Films with fewer white blood cells per field were examined more extensively. Slides were examined in a systematic manner, starting at the top left of the film, and beginning from the periphery of the field, then followed by horizontal movement to the right, field by field. When the other end of the film was reached, the slides would be moved slightly downwards, then to the left, field by field, and so forth. For efficient examination, continuous focus and refocus with the fine adjustment throughout examination of each field was ensured.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantification of Parasitaemia\u003c/strong\u003e: For \u0026ge; 100 parasites counts in 200 white blood cells, counting would be stopped , and results recorded as the number of parasites per 200 white blood cells. For \u0026nbsp;\u0026le; 99 parasites in 500 white blood cells, counting would be \u0026nbsp;stopped as well, and results recorded as the number of parasites per 500 white blood cells. All parasites and white blood cells in the final field were counted, even if the white cell count exceeds 200 or 500.Actual numbers of parasites and white blood cells counted was recorded on an appropriate worksheet. When counting was completed, the parasite density was calculated \u0026nbsp; on the basis of the patient\u0026rsquo;s actual white cell count.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e: Participants\u0026rsquo; demographic was captured by the research assistants. Clinical information such as history of diarrhea, prostration, vomiting, and seizures were filled in the case investigation form by the attending clinician. Laboratory test results on parasitaemia were filled by the Laboratory technicians at Gulu Regional Referral Hospital. Demographic information, \u0026nbsp;clinical information and accompanying meta data were entered into an excel spread sheet by the data clerk. Bar graphs and column plots were done in excel spreadsheet.t test in graph pad prism ver 8 was used to derive statistical significance in box plot for two groups. One way ANOVA in graph pad prism was used to derive statistical significance in means of three groups and above. To compare accuracies of different malaria related signs and symptoms in predicting true Prescence of the etiology, a 2 by 2 contingency table was constructed. Odd ratios were generated by dividing the number of those confirmed to have the parasites and the symptom chosen by the numbers of those who have the symptoms chosen but test negative for malaria (RDT). The higher the odd ratio, the more likely the symptom is better predictor of malaria.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eCases \u0026nbsp;of \u0026nbsp; severe malaria by age category:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to World Health Organization malaria treatment guidelines and Uganda Clinical guidelines, severe malaria is confirmed if a patient has parasitaemia and either diarrhea or vomiting or seizures or prostration (these are the variables included in this study)\u0026nbsp;[17]. This categorization was also adopted for this study. Cases of severe malaria were identified among children below 5 years only (fig 1). Vomiting, diarrhea, seizures, and prostration were found among children under 5 years. Cases of diarrhea and vomiting were more common between the age bracket of 0 to 5 years while prostration and seizures were more pronounced among children between 0 to 2 years (fig 1).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFig 1: severe malaria by age category\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParasitaemia by age category:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThick smear malaria quantification by age\u003c/p\u003e\n\u003cp\u003eParasitemia was quantified using thick smear. In Uganda and so many other African Countries, quantification of malaria parasitemia using + system is routinely done. Its utility is more pronounced in areas with limited equipment, few personnel, high patients\u0026rsquo; volume, and other challenges. +1 equates to low parasitemia, +2 equates to moderate parasitemia and +3 equates to high parasitemia. Children under 5 years (average age of 4.52 years) had highest level of parasitemia (+3) (fig 2) followed by those between 5 to 6 years (average age of 5.82) with parasitaemia quantified as +2. Parasitaemia of different quantification was detected in children below 10 years.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFig 2: Thick smear parasite quantification by age\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantification of parasite load by gender using thin blood smear\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThin smear has the advantage of allowing the exact parasite count \u0026nbsp;thus giving the right quantification. There was no significant difference in the level of parasitemia between male and female (p=0.6966) (fig 3). However, female had slightly higher parasitemia (mean value=361.8) compared to male (mean value=337.7). Parasites were counted per 200 white blood cells while box and whisker plot were used to compared level of parasitaemia between the two gender (fig 3)\u003c/p\u003e\n\u003cp\u003eFig 3: Comparison of parasite load by gender\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTest Performance agreement between thin and thick blood smear as a method of malaria parasite quantification\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study also intended to determine performance agreement between thin and thick blood smear. The gold standard for parasite quantification is a thin blood smear, but as previously mentioned, quantification using thick smear is used across various African countries where resources are limited. There were significant differences in actual parasite counts using thin smear for the three quantification categories of thick smear (+1, +2 and +3). Those quantified as +3 by thick smear had highest number of parasites count/200 WBCs (p\u0026lt;0.0001) followed by those quantified as +2 (p\u0026lt;0.0001) and least parasite count was noted with those quantified as +1 by a thick blood smear. This shows that quantification of parasitemia using a thick blood smear is an effective method in areas with limited resources such as personels, reagents, counting chambers among others.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFig 4: Performance agreement between thin and thick blood smear as a method of malaria diagnosis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParasite load across different age categories\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants were stratified into different age categories. These were (0-5) years, (6-10)years, (11-15) years and \u0026nbsp;\u0026gt;15 years. Categorization was based on projected immunological development and vulnerability to malaria (fig 5). One way Analysis of variance was used to compare parasitemia. The actual parasite count was done using a stained thin blood smear. There was no significant difference in parasite load among different age categories (p=0.3262) (fig 5) . However, children under 5 years of age had highest parasitaemia followed by those between (6-10) years, then those above 15 years and least parasitaemia was noted with children between (11-15 ) years (fig 5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFig 5: Parasite load by different age categories\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAccuracy of different signs and symptoms in predicting malaria among patients seeking treatment at Gulu Regional Referral Hospital\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eThe World Health Organization (WHO) and the Ugandan Ministry of Health rolled out test and treat for malaria. This meant malaria diagnosis could only be confirmed by Prescence of malaria parasites in the blood (either by Rapid diagnostics Test or Microscopy). A 2x2 contingency table was drawn for each malaria related symptom versus test outcome by Rapid diagnostics test (Table 2A, 2B, 2C, 2D \u0026amp; 2E).Joint pain had the highest odd ratio (5.60) meaning its presence was highly associated with \u0026nbsp;malaria (Table 2D \u0026amp; 3). This was followed by Headache (OR=2.04) (Table 2C \u0026amp; 3), then followed by Diarrhea (OR=1.82) (Table 2E\u0026amp; 3), then followed by chills (OR=1.79) (table 2B\u0026amp; 3) and the least Odds ratio was noted with Fever (OR=1.72) (table 2A\u0026amp; 3). Given that odd ratios are above one, all symptoms/ signs are associated with malaria and can be used to clinically predict the disease in areas where laboratories are not easily accessible.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 2: 2x2 contingency table for different malaria signs/symptoms versus test outcome for prediction of a true malaria case\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 2A): 2x2 contingency table for fever versus malaria test outcome\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 2B): 2x2 contingency table for Chills versus malaria test outcome\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 2C): 2x2 contingency table for headache versus malaria test outcome\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 2D): 2x2 contingency table for Joint pain versus malaria test outcome\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 2E): 2x2 contingency table for Diarrhea versus malaria test outcome\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003ePositive RDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eNegative RDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eHad Fever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e324\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eNo Fever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e350\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eB)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003ePositive RDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eNegative RDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eHad Chills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e321\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eHad no Chills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e353\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eC)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003ePositive RDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eNegative RDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eHad Headache\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e280\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eHad no Headache\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e379\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eD)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003ePositive RDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eNegative RDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eHad joint pain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e214\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eHad no joint pain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e354\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eE)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003ePositive RDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eNegative RDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eHad Diarrhea\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eHad no Diarrhea\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e246\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e353\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003cem\u003eTable 3 : odd ratios of different malaria related signs/symptoms in predicting true malaria.\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"642\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.6822429906542%\" valign=\"top\"\u003e\n \u003cp\u003eDisease related sign/symptom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.3177570093458%\" valign=\"top\"\u003e\n \u003cp\u003eOdds of having true malaria\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.6822429906542%\" valign=\"top\"\u003e\n \u003cp\u003eFever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.3177570093458%\" valign=\"top\"\u003e\n \u003cp\u003e1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.6822429906542%\" valign=\"top\"\u003e\n \u003cp\u003eChills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.3177570093458%\" valign=\"top\"\u003e\n \u003cp\u003e1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.6822429906542%\" valign=\"top\"\u003e\n \u003cp\u003eHeadache\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.3177570093458%\" valign=\"top\"\u003e\n \u003cp\u003e2.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.6822429906542%\" valign=\"top\"\u003e\n \u003cp\u003eJoint pains\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.3177570093458%\" valign=\"top\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.6822429906542%\" valign=\"top\"\u003e\n \u003cp\u003eDiarrhea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.3177570093458%\" valign=\"top\"\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eAs the world heads towards the 2030 dateline for global malaria eradication agenda, disease endemic countries such as Uganda continues to deploy all kind of desperate interventions \u0026amp; measures to achieve the ambition[18]. Because countries which have heavy malaria burden also are generally poor, control interventions such as Indoor residual spraying (IRS) are either inconsistently applied or there are no scientific backed up studies to evaluate the effectiveness and the consequences of such long-term interventions on the parasite, vector and human population[19]. This study set out to determine malaria parasite burden in selected areas of northern Uganda where IRS was stopped in 2014.\u003c/p\u003e \u003cp\u003eWe found test positivity rate to be 63.74% while 93.21% of participants had fever at enrollment. BB Tukei et al;2017 reported that IRS was associated with significant reduction in northern Uganda in the first three months of the chemical application, but this effects wanes off followed by increased slide positivity rate at four months and above after cessation of spray. They noted a reduction in slide positivity rate from about 56\u0026ndash;37% in all selected health facilities spread across the ten districts in northern Uganda within the first three months after IRS[20]. A study done in Eastern region of Uganda compared malaria prevalence from two parishes where indoor residual spraying has been sustained for a long period of time and one parish where IRS has never been applied. They found no significant difference in parasite prevalence in IRS exposed and non-exposed study sites. Moreover the incidence of malaria was higher in one parish where IRS has been consistently applied compared to the parish where application has never been done[21]. A similar study done in Eastern Uganda concluded that whilst Malaria burden significantly reduced within the five years of consistent IRS application, a significant proportion of the population remained parasitemic especially in school going children with submicroscopic parasitaemia providing a potential reservoir for malaria transmission[22]. A study done in selected rural areas of India reported that parasite prevalence among patients with acute undifferentiated fever ranged from 19\u0026ndash;35%. The relatively low prevalence of parasitemia in patients with fever means there could be other etiologies causing fever and acute febrile illnesses in these patients and efforts must be put in place to do differential diagnosis.\u003c/p\u003e \u003cp\u003eThe study also found severe malaria among children under five years. All clinical features of severe malaria (Seizures, prostration, vomiting and diarrhea ) were noted in participants under 5 years of age only. A systematic review done using studies conducted across malaria endemic countries of sub-Saharan Africa reported a shift in burden of cerebral malaria towards younger age with increasing intensity of transmission. They concluded that although the peak age of cerebral malaria will increase as transmission intensity decreases in Africa, more than 75% of all pediatric Hospital admissions of severe malaria are likely to remain under 5 years old in most epidemiological settings[23]. Another systematic review analysis reported that Hospital admission with severe malaria were more concentrated among younger children with this effect being even more pronounced for malaria diagnosed deaths. They further reported that for all outcomes, the burden of malaria shifted towards younger ages with increasing transmission intensity[24]. Children under 5 years are more vulnerable to severe malaria because their immune system is still developing. Anti-disease immunity requires a well-developed cell mediated and humoral arm of the immune system which takes time. A child has to be exposed to multiple strains and variants of malaria parasite in order to acquire anti disease immunity. This is projected to take up to 15 years of non-interrupted exposure to malaria parasites[25].\u003c/p\u003e \u003cp\u003eThe study found no significant difference in the level of parasitaemia between male and female (p\u0026thinsp;=\u0026thinsp;0.6966) though mean level of parasitemia in female was slightly higher than males (361.8 for female compared to male\u0026thinsp;=\u0026thinsp;333.7). The finding partially agrees with those from a couple of studies conducted across Africa. One study done in Uganda found incidence of malaria to be higher among females compared to male. They reported incidence of malaria per 1000 person years was 735 among females and 449 among males[26]. Among contributing factors were frequent visits to these facilities independent of malaria and a higher reported rate of seeking care at these facilities for these febrile illnesses. A cross sectional study conducted among patients presenting with fever at health facilities in Kano City of Nigeria reported malaria prevalence among female to be at 54% while among male was 46% [27]. One study conducted across selected health facilities in Kumasi, Ghana used a sequential mixed method design comprising of qualitative and quantitative method. They reported that most malaria cases were among less educated women (62%) with more external factors associated with risk of infections. Amongst factors highlighted by this group is long exposure to mosquito bites by women as they go about with their gender roles.\u003c/p\u003e \u003cp\u003eThere was a perfect agreement in performance between thick and thin smear for quantification of malaria parasites. +1 in thick smear corresponds with low parasite counts in thin smear, while\u0026thinsp;+\u0026thinsp;2 corresponds average parasite counts in thin smear and +\u0026thinsp;3 smear corresponds to high parasite counts in thin smear. There were no easily available studies that directly compared performance agreement between thin and thick smear for malaria parasites quantification. However, a group of researchers compared performance agreement between thick smear and automated computational Techniques. The Techniques discriminates malaria cells from segmented cells using morphological operations and color based pixel discriminator and found this method to have a higher positive predictive rate and lower number of false positives compared to a thick smear[28]. One prospective study tested 853 blood samples using malaria RDT, thick smear and thin smear and reported that RDT had superior performance than the standard Giemsa thick blood smear. They reported that the RDT\u0026rsquo;s sensitivity for all malaria was 97% compared to 85% for thick blood smear and RDT also had a superior Negative predictive value of 99.6% with 98.2% for the tick blood smear.\u003c/p\u003e \u003cp\u003eThis study also found no significant difference in parasitaemia among different age categories much as children under 5 years had slightly higher parasite load. This finding agrees with that of a study done among communities in northern Ghana to determine seasonal profile of malaria infections. They reported malaria prevalence among adults (between 50\u0026ndash;60 years) to be at 38% and 82% among children between 5 to 10 years. They concluded that the assumed protective mechanisms did not reduce susceptibility or blunt infections and that congenital induced resistance to malaria infections may not be realized among children and infants [29]. Another study done in Uganda reported that malaria prevalence significantly rose from children at younger ages (\u0026lt;\u0026thinsp;6 months) by 1.62 times among those between 7 to 12 months and four times among those between 49 to 59 months. They justified their finding of malaria parasitaemia increasing with increase in age as being development of age specific immunity due to continuous exposure to infected mosquito bites [30].\u003c/p\u003e \u003cp\u003eThis study also found joint paint to be most associated with malaria and fever least associated. However, all selected signs and symptoms positively predicts for malaria since all their odds ratio is above one. These symptoms include chills, headache, and diarrhea. A study done among pregnant going for antenatal visits in selected Hospitals in the republic of Benin reported that headache was the only symptom associated with positive thick blood smear for routine antenatal visits while for non-scheduled visits, headache and shivering were associated with malaria infections and almost 90% of the women presented with either of the symptoms [31]. According to Center for Diseases control and prevention, signs and symptoms of uncomplicated malaria include fever, sweat, chills, muscle pain, headaches, nausea, and vomiting. Symptoms of severe malaria include confusion, coma, focal neurological signs, severe anemia, and respiratory difficulties[32]. Joint pain in malaria results from micro-vascular sequestration of parasitized red blood cells, decreasing oxygen delivery leading to obstructed blood flow and Tissue hypoxia. The skeletal muscle microvascular function and its oxygen consumption is significantly impaired in malaria infections[33].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study found malaria test positivity rate to be within the normal range for endemic regions like northern Uganda indicating that the additive effect of Indoor residual spraying is temporal and insignificant in the long run if inconsistently applied. Amidst sustained interventions, malaria test positivity rate is always consistent in areas of stable transmission. We are not certain if the introduction of RTS,S/AS01 or R21 matrix vaccine will come with real additional benefits. Children and infants under 5 years still carry the highest parasite and severe malaria burden. This finding nullifies the hypothesis that cases of severe malaria was shifting from younger to older ages in areas with stable transmission \u0026amp; vector control measures such as northern Uganda.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e: This study was nested in a primary study. The primary study got approval from the Gulu University Research and ethics committee. It was also registered with the National council for Science and Technology. The primary study also got approval by Gulu Regional Referral Hospital Research and Ethics committee. The current study was reviewed and approved by the Makerere University school of Biomedical sciences Research and Ethics committee and registered with the National Council of Science and Technology with registration number SBS-2023-362.\u003c/p\u003e\n\u003cp\u003eParticipants\u0026rsquo; consent: The primary study obtained written consent from\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was not funded by any agency, donor, or Institution. It is part of the PhD work; we have not yet been successful in obtaining financial support to do this work. Attempts are being made to secure one for the remaining objectives. For now, supervisors, Doctoral committee and the student are putting in their personal resources\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data\u003c/strong\u003e: All relevant data will be made available upon request. Data access link will be shared with the editors and scientific editors of the journal.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eOrganization WH. World malaria report 2022: World Health Organization; 2022.\u003c/li\u003e\n \u003cli\u003eDivas S, Opiyo EA, Austin R, Ochaya S. Assessing the Malaria burden and Community Response to the Malaria Control and Management Programs in Omoro District, Northern Uganda. 2023.\u003c/li\u003e\n \u003cli\u003eOcen E, Opito R, Tegu C, Oula A, Olupot-Olupot P. Severe malaria burden, clinical spectrum and outcomes at Apac district hospital, Uganda: a retrospective study of routine health facility-based data. Malaria Journal. 2023;22(1):325.\u003c/li\u003e\n \u003cli\u003eBryce J, Roungou J-B, Nguyen-Dinh P, Naimoli J, Breman JG. Evaluation of national malaria control programmes in Africa. Bulletin of the World Health Organization. 1994;72(3):371.\u003c/li\u003e\n \u003cli\u003eOkullo AE, Matovu JK, Ario AR, Opigo J, Wanzira H, Oguttu DW, et al. Malaria incidence among children less than 5 years during and after cessation of indoor residual spraying in Northern Uganda. Malaria journal. 2017;16:1-10.\u003c/li\u003e\n \u003cli\u003eProietti C, Pettinato DD, Kanoi BN, Ntege E, Crisanti A, Riley EM, et al. Continuing intense malaria transmission in northern Uganda. The American journal of tropical medicine and hygiene. 2011;84(5):830.\u003c/li\u003e\n \u003cli\u003eCohen JM, Smith DL, Cotter C, Ward A, Yamey G, Sabot OJ, et al. Malaria resurgence: a systematic review and assessment of its causes. Malaria journal. 2012;11:1-17.\u003c/li\u003e\n \u003cli\u003eCarter TE, Yared S, Gebresilassie A, Bonnell V, Damodaran L, Lopez K, et al. First detection of Anopheles stephensi Liston, 1901 (Diptera: culicidae) in Ethiopia using molecular and morphological approaches. Acta tropica. 2018;188:180-6.\u003c/li\u003e\n \u003cli\u003eKassam R, Collins JB, Liow E, Rasool N. Narrative review of current context of malaria and management strategies in Uganda (Part I). Acta tropica. 2015;152:252-68.\u003c/li\u003e\n \u003cli\u003eOgwang R, Akena G, Yeka A, Osier F, Idro R. The 2015\u0026ndash;2016 malaria epidemic in Northern Uganda; What are the implications for malaria control interventions? Acta tropica. 2018;188:27-33.\u003c/li\u003e\n \u003cli\u003eStanisic D, Mueller I, Betuela I, Siba P, Schofield L. Robert Koch redux: malaria immunology in Papua New Guinea. Parasite immunology. 2010;32(8):623-32.\u003c/li\u003e\n \u003cli\u003eSherrard-Smith E, Griffin JT, Winskill P, Corbel V, Pennetier C, Dj\u0026eacute;nontin A, et al. Systematic review of indoor residual spray efficacy and effectiveness against Plasmodium falciparum in Africa. Nature communications. 2018;9(1):4982.\u003c/li\u003e\n \u003cli\u003eTrampuz A, Jereb M, Muzlovic I, Prabhu RM. Clinical review: Severe malaria. Critical care. 2003;7(4):1-9.\u003c/li\u003e\n \u003cli\u003ePrudhomme O\u0026rsquo;Meara W, Remich S, Ogutu B, Lucas M, Mtalib R, Obare P, et al. Systematic comparison of two methods to measure parasite density from malaria blood smears. Parasitology research. 2006;99:500-4.\u003c/li\u003e\n \u003cli\u003eHorobin R. How Romanowsky stains work and why they remain valuable\u0026mdash;including a proposed universal Romanowsky staining mechanism and a rational troubleshooting scheme. Biotechnic \u0026amp; Histochemistry. 2011;86(1):36-51.\u003c/li\u003e\n \u003cli\u003eBa EH, Baird JK, Barnwell J, Bell D, Carter J, Dhorda M, et al. Microscopy for the detection, identification and quantification of malaria parasites on stained thick and thin blood films in research settings: procedure: methods manual. 2015.\u003c/li\u003e\n \u003cli\u003eOrganization WH. Management of severe malaria: a practical handbook: World Health Organization; 2000.\u003c/li\u003e\n \u003cli\u003eFeachem RG, Chen I, Akbari O, Bertozzi-Villa A, Bhatt S, Binka F, et al. Malaria eradication within a generation: ambitious, achievable, and necessary. The Lancet. 2019;394(10203):1056-112.\u003c/li\u003e\n \u003cli\u003eRicci F. Social implications of malaria and their relationships with poverty. Mediterranean journal of hematology and infectious diseases. 2012;4(1).\u003c/li\u003e\n \u003cli\u003eTukei BB, Beke A, Lamadrid-Figueroa H. Assessing the effect of indoor residual spraying (IRS) on malaria morbidity in Northern Uganda: a before and after study. Malaria journal. 2017;16:1-9.\u003c/li\u003e\n \u003cli\u003eNankabirwa JI, Bousema T, Blanken SL, Rek J, Arinaitwe E, Greenhouse B, et al. Measures of malaria transmission, infection, and disease in an area bordering two districts with and without sustained indoor residual spraying of insecticide in Uganda. Plos one. 2022;17(12):e0279464.\u003c/li\u003e\n \u003cli\u003eNankabirwa JI, Arinaitwe E, Rek J, Kilama M, Kizza T, Staedke SG, et al. Malaria transmission, infection, and disease following sustained indoor residual spraying of insecticide in Tororo, Uganda. The American journal of tropical medicine and hygiene. 2020;103(4):1525.\u003c/li\u003e\n \u003cli\u003eRoca-Feltrer A, Carneiro I, Smith L, Schellenberg JRA, Greenwood B, Schellenberg D. The age patterns of severe malaria syndromes in sub-Saharan Africa across a range of transmission intensities and seasonality settings. Malaria journal. 2010;9:1-9.\u003c/li\u003e\n \u003cli\u003eCarneiro I, Roca-Feltrer A, Griffin JT, Smith L, Tanner M, Schellenberg JA, et al. Age-patterns of malaria vary with severity, transmission intensity and seasonality in sub-Saharan Africa: a systematic review and pooled analysis. PloS one. 2010;5(2):e8988.\u003c/li\u003e\n \u003cli\u003eLanghorne J, Ndungu FM, Sponaas A-M, Marsh K. Immunity to malaria: more questions than answers. Nature immunology. 2008;9(7):725-32.\u003c/li\u003e\n \u003cli\u003eOkiring J, Epstein A, Namuganga JF, Kamya EV, Nabende I, Nassali M, et al. Gender difference in the incidence of malaria diagnosed at public health facilities in Uganda. Malaria Journal. 2022;21(1):1-12.\u003c/li\u003e\n \u003cli\u003eNas F, Yahaya A, Ali M. Prevalence of malaria with respect to age, gender and socio-economic status of fever related patients in Kano City, Nigeria. Greener Journal of Epidemiology and Public Health. 2017;5(5):044-9.\u003c/li\u003e\n \u003cli\u003eDave IR, editor Image analysis for malaria parasite detection from microscopic images of thick blood smear. 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET); 2017: IEEE.\u003c/li\u003e\n \u003cli\u003eKoram KA, Owusu‐Agyei S, Fryauff DJ, Anto F, Atuguba F, Hodgson A, et al. Seasonal profiles of malaria infection, anaemia, and bednet use among age groups and communities in northern Ghana. Tropical Medicine \u0026amp; International Health. 2003;8(9):793-802.\u003c/li\u003e\n \u003cli\u003eWanzira H, Katamba H, Okullo AE, Agaba B, Kasule M, Rubahika D. Factors associated with malaria parasitaemia among children under 5 years in Uganda: a secondary data analysis of the 2014 Malaria Indicator Survey dataset. Malaria journal. 2017;16(1):1-9.\u003c/li\u003e\n \u003cli\u003eHuynh B-T, Fievet N, Gbaguidi G, Borgella S, M\u0026eacute;vo BG, Massougbodji A, et al. Malaria associated symptoms in pregnant women followed-up in Benin. Malaria Journal. 2011;10(1):1-8.\u003c/li\u003e\n \u003cli\u003eBartoloni A, Zammarchi L. Clinical aspects of uncomplicated and severe malaria. Mediterranean journal of hematology and infectious diseases. 2012;4(1).\u003c/li\u003e\n \u003cli\u003eMarrelli MT, Brotto M. The effect of malaria and anti-malarial drugs on skeletal and cardiac muscles. Malaria Journal. 2016;15(1):1-6.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3854585/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3854585/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e: Northern Uganda has one of the highest malaria burdens in Uganda and globally. Indoor residual spraying (IRS) for mosquito malaria vector control was last conducted in this region in 2014. The Government of the republic of Uganda intends to re-introduce IRS alongside RTS,S/AS01 malaria vaccine in this area as a strategy to achieve the ambitious goal of global malaria elimination by 20230. There is need to ascertain current parasite burden within selected areas of the region in order to predict the impact of future co-deployment of IRS and RTS,S/AS01.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: 3–4 mls of whole blood was collected in an EDTA tube from 353 participants who consented/assented to the blood draw and interviews. Participants were patients seeking for medical care at Gulu Regional Referral Hospital and were drawn from Acholi and Lango districts. Attending clinicians documented disease symptoms and severity. Blood samples were tested for malaria parasite using rapid diagnostics tests and confirmed by microscopy thick smear. Thin blood smears were made, stained, and examined to quantify the parasites. Data was entered into excel and later exported to GraphPad prism (ver8) and STATA where analysis was done. The non-parametric t test was used to compare means of parasitemia across two variables/characteristics while one way ANOVA was used to compare means of parasitemia across three groups and above. 2x2 contingency table of malaria related symptoms versus test outcome was drawn and odd ratios were calculated to identify which symptoms are most associated with malaria in northern Uganda.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Test positivity rate was 63.34% at enrollment with 93.21% of participants having fever as well. 258 of the 353 participants were children below 5 years while majority were females (60.3%). Vomiting, diarrhea, prostration, and seizures (indicators of severe malaria )were identified among children \u0026lt; 5 years only. There was no significant difference in parasite burden between male and female participants. Thick smear is an accurate measure of parasitaemia as there was a performance agreement between its quantification and that of a thin smear. There was no significant difference in the parasite count across different age categories. There is a very high association between joint pain and malaria, while fever is least associated with malaria among symptoms selected.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: This study found malaria test positivity rate to be within the normal range for endemic regions like northern Uganda indicating that the additive effect of Indoor residual spraying is temporal and insignificant in the long run if inconsistently applied. Amidst sustained interventions, malaria test positivity rate always fluctuates within a narrow range in areas of stable transmission. We are not certain if the introduction of RTS,S/AS01 or R21 matrix vaccine will come with real additional benefits. Children and infants under 5 years still carry the highest parasite and severe malaria burden. This finding nullifies the hypothesis that cases of severe malaria was shifting from younger to older ages in areas with stable transmission \u0026amp; vector control measures such as northern Uganda.\u003c/p\u003e","manuscriptTitle":"Malaria Parasite burden in areas of northern Uganda where Indoor Residual spraying has been withdrawn","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-22 11:09:40","doi":"10.21203/rs.3.rs-3854585/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"90d46a14-3b10-4dd7-a3ed-edfee8f6d28d","owner":[],"postedDate":"January 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-01-28T17:34:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-22 11:09:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3854585","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3854585","identity":"rs-3854585","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}