Malaria Risk Assessment Based on the Use of Insecticide Treated Nets in Nsukka Local Government Area, Enugu State, Nigeria

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One of the key interventions that has gained public interest is the use of insecticide treated nets (ITNs) to control the malaria-mosquito vector. This study sought to determine malaria prevalence, ITN ownership and usage and assess the effectiveness of damaged ITNs in malaria prevention in peri-urban communities in Enugu State, South-Eastern Nigeria. Method: This was a cross-sectional study conducted in three communities (Nsukka, Obimo and Edem) of Nsukka Local Government Area. A total of 317 participants from 125 randomly selected households were screened for malaria using rapid diagnostic test (RDT) and confirmed with microscopy. Socio-demographic data and information related to ITN were collected using structured questionnaires and field observation. Bivariate and multivariable logistic regression analyses were performed to determine socio-demographic and ITN characteristics associated with Malaria. Results: Malaria prevalence among participants was 23.7% (RDT) and 14.5% (microscopy). Prevalence of malaria differed significantly among the age group (p<0.01), with 5-15 years having the highest prevalence both by RDT (6.9%) and microscopy (5.4%). Severity of malaria cases revealed 54.3%, 30.4% and 15.3% for moderate, low and severe parasitemia respectively. Of the 125 household surveyed, less than half (44.0%) possessed at least one ITN with only a few (18.2%) in good condition. Members of households without ITN were 1.32 more likely to have malaria using RDT (0.23-3.81, p=0.09). Not using ITN (aOR=1.5, CI=0.45- 3.62) and the use of damaged ITN (aOR=3.81, CI=1.24- 9.71) were significantly associated with having malaria by the use of RDT. Conclusion: Malaria prevalence was high among the study participants particularly the older children. Most of the ITNs used by households were damaged. Participant who did not have ITN were more at risk of being affected with malaria. There is the need for regular provision of ITNs among the risk populations and increased community sensitization on the need to use ITN to prevent human-mosquito contact. Malaria Insecticide Treated Net Assessment Nsukka Figures Figure 1 Figure 2 Introduction Malaria is a life-threatening mosquito-borne disease caused by Plasmodium parasite infection. Over the years the disease has been a leading cause of morbidity and mortality particularly in resource poor countries. In 2015, global estimate of malaria deaths according to the World Health Organization, was 446 000 with 92% of these deaths occurring in the African region (WHO, 2015 ). Malaria is endemic in Nigeria with all year transmission and it is associated with increased ill-health and death. An estimated 97 percent of the country’s approximate population of 173 million residents are at risk of malaria (WHO, 2014 ). In Nigeria, malaria is responsible for the deaths of an estimated 300,000 children per year and contributes to over 4,000 maternal deaths annually and it is the number one cause of absenteeism both at work and in schools, resulting in loss of productivity (Azoma Chikwe, 2017 ). Globally, Nigeria accounted for 27% of the estimated 216 million cases of malaria in 2016 (WHO, 2017 ). The large malaria burden has led to the development and setting of several strategies and targets aimed at malaria control, and where possible its elimination. The Global strategy for Malaria, 2016 to 2030 targets a 90% reduction in the incidence and mortality rates of malaria, as well as elimination of malaria in 35 of its endemic countries by 2030 (World Health Organization, 2016 ). Reports indicate a global decline in malaria cases particularly in endemic regions. The decrease in malaria rates correspond with expanded malaria prevention interventions particularly the use of insecticide treated mosquito nets and indoor residual spraying. Insecticide Treated Nets (ITNs) continue to be an effective tool for malaria prevention, even in areas where mosquitoes have developed resistance to pyrethroids. They are widely accepted as important tools in the prevention of malaria by not only creating a physical barrier between humans and mosquitoes but also by repelling, knocking down and even killing the mosquitoes on contact. Despite ITNs regarded as an important tool in malaria prevention and control, the use of ITNs in Nigeria is still low. In 2015, approximately 69% of households owned at least one insecticide treated net in Nigeria but despite high rates of ownership of insecticide treated nets, only 37% of the households in Nigeria slept under their mosquito nets (National Malaria Elimination Programme, 2015). This study sought to determine malaria prevalence, ITN ownership, ITN usage and assess effectiveness of damaged ITNs in malaria prevention at the study sites. Method Study design This was a cross-sectional study conducted in three randomly selected communities, Nsukka, Obimo and Edem in Nsukka Local Government Area (LGA), Enugu State, Nigeria. The study involved 125 households with 317 participants (125 household heads and 192 household members). Study area Nsukka LGA lies between latitude 6º43’ − 7º00’ and longitude 7º13’ − 7º35’ covering a land area of 1810km in Southeastern Nigeria. Nsukka LGA is characterised by two seasons: the wet and dry seasons. The dry season usually starts in November and recedes in March while the rainy season starts and recedes in April and October respectively. According to Climate-data.org, the average annual rainfall, temperature and relative humidity in Nsukka are 1579 mm, 24.9ºC, and 75% respectively (Fig. 1 ). Sample size determination Sample size for the study was determined by a Daniel, 1999 formula [n = Z2 x P (1- P)/d2] (Naing et al., 2006 ) where n is the sample size, Z = 1.96, P is malaria prevalence from previous study (72.8%) and d is precision = 0.08. This gave a sample size of 119. Taking into account 5% non-response rate, the sample size required was 125 households Sampling procedure The total number of households from each community was obtained from household register at Nsukka Health Centre and were sampled using simple random technique. The number of participants recruited was proportionate to the number of households in each community. Data collection A structured questionnaire was used to take information on participant’s demographic characteristics as well as information on ITN. Field observation was done to determine the extent of damage of ITN. Household heads were the respondents to the questionnaire. Malaria status of participants were determined using rapid diagnostic test kit (RDT); the ONE STEP Malaria P.f Histidine-rich protein II (SD Standard Diagnostics, Inc.). Where participants tested positive on the field, microscopy was done to confirm the presence of Plasmodium parasites. Those diagnosed with malaria were referred to the nearest health centre for treatment. The degree of parasitemia was determined by counting and calculating the percentage of infected Red Blood Cells (RBC). The quantification protocol was as follows: low parasitemia (< 1000 parasites/µl of blood), moderate parasitemia (1000–9999 parasites/ µl of blood) and severe (≥ 10,000 parasites/µl of blood). Data analyses Data entry and analyses were done using SPSS version 20 (SPSS Inc., Chicago, Ill., USA). Descriptive statistics were used for the demographic information of participants. Chi-square test was used to determine the association between demographic, ITN characteristics and malaria status of participants. All variables were used in a bivariate logistic regression and variables with significant associations ( p -value < 0.05) were further considered for multivariate logistic regression to determine independent associations of each variable. Ethical consideration The study was approved by the Ethics Committee of the University of Nigeria Teaching Hospital (UNTH) (NHREC/05/01/2008B-FWA00002458-1RB00002323). Permission was also obtained from the Health Department of Nsukka LGA prior to data collection. Informed consent was sort from all participants prior to data collection. Confidentiality was maintained throughout the study. Results The study involved 317 participants with 125 household heads and 192 household members. Of the 125 households, 62.4% were headed by females and majority (36.0%) were from the age group 41-50 years with the highest number (35.2%) attained secondary education. With respect to occupation, more than half (56.0%) of the household heads were farmers. The socio-demographic data for 192 household members was based on sex and age only. More than half (52.1%) of them were females and majority 26.0% (50/192) were from the age group 40 years and above (Table 1). Table 1: Demographic characteristics of household heads and member, Nsukka LGA, 2018 Characteristics Frequency Percentage Household Heads N=125 Sex Male Female 47 78 37.6 62.4 Age (years) 21-30 31-40 41-50 51-60 >60 9 32 45 23 16 7.2 25.6 36.0 18.4 12.8 Educational level No formal education Primary Secondary Tertiary 37 25 44 19 29.6 20.0 35.2 15.2 Occupation Unemployed Trading Farming Civil servant 6 35 70 14 4.8 28.0 56.0 11.2 Household members N=192 Sex Male Female 92 100 47.9 52.1 Age (years) 40 29 22 44 23 24 50 15.1 11.5 22.9 12.0 12.5 26.0 The prevalence of malaria in the three communities was 23.7% by RDT and 14.5% by microscopy (Figure 2). The community with highest prevalence of malaria by RDT was Nsukka (12.9%), followed by Obimo (6.9%) and Edem (3.8%). Malaria cases significantly differed by communities in terms of RDT (p0.05). (Table 2) Table 2: Malaria prevalence by community for both RDT and Microscopy, Nsukka LGA, 2018 Community RDT p-value Microscopy p-value Positive Negative 0.002 Positive Negative 0.39 Nsukka (224) 41 (12.9) 183 (57.7) 29 (9.1) 195 (61.5) Obimo (60) 22 (6.9) 38 (12.0) 12 (3.8) 48 (15.1) Edem (33) 12 (3.8) 21 (6.6) 5 (1.6) 28 (8.8) Totals 75 (23.7) 242 (76.3) 46 (14.5) 271 (85.5) In terms of severity of malaria cases, more than half (54.3%) constituted moderate (1000–9999 parasites/ μl of blood), low (30.4%) (<1000 parasites/μl of blood) and severe parasitaemia (≥10,000 parasites/μl of blood). Females had higher percentage 32 (69.6%) of malaria parasitaemia than males 14 (30.4%) but there was no significant difference in terms of severity of malaria in the three communities and by sex (Table 3). Table 3: Malaria parasitaemia quantification by communities and sex, Nsukka LGA, 2018 Characteristic Low Moderate Severe Total p-value Community Nsukka 10 (21.7) 15 (32.6) 4 (8.7) 29 (63.0) 0.35 Obimo 3 (6.5) 7 (15.2) 2 (4.4) 12 (26.1) Edem 1 (2.2) 3 (6.5) 1 (2.2) 5 (10.9) Sex 0.08 Male 6 (13.0) 5 (10.9) 3 (6.5) 14 (30.4) Female 8 (17.4) 20 (43.5) 4 (8.7) 32 (69.6) Of the 125 households surveyed, more than half (56.0%) did not have ITNs while 44.0% had ITNs. Of those with ITNs, majority (81.8%) were damaged with a few (18.2%) in good condition. Usage of ITN was observed among most of the participants that had it (58.2%). Sources of ITNs included; those who bought using their own money (5.5%), NGO (29.1%) and FMoH (65.5%). (Table 4). Table 4: Information on Insecticide Treated Nets in Nsukka LGA, 2018 Characteristics Frequency Percentage Household owned ITN Yes No 55 70 44.0 56.0 Condition of ITN Good Damaged 10 45 18.2 81.8 Source of ITN Bought NGO FMoH 3 16 36 5.5 29.1 65.5 Shape of mosquito net Rectangular Circular 53 2 96.4 3.6 Use of ITN Yes No Sometimes 32 12 11 58.2 21.8 20.0 Multivariate logistic regression In a multivariate logistic regression analysis to determine the association of household ITN characteristics and malaria status of participants, it was observed that households without ITN, those with ITNs but not using them and those using damaged ITNs were 1.32 [0.23 - 3.81, CI = 95%, p < 0.09], 1.51 [0.45 - 3.62, CI = 95%, p < 0.03] and 3.81 [1.24 - 9.71, CI = 95%, p < 0.02] respectively more likely to have malaria (RDT) (Table 5). Table 5: Association of Households and ITN characteristics and Malaria status of participants, Nsukka LGA, 2018 RDT Microscopy Variable aOR (CI) p-value aOR (CI) p-value Cracks/Crevices Yes No 0.36 (0.12-1.11) 1 0.08 0.10 (0.12-0.91) 1 0.04 Have ITN Yes No 1 1.32 (0.23-3.81) 0.09 1 0.54 (0.24-1.96) 0.32 Use ITN Yes No 1 1.51 ( 0.45- 3.62) 0.03 1 0.79 (0.86—1.21) 0.47 Condition of ITN Good damaged 1 3.81 (1.24- 9.71) 0.02 1 1.23 (0.50—2.72) 0.32 Discussion In the present study, the overall malaria prevalence was 23.7% by RDT and 14.5% by microscopy, far much lower than the results documented in Nsukka LGA where the prevalence was 72.8% by microscopy (Onyishi et al., 2018 ) and much higher in a study conducted in Umudioka community in Anambra state of Nigeria (Onyido et al., 2011 ). Our study also had a lower malaria prevalence compared to the study performed in Kano (Dawaki et al., 2016 ). However, the malaria prevalence in the current study was similar to other studies conducted at the University of Ibadan Campus in Nigeria (Anumudu et al., 2006 ) and in Iwo community of Southwestern in Nigeria (Igbeneghu et al., 2011 ). The wide range of differences in malaria prevalence in study areas could be attributed to the time when the study was conducted, differences in climatic factors and behavioral patterns of people in the area, which promote mosquito breeding and susceptibility of the people to vector bites (Deredec et al., 2016 ; Paul et al., 2004 ; Qasim et al., 2014 ; Russell et al., 2011 ). The use of ITNs is currently considered the most cost-effective method of malaria prevention in highly endemic areas (National Population Commission, 2013 ). The present study revealed that less than half of the households owned at least one ITN against the National Malaria Strategic Plan 2014–2020 target of at least 80% of households having atleast one ITN for every two persons in the 97% of the population at-risk of malaria. The ITN ownership in the current study was also low compared to the study done in Kano State in Nigeria (Garley et al., 2013 ) and in 23 communities of Amhara and Oromia Regional States of Ethiopia (Baume & Marin, 2007 ). However, the ITN ownership in this study was more than twice the ITN ownership in Makurdi, North Central Nigeria (Jombo, 2010). The ITN utilization in Nsukka LGA was still below the 60% standard recommended by the Roll back Malaria programme and also lower than what was reported in Makurdi – North Central Nigeria (Jombo et al., 2010 ) but higher than the findings obtained in a study conducted in Kano State (Dawaki et al., 2016 ; Garley et al., 2013 ). In this study, one fifth of households did not use their ITNs in the previous night, in agreement with the findings of the study done in Ibadan in Nigeria (Aluko & Oluwatosin, 2012 ) but higher than ITN utilization reported in Calabar Metropolis, Cross River State in Nigeria (Abiodun et al., 2016 ). The current study further demonstrated that one fifth of households used their ITNs sometimes contrary to more than half of the households in study done elsewhere in Nigeria (Aluko and Oluwatosin, 2012 ). The reason for this disparity could be due to low education level (Aluko & Oluwatosin, 2012 ). Use of damaged ITNs In a multivariable logistic regression analysis to determine the association of household and ITNs characteristics and malaria status of participants, the odds of testing positive to malaria using RDT among members of households without ITNs was 1.32 times compared to members of households with ITN [aOR: 1.32, 95% CI (0.23–3.81, p < 0.09)]. Members of households not using ITNs had increased odds of 1.5 times likely to develop malaria compared to those from households using ITN [aOR: 1.5, 95% CI (0.45–3.65, p < 0.03)], and this was in agreement with the study conducted in Hausa communities in Kano State of Nigeria (Dawaki et al., 2016 ). For the household members using damaged ITNs, there was 3.81 odds of having malaria compared to members of households using ITNs in good condition [aOR: 3.81, 95% CI (1.24–9.71, p < 0.02)]. Therefore, not using ITNs and using damaged ITNs were independently associated with having malaria by the use of RDT. This is in agreement with a study in Jimma town, Ethiopia (Alemu et al., 2011 ). Conclusion The prevalence of malaria recorded in this study was high using both RDT and Microscopy. Most households did not have ITNs and of those with ITNs, most were damaged. Participant who did not have ITN or had ITN but were not using them and those that used damaged ITN were more at risk of being affected with malaria. There is the need for regular provision of ITNs in at risk populations such as Nsukka LGA and increased community sensitization on the need to use ITN to control malaria burden. Declarations Conflict of interest The authors declare that there are no conflicts of interest regarding the publication of this paper. Ethical Approval and Consent to participate This study was conducted following ethical approval by University of Nigeria Teaching Hospital with ethical clearance certificate number NHREC/05/01/2008B-FWA00002458-1RB00002323. The informed consent to participate in the study was obtained from the participants prior to data collection process. Consent to publication By virtual of submitting this work in this reputable Malaria Journal, the author and the co-authors of this manuscript have given consent to publish this work. Funding I acknowledge that for this work to be published, I should to pay for it. I will source for funds for publication by myself or seek for sponsorship including the possibility of requesting for a publication waiver from this Journal. Acknowledgement I want to acknowledge the late Prof. F. C. Okafor; Prof. P. O. Ubachukwu and all lecturers in the Department of Zoology and Environmental Biology; my sponsors Deutscher Akademischer Austauschdienst (DAAD) and all my family members and friends for their support during this undertaking. References Abiodun E, Egena R, Irene C. Assessment of the Utilization of Insecticide Treated nets (ITNs) in Calabar Metropolis, Cross River State, Nigeria. J Health Med Nurs. 2016;26:196–205. Alemu A, Tsegaye W, Golassa L, Abebe G. Urban malaria and associated risk factors in Jimma town, south-west Ethiopia. Malar J. 2011;10:1–10. https://doi.org/10.1186/1475-2875-10-173 . Aluko JO, Oluwatosin AO. Utilization of insecticide treated nets during pregnancy among postpartum women in Ibadan, Nigeria : a cross-sectional study. BMC Pregnancy Childbirth. 2012;12(21):1–7. Anumudu CI, A.Adepoju M, Adediran O, Oyewole RIN. Malaria Prevalence and Treatment Seeking Behaviour of Young Nigerian Adults. Ann Afr Med. 2006;5(2):82–8. Azoma Chikwe. (2017). Nigeria accounts for 29% of global malaria burden –NMEP. Nigeria Newspapers , 1. Baume CA, Marin MC. Intra-household Mosquito Net Use in Ethiopia, Ghana, Mali, Nigeria, Senegal, and Zambia : Are Nets Being Used ? Who in the Household Uses Them ? Am Soc Trop Med Hygiene. 2007;77(5):963–71. Dawaki S, Mekhlafi HM, Al, Ithoi I, Ibrahim J, Atroosh WM, Abdulsalam AM, Sady H, Elyana FN, Adamu AU, Yelwa SI, Ahmed A, Areeqi MA, Al, Subramaniam LR, Nasr NA, Lau YL. Is Nigeria winning the battle against malaria ? Prevalence, risk factors and KAP assessment among Hausa communities in Kano State. 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Asian Pac J Trop Med. 2010;1:402–6. https://doi.org/10.1016/S1995-7645(10)60098-3 . Naing L, Winn T, Rusli BN. (2006). Practical Issues in Calculating the Sample Size for Prevalence Studies. Archives of Orofacial Sciences , 1 (June 2014), 9–14. National Malaria Elimination Programme, A. (2015). Malaria Indicator Survey (MIS) 2015 . National Population Commission, Abuja N. (2013). Nigeria Demographic and Health Survey . Onyido A, Agbata V, Umeanaeto P, Obiukwu M. Ecology of Malaria Vectors in a Rainforest Suburban Community of Nigeria. Afr Res Rev. 2011;5(2):293–305. https://doi.org/10.4314/afrrev.v5i2.67328 . Onyishi GC, Aguzie ION, Nwani CD, Obiezue RNN, Okoye IC. Malaria-vector dynamics in a tropical urban metropolis, Nigeria. Pakistan J Zool. 2018;50(3):1035–41. https://doi.org/10.17582/journal.pjz/2018.50.3.1035.1041 . Paul REL, Diallo M, Brey PT. (2004). Mosquitoes and transmission of malaria parasites – not just vectors . 13 , 1–13. https://doi.org/10.1186/1475-2875-3-39 Qasim M, Naeem M, Bodlah I. (2014). Mosquito (Diptera: Culicidae) of Murree Hills, Punjab, Pakistan . 46 (2), 523–529. Russell TL, Govella NJ, Azizi S, Drakeley CJ, Kachur SP, Killeen GF. (2011). Increased proportions of outdoor feeding among residual malaria vector populations following increased use of insecticide-treated nets in rural Tanzania. 1–10. WHO. (2014). World Malaria Report 2014 . WHO. (2015). World Malaria Report 2015 . WHO. (2017). World Malaria Report . World Health Organization. (2016). Global technical strategy for malaria 2016–2030. World Health Organ, 1–35. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4791088","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":335704018,"identity":"a84b3f69-3e24-4b62-84ce-793ed2e386d6","order_by":0,"name":"William Nsemani","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYPACCQY2duaGA0CWHIh74AFRWpgZwVqMwVoSiLIIqAVEJYJJfFrM+Q8f3fBzh0ViHzNj44GfOXXp88MOPwTaYien24Bdi+WMtLSbvWckEtuAthzs3XY4d+PtNAOglmRjswPYtRjc4DG7wdsmkQvScoB324HcjbMTQFoOJG7DpeX8+W83/0K1HPy7rS7dcHb6B/xaDuSw3YbZcph3G3OCvHQOAVtupJndlm2TqAdrkd122HCDdE7BgQQDPH45f/jZzbdtdcby7c2HP77dVicvPzt984cPFXZyuLRgcyqYJFY5CMg3kKJ6FIyCUTAKRgIAAH90Z6myKu8JAAAAAElFTkSuQmCC","orcid":"","institution":"University of Nigeria","correspondingAuthor":true,"prefix":"","firstName":"William","middleName":"","lastName":"Nsemani","suffix":""},{"id":335704019,"identity":"2cad49e9-422b-4867-bcb5-0c74ae10a064","order_by":1,"name":"Patience Ubachukwu","email":"","orcid":"","institution":"University of Nigeria","correspondingAuthor":false,"prefix":"","firstName":"Patience","middleName":"","lastName":"Ubachukwu","suffix":""},{"id":335704020,"identity":"9b9e3d68-218b-4633-808b-553fa0a00e2e","order_by":2,"name":"Isaac Boadu","email":"","orcid":"","institution":"University of Nigeria","correspondingAuthor":false,"prefix":"","firstName":"Isaac","middleName":"","lastName":"Boadu","suffix":""}],"badges":[],"createdAt":"2024-07-23 20:27:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4791088/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4791088/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63373482,"identity":"78450218-ed4c-4a66-bfa1-bf80671a488d","added_by":"auto","created_at":"2024-08-27 12:17:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":109550,"visible":true,"origin":"","legend":"\u003cp\u003eMap of Nsukka LGA showing the Study area, 2018\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4791088/v1/095fee25ac14b73c61031a8e.png"},{"id":63373481,"identity":"2feecf36-aa3b-4926-90d2-732eb7635aa7","added_by":"auto","created_at":"2024-08-27 12:17:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":11218,"visible":true,"origin":"","legend":"\u003cp\u003eMalaria prevalence by RDT and Microscopy, Nsukka LGA, 2018\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4791088/v1/1e3b2b4ca4784bb260f3268c.png"},{"id":64698397,"identity":"e488dceb-1414-4226-a60b-d19b30060247","added_by":"auto","created_at":"2024-09-17 19:29:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":654686,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4791088/v1/d5ae0d88-5105-46d8-96fe-20d7e7f8ae63.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eMalaria Risk Assessment Based on the Use of Insecticide Treated Nets in Nsukka Local Government Area, Enugu State, Nigeria\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMalaria is a life-threatening mosquito-borne disease caused by \u003cem\u003ePlasmodium\u003c/em\u003e parasite infection. Over the years the disease has been a leading cause of morbidity and mortality particularly in resource poor countries. In 2015, global estimate of malaria deaths according to the World Health Organization, was 446 000 with 92% of these deaths occurring in the African region (WHO, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMalaria is endemic in Nigeria with all year transmission and it is associated with increased ill-health and death. An estimated 97 percent of the country\u0026rsquo;s approximate population of 173\u0026nbsp;million residents are at risk of malaria (WHO, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In Nigeria, malaria is responsible for the deaths of an estimated 300,000 children per year and contributes to over 4,000 maternal deaths annually and it is the number one cause of absenteeism both at work and in schools, resulting in loss of productivity (Azoma Chikwe, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Globally, Nigeria accounted for 27% of the estimated 216\u0026nbsp;million cases of malaria in 2016 (WHO, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The large malaria burden has led to the development and setting of several strategies and targets aimed at malaria control, and where possible its elimination. The Global strategy for Malaria, 2016 to 2030 targets a 90% reduction in the incidence and mortality rates of malaria, as well as elimination of malaria in 35 of its endemic countries by 2030 (World Health Organization, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eReports indicate a global decline in malaria cases particularly in endemic regions. The decrease in malaria rates correspond with expanded malaria prevention interventions particularly the use of insecticide treated mosquito nets and indoor residual spraying. Insecticide Treated Nets (ITNs) continue to be an effective tool for malaria prevention, even in areas where mosquitoes have developed resistance to pyrethroids. They are widely accepted as important tools in the prevention of malaria by not only creating a physical barrier between humans and mosquitoes but also by repelling, knocking down and even killing the mosquitoes on contact.\u003c/p\u003e \u003cp\u003eDespite ITNs regarded as an important tool in malaria prevention and control, the use of ITNs in Nigeria is still low. In 2015, approximately 69% of households owned at least one insecticide treated net in Nigeria but despite high rates of ownership of insecticide treated nets, only 37% of the households in Nigeria slept under their mosquito nets (National Malaria Elimination Programme, 2015).\u003c/p\u003e \u003cp\u003eThis study sought to determine malaria prevalence, ITN ownership, ITN usage and assess effectiveness of damaged ITNs in malaria prevention at the study sites.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThis was a cross-sectional study conducted in three randomly selected communities, Nsukka, Obimo and Edem in Nsukka Local Government Area (LGA), Enugu State, Nigeria. The study involved 125 households with 317 participants (125 household heads and 192 household members).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eNsukka LGA lies between latitude 6\u0026ordm;43\u0026rsquo; \u0026minus;\u0026thinsp;7\u0026ordm;00\u0026rsquo; and longitude 7\u0026ordm;13\u0026rsquo; \u0026minus;\u0026thinsp;7\u0026ordm;35\u0026rsquo; covering a land area of 1810km in Southeastern Nigeria. Nsukka LGA is characterised by two seasons: the wet and dry seasons. The dry season usually starts in November and recedes in March while the rainy season starts and recedes in April and October respectively. According to Climate-data.org, the average annual rainfall, temperature and relative humidity in Nsukka are 1579 mm, 24.9\u0026ordm;C, and 75% respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSample size determination\u003c/h2\u003e \u003cp\u003eSample size for the study was determined by a Daniel, 1999 formula [n\u0026thinsp;=\u0026thinsp;Z2 x P (1- P)/d2] (Naing et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) where n is the sample size, Z\u0026thinsp;=\u0026thinsp;1.96, P is malaria prevalence from previous study (72.8%) and d is precision\u0026thinsp;=\u0026thinsp;0.08. This gave a sample size of 119. Taking into account 5% non-response rate, the sample size required was 125 households\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSampling procedure\u003c/h2\u003e \u003cp\u003eThe total number of households from each community was obtained from household register at Nsukka Health Centre and were sampled using simple random technique. The number of participants recruited was proportionate to the number of households in each community.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eA structured questionnaire was used to take information on participant\u0026rsquo;s demographic characteristics as well as information on ITN. Field observation was done to determine the extent of damage of ITN. Household heads were the respondents to the questionnaire. Malaria status of participants were determined using rapid diagnostic test kit (RDT); the ONE STEP Malaria \u003cem\u003eP.f\u003c/em\u003e Histidine-rich protein II (SD Standard Diagnostics, Inc.). Where participants tested positive on the field, microscopy was done to confirm the presence of \u003cem\u003ePlasmodium\u003c/em\u003e parasites. Those diagnosed with malaria were referred to the nearest health centre for treatment. The degree of parasitemia was determined by counting and calculating the percentage of infected Red Blood Cells (RBC). The quantification protocol was as follows: low parasitemia (\u0026lt;\u0026thinsp;1000 parasites/\u0026micro;l of blood), moderate parasitemia (1000\u0026ndash;9999 parasites/ \u0026micro;l of blood) and severe (\u0026ge;\u0026thinsp;10,000 parasites/\u0026micro;l of blood).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData analyses\u003c/h2\u003e \u003cp\u003eData entry and analyses were done using SPSS version 20 (SPSS Inc., Chicago, Ill., USA). Descriptive statistics were used for the demographic information of participants. Chi-square test was used to determine the association between demographic, ITN characteristics and malaria status of participants. All variables were used in a bivariate logistic regression and variables with significant associations (\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were further considered for multivariate logistic regression to determine independent associations of each variable.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eEthical consideration\u003c/h2\u003e \u003cp\u003e The study was approved by the Ethics Committee of the University of Nigeria Teaching Hospital (UNTH) (NHREC/05/01/2008B-FWA00002458-1RB00002323). Permission was also obtained from the Health Department of Nsukka LGA prior to data collection. Informed consent was sort from all participants prior to data collection. Confidentiality was maintained throughout the study.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe study involved 317 participants with 125 household heads and 192 household members. Of the 125 households, 62.4% were headed by females and majority (36.0%) were from the age group\u0026nbsp;41-50 years with the highest number\u0026nbsp;(35.2%) attained secondary education. With respect to occupation, more than half (56.0%) of the household heads were farmers. The socio-demographic data for 192 household members was based on sex and age only. More than half (52.1%) of them were females and majority 26.0% (50/192) were from the age group 40 years and above (Table 1).\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;1: Demographic characteristics of household heads and member, Nsukka LGA, 2018\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.386837881219904%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.386837881219904%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.22632423756019%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.386837881219904%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.386837881219904%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHousehold Heads N=125\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.22632423756019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.386837881219904%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.386837881219904%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.22632423756019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e37.6\u003c/p\u003e\n \u003cp\u003e62.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.386837881219904%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e21-30\u003c/p\u003e\n \u003cp\u003e31-40\u003c/p\u003e\n \u003cp\u003e41-50\u003c/p\u003e\n \u003cp\u003e51-60\u003c/p\u003e\n \u003cp\u003e\u0026gt;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.386837881219904%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.22632423756019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7.2\u003c/p\u003e\n \u003cp\u003e25.6\u003c/p\u003e\n \u003cp\u003e36.0\u003c/p\u003e\n \u003cp\u003e18.4\u003c/p\u003e\n \u003cp\u003e12.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.386837881219904%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducational level\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNo formal education\u003c/p\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003cp\u003eTertiary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.386837881219904%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.22632423756019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e29.6\u003c/p\u003e\n \u003cp\u003e20.0\u003c/p\u003e\n \u003cp\u003e35.2\u003c/p\u003e\n \u003cp\u003e15.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.386837881219904%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupation\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003cp\u003eTrading\u003c/p\u003e\n \u003cp\u003eFarming\u003c/p\u003e\n \u003cp\u003eCivil servant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.386837881219904%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.22632423756019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003cp\u003e28.0\u003c/p\u003e\n \u003cp\u003e56.0\u003c/p\u003e\n \u003cp\u003e11.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.386837881219904%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.386837881219904%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHousehold members N=192\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.22632423756019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.386837881219904%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.386837881219904%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.22632423756019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e47.9\u003c/p\u003e\n \u003cp\u003e52.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.386837881219904%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026lt; \u0026nbsp; \u0026nbsp; 5\u003c/p\u003e\n \u003cp\u003e5 \u0026ndash; 10\u003c/p\u003e\n \u003cp\u003e11 \u0026ndash; 20\u003c/p\u003e\n \u003cp\u003e21 \u0026ndash; 30\u003c/p\u003e\n \u003cp\u003e31 \u0026ndash; 40\u003c/p\u003e\n \u003cp\u003e\u0026gt; \u0026nbsp; \u0026nbsp; 40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.386837881219904%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.22632423756019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e15.1\u003c/p\u003e\n \u003cp\u003e11.5\u003c/p\u003e\n \u003cp\u003e22.9\u003c/p\u003e\n \u003cp\u003e12.0\u003c/p\u003e\n \u003cp\u003e12.5\u003c/p\u003e\n \u003cp\u003e26.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe prevalence of malaria in the three communities was 23.7% by RDT and 14.5% by microscopy (Figure 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe community with highest prevalence of malaria by RDT was Nsukka (12.9%), followed by Obimo (6.9%) and Edem (3.8%). Malaria cases significantly differed by communities in terms of RDT (p\u0026lt; 0.002) but not microscopy (p\u0026gt;0.05). (Table 2)\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;2: Malaria prevalence by community for both RDT and Microscopy, Nsukka LGA, 2018\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"637\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.111459968602826%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommunity\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.373626373626372%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRDT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.361067503924646%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.2574568288854%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMicroscopy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.896389324960754%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.111459968602826%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.186813186813186%\" valign=\"top\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.186813186813186%\" valign=\"top\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.361067503924646%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.1287284144427%\" valign=\"top\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.1287284144427%\" valign=\"top\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.896389324960754%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.84130019120459%\" valign=\"top\"\u003e\n \u003cp\u003eNsukka (224)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.061185468451242%\" valign=\"top\"\u003e\n \u003cp\u003e41 (12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.061185468451242%\" valign=\"top\"\u003e\n \u003cp\u003e183 (57.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.619502868068833%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.208413001912046%\" valign=\"top\"\u003e\n \u003cp\u003e29 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.208413001912046%\" valign=\"top\"\u003e\n \u003cp\u003e195 (61.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.84130019120459%\" valign=\"top\"\u003e\n \u003cp\u003eObimo (60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.061185468451242%\" valign=\"top\"\u003e\n \u003cp\u003e22 (6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.061185468451242%\" valign=\"top\"\u003e\n \u003cp\u003e38 (12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.619502868068833%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.208413001912046%\" valign=\"top\"\u003e\n \u003cp\u003e12 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.208413001912046%\" valign=\"top\"\u003e\n \u003cp\u003e48 (15.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.84130019120459%\" valign=\"top\"\u003e\n \u003cp\u003eEdem (33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.061185468451242%\" valign=\"top\"\u003e\n \u003cp\u003e12 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.061185468451242%\" valign=\"top\"\u003e\n \u003cp\u003e21 (6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.619502868068833%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.208413001912046%\" valign=\"top\"\u003e\n \u003cp\u003e5 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.208413001912046%\" valign=\"top\"\u003e\n \u003cp\u003e28 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.111459968602826%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotals\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.186813186813186%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e75 (23.7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.186813186813186%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e242 (76.3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.361067503924646%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.1287284144427%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e46 (14.5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.1287284144427%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e271 (85.5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.896389324960754%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;In terms of severity of malaria cases, more than half (54.3%) constituted moderate (1000\u0026ndash;9999 parasites/ \u0026mu;l of blood), low (30.4%) \u0026nbsp;(\u0026lt;1000 parasites/\u0026mu;l of blood) and severe parasitaemia (\u0026ge;10,000 parasites/\u0026mu;l of blood). Females had higher percentage 32 (69.6%) of malaria parasitaemia than males 14 (30.4%) but there was no significant difference in terms of severity of malaria in the three communities and by sex (Table 3).\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;3: Malaria parasitaemia quantification by communities and sex, Nsukka LGA, 2018\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.261637239165328%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.088282504012842%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.693418940609952%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModerate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37239165329053%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSevere\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37239165329053%\" valign=\"top\" style=\"width: 14.4955%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.21187800963082%\" valign=\"top\" style=\"width: 8.5261%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.261637239165328%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommunity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.088282504012842%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.693418940609952%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37239165329053%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37239165329053%\" valign=\"top\" style=\"width: 14.4955%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.21187800963082%\" valign=\"top\" style=\"width: 8.5261%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.261637239165328%\" valign=\"top\"\u003e\n \u003cp\u003eNsukka\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.088282504012842%\" valign=\"top\"\u003e\n \u003cp\u003e10 (21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.693418940609952%\" valign=\"top\"\u003e\n \u003cp\u003e15 (32.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37239165329053%\" valign=\"top\"\u003e\n \u003cp\u003e4 (8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37239165329053%\" valign=\"top\" style=\"width: 14.4955%;\"\u003e\n \u003cp\u003e29 (63.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.21187800963082%\" rowspan=\"3\" valign=\"top\" style=\"width: 8.5261%;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.988505747126435%\" valign=\"top\"\u003e\n \u003cp\u003eObimo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.007662835249043%\" valign=\"top\"\u003e\n \u003cp\u003e3 (6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.92337164750958%\" valign=\"top\"\u003e\n \u003cp\u003e7 (15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.54022988505747%\" valign=\"top\"\u003e\n \u003cp\u003e2 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.54022988505747%\" valign=\"top\" style=\"width: 14.4955%;\"\u003e\n \u003cp\u003e12 (26.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.988505747126435%\" valign=\"top\"\u003e\n \u003cp\u003eEdem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.007662835249043%\" valign=\"top\"\u003e\n \u003cp\u003e1 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.92337164750958%\" valign=\"top\"\u003e\n \u003cp\u003e3 (6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.54022988505747%\" valign=\"top\"\u003e\n \u003cp\u003e1 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.54022988505747%\" valign=\"top\" style=\"width: 14.4955%;\"\u003e\n \u003cp\u003e5 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.261637239165328%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.088282504012842%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.693418940609952%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37239165329053%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37239165329053%\" valign=\"top\" style=\"width: 14.4955%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.21187800963082%\" rowspan=\"3\" valign=\"top\" style=\"width: 8.5261%;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.988505747126435%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.007662835249043%\" valign=\"top\"\u003e\n \u003cp\u003e6 (13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.92337164750958%\" valign=\"top\"\u003e\n \u003cp\u003e5 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.54022988505747%\" valign=\"top\"\u003e\n \u003cp\u003e3 (6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.54022988505747%\" valign=\"top\" style=\"width: 14.4955%;\"\u003e\n \u003cp\u003e14 (30.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.988505747126435%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.007662835249043%\" valign=\"top\"\u003e\n \u003cp\u003e8 (17.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.92337164750958%\" valign=\"top\"\u003e\n \u003cp\u003e20 (43.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.54022988505747%\" valign=\"top\"\u003e\n \u003cp\u003e4 (8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.54022988505747%\" valign=\"top\" style=\"width: 14.4955%;\"\u003e\n \u003cp\u003e32 (69.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eOf the 125 households surveyed, more than half (56.0%) did not have ITNs while 44.0% had ITNs. Of those with ITNs, majority (81.8%) were damaged with a few (18.2%) in good condition. Usage of ITN was observed among most of the participants that had it (58.2%). \u0026nbsp; Sources of ITNs included; those who bought using their own money (5.5%), NGO (29.1%) and FMoH (65.5%). (Table 4).\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;4: Information on Insecticide Treated Nets in Nsukka LGA, 2018\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHousehold owned ITN\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e44.0\u003c/p\u003e\n \u003cp\u003e56.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCondition of ITN\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eGood\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eDamaged\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e18.2\u003c/p\u003e\n \u003cp\u003e81.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource of ITN\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eBought\u003c/p\u003e\n \u003cp\u003eNGO\u003c/p\u003e\n \u003cp\u003eFMoH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5.5\u003c/p\u003e\n \u003cp\u003e29.1\u003c/p\u003e\n \u003cp\u003e65.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eShape of mosquito net\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eRectangular\u003c/p\u003e\n \u003cp\u003eCircular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e96.4\u003c/p\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUse of ITN\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eSometimes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e58.2\u003c/p\u003e\n \u003cp\u003e21.8\u003c/p\u003e\n \u003cp\u003e20.0\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003c/strong\u003e\u003cstrong\u003eMultivariate logistic regression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn a multivariate logistic regression analysis to determine the association of household ITN characteristics and malaria status of participants, it was observed that households without ITN, those with ITNs but not using them and those using damaged ITNs were 1.32 [0.23 - 3.81, CI = 95%, p \u0026lt; 0.09], 1.51 [0.45 - 3.62, CI = 95%, p \u0026lt; 0.03] and 3.81 [1.24 - 9.71, CI = 95%, p \u0026lt; 0.02] respectively more likely to have malaria (RDT) (Table 5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;5: Association of Households and ITN characteristics and Malaria status of participants, Nsukka LGA, 2018\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"649\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.889060092449924%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.20647149460709%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eRDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.90446841294299%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMicroscopy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.889060092449924%\" valign=\"top\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.112480739599384%\" valign=\"top\"\u003e\n \u003cp\u003eaOR (CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.093990755007704%\" valign=\"top\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.88597842835131%\" valign=\"top\"\u003e\n \u003cp\u003eaOR (CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.889060092449924%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCracks/Crevices\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.112480739599384%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.36 (0.12-1.11)\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.093990755007704%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.88597842835131%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.10 (0.12-0.91)\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.889060092449924%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHave ITN\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.112480739599384%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.32 (0.23-3.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.093990755007704%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.88597842835131%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e0.54 (0.24-1.96)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.889060092449924%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUse ITN\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.112480739599384%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.51 ( 0.45- 3.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.093990755007704%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.88597842835131%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.79 (0.86\u0026mdash;1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.889060092449924%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCondition of ITN\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eGood\u0026nbsp;\u003c/p\u003e\n \u003cp\u003edamaged\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.112480739599384%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e3.81 (1.24- 9.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.093990755007704%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.88597842835131%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.23 (0.50\u0026mdash;2.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present study, the overall malaria prevalence was 23.7% by RDT and 14.5% by microscopy, far much lower than the results documented in Nsukka LGA where the prevalence was 72.8% by microscopy (Onyishi et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and much higher in a study conducted in Umudioka community in Anambra state of Nigeria (Onyido et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Our study also had a lower malaria prevalence compared to the study performed in Kano (Dawaki et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, the malaria prevalence in the current study was similar to other studies conducted at the University of Ibadan Campus in Nigeria (Anumudu et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) and in Iwo community of Southwestern in Nigeria (Igbeneghu et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The wide range of differences in malaria prevalence in study areas could be attributed to the time when the study was conducted, differences in climatic factors and behavioral patterns of people in the area, which promote mosquito breeding and susceptibility of the people to vector bites (Deredec et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Paul et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Qasim et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Russell et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe use of ITNs is currently considered the most cost-effective method of malaria prevention in highly endemic areas (National Population Commission, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe present study revealed that less than half of the households owned at least one ITN against the National Malaria Strategic Plan 2014\u0026ndash;2020 target of at least 80% of households having atleast one ITN for every two persons in the 97% of the population at-risk of malaria. The ITN ownership in the current study was also low compared to the study done in Kano State in Nigeria (Garley et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and in 23 communities of Amhara and Oromia Regional States of Ethiopia (Baume \u0026amp; Marin, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). However, the ITN ownership in this study was more than twice the ITN ownership in Makurdi, North Central Nigeria (Jombo, 2010).\u003c/p\u003e \u003cp\u003eThe ITN utilization in Nsukka LGA was still below the 60% standard recommended by the Roll back Malaria programme and also lower than what was reported in Makurdi \u0026ndash; North Central Nigeria (Jombo et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) but higher than the findings obtained in a study conducted in Kano State (Dawaki et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Garley et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In this study, one fifth of households did not use their ITNs in the previous night, in agreement with the findings of the study done in Ibadan in Nigeria (Aluko \u0026amp; Oluwatosin, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) but higher than ITN utilization reported in Calabar Metropolis, Cross River State in Nigeria (Abiodun et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The current study further demonstrated that one fifth of households used their ITNs sometimes contrary to more than half of the households in study done elsewhere in Nigeria (Aluko and Oluwatosin, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The reason for this disparity could be due to low education level (Aluko \u0026amp; Oluwatosin, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUse of damaged ITNs\u003c/p\u003e \u003cp\u003eIn a multivariable logistic regression analysis to determine the association of household and ITNs characteristics and malaria status of participants, the odds of testing positive to malaria using RDT among members of households without ITNs was 1.32 times compared to members of households with ITN [aOR: 1.32, 95% CI (0.23\u0026ndash;3.81, p\u0026thinsp;\u0026lt;\u0026thinsp;0.09)]. Members of households not using ITNs had increased odds of 1.5 times likely to develop malaria compared to those from households using ITN [aOR: 1.5, 95% CI (0.45\u0026ndash;3.65, p\u0026thinsp;\u0026lt;\u0026thinsp;0.03)], and this was in agreement with the study conducted in Hausa communities in Kano State of Nigeria (Dawaki et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). For the household members using damaged ITNs, there was 3.81 odds of having malaria compared to members of households using ITNs in good condition [aOR: 3.81, 95% CI (1.24\u0026ndash;9.71, p\u0026thinsp;\u0026lt;\u0026thinsp;0.02)]. Therefore, not using ITNs and using damaged ITNs were independently associated with having malaria by the use of RDT. This is in agreement with a study in Jimma town, Ethiopia (Alemu et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe prevalence of malaria recorded in this study was high using both RDT and Microscopy. Most households did not have ITNs and of those with ITNs, most were damaged. Participant who did not have ITN or had ITN but were not using them and those that used damaged ITN were more at risk of being affected with malaria. There is the need for regular provision of ITNs in at risk populations such as Nsukka LGA and increased community sensitization on the need to use ITN to control malaria burden.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no conflicts of interest regarding the publication of this paper.\u003c/p\u003e\n\u003cp\u003eEthical Approval and Consent to participate\u003c/p\u003e\n\u003cp\u003eThis study was conducted following ethical approval by University of Nigeria Teaching Hospital with ethical clearance certificate number NHREC/05/01/2008B-FWA00002458-1RB00002323. The informed consent to participate in the study was obtained from the participants prior to data collection process.\u003c/p\u003e\n\u003cp\u003eConsent to publication\u003c/p\u003e\n\u003cp\u003eBy virtual of submitting this work in this reputable Malaria Journal, the author and the co-authors of this manuscript have given consent to publish this work.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eI acknowledge that for this work to be published, I should to pay for it. I will source for funds for publication by myself or seek for sponsorship including the possibility of requesting for a publication waiver from this Journal.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAcknowledgement\u003c/p\u003e\n\u003cp\u003eI want to acknowledge the late Prof. F. C. Okafor; Prof. P. O. Ubachukwu and all lecturers in the Department of Zoology and Environmental Biology; my sponsors Deutscher Akademischer Austauschdienst (DAAD) and all my family members and friends for their support during this undertaking.\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbiodun E, Egena R, Irene C. Assessment of the Utilization of Insecticide Treated nets (ITNs) in Calabar Metropolis, Cross River State, Nigeria. J Health Med Nurs. 2016;26:196\u0026ndash;205.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlemu A, Tsegaye W, Golassa L, Abebe G. Urban malaria and associated risk factors in Jimma town, south-west Ethiopia. Malar J. 2011;10:1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1475-2875-10-173\u003c/span\u003e\u003cspan address=\"10.1186/1475-2875-10-173\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAluko JO, Oluwatosin AO. Utilization of insecticide treated nets during pregnancy among postpartum women in Ibadan, Nigeria : a cross-sectional study. BMC Pregnancy Childbirth. 2012;12(21):1\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnumudu CI, A.Adepoju M, Adediran O, Oyewole RIN. Malaria Prevalence and Treatment Seeking Behaviour of Young Nigerian Adults. Ann Afr Med. 2006;5(2):82\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAzoma Chikwe. (2017). 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Socio-Cultural factors influencing insecticide treated bed nets utilization in a malaria endemic city in north-central Nigeria. Asian Pac J Trop Med. 2010;1:402\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S1995-7645(10)60098-3\u003c/span\u003e\u003cspan address=\"10.1016/S1995-7645(10)60098-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaing L, Winn T, Rusli BN. (2006). Practical Issues in Calculating the Sample Size for Prevalence Studies. \u003cem\u003eArchives of Orofacial Sciences\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e(June 2014), 9\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Malaria Elimination Programme, A. 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(2014). \u003cem\u003eMosquito (Diptera: Culicidae) of Murree Hills, Punjab, Pakistan\u003c/em\u003e. \u003cem\u003e46\u003c/em\u003e(2), 523\u0026ndash;529.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRussell TL, Govella NJ, Azizi S, Drakeley CJ, Kachur SP, Killeen GF. (2011). Increased proportions of outdoor feeding among residual malaria vector populations following increased use of insecticide-treated nets in rural Tanzania. 1\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO. (2014). \u003cem\u003eWorld Malaria Report 2014\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO. (2015). \u003cem\u003eWorld Malaria Report 2015\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO. (2017). \u003cem\u003eWorld Malaria Report\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. (2016). Global technical strategy for malaria 2016\u0026ndash;2030. World Health Organ, 1\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e\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":"Malaria, Insecticide Treated Net, Assessment, Nsukka, ","lastPublishedDoi":"10.21203/rs.3.rs-4791088/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4791088/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Malaria remains a global pressing issue despite several interventions to reduce the disease prevalence. One of the key interventions that has gained public interest is the use of insecticide treated nets (ITNs) to control the malaria-mosquito vector. This study sought to determine malaria prevalence, ITN ownership and usage and assess the effectiveness of damaged ITNs in malaria prevention in peri-urban communities in Enugu State, South-Eastern Nigeria.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod:\u003c/strong\u003e This was a cross-sectional study conducted in three communities (Nsukka, Obimo and Edem) of Nsukka Local Government Area. A total of 317 participants from 125 randomly selected households were screened for malaria using rapid diagnostic test (RDT) and confirmed with microscopy. Socio-demographic data and information related to ITN were collected using structured questionnaires and field observation. Bivariate and multivariable logistic regression analyses were performed to determine socio-demographic and ITN characteristics associated with Malaria.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Malaria prevalence among participants was 23.7% (RDT) and 14.5% (microscopy). Prevalence of malaria differed significantly among the age group (p\u0026lt;0.01), with 5-15 years having the highest prevalence both by RDT (6.9%) and microscopy (5.4%). Severity of malaria cases revealed 54.3%, 30.4% and 15.3% for moderate, low and severe parasitemia respectively. Of the 125 household surveyed, less than half (44.0%) possessed at least one ITN with only a few (18.2%) in good condition. Members of households without ITN were 1.32 more likely to have malaria using RDT (0.23-3.81, p=0.09). Not using ITN (aOR=1.5, CI=0.45- 3.62) and the use of damaged ITN (aOR=3.81, CI=1.24- 9.71) were significantly associated with having malaria by the use of RDT.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Malaria prevalence was high among the study participants particularly the older children. Most of the ITNs used by households were damaged. Participant who did not have ITN were more at risk of being affected with malaria. There is the need for regular provision of ITNs among the risk populations and increased community sensitization on the need to use ITN to prevent human-mosquito contact.\u003c/p\u003e","manuscriptTitle":"Malaria Risk Assessment Based on the Use of Insecticide Treated Nets in Nsukka Local Government Area, Enugu State, Nigeria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-27 12:17:23","doi":"10.21203/rs.3.rs-4791088/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":"d8d0abf9-50fa-4f89-b66f-41eba8e672c4","owner":[],"postedDate":"August 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-10T14:23:13+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-27 12:17:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4791088","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4791088","identity":"rs-4791088","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}