The fight against malaria in Edo-North, Edo State, Nigeria: identifying risk factors for effective control (2024)

Malaria in Nigeria

Variations in socio-demographic, environmental, and climatic factors may account for the variation in the prevalence rate of malaria infection among young people, even within a single country (Ibrahim et al., 2023; Ngum et al., 2023). Past research has shown malaria prevalence rates in Nigeria of 66.7% (Dawaki et al., 2016), 64.0% (Nmadu et al., 2015), and 58.0% (Awosolu et al., 2021). Urban and suburban centers have comprehensively recorded the effects of socio-demographic variables or factors, including gender, age, occupation, and level of education, on human exposure and treatment, as well as environmental variables like rainfall, humidity, and temperature, may facilitate the rapid growth of mosquitos’ vectors (Ibrahim et al., 2023; Awosolu et al., 2021). The spread of malaria is more common in Africa’s rural settings than in its urban ones, which may be caused by the region’s increased vector density, subpar housing conditions, and inadequate drainage infrastructure (Ngum et al., 2023; Dawaki et al., 2016; Nmadu et al., 2015; Awosolu et al., 2021; Oladeinde et al., 2012). An increasing amount of research indicates that children and teenagers attending school may potentially be susceptible to malaria (World Health Organization (WHO), 2023; Squire et al., 2016). Furthermore, the prime age of clinical bouts of malaria is moving from young infants and adolescents to children and teenagers due to a drop in spread and exposure in some locations (World Health Organization (WHO), 2023; Squire et al., 2016). Data on P. falciparum infection prevalence and related variables among teenage age groups in rural Nigeria are desperately needed to be able to modify the prevention and control of malaria strategies. For the purpose of malaria prevention and control strategies, decision-makers must identify the major risk factors for malaria infection.

Currently, the most common approaches for managing malaria vectors and related malaria transmission are indoor residual spraying (IRS) and insecticide-treated nets (ITNs), both of which have been shown to decrease malaria (Abubakar et al., 2022; World Health Organization (WHO), 2024). Nonetheless, the primary and most encouraging element of the specific vector control and management practice are long-lasting insecticide-treated nets (LLINs) on sleeping beds, which the Nigerian Ministry of Health uses to combat the disease and its vector.

Nigeria’s malaria control strategy

Nigeria’s fight against malaria has made significant progress, underpinned by a robust control strategy. Key components include surveillance, bed net distribution and health promotion.

Surveillance

Nigeria has a robust surveillance system to monitor the malaria burden and inform interventions. The National Malaria Indicator Survey (NMIS) provides key data on malaria prevalence, risk factors and intervention coverage. This surveillance system is central to identifying hotspots, tracking trends and evaluating the impact of control interventions (Addo et al., 2021; Amede et al., 2022).

Net distribution strategy

The distribution of long-lasting insecticidal nets (LLINs) is a cornerstone of malaria control in Nigeria. Mass distribution campaigns have been instrumental in increasing LLIN ownership. The National Malaria Elimination Programme (NMEP) oversees these campaigns, which target vulnerable populations, particularly pregnant women and children under (Federal Ministry of Health, 2022; World Health Organization (WHO), 2022, 2024).

Health promotion campaigns

Nigeria has implemented extensive health promotion campaigns to raise awareness of malaria prevention and treatment. Messages focus on early diagnosis, prompt treatment with artemisinin-based combination therapies (ACTs) and consistent use of LLINs. Community-based initiatives involving traditional and religious leaders have been effective in disseminating information and changing behavior (Akpan et al., 2023; Mehta, 2024).

Challenges and future directions

Despite progress, Nigeria faces challenges in achieving malaria elimination, including inadequate funding, resistance to antimalarial drugs, and gaps in access to healthcare. Future efforts should focus on strengthening surveillance, expanding access to diagnosis and treatment, and developing innovative strategies to address emerging challenges (Jeremiah et al., 2023).

Therefore, the goal of this study was to ascertain the malaria risk factors and trend in the study populations.

Study location

The study took place in four local government areas of Edo-North, a region located within Edo state, Nigeria. Edo-North includes eight of the state’s eighteen local governments, specifically Akoko-Edo, Etsako-West, Etsako-Central, Etsako-East, Owan-East, and Owan-West (Fig. 1). Edo state is one of the states located in the rain forest zone in Nigeria, with an average annual rainfall between 1,800 and 2,780 millimeters and a mean temperature of 28 degrees Celsius. Malaria continues to be a significant health problem in the region, but transmission intensity varies among the different local governments. Edo North District was chosen for this malaria epidemiology study due to its high malaria burden, lack of previous data, predominantly rural population, and diverse ecological setting. The region’s lowlands, sloping streams, and rainforest vegetation may impact the breeding and distribution of mosquito vectors.

Sampling technique

A systematic sampling approach was employed to select six local government areas in Edo North. Within each selected local government, towns and villages were systematically chosen to ensure representative coverage. Households were then randomly selected from these locations. Data on sociodemographics and environmental risk factors were gathered from 605 participants using questionnaires and blood samples.

Study design

There were two parts to the study. A cross-sectional survey was carried out during August and September 2023 to assess the prevalence of malaria infections and associated risk factors within households, a cross-sectional household survey conducted in the selected communities that took place in September 2023, when most people had returned from their farms, and involved meeting participants at their homes in cool evening to conduct personal interviews. Blood samples were also taken for microscopy and sent for testing at the Irrua Specialists teaching hospital in Irrua, Edo State. In order to determine trends in the prevalence of malaria in Edo-North, data on hospital prevalence from 2021 to 2023 were collected retrospectively from Edo-North’s Public Health Centers as part of the study’s second arm.

Exclusion criteria for the study

Individuals who declined to provide their consent, those who did not reside in Edo-North, those suffering from mental health issues, and those receiving anti-malarial treatment or those who has received anti-malarial treatment 2 weeks before the commencement of this study were also not included in the study.

Determination of minimum sample size

The Swinscow Formula was used to determine the minimum sample size (n) (Charan & Biswas, 2013).

$$\mathrm{n}={\displaystyle \frac{{\mathrm{Z}}^2\times \mathrm{P}\mathrm{r}\mathrm{e}\mathrm{v}\mathrm{a}\mathrm{l}\mathrm{e}\mathrm{n}\mathrm{c}\mathrm{e}\mathrm{f}\mathrm{r}\mathrm{o}\mathrm{m}\mathrm{a}\mathrm{s}\mathrm{i}\mathrm{m}\mathrm{i}\mathrm{l}\mathrm{a}\mathrm{r}\mathrm{s}\mathrm{t}\mathrm{u}\mathrm{d}\mathrm{y}\times \left(1-\mathrm{P}\mathrm{r}\mathrm{e}\mathrm{v}\mathrm{a}\mathrm{l}\mathrm{e}\mathrm{n}\mathrm{c}\mathrm{e}\mathrm{f}\mathrm{r}\mathrm{o}\mathrm{m}\mathrm{s}\mathrm{i}\mathrm{m}\mathrm{i}\mathrm{l}\mathrm{a}\mathrm{r}\mathrm{s}\mathrm{t}\mathrm{u}\mathrm{d}\mathrm{y}\right)}{\mathrm{L}\mathrm{e}\mathrm{v}\mathrm{e}\mathrm{l}\mathrm{o}\mathrm{f}\mathrm{P}\mathrm{r}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{s}\mathrm{i}\mathrm{o}{\mathrm{n}}^2}}$$

$$\mathrm{n}={\displaystyle \frac{{\mathrm{Z}}^2\mathrm{P}\left(1-\mathrm{P}\right)}{{\mathrm{d}}^2}=384.6}$$where n is the minimum sample size for this study, Z is the confidence level, d is set to be 0.05, Z is 1.96 and P is 0.5. Using the minimum sample size formula resulted in approximately 385. A total of 605 participants were sampled in six local government areas. Approximately 101 participants, including men, women and minors, were recruited from each area. Informed consent was obtained from all adult participants and from parents or guardians for minors.

Ethical considerations and participant assent/consent

This research received ethical approval from the Health Research Ethics Committee, Irrua Specialist Teaching Hospital, Irrua, Edo State, Research Ethics Committee, with approval number: 51/23. Before collecting data, we obtained verbal assent for the children below 18 years of age through their parents or guardians. Consent forms were distributed to all participants, ensuring their agreement to donate blood samples for malaria screening. To ensure informed consent, the study objectives were explained clearly explained to each participant individually. For those with literacy limitations, the questionnaires were read aloud and questions clarified to maintain consistency in responses. Participation was entirely voluntary, and participants were informed of their right to withdraw at any time. Written informed consent, obtained through signatures or thumbprints, was collected from all participants. Confidentiality and privacy of all participants were strictly maintained throughout the research. There were no costs associated with participation in the study. In accordance with national treatment guidelines, the respondents who tested positive for malaria parasites were treated with antimalarial drugs.

Data collection and instrumentation process

This study used a semi-structured questionnaire that was developed. A semi-structured questionnaire in English was used in this study as over 90% of the residents in Edo North communicate in English. Data collectors, consisting of final year statistics and medical laboratory science students, were trained for the field work. The questionnaire covered demographics (age, sex, occupation, marital status and educational status) and socio-economic factors (type of house, household size, type of environment, type of toilet, water sources and water storage methods) and malaria prevention practices (ownership and use of insecticide-treated nets and indoor residual spraying). The questionnaire was piloted in several locations (Auchi, Igarra, Ibillo, Ozala, Otuo, Sabongida-Ora, Afuze, Fuga, Jattu, Agenebode and Okpella) before being administered to the entire study population. For the collection of data from young children, their parents or guardians were interviewed in order to have accurate and detailed information.

Sample collection

A digital clinical thermometer was used to take the participants’ body temperatures; the thumb cleaned sterile disposable cotton wool and “lancet” is used to prick the finger in order to collect blood for microscopic analysis in the laboratory. Laboratory records of General Hospitals and Public Health Centers were used to get data on trends in hospital prevalence of malaria in Edo-North.

Laboratory analysis

After being air-dried, the thick blood films were taken to Irrua Specialist Teaching Hospital and stained for 15 to 30 min with 5% Giemsa. They were cleaned, dried by air, and examined under an optical microscope with an X-100 objective. After examining over 100 fields with X-100 high power microscopy and finding no malaria parasite, a thick blood smear was considered negative. By comparing the number of parasites on positive slides to 200 white blood cells (WBC), they computed the parasites/μl of blood using the assumption that there are 8,000 leucocytes per microliter.

The quality control for the malaria microscopy includes regular microscope calibration and cleaning, use of high quality reagents (Giemsa stain), standardized slide preparation, the services of qualified medical laboratory scientists, cross-checking of slides by multiple microscopists, and the use of positive and negative control slides.

Statistical analyses

The data collected was assessed, cleaned, and analyzed with the aid of R 4.3.2 software (R Core Team, 2023). The mean ± standard deviation was used to express the age. Malaria prevalence among participants was obtained using frequencies and percentages. The findings of the Hosmer-Lemeshow test, which was used to test the data’s goodness of fit or normality, reveal a non-normal distribution. Kruskal-Wallis (H) and Mann-Whitney (W) tests were used to analyze the parasite load. The prevalence and factors influencing the parasite load were determined using the binary logistic regression model and Chi-square. Multivariate logistic regression study model variables were selected based on their significance at p-value < 0.05. The selection of variables based on a p-value of 0.05 in multivariate logistic regression aims to balance model complexity and accuracy. By focusing on significant variables, researchers aim to identify key factors influencing malaria prevalence while avoiding over fitting. Two separate multivariate logistic regression models were employed to analyze the associations between demographic factors and malaria prevalence, and between environmental and behavioral characteristics and malaria prevalence. Odd ratios; crude odd ratios (COR) and adjusted odd ratios (AOR) were computed at 95% confidence intervals. p-values less than 0.05 were regarded as statistically significant.

Prevalence of malaria in Edo-North

A total of 605 respondents were under investigation during the study period. The average age of the respondents was 26.40 ± 20.16 years. Table 1 shows that out of the 605 blood samples tested, 94 were microscopically malaria parasites positive, giving a total prevalence of malaria in Edo-North of 15.54%. The percentage of persons who tested positive for malaria parasite but asymptomatic to malaria infection is 13.06% while that of persons with symptomatic malaria infection who also tested positive for malaria parasite is 2.48%. The percentage of persons observed to be febrile but was not as a result of malaria parasite was 6.45%. Approximately 78.02% of the study participants were apparently free of malaria parasites. Hence, from Table 1, the malaria infection prevalence in Edo-North was statistically significant at p-value = 0.011.

The relationship between malaria prevalence and demographic factors

The demographic characteristics are outlined in Table 2. Out of the 605 participants, 94 tested positive for malaria. Among these positive cases, 61.7% were male while 38.3% are female. The age distribution of the study population was as follows: 3.1% infants (9.6% prevalence), 25.5% children (44.7% prevalence), 17.9% teenagers (29.8% prevalence), and 53.6% adults (16.0% prevalence). Individuals residing in rural settlements had a significantly higher infection rate (77.7%) compared to those living in urban areas (22.3%). Akoko-Edo had the highest prevalence, followed by Etsako-West, Owan-East, Owan-West, Etsako-East, and Etsako-Central being the least. Regarding education, individuals with no formal education had the highest malaria prevalence, followed by those with primary, secondary, college, polytechnic, and university education, respectively. Pupils/students had the highest malaria prevalence among occupational groups, followed by farmers and artisans. Business owners and unemployed individuals had similar prevalence rates, while civil servants had the lowest. Marital status was associated with malaria prevalence. Unmarried individuals had the highest rates, followed by married individuals, widowed/widowers, and divorced/single parents. The malaria infection rate was notably higher among residents of mud houses than those living in modern or cement-built houses. Malaria prevalence increased with household size. Households with 10 or more people had the highest risk, while those with 1–5 people had the lowest. Malaria prevalence was highest among those using pit latrines, followed by bush toilets and water systems.

A multivariate logistic regression analysis was then performed on the significant variables, using the response variable (malaria prevalence) and the explanatory variables (socio-demographic factors). The results of the analysis included the following: Individuals with no education were approximately seven times more likely to contract malaria compared to those with higher educational levels (AOR = 6.484, 95% CI: [2.045–20.565]). Children aged 3–12 were about 11 times more likely to contract malaria (AOR = 11.324, 95% CI: [9.827–24.321]). Additionally, residents of Owan-East local government were found to be more susceptible to malaria infection (AOR = 4.617, 95% CI: [1.71–4.281]). Individuals using pit latrine had about 10 times at increased risk of malaria infection (AOR = 10.327, 95% CI: [5.473–19.485]). These variables were significantly associated with the risk of malaria.

Environmental and behavioral characteristics of residents and malaria prevalence

Table 3 summarizes the environmental and behavioral characteristics of the survey respondents. Individuals who used indoor residual spray (IRS) had a lower risk of malaria compared to those who did not. Those who slept without long-lasting insecticidal nets (LLINs) were more susceptible to malaria infection. Residents with bushes in their surroundings exhibited a higher risk of malaria than those without. Malaria prevalence was higher among individuals living near rivers or streams compared to those with other water sources. Additionally, storing water in open containers was associated with a higher risk of malaria compared to other storage methods.

After adjusting for possible confounding odds (AOR) of environmental and behavioural characteristics of the residents and malaria prevalence, the use of long-lasting insecticidal nets (LLINs) is associated with a lower risk of malaria infection. Residents with bushes in their surroundings had a three times at increased risk of malaria infection (AOR = 2.563, 95% CI: [1.947–5.322]). Additionally, those living near streams or rivers were approximately three times more susceptible to malaria (AOR = 2.563, 95% CI: [1.057–2.392]). Individuals who relied on rivers/streams or rainwater as their primary water source were significantly more likely to contract malaria. Compared to those using other sources, their risk was approximately three times higher for river/stream water and four times higher for rain water (AOR = 2.251, 95% CI: [1.234–4.108] and AOR = 4.7388, 95% CI: [2.614–8.588]) respectively. Storing water in open containers was also associated with an increased risk of malaria infection (AOR = 10.016, 95% CI: [2.566–14.764]). Malaria infection was significantly associated with these five environmental and behavioural characteristics of the residents’ variables.

Malaria prevalence and management factors in human populations

From Table 4, the analysis showed that 87.2% of participants had experienced malaria in the past. Of these, 71.3% had received treatment in the past 1–5 months, 20.2% in the past 6–12 months, and 8.5% in the past year. The most common source of treatment was health centers (40.4%), followed by medical/dispensaries centers (22.3%), pharmacies/chemists (20.2%) and traditional/herbal homes (17.0%).

The multivariable logistic regression analysis revealed that individuals who had contracted malaria in the past were significantly more susceptible to future malaria infections, with an odds ratio of 4.839 and a 95% confidence interval of 2.187 to 10.708. Individuals with a history of malaria who received treatment from pharmacies or chemist shops were approximately six times more likely to have recurrent malaria infections (95% CI: [3.232–17.867]). Conversely, those who sought treatment from traditional or herbal healers were less likely to experience recurrent malaria (95% CI: [0.237–0.984]).

Geometric mean malaria parasite density in relation to demographic factors

From Table 5, the average geometric mean parasite density (GMPD) for malaria was approximately 5,146 parasites/μL of blood. At p = 0.002, the GMPD of men (7,765.48 parasites/μL) was higher than that of women (4,786.16 parasites/μL). Similarly, farmers had a higher GMPD of malaria (6,654.43/μL) than people in other occupations or professions (p < 0.001). When marital status was taken into consideration, single persons had a higher GMPD (6,542.32 parasites/μL) than other groups at the significance level of 5%. For age group, teenager had higher GDPM (5,929.00 sites/μL). Those who have live for 11–15 years had higher GDPM (4,126.46 sites/μL) while those who have lived in their locations for over 15 years had lower malaria parasite density load. For location of participants, 8,876.03 parasites/μL, residents of Owan-East had the highest geometric mean malaria parasite density, followed by Owan-West (656.65 parasites/μL), Etsako-Central (6,545.23 parasites/μL), Etsako-East (6,087.65 parasites/μL), Etsako-West (5,876.31 parasites/μL) and Akoko Edo (5,345.87 parasites/μL).

Assessment of long-lasting insecticidal net ownership and utilization in Edo-North

From Fig. 2, LLIN coverage in Edo North is 76.53%, with approximately 463 of the 605 respondents reporting having LLINs at home. From Fig. 3, the majority (61.98%) of people with LLINs rarely sleep under them on a daily basis, while 27.50% sleep under them frequently and 10.41% do not use them. Figure 4 shows that the majority of nets (43.54%) were distributed free of charge by the government, followed by those purchased by individuals (42.62%). A smaller proportion (14.84%) were obtained by other means.

Malaria prevalence patterns in Edo-North between 2000 and 2023

From Fig. 5, records from health facilities and hospitals show that between 2020 and 2023, the prevalence of malaria among residents of Edo North is on a downward trend.