Malaria Hyperendemicity: The Burden and Obstacles to Eradication in Nigeria

Chimezie, Raymond O.

doi:10.4236/jbm.2020.811015

Cited by 8 publications

(9 citation statements)

References 20 publications

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“…All the respondents ( n = 300) said that they have had malaria episodes before and where 46.33% of the respondents had malaria at least once in 6 months ( Figure 1 A). This finding corroborates the fact that Nigeria is a hyper-endemic area for malaria [ 19 ].…”

Section: Resultssupporting

confidence: 88%

Assessment of the Antimalarial Treatment Failure in Ebonyi State, Southeast Nigeria

Egwu¹,

Aloke

Chukwu

et al. 2023

JoX

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The fight against malaria is a continuum as the epidemic is not abating. For proper deployment of tools in the fight against malaria, an assessment of the situation is necessary. This work assessed the level of antimalarial drug treatment failure in Ebonyi State, Nigeria. Both survey and in vitro analyses were adopted. The survey was used to obtain qualitative information from both the malaria subjects and the pharmacies where antimalarial drugs are sourced. The results from the survey were complemented by an in vitro assay of the level of active pharmaceutical ingredients (APIs) in the commonly used artemisinin combination in Nigeria; artemether/lumefantrine. Results from the survey revealed that artemisinin combination therapies (ACTs) remain the mainstay in the treatment of malaria, even though other non-artemisinin drugs are still used. It also revealed that many patients still self-medicate, although, this may not be connected to the treatment failure seen among some malaria subjects. The in vitro assay showed that ACT contains the right quantity of APIs. Further surveillance is, therefore, necessary to understand the real cause of treatment failure among malaria subjects in Nigeria.

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Section: Resultssupporting

confidence: 88%

Assessment of the Antimalarial Treatment Failure in Ebonyi State, Southeast Nigeria

Egwu¹,

Aloke

Chukwu

et al. 2023

JoX

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“…Illiteracy can lead to stigma and resistance to adhere to information. Studies show that lack of health literacy is a reason for resisting the use of insecticide treated bed nets to prevent malaria [49] [50] [51]. In the same vein, when a community has good health education about a disease, it does help in care and management.…”

Section: ) Health Literacy and Awarenessmentioning

confidence: 99%

Health Awareness: A Significant Factor in Chronic Diseases Prevention and Access to Care

Chimezie¹

2023

JBM

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Health literacy and awareness are essential strategies in promoting global health and improving access to care. While seen as an essential tool for promoting population health awareness to improve early detection and treatment of chronic diseases, it is yet to be emphasized in most African countries. Health literacy is an essential practice to promote chronic disease prevention and reduce the growing threat to population health. Incidences and mortalities from chronic diseases commonly arise from limited knowledge of the causative risk factors and access to health facilities. Without knowledge about causes, health impacts, and available health services, people continue to indulge in the habits that worsen their health conditions and fail to access care timely. By using health literacy and awareness as a tool for chronic disease prevention, healthcare professionals will develop strategic health awareness programs that fit the socio-demographics of the population they serve. This article explored the significant role health awareness occupies in individual and community health prevention through health promotion and education. It reviewed the concept and dimensions of chronic disease prevention, cultural beliefs and impact on chronic diseases, gaps created by low health literacy, and the significance of health literacy in disease prevention and health promotion. Furthermore, it recommends that health systems and local communities form partnerships to address common and emerging health problems, and health systems should be properly funded.

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“…Malaria is a life-threatening disease caused by the Plasmodium parasite, transmitted to humans through the bites of infected female Anopheles mosquitoes (Chimezie, 2020).…”

Section: Introductionmentioning

confidence: 99%

Prediction of malaria outcomes using patients’ demographical, environmental and clinical features

Ojurongbe

Afolabi

Akinde

et al. 2023

Preprint

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Background: The current malaria diagnosis methods, which rely on microscopy and Histidine Rich Protein2 (HRP2) based RDT, have drawbacks that necessitate the development of improved and complementary malaria diagnostic methods to overcome some or all the limitations. Consequently, automated detection and classification of malaria can provide patients with a faster and more accurate diagnosis. This study, therefore, used a machine learning model to predict the occurrence of malaria based on socio-demographicbehaviour, environment, and clinical features. Methods:Data from 200 Nigerian patients were used to develop predictive models using nested cross-validation and sequential backward features selection (SBFS), with 80% of the dataset randomly selected for training and optimisation and the remaining 20% for testing the models. Results: Among the three machine learningmodels examined, penalised logistic regression model had the best area under the receiver operating characteristic (ROC) curve performance for the training set (84%; 95% confidence interval (CI) =75–93%) and test set (83%; 95% CI =63–100%). The model included age, BMI, body temperature, bushes in surroundings, body weight, dizziness, fever, headache, mosquito repellant, muscle pain, sex, sore throat, stagnant water in the home, and vomiting. An increased odd of patients having malaria was associated with high body weight (adjusted odd ratio (AOR) = 4.50, 95% CI =2.27-8.01, p-value <0.0001). Even though the association between the odds of having malaria and body temperature was insignificant, patients who had body temperature had higher odds of having malaria than those who did not have body temperature (AOR = 1.40, CI =0.99-1.91, p-value = 0.068). Also, patients who had bushes in the surroundings (AOR = 2.60, 95% CI =1.30-4.66, p-value = 0.006) or experienced fever (AOR = 2.10, CI =0.88–4.24, p-value = 0.099), headache (AOR = 2.07; CI =0.95–3.95, p-value = 0.068), muscle pain (AOR =1.49; CI =0.66–3.39, p-value = 0.333) and vomiting (AOR = 2.32; CI =0.85–6.82, p-value = 0.097) were more likely to experience malaria compared to those without bushes in the surrounding or those who did not experience fever, headache, muscle pain and vomiting. In contrast, decreased odds of malaria were associated with age (AOR = 0.62, 95% CI= 0.41-0.90, p-value = 0.012) or BMI (AOR = 0.47, 95% CI= 0.26-0.80, p-value =0.006). Conclusion:Newly developed routinely collected baseline socio-demographical, environmental, and clinical features topredict malaria types and may serve as a valuable tool for clinical decision making.

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Malaria Hyperendemicity: The Burden and Obstacles to Eradication in Nigeria

Cited by 8 publications

References 20 publications

Assessment of the Antimalarial Treatment Failure in Ebonyi State, Southeast Nigeria

Assessment of the Antimalarial Treatment Failure in Ebonyi State, Southeast Nigeria

Health Awareness: A Significant Factor in Chronic Diseases Prevention and Access to Care

Prediction of malaria outcomes using patients’ demographical, environmental and clinical features

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