Prospects of malaria vaccination in Nigeria: Anticipated challenges and lessons from previous vaccination campaigns

Effiong, Fortune Benjamin; Makata, Victor Chisom; Elebesunu, Emmanuel Ebuka; Bassey, Esther Edet; Salachi, Kyembe Ignatius; Sagide, Martin Rogose; Abdulameed, Hassan Taiye; Uwishema, Olivier

doi:10.1016/j.amsu.2022.104385

Cited by 12 publications

(19 citation statements)

References 19 publications

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“…Nigeria, in particular, has one of the highest malaria burdens globally and is a major contributor to the global malaria mortality rate (Effiong et al, 2022). Approximately 100 million malaria cases are reported annually in Nigeria, resulting in over 300,000 deaths (Effiong et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…Malaria is endemic in sub-Saharan Africa (Dara et al, 2022;Marwa et al, 2022;Arya et al, 2021),where 29 countries account for 96% of global malaria cases (Thornton, 2020). Nigeria, in particular, has one of the highest malaria burdens globally and is a major contributor to the global malaria mortality rate (Effiong et al, 2022). Approximately 100 million malaria cases are reported annually in Nigeria, resulting in over 300,000 deaths (Effiong et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

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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“…Malaria is endemic in sub-Saharan Africa (9); (10); (11),where 29 countries account for 96% of the global malaria cases (12). Nigeria, in particular, has one of the highest malaria burdens globally, and is a signi cant contributor to the global malaria mortality rate (13). Approximately 100 million cases of malaria are reported annually in Nigeria, resulting in over 300,000 deaths (13).…”

Section: Introductionmentioning

confidence: 99%

“…Nigeria, in particular, has one of the highest malaria burdens globally, and is a signi cant contributor to the global malaria mortality rate (13). Approximately 100 million cases of malaria are reported annually in Nigeria, resulting in over 300,000 deaths (13). Along with the Republic of Congo, Nigeria accounts for 36% of the global malaria cases (13).…”

Section: Introductionmentioning

confidence: 99%

“…Approximately 100 million cases of malaria are reported annually in Nigeria, resulting in over 300,000 deaths (13). Along with the Republic of Congo, Nigeria accounts for 36% of the global malaria cases (13). Given the health implications of malaria, Nigeria has joined other African countries to eradicate the disease between 2025-2030 (14).…”

Section: Introductionmentioning

confidence: 99%

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Prediction of malaria positivity using patients’ demographic and environmental features and clinical symptoms to complement parasitological confirmation before treatment

Ojurongbe

Afolabi

Bashiru

et al. 2023

Preprint

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Background Current malaria diagnosis methods that rely on microscopy and Histidine Rich Protein2 (HRP2)-based rapid diagnostic tests (RDT) have drawbacks that necessitate the development of improved and complementary malaria diagnostic methods to overcome some or all of these limitations. Consequently, the automated detection and classification of malaria can provide patients with a faster and more accurate diagnosis. Therefore, this study used a machine-learning model to predict the occurrence of malaria based on sociodemographic behaviour, environment, and clinical features.Method Data from 200 Nigerian patients were used to develop predictive models using nested cross-validation and sequential backward feature 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 learning models examined, the penalised logistic regression model had the best area under the receiver operating characteristic (ROC) curve for the training set (84%; 95% confidence interval (CI) = 75–93%) and test set (83%; 95% CI = 63–100%). Increased odds of malaria was associated with high body weight (adjusted odds ratio (AOR) = 4.50, 95% CI = 2.27–8.01, p < 0.0001). Even though the association between the odds of having malaria and body temperature was insignificant, patients with 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). In addition, patients who had bushes in their 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. In contrast, decreased odds of malaria were associated with age (AOR = 0.62, 95% CI = 0.41–0.90, p-value = 0.012) and BMI (AOR = 0.47, 95% CI = 0.26–0.80, p = 0.006).Conclusion Newly developed routinely collected baseline sociodemographic, environmental, and clinical features to predict malaria types may serve as a valuable tool for clinical decision-making.

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Unlocking the potential of novel RTS, S/AS01, and R21/Matrix‐M™ malaria vaccines in African nations

Oduoye,

Haider,

Marsool

et al. 2024

Health Science Reports

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IntroductionMass malaria vaccination, rather than vaccinating only children below age 5, has been proven to have the potential to reduce morbidity and mortality among those vaccinated, both young and old. Addressing vaccine scepticism and misinformation is crucial in African nations to build public trust in malaria prevention. Therefore, including a wider range of demographics in vaccine trials is necessary for equitable representation and achieving herd immunity against malaria.AimThis present article aims to identify some of the obstacles that impede malaria vaccination usage and acceptability in African Nations in combating malaria in the region as it continues to pose a significant global public health problem.MethodologyA literature search was done on the Malaria vaccine between 2000 and 2023. Past and present articles/studies on this topic were consulted on PubMed, Google Scholar, Scopus and Web of Science using the following keywords; “Malaria,” “Vaccines,” “African Nations,” “Obstacles, Strategies,” and “Public Health.”ResultsThe recently approved RTS, S/AS01, and R21/Matrix‐M™ Malaria vaccines have the potential to prevent numerous deaths and cases of Malaria in Africa. These vaccines Malaria vaccines are cost‐effective in African areas with moderate to high plasmodium falciparum and can be delivered through routine immunization.ConclusionTo combat malaria effectively in African Nations, African leaders need to set up a comprehensive approach that involves; prevention, healthcare access, implementation research strategies towards adoption and acceptance of malaria vaccines in Africa as well as community engagement with the religious leaders, the market women, community heads, schools, as well as students’ union towards the willingness and acceptability of the malaria vaccines among the African populations.

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Prospects of malaria vaccination in Nigeria: Anticipated challenges and lessons from previous vaccination campaigns

Cited by 12 publications

References 19 publications

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

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

Prediction of malaria positivity using patients’ demographic and environmental features and clinical symptoms to complement parasitological confirmation before treatment

Unlocking the potential of novel RTS, S/AS01, and R21/Matrix‐M™ malaria vaccines in African nations

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