Objectives This study aimed to examine the prevalence and pattern of adverse events following childhood immunization and the responses of mothers in Ile-Ife, South West Nigeria.
Methods This descriptive cross-sectional study was conducted among 422 mothers of children aged 0 to 24 months attending any of the 3 leading immunization clinics in Ile-Ife, Nigeria. The respondents were selected using the multi-stage sampling technique. Data were collected using a pretested structured interviewer-administered questionnaire and analyzed using IBM SPSS ver. 26.0. The chi-square test was used to test associations, while binary logistic regression was used to determine the predictors of mothers’ responses to adverse events following immunization (AEFIs). A p-value of <0.05 was considered statistically significant.
Results The mean age of the respondents was 29.99±5.74 years. About 38% of the children had experienced an AEFI. Most mothers believed that the pentavalent vaccine was the most common cause of AEFIs (67.5%). Fever (88.0%) and pain and swelling (76.0%) were the most common AEFIs. More than half of the mothers (53.7%) administered home treatment following an AEFI. Younger mothers (odds ratio [OR], 2.43; 95% confidence interval [CI], 1.20–5.01), mothers who delivered their children at a healthcare facility (OR, 3.24; 95% CI, 1.08–9.69), and mothers who were knowledgeable about reporting AEFIs (OR, 2.53; 95% CI, 1.04–7.70) were most likely to respond appropriately to AEFIs.
Conclusion The proportion of mothers who responded poorly to AEFIs experienced by their children was significant. Therefore, strategies should be implemented to improve mothers’ knowledge about AEFIs to improve their responses.
Objectives Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease 2019 (COVID-19). The COVID-19 incidence and mortality rates are low in Nigeria compared to global trends. This research mapped the evolution of SARS-CoV-2 circulating in Nigeria and globally to determine whether the Nigerian isolates are genetically distinct from strains circulating in regions of the world with a high disease burden. Methods: Bayesian phylogenetics using BEAST 2.0, genetic similarity analyses, and genomewide mutational analyses were used to characterize the strains of SARS-CoV-2 isolated in Nigeria. Results: SARS-CoV-2 strains isolated in Nigeria showed multiple lineages and possible introductions from Europe and Asia. Phylogenetic clustering and sequence similarity analyses demonstrated that Nigerian isolates were not genetically distinct from strains isolated in other parts of the globe. Mutational analysis demonstrated that the D614G mutation in the spike protein, the P323L mutation in open reading frame 1b (and more specifically in NSP12), and the R203K/ G204R mutation pair in the nucleocapsid protein were most prevalent in the Nigerian isolates. Conclusion: The SARS-CoV-2 strains in Nigeria were neither phylogenetically nor genetically distinct from virus strains circulating in other countries of the world. Thus, differences in SARS-CoV-2 genomes are not a plausible explanation for the attenuated COVID-19 outcomes in Nigeria.
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A retrospective whole genome sequencing of SARS-CoV-2 isolate from respiratory sample of an individual from Ota – Ogun State, Nigeria Olajumoke Olufunmilayo Joseph, Samuel Olatunde Dahunsi, Anthony Okoh Gene Reports.2024; 37: 102032. CrossRef
Objectives The study aimed to examine health workers’ perceptions of the coronavirus disease 2019 (COVID-19) vaccine in Nigeria and their willingness to receive the vaccine when it becomes available.
Methods This multi-center cross-sectional study used non-probability convenience sampling to enroll 1,470 hospital workers aged 18 and above from 4 specialized hospitals. A structured and validated self-administered questionnaire was used for data collection. Data entry and analysis were conducted using IBM SPSS ver. 22.0.
Results The mean age of respondents was 40±6 years. Only 53.5% of the health workers had positive perceptions of the COVID-19 vaccine, and only slightly more than half (55.5%) were willing to receive vaccination. Predictors of willingness to receive the COVID-19 vaccine included having a positive perception of the vaccine (adjusted odds ratio [AOR], 4.55; 95% confidence interval [CI], 3.50−5.69), perceiving a risk of contracting COVID-19 (AOR, 1.50; 95% CI, 1.25–3.98), having received tertiary education (AOR, 3.50; 95% CI, 1.40−6.86), and being a clinical health worker (AOR, 1.25; 95% CI, 1.01−1.68).
Conclusion Perceptions of the COVID-19 vaccine and willingness to receive the vaccine were sub-optimal among this group. Educational interventions to improve health workers' perceptions and attitudes toward the COVID-19 vaccine are needed.
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Objectives
This study aims to create a mathematical model to better understand the spread of Ebola, the mathematical dynamics of the disease, and preventative behaviors. Methods
An epidemiological model is created with a system of nonlinear differential equations, and the model examines the disease transmission dynamics with isolation through stability analysis. All parameters are approximated, and results are also exploited by simulations. Sensitivity analysis is used to discuss the effect of intervention strategies. Results
The system has only one equilibrium point, which is the disease-free state (S,L,I,R,D) = (N,0,0,0,0). If traditional burials of Ebola victims are allowed, the possible end state is never stable. Provided that safe burial practices with no traditional rituals are followed, the endemic-free state is stable if the basic reproductive number, R0, is less than 1. Model behaviors correspond to empirical facts. The model simulation agrees with the data of the Nigeria outbreak in 2004: 12 recoveries, eight deaths, Ebola free in about 3 months, and an R0 value of about 2.6 initially, which signifies swift spread of the infection. The best way to reduce R0 is achieving the speedy net effect of intervention strategies. One day's delay in full compliance with building rings around the virus with isolation, close observation, and clear education may double the number of infected cases. Conclusion
The model can predict the total number of infected cases, number of deaths, and duration of outbreaks among others. The model can be used to better understand the spread of Ebola, educate about prophylactic behaviors, and develop strategies that alter environment to achieve a disease-free state. A future work is to incorporate vaccination in the model when the vaccines are developed and the effects of vaccines are known better.
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