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Olorunfemi Akinbode Ogundele,Funmito Omolola Fehintola,Mubarak Salami,Rahmat Usidebhofoh,Mary Aderemi Abaekere 질병관리본부 2023 Osong Public Health and Research Persptectives Vol.14 No.4
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.
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Oluseyi Ademola Adejumo,Olorunfemi Akinbode Ogundele,Cynthia Roli Madubuko,Rosena Olubanke Oluwafemi,Ogochukwu Chinedum Okoye,Kenechukwu Chukwuemeka Okonkwo,Sunday Samson Owolade,Oladimeji Adedeji Jun 질병관리본부 2021 Osong Public Health and Research Persptectives Vol.12 No.4
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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Quality Assessment of Beef Using Computer Vision Technology
Md,Faizur Rahman,Abdullah Iqbal,Md,Abul Hashem,Akinbode A,Adedeji 한국축산식품학회 2020 한국축산식품학회지 Vol.40 No.6
Imaging technique or computer vision (CV) technology has received huge attention as a rapid and non-destructive technique throughout the world for measuring quality attributes of agricultural products including meat and meat products. This study was conducted to test the ability of CV technology to predict the quality attributes of beef. Images were captured from longissimus dorsi muscle in beef at 24 h post-mortem. Traits evaluated were color value (L*, a*, b*), pH, drip loss, cooking loss, dry matter, moisture, crude protein, fat, ash, thiobarbituric acid reactive substance (TBARS), peroxide value (POV), free fatty acid (FFA), total coliform count (TCC), total viable count (TVC) and total yeast-mould count (TYMC). Images were analyzed using the Matlab software (R2015a). Different reference values were determined by physicochemical, proximate, biochemical and microbiological test. All determination were done in triplicate and the mean value was reported. Data analysis was carried out using the programme Statgraphics Centurion XVI. Calibration and validation model were fitted using the software Unscrambler X version 9.7. A higher correlation found in a* (r=0.65) and moisture (r=0.56) with ‘a*’ value obtained from image analysis and the highest calibration and prediction accuracy was found in lightness (r²c=0.73, r²p=0.69) in beef. Results of this work show that CV technology may be a useful tool for predicting meat quality traits in the laboratory and meat processing industries.
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