This study reviewed the distribution of and Nigerian Media responses to the COVID-19 pandemic within the first two months (February 27 to April 30 2020) of its outbreak in Nigeria. Using data collected from the websites of the Nigeria Centre for Disease Control, NCDC and Worldometer, and analysed in simple percentages and presented in tables and charts, the study showed that by April 30, 2020, the total confirmed, active, recovered and death cases stood at 1932, 1555, 319 and 58 respectively; an indication of an alarming spread. The distribution of the disease saw Lagos (epicentre of the disease), Kano and FCT collectively accounting for 71.07% of confirmed cases, 71.45% of active, 73.67% of death, and 46.55% of recovered cases. The initial mode of spread was through contact; however, as of April 30, 2020, 79% of new cases were through contact and incomplete epidemiological link; an indication of possible community transmission. The Nigerian media responded adequately and aggressively to the outbreak by setting the agenda and providing regular updates of government, private and individual efforts in containing the spread of the disease. By the conclusion of this study, there was the issue of transparency of government and equity in providing and distributing palliatives to cushion the effect of lockdowns, and a confirmation of over 40 healthcare workers testing positive to SARS-CoV-2; thus, challenging the government to intensify efforts in curbing community transmission by ensuring effective contact tracing, testing, isolation and treatment, which were barely successful as of April 30.
This data analysis aimed at investigating Basal Metabolic Rate (BMR) of patients around Otuoke region, in Ogbia Local Government Area, and the data were collated at Federal Medical Centre, Otuoke Outreach. The data collated involving 50 patients, of which, 25 are males and 25 female volunteers of different ages. The variables involved in this analysis include age, gender and basal metabolic index, using SPSS version 25. Descriptive analysis was carried out to summarize the data in terms of mean and standard deviation of the gender and age. Biserial correlation was carried out on gender, age and BMR, and Cohen standard was done to investigate the strength of the relationship between the variables. The results of the analysis showed a negative correlation between gender and BMR with a correlation coefficient of -0.70, indicating a large effect size. In addition, it is seen that the linear regression model is significant, F(2,47) = 25.09, p<0.001, and Rsq = 0.52, indicating 52% variance in BMR. The result goes further to reveal that a unit increase in age doesn’t cause an effect on BMR. However, the female category can significantly predict BMR, B = -267.10, t(47) = -7.06, p<0.001. Based on this sample, this suggests that moving from the Male to Female category of Gender will decrease the mean value of BMR by 267.10 units on average.
Prostate cancer is the second most common cause of cancer related deaths in men. It is detected using many screening methods. Like every other cancer, there are risk factors associated with prostate cancer. This include but not limited to, Family History (FH) of the disease, smoking habit, alcohol intake, age and Body Mass Index (BMI). The survival of prostate cancer patients is dependent on many factors such as, early detection of the disease, age of patient and the aggressiveness of the cancer. Gleason score is used to measure the level of aggressiveness of a prostate cancer in a patient. the score ranges from 6 to 10. It is made up of two Gleason grades that ranges from 3 to 5. This study was carried out to determine whether there are significant differences in the mean of Gleason score by the various categories of BMI and FH of patients while controlling for the number of hospital visits. Gleason score was used as the dependent variable while FH and BMI and Number of hospital visits were used as the independent variables. Descriptive statistical measures were used to summarize the basic features of the data. Spearman correlation coefficient was used to measure if there is a significant statistical relationship between the Gleason score, age and BMI, while Analysis of Covariance (ANCOVA) was used to measure the differences in the mean of Gleason score by the categories of FH and BMI while controlling for number of hospital visits. The analysis was done using Statistical Programme for Social Science (SPSS 25.0) and Intellectus Statistics software. Results from the analyses were presented in tabular form. The results showed a significant effect of Body Mass Index (BMI) on Gleason score and that Gleason score increases, as age tends to increase.
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