Age Group-Based Mean Differences in Recovery Speed from COVID-19 Infection Mpumalanga Province

The aim of this analysis was to examine mean differences in the speed of recovery (days) from COVID-19 infection by case-patients among age groups in Mpumalanga province. A sample of 5723 case-patients distributed among ten distinct age groups, beginning from 0-9 years. Using the date at which the result confirming positivity for each case was received and the date at which discharge occurred, the DATEDIF() function in Microsoft Excel was used to calculate the speed of recovery, measured by number of days. Time from infection to recovery was therefore measured as the number of days from first positive to first negative SARSCoV-2 PCR test result. Data was processed in both Excel and Statistical Package for Social Sciences (SPSS) prior to conducting statistical data analysis. The mean differences in the speed of recovery among age groups were analysed using the mean comparison Analysis of Variance (ANOVA) method. Age groups 0-9 years and 40-49 years had the longest average recovery speed of 17.32 ±8.84 days and 17.22 ±7.88 days; respectively, while the age group 70-79 years had the fastest average recovery speed of 15.43 ±4.51 days. ANOVA results show no statistical evidence that the mean recovery speed (days) from differed significantly among age groups (F (8, 5714) statistic (= 0.932; p > 0.05) at 5% significance level. Key words: Age group mean differences, recovery speed, Covid 19, Mpumalanga Province DOI: 10.7176/JHMN/80-19 Publication date:September 30th 202

Testing for Correlation Between Age and Recovery Speed from COVID-19 Infection in Mpumalanga Province, South Africa

The major aim of this research study was to test for correlation between age and recovery speed from COVID-19 infection among case-patients in Mpumalanga province. A sample of 5723 case-patients in the province was used. Using the date at which the result confirming positivity for each case was received and the date at which discharge occurred, the suitable date function in Excel was used to calculate the speed of recovery, measured by number of days. The speed of recovery from infection was calculated as the number of days from first positive to first negative SARSCoV-2 PCR test result. Data was processed in Statistical Package for Social Sciences (SPSS) version 21 for windows prior to conducting statistical data analysis. The correlation between age and recovery speed was tested using a Pearson’s correlation method.  Frequencies show that the largest proportions of cases were 32% (n = 1831) aged 30-39 years, and 21% (n = 1208) aged 40-49 years. Descriptive statistics show that the mean (standard deviation) age and average recovery speed were 38.3 ± 14.6 years and 16.9 ± 7.4 days. The calculated Pearson correlation coefficient (ρ = 0.008; p > 0.05) show that there is a positive but statistically insignificant correlation between recovery speed and age, confirming that recovery speed does not correlate strongly with age of case-patients. Keywords: Age, Recovery Speed, COVID-19 Infection DOI: 10.7176/JHMN/80-12 Publication date:September 30th 202

Assessing Gender-Based Mean Differences in the Recovery Speed among COVID-19 Case Patients in Mpumalanga Province, South Africa

The primary objective of this research study was to assess whether there exist gender-based mean differences in the recovery speed from COVID-19 infection among case-patients in Mpumalanga province. A sample of 5723 case-patients in the province was used. Using the date at which the result confirming positivity for each case was received and the date at which discharge occurred, the date function in Excel was used to compute the speed of recovery, measured by the number of days. The speed of recovery from infection was computed as the number of days from first positive to first negative SARSCoV-2 PCR test result. Data was processed in Excel and Statistical Package for Social Sciences (SPSS) software prior to conducting statistical data analysis. Results show that female case patients in each age group were larger than their counterpart male case patients, while Nkangala district had the largest numbers of case patients across all age groups. The average recovery speed for male case patients was 16.89 days, with a standard deviation of ±7.23 days, while the mean recovery speed for female case patients was marginally lower at 16.87 days, but with a marginally higher standard deviation of ±7.47 days. The Levene’s test for equality of variance statistics (F-statistic = 0.686; p-value > 0.05) reveals no evidence of variability in the recovery speed (days) between male and female case patients. Key words: Gender mean differences, recovery speed, Covid 19, Mpumalanga Province DOI: 10.7176/JHMN/80-18 Publication date:September 30th 202

District Level-Based Mean Differences in Recovery Speed from COVID-19 Infection in Mpumalanga Province

The aim of this analysis was to examine mean differences of the speed of recovery (days) from COVID-19 infection by case-patients based in three different districts in Mpumalanga province. A sample of 5723 case-patients distributed across three districts in the province; namely Gert Sibande, Ehlanzeni and Nkangala. Using the date at which the result confirming positivity for each case was received and the date at which discharge occurred, the DATEDIF() function in Microsoft Excel was used to calculate the speed of recovery, measured by number of days. The speed (time) from infection to recovery was thus measured as the number of days from first positive to first negative SARSCoV-2 PCR test result. Data was processed in Statistical Package for Social Sciences (SPSS) version 21 for windows prior to conducting statistical analysis. The mean differences in the speed of recovery across the three districts were analysed using the mean comparison Analysis of Variance (ANOVA) technique. Mean and standard deviation (mean ± sd) statistics show that cases in Gert Sibande had the quickest average recovery speed of 16.43 ± 7.14 days, followed by Ehlanzeni with 17.07 ± 7.18 days, while cases in Nkangala had the marginally longest recovery time of 17.09 ± 7.56 days at 95% confidence interval. The F (2, 531.96) statistic (= 4.297; p < 0.05) and the Tukey HSD post-hoc results indicate significant differences in the speed of recovery by case-patients in the three districts. The mean statistics results demonstrate that the mean recovery speed of case-patients in Gert Sibande differed significantly from average recovery speeds of case-patients in Ehlanzeni and Nkangala. Keywords: Recovery Speed from COVID-19, Mpumalanga DOI: 10.7176/JHMN/80-17 Publication date:September 30th 202

Risk factors for COVID-19-related in-hospital mortality in a high HIV and tuberculosis prevalence setting in South Africa : a cohort study

BACKGROUND : The interaction between COVID-19, non-communicable diseases, and chronic infectious diseases such as HIV and tuberculosis is unclear, particularly in low-income and middle-income countries in Africa. South Africa has a national HIV prevalence of 19% among people aged 15–49 years and a tuberculosis prevalence of 0·7% in people of all ages. Using a nationally representative hospital surveillance system in South Africa, we aimed to investigate the factors associated with in-hospital mortality among patients with COVID-19. METHODS : In this cohort study, we used data submitted to DATCOV, a national active hospital surveillance system for COVID-19 hospital admissions, for patients admitted to hospital with laboratory-confirmed SARS-CoV-2 infection between March 5, 2020, and March 27, 2021. Age, sex, race or ethnicity, and comorbidities (hypertension, diabetes, chronic cardiac disease, chronic pulmonary disease and asthma, chronic renal disease, malignancy in the past 5 years, HIV, and past and current tuberculosis) were considered as risk factors for COVID-19-related in-hospital mortality. COVID-19 in-hospital mortality, the main outcome, was defined as a death related to COVID-19 that occurred during the hospital stay and excluded deaths that occurred because of other causes or after discharge from hospital; therefore, only patients with a known in-hospital outcome (died or discharged alive) were included. Chained equation multiple imputation was used to account for missing data and random-effects multivariable logistic regression models were used to assess the role of HIV status and underlying comorbidities on COVID-19 in-hospital mortality. FINDINGS : Among the 219 265 individuals admitted to hospital with laboratory-confirmed SARS-CoV-2 infection and known in-hospital outcome data, 51 037 (23·3%) died. Most commonly observed comorbidities among individuals with available data were hypertension in 61 098 (37·4%) of 163 350, diabetes in 43 885 (27·4%) of 159 932, and HIV in 13 793 (9·1%) of 151 779. Tuberculosis was reported in 5282 (3·6%) of 146 381 individuals. Increasing age was the strongest predictor of COVID-19 in-hospital mortality. Other factors associated were HIV infection (adjusted odds ratio 1·34, 95% CI 1·27–1·43), past tuberculosis (1·26, 1·15–1·38), current tuberculosis (1·42, 1·22–1·64), and both past and current tuberculosis (1·48, 1·32–1·67) compared with never tuberculosis, as well as other described risk factors for COVID-19, such as male sex; non-White race; underlying hypertension, diabetes, chronic cardiac disease, chronic renal disease, and malignancy in the past 5 years; and treatment in the public health sector. After adjusting for other factors, people with HIV not on antiretroviral therapy (ART; adjusted odds ratio 1·45, 95% CI 1·22–1·72) were more likely to die in hospital than were people with HIV on ART. Among people with HIV, the prevalence of other comorbidities was 29·2% compared with 30·8% among HIV-uninfected individuals. Increasing number of comorbidities was associated with increased COVID-19 in-hospital mortality risk in both people with HIV and HIV-uninfected individuals. INTERPRETATION : Individuals identified as being at high risk of COVID-19 in-hospital mortality (older individuals and those with chronic comorbidities and people with HIV, particularly those not on ART) would benefit from COVID-19 prevention programmes such as vaccine prioritisation as well as early referral and treatment.DATCOV, as a national surveillance system, is funded by the South African National Institute for Communicable Diseases (NICD) and the South African National Government.http://www.thelancet.com/hivam2022School of Health Systems and Public Health (SHSPH

Exchange rate pass-through to domestic prices under the common monetary area of Southern Africa : the case of Eswatini

Abstract: The study conducts an investigation that seeks to evaluate the nature of exchange rate pass-through on consumer prices for Eswatini (formerly known as Swaziland). As a small, open economy, the country is susceptible to exogenous shocks. The exchange rate acts as a medium through which ii external shocks get transmitted to the real economy. These shocks are expected to be mitigated through the Common Monetary Area arrangement with South Africa and other member countries. However, there is limited evidence regarding the effectiveness of the Common Monetary Area arrangement in mitigating exchange rate costs, and it is non-existent in the case of Eswatini. The study was guided by the Johansen cointegration methodology to analyse the dynamic link between the Consumer Price Index, the exchange rate and Gross Domestic Product, utilizing time series data from 1989 to 2018. The cointegration test confirmed the presence of a long-term relationship in the model. Results show incomplete exchange rate pass-through, in which a one per cent increase in the exchange rate would increase the consumer price index by about 0.43 per cent in the long run. In addition, Eswatini’s gross domestic product is negatively related to the consumer price index. Granger causality tests that are based on the Vector Error Correction Model, show a unidirectional causality moving along from the exchange rate to the consumer price index in Eswatini. These results imply that the country’s exchange rate pass-through is relatively higher than its domestic prices. To mitigate the impact of exchange rate pass-through, Eswatini monetary authorities should establish a partial monetary policy such as a required reserve, credit control, and open market operations. Local demand should be managed, and supply challenges should be addressed.M.Phil. (Industrial Policy

Changes in the humoral immune responses after chemotherapy in single and co-infected individuals with Schisosoma haematobuim and Plasmodium falciparum

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