Background: The coronavirus disease 2019 (COVID-19) pandemic has placed the psychological functioning of mental healthcare practitioners under severe strain. Coping methods may affect mental health outcomes.Aim: The study examined the relationship between depression, anxiety, stress, and coping styles utilised by mental healthcare practitioners during the COVID-19 pandemic.Setting: Three private and one public mental healthcare facility in Bloemfontein, South Africa.Methods: Respondents completed the Depression, Anxiety and Stress Scale (DASS-21) and Brief Coping Orientation to Problems Experienced (Brief-COPE) inventories. An ordinal regression model was used to assess the relationship between coping styles, anxiety and depression.Results: A total of 212 practitioners were included in the analysis. According to DASS-21 measures, approximately 41% and 28% of respondents had moderate to severe depressive and anxiety symptoms, respectively, with the highest prevalence among younger female respondents and nurses. The association between stress severity, anxiety and depression was significant. Avoidant coping methods and two approach coping strategies (planning and acceptance) were associated with depression and anxiety. Anxiety was linked to an increased likelihood of transitioning to higher avoidant categories, while participants with depression were less likely to move to higher avoidant or approach categories.Conclusion: Mental healthcare practitioners, especially nurses, experienced significant COVID-19-related psychological distress during the pandemic. Avoidant coping mechanisms may increase the risk of poor mental health outcomes.Contribution: This study added data on the mental health effects of COVID-19 on mental healthcare practitioners, as well as psychological methods used to cope during the pandemic.

Various studies have been conducted on children using the Ellisras Longitudinal Study Survey in South Africa, but none of these has addressed growth variations of children in this rural location. The aim of this paper is to assess the age at peak height velocity using the superimposition by translation and rotation (SITAR) method for both boys and girls in rural South Africa. The study is part of the on-going Ellisras Longitudinal Study, and has employed secondary data during the period from November 1996 to November 2003. The data was collected biannually in May and November each year. The Ellisras study initially followed a cluster sampling method. All participating children underwent a series of anthropometric measurements of height and weight according to the standard procedures recommended by the International Society for the Advancement of Kinanthropometry. The analysis was done using the SITAR model. Age at peak height velocity for Ellisras rural children was at 14.45 years for boys at 11.82 years for girls. Ellisras rural girls had their age at peak height velocity way earlier than Ellisras rural boys did by an estimated 2.63 years. Ellisras rural children and their growth variations were comparable to other studies.

Malaria infects and kills millions of people in Africa, predominantly in hot regions where temperatures during the day and night are typically high. In South Africa, Limpopo Province is the hottest province in the country and therefore prone to malaria incidence. The districts of Vhembe, Mopani and Sekhukhune are the hottest districts in the province. Malaria cases in these districts are common and malaria is among the leading causes of illness and deaths in these districts. Factors contributing to malaria incidence in Limpopo Province have not been deeply investigated, aside from the general knowledge that the province is the hottest in South Africa. Bayesian and classical methods of estimation have been applied and compared on the effect of climatic factors on malaria incidence. Credible and confidence intervals from a negative binomial model estimated via Bayesian estimation and maximum likelihood estimation, respectively, were utilized in the comparison process. Overall assumptions underpinning each method were given. The Bayesian method appeared more robust than the classical method in analysing malaria incidence in Limpopo Province. The classical method identified rainfall and temperature during the night to be significant predictors of malaria incidence in Mopani, Vhembe and Waterberg districts. However, the Bayesian method found rainfall, normalised difference vegetation index, elevation, temperatures during the day and night to be the significant predictors of malaria incidence in Mopani, Sekhukhune and Vhembe districts of Limpopo Province. Both methods affirmed that Vhembe district is more susceptible to malaria incidence, followed by Mopani district. We recommend that the Department of Health and Malaria Control Programme of South Africa allocate more resources for malaria control, prevention and elimination to Vhembe and Mopani districts of Limpopo Province.

Keeping a low steady rate of inflation is one of the government’s most important responsibilities. Inflation is an important determinant of economic growth. Consequently, it has been one of the most examined areas in economics, from both theoretical and empirical perspectives. Indeed, economists have shown continued interest in this essential economic variable. The most important question related to inflation is: Does non-linearity exist in inflation? The answer to this question, which has important policy implications, can support or endanger the validity of several important economic models. Hence, a clear understanding of the changing aspects of inflation is crucial to any economy because it is regarded as a significant variable in a number of economic models, whose legitimacy critically relies on whether or not this variable is stationary. In practice, many economic time series models rely on linearity. Nonetheless, it has often been found that simple linear time series models regularly leave certain aspects of economic and financial data inexplicable. This paper proposes a model that combines fractional integration with non-linear deterministic terms based on the Chebyshev polynomials in time for the analysis of CPI inflation rates of Ghana and South Africa in Sub-Saharan Africa. Firstly, we tested for non-linear deterministic terms in the context of fractional integration. The estimates of the differencing parameter, d, were found to be 1.11 and 1.32, respectively for the Ghanaian and the South African inflation rates, but the non-linear trends were found to be statistically insignificant in the two series. A linear model was then investigated and the results indicated that the order of integration for the Ghanaian inflation series was slightly above 1. However, for the case of South Africa, a cyclical I(d) process was found to be more appropriate, with an order of integration below 1, thus showing mean reversion and a cyclical structure of approximately 80 periods (months) per cycle. The implication of these findings could assist in a decision-making process regarding adjusting monetary policy instruments such as inflation targeting (IT) or adopting different monetary policies, to achieve the desired target.