Using the SITAR Method to Estimate Age at Peak Height Velocity of Children in Rural South Africa: Ellisras Longitudinal Study.

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.

Assessing joint spatial autocorrelations between mortality rates due to cardiovascular conditions in South Africa.

South Africa is experiencing an increasing burden of noncommunicable diseases (NCDs). There is evidence of co-morbidity of several NCDs at small geographical areas in the country. However, the extent to which this applies to joint spatial autocorrections of NCDs is not known. The objective of this study was to derive and quantify multivariate spatial autocorrections for NCDrelated mortality in South Africa. The study used mortality attributable to cerebrovascular, ischaemic heart failure and hypertension captured by the country's Department of Home Affairs for the years 2001, 2007 and 2011. Both univariate and pairwise spatial clustering measures were derived using observed, empirical Bayes smoothed and age-adjusted standardised mortality rates. Cerebrovascular and ischaemic heart co-clustering was significant for the years 2001 and 2011. Cerebrovascular and hypertension co-clustering was significant for the years 2007 and 2011, while hypertension and ischaemic heart co-clustering was significant for the year 2011. Co-clusters of cerebrovascular-ischaemic heart disease are the most profound and located in the south-western part of the country. It was successfully demonstrated that bivariate spatial autocorrelations can be derived for spatially dependent mortality rates as exemplified by mortality rates attributed to three cardiovascular conditions. The identified co-clusters of spatially dependent health outcomes may be targeted for an integrated intervention and monitoring programme.

Modelling non-stationary annual maximum flood heights in the lower Limpopo River basin of Mozambique.

In this article we fit a time-dependent generalised extreme value (GEV) distribution to annual maximum flood heights at three sites: Chokwe, Sicacate and Combomune in the lower Limpopo River basin of Mozambique. A GEV distribution is fitted to six annual maximum time series models at each site, namely: annual daily maximum (AM1), annual 2-day maximum (AM2), annual 5-day maximum (AM5), annual 7-day maximum (AM7), annual 10-day maximum (AM10) and annual 30-day maximum (AM30). Non-stationary time-dependent GEV models with a linear trend in location and scale parameters are considered in this study. The results show lack of sufficient evidence to indicate a linear trend in the location parameter at all three sites. On the other hand, the findings in this study reveal strong evidence of the existence of a linear trend in the scale parameter at Combomune and Sicacate, whilst the scale parameter had no significant linear trend at Chokwe. Further investigation in this study also reveals that the location parameter at Sicacate can be modelled by a nonlinear quadratic trend; however, the complexity of the overall model is not worthwhile in fit over a time-homogeneous model. This study shows the importance of extending the time-homogeneous GEV model to incorporate climate change factors such as trend in the lower Limpopo River basin, particularly in this era of global warming and a changing climate.