Serum procalcitonin as an early marker of neonatal sepsis

Background. It has recently been suggested that procalcitonin (PCT) is of value in the diagnosis of neonatal sepsis, with varying results. This study was to evaluate the role of PCT as a single early marker of neonatal sepsis. Setting. Neonatal Unit, Johannesburg Hospital, and Microbiology Laboratory, National Health Laboratory Service (NHLS), South Africa. Subjects and methods. Neonates undergoing evaluation for sepsis between April and August 2002 were eligible for inclusion. Patients were categorised into ‘no infection', ‘possible infection' and ‘definite infection' on the basis of C-reactive protein (CRP), white cell count (WCC), platelet count and blood culture results. PCT was correlated with infection categories. Results. One hundred and eighty-three neonates were enrolled. One hundred and eighteen had no infection, 52 possible infection and 13 definite infection. PCT differed significantly among infection categories (p < 0.0001) and correlated significantly with CRP at presentation (correlation coefficient 0.404, p < 0.001) and CRP at 24 hours (correlation coefficient 0.343, p < 0.001). PCT predicted 89.5% of definite infection. Receiver operating characteristic (ROC) analysis for PCT to predict definite infection showed odds ratio (OR) 1.145 (95% confidence interval (CI): 1.05 - 1.25) with an area under the curve of 0.778. PCT had a negative predictive value of 0.95 (95% CI: 0.915 - 0.988) for definite infection. Conclusions. Although PCT was significantly related to the category of infection, it is not sufficiently reliable to be the sole marker of neonatal sepsis. PCT would be useful as part of a full sepsis evaluation, but is relatively expensive. A negative PCT on presentation may rule out sepsis, but this needs to be evaluated further. S Afr Med J 2004; 94: 851-854

Staphylococcus aureus bacteraemia at two academic hospitals in Johannesburg

Objectives and methods. Staphylococcus aureus bacteraemia (SAB) remains a major problem worldwide. A retrospective study of patients with SAB seen from November 1999 to October 2002 was conducted at two academic hospitals in Johannesburg to determine mortality rates (death within 14 days of submission of blood culture) in patients bacteraemic with methicillin-sensitive (MSSA) and resistant S. aureus (MRSA) and to identify risk factors associated with mortality. Results. Of 449 patients with SAB, 104 (23.2%) died within 14 days of clinically suspected SAB. Of the 204 patients who acquired SAB in hospital, 6 patients died within 2 days, 39 between 2 and 14 days, and 41 more than 14 days after onset of SAB. One hundred and five patients (23.4%) had MRSA bacteraemia, 21 (20%) originating from the community. The MRSA bacteraemia rate among patients with hospital-acquired infection was 41.1%, significantly higher (p < 0.0001) than the 10.3% community-acquired MRSA bacteraemia. Thirty-five (33.3%) of the 105 patients with MRSA bacteraemia died within 14 days, compared with 69 (20.1%) of 344 MSSA patients (p = 0.0048). Admission to the intensive care unit (ICU) was significantly associated with mortality (p < 0.001) – 30 of 79 patients admitted to ICU died (38%). Among 222 patients whose HIV status was known, 117 (52.7%) were positive, and of these 32 died (27.4%), a rate not significantly higher than that among HIVseronegative patients (18 of 105 patients, p = 0.69). Conclusions. Compared with MSSA, MRSA was shown to be significantly associated with mortality. Stay in ICU and infection with strains resistant to oxacillin, ofloxacin and rifampicin were highly significant predictors for mortality. South African Medical Journal Vol. 96(8) 2006: 714-71

Density forecasting for long-term electricity demand in South Africa using quantile regression

Background: This study involves forecasting electricity demand for long-term planning purposes. Long-term forecasts for hourly electricity demands from 2006 to 2023 are done with in-sample forecasts from 2006 to 2012 and out-of-sample forecasts from 2013 to 2023. Quantile regression (QR) is used to forecast hourly electricity demand at various percentiles. Three contributions of this study are (1) that QR is used to generate long-term forecasts of the full distribution per hour of electricity demand in South Africa; (2) variabilities in the forecasts are evaluated and uncertainties around the forecasts can be assessed as the full demand distribution is forecasted and (3) probabilities of exceedance can be calculated, such as the probability of future peak demand exceeding certain levels of demand. A case study, in which forecasted electricity demands over the long-term horizon were developed using South African electricity demand data, is discussed.   Aim: The aim of the study was: (1) to apply a quantile regression (QR) model to forecast hourly distribution of electricity demand in South Africa; (2) to investigate variabilities in the forecasts and evaluate uncertainties around point forecasts and (3) to determine whether the future peak electricity demands are likely to increase or decrease.   Setting: The study explored the probabilistic forecasting of electricity demand in South Africa.   Methods: The future hourly electricity demands were forecasted at 0.01, 0.02, 0.03, … , 0.99 quantiles of the distribution using QR, hence each hour of the day would have 99 forecasted future hourly demands, instead of forecasting just a single overall hourly demand as in the case of OLS.   Results: The findings are that the future distributions of hourly demands and peak daily demands would be more likely to shift towards lower demands over the years until 2023 and that QR gives accurate long-term point forecasts with the peak demands well forecasted.   Conclusion: QR gives forecasts at all percentiles of the distribution, allowing the potential variabilities in the forecasts to be evaluated by comparing the 50th percentile forecasts with the forecasts at other percentiles. Additional planning information, such as expected pattern shifts and probable peak values, could also be obtained from the forecasts produced by the QR model, while such information would not easily be obtained from other forecasting approaches. The forecasted electricity demand distribution closely matched the actual demand distribution between 2012 and 2015. Therefore, the forecasted demand distribution is expected to continue representing the actual demand distribution until 2023. Using a QR approach to obtain long-term forecasts of hourly load profile patterns is, therefore, recommended

Analysis of rain quality data from the South African interior

Rain acidity may be ascribed to emissions from power station stacks, as well as emissions from other industry, biomass burning, maritime influences, agricultural influences, etc. Rain quality data are available for 30 sites in the South African interior, some from as early as 1985 for up to 14 rainfall seasons, while others only have relatively short records. The article examines trends over time in the raw and volume weighted concentrations of the parameters measured, separately for each of the sites for which sufficient data are available. The main thrust, however, is to examine the inter-relationship structure between the concentrations within each rain event (unweighted data), separately for each site, and to examine whether these inter-relationships have changed over time. The rain events at individual sites can be characterized by approximately eight combinations of rainfall parameters (or rain composition signatures), and these are common to all sites. Some sites will have more events from one signature than another, but there appear to be no signatures unique to a single site. Analysis via factor and cluster analysis, with a correspondence analysis of the results, also aid interpretation of the patterns. This spatio-temporal analysis, performed by pooling all rain event data, irrespective of site or time period, results in nine combinations of rainfall parameters being sufficient to characterize the rain events. The sites and rainfall seasons show patterns in these combinations of parameters, with some combinations appearing more frequently during certain rainfall seasons. In particular, the presence of the combination of low acetate and formate with high magnesium appears to be increasing in the later rainfall seasons, as does this combination together with calcium, sodium, chloride, potassium and fluoride. As expected, sites close together exhibit similar signatures. Copyright © 2002 John Wiley & Sons, Ltd

A unified approach on residuals, leverages and outliers in the linear mixed model

Best linear unbiased predictor (BLUP), Case deletion, Leverages, Outliers, Predicted residuals, Variance ratios, 62J20, 62J99,