Iron and manganese mobilisation due to dam height increase for a tropical reservoir in South East Asia.

The aim of this research was the analysis of the effect of a dam height raise on the water quality of a tropical reservoir used for drinking water purposes in South East Asia. Analyses of iron, manganese, pH and ammonia were performed over a 5-year period from daily water sampling at the reservoir. In addition, high-frequency monitoring data of nitrate, ammonium, pH and blue-green algae were obtained using a monitoring probe. The results showed that due to the raising of the reservoir water level, previously oxic sediments became submerged, triggering an increase in iron and manganese in particular due to the establishment of reducing conditions. Manganese concentrations with values up to 4 mg L-1 are now exceeding guideline values. The analysis strongly indicated that both iron and manganese have a seasonal component with higher iron and manganese concentrations during the wet season. Over a three-year period afterwards, concentrations did not go back to pre-raise levels. The change in water quality was accompanied by a change in pH from previous values of around 5 to pH values of around 6.5. Geochemical simulations confirmed the theory that the increasing concentrations of iron and manganese are due to the dissolution of MnO2 and ferric oxyhydroxides oxidising organic matter in the process. This study showed that changes in reservoir water levels with the establishment of reducing conditions can have long-term effects on the water quality of a reservoir.

Pairwise likelihood estimation for confirmatory factor analysis models with categorical variables and data that are missing at random.

Methods for the treatment of item non-response in attitudinal scales and in large-scale assessments under the pairwise likelihood (PL) estimation framework and under a missing at random (MAR) mechanism are proposed. Under a full information likelihood estimation framework and MAR, ignorability of the missing data mechanism does not lead to biased estimates. However, this is not the case for pseudo-likelihood approaches such as the PL. We develop and study the performance of three strategies for incorporating missing values into confirmatory factor analysis under the PL framework, the complete-pairs (CP), the available-cases (AC) and the doubly robust (DR) approaches. The CP and AC require only a model for the observed data and standard errors are easy to compute. Doubly-robust versions of the PL estimation require a predictive model for the missing responses given the observed ones and are computationally more demanding than the AC and CP. A simulation study is used to compare the proposed methods. The proposed methods are employed to analyze the UK data on numeracy and literacy collected as part of the OECD Survey of Adult Skills.

Latent Class Analysis: Insights about design and analysis of schistosomiasis diagnostic studies.

Various global health initiatives are currently advocating the elimination of schistosomiasis within the next decade. Schistosomiasis is a highly debilitating tropical infectious disease with severe burden of morbidity and thus operational research accurately evaluating diagnostics that quantify the epidemic status for guiding effective strategies is essential. Latent class models (LCMs) have been generally considered in epidemiology and in particular in recent schistosomiasis diagnostic studies as a flexible tool for evaluating diagnostics because assessing the true infection status (via a gold standard) is not possible. However, within the biostatistics literature, classical LCM have already been criticised for real-life problems under violation of the conditional independence (CI) assumption and when applied to a small number of diagnostics (i.e. most often 3-5 diagnostic tests). Solutions of relaxing the CI assumption and accounting for zero-inflation, as well as collecting partial gold standard information, have been proposed, offering the potential for more robust model estimates. In the current article, we examined such approaches in the context of schistosomiasis via analysis of two real datasets and extensive simulation studies. Our main conclusions highlighted poor model fit in low prevalence settings and the necessity of collecting partial gold standard information in such settings in order to improve the accuracy and reduce bias of sensitivity and specificity estimates.