The prevalence of chronic kidney disease of uncertain etiology (CKDu) in the Uva Province (UP) of Sri Lanka has received much attention over the past two decades. Many scientists assumed that prolonged consumption of drinking water with high levels of contaminants may be the causative factor. Thus, the prime objective of this study is to develop a binary logistic regression model based on water quality parameters and the prevalence of CKDu to find out the geochemical risk factors that affect the CKDu. For this, 260 groundwater samples were collected following a stratified random sampling method and analyzed for its major cations, anions, and selected trace element contents. In the model, the dichotomous dependent variable defines the availability of CKDu patients, and explanatory variables define groundwater quality parameters. According to the best-fit model, F− and PO43- levels of the groundwater were found to be the geochemical risk factors that were significantly associated with the progression of CKDu in the study area. Furthermore, it was shown that geochemical processes such as dissolution of bedrocks are the causative phenomenon of the enhancement of F− levels in the groundwater sources. It has also been observed that the PO43- concentrations of the groundwater possibly increase because of the intensive application of agrochemicals in addition to geogenic sources. The results of this study can be used by the government authorities in groundwater management and the management of the prevalence of CKDu disease in the study area. Furthermore, the findings of this study will contribute to the policy-makers in Sri Lanka for providing safe drinking water to meet the sustainable development goals (SDGs).

This study aimed in identifying and comparing the stride pattern in the 400mH event of the top 10 Sri Lankan, Asian, and World levels athletes of 2019 top list. A retrospective research design was used and following a selective sampling method top 10 athletes were selected from each group as subjects (N 30). Each athlete’s 400 mH 2019 season best video was analysed. 400m event timings were recorded from World Athletics. Kinovea software version 0.8.26 and Minitab software version 19 were used for data analysis. One-way ANOVA, Tukey test and Pearson’s correlation coefficient tests were performed. It was significantly different from the 1st hurdle to the 6th hurdle in all three groups. Tukey test further revealed a significant difference in Sri Lankan athletes from the start to the 1st hurdle, and from the 6th hurdle to the 10th hurdle. Moreover, only the world-level athletes were significantly different from the 10th hurdle to the finish line. The 400m time was significantly different in all three levels. World-level athletes’ group have a moderate, and the Asian-level athlete group have a very weak correlation while the Sri Lankan athlete group have a strong correlation between 400m time and 400 mH time (r= 0.607, 0.135, 0.849) respectively. In conclusion, to improve the level of performance among Sri Lankan 400mH athletes compared with the other levels, the times taken from start to the first hurdle, between hurdles, and from last hurdle to the finish line needed to be improved while improving 400m performance.

In different fields of study, multivariate binary data is often found, especially when several different qualitative characteristics or attributes are measured in the same unit or from the same person. These bivariate or multivariate responses observed from the same individual or a unit are likely to be correlated. This study aimed to evaluate the influence on the regression estimates of the parameters when binary responses are modeled jointly. The correlation between binary outcomes was captured by incorporating random effects. Normal and bridge distributions were assumed for the random effects. A simulation study was performed to illustrate the impact on the marginal parameter estimates of the joint response model when using the bridge and normal distributions for the random effects. The simulation study revealed that the joint model with either normal or bridge random effects provides a better gain in efficiency in the parameter estimates compared to the individual models which assume responses are independent. Furthermore, the parameter estimates of the joint model are more or less the same under the normal distribution and bridge distribution of the random effects when outcomes are correlated. However, slight differences are noted in the standard errors of the parameter estimates. In addition, when two outcomes are not correlated there is no gain in the fitting joint model over separate univariate models. Finally, these methods were applied to the Bangladesh Demographic and Health Survey 2011 (BDHS 2011) data.