Derivation of a frailty index from the interRAI acute care instrument

Background: A better understanding of the health status of older inpatients could underpin the delivery of more individualised, appropriate health care

Diarrhoeal health risks attributable to water-borne-pathogens in arsenic-mitigated drinking water in West Bengal are largely independent of the microbiological quality of the supplied water

Abstract: There is a growing discussion about the possibility of arsenic mitigation measures in Bengal and similar areas leading to undesirable substitution of water-borne-pathogen attributable risks pathogens for risks attributable to arsenic, in part because of uncertainties in relative pathogen concentrations in supplied and end-use water. We try to resolve this discussion, by assessing the relative contributions of water supply and end-user practices to water-borne-pathogen-attributable risks for arsenic mitigation options in a groundwater arsenic impacted area of West Bengal. Paired supplied arsenic-mitigated water and end-use drinking water samples from 102 households were collected and analyzed for arsenic and thermally tolerant coliforms [TTC], used as a proxy for microbiological water quality, We then estimated the DALYs related to key sequelae, diarrheal diseases and cancers, arising from water-borne pathogens and arsenic respectively. We found [TTC] in end-use drinking water to depend only weakly on [TTC] in source-water. End-user practices far outweighed the microbiological quality of supplied water in determining diarrheal disease burden. [TTC] in source water was calculated to contribute <1% of total diarrheal disease burden. No substantial demonstrable pathogen-for-arsenic risk substitution attributable to specific arsenic mitigation of supplied waters was observed, illustrating the benefits of arsenic mitigation measures in the area studied

Bootstrapping for highly unbalanced clustered data

We apply the generalized cluster bootstrap to both Gaussian quasi-likelihood and robust estimates in the context of highly unbalanced clustered data. We compare it with the transformation bootstrap where the data are generated by the random effect and transformation models and all the random variables have different distributions. We also develop a fast approach (proposed by Salibian-Barrera et al. (2008)) and show that it produces some encouraging results. We show that the generalized bootstrap performs better than the transformation bootstrap for highly unbalanced clustered data. We apply the generalized cluster bootstrap to a sample of income data for Australian workers

Predicting Upcoming Glucose Levels in Patients with Type 1 Diabetes Using a Generalized Autoregressive Conditional Heteroscedasticity Modelling Approach

Continuous blood glucose monitoring systems (CGMS) capture interstitial glucose levels at frequent intervals over time, and are used by people with diabetes and their health care professionals to assess glycaemic variability. This information helps to adjust treatment to achieve optimum glycaemic control, as well as potentially providing early warning of imminent and dangerous hypoglycaemia. Although a number of studies has reported the possibilities of predicting hypoglycaemia in insulin dependent type 1 diabetes (T1DM) patients, the prediction paradigm is still unreliable, as glucose fluctuations in people with diabetes are highly volatile and depend on many factors. Studies have proposed the use of linear auto-regressive (AR) and state space time series models to analyse the glucose profiles for predicting upcoming glucose levels. However, these modelling approaches have not adequately addressed the inherent dependencies and volatility aspects in the glucose profiles. We have investigated the utility of generalized autoregressive conditional heteroscedasticity (GARCH) models to explore glucose time-series trends and volatility, and possibility of reliable short-term forecasting of glucose levels. GARCH models were explored using CGMS profiles of young children (4 to &lt;10 years) with T1DM. The prediction performances of GARCH approach were compared with other contemporary modelling approaches such as lower and higher order AR, and the state space models. The GARCH approach appears to be successful in both realizing the volatility in glucose profiles and offering potentially more reliable forecasting of upcoming glucose levels

Assessment of self-perceived knowledge in e-health among undergraduate students

Growing research evidence shows the value of e-health in healthcare delivery. While efforts are made to implement e-health in mainstream healthcare, relatively modest attention has been paid to develop e-health knowledge and skills in health practitioners. Using a pre-post design, in this study, we aimed to examine self-reported knowledge and perception changes associated with an e-health course offered to university undergraduate students in Australia.Pre- and postsurveys were used to examine self-reported knowledge and perception changes relating to e-health among undergraduate students. All students enrolled in an e-health course (n = 165) were asked to complete an identical survey in the first and last week of the semester.The response rates were 53% (n = 87) for the presurvey and 52% (n = 85) for the postsurvey. For all items, changes in self-reported knowledge and perception were statistically significant in pre/post median scores and dichotomized negative/positive proportions.Students believed the course helped them to improve their knowledge regarding key aspects of e-health. It is important to design an e-health curriculum targeting competencies to provide necessary knowledge and skills to help students practice e-health in their professional careers

A robust statistical approach to analyse population pharmacokinetic data in critically ill patients receiving renal replacement therapy

Background and Aim: Current approaches to antibiotic dose determination in critically ill patients requiring renal replacement therapy are primarily based on the assessment of highly heterogeneous data from small number of patients. The standard modelling approaches limit the scope of constructing robust confidence boundaries of the distribution of pharmacokinetics (PK) parameters, especially when the evaluation of possible association of demographic and clinical factors at different levels of the distribution of drug clearance is of interest. Commonly used compartmental models generally construct the inferences through a linear or non-linear mean regression, which is inadequate when the distribution is skewed, multi-modal or effected by atypical observation. In this study, we discuss the statistical challenges in robust estimation of the confidence boundaries of the PK parameters in the presence of highly heterogenous patient characteristics. Methods: A novel stepwise approach to evaluate the confidence boundaries of PK parameters is proposed by combining PK modelling with mixed-effects quantile regression (MEQR) methods. Results: This method allows the assessment demographic and clinical factors’ effects at any arbitrary quantiles of the outcome of interest, without restricting assumptions on the distributions. The MEQR approach allows us to investigate if the levels of association of the covariates are different at low, medium or high concentration. Conclusions: This methodological assessment is deemed as a background initial approach to support the development of a class of statistical algorithm in constructing robust confidence intervals of PK parameters which can be used for developing an optimised antibiotic dosing guideline for critically ill patients requiring renal replacement therapy

Trends and predicted trends in presentations of older people to Australian emergency departments: effects of demand growth, population aging and climate change

Objectives The aim of the present study was to describe trends in and age and gender distributions of presentations of older people to Australian emergency departments (EDs) from July 2006 to June 2011, and to develop ED utilisation projections to 2050