Smoking and Second Hand Smoking in Adolescents with Chronic Kidney Disease: A Report from the Chronic Kidney Disease in Children (CKiD) Cohort Study

The goal of this study was to determine the prevalence of smoking and second hand smoking [SHS] in adolescents with CKD and their relationship to baseline parameters at enrollment in the CKiD, observational cohort study of 600 children (aged 1-16 yrs) with Schwartz estimated GFR of 30-90 ml/min/1.73m2. 239 adolescents had self-report survey data on smoking and SHS exposure: 21 [9%] subjects had “ever” smoked a cigarette. Among them, 4 were current and 17 were former smokers. Hypertension was more prevalent in those that had “ever” smoked a cigarette (42%) compared to non-smokers (9%), p\u3c0.01. Among 218 non-smokers, 130 (59%) were male, 142 (65%) were Caucasian; 60 (28%) reported SHS exposure compared to 158 (72%) with no exposure. Non-smoker adolescents with SHS exposure were compared to those without SHS exposure. There was no racial, age, or gender differences between both groups. Baseline creatinine, diastolic hypertension, C reactive protein, lipid profile, GFR and hemoglobin were not statistically different. Significantly higher protein to creatinine ratio (0.90 vs. 0.53, p\u3c0.01) was observed in those exposed to SHS compared to those not exposed. Exposed adolescents were heavier than non-exposed adolescents (85th percentile vs. 55th percentile for BMI, p\u3c 0.01). Uncontrolled casual systolic hypertension was twice as prevalent among those exposed to SHS (16%) compared to those not exposed to SHS (7%), though the difference was not statistically significant (p= 0.07). Adjusted multivariate regression analysis [OR (95% CI)] showed that increased protein to creatinine ratio [1.34 (1.03, 1.75)] and higher BMI [1.14 (1.02, 1.29)] were independently associated with exposure to SHS among non-smoker adolescents. These results reveal that among adolescents with CKD, cigarette use is low and SHS is highly prevalent. The association of smoking with hypertension and SHS with increased proteinuria suggests a possible role of these factors in CKD progression and cardiovascular outcomes

Comparative study of an HIV risk scorecard and regression models to rank effects of demographic characteristics on risk of aquiring an HIV infection

IEEE international conference on bioinformatics and biomedicine (BIBM2013), Shanghai, China, 18-21 December 2013This research paper covers the development of an HIV risk scorecard using SAS Enterprise MinerTM. The HIV risk scorecard was developed using the 2007 South African annual antenatal HIV and syphilis seroprevalence data. Limited comparisons are made with a more recent 2010 antenatal database. Antenatal data contains various demographic characteristics for each pregnant woman, such as pregnant woman’s age, male sexual partner’s age, population group, level of education, gravidity, parity, HIV and syphilis status. The purpose of this research was to use a scorecard to rank the effects of the demographic characteristics on influencing a pregnant woman’s risk of acquiring an HIV infection. The project encompassed the selection of the data sample, classing, selection of demographic characteristics, fitting of a regression model, generation of weights-of-evidence (WOE), calculation of information values (IVs), creation and validation of an HIV risk scorecard. The educational level and syphilis status of the pregnant women produced information values below 0.05 and were rejected from inclusion in the final HIV risk scorecard. Based on their respective information values, the following four demographic characteristics of the pregnant women were found to be of medium predictive strength and thus included in the final HIV risk scorecard; pregnant woman’s age, age of male sexual partner, gravidity and parity. The age of the pregnant woman had the highest information value and Gini coefficient. The final objective of this research was to demonstrate that a binned variable HIV risk scorecard can provide as much risk ranking as any other regression based model.http://bibm2013.tongji.edu.cn/http://dx.doi.org/10.1109/BIBM.2013.6732736http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=673273

An empirical investigation in the decision-making processes of new infrastructure development

The aim of this research is to present and discuss the development and deployment of Lean thinking models and techniques applied to improve the decision-making within the planning and design processes of new infrastructures, within a healthcare organisation. In the UK, healthcare organisations are responsible for planning, designing, building and managing their own infrastructures, through which their services are delivered to the local population (Kagioglou & Tzortzopoulos, 2010). These processes are long and complex, involving a large range of stakeholders who are implicated within the strategic decision-making. It is understood that the NHS lacks models and frameworks to support the decision-making associated with their new infrastructure development and that ad-hoc methods, used at local level, lead to inefficiencies and weak performances, despite the contractual efforts made throughout the PPP and PFI schemes (Baker & Mahmood, 2012; Barlow & Koberle-Gaiser, 2008). This is illustrated by the long development cycle time – it can take up to 15 years from conception to completion of new infrastructure. Hence, in collaboration with an NHS organisation, an empirical action research embedded within a mixed-methodology approach, has been designed to analyse the root-cause problems and assess to what extent Lean thinking can be applied to the built environment, to improve the speed and fitness for purpose of new infrastructures. Firstly, this multiphase research establishes the main issues responsible for the weak process performances, via an inductive-deductive cycle, and then demonstrates how Lean thinking inspired techniques: Multiple Criteria Decision Analysis (MCDA) using ER and AHP, Benchmarking and Quality Function Deployment (QFD), have been implemented to optimise the decision-making in order to speed up the planning and design decision-making processes and to enhance the fitness for purpose of new infrastructures. Academic literatures on Lean thinking, decision theories and built environment have been reviewed, in order to establish a reliable knowledge base of the context and to develop relevant solutions. The bespoke models developed have been tested and implemented in collaboration with a local healthcare organisation in UK, as part of the construction of a £15 million health centre project. A substantial set of qualitative and quantitative data has been collected during the 450 days, which the researcher was granted full access, plus a total of 25 sets of interviews, a survey (N=85) and 25 experimental workshops. This mixed-methodology research is composed of an exploratory sequential design and an embedded-experiment variant, enabling the triangulation of different data, methods and findings to be used to develop an innovative solution, thus improving the new infrastructure development process. The emerging developed conceptual model represents a non-prescriptive approach to planning and designing new healthcare infrastructures, using Lean thinking principles to optimise the decision-making and reduce the complexity. This Partial & Bespoke Lean Construction Framework (PBLCF) has been implemented as good practice by the healthcare organisation, to speed up the planning phases and to enhance the quality of the design and reduce the development cost, in order to generate a competitive edge. It is estimated that a reduction of 22% of the cycle time and 7% of the cost is achievable. This research makes a contribution by empirically developing and deploying a partial Lean implementation into the healthcare‟s built environment, and by providing non-prescriptive models to optimise the decision-making underpinning the planning and design of complex healthcare infrastructure. This has the potential to be replicated in other healthcare organisations and can also be adapted to other construction projects