EuroQol (EQ-5D) measure of quality of life predicts mortality, emergency department utilization, and hospital discharge rates in HIV-infected adults under care

BACKGROUND: Health-related quality of life (HR-QOL) is a relevant and quantifiable outcome of care. We implemented HR-QOL assessment at all primary care visits at UCSD Owen Clinic using EQ-5D. The study aim was to estimate the prognostic value of EQ-5D for survival, hospitalization, and emergency department (ED) utilization after controlling for CD4 and HIV plasma viral load (pVL). METHODS: We conducted a retrospective analysis of HIV clinic based cohort (1996–2000). The EQ-5D includes single item measures of: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. Each item is coded using 3-levels (1 = no problems; 2 = some problems; 3 = severe problems). The instrument includes a global rating of current health using a visual analog scale (VAS) ranging from 0 (worst imaginable) to 100 (best imaginable). An additional single item measure of health change (better, much the same, worse) was included. A predicted VAS (pVAS) was estimated by regressing the 5 EQ-5D health states on VAS using reference cell coding of health states and random effects linear models. Survival models were fit using Cox modelling. Hospitalization and ED rate models were estimated using population-averaged Poisson models. RESULTS: 965 patients met eligibility criteria. 12% were female; 42% were non-white. Median time-at-risk was 1.2 years. Median CD4 was 233. Median log(10)(pVL) was 4.6. 47 deaths occurred. In two Cox models controlling for CD4 and pVL, the adjusted hazard ratios (aHR) for VAS and pVAS as time-varying covariates were 0.73 (95% CI: 0.63–0.83) and 0.66 (95% CI: 0.56–0.77) respectively, for every 10 point increase in (p)VAS rating. In Poisson regression models predicting ED visit rates and hospital discharge rates controlling for current CD4 and pVL, each of the EQ-5D health dimensions, VAS, and health change items were significantly (p < 0.05) associated with the outcomes. For ED visit rates, the adjusted incidence rate ratios (aIRR) were 0.86 (0.83–0.89) and 0.79 (0.75–0.82) for VAS and pVAS, respectively. For hospital discharge rates, the aIRR's were 0.85 (0.82–0.88) and 0.79 (0.75–0.82) for VAS and pVAS, respectively. CONCLUSION: EQ-5D is a brief and prognostically useful predictor of mortality, hospitalization, and ED utilization among adults under care for HIV infection, even after adjusting for CD4 and HIV plasma viral load

Outcome mapping for health system integration

Peter Tsasis,1 Jenna M Evans,2 David Forrest,3 Richard Keith Jones4 1School of Health Policy and Management, Faculty of Health, York University, Toronto, Canada; 2Institute of Health Policy, Management and Evaluation, Faculty of Medicine, University of Toronto, Canada; 3Global Vision Consulting Ltd, Victoria, Canada; 4R Keith Jones and Associates, Victoria, Canada Abstract: Health systems around the world are implementing integrated care strategies to improve quality, reduce or maintain costs, and improve the patient experience. Yet few practical tools exist to aid leaders and managers in building the prerequisites to integrated care, namely a shared vision, clear roles and responsibilities, and a common understanding of how the vision will be realized. Outcome mapping may facilitate stakeholder alignment on the vision, roles, and processes of integrated care delivery via participative and focused dialogue among diverse stakeholders on desired outcomes and enabling actions. In this paper, we describe an outcome-mapping exercise we conducted at a Local Health Integration Network in Ontario, Canada, using consensus development conferences. Our preliminary findings suggest that outcome mapping may help stakeholders make sense of a complex system and foster collaborative capital, a resource that can support information sharing, trust, and coordinated change toward integration across organizational and professional boundaries. Drawing from the theoretical perspectives of complex adaptive systems and collaborative capital, we also outline recommendations for future outcome-mapping exercises. In particular, we emphasize the potential for outcome mapping to be used as a tool not only for identifying and linking strategic outcomes and actions, but also for studying the boundaries, gaps, and ties that characterize social networks across the continuum of care. Keywords: integrated care, integrated delivery systems, complex adaptive systems, social capital, collaborative capita

Reconsidering person-centered service models as social ecology networks

Efforts to address problems such as mental health, poverty, social exclusion, and chronic disease have often proven resistant to traditional policies or interventions. In this paper, we take up the challenge and present a pioneering new method of analysis in drawing on theoretical and methodological extensions of two prominent approaches, namely, social network analysis and developmental social ecology. Considered in combination, these two seemingly disparate approaches frame a powerful new way of thinking about person-centred care, as well as offer a methodologically more rigorous set of analytical tools. The conceptual model developed from this combination offers to bridge the apparent disconnect between service integration levels and patient needs in such a way as to direct optimal effort to interventions at the individual level and to provide a new innovative approach to the delivery of integrated care.Fil: Woolcott, G.. Southern Cross University; AustriaFil: Keast, R.. Southern Cross University; AustriaFil: Tsasis, P.. University of York; Reino UnidoFil: Lipina, Sebastián Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. CEMIC-CONICET. Centro de Educaciones Médicas e Investigaciones Clínicas "Norberto Quirno". CEMIC-CONICET; ArgentinaFil: Chamberlain, D.. La Trobe University; Australi

Conceptualizing type 2 diabetes and its management

Peter Tsasis,1 Jianhong Wu,2 Aijun An,3 Hannah J Wong,1 Xiandong An,3,4 Zhen Mei,4 Ted Hains4 1School of Health Policy and Management, York University, Toronto, ON, Canada; 2Centre for Disease Modelling, York University, Toronto, ON, Canada; 3Department of Electrical Engineering of Computer Science, York University, Toronto, ON, Canada; 4Manifold Data Mining Inc., Toronto, ON, Canada Abstract: Type 2 diabetes is growing worldwide due to population growth, increased rates of obesity, unhealthy diet, and physical inactivity. Risk assessment methods can effectively evaluate the risk of diabetes, and a healthy lifestyle can significantly reduce risk or prevent complications of type 2 diabetes. However, risk assessment alone has not significantly improved poor adherence to recommended medical interventions and lifestyle changes. This paper focuses on the challenge of nonadherence and posits that improving adherence requires tailoring interventions that explicitly consider the social determinants of health. Keywords: type 2 diabetes, nonadherence, tailored interventions, data mining and cluster analysis&nbsp