Health Technology in Ontario: Report to the Ontario Health Review Panel

Technology refers to the body of knowledge concerning the conversion of inputs into outputs. The health-care system produces health-care services. Health-care services in turn, in conjunction with the activities of the patient, her genetic endowment, and a considerable amount of random variation, produce changes in health status in the patient. Improvements in health status are valued by the patient, both intrinsically because health is a basic component of quality of life and because improved health status enhances other production and consumption activities. The desired outcome of the use of health-care resources is improvements in health status that are important to the consumers of health-care services, the patients. Health technology affects this outcome indirectly through the production of health-care services. The indirect nature of the impact invites a focus on process, rather than outcome, that is reflected in practice throughout most of the health-care system. It also suggests challenges in evaluating health-care technologies. New health-care technologies may represent improvements over existing technologies in one of several ways. Potentially favourable effects may occur when the new technology represents an improvement in quality of existing services or when it represents a less costly method of obtaining the same outcome with no sacrifice in quality. The new technology may also add a whole new capability to the system. Less favourable effects are obtained when new technologies represent no improvement, are more costly yet generate no improvement over existing methods, or add new capabilities that have no effect on health outcomes. A major challenge for the Ontario health-care system is the early identification of which pattern among these is likely to occur as each new technology becomes available. Although the Ontario health-care system has done relatively well at containing excessive adoption of many important new technologies, it is not clear that the mix of technologies chosen and the subsequent utilization patterns have obtained the greatest value for the level of expenditures. One of the major themes developed in this Report is the need for more thorough clinical and economic evaluation before widespread adoption. The second and third themes follow as implications of that strategy. A heavier reliance on evaluation will generate demands for rigorous and timely information. That information, however, will have little impact unless key actors within the health-care system are given the incentives to use it wisely. Because technology is at the core of the production process for health services, technology policy cannot be divorced from other policy initiatives aimed at rationalizing health-care delivery. In the sections that follow, a foundation will be laid by discussing definitions and characteristics of technologies, examining typical patterns of diffusion of new technologies, briefly reviewing current policy in Ontario, and considering the role of new technologies in increasing health-care costs. The fundamentals of an evaluative strategy to establish clinical effectiveness and economic efficiency will then be developed, and their implications explored. Social and ethical issues are then discussed and finally, conclusions are drawn.

The Health Utilities Index (HUI(®)): concepts, measurement properties and applications

This is a review of the Health Utilities Index (HUI(®)) multi-attribute health-status classification systems, and single- and multi-attribute utility scoring systems. HUI refers to both HUI Mark 2 (HUI2) and HUI Mark 3 (HUI3) instruments. The classification systems provide compact but comprehensive frameworks within which to describe health status. The multi-attribute utility functions provide all the information required to calculate single-summary scores of health-related quality of life (HRQL) for each health state defined by the classification systems. The use of HUI in clinical studies for a wide variety of conditions in a large number of countries is illustrated. HUI provides comprehensive, reliable, responsive and valid measures of health status and HRQL for subjects in clinical studies. Utility scores of overall HRQL for patients are also used in cost-utility and cost-effectiveness analyses. Population norm data are available from numerous large general population surveys. The widespread use of HUI facilitates the interpretation of results and permits comparisons of disease and treatment outcomes, and comparisons of long-term sequelae at the local, national and international levels

Patient-focused measures of functional health status and health-related quality of life in pediatric orthopedics: A case study in measurement selection

The objectives of this report are to review the assessment of patient-focused outcomes in pediatric orthopedic surgery, to describe a framework for identifying appropriate sets of measures, and to illustrate an application of the framework to a challenging orthopedic problem. A detailed framework of study design and measurement factors is described. The factors are important for selecting appropriate instruments to measure health status and health-related quality of life (HRQL) in a particular context. A study to evaluate treatment alternatives for patients with neurofibromatosis type 1 and congenital tibial dysplasia (NF1-CTD) provides a rich illustration of the application of the framework. The application involves great variability in the instrument selection factors. Furthermore, these patients and their supportive caregivers face numerous complex health challenges with long-term implications for HRQL. Detailed summaries of important generic preference-based multi-attribute measurement systems, pediatric health profile instruments, and pediatric orthopedic-specific instruments are presented. Age-appropriate generic and specific measures are identified for study of NF1-CTD patients. Selected measures include the Activities Scale for Children, Gillette Functional Assessment Questionnaire Walking Scale, Health Utilities Index, and Pediatric Inventory of Quality of Life. Reliable and valid measures for application to pediatric orthopedics are available. There are important differences among measures. The selected measures complement each other. The framework in this report provides a guide for selecting appropriate measures. Application of appropriate sets of measures will enhance the ability to describe the morbidity of pediatric orthopedic patients and to assess the effectiveness of alternative clinical interventions. The framework for measurement of health status and HRQL from a patient perspective has relevance to many other areas of orthopedic practice

Health utilities index mark 3 scores for major chronic conditions : population norms for Canada based on the 2013-2014 Canadian Community Health Survey

Background: Utility scores are frequently used as preference weights when estimating quality-adjusted life-years within cost-utility analyses or health-adjusted life expectancies. Though previous Canadian estimates for specific chronic conditions have been produced, these may no longer reflect current patient populations. Data and methods: Data from the 2013-2014 Canadian Community Health Survey were used to provide Canadian utility score norms for seventeen chronic conditions. Utility scores were estimated using the Health Utilities Index Mark 3 (HUI3) instrument and were reported as weighted average (95% confidence intervals [95% CI]) values. In addition to age and sex-stratified analyses, results were also stratified according to the number of reported chronic conditions (i.e., “none” to “≥5”). All results were weighted using sampling and bootstrapped weights provided by Statistics Canada. Results: Utility scores were estimated for 123,654 (97.2%) respondents (weighted frequency = 29,337,370 [97.7%]). Of the chronic conditions that were examined, “Asthma” had the least detrimental effect (weighted average utility score = 0.803 [95%CI 0.795 – 0.811]) on respondents’ utility scores and “Alzheimer’s disease or any other dementia” had the worst (weighted average utility score = 0.374 [95%CI 0.323 – 0.426]). Respondents who reported suffering from no chronic conditions had, on average, the highest utility scores (weighted average utility score = 0.928 [95%CI 0.926 – 0.930]); estimates dropped as a function of the number of reported chronic conditions. Interpretation: Utility score differed between various chronic conditions and as a function of the number of reported chronic conditions. Results also highlight several differences with previously published Canadian utility norms

Different IS Research Communities: Are They Competitors, Complements, or Ignoring Each Other?

The paper is based on an ICIS 2002 panel on the role of four different IS Research communities with regard to topic choice, project/study acquisition, research strategy, respondents and site access, and expected, measurable outcome and dissemination channel. Although differences are clear and although a probably healthy degree of competition among the communities cannot be denied, at the end all panelists expressed the need for more complementarity and thus cooperation among the different communities

Misinterpretation with norm-based scoring of health status in adults with type 1 diabetes

BACKGROUND: Interpretations of profile and preference based measure scores can differ. Profile measures often use a norm-based scoring algorithm where each scale is scored to have a standardized mean and standard deviation, relative to the general population scores/norms (i.e., norm-based). Preference-based index measures generate an overall scores on the conventional scale in which 0.00 is assigned to dead and 1.00 is assigned to perfect health. Our objective was to investigate the interpretation of norm-based scoring of generic health status measures in a population of adults with type 1 diabetes by comparing norm-based health status scores and preference-based health-related quality of life (HRQL) scores. METHODS: Data were collected through self-complete questionnaires sent to patients with type 1 diabetes. The RAND-36 and the Health Utilities Index Mark 3 (HUI3) were included. RESULTS: A total of 216 (61%) questionnaires were returned. The respondent sample was predominantly female (58.8%); had a mean (SD) age of 37.1 (14.3) years and a mean duration of diabetes of 20.9 (12.4) years. Mean (SD) health status scores were: RAND-36 PHC 47.9 (9.4), RAND-36 MHC 47.2 (11.8), and HUI3 0.78 (0.23). Histograms of these scores show substantial left skew. HUI3 scores were similar to those previously reported for diabetes in the general Canadian population. Physical and mental health summary scores of the RAND-36 suggest that this population is as healthy as the general adult population. CONCLUSION: In this sample, a preference-based measure indicated poorer health, consistent with clinical evidence, whereas a norm-based measure indicated health similar to the average for the general population. Norm-based scoring measure may provide misleading interpretations in populations when health status is not normally distributed

Comparison of the EQ-5D-5L and the patient-reported outcomes measurement information system preference score (PROPr) in the United States.

BackgroundIn contrast to prior research, our study presents longitudinal comparisons of the EQ-5D-5L and Patient-Reported Outcomes Measurement Information System (PROMIS) preference (PROPr) scores. This fills a gap in the literature, providing a much-needed understanding of these preference-based measures and their applications in healthcare research. Furthermore, our study provides equations to estimate one measure from the other, a tool that can significantly facilitate comparisons across studies.MethodsWe administered a health survey to 4,098 KnowledgePanel® members living in the United States. A subset of 1,256 (82% response rate) with back pain also completed the six-month follow-up survey. We then conducted thorough cross-sectional and longitudinal analyses of the two measures, including product-moment correlations between scores, associations with demographic variables, and health conditions. To estimate one measure from the other, we used ordinary least squares (OLS) regression with the baseline data from the general population.ResultsThe correlation between the EQ-5D-5L and PROPr scores was 0.69, but the intraclass correlation was only 0.34 because the PROPr had lower (less positive) mean scores on the 0 (dead) to 1 (perfect health) continuum than the EQ-5D-5L. The associations between the two preference measures and demographic variables were similar at baseline. The product-moment correlation between unstandardized beta coefficients for each preference measure regressed on 22 health conditions was 0.86, reflecting similar patterns of unique associations. Correlations of change from baseline to 6 months in the two measures with retrospective perceptions of change were similar. Adjusted variance explained in OLS regressions predicting one measure from the other was 48%. On average, the predicted values were within a half-standard deviation of the observed EQ-5D-5L and PROPr scores. The beta-binomial regression model slightly improved over the OLS model in predicting the EQ-5D-5L from the PROPr but was equivalent to the OLS model in predicting the PROPr.ConclusionDespite substantial mean differences, the EQ-5D-5L and PROPr have similar cross-sectional and longitudinal associations with other variables. We provide the OLS regression equations for use in cost-effectiveness research and meta-analyses. Future studies are needed to compare these measures with different conditions and interventions to provide more information on their relative validity

Comparison of health state utility estimates from instrument-based and vignette-based methods: a case study in kidney disease.

OBJECTIVE: We take advantage of a rare occurrence when two different studies report on the estimation of quality of life utilities for the same health states to assess convergence of the reported measures. Health state utilities are important inputs into health economic models that estimate the impact of new medical technologies using a common metric of health gain-the quality adjusted life-year. RESULTS: We find low concordance between the two measures which is concerning in that this could have important ramifications for health care decision making based on estimated cost-effectiveness. We explore possible reasons for the discrepancy between the two measures and draw implications for the design of future studies