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The Evidence Speaks

The Evidence Speaks (January 2019)

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The Evidence Speaks Series is a recurring feature highlighting the latest in CHÉOS research. This series features summaries of select publications as well as in-depth features on the latest work from our investigators.
In the early days of CHÉOS, the Centre had a series known as “The Evidence Speaks,” a monograph series to keep media and the research community up-to-date with CHÉOS’ current research results in the health outcomes field.

Carbert NS, Brussoni M, Geller J, Mâsse LC. Moderating effects of family environment on overweight/obese adolescents’ dietary behaviours. Appetite. 2018 Dec 24;134:69-77.

There has been considerable research into how parents influence the eating behaviours of overweight or obese adolescents but the ways in which other family factors modify these influences has not been thoroughly explored and are the focus of a recent publication co-authored by CHÉOS Scientist Dr. Josie Geller. Dr. Geller, who is the Director of Research at the St. Paul’s Hospital Eating Disorders Program, teamed up with researchers from UBC and the BC Children’s Hospital Research Institute to complete this cross-sectional study. Specifically, the study looked at how the effects of parenting practices and modelling of food behaviours (ex. displaying healthy eating habits) are modified by parenting style and family functioning (spousal, parent-child, and sibling relationships). Overweight or obese adolescents between the ages of 11 and 16 and one parent were enrolled; each pair completed three 24-hour dietary recalls and parents completed a number of surveys to measure parenting and family factors. The average dietary quality of both parents and adolescents was rated as inadequate (65% using the Health Eating Index). Parents scored high on the authoritative parenting style scale, moderately on the permissive parenting scale, and showed a balance of cohesion and flexibility with respect to family functioning. Analysis revealed that when both parenting style and family functioning are included in the model, a more authoritative parenting style in combination with modelling of healthy dietary habits was associated with a higher-quality diet in the children. (Note: an authoritative parenting style is not the same as an authoritarian approach, in which strict obedience is the goal; see here) As this sample was balanced in terms of family functioning (cohesion and flexibility), further research on the impact of families that are high or low in these components is needed to understand their impact on eating behaviours. The study demonstrates the importance of considering the effects of parental factors as well as the family context on dietary behaviours of obese or overweight children.

Harrison M, Han PKJ, Rabin B, Bell M, Kay H, Spooner L, Peacock S, Bansback N. Communicating uncertainty in cancer prognosis: A review of web-based prognostic tools. Patient Educ Couns. 2018 Dec 12 epub ahead of print.

CHÉOS Scientists Drs. Mark Harrison and Nick Bansback recently published their review of currently available online cancer prognostic tools and how these tools communicate risk about future uncertainties. Specifically, they were interested in the communication of the random nature of future outcomes (“aleatory uncertainty”) and imprecision of probability estimates of future outcomes, often communicated through the use of confidence intervals or ranges (“epistemic uncertainty”). Importantly, prognostic tools differ from risk tools in that they involve prediction of cancer outcomes (progression, regression etc.) rather than the risk of developing cancer. In their review of available tools, the team included 222 different prognostic risk tools which produced 772 distinct prognostic estimates. Over 80 per cent of tools directly referenced at least one statistical model used for estimation and most estimates were related to mortality (62%) and progression or recurrence (39%). Aleatory uncertainty was communicated in 90% of tools, often through the use of statements of risk (“my chances of survival are 15%”). Only 14% of tools (32 tools) described epistemic uncertainty with 25 tools using qualitative language and 22 using quantitative representations (ranges or confidence intervals): only 4% of the 222 tools (9 tools) used additional qualitative language to state the limitations of knowledge about risk. For the 772 risk estimates, there were 16 combinations of aleatory and epistemic uncertainty risk estimates. Ninety-three tools (42%) used additional graphical representations of risk. This review demonstrates the wide variation in the communication of aleatory uncertainty, the lack of communication about epistemic uncertainty, and the need for consistency in the way available prognostic tools discuss risk. The researchers also highlighted the need to understand the outcomes of communicating this type of risk to patients and to recognize the different approaches to delivering the same risk information and how their success may vary between different patients.

Ling DI, Lynd LD, Harrison M, Anis AHBansback N. Early cost-effectiveness modeling for better decisions in public research investment of personalized medicine technologies. J Comp Eff Res. 2019 Jan;8(1):7-19.

In a recent publication, a group of CHÉOS Scientists proposed a decision analysis approach to developing new personalized medicine technology. Personalized medicine approaches are becoming more common and include diagnostic tests and biomarkers however, many of the proposed tests may not be cost effective. Drs. Larry Lynd, Mark Harrison, Aslam Anis, and Nick Bansback, with former CHÉOS post-doctoral fellow Dr. Daphne Ling, propose that economic evaluations of new technologies be moved further upstream in the development pathway to limit the financial risks of research investment thereby increasing the efficiency of the pathway. Currently, research into how a technology will be used according to the preferences of patients and their care providers is completed after a technology is approved — this research should be done early in the stages of development. The researchers propose a decision analytic modelling approach, an approach that incorporates multiple sources of evidence (e.g. cost of the test, diagnostic accuracy) into a single framework to assess costs and benefits of implementing a new test. This modelling approach allows researchers to continuously change the level of different variables in the model (e.g. test sensitivity) to determine the key components that impact cost-effectiveness. In general, the researchers propose that economic evaluation can be used as a “go/no go checkpoint” regarding further development of a personalized medicine technology. They propose that a technology should not be funded if it is found to be unlikely to meet cost effectiveness requirements. Economic analysis can be used to guide funding decisions when multiple grant proposals are being considered for a limited funding opportunity. This type of analysis, termed value of information (VOI) analysis, can determine whether or not future research is cost-effective i.e. is the improvement of a specific aspect of a technology (e.g. diagnostic accuracy) worth the cost? VOI analysis can also be useful to funding agencies when identifying priority areas for targeted research calls. Overall, the researchers outline how cost-effectiveness studies can result in better use of public research and health care resources and to maximize the benefit of the personalized medicine movement in Canada.

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