Person-centred healthcare and public health policy: the role of Multi-Criteria Decision Analysis (MCDA) and Cluster Analysis (CA) in responding to preference heterogeneity
Professor Jack Dowie
|A Medicine seminar|
|Date||4 March 2014|
|Time||12:30 to 13:30|
|Place||Veysey Lecture theatre|
1st Floor, Veysey Building, Salmon Pool Lane, Exeter, EX2 4SG
Professor Emeritus of Health Impact Analysis, Department of Social and Environmental Health Research, London School of Hygiene and Tropical Medicine
The growing interest in addressing heterogeneity formally and analytically is a reflection of the move - or at least talk about moving - to more ‘personalised’ heath care. At the clinical level the existence of biological-clinical and/or sociodemographic heterogeneity and use of such variables reflecting this, is not at all controversial in either research or practice. Use of subgrouping based on such variables is also fairly well accepted at the health policy level, such as in NICE-type coverage/reimbursement decisions, where any debate is about the use of particular variables, rather than the principle of subgrouping. Acknowledgement of preference heterogeneity and use of preference variables is a totally different story. It is becoming less controversial at the clinical level, where the role of preferences in influencing patient behaviour, especially treatment adherence, is well-accepted; however as yet only a few decision aids attempt to process patient’s preferences explicitly and analytically at the point of care. But major controversy surrounds the use of individual person’s preferences – necessarily subgrouped - in the forming of public health policy. The debate reveals that an essentially reductionist view is still pretty pervasive: preferences are epi-phenomena that are either actually ‘caused by’ biological-clinical and/or sociodemographic variables, or in practice need to be treated as such. This presentation falls into two section. The first shows how MCDA-based decision support can provide the basis for better individual level decision making, using an Australian trial on prostate cancer screening. The second shows, at a
proof of method level, how the same data could contribute to a more preference-sensitive analysis of public health policy on prostate cancer screening, via the use of CA to produced preference-based public subgroups (not patient subgroups). But do we want that? The debate is reviewed.
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