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Patients’ preferences for preventive osteoporosis drug treatment EW de Bekker-Grob ML Essink-Bot WJ Meerding HAP Pols BW Koes EW Steyerberg Dept. Public.

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Presentation on theme: "Patients’ preferences for preventive osteoporosis drug treatment EW de Bekker-Grob ML Essink-Bot WJ Meerding HAP Pols BW Koes EW Steyerberg Dept. Public."— Presentation transcript:

1 Patients’ preferences for preventive osteoporosis drug treatment EW de Bekker-Grob ML Essink-Bot WJ Meerding HAP Pols BW Koes EW Steyerberg Dept. Public Health, Dept. Internal Medicine, and Dept. General Practice, Erasmus MC Rotterdam, the Netherlands e.debekker@erasmusmc.nl

2 BACKGROUND Osteoporosis  Risk factor for fractures (low bone mass)  75.000 osteoporotic fractures annually in NL  15.000 hip fractures  60.000 other fractures  Age risk factor  Preventive medication (bisphosphonates) Study  Active case finding important to identify patients who benefit from preventive drug treatment.  Are subjects willing to take preventive drug treatment?

3 AIM 1.To elicit relative weight that patients place on various aspects of preventive osteoporosis drug treatment 2.To investigate whether high risk patients had other preferences than low risk patients.

4 METHODS I Respondents Women aged over 60 years Identified by case finding in 34 GP-practices in NL Overrepresentation high risk (10-yrs risk hip # > 6%) DCE Respondents choose between different options described by drug treatment attributes at different levels

5 METHODS II Background information DCE  Survey method  Good and services can be described by their characteristics (attributes)  Used to estimate:  Whether attribute is important  Relative importance of attributes  Trade-off between attributes  Willingness to pay (monetary measure of benefit)

6 ATTRIBUTES and ATTRIBUTE LEVELS

7 CHOICE SETS  Fractional factorial design (main effects only design)  16 drug profiles  Folder-over technique (0  1, 1  2, etc) for minimal overlap

8 ANALYSES  Patient preferences  conditional logit regression V = β 0 + β 1 TABLETweekly + β 2 INJECTIONfourmonths + β 3 INJECTIONmonthly + β 4 EFFTIVENSS + β 5 NAUSEA + β 6 TIME + β 7 COST  Time and monetary trade-offs  ratios of coefficients  High vs low risk  conditional logit regression with interaction

9 RESULTS: RESPONDENTS

10 RESULTS: CONDITIONAL LOGIT REGRESSION

11 RESULTS: TRADE-OFFS

12 RESULTS: EXAMPLE Bisphosphonate  Weekly tablet  Nausea  Duration 5 years Women prefer this drug treatment above no drug treatment, if WTP is positive, thus 0 < €constant + €weekly tablet + €nausea + €time + €effectiveness

13 WTP = 847 – 212 – 752 – 5*26 + 20*X If risk reduction is larger than 12%, than out-of-pocket payment becomes acceptable

14 LOW VS HIGH FRACTURE RISK PATIENTS

15 CONCLUSIONS 1.Women exhibited a very positive attitude towards preventive osteoporosis drug treatment 2.Important message for policy decision-making on introduction of active osteoporosis case finding at large scale (in addition to cost-effectiveness considerations) 3.This study demonstrates feasibility of DCE in older patients

16 We would like to thank the Netherlands Organization for Health Research and Development (ZonMw) for funding the research.


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