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Patients’ preferences for preventive osteoporosis drug treatment

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.

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Patients’ preferences for preventive osteoporosis drug treatment

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  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 • To elicit relative weight that patients place on various aspects ofpreventive osteoporosis drug treatment • To investigate whether high risk patients had other preferences than low risk patients.

  4. METHODS I RespondentsWomen 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 = β0 + β1TABLETweekly + β2INJECTIONfourmonths + β3INJECTIONmonthly + β4EFFTIVENSS + β5NAUSEA + β6TIME + β7COST • 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 • Women exhibited a very positive attitude towards preventive osteoporosis drug treatment • Important message for policy decision-making on introduction of active osteoporosis case finding at large scale (in addition to cost-effectiveness considerations) • 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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