Authors: Akkazieva, Baktygul¹; Gulacsi, Laszlo²; Brandtmuller, Agnes²; Péntek, Márta³; Bridges, John F.P.⁴

Source: Applied Health Economics and Health Policy, Volume 5, Number 3, 2006 , pp. 189-198(10)

Publisher: Adis International

Abstract:

Objectives: To illustrate how conjoint analysis can be used to identify patient preferences for healthcare policies, and to measure preferences for healthcare reforms in Hungary.

Data source/study setting: Data was collected via a mail-based survey and a direct survey administered in a rheumatology out-patient centre in Flór Ferenc County Hospital, Budapest, Hungary (n = 86).

Study design: We designed and administered a conjoint analysis to the study population. Attributes and attribute levels were developed on the basis of key informant interviews and a literature review. Additional demographic, occupation and healthcare utilisation data were also collected using surveys. A mixed effects linear probability model was estimated holding respondent characteristics constant and correcting for clustering.

Data collection: Conjoint analysis questionnaires were administered by a physician to 50 consecutive rheumatology patients in a clinic and an additional 36 were mailed by post.

Principal findings: The response rate for the physician-administered survey was 98% (but 18% of these were excluded for inconsistent preferences) and 53% for the mail survey, leaving a final sample of 59. Regression results (R² = 56.8%) indicated that patients preferred a health system that was not cost constrained (p = 0.003), was based on solidarity (p < 0.001) and where patients were empowered (p = 0.024). Further, they would choose a system with no choice of provider to avoid co-payments (p = 0.005).

Conclusions: This study demonstrates that patients have clear preferences for healthcare system policy. In order to develop evidence-based healthcare policy and to empower patients in the healthcare system, methods such as conjoint analysis offer a simple yet theoretically grounded basis for policy making.

Keywords: Data collection; Health policy; Patient preference

Document Type: Research article

Affiliations: 1: 1 Department of Tropical Hygiene and Public Health, University of Heidelberg, Heidelberg, Germany 2: 2 Unit of Health Economics and Health Technology Assessment, Department of Public Policy and Management, Corvinus University of Budapest, Budapest, Hungary 3: 3 Flor Ferenc County Hospital, Rheumatology Ward, Kistracsa, Hungary 4: 4 Johns Hopkins Bloomberg School of Public Health, Health Policy and Management, Baltimore, Maryland, USA

Publication date: 2006-01-01

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