Abstract
Background: Generic preference-based measures (pbms), though commonly used, may not be optimal for use in the economic evaluations assessing the impact of breast cancer interventions. Concerns that are unique to women with breast cancer (for example, body image, appearance, treatment-specific adverse effects) are not adequately captured by the existing generic measures. No breast cancer–specific pbm exists. The objective of this study was to construct a health state classification system specific to breast cancer which is amenable to valuation. Methods: We conducted semi-structured interviews in a heterogeneous sample of women with breast cancer [stages 0–4, any stage of treatment(s)]. Interviews were audio recorded, transcribed verbatim, and coded using the constant comparison approach to develop the conceptual framework. Patients were also asked to describe their most and least important concerns during the interview and to rate items in the related breast-q module (that is, mastectomy, breast-conserving therapy, or reconstruction) on a modified 5-point Likert scale (ranging from Not important to Very important). A faceto- face meeting with an expert panel of health care professionals, health economists, and hrqol researchers was used to obtain feedback on the health state classification system, response levels, and wording of the items. Results: Interviews (n = 59) with patients aged 59.9 years were completed. The resultant conceptual framework included site-specif ic (that is, abdomen, arm, breast) and overall (that is, body image, appearance, cancer, psychological, sexual, and social) domains. Triangulation of the qualitative and quantitative evidence led to the selection of key constructs for inclusion in the new pbm. The field test version of the breast-q utility health state classification system consisted of 13 attributes with 4 response levels each. Conclusions: The health state classification system for the preference-based module of the breast-q (breast-q-u) was derived using patient and expert feedback. The next phase will involve establishing psychometric properties of the breast-q-u, followed by a valuation study to generate utility weights.