Does economic burden influence quality of life in breast cancer survivors?

Abstract

Goals: Economic burden is emerging as a crucial dimension in our understanding of adjustment to cancer during treatment. Yet, economic burden is rarely examined in cancer survivorship. The goal of this paper is to describe the effect of economic hardship and burden among women with breast cancer.

Methods: We examined baseline and follow-up (3 and 6 month) data reported by 132 stage I and II breast cancer survivors assigned to the Wait Control arm of the Breast Cancer Education Intervention (BCEI), a clinical trial of education and support interventions. Repeated measures models fitted with linear mixed models were used to examine relationships between aspects of economic burden and overall quality of life (QOL) scores. Structural equation models (SEM) were used to examine the relationship between overall economic burden and QOL.

Results: Nineteen economic events were reported. The proportion of survivors who reported increase in insurance premiums increased in the 6-month study period (p=.022). The proportion of survivors reporting change in motivation (p=.016), productivity (p=.002), quality of work (p=.01), days missed from work (p<.001) and sacrificing other things (p=.001) declined. An increase in economic events was significantly associated with poorer quality of life at each of the study time points.

Conclusion: Economic burden of breast cancer extends into post-treatment survivorship. Better understanding of economic impact and managing economic burden may help maintain QOL.

Figure 1

Final Structural Equation Model for the relationship between QOL, baseline characteristics, and the reported number of economic burden-related events. Standardized path coefficients are shown. Coefficient values estimated with asymptotically distribution-free methods. All path coefficients shown are statistically significant. The number of economic burden events reported (Num. Events) serves as proxy for overall economic burden. Higher scores in QOL indicate lower perceived quality of life. The model provides good fit to the data, according to the χ² goodness-of-fit test, and three common fit indices (RMSEA, GFI, and CFI).