BACKGROUND: When making treatment decisions, cancer patients must make trade-offs among efficacy, toxicity, and cost. However, little is known about what patient characteristics may influence these trade-offs. METHODS: A total of 400 cancer patients reviewed 2 of 3 stylized curative and noncurative scenarios that asked them to choose between 2 treatments of varying levels of efficacy, toxicity, and cost. Each scenario included 9 choice sets. Demographics, cost concerns, numeracy, and optimism were assessed. Within each scenario, we used latent class methods to distinguish groups with discrete preferences. We then used regressions with group membership probabilities as covariates to identify associations. RESULTS: The median age of the patients was 61 years (range, 27-90 y). Of the total number of patients included, 25% were enrolled at a community hospital, and 99% were insured. Three latent classes were identified that demonstrated (1) preference for survival, (2) aversion to high cost, and (3) aversion to toxicity. Across all scenarios, patients with higher income were more likely to be in the class that favored survival. Lower income patients were more likely to be in the class that was averse to high cost (P<0.05). Similar associations were found between education, employment status, numeracy, cost concerns, and latent class. CONCLUSIONS: Even in these stylized scenarios, socioeconomic status predicted the treatment choice. Higher income patients may be more likely to focus on survival, whereas those of lower socioeconomic status may be more likely to avoid expensive treatment, regardless of survival or toxicity. This raises the possibility that insurance plans with greater cost-sharing may have the unintended consequence of increasing disparities in cancer care.
BACKGROUND: When making treatment decisions, cancerpatients must make trade-offs among efficacy, toxicity, and cost. However, little is known about what patient characteristics may influence these trade-offs. METHODS: A total of 400 cancerpatients reviewed 2 of 3 stylized curative and noncurative scenarios that asked them to choose between 2 treatments of varying levels of efficacy, toxicity, and cost. Each scenario included 9 choice sets. Demographics, cost concerns, numeracy, and optimism were assessed. Within each scenario, we used latent class methods to distinguish groups with discrete preferences. We then used regressions with group membership probabilities as covariates to identify associations. RESULTS: The median age of the patients was 61 years (range, 27-90 y). Of the total number of patients included, 25% were enrolled at a community hospital, and 99% were insured. Three latent classes were identified that demonstrated (1) preference for survival, (2) aversion to high cost, and (3) aversion to toxicity. Across all scenarios, patients with higher income were more likely to be in the class that favored survival. Lower income patients were more likely to be in the class that was averse to high cost (P<0.05). Similar associations were found between education, employment status, numeracy, cost concerns, and latent class. CONCLUSIONS: Even in these stylized scenarios, socioeconomic status predicted the treatment choice. Higher income patients may be more likely to focus on survival, whereas those of lower socioeconomic status may be more likely to avoid expensive treatment, regardless of survival or toxicity. This raises the possibility that insurance plans with greater cost-sharing may have the unintended consequence of increasing disparities in cancer care.
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Authors: Gita Thanarajasingam; Lori M Minasian; Frederic Baron; Franco Cavalli; R Angelo De Claro; Amylou C Dueck; Tarec C El-Galaly; Neil Everest; Jan Geissler; Christian Gisselbrecht; John Gribben; Mary Horowitz; S Percy Ivy; Caron A Jacobson; Armand Keating; Paul G Kluetz; Aviva Krauss; Yok Lam Kwong; Richard F Little; Francois-Xavier Mahon; Matthew J Matasar; María-Victoria Mateos; Kristen McCullough; Robert S Miller; Mohamad Mohty; Philippe Moreau; Lindsay M Morton; Sumimasa Nagai; Simon Rule; Jeff Sloan; Pieter Sonneveld; Carrie A Thompson; Kyriaki Tzogani; Flora E van Leeuwen; Galina Velikova; Diego Villa; John R Wingard; Sophie Wintrich; John F Seymour; Thomas M Habermann Journal: Lancet Haematol Date: 2018-06-18 Impact factor: 18.959