Importance
In the US, thyroid cancer has been identified as a cancer type with a high degree of associated financial burden on patients, and survivors of thyroid cancer report higher rates of bankruptcy than those of other cancer types. However, the available literature on the financial burden of thyroid cancer has not yet been described.
Observations
Estimates of the out-of-pocket costs of initial thyroid cancer diagnosis and treatment range widely ($1425-$17 000) and are influenced by age, surgical treatment type, and health insurance coverage. The rates of patient-reported financial burden are heterogeneous (16%-50%) and are rarely compared with those of other cancer types. Independent risk factors of financial burden have included younger age, lack of health insurance, and annual household income of less than $49 000. Two studies measured medical debt associated with thyroid cancer diagnosis and treatment at notably different rates (2.1% vs 18.7%). The bankruptcy incidence at 1 year after cancer diagnosis is highest for thyroid cancer (9.3 per 1000 person-years) than other studied cancer types (ie, lung, uterine, leukemia/lymphoma, colorectal, melanoma, breast, prostate) and 4.39-fold higher than control individuals among those aged 35 to 49 years.
Conclusions and Relevance
Current estimates of the financial burden of thyroid cancer are methodologically limited and are based on cross-sectional analyses of patient-reported data. We propose novel frameworks for new research by improvements in (1) data sourcing and utilization, (2) study design, and (3) pilot interventions. To understand how out-of-pocket thyroid cancer-related expenditures transition to various forms of debt, how households finance ongoing costs of care, and rates at which debts are sent to collection agencies, future research should focus on integrating underutilized sources of primary data, including credit reports, public records, and mortgage-backed securities loan-level data. Improvements in study design, such as the development of prospective cohorts, can allow for more objectively measured estimates of out-of-pocket costs, and robust covariate analysis can further reveal the influence of demographic factors, including age, sex, race, income, and health insurance coverage. Finally, new pilot interventions on cost controls can both enable further study and alleviate financial burden.