Economic burden of cancers in Taiwan: a direct and indirect cost estimate for 2007–2017

Abstract

Objective Cancers result in significant economic burdens on patients, health sectors and society. Reliable burden estimates will help guide resource allocation. This study aimed to perform a nationwide cost analysis of the direct and indirect costs of the top ten most costly cancers, and acute coronary syndrome (ACS), as a comparison, in Taiwan.Setting A population-based cohort study.Participants In total, 545 221 patients with newly diagnosed cancer (lung cancer, female breast cancer, colorectal cancer, liver cancer, oral cancer, leukaemia, prostate cancer, non-Hodgkin's lymphoma, gastric cancer and oesophageal cancer) and 170 879 patients with ACS between 2007 and 2014 were identified.Primary and secondary outcome measures Direct medical costs were calculated from claims recorded in the National Health Insurance Research Database . Indirect costs, comprising morbidity-associated and mortality-associated productivity losses, were estimated from public life expectancy, average wage and employment data. The costs incurred in the 3 years after diagnosis were assessed. As a comparison, the cost of ACS was also estimated using the same study frame. A cost driver analysis was conducted to identify factors impacting cancer costs.Results The cancers with the highest mean direct medical costs and total costs were leukaemia (US$28 464) and oesophageal cancer (US$81 775), respectively. Indirect costs accounted for over 50% of the total economic burden of most cancers, except for prostate cancer and female breast cancer. The costs of ACS were lower than those of most cancers. From the cost driver analysis, older age at diagnosis significantly (p<0.05) decreased the total cost of cancer; in contrast, male, tumour metastasis, comorbidities and treatment in medical centres increased the costs.Conclusions This study demonstrates the comprehensive economic burden of the top 10 most costly cancers in Taiwan. These results are valuable for optimising healthcare resource allocation.