Feasibility of the International Wealth Index and the Gapminder Tool As Instruments to Assess Household Income and Estimate Catastrophic Expenditure: A Prospective Patient-Level Cohort Study in India.
PLOS ONE(2023)
All India Inst Med Sci Rishikesh | Univ Birmingham | Christian Med Coll & Hosp | Tata Med Ctr | Tata Mem Hosp
Abstract
BackgroundPatient income assessment is required to assess healthcare catastrophic expenditure (Sustainable Development Goal) but self-reported income has several biases. This study aimed to assess the feasibility of the International Wealth Index (IWI) and the Gapminder tool as indirect instruments to assess household income. MethodsProspective cohort study of colorectal cancer patients in five tertiary care hospitals in India (Dec 2020-August 2021). Patient self-reported household income was compared to income estimated from the IWI (twelve questions about household goods) and the Gapminder tool (five pictures of household assets). Agreement between instruments was explored with Bland-Altman methods. Cancer care expenditure from the same cohort was used to illustrate the impact of these tools in catastrophic expenditure rates. ResultsFrom the 226 patients included, 99.5% completed the IWI and the Gapminder tool. Overall, self-reported incomes were lower than the estimated from the IWI and Gapminder tools (median incomes: 17350(sic) for self-reported, 37491(sic) for IWI and 51520(sic) for Gapminder). The IWI showed better agreement with the self-reported income than the Gapminder tool. For both instruments, the agreement was better for low income earning households. Illustrative catastrophic expenditure rates range from 71% to 56% to 43% when using self-reported, IWI and Gapminder incomes respectively. DiscussionIt is feasible to use the IWI and the Gapminder tools to estimate household income although they might overestimate income, with an impact on catastrophic expenditure rates. Further refinement of these tools could enable global monitoring and modelling of catastrophic expenditure from real-world data, at low burden for patients.
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