ArticleCancer medicine2023
A multi-dimensional assessment of financial hardship of cancer patients using existing health system data.
Article in Cancer medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed, 2 citations in OpenAlex.
- A multi-dimensional assessment of financial hardship of cancer patients using existing health system data.Cancer medicine · 2023Article
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
backgroundExisting financial hardship screening does not capture the multifaceted and dynamic nature of the problem. The use of existing health system data is a promising way to enable scalable and sustainable financial hardship screening.
methodsWe used existing data from 303 adult patients with cancer at the University of Virginia Comprehensive Cancer Center (2016-2018). All received distress screening and had a valid financial assistance screening based solely on household size-adjusted income. We constructed a composite index that integrates multiple existing health system data (Epic, distress screening, and cancer registry) to assess comprehensive financial hardship (e.g., material conditions, psychological responses, and coping behaviors). We examined differences of at-risk patients identified by our composite index and by existing single-dimension criterion. Dynamics of financial hardship over time, by age, and cancer type, were examined by fractional probit models.
resultsAt-risk patients identified by the composite index were generally younger, better educated, and had a higher annual household income, though they had lower health insurance coverage. Identified periods to intervene for most patients are before formal diagnosis, 2 years, and 6 years after diagnosis. Within 2 years of diagnosis and more than 4 years after diagnosis appear critical for subgroups of patients who may suffer from financial hardship disparities.
conclusionExisting health system data provides opportunities to systematically measure and track financial hardship in a systematic, scalable and sustainable way. We find that the dimensions of financial hardship can exhibit different patterns over time and across patient subgroups, which can guide targeted interventions. The scalability of the algorithm is limited by existing data availability.
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