Approximately 30% of the global burden of disease is surgical, but two-thirds of the world's population cannot access safe, affordable, and timely surgical care. An ambulatory surgical center (ASC) in Uganda offers subsidized and free care to some patients, but identifying patients in greatest need is challenging. We aimed to develop an unbiased tool appropriate for medical settings to determine subsidy eligibility to prevent catastrophic healthcare expenditure (CHE) for ASC's patients. Utilizing a modified simple poverty scorecard (SPS), expert opinions, and literature review, the center developed a new poverty assessment tool (PAT), the healthcare subsidy assessment (HSA). The HSA was implemented alongside the SPS at ASC. Principal component analysis (PCA) was used to refine the HSA and establish a correlation between the original HSA and SPS to predict CHE likelihood. A 21-question HSA was developed, and 175 patients completed both the HSA and SPS. The questionnaires had a correlation of R2=0.294, p<0.001. PCA identified six distinct components, and the HSA was refined to an 11-question survey (rHSA). After rescaling to 100 for comparison to the SPS and the original HSA, rHSA scores were significantly different between both (p<0.001) but had an improved correlation with the SPS (R2=0.457, p<0.001) and a strong correlation with the original HSA (R2=0.621, p<0.001). We successfully developed and validated the rHSA, a novel PAT tailored for a healthcare setting to better identify and support patients at risk.
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