Introduction: Sickle cell disease (SCD) is a complex genetic disease with a multifactorial pathophysiology including multi-cell adhesion between red blood cells, white blood cells, platelets and endothelial cells, ultimately resulting in vaso-occlusive crises (VOCs). VOCs are the hallmark of SCD and are the primary cause for hospitalization. These recurrent episodes induce severe pain, decrease quality of life, can cause life-threatening complications, and are associated with increased risk of organ damage and mortality. While the clinical burden of SCD is well-documented, less evidence exists surrounding how the severity of SCD affects patients economically. The number of VOCs and other complications not only affect the likelihood of patients being able to work on a given day, but may also affect their long-term economic prospects. This analysis aims to better understand the association between SCD disease severity and its impact on the likelihood of collecting Supplemental Security Income (SSI) and patient income.

Methods: A web-based survey was administered to adult patients (≥18 years) living in the United States with a self-reported diagnosis of SCD. The outcomes of interest for this analysis were Social Security Income (SSI) receipt and self-reported total household annual income. These outcomes were stratified by SCD disease severity. Our first measure of disease severity was the number of self-reported VOCs in the prior year. Our second measure of disease severity was developed through clinical expert opinion and relied on an algorithm for dividing patients into 3 disease severity classes. Severity Class I was defined as having no VOCs requiring treatment by health care providers in the past year; Severity Class II was defined as ≥1 emergency department visit or hospital admission for a VOC, or complication in the past year without any organ damage; and Severity Class III was defined as long-term organ damage (such as stroke or renal disease). Generalized linear models (GLM) with a binomial link function were used to analyze the association between SCD disease severity and SSI collection (one model used VOC frequency; a second model used severity classes). A linear regression model was used to analyze the relationship between VOC frequency and income level, while an ordered logistic regression model was used to analyze the association between SCD disease severity classes and income level.

Results: The final sample was comprised of 303 individuals who completed the survey. The average age was 34.4 years (range 18 - 72) and 221 (72.9%) were female. The probability of SSI collection among patients with SCD varied across VOC frequency in the previous year. The probability of collecting SSI for patients having 0 and ≥4 VOCs in the past year, was 12% (95% confidence interval (CI): 4% to 31%) and 47% (39% to 55%), respectively. A chi-squared p-value of 0.002 indicated a statistically significant association between a greater number of VOCs and probability of SSI collection. The probability of SSI collection among SCD patients with Severity Class II and Severity Class III SCD was 16% (7.5% to 32%), and 39% (32.9% to 45%), respectively. A chi-squared p-value of 0.03 indicated a statistically significant association between SCD disease severity class and SSI collection. The predicted mean income for patients with SCD experiencing 0 and ≥4 VOCs in the past year was $47,488 and $34,569, respectively. A linear association test p-value of 0.06 indicated weak evidence of a lower mean income in relation to number of VOCs experienced. The predicted mean income among patients with class II and class III SCD was $42,443 and $36,842, respectively. There was no evidence of association between SCD severity class and mean income (p=0.29).

Conclusion: Among patients with SCD, having more VOCs was strongly associated with the probability of collecting SSI and weakly associated with lower income. Disease severity class was strongly associated with the probability of collecting SSI.

Disclosures

Shafrin:Precision Health Economics, part of Precision Medicine Group: Employment, Equity Ownership. Thom:Bayer AG: Consultancy; Hoffman-La Roche: Consultancy; Pfizer: Consultancy; Novartis Pharma AG: Consultancy. Gaunt:Novartis Pharma AG: Consultancy. Zhao:Precision Health Economics, part of Precision Medicine Group: Employment. Joseph:Cigna: Equity Ownership; Pfizer: Equity Ownership; Amgen: Equity Ownership; Novartis: Employment, Equity Ownership. Bhor:Novartis: Employment, Equity Ownership. Rizio:Optum: Employment. Bronté-Hall:bluebird bio: Research Funding. Shah:GBT: Research Funding; Alexion: Speakers Bureau; Novartis: Consultancy, Research Funding, Speakers Bureau.

Author notes

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Asterisk with author names denotes non-ASH members.

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