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Evaluating US Multiple Listing Practices in Lung Transplantation: Unveiling Hidden Disparities
Adora N Moneme1, Mallory Hunt1, Jacqueline Friskey1
1Division of Cardiovascular Surgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA.
Chest
|August 18, 2024
Summary
Multiple listing (ML) is uncommon in lung transplants and shows disparities based on social deprivation. While ML increases transplant likelihood, it only speeds up the process if initiated early in candidacy.
Area of Science:
- Organ transplantation research
- Health equity studies
- Socioeconomic determinants in healthcare
Background:
- Multiple listing (ML) is a strategy to enhance transplant opportunities but faces scrutiny for potentially favoring patients with better healthcare access.
- Concerns exist regarding equitable distribution of organs and potential biases in ML practices.
Purpose of the Study:
- To investigate disparities in ML practices in the United States based on social deprivation.
- To determine if ML utilization leads to a reduced time to lung transplantation.
Main Methods:
- A retrospective cohort study analyzed adult lung transplant candidates from 2005-2018 using the UNOS STAR File.
- Social Deprivation Index was used to assess disparities between ML and single listing (SL) patients.
- Time to transplant was evaluated as a primary outcome in relation to ML status.
Main Results:
- Out of 35,890 patients, 2.2% were multiple listed (ML) and 97.8% were single listed (SL).
- ML patients exhibited lower social deprivation levels and were more likely to receive a transplant (OR, 1.42).
- A significantly quicker time to transplant was observed only for patients with early ML status (within 6 months of listing).
Conclusions:
- Multiple listing is an infrequent practice with notable disparities linked to social deprivation and other factors.
- While ML increases transplant probability, its benefit in reducing wait times is contingent on early implementation.
- Future research will assess the impact of evolving organ allocation guidelines on ML practices.
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