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Advisory Committee Chair

Bertha Hidalgo

Advisory Committee Members

Babak J Orandi

Russell Griffin

Document Type

Thesis

Date of Award

2022

Degree Name by School

Master of Science in Public Health (MSPH) School of Public Health

Abstract

Transplant physicians review clinical data ranging from laboratory values to substance use history and use prediction models combining these factors, along with previous recipient outcomes, to evaluate deceased organ donors for organ suitability. Factors such as social determinants of health (SDOH) are not currently included in prediction models, despite evidence of relationships between SDOH and health outcomes in the general population. Occupational status is a SDOH routinely collected at the time of donation and stored in DonorNet, but not yet characterized in the deceased donor population. Despite well-documented relationships between occupational status and health outcomes in the general population, these relationships had not been explored in the deceased organ donor population prior to this study. This retrospective observational study is the first to characterize the occupational status of deceased donors. Disparities in factors of interest by the occupational status of deceased donors were studied using ANOVA and chi-square tests. Multivariable regression models were used to model relationships between occupational status and factors of interest in the deceased donor population. We found statistically significant disparities in organ quality factors and their association with occupational status within the deceased donor population, with a higher proportion of behavioral risk factors and premature mortality in lower skill level occupations. Adjusted iv regression models exposed an increased odds of poorer outcomes of interest associated with lower occupational status. These associations between occupational status and important factors that influence organ quality establish a foundation to propose leveraging occupational status—an important social determinant of health—to improve prediction models.

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