4508 Contextual Predictors of Hospitalization and Quality of Life Among Patients on Hemodialysis

Taylor, K; Crews, D; Davidson, P

4508 Contextual Predictors of Hospitalization and Quality of Life Among Patients on Hemodialysis

Taylor, K Crews, D Davidson, P

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Publisher:: Cambridge University Press (CUP)
Publication Type:: Journal Article
Citation:: Journal of Clinical and Translational Science, 2020, 4, (s1), pp. 83-83
Issue Date:: 2020-06-01

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Field	Value	Language
dc.contributor.author	Taylor, K
dc.contributor.author	Crews, D
dc.contributor.author	Davidson, P https://orcid.org/0000-0003-2050-1534
dc.date.accessioned	2022-10-05T01:53:36Z
dc.date.available	2022-10-05T01:53:36Z
dc.date.issued	2020-06-01
dc.identifier.citation	Journal of Clinical and Translational Science, 2020, 4, (s1), pp. 83-83
dc.identifier.issn	2059-8661
dc.identifier.issn	2059-8661
dc.identifier.uri	http://hdl.handle.net/10453/162313
dc.description.abstract	<jats:p>OBJECTIVES/GOALS: People engaging in high-risk substance use or experiencing food insecurity or housing instability are at increased risk to develop end-stage kidney disease. This study will examine associations between these risk factors, patient indicators of socioeconomic position, and hospitalization rates and quality of life after initiation of hemodialysis. METHODS/STUDY POPULATION: The proposed study will leverage a prospective cohort design. We will enroll a convenience sample of 330 participants from the same large dialysis organization. Participants will complete measures of socioeconomic position (age, gender, race, ethnicity, education, income, occupation and community poverty); substance use; food insecurity; housing instability; and quality of life at baseline. We will follow participants for 6 months and extract hospitalization counts from the dialysis facility medical record. RESULTS/ANTICIPATED RESULTS: We will generate risk scores (low, medium, high) from measures of substance use, food insecurity and housing instability. We will conduct multiple logistic regression to generate odds ratios comparing risk group membership by indicators of socioeconomic position. We anticipate that low or medium-risk groups will differ from high risk groups by indicators of socioeconomic position. We will conduct Poisson regression to generate incidence rate ratios for 6-month hospitalization rates comparing low or medium-risk and high-risk groups. Lastly, we will conduct multiple linear regression to generate beta coefficients for changes in quality of life scores comparing low or medium-risk and high-risk groups. We anticipate that high-risk groups will have higher hospitalization rates and lower quality of life scores. DISCUSSION/SIGNIFICANCE OF IMPACT: As the prevalence of end-stage kidney disease continues to increase, there is a need for tertiary prevention interventions that reduce costly inpatient utilization and improve health-related quality of life. The proposed study will lay groundwork for the development of interventions to improve patient outcomes and reduce Medicare spending.</jats:p>
dc.language	en
dc.publisher	Cambridge University Press (CUP)
dc.relation.ispartof	Journal of Clinical and Translational Science
dc.relation.isbasedon	10.1017/cts.2020.264
dc.rights	info:eu-repo/semantics/openAccess
dc.title	4508 Contextual Predictors of Hospitalization and Quality of Life Among Patients on Hemodialysis
dc.type	Journal Article
utslib.citation.volume	4
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Health
utslib.copyright.status	open_access	*
dc.date.updated	2022-10-05T01:53:35Z
pubs.issue	s1
pubs.publication-status	Published
pubs.volume	4
utslib.citation.issue	s1

Abstract:

OBJECTIVES/GOALS: People engaging in high-risk substance use or experiencing food insecurity or housing instability are at increased risk to develop end-stage kidney disease. This study will examine associations between these risk factors, patient indicators of socioeconomic position, and hospitalization rates and quality of life after initiation of hemodialysis. METHODS/STUDY POPULATION: The proposed study will leverage a prospective cohort design. We will enroll a convenience sample of 330 participants from the same large dialysis organization. Participants will complete measures of socioeconomic position (age, gender, race, ethnicity, education, income, occupation and community poverty); substance use; food insecurity; housing instability; and quality of life at baseline. We will follow participants for 6 months and extract hospitalization counts from the dialysis facility medical record. RESULTS/ANTICIPATED RESULTS: We will generate risk scores (low, medium, high) from measures of substance use, food insecurity and housing instability. We will conduct multiple logistic regression to generate odds ratios comparing risk group membership by indicators of socioeconomic position. We anticipate that low or medium-risk groups will differ from high risk groups by indicators of socioeconomic position. We will conduct Poisson regression to generate incidence rate ratios for 6-month hospitalization rates comparing low or medium-risk and high-risk groups. Lastly, we will conduct multiple linear regression to generate beta coefficients for changes in quality of life scores comparing low or medium-risk and high-risk groups. We anticipate that high-risk groups will have higher hospitalization rates and lower quality of life scores. DISCUSSION/SIGNIFICANCE OF IMPACT: As the prevalence of end-stage kidney disease continues to increase, there is a need for tertiary prevention interventions that reduce costly inpatient utilization and improve health-related quality of life. The proposed study will lay groundwork for the development of interventions to improve patient outcomes and reduce Medicare spending.

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http://hdl.handle.net/10453/162313