A Logistic Regression Analysis of Multiple Independent Variables Impacting Psychiatric Readmissions

<p> This dissertation explored several internal and external factors in relation to psychiatric readmissions. Internal factors are directly related to the individual i.e., demographic information, diagnosis, admission history and status. External factors are factors outside of the individuals...

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Main Author: Simmons, Carol Ivy
Language:EN
Published: Capella University 2018
Subjects:
Online Access:http://pqdtopen.proquest.com/#viewpdf?dispub=10289773
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spelling ndltd-PROQUEST-oai-pqdtoai.proquest.com-102897732018-04-05T15:57:08Z A Logistic Regression Analysis of Multiple Independent Variables Impacting Psychiatric Readmissions Simmons, Carol Ivy Mental health <p> This dissertation explored several internal and external factors in relation to psychiatric readmissions. Internal factors are directly related to the individual i.e., demographic information, diagnosis, admission history and status. External factors are factors outside of the individuals control i.e., length of hospital stay and reimbursement processes. The goal of the study was to explore the impact of multiple factors in relation to the phenomenon of psychiatric readmissions. Dynamic Systems Theory (1994) was used as a theoretical foundation to understand the complexities associated with psychiatric readmissions. The study utilized state archival data provided by the Maryland Health Services Cost Review Commission; an agency charged with collecting statewide hospital data on hospital admissions. </p><p> A quasi experimental study was conducted using a logistic regression design to answer the research question: When taken together do age, sex, ethnicity, diagnosis, insurance type, admission status and length of stay predict psychiatric readmission? This researcher predicted that the null hypothesis will be rejected. The sample included a large state-wide data set of over 130,000 individuals who fell under the criteria of being over the age of 18 when readmitted for psychiatric care in Maryland in 2015. The research methodology includes a logistic regression research design, exploring multiple factors, simultaneously, that impact psychiatric readmissions. </p><p> The results of the study indicate that length of stay is the most important factor impacting psychiatric readmissions. The second most important factor associated with psychiatric readmission, is a psychiatric readmission within 30 days. Medicare and Medicaid were also found to be significant factors associated with psychiatric readmission. Additionally, affective disorders were found to be the primary diagnosis associated with psychiatric readmissions. Lastly, individuals at greatest risk for psychiatric readmissions are between the age of 18-39, are non-Hispanic, are enrolled in Medicare, most likely to be disabled, are diagnosed with an affective disorder and have had a previous psychiatric readmission.</p><p> Capella University 2018-03-30 00:00:00.0 thesis http://pqdtopen.proquest.com/#viewpdf?dispub=10289773 EN
collection NDLTD
language EN
sources NDLTD
topic Mental health
spellingShingle Mental health
Simmons, Carol Ivy
A Logistic Regression Analysis of Multiple Independent Variables Impacting Psychiatric Readmissions
description <p> This dissertation explored several internal and external factors in relation to psychiatric readmissions. Internal factors are directly related to the individual i.e., demographic information, diagnosis, admission history and status. External factors are factors outside of the individuals control i.e., length of hospital stay and reimbursement processes. The goal of the study was to explore the impact of multiple factors in relation to the phenomenon of psychiatric readmissions. Dynamic Systems Theory (1994) was used as a theoretical foundation to understand the complexities associated with psychiatric readmissions. The study utilized state archival data provided by the Maryland Health Services Cost Review Commission; an agency charged with collecting statewide hospital data on hospital admissions. </p><p> A quasi experimental study was conducted using a logistic regression design to answer the research question: When taken together do age, sex, ethnicity, diagnosis, insurance type, admission status and length of stay predict psychiatric readmission? This researcher predicted that the null hypothesis will be rejected. The sample included a large state-wide data set of over 130,000 individuals who fell under the criteria of being over the age of 18 when readmitted for psychiatric care in Maryland in 2015. The research methodology includes a logistic regression research design, exploring multiple factors, simultaneously, that impact psychiatric readmissions. </p><p> The results of the study indicate that length of stay is the most important factor impacting psychiatric readmissions. The second most important factor associated with psychiatric readmission, is a psychiatric readmission within 30 days. Medicare and Medicaid were also found to be significant factors associated with psychiatric readmission. Additionally, affective disorders were found to be the primary diagnosis associated with psychiatric readmissions. Lastly, individuals at greatest risk for psychiatric readmissions are between the age of 18-39, are non-Hispanic, are enrolled in Medicare, most likely to be disabled, are diagnosed with an affective disorder and have had a previous psychiatric readmission.</p><p>
author Simmons, Carol Ivy
author_facet Simmons, Carol Ivy
author_sort Simmons, Carol Ivy
title A Logistic Regression Analysis of Multiple Independent Variables Impacting Psychiatric Readmissions
title_short A Logistic Regression Analysis of Multiple Independent Variables Impacting Psychiatric Readmissions
title_full A Logistic Regression Analysis of Multiple Independent Variables Impacting Psychiatric Readmissions
title_fullStr A Logistic Regression Analysis of Multiple Independent Variables Impacting Psychiatric Readmissions
title_full_unstemmed A Logistic Regression Analysis of Multiple Independent Variables Impacting Psychiatric Readmissions
title_sort logistic regression analysis of multiple independent variables impacting psychiatric readmissions
publisher Capella University
publishDate 2018
url http://pqdtopen.proquest.com/#viewpdf?dispub=10289773
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