IDENTIFICATION OF FALL RISK FACTORS AND DEVELOPMENT OF A FALLS PREDICTION MODEL FOR COMMUNITY-DWELLING OLDER ADULTS RECEIVING PUBLIC HEALTH NURSING SERVICES IN HAWAI`I

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dc.contributor.advisor Tse, Alice M.
dc.contributor.author Chock, Johnelle
dc.contributor.department Nursing
dc.date.accessioned 2024-02-26T20:14:21Z
dc.date.available 2024-02-26T20:14:21Z
dc.date.issued 2023
dc.description.degree Ph.D.
dc.identifier.uri https://hdl.handle.net/10125/107945
dc.subject Nursing
dc.subject Fall Risk Factors
dc.subject MAHC-10
dc.subject Older Adult
dc.title IDENTIFICATION OF FALL RISK FACTORS AND DEVELOPMENT OF A FALLS PREDICTION MODEL FOR COMMUNITY-DWELLING OLDER ADULTS RECEIVING PUBLIC HEALTH NURSING SERVICES IN HAWAI`I
dc.type Thesis
dcterms.abstract Nationally, one in four seniors fall each year, with the incidence of falls increasing with age. It is estimated that there are 26 fall-related injuries per day. For older adults 65 years and older, falls are the leading cause of hospitalizations, traumatic brain injury, and the leading cause of injury Emergency Department visits. The Centers for Disease Control and Prevention estimates that in 2030, 73 million older adults will live in the United States, leading to 52 million falls and 12 million fall-related injuries annually. Fall risk factors have been identified as intrinsic or extrinsic. The overall purpose of this study is to discover which fall risk factors (Missouri Alliance for Home Care Fall Risk Assessment Tool (MAHC-10), age, living arrangement, gender, number of medications, medication type, Charlson Comorbidity Index (CCI) score, and the Age-Adjusted Charlson Comorbidity Index (ACCI) score) provide the strongest indicators of falls among community-dwelling older adults in Hawaii. A fall prediction model was developed using the strongest fall indicators for community-dwelling older adults in Hawaii. This study utilized a secondary analysis of the Hawaii Department of Health Public Health Nursing Branch fall database. The sample size was 211 community-dwelling older adults 60 years and older living in Hawaii. Data was collected for the model and included the MAHC-10 score, age, living arrangement, gender, number of medications, medication type, the CCI score, and the ACCI score. A nonparametric two-sample Wilcoxon test, a nonparametric Pearson chi-square test, a Fisher’s exact test, a Cochran-Armitage test for trend, or a univariate logistic regression was performed on the independent variables. A receiver operating characteristic (ROC) curve, sensitivity, and specificity were completed for the screening tools. Multiple logistic regression was completed to develop a prediction model for falls among Hawaii community-dwelling older adults. The statistical modeling was performed using SPSS version 25. This study found that there were individual significant associations between community-dwelling older adult falls and the MAHC-10 total score of six or more, prior history of falls, environmental risk factors, CCI total score of two or more, the ACCI total score of six or more, depression, cancer, myocardial infarction (MI), peripheral vascular disease (PVD), and antispasmodic medication. This study also developed a parsimonious model with five variables and a c-statistic of 0.70 (95% CI= 0.61- 0.79). The variables that were included in the model were prior history of falls, MI or PVD, antispasmodic medication, depression, and environmental hazards. This study identified significant fall risk factors and developed a parsimonious model for this sample of community-dwelling older adults in Hawaii. This information could be used in the community to identify older adults at risk for falling and provide evidence-based fall prevention interventions in the future.
dcterms.extent 200 pages
dcterms.language en
dcterms.publisher University of Hawai'i at Manoa
dcterms.rights All UHM dissertations and theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission from the copyright owner.
dcterms.type Text
local.identifier.alturi http://dissertations.umi.com/hawii:12020
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