Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
IntroductionIn 2021, among all age groups, falls ranked as the third leading cause of unintentional injury death in the USA. Unlike fatal data, which rely on death certificates as the gold standard, there is not a gold standard for non-fatal data. Non-fatal falls data are often based on insurance claims or administrative billing data. The purpose of our study is to compare three claims databases to estimate rates of unintentional fall-related hospitalisations in 2019, the most recent year of available data across the three sources.MethodsThree databases were used to produce incidence rates of fall-related hospitalisations for the year 2019: (1) Merative MarketScan research databases, (2) Centers for Medicare and Medicaid Services (CMS) data and (3) Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample. Inpatient falls were identified using International Classification of Diseases, 10th Revision, Clinical Modification codes. Incidence rates per 100 000 people were then produced across all three datasets by payer type. Unadjusted incidence rate ratios were estimated with corresponding 95% CIs.ResultsThere were wide disparities among fall rates between the three datasets by payer type. HCUP had the highest rate of falls among Medicare (1087.6 per 100 000) and commercial enrollees (74.7 per 100 000), while CMS had the highest rates of falls among Medicaid enrollees (148.0 per 100 000).ConclusionsThis study shows wide variation in fall hospitalisation rates based on the claims data used to estimate rates. This study suggests that database selection is an important consideration when determining incidence of non-fatal falls.
IntroductionIn 2021, among all age groups, falls ranked as the third leading cause of unintentional injury death in the USA. Unlike fatal data, which rely on death certificates as the gold standard, there is not a gold standard for non-fatal data. Non-fatal falls data are often based on insurance claims or administrative billing data. The purpose of our study is to compare three claims databases to estimate rates of unintentional fall-related hospitalisations in 2019, the most recent year of available data across the three sources.MethodsThree databases were used to produce incidence rates of fall-related hospitalisations for the year 2019: (1) Merative MarketScan research databases, (2) Centers for Medicare and Medicaid Services (CMS) data and (3) Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample. Inpatient falls were identified using International Classification of Diseases, 10th Revision, Clinical Modification codes. Incidence rates per 100 000 people were then produced across all three datasets by payer type. Unadjusted incidence rate ratios were estimated with corresponding 95% CIs.ResultsThere were wide disparities among fall rates between the three datasets by payer type. HCUP had the highest rate of falls among Medicare (1087.6 per 100 000) and commercial enrollees (74.7 per 100 000), while CMS had the highest rates of falls among Medicaid enrollees (148.0 per 100 000).ConclusionsThis study shows wide variation in fall hospitalisation rates based on the claims data used to estimate rates. This study suggests that database selection is an important consideration when determining incidence of non-fatal falls.
Vision is critical for human balance. Visual impairment (VI) decreases the ability of individuals to maintain balance and greatly impacts the activities of daily living. Hence, the purpose of this study is to assess balance in individuals with VI using the Berg Balance Scale (BBS) and Activities-specific Balance Confidence (ABC) Scale regarding its correlation with fall risk. A total of 88 participants were eligible for this study. We administered pre-tested questionnaires for demographic conditions and history of falls after recruiting participants; following that, BBS and ABC Scale tests were conducted for the participants. A t-test was used to determine statistically significant differences between the means in the fall and non-fall groups. A Pearson bivariate correlation test and linear regression were used to determine the existence of relationships between BBS and ABC variables. In all, 51 participants in this study had experienced falls within 1 year (fall group), while 37 participants had falls over the past 1 year (non-fall group). The fall group had a lower mean of BBS (49) and ABC (69) scores than the non-fall group; also, there was a significant correlation between the BBS and ABC Scales ( p < .05). The type of VI and sex also had significant differences in the risk of falls. These findings suggest that individuals with VIs should raise their awareness about improving aspects of balance in the body with specific exercise and training to minimize the risk of falls.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.