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To examine the relationship between individual characteristics and potential correlates of elder abuse in older adults who present with fractures.Cross-sectional analysis of deidentified data extracted from medical records.Academic medical center.Individuals aged 65 and with a primary diagnosis of any fracture admitted to an outpatient department or emergency department (ED) in a single southern California medical center over a 36-month period (N = 652).Participant characteristics included demographic characteristics, number of medical visits, and point of service. Corresponding International Classification of Diseases, Ninth Revision (ICD-9) codes, E-codes, and V-codes were extracted to identify cause, location, and type of fracture. The presence of 13 potential correlates of abuse as captured by ICD-9 codes were extracted and summed. Descriptive statistics and regression models were used for analyses.Mean age of participants was 77.2, 58

作者:Melanie W, Gironda;Annie L, Nguyen;Laura M, Mosqueda

来源:Journal of the American Geriatrics Society 2016 年 64卷 8期

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作者:
Melanie W, Gironda;Annie L, Nguyen;Laura M, Mosqueda
来源:
Journal of the American Geriatrics Society 2016 年 64卷 8期
标签:
elder abuse elder mistreatment fractures neglect
To examine the relationship between individual characteristics and potential correlates of elder abuse in older adults who present with fractures.Cross-sectional analysis of deidentified data extracted from medical records.Academic medical center.Individuals aged 65 and with a primary diagnosis of any fracture admitted to an outpatient department or emergency department (ED) in a single southern California medical center over a 36-month period (N = 652).Participant characteristics included demographic characteristics, number of medical visits, and point of service. Corresponding International Classification of Diseases, Ninth Revision (ICD-9) codes, E-codes, and V-codes were extracted to identify cause, location, and type of fracture. The presence of 13 potential correlates of abuse as captured by ICD-9 codes were extracted and summed. Descriptive statistics and regression models were used for analyses.Mean age of participants was 77.2, 58