ArticleThe American journal of medicine2017
Predicting Falls in People Aged 65 Years and Older from Insurance Claims.
Article in The American journal of medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 3 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- A systematic review of fall prediction models for community-dwelling older adults: comparison between models based on research cohorts and models based on routinely collected data.Age and ageing · 2024Pooled it
- Cardiovascular Disorders and Falls Among Older Adults: A Systematic Review and Meta-Analysis.The journals of gerontology. Series A, Biological sciences and medical sciences · 2024Pooled it
- A Systematic Review and Meta-Analyses of the Association Between Anti-Hypertensive Classes and the Risk of Falls Among Older Adults.Drugs & aging · 2018Pooled it
- A scoping review of machine learning models to predict risk of falls in elders, without using sensor data.Diagnostic and prognostic research · 2025Review
- Development and Validation of an Intracranial Hemorrhage Risk Score in Older Adults with Atrial Fibrillation Treated with Oral Anticoagulant.Clinical epidemiology · 2024Article
- Development and validation of a machine learning-based fall-related injury risk prediction model using nationwide claims database in Korean community-dwelling older population.BMC geriatrics · 2023Article
- Musculoskeletal adaptation of young and older adults in response to challenging surface conditions.Journal of biomechanics · 2022Article
- Development of the ADFICE_IT Models for Predicting Falls and Recurrent Falls in Community-Dwelling Older Adults: Pooled Analyses of European Cohorts With Special Attention to Medication.The journals of gerontology. Series A, Biological sciences and medical sciences · 2022Article
- Development and Internal Validation of a Risk Prediction Model for Falls Among Older People Using Primary Care Electronic Health Records.The journals of gerontology. Series A, Biological sciences and medical sciences · 2022Article
- Risk Factors Associated with Falls among Nursing Home Residents: A Case-Control Study.Portuguese journal of public health · 2022Article
- Determining the relative risk of hospitalisation and surgery of fall injury patients.Health systems (Basingstoke, England) · 2022Article
- Safety and effectiveness of oral factor Xa inhibitors versus warfarin in nonvalvular atrial fibrillation patients at high-risk for falls.Journal of thrombosis and thrombolysis · 2019Article
- Clinical Factors and Social Determinants of Health Predict Elder Mistreatment at Two Years Among Older Adults: A Preliminary Prediction Model.Inquiry : a journal of medical care organization, provision and financingObservational
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundAccidental falls among people aged 65 years and older caused approximately 2,700,000 injuries, 27,000 deaths, and cost more than 34 billion dollars in the US annually in recent years. Here, we derive and validate a predictive model for falls based on a retrospective cohort of those 65 years and older.
methodsInsurance claims from a 1-year observational period were used to predict a fall-related claim in the following 2 years. The predictive model takes into account a person's age, sex, prescriptions, and diagnoses. Through random assignment, half of the people had their claims used to derive the model, while the remaining people had their claims used to validate the model.
resultsOf 120,881 individuals with Aetna health insurance coverage, 12,431 (10.3%) members fell. During validation, people were risk stratified across 20 levels, where those in the highest risk stratum had 10.5 times the risk as those in the lowest stratum (33.1% vs 3.1%).
conclusionsUsing only insurance claims, individuals in this large cohort at high risk of falls could be readily identified up to 2 years in advance. Although external validation is needed, the findings support the use of the model to better target interventions.
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