Article Text
PDF
Abstract
Objective A major preventable contributor to healthcare costs among older individuals is fall-related injury. We sought to validate a tool to stratify such risk based on readily available clinical data, including projected medication adverse effects, using state-wide medical claims data.
Design Sociodemographic and clinical features were drawn from health claims paid in the state of Massachusetts for individuals aged 35–65 with a hospital admission for a period spanning January–December 2012. Previously developed logistic regression models of hospital readmission for fall-related injury were refit in a testing set including a randomly selected 70% of individuals, and examined in a training set comprised of the remaining 30%. Medications at admission were summarised based on reported adverse effect frequencies in published medication labelling.
Setting The Massachusetts health system.
Participants A total of 68 764 hospitalised individuals aged 35–65 years.
Primary Measures Hospital readmission for fall-related injury defined by claims code.
Results A total of 2052 individuals (3.0%) were hospitalised for fall-related injury within 90 days of discharge, and 3391 (4.9%) within 180 days. After recalibrating the model in a training data set comprised of 48 136 individuals (70%), model discrimination in the remaining 30% test set yielded an area under the receiver operating characteristic curve (AUC) of 0.74 (95% CI 0.72 to 0.76). AUCs were similar across age decades (0.71 to 0.78) and sex (0.72 male, 0.76 female), and across most common diagnostic categories other than psychiatry. For individuals in the highest risk quartile, 11.4% experienced fall within 180 days versus 1.2% in the lowest risk quartile; 57.6% of falls occurred in the highest risk quartile.
Conclusions This analysis of state-wide claims data demonstrates the feasibility of predicting fall-related injury requiring hospitalisation using readily available sociodemographic and clinical details. This translatable approach to stratification allows for identification of high-risk individuals in whom interventions are likely to be cost-effective.
- fall-related injury
- adverse effects
- risk stratification
- prediction
- precision medicine
- health claims
This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Statistics from Altmetric.com
Footnotes
Contributors THMJ helped design the study, analysed the data and drafted the manuscript. VMC and AC imported and cleaned the data and generated all derived variables. AMR contributed to preparation of the manuscript. RHP initiated the project, designed the study, monitored the analyses, created the tool for risk stratification and drafted the manuscript. He is a guarantor.
Funding This work was supported in part by 1P50MH106933 from NIMH and NHGRI, and by 1R01HL124262-01A1 from NHLBI.
Competing interests All authors will complete the Unified Competing Interest form at http://www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare that: THMJ, VMC, AC and AMR have read and understood BMJ policy on declaration of interests and declare that we have no competing interests. RHP has read and understood BMJ policy on declaration of interests and declares the following interests: RHP has served on advisory boards or provided consulting to Genomind, Healthrageous, Pamlab, Perfect Health, Pfizer, Psybrain and RIDVentures.
Ethics approval This study obtained IRB approval from the Partners Human Research Committee under protocol number 2012-P-002527, and from the Massachusetts Department of Public Health under protocol number 533160-1. No informed consent was required, as this project is a retrospective healthcare usage/clinical study involving thousands of patients and multiple years of data—that is, consent could not feasibly be obtained from all subjects.
Provenance and peer review Not commissioned; externally peer reviewed.
Data sharing statement No additional data are available.