Morgan Henderson1, Fei Han1, Chad Perman2, Howard Haft2, Ian Stockwell1. 1. The Hilltop Institute, University of Maryland, Baltimore County (UMBC), Baltimore, Maryland, USA. 2. Maryland Primary Care Program, Maryland Department of Health, Baltimore, Maryland, USA.
Abstract
OBJECTIVE: To develop and validate a prediction model of avoidable hospital events among Medicare fee-for-service (FFS) beneficiaries in Maryland. DATA SOURCES: Medicare FFS claims from Maryland from 2017 to 2020 and other publicly available ZIP code-level data sets. STUDY DESIGN: Multivariable logistic regression models were used to estimate the relationship between a variety of risk factors and future avoidable hospital events. The predictive power of the resulting risk scores was gauged using a concentration curve. DATA COLLECTION/EXTRACTION METHODS: One hundred and ninety-eight individual- and ZIP code-level risk factors were used to create an analytic person-month data set of over 11.6 million person-month observations. PRINCIPAL FINDINGS: We included 198 risk factors for the model based on the results of a targeted literature review, both at the individual and neighborhood levels. These risk factors span six domains as follows: diagnoses, pharmacy utilization, procedure history, prior utilization, social determinants of health, and demographic information. Feature selection retained 73 highly statistically significant risk factors (p < 0.0012) in the primary model. Risk scores were estimated for each individual in the cohort, and, for scores released in April 2020, the top 10% riskiest individuals in the cohort account for 48.7% of avoidable hospital events in the following month. These scores significantly outperform the Centers for Medicare & Medicaid Services hierarchical condition category risk scores in terms of predictive power. CONCLUSIONS: A risk prediction model based on standard administrative claims data can identify individuals at risk of incurring a future avoidable hospital event with good accuracy.
OBJECTIVE: To develop and validate a prediction model of avoidable hospital events among Medicare fee-for-service (FFS) beneficiaries in Maryland. DATA SOURCES: Medicare FFS claims from Maryland from 2017 to 2020 and other publicly available ZIP code-level data sets. STUDY DESIGN: Multivariable logistic regression models were used to estimate the relationship between a variety of risk factors and future avoidable hospital events. The predictive power of the resulting risk scores was gauged using a concentration curve. DATA COLLECTION/EXTRACTION METHODS: One hundred and ninety-eight individual- and ZIP code-level risk factors were used to create an analytic person-month data set of over 11.6 million person-month observations. PRINCIPAL FINDINGS: We included 198 risk factors for the model based on the results of a targeted literature review, both at the individual and neighborhood levels. These risk factors span six domains as follows: diagnoses, pharmacy utilization, procedure history, prior utilization, social determinants of health, and demographic information. Feature selection retained 73 highly statistically significant risk factors (p < 0.0012) in the primary model. Risk scores were estimated for each individual in the cohort, and, for scores released in April 2020, the top 10% riskiest individuals in the cohort account for 48.7% of avoidable hospital events in the following month. These scores significantly outperform the Centers for Medicare & Medicaid Services hierarchical condition category risk scores in terms of predictive power. CONCLUSIONS: A risk prediction model based on standard administrative claims data can identify individuals at risk of incurring a future avoidable hospital event with good accuracy.
Authors: Bradley G Hammill; Lesley H Curtis; Gregg C Fonarow; Paul A Heidenreich; Clyde W Yancy; Eric D Peterson; Adrian F Hernandez Journal: Circ Cardiovasc Qual Outcomes Date: 2010-12-07
Authors: Ewout W Steyerberg; Andrew J Vickers; Nancy R Cook; Thomas Gerds; Mithat Gonen; Nancy Obuchowski; Michael J Pencina; Michael W Kattan Journal: Epidemiology Date: 2010-01 Impact factor: 4.822