Neil J Mackinnon1, Charles D Hepler. 1. Dalhousie University, College of Pharmacy, 5968 College St., Halifax, Nova Scotia, Canada B3H 3J5. neil.mackinnon@dal.ca
Abstract
OBJECTIVE: To determine the incidence of preventable drug-related morbidity (PDRM) in older adults in a provider-sponsored network and identify risk factors for PDRM. METHODS: The study was based on a retrospective review of an integrated health care database, using 52 newly developed clinical indicators of PDRM. The incidence of PDRM was determined by identifying individuals in the database who matched an outcome and pattern of care associated with an indicator. Risk factors were determined through a forward inclusion logistic regression model. The subjects in this study were 3,365 older adults enrolled in a hospital-based health care system in Florida in 1997. The principal outcome measure was identification of individuals who matched a PDRM indicator and risk factors for PDRM. RESULTS: Ninety-seven enrollees who matched one or more of 52 PDRM indicators were found in 3,365 older adults, for an overall incidence rate of 28.8 per 1000. The top 5 indicators of PDRM were responsible for 46.8% of all PDRMs found. Regression analysis identified 5 risk factors: 4 or more recorded diagnoses, 4 or more prescribers, 6 or more prescription medications, antihypertensive drug use, and male gender. CONCLUSION: This study demonstrated that clinical indicators can be used in a managed care organization to identify seniors who have experienced a PDRM. The risk model should better prepare managed care organizations to proactively identify patients at risk for PDRM and to optimize medication use in older adults.
OBJECTIVE: To determine the incidence of preventable drug-related morbidity (PDRM) in older adults in a provider-sponsored network and identify risk factors for PDRM. METHODS: The study was based on a retrospective review of an integrated health care database, using 52 newly developed clinical indicators of PDRM. The incidence of PDRM was determined by identifying individuals in the database who matched an outcome and pattern of care associated with an indicator. Risk factors were determined through a forward inclusion logistic regression model. The subjects in this study were 3,365 older adults enrolled in a hospital-based health care system in Florida in 1997. The principal outcome measure was identification of individuals who matched a PDRM indicator and risk factors for PDRM. RESULTS: Ninety-seven enrollees who matched one or more of 52 PDRM indicators were found in 3,365 older adults, for an overall incidence rate of 28.8 per 1000. The top 5 indicators of PDRM were responsible for 46.8% of all PDRMs found. Regression analysis identified 5 risk factors: 4 or more recorded diagnoses, 4 or more prescribers, 6 or more prescription medications, antihypertensive drug use, and male gender. CONCLUSION: This study demonstrated that clinical indicators can be used in a managed care organization to identify seniors who have experienced a PDRM. The risk model should better prepare managed care organizations to proactively identify patients at risk for PDRM and to optimize medication use in older adults.
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