BACKGROUND: Benzodiazepines (BZD) are one class of medications that are generally acknowledged to be a risk factor for injuries. OBJECTIVE: Our objective was to link outpatient prescription data with clinical data in order to develop a risk adjusted binary model that associates BZD usage with the risk for a healthcare encounter for an injury. METHODS: In total, 3 years of outpatient BZD prescription data, totaling 133 872 outpatient BZD prescriptions for 13 745 patients for a VA medical center, were combined with data from inpatient and outpatient administrative databases. The model incorporated Elixhauser comorbidity measures with 1-year look back period, along with hospital discharges, marital status, age, mean arterial pressure and body mass index. The model also included the dose of the drug, converted to valium equivalents and its duration. The model was analyzed using generalized estimation equations (GEE). RESULTS: Dose, duration, discharges and various comorbidities were associated with an increased risk for injury, while being married reduced the risk. Increased body mass was associated with increased injury risk. Increased mean arterial pressure was associated with decreased risk. CONCLUSIONS: These findings offer guidance on how specific combinations of risk factors and potential protective effects may impact accidental injury risk. Clinicians prescribing or adjusting BZDs can use these results to more accurately tailor medication regimens for a patient. Our findings suggest that clinicians should also consider the nature of the social support system available to the patient in assessing total injury risk. Copyright (c) 2004 John Wiley & Sons, Ltd.
BACKGROUND:Benzodiazepines (BZD) are one class of medications that are generally acknowledged to be a risk factor for injuries. OBJECTIVE: Our objective was to link outpatient prescription data with clinical data in order to develop a risk adjusted binary model that associates BZD usage with the risk for a healthcare encounter for an injury. METHODS: In total, 3 years of outpatientBZD prescription data, totaling 133 872 outpatientBZD prescriptions for 13 745 patients for a VA medical center, were combined with data from inpatient and outpatient administrative databases. The model incorporated Elixhauser comorbidity measures with 1-year look back period, along with hospital discharges, marital status, age, mean arterial pressure and body mass index. The model also included the dose of the drug, converted to valium equivalents and its duration. The model was analyzed using generalized estimation equations (GEE). RESULTS: Dose, duration, discharges and various comorbidities were associated with an increased risk for injury, while being married reduced the risk. Increased body mass was associated with increased injury risk. Increased mean arterial pressure was associated with decreased risk. CONCLUSIONS: These findings offer guidance on how specific combinations of risk factors and potential protective effects may impact accidental injury risk. Clinicians prescribing or adjusting BZDs can use these results to more accurately tailor medication regimens for a patient. Our findings suggest that clinicians should also consider the nature of the social support system available to the patient in assessing total injury risk. Copyright (c) 2004 John Wiley & Sons, Ltd.
Authors: Dustin D French; Thomas N Chirikos; Andrea Spehar; Robert Campbell; Heidi Means; Tatjana Bulat Journal: Drug Saf Date: 2005 Impact factor: 5.606
Authors: Els van den Ban; Patrick Souverein; Willemijn Meijer; Herman van Engeland; Hanna Swaab; Toine Egberts; Eibert Heerdink Journal: Eur Child Adolesc Psychiatry Date: 2013-06-04 Impact factor: 4.785
Authors: Yannick Fortin; James A G Crispo; Deborah Cohen; Douglas S McNair; Donald R Mattison; Daniel Krewski Journal: PLoS One Date: 2017-03-28 Impact factor: 3.240