OBJECTIVES: Antihistamines have been reported to be the most widely used drug category in the treatment of allergic rhinitis despite the recommendation to use intranasal steroids (INS) as the first line therapy. The purpose of this study was to examine the predictors of receiving an antihistamine prescription in ambulatory care setting in the United States.
METHODS: A retrospective, cross-sectional study of the data from National Ambulatory Medical Care Survey (2006, 2007) was performed. Variables examined included whether the physician was a primary care physician, age (children: 0–18 yrs vs adults), gender, race, geographic region, metropolitan statistical area, major reason for visit (chronic vs non chronic), insurance status, patient visit status and presence of co-morbidity (asthma, nasal polyps, sinusitis and COPD). Allergic rhinitis visits were identified using ICD9-CM codes, and prescribed medications using the Multum Lexicon codes. Covariates were selected based upon the Anderson Behavior Model. Descriptive statistics and multivariate logistic regression were carried out to identify predictors of receiving an antihistamine prescription.
RESULTS: A total of 40.56 million ambulatory office visits were diagnosed with allergic rhinitis. Antihistamines were prescribed in 45.6% (18.52 million) of these visits. Multivariate analysis showed that, children suffering from allergic rhinitis were 1.6 times more likely (OR 1.611, 95% CI 1.067–2.432) to receive a prescription of anti histamines than adult patients. Physician specialty, insurance status, geographic region and presence of comorbidity were not found to be significant predictors of an antihistamine prescription.
CONCLUSIONS: The result of this study suggests that patient’s age is a significant predictor of antihistamine prescription. This may partly be explained by the intranasal route of administration for the recommended first line therapy (INS) which may be difficult in children. Future research is needed to evaluate other factors like patient socioeconomic status and disease severity, which could not be assessed due to data limitation.
