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Ann Fam Med
2015[Nov]; 13
(6
): 529-36
PMID26553892
show ga
PURPOSE: Prior studies have evaluated factors predictive of inappropriate
antibiotic prescription for upper respiratory tract infections (URIs). Community
factors, however, have not been examined. The aim of this study was to evaluate
the roles of patient, clinician, and community factors in predicting appropriate
management of URIs in children. METHODS: We used a novel database exchange,
linking electronic health record data with community statistics, to identify all
patients aged 3 months to 18 years in whom URI was diagnosed in the period from
2007 to 2012. We followed the Healthcare Effectiveness Data and Information Set
(HEDIS) quality measurement titled "Appropriate treatment for children with upper
respiratory infection" to determine the rate of appropriate management of URIs.
We then stratified data across individual and community characteristics and used
multiple logistic regression modeling to identify variables that independently
predicted antibiotic prescription. RESULTS: Of 20,581 patients, the overall rate
for appropriate management for URI was 93.5%. Family medicine clinicians (AOR =
1.5; 95% CI 1.31, 1.71; reference = pediatric clinicians), urgent care clinicians
(AOR = 2.23; 95% CI 1.93, 2.57; reference = pediatric clinicians), patients aged
12 to 18 years (AOR = 1.44; 95% CI 1.25, 1.67; reference = age 3 months to 4
years), and patients of white race/ ethnicity (AOR = 1.83; 95% CI 1.41, 2.37;
reference = black non-Hispanic) were independently predictive of antibiotic
prescription. No community factors were independently predictive of antibiotic
prescription. CONCLUSIONS: Results correlate with prior studies in which
non-pediatric clinicians and white race/ethnicity were predictive of antibiotic
prescription, while association with older patient age has not been previously
reported. Findings illustrate the promise of linking electronic health records
with community data to evaluate health care disparities.
|Adolescent
[MESH]
|Age Factors
[MESH]
|Ambulatory Care/statistics & numerical data
[MESH]
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