4.3 Article

Unexplained Practice Variation in Primary Care Providers' Concern for Pediatric Obstructive Sleep Apnea

Journal

ACADEMIC PEDIATRICS
Volume 18, Issue 4, Pages 418-424

Publisher

ELSEVIER SCIENCE INC
DOI: 10.1016/j.acap.2018.01.011

Keywords

computer decision support; obstructive sleep apnea; pediatric; primary care provider; sleep disorders

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OBJECTIVE: To examine primary care provider (PCP) screening practice for obstructive sleep apnea (OSA) and predictive factors for screening habits. A secondary objective was to describe the polysomnography completion proportion and outcome. We hypothesized that both provider and child health factors would predict PCP suspicion of OSA. METHODS: A computer decision support system that automated screening for snoring was implemented in 5 urban primary care clinics in Indianapolis, Indiana. We studied 1086 snoring children aged 1 to 11 years seen by 26 PCPs. We used logistic regression to examine the association between PCP suspicion of OSA and child demographics, child health characteristics, provider characteristics, and clinic site. RESULTS: PCPs suspected OSA in 20% of snoring children. Factors predicting PCP concern for OSA included clinic site (P <.01; odds ratio [OR] = 0.13), Spanish language (P <.01; OR = 0.53), provider training (P =.01; OR = 10.19), number of training years (P =.01; OR = 4.26) and child age (P <.01), with the youngest children least likely to elicit PCP concern for OSA (OR = 0.20). No patient health factors (eg, obesity) were significantly predictive. Proportions of OSA suspicion were variable between clinic sites (range, 6-28%) and between specific providers (range, 0-63%). Of children referred for polysomnography (n = 100), 61% completed the study. Of these, 67% had OSA. CONCLUSIONS: Results suggest unexplained small area practice variation in PCP concern for OSA among snoring children. It is likely that many children at risk for OSA remain unidentified. An important next step is to evaluate interventions to support PCPs in evidence-based OSA identification.

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