A J M Hirsch Allen1, Ofer Amram2, Hamid Tavakoli1, Fernanda R Almeida3, Mona Hamoda3, Najib T Ayas1,4. 1. 1 Division of Respiratory Medicine, and. 2. 2 Department of Health Geography, Simon Fraser University, Vancouver, British Columbia, Canada; and. 3. 3 Department of Dentistry, University of British Columbia, Vancouver, British Columbia, Canada. 4. 4 Sleep Disorders Program, University Hospital, Vancouver, British Columbia, Canada.
Abstract
RATIONALE: In the majority of people with obstructive sleep apnea, the disorder remains undiagnosed. This may be partly a result of inadequate access to diagnostic sleep services. We thus hypothesized that even modest travel times to a sleep clinic may delay diagnosis and reduce detection of milder disease. OBJECTIVES: We sought to determine whether travel time between an individual's home and a sleep clinic is associated with sleep apnea severity at presentation. METHODS: We recruited patients referred for suspected sleep apnea to the University of British Columbia Hospital Sleep Clinic between May 2003 and July 2011. The patient's place of residence was geocoded at the postal code level. Travel times between the population-weighted dissemination areas for each patient and the sleep clinic were calculated using ArcGIS (ESRI, Redlands, CA) network analyst and the Origin-Destination matrix function. All patients underwent full polysomnography. MEASUREMENTS AND MAIN RESULTS: There were 1,275 patients; 69% were male, the mean age was 58 years. (SD = 11.9), and the mean apnea-hypopnea index was 22 per hour (SD = 21.6). In the univariate model, travel time was a significant predictor of obstructive sleep apnea severity (P = 0.02). After controlling for confounders including sex, age, obesity, and education, travel time remained a significant predictor of sleep apnea severity (P < 0.01). In the multivariate model, each increase in 10 minutes of travel time was associated with an increase in the apnea-hypopnea index of 1.4 events per hour. CONCLUSIONS: For reasons that remain to be determined, travel times are associated with the severity of obstructive sleep apnea at presentation to a sleep clinic. If the results can be verified at other centers, this may help guide the geographic distribution of sleep centers within a health care system.
RATIONALE: In the majority of people with obstructive sleep apnea, the disorder remains undiagnosed. This may be partly a result of inadequate access to diagnostic sleep services. We thus hypothesized that even modest travel times to a sleep clinic may delay diagnosis and reduce detection of milder disease. OBJECTIVES: We sought to determine whether travel time between an individual's home and a sleep clinic is associated with sleep apnea severity at presentation. METHODS: We recruited patients referred for suspected sleep apnea to the University of British Columbia Hospital Sleep Clinic between May 2003 and July 2011. The patient's place of residence was geocoded at the postal code level. Travel times between the population-weighted dissemination areas for each patient and the sleep clinic were calculated using ArcGIS (ESRI, Redlands, CA) network analyst and the Origin-Destination matrix function. All patients underwent full polysomnography. MEASUREMENTS AND MAIN RESULTS: There were 1,275 patients; 69% were male, the mean age was 58 years. (SD = 11.9), and the mean apnea-hypopnea index was 22 per hour (SD = 21.6). In the univariate model, travel time was a significant predictor of obstructive sleep apnea severity (P = 0.02). After controlling for confounders including sex, age, obesity, and education, travel time remained a significant predictor of sleep apnea severity (P < 0.01). In the multivariate model, each increase in 10 minutes of travel time was associated with an increase in the apnea-hypopnea index of 1.4 events per hour. CONCLUSIONS: For reasons that remain to be determined, travel times are associated with the severity of obstructive sleep apnea at presentation to a sleep clinic. If the results can be verified at other centers, this may help guide the geographic distribution of sleep centers within a health care system.
Entities:
Keywords:
epidemiology; geographical information systems; health services accessibility; sleep
Authors: Sachin R Pendharkar; Marcus Povitz; Nick Bansback; Charles F P George; Debra Morrison; Najib T Ayas Journal: CMAJ Date: 2017-12-11 Impact factor: 8.262
Authors: Jennifer Corrigan; Willis H Tsai; Ada Ip-Buting; Christopher Ng; Imhokhai Ogah; Peter Peller; Heather Sharpe; Cheryl Laratta; Sachin R Pendharkar Journal: J Clin Sleep Med Date: 2022-04-01 Impact factor: 4.062
Authors: Jennifer Corrigan; Imhokhai Ogah; Ada Ip-Buting; Heather Sharpe; Cheryl R Laratta; Peter Peller; Willis H Tsai; Sachin R Pendharkar Journal: ERJ Open Res Date: 2020-10-05