Laura Barrie Smith1, Zhiyou Yang2, Ezra Golberstein3, Peter Huckfeldt3, Ateev Mehrotra4, Hannah T Neprash3. 1. Health Policy Center, Urban Institute, Washington, District of Columbia, USA. 2. Health Policy Research Center, Mongan Institute, Massachusetts General Hospital, Boston, Massachusetts, USA. 3. University of Minnesota School of Public Health, Minneapolis, Minnesota, USA. 4. Harvard Medical School, Boston, Massachusetts, USA.
Abstract
OBJECTIVE: To test whether there were fewer missed medical appointments ("no-shows") for patients and clinics affected by a significant public transportation expansion. STUDY SETTING: A new light rail line was opened in a major metropolitan area in June 2014. We obtained electronic health records data from an integrated health delivery system in the area with over three million appointments at 97 clinics between 2013 and 2016. STUDY DESIGN: We used a difference-in-differences research design to compare whether no-show appointment rates differentially changed among patients and clinics located near versus far from the new light rail line after it opened. Models included fixed effects to account for underlying differences across clinics, patient zip codes, and time. DATA EXTRACTION METHODS: We obtained data from an electronic health records system representing all appointments scheduled at 97 outpatient clinics in this system. We excluded same-day, urgent care, and canceled appointments. PRINCIPAL FINDINGS: The probability of no-show visits differentially declined by 0.5 percentage points (95% confidence interval [CI]: -0.9 to -0.1), or 4.5% relative to baseline, for patients living near the new light rail compared to those living far from it, after the light rail opened. The effects were stronger among patients covered by Medicaid (-1.6 percentage points [95% CI: -2.4 to -0.8] or 9.5% relative to baseline). CONCLUSIONS: Improvements to public transit may improve access to health care, especially for people with low incomes.
OBJECTIVE: To test whether there were fewer missed medical appointments ("no-shows") for patients and clinics affected by a significant public transportation expansion. STUDY SETTING: A new light rail line was opened in a major metropolitan area in June 2014. We obtained electronic health records data from an integrated health delivery system in the area with over three million appointments at 97 clinics between 2013 and 2016. STUDY DESIGN: We used a difference-in-differences research design to compare whether no-show appointment rates differentially changed among patients and clinics located near versus far from the new light rail line after it opened. Models included fixed effects to account for underlying differences across clinics, patient zip codes, and time. DATA EXTRACTION METHODS: We obtained data from an electronic health records system representing all appointments scheduled at 97 outpatient clinics in this system. We excluded same-day, urgent care, and canceled appointments. PRINCIPAL FINDINGS: The probability of no-show visits differentially declined by 0.5 percentage points (95% confidence interval [CI]: -0.9 to -0.1), or 4.5% relative to baseline, for patients living near the new light rail compared to those living far from it, after the light rail opened. The effects were stronger among patients covered by Medicaid (-1.6 percentage points [95% CI: -2.4 to -0.8] or 9.5% relative to baseline). CONCLUSIONS: Improvements to public transit may improve access to health care, especially for people with low incomes.
Keywords:
access; demand; determinants of health; health care organizations and systems; population health; socioeconomic causes of health; utilization of services
Authors: Krisda H Chaiyachati; Rebecca A Hubbard; Alyssa Yeager; Brian Mugo; Stephanie Lopez; Elizabeth Asch; Catherine Shi; Judy A Shea; Roy Rosin; David Grande Journal: JAMA Intern Med Date: 2018-03-01 Impact factor: 21.873
Authors: Shreya Kangovi; Frances K Barg; Tamala Carter; Judith A Long; Richard Shannon; David Grande Journal: Health Aff (Millwood) Date: 2013-07 Impact factor: 6.301
Authors: Andrew S Hwang; Steven J Atlas; Patrick Cronin; Jeffrey M Ashburner; Sachin J Shah; Wei He; Clemens S Hong Journal: J Gen Intern Med Date: 2015-03-17 Impact factor: 5.128
Authors: Abigail L Cochran; Noreen C McDonald; Lauren Prunkl; Emma Vinella-Brusher; Jueyu Wang; Lindsay Oluyede; Mary Wolfe Journal: BMC Public Health Date: 2022-09-20 Impact factor: 4.135