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New Method for Assessing the Effect of Driving Distance to Hospital Care

2017; Lippincott Williams & Wilkins; Volume: 10; Issue: 9 Linguagem: Inglês

10.1161/circoutcomes.117.003850

1941-7705

Daniel Lindholm, Stefan James, Bo Lagerqvist, Mark A. Hlatky, Christoph Varenhorst,

Blood Pressure and Hypertension Studies

HomeCirculation: Cardiovascular Quality and OutcomesVol. 10, No. 9New Method for Assessing the Effect of Driving Distance to Hospital Care Free AccessResearch ArticlePDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissionsDownload Articles + Supplements ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toSupplemental MaterialFree AccessResearch ArticlePDF/EPUBNew Method for Assessing the Effect of Driving Distance to Hospital CareUsing OpenStreetMap Routing in Cardiovascular Research Daniel Lindholm, MD, PhD, Stefan James, MD, PhD, Bo Lagerqvist, MD, PhD, Mark A. Hlatky, MD and Christoph Varenhorst, MD, PhD Daniel LindholmDaniel Lindholm From the Department of Medical Sciences, Cardiology (D.L., S.J., B.L., C.V.), and Uppsala Clinical Research Center (D.L., S.J., B.L., C.V.), Uppsala University, Sweden; and Department of Health Research and Policy, Stanford University School of Medicine, Stanford University, CA (D.L., M.A.H.). , Stefan JamesStefan James From the Department of Medical Sciences, Cardiology (D.L., S.J., B.L., C.V.), and Uppsala Clinical Research Center (D.L., S.J., B.L., C.V.), Uppsala University, Sweden; and Department of Health Research and Policy, Stanford University School of Medicine, Stanford University, CA (D.L., M.A.H.). , Bo LagerqvistBo Lagerqvist From the Department of Medical Sciences, Cardiology (D.L., S.J., B.L., C.V.), and Uppsala Clinical Research Center (D.L., S.J., B.L., C.V.), Uppsala University, Sweden; and Department of Health Research and Policy, Stanford University School of Medicine, Stanford University, CA (D.L., M.A.H.). , Mark A. HlatkyMark A. Hlatky From the Department of Medical Sciences, Cardiology (D.L., S.J., B.L., C.V.), and Uppsala Clinical Research Center (D.L., S.J., B.L., C.V.), Uppsala University, Sweden; and Department of Health Research and Policy, Stanford University School of Medicine, Stanford University, CA (D.L., M.A.H.). and Christoph VarenhorstChristoph Varenhorst From the Department of Medical Sciences, Cardiology (D.L., S.J., B.L., C.V.), and Uppsala Clinical Research Center (D.L., S.J., B.L., C.V.), Uppsala University, Sweden; and Department of Health Research and Policy, Stanford University School of Medicine, Stanford University, CA (D.L., M.A.H.). Originally published27 Aug 2017https://doi.org/10.1161/CIRCOUTCOMES.117.003850Circulation: Cardiovascular Quality and Outcomes. 2017;10:e003850IntroductionGeography is a key determinant of access to health care. Patient outcomes cannot be improved if novel therapeutic and preventive strategies cannot be delivered to patients in a timely and equitable fashion.We developed a new method to calculate driving distances to hospital care by integrating an open-source geographic database with a standard statistical software package to create a tool for outcomes research. We illustrate its application to cardiac care in Sweden; yet, the methodology is readily applicable to other geographical regions and other healthcare services.Time to reperfusion in ST-segment–elevation myocardial infarction affects patient outcomes, and so guidelines recommend reperfusion be achieved within 120 minutes of symptom onset.1 Delayed percutaneous coronary intervention (PCI) has been associated with worse outcomes also in patients with non–ST-segment–elevation myocardial infarction.2 Furthermore, large distance to a center providing cardiac care could not only impede acute care but might also reduce access to other services, such as follow-up with a cardiac specialist, advanced cardiac testing, and cardiac rehabilitation.3 Therefore, it would be of value to assess geographic barriers to cardiac care, and link this data to clinical and quality of care registries.We obtained map data for Sweden from the OpenStreetMap project (http://www.openstreetmap.org), set up a local OpenStreetMap Routing Machine (http://project-osrm.org),4 and programmed a custom R interface (code available in the Data Supplement) to obtain driving distance information from each zip code area centroid (n=10 007) to different levels of cardiac care. We then combined these data with information about residential locations of the Swedish population and plotted the shortest distance from each zip code to each of the hospital categories by layering distances over a background map freely available from Natural Earth (http://www.naturalearthdata.com/). We summarize distance to different levels of cardiac care with empirical cumulative distribution function plots. For additional details, see extended methods description in the Data Supplement.We determined the driving routes from all 10 007 zip codes to all 73 hospitals in Sweden. Calculation of these 730 511 routes took about 53 minutes to complete or 4.3 ms per route. The software failed to find routes from 7 of the zip code areas (0.07%), all of which were small islands off the coast of Sweden that lack any road connections to the mainland. As of December 31, 2015, there were N=9 834 211 inhabitants in Sweden. The analysis population included 9 772 322 individuals, excluding 57 316 individuals (0.6%) living on the island of Gotland and 4573 individuals (0.05%) living in the 7 zip code areas from which routing failed.We assessed driving distance to coronary revascularization (Figure [A]), categorizing the shortest distances from each zip code area to (1) any hospital providing acute cardiac care, (2) to any PCI-capable hospital, (3) to a PCI-capable hospital providing primary PCI 24 hours a day 7 days a week (24/7), and (4) to a hospital capable of both PCI and coronary artery bypass graft surgery. Driving distances for the population to different levels of cardiac care varied greatly (Figure [B]). The median (25th–75th percentiles) distance to any hospital was 10.6 (4.2–24.9) km, with mean 17.7 km, and a maximum distance of 292.1 km. For distance to any PCI-capable hospital, the median was 19.0 (6.2–45.0) km, mean 32.3 km, and maximum 464.6 km. The median distance to a PCI-capable hospital open 24/7 was 24.2 (8.6–59.6) km, mean 38.7 km, and maximum 464.6 km, whereas the median distance to a hospital capable of both PCI and coronary artery bypass graft was 49.6 (15.5–107.8) km, mean 76 km, and maximum 719.6 km. A total of 3 348 764 (34%) individuals had a non-PCI hospital as their nearest hospital, 1 983 264 (20%) lived closest to a PCI-capable hospital without 24/7 capability, 2 391 953 (24%) lived closest to a center capable of PCI 24/7, and 2 048 341 (21%) lived closest to a PCI/coronary artery bypass graft-capable hospital.Download figureDownload PowerPointFigure. A, Distance to (1) any hospital providing acute cardiac care, (2) any percutaneous coronary intervention (PCI)–capable hospital, (3) a PCI-capable hospital with PCI capability 24/7, and (4) a hospital capable of providing both PCI and CABG, from all zip code areas in Sweden (except islands lacking road connection to the mainland). B, Empirical cumulative distribution functions of distance to (1) any hospital providing acute cardiac care, (2) any PCI-capable hospital, (3) a PCI hospital open 24/7, and (4) a hospital capable of providing both PCI and CABG, for the Swedish population (excluding people living on islands without road connection to the mainland). CABG indicates coronary artery bypass graft.In summary, our new open-source tool to estimate the driving distance to hospital care seems useful in identifying geographic areas where timely access to health care is unlikely and in visualizing potential repercussions of policy changes. Although we used Swedish cardiac care in this study to illustrate the application of this tool, this method is generalizable to any geographic area in which coordinates of patients' neighborhoods (or proxies thereof) and of care facilities are available. Furthermore, because the software uses parallel computing to determine distances, this method can be readily scaled-up in a high-performance computing environment and applied to larger territories and populations, such as the United States. Data on driving distances could be linked to data from clinical or quality of care registries, such as the nationwide SWEDEHEART registry (Swedish Web-System for Enhancement and Development of Evidence-Based Care in Heart Disease Evaluated According to Recommended Therapies) on coronary heart disease in Sweden, or the national cardiovascular data registries in the United States, which would provide the opportunity to assess the impact of driving distance on a wide array of cardiac services and outcomes, not just acute cardiac care and coronary revascularization. This method could just as easily be applied to other healthcare services in which geography could limit access to care, such as oncology, burn care, and infectious diseases.Sources of FundingDr Lindholm was supported by the Swedish Society of Medicine, the Swedish Cardiac Society, and the Royal Society of Arts and Sciences of Uppsala. No other source of funding was used for this study.DisclosuresDr Lindholm reports institutional research grants from AstraZeneca and GlaxoSmithKline and lecture fees from AstraZeneca. Dr James reports institutional research grants from AstraZeneca, Bayer, Abbot, Boston Scientific, and The Medicines Company and honoraria from Boston Scientific, Bayer. Dr Lagerqvist reports institutional research grants from AstraZeneca. Dr Hlatky reports institutional research grants from Milestone Pharmaceuticals, Inc, and HeartFlow, Inc; honoraria as a Scientific Advisor to the Center for Clinical Effectiveness of the Blue Cross Blue Shield Association, for consultation with the George Institute and with Acumen, Inc., and as Associate Editor of the Journal of the American College of Cardiology. Dr Varenhorst reports institutional research grants from AstraZeneca and The Medicines Company, lecture and advisory board fees from AstraZeneca, The Medicines Company and Boeringer Ingelheim, lecture fees from Bayer, Bristol Myers Squibb, Pfizer, and CSL Behring, and is or has been on the Clinical End Point Committee for Pfizer, Bristol Myers Squibb, Philips, and AstraZeneca.FootnotesThe Data Supplement is available at http://circoutcomes.ahajournals.org/lookup/suppl/doi:10.1161/CIRCOUTCOMES.117.003850/-/DC1.Correspondence to Daniel Lindholm, MD, PhD, Uppsala Clinical Research Center, Dag Hammarskjölds väg 14B, SE-752 37 Uppsala, Sweden. E-mail [email protected]References1. The Task Force on the management of ST-segment elevation acute myocardial infarction of the European Society of Cardiology (ESC); Steg PG, James SK, Atar D, Badano LP, Blömstrom-Lundqvist C, Borger MA, Di Mario C, Dickstein K, Ducrocq G, Fernandez-Aviles F, Gershlick AH, Giannuzzi P, Halvorsen S, Huber K, Juni P, Kastrati A, Knuuti J, Lenzen MJ, Mahaffey KW, Valgimigli M, van't Hof A, Widimsky P, Zahger D. ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation.Eur Heart J. 2012; 33:2569–2619. doi: 10.1093/eurheartj/ehs215.CrossrefMedlineGoogle Scholar2. Lindholm D, Alfredsson J, Angerås O, Böhm F, Calais F, Koul S, Lagerqvist B, Renlund H, Sarno G, Varenhorst C. Timing of percutaneous coronary intervention in patients with non-ST-elevation myocardial infarction: a SWEDEHEART study.Eur Heart J Qual Care Clin Outcomes. 2017; 3:53–60. doi: 10.1093/ehjqcco/qcw044.CrossrefMedlineGoogle Scholar3. van Engen-Verheul M, de Vries H, Kemps H, Kraaijenhagen R, de Keizer N, Peek N. Cardiac rehabilitation uptake and its determinants in the Netherlands.Eur J Prev Cardiol. 2013; 20:349–356. doi: 10.1177/2047487312439497.CrossrefMedlineGoogle Scholar4. Luxen D, Vetter C. Real-time routing with OpenStreetMap data.The 19th ACM SIGSPATIAL International Conference, New York, NY: ACM; 2011.CrossrefGoogle Scholar Previous Back to top Next FiguresReferencesRelatedDetailsCited By Sritart H and Miyazaki H (2022) Geographic Information System (GIS) and Data Visualization Disaster Nursing, Primary Health Care and Communication in Uncertainty, 10.1007/978-3-030-98297-3_26, (297-307), . Hoedemaker N, de Winter R, Kommer G, Giesbers H, Adams R, van den Bosch S and Damman P (2020) Expansion of off-site percutaneous coronary intervention centres significantly reduces ambulance driving time to primary PCI in the Netherlands, Netherlands Heart Journal, 10.1007/s12471-020-01466-2, 28:11, (584-594), Online publication date: 1-Nov-2020. Borg S, Öberg B, Leosdottir M, Lindolm D, Nilsson L and Bäck M (2019) Factors associated with non-attendance at exercise-based cardiac rehabilitation, BMC Sports Science, Medicine and Rehabilitation, 10.1186/s13102-019-0125-9, 11:1, Online publication date: 1-Dec-2019. September 2017Vol 10, Issue 9 Advertisement Article InformationMetrics © 2017 American Heart Association, Inc.https://doi.org/10.1161/CIRCOUTCOMES.117.003850PMID: 28844994 Originally publishedAugust 27, 2017 Keywordstransplantationmyocardial infarctionrevascularizationgeographic mappingpercutaneous coronary interventionhealthcare accessPDF download Advertisement SubjectsHealth ServicesPercutaneous Coronary InterventionRevascularizationTransplantation