The use of geographical analysis in assessing the impact of patients’ home addresses on their participation in outpatient cardiac rehabilitation: a prospective cohort study

Atsuko Nakayama1, Masatoshi Nagayama2, Hiroyuki Morita1, Takuya Kawahara3, Issei Komuro1, Mitsuaki Isobe2
1Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
2Sakakibara Heart Institute, Tokyo, Japan
3Clinical Research Promotion Center, The University of Tokyo, Tokyo, Japan

Tóm tắt

Abstract Purpose Geographical analysis is becoming a powerful tool for evaluating the quality of medical services and acquiring fundamental data for medical decision-making. Using geographical analysis, we evaluated the impact of the distance from patients’ homes to the hospital on their participation in outpatient cardiac rehabilitation (OCR). Methods All patients hospitalized for percutaneous coronary intervention, coronary artery bypass grafting, valvular surgery, congestive heart failure, and aortic diseases were advised to participate in an OCR program after discharge. Using the dataset of our cohort study of OCR from 2004 to 2015 (n = 9,019), we used geographical analysis to investigate the impact of the distance from patients’ homes to hospital on their participation in our OCR program. Results Patients whose road distance from home to hospital was 0–10 km, 10–20 km, and 20–30 km participated more in OCR than those whose road distance was ≧ 30 km (OR 4.34, 95% CI 3.80–4.96; OR 2.98, 95% CI 2.61–3.40; and OR 1.90, 95% CI 1.61–2.23, respectively). Especially in patients with heart failure, the longer the distance, the lesser the participation rate (P < .001). Conclusions Using geographical analysis, we successfully evaluated the factors influencing patients’ participation in OCR. This illustrates the importance of using geographical analysis in future epidemiological and clinical studies. Trial registration UMIN000028435.

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Tài liệu tham khảo

Shanthi M, Pekka P, Bo N. Global Atlas on cardiovascular disease prevention and control, World Health Organization in collaboration with the World Heart Federation and the World Stroke Organization, 2011. https://www.who.int/cardiovascular_diseases/publications/atlas_cvd/en/. Archived from the original on 2019-03-11.

Leon AS, Franklin BA, Costa F, et al. American Heart Association; Council on Clinical Cardiology (Subcommittee on Exercise, Cardiac Rehabilitation, and Prevention); Council on Nutrition, Physical Activity, and Metabolism (Subcommittee on Physical Activity); American association of Cardiovascular and Pulmonary Rehabilitation, Cardiac rehabilitation and secondary prevention of coronary heart disease: an American Heart Association scientific statement from the Council on Clinical Cardiology (Subcommittee on Exercise, Cardiac Rehabilitation, and Prevention) and the Council on Nutrition, Physical Activity, and Metabolism (Subcommittee on Physical Activity), in collaboration with the American Association of Cardiovascular and Pulmonary Rehabilitation. Circulation. 2005;111:369–76. https://doi.org/10.1161/01.CIR.0000151788.08740.5C.

Shanmugasegaram S, Oh P, Reid RD, McCumber T, Grace SL. Cardiac rehabilitation barriers by rurality and socioeconomic status: a cross-sectional study. Int J Equity Health. 2013;12:72. https://doi.org/10.1186/1475-9276-12-72.

Leung YW, Brual J, Macpherson A, Grace SL. Geographic issues in cardiac rehabilitation utilization: a narrative review. Health Place. 2010;16(6):1196–205. https://doi.org/10.1016/j.healthplace.2010.08.004.

Nakayama A, Nagayama M, Morita H, et al. A large-scale cohort study of long-term cardiac rehabilitation: a prospective cross-sectional study. Int J Cardiol. 2020;309:1–7. https://doi.org/10.1016/j.ijcard.2020.03.022.

Dobbs RW, Malhotra NR, Caldwell BM, Rojas R, Moreira DM, Abern MR. Determinants of clinic absenteeism: a novel method of examining distance from clinic and transportation. J Community Health. 2018;43(1):19–26. https://doi.org/10.1007/s10900-017-0382-z.

Lally P, van Jaarsveld CHM, Potts HWW, Wardle J. How are habits formed: modelling habit formation in the real world. Eur J Soc Psychol. 2010;40:998–1009. https://doi.org/10.1002/ejsp.674.

Sommerhalter KM, Insaf TZ, Akkaya-Hocagil T, McGarry CE, Farr SL, Downing KF. Proximity to pediatric cardiac surgical care among adolescents with congenital heart defects in 11 New York Counties. Birth Defects Res. 2017;109(18):1494–503. https://doi.org/10.1002/bdr2.1129.

Kleinbaum DG, Klein M. Modeling strategy guidelines, in: Logistic regression: a self-learning text. 3rd ed. New York: Springer; 2010. p. 165–202.

Malhotra R, Bakken K, D'Elia E, Lewis GD. Cardiopulmonary exercise testing in heart failure. JACC Heart Fail. 2016;4:607–16. https://doi.org/10.1016/j.jchf.2016.03.022.

Fradelos EC, Papathanasiou IV, Mitsi D, Tsaras K, Kleisiaris CF, Kourkouta L. Health based geographic information systems (GIS) and their applications. Acta Inform Med. 2014;22(6):402–5. https://doi.org/10.5455/aim.2014.22.402-405.

Anderson DJ, Rojas LF, Watson S, et al. CDC Prevention epicenters program. Identification of novel risk factors for community-acquired Clostridium difficile infection using spatial statistics and geographic information system analyses. PLoS One. 2017;12(5):e0176285. https://doi.org/10.1371/journal.pone.0176285.

Sepehrvand N, Alemayehu W, Kaul P, Pelletier R, Bello AK, Welsh RC. Ambulance use, distance and outcomes in patients with suspected cardiovascular disease: a registry-based geographic information system study. Eur Heart J Acute Cardiovasc Care. 2020;9(1_suppl):45–58. https://doi.org/10.1177/2048872618769872.

Nakayama A, Takayama N, Kobayashi M, et al. Remote cardiac rehabilitation is a good alternative of outpatient cardiac rehabilitation in the COVID-19 era. Environ Health Prev Med. 2020;25(1):48. https://doi.org/10.1186/s12199-020-00885-2.