Authors: Teppo Repo*, University of Eastern Finland, Markku Tykkyläinen, University of Eastern Finland, Tiina Laatikainen, Institute of Public Health and Clinical Nutrition; University of Eastern Finland, National Institute for Health and Welfare (THL), Joint municipal authority for North Karelia social and health services (Siun sote)
Topics: Medical and Health Geography, Geographic Information Science and Systems, Spatial Analysis & Modeling
Keywords: Coronary heart disease, disease clustering, health geography, risk factors of coronary heart disease, rural health, electronic medical records, geospatial health, health-care planning, quality of care, primary care
Session Type: Paper
Start / End Time: 2:00 PM / 3:40 PM
Room: Tyler, Marriott, Mezzanine Level
Presentation File: No File Uploaded
Despite significant improvement in coronary heart disease (CHD) morbidity and mortality rates in Finland, meeting the goals for prevention of CHD have been suboptimal, especially in rural areas like the studied North Karelia Hospital district catchment area. The study aim was to detect and explain spatial variation in diagnosed acute coronary syndrome incidence rates and compare those with rates of operated coronary heart disease (CHD) patients at as fine spatial scale as possible. Earlier studies have shown evidence of spatial variation of CHD incidences. Also socioeconomic and gender disparities of getting preventive CHD operation has been reported earlier.The regional electronic medical records provided up-to date individual level information of the CHD patients, including e.g. geocodable addresses. Spatial zones were created for the neighbourhood level sociodemographic variables. Incidence rates for 1344 patients with acute coronary syndrome diagnose and 1016 patients with percutaneous coronary intervention or coronary artery bypass grafting as a first coronary event were calculated. Clustering of the different patient groups were compared to each other and with the regressed rates. Regression models with the rates of CHD as a dependent variable were made and different clustering methods were tested and compared to detect disparities and find causes for high- and low CHD risk areas.Variation in spatial risk clustering between the two patient groups were found. Sociodemographic differences explained some of these disparities, but partly the differences might be due e.g. suboptimal clinical pathways. The results will help organizing targeted primary and secondary prevention strategies in the region.