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Geospatial Health as Interdisciplinary Research for Health Care Reform and Planning

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Geospatial Health as Interdisciplinary Research for Health Care Reform and Planning. Markku Tykkyläinen – Mikko Pyykönen – Sami Sieranoja – Pasi Fränti – Tiina Laatikainen
University of Eastern Finland, IMPRO, Joensuu

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Geospatial Health as Interdisciplinary Research for Health Care Reform and Planning

  1. 1. 15.4.2019 1 Geospatial Health as Interdisciplinary Research for Health Care Reform and Planning 1 Annual Meeting , American Association of Geographers, Washington DC, 3-7. April 2019 Markku Tykkyläinen – Mikko Pyykönen – Sami Sieranoja – Pasi Fränti – Tiina Laatikainen University of Eastern Finland, IMPRO, Joensuu https://www.stnimpro.fi/ https://www.stnimpro.fi/contact-information/ https://www.uef.fi/web/geospatial-health AAG, April 5th, 2019, 3.05 PM- 4:45 PM, Marshall South, Marriot Mezzane Level
  2. 2. 2 Aims of IMPRO - Improved knowledge base and service optimisation to support health and social services reform • Quality of care - better outcomes of care more cost- efficiently - equal outcomes - new ways of care • Need of care – where and how? • Cost savings: care & patients • Optimal spatial allocation of services • To sum up: Allocation of scarce resources over space cost-efficiently to produce better care in the region Research related to IMPRO’s geospatial approach: Oulu/Geography/(WP1): Geography of health and well- being as focus area in research of the dept. (WP1) Joensuu/Geographies/Research group: Geospatial Health, Geoinformatics. (WP4) Joensuu: School of Computing (CS): optimization tools. (WP6) HEL/Aalto University/ Institute of Healthcare Engineering, Management and Architecture (Economics): costs, data bases & research. (WP5) Kuopio/Institute of Public Health and Clinical Nutrition: “heavy users” of health care and social services. (WP3) HEL/National Instit. for Health and Welfare THL (Health sc): emergency and specialist care services. (WP2) Hospital districts: databases, registers; indicators of care Focus on chronic diseases AAG 2019
  3. 3. Reform of health and social services • At the moment the 311 municipalities are responsible for organizing health and social services. This responsibility will be transferred to about 18-22 new regions and cities when the health and social services reform will be implemented in the early 2020s on. Study area: North Karelia 3
  4. 4. AAG 2019 4 Where ”geospatial health” comes from? Operations Research (complex systems, CP, LP, netw.) Spatial Statistics (n-dim. space, regr., hot spots) Computational sciences (algorithms, clust., sim., progr., db) GPS, georef info (coordinates, orientation) GIScience/ Geoinformatics (db, analysis, rem sens) Health Sciences Spatial economics (space-time dynamics) 1990s- 1950-60s 1990s Geospatial Health Spatial analysis (geogr., reg. sc.; math. space) 1990s- NEGEarly location and spatial economic theoretizations 1900-40s
  5. 5. Atrial fibrillation medications Drugs: Warfarin + lab visits (up 20 p.a.) Or new DOAC The locations of health care centers (labs) are on the map on the right. Warfarin should be used near the health care centers. Now the consumption patterns of alternative drugs are NOT cost- efficient at all (on the map on the left). Warfarin should not be used in the peripheries. Derivation of the market areas of two alternative drugs – varfarin vs. DOAC (Direct-Acting Anticoagulants) AAG 2019 5
  6. 6. • Drugs: Warfarin & DOAC • Number of lab visits up 20 annually if Warfarin is used • Out-of-pocket costs Fixed costs = DOAC W is cheaper W lab Market areas - a bird's eye view DOAC W 2 W 1 Least-cost optimization of AF atrial fibrillation treatment - derivation of market areas TCwcv1 Areawcv1Areawcv2 cv1 cv2 Pw TCwcv2 Distance d Gradients of the total costs of Warfarin to the patient A € AreaDOAC The sizes of market areas are influenced by drug prices and additionally from one to two laboratory visits if the patient uses W. TCwcv2 TCwcv1 cDOAC TCDOAC 6
  7. 7. Variables: Distance between home and health care center Travel time between home and health care center Travel modes: car bus taxi walking Parameter Description Value Used in Analysis VOT The value of time based on average hourly gross wage of North Karelia. 10.3 €/h P The productivity coefficient of patient. Working time is valued as 100 percent of VOT and leisure time of a retired person is valued as 35 percent of VOT. Working persons: 1 Retired persons: 0.35 Tm The time spent in the INR monitoring visit. 20 min = 0.33 h Td The time spent for dose adjustment of warfarin after monitoring. 10 min = 0.167 h Tp The time spent for private car parking. 5 min = 0.083 h Tt The service time of taxi. 5 min = 0.083 h Tbw1 The waiting time in a bus stop. 7 min = 0.117 h Tbw2 The walking time to bus stop. 5 min = 0.083 h VOCc The vehicle operating cost for private car. 0.45 €/km VOCt The vehicle operating cost for taxi. 1.59€/km Ft The initial fare paid for the journey with taxi. 5.9 € Fb The fare paid for the journey with bus. 3.8 € Sb The average speed of bus. 30 km/h Sw The average speed of walking. 3.5 km/h Cwar The annual cost of warfarin after reimbursement. 25.5 € CNOAC The annual cost of NOAC after reimbursement. Dabigatran and Apixaban 369.3 € Rivaroxaban and Edoxaban 338.3 € FREQ The frequency of monitoring visits per year. From 6 to 30 15.4.2019 AAG 2019 7 Mikko Pyykönen
  8. 8. The least-cost market areas of anticoagulation therapies for working persons. Three market area classes (colored by the shades of grey) having 10, 14 and 18 annual INR monitoring visits indicate where warfarin is most affordable. DOAC therapy brings about the lowest costs outside of the respective warfarin market areas. Mikko Pyykönen mikko.pyykonen@uef.fi AAG 2019 Warfarin is most affordable to use in centers. 8
  9. 9. Resulting clusters (2 out of 30) Gout Cardiomyopathy Heart failure Diverticular disease of intestine Chronic kidney disease (CKD) 2.5 3.6 4.9 3.7 4.6 4.1 2.4 1.9 Asthma Sleep disorders Abnormalities of breathing Overweight and obesity Hypertensive heart disease 3.6 2.0 1.6 3.3 2.8 2.4 2.0 5.1 2.6 2.2 = observed/expected ("correlation") weights Sami Sieranoja Total number of patients​ = 9149 Clustering: Maximize the sum of weights/pairwise links Multimorbidity AAG 2019 9
  10. 10. Conclusions • Currently different diagnoses are treated separately • Treating a related diseases together can be more cost effective • In training new doctors, could optimize skill sets based on how diagnoses are connected Asthma Sleep disorders Abnormalities of breathing Overweight and obesity Hypertensive heart disease 3.6 2.0 1.6 3.3 2.8 2.4 2.0 5.1 2.6 2.2 Sami Sieranoja sami.sieranoja@uef.fi AAG 2019 10
  11. 11. Multimorbidity AAG 2019 11 Local optima Global optimum Treatment outcomes per cost by multimorbid patient *) Unknown, more cost-efficient outcomes • How do we reach globally optimal, more cost efficient results in multimorbidity care? • So, do we simulate different combinations? • Any good examples? *) it indicates cost savings compared to usual care practices
  12. 12. Thank you for listening! Comments, please. Esityksen nimi / Tekijä 12 markku.tykkylainen@uef.fi 12

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