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Geospatial costs of delivering health in Finland

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IMPRO project's presentation at Understanding Present and Future Public Service Delivery Costs, a kick-off workshop hosted by OECD, Brussels, June 28th, 2019.

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Geospatial costs of delivering health in Finland

  1. 1. 10.7.2019 1 Geospatial costs of delivering health in Finland 1 Understanding Present and Future Public Service Delivery Costs, a kick-off workshop hosted by OECD, Brussels, June 28th, 2019. Markku Tykkyläinen – Aapeli Leminen – Mikko Pyykönen – Teppo Repo – Maija Toivakka – 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
  2. 2. markku.tykkylainen@uef.fi 2 At the moment, the 311 municipalities are responsible for organizing health and social services. This responsibility will be transferred to about 17 NUTS3 regions and capital city when the health and social services reform will be implemented in the 2020s. Health and social services reform Pilot area Pilot area
  3. 3. markku.tykkylainen@uef.fi 3 Geospatial Health as an interdisciplinary research in UEF (Univ of Eastern Finland) Joint aims Inventive results for more efficient health care Interaction and discovery- oriented research Geographies, GIScience Computer Science, Statistics, Geostatistics Health Sciences Machine Learning Research Group Geospatial Health Research Group Research Groups in Health Sciences
  4. 4. markku.tykkylainen@uef.fi 4 Scientific background of geospatial health research Operations Research (complex systems, CP, LP, netw.) Spatial Statistics (n-dim. space, regr., hot spots) Computer science (algorithms, clust., sim., progr., db) GPS, Georef. info (coordinates, orientation) GIScience/ Geoinformatics (data produc- tion, rem. sens.) Health Sciences Spatial economics (space-time dynamics) 1990s- 1950-60s 1990s Geospatial Health Spatial analysis (geography, regional sc.; math. space) 1990s- NEGEarly location and spatial economic theorizations 1900-40s
  5. 5. Optimal structure and locations of health care centres Joint use of patient registers and geospatial databases in health care 5 Toivakka, M., Repo, T., Leminen, A., Pyykönen, M., Laatikainen, T. & Tykkyläinen, M. (2018). Potilastieto ja paikkatieto kohtaavat. Terra 130: 4, 201–205. Geospatial data Digiroad Location of health services Statistical data Sociodemographics Environment Location information according to EUREF-FIN terrestrial coordinate system markku.tykkylainen@uef.fi Patient database Domicile Diagnoses Visits Lab results Background variables Scale and accuracy Data Location Individuals Postal code area Municipalities LAU-2 or health care centre areas Statistical squares 250 m x 250 m, 1 km x 1 km Health care district Ad hoc or NUTS, LAU Prevalence of diseases where, how much and diffusion Accessibility, travel modes, travel time, costs Management and operational use Indicators Maps & visualizations Computational models Causal models Predictions Alternatives Scenarios Outcome of care, where, why, how well, optimal treatments Strategic planning Tactical planning
  6. 6. Geospatial Health - expertise in IMPRO (UEF-Geography) markku.tykkylainen@uef.fi 6 Geospatial optimization; minimizing costs of travel and time loss • Less traveling > less costs • Savings in health care with using more self-monitoring and remote health consultacy • Relocation of services based on patients’ locational data and the expected use of care • Considering current & future prevalence and care in plans Market area analysis and minimization of the costs of medication and care • Determining least-cost medication alternatives by locations of patients • Making least-cost clinical guidelines geospatially based on the total costs • Societal costs and/or out-of- pocket costs • Patient data and areal data combined • Predicting prevalence and the need of care by statistical square, postal code area data and health centre area • Follow-up of the outcomes of care to improve care by area • Considering the impacts of SES, environment etc. on prevalence and care • Multifaceted data for planning Prevalence and hot spots; allocating care accordingly Small area analyses for prevalence and disease management • Detecting - hot spots - the highest prevalences and needs of care and research • Showing the best practises • Minimum maximal distances (or time) Patient records, geocoded locations statistical square data, postal code areas, municipalities, satellite data, individual statistical data Patient records, geocoded locations, Digiroad, small-area data More efficient health care system ”From where to nowhere” ”Where and why” ”What, where and whereabouts”
  7. 7. Observing needs and modeling costs by health centre area, postal code areas and statistical squares maija.toivakka@uef.fi; markku.tykkylainen@uef.fi 7 Patients outside the ordinary commuter distance (here 25 km) 358 (3.7 %) non- shaded If ≥ 10 km 1958 (20.4 %) If ≥ 5 km 2923 (30.4 %) Society would save money if there were less visits and less costs of travel and lost time especially in remote areas. Localizations randomized
  8. 8. Increasing self-monitoring and increasing remote consultancy to lower the costs of care and traveling - a model and results • Assumptions in the model: - Travels: home->HC lab and back - Patients always go to the same HC - Travels done at a ”normal day time”; no rush times considered in HC (and traffic) aapeli.leminen@uef.fi; markku.tykkylainen@uef.fi Leminen, A. 2016. Potilaiden liikkumiskustannukset ja omaseurannan kehittämisen kustannusvaikutukset tyypin 2 diabeteksen seurannassa Pohjois-Karjalassa. http://urn.fi/urn:nbn:fi:uef-20160917 Flow chart and data flows: HC= Health Centre
  9. 9. Mean cost of one trip in a zip area Mean cost of one trip by HC T2D patient: mean travel costs of one follow-up trip to HC Mean cost of one trip by HC Mean cost of one trip in a 2kmx2km square Mean costs of one trip in a 2kmx2km square: 1.70 - 340.00€ Mean costs one trip in a zip area: 4.80 - 114.60€ One trip by HC: 6.40 - 19.60€ BASELINE Calculations by individual is possible HC= Health Centre
  10. 10. aapeli.leminen@uef.fi; markku.tykkylainen@uef.fi Cost savings: -Health care and travels from 2,5 mill. € to 1,1 mill. € ( = 56.3 %) of which: - Health care costs from 2 128 412 € to 942 587 € (= 55.7%) -Travelling from 406 967 € to 166 767 € (= 59.0 %) Results of simulation: the cost savings of type 2 diabetes (T2D) by increasing self-monitoring and increasing remote consultancy The highest travel costs Lakes Health care centre Self-monitoring reduces cumulative travel costs (but not the costs of one trip). 25 x higher compared with cities (or if using own car) A. Leminen, M. Tykkyläinen & T. Laatikainen, Self-monitoring induced savings on type 2 diabetes patients’ travel and healthcare costs, International Journal of Medical Informatics, 115 (2018), 125.
  11. 11. Atrial fibrillation (AF) medications Drugs: Warfarin + lab visits (up 20 p.a.) Or new DOAC The locations of health care centres (labs) are on the map on the right. Warfarin should be used near the health care centres. 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 – warfarin vs. DOAC (Direct-Acting Anticoagulants) mikko.pyykonen@uef.fi; markku.tykkylainen@uef.fi 11
  12. 12. • Drugs: Warfarin & DOAC • The number of annual lab visits up to 20 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 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 Warfarin. TCwcv2 TCwcv1 cDOAC TCDOAC 12mikko.pyykonen@uef.fi; markku.tykkylainen@uef.fi LAB LAB
  13. 13. The least-cost market areas of anticoagulation therapies for working persons by 4 travel modes. Three market area classes (coloured 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 for the patient. Warfarin is most affordable to use in centres (= in grey areas) and DOAC in peripheries. 13mikko.pyykonen@uef.fi; markku.tykkylainen@uef.fi A patient’s out-of-pocket cost includes the cost of both travels and lost time. M. Pyykönen, A. Leminen, J. Tynkkynen, M. Tykkyläinen & T. Laatikainen, A geospatial model to determine the spatial cost-effectiveness of anticoagulation drug therapy; patients’ perspective. Forthcoming article DOAC W DOAC W
  14. 14. Coronary heart disease Population in 2kmx2km statistical square Coronary Heart Disease Hot Spots - Cost-efficiency of emergency services - Minimize max distance (time, transport mode) to save lives - Adjust follow- up and treatment according to distance (time, transport mode) Central Hospital teppo.repo@uef.fi; markku.tykkylainen@uef.fi Repo, T., Tykkyläinen, M., Mustonen, J., Rissanen, T. T., Ketonen, M., Toivakka, M. & Laatikainen, T. (2018). Outcomes of Secondary Prevention among Coronary Heart Disease Patients in a High-Risk Region in Finland. Int. J. Environ. Res. Public Health 2018, 15(4), 724 Non-adjusted cumulative incidence hot-spots of CHD
  15. 15. Rural-urban classification is based on 250m x 250m statistical square data. Classification is based on population density, area density rate, land use, industrial diversity and accessibility. 15 7-class classification of areas Numbers of patients and their areal percentage distribution Proportions of HbA1c measured patientsa to the diagnosed Proportions of HbA1c < 7% patientsa to the measured Patients’ mean driving distances and the rangesb in km Inner urban area 849 (8.8%) 82.8% 74.8% 2.0 (0 – 4.0) Outer urban area 1433 (14.9%) 80.5% 75.6% 2.1 (0 – 9.5) Peri-urban area 644 (6.7%) 85.6% 74.8% 5.0 (0.1 – 27.1) Local centers in rural areas 1414 (14.7%) 79.9% 69.2% 1.8 (0 – 5.7) Rural areas close to urban areas 725 (7.5%) 84.6% 71.8% 7.8 (0 – 27.9) Rural heartland areas 2376 (24.7%) 84.9% 73.1% 6.0 (0 – 36.0) Sparsely populated rural areas 2165 (22.5%) 83.5% 66.7% 12.1 (0 – 91.8) Total 9606 (100%) 5.9 (0 – 91.8) a χ² p-value < 0.05 b Minimum and maximum values in brackets Toivakka, M., Laatikainen, T., Kumpula, T. & Tykkyläinen, M. (2015). Do the classification of areas and distance matter to the assessment results of achieving the treatment targets among type 2 diabetes patients? International Journal of Health Geographics 14: 27. doi: 10.1186/s12942-015-0020-x. An example of a tailored area classification – Rural-urban Follow-up % In therapeutic equilibrium % maija.toivakka@uef.fi; markku.tykkylainen@uef.fi local centre local centres Type 2 diabetes
  16. 16. Targeting care where needed Patient characteristics (individual-level variables) • Female gender is associated with higher HbA1c follow-up rates and a higher proportion of achieving the recommended HbA1c level. • The probability of HbA1c measurements increases with ageing. However, younger age increases the probability of achieving the recommended HbA1c level. • Distance as such is not a barrier to good control or to achieve treatment targets. Socioeconomic variables (by postal code area) • Both of the patient’s education level and the area-level predictor of educational status explains the outcomes of care. 7-class classification of urban and rural areas • Best follow-up rates: peri-urban area (suburbs), 2nd in rural heartland areas (farming areas), 3nd rural areas close to urban areas (commuter belt of Joensuu). • Worst follow-up rates: in local centres, 2nd in outer urban areas (detached urban housing). • Best outcomes: outer urban area (detached urban housing), 2nd in peri-urban area (suburbs), and inner urban area (city) • Worst outcomes: in sparsely pop. rural areas and 2nd local centres maija.toivakka@uef.fi; markku.tykkylainen@uef.fi 16 This information is useful in targeting care and the selection of the most cost-effective ways of care.
  17. 17. 17 Wikström, K., Toivakka, M., Rautianen, P., Tirkkonen, H., Repo, T. & Laatikainen, T. (2019). Electronic health records as valuable data sources in health care quality improvement process. Health Services Research & Managerial Epidemiology. Health care professionals were notified of the prevalence of T2D by municipality (the influence areas of health centres) and counselled. Early detection of T2D improved and differences between municipalities decreased (2012 -> 2017). Best-practise health centre (Outokumpu) was a milestone. T2D was more thoroughly diagnosed. How do follow-up information and training improve the quality of care locally? maija.toivakka@uef.fi; markku.tykkylainen@uef.fi
  18. 18. IMPRO consortium 18 Geospatial Health Research Group2019-20, 2021-23 https://www.uef.fi/web/geospatial-health markku.tykkylainen@uef.fi IMPRO-consortium Deputy PI heading WP4 aapeli.leminen@uef.fi maija.toivakka@uef.fi teppo.repo@uef.fi (Stockholm) mikko.pyykonen@uef.fi tiina.laatikainen@uef.fi IMPRO-consortium PI heading WP3 https://www.stnimpro.fi/ tiina.laatikainen@uef.fi; markku.tykkylainen@uef.fi
  19. 19. Publications: • Pyykönen M., Leminen A., Tynkkynen J., Tykkyläinen, M & Laatikainen T. (2019). A geospatial model to determine the spatial cost-effectiveness of -anticoagulation drug therapy; patients’ perspective. Submitted. • Leminen, A., Pyykönen, M., Tynkkynen, J., Tykkyläinen, M. & Laatikainen, T. (2019). Modeling patients’ time, travel, and monitoring costs in anticoagulation management: societal savings achievable with the shift from warfarin to direct oral anticoagulants. Submitted. • Toivakka, M., Pihlapuro, A., Tykkyläinen, M., Mehtätalo, L., & Laatikainen, T. (2018). The usefulness of small-area-based socioeconomic characteristics in assessing the treatment outcomes of type 2 diabetes patients: a register-based mixed-effect. BMC Public Health 18:1258. https://doi.org/10.1186/s12889-018-6165-3. • Repo, T., Tykkyläinen, M., Mustonen, J., Rissanen, T. T., Ketonen, M., Toivakka, M. & Laatikainen, T. (2018). Outcomes of Secondary Prevention among Coronary Heart Disease Patients in a High-Risk Region in Finland. Int. J. Environ. Res. Public Health 2018, 15(4), 724; https://doi.org/10.3390/ijerph15040724 • Leminen, A., Tykkyläinen, M. & Laatikainen, T. (2018). Self-monitoring induced savings on type 2 diabetes patients’ travel and health care costs. International Journal of Medical Informatics 115, 120-127. • Toivakka, M., Laatikainen, T., Kumpula, T. & Tykkyläinen, M. (2015), Do the classification of areas and distance matter to the assessment results of achieving the treatment targets among type 2 diabetes patients? International Journal of Health Geographics, 14:27. • Sikiö, M., Tykkyläinen M., Tirkkonen H., Kekäläinen P., Dunbar J., Laatikainen T. (2014),Type 2 diabetes care in North Karelia Finland: Do area-level socio-economic factors affect processes and outcomes? Diabetes Research and Clinical Practice 106, 296-503. 10.7.2019 Esityksen nimi / Tekijä 19
  20. 20. Thank you for attending! Comments, pls Esityksen nimi / Tekijä 20 markku.tykkylainen@uef.fi

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