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19.4.2018 Esityksen nimi / Tekijä 1
Efficient allocation of health care service delivery
using geospatial analysis and opt...
tiina.laatikainen@thl.fi; markku.tykkylainen@uef.fi
PILOT
REGION
Aims
• To develop data systems and follow-up and care methods for more efficient health care,
especially for chronic disea...
Focus on
• Targeting health care efficiently according to demand and needs (equal marginal utility)
• Geospatial optimizat...
Aim: How different factors affect the outcomes of care among type 2 diabetes patients?
10 204 T2DM patients
Outcomes of ca...
Results so far..
Patient characteristics
• Female gender is associated with higher HbA1c follow-up rates and a higher prop...
antti.leminen@uef.fi; markku.tykkylainen@uef.fi
Cost savings:
-PHC and travels from 2,5 mill. € to 1,1 mill. € ( = 56.3 %)...
Coronary heart disease (CHD) burden in North Karelia Hospital District
(CHD patients consist of the patients of ACS= acute...
STUDY 3
The municipality level dependencies of CHD patients’
body mass index (BMI) follow-up rates and LDL-C
management ra...
teppo.repo@uef.fi
STUDY 3
Summary of study 3 and further research
Summary of study 3.
1. Eastern part of Finland continue ...
Comments from you? Next step: Does the greenness of the
living environment affect the care outcomes of T2DM?
• People who ...
Comments from you to improve cost-efficency of care:
• The next stage in our research project:
markku.tykkylainen@uef.fi
E...
Publications:
• Repo, T., Tykkyläinen, M., Mustonen, J., Rissanen, T., Ketonen M., Toivakka, M. & Laatikainen T. (2018), O...
https://www.stnimpro.fi/impro-improved-knowledge-base-and-service-optimisation-to-support-health-and-social-services-refor...
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Markku Tykkyläinen, Maija Toivakka, Aapeli Leminen, Teppo Repo, Tiina Laatikainen: Efficient allocation of health care service delivery using geospatial analysis and optimization at a regional level

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Markku Tykkyläinen, Maija Toivakka, Aapeli Leminen, Teppo Repo, Tiina Laatikainen: Efficient allocation of health care service delivery using geospatial analysis and optimization at a regional level

  1. 1. 19.4.2018 Esityksen nimi / Tekijä 1 Efficient allocation of health care service delivery using geospatial analysis and optimization at a regional level Markku Tykkyläinen - Maija Toivakka - Aapeli Leminen - Teppo Repo - Tiina Laatikainen TUE-022-8:00 a.m Balcony N, River Tower, Marriott, 4th Floor Annual Meeting of the American Association of Geographers in New Orleans, April 10-14, 2018 Project duration 2018-20, 2021-23
  2. 2. tiina.laatikainen@thl.fi; markku.tykkylainen@uef.fi PILOT REGION
  3. 3. Aims • To develop data systems and follow-up and care methods for more efficient health care, especially for chronic diseases. markku.tykkylainen@uef.fi A pilot region for other 17 social and health care regions • Social and health care reform: responsibilities will be transferred to 18 regions from municipalties - 1.1.2020 -> • A pilot region North Karelia – Regionwide patient register already exists (instead of the numerous municipal patient registers elsewhere) • The area contains urban areas, industrial communities and rural, remote areas
  4. 4. Focus on • Targeting health care efficiently according to demand and needs (equal marginal utility) • Geospatial optimization cost-efficiently • Self-monitoring (cost savings) • Mobile phone application (cost savings) • Feedback to patients (better care) • Type 2 Diabetes – 3 papers published + 2 submitted + 2 in Finnish Medical Journal in Finnish • Cardiovascular diseases (focusing on coronary heart diseases) – 1 published • Artrial fibrillation – not yet started tiina.laatikainen@siunsote.fi; markku.tykkylainen@uef.fi
  5. 5. Aim: How different factors affect the outcomes of care among type 2 diabetes patients? 10 204 T2DM patients Outcomes of care: How often is the hemoglobin A1c (HbA1c) measured from the patients? How patients achieve the recommended level of HbA1c? Factors Data Source Patient characteristics • Age, gender, laboratory results, home address Electronic patient database Socio-economic variables (individual level) • Income, education, unemployed Socio-economic variables (postal code level) • Median income (thousands / €), Educated (%), Unemployed (%) Socio-economic variables (250m x 250m grid level) • Median income, (thousands / €), Educated (%), Unemployed (%) Database of Statistics Finland (restricted access, subject to a charge) Database of Statistics Finland (open data, free of charge) Database of Statistics Finland (restricted access, subject to a charge) 7-class classification of urban and rural areas • Inner urban area, Outer urban area, Peri-urban area, Local centers in rural areas, Rural areas close to urban areas, Rural heartland areas, Sparsely populated rural areas Grid-based classification by Finnish Environment Institute (SYKE) Greenness of the living environment Satellite images or such? maija.toivakka@uef.fi 5 STUDY 1
  6. 6. Results so far.. Patient characteristics • 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 is not a barrier to good control or to achieve treatment targets. Socioeconomic variables • The association of the area-level predictor of educational level and the outcomes of care is closely comparable with the respective association of the individual-level predictor of educational status. 7-class classification of urban and rural areas • Best follow-up rates: peri-urban area, rural areas close to urban areas, rural heartland areas. • Worst follow-up rates: local centers in rural areas. maija.toivakka@uef.fi 6 How to improve the situation and reduce costs? STUDY 1
  7. 7. antti.leminen@uef.fi; markku.tykkylainen@uef.fi Cost savings: -PHC 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 %) Simulation of cost savings of T2DM self- monitoring and closing down three small heath care centres 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 in a city (or if using own car) STUDY 2 We will develop the model for more cost-efficient care of artrial fibrillation
  8. 8. Coronary heart disease (CHD) burden in North Karelia Hospital District (CHD patients consist of the patients of ACS= acute coronary syndrome, PCI=percutaneous coronary intervention, CABC=coronary artery bypass grafting ) High and low risk clusters of ACS and/or inv. treat. in 2011-14 at cluster size of max. 8% of popMale and female age adjusted cumulative incidence rate of acute coronary heart disease events and patients undergone preventive cardiac operation in 2011-2014 Heat map of non-adjusted Empirical Bayes (EB) Smoothed CHD incidence rates SPATIAL SCAN STATISTICS LAKES UNINHAB STUDY 3 MALE FEMALE teppo.repo@uef.fi; markku.tykkylainen@uef.fi A FEW PATIENTS IN LOW RISK CLUSTERS
  9. 9. STUDY 3 The municipality level dependencies of CHD patients’ body mass index (BMI) follow-up rates and LDL-C management rates on the proportion of multimorbid CHD patients. BMI= body mass index, LDL-C= low-density lipoprotein cholesterol, ACS=acute coronary syndrome, PCI= percutaneous coronary intervention, CABG=coronary artery bypass grafting LDL-C treatment target attainment among patients with ACS or PCI/CABC in North Karelia Hospital District in 2011–2014. BMI measured Proportion of multimorbid CHD patients -> LDL-C < 1.8 Disparities in processes of care, probably teppo.repo@uef.fi; markku.tykkylainen@uef.fi
  10. 10. teppo.repo@uef.fi STUDY 3 Summary of study 3 and further research Summary of study 3. 1. Eastern part of Finland continue to suffer a heavy CHD burden 2. Large disparities in CHD incidence rates between genders and SES groups (- > geography) 3. Spatial differences in the processes of care 4. Population dynamics / selective migration has increased spatial disparities in health 5. - > Rural-urban disparities may still be growing due to changing demography and SES. Next steps: 1.What factors are causing the elevated clustering of CHD risk? The spatial clustering of obesity: does the built environment matter? R. Huang et al. 2015 2. Is there improvement in the secondary prevention of CHD after 2014? 3. Is there a correlation between primary prevention of CHD (management of modifiable CHD risk factors in the population) and CHD incidence rates?
  11. 11. Comments from you? Next step: Does the greenness of the living environment affect the care outcomes of T2DM? • People who live in greener neighborhoods have smaller risk of getting type 2 diabetes (Astell-Burt et al. 2014; Bodicoat et al. 2014; Müller et al. 2018). - Does the neighborhood green space affect the care outcomes in T2DM patients? • Data for green space - Satellite image data (e.g. Normalized Difference Vegetation Index (NDVI)) - Land-use databases (e.g. calculating the percent of an area covered by parks/forests or measuring the distance from a patient’s home to the nearest park) - Comparison of indexes, Trabelsi 2018 • Is it possible to find an association in rather remote and green study region? (Forest covers 89% of the land area in the study region.) maija.toivakka@uef.fi 11 Astell-Burt et al. (2014). Is neighborhood green space associated with a lower risk of type 2 diabetes? Evidence from 267,072 Australians. Diabetes Care 37(1):197-201. doi: 10.2337/dc13-1325. Bodicoat et al. (2014). The association between neighbourhood greenspace and type 2 diabetes in a large cross-sectional study. BMJ Open 4(12):e006076. doi: 10.1136/bmjopen-2014-006076. Müller et al. (2018). Inner-city green space and its association with body mass index and prevalent type 2 diabetes: a cross-sectional study in an urban German city. BMJ Open 8(1):e019062. doi: 10.1136/bmjopen-2017-019 Trabelsi, Sonia (2018), On the measures of the Green, AAG 2018-04-10, Namur BE, U catolique de Louvain. STUDY 1
  12. 12. Comments from you to improve cost-efficency of care: • The next stage in our research project: markku.tykkylainen@uef.fi Easy-to-use solutions in an interactive way, e.g. mobile-solutions to be tested in practise T2DM, CHD, Artrial Fibrillation Introducting cost-efficient solutions; 1) self-monitoring -> 2) telehealth (mobile), -> 3) e-feedback to patients 1. self-monitoring: good devices?, good practices? 2. telehealth (mobile): good apps? 3. e-feedback: what would be the best way to communicate? Virtual communities? Via social media the patients used (tested in Uganda)?
  13. 13. Publications: • Repo, T., Tykkyläinen, M., Mustonen, J., Rissanen, 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, 724; doi:10.3390/ijerph15040724. • 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? Int. J. of Health Geographics, 14:27, doi: 10.1186/s12942-015- 0020-x • 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 (3), 496-503. • Tirkkonen, H., Sikiö, M., Kekäläinen, P. & Laatikainen, T. (2014). Tyypin 2 diabeteksen hoidossa merkittävää kuntakohtaista vaihtelua, Suomen Lääkärilehti 69: 34, 2027–2032. • Laatikainen, T., Sikiö, M., Tirkkonen, H., Niemi, A., Kekäläinen, P., Turunen, A., Mustonen, J., Ketonen, M., Kumpula, T., Colpaert, A. & Tykkyläinen, Markku (2013). Alueellisesti yhtenäinen potilastietojärjestelmä tukee hoidon laadun arviointia, Suomen Lääkärilehti 68 (33), 1986-1988. • Submitted; • Leminen, A., Tykkyläinen, M. & Laatikainen, T. (2018), Self-monitoring induced savings on type 2 diabetes patients' travel and healthcare costs, Self-monitoring induced savings on type 2 diabetes patients' travel and healthcare costs, (submitted). • Toivakka, M., Pihlapuro, A., Tykkyläinen, M., Mehtätalo, L. & Laatikainen, T. (2018). Do individual socioeconomic factors have predictive value over area-level characteristics in the outcomes of chronic disease care? (submitted). Geospatial Health Group: http://www.uef.fi/en/web/geospatial-health/home
  14. 14. https://www.stnimpro.fi/impro-improved-knowledge-base-and-service-optimisation-to-support-health-and-social-services-reform/ Thank you!

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