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. tiina.laatikainen@thl.fi; markku.tykkylainen@uef.fi
PILOT
REGION

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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. https://www.stnimpro.fi/impro-improved-knowledge-base-and-service-optimisation-to-support-health-and-social-services-reform/
Thank you!