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

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
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. 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. 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. 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. • 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. 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. 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. 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. 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. 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. Thank you
for
listening!
Comments,
please.
Esityksen nimi / Tekijä 12
markku.tykkylainen@uef.fi
12