Economic Evaluation of eHealth in JapanMeasurement of blood pressure, ... ¾Over 70% of users are using the system at least once a week. 8th July 2008, Healthcom 2008. ... University

Economic Evaluation of eHealthin Japan

Masatsugu Tsujiand

Yuji Akematsu, Graduate School of Applied Informatics,

University of Hyogo

Graduate School of Applied InformaticsUniversity of Hyogo

APuHC Workshop, UNSW, Australia, October 3, 2008


Outline1. Methods of Measuring Benefits

2. Receipt data analysis

3. Research Site and eHealth System

4. Data, Hypothesis

5. Result of Estimation

6. Conclusion

Methods of Measuring Benefits

➢ Travel cost method (Evaluation I)

➢ Replacement cost method (Evaluation II)

➢ Contingent valuation method (CVM)WTP approach (Evaluation III)

➢ Hedonic method


Travel cost method


Evaluation of Teleradiology(D2D)

Local clinic University hospital

Traditional method

(once a week)

Salary: US$ 750

Traveling costs:

US$ 130

Total costs: US$ 44,000

Telemedicine

Initial costs: US$ 7,500

Monthly fees: US$ 500

fees per picture: US$ 20

Telephone bills (year):US$ 3,150

Total costs: US$ 32,000

ISDN 128Kbps

Contingent Valuation Method


Estimation Method III :CVM Approach

Contingent Valuation Method

Goods or services: are not evaluated at the market.EnvironmentsMedical services

Widely recognized as a standard method


Concept of WTP I (Willingness to pay)

Benefits should include：(1) Less anxiety in day-to-day life

(2) Enhanced consciousness towards health

(3) Stabilization of illness

(4) Decrease in medical expenses, etc.

WTP


Concept of WTP II

Demand function

price

quantity

Consumer’s surplus

Graduate School of Applied Informatics University of Hyogo

Supply curveprice

Demand curve

quantity

Consumer's surplus

Supply curve

Estimated of WTP

None106,520,000$887,700

2,955 yen$24.53

551Sangawa

None107,700,000$888,000

3,177 yen$27.81

518Nishiaizu

None99,360,000$828,000

1,640 yen$13.67

926Katsurao

2,500 yen/month

102,900,000$857,500

4,519 yenUS$38.66

348Kamaishi

FeeTotal WTP(6 years) yen

WTP (per user/month)

No. of users


Receipt Data Analysis



Objectives

This paper calculates exact monetary medical expenditures reduced by eHealth.

H1: Medical expenditures of life-style related diseasesof users are lower than those of non-users.

H2: The longer they use the system, the more their medical expenditures will be reduced.

ＮｉｓｈｉａｉｚｕＴｏｗｎ

Nishiaizu Town, Fukushima Prefecture


Research site

Nishiaizu Town, Fukusima Prefecture

Population 10,000; 3,000 householdselderly ration; 35.9%

15 years Implementation of eHealth systemLong enough to analyze its effects

Graduate School of Applied Informatics University of Hyogo


eHealth System in Nishiaizu TownSenior people

at homeTown’s care supporting

center

Town’s CATV networkTelephone network

Host computer at centerRead and store of medical dataAdvice on health care

Peripheral device at homeMedical examinationMeasurement of blood pressure,pulse, ECG…

Input of temperature, weight…

Senior people at home

Senior people at home

Medical Expenditures per capita over age 75 years old paidby National Health Insurance

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

900,000

1985 87 89 91 93 95 97 99

2001

2003

Nishiaizu

National Average

1994: Introduction of eHealth system

Medical Expenditures of over 70 Years Old per Capita paid by National Health Insurance

Prefectural average



Making DatabaseⅠ

Selection of user and non-user groups

User： from the list of town officeNon-user： from the list of National Health Insurance

Send them questionnairesCharacteristics (age, sex, chronic disease…)Usage of the system (years of using…)Number of sample

User 199 vs. Non-user 209

Check their 408 receipts of medical expenditures 2002-2006


Making Database II

7

Checked items from the receipts

1. Name2. Date of birth3. Outpatient or hospitalized patient treatment4. Name(s) of major disease(s)5. Date of initial treatment6. Number of days needed for treatment7. Amount of medical expenditures

8th July 2008, Healthcom 2008


Year start using the system Male Female Total Users selected

as Sample1994 9 11 20 201995 13 11 24 241996 8 14 22 221997 30 36 66 661998 13 15 28 281999 4 6 10 102000 8 11 19 192001 3 3 6 62002 6 7 13 132003 91 88 179 952004 53 69 122 952005 6 6 12 122006 2 0 2 2Total 246 277 523 412

User Non-userTotal 523 3,528

Number of sent questionnaires 412 450

Number of respondents 311 239

Number of valid respondents 199 209

Rate of valid respondents 38.05% 46.44%

Two Groups User Group


Sex distribution Age distribution

User Non-user Total

40 - 49 2 0 250 - 59 14 23 3760 - 69 45 67 11270 - 79 92 76 16880 - 89 46 37 83Over 90 0 6 6

Total 199 209 40890

90

119

109

MaleFemale


10295 10598

6

9072

3781

90

38

2

Having chronic diseases

Working situation

WorkingNot workingNo reply

YesNoNo reply


11

Years of Using eHealth system Frequency of use

Less than 1 year 6 3.0%

1 - 3 38 19.1

3 - 5 45 22.6

5 - 7 35 17.6

7 - 10 39 19.6

Over 10 years 36 18.1

Total 199

Almost every day 76 38.2%

3 - 4 times a week 47 23.6

1 - 2 times a week 20 10.11 - 2 times a month 23 11.6

Not use 25 12.7No reply 8 4

Total 199

We could get respondent evenly on years of using except less than 1 year.

Over 70% of users are using the system at least once a week.



Diseases Having Treated within 5 years

User Non-user TotalHeart diseases 44 23 67High blood pressure 100 74 174Diabetes 15 21 36Strokes 14 10 24Respiratory diseases 9 10 19Cancer 8 3 11Gastropathy 25 13 38Lumbago, Arthritis 45 43 88Ophthalmic diseases 57 46 103Kidney diseases 3 1 4Anal diseases 9 7 16Others 19 7 26

Whether are medical expenditures life-style related diseases of users lower than those of non-users, or not?



Medical expenditures (Outpatient: All diseases)

User Non-user


Medical expenditures(Outpatient: Life-style related diseases)

User Non-user


Estimation I: medical expenditures of all diseases and life-style related diseases

Model for estimation (one-way fixed effect model)

yit: medical expenditures of life-style related diseasesXit: sex, age, education, working, no. of family,

income, chronic disease, user (dummy variable)

ititit uXy ++= βα

)),0(~( 2vitittit iidvvu σλ 　　+=


Result IVariable Coef. Std. Err. t-value p-value

Sex 1467.36 473.55 3.10 0.002 ***Age 219.67 29.12 7.54 0.000 ***

Education 309.45 315.10 0.98 0.326 Working 95.86 501.79 0.19 0.849

No. of family 289.24 126.34 2.29 0.022 **Income -19.09 4.08 -4.68 0.000 ***

Chronic disease 3344.00 476.34 7.02 0.000 ***User -1568.79 478.90 -3.28 0.001 ***

Constant -10517.63 2378.58 -4.42 0.000 ***Number of obs. 1820

Adjusted R2 0.0819

***, **, and * indicate the 1%, 5%, and 10% significant level, respectively.

Medical expenditures for lifestyle-related diseases of user group is smaller than those of non-user group by 15,688 yen (US$ 144.60, 21.2% of average) per year.

The longer they use the system, the more their medical

expenditures will be reduced?



Years of Using the System and Medical Expenditures (All diseases)

Over 10


Years of Using and Medical Expenditures(Life-style Related Diseases)

Over 10Less than 1


Result II

Medical expenditures of lifestyle-related diseases can be reduced by 1,133 yen (US$ 10.05, 1.5% of average) per year, if they extend using the system one more year.

Variable Coef. Std. Err. t-value p-valueSex 1542.36 474.40 3.25 0.001 ***Age 223.57 29.65 7.54 0.000 ***

Education 302.22 315.90 0.96 0.339 Working 127.48 503.50 0.25 0.800

No. of family 257.77 126.99 2.03 0.043 **Income -19.24 4.09 -4.70 0.000 ***

Chronic disease 3315.44 477.95 6.94 0.000 ***Years of using system -113.32 66.18 -1.71 0.087 *

Constant -11250.42 2411.04 -4.67 0.000 ***Number of obs. 1820

Adjusted R2 0.0780



Result IIIYears of using Predicted (Coef.)×(Years) Elasticity

Non-user 6652.982 0 00 - 2 5931.742 -78.480 -0.013232 - 4 6077.795 -252.872 -0.041614 - 6 6465.005 -470.608 -0.072796 - 8 6983.062 -695.453 -0.099598 - 10 7173.224 -918.604 -0.1280610 - 12 7416.090 -1115.710 -0.15044Over 12 7239.115 -1326.310 -0.18321

Elasticity of the reduction of medical expenditures with respect to years of utilization of the e-Health system is not elastic, but it becomes larger for longer users.

Medical expenditures between users and non-users who have

or have no chronic diseases



What diseases does eHealth system effect?

17

Difference of medical expenditures of each disease• Model (one-way fixed effect model)

yit: medical expenditure, Xit: age, sex, income,…diseaseit: dummy variable showing 1 if disease is jUseri: dummy variable showing 1 if i is user

itj

ji

jjititit uUserdiseaseXy ++++= ∑

12

)}({ δγβα

)),0(~( 2vitittit iidvvu σλ 　　+=



Result IVCharacteristics, diseases Cross effect

Variable Coefficient (t-value) Coefficient (t-value)Sex -747.67 (-0.74) Age 222.37 (3.58) ***Education 456.51 (0.70) Working -3427.97 (-3.29) ***No. of family 1068.60 (4.06) ***Income -12.09 (-1.42) Heart diseases 20171.06 (7.14) *** -6391.33 (-1.79) *High blood pressure 10181.37 (7.40) *** -3080.34 (-1.71) *Diabetes 16166.76 (6.83) *** -8837.79 (-2.49) **Strokes 9254.46 (2.92) *** 9313.10 (2.17) **Respiratory diseases 1668.48 (0.79) -692.95 (-0.22) Cancer 16843.44 (6.25) *** -1165.97 (-0.31) Gastropathy 5257.63 (2.83) *** -5701.81 (-2.15) ***Lumbago, Arthritis 6193.50 (3.22) *** 478.86 (0.17) Ophthalmic diseases 6117.03 (3.45) *** 3358.96 (1.37) Kidney diseases 1697.60 (0.42) 76684.07 (12.23) ***Anal diseases 543.66 (0.10) -11084.02 (-1.04) Others 4793.56 (3.49) *** 2664.82 (1.34) Constant -14600.09 (-2.93) ***

Adjusted R-squared 0.3675Number of observation 1820



Result VWithout chronic diseases With chronic diseases

Variable Coef. Std. Err. t-value p-value Coef. Std. Err. t-value p-valueSex 1424.65 553.25 2.58 0.010 ** 1681.68 815.62 2.06 0.040 **Age 281.14 32.95 8.53 0.000 *** 112.32 52.40 2.14 0.032 **

Education 13.26 390.09 0.03 0.973 511.28 508.33 1.01 0.315 Working 565.58 579.25 0.98 0.329 -592.03 879.79 -0.67 0.501

No. of family 110.53 145.50 0.76 0.448 588.46 222.25 2.65 0.008 ***Income -14.40 4.75 -3.03 0.002 *** -26.23 7.19 -3.65 0.000 ***

User 65.60 557.77 0.12 0.906 -3794.18 827.54 -4.58 0.000 ***Constant -14725.31 2720.33 -5.41 0.000 *** 324.68 4177.35 0.08 0.938

Number of obs. 1030 790Adjusted R2 0.0933 0.0485


Medical expenditures for lifestyle-related diseases of users with chronic diseases is smaller than those of non-users by 37,942 yen (US$ 349.73) per year, but there is no difference in medical expenditures between users and non-users who have no chronic diseases.


Hypotheses Proved

H1: Medical expenditures of life-style related diseasesof users are lower than those of non-users.

H2: The longer they use the system, the more their medical expenditures will be reduced.

H3: The longer they use the system, the larger elasticity of reduction of medicalexpenditures would be.

H4: Effects of reduction of medical expenditures arelager especially to the people with chronicdiseases.

0.158 1.42 348.5 493.3 Reduction of medical expenditures

**0.014 2.48 366.7 909.6

Less anxiety in day-to-day life

***0.000 3.93 340.4 1340.5

Enhancement of health consciousness

**0.012 2.51 299.3 753.1 Stabilization of illness

p-valuet-valueStd. Err.Coef.Effects

Why the eHealth System reduces Medical Expenditures of Life-style related Diseases


Conclusion


Obstacles for Further Implementation of Telemedicine from our Survey

For requesting institution• Reliability• Require time• Quality

For undertaking institution• Responsibility• Have no time• Shortage of manpower


Issues of eHealth: from Special Issue of Journal eHealth

Technology and Application• Financial support :

Tax money“Public policy model”Cost-benefit analysis

Reimbursement from medical insurance Tele-radiology, chronic diseases werealready admitted calculate benefits of eHealth


Issues of eHealth II

• Enhance efficiency of health industryeHealth promote efficiency in hospital management

• Legal issuesMedicine is a business model with several

thousand year historyDifficulties for private firms to enter the market


Issues of eHealth III

• Collaboration of eHealth networksCombining medical, health, welfare and

home-care networksApplication of Wireless broadband network

• Capacity building of eHealth in developing countries

• ICT infrastructure, HRD


Documents

Economic Evaluation of eHealth in JapanMeasurement of blood pressure, ... ¾Over 70% of users are using the system at least once a week. 8th July 2008, Healthcom 2008. ... University