International Health Policy Program -Thailand Tracking progress in universal health access: Monitoring effectiveness of universal coverage in Thailand.

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Tracking progress in universal health Tracking progress in universal health

access:access:

Monitoring effectiveness of Monitoring effectiveness of

universal coverage in Thailanduniversal coverage in Thailand

Supon Limwattananon, Supon Limwattananon, MPHM, PhDMPHM, PhD

Viroj Tangcharoensathien, Viroj Tangcharoensathien, MD, PhDMD, PhD

Prince Mahidol Award Conference, Prince Mahidol Award Conference, BangkokBangkok

Parallel Session 2.3 Parallel Session 2.3

29 January 201029 January 2010

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Objectives

1. To describe the four-decade trend of key health

indicators

2. To demonstrate relationship between health resource

inputs, service outputs and health and financial

outcomes

3. To assess existing data sources for tracking the UC

progress

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T00T01 T02T03T04T05

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0

20

40

60

80

100

120

140

160

180

200

220

240

260

U5

MR

per

1,0

00

live

bir

ths

5 10 20 50 100 200 400Total health expenditure per capita (USD, logarithm)

Top ten MDG4 performersTop ten MDG4 performers

Source: Analysis of World Health Statistics

Thailand 2000-05

Source: Rohde et al. (Lancet 2008)

Good Health at Low Cost !Good Health at Low Cost !

Where is Thailand standing at?Where is Thailand standing at?

* GNI < USD5,000 per capita; Births > 100,000/year

U5MR vs. THE per capitaLow- and middle-income countries

Rank

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50,000

Co

nsu

mp

tion

exp

en

ditu

re p

er

cap

ita (

Ba

ht)

0 10 20 30 40 50 60 70 80 90 100Cumulative percent (in ascending order of consumption expenditure)

Post-health OOP Pre-health OOP Poverty line

Health impoverishment -Thailand, 2008

0

1,000

2,000

3,000

4,000

5,000

0 5 10 15 20 25 30 35 40

11.0% 3.0%

0.71%

0.38%

0.75%

0.22%

0.57%

0.22%

0.37%

0.15%

0.56%

0.15%

17.1% 6.0% 12.8% 4.5% 11.1% 3.6% 10.3% 3.1%

Rural Urban Rural Urban Rural Urban Rural Urban Rural Urban

2002 2004 2006 2007 2008

Pre-health OOP impoverished Post-health OOP impoverished

Source: Analysis of Socio-Economic Surveys (SES, various years)

8.4%

0.4%

MDG1MDG1

0

1

2

3

4

5

6

1995 2000 2005

Total health expenditure per GDP (%)

0

1

2

3

4

5

6

2000 2002 2004 2006

Poorest quintile Richest quintile

Catastrophic health expenditure (%)

0

10

20

30

40

50

60

70

80

1995 2000 2005

Government Household OOP

Health expenditure share (%)

0

1

2

3

4

5

6

7

8

1992 1994 1996 1998 2000 2002 2004 2006

Poorest decile Richest decile

OOP health expenditure per income (%)

UC scheme 2001

UC scheme 2001

UC scheme 2001

* Health expenditure > 10% of total expenditure per household

*

Source: National Health Accounts (NHA) and analysis of Socio-Economic Surveys (SES, various years)

Asian economic crisis

Population coverage of health insurancePopulation coverage of health insurancebefore and after the UC reform in 2001before and after the UC reform in 2001

Source: Analysis of Health and Welfare Surveys (HWS, various years)

5.1% 5.7% 4.9% 4.0% 3.7%

66.5%

29.0%

54.5%

14.2% 27.9% 52.3% 74.7% 73.4% 72.2% 74.3% 73.6%

0%

20%

40%

60%

80%

100%

1991 1996 2001 2003 2004 2005 2006 2007

Uninsured LIC/VHC UC SS CSMB Other

LIC: Low-Income Card Scheme Tax-funded, public welfare program (defunct)

VHC: Voluntary Health Card Scheme Subsidized, voluntary, community-based health insurance (defunct)

UC: Universal Coverage Scheme Tax-funded, entitlement scheme for the rest of all Thai population

SS: Social Security Scheme Compulsory, contributory, social health insurance for formal private employees

CSMB: Civil Servant Medical Benefit Scheme Tax-funded, fringe benefit for government employees/pensioners, dependants

2000

19701st-3rd NHP (1962-76)

100% provincial hospitals

1. Infrastructure development1. Infrastructure development

The path of health care coverageThe path of health care coverage

LIC1975

1990

CSMB1980

CHF1983

SS1991

4th -5th NHP (1977-86)

Expansion of district hospitals

and health centers

UC2001

VHC1994

1980

MOPH established 1942

15 provincial hospitals 300+ health centers

2. Innovative financing2. Innovative financing

Source: Adapted from Srithamrongsawat

Prospective payment system (PPS)- Capitation for SS (OP-IP)

- Diagnostic-related groups (DRG) for LIC/VHC (IP)

PPS expansion- Capitation for UC (OP)

- DRG for UC (IP)

- DRG for CSMB (IP)

- Direct billing for CSMB (OP)

LIC+1996

SS+1994

SS+2002

0

100

200

300

400

500

600

700

800

900

1,000

1,100

1,200

1,300

1,400

1965 1970 1975 1980 1985 1990 1995 2000 2005

All District Other public Private

Hospitals

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

100,000

110,000

120,000

1965 1970 1975 1980 1985 1990 1995 2000 2005

Doctors Nurses

Doctors and nurses

400

500

600

700

800

900

1,000

1,100

1,200

1,300

1,400

1965 1970 1975 1980 1985 1990 1995 2000 2005

Population per bed

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000

1965 1970 1975 1980 1985 1990 1995 2000 2005

Doctor Nurse

Population per doctor and nurse

National Health Plans

1-th 2-th 3-th 4-th 5-th 6-th 7-th 8-th 9-th 10-th

District hospitals

MD mandated rural service

Technical nurses

Asian economic crisis

Source: Analysis of Health Resource Surveys (HRS, various years)

Four decades of health infrastructure Four decades of health infrastructure developmentdevelopment

-4.2/year

-3.3/year

-2.2/year

-1.3/year

-1.0/year-0.8/year

0

10

20

30

40

50

60

70

80

90

100

1970 1977 1982 1991 1997 2001 2007

Under-five mortality per 1,000 live births

• District hospitals 1977 • Village health volunteers 1977• National EPI 1978

Social Security Act 1991

Universal Coverage scheme 2001

Asian economic crisis 1997

Technical nurses 1982

Low-Income Card scheme 1975

Community health funds 1983

Voluntary Health Card scheme 1994

Civil Servant Medical Benefit scheme 1980

National Health Plans:

3-th 4-th 5-th 6-th 7-th 8-th 9-th 10-th

MD mandatory rural service 1972

Source: Analysis of IHME data

Child mortality trends and health systems developmentChild mortality trends and health systems development

197019711972

19731974

19751976

19771978

19791980

19811982

19831984

1985198619871988198919901991199219931994199519961997199819992000200120022003200420052006200710

20

30

40

50

60

70

80

90

U5M

R p

er 1

,000

live

birt

hs

3,000 4,000 5,000 6,000 7,000 8,000 9,000Population per doctor

19701971

19721973

19741975

19761977

19781979

19801981

19821983

19841985198619871988198919901991199219931994199519961997199819992000200120022003200420052006200710

20

30

40

50

60

70

80

90

U5M

R p

er 1

,000

live

birt

hs

500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500Population per nurse

19701971

19721973

19741975

19761977

19781979

19801981

1982198319841985

198619871988198919901991199219931994199519961997199819992000200120022003200420052006200710

20

30

40

50

60

70

80

90

U5M

R p

er 1

,000

live

birt

hs

400 500 600 700 800 900Population per bed

ThailandChild mortality and population-health resource ratios

U5MRt = – 16.75

+ 2.9 * 103-Population per doctort

+ 12.2 * 103-Population per nurset

+ 38.1 * 103-Population per bedt

– 0.1 * 103-USD GNI per capitat

AR(1) time-series analysisAR(1) time-series analysis

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

2001 2003 2005 2007

Health ctr. District hosp. Province hosp. Private hosp.

Ambulatory visit (Concentration Index)

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

2001 2003 2005 2007

District hosp. Province hosp. Private hosp.

Hospitalization (Concentration Index)

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

2000 2002 2004 2006

OOP payment Direct tax Indirect tax

Financing source (Concentration Index)

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

2000 2002 2004 2006

OOP payment Direct tax Indirect tax

Financing source (Kakwani Index)

Concentration in health utilization and financing -Thailand

Source: Analysis of HWS (on health utilization) and SES (on health financing)

Pro-rich

Pro-poor

Progressivefinancing

utilization

0.0

0.2

0.4

0.6

0.8

1.0

Cu

mula

tive p

rop

ort

ion o

f ca

se

s

0.0 0.2 0.4 0.6 0.8 1.0Cumulative proportion by wealth index rank

Weight for age

0.0

0.2

0.4

0.6

0.8

1.0

Cu

mula

tive p

rop

ort

ion o

f ca

se

s

0.0 0.2 0.4 0.6 0.8 1.0Cumulative proportion by wealth index rank

Height for age

Inequality in child malnutrition -Thailand

CI – 0.372CI – 0.372

Source:

Analysis of MICS2006

CI – 0.260CI – 0.260

Factors contributing tochild malnutrition

Concentration index

(negative) Weight for age

(negative) Height for age

Elasticity

Contribution 54.7%

Elasticity

Contribution 51.8%

1. Child’s age 0.006 1.386 0.8% 0.521 0.3%

2. Child’s squared age 0.010 -0.665 -0.7% -0.195 -0.2%

3. Male child -0.004 -0.047 -0.05% 0.057 0.08%

4. (log) Income per capita 0.026 -4.779 33.7% -3.001 30.3%

5. Mother’s college education 0.604 -0.047 7.7% -0.033 7.7%

6. Number of children in household

-0.024 0.432 2.7% 0.346 3.1%

7. Living in urban area 0.264 -0.154 10.9% -0.105 10.7%

InequityInequitydecompositiondecomposition

negative negative

Underweight = 9.3% Stunting = 11.9%

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0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000

11,000

1995 2000 2005

Bangkok North-East

Population per doctor

0

100

200

300

400

500

600

700

800

900

1,000

1,100

1,200

1,300

1995 2000 2005

Bangkok North-East

Population per nurse

0

100

200

300

400

500

600

700

800

900

1995 2000 2005

Bangkok North-East

Population per bed

0

2

4

6

8

10

12

14

16

18

20

22

1980 1985 1990 1995 2000 2005

Doctors-Pop. Nurses-Pop. Beds-Pop.

Bangkok-NorthEast ratio

Health resource disparity -Thailand

Source: Health Resource Surveys; Civil Registration

(Richest : Poorest)

Provincial economic status Provincial economic status Two distinctive indicatorsTwo distinctive indicators

GPP per capita (Baht) 1-30,00030,001-50,00050,001-70,00070,001-100,000100,001+

Poverty head count ratio (%)0-56-1011-1516-2021+

Source: National Economic and Social Development Board (NESDB)

1. Administrative reports 2. HH SES surveys + Pop. census

Gross Provincial Product 2004 Small Area Estimation (GPP) Poverty Map 2004

rr = – 0.4 = – 0.4

13

141516

1718

19

20 21

22

23

24

25

26

27

30

31

32

33

34

35

36

37

39 40

41

42

43

44

45

46

47

4849

50

51

5253

54

55

56

57

60

61

62

63

64

65

66

67

70

71

72

73

7475

76

77

80

81

8384

85

86

90

91

92

93

94

95

96

10

15

20

25U

5MR

per

1,0

00 li

ve b

irths

10000 100000 200000 300000 400000 500000 600000 700000GPP (Baht) per capita

U5MR vs. Gross Provincial Product U5MR vs. Gross Provincial Product 20042004

R2 = 0.064

72 Provinces (Greater Bangkok excluded)Three deep southprovinces

13

14 1516

1718

19

2021

22

23

24

25

26

27

30

31

32

33

34

35

36

37

3940

41

42

43

44

45

46

47

4849

50

51

5253

54

55

56

57

58

60

61

62

63

64

65

66

67

70

71

72

73

7475

76

77

80

81

8384

85

86

90

91

92

93

94

95

96

10

15

20

25U

5MR

per

1,0

00 li

ve b

irths

0 5 10 15 20 25 30 35 40Poverty head count ratio (%)

U5MR vs. Provincial poverty rate U5MR vs. Provincial poverty rate 2004200472 Provinces (Greater Bangkok excluded)

R2 = 0.036

Three deep south provinces

13

141516

1718

192021

22

23

24

25

26

27

30

31

32

33

34

35

36

37

39 40

41

42

43

44

45

46

47

4849

5051

5253

54

55

56

57

58

60

61

62

63

6465

66

67

70

71

72

73

7475

76

7780

81

8384

85

86

9091

92

93

9495

96

10

15

20

25

U5M

R p

er 1

,000

live

birt

hs

5 15 25 35 45 55 65 75Physicians per 100,000 pop.

13

14 1516

1718

192021

22

23

24

25

26

27

30

31

32

33

34

35

36

37

39 40

41

42

43

44

45

46

47

4849

5051

5253

54

55

56

57

58

60

61

62

63

6465

66

67

70

71

72

73

74 75

76

7780

81

8384

85

86

9091

92

93

9495

96

10

15

20

25

U5M

R p

er 1

,000

live

birt

hs

50 100 150 200 250 300Nurses per 100,000 pop.

13

14 1516

1718

192021

22

23

24

25

26

27

30

31

32

33

34

35

36

37

39 40

41

42

43

44

45

46

47

4849

5051

5253

54

55

56

57

58

60

61

62

63

6465

66

67

70

71

72

73

7475

76

7780

81

8384

85

86

9091

92

93

9495

96

10

15

20

25

U5M

R p

er 1

,000

live

birt

hs

50 100 150 200 250 300 350 400Hospital beds per 100,000 pop.

U5MR vs. Health facilities/workers-Population Ratio, 2004

R2 = 0.089 R2 = 0.104

R2 = 0.136

Provincial variations

Health Health

statusstatus

Financing & Financing &

risk protectionrisk protection

Health Health

utilizationutilization

Health Health

resourceresource

1. Population & household surveys

- DHS 1987

- MICS 2006

- NHES 1991, 1996, 2003, 2008

Socio-Economic Survey (SES) 1957-1986 (q 5 y)

1988-2006 (q 2 y)

2007+ (q 1 y)

Health and Welfare Survey (HWS) 1974-1978 (q 1 y)

1981-2001 (q 5 y)

2003-2007 (q 1 y)

2009+ (q 2 y)

2. Administrative & facility-based datasets

Vital RegistryVR 1957+

National Health AccountsNHA 1994-2008

Health insurance electronic IP data2002+

Health Resource SurveyHRS 1962+

Data available for trackingData available for tracking

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SummarySummary

• Four-decade investment in public health infrastructure in rural areas results in – High and equitable level of population health outcomes

• Functioning health service is a prerequisite of

extension of health insurance• Targeting scheme public welfare • CBHI for informal sector

Universal coverage extending to the uninsured• SHI and CSMB for formal sector

results in very low catastrophic and poverty impacts

• Comprehensive national datasets plus analytical

capacities facilitate tracking the progress

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ndConclusionsConclusions

• Tracking a progress in the universal health

access need to exploit wide variations in health

outcomes, service outputs, and resource inputs

– This requires regular information from (demand-side)

household surveys and (supply-side) facility-based

administrative reports at the national and sub-national

levels

– Analysis of long time-series, multiple cross-sectional,

and panel data would help increase validity in claiming

health systems improvement as a result of health care

reforms