the impact of universal healthcare coverage_1juli2013

7/30/2019 The Impact of Universal Healthcare Coverage_1Juli2013

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The Impact of Universal Healthcare

Coverage (Jamsoskes)in South Sumatera

23rd Pacific Conference of The Regional Science Association International

(RSAI) & 4th Indonesian Regional Science Association (IRSA) Institute

Hotel Savoy Homan, Bandung, 2-4 July 2013

Institute for Economic and Social Research

Faculty of Economics University of Indonesia (LPEM FEUI )

Ari Kuncoro, Ifa Isfandiarni and Hengky Kurniawan


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Background

2

South Sumatera ranks in the bottom third life expectancy, infant and maternal mortality rates, and

malnourishment among children (UNDP, 2010).

In January 2009, the provincial government of South

Sumatera institutedJaminan Sosial Kesehatan (Jamsoskes) universal healthcare coverage

free basic health services to the 4 million citizens

excludesJamkesmas,ASKES, Jamsostek, etc.

eligible population constitutes about a half of the totalpopulation.


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Research Questions

3

DoesJamsoskes increase the number of inpatient, outpatient,and prenatal care visits?

Do families increase their utilization of all healthcare services?

Are levels of utilization different by age, education, and gender?

Is there any variation on utilization ofJamsoskes by urbanversus rural areas, and income group?

What are the effects on health outcomes (infant and adult)?

What are the effects on days of normal activity that was

disrupted due to illness?


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Region and Context

4

Area: 99,888.28 kmsq

11 districts and 4 cities,

district of Ogan Komering Ilir

(biggest area) and city of

Palembang (highest density) Border: Jambi, Bangka

Belitung, Bengkulu and

Lampung

Population: 7.4 millions

Main sector: mining and

services


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Evolution of Jamsoskes in South

Sumatera

2002: Askes Perdana, District ofMusi Banyuasin (Muba) (selectedpoors)

2003-2004: Gakin Muba (poors)

2005: Gakin Muba joined AskeskinNasional (poors)

2007: Mubas Jamkesda (universalhealth care)

2009: South Sumatera Provinces

Jamsoskes Covers more than half population

Cost shares between provincialgovt. and districts/cities govts (30-50% paid by districts/cities)

5

ASKES PNS

6%JAMSOSTEK

1%

JAMKESMAS39%

TNI/POLRI

1%JAMKES

SWASTA

1%

JAMSOSKESSUMSEL 52%

Proportion of Health Insurance by

Type (2012)


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Demand for Health and Health Care (1)

6

Health as capital (Grossman, 1974)

People invest when marginal benefit exceeds the marginal cost

Health insurances are rare in developing countries (Gertler and Gruber,2002)

Households face big portion of out-of-pocket expenditures Financial catastrophic endangers the familys ability to maintain its

standard of living (Berki, 1986)

There are varieties of catastrophic expenditures among countries (Xu et.al,2003)

Households spend less on food following a health shock (Wagstaff, 2007)

Health insurance increases the number of visits in health care center (Ekowati,

2009)


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Demand for Health and Health Care (2)

7


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Methodology (1)

8

Data Riskesdas 2007 & 2010, Susenas 2006 &2011, PODES 2008

Model to be estimated

edisturbanccomponent/error:v

Jamsoskes)afterand(beforedummyvariabel:D

puskesmas)hospital,(e.g.facilityhealthofchoice:F

rural)urban,(e.g.variablecontrol:C

sticcharacterihousehold:Xsticcharacteriindividual:I

estimatedbetoparameter:

indicatorhealthoroutcome:h

ik

ik

k

ik

ik

ikikkikkikikik

ikikkikik

vCDDFDXDIFCXIh

vDFCXIh

87654321

54321


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Methodology (2)

9

By adding other variables we get:

Interaction among the variables:

Above equations are estimated by maximum likelihood method

see annexes for detail tables of estimation results

jj'everyfor}{[

jj'everyfor}'{[:(j)facilitieshealthusingin(i)individualanof)(yProbabilit

'

ijijijijr

ijijrij

ij

euevP

uuPPP

ijkikkikijk DFCXIP 54321

ijkikkikkikikijk vCDDFDXDIFCXIP 87654321


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Results and Interpretation (1)

10

Multinomial Outpatient Choices - Basic regression

(government hospital as a base): The elder patient is more likely to visit government hospital compared

to other health facilities, while household income has mixed result Distance to hospital & percentage of paved road persistently affect the

choice of government hospital

Multinomial Outpatient Choices - Interactive terms

(government hospital as a base) The poor family tends to useJAMSOSKES to the utmost. The coefficients of all choices are negative and significant which means

there is a huge shift to government hospital for outpatient treatment Compared to Sumatra with no health care program; there is an increase

of demand for private doctor/polyclinic. People go to private doctor to get good diagnosis before joining free

health care despite having to pay fees.


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11

Multinomial Inpatient Choices Basic Regressions

(government hospital as a base) Age variable is the only significant for PUSKESMAS.

The negative coefficient suggests that as a person gets older the lesslikely he or she chooses PUSKESMAS relative to government hospital.

Education is not important in the choice. Richer family tends to use private relative to private hospital.

Multinomial Inpatient Choices Interacted Terms (governmenthospital as a base)

Urban people tend not choose PUSKESMAS but rather to go to government

hospital. In the choice of private health worker both urban dummy and its interaction

with time dummy are significant.

The coefficient sign indicates that after the program is introduced in 2009 more

urban dweller move away from private health worker to government hospital.


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12

Multinomial Choices of Birth Helper BasicRegressions (doctor as a base) The impact of age is uniform across all choices.

Higher household incomes prefer doctor for birth helper.

The introduction ofJAMSOSKES makes people less likely to choosemidwife and traditional midwife over doctor

As more and more midwife available locally paramedic and traditionalmidwife will no longer an attractive option.

Multinomial Choices of Birth Helper InteractiveTerms (doctor as a base)

The Jamkesmas benefit the poor family The urban people are also benefitting in the same sense that they are

moving to doctor as a birth helper and leaving out midwife, paramedic andtraditional midwife.


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13

Probability of Being Sick Impact ofJAMSOSKES is reducing the probability of being sick for all

cases considered.

The probability of being sick is related positively with age.

While higher education attainment tends to reduce the probability ofsickness.

But high household apparently does not prevent someone from beingsick.

Poisson Regression: South Sumatra Impacts on Daysof Illness

Older people seem to suffer longer than younger when they get ill. It suggests that marriage people tend to have fewer days of illness

None of the interactive terms is significant suggesting that the sicknessof this type does not prevent one from continuing to work.


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Conclusions

14

Increased demand for government hospital appears to

overburden

But Puskesmas as sorting system is working

Does not diminish the role of private doctor

In the inpatient case, non-doctor health worker is preferred.

In remote area, this reduce the number of visits in Puskesmas

In the case of woman in childbirth, medical doctor is preferred,

causing in reduction of other facilities

Good infrastructure plays an important role

Jamsoskes does not reduce normal activity that was disrupted

due to illness

Absorption of light illness


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Policy Implications

15

Health workers at remote areas should be improved as they serve as

a first stop before continuing to government hospital. This will reduce excess demand for government hospital at district level. PUSKESMAS should also add more staffs to anticipate the higher

utilization of this health facility by the expecting mothers.

In term of budgeting policy the local government could focus on

building and maintaining good roads while at the same time optimizingthe use of PUSKESMAS system by upgrading their capabilities and/orbuilding hospital. This is because PUSKESMAS are in effect in competition with each other

in attracting patient.

The socialization of staying at home if sick should be carried outmore frequent to all people so as to reduce spreading disease tofellow workers.

The role of modern trained midwife should be strengthened inreducing IMR among the poor.


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16

(1) (2) (3) (4)VARIABLES Private

Hospital PrivateDoctor/Polyclinic

PUSKESM

AS

/PUSTUPrivate

Health

WorkerAge 0.00404 -0.00931*** -0.0171*** -0.0132***

(0.930) (-3.321) (-6.361) (-4.811)H. school and

above0.332 -0.200 -0.838*** -0.841***

(1.427) (-1.246) (-5.123) (-5.058)Household

income 0.403** 0.250** -0.495*** -0.307***(2.339) (2.147) (-4.321) (-2.639)

Year 2011 -4.023** 0.568 3.129** 1.373(-2.270) (0.466) (2.572) (1.053)

% paved road -0.00341 -0.00192 -0.00595* -0.0112***(-0.721)

(-0.587)

(-1.908)

(-3.563)

Hilly region -0.0216** -0.000360 -0.00360 -0.00273

(-2.314) (-0.0966) (-0.993) (-0.749)N. of hospital 2.818** 1.199 -1.227 -0.00865

(2.029) (1.361) (-1.414) (-0.00946)N. of polyclinic -0.744 -0.132 0.468 -0.466

(-1.057) (-0.276) (0.987) (-0.962)N. of puskesmas 0.410 -0.905 -1.064* 0.528

(0.456) (-1.574) (-1.891) (0.896)Dist. to hospital -0.00182 0.0133** 0.00934* 0.0370***(-0.190) (2.277) (1.661) (6.640)Dist. to polyclinic 0.00714 -0.0177** 0.00303 -0.0149**

(0.649) (-2.429) (0.445) (-2.194)Dist. to

puskesmas 0.0217 -0.00587 0.00889 -0.0346**(1.028) (-0.344) (0.589) (-2.186)

Constant -6.810*** -2.015 9.752*** 5.797***(-2.753) (-1.204) (5.947) (3.487)

Observations 5,981 5,981 5,981 5,981

Table 1:

South Sumatra:

Multinomial Outpatient Choices

Basic Regressions

z-statistics in parentheses

*** p


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17

OutpatientChoices

VARIABLES PrivateHospital PrivateDoctor

/PolyclinicPUSKESM

AS

/PUSTUPrivate

Health

WorkerPoor Family

Poor Family 0.758** 0.473* 0.981*** 0.914***(2.038) (1.814) (4.121) (3.529)

2011 X Poor -1.143** -0.817*** -0.946*** -1.002***(-2.532) (-2.640) (-3.263) (-3.271)

MotherMother -0.555 0.530 0.396 0.638

(-0.494) (1.031) (0.817) (1.238)2011 X Mother -27.56 -0.567 -0.489 -0.170

(-4.18e-05) (-0.874) (-0.788) (-0.264)Children

under 5Age 5 and less 0.305 0.360 -0.00924 0.255

(0.561) (1.067) (-0.0293) (0.746)2011 X Age 5 or less -0.329 -0.0940 0.171 0.497

UrbanUrban -0.247 -0.383 -0.454* -2.138***

(-0.573) (-1.403) (-1.814) (-6.753)2011 X urban 0.904* 0.952*** 0.521* 1.424***(1.798) (2.958) (1.711) (3.831)

SumatraContext

South Sumatra 0.0815 -0.0604 0.151 -0.221**(0.498) (-0.551) (1.533) (-2.031)

2011 X South

Sumatra0.126 0.389*** -0.155 0.415***

(0.599) (2.780) (-1.182) (3.008)

Table 2:

South Sumatra Multinomial Choices of

Outpatient Interactive Terms

base: government hospital

The poor family tends to use

JAMSOSKESto the utmost.

The coefficients of all choices are

negative and significant which means

there is a huge shift to government

hospital for outpatient treatment

Compared to Sumatra with no health

care program; there is an increase of

demand for private doctor/polyclinic.

People go to private doctor to get good

diagnosis before joining free health

care despite having to pay fees.


18/23

18

Inpatient ChoicesVARIABLES Private

Hospital PUSKESMAS/PUSTU Private HealthWorkerAge 0.00415 -0.0203*** -0.00994

(1.055) (-2.857) (-1.636)H. school and

above 0.159 -0.730 -0.535

(0.819) (-1.560) (-1.594)Household income 0.829*** -0.306 0.0862

(5.568) (-1.072) (0.372)Year 2011 1.778 -8.952** 8.807***

(1.010) (-2.126) (2.674)% road paved 0.000947 -0.0126** -0.0209***

(0.233) (-2.090) (-4.074)Hilly region -0.0468*** -0.00575 -0.0143

(-3.716) (-0.606) (-1.462)N. of hospital 0.695 9.733*** -1.419(0.596) (2.937) (-0.706)

N. of polyclinic -0.449 -0.177 0.185(-0.693) (-0.130) (0.193)

N. of puskesmas -0.948 -6.084*** -3.668***(-1.139) (-3.416) (-3.004)

Dist. to hospital -0.0124 0.0203** 0.0266***(-1.457) (2.087) (3.393)

Dist. to polyclinic 0.0114 -0.000942 -0.0402***(1.117) (-0.0858) (-3.194)

Dist. to puskesmas 0.00868 -0.0189 -0.0381(0.411) (-0.758) (-1.042)

Constant -12.12*** 4.321 -0.222(-5.548) (1.064) (-0.0667)

Observations 778 778 778

Table 3:

South Sumatra: Multinomial

Inpatient Choices Basic Regressions


*** p


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19

Inpatient

ChoicesVARIABLES Private

Hospital PUSKESMAS/PUSTU PrivateHealthWorker/Poly

Poor FamilyPoor Family 0.508 -0.139 0.360

(1.192) (-0.233) (0.504)2011 X poor -0.521 0.155 -0.0660(-1.123) (0.221) (-0.0853)Mother

Mother 0.923 0.993 0.810(0.954) (0.886) (0.538)

2011 X mother -0.999 -33.40 -0.554(-0.904) (-4.78e-06) (-0.341)

Children under

5Age 5 and less -0.373 -0.478 -1.309

(-0.562) (-0.669) (-1.013)2011 X age 5 0.849 -0.109 0.978

(1.222) (-0.131) (0.739)Urban

Urban 0.710 -2.174** 1.956**(1.481) (-2.225) (2.422)

2011 X urban -0.545 0.458 -2.452***(-1.041) (0.365) (-2.735)Sumatra

ContextSouth Sumatra 0.129 -0.0207 -0.272

(0.724)

(-0.0783)

(-0.872)

2011 X S. Sumatra 0.0524 -0.830** 0.230(0.256) (-2.511) (0.665)

Table 4:

South Sumatra: Multinomial Inpatient

Choices Interacted Terms


*** p


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20

(1) (2) (3) (4)VARIABLES Midwife Paramedic Dukun RelativeAge 0.0751** 0.352** 0.0859** 0.199*

(2.328) (2.362) (2.293) (1.768)Household income -0.694*** -0.839** -1.268*** -1.074***

(-8.565) (-2.122) (-12.73) (-3.389)Year 2011 -2.194* 15.70* -9.123*** -8.924

(-1.752) (1.769) (-5.129) (-1.509)% road paved 0.00143 -0.0228** -0.000639 0.0103

(0.628) (-2.487) (-0.253) (1.293)Hilly region 0.0133*** 0.0119 0.0173*** 0.0173**

(3.181) (0.969) (3.949) (2.218)N. of hospital -1.322* -14.95*** -0.239 2.422

(-1.873) (-2.824) (-0.238) (0.723)N.of maternity hosp. 0.976** 7.761* 1.140* -1.062

(2.016) (1.775) (1.783) (-0.520)N. of puskesmas 1.208** -0.861 3.118*** 3.474*

(2.479) (-0.233) (4.996) (1.651)N. of doctor -0.0400 0.559 -0.234* -1.558**

(-0.395) (0.479) (-1.714) (-2.149)N. of midwife -0.135 -3.043*** -0.488*** 0.745

(-1.199) (-3.199) (-3.519) (1.495)N. of polindes -0.122 -0.374 0.516** 0.497

(-0.609) (-0.418) (2.287) (0.758)Dist. to hospital 0.00633 0.0319 0.0178*** 0.0285**

(1.361) (1.515) (3.511) (2.398)Dist. to amaternity_hosp. 0.000412 -0.00323 0.00719 -0.00252

(0.101) (-0.161) (1.610) (-0.229)Dist. to puskesmas 0.00831 -0.0196 -0.0133 -0.0268

(0.609) (-0.376) (-0.936) (-0.870)Dist. to doctor 0.0178* -0.0800 0.0285*** 0.0176

(1.810) (-1.614) (2.813) (0.860)Dist to midwife -0.0183 0.0808* -0.00110 0.0273

(-1.540) (1.742) (-0.0902) (1.256)Dist. to polindes 0.00413 -0.0419 -0.00513 0.00547

(1.499) (-1.506) (-1.632) (0.686)Constant 11.35*** 11.35** 17.56*** 9.600**

(9.774) (2.055) (12.50) (2.168)Observations 6,577 6,577 6,577 6,577

Table 5:South Sumatra: Multinomial Choices of

Birth Helper Basic Regressions

z-statistics in parentheses*** p


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21

(1) (2) (3) (4)VARIABLES Midwife Paramedi

c Dukun Relative

Poor

FamilyPoor Family 0.523** 0.878 0.957*** 1.044*

(2.365) (1.463) (4.051) (1.836)Year 2011 X Poor -0.420* 0.175 -0.779*** -1.258*

(-1.706) (0.188) (-2.898) (-1.845)Urban

Urban 0.330 1.549 -0.391 -2.258*(1.334) (1.416) (-1.394) (-1.761)

Year 2011 X urban -0.475* -8.543*** -0.795** -1.050(-1.738) (-3.193) (-2.368) (-0.606)

Sumatra

ContextSouth Sumatra 0.0280 0.322 0.0778 -1.442***

(0.324) (1.210) (0.802) (-5.340)Year 2011 X South

Sumatra -0.01000 -0.881** -0.0519 0.544(-0.0968) (-2.000) (-0.437) (1.640)

Table 6:South Sumatra: Multinomial Choices of

Birth Helper Interacted Terms

z-statistics in parentheses*** p


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22

(1) (2) (3) (4)VARIABLES Days of illness Days of illness Days of illness Days of illness

Age 0.00276*** 0.00427*** 0.00290*** 0.00276***(11.40) (18.02) (17.07) (11.37)

Marriage -0.0285*** -0.0195** -0.0292***(-3.453) (-2.330) (-3.583)

High school -0.0687*** -0.0471*** -0.0643*** -0.0694***(-9.442) (-6.327) (-8.654) (-9.565)

Household

income 0.0390*** 0.0330*** 0.0326*** 0.0348***(4.082) (3.628) (3.652) (4.170)

Year 2011 0.536*** 0.536*** 0.532*** 0.552**(3.072) (3.050) (2.988) (2.296)

Poor Family 0.0500***(2.863)

2011 X poor -0.0245(-1.290)Age 5 and less 0.286***

(13.90)2011 X age 5 -0.0609***

(-0.636)Mother 0.238***

(11.32)2011 X mother -0.0468**

(-2.112)Urban -0.0337

(-0.808)2011 X urban -0.000171

(-0.00392)Geo-topography Yes Yes Yes Yes

Facility

availability Yes Yes Yes

Distance to

facility Yes Yes Yes Yes

Observations 73,171 73,171 73,171 73,171Wald-Chisq 365.68*** 1008.31*** 267.16*** 346.37***

Table 7:

South Sumatra: Probability of Being

Sick

Impact ofJAMSOSKES is reducing the

probability of being sick for all cases

considered.

The probability of being sick is

related positively with age.

While higher education attainment

tends to reduce the probability ofsickness.

But high household apparently does

not prevent someone from being sick.

(1) (2) (3) (4)


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23

(1) (2) (3) (4)VARIABLES Days of illness Days of illness Days of illness Days of illness

Age 0.00943*** 0.0104*** 0.00922*** 0.00942***(14.02) (14.81) (14.13) (13.91)

Marriage -0.0736** -0.0548* -0.0727**(-2.423) (-1.741) (-2.374)

High school -0.0426 -0.0207 -0.0433 -0.0441(-0.873) (-0.419) (-0.897) (-0.885)

ln_HH expenditure -0.0436 -0.0375 -0.0393 -0.0377(-1.404) (-1.273) (-1.327) (-1.308)

Year 2011 -0.407 -0.358 -0.396 -0.590(-1.031) (-0.929) (-1.018) (-1.450)

Poor Family -0.0589 -0.329(-1.034) (-0.919)

2011 X poor 0.0643(0.987)

Age 5 and less 0.174***(3.100)

2011 X age 5

-0.0396

(-0.636)Mother 0.00165

(0.0195)2011 X mother -0.000714

(-0.749)Urban -0.0436

(-0.474)2011 X urban 0.0735

(0.732)Geo-topography Yes Yes Yes Yes

Facility availability Yes Yes Yes YesDistance to facility Yes Yes Yes Yes

Constant 2.145*** 1.971*** 2.238*** 2.056***(4.955) (4.804) (5.550) (5.073)

Observations 9,248 9,248 9,248 9,248Wald-Chisq 269.69*** 305.88*** 267.16*** 246.00***

Table 8:

Poisson Regression: South Sumatra

Impacts on Days of Illness

Older people seem to sufferlonger than younger when they

get ill.

It suggests that marriage people

tend to have fewer days of

illness

None of the interactive terms is

significant suggesting that the

sickness of this type does not

prevent one from continuing to

work.

the impact of universal healthcare coverage_1juli2013

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