Conference Health and social protection: Meeting the needs of the poor Vientiane 2008

Cambodia, China and Lao PDRInitial thoughts from POVILL

www.povill.com

CambodiaChean Rithy MenCentre for Advanced Studies

We will report on…

• Different types of major illness affecting household livelihoods

• Health-seeking behaviors

• Coping strategies to finance health care expenditure

• Impact of HEF on hospital utilization

• Three level health system with first level organized in health district (health centers and referral hospital)

• Public health services are highly subsidized• Public health facilities adopt “flat fees” charging

system• Staff working in public health facilities have modest

economic incentive • Most staff earn their living by dual practices• Private practices are loosely regulated

Cambodian Health Care System

THE STUDY: DESIGN AND DATA COLLECTION

Research sample

Sites 

Rapid Household SurveyIn-depth

study

 Village HH Person HH

Mongkol Borei 80 2,000 11,495 110 

Sonikum 80 2,000  10,950    110

Kirivong80

 1,975 10,716   110

Total 240   5,975 

33,161 330 

PRELIMINARY FINDINGS

Self-reported serious illness last year

N= 33,161 Total number of individual in sample

Percentage of reported serious illness

Mongkol Borei 11,495 13.82%

Sotr Nikum 10,950 14.94%

Kirivong 10,716 16.48%

Average over three ODs   15.05%

Major illness includes more than inpatient care

N=4992 Total number of Individual in Sample (M.I.)

Received Inpatient treatment

Mongkol Borei 1589 29.64%

Sotr Nikum 1637 30.05%

Kirivong 1766 29.38%

Average over three ODs   29.68%

Working days lost due to serious illness

 N= 4992 Frequency Percentage

no working days lost 426 11.51%

1-5 workdays lost 343 9.26%

6-10 workdays lost 550 14.86%

11-15 workdays lost 421 11.37%

16-30 workdays lost 696 18.80%

>30 workdays lost 1265 34.17%

Children 1291 25.86%

A highly fragmented health systemDistribution of health seeking behaviors over respective providers (30 days recall period), RHS

Public sector: 18%

Private facility

Health center

NGO clinic

Private practitioner

NGO/religious facility

Did not seek care

District hospital

Pharmacy

Provincial hospital

Kru Khmer

Drugstore/shop

National hospital in Phnom Penh

Provincial hospital

District hospital

Health center

Health post

Outreach

Private facility

Pharmacy

NGO/religious facility

TBA/VHW

Drugstore/shop

Kru Khmer

Monk/religious healer

Did not seek care

NGO clinic

Private practitioner

Mobile drug seller (by moto/boat/car)

Health facility (factory)

Seek treatment in Vietnam/Thailand

Chinese drug shop

Don't know

Different incentives for health professionals with dual practices in public and private settings (n=55)

Factors influencing providers' medical decision in public facilities

0%

20%

40%

60%

80%

100%

No influence Low influence Moderate influence High influence

Factors influencing providers' medical decision in private facilities

0%

20%

40%

60%

80%

100%

No influence Low influence Moderate influence High influence

An example of irrational practices

Coping strategies with major illness

  Frequency Percent

Using saving 86 1.4

Reduce food expenditures 24 0.4

Remove children from school 19 0.3

Sell stored food 319 5.3

Sell household assets 99 1.7

Sell production tools 206 3.4

Sell livestock 317 5.3

Sell land 93 1.6

Borrow money from friends/relatives 911 15.2

Borrow money from informal money lender 1,594 26.7

Borrow money from credit institute 234 3.9

Seek additional work 615 10.3

Total of HH reported severe financial problem due serious illness 3,068

(51% total sample)

Redressing health seeking behaviors:HEF as part of the solution?

HEF is a mechanism or fund that• is operated by an independent organisation• in the interest of poor people, • purchases health care for those poor people (from a

public health care provider), • and also pays for all the associated costs (from

non-medical providers).

• Independent = purchaser-provider split , the organisation does not belong to the Ministry of Health.

Functions of HEF

→ Local NGOs are particularly suited to perform these various functions

Targeting for HEF

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00

45.00

no HEF card (n=4782) HEF card (n=1113)

Q1

Q2

Q3

Q4

Q5

HEF boosts utilization of public hospitals(Logistic regression (of likelihood to go to public hospitals (vs other option) for seriously ill people who got the advice from a qualified expert to seek inpatient care (N=1567) RHS)

Odds for a HEF card holder to go to the public hospital are 2.4 higher than someone with a same profile without a HEF card!

,257,000123,437,280-1,358Constant

2,374,000132,045,153,865HEFbook

,899,03014,730,049-,106SES (quintiles ) (=considered as intervalvariable)

2,189,000114,162,208,784Agegroup (more than 45)

1,833,00319,022,202,606Agegroup (16-45)

1,104,6881,161,248,099Agegroup (6-15)

,000320,783Agegroup (refcat: 0-5)

,976,000123,248,005-,024Distance to public hospital

2,122,000124,040,153,752PROVINCE(Takeo dummy)

,930,7011,147,189-,072PROVINCE(Siem Reap dummy)

,000238,727PROVINCE (refcat: BMC)

Exp(B)Sig.dfWaldS.E.B

Variables in the Equation

,257,000123,437,280-1,358Constant

2,374,000132,045,153,865HEFbook

,899,03014,730,049-,106SES (quintiles ) (=considered as intervalvariable)

2,189,000114,162,208,784Agegroup (more than 45)

1,833,00319,022,202,606Agegroup (16-45)

1,104,6881,161,248,099Agegroup (6-15)

,000320,783Agegroup (refcat: 0-5)

,976,000123,248,005-,024Distance to public hospital

2,122,000124,040,153,752PROVINCE(Takeo dummy)

,930,7011,147,189-,072PROVINCE(Siem Reap dummy)

,000238,727PROVINCE (refcat: BMC)

Exp(B)Sig.dfWaldS.E.B

Variables in the Equation

Conclusion• Illness is a major burden for rural households (suffering, health care

expenditures, lost days…).

• Several factors have led to a fragmentation of the Cambodian health system. Many providers are loosely regulated; this leads to unsatisfactory quality of care, irrationnal prescription and unnecessary health care expenditure.

• Due to coping mechanisms adopted by households, households can be tipped into poverty.

• Health equity funds (and the civil society) can be part of the solution to this problem.

• Yet, other measures are needed: improve quality of service and care in public facilities to attract users, maintain a system of public hospitals close to the rural population and regulate private health care facilities, including informal providers.

More to come

• More analysis on RHS and In-depth data• Further analysis or follow-up study on households with

chronic diseases• More analysis on informal health providers

ChinaProfessor JinInstitute of Social and Public Policy

Quantitative Research:Major Research questions and methods

Research questions Dependent variables

Independent variables

Samples collected

Impact of Major illness on household livelihood

Household livelihood

Major illnessCoping strategies

Rapid Household survey: 12000 HH In-depth Interview: 600HH

NCMS’ effect on the out-of-pocket inpatient care expenses.NCMS’ effect on utilization of inpatient service among rural residents.

Medical expenditureOut-of pocket paymentInpatient care

schemes As above

Unnecessary care and drug, and unnecessary cost to the poor

Unnecessary drug, tests, services

Poor/non-poor

3 tracer conditions628 inpatient care

Impact of scheme on unnecessary care, drug

Unnecessary drug, test and services

With/wo scheme

As above

Major Preliminary Findings---1. Household Survey

1 Diversity of Major IllnessConcept/definition, Perception from different actors

– Complicated concept: economically, socially, medically

• Household perception in terms of – inpatient care; – large amount of money spent; – long time drugs-taken; disabled; – great amount of working days lost

• NCMS: not adequate response

Outpatient and inpatient use for selected serious illness groups

Type of serious illness

Percent using inpatient treatment

Percent using only outpatient services

Percent other

Circulatory 13.5 60.5 25.9

Respiratory 16.6 62.6 20.9

Digestive 16.5 57.8 25.7

Urinogenital 15.2 65.2 19.6

2 Demographic changes and its implication for healthcare intervention– Household composition and out migration

• Changing patterns of household composition: – Unit of analysis

• Migrant labor and their health seeking behavior: – Scheme: population targeted

• Impact of changing demographic pattern on household health seeking behavior and their livelihoods

Preliminary findings from household study in China

Major Preliminary Findings---2. Impact of Schemes

With NCMS Social-economic

Situation Yes Ratio (%) No Ratio (%) Total Ratio (%)

Poorest 1587 84.15 299 15.85 1886 100.00

Second 1622 89.42 192 10.58 1814 100.00

Middle 1555 90.78 158 9.22 1713 100.00

Fourth 1607 91.57 148 8.43 1755 100.00

Richest 1616 92.03 140 7.97 1756 100.00

Total 7987 89.50 937 10.50 8924 100.00

The distribution of the social economic situation of households by NCMS

● The poorest were less inclined to be covered by NCMS

• Method– Multiple Linear regression

• Result – The effect of NCMS participation on out-

of-pocket expenses of hospitalization of households with major illness is not statistically significant (P>0.05).

• Method– Two-level logistic regression

• Result

– The effect of NCMS on utilization of hospital service of households with major illness is not statistically significant (P>0.05)

Different household social economic status of MFA targets

• 0.66% of the poorest households were covered by MFA• The overall coverage rate(0.31%) is low

MFA target Social economic status

Number of households N Ratio (%)

Poorest 2410 16 0.66 Second 2410 6 0.25 Middle 2408 5 0.21 Fourth 2407 7 0.29

Richest 2414 3 0.12 Total 12049 37 0.31

Major Preliminary Findings---3. Provider’s performance, Unnecessary Care and Unnecessary cost

Model 1 Model 2 Model 3 Model 4

Economic status (Ref.: Low)

Middle 0.240* 0.230* 0.176+ 0.195*

High 0.386** 0.375** 0.279* 0.312*

Facility level (Ref.: County hospital)

Township Health centre -0.801** -0.701* -0.652*

Health insurance (Ref.: No insurance)

NCMS -0.299* -0.273*

Other insurance -0.234+ -0.166

Doctor education level (Ref.: <3)

3 -0.080

>=5 0.000

Age of the patient -0.114*

Squared age 0.015*

_cons 6.491** 6.533** 6.782** 6.777**

N 207.000 207.000 199.000 201.000

Regression of log transformed total cost of pneumonia

+: P<0.1; *: P<0.05; **: P<0.01; ***: P<0.001 NCMS: New Cooperative Medical Scheme

53.68

1.3

43.72

50.22

2.6

48.48

020

4060

unnecessary nucessary more needed

Percent of Unnecessary Care for Treating Pneumonia

drug test_exam

37.4

42.040.4

010

20

30

40

Un

ne

cess

ary

co

st o

f dru

g

1 2 3

Median unnecessary cost of drug treating pneumonia by economic status (RMB Yuan)

Major Preliminary Findings---4. Institutional Analysis

Qualitative researchMajor Research questions and methods

Research Questions MethodsPolicy process of the NCMS and MFA at national level;

Impact of the policy context and the interplay of relevant stakeholders on policy process

- Literature and record review: documents/published paper/gray report/ news;- Key informants interview : officials from MOH, MOCA, MOF; hospital managers; - Focus group discussion: rural residents- Participatory observation: policy seminar by MOCA, workshops

Qualitative research --Main findings

Rural health policy process: response to the transitional context of China - unequal share of the resources distributed; - unequal access to essential health care; - political priority shift to harmonious development; - rising concern on rural health development; - more revenues to support

Stakeholder analysis: - political elites & academic elites: significant role - the media: active in shaping public opinion - rural residents: passive recipients of policies

Formal/informal mechanisms: not sufficiently to voice out the interest of rural residents

“Its not the end, its just the end of beginning” ----Churchill

With the unique datasets, More findings are coming

Medical care used:Medical records

In hospital

Patient and household Social and

economic data:Exit interview

Medical Cost data:hospital account

Linked

6000 householdsurvey

600 householdIn-depth interview

Next steps

• To Provide the evidence by– Dealing with selection bias– Dealing with confounding factors

• To Influence evidence-based policy making process to– Improve better targeting of scheme– Improve design and implementation of schemes– Improve provider’s performance for cost-effective

services

Lao PDRAnonh XeuatvongsaMinistry of Health

Topics to be covered in the presentation

• Country profile• Enforcement of medical law• Findings from the research into the level and causes of household poverty and health seeking behaviour• Analysis of the way in which the Health Equity Fund is working• Some findings related to provider performance• Further issues to be explored

Country Profile & Health Indicators

• Population: 5.82 millions (2007)

• GDP 701US$ per capita (2007)

• GDP annual growth rate: 7.9%

(2007)

• 30% of the population under

poverty line (2005)

• Life expectancy 61 years (female

62, male 59) (2005)

• IMR 70 per 1000 live births

(2005)

• U5MR 98 per 1000 live births

(2005)

• MMR 405 per 100.000 live births

(2005)

Data source: National report 2006-07 and National census 2005.

National Health Expenditure

• 3.6% of GDP in 2005

• 17.5 USD per capita in

2005

- Out of pocket: 79.8% of

THE

- Donor: 11.3% of THE

- Domestic Gov. : 8.9% of

THE

• GGE on Health as % of

GGE: 4.6%

• Social security fund as %

of GGHE: 11.2%Data source: NHA unit, EIP/HSF/CEP, WHO, Geneva 2007

Transform of Medical Law into practice

Lao has a strong legal framework to protect poor people from catastrophic illness. However,• Law dissemination is yet saturated in public and whose responsibility is not clear,• Institutional arrangement to enforce the Law’s implementation is yet sufficient, • Fee exemption for poor is not standardized,• Inconsistent in identifying poverty level in different sectors.

Graph 1: Percentage of number of poor households officially recognized ( N= 3000 HHs )

91%

9%

No

Yes

Graph 2 : Comparison of Percentage of main reasons of being poor among 9% of poor (n = 270 recognized as poor households)

0.00% 20.00% 40.00% 60.00% 80.00%

a

b

c

d

e

e

d

c

b

a

a = Poor environment (e.g. unfertile soil, no land, natural disaster, crops damage by wild animals e.g. insects and mice…)b = Labor shortagec = Many dependentsd = Illness / disabilitye = Other

 

Rapid HH Survey [ in general ] (n=3000hh) : In-Dept Studies [ serious illness ] (n=150hh) .

Self Treatm. Out Patient In Patient .

Type of facility No ( n=809) (%) No (n=70hh) (%) No(n=99hh) (%) No(n=99hh) (%)

1. Govt. hospital : 353 43.64 15 21.5 65 65.6 65 65.6

a. Provincial Hospital : 138 17.1 9 12.9 44 44.4 44 44.4

b. District Hospital ] : 215 26.6 6 8.6 21 21.2 21 21.2

2. Govt. primary facility: 101 12.5 3 4.3 11 11.1 11 11.1[ Health center ]

3. Private facility : 30 3.7 7 10 2 2 2

4. Pharmacy: 182 22.5 33 47.1 3 3 3

5.. TBA/VHW : 19 2.3 1 1.4 3 3 3 3

6. Drugstore/shop/trader : 4 0.5 0 0 0 0 0 0

7. Traditional healer : 14 1.7 9 12.9 4 4.04 4 4.04

8. Religious faith healer : 4 0.5 1 1.4 2 2 2 2

9. Other : 94 11.6 1 1.4 9 9.1 9 9.1

10. Did not seek care: 8 0.9 0 0 0 0 0 0

Total 809 100 70 100 99 100 99 100

Remarks : 1. Poor households : 9% ( out of 3000 hh )

2. Death: n = 90 persons =>0.53% out of 17 093 persons

3. Serious health problem no treated because of cost: n = 102hh =>47.67

(out of 214 hh get serious health problem [ n = 219 persons] )

Health seeking behavior of people in the last month before the survey

Health seeking behavior of people with severe illnesses

Type of facilities Number of Households (n = 3000 ) Percent ( %) 1. Central hospital : 51 1.7 2. Provincial hospital : 712 23.7 3. District Hospital : 1545 51.5 4. Health Centre : 440 14.675. Private clinic : 75 2.5 6. Outside country : 10 0.3 7. Other ** : 167 5.63 Total 3000 100 .Remark : Specified places ** : 1. Military hospital : 81 HH => 48.51% out of 167 HH 2. Traditional medicine : 40 HH => 23.96 % ; 3. Pharmacy : 12 HH => 7.19% .

Coverage of Health Equity Fund

Number Percent Number Percent Number Percent % Year

Nambak District 6,535 11 4,652 8 4,652 7 20 2002

Vientiane province 10,148 3 11,230 3 11,230 3 6 2008

Sepone 7,984 23 10,890 24 34 2002

Location Pre-identified HEF member

2006 2007 2008Poverty rate

(NGPES)

103

23

407

230

462

253 258

55

289

125

272

104

26

200

397

102

1067

130

36

289

1076

468

1% 0%3% 3%

8% 7%

2% 1% 2% 2% 2%1% 1% 2%

8%

2%

28%

1% 1% 3%

27%

6%

0%

20%

40%

60%

80%

100%

0

200

400

600

800

1,000

1,200

1,400

SNK VKH FEU KAS HHP MAE TOU KEO PHG HOM VVG

% o

f poo

r hou

seho

lds

in p

rovi

nce

Poor

hou

seho

lds

in p

rovi

nce

HEF pre-identified Poor-Households in Vientiane Province

HEF HH (2007)

Poor Prov HH (2008)

%HEF HH (2007)

% Poor Prov HH (2008)

HEF: 2.522 HH (11.230 pers) pre-dentified = 3% of HH (3% pers.)vs

Poor Province: 3.895 HH (23.412 pers) pre-dentified = 5% of HH (6% pers.)

Excluding Saysomboun district

• General poverty level:– Three different situation from Sepone (very poor but changing very fast) to

average rural situation (Nambak) and low poverty rate (in Vientiane Province but ranging from 1 to 15% across districts)

• HEF coverage:– Lower rate of HEF pre-identification in Nambak and Vientiane Province

versus general poverty level defined by government (30% in 2005)– Decrease HEF in Nambak, and stable in Vientiane and Sepone

* NGPES = National Growth and Poverty Eradication Strategy

HEF by Wealth index. Study sites: Nambak, Vangvieng and Sepone

0

10

20

30

40

50

60

70

80

90

100

HEFNB

Poorest: 15,7%

Second: 23,4%

Middle: 23,2%

Fourth: 22%

Richest: 15,7%

0

10

20

30

40

50

60

70

80

90

100

HEFB

Poorest: 59,1%

Second 26,1%

Middle: 6,8%

Fourth: 8%

HEF Bénéficiaires (n=88) HEF Non Bénéficiaires (n=1412)

Source: RHS

Utilization of HEF

OPD: Visible positive impact of HEFB in Nambak and Vientiane ProvinceIPD: Visible positive impact of HEFB in the 3 HEF Schemes

Costs

Yearly data (Nambak, Vientiane province:

2007, Sepone: 2007/08)

Nambakdistrict

VientianeProvince (11 districts)

Sepone district

Total benefits/year $19,717 $54,896 $19,108

total benefits/HEFB capita

HEF Pre-id: $2,3; HEF Post-id: $1,9

HEF Pre-id: $2,8; HEF Post-id: $2,2 HEF Pre-id: $1,7

% OPD-IPD 12% vs 88% 19% vs 81% 18% vs 82%

% medical fees-transport-others 82% vs 16% vs 2% 74% vs 13% vs

13% 82% vs 11% vs 7%

Knowledge on types of services for free with the HEF members

Knowledge on benefits of HEF HEF Beneficiaries NEF NB

N % N %

Free medical services 78 98.7 470 98.5

Free food and soap while hospitalized 24 30.4 213 44.7

Free ambulance transportation to upper level

36 45.6 188 39.4

Free transportation back home of a relative’s body dead while hospitalized

34 43.0 147 30.8

Other (Room) 1 2.6 3 0.6

Provider performance

• No significant differences in treating poor and non poor patients

• Use of Essential Medicines is high in treating pneumonia 95% in poor patient (T1), 94% in near poor (T2) and 100% in non poor (T3)

• Unnecessary cost may not be high since many Essential Medicines prescribed

Provider performance continued

• However informal payment was 32% considered as unnecessary cost

• Access to health care for the poor may be a problem as expressed by one villager: “having no money for the care cost I would prefer dying at home rather than going to hospital”

Issues for further exploration

• How do we increase knowledge amongst potential users of the benefits of the Health Equity Fund beyond free medical care?

• Should we extend the Health Equity Fund to the 15% of the population classed as very poor who are not currently covered? How?

• How can we encourage patients to utilise health centres?

• What is the optimum strategy for preventing the misuse of the Health Equity Fund?

• What institutional arrangement should be seriously made to enforce the medical law?