PhD Thesis Proposal Review, NCEPH-ANU

Vasoontara Yiengprugsawan

1 February 2006

Health in transition: Decomposing health inequalities in Thailand

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Funding from the Wellcome Trust, United Kingdom andThe National Health and Medical Research Council, Australia

Partnership led by the National Centre for Epidemiology and Population Health of the Australian National University and The Sukhothai Thammatirat Open University

Part of the research capacity building: five PhD students

Thai-Health Risk Transition:A National Cohort Study

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Outline of the talk

Part 1: Background and Rationale

Part 2: Aim/ Objectives and Methodology

Part 3: Anticipated PhD contributions / Timeline

Questions and suggestions

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Economic Growth vsEconomic Development

Economic growth emerged after the World War II

Concentrated in increasing GNP (Gross National Product) and full employment but leaves out the question of the distribution of GNP

Economic development

= Economic growth + distribution

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Economic development

Handbook of development economics (1988)- Increasing productive capacity

- Eliminating absolute poverty

- Minimising income inequality

- Improving literacy

- Reducing infant and maternal mortality

- Increasing life expectancy

Macro economy

education

health

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Health inequalities

Inequalities in health have been defined as:

“The virtually universal phenomenon of variation in health indicators (infant and maternal mortality rates, mortality incidence rates of many diseases, etc.) associated with socio-economic status”

J. Last (1995) A Dictionary of Epidemiology

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Why am I doing this?

Thailand

Economic, Inequality, Health

BACKGROUND: Thailand

Economic and Health in transition

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Gross Domestic Products per Capita in US Dollars, 1960-2003

Source: NESDB, cited in Thailand Health Profile 2001-2004, Ministry of Public Health

Thailand: Rapid economic changes

BoomBust

Beyond

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Source: Thailand Health Profile 2001-2004, Ministry of Public Health

Relative poverty(inequality)

Absolute poverty

(20% highest) income group

(20% lowest) income group

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Source: P. Faramnuoyphol (2005). Multilevel analysis for measuring inequality in health. Presented at Conference on "Health equity: lesson learnt from universal health coverage (translated title)", Miracle Grand Hotel, Thailand.

Socio-economic score (provincial and district)

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Health in transition: demographic and epidemiological changes

Demographic transition Life expectancy Population growth stabilise at around 1% Household size has shrunk from 5.5 (1960) 3.5 (2005) Ageing population

Epidemiological transition Child and maternal mortality Communicable and vaccine preventable diseases Chronic diseases, injuries and mental health Cigarettes, alcohols, and unsafe sexual practices

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Thai Health System

Disparities in health status (geographical area) Variation in life expectancy (highest in BKK and lowest in the

Northeast) Infant Mortality rate in non-municipal 1.85 times > municipal areas UNDP human development index: BKK has the highest health index,

while the Northeast and the North have the lowest

Unequal distribution of health services (during boom period) Human resources and medical technologies Healthcare budget (from centralised system)

Unequal access to healthcare and burden of health expenditures Greater utilisation by the rich Greater burden of health expenditure to the poor

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Recent policy developments

Universal Coverage of Healthcare Scheme

“30 Baht Scheme” implemented since 2001

Ongoing implementation of Decentralisation Act 1999 in basic services including health: increase the proportion of revenue of overall local administration

from 9% to 20% in 2001 35% in 2006

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PART II

Rationale

Aim and objectives

Conceptual framework

Datasets

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Underlying research questions

By applying population health approach, three main questions are being raised:

Are there any links between economic and health inequalities?

Are these health inequalities systematically distributed across identifiable social and economic characteristics?

How could public policy ought to be deployed to maximise health related-outcomes and minimise these variations in general population?

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Rationale

Thailand has gone through rapid economic and heath transition.

Inequality in health-related outcomes is according to overall social and economic structure, household characteristics, as well as the characteristics of health system.

While downstream interventions are important, their effect will be limited if ignore the underlying determinants of health which have derived from their socio-economic status (income, education, occupation, etc.)

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RationaleWork already been done in Thailand:

Ministry of Public Health’s International Health Policy Program and Health Systems Research Institute:

Allocation of human resources for health Equity in the financing and delivery of health system Benefit- incidence analysis of government spending Pre-post analysis of Universal Health Scheme

My study may have a slight different focus and will thus attempt to look at the underlying determinants of health inequalities at both household and area levels.

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Aim and Objectives

AIM: To quantify and determine underlying (socio-economic) factors responsible for health inequalities in Thailand and to relate this to an observable trend

Obj 1: To measure overall health inequalities using concentration indices

Obj 2: To decompose health inequalities into underlying determinants at household level

Obj 3: To decompose health inequalities into underlying determinants at both household and area levels

Obj 4: To measure the magnitude of changes in health inequalities over time

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Conceptual framework

Key outcomes 

Health status  

Use of health services

Out-of-pocket health expenditures

Proximate determinants

Household level

Use of preventive/ curative services

Dietary and nutrition intake

Lifestyle – Smoking and alcohol

Lifestyle- Sexual practices

Occupation and transport hazard

Community level

Quality of water and sanitation

Availability of health facilities

Access to health facilities

Ambient environment

Health system and government policies

Health service provision (prices and quality)

 Health finance (public health facilities, insurance)

Supply in related sectors (e.g. electricity, roads, food)

Underlying determinants

Household level

Financial capital (income, expenditure, asset)

Human capital (knowledge, literacy, education)

Household characteristics (size, gender, age)

Physical setting (in relation to health services)

Area level

Ecology and geography setting

Social capital

Local government spending

Obj 1Obj 2

Obj 3

Obj 4 trend

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Datasets: HWS and SES

Household Socio-Economic Survey (SES)Conducted by National Statistical Office  

Unit data available: 1986, 1988, 1990, 1992, 1994, 1996, 1998, 2000, 2002, 2004

 Used a stratified two-stage sampling method  

Private sample households: 32,828 Relevant categories of questions are:  a) Expenditure on health care and personal

supplies and services b) Medical treatment expense (outpatients) c) Medical treatment expenses (inpatients) and

optometric

Health and Welfare Survey (HWS)Conducted by National Statistical Office  

Unit data available: 1986, 1991, 1996, 2001, 2003, 2004, 2005

 Used a stratified two-stage sampling method Private sample households: 26,520

 Relevant categories of questions are:

a) Illness during 1 month before interview’s date

b) Using health services due to chronic disease

c) Admission in hospital during 12 month before interview’s date (inpatient care)

Plus utlisation of healthcare and out-of-pocket expenditure

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Methodology

Obj 1:Measuring health inequalities ( concentration indices)

Obj 2:Decomposing underlying h/h determinants

Obj 3:Decomposing underlying h/h + area level determinants

Obj 4:Measuring magnitude of changes in health inequalities

Socio-economic rank using asset index

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Objective 1: Method

Obj 1: To measure socio-economic health inequalities

Alternative measures:

Pure health inequalities

- Lorenz curve, Range measure, Index of Dissimilarity

Socio-economic health inequalities

- Slope Index of Inequality, Concentration Curve

Selected measure: Concentration curve and index

(Source: Wagstaff, A., P. Paci, and E. van Doorslaer 1991)

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Objective 1: Method

Concentration curve

Plots the cumulative proportions of the population (worst off to better off) against the cumulative proportions of health indicators

 

If health is equally distributed across socio-economic groups, the concentration curve will coincide with the diagonal

Source:

Kakwani 1981; Wagstaff, A., P. Paci, and E. van Doorslaer 1991

Kakwani, N., A. Wagstaff, and E. van Doorslaer 1997

Cu

mu

lati

ve %

pop

ran

k b

y h

ealt

h-r

elat

ed

outc

omes

Cumulative % pop rank by SES

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Objective 1: Method

Concentration index: area between

the diagonal and concentration curve

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1

i

n

i iRynC

Mean of y

Health-related outcomes-Self-reported health

- Healthcare utilisation-Health expenditures

Rank of person/household into socio-economic distribution

Cu

mu

lati

ve %

pop

ran

k b

y h

ealt

h-r

elat

ed o

utc

omes

Cumulative % pop rank by SES

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Y Variable: Self-reported health

(from HWS)

Acute“During 1 month before interview, how many times did you get ill?”

“In last time of illness, what was the kind of sickness or symptoms?”

“Did the illness keep you away from regular activities and how long?”

Chronic

“Within 12 month, have you had any chronic diseases?”

“If yes, record the name of the chronic disease"

In-patient

“In last admission, what kind of diseases did you have?”

“How long did you spend in last admission?”

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Y Variable: Self-reported health

(from HWS)

Report of Health and Welfare Survey 2005, National Statistical Office (NSO) Symptoms and illnesses are being reported in 15 broad categories: Diseases of the respiratory system Diseases of the digestive system Diseases of urinary system Cardiovascular diseases Infectious diseases Diseases of skin Allergic condition Diseases of oral cavity, ear, throat, nose, eye Disease of female genital organ Condition related to delivery Diseases of endocrine system, metabolic diseases and nutritional status Diseases of the musculoskeletal system and connective issue Diseases of the nervous system and mental disorder Others Unknown

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Y Variable: Health-care utilisation

(from HWS)

Acute “How were you treated in last time of illness?” “In each treatment, did you use the health welfare scheme?”

Chronic “Did you obtain any healthcare services due to your chronic disease?" "Which type of the health care provider did you seek?" “For the last service, did you use the health welfare scheme?”

In-patient “Have you been admitted (including delivery), how many times?” “What type of health facilities were you admitted in?” “In last admission, did you use the health welfare scheme?”

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Y Variable: Health-care utilisation

(from HWS)

Acute “How were you treated in last time of illness?” “In each treatment, did you use the health welfare scheme?”

Chronic “Did you obtain any healthcare services due to your chronic disease?" "Which type of the health care provider did you seek?" “For the last service, did you use the health welfare scheme?”

In-patient “Have you been admitted (including delivery), how many times?” “What type of health facilities were you admitted in?” “In last admission, did you use the health welfare scheme?”

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Y Variable: Health-care utilisation

(from HWS)Type of healthcare utilisation None Use herb medicine Traditional healer Self-treatment Government health office/

Primary care unit Community hospital General/ Center hospital University hospital Other government hospital Private clinic Private hospital Others Unknown

Health Welfare Scheme Does not have health insurance Use health insurance from

• Government office welfare/ pensioner/ state enterprise welfare

• Social security/ worker compensation fund

• Universal coverage card (UC card)

• Not pay fee 30 Baht

• Pay fee 30 Baht

• Private health insurance

• Employer welfare

• Others Have health insurance but not used Unknown

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Y Variable:Health-care expenditure

Acute “For each treatment, how much did you spend in your last

ambulatory care?”

Chronic “How much did you spend in the last service?”

In-patient “How much did you spend for your expenditure in last admission?” “How much did you spend for your travelling between home and

hospital in last admission?”

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Method: ranking households

Alternatives for ranking households Indicator in money terms vs. non-monetary terms Actual consumption – best but data not easy to obtain Income and expenditure – but these may exclude non market-based

transactions and reporting bias

Asset index Asset-based index (non-monetary less likely to contain reporting bias

and easier for validation)

Good proxy for long-term wealth

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Method: ranking households

Method Principal Components Analysis (PCA) to determine the weights for

an index of asset variables

Dataset Health and Welfare Survey 2005

Variables Household characteristics (i.e. type of household, no. of rooms) Source of water and method for garbage disposal Household ownership of durable goods (i.e. kitchen facilities, TV, DVD,

type of automobiles)

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Objective 2: Method

Obj 2: To decompose underlying household determinants

1) The various underlying determinants (i.e. income, education, occupation, housing) on health-related outcomes

2) The distribution of these underlying health determinants across socio-economic groups

Dataset: HWS 2005

Variables: Self-reported health, health-service utilisation and health care expenditure

Method: Multivariate regression analysis

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Objective 2: Method

Obj 2: Decompose underlying household determinants

Health-related outcomes

Self-reported healthHealthcare utilisationHealth expenditure

kik ki xy

Coefficient vector

Underlying determinants (income, education, occupation, housing)

An error term

12

1

i

n

i iRynC

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Objective 3: Method

Obj 3: Decompose underlying household + area determinants

To what extent are socio-economic inequalities in health due to inequalities in the area rather than the household?

“Composition” vs “Contextual” socio-economic effects

Method: Possibility of 1) adding fixed-area effect or 2) using multi-level analysis

Dataset: Census of Population and Housing 2000 (to be confirmed)

Local government health-related spending

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Objective 3: Method

Obj 2: Decompose underlying household determinants

Variable of interestSelf-reported healthHealthcare utilisationHealth expenditure

kik ki xy

Coefficient vector

Underlying determinants (income, education, occupation, housing)

Fixed effect at area level

An error term

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1

i

n

i iRynC

Obj 3

Health-related outcomes

Self-reported healthHealthcare utilisationHealth expenditure

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Objective 4: Method

Obj 4: To measure magnitude of changes in health inequalities

Trade-off between improvement in overall average level of health vs. increases in health inequality over time

Changes in mean of the underlying determinants Changes in the distribution of these determinants

To understand the impacts of government health-related policies (i.e. Universal Health Scheme) in reducing health disparities

Method: Comparing changes in concentration indices and their underlying determinants from survey in different years (Oaxaca’s method)

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Objective 4: Method

Obj 4: To measure the magnitude of changes over time Dataset:

HWS: 1986, 1991, 1996, 2001, 2003, 2004, 2005

The criterion in selecting years for the analysis: At the peak of the growth 1996 Comparing before/after the Universal Health Scheme in 2001 Variables: - Household variables may be limited in the earlier surveys- Questions from the surveys have been changed over time- Area level-data still to be confirmed for earlier years

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Anticipated PhD contributions

Understanding of health and economic transition in Thailand

Understanding of socio-economic and geographic determinants of health inequalities

Understanding of the changes in health inequalities over time

Understanding of the possible impact of government health-related policies

Contribution to the further use of existing national surveys

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Timeline

2005 (March – Dec) Thesis proposal

2006

Jan – April: Fieldtrip I (obtain dataset and consult experts)

May-December: Data analysis

2007

Jan-April: Fieldtrip II (interview experts)

May-December: Data analysis

2008

Jan-April: Write up

Overall plan

Part I:

Literature review

1978-2004

(Thailand: the linkage & transition of economic & health inequalities)

Part III:

Qualitative analysis

Interview experts

(Delphi technique)

Part II:

Quantitative analysis

Health and Welfare Survey

Cross-sectional 2005

Changes over time 2005, 2001, 1996

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NCEPH – Australian National University Prof. Adrian Sleigh (Chair, overall project)

Dr. Alexandra Sidorenko (Supervisor, health economics)

Dr. Lynette Lim (Supervisor, statistics)

Collaborators in Thailand National Statistical Office

Ministry of Public Health

National Economic and Social Development Board

Sukhothai Thammatirat University

Supervisory panel and experts

Thank you very much

Comments and suggestions please