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

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International Health Policy Program -Thailand Tracking progress in universal health access: Monitoring effectiveness of universal coverage in Thailand Supon Limwattananon, MPHM, PhD Viroj Tangcharoensathien, MD, PhD Prince Mahidol Award Conference, Bangkok Parallel Session January 2010

International Health Policy Program -Thailand 2 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

Top ten MDG4 performers Source: Analysis of World Health Statistics Thailand Source: Rohde et al. (Lancet 2008) Good Health at Low Cost ! Where is Thailand standing at? * GNI 100,000/year U5MR vs. THE per capita Low- and middle-income countries Rank

Source: Analysis of Socio-Economic Surveys (SES, various years) 8.4% 0.4% MDG1

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 insurance before and after the UC reform in 2001 Source: Analysis of Health and Welfare Surveys (HWS, various years) 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

st -3 rd NHP ( ) 100% provincial hospitals 1. Infrastructure development The path of health care coverage LIC CSMB 1980 CHF 1983 SS th -5 th NHP ( ) Expansion of district hospitals and health centers UC 2001 VHC MOPH established provincial hospitals 300+ health centers 2. 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 SS SS+ 2002

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 development

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 development

U5MR t = – * Population per doctor t * Population per nurse t * Population per bed t – 0.1 * USD GNI per capita t AR(1) time-series analysis

Source: Analysis of HWS (on health utilization) and SES (on health financing) Pro-rich Pro-poor Progressive financing utilization

CI – Source: Analysis of MICS2006 CI – Factors contributing to child malnutrition Concentration index (negative) Weight for age(negative) Height for age Elasticity Contribution 54.7% Elasticity Contribution 51.8% 1. Child’s age % % 2. Child’s squared age % % 3. Male child % % 4. (log) Income per capita % % 5. Mother’s college education % % 6. Number of children in household % % 7. Living in urban area % % Inequitydecomposition negative Underweight = 9.3% Stunting = 11.9%

International Health Policy Program -Thailand 13 Source: Health Resource Surveys; Civil Registration (Richest : Poorest)

Provincial economic status Two distinctive indicators GPP per capita (Baht) 1-30,000 30,001-50,000 50,001-70,000 70, , ,001+ Poverty head count ratio (%) 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 r = – 0.4

U5MR vs. Gross Provincial Product 2004 R 2 = Provinces (Greater Bangkok excluded) Three deep south provinces

U5MR vs. Provincial poverty rate Provinces (Greater Bangkok excluded) R 2 = Three deep south provinces

R 2 = 0.089R 2 = R 2 = Provincial variations

Healthstatus Financing & risk protection HealthutilizationHealthresource 1. Population & household surveys - DHS MICS NHES 1991, 1996, 2003, 2008 Socio-Economic Survey (SES) (q 5 y) (q 2 y) (q 1 y) Health and Welfare Survey (HWS) (q 1 y) (q 5 y) (q 1 y) (q 2 y) 2. Administrative & facility-based datasets Vital Registry VR National Health Accounts NHA Health insurance electronic IP data Health Resource Survey HRS Data available for tracking

International Health Policy Program -Thailand 19 Summary 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

International Health Policy Program -Thailand 20 Conclusions 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