The Benchmark of Fairness for Health System Reform: the Tool for National and Provincial Health Development in Thailand (Phase II)

BM9 1 BM1 Average income per capita Gini coefficient 30 2 7,000 0.55 29 4 3 28 4 0.5 6,000 27 2 5 0.45 BM7 5,000 26 - 6 0.4 4,000 25 7 0.35 (2) 3,000 24 8 0.3 (4) 23 9 2,000 0.25 BM2 22 10 1,000 0.2 0 50,000 100,000 150,000 200,000 250,000 300,000 21 11 GPP per capita BM6 20 12 19 13 18 14 Gini coefficient Average income per capita 17 15 BM3 BM5 16 BM4

Pinij Faramnuayphol Supasit Pannarunothai

By The Centre for Health EquityMonitoring, Fuculty of Medicine Naresuan University

With support from Rockefeller Foundation Health System Research Institute Content Foreword Acknowledgement Figure and Table Abstract

Chapter 1 Introduction 1 1.1 Why health system must be equitable? 1 1.2 Principles and trends of health equity 3 1.3 Health equity status 6 1.4 Various tools to measure health equity 10

Chapter 2 Equity in health and measurement tool 11 2.1. The benchmarks of fairness for health care reform 11 2.2. Experiences of the benchmarks in Thailand 14 2.3. The benchmarks for Phase 2 and analysis 15

Chapter 3 Equity of health status: quantitative data 19 3.1. Distribution of health status 19 3.2. Distribution of health determinants 23 3.3. Relationship with health status 28 3.4. Summary of equity in health status 29

Chapter 4 Equity in health finance: Quantitative data 31 4.1. Financial barrier to equitable services 31 4.2. Financial burden from health spending in various groups 33 4.3. Summary on equity in health finance 34 Chapter 5 Equity in health delivery: Quantitative data 35 5.1. Distribution of health resource 35 5.2. Accessibility and utilization 38 5.3. Efficiency and quality of service 44 5.4. Efficiency in management 50 5.6. Relationship between factors influencing health service utilization 60 5.7. Summary on equity of health delivery 61

Chapter 6 Links between equity benchmarks 64 6.1. Influence across equity benchmarks 64 6.2. Integrated links between indicators 67 6.3. Summary of the links between equities 68 6.4. Total score of equity 69

Chapter 7 Equity in health in 10 provinces: Qualitative data 72 7.1. Qualitative data from 10 provinces 73 ! ChiangMai 73 ! Phayao 79 ! Phare 85 ! Khon Kaen 91 ! NaKhon Ratchasima 97 ! Phuket 103 ! Songkhea 109 ! Pattani 115 ! Ayuthaya 121 ! Ratchaburi 127 7.2. Overall scores for equity by qualitative technique 133 Chapter 8 Overall picture of equity 134 8.1 Strengths and weaknesses of equity in health 136 8.2 Influences of other factors on health system 139 8.3 Direction and distribution of equity in health 140

Chapter 9 Systems recommendations 141 9.1. The benchmarks as policy tool 141 9.2. The benchmarks as a tool to monitor the impact of development programmes 145 9.3. Equity as a tool to mobilize participation and encourage decentralization 148 9.4. Information system to monitor equity in health 149

Chapter 10 Discussion and conclusion 150 Part 1 The measurements of health equity 150 10.1. Methods for expressing equity 150 10.1.1. Composite index for inequity 151 10.1.2. Comparison of Variables among groups and areas 153 10.1.3. Scoring for inequity 154 10.2. Strengths and weaknesses of health equity measurements 154 10.3. Data for the measurement of equity in health 158 10.4. Appropriateness and uses in policy and health reform 160 Part 2 The appropriateness of the tools used to measure health equity 162 10.5. Adaptation of quantitative tool 162 10.6. Adaptation of qualitative tool 162 10.7. Next steps for health equity 168 10.8. Conclusion 174 References 165 Appendix Figures and Tables Figures

Figure 1.1 Relationship between wealth by GNP per capita and income distribution by Gini Coefficient 1 Figure 1.2 Equity principles of finance and utilisation by risk pooling and income share of insurance mechanisms (WHO 2000) 4 Figure 1.3 Trend of Gini coefficient of income distribution 6 Figure 1.4 Population to doctor ratio by region 1999 6 Figure 1.5 Illness rate by income quintiles (Quintile 1 was the poorest 20%) 7 Figure 1.6 Kakwani index of health finance 1996 8 Figure 1.7 Percentage of health spending to household income by income quintile 8 Figure 1.8 Proportion of health care consumption by income quintiles 9 Figure 1.9 Per capita spending by health benefit scheme 9 Figure 2.1 Geographical distribution of Z-scores for 75 provinces for age-adjusted death rate 16 Figure 3.1 Crude and age-sex adjusted death rates by 75 ranked by crude death rate 19 Figure 3.2 Life expectancy at birth by Z-score 20 Figure 3.3 Relationship between life expectancy at birth (x-axis) and age-specific mortality Rates 20 Figure 3.4 Distribution of traffic car crashes 21 Figure 3.5 Relationship between traffic car crashes (X-axis) and deaths in 30-40 year old (Y-axis) 21 Figure 3.6 Malnutrition rate in children and low birth weight by province 21 Figure 3.7 Distribution of malnutrition rate in children 22 Figure 3.8 Distribution of low birth weight rate 22 Figure 3.9 Coverage of antenatal care by province 22 Figure 3.10 Access of household to clean drinking water 23 Figure 3.11 Proportion of household with good housing sanitation 23 Figure 3.12 Distribution of size of household 24 Figure 3.13 Distribution of dependency ratio 24 Figure 3.14 Shows the relationship between household size and dependency ratio by province. 25 Figure 3.15 The prevalence of poverty by per capita income (dot) and household debt (diamond) 25 Figure 3.16 Distribution of income per capita 26 Figure 3.17 Distribution of people under poverty 26 Figure 3.18 Income per capita (x-axis) and household expenditure to income ratio (y-axis) 26 Figure 3.19 Relationship between gross provincial product (x-axis), income per capita (diamond) And income distribution by Gini coefficient (dot) 27 Figure 3.20 Overal deprivation index 27 Figure 3.21 Relationship between income per capita (x-axis) and malnutrition among children (y-axis) 28 Figure 3.22 Relationship between standardized mortality (x-axis) and the dependency ratio (y-axis) 28 Figure 3.23 Relationship between dependency ratio (x-axis) and the proportion of population Aged 0-14 (diamond), 15-64 (dot) and 65 and above 29 Figure 4.1 The coverage of health insurance by province 31 Figure 4.2 The coverage of the better-off insurance schemes 31 Figure 4.3 Relationships between the percentage of uninsured with the coverage of the better-off (dot) and moderate schemes (diamond) 32 Figure 4.5 Distribution of the relative burden of health spending among the unemployed 33 and Businessmen Figure 5.1 Distribution of population to doctor ratio 35 Figure 5.2 Distribution of population to bed ratio by province 36 Figure 5.3 The relationship between population to doctor and population to bed 36 Figure 5.4 Relationship between population to bed (x-axis) and bed to doctor (y-axis) by 4 groups of provinces 36 Figure 5.5 Distribution of outpatient to doctor ratio by province 37 Figure 5.6 Distribution of inpatient to doctor ratio by province 37 Figure 5.7 Relationship between population to doctor ratio (x-axis) and outpatient (dot) and inpatient to doctor ratio (diamond) 37 Figure 5.8 Distribution of the rate of new outpatient attendants 38 Figure 5.9 Correlation between population to doctor ratio (x-axis) and new outpatient attendant (y-axis) 38 Figure 5.10 Correlation between population to bed ratio (x-axis) and admission rate (y-axis) 38 Figure 5.11 Distribution of outpatient uses in MOPH infrastructure 39 Figure 5.12 Distribution of inpatient uses in MOPH hospitals 39 Figure 5.13 Correlation between outpatient (x-axis) and inpatient uses in MOPH infrastructure (y-axis) 39 Figure 5.14 Distribution of ratio of health centre use to hospital utilization 40 Figure 5.15 Correlation between rate of overall health utilization (x-axis) and rate of health centre utilization (y-axis) 40 Figure 5.16 Distribution of proportion of services at the provincial level 41 Figure 5.17 Distribution of ratio of admission to community hospital to provincial hospital 41 Figure 5.18 Distribution of number of repeated visits by new attendant 42 Figure 5.19 Distribution of proportion in health promotion activities to all activities at health centre 43 Figure 5.20 Relationship between use rate at health centre (x-axis, visit/person/year) and proportion of health promotion activity at health centre (y-axis) 44 Figure 5.21 Bed occupancy rate (x-axis) and bed turnover rate (y-axis) for regional and general Hospital 44 Figure 5.22 Distribution of occupancy rate of provincial hospital 45 Figure 5.23 Relationship between inpatient to outpatient ratio (x-axis) and admission rate (y-axis) 45 Figure 5.24 Relationship between casemix index (x-axis) and the average length of stay (y-axis) of regional and general hospitals 46 Figure 5.25 Relationship between unit cost by outpatient (x-axis) and unit cost by inpatient (y-axis) 47 Figure 5.26 Distribution of unit cost by inpatient 47 Figure 5.27 Relationship between cost per day (x-axis) and cost per inpatient (y-axis) 48 Figure 5.28 Relationship between length of stay (x-axis) and cost per inpatient (y-axis) 48 Figure 5.29 Relationship between casemix index and case fatality and length of stay 49 Figure 5.30 Relationship between unit cost for inpatient (x-axis) and case fatality (y-axis) 49 Figure 5.31 Distribution of public health spending per capita 50 Figure 5.32 Public health spending per capita (x-axis) and outpatient uses (y-axis) at public Sector 51 Figure 5.33 Relationship between public health spending per capita (x-axis) and bed to population ratio (y-axis) 51 Figure 5.34 Relationship between population (x-axis) and total spending per capita (y-axis) 52 Figure 5.35 Distribution of proportion of spending at health centre 53 Figure 5.36 Distribution of spending at provincial hospital per capita 53 Figure 5.37 Distribution of expected health spending per capita 54 Figure 5.38 Distribution of ratio of real to expected health spending 54 Figure 5.39 Distribution of provincial hospital expenditure per bed 55 Figure 5.40 Distribution of proportion of labour cost at provincial hospital 56 Figure 5.41 Distribution of proportion of administrative cost of provincial hospital 56 Figure 5.42 Distribution of proportion of use in public health sector 58 Figure 5.43 Distribution of ratio of private to public sector use for outpatient service 59 Figure 5.44 Relationship between income per capita (x-axis) and ratio of private (star) to public sector use (diamond) 59 Figure 5.45 Relationship between health spending per cap (x-axis) and use rate for inpatient care (y-axis) 60 Figure 6.1 Relationship between gross provincial product (x-axis) and ratio of population to Doctor (y-axis) 64 Figure 6.2 Relationship between income per capita (x-axis) and coverage of good insurance Schemes (y-axis) 64 Figure 6.3 Relationship between income per capita (x-axis) and use rate of new case at outpatient service (y-axis) 65 Figure 6.4 Relationship between income per capita (x-axis) and proportion of choice at private hospital for inpatient service (y-axis) 65 Figure 6.5 Relationship between total spending per capita (x-axis) and proportion of use at provincial hospital (y-axis) 66 Figure 6.6 Relationship between total spending per capita (x-axis) and ratio of admission at community to provincial hospital (y-axis) 66 Figure 6.7 Relationship between the total health spending per capita (x-axis) and the provincial hospital spending per capita (y-axis) 66 Figure 6.8 Inter-link between factors affecting health equity 66 Figure 6.9 Links between the equity status by 30 indicators and Benchmarks of fairness by 8 benchmarks (except benchmark 8) by quantitative approach 68 Figure 6.10 Radar graph showing strengths and weaknesses of province by 30 indicator 69 Figure 7.1 Radar graph for 30 indicators for Chiang Mai 75 Figure 7.2 3-dimentional surface graph showing scores for 46 items on equity rated by 8 focus group discussions in Chiang Mai 78 Figure 7.3 Graph of the scores by 30 indicators, Phayao 80 Figure 7.4 The 3-dimensional surface graph for 46 items by 8 focus group discussions in Phayao 85 Figure 7.5 Graph for score by 30 indicators, Phrae 86 Figure 7.6 The 3-dimensional surface graph for 46 items by 8 focus group discussions in Phrae 90 Figure 7.7 Radar graph for 30 indicators Khon Kaen 92 Figure 7.8 3-dimensional surface graph of 46 indicators by 8 focus group discussions in Khon Kaen 96 Figure 7.9 Scores by 30 indicators for Korat 98 Figure 7.10 Contour graph showing results from 46 indicators by 8 focus groups in Nakhon Ratchasima 102 Figure 7.11 Radar graph of 30 indicators for Phuket 104 Figure 7.12 3 dimension surface graph for detailed scores of 46 items given by 8 focus group discussions in Phuket 108 Figure 7.13 The scores by 30 indicators for Songkhla 110 Figure 7.14 Three-dimensional surface graph shows details of scores by 46 items by 8 focus group discussions in Songkhla 114 Figure 7.15 Scores by 30 indicators in Pattani 116 Figure 7.16 3-dimensional surface graph for 46 items from 8 focus group discussion in Pattani 120 Figure 7.17 Graph of scores for 30 indicators in Ayuthaya 122 Figure 7.18 3-dimensional surface graph by 46 items 46 from 8 focus group discussion in Ayuthaya 126 Figure 7.19 Graph of score by 30 indicators in Ratchaburi 128 Figure 7.20 3-dimensional surface graph of score by 46 itmes from 8 focus group discussions in Ratchaburi 132 Figure 7.21 Difference of summary scores before and after discussion (score post – pre discussion) 135 Figure 8.1 Relationships between determinants of equity in health 139 Figure 9.1 Setting target for equity by improving the average level and reducing the difference 142 Figure 9.2 Cost per relative population for outpatient and inpatient by age group 142 Figure 9.3 Cost per capita at community hospital for population size in central provinces and adjustment 143 Figure 9.4 The chance of being surplus or deficit for the different uses of private services 144 Figure 9.5 Strengths and weaknesses according to development strategies using quantitative tool of the benchmark 146 Figure 9.6 Changes of equity score when data changes while the average and standard deviation constant 147 Figure 9.7 Changes of equity score over time with changes of mean and standard deviation 147 Figure 9.8 Use of qualitative approach of the benchmark as the entry point for people Participation 148 Figure 9.9 Information systems related to the benchmarks by 7 subsystems and 4 levels 149 Figure 10.1 Relationship between maximum and minimum scores and coefficient of variation by 81 indicators 163 Tables Table 1.1 Definitions of horizontal and vertical equity in health delivery and finance 3 Table 1.2 Equality of child survival index for Southeast Asia countries (WHO 2000) 7 Table 2.2 The scores according to 9 benchmarks in Phayao and Yasothon 14 Table 2.3 Age-adjusted mortality rate by top and bottom 3 provinces ranked by Z-score 17 Table 3.1 Overall Z-score of the benchmark 1 for the first top 3 and the bottom 3 ranks 30 Table 4.1 The average score by benchmarks 2 and 5 34 Table 5.1 Scores of health equity by benchmarks 3, 4, 6, 7 and 9, for the first 3 ranks and the bottom 3 ranks 62 Table 6.1 Total score of equity and rank by province according to 3 approaches 70 Table 7.1 Average score of 30 indicators for Chiang Mai 75 Table 7.2 Scores for equity by 9 benchmarks and summary scores from 8 focus group discussions in Chiang Mai 77 Table 7.3 Average scores for 30 indicators for Phayao 80 Table 7.4 Equity scores for 9 benchmarks by 8 focus group discussions in Phayao 83 Table 7.5 Average score for 30 indicators of Phrae 86 Table 7.6 Equity scores for 9 benchmarks and overall scores by 8 focus group discussion in Phare 89 Table 7.7 Average score by 30 indicators, Khon Kaen 92 Table 7.8 Equity scores by 9 benchmarks and summary scores by 8 focus group discussion, Khon Kaen 95 Table 7.9 Average score by 30 indicators, Korat 98 Table 7.10 Equity score for 9 benchmarks and summary score by 8 focus group discussion, Nakhon Ratchasima 101 Table 7.11 Average scores of 30 indicators for Phuket 104 Table 7.12 Health equity scores for 9 benchmarks by 8 focus group discussions in Phuket 107 Table 7.13 The average scores by 30 indicators for Songkhla 110 Table 7.14 Equity score by 9 benchmarks and overall scores by 8 focus group discussions in Songkhla 113 Table 7.15 Average scores by 30 indicators in Pattani 116 Table 7.16 Health equity score by 9 benchmarks from 8 focus group discussions in Pattani 119 Table 7.17 Average scores by 30 indicator in Ayuthaya 122 Table 7.18 Equity scores of 9 benchmarks by 8 focus group discussions in Ayuthaya 125 Table 7.19 Average scores by 30 indicators in Ratchaburi 128 Table 7.20 Overall scores of 9 benchmarks by 8 focus group discussion from Ratchaburi 131 Table 7.21 Equity score by benchmark and by province, the average score for 10 provinces 133 Table 7.22 Equity score by focus group discussion and by province in 10 provinces 134 Table 7.23 Changes of equity scores after focus group discussion by province in 10 provinces 135 Table 8.1 Average health equity scores by 46 items in 10 provinces 137 Table 8.2 Strengths and weaknesses of 9 benchmarks by province 140 Table 10.1 Objective and process to measure equity in health by composite index 151 Table 10.2 Objective and process for measuring health equity by comparison between groups or areas 153 Table 10.3 Objective and process in measuring equity in health by scoring 154 Table 10.4 Strengths and limitations for measuring equity in health by composite indicator 155 Table 10.5 Strengths and limitations for measuring equity in health by comparison between group or area 156 Table 10.6 Strengths and weaknesses of health quity measurement by scoring 157 Table 10.7 Data requirement for measuring health equity by composite index 158 Table 10.8 Data requirement for measuring equity in health by comparison between groups and geographical area 159 Table 10.9 Data requirement for measuring equity in health by scoring 160 Table 10.10 Comparison of methods for measuring equity in health 161 Table 10.11 Proposed 22 questions and quantitative data to support decision 171 Abstract

Abstract

The trend of health care reform has given stronger emphasis on health equity. Since health care resources are scarce, distribution of these resources should promote equal access to all alike. There are two important aspects of health equity: equity in terms of health delivery and in terms of health financing. Measurements by available tools on equity have shown that inequities in health exist. This study provided another alternative of using the measurement tool called ‘the benchmark of fairness for health system reform’ with extensive community involvement to assess the final effects of the reforms. The tool composed of 9 benchmarks covered from intersectoral health elements, to equitable access and financing to comprehensive health care, efficiency of quality health services, accountability and empowerment to autonomy of consumer and provider. Two kinds of tools used in this study were based on quantitative and qualitative techniques. The quantitative tool was packed with a set of indicators that collected from various sources of data. There were 81 indicators within 30 groups covered 8 benchmarks except accountability (benchmark 8). The summary statistics were calculated to present variations of data among 75 provinces. Z-score was used as a score of benchmarks for this quantitative tool. Various techniques of analysis were applied such as z-scoring, ranking, mapping and zoning, and area inside the spider graph. The overall benchmark of fairness in 75 provinces was represented and ranked by summation of z-scores, of ranks, and of area inside spider graph. The qualitative tool employed focus group discussions and the scoring of the benchmarks before and after focus group discussion. Ten provinces involved in health decentralization project were selected for testing this qualitative tool covering four regions in Thailand. Eight groups of participants were invited into the focus group discussions. The target participants were health managers, health care providers, local governments, and civil societies. The research gave equal importance of qualitative contents as same as the scores at the end of the discussions. All participants responded to a set of 46 questions covered 9 benchmarks of fairness before and after discussions. The participants assessed the benchmarks compared the present situation with the situation of three years ago, on a scale of -5 to 5, with zero as baseline (situation at 3 years ago). The results showed the different perceptions from different groups of targets. There were two dimensions of assessing benchmarks of fairness, one was the benchmarks across the provinces from quantitative data and the other was the benchmarks along the time from qualitative discussion and scoring. A balance between two sources of scores had to be made to make this evaluation tool more scientific, reasonable and acceptable. However these tools should be developed continuously to be more sensitive, effective and comparative in both cross-sectional and longitudinal dimensions.

Keywords: health system reform, benchmarks, fairness, equity, efficiency, quality, accountability, autonomy, civil society I. Introduction 1

I Introduction

This chapter will introduce the importance of health equity in both health delivery and finance. Some indicators for assessing equity both internationally and locally will be presented which will lead to framing the extent of this study.

1.1. Why health system must be equitable?

There are ample evidences showing that any countries under-perform in distribution of income, they would face unsustainable economic growth, no matter how high economic growth they achieved. This strong message warns the countries to pay more attention on ‘equity’. Thailand was no exception, recent economic crisis in 1997 was a good example that heated economic growths early 1990s with bad income distribution led to a new round of economic development cycle(1). Furthermore, main finding from international comparison between income distribution and per capita income showed an inverted U curve (see figure 1.1). In the first place, Gini coefficient rises as income per capita rise. However, income inequality reaches its peak and later lowered down to reach higher per capita income. It can be interpreted that only the lowest and the richest income countries could have good income distribution. The middle income countries could not pass to be a high-income country if they failed to distribute income from higher income quintile to the lower income quintiles.

Fig 1.1 Relationship between wealth by GNP per capita and income distribution by Gini coefficient 2 Benchmarks of Fairness for health reform

The social justice has broader implications than just income distribution. The new Constitution of Thailand in 1997 portrays many aspects of equity or fairness, from equal basic human rights for all Thais, to equal political rights, equal access to public information and equality in health. The Constitution, Act 52 states that ‘every individual has equal rights in getting good quality health services, and the underprivileged have access to services at public facilities without paying charges, according to the subsequent organic laws’ (1). What was not thought of in the Constitution was the impact of income inequality. If people still have low income, exempting them from paying fees at public facilities is a minimal solution to poverty. If severe income inequality has been remedied, people will have higher disposable income and perhaps can pay for services better. However, the best solution is raising people’s wealth and the government gives equal opportunity to all to get access to health services regardless of ability to pay.

One more reason supporting the importance of equity. Since health resources are scarce, distribution of these resources for benefits of all people according to need was the ultimate goal of the good health systems. In the World Health Report 2000, health systems performance was assessed according to 3 factors: health, health delivery and financing. Health was the outcome of the health system, it was assessed in terms of level of health and distribution of health between individuals. Health delivery was called responsiveness and was assessed in terms of level of responsiveness and equitable distribution of responsiveness to individuals. Financing was assessed by fairness of financial contribution for health by individual families. The report confirmed strong emphasis on fairness as well as goodness of the health systems(3).

In the rapid transition of health systems, there is a need to monitor and evaluate health systems in order to be aware of the impacts of changes on health either positive or negative. The impacts of health systems transition on equity, efficiency and quality of care and social accountability have to be closely monitored. These aspects are important in guiding the direction and pace of health reforms in Thailand as well as in other countries(4)(5) .

There are quite a number of quantitative tools to assess equity in health but little knowledge on the qualitative tool. This research wanted to explore the use of qualitative assessment and see how it complements the quantitative tool. Since we know the limitations of quantitative technique as well as the limitations of qualitative technique, combining both techniques in this study would expect to give better insights on measurement of equity. The objectives of the study were as follows: 1) To develop the tools in measuring equity in health both quantitative and qualitative, 2) To study the distribution of health equity measurement and the relationship of those equity indicators, 3) To study the score and views on the equity indicators from qualitative technique, and 4) To study the feasibility of using the quantitative and qualitative tools in monitoring health equity in the future. I. Introduction 3

1.2. Principles and trends of health equity

There have been debates on the interpretation of equality and equity. Equality simply means the status of being equal, e.g. having equal income, equal opportunity. Equity is more complicate, many inequalities can lead to equities. Equity therefore is interpreted on some principles of distribution. In terms of health delivery, health services should be distributed according to health needs. In terms of health care finance, financial contribution should be distributed according to ability to pay. In operational terms, equity can be assessed at horizontally or vertically (see table 1.1). Horizontal equity adopts equality principle while vertical equity adopts inequality principle(6).

Table 1.1 Definitions of horizontal and vertical equity in health delivery and finance Health equity Health delivery Health finance Horizontal equity Those who have the Those who have the same needs should same ability to pay, get the same services pay the same amount (equal treatment for (equal payment for equal need) equal ability to pay) Vertical equity Those who do not Those who have have the same need different ability to pay, get different services pay according to their (unequal treatment for ability (unequal unequal need) payment for unequal ability to pay)

Donaldson and Gerard summarised the concept of horizontal and vertical equity as follows(7):

Horizontal equity criteria 1. Equal expenditure for equal need, 2. Equal utilization for equal need, 3. Equal access for equal need, 4. Equal health status. Vertical equity criteria 1. Unequal treatment for unequal need, 2. Progressive financing based on ability to pay.

It is a consensus that equity of health finance is judged on the vertical equity principle according to ability to pay rather than who benefit who pay. To be more specific, those who earn more should pay more by the prepayment methods. For tax-financed health systems, equitable taxes are levied on an ability to pay basis, the high income brackets pay a higher tax rate than the poor, in other words, progressive tax rate. On the contrary, health services are delivered on a horizontal principle. In countries with immature health insurance systems, health care is delivered according to ability to pay, rather than health need. This contradicts with the principles describe in World Health Report 2000(3) summarised in figure 1.2. 4 Benchmarks of Fairness for health reform

Risk pooling (Same income

Low Risk High

Payment Shifting Utilisation

Low Income

High

Income share For same risk

Figure 1.2 Equity principles of finance and utilisation by risk pooling and income share of insurance mechanisms (WHO 2000) From figure 1.2, health utilisation is according to risk or need, but insurance premium is according to income. The risk is transferred from the high risk group to low risk group without linking to income level, while financial risk is transferred from the low income to high income groups without linking to health risks.

Mooney gave 7 operational definitions of equity employing the principles of equality(8): 1) Equality of expenditures per capita, 2) Equality of inputs per capita, 3) Equality of inputs for equal need, 4) Equality of access for equal need, 5) Equality of utilisation for equal need, 6) Equality of marginal met need, 7) Equality of health.

Culyer gave detailed accounts of horizontal and vertical equity using 3 steps of analysis (9): Horizontal equity (H) H1: Equal treatment of those with equal initial health. H2: Equal treatment for equal need. H3: Equal treatment for those with equal expected final health. Vertical equity (V) V1: More favourable treatment for those with worse initial health. V2: More favourable treatment for those with greater need. V3: More favourable treatment for those with worse expected final health.

Mooney’s and Culyer’s definitions focussed on health delivery rather than on financing. Mooney’s definition stressed on horizontal equity much more than vertical equity. However, for comprehensive assessment, both equity of delivery and equity of finance must be considered.

Concerns on equity in health were first addressed by public health specialists called Resource Allocation Working Party (RAWP) in the UK around 1970s. They focussed I. Introduction 5 their interests on allocating scarce health resources on a mathematical formula. They proposed that health budget should be allocated on the grounds of health needs. Important determinants of health need included age and sex structures of the populations, standardised mortality ratio, fertility rate and the cross-boundaries, etc.

Economists contributed their knowledge on equity of finance when arguing on the progressivity of taxation. Kakwani index of progressivity named after an econometrician was employed to evaluate the overall patterns of health finance(10)(11) and further employed the Concentration index (CI) for evaluating equity of health delivery according to health need(12). Social scientists used their arguments on human rights to advocate for health rights for all regardless of socioeconomic differences.

The concerns on health equity have been competed by concerns on efficiency once the developed countries wanted to contain escalating health care costs through the health care reforms(1). But, at the turn of the millennium, WHO as well as the World Bank, has stressed on equity. The Equality of child survival of the UNICEF and the Fairness of financial contribution of World Health Report 2000 are a few examples of concerns (3).

Learning from international trends of equity in health, the Thai health care systems, which proved to be less efficient and less equitable, have proposed series of health care reform efforts. The Health Care Reform Office was set up in the Ministry of Public Health in 1996 to pursue research activities and field trials. The explicit four objectives of health reforms learned by heart (‘EQESa’) by most health personnel included ‘equity’, ‘quality’, ‘efficiency’ and ‘social accountability(13).

1.3. Health equity status

The studies on health equity in Thailand will be presented in this section. The studies included a rinsing of income inequality in Thailand, inequitable distribution of health resources, unequal health status, inequity of health care finance and inequity of health delivery. Some studies reflected the inequity between areas and some reflected inequity between individuals (inter-individual).

Income distribution The prevalence of people under poverty line decreased from 22% (or 17.9 million people) in 1988 to 13% (or 7 million) in 1992. Despite the fall of absolute poverty, income distribution by Gini coefficient became worst. Gini coefficient increased from 0.41 in 1962 (the start of the first economic development plan) to the peak of 0.54 in 1992. In 1992, the richest 20% had almost 59% of the total income while the poorest 20% had only 3.9% of the total income(1), see figure 1.3. These indices of income inequalities reflected inequity among individuals or group of individuals. 6 Benchmarks of Fairness for health reform

GINI coefficient

0.6

0.5

0.4

0.3

0.2

0.1

0 2505 2512 2518 2524 2529 2531 2533 2535 2537 2539 2541

Fig 1.3 Trend of Gini coefficient of income distribution

Distribution of health resources

Health resource distribution shows stronger inequalities between areas. Health personnel were heavily concentrated in Bangkok, hence wide disparity when compared the ratio of personnel to population between Bangkok and the northeast, the poorest region in Thailand. In 1997, disparity by doctor to population ratio between Bangkok and the northeast was 13.8, while disparity by bed to population ratio was 4.0(1). In 1999, one doctor was taking care of 3,394 people for the whole country, but in Bangkok the figure was on 762, and in the northeast it was 8,110(14), see figure 1.4.

Ratio of population to doctor

10,000

8,000

6,000

4,000

2,000

0 !"BKK$# %&' !()Central$ South*+, North%-.00/ NE21 ). Tota"34l 762 3,654 4,888 4,869 8,110 3,394

Figure 1.4 Population to doctor ratio by region 1999 I. Introduction 7

Health status

In the World Health Report 2000, an index to present the equality of child survival was published for the member countries. The higher the index (highest=1), the more equality among individuals. Thailand was ranked 74 from 191 member countries. If compared with countries in the Southeast Asia, Thailand was the 4th after Singapore, Malaysia and the Philippines(3), see table 1.2. The figures suggested some relationship between equality of health status with economic status of the country. The poorer the country, the more unequal child survival.

Singapore Malaysia Philippines Thailand Vietnam Laos Cambodia Indonesia Myanmar Index 0.971 0.901 0.892 0.845 0.779 0.624 0.606 0.599 0.579 Rank 29 49 50 74 104 147 150 156 162

Table 1.2 Equality of child survival index for Southeast Asia countries (WHO 2000)

The household surveys on socioeconomic status and health and welfare of the National Statistical Office in Thailand showed that households with lower income reported higher illness rates than the higher income households. This inequality existed for urban and rural areas(1), see figure 1.5.

Illness rate (per person per year)

urban 2.5 Semi- Rural 2

1.5

1

0.5

0 Quintile1 Quintile2 Quintile3 Quintile4 Quintile5 Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 By income quintiles

Fig 1.5 Illness rate by income quintiles (Quintile 1 was the poorest 20%)

Equity of health finance

Equity of health finance in terms of progressivity of all sources of finance for health, and the summary index is Kakwani index. If the index is positive, it means that source of finance is progressive to Direct tax income, therefore equitable. On the !"#$%"&'(& 0.237 other hand, if it is negative, the raising of money is regressive to income and Indirect!"#$%tax"& -0.241 inequitable. Figure 1.6 shows that only direct tax was progressive to )*"+,-+'(& -0.151 income, while the rest (indirect tax and Payment

(.All/%01&2/- -0.122

-0.4 -0.2 -0.1 0 0.1 0.2 0.3 0.4 -0.3 Fig 1.6 Kakwani index of health finance 1996 8 Benchmarks of Fairness for health reform

out-of-pocket payment) was regressive and inequitable. The summary Kakwani index for all sources of finance in 1996 was negative (-0.122)(11) . This confirmed the inequity that has long driven for the reforms on universal health coverage. % health spending to household income More simple calculation for progressivity or 6 poorest quintile regressivity of health expenditure is presented in second quintile figure 1.7. This time, health expenditure was 5 third quintile fourth quintile compared with the level of income by income quintile. richest quintile The data from household surveys could only be used 4 to present the burden of out-of-pocket expenditure. From the figure, the poorest quintile spent on health 3 about 5% of their household income, while the richest quintile spent on health only 2% of their household 2 income(15). The pattern in the graph reflects regressivity to income. 1

0 Fig 1.7 Percentage of health spending to household income by income quintile

In the World Health Report 2000, another index was presented: the fairness of financial contribution (FFC) on health. The index for Thailand was 0.913, ranked 128-130 from 191 member countries(3). This poor index reflected ineffectiveness of financial protection, even the health benefits had reached 80.3% of the total population in 1998. The main cause could be high reliant on out-of-pocket payment, as high as 63.9% of the total health expenditure by some authors(16) .

Equity of health delivery

By the national household surveys, consumption of health care from drug store, private clinic to formal health facility was calculated by household income quintile. The pattern was the same as income distribution. The richest income quintile consumed almost 47% of the total health consumption, while the poorest quintile consumed 8% of the total consumption in 1995(15). This reflects higher access to care among the rich than the poor (figure 1.8). If health spending was compared among health benefit coverage, inequalities were obvious. The civil servant medical benefit scheme (CSMBS) spent on health about 2,491 baht per capita, while private insurance was 3,000 baht per capita. The government CSMBS spent 2 times higher than the compulsory social security scheme (SSS), and 4-5 times higher than other government schemes for the poor and near poor (low income card scheme, LICS and voluntary health card scheme, VHCS)(16) see figure 1.9. I. Introduction 9

% health consumption Per capital spending (baht) ,

50 3,500 3,000 poorest quintile second quintile 3,000 2,491 third quintile 40 2,500 fourth quintile richest quintile 2,000 1,468 30 1,500 1,000 667 750

20 500 0

10 LICS VHCS SSS CSMBS PI

0

Fig 1.8 Proportion of health care consumption by income quintiles Fig 1.9 Per capita spending by health benefit scheme

From the described inequities, income disparity could be the fundamental problem leading to inequitable distribution of health resources, and therefore unequal access to care. At the individual level, the rich had better access to health care, but the poor had higher burden from out-of-pocket spending. The final outcome was inequity of health status.

1.4 Various tools to measure health equity

From the above presentation, there are many indices to measure equity. Example of summary index includes equality index measuring equality between individual as in the case of WHO’s child survival, distribution of health expectancy, individual / mean differences and inter-individual differences(17). Index for equity in health includes Kakwani and WHO’s fairness of financial contribution. Index for equity of health delivery is the concentration index, to evaluate whether health services are favouring the rich or favouring the poor taking into account health need. These indices focus on inequalities among individuals. Less emphasis has been put on inequality between areas. The shortcoming of summary index is that it does not show how to solve the problem.

Moreover, the United Nations Development Programme, Thailand has developed a composite index of deprivation to rank social situations in 76 provinces. The composite index derived from 8 groups of 48 indicators: income, health, education, employment, housing and environment, communication, utility and gender issues. For health deprivation, 10 indicators were used to calculate the composite index: health (infant mortality, under 5 mortality, maternal mortality, malnutrition in children, rate of notifiable diseases, malaria, sexually transmitted disease), and health resources (population to doctor, population to nurse, population to hospital bed). By this approach, provinces 10 Benchmarks of Fairness for health reform identified as severely deprived in health were: Maehongson, Chiangrai, Phayao, Lamphun, Tak, Kampaengphet, Loei, Kalasin, Nakhon Phanom, Mukdaharn, Burirum and Surin. The provinces severely deprived in general were: Tak, Maehongson, Surin, Nhongkhai, Kampaengphet, Nakhon Phanom, Burirum, Sakon Nakhon, Sisaket and Mahasarakam. Five provinces were identified by both deprivation in general and in health (18) .

Recent development on the approach of Benchmarks of Fairness was comprehensive and extensive covering steps of health reforms. This study used the same approach to combine the quantitative index and the qualitative methods. Details are presented in the next chapters. 2. Equity in health and measurement tool 11

2 Equity in health and measurement tool

This chapter presents the backgrounds and elements of the benchmarks of fairness for health care reform. The background describes how the benchmarks were introduced in Thailand, and how it was used in this phase that combined both quantitative and qualitative techniques. 2.1. The benchmarks of fairness for health care reform

The original version of the Benchmarks of fairness for health reform was developed by Daniels et al (1996). It was developed to assess the proposal of the medical insurance reforms in the US during the first Clinton administration. Though the reforms in the US had failed, but the tool has been further developed to developing countries with a grant support from the Rockefeller Foundation. Four countries involved in the first adaptation were Columbia, Mexico, Pakistan and Thailand. The original 10 benchmarks were adapted into 9 benchmarks, with a strong emphasis on intersectoral public health(19) .

Fairness in this context carries wider connotation than equity. Fairness rather covers 3 main aspects that are important for reforms: equity, efficiency and transparency. The details of 9 benchmarks are as follows (19) (20):

1) Intersectoral public health This benchmark focuses on population health outcomes covering morbidity, mortality, overall health status and other factors influencing health outcomes such as economic status, education, environment and intersectoral coordination to solve social problems.

2) Financial barrier to equitable access This benchmark measures access to care in relation to financial access, such as population coverage of health insurance, burden of financial payment for access to health care.

3) Non-financial barrier to access This benchmark focuses on non-financial factors that can inhibit access to care, such as distribution of health resources, acceptability to quality of services and socio-cultural aspects (language, health belief) of access to care. 12 Benchmarks of Fairness

4) Comprehensiveness of care and tiering This benchmark emphasizes the comprehensiveness of services, especially the provision of primary care, continuity of services by referral system. This benchmark involves equal access to the comprehensive package without discrimination.

5) Equitable financing This benchmark proposes the principle that health care finance should be based on ability to pay to share financial risk. The rich should pay higher than the poor.

6) Efficiency and quality of care This benchmark sends the signal that health care should be delivered at a high quality according to professional standards, value for money and affordable to the country situation.

7) Administrative efficiency This benchmark sets the goal that administrative cost for health care should be efficient and resource allocation should be responsive to health needs and reach the lowest total health care cost.

8) Democratic accountability and empowerment This benchmark offers opportunity to community to participate in health system development including access to data, participating in health delivery and involving in decision making in resource allocation. This benchmark also involves empowerment of people in taking care of self-health.

9) Patient and provider autonomy This benchmark provides choice to both patient and provider in seeking and delivering appropriate care.

From these 9 benchmarks, details can be elaborated for higher specificity into 46 items of assessment as presented in table 2.1. The benchmarks fit well with the health care reform objectives in Thailand: equity, efficiency, quality and social accountability. One benchmark can serve as an assessment of many objectives (see table 2.1). 2. Equity in health and measurement tool 13

Equity in y it il

Sub-items b e y h nanc i Healt Access Service F Efficiency Qualit Social Accounta Benchmark 1 : Intersectoral public health 1.1 Overall health status ! 1.2 Particular health status ! 1.3 Coverage of basic health care !!! 1.4 Environmental condition ! 1.5 Demographic pattern ! 1.6 Economic status !! 1.7 Educational status !!! 1.8 Public utilities distribution !! 1.9 Intersectoral effort and cooperation !! Benchmark 2 : Financial barrier to equitable access 2.1 Coverage of health insurance !! 2.2 Coverage of benefit package !! 2.3 Household spending for health !! Benchmark 3 : Non-financial barrier to access 3.1 Distribution of health resources !! ! 3.2 Adequate resources for good care !! ! 3.3 Factors affecting access to care !! ! 3.4 Overall service utilization !! Benchmark 4 : Comprehensiveness of benefits and tiering 4.1 Coverage of service provision !! 4.2 Utilization of primary care !! ! 4.3 Continuity of care and referral system !! ! 4.4 Comprehensiveness of care !! 4.5 Equality of health care !! Benchmark 5 : Equitable financing 5.1 Public funding for the poor ! 5.2 Equity in financial contribution ! 5.3 Out-of-pocket payment reduction ! Benchmark 6 : Efficiency and quality of care 6.1 Technical efficiency of care !!! 6.2 Unit cost of health service !! 6.3 Standard practice based on evidence !! 6.4 Overall quality of care !! 6.5 Process for quality improvement !! Benchmark 7 : Administrative efficiency 7.1 Overall health expenditure ! 7.2 Allocative efficiency for primary care !! 7.3 Administrative cost control ! 7.4 Unnecessary cost reducing mechanism ! 7.5 Medical technology optimization !!! Benchmark 8 : Democratic accountability and empowerment 8.1 Explicit public health report ! 8.2 Transparency in resource allocation !! 8.3 Fair grievance procedure !! 14 Benchmarks of Fairness

Equity in y it il

Sub-items b e y h nanc i Healt Access Service F Efficiency Qualit Social Accounta 8.4 Adequate privacy protection !! 8.5 Compliance with rules and laws ! 8.6 Strengthening civil society ! Benchmark 9 : Patient and Provider autonomy 9.1 Consumer choice for primary care ! 9.2 Consumer choice for specialized care ! 9.3 Consumer choice in public sector ! 9.4 Consumer choice in private sector ! 9.5 Consumer choice for alternative care ! 9.6 Autonomy of provider !

It can be seen that many detailed items of the Benchmarks of fairness are related to equity, efficiency and quality, e.g. technical efficiency of care, continuity of care and referral system, medical technology optimization. However, most items serve the one-to-one objective of health care reform.

2.2. Experiences of the benchmarks in Thailand The experience on the Benchmark of fairness in Thailand was presented by Pannarunothai and Srithamrongsawat. The test was carried out in 2 provinces involved in health care reform activities (Phayao and Yasothon). Interviews to the chief provincial medical officers and focus group discussions with health managers at district levels gave the scores on these 9 benchmarks. The score system of -5 to +5 was given by participants asking them to compare the situation at the moment with the situation before the health care reforms(20) . The average scores of the two provinces are shown in table 2.2. The Benchmarks of Fairness Phayao Yasothon 1. Intersectoral public health 1.8 2.0 2. Financial barriers to equitable access 2.6 2.2 3. Non-financial barriers to access 2.7 2.0 4. Comprehensiveness of benefits and tiering 1.4 2.1 5. Equitable health financing 1.5 1.5 6. Efficiency and quality of health care 2.1 2.0 7. Administrative efficiency 1.8 1.5 8. Democratic accountability and empowerment 3.8 1.9 9. Patient and provider autonomy 1.6 0.8 Overall score 2.1 1.8

Table 2.2 The scores according to 9 benchmarks in Phayao and Yasothon 2. Equity in health and measurement tool 15

The scores show that most benchmarks had a slight to moderate improvement score (from +1 to +2). Phayao achieved the highest improvement score on the democratic accountability and empowerment because the system involved community participation to solve the province’s health problems (3.8). However, the lowest score was found in the patient and provider autonomy because they felt that they had more limited choice (0.8).

2.3. The benchmarks for Phase 2 and analysis

This study was the continuation of the previous works described above. In phase 2, the study aimed to base as much as possible on the evidence at provincial level and not only among health personnel. The benchmarks were elaborated into a quantitative tool containing 46 items in 9 benchmarks, and a qualitative tool to grab ideas and assessment score from group discussions.

1) Quantitative tool

The quantitative part of the benchmarks compiled secondary data from many data sources. Firstly, the benchmarks were interpreted on a quantitative basis, 46 items were possible to measure quantitatively. These 46 items were expanded into 81 indicators and grouped into 30 groups. The 30 groups fit well with 46 items of quantitative elements. The 30 groups of indicators would be used later for qualitative data collection.

Data used for calculating 81 indicators included routine reports and individual records of health data at the MOPH (e.g. provincial health surveys conducted by MOPH(21)) and data sets from other organisations, e.g. the National Statistical Office(22)(23), people registry and report on the basic minimum need of the Ministry of Interior(24). Most of the data sources gave detailed account of 75 provinces except the commonly missing was data on Bangkok. Therefore, data from 75 provinces were analysed to get arithmetic mean and standard deviation (S.D.) and finally, Z-score could be produced for each province. The formula for Z-score is as follows:

Z-score for province = (data of the province - mean of 75 province) Standard deviation 16 Benchmarks of Fairness

Z-score represents the deviation from mean. Any province with data higher than the mean, the Z- score will be positive, and vice versa. However, the positive or negative Z-scores depend on the meaning of each question, if the higher the figure means worse situation, the Z-score would turn to be negative, e.g. mortality rate. Z-score is used here as the benchmark score for health equity. Quantitative data yielded Z-score for 2 levels: the national and provincial levels. Z-scores of each item by individual provinces are used to plot in the map of 75 provinces to see equity at the national level (75 ranks by Z-scores). Z-scores by 81 indicators of 30 groups will be calculated for each province to reflect equity at provincial level. This reflects strengths and weaknesses of each province by the positive and negative of Z-scores. Figure 2.1 shows the geographical distribution of Z-scores to pinpoint strengths and weaknesses of provinces by each indicator. This kind of presentation should help policy development at a glance. Z- scores vary from –4 to +4. Figure 2.2 presents inequalities of age-adjusted mortality data by province, one indicator in the benchmark 1 intersectoral public health. The mortality data were obtained for 2000 and age-sex population structure for 2000 obtained from the census survey. These two sources of data were used to calculate age-adjusted mortality. The black-shady areas (Z-score lower than –2) were mostly in the northern part of Thailand reflecting higher than average the mortality rate. The map is used in conjunction with the data presented in table (see table 2.3).

Figure 2.1 Geographical distribution of Z- scores for 75 provinces for age-adjusted death rate 2. Equity in health and measurement tool 17

Rank Province Mortality rate (per1,000) Z-score 1 Chainat 4.46 1.48 2 Pattalung 4.53 1.40 3 Nakhon Sithammarat 4.57 1.36 73 Chiangrai 8.14 -2.53 74 Chiangmai 8.44 -2.86 75 Phayao 9.11 -3.59

Table 2.3 Age-adjusted mortality rate by top and bottom 3 provinces ranked by Z-score

2) Qualitative tool

The quantitative tool developed in this research was to provide opportunities to people in the provinces to evaluate equity in health in their own provinces. The tool employed open questions related to the quantitative indicators (46 indicators) from the previous section. In each open question, the assessment score was given by each respondent to reflect the present situation as compared to the situation 3 three ago by each indicator. The assessment score varied from –5 (the very worst situation) to +5 (the very best situation), while 0 meant no changes observed. Within each benchmark (for all 9 benchmarks) the overall assessment score was asked again from each respondent to avoid explicit weighting process when summary assessment score was needed. Each member of the focus group could give different emphasis for each indicator within each benchmark(20).

The focus group discussion was used to find out interactions between people’s perception and interpretation on equity aspect of each benchmark. Focus group discussion could provide opportunity to clarify and discuss interactively between researchers and respondents and among respondents themselves. In this research, 10 provinces were picked up for study and 8 focus group discussions were held. The selected groups ranged from health to non-health and from urban to rural areas. Group 1 was composed of administrators at the provincial health office, group 2 health providers at provincial and district level, group 3 health providers at subdistrict level, group 4 administrators in local governments, groups 5 and 6 civic groups related to health in urban and rural areas and groups 7 and 8 civic groups not related to health in urban and rural areas respectively. 18 Benchmarks of Fairness

People recruited to the focus group discussions were free to give their assessment scores and comments immediately before and after the discussions. During focus group discussions, quantitative data as presented in the previous section were presented by visual aid and by paper form. Participants could change their assessments after the discussions whatever they felt. It is worth emphasizing that the comparison on quantitative data nationwide was between provinces, but the comparison under qualitative method was only in a province within different time frame (about 3 year difference).

Assessment scores from the focus groups were analyzed by items and by overall scores, comparing among 8 groups before and after the discussions, and comparing with the quantitative data. Other qualitative data gained from focus group discussions were used for setting the context of the provinces, for interpretation and explanation of the research results, those of which could not be given by assessment score or the quantitative benchmarks.

The results on equity interpreted by 8 focus groups were presented simultaneously by 3-dimentional surface graph (3-D surface graph). The first axis represented the 9 benchmarks of fairness, the second axis represented the target groups of focus group discussions, and third axis presented the average assessment scores given by the target groups. The 3-D surface graph was used because it intuitively gave a quick impression on strengths and weaknesses by individual indicators and by target groups. The positive assessment scores (the much improvements perceived) reflected strengths while the negative assessment scores (the much worst conditions) reflected weaknesses in the province. The 3-D surface graphs could show the changes of assessments before and after the discussion. 3. Equity of health status: quantitative data 19

3 Equity of health status: quantitative data

Chapters 3 to 6 present quantitative data according to the benchmarks. We divided them into 3 parts: equity of health status, equity of health finance and equity of health delivery. Most of equity of health status contained in the Benchmark 1 intersectoral public health, but could be found in other benchmarks or could be the impacts of other development programmes apart from health. The health needs and outcomes are proved to be related to socioeconomic status. This chapter therefore presents the disparities in overall and specific health status in relation with socioeconomic status, demographic and environmental conditions of 75 provinces (except Bangkok). Data were obtained from many sources. Midyear populations and the number of deaths were obtained from the Ministry of Interior. Socioeconomic data were from the surveys by the National Statistical Office. Health status data were from the Ministry of Public Health and the report on human resource developments of the United Nations Development Programme, Thailand, etc.

3.1. Distribution of health status

Overall health status was measured by mortality data by province using 2000 data from the Ministry of Interior. Crude death rates varied from 3.6 per 1,000 (Ranong province) to 9.9 per 1,000 (Chiang Mai), almost three-time difference. To remove confounding effect of age and sex, the standardization was Mortality rate done. The disparity between the ÍѵÃÒµÒ 0.011 highest the lowest became lower, and 0.01 0.009 many provinces with low crude death 0.008 0.007 rates (lower than 5.5 per 1,000)were 0.006 adjusted up and many of high rates 0.005 0.004 0.003 (higher than 5.5 per 1,000) were 020406080 adjusted down (see figure 3.1). Rank of age-sex adjstedÅÓ´ Ѻ·morÕè¢Ítalit§Íy ѵrateÃÒµ(diamondÒÂ Í Â èÒ§) andË ÂcruÒº de death (dot) ÍÑStandardisdµ ÃÒµ Ò » ÃѺ¤mortalitèÒÁ Òµy rÃate° Ò¹ Í µÑCrudeÃÒµ ÒÂdeÍ ath ҧèratËe Һ

Fig3. 1Crude and age-sex adjusted death rates by 75 province 20 The Benchmarks of fairness for health reforms

After standardization, Chiang Mai rate was reduced to 8.44 per 1,000, but Phayao, another province in the North with high HIV prevalence, became the highest mortality rate (9.11 per 1,000). The next 3 highest mortality were in the North: Chiangrai (8.14), Phrae (7.89) and Lamphun (7.49). The lowest standardized mortality provinces were Chainat (4.46), Pattalung (4.53), Nakhon Srithammarat (4.57), Chaiyaphum (4.71) and Ranong (4.71).

When converting age-sex specific mortality data into life table to get life expectancy at birth by province, the results were similar to age-sex standardized mortality rate. Z-scores of life expectancies at birth were calculated by province and presented in a map to help quick comprehension through graphic (see figure 3.2).

Exploring details of how age-sex mortality rate influenced the life expectancy, it was found that age- specific mortality rate of 30-39 year old was highly correlated with life expectancy (linear correlation, r= – 0.8936). The relationship with under 5 mortality and life expectancy was much lower (see figure 3.3). Premature deaths at 30-39 years were largely contributed by AIDS epidemics in the North, this gave a higher impact than infant mortality. Figure 3.2 Life expectancy at birth by Z-score

Death rate 0.014 0.012 0.01 0.008 0.006 0.004 0.002 0 69 69.5 70 70.5 71 71.5 72 Life expectancy at birth

Death rate 30-40 yrs Death rate 0-5yrs

Figure 3.3 Relationship between life expectancy at birth (x-axis) and age-specific mortality rates 3. Equity of health status: quantitative data 21

Although the under 5 mortality rates had a wide disparity from 1.0 per 1,000 (Mahasarakam) to 5.18 per 1,000 (Singburi), but this did not influence a high variation in life expectancy. If the difference of mortality in age group 30-39 was not high, the life expectancy would not vary so much.

Looking at some specific health problems e.g. deaths from traffic car crashes(25), malnutrition among under 5, and low birth weight, the strong trend could not be established. Deaths from traffic car crashes were high in the central, the eastern and southern provinces. Phuket in the South had the highest rate from car crashes of 5,103 per 100,000, but Samutprakarn in the central area had the lowest 1,318 per 100,000. The different of car crashes did not influence the difference of death among 30-39 year old, this support previous statement that high mortality among 30-39 was the cause of AIDS. Death rate 30-40 yrs Íѵ ÃÒµ ÒÂ Í ÒÂØ30-40»Õ 0.014

0.012

0.01

0.008

0.006

0.004

0.002 Figure 3.4 Distribution of traffic car crashes 1,000 2,000 3,000 4,000 5,000 6,000 TraÍѵÃffiÒc¡ ÒcaÃàr¡ Ô´ÍcrasºÑصÔheàËsµ ratبÃÒe¨Ã

Figure 3.5 Relationship between traffic car crashes (X-axis) and deaths in 30-40 year old (Y-axis)

The incidence of malnutrition among under five and the incidence of low birth weight (lower than 2,500 (21) gm) was quite correlated but not Malnutrition strong. Provinces in the North and the 0.2

Northeast had both high malnutrition and 0.15 11 !"#$%"& high low birth weight, and higher than the 0.1 Central and the South. 11 provinces in figure 3.5 showed high malnutrition rate 0.05 (higher than 10%) and high low birth 0 2 4 6 8 10 12 14 16 weight higher than 7%). Low birt h weight

Figure 3.6 Malnutrition rate in children and low birth weight by province 22 The Benchmarks of fairness for health reforms

The list of 11 provinces were Nan (17.6%), Tak (15.4%), Maehongson (15.2%), Chiangrai, Mukdaharn, Ubon, Sakon Nakhon, Chiangmai, Phayao, Amnat Charoen and Roiet. The highest low birth weight rates were found in Chiangrai (14.17%) and Phayao (12.5%).

Figure 3.7 Distribution of malnutrition rate in children Figure 3.8 Distribution of low birth weight rate

Intermediate data on the coverage of basic health services, e.g. antenatal care4 visits and coverage of immunization(21), problems existed in a few provinces, especially the provinces at the border in the South (Narathiwas, Yala, Pattani) and in the North (Maehongson and Tak).

In summary, there were differences of health status by provinces, that meant inequity of health status existed. The provinces with high health risks, may be the contributions of other non-health as well as health services factors.

Figure 3.9 Coverage of antenatal care by province 3. Equity of health status: quantitative data 23

3.2. Distribution of health determinants Apart from health service factors, there are other direct and indirect factors influencing health status. This section explores other potential health determinants by province, they are environmental factor, demographic, economic factors.

Environmental factors Environmental factors influence health status in many ways. Ill-environment makes people ill, then erodes economic potential of household. Data related to environmental health cover the proportion of households accessible to clean drinking water and good house sanitation. The 1996 Health and Welfare survey showed that 21 provinces had lower than 70% of households accessible to clean drinking water. Most of them were in the Northeast, North and South. Provinces with very low access to clean drinking water were Maehongson (35.44%), Sisaket (41%) and Surin (47.93%).

Figure 3.10 Access of household to clean drinking water

Data on household with good housing sanitation and free from toxic substances were derived from the 1999 Basic Minimum Need survey. Province with the lowest housing sanitation was Maehongson (63.3%), and province with the lowest free from toxic substance was Kamphaengphet (82.7%). Poor housing sanitation was prevalent along the upper North and the South. Households with risk to toxic substance were prevalent in the lower North and the South. However, the data obtained were self-reporting and should be used with caution.

Figure 3.11 Proportion of household with good housing sanitation 24 The Benchmarks of fairness for health reforms

Demographic factors Demographic factors include crowding index. Over-crowded provinces may have higher risk from poor sanitation. Provinces with overcrowding index were prevalent in peri-Bangkok Metropolitan and Phuket. 7 provinces with Z-score lower than –1.00, the leading 5 provinces of high density population were Nonthaburi (1,302 per sq. km., Z-score = -5.79), Samutprakarn (1,010 per sq. km., Z-score = -4.31), Samutsakhon (524 per sq. km., Z-score = -1.83), Samutsongkram (489 per sq. km., Z-score = -1.65) and Phuket (440 per sq. km., Z-score= -1.40). Provinces with very sparse population were Maehongson, Tak and Kanchanaburi (16.5, 29.5 and 37.6 population per sq. km. respectively). The difference between the densely and sparsely populated province was at the order of 80(22).

Two more demographic factors were household size (population/household)(22) and dependency ratio (the number of population 0-15 years old and more than 65 years as compared with the 15-65 years old. Provinces with high dependency index will carry higher burden of the non-income earner populations. The two indices were quite correlated. The South and the Northeast had bigger family size and higher dependency ratio, while the North, the East and the Central had smaller family size and lower dependency ratio (see figures 3.12 and 3.13).

Figure 3.12 Distribution of size of household Figure 3.13 Distribution of dependency ratio 3. Equity of health status: quantitative data 25

Age dependency ratio 0.65

0.6

0.55

0.5

0.45

0.4

0.35

0.3 33.544.55 Size of household Figure 3.14 shows the relationship between household size and dependency ratio by province. The correlation coefficient between the two factors was 0.70. The implication of bigger family size and higher dependency ratio were that provinces with these high index would need more budget because both the elderly and young children need higher resources for health.

Economic factor Economic factors have shown great influence in health status. Households with good economic status show better access to health care and better health status. Overall health status of the poor was lower than of the rich. Among the elderly, invalidity score among the underprivileged elderly was 34.6% higher than the invalidity score among the non-underprivileged elderly 22.2%. This proved the influence of economic status on health(1). Data on income per capital by province had high correlation with the prevalence of the poor in the province (household with income lower than 1,000 baht a month), and also correlated with the number of household with debt. Provinces with low income had a higher poverty rate and a higher proportion of household with debt. These provinces were in the Northeast, the border of the South and Maehongson(26). % 100

80

60

40

20

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 Average income

% poverty % debt

Figure 3.15 The prevalence of poverty by per capita income (dot) and household debt (diamond) 26 The Benchmarks of fairness for health reforms

Figure 3.15 shows the inverse relationship between per capita income and proportion of household defined as poor (r= -0.786). Relationship between per capita income and household with debt was not strong since some provinces had no very low household with debt in spite of low income per capita.

Figure 3.16 Distribution of income per capita Figure 3.17 Distribution of people under poverty

Another aspect of household economy is household saving. There were 13% of the provinces with monthly per capita income lower than 3,000 baht that had household spending higher than household income (the ratio of spending to income higher Expenditure/ income (% ) than 100%). This was not a good sign. Even 120 though, the relationship between household 110 income and household saving was not 100 established, but it could have played an 90 important role if health services was determined 80 by household ability to pay. Provinces with 70 60 incapacity to save were Yala (the ratio of 111%), 1,000 2,000 3,000 4,000 5,000 6,000 7,000 Phanga (109.9%), Nakhon Sawan (109.5%), Average monthly income Narathiwat (105.1%), Surin (102.2%), and Kalasin (101.8%),

Figure 3.18 Income per capita (x-axis) and household expenditure to income ratio (y-axis) 3. Equity of health status: quantitative data 27

When looking at the overall economy of the province by gross provincial product(27) and compared with income per capita or income distribution (by Gini coefficient)(26) , the graph (figure 3.10) shows that provinces with high provincial product would have a higher income per capital and a lower Gini coefficient. This implied that provinces with better economy lead to better people wealth and better income equality.

Average income per capita Gini coefficient 7,000 0.55 6,000 0.5 0.45 5,000 0.4 4,000 0.35 3,000 0.3 2,000 0.25 1,000 0.2 0 50,000 100,000 150,000 200,000 250,000 300,000 GPP per capita

Gini coefficient Average income per capita

Figure 3.19 Relationship between gross provincial product (x-axis), income per capita (diamond) and income distribution by Gini coefficient (dot) Provinces with high gross provincial product included Chonburi, Pathumthani, Samutsakhin and Samutprakan. Provinces with good income equality (low Gini coefficient) included Rayong, Samutparakan, Chonburi, Samutsongkram and Suratthani.

Deprivation index The study by UNDP, Thailand on deprivation by 8 elements, reported the final human deprivation index by province is worth mentioning here. When apply the Z-score to this index, the ranking of deprivation is presented in figure 3.20. The most deprived provinces were concentrated in the Northeast, the North, upper part of the Central and lower part of the South. The overall deprivation was similar to deprivation in health and education.

Figure 3.20 Overal deprivation index 28 The Benchmarks of fairness for health reforms

From data presented above, inequity existed in health status, environmental factor, demographic, economic factors and overall deprivation. Disparities by provinces may be good or bad for development, depending on what value was put on these indices.

3.3. Relationship with health status One possible health determinant other than health services factor was the nutrition status of children. When study the relationship between income per capita and malnutrition rate by province the r= -0.6358. Provinces with high income would have a lower rate of child malnutrition. However, income per capita had a weak relationship with the low birth weight rate. It may imply that the low birth weight was perhaps not the consequence of malnutrition in pregnant mother.

% Malnutrition in children 0.2

0.15

0.1

0.05

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 Average income per capita Figure 3.21 Relationship between income per capita (x-axis) and malnutrition among children (y-axis) The relationship between standardized mortality and the dependency ratio was again an inverse relationship (r= -0.623). Provinces with a higher standardized mortality had a lower dependency ratio. It was perhaps the high death rate did not disproportionately occur among the working age, therefore the denominator did not become smaller than the dependent populations.

Age dependency ratio 0.65 0.6 0.55 0.5 0.45 0.4 0.35 0.3 0.003 0.004 0.005 0.006 0.007 0.008 0.009 0.01 0.011 Age standardized death rate Figure 3.22 Relationship between standardized mortality (x-axis) and the dependency ratio (y-axis) 3. Equity of health status: quantitative data 29

The composition of dependency ratio was different between provinces. The provinces in the South had a higher proportion of population under 15 years old. But provinces in the North had lower population under 15 years old, therefore dependency ratios of provinces in the South were higher than the ratios of provinces in the North.

Proportion of age group 0.8

0.6

0.4

0.2

0 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 Age dependency ratio

Age 0-15 Age > 65 Age 15-65

Figure 3.23 Relationship between dependency ratio (x-axis) and the proportion of population aged 0-14 (diamond), 15-64 (dot) and 65 and above

3.4. Summary of equity in health status Data presented in this chapter display inequality between provinces. These inequalities contributed to unequal risks to health, especially, inequalities in economic status led to unequal utilization of health services. However, improving economic status alone may not lead to improved health status. Provincial data show that provinces with high economic status did not necessarily have low mortality, since the two variables were not directly correlated. The provisional conclusion was that provinces with multiple factors showing deprived status, were likely to have low economic status, under coverage of infrastructure, poor health status related with poverty and low environmental sanitation. With low Z-score for each item, the overall Z-score of these provinces according to the benchmark 1 were ranked as presented in table 3.1. 30 The Benchmarks of fairness for health reforms

Table 3.1 Overall Z-score of the benchmark 1 for the first top 3 and the bottom 3 ranks

Rank 1 2 3 73 74 75 Province Chainat Pathumthani Nonthaburi Yala Narathiwat Maehongson 1. Overall health status 0.94 -0.33 -0.49 -0.45 -0.52 -0.00 2. Health status by subgroup 0.57 0.98 0.66 -0.05 0.48 -0.45 3. Service coverage 0.64 0.02 0.65 -3.34 -3.52 -3.27 4. Environmental factors 0.89 0.73 1.12 -0.36 -0.39 -2.26 5. Demographic factors 0.56 0.39 -0.89 -0.83 -1.29 -0.13 6. Economic status -0.03 1.51 1.39 -0.75 -1.08 -0.70 7. Deprivation 1.11 0.79 1.20 -0.86 -1.41 -1.66 Benchmark 1 0.67 0.59 0.52 -0.95 -1.10 -1.21

The high overall score of benchmark 1 (see table 3.1) was the result of positive score for each benchmark. Provinces at the bottom three rank showed negative Z-score in almost all benchmarks. This reaffirmed that inequalities existed in a cluster of provinces. The overall scores by 75 provinces were not much different from each other because the overall scores were the summation of positive and negative score. Details of score on each benchmark had its own virtue and should not be overlooked. 4. Equity in health finance: Quantitative data 31

4 Equity in health finance: Quantitative data

There are 2 benchmarks concerning equity in health care finance: no financial barrier to equitable health delivery and equitable financing. These two aspects are closely linked to the principles of health insurance. There are various sources of data that can be used to evaluate equity of health finance. This chapter illustrates the results on equity in health finance using data from the Health and Welfare Survey and the Provincial Health Survey of the MOPH. 4.1. Financial barrier to equitable services Health insurance coverage is an important indicator to show how large the people are accessible to health services without paying high user fee. Data from the 1996 Health and Welfare Survey revealed that provinces with high coverage of health insurance were Nan (93.72% Z-score = +3.02), Loei (93.27% Z-score = +2.96) and Phayao (89.01% Z-score = +2.42). Provinces with the lowest coverage included Cholburi (49.13% Z-score= -2.40), Petchaburi (41.50% Z-score = -1.44) and Samut Sakhon (41.39% Z-score = -1.42).

Figure 4.1 The coverage of health insurance by province Figure 4.2 The coverage of the better-off insurance schemes 32 Equity of Health status: quantitive data

If the insurance coverage was divided into 2 categories, the better-off schemes were the CSMBS, the SSS and private insurance; and the moderate coverage schemes covered the public assistance scheme and the VHCS. The distributions of these schemes were rather different. The better-off schemes concentrated in the central region and the moderate schemes were more prevalent in the north and northeast. There seems to be a relationship between the prevalence of the uncovered and the coverage of the moderate scheme rather than with the coverage of the better-off scheme. Provinces with high percentage of uninsured tended to have lower coverage of the moderate schemes and perhaps high incidence of the better-off schemes.

% of insurance 100

80

60

40

20

0 0 102030405060 % of no insurance

High benefit Moderate benefit

Figure 4.3 Relationships between the percentage of uninsured with the coverage of the better-off (dot) and moderate schemes (diamond)

After the universal coverage policy, the coverage of health insurance should reach 100% in all provinces. The coverage of the better-off schemes should not much change because the CSMBS and the SSS were still unchanged. Hence inequity in health beneficiary did exist. Considering the impact of health insurance on financial burden to household wealth (% of health spending to household spending), it was observed that the distribution of household financial burden on health (out of household spending) was not related to the coverage rate of health insurance. This may be implied that health insurance did not protect high household health expenditure since there were many other spending not covered by the schemes, e.g. Figure 4.4 Distribution of burden of health spending services at private clinic, private hospital and drug store. 4. Equity in health finance: Quantitative data 33

Household financial burden on health according to the Socioeconomic Survey 1999 ranged from 2.94% (Petchaburi) to 8.24% of household spending (Nongkhai). The high burden confirmed that in spite of health insurance coverage, household still spent on health services out of their income.

From the above data, no financial barrier to equitable services varied between province. However, lower insurance coverage did not mean that people lacked access, as long as they were able to pay out of their own pocket. Next section deals with the inequitable health financing as the consequence of out-of-pocket payment especially for provinces that had lower insurance coverage.

4.2. Financial burden from health spending in various groups The benchmark 5 emphasizes on the difference of financial burden from health expenditure between the rich and the poor. Kakwani index has been used in many countries to illustrate progressivity of health finance, and the index for Thailand has also been estimated. Due to data limitation at provincial level, a modified index was calculated. According to the Socioeconomic Survey 1999, the financial burden from health spending among the unemployed was compared with the burden among businessmen. The relative burden ranged from 0.24 to 13.01. Most provinces (81.3% of the total) had the relative burden higher than 1, implying that the unemployed faced the higher burden than the businessmen. The average relative burden was 2.71(26), i.e. the unemployed had a higher chance of paying out-of- pocket for health than the richer population. The poor were mostly prevented from seeking care because they faced higher burden. (Readers must keep in mind that financial data used here were out- of-pocket payment only, not including how equitable Figure 4.5 Distribution of the relative burden of health spending the government taxation). among the unemployed and businessmen 34 Equity of Health status: quantitive data

4.3. Summary on equity in health finance The data presented in this chapter show differences of health insurance coverage from province to province. This inequity gap is likely to be closed by the universal coverage policy, since the government would like to provide equal opportunity to get access to care. Financial burden from health expenditure was also inequitable due to limited health coverage of the insurance schemes, people therefore paid out-of-pocket for health care.

Quantitatively, when the average scores of the benchmarks 2 and 5 were reanalyzed, the results were the overall average score. Table 4.1 shows the top 3 and the bottom 3 provinces according to the overall average score.

Table 4.1 The average score by benchmarks 2 and 5 The benchmark 2 No financial barrier to equitable health services Rank 1 2 3 73 74 75 Province Phrae Loi Nan Suphan Chachoeng sao Nongkhai 1. Health insurance coverage 1.24 1.29 1.68 -0.21 -0.27 -0.17 2. Ratio of health insurance 1.11 2.29 2.98 -0.38 -0.13 -0.61 3. Total health spending 2.04 0.53 -0.63 -2.47 -2.80 -3.27 Benchmark 2 1.46 1.37 1.34 -1.02 -1.07 -1.35

The benchmark 5: Equitable health financing Rank 1 2 3 73 74 75 Province Udon N. Phanom Trang Patthalung Trad Phatumthani 1. Difference in health 1.06 0.97 0.94 -2.20 -2.33 -4.45 spending * Benchmark 5 1.06 0.97 0.94 -2.20 -2.33 -4.45

In terms of inequity in benchmark 2, including health insurance coverage and financial burden from health spending, the average score was the result of burden of health spending. 5. Equity in health delivery: Quantitative data 35

5 Equity in health delivery: Quantitative data Previous chapters have shown unequal distribution of health determinants and outcomes by province. Those determinants were mainly outside health sectors. This chapter focuses on factors related to health delivery system. The benchmark 3 emphasizes on all other barriers to equitable health services except financial terms. The benchmark 4 emphasizes on the coverage of comprehensive services. The benchmark 6 emphasizes on efficiency and quality of services. The benchmark 7 deals with efficiency of the delivery system. Finally, the benchmark 9 emphasizes on autonomy of providers and consumers of health care. Data shown in this chapter mainly derived from the Bureau of Health Policy and Plan, the Provincial Hospital Division, the Rural Health Division, and the Health Insurance Office of the Ministry of Public Health and the Health Systems Research Institute.

5.1. Distribution of health resource Unequal distribution of health resources in Thailand is no exception. The unequal distribution of doctor in 1999 is shown in figure 5.1. The national average population to doctor ratio was 3,654, while the ratio for the northeast was the highest at 8,110(14). There were high variations of the ratios when analyzed by provinces even in the same region. Provinces with medical school showed lower population to doctor ratios, implying better health resources than provinces with no medical school.

Apart from Bangkok, provinces with good population to doctor ratio included Chiang Mai (1,884: 1), Pathumthani (2,149:1), Cholburi (2,229:1), Samutprakan (2,264: 1), Songkhla (2,280: 1), Saraburi (2,699:1), Phuket (2,717:1) and Khon Kaen(2,844:1). Provinces with high ratio included Sisaket (14,878: 1), Petchabun (14,123:1), Nongbualamphu (14,079: 1), Chaiyaphum (12,795: 1), Roi Et (12,345:1), Surin (11,326: 1), Kalasin (11,176: 1) and Burirum (10,915:1). The maximum to minimum ratio was as high as 8. Figure 5.1 Distribution of population to doctor ratio 36 Tool to measure health equity

Health resource input as measured by population to bed ratio had the same pattern as population to doctor ratio. Provinces with the best ratio included Nonthaburi (152: 1 bed), Chiang Mai (235: 1), Pathumthani (272: 1), Cholburi (297: 1) and Suratthani (297: 1). Provinces with high population to bed ratio were Nongbualumphu (1,647: 1), Chaiyaphum (1,151: 1), Sisaket (1,141: 1), Mahasarakam (1,070: 1) and Narathiwat (1,014: 1). The maximum to minimum ratio was as high as 11. The population to doctor ratio had strong correlation with the population to bed ratio (r = 0.9077) as shown in figure 5.3. Figure 5.2 Distribution of population to bed ratio by province Population per bed Health resource by bed to doctor ratio, if 2,000 high it implies that doctor has to take 1,500 care of larger number of inpatient, not considering population dimension. This 1,000 ratio ranged from 5.09 bed to 1 doctor 500 (Samutprakan) to 21.71 (Nonthaburi). 0 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 When taking account of population (see Population per doctor figure 5.4), provinces were divided into Figure 5.3 The relationship between population to doctor and population to bed 4 groups according to the population to doctor Bed per doctor ratio and the bed to doctor ratio. Provinces 25 classified as A had more doctors and more beds, nonthabure 20 Ae B petchabun while provinces classified as B had fewer doctors 15 by more beds (compared with doctor). Provinces classified as C had more doctors but fewer beds, 10 5 and D had both fewer doctors and fewer beds. DNongbualumphu samutprakan C This shows the imbalance of health resources 0 0 4,000 8,000 12,000 16,000 distribution. 2,000 6,000 10,000 14,000 Population per doctor

Figure 5.4 Relationship between population to bed (x-axis) and bed to doctor (y-axis) by 4 groups of provinces 5. Equity in health delivery: Quantitative data 37

The imbalance distribution of resources further complicated the difference in workloads to health personnel in the province. Comparing the load of outpatients and inpatients to doctor, the results looked the same as the distribution of doctor. Provinces with the lowest load of outpatients were Chiang Mai (1,080), Songkhla (1,178) and Nakhonpatom (1,260). Provinces with the lowest load of inpatients were Songkhla (293), Samutprakan (324) and Pathumthani (328). On the other hand, provinces with high outpatient load included Sisaket (5,244), Burirum (5,192) and Petchabun (4,682); and high inpatient load Surin (1,492), Ubonratchathani (1,445) amd Sisaket (1,279). The strong relationships between outpatient to doctor and inpatient to doctor with the population to doctor (r = 0.8518 and 0.7777 respectively) were shown in figures 5.5 and 5.6.

Figure 5.5 Distribution of outpatient to doctor ratio by province Figure 5.6 Distribution of inpatient to doctor ratio by province

Patient per doctor 6,000 5,000 4,000 3,000 2,000 1,000 0 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 Population per doctor

Out-patient In-patient

Figure 5.7 Relationship between population to doctor ratio (x-axis) and outpatient (dot) and inpatient to doctor ratio (diamond) 38 Tool to measure health equity

5.2. Accessibility and utilization

Overall utilization The unequal distribution of health resources somehow affects accessibility and utilization of services. Data from the Health Policy and Plan Bureau showed wide variations of outpatient and inpatient services. The annual rate of new outpatient attendant (proportion of population who at least attended outpatient service in the year) (14) was highest in Samutsakhon (0.87) and lowest in Nongbualumphu (0.21) see figure 5.8. The overall admission rate (number of admissions divided by population) was highest Trad (28.4%) and lowest in Mahasarakam (5.2%). There was a high correlation between distribution of health resource and utilization. Province with higher population to doctor ratio had lower rate of new outpatient attendants (r= -0.6559 see figure 5.9), i.e. holding population constant, the fewer the doctor the lower outpatient use. Admission rate had a better correlation with population to bed ratio (r = -0.7239, see figure 5.10). Holding population constant, province with fewer beds had lower admission rate. Figure 5.8 Distribution of the rate of new outpatient attendants

Figure 5.9 Correlation New outpatient attendent rate between population to doctor ÍѵÃÒ¡ ÒÃ㪠éºÃÔ¡Òü».¹Í¡ÃÒÂãËÁèµèÍ»Õ 1 ratio (x-axis) and new outpatient attendant (y-axis) 0.8

0.6 Admission rate ÍѵÃÒ¡ ÒÃ㪠éºÃÔ¡Òü».ã¹µèÍ»Õ 0.3 0.4

0.25 0.2

0.2 0 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 0.15 Population»ÃЪҡtoõdoctorèÍá¾·rÂateì

0.1

0.05 Figure 5.10 Correlation between

0 population to bed ratio (x-axis) and 0 500 1,000 1,500 2,000 admission rate (y-axis) Population»ÃЪҡà µtoèÍà bedµÕ§ rate 5. Equity in health delivery: Quantitative data 39

The impact of unequal distribution of health resources on health care utilization is further elaborated. Province with doctor to population ratio worse than 1: 8,000 had new outpatient attendant rate lower than 0.5, implying that fewer than 50% of population in these provinces had visited outpatient service at least once in a year. Province with bed to population worse than 1: 800, had admission rate lower than 10%, i.e. less than 1 in 10 of the population had been admitted to the hospital.x

Looking at utilization only within the MOPH infrastructure (health centre, community, general and regional hospitals)(28)(29) the uses in outpatient services ranged from 1.13 visits/cap/year (Petchabun) to 3.67 (Phrae). Admission rate ranged from 4.86% (Nonthaburi) to 20.25% (Mukdahan). The distributions of the two utilization rates were presented in figures 5.11 and 5.12.

Figure 5.11 Distribution of outpatient uses in MOPH infrastructure Figure 5.12 Distribution of inpatient uses in MOPH hospitals Admission rate 0.25 Use rates of outpatient and inpatient 0.2 services in MOPH infrastructure had good correlation. Province with 0.15 highoutpatient rate also had a high 0.1 admission rate (see figure 5.13). 0.05

0 1 1.5 2 2.5 3 3.5 4 OPD Utilization rate Figure 5.13 Correlation between outpatient (x-axis) and inpatient uses in MOPH infrastructure (y-axis) 40 Tool to measure health equity

Use of primary level services High utilization rate should reflect better access to health services. If the services closed to people’s home are of acceptable standards, the use rate of primary level is high, with the lower use rate of secondary to tertiary care level. The primary care level here included health centre and community hospital. The proportion of care from primary level to the total services in the province was developed here as an index to measure access to lower level of care and the popularity of using lower level of care. This index of using lower level of care was similar to the use of overall public health facilities. Therefore, the variations of using the mix of public health services were explained by the utilization at health centre.

Provinces with high use rate at health centre included Nongbualumphu (1.82), Kalasin (1.63) and Phrae (1.61). Provinces with the lowest rate were Phuket (0.29), Chainat (0.32) and Songkhla (0.36). In general, provinces in the northeast and the north had higher ratio of utilization at health centre, especially higher than provinces in the south.

Plots on the correlation between uses of overall services and different levels of health services were shown in figure 5.15. It can be seen that uses at health centre had the highest correlation with the overall uses (r = 0.8395),

Figure 5.14 Distribution of ratio of health centre use to hospital utilization Utilization rate at health center 2.5

2

1.5 Figure 5.15 Correlation between rate of 1 overall health utilization (x-axis) and rate of health centre utilization (y-axis) 0.5

0 1 1.5 2 2.5 3 3.5 4 Overall utilization rate 5. Equity in health delivery: Quantitative data 41

Proportion of service uses at regional and general hospitals to overall use reflect the utilization at higher level of care. If the proportion is high, it reflects under-utilization of lower level of care. Provinces in the northeast and the north had lower access to higher level of care than provinces in the central and the south. Provinces with lower access to high level of care were Kalasin (6.27%), Sisaket (8.01%), Burirum (8.75%), Roi-et (8.95%) and Sakolnakhon (9.04%). Provinces with higher access to higher level of care were Phuket (48.59%), Yala (47.80%), Singburi (46.78%), Samutsongkram (45.20%) and Ratchaburi (44.61%).

Figure 5.16 Distribution of proportion of services at the provincial level Admission rate at community hospital to the rate at provincial hospital is another index showing accessibility to services closed to people’s home. If the ratio higher than 1, the higher the probability of using community hospitals. Provinces with high ratio were concentrated in the northeast. They were Nakhon Ratchasima (2.84), Khon Kaen (2.61), Sakolnakhon (2.60), Chiang Mai (2.58) and Sisaket (2.47). Provinces with the lowest ratio were Ratchaburi (0.21), Singburi (0.27), Phuket (0.32), Phayao (0.33) and Yala (0.35).

Figure 5.17 Distribution of ratio of admission to community hospital to provincial hospital 42 Tool to measure health equity

Continuing access of service Continuing access of service is measured by averaging total outpatient visits with the new outpatient cases of the hospital. The higher number of visit for each new patient in a year reflects the repeat visits or the continuity of accessing to services, which is preferable among chronically illed patients. Data from the MOPH revealed that provinces with high repeated visits were Nakhonpathom (4.19), Angthong (4.09) and Samutsongkram (3.86). Provinces with the lowest repeated visits included Ayuthaya (2.15), Phuket (2.16) and Nakhonnayok (2.20). Provinces with high repeated visits were mostly in the central and the south. However the differences were not very obvious. Figure 5.18 Distribution of number of repeated visits by new attendant

The referral rate from community hospital to provincial hospital is another indicator of continuity of access for patients at community hospital. If the referral rate is high, it may mean that patients have to be transferred far away from home. On the other hand, it may be interpreted that health system in the province provides good care with good accessibility to high level of care if needed. The referral rates ranged from 0.28% (Samutsakhon) to 3.55% (Lampang).

Comprehensiveness of service Comprehensiveness of service in a health facility is judged according to missions of each facility. For example, the primary care level at health centre should focus on health promotion and disease prevention activities, but provincial hospital should be able to provide surgical services which cannot be delivered by other level of care. Proportion of surgery to total inpatients in general and regional hospitals is an indirect indicator for comprehensive services provided. Provinces with high surgical patients included Khon Kaen (42.18%), Chiangrai (32.61%) and Ubonratchathani (27.92%). Provinces with low surgical patients included Sisaket (10.62%), Yala (12.72%) and Satoon (12.79%). Provinces with regional hospitals usually had a high proportion of surgical patients. 5. Equity in health delivery: Quantitative data 43

Proportion of health promotion activities at health centre is an indicator for comprehensiveness of services at health centre. Provinces with high proportion of health promotion activity at health centre were Nakhonpanom (44.86%), Petchabun (36.64%) and Saraburi (34.24%). Provinces with low health promotion activity were Nakhon Ratchasima (4.15%), Trad (5.28%) and Nan (6.11%). Provinces in the south and northeast had a higher proportion of health promotion activity than in the central and north.

Finding the correlation between visits at health centre (visit/capita/year) and proportion of health promotion activity, it revealed that provinces with high visits at health centre had a tendency of having lower health promotion activity. The higher the activities at health centre, the more likely to be for curative services. Except was found in Nakhonpanom with rather high visit rate and the highest proportion on health promotion (see figure 5.20).

Figure 5.19 Distribution of proportion in health promotion activities to all activities at health centre Proportion of health promotion service 0.5

0.4

0.3

0.2

0.1

0 00.511.522.5 Utilization rate at health center

Figure 5.20 Relationship between use rate at health centre (x-axis, visit/person/year) and proportion of health promotion activity at health centre (y-axis) 44 Tool to measure health equity

5.3. Efficiency and quality of service Efficiency and quality of service are the indicator to flag the unnecessary and high cost services when related to value for money or outcome. Assessing what service is not a necessary is difficult, as there is no distinct line for what is appropriate. In real life we can only analyze the mean or median and compare the means of the two comparative groups. This comparison is a relative scale, and cannot be sure in absolute term, what is the most efficient level. In this part, efficiency and quality of services are presented in terms of efficiency of hospital bed use, patterns of inpatient service delivery, unit cost and quality of service.

Efficiency of bed use There are many indicators of bed use and they must be used complementarily. Considering admitting a patient to the hospital, if most of hospital beds are occupied (high bed occupancy rate), the patient will get little chance of having good care, because the ward is too congested. High bed occupancy reflects imbalance between health need and available resource. If the resource is adequate, bed occupancy should be at an appropriate level so that the hospital can take new patient and provide good quality care. Considering the bed occupancy should be in conjunction with the bed turnover rate. This index measures how many patients are treated for a hospital bed in a year. A hospital with high bed occupancy and high bed turnover will have high number of inpatients with long stays (in quadrant A). A hospital with low occupancy rate but high bed turnover will have non-severe cases that can be treated as outpatients, therefore, admissions may not be justified (in quadrant B). A hospital with high occupancy rate but low bed turnover usually has long stay patients who may not need that long stays or the more severe cases l (quadrant C). A hospital with both low occupancy and low bed turnover rate shows the excess bed supply with low bed utilization (quadrant D). Figure 5.21 shows no relationship between occupancy rate and bed turnover rate. The diagram is divided into 4 quadrants according to the average values.

Bed Turnover rate 100 Buriru 90 B Satune 80 A

70 Ubonratchathani 60 D C Nakornrachasema 50 Phangnga 40 50 60 70 80 90 100 110 120 Bed Occupancy rate Figure 5.21 Bed occupancy rate (x-axis) and bed turnover rate (y-axis) for regional and general hospital 5. Equity in health delivery: Quantitative data 45

These bed use data represented the regional and general hospitals. Some provincial hospitals were overcrowded (in A), while some were rather empty (in D). The most preferable should be B where bed turnover rate was high and short stay, therefore new cases could be admitted. If considering only occupancy rate, the highly occupied beds were found in the northeast. These included Ubonratchathani (115.8%), Chaiyaphum (114.4%) and Khon Kaen (111.9%). The least occupied provincial hospital beds were found in Amnatcharoen (58.6%), Panga (64.0%) and Chumporn (65.9%).

Inpatient services Figure 5.22 Distribution of occupancy rate of provincial hospital Indicators for measuring inpatient services cover the whole range: from the inpatient to outpatients ratio (describing the likelihood of severe patient that needs hospitalization), the casemix index (the average relative weight by diagnosis related group, DRG, describing the extent of resource uses to treat inpatient) and the average length of stay (describing indirectly the severity of inpatient). Using these 3 indicators together provide better description of appropriateness of inpatient services. If the inpatient to outpatient ratios of community, general and regional hospitals were high, they showed the likelihood of outpatients being Admission rat e treated as inpatients that if not 0.25 necessary, led to higher cost. 0.2 Provinces with high ratios were 0.15 Mukdahan (17.1%), Nakhonnayok (12.6%) and Mahasarakam 0.1

(11.5%). Provinces with low ratios 0.05 were Nonthaburi (5.4%), 0 Pathumthani (6.5%) and 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 In-patient : Out-patient ratio Samutsongkram (6.6%). Figure 5.23 Relationship between inpatient to outpatient ratio (x-axis) and admission rate (y-axis) 46 Tool to measure health equity

If the inpatient to outpatient ratio was compared with the admission rate of the population in the province (admission/capita/year), figure 5.23 reveals that hospitals with high inpatient to outpatient ratio also had the high admission rate. Interpretation should be cautious whether the admissions were inappropriate or whether the admission rate was influenced by the high inpatient to outpatient ratio.

In principle, the outpatients who turned to be inpatients suffered from more severe conditions that required hospitalization, therefore the casemix index should be high. Casemix index further related with the average length of stay. The efficiency consideration gives more emphasis on shorter length of stay for the same casemix index. Considering the casemix index and the average length of stay by province, the problems of inefficiency scattered with no specific conclusion on cluster of provinces.

Regional and general hospitals with high casemix index in 1999 (excluding cases with relative weight 0) ranged from Nakhonsrithammarat (1.410), Khon Kaen (1.092) and Chiang Mai (1.040) to Srakaew (0.605), Samutprakan (0.587) and Amnatcharoen (0.550). The highest length of stay hospitals were found in Nakhonratcahsima (6.95 days), Samutsongkram (6.94) and Nakhonsrithammarat (6.86 days). The shortest stays were found in Satoon (3.11 days), Amnatcharoen (3.22) and Roi-et (3.68 days). Figure 5.24 plotted casemix index with average length of stay by province, the higher the casemix index the longer the stay. It can be concluded that length of stay in hospital could be explained by severity of inpatient, not the behaviour of discretionary keeping patient too long in hospital.

Average length of stay 8

7

6

5

4

3

2 0.4 0.6 0.8 1 1.2 1.4 1.6 Relative w eight I PD

Figure 5.24 Relationship between casemix index (x-axis) and the average length of stay (y-axis) of regional and general hospitals 5. Equity in health delivery: Quantitative data 47

Unit cost Unit cost of outpatient and inpatient can be estimated from the hospital financial report (total spending on labour and other cost from government budget and hospital revenue). The assumptions on proportion of cost for outpatient and inpatient services were made. Adopting the proportion of outpatient and inpatient costs of 55% and 45% from the Social Security Scheme, 55% of the total hospital spending was divided by the number of outpatients, unit cost for outpatient visit was derived. On the other hand, dividing 45% of the total hospital spending with number of inpatients, unit cost for inpatient was derived.

Unit cost by inpatient ¤èÒã ª é¨èÒ µ èÍ˹èÇ º ÃÔ¡Òü».ã¹ Provinces with high cost per 8,000 outpatient were likely to have high 7,000 cost per inpatient (see figure 5.25 6,000 5,000 because they had the same inpatient 4,000 to outpatient ratio). Hence, they 3,000 tended to be high health spending 2,000 1,000 provinces. 200 400 600 800 1,000 1,200 1,400 ¤èÒ㪠Unité¨èÒ µcostèÍË ¹èbyÇ ouº tÃpÔ¡Òatientü».¹Í¡

Figure 5.25 Relationship between unit cost by outpatient (x-axis) and unit cost by inpatient (y-axis)

Considering the unit cost by inpatient, hospitals in the central and southern provinces were costlier than hospitals in the northeast and northern provinces. The most costly hospitals were Chantaburi (7,118 baht/case), Narathiwas (7,023) and Nakhonpathom (6,894). Provinces with lowest unit cost were Roi-et (2,222), Surin (2,375) and Burirum (2,725 baht/case). The maximum to minimum ratio was about 3 times. There were many influencing factors such as labour cost (there were more health personnel per population in the central and the south than in the northeast and the north).

Figure 5.26 Distribution of unit cost by inpatient 48 Tool to measure health equity

Cost per inpatient case can be the Average cost per admission influence of both cost per day and length 8,000 7,000 of stay. If cost per day is held constant, 6,000 the cost per case is the influence of 5,000 length of stay. Figures 5.27 and 5.28 4,000 show that cost per inpatient was 3,000 2,000 influenced by both factors. Hospitals with 1,000 400 600 800 1,000 1,200 1,400 1,600 high cost per case had high cost per day Average cost per admission day and longer stays in hospital. However, Figure 5.27 Relationship between cost per day (x-axis) and cost per inpatient (y-axis) length of stay was suspected to have lower Average cost per admission influence than the cost per day. 8,000 7,000

6,000

5,000

4,000

3,000

2,000

1,000 2345678 Average length of stay

Figure 5.28 Relationship between length of stay (x-axis) and cost per inpatient (y-axis) Quality of service There are many indicators to measure quality of service, most of them require data that are not available routinely. Few examples of indicators using routinely available data are the overall case fatality in the hospital (indicates the probability of dying in the hospital), the ratio of abnormal labour to normal labour (indicates the standards of antenatal care and delivery services). However high abnormal labour may indicate different choices of baby delivery, the consequences are higher risks and longer stay in hospital.

The overall case fatality rate in regional and general hospital in 2000 ranged from 1.16% (Nonbuallumphu) to 7.23% (Nakhonratchasima), with point average at 2.9%. The ratio of abnormal to normal delivery ranged from 0.20 (Nakhonpanom) to 2.01 (Angthong), with point average at 0.71. There were a considerable number of provinces (12 provinces) that had high number of abnormal delivery than normal delivery (the ratio higher than1). To what extent the abnormal delivery brings in good quality of care is unclear, but the data showed inequality of delivery across provinces. Case fatality rate of inpatients in regional and general may be the influence of severity of cases admitted to the hospital. This hypothesis could be tested by comparing case fatality rate with relative weight and 5. Equity in health delivery: Quantitative data 49 length of stay by hospital. Province with severe cases (high relative weight) tended to have higher case fatality and longer stays in hospital (figure 5.29). Comparing case fatality with unit cost of inpatient, it showed that province with higher unit cost slightly had higher case fatality rate (figure 5.30). Hence, it was most likely that high case fatality rate was associated with severity of cases, longer stay in hospital and higher cost. Death rate IPD Length of stay 0.08 8 0.07 7 0.06 0.05 6 0.04 5 0.03 4 0.02 0.01 3 0 2 0.4 0.6 0.8 1 1.2 1.4 1.6 Relative weight IPD

Death rate IPD LOS

Figure 5.29 Relationship between casemix index and case fatality and length of stay

Death rate I PD 0.08 0.07 0.06 0.05 0.04 0.03 0.02 0.01 0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 Average cost per adm ission

Figure 5.30 Relationship between unit cost for inpatient (x-axis) and case fatality (y-axis) 50 Tool to measure health equity

5.4. Efficiency in management Efficiency of management can be looked at in both levels: the efficiency of overall health service system and the efficiency of each provider. Efficiency of overall health service system is assessed by the level of health expenditure out of the overall country economy. Country should have adequate resources for health. Moreover, the share of spending to primary care services should also be adequate because it is the most cost-effective health care. If spending is concentrated in the higher level of care, the overall spending is likely to be high. Second level of efficiency, the provider level looks at appropriateness in management by cost of management in comparison with size and output. The proportion of management cost will be compared. However, the conclusion may not be very conclusive since the variations may mean inequality between provider and lower spending provider may not be the more efficient health care, despite it may mean under spent. The inequity of distribution of health spending may have relationship with efficiency with efficiency of management.

Overall health care cost per capita The overall health care cost can be assessed only in public sector (the Ministry of Public Health). The spending at health centre, community hospital and provincial hospital per capita was compared among 75 provinces in figure 5.31. The average was 895 baht per capita in 2000. High spending provinces were in the central, the east and some of the south. Lowest spending was found in the northeast. Provinces with the highest spending were Panga (1,842 baht), Singburi (1,789 baht), Chantaburi (1,773 baht), Saraburi (1,587 baht) and Ratchaburi (1,480). Provinces with the lowest spending were Nongbualumphu (349 baht), Samutprakan (415 baht), Surin (465 baht), Roi-et (471 baht) and Burirum (471 baht). Figure 5.31 Distribution of public health spending per capita 5. Equity in health delivery: Quantitative data 51

Though provinces in the central had high public health spending per capita but peri-Bangkok provinces had lower spending per capita. This may be explained by the low utilization rate of public health sector in these provinces. Figure 5.32 compare utilization rate and spending per capita, most provinces with lower than average spending had lower than average use of services in the MOPH. A few provinces had high health spending per cap but low utilization, with suspicion that low spending was not the same as high efficiency but rather low utilization. Provinces with higher utilization rate than the average had wide range of health spending per capita. There should be other factors explaining this variation except utilization rate. Utilization rate OPD 4 Phrae 3.5 Nakornphanom

3

2.5 Phangnga 2 Nongbualumpu 1.5 Samuthprakarn Nakornnayok 1 0 500 1,000 1,500 2,000 Cost per capita Figure 5.32 Public health spending per capita (x-axis) and outpatient uses (y-axis) at public sector

Other factors include available health resources especially hospital bed. Provinces with high bed to population ratio were likely to have high spending. There were 4 provinces in peri-Bangkok area with low spending but high bed to population ratio, this was explained by the available beds were largely in the private sector.

Population per bed 2,000

Nongbualampu 1,500

1,000

Samutprakan 500 Pathurmthani Nonthaburi Chiang mai 0 0 500 1,000 1,500 2,000 Cost per capita

Figure 5.33 Relationship between public health spending per capita (x-axis) and bed to population ratio (y-axis) 52 Tool to measure health equity

The size of population in each province might be some relationship with health spending per capita in terms of economies of scale. Provinces with large population tended to have lower spending per capita. There were a number of provinces with small population size but had low spending per capita. Most of these provinces were in the northeast. This suggested that size was not the only factor determining the spending as perceived in the economies of scale principle. Cost per capita 2,000

1,500

1,000

500

0 0 500,000 1,000,000 1,500,000 2,000,000 2,500,000 3,000,000 Population

Figure 5.34 Relationship between population (x-axis) and total spending per capita (y-axis)

Other factors influencing health spending per capital included health need and demand for health care, especially demand for health care in public sector. Since choice in private sector differed by province and higher choice may be related with higher spending. Population size, which displayed the effect of economies of scale, was the external factor outside the control of health manager. One of the manageable factor was the level of health service available. If the manager wanted to lower the overall cost, shifting service provision to primary care level would be an attractive option because good primary care system could provide good quality of care at the lowest cost. Making clients having faith with primary care services should be one of the cost-effective interventions.

Proportion of spending on primary care Proportion of health spending on primary care was an indicator for cost-effective health utilization. If the proportion of spending at health centre to the total spending was high, it meant that the pattern of utilization favoured primary care level and finally the overall cost may be reduced. The average proportion was 11.85%. Provinces with high proportion of health centre spending were Roi-et (20.75%), Kampaengphet (18.15%), Pattani (17.95%), Chaiyapum (17.50%) and Siraket (17.46%). Provinces with 5. Equity in health delivery: Quantitative data 53 the lowest proportion were Samutsakhon (2.22%), Samutsongkram (3.38%), Ratchaburi (3.80%), Samutprakan (4.88%) and Singburi (6.10%). The data showed little emphasis on primary care level. Patterns of resource allocation (health spending) showed strong biases to the secondary and tertiary care. Provinces with high proportion of spending at health centre were concentrated in the northeast where there were sparse health facilities hence lower health spending. The lower cost may be interpreted as high efficiency when compared with other costly provinces. However, this efficiency may be the opposite with the opportunity or accessibility to health services, since higher access may mean higher cost. Figure 5.35 Distribution of proportion of spending at health centre If the cost of providing care at regional and general hospital was divided by the population, the obtained indicator reflected indirectly the choice for higher level of care. Higher level facilities were associated with higher capital cost. The high investment in these facilities although increased the opportunity of access to highly specialized care, but the point of over-supply and unnecessary use had to be considered. The hardest question to answer was what is the appropriate level. The per capita health spending at regional and general hospitals by province was pretty the same as proportion of spending at health centre. Provinces in the northeast had the lowest health spending per capita at regional and general hospitals. The highest spending was found in Singburi (1,590 Figure 5.36 Distribution of spending at provincial hospital per capita 54 Tool to measure health equity baht), Ratchaburi (1,509 baht), Panga (1,251 baht), Saraburi (1,220 baht) and Phuket (1,210 baht). Provinces with the lowest spending were Sisaket (175 baht), Roi-et (186 baht), Nongkhai (199 baht), Buriru (199 baht) and Chiang Mai (204 baht). There was a discrepancy of 9 between the lowest and the highest. Health spending compared with expected spending Expected health spending was calculated from the average utilization rate derived from the Health and Welfare survey (use of outpatient and inpatient services by health facility(30) ) and unit cost of services at public and private health facilities(31). When the expected spending per capita was compared with public health spending per capita, we could see the difference between the expected total health spending and the ‘real’ spending within public health sector. Limitations of this comparison had to be realized. The total spending covered uses of services in private sector. Since spending in private sector was hard to collect, the low index of public health spending to total spending was the effect of high spending in private sector. However, if the public health spending was higher than the expected spending, it could be either the expected value was too low or the cost of public facility in the province was too high. The results showed that the real spending in public health sector and expected spending varied to a certain extent. The values for the central region were not remarkably higher than other regions as could be seen in figure 5.36 showing distribution of real expenditure. The ratio of real to expected health spending was high in the central and the south. Provinces with the highest ratios were Narathiwat (2.70), Saraburi (2.59), Prachinburi (2.35), Samutsongkram (2.27) and Chanthaburi (2.17). Provinces with the lowest ratios were Nongbualumphu (0.35), Nakhonpanom (0.52), Chaiyaphum (0.54), Amnatcharoen (0.59), and Chainat (0.60).

Hospital expenditure and size of the hospital Figure 5.37 Distribution of expected health spending per capita Figure 5.38 Distribution of ratio of real to expected health spending 5. Equity in health delivery: Quantitative data 55

The detailed analysis on expenditure at hospital level could reveal another aspect of cost in relation to size of the hospital. The total hospital expenditure per bed was an indicator to show efficiency of the hospital but it was influenced by bed utilization. The distribution of hospital spending per bed was presented in figure 5.39. The high spending per bed was concentrated in the central region. Provinces with the highest spending per bed were Samutprakan (1,003,900 baht), Nonthaburi (992,300 baht) and Nakhonpathom (955,200 baht). The lowest spending per bed were Chumphon (403,100 baht), Surin (404,800 baht) and Amnatcharoen (431,200 baht).

Figure 5.39 Distribution of provincial hospital expenditure per bed Proportion of administrative cost The expenditure of health facility could be broken down to various categories. Some expenditures were defined as fixed cost because they did not change according to the number of services produced. These include salary, special allowance, public utility, general administration and maintenance. Other than fixed cost was the variable cost, which included spending on drug, medical supply, general supply, payments related to performance, etc. To achieve high efficiency, the unnecessary costs should be managed. The unnecessary costs included administrative overhead and other controllable costs. The proportion of administrative cost to the total cost was monitored. The administrative costs included labour cost (salary and allowance). If the proportion was high, it may reflect that there were too many people working in the hospital (because the proportion of the variable cost was low). The proportion of labour cost was highest in Satoon (67.39%), Pattani (62.87%) and Nan (61.28%). The lowest proportion of labour cost to the total cost was found in Sakaew (33.85%), Nongbualumpu (35.10%) and Supanburi (39.28%). Provinces with high proportion of administrative costs (spending on public utility) were Petchabun (12.14%), Chiang Mai (11.57%) and Pathumthani (9.52%). Provinces with the lowest proportion of administrative cost were Nakhonnayok (3.76%), Kampaengphet (3.97%) and Panga (3.99%)(28). 56 Tool to measure health equity

Figures 5.40 and 5.41 show the distribution of the two proportions. The maximum to minimum portion on labour cost was 2 times, averaged at 50.26%. The maximum to minimum proportion of administrative cost was about 3 times, averaged at 6.81%.

The difference of proportions did not only show the different burden of expenditure, but did suggest the appropriateness of the spending. For example, the proportions of administrative cost (max to min 3 times) ranged from 3.76% - 12.14%, or 8.38% difference. The proportion of labour cost had a difference of 33.54% (ranged from 33.85% - 67.39%). The different burden or cost structure may suggest the different efficiency. Some of these expenditures may not be necessary and did not help increase number of outputs or increase quality of outputs.

Figure 5.40 Distribution of proportion of labour cost at provincial Figure 5.41 Distribution of proportion of administrative cost of hospital provincial hospital

Difference in spending for health reflected the variation of consumption on health resources. In terms of equity in health, we concern that the difference in spending should be related to the difference in health need and factors related to the total cost, rather than the difference in the ability to pay of people. If based on the ability to pay, it was less likely to achieve equity. High spending will still be observed in the provinces with high proportion of private health sector but low spending in low purchasing power provinces. One confounding factor was the province with regional hospital established the high spending because of the cross-boundaries from neighbouring provinces. 5. Equity in health delivery: Quantitative data 57

5.5. Opportunity of people to exercise choice of service Previous discussions on equity were based on provider’s perspective and management’s perspective, imposing on consumer’s behaviour to achieve the goals of equity, efficiency and quality. Few emphasis was given on consumer’s perspective. This part raises the issue to what extent consumer should be given consumer sovereignty or choice. Asking people themselves, it is likely that they prefer full autonomy of free access to any facility at anytime for any disease regardless of referral line. On the other hand, asking managers of health system, they tend to limit consumer choice by imposing registration system as gatekeeper, and strictly following the referral line. Therefore balancing the consumer autonomy with the efficiency of the health system is the issue of concern. At this period, it is very unlikely to deny the citizen’s freedom, as it is the basic human right. The trade-off of human right and affordability of the national health system must be put forward.

Consumer choice can be considered on the following steps: ! Availability of services if freedom to choose is given. ! Accessibility to the services of choice. ! Autonomy of choices selection. These steps are sequential. To declare consumer choice, the first step is to have alternative services for people to choose, and they should have good accessibility without financial and non-financial barriers. Finally, they are free to choose without any constraining regulations. We can see that in many situations, people have limited choice, the choices are difficult to get access and people face administrative rules from health insurance principle or health service regulations.

Freedom to choose health services according to health insurance regulations, three levels of freedom are made possible: ! People register with health facility on the geographical or the local administrative basis with limited choice. When they get ill, they attend at the registered facility and they follow the referral line when need higher level of care. ! People register with health facility on their own choice, with or without guideline for making choice (e.g. suggested level and distance). When they get ill, they attend at their registered facility and follow the referral line. ! No registration is required, people can make use at any facility, with or without conditions for use (e.g. suggested level or distance). 58 Tool to measure health equity

The first level people have the least freedom. However, this arrangement provides strong support for primary care principle and brings in the most effective referral system. The weakness is less incentive for quality. The second level provides moderate people’s freedom to choose, but they must attend the facility of their choice first. This arrangement supports primary care concept to some extent. The weakness is the financial viability of the health facility if very limited number of people choose it. This option may be too early for Thailand, because many small health facilities faces inadequate resource allocation in the past and they were unattractive to people as compared to big facilities. The third level of choice is the most ideal for people to choose. People make their own choice each time they get ill. The weakness is the uncontrollable cost of the whole system if people still choose costly higher level of care instead of choosing a good primary care. This weakness could be overcome by improving the quality of health services at the lowest level of health facility. In principle, if people are provided with choice, the whole health system could be more equitable. People’s choice should be weighed together with other aspects of health service system. Efficiency had to consider quality to ensure that equity is achievable. People’s choice could also been interpreted by the choice of public and private health facilities in the province. The Health and Welfare survey provided estimates of proportions of utilization in public health sector as compared to total. High proportion of use in public sector meant that people had high preference in public sector. Low proportion of use in public sector meant that people preferred to use other services from drug store to clinic and private hospital or even self-care. In other means, the ratio of private to public sector use could indicate the popularity of private sector as compared to public sector. High ratio meant that people had more choice going to private sector. Figure 5.42 Distribution of proportion of use in public health sector Data from the survey showed high proportion of public sector use when people were ill(30)(31). The highest proportion was found in the northeast, north and lower south. The proportion was low in the central region. Provinces with the highest proportion were Nan (60.40%), Phitsanulok (60.19%), Uthaithani (58.97%), Yasothon (58.06%) and Chaiyaphum (56.77%). Provinces with the lowest proportion were Samutprakan (22.44%), Nonthaburi (23.33%), Krabi (24.47%), Pathumthani (25.23%) and Kanchanaburi (25.66%). 5. Equity in health delivery: Quantitative data 59

Ratio of private to public sector use was the reciprocal index to the proportion of use in public sector. Where the use in public sector was low, the use of private sector was high. The high ratio meant that people had more choice in private sector. Provinces with the highest ratio of use in private to public sector were Nonthaburi (1.96), Krabi (1.83), Samutprakan (1.46), Kanchanaburi (1.43) and Samutsonkram (1.36). On the other hand, provinces with the lowest ratio were Yasothon (0.19), Maehongson (0.20), Patthalung (0.22), Khon Kaen (0.23) and Pattani (0.23). There were 9 provinces with ratio higher than 1 (use private sector more than public sector). The country average was 0.58, private sector use was only 0.58 of public sector(30)(31). Figure 5.43 Distribution of ratio of private to public sector use for outpatient service

The data showed different distribution of private sector and therefore choice available in the province. Province with high private health sector, the use rate in public sector was lower and public health spending lower. However, high use in private sector would result in high spending from private source, and may be high total health spending. Therefore higher choice may come with high spending because people with high ability to pay could exercise their choice in private sector than people with limited ability to pay. Provinces with high income per capita were likely to have higher use in private sector, and lower use in public sector, as shown in figure 5.44. Proportion of seeking care 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 Average income per capita

Private service Public service

Figure 5.44 Relationship between income per capita (x-axis) and ratio of private (star) to public sector use (diamond) 60 Tool to measure health equity

5.6. Relationship between factors influencing health service utilization To summarize the factors influencing health service utilization, these factors were interconnected: ! Utilization rate was the influence of distribution of health resources. Utilization rate was higher in provinces with higher health resources. ! High utilization in public sector was attributable by high use at health centre, rather than the use at hospital. High utilization at health centre, and high total utilization, meant that the distribution of primary care service was adequate. ! Proportion of health promotion at health centre decreased when the total utilization at health centre increased. This was a warning sign, if the service at health centre was too high, the preventive function of primary care level may be diluted. Holistic and integrated services should be maintained. ! Admission rate was influenced by the pattern service behaviour. Hospital that increased the proportion of inpatient service would certainly increase admission rate, while the need was constant. ! Length of stay was influenced by casemix and severity illness (by relative weight). The differences in length of stay, therefore, reflected differences in inpatient care need. ! Unit cost per inpatient was the function of average length of stay and cost per day. Higher unit cost per inpatient was the result of the difference in casemix (relative weight) and length of stay. ! Case fatality rate was influenced by disease group and severity of illness (by relative weight). Hence, outcome of treatment was partly a function of difference in patient severity. ! Health spending per capita was influenced by distribution of health resources. Provinces with high concentration of resources tended to have high health spending. ! Health spending per capita was influenced by health utilization, especially admission rate. The higher the admission rate, the higher the per capita spending. ! Health spending per capita was also influenced by population size of the district. The bigger the province, the lower the spending per capita, according to economies of scale principle.

Admission rate 0.25

0.2

0.15

0.1 Figure 5.45 Relationship between health spending per 0.05

cap (x-axis) and use rate for 0 0 500 1,000 1,500 2,000 inpatient care (y-axis) Cost per capita 5. Equity in health delivery: Quantitative data 61

From the interconnected factors described above, a change in one factor induced changes in other factors. If the impact on equity was important, any changes should be traced for the impact on equity. The most desirable change was the change that attained better equity. The sum of positive changes should outweigh the sum of negative changes and should keep the balance between these influencing factors.

For instance, increasing the use rate of health service by increasing resource distribution, the impact was the increase in total health spending and reduction in efficiency if health outcome did not increase. The second example, an increase in use at health centre should be offset by a reduction in use at hospital. If not the total health spending would not reduce, or an increase in use at health centre would concentrate on curative services that overlapped with services at hospital. The total health spending which was the function of a) utilization rate (related with illness rate and pattern of service use) b) the unit cost (related with disease severity and economies of scale). The first factor was the demand factor while the second was supply side factor. The resource allocation should take account of these factors to promote allocative efficiency and equity without compromising quality.

5.7. Summary on equity of health delivery

Data analyses led to the conclusion that health inequity between provinces originated from disparity in health resource distribution, service utilization and ultimately total health spending. In remedying inequity, health resources should be evenly distributed, the accessibility to service be equalized and utilization be met with equal opportunity to choose. If health resource distribution is more even, the distribution of health expenditure will be more equalized. The other option was to equalize health spending in order to mobilize more even health resources. These two approaches would bring in different strains on health services adjustment. The aim of achieving more equitable health delivery may cause turbulence in reducing the overall health achievements because moving resources from one area to another would cause under production in one area. Keeping the balance and maintaining the mechanism to equalize health impact were the main objectives. 62 Tool to measure health equity

Considering the scores for equity in health delivery by Z-score of each benchmark, details are presented in table 5.1. Table 5.1 Scores of health equity by benchmarks 3, 4, 6, 7 and 9, for the first 3 ranks and the bottom 3 ranks Benchmark 3 Non-financial barrier to equitable services Rank 1 2 3 73 74 75 Province Chiang Cholburi Pathum Petchabu Sisaket Nongbualum Mai thani n phu 1. Resource distribution 1.44 1.27 1.33 -1.77 -2.32 -3.15 2. Workload to Dr. 1.38 1.36 1.26 -1.82 -1.79 -0.05 3. Use of curative care 0.82 0.71 0.69 -0.74 -0.57 -1.51 Benchmark 3 1.21 1.11 1.09 -1.44 -1.56 -1.57

Benchmark 4 Comprehensive and equitable health services Rank 1 2 3 73 74 75 Province Nakhon Khon Nan Yala Nakhon Nonthaburi panom Kaen Nayok 1. Utilization rate 0.27 0.09 1.96 0.46 -0.14 -1.80 2. Proportion of primary care 1.44 1.43 0.99 -1.60 -0.43 -1.00 3. Continuity of care 0.13 -0.39 0.47 -0.53 -0.71 0.65 4. Comprehensiveness 1.59 2.16 -0.48 -0.26 -0.74 -0.57 Benchmark 4 0.86 0.82 0.73 -0.48 -0.51 -0.68

Benchmark 6 Efficiency and quality of care Rank 1 2 3 73 74 75 Province Amnatchar Nakhonp Satun Chanth Nokhonratc Samutsongk oen anom aburi hasima ram 1. Length of stay 1.57 1.00 1.11 -0.70 -0.63 -2.45 2. Bed use 1.19 0.52 1.39 -0.40 -1.11 -0.88 3. Admission rate -0.91 0.18 -0.65 0.57 0.94 -0.26 4. Unit cost 0.78 0.64 0.60 -1.94 0.15 -0.43 5. Quality of care 1.47 1.32 0.89 -0.85 -2.62 -0.00 Benchmark 6 0.82 0.73 0.67 -0.66 -0.66 -0.81 5. Equity in health delivery: Quantitative data 63

Benchmark 7 Administrative efficiency Rank 1 2 3 73 74 75 Province Nongbualum Roiet Kampaeng Ratcha Sarabu Chanthaburi phu phet buri ri 1. Total spending per capita 0.79 0.79 0.33 -0.89 -0.94 -1.14 2. Total spending per service -0.09 1.07 0.99 -0.62 -0.35 -1.80 3. Proportion of primary care 0.50 0.86 1.11 -1.85 -0.54 -0.69 4. Proportion of admin cost 1.43 0.00 0.11 0.30 -0.15 0.74 5. Spending vs. expected 1.68 0.93 1.07 -1.52 -3.04 -2.15 Benchmark 7 0.86 0.73 0.72 -0.92 -1.00 -1.01

Benchmark 9 Patient and provider autonomy Rank 1 2 3 73 74 75 Province Phitsanul Nonthabu Samutpraka Tak Ubonratcha Chumpho ok ri n thani n 1. Choice of public service 0.53 -1.65 -2.03 -0.15 -0.48 -0.70 2. Use of private to public 0.59 2.77 2.99 -0.57 -0.29 -0.28 Benchmark 9 0.56 0.56 0.48 -0.36 -0.38 -0.49

The benchmark of non-financial barrier to health service showed a better trend of resource distribution, the workload to doctor and the use of public health sector. Provinces with high score had almost high score for other items. The disparity between the highest and the lowest scores were rather wide (from –1.57 to +1.21). The benchmarks on coverage and equality of utilization and efficiency and quality of care, the small item score were quite different. Each province had different strengths and weaknesses. The scores were close to 0 (range from –0.68 !"# +0.86 for benchmark 4 and from 0.81 !"# +0.82 for benchmark 6. The scores for benchmark 7 - administrative efficiency, small items had the same directions; total spending per capita, proportion of use in primary care, and total spending to expected spending. The score for administrative efficiency calculated only from regional and general hospitals, the score for small items showed different directions. The average score was from –1.01 to +0.86. For benchmark 9 – patient and provider autonomy, people in most provinces chose to use public services, and lesser rartio in private sector use. The scores were in different directions. Therefore, the score was around 0. Some provinces had the same direction of benchmark 9, the score was around 0 and had the average of –0.49 to +0.56. 6. Links between equities 64 6 Links between equity benchmarks

6.1. Influence across equity benchmarks Some factors in one benchmark may have influences over other factors in different benchmarks. To set some examples. The economic factor in benchmark 1 may have influences over other factors in other benchmarks. Distribution of resource in benchmark 3 (non-financial factors for equitable services) had impact on accessibility of service. Figure 6.1 shows that provinces with good economy (high provincial product per capita) tended to have lower load of population to doctor ratio.

Population per doctor 16,000 14,000 12,000 10,000 8,000 6,000 4,000 2,000 0 0 50,000 100,000 150,000 200,000 250,000 300,000 GPP per capita

Figure 6.1 Relationship between gross provincial product (x-axis) and ratio of population to doctor (y-axis) Moreover, economy also influenced on the health insurance coverage (benchmark 2), overall use rate (benchmark 3) and choice of service utilization (benchmark 9). Province with high income tended to have better type of insurance coverage (CSMBS, SSS, private insurance) more than provinces with low income. They also had high use rate for new case and the proportion of choosing private facility was also higher. This confirmed that economy had influences over accessibility and choice for services.

Proportion of high benefit insurance 40 35 30 25 20 15 10 5 0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 Average income per capita

Figure 6.2 Relationship between income per capita (x-axis) and coverage of good insurance schemes (y-axis) 65 Benchmarks of fairness

Overall utilization OPD (new case) 1

0.8

0.6

0.4

0.2

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 Average income per capita

Figure 6.3 Relationship between income per capita (x-axis) and use rate of new case at outpatient service (y-axis)

Proportion of private seeking care (I PD) 0.6

0.5

0.4

0.3

0.2

0.1

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 Average income per capita

Figure 6.4 Relationship between income per capita (x-axis) and proportion of choice at private hospital for inpatient service (y-axis)

The factors on health resources (by benchmark 3) played important roles on health service utilization (benchmarks 3 and 4) and health spending (benchmark 7). Both the population to doctor ratio and the population to bed ratio had influences on utilization both outpatient and inpatient services and on health expenditure. Provinces with high health resources had high utilization and health spending as described in Chapter 5.

Apart from the influence of health service utilization (benchmark 4) on the total health spending (benchmark 7), the level of health infrastructure or proportion of service utilization at regional hospital and provincial hospital, or proportion of use at community hospital to the total use (benchmark 4) also influenced on the total health spending. Provinces with higher proportion of use at provincial hospital or low proportion of use at community hospitals would have higher health spending per capita. This confirmed the association that pattern of health service use had effects on total health spending. 6. Links between equities 66

Proportion of utilization at provincial hospital 0.6

0.5

0.4

0.3

0.2

0.1

0 0 500 1,000 1,500 2,000 Total cost per capita

Figure 6.5 Relationship between total spending per capita (x-axis) and proportion of use at provincial hospital (y-axis)

Admission at district hospital : provincial hospital ratio 3

2.5

2

1.5

1

0.5

0 0 500 1,000 1,500 2,000 Total cost per capita

Figure 6.6 Relationship between total spending per capita (x-axis) and ratio of admission at community to provincial hospital (y-axis)

The spending at regional and provincial hospital per capita was highly correlated with total health spending per capita because the provincial hospital spending accounted for a high proportion of total spending in the province. Hence, changing the pattern of use to cheaper health facility should reduce the high cost in big hospital and finally the total health spending.

Cost at provincial hospital per capita 1,800 1,600 1,400 1,200 1,000 800 600 400 200 0 0 500 1,000 1,500 2,000 Total cost per capita

Figure 6.7 Relationship between the total health spending per capita (x-axis) and the provincial hospital spending per capita (y-axis) 67 Benchmarks of fairness

6.2. Integrated links between indicators The above data displaying relationships between different equity indicators can be summarized as follows. ! Economic status had influences on environment, health status and distribution of resources. ! Environment had influence on health. ! Health status had influence on service utilization. ! Health resources had influence. ! Health insurance had influences on health utilization and choice of services. ! Choice of service had influence on service utilization. ! Use of service had influences on health spending. ! Service provision had influences on health spending and health status. ! Use of service was influenced by health resources, health insurance, choice and health status. ! Health status was influenced by economic status, environment and service provision. The inter-link between these factors convinced us that if we plan to achieve equity in health, the inter- related factors should be managed simultaneously to support the change in the same direction. Economic status Environment

Health resources Health status

Health insurance Service utilization Service provision

Choice of service Health spending

Figure 6.8 Inter-link between factors affecting health equity 6. Links between equities 68

6.3. Summary of the links between equities The inter-links between equity indicators encompass very broad range from input, process, output and outcome. Some are contextual factors such as environmental factors affecting health status. Some factors ensure the early stages of equity of input of the system, some focus on transparent and efficient processes. The final outputs and outcomes are the ultimate equity goals. The links between the 9 benchmarks are closely integrated and summarized in figure 6.9.

!"#$%&'()**$+,- 1 !"#$%&'()**$+,- 2 !"#$%&'()**$+,- 3 !"#$%&'()**$+,- 4 - 5 !"#$%&'()**$+,- 6 !"#$%&'()**$+,- 7 !"#$%&'()**$+,- 9

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,.- ./0%&'()*+ !"#$%&'()*+ - !/B;+ ,.- .0'/ #) 1,234#52 - 672895:. "#&3672895:. ;&#+

Figure 6.9 Links between the equity status by 30 indicators and Benchmarks of fairness by 8 benchmarks (except benchmark 8) by quantitative approach The final outcome of health status as described in figure 6.9 could also be considered as the input of the health system since the health system should be responsive to health needs, therefore different health needs indicated different health resources. The contextual data were also important in considering how equitable the health system because different areas had different problems due to different context. There were no two areas with identical context and same health problems. The next chapter will illustrate different contextual data at provincial level by qualitative and quantitative approaches using the frame of the 9 benchmarks of fairness.

Before turning to the next chapter, the framework of how to deal with quantitative data at the provincial level is described. The overall equity score derived from comparing quantitative data between provinces. Different approaches were applied to get the final score and the ranking of equity by province. 69 Benchmarks of fairness

6.4. Total score of equity There were many approaches to estimate the total equity score from the data used for calculating intermediate indicators. However, the total equity score did not intend to reflect the overall performance of the provincial health system, but to reflect the equity aspects within the province as far as available data allowed, subject to incomparability in many aspects. The approaches were listed as follows: Approach 1 Sum of average score, from selected 30 clusters of indicators. Approach 2 Average rank of 81 indicators. Approach 3 Sum of area from the radar plot from average score of 30 clusters of indicators. The formula to estimate area within the radar graph (figure 6.10) is as follow:

30 Area = " [(average score of x )+ 4] * [(average score of x+1)+4] * sin(12#)

X=1 2

The three approaches gave different results. Approaches 1 and 3 led to similar conclusions because they used the same average score, but the area by radar graph was strongly influenced sequencing of the indicators (neighbouring effect). Approach 2 gave different result because it adopted non-parametric data analysis, neglecting the differences in value. The first approach was considered to be the best because it took account of quantitative value, while dismissing the effect of sequencing. The results of the score varied from –17.97 to +9.46. The second approach resulted in the average ranks between 32.02 to 48.05. The third approach gave area from 34.46 to 54.40. All three approach did not pay attention to differential weighting of different indictors.

The average score had limitation as the score did not BM9 1 BM1 describe the weaknesses and strengths of each 30 2 29 4 3 28 4 province by each indicator. Looking at the distribution 27 2 5 BM7 26 - 6 or the pattern of the graph (radar) by 30 indicators 25 7 (2) 24 8 should provide details on the strengths and (4) 23 9 BM2 weaknesses of specific equity indicators. 22 10 21 11 BM6 20 12 19 13 18 14 17 15 BM3 BM5 16 BM4

Figure 6.10 Radar graph showing strengths and weaknesses of province by 30 indicator 6. Links between equities 70

Table 6.1 shows the concordance and discordance of the total equity scores by 3 approaches.

Table 6.1 Total score of equity and rank by province according to 3 approaches

!"#$$%&' (*) +-., 10/ 2(30/ 4$65 .780/ :9 %*; (*) +-., 0/ <,=>&,+ 30:('<@? ,=>&,+ 81 8,&A/6&,+<,=>&,+%,B'/ 30 :('@? 1 !"# 9.46 $%'(& )*+ 32.02 *,-.*' 58 .40 2 *,-.*' 9.35 !"# 32.07 !"# 58 .17 3 /0*12*3 7.41 *,-.*' 33.07 /0*12*3 56 .09 4 4'%& -4),- 6.80 4'%& -4),- 33.16 1.-3 56 .06 5 1.-3 6.71 4'%& -$-)2)- 33.37 4'%& -4),- 55 .48 6 4'%& -$-)2)- 6.46 .-5*,-6-0#+ (& #) 33.60 07)*)8!8-9: 54 .99 7 !.;-<&-+ 5.70 **%<&-+ 33.96 !.;-<&-+ 54 .68 8 07)*)8!8-9: 5.55 ;&'.- 34.28 4'%& -$-)2)- 54 .39 9 ".<&-+ 4.89 $-58=<,+-+/>*(? 34.51 .-5*,-6-0#+ (& #) 54 .06 10 .-5*,-6-0#+ (& #) 4.65 ".<&-+ 34.63 *)3 * 53 .97 11 *)3 * 4.46 1.-3 34.63 ".<&-+ 53 .82 12 $%'(& )*+ 4.19 !.;-<&-+ 34.64 $%'(& )*+ 53 .62 13 $-58=<,+-+/>*(? 4.01 ")7 .*@ 34.78 -A0#!0BC 53 .18 14 ;&'.- 3.74 /0*12*3 34.88 $-58=<,+-+/>*(? 53 .11 15 2-5<+ 3.69 !;#+ DEF'3 35.22 ")7 .*@ 53 .08 16 ")7 .*@ 3.60 G@!2BH 35.48 -5#0D 53 .06 17 '2& C)F)- 3.59 07)*)8!8-9: 35.51 ;&'.- 53 .06 18 -5#0D 3.34 ;#> *)% 35.67 '2& C)F)- 53 .00 19 -A0#!0BC 3.33 ;>#G@'9 35.72 ;#> *)% 52 .82 20 ;#> *)% 2.88 -5#0D 35.89 2-5<+ 52 .82 21 -5*0D 2.78 4I& /%#> 36.19 4I& /%#> 52 .09 22 4I& /%#> 2.78 -5*0D 36.22 G@!2BH 52 .05 23 H->D 2.41 *)3 * 36.47 H->D 51 .94 24 G@!2BH 2.39 .JK9 I& "2 36.53 -5*0D 51 .83 25 0%& #> ()*+ 2.22 ")7 $)D 36.72 0%& #> ()*+ 51 .43 26 'F)4)-,)' 1.70 4D9 F#G@'9 1.19 ;"<&-+ 36.81 ;>#G@'9 50 .96 29 .JK9 I& "2 1.02 0%& #> ()*+ 36.98 !;#+ DEF'3 50 .93 30 ")7 $)D 0.84 H->D 36.98 ")7 $)D 50 .73 31 2):8*<-& + 0.71 H-)C 37.00 'F)4)-,)' 50 .64 32 *,-$L' 0.62 'F)4)-,)' 37.01 2)M4*9 (? 50 .55 33 **%<&-+ 0.58 2)7 1.D!.;- 37.09 *,-$L' 50 .54 34 0&C-()*+ 0.50 *,-$L' 37.31 2):8*<-& + 50 .48 35 4-5<&-+ 0.49 '2& C)F)- 37.35 4-512=A 50 .43 36 2)M4*9 (? 0.40 0)3 D%0D 37.52 #I4(- 50 .39 37 #I4(- 0.40 2):8*<-& + 37.59 0&C-()*+ 50 .33 38 0)3 D%0D 0.35 2-5<+ 37.70 H-)C 50 .22 39 H-)C 0.29 4-5<&-+ 37.83 0)3 D%0D 50 .14 40 4-512=A 0.04 4'%& -4D,-)' 38.10 ;"<&-+ 50 .00 71 Benchmarks of fairness

!"#$$%&' (*) +-., 10/ 2(30/ 4$65 .780/ :9 %*; (*) +-., 0/ <,=>&,+ 30:('<@? ,=>&,+ 81 8,&A/6&,+<,=>&,+%,B'/ 30 :('@? 41 ;"<&-+ (0 .34) N5!;9D!%-) 38.26 **%<&-+ 49 .99 42 27)1.D!.;- (0 .70) .>DD) 38.56 27)1.D!.;- 49 .86 43 *,-4=--,? (0 .82) 4&-)JO-?()*+ 38.57 F*0D<>="7)G@ 49 .41 44 F*0D<>="7)G@ (1 .19) *,-*)#2 38.58 4&-9*%-? 49 .20 45 .989H- (1 .19) 4-512A= 38.58 *,-4=--,? 49 .13 46 42"*,- (1 .41) F*0D,)# 38.62 .989H- 48 .95 47 .5!#) (1 .60) 8>*%<&-+ 38.95 .5!#) 48 .89 48 *,-6-+(--'-); (1 .71) #I4(- 39.09 42"*,- 48 .70 49 .>DD) (1 .91) 4D/") 39.12 <&-+->'#? 48 .55 50 4&-9*%-? (2 .28) *,-4=--,? 39.17 *,-*)#2 48 .32 51 *,-*)#2 (2 .34) .>%"&D 39.35 *,-6-+(--'-); 48 .31 52 4&-)JO-?()*+ (2 .42) 0&C-()*+ 39.37 0&H-C9HP? 48 .14 53 0&H-C9HP? (2 .52) $QHH)*+ 39.49 .>DD) 48 .10 54 <&-+->'#? (2 .55) !;+#D-)# 39.51 4&-)JO-?()*+ 48 .07 55 0&<"-);()*+ (2 .61) .989H- 39.51 4H@" 48 .02 56 4D/") (2 .79) 4&.--K<&-+ 39.75 4D/") 47 .87 57 4H@" (2 .89) *,--);4+') 39.81 0&<"-);()*+ 47 .78 58 *,--);4+') (3 .16) 0&H-C9HP? 39.85 !;+#D-)# 47 .25 59 4'&%-4D,-)' (3 .29) -);<&-+ 39.93 8>*%<&-+ 47 .16 60 49DF?<&-+ (3 .30) 2)M49*(? 39.98 49DF?<&-+ 47 .07 61 .>%"&D (3 .39) $-)8+*<&-+ 40.21 F*0D,)# 47 .06 62 4&.--K<&-+ (3 .45) .5!#) 40.40 .>%"&D 47 .03 63 !;+#D-)# (3 .48) 42"*,- 40.47 6-+45!2J 47 .02 64 -);<&-+ (3 .83) F*0D<>="7)G@ 40.64 4&.--K<&-+ 47 .01 65 8>*%<&-+ (3 .97) <&-+->'#? 40.72 *,--);4+') 47 .01 66 !.;-<@-K? (4 .02) 4H@" 40.99 !.;-<@-K? 46 .98 67 H)2 (4 .22) 0&<"-);()*+ 41.14 4'&%-4D,-)' 46 .90 68 6-+45!2J (4 .52) 1'3R30D40* 41.22 -);<&-+ 46 .78 69 N5!;9D!%-) (4 .87) !.;-<@-K? 41.40 N5!;9D!%-) 46 .37 70 F*0D,)# (4 .89) 6-+45!2J 41.63 H)2 46 .35 71 $-)8+*<&-+ (5 .06) 4&-9*%-? 41.67 $-)8+*<&-+ 45 .94 72 $QHH)*+ (5 .91) *,-6-+(--'-); 41.88 1'3R30D40* 45 .32 73 1'3R30D40* (6 .49) H)2 42.96 $QHH)*+ 45 .02 74 #5") (9 .67) #5") 43.47 #5") 42 .10 75 *-)(9=)4 ( 17.97) *-)(9=)4 48.05 *-)(9=)4 34 .46 Benchmarks of Fairness 72

7 Equity in health in 10 provinces: Qualitative data

This chapter presents another method for monitoring equity in health. Since quantitative data provided according to the Benchmark of Fairness in previous chapter faced limitations in comparability between provinces, contextual data and interpretation of these data for each province may be more important in reaching decision on fairness. This chapter therefore picks up 10 provinces to study in details, getting contextual data of the provinces, providing quantitative data by each benchmark and obtaining qualitative data from the interpretation of the quantitative data. It was hypothesized that this technique be one of the monitoring tool for health equity. In each province, we sought opinion from 8 focus group discussions (each group not more than 10 participants). The eight groups covered: 1) health administrators one from provincial level and the other from district levels, 2) health care providers at district level and local government politicians, 3) civic groups related to health in urban and rural areas, and 4) civic groups not related to health in urban and rural areas. Each focus group discussion was preceded by the quantitative scoring of 9 benchmarks by each participant on 46 items. The overall score for each benchmark was obtained at the end of the detailed items. Each participant scored independently before the discussion, and did that again independently after discussion. The value of –5 to +5 was used to compare the current situation with the situation of 3 years ago. The value 0 stood for no change occurred, +5 was extremely better and –5 extremely worse than 3 years ago.

Provinces selected for this research were mostly the provinces participating in the decentralization activities. This included Chiang Mai, Phayao,!khon Kaen, Nakhon Ratchasima, Phuket, Songkhla, Pattani, Ayuthaya and Ratchaburi. This chapter presents the results of these 10 provinces, beginning with quantitative data indicating strengths and weaknesses, then following by qualitative data from the focus group discussions. The qualitative data covered from the context issues raised during the discussions, the scores given by each member before and after the discussions, as well as detailed reasons why extreme scores were given to any benchmarks. Detailed reasons could be further used for identifying opportunity for provincial development. The presentation started from provinces in the north, in the northeast, the south and the central part of Thailand respectively. 73 7. Equity in health in 10 provinces: Qualitative data

7.1. Qualitative data from 10 provinces 1) Chiang Mai Chiang Mai in 2001 had 21 districts and 3 pre-districts (they would be upgraded to be district later). Chiang Mai was the second biggest in terms of land area (20,107 sq. km.) after Nakhon Ratchasima. The population census in 2000 revealed than Chiang Mai had 1,472,403 population, the seventh rank. Hence, the population density was 73.2 per sq. km. The gross provincial product per capita ranked at 22nd with value of 57,015 baht (but the second rank of the north, after Lamphun). The income of the population was 3,179 baht per month, at 27th rank. Almost 70% of area in Chiang Mai was mountainous, and the rest was the plain along Mae Ping river. Chiang Mai is the centre for commercial business, transportation, communication, finance and international tourism. Important problems were lists as deforestation, and invasion of land along the river, refuse disposal, labour migration off-agricultural season, traffic and most important drug addict(32). Data on health, Chiang Mai had the highest crude death rate 9.9 per 1,000 (rank 1 in 2000). However, after age and sex standardization, the rate was 8.4 per 1,000 (rank 2 after Phayao). Dependency ratio (those aged 0-14 and over 65 to the working age) was 0.39 (the bottom 6th rank). In terms of health resources, population to doctor ratio was the lowest in Chiang Mai (excluding Bangkok), one doctor took care of 1,844 population. Population to bed ratio was the second lowest after Nonthaburi (236 population per bed). There were one provincial hospital with 421 beds and 20 community hospitals. Apart from health resources under the MOPH, there were 19 more hospitals with 4,225 beds. One was a teaching hospital, and 16 were private hospitals (in 1999). Hence, Chiang Mai was the most resourceful province, matched with the good economy level. Access to health care should be the top compared to other provinces, especially access to various choices of health services. The use rate in private sector was 22% for ambulatory services, and 30% for inpatient services. The main problems of Chiang Mai included the high death rate, which could be the consequence of AIDS, the high rate of malnutrition in children (12.5% in 0-5 years old, the 10th rank), and high low birth weight rate (9.8%, the 5th rank). However, these average figures did not pinpoint the area with high problems. Considering the health service use rate, Chiang Mai population had high accessibility. The rate of new attendants of ambulatory service (both public and private) was 57% (the 13th rank) and utilization of inpatient service was 17% (the 7th rank). If taking account only the uses in public service (only MOPH), the ambulatory services was1.67 visits per capita per year (rank 64), and admission rate was 7.6% (rank 69). Benchmarks of Fairness 74

The wider choices of making use of services other than the MOPH produced a very low per capita health spending of the MOPH (614 baht per capita in 2000, rank 18 from the bottom). The quantitative score provided the evidence that Chiang Mai had the average score higher than +1 for demographic factor, health resource distribution, workload per doctor, proportion of use at primary care and admission rate. This was because of the low population density, low dependency rate and high supply of health resources. Furthermore, health resources in public sector stressed on services at health centres and community hospitals rather than 1 Overall health status ( 2.23 ) the higher level, admission rate was not too 2 Specific health status ( 0.74 ) 3 Coverage of health service ( 0.27 ) high and the criteria for admission was rather 4 Environment ( 0.44 ) appropriate (judged from high casemix index). 5 Demographic condition 1.05 The negative score (lower than –1) was found 6 Economic status 0.16 7 Resource deprivation 0.45 in overall health status and the use in public 8 Coverage of health insurance ( 0.74 ) services. This was because of the high 9 Ratio of health insurance ( 0.24 ) 10 Household health expenditure 0.19 mortality rate and low uses in public sector (or 11 Distribution of resource 1.44 higher choice to go for private sector). The 12 Workload of health service 1.38 overall equity score by the 30 indicators was 13 Institutional seeking care 0.82 14 Utilization rate ( 1.04 ) +1.20, rank 21. 15 Proportion of primary care utilization 1.01 16 Continuity of care ( 0.34 ) 17 Comprehensiveness of care ( 0.25 ) 18 Difference of health expenditure 0.48 BM9 1 BM1 19 Average length of stay ( 0.94 ) 30 2 29 4 3 20 Bed utilization 0.14 28 4 27 2 5 21 Admission pattern and case mix 1.34 BM7 26 - 6 22 Unit cost of health service 0.34 25 7 (2) 23 Quality of care ( 0.82 ) 24 8 (4) 24 Overall health care cost 0.78 23 9 BM2 22 10 25 Health care cost per service ( 0.19 ) 21 11 26 Proportion of primary care cost 0.69 BM6 20 12 19 13 27 Proportion of administrative cost ( 0.92 ) 18 14 BM3 17 15 28 Real cost per expected cost 0.14 BM5 16 BM4 29 Public health service seeking care 0.09 30 Private per public seeking care 0.18

Figure 7.1 Radar graph for 30 indicators for Chiang Mai Table 7.1 Average score of 30 indicators for Chiang Mai 75 7. Equity in health in 10 provinces: Qualitative data

Comments on equity of health status and related factors In terns of health, deaths from AIDS and injury were big problems. Injuries were related to alcohol drinking and increased among adolescent. HIV problem saw high deaths among parents of the students. Sometimes, the students were the cause of HIV spread. Since disclosure of HIV infection to public was not popular in Chiang Mai, the spread of HIV was still high. When the public was made aware of the problem, deaths became decline. HIV infected children were in better health because of better care than before. AIDS was one important cause of suicide. The incidence of suicide was higher because of AIDS and financial problems. However, some participants blamed the child rearing of Chiang Mai as the cause of suicide. Children were spoilt, brittle and reacted unhealthily with problems. The old habit could be one cause of health problem, such as the habit of smoking since childhood was the cause of chronic lung disease. The old post-natal habit (staying in a warm room for a month, about 90% of the mothers in one district did that) could cause malnutrition and infection. Health problems in urban area included allergy from air pollution, the habit of taking junk food and toxic substance. People had little interest in health. Prevalence of chronic disease was rising for diabetes, cancer, kidney and heart disease. Urbanization and modernization led to higher household spending therefore people needed to have higher income. However, they did so at the expense of deforestation, taking items from forest to earn income. Most participants believed that modernization led to the decline in traditional culture, environment, drug and HIV. Adolescent disobeyed adults, they listened to messages from the media. Decline in environment such as refuse disposal, traffic led to increase in air pollution, this was so severe that rain water was not suitable for drinking. Congestion of urban and suburban community led to the decline in health status, while rural people were believed to be better off in terms of health and environment than urban people. The malnutrition rate was also lower in rural area. The hill tribe minority were in better living conditions because there was government budget support, but the coordination between related organizations was slowly getting better. The local government became more involved in giving budget support, to a limited scale; e.g. control of dengue haemorrhagic fever, AIDS. People sectors started to set up interest groups: the elderly Gong Fu exercise, foundation for the elderly, women’s group, health volunteer group, aerobic exercise group. In urban area, setting up these group activities was a bit difficult. Some communities set up a group to improve housing and environmental conditions.

Comments on equity in health finance and delivery

Health providers commented that the health coverage provided was unable to protect all financial health expenditure. People still seek care from private clinic, drug store and private hospital. Even for severe Benchmarks of Fairness 76 illness, the 30 baht scheme could not convince the rich to come and use services provided in the scheme. When people paid for health from their own pocket, the difference of health utilization between the poor and the rich was high. Some participants agreed that contribution for health care should be proportionate to income, collected at the health insurance fund, and paid equitably for the benefit of all citizens. The budget to health provider should be paid prospectively.

People from the local government proposed that assignment of people to the main contractor of health care on a geographical basis led to problem because people were not well aware of the facility they were assigned to. The specialist hospitals and private hospitals showed discrimination policy for the 30 baht scheme to be operated separately. Many people chose to pay cash (almost at full cost) for health care, as they thought paid service was better in quality. Furthermore, the details on geographical area may not be correct, as the school children were literally staying in urban area, while the house registry showed differently. The civic society in urban area commented that people used services in private sector were asked to pay higher copayment for private ward. Private sector that joined the universal coverage scheme sometimes asked people to pay higher copayment by explaining that common ward was not available. The civic society in rural areas admitted that people still worried about hospital charges, even after they were covered by the new health benefit. Other financial budget included travel expenses, food and financial constraints for using private sector though they were satisfied with public sector.

In terms of health delivery, health providers commented that health facilities attempted many efforts of quality improvement (e.g. QA, HA, ISO), and they were open for feedback on health expenses. They thought expenditure on drug would be reduced because they would like to save spending. However, they were afraid that health providers would go for non-essential drugs and asked people to pay more, hence as increase especially in private sector. High spending could be the result of better access to services and higher utilization. Health facilities improved their quality to satisfy clients by introducing expensive services. Provider autonomy was thought to increase because providers were free to use their own budget plan. Some thought provider autonomy was reduced because they had to comply with national policy and local people. Local government thought that private sector was better than public sector, but they agreed that public sector was fast improving especially provincial hospital. Some areas of services were thought to be inadequate, e.g. dental service at health centre. The visiting medical team to health centre was also good. The manner of health personnel at health centre was good, perhaps because they were closed to local people. The existing health problems included varied standards of community hospitals, inadequate doctors at community hospitals and doctors making business. 77 7. Equity in health in 10 provinces: Qualitative data

Civic groups in urban areas supported that quality of care became better. However, private services were quicker, more modern with more famous doctors, but more expensive (only for the rich or the insured). Public services were thought to provide different care to general public and to the rich who donated to hospital. Same doctors behaved differently, while working in public and in private hospital. They were less thorough in physical exam. Patients waited too long till losing their lives. Patient wanted more explanation on their illness. Civic groups in rural areas complained of having fewer doctors in rural area. They demanded higher production of doctor. The groups raised the issue that hill tribes were not accessible to health care, had no identification card and had unusual health belief. The groups supported extension of the benefit to cover medical services for chronic renal failure. Summary scores on equity in health by focus group discussion Summary scores for Chiang Mai from 8 focus group discussions were averaged at +1.82. The highest score was found for benchmark 6 (efficacy, efficiency and quality of care) +2.12. The lowest score was found for benchmark 7 (administrative efficiency) +0.89. The group that gave the highest score was non- health civic group (+2.80). The lowest rater was health provider at subdistrict level (+0.63). Table 7.2 Scores for equity by 9 benchmarks and summary scores from 8 focus group discussions in Chiang Mai Health Provider Provider Local Civil society Civil society Civil society Civil society Overall Manager Prov. Dist Sub-dist Government Health (urban) Health (rural other (urban) other (rural) average Benchmark 1 0.82 1.44 1 .00 2.70 1 .20 2. 71 3.00 3.00 1.85 Benchmark 2 2.27 1.67 0 .63 2.60 1 .00 2. 33 2.50 1.80 1.87 Benchmark 3 0.64 1.44 1 .33 2.50 0 .80 2. 29 1.86 2.40 1.56 Benchmark 4 1.73 2.11 1 .00 2.70 1 .00 2. 63 1.71 3.20 1.93 Benchmark 5 1.00 1.67 0 .78 2.70 1 .00 1. 33 1.83 2.75 1.54 Benchmark 6 1.55 2.00 1 .33 3.00 1 .90 3. 00 2.00 2.50 2.12 Benchmark 7 0.45 1.56 0 .75 0.89 Benchmark 8 0.09 2.00 0 .38 2.50 1 .90 2. 13 2.43 3.17 1.72 Benchmark 9 1.36 1.22 0 .50 2.50 1 .89 1. 67 2.43 2.40 1.68 Benchmark 1.09 2.00 0 .63 2.25 1 .75 2. 60 2.43 2.80 1.82

Focus group discussion of health providers rated the highest score for benchmark 2 (+2.27) because the universal coverage policy is likely to provide equitable access to care. The lowest score was found for benchmark 8 (+0.09), benchmark 7 (+0.45), benchmark 3 (+0.64) and benchmark 1 (+0.82), because of low participation from the public, high health care cost, unequal distribution of health resources and high mortality. Health providers at provincial and district levels gave high scores to benchmarks 4,6 and 8 (+2.11, +2.00 and +2.00) because they saw comprehensive improvements in health provision, with quality improvement and empowerment of community. Health providers at subdistrict level gave low score (+0.63) because they realized that health system was full of problems. The lowest score was for benchmark 8 (+0.38), because of low people participation. Local politicians gave higher score, ranged Benchmarks of Fairness 78 from +2.50 and +3.00 (summary score was +2.25). They agreed that health systems were better. Civic groups in urban area gave low score (less than +2.00, because health delivery system in urban area had problem. This contrasted with civic groups in rural area, the score was +2.60, and highest score was +3.00 for efficacy, efficiency and quality of service. Non-health civic groups in both urban and rural areas gave higher scores (+2.43 and +2.80), while accepting that health equity improved. Looking into scores of individual 46 items in the 3-dimensional surface graph and different colours, the scoring for Chiang Mai was low (-1.00 to +1.00) for overall health status in the administrators and health related civic groups. Environmental problems, demographic and economic problems were the matter of concerns among the administrators, health providers, health and non-health civic groups. Accessibility to health services that related to distribution and work overloaded were the concerns among health providers and health civic groups in urban area. Providers at subdistrict level gave low scores for many items, while administrators gave low score for people participation in benchmark 8. Items gaining high scores (higher than +3.00) included the coverage of health insurance and health benefit packages. Administrators of the local government and civic groups were advised not to score the benchmark 7 and 9.6, therefore the scores were 0.

Figure 7.2 3-dimentional surface graph showing scores for 46 items on equity rated by 8 focus group discussions in Chiang Mai 79 7. Equity in health in 10 provinces: Qualitative data

2) Phayao

Phayao is the 72nd province established in 1977 by separating from Chiang Rai. In 2000, Phayao had 7 districts, 501,815 population in 6,335 sq.km. (population density 79.2 per sq. km.). The average income was 2,344 baht per month, at the 53rd rank of the country. Gross provincial product per capita was 28,420 baht, the 58th rank. Proportion of population with income lower than 1,000 baht a month was 12.7%. The physical geography of Phayao is the same as other northern province, i.e. the valley surrounded by mountains. In the cold season, the weather sometimes reaches minus degree Celsius. In summer, the weather is hot and humid during daytime, and rather cold at night. Phayao was the peaceful and not complicated province, with rich natural resources. It has 4 big water reservoirs: 2 rivers called Yom and Ing, and 2 fresh water lake called Kwan Phayao and Nong Mae Jai (or Nong Leng Sai). Kwan Phayao is the second biggest fresh water lake, after Bueng Boraphet, covered the area of 12,831 rai (2.5 rai equal to 1 acre). The lake is the rich resources for agriculture and fishery. The main occupations in Phayao covered agriculture, rice farming, crop farming, fishery and some manufacturing. The main problems for Phayao were the production infrastructure, employment and income, land use, labour force, and education level. The most important health problem was AIDS epidemics(32). Health statistics for Phayao were as follows: crude death rate 9.7 per 1,000, the second rank after Chiang Mai, after age-sex adjustment, the rate was 9.1 per 1,000 – the first rank! Life expectancy at birth was the lowest. The natural rate of increase was therefore minus for 3 consecutive years. This could be the impact of AIDS epidemics, as most deaths were observed among the 30-40 age group, the age most affected by AIDS, the same as Chiang Mai. Phayao still faced high prevalence of malnutrition among children under 5 years old (12.2% ranked 12th), and high prevalence of low birth weight (12.5%, ranked 2nd). The dependency ratio was 0.41, almost the same as other northern provinces. The Health and Welfare Survey in 1996 showed that the health benefit coverage in Phayao was as high as 89%, the 3rd rank after Nan and Loei. Most (81.7%) were covered by the schemes with moderate benefits (the VHCS and LICS). In terms of health resources, Phayao ranked 43rd and 42nd for doctor and bed to population ratios (1:6,621 and 1:568 respectively. There were 2 hospitals not belonged to the MOPH, one was a private hospital. Because of limited choice and high health benefit coverage, the uses of public services were as high as 2.7 visits per capita per year, the 9th rank, and the share for private health sector was 16.3% for outpatients and 1.8% for inpatients. Benchmarks of Fairness 80

Health expenditure for public health sector was 969 baht per capita per year. The spending was high at provincial hospital, 698 baht per capita per year. The proportion for health cente was 6.4%, the least 7th rank, even though ratio of health utilization at health centre to hospital was 1.28, the 7th highest rank. The ratio of hospitalization in community hospitals to provincial hospitals was 0.33, the least 4th rank after Ratchaburi, Singburi and Phuket. This was explained by 2 provincial hospitals in Phayao owned the total beds of 623, while the total beds in 5 community hospitals was only 150. Or the ratio of community hospital bed to provincial hospital bed was 0.24, not much different from the utilization rate. The quantitative scores for health equity in Phayao were as follows. The average positive score was found (higher than +1) for health insurance coverage (very minimal population was left out). The average negative scores were obvious (lower than –1) 1 Overall health status ( 2.30 ) 2 Specific health status ( 1.43 ) for overall health status and health status for 3 Coverage of health service 0.67 specific populations. The moderate positive 4 Environment ( 0.63 ) 5 Demographic condition 0.95 scores (higher than +0.5) was found for the 6 Economic status ( 0.24 ) coverage of services, demographic factors, 7 Resource deprivation ( 0.76 ) uses of public services and choice for public 8 Coverage of health insurance 1.28 9 Ratio of health insurance 0.84 services. The moderate negative scores 10 Household health expenditure ( 0.04 ) (lower than –0.5) were found for environment, 11 Distribution of resource 0.07 12 Workload of health service 0.07 overall deprivation and the proportion for 13 Institutional seeking care ( 0.37 ) health spending at primary care 14 Utilization rate 0.60 15 Proportion of primary care utilization 0.03 16 Continuity of care ( 0.66 ) 17 Comprehensiveness of care ( 0.54 ) 18 Difference of health expenditure 0.08 BM9 1 BM1 19 Average length of stay ( 0.02 ) 30 2 29 4 3 20 Bed utilization 0.19 28 4 27 2 5 21 Admission pattern and case mix 0.21 BM7 26 - 6 22 Unit cost of health service 0.36 25 7 (2) 23 Quality of care 0.54 24 8 (4) 24 Overall health care cost ( 0.23 ) 23 9 BM2 22 10 25 Health care cost per service 0.19 21 11 26 Proportion of primary care cost ( 0.76 ) BM6 20 12 19 13 27 Proportion of administrative cost ( 0.08 ) 18 14 17 15 BM3 28 Real cost per expected cost 0.60 BM5 16 BM4 29 Public health service seeking care 0.66 30 Private per public seeking care ( 0.81 ) Figure 7.3 Graph of the scores by 30 indicators, Phayao Table 7.3 Average scores for 30 indicators for Phayao 81 7. Equity in health in 10 provinces: Qualitative data

Comments on equity of health status and related factors

From health statistics, deaths in Phayao were high for AIDS and injury. AIDS has been epidemic among age 25 years old and death occurred at age 30. This problem was severe in some areas. Dok Kum Tai hospital had 80% of inpatients with AIDS. Those suffered from AIDS acquired the virus elsewhere and returned home when disease developed. They committed suicide to escape AIDS. The mental health state of urban people ran down. The negative population growth was found over the past 3 years, therefore, working populations decreased. The elderly and children were left without care-takers. Houses were deserted. Rates of other infectious diseases declined but the prevalence of chronic disease increased. This included diabetes, hypertension, joint diseases and kidney diseases. In general, health of the elderly was better, with group exercise. Food habits rather remained the same, eating local and healthy food. However, people in rural area were still consumed raw food.

In general, there were efforts of making Phayao a healthy city. The weather was good with less pollution. People were in agriculture work more than manufacture. There were campaigns on insecticide-free vegetables, planting in the farm net. People were more educated and accessible to media. However, the economic problems led to higher unemployment. Markets filled with more sellers than buyers. General living of the hill tribes were better, with good employment, free schooling and healthier with big government subsidy. The community situation was rather preserved for family tie and simple lifestyle. Anyway, social problems were increasing with more drug use, nightlife and gangster. In rural areas, we still saw some home deliveries, some spiritual believes. Health seeking behaviour with spiritual healer was on an increase, e.g. from 10 visits a day in 1984 to 150 visits a days in 2000. Most of them were well- educated people. They practiced the spiritual procedures. Furthermore, the use of traditional healers before going to hospital was about 5%. They still used rubbing oil, herbs and spiritual processes to treat illness.

Comments on equity in health finance and delivery Since Phayao nearly achieve universal coverage before the government policy, therefore, the people did not feel any dramatic change of the policy. Some of them preferred the VHCS to the 30 baht scheme. Particularly the chronically ill who regularly came to hospital thought that paying one time 500 baht for a family was better than paying 30 baht a visit. Moreover, new members of the 30 baht scheme did not use their benefit, instead they went to clinic because of prompt service at private clinic; or they went to private hospital for emergency care to save life. Seeking care elsewhere did not contain overall health care cost. Benchmarks of Fairness 82

The civic groups thought that the out-of-pocket burdens were still a wide gap between the rich and the poor. Health providers thought that people still paid out-of-pocket for cross-boundary services. Health administrators commented that Phayao’s experiences from the Social Improvement Project (SIP) empowering management at district level prevented management problems when implementing the 30 baht scheme. They agreed that management of funds at district level and allocation of inpatient budget on DRG basis supported risk sharing principle between hospitals and health centre.

In terms of health service delivery, health administrators commented that ratio of doctor to population was inadequate. There were all sorts of specialists in provincial hospital but only 2 generalists. There were lots of patients at health centres and hospitals not knowing whether they were “shopping around”. Public health services were more interested in providing alternative medicine, integrating western medicine and traditional medicine. Health providers also agreed that number of doctors and patients were inappropriate. A high use rate at health centre about 20-30 visits per day may be explained by a high dependency ratio, or high unemployment, leaving them lots of time to attend services. Health providers proposed an important strategy of improving primary care by strengthening capacity of caring for the chronically ill with same standard of medicine. Health centre should be upgraded as hospital’s outpatient service with good referral system. Special clinic should be set up for cross-boundary cases. Health provision opened opportunity for people participation, e.g. non-executive board member of the hospital. The local government officers suggested more services provided during weekends and after office hours. There should be more outpatient services with doctors at the suburban areas, prescribing same medicine for rich and poor, giving patients more opportunity to ask about what medicine they were taking. Health promotion activities needed higher group participation from people.

The civic groups commented that services at health centre and hospital were better, however they suggested having more number of primary care units with medical doctor providing service and more medical equipment. Problems they listed were differential grading of medicine and service users, discontinuity care for chronic patients, services inferior to private sector, doctor diagnosis and doctor working part-time in private sector. The groups suggested more annual health check up and covering larger number of people instead of waiting until becoming ill, e.g. check up for diabetes, malignancy. The check up service should be available at health centre and community level. 83 7. Equity in health in 10 provinces: Qualitative data

Summary scores on equity in health by focus group discussion The summary score for Phayao by 8 focus group discussions averaged at +2.04. The highest score was for benchmarck 1 (intersectoral public health) +2.43, and the lowest score for benchmark 7 (administrative efficiency) +1.27. The group gave the highest scores was health civic group in rural area (+3.25), and the lowest group was the local government officers (-0.20).

Table 7.4 Equity scores for 9 benchmarks by 8 focus group discussions in Phayao Health Provider Provider Local Civil society Civil society Civil society Civil society Overall Manager Prov. Dist Sub-dist Government Health (urban) Health (rural other (urban) other (rural) average Benchmark 1 2.83 1.80 0 .50 2.60 2 .63 2. 60 2.00 3.20 2.43 Benchmark 2 2.57 3.25 1 .67 1.80 2 .50 1. 90 1.38 1.83 2.08 Benchmark 3 2.29 2.40 - 1.80 3 .13 2. 50 2.25 1.43 2.21 Benchmark 4 2.43 2.75 1 .33 2.00 3 .25 2. 56 2.25 0.80 2.31 Benchmark 5 2.57 2.60 1 .00 0.40 2 .75 2. 10 2.33 1.20 2.02 Benchmark 6 2.00 2.40 2 .33 1.80 3 .25 2. 38 2.00 1.80 2.29 Benchmark 7 1.57 1.80 - 0 .33 1.27 Benchmark 8 2.43 2.00 1 .33 2.00 1 .71 3. 20 2.43 2.71 2.37 Benchmark 9 2.14 1.00 0 .33 1.80 3 .20 2. 70 2.50 2.29 2.17 Benchmark 2.00 2.60 0 .67 - 0.20 2 .50 3. 25 2.00 2.13 2.04

Health administrators gave high scores +2.00 to all except the benchmark 7 administrative efficiency (+1.57), because the impact of the SIP’s efforts and health care reform project in improving equity. Health providers at district level also gave high scores, especially the highest score for benchmark 2: financial barrier to equitable access (+3.25), and the lowest score for benchmark 9: patient and provider autonomy (+1.00) because they thought that people were given limited choice. Providers at subdistrict level gave generally low scores (lower than +2.00) for all, with low score for benchmark 9 (only +0.33), and the lowest for benchmark 7 (-0.33). The highest score was given to benchmark 6 (+2.33). The overall average was only +0.67 because they thought health system still faced many problems. The local government officers gave positive scores, the highest for benchmark 1 (+2.60). The health-related and non-health civic groups gave almost high scores, from +2.00 (non-health in urban area) to +3.25 (health civic group in rural area). Health civic group in urban area gave the highest score for benchmark 4 (comprehensiveness of benefit and tiering) and benchmark 6 (efficacy, efficiency and quality of health care). Health civic group in rural area gave the highest score for benchmark 8 (+3.20) because they saw increasing number of group activities. Non-health civic group in urban area gave lowest score for benchmark 2 (+1.38), inspite of universal coverage, because they thought that people still face high burden of health spending. Benchmarks of Fairness 84

Detailed scores were presented by 3- dimensional surface graph, the overall and individual scores were low for health status (-1.00 "$# +1.00) given by health providers at district and subdistrict level and the local government officers. The general economic condition also received low score from providers, local government and health civic group in urban area. The problems of health resources distribution and workload by health personnel were raised by health providers and local government officers. Providers at provincial, district and subdistrict levels gave rather low scores for administrative efficiency and unit cost. Providers at subdistrict level gave negative score (– 1.00) for administrative cost and unnecessary service. Local government officers gave low score on listening to client’s need and patient right. Providers at subdistrict level gave low score for choice of private service. Health administrator gave high score (more than+3.00) for comprehensiveness of service, health coverage, access and use of primary medical services

Figure 7.4 the 3-dimensional surface graph for 46 items by 8 focus group discussions in Phayao 85 7. Equity in health in 10 provinces: Qualitative data

3) Phrae

Phrae in 2000 had 7 districts and 1 pre-district with 491,516 population, 6,538 sq.km. The population density was 75.2 persons per sq.km., nearly the same as Chiang Mai and Phayao. The average income was 2,553 baht a month, the 46th rank, a little better than Phayao. The gross provincial product was 26,106 baht per person, the 62nd rank. But the proportion of people having income lower than 1,000 baht a month was only 5.6%, the bottom 18th rank, suggesting that income distribution was rather even, the Gini coefficient for income distribution ranked the 19th.

Geographically, Phrae is in the northern Thailand on the Yom river, and in the midst of mountains, rich with natural resources and deep forest. Phrae was in the lower northern part so the weather was not as cold as Chiang Mai. Phrae ever had more than 3 million rai of forest with valuable teak and other economic treasure. Logging and timber have made many Phrae people prosper, including employees, national investors and foreign companies that won franchise to trade timber. Recently, the forest in Phrae has shrunken to only 1 million rai. Apart from timber, Phrae has been known as tobacco growing area. The main problems of Phrae were listed as follows: deforestation, poverty, limited land for earning a living and lack of water for agriculture(32).

In terns of health, the crude death rate was the 4th rank (9.0 per 1,000) after Chiang Mai, Phayao and Lamphun. After age sex adjustment, the mortality rate was 7.89 per 1,000 (still the 4th rank). Life expectancy for Phrae was therefore short like other provinces in the north. Mortality rate among the under five was 3.9 per 1,000, at the 2nd rank. Malnutrition rate among the under five was 9.25% and the low birth weight rate 8.13%, the top 27th and 14th rank respectively. Dependency ratio was low at 0.40, nearly the same as Chiang Mai and Phayao.

The health insurance coverage in Phrae was high at 86%, the 4th rank. Most were covered by intermediate schemes (VHCS and LICS) about 72%, at the 8th rank. The Health and Welfare Survey 1996 and Socioceonomic Survey 1998 found the low health expenditure only 3.2% of income, the second lowest after Petchaburi. In terms of health resource, the population to doctor and population to bed ratios were Benchmarks of Fairness 86

similar to Phayao (6,335 and 588 the 41st and 48th rank respectively). There were 5 private hospitals (198 beds), 1 provincial hospital (402 beds) and 7 community hospitals (240 beds).

Use rate of public health sector was very high 3.67 visits/person/year, the first rank. The ratio of use at health centre to hospital was 1.61, the top 3rd rank. Even though uses at health centre were higher than hospital, the cost at health centre was only 8.63% of the total cost. The overall cost per capital and public hospital cost per capita were similar to Phayao (977 baht and 651 baht respectively). However, the total cost including private sector should be higher because people in Phrae had more choices at private sector, the share was 35% for outpatient services (the top 8th rank) and 32% for inpatient services (the top 10th rank). 1 Overall health status ( 2.34 ) 2 Specific health status ( 0.44 ) 3 Coverage of health service 1.21 The scores for health equity, quantitatively, 4 Environment 0.39 Phrae had almost all positive score (higher 5 Demographic condition 1.11 6 Economic status 0.18 than +1) for the coverage of services, 7 Resource deprivation 0.10 demographic factor, health insurance 8 Coverage of health insurance 1.24 coverage, health spending and uses of 9 Ratio of health insurance 1.11 10 Household health expenditure 2.04 public services. The scores were negative 11 Distribution of resource 0.07 (lower than –1) for overall health status, 12 Workload of health service ( 0.55 ) health status for specific groups and quality 13 Institutional seeking care 0.58 14 Utilization rate 1.80 of service. The negative score for quality of 15 Proportion of primary care utilization 0.83 care was due to the ratio of abnormal labour 16 Continuity of care 0.19 to normal labour was very high 1.88 for 17 Comprehensiveness of care ( 0.36 ) 18 Difference of health expenditure 0.60 provincial hospital, the 2nd rank after 19 Average length of stay 0.51 20 Bed utilization 0.20

BM9 1 BM1 21 Admission pattern and case mix 0.53 30 2 29 4 3 22 Unit cost of health service ( 0.13 ) 28 4 27 2 5 BM7 23 Quality of care ( 1.59 ) 26 - 6 25 7 24 Overall health care cost ( 0.13 ) (2) 24 8 (4) 25 Health care cost per service ( 0.39 ) 23 9 BM2 22 10 26 Proportion of primary care cost ( 0.10 ) 21 11 BM6 20 12 27 Proportion of administrative cost ( 0.16 ) 19 13 18 14 17 15 BM3 28 Real cost per expected cost 0.16 BM5 16 BM4 29 Public health service seeking care ( 0.61 ) 30 Private per public seeking care 0.95 Figure 7.5 Graph for score by 30 indicators, Phrae Table 7.5 Average score for 30 indicators of Phrae 87 7. Equity in health in 10 provinces: Qualitative data

Angthong. Case fatality in provincial hospital was 2.74%, only the 35th rank. Scores at intermediate level (higher than +0.5) were found in the proportion of primary care use and choice of private service to public services, implying that people in Phrae used lots of services in public sector but also had good choice at private sector. The overall score summing up 30 indicators was +6.71, at the 5th rank.

Comments on equity of health status and related factors In terms of health, AIDS was a big killer in Phrae like other provinces in the north. Deaths from injury, cancer (especially liver) were the top rank of the country. This could be related to the habit of eating raw fish and alcohol drinking. Tung Hong was the village where there were lots of Lao Pwan people with the habit of eating raw prawn, fish, and drinking locally produced alcohol. Deaths from liver cancer were 25 within 9 months. The most common ages were 40-50 years. The prognosis was poor, death occurred within 3 months. The other common condition was paralysis in young adults due to injury. The trend of suicide was increasing because of AIDS. Some AIDS patients died while they were studying, at 17-18 years old. Some of AIDS patients acquired the virus when they emigrate to work in other provinces.

Social and economic factors were important for health and health behaviour of Phrae people. Previously, timber was the main income earning but now deforestation made many people unemployed. The economic crisis left people with limited occupations. Agriculture lacked irrigation. The average land holding for agriculture earning was less than 5 rai. Phrae people were used to having good income and spending easily. After work everyday, they would go for entertainment and some involved with amphetamine use (more than 10 years). When the country faced economic crisis, there were more social problems, more addict cases, more stealing of money, not peaceful society. The adolescent were the major group of drug addict, they lived in gang at discotheque, dormitory. The elderly were left behind. More environmental problems were realized especially air pollution, refuse disposal, insecticide use. These problems stimulated the group processes in finding solution to the problems. Community groups solved the problem of deforestation by planting more forest in Song and Wang Chin for example.

Comments on equity in health finance and delivery In terms of finance, universal coverage policy would reach all people, but some people still preferred the VHCS paying 500 baht a year, because no copayment needed for each visit. Private hospitals the joined the scheme was likely to underprescribe, claiming full bed occupancy and denying contraceptive operation. The main problem of insurance scheme was the gap between the schemes, these health Benchmarks of Fairness 88 benefit gaps led to implementation problem. The 30 baht scheme did not cover high cost of haemodialysis 3,000 baht a visit. People had to pay themselves. The groups thought the poor should not bear the cost more than the rich. The healthy should not pay for those who still practiced risk behaviour (e.g. smoking). Most agreed that the 30 baht scheme provided easy access, reduced worries. They wanted to have free choice within the province. The provincial health board should be responsible for setting the bill. Pathological investigation for liver cancer was a problem because of bill clearance.

In terms of health delivery, the administrators commented that during the economic crisis, private sectors had fewer patients, but in 2001 they recovered. The 30 baht scheme has yet no impact on private sector. Some of them explained that higher health spending was not the result of higher drug cost but the higher cost of other services. Health facilities tried to increase income from the civil servant medical benefit scheme. Budget allocation to health facilities adopted the criteria agreed at the provincial level. In terms of choice, Phrae had a number of choices. Private hospitals were established 10-20 ago. Health providers commented on inequalities in health. Doctors in urban areas were 8 times higher than in rural areas given the same population. Some districts relied on interns with limited experiences. Sometimes, the number of deaths increased. People complained about doctor’s manner and professional standards of interns. They sensed that health facilities were controlling costs, reducing the uses of expensive drugs and limiting the use of necessary investigations. Local government officers commented on the long distance to access hospital care. Health centre had limited capability with few personnel and low utilization. The poor were the main clients at health centre. The better off by-passed to hospitals especially hospitals in other provinces.

Civic groups expressed their worries on the standards of private hospitals; queue jumping, drug effectiveness and quality of care in public hospitals and high cross-boundaries at health centre level including neighbourhood areas. About 10% of the population in remote areas used traditional medicine and spiritual healers. Some claimed that cancer patients diagnosed as incurable with modern medicine approach, became cured by spiritual healer. Some commented that health centre and hospital made few home visits. There were still high demands for bypassing the community to provincial hospital especially the emergency case. They did so by paying from their own pocket. Some commented that services in primary health centre should be promoted, e.g. household drug kit, which was cheaper than paying 30 baht at health centre with same medicine. Other civic groups commented that people put more emphasis on the doctor than the hospital, but some valued hospital rather than doctor. They admitted that the overall quality both public and private became improved. Main problems of Phare were lack of specialists 89 7. Equity in health in 10 provinces: Qualitative data

(e.g. diseases of the brain had to go to Uttaradit, Chiang Mai; bone diseases to Chiang Mai and Lampang; heart diseases to Uttaradit and Nan; eye to Lampang, etc.). Some expressed that doctors were taking benefit from society by seeing patients at their private clinic then coming to hospital too late. Some liked the ideas of urban health centre with doctor rotation for clinical services, and more choice for alternative medicine. Some supported the ideas of provincial health board rather than transferring health centre to Tambon Administrative Organization, community hospital to municipality and provincial hospital to Provincial Administrative Organization.

Summary scores on equity in health by focus group discussion The summary scores for Phrae by 8 focus group discussion averaged at +1.96, with highest scores for benchmark 4 (comprehensiveness of benefit and tiering) +2.16, and lowest for benchmark 9 (provider and patient autonomy) +1.59. The group giving highest score was health civic group in urban area (+3.50) and lowest was providers at province and district (+1.00).

Table 7.6 Equity scores for 9 benchmarks and overall scores by 8 focus group discussion in Phare

Health Provider Provider Local Civil society Civil society Civil society Civil society Overall Manager Prov. Dist Sub-dist Government Health (urban) Health (rural other (urban) other (rural) average Benchmark 1 2.80 1.40 1 .73 2.20 2 .70 2 .33 1.38 1.44 2.00 Benchmark 2 2.90 1.56 1 .91 2.33 3 .00 1 .67 1.63 1.78 2.11 Benchmark 3 2.40 1.70 1 .55 1.67 2 .60 1 .33 1.75 1.89 1.88 Benchmark 4 2.70 2.00 2 .55 1.83 3 .22 1 .11 1.38 2.13 2.16 Benchmark 5 2.40 1.50 1 .73 1.67 2 .60 0 .88 1.75 1.25 1.76 Benchmark 6 3.10 1.50 2 .45 2.17 2 .89 1 .56 1.43 1.63 2.13 Benchmark 7 2.40 1.30 1 .10 1.60 Benchmark 8 2.80 1.60 1 .78 1.83 2 .89 1 .33 1.50 1.78 1.99 Benchmark 9 2.20 1.40 1 .00 1.33 3 .11 1 .29 1.13 1.00 1.59 Benchmark 2.40 1.00 1 .70 2.67 3 .50 1 .71 1.25 1.75 1.96

Administrators gave rather high score from +2.00 to +3.00, averaged at +2.40. The highest score was for benchmark 6 (+3.10) and the lowest for benchmark 9 (+2.20). Providers at provincial and district levels gave score from +1.00 to +2.00, averaged at +1.00, and highest for benchmark 4 (+2.00), lowest for benchmark 7 (+1.30). Providers at subdistrict level gave higher score, that is, averaged +1.70, the highest for benchmark 4 (+2.55) and lowest for benchmark 9 (+1.00). The benchmark 9 got the lowest score from local government officers (+1.33), non-health civic group in urban area (+1.13) and in rural area (+1.00). This near consensus reflected limited choice that they put high concern, especially when they were assigned to private contractor unit for primary care. Local government officers gave the highest score for benchmark 2 (+2.33). Urban health civic group gave the highest score for benchmark 4 (+3.22), while rural health civic group valued benchmark 1 (+2.33). Urban non-health civic groups gave highest Benchmarks of Fairness 90 scores for benchmark 3 and 5 (+1.75) rather low. The overall score was +1.25 and the overall for rural non-health civic group was +1.75, and the highest score for benchmark 4 (+2.13).

Details of scoring as presented by 3- dimensional surface graph show that overall and specific health status had rather low scores (-1.00 to +1.00) from providers, health and non-health civic groups. Especially, economic status received low scores for all group, that is lower than –1.00 from provider at subdistrict level, urban health civic group, rural non-health civic group. The scores for distribution of health resource were also low for subdistrict providers, rural health civic group. Providers at provincial and district levels gave low scores for overall health spending and administrative cost. The benchmark on autonomy, or freedom to choose primary care and specialist care, public and private, got rather low scores from many groups; i.e. providers at all levels, local government officers, rural health civic group. The high scores of higher than +3.00 were not common, but found in the coverage of service, education, health insurance. Most scores varied from +1.00 to +3.00.

Figure 7.6 The 3-dimensional surface graph for 46 items by 8 focus group discussions in Phrae 91 7. Equity in health in 10 provinces: Qualitative data

4) Khon Kaen

Khon Kaen or “the capital of the northeast” was divided into 20 districts and 4 predistricts, with 1,727,464 population, the 3rd rank after Bangkok and Nakhon Ratchasima. It had 10,886 sq. km. and the population density of 158.7 person/sq.km. The average income was 2,965 baht amonth, at the 31st rank. About 14.7% of the population had income lower than 1,000 baht a month. The proportion of households with debt higher than 70% of income was on the top 12th rank. The gross provincial product was the 39th rank, 40,985 baht per person per year.

Khon Kaen is situated on the Korat highland, with plain area in the east that futile for rice growing ant other agriculture, with 2 rivers Pong and Chi. Khon Kaen is the centre for commercial business and banking for the region, and is the market for exchange of locally produced goods. The rate of growth for manufacture was 16.7% a year, the 1st rank in the region. There were more than 5,000 factories. Most were agro- industries. The main problems for Khon Kaen were: income distribution, people in farm had low income, unsatisfied quality of life, emigration of workforce because low absorbing capacity of the province and inadequate irrigation for agriculture(33) .

In terms of health, age-sex adjusted death rate was 6.49 per 1,000, the top 13th rank. Malnutrition among 0-5 was 13.25%, the top 8th rank, but low birth weight rate was 5.99% (only the 28th rank). The coverage for antenatal care and child vaccination were high, at the top 13th and 10th rank. From the Health and Welfare Survey in 1996, dependency ratio was 0.46, slightly lower than the average. Health insurance coverage was good, the coverage was 71.6% in 1996, the top 28th rank, with intermediate health insurance schemes 61.3%. Household health spending by the socioeconomic survey in 1998 was low, only 3.9%, the 5th rank from for the lowest. Doctor to population ratio for Khon Kaen was rather good, at 1:2,844, the top 8th rank. Bed per population was 1:512, the top 32nd rank. The bed to doctor ratio was low, at the 2nd rank, because 1 doctor was responsible for only 5.57 beds. With high number of doctor, the ratios of outpatients and inpatients to doctor were rather low. One doctor was taking care of 1,326 outpatients, and 355 inpatients. It was worth noting that 61% of the total doctors were not the MOPH doctors, they belonged to the teaching university as in Chiamg Mai. There were 4 hospitals other than MOPH hospitals, with 957 beds. Two private hospitals had 150 beds. A regional hospital had 714 beds and 19 community hospitals had 870 beds. Benchmarks of Fairness 92

Uses of public services were high. People made 2.35 visits per year, at the 21st rank. Ratio of uses at health centre to hospital was 1.2, at 11th rank. Ratio of hospitalizations at community to provincial hospitals was 2.61, the 2nd rank after Nakhon Ratchasima. The use at provincial hospital was 10.4% of the total outpatient services. Khon Kaen Hospital acted as provincial and regional hospital, the surgical cases accounted for 42% of total, the top 1st rank. The average relative weight according to DRG was 1.092, the second highest, and the average length of stay was 6.16, the top 4th rank. The average bed occupancy rate of 112%, the top 3rd and case fatality of 3.7%, the top 13th rank. The total health spending in the MOPH sector was 614 baht per capita per year, the 17th rank from the bottom. The spending at provincial hospital was only 340 baht per capita per year. Although Khon Kaen had a number of prrivate sector, the uses of public sector were high at 53% of the total, and only 12% used private sector (1996 survey).

Equity scores from quantitative tool show that 1 Overall health status ( 0.41 ) Khon Kaen had obvious positive scores 2 Specific health status ( 0.36 ) (higher than +1) for household health 3 Coverage of health service 0.77 4 Environment 0.27 spending, workload by doctor, proportion of 5 Demographic condition ( 0.01 ) use at primary care, comprehensiveness of 6 Economic status ( 0.17 ) care, admission rate. The obvious negative 7 Resource deprivation 0.28 8 Coverage of health insurance 0.09 scores (lower than –1) were found for average 9 Ratio of health insurance ( 0.28 ) length of stay, bed use. People used services 10 Household health expenditure 1.27 at health centre and community hospital, and 11 Distribution of resource 0.77 12 Workload of health service 1.72 the severe cases at provincial hospital. The 13 Institutional seeking care ( 0.48 ) long stay and limited number of bed led to 14 Utilization rate 0.09 high occupancy rate. Equity score from 30 15 Proportion of primary care utilization 1.43 rd 16 Continuity of care ( 0.39 ) indicators averaged at +7.41, at the 3 rank. 17 Comprehensiveness of care 2.16 18 Difference of health expenditure 0.71 19 Average length of stay ( 1.05 ) 20 Bed utilization ( 1.02 ) B9 1 B1 29 30 4 2 3 21 Admission pattern and case mix 1.24 28 4 27 2 5 22 Unit cost of health service ( 0.21 ) B7 26 - 6 23 Quality of care 0.02 25 (2) 7 24 8 24 Overall health care cost 0.52 (4) 23 9 25 Health care cost per service ( 0.91 ) 22 10 B2 21 11 26 Proportion of primary care cost 0.28 20 12 B6 19 13 27 Proportion of administrative cost 0.43 18 17 15 14 B3 28 Real cost per expected cost 0.85 16 B5 B4 29 Public health service seeking care 0.66 30 Private per public seeking care ( 0.85 ) Figure 7.7 Radar graph for 30 indicators Khon Kaen Table 7.7 Average score by 30 indicators, Khon Kaen 93 7. Equity in health in 10 provinces: Qualitative data

Comments on equity of health status and related factors Health problems in Khon Kaen included injury, cancer, heart disease and AIDS. The trend of cases and deaths for injury was increasing. Deaths from communicable diseases decreased except leptospirosis. Liver cancer, once the high endemic in the northeast, now thought to be controlled because campaign against eating raw fish and screening for liver fluke. Malnutrition in children also decreased. Death rates were rather stable, but birth rates reduced. Chronic diseases needed long stay in hospital increased. The trend had changed. The working age found more diabetes and hypertension, and more among the poor. People changed their occupation from farm to employee, lifestyle changed. Suicide and violence increased.

Factors influencing health included environment, refuse disposal and water pollution became big problem, especially pollution in Pong river. Farmers used more chemicals, e.g. sugar cane farm. Most commented that Khon Kaen was the centre of the region with mix of people, some indigenous and others immigrants. The ways of thinking differed. Social development provided better opportunity. However, high technology led to social disruption, e.g. drug addict, congested urban area, illegal immigrants from Myanmar and Pakistan. Unemployment increased. Economy run down. Small enterprises earned less, because people’s purchasing power decreased. Only 70-80% of urban people owned their houses, the rest lent the houses or dormitory. Some children were homeless. Slums were scattered in urban areas, there were 16 communities along the railways with 1,800-1,900 families and about 224 people in slum near the refuse disposal area. However, the principle of sufficient economy began to work. More village funds with 100,000 baht a village or 1,000,000 baht a village. More education opportunity by free interest loan from government. Khon Kaen had better communication system. There were more people networks and more participation from local government, more cooperation against drug addict and injury from public, private sectors and mass media.

Comments on equity in health finance and delivery The insurance coverage of people in Khon Kaen was better, when the 30 baht scheme was introduced, nearly all were covered, except some areas, e.g. suburban and boundary areas, immigrants and foreign immigrants, the marginal poor as excluded group from society, nomad people and people in slum areas. People who had no house registry were excluded, this group constituted around 5% of total population. The existing schemes still overlapped in terms of target groups. Choice of services at district and subdistrict level was limited. People had to pay if they exercised their choice. The ideal insurance scheme in their mind was the one with a card that could be used anywhere. Though the burden of health Benchmarks of Fairness 94 spending was reduced, the travel cost and other indirect costs were still high. Civic groups commented on limited choice in the VHCS, low quality drugs and long waiting. Some private hospitals in the social security scheme under-dispensed.

Health administrators commented on lack of doctors at community hospitals and small number of health personnel at health centre (average 3 doctors per hospital and 2.9 persons per health cenre). Contrast with the coverage of services. The 30 baht scheme put emphasis on family medicine and tried to reduce unnecessary referrals. Home visits were not so many. There was a common drug list for health centre and community hospital. The reform pilot period saw the decreasing trend of cross-boundaries and referrals. Lengths of stay were high at regional and teaching hospitals. More budget was allocated to district level. Cost data and DRG were used as a basis for paying referral cases. Cost containment led to a reduction in unnecessary services. Health providers still saw cross-boundary cases at the regional hospital, only 10% came through referral line. Most by-pass cases could not pay the fees. Some were reimbursed by community hospital. The number of doctors and beds at the regional hospital increased but was rather stable at community hospital. Number of services at health centre slightly increased. The regional hospital had a system to provide continuity care. Patients still believed that they were dispensed with different quality of drug. There were some limitations in care for some insurance schemes, e.g. some surgical procedures. Hospitals had a tendency to reduce length of stay, by shortening waiting time to surgery. At the same time, community hospital tried not to refer by keeping patients longer. The total cost per case became higher. Cost of the regional hospital increase from 400 million baht to 600 million baht within a few years. This may be the high use of high technology equipment. The local government officers commented on the ratio of doctor to patient that was inadequate and not equal within the province. There should be rotation scheme for doctor to health centre. Rural areas had limitations in access to specialist care, while people in urban area had better opportunity.

Urban civic group commented that the rich preferred the services in private sector because of convenience, rapidity and technology. The middle income used services in public sector. People believed that treatment problems were related with the insurance schemes, e.g. complications from surgery, misdiagnosis. They believed that the VHCS had better care than the LICS. Some people said they did not want to rely totally on doctor, but self-care. People in slum seldom came to hospital. The civic group thought that the number of doctor increased, but they started examining patients at 10.00 while patients started waiting since 6.00 am. When there were 4 urban health centres at each corner, hospital became a luxury. Urban health centre had 30 patients a day. Recently, hospital provided prompt 95 7. Equity in health in 10 provinces: Qualitative data diagnosis, admitted only necessary cases, e.g. acute appendicitis case stayed only 2 days. Choice of private sector became limited because of bankruptcy in private sector. Civic group in rural area used services at clinic and private hospital because of prompt service. Lay people were minimally interested in their health. Most of them relied on health facility. Issues on autonomous hospital, the 30-baht scheme and provincial health board were raised in order to explore involvement of the local government.

Summary scores on equity in health by focus group discussion Summary scores for Khon Kaen by 8 focus group discussion averaged at +2.13. The highest was for benchmark 6 (efficacy, efficiency and quality of service) +2.76, and the lowest for benchmark 7 (administrative efficiency) +1.62. The highest summary score was found in the urban health civic group (+3.63), and the lowest in non-health civic group in rural area (+0.25). Table 7.8 Equity scores by 9 benchmarks and summary scores by 8 focus group discussion, Khon Kaen

Health Provider Provider Local Civil society Civil society Civil society Civil society Overall Manager Prov. Dist Sub-dist Government Health (urban) Health (rural other (urban) other (rural) average Benchmark 1 2.40 1.50 2 .11 2.75 2 .57 0. 44 3.50 0.80 1.88 Benchmark 2 2.80 1.80 3 .11 2.75 2 .33 1. 50 2.80 2.50 2.38 Benchmark 3 2.40 0.90 2 .67 2.50 2 .43 1. 90 2.40 1.67 2.05 Benchmark 4 2.70 1.90 2 .00 2.50 2 .50 2. 70 3.20 0.33 2.23 Benchmark 5 2.00 1.30 2 .22 2.50 3 .57 1. 80 1.20 1.50 1.98 Benchmark 6 2.20 2.11 3 .00 3.25 3 .57 3. 25 2.83 2.33 2.76 Benchmark 7 2.00 1.00 1 .89 1.62 Benchmark 8 2.00 2.10 2 .56 2.25 2 .63 2. 00 2.40 - 0.40 2.03 Benchmark 9 2.20 1.70 1 .67 1.75 4 .00 2. 78 2.20 - 0.75 2.14 Benchmark 2.09 1.60 2 .11 2.25 3 .63 2. 10 2.33 0.25 2.13 The scores given by health administrators ranged from +2.00 to +3.00, averaged at +2.09. The highest score was for benchmark 2 (+2.80) due to the 30 baht scheme. Health providers at provincial and district levels gave average score +1.60, the highest for benchmark 6 (+2.11) because of continuous quality improvement, and lowest for benchmark 3 (+0.90) because of limited number of personnel, and benchmark 7 (+1.00) because of high health expenditure. The average score by health provider at subdistrict level was +2.11, highest for benchmark 2 (+3.11) and lowest for benchmark 9 (+1.67) because of limited choice especially in private sector. Summary score for local government officer was also lowest for benchmark 9 (+1.75) and highest for benchmark 6 (+3.25) because of quality development in health facilities. Urban health civic group gave high average score +3.63, with the highest for benchmark 9 (+4.00) because of having lots of choice in urban area. Rural health civic group gave the highest score for benchmark 6 (+3.25), lowest for benchmark 1 (+0.44), contrast with non-health civic group in urban area giving highest score for benchmark 1 (+3.50). This maybe because the health status in urban and Benchmarks of Fairness 96 rural areas were different. Non-health civic group in rural areas gave the average low score, especially for benchmarks 8 and 9 (-0.40 and -0.70).

Details of equity score by 3-dimensional surface graph show that overall health status score and by specific group were rather low for subdistrict health providers, health and non-health civic groups in rural area. Non- health civic group in rural area gave low score lower than –2.00. Environment, population and economic status got rather low score from all (lower than 0.00). Non-health civic group in urban area gave score lower than –1.00 for demographic factor. Administrators gave score for economic condition. Rural non- health civic group gave low score for environment, population (<–1.00) and score on economy –3.00. Equity for distribution of health resource had low score from administrators and providers. Lower resources got score lower than –1.00. Transparent management and health care costs. Choice did not get high scores. Benchmarks 7, 8 got moderate grade. Rural non-health also had moderate score.

Figure 7.8 3-dimensional surface graph of 46 indicators by 8 focus group discussions in Khon Kaen 97 7. Equity in health in 10 provinces: Qualitative data

5 Nakhon Ratchasima

Nakhon Ratchasima or Korat was the biggest province in land area, with 20,494 sq.km. and the 2nd largest population after Bangkok, 2,550,204. Population density was 124.4 people/sq.km. Korat was divided into 24 districts and 3 pre-districts. The average income was 2,437 baht a month, at 49th rank. The gross provincial product was 39,826 baht, 41st rank with households earned less than 1,000 baht a month 24.5% of the total and 62% of total households were in debt.

Nakhon Ratchasima was on the Korat highland about 185 m. above sea level. Korat has promoted industrial development to be the centre for the lower northeast in trade, finance and education. Main problems of Korat were the exploitation of natural resources, inadequate resources for health, migration of workforce to other provinces, inadequate health infrastructure, lack of educational opportunity and drug problems(33).

The age-sex standardized mortality rate was 5.51 per 1,000, at the 33rd rank. Malnutrition rate among the under 5 was 11%, at the top 17th rank. Low birth weight was 6.42%, the 39th rank. Dependency ratio was 0.50, slightly higher than the average. Main health problems of Korat were health status and factors related to health which were similar to the national average.

Coverage rate of health insurance by 1996 survey was only 61.2%, at the 66th rank. The intermediate schemes covered 51.3% of the total population. In 1998, household health spending was 5.5% of the household income, at the 22nd rank. The ratio of doctor to population was at the 47th rank 1:7,068, and bed to population the 52nd rank at 1:701. Workloads of outpatients and inpatients to doctor were at the average. There were 25 hospitals in Korat that belonged to the MOPH hospitals, one regional with 1,082 beds, 24 community hospitals with 1,200 beds. Moreover, there were 5 more hospitals not belonged to the MOPH with 1,012 beds. Three of them were private hospitals with 562 beds,

Use rate for new outpatients (at least once a year) was only 35% of the total population, the bottom 10th rank. Use rate in public sector was also low 1.77 visits/capita/year, the bottom 15th rank. Ratio of use at health centre to hospital was 0.86, similar to the average. Ratio of inpatients at community hospital to provincial hospital was the 1st rank. The use of provincial hospital was only 11.5% of the total outpatients. Benchmarks of Fairness 98

Provincial hospital had high surgery rate for inpatients 22%, the top 22nd rank. The casemix index was 1.016, the top 4th rank. Length of stay was the 1st rank, average 6.95 days. Bed occupancy rate was 97%, the top 16th rank. Case fatality rate was 7.2%, the 1st rank. Ratio of abnormal to normal delivery was 1.12, the top 7th rank. High proportion of community hospitals led to high proportion of admissions in these hospitals, with more severe cases of long stay and high case fatality treated in the regional hospital.

The total spending in public sector (MOPH) was 566 baht/capita, the bottom 13th rank. Spending at provincial hospital was only 254 baht/capita. Choice for public sector was high 48.6%, while use at formal private sector was 13.7% (from 1996 survey).

The equity scores from quantitative tool for Korat averaged at 0. Some items had positive score (higher than +1) such as proportion of use at primary level, admission in community hospitals, severe cases

admitted in provincial hospital, and overall 1 Overall health status 0 .41 low spending per capital. The negative 2 Specific health status 0 .09 3 Coverage of health service 0 .60 scores (lower than –1) were for bed use 4 Environment 0 .92 because of high occupancy, quality of 5 Demographic condition ( 0 .16 ) 6 Economic status ( 0 .24 ) service because of high mortality and high 7 Resource deprivation ( 0 .08 ) 8 Coverage of health insurance ( 0 .68 ) abnormal delivery. Use rates were high at 9 Ratio of health insurance ( 0 .52 ) health centre and community hospitals. 10 Household health expenditure ( 0 .39 ) 11 Distribution of resource ( 0 .26 ) Severe cases treated at regional hospital 12 Workload of health service 0 .29 with long stay and high fatality. The overal 13 Institutional seeking care ( 0 .64 ) 14 Utilization rate ( 0 .80 ) equity score by 30 indicators was -3.16, at 15 Proportion of primary care utilization 1 .16 the 58th rank. 16 Continuity of care 0 .03 17 Comprehensiveness of care ( 0 .49 ) 18 Difference of health expenditure ( 0 .11 ) 19 Average length of stay ( 0 .63 ) BM9 1 BM1 20 Bed utilization ( 1 .11 ) 30 2 29 4 3 21 Admission pattern and case mix 1 .09 28 4 27 2 5 22 Unit cost of health service 0 .15 BM7 26 - 6 23 Quality of care ( 2 .62 ) 25 7 (2) 24 Overall health care cost 0 .98 24 8 (4) 23 9 BM2 25 Health care cost per service 0 .18 22 10 26 Proportion of primary care cost 0 .16 21 11 27 Proportion of administrative cost ( 0 .14 ) BM6 20 12 19 13 18 14 28 Real cost per expected cost 0 .14 17 15 BM3 29 Public health service seeking care 0 .21 BM5 16 BM4 30 Private per public seeking care ( 0 .52 ) rat Table 7.9 Average score by 30 indicators, Korat 99 7. Equity in health in 10 provinces: Qualitative data

Comments on equity of health status and related factors Health problems in part came from injury with increasing deaths from 4-lane roads connecting Pak Chong to Sikiew, but the road on the left side was bad; and from Pak Chong to Chokechai. More over, high disabilities were from motorcycle accidents, only 20% of them wearing safety helmet. AIDS was also accumulating, most of them were inmates in jail. Diabetes and hypertension were on the rise. Some communicable diseases were prevalent such as leptospirosis, dengue haemorrhagic fever, rabies (deaths found every year). Health problems were also found among workers in factories. Civic group commented on better health conditions especially exercise helped better health without from taking medicine.

Factors influencing health included economic and expanded manufacturing industries. Foreign labour was higher. Agriculture business include vineyard custard apple, maize at Pak Chong, which were the tourist attractions. Economy of districts in the south was better than in the north. Environmental problems were water pollution from factories (in Pak Chong, Si Kiew, Sung Noen, Kaen Sanamnang), fume from factories (Sung Noen), rock mill (Pak Chong, Chokechai). Health problems included skin rash, respiratory diseases. Refuse dispose became big problem for municipality. Use of chemicals in agriculture increased, e.g. use herbicide instead of manual removal, leading to 50% of farmers with high chemical in blood. Soil salt farm was problem in Dan Khuntod, Nonthai and Bua Yai. Salty water contaminated farm and non-futile. Education level was better, but the 50-60 was still illiterate. Delivery of health messages was better, through ‘Chaturamit’ (four-friends), to fight against epidemics, to increase intersectoral cooperation, to make health for all sustainable. However, people sometimes had no consensus in solving problems, such as the water reserve Lamtakhong problem. The ideas of using nature to promote health became a big trend. They were uses of organic fertilizers to replace chemical fertilizers, uses of enzymes to ferment refuse to control mosquito and fly, promotion of chemical-free vegetables and uses of herbs to correct the biological host balance as discovered in the local wisdom long time ago.

Comments on equity in health finance and delivery The coverage of health insurance was better, but there were problems among workers in sugar cane farms, alien workers from Myanmar, Cambodia in rice mills, pig farms. People thought universal health insurance policy was good because all could use services even the poor. Some commented against limit of access by geographical area and setting steps for services. They were not sure about the quality of drug as they thought that the hospital received only 30 baht therefore the quality could not be good. Some people complained that they did not get better from the medicine prescribed. Some people complained that private sector collected fees, however, they normally seek care and pay at private sector. Benchmarks of Fairness 100

Health providers commented that doctors were concentrated in regional hospital, while more beds were put in community hospitals. Distribution of resources was still not appropriate. It was better that nurses were working at health centre in the network. People felt that they were the owner of health centre. The system of provincial purchasing of drug has been for 10 years, making community hospital and health centre have same drug lists. The regional hospital had 3 drug lists. There was a big gap in medical technology. Many CT scanners were in the city, and an MRI in a hospital. People were more demanding, had higher expectations and participated in accreditation. Health facilities had to improve both quality and efficiency especially cost. They thought that if choices were provided, people would choose private sector because private sector provided better services as seen in the social security scheme. Public sector could form a network with private sector, such as Pak Chong signed a contract with clinics in the social security scheme. Providers commented that the number of personnel was not appropriate with the workload because of more patients but constant personnel. People had the attitude of dying in hospital, therefore the regional hospital was congested. Even there were 160 doctors, but some departments were crowded e.g. medicine. There were 14 urban health centres, but the number of outpatient attendants at regional hospital was not reduced. Payment to regional hospital was on DRG basis. The total health spending was not reduced. The local government officers emphasized on health promotion for the under privileged. The work of primary care unit convinced the importance of family medicine. Though people had choice, they thought that assigning areas to health facility promoted relationship between people and health service.

Urban civic groups commented on better public services, but some delayed referrals, e.g. cancer, coronary heart disease, leptospirosis. Misdiagnosis was one reason for delay. Different drugs at different levels were a problem. Some thought that the 30 baht increased the number of users especially at primary care unit, but did not reduce the use at private sector. Most of them accepted the quality at the regional hospital, with good doctor but overcrowded. Sometimes two patients shared the same hospital bed. The system for health delivery was better, patients need not carry the card, queue for dispensed drug was quick, and personnel welcomed clients. Sometimes there were complications because of inexperienced interns. In primary care unit, doctors had more time to talk to patient because there were not so many patients, about 50 during daytime and 30 in the evening. Rural civic groups commented on ethical problem of doctors. They provided good services to those whom they know, and maybe careless or delay for general patients with death as a consequence. The trend of lawsuit would increase which would jeopardize the relationship between provider and user. The malpractice insurance and accreditation was good but more concern should be paid on clients. 101 7. Equity in health in 10 provinces: Qualitative data

Summary scores on equity in health by focus group discussion The summary score for Korat from 8 focus group discussions averaged at +2.25, the highest for benchmark 2 (financial barrier to equitable access) +2.32, and the lowest for benchmark 7 (administrative efficiency) +1.55. Rural health civic group gave the highest score (+3.40) and non-health civic group in urban area gave the lowest score (0.83).

Table 7.10 Equity score for 9 benchmarks and summary score by 8 focus group discussion, Nakhon Ratchasima

Health Provider Provider Local Civil society Civil society Civil society Civil society Overall Manager Prov. Dist Sub-dist Government Health (urban) Health (rural other (urban) other (rural) average Benchmark 1 2.13 2.00 2 .56 2.44 2 .50 1. 50 0.86 2.13 2.07 Benchmark 2 2.75 2.80 3 .11 1.56 2 .25 1. 50 2.00 2.50 2.32 Benchmark 3 2.14 0.83 2 .44 1.33 2 .00 2. 33 0.86 2.00 1.77 Benchmark 4 2.29 2.00 2 .33 2.67 2 .38 1. 20 1.86 1.75 2.12 Benchmark 5 1.71 3.17 2 .78 2.11 2 .38 2. 20 1.14 1.75 2.15 Benchmark 6 2.43 2.50 2 .33 2.22 2 .29 2. 20 1.86 1.88 2.21 Benchmark 7 1.86 0.67 1 .89 1.55 Benchmark 8 2.29 1.50 2 .44 2.63 3 .25 1. 00 1.57 1.86 2.16 Benchmark 9 2.00 1.17 2 .00 2.88 1 .88 1. 80 1.57 1.86 1.93 Benchmark 2.00 2.67 2 .00 3.00 2 .50 3. 40 0.83 1.71 2.25

Health administrators gave the average score at +2.00. Detailed scores were not different from the average. The highest score was for benchmark 2 (+2.75) health insurance coverage, and benchmark 5 the lowest (+1.71) equitable source of finance. Providers at provincial and district levels in contrast gave highest score for benchmark (+3.17) because of universal health coverage, and score for benchmark 2 was also high (+2.80), while benchmark 7 was the lowest (+0.67). Providers at subdistrict level agreed that the lowest score was benchmark 7 (+1.89), and the highest was benchmark 2 (+3.11). The similarity between health administrators and health providers was that they gave high score for health insurance. Local government officer gave high score for benchmark 9 (+2.88), while the lowest for benchmark 3 (+1.33) because of gap in health resource distribution. Urban health civic group gave the highest score for benchmark 8 (+3.25) because they realized high participation in health from people. The score for benchmark 9 was the lowest (+1.88). Non-health civic groups in urban and rural areas gave the highest score for benchmark 2 (+2.00 and +2.50), while non-health civic group in urban area gave the lowest score for benchmarks 1 and 3 (both +0.86). Benchmarks of Fairness 102

Details on equity scores by the 3- dimensional surface graph show that health status score was low (lower than +1.00) for local government officers and urban health civic group. Environment, demographic and economic status had the lowest score from all groups, especially score on economic condition was lower than 0.00. Health providers and health civic groups especially rural health civic group gave lowest score for economic status –2.00, but urban health civic group also gave low score for economic factor (lower than 0.00) and workload to health resources (-1.00 "$# +1.00) got low score from health providers, local government and civic groups. Providers at provincial and district levels gave low score for administrative efficiency and choice of services. Health providers at subdistrict level gave low score for choice at private sector. The high scores of higher than +3.00 were obtained for coverage of service, health insurance, equity of payment. Most of the high scores were given by health administrators and health providers.

Figure 7.10 Contour graph showing results from 46 indicators by 8 focus groups in Nakhon Ratchasima 103 7. Equity in health in 10 provinces: Qualitative tool

6) Phuket Phuket is the only island province in Thailand. Phuket is called ‘Pearl of Andaman’ for its world-renowned tourist spot. Phuket is the largest island but the second smallest province after Samut Songkram. Phuket had 543 sq.km., with 47.8 km. length and 21.3 km. width(34). There were 3 districts in Phuket: Muang, Thalang and Kratu. There were 238,997 resident population, the population density was 440.1 persons/sq.km. in 2000, the fifth densely populated province (not included Bangkok). The average monthly income was 5,874 baht, the 3rd highest after Nonthaburi and Pathumthani. Gross provincial product per year was 173,026 baht, the 6th rank from top. Only 0.6% of total households survive with income under 1,000 baht per person per month.

Geographically, Phuket is a mountainous island with small plain area. There is no river except small canals. Phuket had only 2 seasons: summer and rainy. Economy and tourism of Phuket was the big magnet to tourists and foreign investors. This led to problems of urbanization, too crowded with entertainment businesses and socio-cultural problems.

In terms of health, age standardized mortality rate was 7.17 per 1,000, the top 7th rank. Life expectancy was as short as people in the north and the east. Major killers were also HIV. The under 5 mortality was high, about 2.65 per 1,000, the top 15th rank. Traffic accident rate was the top 1st rank, 5,103 per 1,000 population. Malnutrition rate in children was low 3.44%, the 10th from bottom. Dependency ratio was 0.48, the country average. Good economy of Phuket could reduce disease with poverty, i.e. malnutrition, but increase other problems such as deaths from accidents and HIV.

The coverage of health insurance in Phuket was 72.6%, the top 23rd rank. The coverage for high benefit insurance (CSMBS, SSS, private insurance) was high 22.4%, the top 6th rank and the coverage of medium benefit was 49%, 62nd rank. This suggested that Phuket people had little financial barrier to health care. The distribution of health resources was good. Doctor to population ratio was 1:2,717, the top 7th rank. Bed to population ratio was 1:352, the top 10th rank. Workload of patient to doctor was low at the bottm 13th rank for outpatient load and bottom 14th rank for inpatient load. There were 3 public hospitals under the MOPH in Phuket, one with 420 beds, and 2 community hospitals with 90 beds. There were 6 private hospitals with 189 beds. Benchmark of Fairness 104

With high accessibility, 66% of Phuket people used services at least 1 visit/person/year, the top 7the rank, and 18.8% admitted to the hospital, the 5th rank. However the use at public service was 0.214 visit/person/year, at 33rd rank, but admission to public hospital was high at 14.74%, the top 6th rank. Since Phuket is a small province, primary care services were limited, and the distance to hospital was not too far. The ratio of use at health centre to hospital was the lowest 0.29, the same as the ratio of admission to community hospital to provincial hospital was 0.32, the bottom 3rd rank after Ratchaburi and Singburi. The proportion of use at provincial hospital was the highest at 48.6% of the total outpatient visits. The high uses at provincial hospital led to high cost at public health services, at 1,357 baht, the top 6th rank. The per capita cost at provincial hospital was 1,210 baht. When compared the cost of provincial hospital to the number of bed, the cost per bed was not high, as it was the 40th rank. The choice of use at private sector to public sector was 0.68 for outpatient services, and 0.38 for inpatient services, the 20th and 16th respectively.

The equity scores from quantitative tool show 1 Overall health status ( 1.11 ) 2 Specific health status ( 0.65 ) Phuket the better (score higher than +1) situation 3 Coverage of health service 0.26 on economy and distribution of health resources. 4 Environment 1.00 5 Demographic condition ( 0.23 ) The average worse situations (lower than –1) were 6 Economic status 1.08 found on general health status and proportion of 7 Resource deprivation 0.29 8 Coverage of health insurance 0.42 use at primary care. It is a proof that worse health 9 Ratio of health insurance 0.73 status could be seen with good economy. The high 10 Household health expenditure 0.04 11 Distribution of resource 1.09 use of provincial hospital led Phuket to high per 12 Workload of health service 0.98 13 Institutional seeking care 0.47 capita expenditure for public health sector. The 14 Utilization rate 0.77 overall score from 30 indicators was +2.39 at the 15 Proportion of primary care utilization ( 1.89 ) th 16 Continuity of care ( 0.41 ) 24 rank. 17 Comprehensiveness of care 0.49 18 Difference of health expenditure 0.94 BM9 BM1 1 19 Average length of stay 0.29 30 2 29 4 3 20 Bed utilization 0.09 28 4 27 2 5 21 Admission pattern and case mix 0.03 BM7 26 - 6 22 Unit cost of health service ( 0.25 ) 25 7 (2) 23 Quality of care 0.19 24 8 (4) 24 Overall health care cost ( 0.94 ) 23 9 BM2 22 10 25 Health care cost per service 0.19 21 11 26 Proportion of primary care cost ( 0.73 ) BM6 20 12 27 Proportion of administrative cost 0.41 19 13 18 14 17 15 BM3 28 Real cost per expected cost ( 0.48 ) BM5 16 29 Public health service seeking care ( 0.93 ) BM4 30 Private per public seeking care 0.35

Figure 7.11 Radar graph of 30 indicators for Phuket Table 7.11 Average scores of 30 indicators for Phuket 105 7. Equity in health in 10 provinces: Qualitative tool

Comments on equity of health status and related factors Main health problems of Phuket were accidents and AIDS. Death rate of accidents was still high, but some people commented that it was decreasing because of strong law enforcement. For example, enforcement of safety helmet over 3 years still saw average 25 deaths a month. AIDS was more significant health problem. New cases were increasing. Last years experienced 164 deaths. There were more deaths that did not record AIDS as the leading cause. Some clusters of new cases were fishermen, seasonally migrated from central and northeastern part of Thailand. AIDS was prevalent because of risky sex behaviours. Low condom use and extramarital sex practice among adolescents were the cause. Abortions were also high. Other health problems included liver cancer, breast cancer, chronic diseases (diabetes, hypertension). Communicable diseases were found among migrant workers such as malaria, filariasis. Other non-communicable diseases were pollution, occupation diseases. However health of the 50-60 year old were believed to be better, because they joined exercise programme of the health club.

The general economy of Phuket was still good, noting that private hospitals were still full. Most people were rich and middle income. Only some were poor, most of them were immigrants. Some lived on only 100 baht. Slum community called ‘new Thai community’. Recently foreigners have invaded Phuket’s economy, e.g. they became owner of entertainment’s places. Phuket dwellers were classified into 3 groups: local people, migrant worker from other provinces or abroad and tourists. Migrant workers from Myanmar could be more than 50,000 but the registered number was 20,000. Most of them worked in fishery, rubber field, construction, trade, transport and household work. Migrant workers were often found with communicable diseases, ill health and did not use proper services. The uses of contraception, vaccination services were low. Deforestation of mangrove by immigrants was prevalent. Phuket became congested because of tourism. Social problems came with night entertainment especially among adolescent, leading to family problem. Some families sold their land but the adolescent used that money for entertainment, no schooling. They bought motor vehicles for motor racing leading to high road traffic injury. Social cohesion became isolated, having less time for community issues, high addiction problems, factories drained pollution into the sea, flooding in some areas, e.g. Patong, Kamala, and deforestation.

Comment on equity in health finance and delivery In terms of health finance, most Phuket people had health insurance, if not they were able to pay for health services. Most of the uninsured were construction workers from other provinces, who previously had temporary health card to get access to care. However, those who had the universal coverage (UC) card, not all of them used services provided by the scheme. If they had enough money they still used services Benchmark of Fairness 106 in private sector (clinics and hospitals). The services under the UC that were often used includeed dental care, surgery, obstetric care. Many people still preferred the health card to the 30 baht scheme.

For health delivery, the second round of issuing the UC card saw a 30% increase of general health service use and a 20% increase of dental care. This may be because people wanted to try the new system. There was evidence that the uses at health centre free of charge increased but the revenue still maintained. Moreover, people still used services in private sector that they had to pay themselves. Most of the complaints were related to service behaviour rather than the charges of health services. People who had private insurance were not interested to have the UC card because they could get access to private sector. Distribution of health resources was a problem especially in community hospital. One had only 4 doctors and the other only 2 doctors serving for outpatient and inpatient services to local people and tourists. There were about 4-5 health personnel at health centre, rather higher than other provinces. The municipal health centre had medical doctor. There was a weekly service of doctor to small remote island, with helicopter service for referral. The access to health services in public sector was free they did not have to follow the referral line since the island was not big. People had many choices. The providers commented that there were many doctors working in private sectors. They commented that hospital beds in provincial hospital were not enough because occupancy rate was too congested. They agreed that sending doctor to serve part-time in health centre would reduce travel expenses of local people and reduce absenteeism from work. They commented that home health service was still inadequate. They commented that people still preferred high tech-investigations in private sector. Some patients underwent investigations in private sector but shifted for treatment in public hospital. Local governments commented that people did not know their rights. Foreign workers used services in health centre more than hospital because they were afraid of being caught by law. Doctors were concentrated in urban area. Health services were not outreach to the needy, lack of continuity of care, post natal and vaccination was under coverage. They suggested more doctors working in health centre. Overall they thought public health sector was at food quality. Provincial health board was a good body for balancing the power.

Health civic group commented on the congested public hospital, especially medical and surgical departments. Doctors in public hospitals worked in both public hospital and private clinic. If possible, they would like to see doctors working only in public sector, especially when they were on call. People still preferred the 500 baht health card. Some commented that health facility gave higher priority to the civil servant than other schemes, e.g. booking for private ward. People preferred to go to private hospital for emergency care. However, people became more involved with public sector to provide non-clinical 107 7. Equity in health in 10 provinces: Qualitative tool services to the people. Non-health civic groups commented on the quality of care, e.g. low quality and quantity of prescribed drug. However, there were more clients at health centre where doctors came to service. Hotline for health and the phone-in radio programme were good ways to get people’s comments.

Overall score of health equity from focus group discussion The average of the overall scores of health equity for 8 group discussions was +1.95. The highest score was for benchmark 2 (no financial barrier for access) at +2.25. The lowest was for benchmark 7 (administrative efficiency) +0.76. Non-health civic group of urban area gave the highest score (+3.00). The local government group gave the lowest score (+0.86).

Table 7.12 Health equity scores for 9 benchmarks by 8 focus group discussions in Phuket

Health Provider Provider Local Civil society Civil society Civil society Civil society Overall Manager Prov. Dist Sub-dist Government Health (urban) Health (rural other (urban) other (rural) average Benchmark 1 2.00 1.14 0 .25 1.71 1 .78 2. 75 2.83 1.80 1.70 Benchmark 2 2.56 1.29 2 .25 2.29 2 .00 3. 75 2.50 2.00 2.25 Benchmark 3 2.40 1.14 1 .88 2.17 1 .40 3. 33 2.33 0.33 1.79 Benchmark 4 2.22 1.86 2 .38 0.14 1 .10 3. 25 3.00 2.00 1.88 Benchmark 5 2.00 1.14 2 .13 1.86 1 .78 3. 50 2.80 2.83 2.13 Benchmark 6 2.00 1.00 1 .63 1.43 2 .22 3. 00 2.83 2.50 2.00 Benchmark 7 1.20 0.43 0 .50 0.76 Benchmark 8 2.30 0.86 0 .14 0.67 1 .30 3. 75 2.67 1.83 1.59 Benchmark 9 2.80 1.00 2 .13 1.86 2 .63 2. 67 3.00 1.20 2.19 Benchmark 2.60 1.29 1 .75 0.86 2 .00 2. 67 3.00 1.67 1.95

The administrators gave the average score at +2.60, most of them higher than +2.00, except for benchmark 7 only +1.20 (the lowest). Interestingly, providers at all levels also gave low scores for benchmark 7 (between +0.43 and +0.50), suggesting the agreement on high cost of health care. Providers at provincial and district level gave high score for benchmark 4 (+1.86), with average score at +1.29. Providers at subdistrict level also ranked the highest for benchmark 4 (+2.38) after realizing that wider people had good access, and the average was +1.75. Local government group gave highest score for benchmark 2 (+2.29) and the lowest for benchmark 4 (+0.14) since they did not see the continuity of care, and the average was +0.86. Civic group in urban area gave highest score for benchmark 9 (+2.63) and the lowest for benchmark 4 (+1.10). Health civic group in rural area gave rather high score (+2.67), the highest for benchmarks 2 and 8 (+3.75). Non-health civic group in urban area also gave high score (+3.00), the highest for benchmarks 4 and 9 (+3.00). Non-health civic group in rural area gave the average score of +1.67, the highest for benchmark 5 (+2.83) and the lowest for benchmark 3 (+0.33). This suggested inequity in resource distribution and problem of access to health care. Benchmark of Fairness 108

Details of equity scores by 3-dimension surface graph show that health status of all and by subgroup was a big problem with low scores (lower than -.00 !#" +1.00) from administrators at provincial to sub-district levels, local government and rural non- health civic group. Environment and demography had rather low scores from all groups (-1.00 for environment from providers at sub-district and –1.00 for demography from local government). Economic condition had low score (lower than 0.00) from non-health civic group in rural area. Issues on resource distribution and workload got low score from administrators and providers. The continuity of care got low score from administrators, providers at provincial level and local governments. Moreover, the efficiency of services, administrative cost and people participation got rather low scores like the scores given by health providers. Issues with score higher than +3.00 were found in health insurance, comprehensiveness and coverage of services, equitable health financing and the choice of health services.

Figure 7.12 3 dimension surface graph for detailed scores of 46 items given by 8 focus group discussions in Phuket 109 7. Equity in health in 10 provinces: Qualitative tool

7) Songkhla

Songkhla was a province with 16 districts, 7,393 sq.km. and 1,250,903 population. The population density was 169.3 per sq.km. the 14th rank of the most densely population. The income was high at the 8th rank, average 4,275 baht/person/month. Proportion of household with income less than 1,000 baht a month was only 4.2%, the 13th rank from the bottom. Provincial product was the top 17th about 64,168 baht per capital.

Songkhla is the commercial and touristic attraction for Thais and foreigners. Hat Yai is the big cmmercial district bringing in income for Songkhla. The big water reservoir is Songkhla lake covered 1,040 sq.km. The lake was the mix of fresh water with large area of brackish and sea water in the south. The lake was the rich water resources especially for fishermen living around the lake. Most of Songkhla people were Buddhist and some Thai Muslims were living along Songkhla lake and the east coast of the Gulf of Thailand, in Tepa and Jana districts. Chainese-Thai lived in Hat Yai doing bussiness(35).

In terms of health, age-sex adjusted mortality rate for Songkhla was 6.32 per 1,000 population, the top 14th rank. The under 5 malnutrition rate was 6.94%. Low birth weight rate was 4.63%, the 6th rank from the bottom. Dependency ratio was 0.53, the top 19th rank. Good economy of Songkhla led to low malnutrition rate. However high mortality and high dependency suggested that there were many children in the province.

People were covered with good insurance schemes (CSMBS, SSS and private insurance) about 16.7%, the 14th rank but the overall insurance rate wasthe 35th rank (coverage rate was 70%). Doctor to population ratio was the top 5th rank, with ratio of 1:2,280. Bed to population ratio was high at the top 12th rank, ratio 1:368. Hence the bed to doctor ratio was the 3rd rank, 1 doctor looked after only 6.24 patient beds. Outpatient visits per doctor was the 2nd lowest, 1 doctor for 1,178 outpatient visits, Inpatients per doctor was the 1st lowest, 1 for 298 inpatients only. The proportion of doctors working outside the MOPH was high about 55% the same as found in Chiang Mai and Khon Kaen, Most of them worked in the teaching hospital. There were 10 hospitals (with 1,346 beds) not belonged to the MOPH. Out of these, 6 hospitals and 479 beds belonged to private sector. The MOPH owned 1 regional hospital with 640 beds, 1 provincial hospital with 515 beds and 15 hospitals with 430 beds. Benchmark of Fairness 110

The use rate for new case outpatient service was 50%, the top 21st rank. The use rate at government hospital (MOPH) was low about 1.57 visits per person per year, at the 68th rank. The ratio of uses at health centre to the hospital was very low only 0.36 at the 73rd rank better than Phuket and Chainat. The proportion of uses at regional and provincial hospital was 33.6%, the 10th highest. Therefore the proportion of uses at health centre for health promotion activities were high at 30.6%, the top 5th rank.

Admission rate was about 9.6% of the outpatient cases, the bottom 9th rank. The average length of stay at provincial hospitals was 5.88 days, the top 7th rank. The unit cost for inpatient service was high about 6,740 baht a case, the top 7th rank. Spending be a regional hospital bed was also high about 809,300 baht a bed, the top 10th rank. The total spending of public sector per person was 902 baht, not very high. About 758 baht was the spending at regional hospital, about 70 baht for health centre, a 10 time difference, at the top 10th rank. 1 Overall health status ( 0.26) 2 Specific health status 0.30 3 Coverage of health service 0.40 The health equity scores from the quantitative 4 Environment ( 0.02) tool show that Songkhla gained the clear 5 Demographic condition ( 0.40) 6 Economic status 0.58 positive score (higher than +1) for health 7 Resource deprivation 0.42 resource distribution, workload for doctor. The 8 Coverage of health insurance 0.09 items with clear negative scores (lower than – 9 Ratio of health insurance 0.16 10 Household health expenditure 0.32 1) were the proportion of uses at the primary 11 Distribution of resource 1.13 level of care, unit cost of health service, and 12 Workload of health service 1.77 13 Institutional seeking care ( 0.07) the real health expenditure as compared with 14 Utilization rate ( 0.76) expected expenditure. The average score for 15 Proportion of primary care utilization ( 1.04) 30 indicators was about -2.79, at the 56 rank. 16 Continuity of care 0.30 17 Comprehensiveness of care 0.80 18 Difference of health expenditure ( 0.84) BM9 1 BM1 19 Average length of stay ( 0.69) 30 2 29 4 3 20 Bed utilization ( 0.55) 28 4 27 2 5 21 Admission pattern and case mix 0.09 BM7 26 - 6 22 Unit cost of health service ( 1.35) 25 7 (2) 23 Quality of care ( 0.02) 24 8 (4) 23 9 BM2 24 Overall health care cost 0.36 22 10 25 Health care cost per service ( 0.89) 21 11 BM6 20 12 26 Proportion of primary care cost ( 0.92) 19 13 18 14 27 Proportion of administrative cost 0.08 17 15 BM3 BM5 16 28 Real cost per expected cost ( 1.44) BM4 29 Public health service seeking care 0.69 30 Private per public seeking care ( 0.75)

Figure 7.13 The scores by 30 indicators for Songkhla Table 7.13 The average scores by 30 indicators for Songkhla 111 7. Equity in health in 10 provinces: Qualitative tool

Comments on health equity for health status and related issues Songkhla faced with high road traffic injury because of inter city network of road. The victims were people from outside Songkhla. More injuries occurred when raining, e.g. the Tepa incidence, the victims were people travelling from Yala and Narathiwas, provinces further south. The incidence on the road from provincial city to Hat Yai was also high but declining. AIDS problem was increasing, among fishermen along Ranote, Sathingphra districts. AIDS spread through night-life café and entertainment, some by people from outside Songkhla, as seen in Nathawee district. The mother to child transmission was about 0.5-0.6%. Chronic disease, e.g. diabetes and hypertension became more prevalent. Some communicable diseases, e.g. dengue haemorrhagic disease increased in urban area. Occupational diseases among industrial and agricultural workers increased. Drug addiction among adolescent in slum increased. Civic groups were more concerned about exercise and eating non-polished rice.

For factors related to health, environment was a problem, e.g. bad smell from rubber factory in Jana, Hat Yai and Sadao. Water pollution from household and small industry including from prawn farm made Songkhla lake polluted. In general economy, people income dropped. Rubber price was down, with 2 baht loss per kg. But spending remained the same, purchasing power dropped, unemployment increased. Local workers were selective on their work bringing in foreign workers from Myanmar, Laos, Cambodia doing hard work in rubber forest and fishing. Suicide caused by economic problem. Thefts were here and there. Education level seemed to be better, illiteracy rate dropped. Rural people did not pass their high school because they helped their parents to earn a living. Cooperation between public and private sectors was better, e.g. AIDS network. People were more organized, more health clubs for herbs for medicinal uses, pesticide-free vegetables, traditional medicine clinic (Dharmaraksa).

Comments on equity of health finance and delivery The coverage of health insurance was better. Before the UC scheme, there were 20% of the uninsured. Most schemes were the LICS and the CSMBS because of the headquarters of many organizations. After the UC scheme, very few were left uninsured. But many people preferred the VHCS, they thought paying 30 baht copayments many times would be costlier than 500 baht. The cross-boundaries were higher. Some commented that the UC scheme overlapped with the previous schemes. Problems of using house registry as the document for card issuing occurred with the internal immigrants. Budget allocation on capitation basis caused cash-flow problem to big hospital. Urban civic group commented on less Benchmark of Fairness 112 freedom in choosing facility under the UC scheme because they were forced to accept the low quality service, inappropriate referral rule. The list of diseases covered limited and they must pay from their own.

Health administrators commented on good supply side in the province: one regional and one provincial hospitals, one teaching hospital and 3 military hospitals. There were 18 municipal health centres. After the UC scheme, more people used health services, e.g. surgery (wait until getting the card), some thought the use in private clinic would reduce. Some commented that the UC scheme missed to encourage health promotion. Health system problems were as follows: in appropriate distribution of health resources, limited choice of rural people, administrative structure of health centre, encouraging health promotion not only reducing health care cost but increasing effectiveness, promoting health care decentralization, performance related payment to health personnel and providing better access, verifying using the national identification card. Other comments were: the district with population lower than 10,000 population would face budget problem, the income in private sector decreased, some people shifted their choice from private hospital to the public regional hospital, people supported the programme that doctors and nurses worked in health centre. Health providers commented that curative services were more popular than health promotion, more people seek care after office hours, the hospital revenue decreased, capitation budget overlooked local problem was not appropriate. Many facilities tried to have extended primary care services, putting more personnel in health centre. They reduced the costs by bulk purchasing of medicine (practice before the UC scheme). The local governments commented on high health care cost borne by people, some groups of people did not have access to care especially the Muslim Thais with different language and culture. There was inequity problem especially the use of medicine between health insurance schemes.

Civic group in rural area commented on inadequate specialists, health personnel and beds. Health centre had low quality, the diagnosis not the same as hospital. People were not allowed to bypass. There was language barrier (they used Malayu). Inequity occurred when compared services provided to the rich and the poor. Non-health civic group in urban area commented on long waiting time, queue jump, inappropriate care (dead foetus in utero), delayed diagnosis (appendicitis in pregnant woman), mis- treatment (fracture of leg, treated with plaster cast but later amputated because of gangrene leg), medical negligence and doctor in public hospital with private clinic. Non-health civic group in rural area commented on increasing awareness of people interest groups. There were more community funds covering health expenditure of the members as well as other welfare issues. More emphasis on using local wisdom e.g. herbal medicine. More need to participate and involve in the development of the 113 7. Equity in health in 10 provinces: Qualitative tool hospital. The network of people with the hospital increased. They believed that doctor could be good liaison. Overall health equity score from focus group discussion The overall equity scores of Songkhla from 8 focus group discussions averaged at +1.08. The highest score was for benchmark 6 (efficiency and quality of care) and the lowest was for benchmark 5 (equitable financing) +1.00. Those who gave the highest score were urban health civic group (+2.00), and who gave the lowest score were non-health civic group in urban area (-2.86). Table 7.14 Equity score by 9 benchmarks and overall scores by 8 focus group discussions in Songkhla Health Provider Provider Local Civil society Civil society Civil society Civil society Overall Manager Prov. Dist Sub-dist Government Health (urban) Health (rural other (urban) other (rural) average Benchmark 1 2.14 1.60 2. 11 1.50 1.71 2. 50 - 2.22 1.86 1.34 Benchmark 2 1.86 1.80 2. 22 1.75 1.29 1. 60 - 1.00 1.86 1.31 Benchmark 3 2.00 1.00 1. 56 1.75 1.57 1. 90 - 0.56 1.86 1.33 Benchmark 4 1.86 1.00 2. 11 1.75 2.14 1. 78 - 1.20 2.14 1.34 Benchmark 5 1.43 2.20 1. 22 1.50 1.57 1. 63 - 1.20 1.00 1.00 Benchmark 6 1.86 2.20 1. 67 1.75 2.14 1. 70 - 0.60 2.00 1.46 Benchmark 7 1.00 1.20 1. 89 1.43 Benchmark 8 0.86 0.40 1. 56 1.50 2.14 2. 56 - 1.25 2.33 1.27 Benchmark 9 1.29 - 0.40 1. 67 1.00 2.20 1. 60 - 0.43 2.67 1.25 Benchmark 1.00 0.60 1. 89 1.00 2.00 1. 80 - 2.86 3.00 1.08

Health administrator gave the average equity score at +1.00. Most of the score not higher than +2.00, except benchmark 1 got +2.14. The lowest score was benchmark 8 (+0.86). Providers at provincial and district levels gave the average scores +0.60. The highest was the benchmarks 5 and 6 (+2.20), but the lowest was benchmark 9 (-0.40) because people commented that they had more limited choice under the UC scheme. Providers at subdistrict level gave higher score, +1.89, where benchmark 2 got the highest score (+2.22), and benchmark 5 the lowest (+1.22). The local governments gave the scores between +1.00 and +1.75 with low variations between items, and benchmark 9 had the lowest score (+1.00). Urban health civic group gave high average +2.00, but the highest was benchmark 9 (+2.20) because they had many choices, and benchmark 2 the lowest (+1.29). Rural health civic group gave the score close to the urban civic group +1.80, the highest score was for benchmark 8 (+2.56), the lowest was for benchmarks 2 and 9 (+1.60) because they commented on limited choice. Non-health civic group in urban area gave the negative scores for all benchmarks, averaged –2.86. The benchmark 1 got the lowest score (-2.22), while benchmark 9 got the highest but still negative (-0.43). They commented that the health equity situation was not acceptable from the experiences of health service uses and the UC scheme not really covered all diseases. Non-health civic in rural area gave rather high average score (+3.00). The highest was benchmark 9 (+2.67) and the lowest was for benchmark 5 (+1.00). Benchmark of Fairness 114

Details of equity scores are presented in the 3 dimensional surface graph (figure 7.14). It is clearer that non-health civic group in urban area gave negative scores for almost all items (range from 0.00 to – 2.00). For other groups, the lowest scores (range from -1.00 to +1.00) were found for environment (by local government, health and non-health civic groups), demo-graphic and economic situation (for most groups, economic situation had very low score –1.00 from providers at provincial and district levels, local government and civic groups), health resources distribution and workload to health resource (got low score from providers at provincial, district and sub-district levels). The continuity of care got the low score as well as the administrative efficiency, people participation, choice of service. The transparency in resource allocation and choice of services got the score lower than 0.00. The local governments gave low scores for benchmarks 8 and 9. The items of score higher than +3.00 were scattered.

Figure 7.14 Three-dimensional surface graph shows details of scores by 46 items by 8 focus group discussions in Songkhla 115 7. Equity in health in 10 provinces: Qualitative tool

8) Pattani

Pattani had 12 districts covered 1,940 sq.km. There were 594,367 population, the density was 306.3 people per sq.km. the 8th rank from top (not include Bangkok). Their income averaged 2,486 baht/month, the 48th rank. Population earning under 1,000 baht a month 1,000 were 29% of total, the 15th rank for the poorest. Provincial per capita income was 49,816 baht, at the 29th rank.

Most of Pattani was low land suitable for rice growing, especially along the bank of Pattani river. This river originated from Kalakiri mountain in Betong district. The river sediments formed the futile mangrove forest. Pattani had long beach of 116.4 km. Saiburi river was the main river in the east, but the west was mountainous and forest. About 80% of Pattani people were Muslim Malayu. They spoke the same accent as Malaysian in Kalantan. Writing language was Yawi. Moreover, there were Arabian Muslim and Indian in many districts. Chinese Thais were in commerce (36).

In terms of health, age-sex adjusted mortality rate was 5.98 per 1,000. The under 5 mortality was 2.64 per 1,000, the top 16th. Road traffic injury rate was 2,000 per 100,000, the 11th from bottom. Malnutrition rate was 7.9. Low birth weight was 5.6 not high. The coverage of complete antenatal care (4 visits) was low 67.9% (the bottom 3rd). Vaccination rate in 1996 was the lowest 47%. Clean drinking water was available to only 60.9% of households, the least 12th rank (from Basic Minimal Need database 1999). Household size in Pattani was the largest 4.8 persons per household. Dependency ratio was 0.62, the 3rd highest after Satun and Nakhon Sithammarat. By UNDP’ deprivation score, Pattani was the second highest deprived in education (after Narathiwas). The overall deprivation score was 0.750, highly deprived.

Health insurance coverage of Pattani was the top 15th highest. The medium quality insurance was 64% of the total insurance coverage (75% of total population). The ratio of doctor to population was 1:8,159, the 53rd rank. Bed to population ratio was 1:841, the 60th rank. The rate of new outpatient attendant was only 35% of population, the lowest 14th rank. Use of inpatient was 7.74%, the lowest 4th rank. There was accessibility problem especially access to public services was 1.86 visits per person per year, the 54th rank. The ratio of use at health centre to the hospital was 0.93, the top 26th rank. The ratio of admission at community hospital to provincial hospital was 1.47, the top 21st rank. Proportion of use at provincial hospital was only 13.8%. Benchmark of Fairness 116

Pattani had limited health resource distribution so the use rate was not high. There was 1 provincial hospital with 355 beds, and 11 community hospitals with 310 beds. Apart from the MOPH hospitals, there was only 1 public hospital (with 60 beds) and no private hospital, the choice of private health sector for outpatient service was low 12.8%, the lowest 10th rank and 0% for inpatient service.

Public health spending in Pattani was 689 baht per capita, the lowest 24th rank. The proportion of spending at health centre was 17.9%, the top 3rd rank. Spending at provincial hospital was 271 baht per capita, the lowest 13th rank. Discrepancy of spending at hospital to health centre was 2.2 times, the lowest 4th rank. Spending per hospital bed was 496,100 baht, the lowest 5th rank. Because of low total spending, the proportion on labour cost of provincial hospital was high 62.8%, the top 2nd rank after Satun. The low cost of provincial hospital was explained by low DRG relative weight 0.715, the lowest 19th rank. And the lowest 19th rank was also for unit cost. Cost per day was the lowest 5th rank. These amounted to low public health spending in Pattani. Finally death rate in provincial hospital was only 1.77% the lowest 10th rank. 1 Overall health status ( 0.11 ) 2 Specific health status 0.54 3 Coverage of health service ( 3.19 ) The scores from quantitative tool show that on 4 Environment ( 0.13 ) 5 Demographic condition ( 1.89 ) average Pattani got the clear positive scores 6 Economic status ( 0.28 ) (higher than +1), e.g. choice for public service 7 Resource deprivation ( 1.36 ) 8 Coverage of health insurance 0.31 (about 55%). The items with negative scores 9 Ratio of health insurance ( 0.19 ) (lower than –1) were for coverage of service, 10 Household health expenditure 0.32 11 Distribution of resource ( 0.69 ) demographic factor, deprivation, length of stay 12 Workload of health service 0.26 13 Institutional seeking care ( 0.62 ) (at community and provincial hospitals). The 14 Utilization rate ( 0.67 ) overall health equity score by 30 indicators 15 Proportion of primary care utilization 0.47 nd 16 Continuity of care 0.02 was -5.91, the lowest 72 rank 17 Comprehensiveness of care ( 0.20 ) 18 Difference of health expenditure ( 0.26 ) BM9 BM1 1 19 Average length of stay ( 1.11 ) 30 2 29 4 3 20 Bed utilization ( 0.50 ) 28 4 27 2 5 21 Admission pattern and case mix ( 0.15 ) BM7 26 - 6 22 Unit cost of health service 0.86 25 7 (2) 23 Quality of care 0.18 24 8 (4) 24 Overall health care cost 0.30 23 9 BM2 22 10 25 Health care cost per service 0.91 21 11 26 Proportion of primary care cost 0.63 BM6 20 12 27 Proportion of administrative cost 0.07 19 13 18 14 17 15 BM3 28 Real cost per expected cost 0.54 BM5 16 29 Public health service seeking care 1.13 BM4 30 Private per public seeking care ( 0.97 )

Table 7.15 Average scores by 30 indicators in Pattani Figure 7.15 Scores by 30 indicators in Pattani 117 7. Equity in health in 10 provinces: Qualitative tool

Comments on equity of health status and health related factors Health problem in Pattani started from preventable communicable disease, e.g. tetanus neonatorum which found in a pregnant Pattani woman working elswhere came back at 8 month-pregnancy, had inadequate tetanus vaccination. Some pregnant women were malnourished, anaemic and faced the risk of dying from postpartum haemorrhage. Infant had vitamin A deficiency because it was fed with condensed-sweetened milk. Malnutrition in children existed but decreasing. Increasing problems were diabetes, hypertension and AIDS (or “Yakeawa’ in Malayu, means old disease). Most Pattani people did not prefer night life. AIDS sufferers were those from other provinces working in fishery or worked in Malaysia. Muslim Pattani preferred to have big family with 3-4 sons and daughters. Sudden deaths were found in heart disease or stroke (or “Retaksae’). More survivors were hemiplegic. Home delivery was prevalent in Pattani (about 40% of total) with assistance from traditional birth attendant (or ‘Tohbedae”) because people preferred to have relatives around and after delivery they could bury placenta according to their beliefs. People still attended traditional healer for stroke problem claiming that the condition could be cured.

Other health related factors were quite unchanged, e.g. economic status. Some got worse, e.g. fishermen had lower income because of low sale and price drop. Construction business slowed down. Rich men were limited in number. Population density was high because Pattani was a small province with big household size (about 5). Some were homeless. Urban problems increased. Congested families along the sea caused pollution. Bad smell came from factories especially around Pattni gulf. Rural area had fewer problems, no pollution and low pesticide use. The majority Muslim (80% of total population) had good lifestyles, abstained from alcohol drinking, low injury and violence. They admired that Pattani was peaceful, culture rich with few night lives, so a few adolescent exposed to night life. Pattani people had high tolerance because they accepted what god had given to them. There were not many complaints filed. Suicide problem was low because it was forbidden. Muslim believed that they would not take self- interest advantages. They denied interest from money deposit. Education opportunity was better. More people were aware of the benefit of education. Civic groups became strong, e.g. environment

Comments on equity of health finance and delivery Health insurance coverage reached the high level. People explained that they need not pay cash while they were ill if they had health insurance. However, some groups were the uncovered especially newborns had not been registered since birth because they afraid of giving details that fathers were Benchmark of Fairness 118

Malaysian, or the mothers had no house register. Some people preferred the health card to UC card. Some people had self-treatment before going to traditional healer or private clinic. For service provision, health administrators commented that people preferred self-treatment as the first choice so the hospitals were not too crowded. If the case was too serious, Pattani hospital would refer to regional hospitals in Yala or Hat Yai. There were private clinics in 4 districts out of 12, no private hospital. People used health centre before coming to hospital. There were few complaints on verbal communication of personnel, perhaps because they did not fully understand Thai. People in urban area were more accessible to caesarean section. Health providers commented on more personnel and better access. But higher workload may compromise quality of care. They prescribed fewer medicines, and discharge patients earlier. Overall the quality of care was better. No overuse of high-tech care (no CT- scanner). Local governments commented on the use at health centres, more people used then the cost at health centre was high. Quality of medicine varied to payment. Public services were inequitable. People had limited choices for care, they dare not file their complaints. Some people bypassed to Yala for medical care. They suggested on decentralization to local government, especially budget for health promotion and disease prevention, and higher accountability.

Health civic group in urban area commented on overcrowded facilities. Patient’s rights were not well observed, e.g. the noise from television programme filled up the room while patients reading Qoran, or dying. Services provided to civil servants and ordinary people were unequal. Overall standard of care by health personnel was not acceptable, especially the verbal communication. People went to clinics. After 2 primary care units were set up, people had more choice. In general the quality of care became better. Other civic group commented on the non-prioritized services (patients with mild condition, attending for incision and drainage, got better care than asthmatic patients). Treatments had complication risk related to death (a patient had appendectomy twice, soon died). Delayed diagnosis, mis-diagnosis, bad communication were a few complaints. People went to Yala for medical care. Health civic group in rural area commented that people used more services at health centre, they delivered baby in hospital because health volunteers suggested pregnant mothers. However, they commented that the fathers of the babies were not allowed to enter labour room to perform religious procedure. They also wanted to take placenta to be buried. Some health personnel were asked to attend birth at home. Some services at some hospitals erred (prescription and dispensing errors, mismatch of blood group). They commented on civil servant had better care than general population. Health volunteers asked for higher privileged, they wanted freer choice. Civic group should be consulted to improve quality of care, e.g. the health council should have lay people as a member of committee. There should be a programme to upgrade or 119 7. Equity in health in 10 provinces: Qualitative tool legalize traditional healer because people still seek care from them. They suggested that there should be more health personnel and volunteers to take care of people attending religious rite in Mecca. Overall health equity score from focus group discussion The overall equity score for Pattani from 8 focus group discussion averaged at +1.95. The highest score was for benchmark 4 (comprehensive package and tiering) and benchmark 8 (Accountability and community participation) got +1.98. The lowest score was benchmark 3 (non-financial barrier to health care) +1.14. Health civic group in urban area gave the highest score (+3.57), but the local governments gave the lowest score (+0.67).

Table 7.16 Health equity score by 9 benchmarks from 8 focus group discussions in Pattani

Health Provider Provider Local Civil society Civil society Civil society Civil society Overall Manager Prov. Dist Sub-dist Government Health (urban) Health (rural other (urban) other (rural) average Benchmark 1 0.90 2.40 2 .88 - 1.00 3 .80 1. 89 2.50 0.83 1.81 Benchmark 2 1.36 3.33 3 .38 - - 2 .00 1. 60 1.17 1.33 1.45 Benchmark 3 0.45 2.43 3 .00 - - 0 .33 0. 89 0.33 1.33 1.14 Benchmark 4 1.55 2.67 2 .63 - 0.33 3 .43 1. 60 2.00 1.33 1.98 Benchmark 5 0.36 2.83 2 .63 - 0.67 3 .40 1. 60 2.33 1.00 1.69 Benchmark 6 1.18 2.57 2 .44 0.33 2 .57 1. 30 2.67 1.17 1.83 Benchmark 7 1.18 2.00 1 .56 1.52 Benchmark 8 0.67 2.00 1 .78 1.67 3 .86 1. 50 3.67 1.00 1.98 Benchmark 9 1.27 2.29 1 .67 1.67 3 .17 2. 43 2.17 1.00 1.91 Benchmark 1.45 2.43 2 .11 0.67 3 .57 1. 71 2.00 1.00 1.95

Health administrator gave the average score of +1.45 rather low for the benchmarks 5,3 and 8 (+0.36, +0.45 $%& +0.67 respectively), because the problems on equitable financing, access to care and people participation existed. The highest score was for benchmark 4 (+1.55). Providers at provincial, district and sub-district levels gave higher score, +2.43 (provincial level), the highest for benchmark 2 (+3.33 provincial level and +3.38 sub-district level). The benchmarks 7 got the lowest score (+2.00 for provincial level and +1.56 for sub-district level). The local governments gave negative scores for benchmarks 1, 4 and 5 (-1.00, -0.33 and –0.66), resulted in low average score (0.67). Health civic group in urban area gave rather high score (+3.57), but some negative value for benchmarks 2 and 3 (-2.00 and –0.33), suggested that there were problems on access to care. They gave highest score for benchmark 8 (+3.86). Health civic group in rural area and non-health civic group in urban area gave the lowest score for benchmark 3 (0.89 and 0.33) because there were problems on inadequate health resources and inequitable distribution as well as non-financial barrier to health care (factors related to language, religion and culture of local people). Health civic group in rural area gave the highest score for benchmark 9 Benchmark of Fairness 120

(+2.43). Non-health civic group in urban area gave the highest score for benchmark 8 (+3.67). Non- health civic group in rural area gave almost the same scores of +1.00 for all benchmarks.

Details of health equity scores are presented in figure 7.16 by the 3- dimensional surface graph. Overall health status and by specific group got rather low scores (-1.00 to+1.00) from administrators, providers and local governments. The coverage of services also got low score from administrator. Environment, demographic and economic problems got low scores from almost all groups. Economic issue got lower than 0.00 from all. Intersectoral collaboration got negative score from local governments. Comprehensive coverage and burden on health spending got negative score from health and non- health civic groups in urban area. The benchmarks 3 got negative score from health and non-health civic groups in rural and urban areas. Moreover, some of civic groups gave rather low score for administrative efficiency and quality of services. Health administrators gave low score for benchmark 8 and providers at sub-district level gave low score for benchmark 9 especially the choice for private health sector. The area with high score (+3.00) was the health insurance coverage.

Figure 7.16 3-dimensional surface graph for 46 items from 8 focus group discussion in Pattani 121 7. Equity in health in 10 provinces: Qualitative tool

9) Ayuthaya

Ayuthaya was once the capital of Thailand. Ayuthya had 16 districts covered 2,556 sq.km. There were 721,641 population, the density was 282.3 persons per sq.km. the 10th dense province. The average income was 3,411 baht/month, the 19th rank and gross provincial product was 102,018 baht per capita, the 10th rank. Household with income lower than 1,000 baht a month was only 4.9% of total, the lowest 16th rank.

Ayuthaya was an inland island province because 3 rivers (Chao Phraya, Lopburi and Pasak rivers) surrounded it. Apart from rivers, there were man-made canals adjoining the rivers. The rivers and canals networks made Ayuthaya the centre of inland water transport. Almost 92% of the province area were used for rice farm. Fishery was around the island province. Ayuthaya people were Thai, Muslims, Chinese, Mon (Myanmar), Vietnamese and Laos. Main problems of Ayuthaya were land ownership, inadequate labour force, post-fertile land, pollution from industry leading to pollution(37)(38).

In terms of health status, the age-sex adjusted death rate for Ayuthaya was 4.84 per 1,000, the lowest 7th rank. Malnutrition rate in the under 5 was 3.5%, the lowest 11th rank. Low birth weight rate was 5.6%, the 17th rank from the bottom. But the road traffic injury was 4,482 per 100,000 the 2nd highest after Phuket. Dependency ratio was 0.5, close to the country average. Overall deprivation and education deprivation for Ayuthaya was the 3rd lowest. In summary, health status and other health related status for Ayuthaya was rather good except some aspects, e.g. road traffic injury.

Health insurance coverage especially the better schemes (CSMBS, SSS and private insurance) was as high as 23.2%, the top 4th rank after Samut Prakan, Nonthaburi and Pathumthani. The coverage for the intermediate scheme was 50%, the 59th rank. The overall coverage was 73%, the 21st rank. Doctor to population ratio was 1:4,870 and bed to population ratio was 1:544, the 31st and the 39th rank respectively. There were 4 hospitals of 410 beds not under the MOPH, 3 of them were privates hospitals with 400 beds. Ayuthaya Hospital was recently upgraded to be a regional hospital with 396 beds. There was 1 more provincial hospital with 160 beds and 14 community hospitals with 380 beds. The use rate for outpatient services (new cases) was 53%, the 20th rank. Use rate for outpatient services in public sector was 2.57 visits per person per year, the 13th rank. Use rate for inpatient services was 12%, the top 21st rank. Benchmark of Fairness 122

Choices of services in Ayuthaya was better than average, use rate in private sector was 33.5% for outpatient service (the top 11th rank), and 26.8% for inpatient services (the top 19th rank). The ratio of uses in private sector to public sector was high at 0.82, the 13th rank. The uses at primary care was close to country average, i.e. the ratio of uses at health centre to hospital was the 30th rank, and the ratio of admission at community to provincial hospitals was the 33rd rank. In terms of continuity of care, the frequency of service use was 2.15 visits per person per year, the lowest rank. Referral rate from community hospital was 1.13%, the lowest 4th rank. Length of stay at community hospitals was 2.42 days, the lowest 6th rank. Length of stay at provincial hospitals was longer at 4.4 days, the lowest 19th rank. This was according to the average relative weight of inpatients at provincial hospital was 0.692, the lowest 13th rank, but the cost per day at provincial hospital was high at 1,159 baht per day, the top 10th rank.

Health spending in public sector was 992 baht per person. Spending at health centre was 14.7%, the top 20th rank. Spending per bed at provincial hospital was high about 843,500 baht, the top 6th rank. Proportion of labour cost was only 43.4% of 1 Overall health status 0.29 th 2 Specific health status ( 0.16 ) total, the lowest 15 rank. 3 Coverage of health service 0.07 4 Environment 0.83 5 Demographic condition ( 0.21 ) Health equity scores from quantitative tool 6 Economic status 0.57 show that Ayuthaya got the positive average 7 Resource deprivation 1.16 8 Coverage of health insurance 0.54 score (higher than +1), e.g. score for 9 Ratio of health insurance 0.85 10 Household health expenditure ( 0.59 ) deprivation. The items with medium positive 11 Distribution of resource 0.39 scores were the use rate, length of stay, 12 Workload of health service 0.61 13 Institutional seeking care 0.55 choice in private sector to public sector. The 14 Utilization rate 0.72 overall score for 30 indicators was +4.65, the 15 Proportion of primary care utilization 0.08 16 Continuity of care ( 0.21 ) th 10 rank. 17 Comprehensiveness of care ( 0.88 ) 18 Difference of health expenditure 0.42 BM9 1 BM1 19 Average length of stay 0.74 30 2 20 Bed utilization 0.24 29 4 3 28 4 21 Admission pattern and case mix ( 0.27 ) 27 2 5 BM7 26 - 6 22 Unit cost of health service ( 0.67 ) 25 7 23 Quality of care ( 0.26 ) (2) 24 8 24 Overall health care cost ( 0.47 ) (4) 23 9 BM2 25 Health care cost per service ( 0.78 ) 22 10 26 Proportion of primary care cost 0.38 21 11 BM6 20 12 27 Proportion of administrative cost 0.55 19 13 18 14 28 Real cost per expected cost ( 0.04 ) 17 15 BM3 29 Public health service seeking care ( 0.22 ) BM5 16 BM4 30 Private per public seeking care 0.50

Figure 7.17 Graph of scores for 30 indicators in Ayuthaya Table 7.17 Average scores by 30 indicator in Ayuthaya 123 7. Equity in health in 10 provinces: Qualitative tool

Comments on equity of health status and related issues Road traffic injury in Ayuthaya was a decreasing trend. Big casualties were 1-2 times a year. Motorcycle was the main causes (about 75% of road traffic injury) because only 30% motorcyclists put on safety helmet. Bang Pa-in had 2 industrial estates with 10-lane road filled with 10-wheel trucks, considered a high risk of road traffic injury. Chronic diseases were more prevalent, e.g. diabetes, kidney, cancer (cervical cancer), AIDS, especially among migrant workers. Entertainment places with casual sex workers led to reproductive health problems among workers. Death from opportunistic infection occurred every 3- 4 days. Many of them died at home, and they may be specified with other causes of death rather than AIDS. Suicide, leptospirosis, dengue haemorrhagic fever, drug addiction were the prevalent problems. People became aware of health problems, setting up health club with exercise, Tai-Chi and jockeying.

Other health-related factors: our communities became overcrowded with more slums, and migrant construction worker. Because of overcrowded, people invaded the ancient town. Urbanization was eminent, there were more factories with pollution, e.g. tapioca, paper, fertilizer, concrete, beer factories. Rivers became more polluted with bad smell. Sanitation problems were higher especially in the market. The modern way of life brought out adolescent from their houses leading to sex problems, AIDS and drug addiction. Most people complained on worse economy, observing from closing down of factories (workers reduced from 30,000 to 10,000). Gap between the rich and poor was wide. The poor used the market or railways as their homes. Ayuthaya was houses of many races and culture. Muslims stayed in Muang and Ladbualuang. There were lots of foreign workers (only 3,000 were on the registered list).

Comments on equity in health finance and delivery In terms of finance, the coverage of insurance was increasing. Before the 30 baht scheme, about 40% of population were uninsured. About 13% were covered by the SSS, 7% by the CSMBS and 80% by the UC scheme. People used more health services when they were insured. Only 50% followed the referral line as mentioned in the card. The rest 50% used services at private clinic as before. Services at public sector, health centre, community hospitals and provincial hospitals, should have been expanded while services at private health sector remained unchanged. Most commented that tax should be the main source of finance and compulsory contribution to the insurance fund should be imposed to make people more aware that they also contributed to the scheme. The services provided to different schemes were thought inequitable. Some preferred the 500 baht health card to the 30 baht because, it covered the whole family and need not pay 30 baht for every prescription. Benchmark of Fairness 124

Health administrators commented that the number of doctors should be adequate but had continuity problem especially at community hospital. There should be 3 types of doctors: specialist, family doctor and public health doctor. Community hospital in populated district should be upgraded to provincial hospital, while the hospital in less populated district should have only primary care services. Big health centre in urban area should have doctor, if not, nurses could do the job. They should be trained in applying social skills. Specialists should not only work at provincial hospital, but should be placed in some community hospitals, e.g. Bang Pa-in. Quota on number of doctors in a district should be used to mobilize doctor, not only financing mechanism. After the 30 baht scheme people used 10-20% more services. Health centre expanded home visiting service to hard to reach area. Medicine standards were the same for all providers. Home health care was the strategy to reduce length of stay. People filed fewer complaints, while they were more open in expressing ideas. The development of primary care was not quite ready. People should be given more choice, e.g. choice for primary level and for secondary level. Health providers commented on more number of users, and people’s preference for specialists rather than family physicians or newly graduated doctors. The rich travelled to Bangkok for treatment. Providers had limited experiences on the expanding home visit services. There were no problems on referral since all public providers reached common agreements in paying for referral. Providers defended that they prescribed indifferent medicine for all insurance schemes. Only the rich were afraid of receiving substandard medicine. Inefficiency in administration was seen in the allocation of capital budget. The upgrading of health facility was inappropriate since some areas seem to have inadequate health resources. There was a scheme of provincial purchase of drug. 2 CT-scanners were in private hospitals. The local governments commented on congested health services, while there were high number of primary care units, health centre and provincial hospitals. They commented on setting up specialized hospital. Though the services became better but there were inequities on queuing and dispensing.

The urban civic group commented on inadequate public health resources but over supply in private services. The rich preferred to go to private sector, but private hospitals seem to lose income. They admitted that primary care unit became better, more accessible and more home visit, but poor public relations. Four centres in urban area had more than 50 visits a day, providing traditional medicine. Hospitals delayed diagnosis or mis-diagnosed (ruptured appendicitis after 3 day treatment as peptic ulcer). Hospitals discharged patients too early. Sometimes lack of medicine and supply drove the patients to seek care elsewhere. Civic group in rural area commented that people were less confident in doctors at community hospitals compared with provincial hospital. Doctors and beds were lacking. Quality of care at community hospitals was better. Primary care unit could solve quality problems. 125 7. Equity in health in 10 provinces: Qualitative tool

Overall health equity score from focus group discussion

Overall health equity score for Ayuthaya by 8 focus group discussion was +2.09. The highest score was for benchmark 4 (comprehensiveness and tiering) +2.45. The lowest score was benchmark 7 (Administrative efficiency) +1.63. The group that gave the highest score was non-health civic group in urban area (+2.88), the lowest score was health providers at sub-district level (+1.40).

Table 7.18 Equity scores of 9 benchmarks by 8 focus group discussions in Ayuthaya Health Provider Provider Local Civil society Civil society Civil society Civil society Overall Manager Prov. Dist Sub-dist Government Health (urban) Health (rural other (urban) other (rural) average Benchmark 1 2.13 2.00 1 .67 2.17 2. 64 2. 67 3.50 2.33 2.38 Benchmark 2 2.38 1.55 2 .40 2.67 2. 18 3. 00 - 0.25 1.00 1.85 Benchmark 3 2.50 1.45 2 .40 2.17 1. 80 2. 44 2.38 2.40 2.17 Benchmark 4 2.88 2.18 2 .00 2.33 2. 10 3. 78 2.50 2.00 2.45 Benchmark 5 1.75 1.91 2 .80 2.33 1. 11 3. 89 1.38 2.30 2.20 Benchmark 6 2.50 2.09 1 .90 2.17 1. 90 2. 56 3.29 1.50 2.18 Benchmark 7 2.67 1.64 1 .00 1.63 Benchmark 8 2.75 1.70 1 .80 1.50 2. 20 2. 78 2.00 0.60 1.91 Benchmark 9 2.50 1.91 1 .30 1.50 1. 82 3. 29 2.38 1.20 1.93 Benchmark 2.38 1.64 1 .40 1.50 1. 89 2. 86 2.88 2.40 2.09

Health administrators gave average score higher than +2.00, at score of +2.38. The benchmark 5 had low score +1.75 because people still paid out-of-pocket for health even though they were covered by insurance. The gap between the rich and poor was observed. The highest score was for benchmark 4 (+2.88). Providers at provincial and district level gave average score at +1.64, the highest was for benchmark 4 (+2.18), because health administrator and providers had put effort in continuous development of primary care. Providers at sub-district level gave average score of +1.40, the highest was for benchmark 5 (+2.80) and the lowest for benchmark 7 (+1.00). The local government gave rather low scores for benchmarks 8 and 9 (+1.50). The highest score was for benchmark 2 (+2.67). Health civic group in urban area gave the highest score for benchmark 1 (+2.64) and the lowest score for benchmark 5 (+1.11), therefore the average score was +1.89. Health civic group in urban area gave rather high score +2.86, the highest score was benchmark 5 (+3.89) rather the same as benchmark 4 (+3.78). Non- health civic group in rural areas gave the average score of +2.88, the highest was for benchmark 1 (+3.50), and the lowest was for benchmark 2 (-0.25) because there were problems on spending on health rather than the problems of health insurance. Non-health civic group in rural area gave average score of +2.40, the highest score was for benchmark 3 (+2.40) and the lowest for benchmark 8 (+0.60). Benchmark of Fairness 126

Details of equity scores in Ayuthaya were shown in a 3-dimensional surface graph (figure 7.18). Lower scores were found in environmental, demographic and economic conditions among health administrators, providers, health and non-health civic groups in urban area. Providers at sub-district level gave score lower than –2.00 for environmental and economic conditions. Non-health civic group in urban area gave low score lower than –1.00 for demographic and economic conditions, and score of lower than 0.000 for resource inadequacy. Providers at provincial and district levels gave rather low score (0.00 to +1.00) for resource distribution. Providers gave low score for people participation and choice of primary and specialized care and autonomy of providers. Issues with high scores (higher than +3.00) included coverage of services, coverage of insurance schemes, benefit package and benchmark 4 (comprehensiveness and continuity of care) and uses of primary care.

Figure 7.18 3-dimensional surface graph by 46 items 46 from 8 focus group discussion in Ayuthaya 127 7. Equity in health in 10 provinces: Qualitative tool

10) Ratchaburi

Ratchaburi had 9 districts with 788,525 population in 5,196 sq.km. The population density was 151.7 persons per sq.km., the 21st densely populated province. Average income was 3,454 baht a month, the top 18th rank. There were only 2.8% of household with income lower than 1,000 baht a month, the lowest 7th rank. Gross provincial product was 59,914 baht per person, the top 19th rank.

Ratchaburi is on the Tha Chin River. The futile land was good for agriculture. The Mae Klong River is another river feeding Ratchaburi people over 100 years. They lived on rice growing, fruit orchard, husbandry and fishery. Small industry included pottery. The ethnic groups were diverse, central Thai, Chinese, Mon, Lawa and Karen. Each had long time own culture. Ratchaburi was faced with environmental pollution, water and air, because of substandard control of factories(37).

Age-sex standardized mortality rate for Ratchaburi was 5.84 per 1,000. Under 5 mortality was high 3.44 per 1,000, the top 4th rank. Road traffic injury rate was 3,337 per 100,000 the top 16th rank. Malnutrition among under 5 was 5.38%, and low birth weight rate was 6.98%. Dependency ratio was 0.46, the lowest 25th rank. Ratchaburi was not a deprived province, deprivation index was 0.185, the lowest 11th rank. Health services were also not deprived.

Health insurance coverage was the 52nd rank. The coverage of good schemes was high 15.5%, the top 16th rank. Doctor to population ratio was 1:3,124, the 11th rank. Hospital bed to population was the top 6th rank, 1 bed was for 301 population. There were 16 hospitals with 646 beds not belonged to the MOPH, 15 of them were private. Under the MOPH, one regional hospital had 717 beds, 3 provincial hospitals had 950 beds and 6 community hospitals had only 270 beds. About 41.8% of Ratchaburi people used public services when they were ill, 26.5% used private sector for outpatient services and 22.7% for inpatient services. The choice of using private to public service was the top 24th rank, that means they had more choice. The overall use rate for new outpatient cases was 69%, the top 5th rank, i.e. 69% of people ever used the service at least once in the year. Overall use rate for inpatient service was 23.9%, the top 2nd rank after Trad. Use rate for outpatient services in public sector was 2.09 visits per person per year the top 39th rank, admission rate was 13%, the top 16th rank. Benchmark of Fairness 128

Ratio of uses at health centre to hospital was low (0.6), at the 56th rank. Ratio of admission in community hospital to provincial hospital was the lowest, 0.21. Proportion of outpatients at provincial hospitals was 44.6% suggested the inappropriate uses at big hospitals. This caused by supply side dominated by provincial hospitals (total beds were 6 times of the beds in community hospitals). The uses at hospital was 3.43 visits per person per year, the top 10th rank. Referral rate from community hospital was 2.62%, the top 8th rank. Length of stay in provincial hospitals was 5.5 days, the 16th longest stay. Occupancy rate was 71.5%, the lowest 6th rank. Bed turnover rate was the lowest 3rd rank because a bed could serve a few patients. High use rates and long stays but low occupancy and low bed turnover suggested that there were oversupply of hospital beds in Ratchaburi.

The average cost per inpatient at provincial 1 Overall health status ( 0.71) hospital was rather high 6,313 baht per case, 2 Specific health status ( 0.07) 3 Coverage of health service ( 0.09) th the top 8 rank. The spending of provincial 4 Environment 0.27 hospitals was high 1,509 baht per person, the 5 Demographic condition 0.11 2nd highest after Singburi. Proportion of 6 Economic status 0.36 7 Resource deprivation 0.81 spending at health centre was low 3.8%, the 8 Coverage of health insurance ( 0.23) rd lowest 3 rank. But, spending per provincial 9 Ratio of health insurance ( 0.02) hospital bed was not high. 10 Household health expenditure ( 0.09) 11 Distribution of resource 1.12 12 Workload of health service 0.30 The health equity scores from quantitative tool 13 Institutional seeking care 1.33 displayed positive sign for resource 14 Utilization rate 0.41 distribution and use of service. Negative 15 Proportion of primary care utilization ( 1.50) 16 Continuity of care ( 0.05) scores were for proportion of use at primary 17 Comprehensiveness of care ( 0.18) care, unit cost, spending at primary care. The 18 Difference of health expenditure 0.57 overall equity score from 30 indicators was - 19 Average length of stay ( 0.38) 3.83, at the 64th rank. 20 Bed utilization ( 0.22) 21 Admission pattern and case mix ( 0.38)

BM9 22 Unit cost of health service ( 1.29) 1 BM1 30 2 23 Quality of care 0.44 29 4 3 28 4 27 2 5 24 Overall health care cost ( 0.89) BM7 26 - 6 25 7 25 Health care cost per service ( 0.62) (2) 24 8 (4) 26 Proportion of primary care cost ( 1.85) 23 9 BM2 22 10 27 Proportion of administrative cost 0.30 21 11 BM6 20 12 28 Real cost per expected cost ( 1.52) 19 13 18 14 17 15 BM3 29 Public health service seeking care ( 0.16) BM5 16 BM4 30 Private per public seeking care 0.13

Figure 7.19 Graph of score by 30 indicators in Ratchaburi Table 7.19 Average scores by 30 indicators in Ratchaburi 129 7. Equity in health in 10 provinces: Qualitative tool

Comments on equity of health status and health-related factors Health problems were seen in injury, AIDS and chronic diseases (e.g. diabetes, hypertension, cancer and kidney diseases). Suicide and drug addict were increasing. AIDS was caused by drug addiction, run out of money and consequently fell into sex business. Some caused by family problem and economic hardship. Next to the border, elephantiasis malaria and polio were endemic among immigrants. Burmese workers in Suan Phueng lacked health services. Allergic problem caused by lignite combustion in electric plant and cistern factory. Alcoholic cirrhosis was a non communicable disease problem. Infectious diseases and malnutrition in children were decreasing. The elderly health became better because they joined the club for exercise at the primary care unit.

Environmental problems contributed to people’s health. Pollution from factory and electric plant caused polluted and undrinkable rain-water. Lime and sugar cane factories drained pollution into the rivers, made Mae Klong, the main river of Ratchaburi polluted. Rapid urbanization caused accumulation of refuse, air pollution and congested community. Occupational diseases were found in the form of lung diseases and pesticide uses. Economic conditions did not improve or became a bit worse. The unemployed rose. However, the opportunity for education was better. Prominent problems could be observed in the depletion of natural resources, many kinds of fish became endangered. Prawns and shells reduced in number, because pollution from factory. These health problems created the civic group gathering. The province had 5 common strategies to tackle the problems. First: Strategy Mae Klong River, second: public health strategy, the elderly strategy, strategic village public addressing system and leader for change strategy. Problems of Mae Klong River were tackled by 3 neighbouring provinces: Ratchaburi, Samut Songkram and Kanchanaburi. They set the surveillance system for water quality monitoring. The cooperation between public sector and people sector existed. This cooperation supported the idea of the “people’s state”

Comments on equity in health finance and delivery Health insurance coverage became increased from the coverage of the 30 baht scheme. This scheme did not covered some disease package, e.g. renal failure, AIDS and suicide. People therefore paid the fee out-of their pocket. People still had limited choice. They thought the 30 baht scheme made people paid more than what they paid in the 500 baht health card scheme. For example, if people under the 30 baht scheme visited the doctor twice a month, the whole year they would pay higher than 500 baht. Health providers were aware of limited budget, instead of getting 700 million baht, the capitation budget allocated only 300 million baht. 7 hospitals had to submit the proposal to get grant from contingency fund. Benchmark of Fairness 130

Health administrator commented on coverage of health services. Length of stay in hospital was shorter. But the spending on civil servant schemes was more expensive while on the 30 baht scheme was cheaper. Quality of health facility became better but the ISO may not reflect the continuous quality improvement. Health providers commented that most services provided were for fixing health as usual. People were more accessible. Some preferred private clinic because of prompt service though more expensive. They commented on inadequate health personnel in rural area (in certain community hospitals e.g. Suan Phueng). Provincial hospitals had too many doctors. Referral system would disrupt if primary care stop referring patients because primary care did not want to pay for referrals. If money followed the referral too much there would be little money left for allocating to health centre. Home visit programme helped improve communication between patients and personnel. Inequity was observed in drug dispensed to patients. Poor patients were less accessible to some services as compared with the rich, e.g. CT scanning, injury care, special case, VIP card to classify privileged care. This considered unfair because the special cases also consumed public resources. The rich were less likely using the 30 baht scheme as they continued to use private health sector. Length of stay for the 30 baht patients was shorter, but for the CSMBS longer. They were concerned about the drop of quality of service if there was strong budget constraints. People were not free to choose their preferred health facility. The local governments commented on the quality of care for the 30 baht scheme. In general they admitted that the quality of service was better, with adequate personnel (at provincial hospitals), but no specialists in community hospitals. Problem in private hospital was too expensive the care, in public hospital was long waiting time. Civic group in urban area commented on limited choice of services. They suggested that hospitals should have the same standards. Public facilities were overcrowded, substandard, delayed referrals. Sometimes they had poor decision (a patient fell from stairs, the relatives wanted the case admitted in the hospital but the hospital refused, later died of fracture neck; a pregnant mother died with baby in uterus). Service providers were not careful concerning patient’s rights. Patients preferred to be examined by specialists, leading to congested provincial hospitals. They commented on different drug quality prescribed but overall quality was better. Civic group in rural area commented that people preferred provincial hospitals to community hospitals because of specialists. Many patients came from other provinces, e.g. Petchaburi, Prachuabkirikan amd Kanchanaburi. The problems in hospitals were long waiting time, specialists did not communicate with each other, not the same as specialist in private hospitals Overall quality was better, personnel were good, people were able to express their ideas through the meeting between volunteers. They suggested that primary health care centre should be improved and uses of herbal medicine should be promoted. 131 7. Equity in health in 10 provinces: Qualitative tool

Overall health equity scores from focus group discussion Overall health equity score by 8 focus group discussions averaged at +2.03. The highest was for benchmark 6 (efficiency and quality of care) +2.79. The lowest score was benchmark 9 (autonomy of patients and providers) +1.53. Health civic group in urban area gave the highest average score (+4.00), while non-health civic group gave the lowest score (-0.75).

Table 7.20 Overall scores of 9 benchmarks by 8 focus group discussion from Ratchaburi Health Provider Provider Local Civil society Civil society Civil society Civil society Overall Manager Prov. Dist Sub-dist Government Health (urban) Health (rural other (urban) other (rural) average Benchmark 1 2.36 1.14 2 .33 2.17 3 .40 2 .57 0.86 2.86 2.19 Benchmark 2 2.45 2.29 2 .78 1.67 3 .60 2 .57 0.25 2.00 2.16 Benchmark 3 2.36 2.14 1 .90 2.80 3 .40 3 .43 0.25 1.88 2.16 Benchmark 4 3.00 2.14 2 .20 3.67 4 .00 3 .57 0.13 2.50 2.55 Benchmark 5 2.00 2.43 2 .40 3.00 3 .60 3 .57 1.13 2.75 2.50 Benchmark 6 3.45 2.29 2 .70 3.33 4 .20 4 .00 0.50 2.38 2.79 Benchmark 7 3.09 1.29 1 .60 2.11 Benchmark 8 3.00 2.00 2 .00 3.33 3 .60 3 .71 1.50 2.25 2.60 Benchmark 9 2.36 - 0.14 0 .89 2.80 3 .75 2 .00 - 0.25 2.00 1.53 Benchmark 2.64 1.43 1 .67 2.80 4 .00 3 .71 - 0.75 2.00 2.03

Health administrator gave overall high score more than +2.00. Highest score of +3.45 was for benchmark 6, and the lowest was +2.00 for benchmark 5. Providers at all levels gave lower score than the health administrators. Providers at provincial and district levels gave average score of +1.43, the highest score for benchmark 5 (+2.43) followed by benchmarks 2 and 6 (+2.29). The lowest score was for benchmark 9 (-0.14) because they saw people did not have adequate choice. Providers at sub-district level also gave lowest score for this benchmark (+0.89), but higher score for benchmark 2 (+2.78) and higher for overall average +1.67. The local governments gave rather high score, the average was +2.80. The highest score was for benchmark 4 (+3.67) and the lowest for benchmark 2 (+1.67) because they realized high burden on health expenditure in spite of having health insurance. Civic groups in urban and rural areas gave very high score. Health civic group in urban area gave average score of +4.00, while health civic groups in rural areas gave +3.71. The lowest score among health civic group in rural area was for benchmark 9 (+2.00). Non-health civic group in urban area gave rather low score, the average was negative (–0.75). The highest score was for benchmark 8 (only +1.13) and the lowest score was for benchmark 9 (–0.25). Non-health civic group in rural area gave moderate, positive score, the average was +2.00, the highest was for benchmark 1 (+2.86). Benchmark of Fairness 132

Details of health equity scores are presented in figure 7.20 in a 3- dimensional surface graph. Low scores were observed in environmental, demographic and economic conditions (score from -2.00 to +1.00) given by providers at all levels, health civic group in rural area, non-health civic group in urban area. The non-health group gave almost all low scores (lower than 0.00) especially for continuity of care, transparency in resource allocation, patient autonomy and choice of use at primary care. Health administrator gave low score for unit cost. Providers gave low score for total cost of service and proportion of spending at primary care, choice of use at primary care and specialist care. Providers at provincial and district level gave low score, lower than –1.00. The areas with high scores +3.00 were found in coverage of service, opportunity for education, coverage of health insurance, and other factors from health civic groups in urban and rural areas for benchmark 3, 4, 5, 6 and 8.

Figure 7.20 3-dimensional surface graph of score by 46 itmes from 8 focus group discussions in Ratchaburi 133 7. Equity in Health in 10 provinces: Quantitative tool

7.2. Overall scores for equity by qualitative technique

The equity scores for 10 provinces were averaged between +1.08 and +2.25, implying that most people were aware of the changes in terms of equity, and most valued slightly and moderately better. The province with the highest score was Nakhon Ratchasima (+2.25), and the lowest average score was Songkhla (+1.08). The highest scores were obtained for benchmark 6 (4 provinces), benchmark 4 (3 provinces) and benchmark 2 (2 provinces). The lowest score was for benchmark 7 (the lowest in 7 provinces) perhaps because people from local governments skipped giving score for this benchmark, since it dealt with internal affairs. From the summary table of 10 benchmarks in 10 provinces (table 7.21), the scores describing considerable betterment were found in benchmarks for health delivery. Fore example, efficacy, efficiency and quality of health care in benchmark 6 had average score 2.18, comprehensiveness of benefits and tiering in benchmark 4 averaged 2.10), and financial barriers to equitable access in benchmark 2 average 1.98. The issues with less improvement were administrative efficiency in benchmark 7 (average 1.44), non-financial barriers to access in benchmark 3 (average 1.81), patient and provider autonomy in benchmark 9 (average 1.83) and equitable financing in benchmark 5 (average 1.90).

Province Mean CM. PY PR KK NR PK SK PT AY RB Benchmark 1 1.85 2.43 2.00 1.88 2.07 1.70 1.34 1.81 2.38 2.19 1.97 Benchmark 2 1.87 2.08 2.11 2.38 2.32 2.25 1.31 1.45 1.85 2.16 1.98 Benchmark 3 1.56 2.21 1.88 2.05 1.77 1.79 1.33 1.14 2.17 2.16 1.81 Benchmark 4 1.93 2.31 2.16 2.23 2.12 1.88 1.34 1.98 2.45 2.55 2.10 Benchmark 5 1.54 2.02 1.76 1.98 2.15 2.13 1.00 1.69 2.20 2.50 1.90 Benchmark 6 2.12 2.29 2.13 2.76 2.21 2.00 1.46 1.83 2.18 2.79 2.18 Benchmark 7 0.89 1.27 1.60 1.62 1.55 0.76 1.43 1.52 1.63 2.11 1.44 Benchmark 8 1.72 2.37 1.99 2.03 2.16 1.59 1.27 1.98 1.91 2.60 1.96 Benchmark 9 1.68 2.17 1.59 2.14 1.93 2.19 1.25 1.91 1.93 1.53 1.83 All benchmarks 1.82 2.04 1.96 2.13 2.25 1.95 1.08 1.95 2.09 2.03 1.93 Table 7.21 Equity score by benchmark and by province, the average score for 10 provinces

* Note Bold and italic means the highest score for each province. Bold and underlined means the lowest score for each province Benchmark of Fairness 134

People who gave high scores were from health civic groups (6 provinces) in urban areas of 4 provinces and rural areas of 2 provinces. The non-health civic groups (4 provinces) in urban areas of 2 provinces and rural areas of 2 provinces also gave high scores. The lowest scores were found among the local government officers (3 provinces), non-health civic groups in urban areas (3 provinces) and providers at subdistrict level (2 provinces). The table summarising scores by 8 types of focus group discussions in 10 provinces (table 7.22) shows that health civic groups in urban areas expressed their best scores (2.73), followed by health civic groups in rural areas (2.58) and health administrators (1.96). Groups with the lowest scores were non-health civic groups in urban areas (1.31), followed by providers at subdistrict level (1.59) and local government officers (1.68). Groups with very wide range scores were non-health civic groups in urban areas (from –2.86 in Songkhla to 3.00 in Phuket). Groups with narrowest range score were health providers at subdistrict level (from 0.63 in Chiang Mai to 2.11 in Khon Kaen and Pattani). Groups showing high variations of scores between provinces were non-health civic groups in urban areas (highest -lowest=5.86), local government officers (highest-lowest=3.20) and non-health civic groups in rural areas (highest - lowest=2.75). Most of them were not very close to health issues. Concerns of people in different provinces differed. The scores were dependent on the type of participants, experiences and personal attitudes towards health system and the influences from group discussions that had changed their views.

Province Mean CM. PY PR KK NR PK SK PT AY RB Health administrat 1.09 2.00 2.40 2.09 2.00 2.60 1.00 1.45 2.38 2.64 1.96 Health provider 2.00 2.60 1.00 1.60 2.67 1.29 0.60 2.43 1.64 1.43 1.72 Administrator 0.63 0.67 1.70 2.11 2.00 1.75 1.89 2.11 1.40 1.67 1.59 Local government 2.25 -0.20 2.67 2.25 3.00 0.86 1.00 0.67 1.50 2.80 1.68 Urban health civic 1.75 2.50 3.50 3.63 2.50 2.00 2.00 3.57 1.89 4.00 2.73 Rural health civic 2.60 3.25 1.71 2.10 3.40 2.67 1.80 1.71 2.86 3.71 2.58 Non-health, urban 2.43 2.00 1.25 2.33 0.83 3.00 -2.86 2.00 2.88 -0.75 1.31 Non-health, rural 2.80 2.13 1.75 0.25 1.71 1.67 3.00 1.00 2.40 2.00 1.87 Table 7.22 Equity score by focus group discussion and by province in 10 provinces

* Note Bold and italic means the highest score for each province. Bold and underlined means the lowest score for each province 133 7. Equity in Health in 10 provinces: Quantitative tool

The differences of scores before and after focus group discussions were shown in figure 7.21. Most provinces had better score after discussions from +0.02 (Songkhla) to +0.50 (Phuket), except Phayao the score dropped after discussions -0.12. The average difference of scores for 10 provinces was +0.26.

0.6 0.5 0.42 0.43 0.43 0.44 0.4

0.2 0.16 0.17 0.13

0.02 0

-0.12 -0.2 Phayao Phrae Ayut haya Chiengmai Khonkaen Songkhla Pat t an ee Nakornrachasema Rat chabur i Phuket

Figure 7.21 Difference of summary scores before and after discussion (score post – pre discussion)

Details of score differences for 9 benchmarks were presented in table 7.23. The scores in some provinces show worse scores after focus group discussions. Phayao had lower scores for benchmarks 4, 5, 8 and 9. Songkhla has lower scores for benchmarks 1, 2, 6 and 8. Pattani had lower scores for benchmarks 1, 2, 3 and 4. However, the overall scores were higher after discussions, for example slightly higher +0.15 for benchmarks 1 and 4), and much higher 0.28 for benchmark 9. Province Mean CM. PY PR KK NR PK SK PT AY RB Benchmark 1 0.18 0.22 0.17 0.48 0.14 0.44 -0.18 -0.05 0.05 0.02 0.15 Benchmark 2 0.39 0.08 0.07 0.52 0.41 0.50 -0.10 -0.10 0.06 0.21 0.20 Benchmark 3 0.08 0.09 0.20 0.36 0.29 0.36 0.19 -0.28 0.45 0.37 0.21 Benchmark 4 0.21 -0.31 0.03 0.15 0.32 0.34 0.29 -0.05 0.21 0.31 0.15 Benchmark 5 0.24 -0.17 0.05 0.40 0.15 0.58 0.03 0.21 0.30 0.55 0.23 Benchmark 6 0.15 0.11 0.07 0.45 0.28 0.59 -0.28 0.19 0.13 0.36 0.21 Benchmark 7 0.04 0.13 0.12 0.07 0.20 0.36 - 0.13 0.31 1.11 0.27 Benchmark 8 0.37 -0.01 0.29 0.18 0.23 0.23 -0.26 0.61 0.19 0.52 0.24 Benchmark 9 0.46 -0.01 0.16 0.37 0.42 0.40 0.10 0.33 0.20 0.34 0.28 All benchmarks 0.43 -0.12 0.13 0.44 0.42 0.50 0.02 0.16 0.17 0.43 0.26 Table 7.23 Changes of equity scores after focus group discussion by province in 10 provinces Benchmarks of fairness 136

8 Overall picture of equity

This chapter analyses the equity in health in all aspects using scores from qualitative methods combining with some data from qualitative discussions and quantitative methods. The chapter presents only data from 10 provinces that we collect qualitative data. The analyses started from reviewing strengths and weaknesses of equity in health of the province as illustrated by quantitative scores of equity in health in 46 items. The quantitative scores for slow and regressive situations will be presented. These causes will be linked to see their relationships and effects on equity in health. The 10 province data will show the special conditions by province or area. Finally, the chapter presents the trends and ways forward in achieving equity in health according to the situations that need changes.

8.1. Strengths and weaknesses of equity in health

The scores on equity in health from focus group discussions in 10 provinces show variations in different issues. When considering the 46 items of 10 provinces, we could see some consistencies in equity scores, both the better and the worse conditions. Table 8.1 shows the equity scores by 46 items for 10 provinces, the colours highlight the 4 groups of scores: 1) the worse condition or minus score, 2) the average score between 0-1.5, 3) the average score between 1.5-2.5, and 4) the score of 2.5 and above. From the patterns of score distribution, we observed that, the item with average minus scores was the economic conditions average minus score for 6 provinces, and the rest 4 had score lower than 1.5. The items with average score of 0-1.5 (common feature for 4 and more provinces) covered the overall health status, health status for specific group, environmental condition, demographic and economic conditions, resource distribution, workload by resource, the difference in health spending, efficiency in provision, total health spending, administrative cost, unnecessary service, transparency in resource management, choice of primary care services, choice of specialized care, choice for private sector and autonomy for provider. The items with score from 2.5 and above (for 4 and more provinces) covered 6 areas: coverage of services, education condition, coverage of health insurance, coverage of benefit, access of service and use of primary care services. 137 8. Overall picture of equity

Table 8.1 Average health equity scores by 46 items in 10 provinces Benchmarks of fairness 138

From the above data, we can see that problem issues clustered around benchmarks 1, 3, 7 and 9, whereas issues with high score were benchmarks 2 and 4. If we take the average of 10 provinces to average again, there were 3 benchmarks with good score (higher than 2.5) for 10 provinces as listed below: 1) Coverage of services under benchmark 1, scored 2.92. 2) Coverage of health insurance under benchmark 2, scored 2.75. 3) Education status under benchmark 1, scored 2.53. There were 11 items with average scores lower than 1.5: 1) Economic condition under benchmark 1, scored 0.13. 2) Demographic condition under benchmark 1, scored 0.76. 3) Environmental condition under benchmark 1, scored 0.91. 4) Workload of service by health resource under benchmark 3, scored 1.07. 5) Administrative cost under benchmark 7, scored 1.21. 6) Over-servicing under benchmark 7, scored 1.23. 7) Total cost of health services under benchmark 7, scored 1.28. 8) Overall health status under benchmark 1, scored 1.28. 9) Choice for private health sector under benchmark 9, scored 1.32. 10) Distribution of health resources under benchmark 3, scored 1.37. 11) Health status for subgroup of population under benchmark 1, scored 1.45. The scores presented were compatible with data from qualitative method. Most of the focus group discussions agreed that social improvements provided better opportunity to the whole population. Especially the opportunity on education and accessibility to necessary public utilities provided people better access to health care. Improvements in transportation, coverage of health insurance, higher number of supply side led to better coverage of primary care. However, we faced problems that were the consequences of social developments, e.g. environmental problems, population and overcrowded, industrial pollution. Economic crisis also created social problems such as crime, drug addiction, mental health problems and HIV/AIDS. High mortality was the consequence of high accidents, chronic diseases and non-communicable diseases, cancer. In some areas like rural area still faced scarcity in health resources, mal-distribution of human resource for health. High services concentrated in hospital led to overcrowded patients in hospital, higher total health care cost while people still lacked opportunity to seek appropriate health care. 139 8. Overall picture of equity

8.2. Influences of other factors on health system If the 9 benchmarks of fairness were divided into 16 items, we will see the close linkages between those items. These factors were both controllable and non-controllable by the health system. Figure 8.1 shows that health status was influenced by economic, social and environmental factors that were non-health system factors. It was also influenced by community participation and by the capacity of the community. Any community which united for health of the community, and integrated that with local wisdom in taking care of health issues, that community would gain health in return. Moreover, health status was dependent on health service system, in terms of coverage, comprehensiveness, quality and efficiency of service. These factors would reduce mortality and disability of users of services. The socioeconomic factors not only involved directly on health of individuals but affected on financial access and distribution of health resources and other factors determining access to care, as well as choice of services. People in wealthy area would have better opportunity in choosing between public and private health services, increasing the level of use, and finally health status. Factors on health service system were also important in increasing or decreasing uses of services. Good quality health services would increase the uses of services because people accepted the value of use. Use of service, efficiency of service and efficiency of management were important factors affecting cost of service and the degree of accountability by the community. They also stimulated quality and efficiency of service and efficiency of management. Autonomy aspect also had impact on efficiency of management.

Health status

Economic factor Participation and accountability

Equity of health Equity of delivery Financial access Coverage & finance to care comprehensive

Distribution of Service Quality of service resources provision

Accessibility to Cost of care Efficiency of service service

Autonomy of Efficiency in Autonomy of client management provider Figure 8.1 Relationships between determinants of equity in health Benchmarks of fairness 140

8.3. Direction and distribution of equity in health Considering quantitative data of 10 provinces or of the total 75 provinces (except Bangkok), there was no single province with all best indicators, or positive scores in all items compared with other provinces. Hence, the aim for equity is to keep the balance between all equity items, as well as improving the weak areas. It should be cautioned that increasing equity in one area might decrease the equity score in other areas. Such as, increasing equity in choice of services would jeopardize the continuity of care as well as the efficiency in total health expenditure. The aim for improvement should take all items into account. The quantitative data from 10 provinces could be summarized to highlight the aim to improve equity as follows. If we grouped the 9 benchmarks scores by equal interval of 0.5, the distributions of scores both + and – intervals were presented in table 8.2 below.

Chiang mai Phayao Phrae Khon Kaen Khorach Phuket Songkhla Puttani Ayuthaya Ratchaburi Intersectoral public health - -- + + + + + -- + + Financial banier to equitable access - ++ +++ + -- + + + + - Non-financial banier to access +++ - + ++ - ++ ++ - ++ ++ Comprehensiveness of care and tiering - - ++ ++ ------Equitable financing + + ++ ++ - ++ -- - + ++ Efficiency and quality of care - + - - -- + -- - - - Administrative efficiency + - - + + - -- + - -- Democratic accountability and empowerment ?????????? Patient and provider autonomy + - + ----++-

Table 8.2 Strengths and weaknesses of 9 benchmarks by province From the table, each province had combination of strengths and weaknesses. Strengths on financial access to health care were found in Phayao and Phrae. Strengths on non-financial access to health care were found in Chiang Mai, Khoh Kaen, Phuket, Songkhla, Ayuthaya and Ratchaburi. Weaknesses on health status and determinants were at Phayao and Pattani, weaknesses on administrative efficiency (total health spending) were at Songkhla and Ratchaburi. From the inequities between provinces, only some aspects could be improved. Providing universal coverage would increase equity of financial access to health care for all provinces to the same level. Creating primary care to all provinces would expand accessibility to good quality and continuity of services, and would ultimately increase national administrative efficiency (allocative efficiency)(39). It was important to find other mechanisms apart from financial mechanisms to improve equity, and balancing with the efficiency and quality goals. Providing choice of services must be balanced with maintaining overall efficiency while community must be empowered to increase participation in accountability of health care system. 141 9 Systems recommendations

9 Systems recommendations

From previous chapters, the equity assessments by 9 benchmarks were complicate and interconnected. It is difficult to collect data only once and interpret. This chapter shows how the tools or data on health equity could be apllicable for uses at different levels. The quantitative and qualitative tools were applicable at 3 levels: policy, management and operational levels. Each level has its own specific objectives as follows.

Policy level : The data by the benchmark of fairness could be used at policy level, by monitoring the impact of health policy, and measuring the size and distribution of health problems. The benchmark could be used to set policy to promote equity in health, especially in problematic areas, such as allocation of health resources according to health needs and health care cost, to attain equity, efficiency and quality. Management level: Provinces could use data from the benchmark of fairness to improve equity in various aspects with good balance. The data could be used as the entry point to improve equity in health. Weaknesses in health systems could be remedied. Strengths identified could be maintained. The equity scores by province could be used to share experiences of each province and to correct discrepancies to the most equitable situations. Operational level: The processes to obtain equity scores mobilized mental efforts from people in the province. These helped stimulate participation and data exchanges in the community. Further improvement processes started from the meeting and target setting with detailed plans to reach the target.

Systems recommendations emerged from the benchmark research ranged from the mentioned levels. Detailed discussions focused on how to apply these benchmarks as the policy tools, as the tool to monitor impact of development programmes, and the tool to mobilize participation and encourage decentralization processes.

9.1. The benchmarks as policy tool At the policy level, the benchmarks of fairness could be the target of health system development. Data from comparison between areas could be used to achieve equity in health, to assess health need and to allocate health resources. These issues should go along with each other. I.e. budget allocation should go in line with different health needs. However, resource allocation alone could not solve all problems on equity. Other measures should complement the limitations of budget allocation. Other mechanisms to improve equity included administration, system to monitor and evaluate and community participation. Benchmark of Fairness 142

Setting target for equity Data on inequalities between provinces could lead to the policy to reduce inequalities. In setting for equity improvement, important indicators should be set up and situation analysis done accordingly to 1) set appropriate average level for standard equity benchmark, and 2) set target of reducing discrepancies between the maximum and minimum values (see figure 9.1). For example, mortality target if set to reduce the national average mortality rate from 5.5 per 1,000 to 4.5 per 1,000, the difference between the highest and lowest provinces should reduce from 6 per 1,000 to 4 per 1,000 as well.

Upper

Average Reduce difference Increase level Lower

Figure 9.1 Setting target for equity by improving the average level and reducing the difference Monitoring health needs Indicators for health needs will reflect the risks or opportunities to use health services. The health needs vary according to age structure, morbidity and mortality patterns and other determinants of health. When considering health needs and utilization of health care in public and private sectors including alternative medicines the supply side did influence the use. Data on the ranking of accessibility and geographical distribution of health resources could indicate the regions with high or low health needs and the level of accessibility at the same time. These could also be used for resource allocation. Provinces with different age structure would have different risks in the uses and costs of health care, since infants and the elderly tended to display higher uses than the working age (the U-shape graph)(40). However, when the use rates were multiplied by the unit costs of services, the annual cost per person by ages group increased drastically for the elderly, turning the graph of cost per person by age group a J-shape graph (see figure 9.2). Data from a community hospital 4 Cost per relative population 3.5 to draw figure 9.2 show that the cost of 3 2.5 using inpatient services increased a 2 bit for age group 20-30 years old, 1.5 1 suggesting that the increase was 0.5 0 related to deliveries of newborn 0-5 10-15 20-25 30-35 40-45 50-55 60-65 70-75 5-10 15-20 25-30 35-40 45-50 55-60 65-70 >75 babies. OPD I PD 1=average

Figure 9.2 Cost per relative population for outpatient and inpatient by age group 143 9 Systems recommendations

Resource allocation and budget for health Appropriate resource allocation should take account of a number of factors that influence on cost of providing care. Data on equity in health could be used as one determinant factor such as data on health need from benchmark 1 (demand-side factor). If the data on health status and inter-public health functions showed poor health, the province would face higher risks of morbidity and mortality which would lead to higher cost. Furthermore, age structure of higher dependency ratio would also face higher cost of care. Socioeconomic factors suggested the accessibility of people to health services; the poorer provinces would have higher household financial barrier to health care than provinces with better socioeconomic status. Hence, poorer provinces should gain higher resource allocation.

Apart from health need, there are other factors influencing costs such as size, population density. If these factors matter, they should be present in the allocation formula. For the transition period, there are many ways to implement, and the approach should be gradual. First, by drawing the service unit to the most optimal and equal size to achieve the same cost and outcome, but this approach has limitation especially on the administrative boundary. Second, putting factor on population size in the allocation formula by reducing the weight for bigger provinces. The weight adjustments are expected to take long time to reach equity in resource mobilization. Example Cost per capita from 4 provinces in the central part of 1) adjust by population 1,500 Thailand supported the mentioned proposal

(see figure 9.3). Some provinces needed 1,000 time to expand health infrastructure, to 2) adjust by cost/cap balance between revenue and expenditure, 500 and to protect the shock of under- or over 0 funding. 0 20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000

Population Apart from population factors, administrative Figure 9.3 Cost per capita at community hospital for population efficiency according to benchmark 7 also size in central provinces and adjustment influenced overall cost of health services. The supply-side factors included the over-sized health facilities. Big hospitals faced the risk of high cost while getting the same capitation budget. The allocation formula should therefore take account of the size of health service to balance the cost of providing services, with the step to reduce the weight of size. This process should be gradual while people’s health seeking behaviour should also be changed. Benchmark of Fairness 144

Another interesting factor was the use in private sector. Whether the allocated budget was for total health spending or only public health spending, the use in private services always mattered. Areas with low cost could be confounded by high uses in private sectors. Budget allocation should aware of the differences in private health consumption. If the policy was for spending in public sectors only, giving the same budget to areas with high uses of private sector created surplus, and the opposite- deficit was true for the same budget to areas with low uses of private sector (see figure 9.4).

Area A Area B Private Private Deficit Allocated budget Public Public Surplus spending spending Figure 9.4 The chance of being surplus or deficit for the different uses of private services Example of allocation formula in England took account of age-sex population structure, health need factors as well as cost factors. Detailed formula was as follows: WP = POPx(1+a)x(1+n)x(1+c) where a was age adjustment, n was needs adjustment and c cost adjustment. This example reflected the equitable allocation formula in taking account of all determinants of health care costs including population factor(41). Further proposal for equitable allocate formula at district level taking account of differences in small areas should cover these 4 factors at the transitional period: ! Adjustment factor for age structure. This adjustment should be calculated from the proportion of people in different age structure and the relative cost of services provided to that age group (the product of utilization rate and unit cost by age group). Summing up the above weight for all age group and comparing with the average are to be used for adjustment. ! Adjustment factor for economies of scale. This adjustment factor should be derived from the regression between population and cost per person as the cost per person tended to be lower if the population size was big. This adjustment would increase budget for small provinces. ! Adjustment factor for utilization rate. This adjustment accepted that areas with high utilization should receive higher budget because of a high burden of cost. Accessibility, popularity of the service and choice of available services were determinants of utilization rate. ! Adjustment factor for real cost per capita. This adjustment accepted true cost of providing care at the starting point. This adjustment should later be changed to other output factors. The adjusted population for allocating budget to each area should include the following factors: Adjusted capitation = capitation * [(w1*a)+(w2*s)+(w3*u)+(w4*c)] 145 9 Systems recommendations where w1 to w4 are the weights of individual factors, e.g. w1 is the weight for age structure and w1+w2+w3+w4 = 1, a is the age adjustment, s is population size adjustment, u is the utilization adjustment and c is the real cost adjustment. At the first year, w4 should reflect the cost, however w4 should be reduced as the transition period has passed. The reduction of w4 is to send the signal to providers that their higher costs should be reduced. On the other hand, w1, w2 and w3 should further reflect the situation on demand side. Payment mechanisms should be combined to get the highest efficiency, quality and equity(42).

The above describe process is an example of giving concerns to data that reflect different health needs between geographical areas. The use is at policy level for allocation of resources and health budget.

9.2. The benchmarks as a tool to monitor the impact of development programmes

The use of benchmarks to monitor the impact of development programmes can be established at the local level. This tool can suggest the management of the programmes by identifying strengths, weaknesses, strategic planning and evaluation. If equity in health is the aim of health system at the local level, managers of health system at the local level should put all efforts in improving the level of health especially when the situation of the area is below the average. Quantitative data in terms of ranking are helpful in signaling how much inequity should be improved. Holding mean and standard deviation constant, the equity score will be better if own status is improved. The uses of quantitative score can be twofold: first to improve equity score at local level and second to monitor general equity in health.

Improving equity at the local level To improve equity at the local level is to uplift the overall equity level and to keep the balance of equity of the individual items, i.e. the level is higher and not jeopardize any individual items. The scores by quantitative benchmark tool are useful in setting equity targets. The positive and negative equity scores are able to demonstrate the situations, strengths, weaknesses, targets and strategic plans.

Example from Chiang Mai, scores of 30 indicators of 8 benchmarks showed the negative average, that is, Chiang Mai had many weaknesses as compared with other provinces. Negative scores were found for overall health status and health of specific groups, coverage of services, environmental factors, coverage of health insurance and ratio of high and low insurance, use rate of public services, continuity and integration of services, length of stay, quality of service, expenditure by services and administrative costs. Benchmark of Fairness 146

These weaknesses have to be turned out as strategies to improve equity, such as reducing morbidity and mortality in principle causes to increase scores on health status. AIDS and accidents are the example of diseases control strategies as presented in figure 9.5.

2 Equity score 1.44 1.38 1.34 1.05 1.01 0.82 1 0.78 0.69 0.48 0.45 0.34 0.16 0.19 0.14 0.14 0.09 0.18 0 -0.27 -0.24 -0.25 -0.19 -0.44 -0.34 -0.74 -0.74 -0.82 -0.92 -1.04 -0.94 -1

-2-2.23

-3 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Overall health status Health insurance Utilization rate Quality of care Specifc health status Prop of high scheme Continuity of care Cost by size of facility Coverage of care Comprehensiveness Administrative cost Environment factor Average length of stay

Reduce death rate Expand insurance Expand primary care Quality improvement Civil society Expand package Continuity care to home Facility restructuring Out reach services Strengthen services Reduce wastage in system Reduce pollution Health promotion focus Home health care

Strategy to improve

Figure 9.5 Strengths and weaknesses according to development strategies using quantitative tool of the benchmark

It can be seen that quantitative data can be use for strategy formulation. To be effective the data need to be updated frequently to reflect current situations. 147 9 Systems recommendations

To monitor equity in health The data to reflect equity in health are time-specific and need to be updated in order to reflect the situations of the local areas after intervention of any programme. Hence, data collected are not only for strategic planning but also for monitoring changes according to intervention programmes. The changes of z-scores for equity score reflect the changes of inequity. Since z-score is the standardized deviation of the score from mean and standard deviation, the changes of z-score reflect the changes of situations better than the changes of means alone. Figure 9.6 shows that the changes of data from year 1 to year 4 have been dramatic by the change of z-scores from less than –1 to +2.

Yr1 Yr 2 Yr 3 Yr 4 Data

Z-score -4 -3 -2 -1 0 +1 +2 +3 +4 Figure 9.6 Changes of equity score when data changes while the average and standard deviation constant Comparisons of z-scores at the country level have more dimensions to consider. If all provinces show better improvements, the z-scores for all provinces will shift to the positive side, and after a few years, the differences between provinces will be masked by the positive sign. In this situation, the mean and standard deviation to compute z-scores should be renewed, in other word the recalibration of z-scores should be undertaken (see figure 9.7). The recalibration of z-scores will result in provinces showing either positive or positive z-scores. Averate -5 0 +5 -5 0 +5 Province 1 Province 1 . . Shift of . . score by . . new mean . . and new . . SD . . Start year 3 years later

Figure 9.7 Changes of equity score over time with changes of mean and standard deviation It is shown that equity scores at local and national levels can be used to monitor the changes after interventions. It the changes become worse, they warrant special attention. Benchmark of Fairness 148

9.3. Equity as a tool to mobilize participation and encourage decentralization

The practice of applying qualitative tool to assess equity in health has stimulated participation in expressing their viewpoints in focus group discussion and in questionnaire. Participants in the focus group discussion should have similar characteristics (homogeneous) so that they feel free in expressing their viewpoints. However, if diversity of viewpoint is the aim of the process, next step is to try mixing more different characteristics of participants. This approach is the foundation for expanding the participation to wider partners for health. The structural network linking various groups of populations to evaluate equity in health would support the trend of health decentralization that requires more people involvement. If the proposed area health board (AHB)(43) (consisted of 5 representative groups: health administrator, health care provider, local government, civil society and expert) were to oversee the decentralization process, each member should seek opinions from various sorts of people. The similar and different opinions could be used for strategic formulation to improve health equity. Quantitative and qualitative data are both important and can be used to illustrate how inequity can be improved. The participation approach needs open-mindedness of the participants and the common goal for achieving better health equity.

Focus group discussions in 8 Analyze, assess target groups on and plan by area health equity health board

Improving health Qualitative aspects Health equity score equity

Focus group disc Continuity of in different mixes knowledge exchange

Figure 9.8 Use of qualitative approach of the benchmark as the entry point for people participation 149 9 Systems recommendations

9.4. Information system to monitor equity in health To evaluate and monitor equity in health, information system is very crucial in illustrating the changes of equity situations. The benchmarks of fairness, both quantitative and qualitative approaches lead to ranking as data per se and the ranking on people judgement. The information system should be good enough that it generates information that can be used at the operational, the local area management and the national policy levels. The linkages of information systems on equity cover 7 subsystems: 1) system for compiling local quantitative data, 2) data networking system, 3) analysis and statistical system, 4) geographical information system, 5) system for managing data from quantitative tool, 6) system for surveying individual viewpoints and 7) system for internet survey (see figure 9.9).

National level Geographical distribution

GIS 4) Geographical data

Equity score by Z-score and ranking Equity score at national level

Spread sheet Statistics 3) Data analysis system 5) Score handling system Graphics Spread sheet Data at national level Graphics

Equity score FTP Network 2) Network system, data send

Level to province Qualitative at local level

Benchmarks of Fairness Group discuss

Databases 1) Routine data compilation 6) Opinion survey CAI

Report Database Surveys Survey to public

7) Survey of ideas through web Operational WEB Population

Figure 9.9 Information systems related to the benchmarks by 7 subsystems and 4 levels Benchmarks of Fairness 150

10 Discussion and conclusion

This chapter discusses 2 main issues. First, it discusses various measurements of health equity, starting from data that used for calculation of equity indices and the interpretation of the indices. If the measurement involves value judgement of assessor, experiences of the assessors play important part in the measurement. Strengths and weaknesses of the approaches were discussed. The discussions proceed on the availability and sources of data, time frame for data collection and the appropriateness of the approaches to developing countries in measuring the impact of health care reforms. The second part discusses the tools used to measure health equity in this study, i.e. quantitative tool compiling secondary data and qualitative tool collecting data from focus group discussion and scoring to express judgement. This discussion focuses on the appropriateness of the tools and recommendations for further uses.

Part 1 The measurements of health equity

There are many ways to measure health equity. Each method is linked with the objective. The methods of expressing equity are discussed: composite index, comparison among subgroups and areas, and scoring. The discussion will go on to details of objective, procedure, strength and weakness and data that are essential for the measurement.

10.1. Methods for expressing equity This section of discussion compares 3 ways of expressing equity: ) Composite index ) Comparison of values among subgroups and areas ) Scoring method 151 10. Discussion and conclusion

10.1.1. Composite index for inequity

This method calculates data to arrive at a single index to reflect equity status among individuals. Often, the inequalities are used to portray health inequities, such as health status, health care finance and health utilization. Some composite indices reflect the horizontal equity, and some reflect vertical equity (see chapter 1 for details of horizontal and vertical equity). Examples of composite indices shown in table 10.1 cover equity index of health status, health care finance and health care delivery. The table shows details on objective and process to arrive at the index.

Table 10.1 Objective and process to measure equity in health by composite index Composite index Objective Process 1. Equality Index of Child To measure the chance of Use data on survival of children under 5 Survival(3) survival among children. years, calculate variations of survival time of Theoretically all children each individual around the mean by the should have the equal following formula: chance of survival.

if ! is survival year of each child, and x is the average survival time. If the index is 1, it means that there is and absolute equality of survival among individuals. 2. Fairness of Financial To measure equity of health Use data on the proportion of household Contribution(3) finance at household level, income spending for health, compare the each household should pay variations by the following formula: for health equally proportionate to household income. if HFC is the proportion of household income spending on health, HFC is the average proportion of all households. The index maximum value 1 suggests the absolute equality of household spending for health. Benchmarks of Fairness 152

Composite index Objective Process 3. Kakwani Index(11) To measure equity of health Use individual data with income and tax data. care finance using the Sort individuals by income, grouped them concept of progressivity of into 5 (quintile) or 10 (decile) subgroups. Plot taxation. Those who have the Lorenz curve, x-axis is income ranking high income should pay for and y-axis the cumulative income or tax. The tax at a higher rate than area between diagonal line and tax curve those with low income (this subtracted by the area between diagonal line is a vertical equity) and income curve describes the value of Kakwani index. If the former is bigger, Kakwani index is positive and can be called progrssive to income. If the latter is bigger, Kakwani index negative and regressive. 4. Concentration Index for To measure equity of health The calculation is almost the same as health care utilization(12) care utilization, which calculation of Kakwani index. X-axis is should be distributed income ranking, Y-axis plots on cumulative according to health need. illness and health utilization. The area Those who are sicker between diagonal line and utilization curve should use services more subtracted by the area between diagonal line than those who are and illness curve describes horizontal equity healthier. of health care delivery. If the concentration index is positive, it means that health services favour the rich. If the index is negative, health services favour the poor. 153 10. Discussion and conclusion

10.1.2. Comparison of Variables among groups and areas

This method compares the certain indices by socioeconomic groups or by geographical areas. Most often socioeconomic characteristics used is income. If income is used, the populations are grouped into quintile or decile, and the rates by quintile or decile are compared. Such rates for comparisons are the proportion of income spending for health, mortality or survival, etc. If geographical area is used the characteristics for comparison are health input per capita, utilization rate, health spending per capita, etc. Such comparisons may follow the framework set in the benchmarks of fairness. See details of the two methods in table 10.2. Table 10.2 Objective and process for measuring health equity by comparison between groups or areas Comparison Objective and variable Process 1. Comparison between To compare equity in health Use data for ranking of socioeconomic socioeconomic group among different socio- status, e.g. rank by income quintile or decile. economic groups. To show The dependent variables for comparison can the trends of equality. be the proportion of income spending for health. It the proportions for the poorer subgroups are higher than the richer, it means that health spending by income group is not equitable. 2. Comparison between To compare different Use data from subnational levels e.g. geographical area using characteristic of health province, district, for comparison. Sort the the quantitative framework system by geographical characteristics of interest by geographical of Benchmark of Fairness area. The principle is area, compute the deviation from the mean, distribution should be equal e.g. Z-score (by subtracting the value by by area, such as health arithmetic mean and divided by standard resources, access, health deviation) of each individual area. If Z-scores spending, health status. are very wide (from negative to positive The dependent variables value), it means that the inequality is huge. can be listed according to The Z-scores can be plotted on the GIS the 9 Benchmarks of (Geographical Information System) map for Fairness. quick comprehension Benchmarks of Fairness 154

10.1.3. Scoring for inequity

This method needs individual or group of individuals act as assessors. The assessment may or may not provide evidence for scoring. The score is assumed to be the judgement for health situation as compared with reference situation, e.g. compared with the past or ideal. This method is widely applicable because it is based mainly on people’s perception. The framework for scoring follows the 9 Benchmarks of Fairness as seen in table 10.3.

Table 10.3 Objective and process in measuring equity in health by scoring Scoring Objective and framework Process 1. Give scores for items of To obtain assessment of Prepare the tool for assessor to assess the equity according to the health system by related situation according to the 9 benchmarks, Benchmark of Fairness. people. The assessment either by interview, focus group discussion or covers 9 Benchmarks of questionnaire for scoring. The assessors Fairness. should be those who directly or indirectly involved in health system of that area. Assessment is done by comparing present situation with the past or the ideal situation.

10.2. Strengths and weaknesses of health equity measurements

Each equity measurement has strengths and weaknesses. The composite index requires individual data, employs sophisticate analysis and is difficult to interpret and comprehend. The method of comparison between group of people or geographical area is more feasible in terms of data availability, but the scale may be less sensitive. The scoring method is the most feasible. It may not require intensive data but validity of scores obtained can be problematic as they are dependent on subjective perception of assessors. Strengths and weaknesses of each measurement are presented in tables 10.4 to 10.6. 155 10. Discussion and conclusion

Table 10.4 Strengths and limitations for measuring equity in health by composite indicator

Index Strengths Weaknesses or limitations 1. Equity index of health - Present the equality - It measures inequality but status e.g. equality index situation in just one single does not indicate of child survival figure. socioeconomic differences. - Can be used for - It requires individual data international comparison as from good sampling method. used by the WHO. - It employs difficult formula. 2. Index of Fairness of - - It presents inequality of - It measures inequality but Financial Contribution health finance by one denies the principle of figure. progrssivity of health finance. - Can be used for - It requires individual data international comparison as from good sampling method. used by the WHO. - It employs difficult formula. 3. Kakwani Index - It presents inequity of health - It requires individual data on finance by one figure. income, tax that obtained - Can be used for from good sampling method. international comparison. - It employs difficult formula - Reflect vertical equity in and difficult to understand. health, i.e. progressivity to income. 4. Concentration Index for - It presents inequity of health - It requires individual data on health care utilization delivery by one figure. illness and use of services - Can be used for that obtained from good international comparison. sampling method. - Reflect the trend of inequity, - It employs difficult formula whether the services favour and difficult to understand. the rich or the poor. Benchmarks of Fairness 156

Table 10.5 Strengths and limitations for measuring equity in health by comparison between group or area Comparison Strengths Weaknesses or limitations 1. Comparison by socio- - Present inequity between - Require individual data to economic group individual groups, can be compute the average by used for planning and income groups or targeting to improve equity. socioeconomic groups. - Can be presented with - Applicable only for simple graph comparing characteristics by person different socioeconomic groups. status. - Easily comprehensible, does not require sophisticate data 2. Comparison by - Present inequity by area, - Applicable only for geographical areas by the can be used for planning comparison between areas. Benchmark of Fairness and targeting inequity. If comparison between - Can be presented by individual is sought, the simple graph and compare previous comparison is between area, using GIS. advised. - Easily comprehensible, not - Limitation in the interpretation require intensive data. because the inequality - Use local area data, already between area may be existed in routine reporting influenced by other factors system. not necessary inequitable. - Cover all elements to Correlation between various compare between area as related factors should be listed in the benchmarks. examined. 157 10. Discussion and conclusion

Table 10.6 Strengths and weaknesses of health quity measurement by scoring Scoring Strengths Weaknesses or limitations 1. Having assessors to - Involve people in the - The scores obtained are score health equity assessment of health equity based on individual according to the and evaluate health system subjective judgement that Benchmark of Fairness by qualitative method. cannot be compared - Get good understanding of between different areas health system of the local because they may have area. This will lead to the different criteria for scoring. improvement of the local - The average scores do not health system. indicate ways of - The method is simple. It improvement. In case the may not require any average is 0, from some quantitative data or may groups give positive scores have data provided in the and some give negative focus group discussion. scores. - Selection of assessors tend to be bias and cannot claim that the scores are representative. Benchmarks of Fairness 158

10.3. Data for the measurement of equity in health To measure equity in health employing different methods requires different sets of data. The composite index requires data of individuals on key variables, e.g. age, survival, income, tax, etc. To compare equity between group or geographical area requires average statistics on data from each group or area. To measure equity using scoring technique may not require any quantitative data. Details of data requirements are presented in tables 10.7 to 10.9.

Table 10.7 Data requirement for measuring health equity by composite index Index Data required Time frame 1. Equality index of child - Individual data from children aged 0-5 - If population survival years on survival time, i.e. date of registry is good, birth, death or alive, date of death. yearly is possible. - Data from all children or by sampling - If sampling method. If all children are needed, survey, it should population registry on births and be conducted deaths can be a good data source. every 3-5 years to monitor trend. 2. Fairness of financial - Household survey data on income - It is possible contribution and spending on all items including except food and health. conducting a - Need large number and sampling survey, it should techniques for representativeness. be conducted - If small area is the aim for every 3-5 years to comparison, the sampling must establish trend. represent that area level. 3. Kakwani index - Individual data on income and - The data spending, esp. tax, insurance, health. obtained from - The data are from survey. If tax data survey, the are from revenue department, they frequency should have to be linked for the same year. be 3-5 years to (If data on tax payers are only used, show trends. they will not cover those not paying tax. 159 10. Discussion and conclusion

Index Data required Time frame 4. Concentration index for - Data on individual income are from - The data health care utilization survey. obtained from - Data on individual illness and survey, the utilization are also from survey. frequency should - If those data are not from the same be 3-5 years to survey, they have to be linked with show trends. appropriate household index.

Table 10.8 Data requirement for measuring equity in health by comparison between groups and geographical area Comparison Data required Time frame 1. Comparison by socio- - Data on household income and other - The data economic group variables of socioeconomic status, obtained from variables under study, e.g. household survey, the spending on health. frequency should - Data are obtained from surveys, be 3-5 years to sometime, different surveys. show trends. 2. Comparison of - Data at area level on study variables, - Can be compiled quantitative data by e.g. health resource, access, annually because geographical area using utilization, spending, health status. most data used the framework of the These data are available in routine are routinely Benchmark of Fairness report or survey. reported data. If - Data by area on population or other use survey data, variables. The source of data is it depend on the compiled routinely. frequency of the - Geographical data to establish survey. distribution by area. Benchmarks of Fairness 160

Table 10.9 Data requirement for measuring equity in health by scoring Scoring Data required Time frame 1. Scoring by different - Do not require any data. However, it - Can be groups of people using the will be more evidence based if some conducted any Benchmark of Fairness data are provided in the focus group time, starting from discussion. some civic groups and expand to others. To know the trend, it should be conducted every 1-3 years.

10.4. Appropriateness and uses in policy and health reform

Since we have various approaches in measuring equity in health, we should use them to complement each other. They have different strengths and weaknesses. Combining them we will have well round measure of equity. In developing countries, data are scarce. The methods required detailed data to measure equity may not be possible. In choosing any approach to measure equity, the following principles may help: academic value, feasibility in terms of data availability, simplicity in calculation and relevancy to the real situation.

Although the composite index ranks high in terms of academic value, it has been used for international comparison, but this approach is data intensive. Household survey is the only data source. The computation is complex and not easily comprehensible. Comparison by groups of people also requires household survey data. This approach is comparatively easier because the data for comparison are more readily available within the ministry of health, the computation is also easier. The results can be used for planning distribution of resources. The scoring approach requires the least data. It can be use to test the understanding and interpretation of community members to the health system at the local level. This is the starting point for development of local health system in the long run. See the summary of comparison in table 10.10. 161 10. Discussion and conclusion

Table 10.10 Comparison of methods for measuring equity in health Issues for Composite index Comparison Comparison Scoring by related comparison between groups between areas civic groups Academic value +++ ++ ++ +

Feasibility in terms + + +++ +++ of data Simplicity in + ++ ++ +++ calculation Relevancy to the ++ ++ ++ + real situation Participation of ++++++ people

Therefore, at the beginning when data constraint is obvious, we can adopt the scoring method by involving various interest groups. The scoring process will lead the groups to learn the concepts of equity and link to the real situation by 9 items of the Benchmark of Fairness. These groups of people can be revisited to gain the long term benefit. At the same time comparison between area should be used because the data should be available for taking off. Mapping the distribution of resources at the local level and comparing with the national level can be a good start for maintaining the data bank. This logic of comparison can be expanded to comparison between individual groups, such as income level. This can be further expanded to the computation of composite index in the future.

The comparison by quantitative approaches and scoring by the qualitative discussion with scoring according to the Benchmark of Fairness can be used for formulating policy and strategic plan. The data from these approaches can be incorporated into the plan and raise the issues on equity especially equity of resource distribution, and distribution according to health needs. The data can also be used to monitor effectiveness according to policy and the progress of health care reform. The processes should be used in dynamic to improve the relevancy of the tools to the real situation. Benchmarks of Fairness 162

Part 2 The appropriateness of the tools used to measure health equity

The last chapter shows the uses of the Benchmarks of Fairness, both quantitative and qualitative tools, at the policy level and operational level. However, the uses also inherit with limitations and should do so with cautious. The limitations from the tools are the main focus of this section. Quantitative and qualitative tools are discussed for the way to improve validity if they are to be used in the future. The final part of this section summarizes experiences learnt from this study.

10.5. Adaptation of quantitative tool

Data from the quantitative tool can be used to describe the equitable distribution of interested variables at the national and provincial levels by comparing the Z-score and the ranking of each province. Comments for the quantitative tool are as follows:

Possibility for using Z-score to describe equity situation To answer how relevant using Z-score to describe equity situation, we need to look at the distribution of data. If the distribution of data is normal, the use of Z-score is appropriated. Quantitative data for 81 indicators are almost normally distributed. Some data are skewed.

The coefficient of variation (C.V.) is a statistics to describe distribution of data around the mean. C.V. is calculated by dividing standard deviation with the mean. If the C.V. is high, it means that the individual observations are distributed far from the mean. The high C.V. may suggest the asymmetrical distribution of the data.

Data from 81 indicators were found that 88.9% of them had the C.V.s lower than 0.5. It means that most of the quantitative data were closely distributed around the means. Considering the maximum and minimum values of each indicators, only 5 had the Z-score lower than -4 and only 11 had the Z-score higher than +4. Comparing the C.V.s of 81 indicators we found that for indicators with the lowest and highest Z- scores, the C.V.s were very high, suggesting the asymmetrical distribution of data (see figure 10.1). We therefore summarize that the Z-score is valid for measuring equity distribution. However, for data with high C.V.s, interpretation should not go too far. 163 10. Discussion and conclusion

Score 8

6 4 Maximum score 2

0 -2 Minimum score -4

-6

-8 0 0.2 0.4 0.6 0.8 1 1.2 1.4

Coefficient of variation (C.V.) Figure 10.1 Relationship between maximum and minimum scores and coefficient of variation by 81 indicators

Quality of quantitative data There are limitations in using secondary data. Though low quality data were excluded from the study, the remaining data may contain some errors. When using data from routine report, errors are inherited during data collection. When using data from population registry, mortality records especially infant deaths are always under report. When using survey data, the sample may not be sufficient for each province.

Moreover there is a time-frame problem. Some data are not available annually. When linking them together, the nearest years are compiled. However, this problem is not serious if the quality of data are acceptable.

Limitation of the tool The limitations of using Z-score for comparing equity in health are as follows: a) Asymmetric distribution: The skewed data or non-normal distribution, the negative and positive values are not equally distributed. Most provinces have almost the same Z-scores, therefore non- parametric statistics or ranks should be used instead or complementarily. b) Ceiling of possible maximum or minimum score: The highest and lowest scores are not equal for all items. For example, comparing the coverage of insurance between provinces the maximum Z-score was +2 where the possible maximum was +4. The score depends on deviation of the value from the mean and the standard deviation. c) Gap ignorance: Z-scores do not directly present the true values therefore are not good for describing the different of real values. Some items had pretty similar values but the Z-scores were very large. Using Z-score should be complemented by the real values. Benchmarks of Fairness 164

Interpretation aspect Many indicators are the results of various determinants, the interpretation must take into account other aspects. Such as, bed occupancy rate, it is meant for measuring efficiency of bed use. More number of inpatients and the longer stay result in high occupancy. Too Long stay in the hospital is not good. High occupancy is good but too high means too crowded and may compromise the quality of services. High occupancy reflects good accessibility to hospital care and reflects the popularity of the hospital if the hospital has adequate number of beds. Interpretations toward equity are related to many aspects.

Adjustment to related factors Since many indicators are influenced by other uncontrollable factors. The interpretation of a single indicator must be cautious. The ratio of admission to community hospitals to provincial hospitals can be influenced by the ratio of available beds in community hospitals to provincial hospitals. To reflect the true popularity of the community hospitals, adjustment of the available beds should be made before comparison of popularity between provinces.

Establishment of continuous monitoring system The monitoring of health equity index should be systematic and continuous. For the continuity a system should be established to develop the indicators with the same standards. The acceptable indicators should have data available for the long-term evaluation. If possible the data should be built-in to the regular databases. A special organization should be assigned to undertake this long-term monitoring of health equity.

10.6. Adaptation of qualitative tool Qualitative tool is to obtain the equity scores given by wider groups of people and to obtain comments from the discussions. These are important issues for consideration.

Steps in tool development The qualitative tool for obtaining scores from 8 focus group discussions in 10 provinces was developed according to the framework set in the Benchmark of Fairness proposed by Daniels et al (2000). The original Benchmark of Fairness was composed of 10 benchmarks and later revised to 9 benchmarks. Even though the items have been reduced, both versions cover the same main issues, while the second version has covered wider concern in developing country, the inter-related public health issues. The tool used in this study finally had 46 questions. 165 10. Discussion and conclusion

Each question sets the scores into 11 levels from –5,-4,-3,-2,-1,0,+1,+2,+3,+4 to +5. These score levels had been tried by Pannarunothai & Srithamrongsawat (2000) since the first phase in Phayao and Yasothorn provinces. The participants in focus group discussions compared the situation today with the situation from the previous 3 years. Participants gave score for each question and the overall score for each benchmark. At the end of each question, space was provided to encourage free comments. Participants did not put their names, but entered their characteristics such as sex, position and workplace.

After drafting 45 questions, the tool was tested with 10 health personnel at a provincial health office. The purpose was to revise the questions in order to obtain high content validity. Due to time limitation, the tool had not been tried with the civic groups or the local governments.

Completeness of data Each participant in the focus group discussion gave the score individually for 46 questions. We found that some questions were not answered, and some had only before discussion but not after discussion. The incomplete scores were found most often among the civic groups. This probably because they did not understand the questions, or had no knowledge on those issues, or were not aware of filling in complete scores. Further uses of the tool must pay attention to the clarity of the question and the completeness of answer.

Adaptation of the tool The qualitative tool, asking participant to score after each question should be revised. Wording should be rephrased to suit local interpretation in different areas. Some questions merit the split while some should be combined to be clearer. The score levels should also be reduced to more natural language, e.g. very worse than before, worse, the same as before, a little better than before and the most better than before.

Sensitivity and specificity The tool becomes more sensitive if it has been revised for clearer understanding and the participants have knowledge on the issues. The specificity will increase because the questions become narrower. However, if the questions become too specific, there will be some weaknesses, such as the tool will have more number of questions, and the questions will also get answer from narrower group of participants.

Process of focus group discussion Conducting a focus group discussion, important processes are before, during and after the discussion as follows: Benchmarks of Fairness 166

a) Steps of conducting discussion The management of focus group discussion in this study, we asked help from health personnel at the provincial health offices. Telephone was the main mode for coordination with the coordinator in the province. The coordinator invited people to the focus group discussion as specified by the researchers. They were classified into 8 groups. 1) Health administrators at provincial and district levels (provincial chief medical officer and deputies, heads of departments of the provincial and district health offices). 2) Health providers at provincial and district levels (director and health personnel at provincial and community hospitals). 3) Health providers at sub-district level (health personnel in health centre). 4) Local government (the president or representative of provincial or tambon administrative organization, municipality). 5 and 6) Health civic groups in urban and rural areas (village health volunteer, interest groups for health). 7 and 8) Non-health civic groups in urban and rural areas (interest groups, non-governmental organizations and respectable people).

b) Selection of participants The criteria for each group were not very detailed. Most were selected from their position in the community. No stringent criteria were specified such as years in the position, leadership, membership of the interest groups. The coordinator listed the people eligible for each group with no public announcement. The persons came to focus group discussion were based on convenience, i.e. the research team came on a particular day therefore, it was by chance the administrators who were present on the day were included in the study. For civic groups, we aimed to have varieties of people invited for the discussion to get various views, but most often we had 3-5 people in the same group came from the same community. The community leader came with his or her followers to the focus group discussion, this ended up with only 1-2 people actively participated in the group. The scores after the discussion could be easily influenced by the community leader in the group. This experience suggested that future activity should pay attention to the proportion of variety of people invited to the discussion. The scores obtained will be more reliable and view points wider.

Different selection criteria in different provinces made great influence in the scores obtained. Village health volunteers who were selected had good relationship with the coordinator, therefore they gave high scores on the health care reform. Detailed analysis should be made to prove 167 10. Discussion and conclusion

whether this was true by having the characteristics of participants in the analysis framework. Another solution is to make a systematic or random sampling of the participants.

c) Scoring before discussion The scores obtained at the beginning of focus group discussion may not be reliable because many issues were not well understood by the participants. The worth of scoring before discussion was to introduce the issues for considerations not the scores obtained.

d) The role of quantitative data Experiences from the discussions revealed that giving quantitative data did not directly influence on the scores gave by the participants since they based their judgements on qualitative data from the discussions. Quantitative data were often used in the discussions among the administrators and health providers as they referred to during their discussions. However, the provided data were a cross-sectional comparative data, not the same as the time frame for scoring comparing situation today with the situation in the past 3 years.

e) Sequence of the discussion The sequence of discussion is to facilitate good flows of thoughts during the discussion. The related issues but from different benchmarks may benefit from making a new sequence of discussion that are different from the benchmarks 1 to 9. Such as, starting from benchmark 1 health status and public health, followed by benchmark 8 accountability and community participation, benchmarks 2 and 5 the financial aspects of access to care, then the benchmarks 3, 4, 6, 7 and 9 respectively. This new sequence will deliver continuity of discussions.

f) Scoring after discussion The scores after discussion are very important as they are the final decisions on health situations of the province combining own opinion with information raised during group discussion. Comparing scores given before and after discussion, the differences were not high (the average increase of 0.5 after discussion). The minimal increase may suggest that focus group discussion had little effect on own decision on the scores.

Content analysis of qualitative data Qualitative data from focus group discussion are equally important, because they help explain why people give very high or very low scores. Socio-cultural aspects are raised and help explain the situations of Benchmarks of Fairness 168 health equity. The analysis of qualitative is not easy, because there are numerous factors involved with the obtained data. Our experiences found that often the data from focus group discussion did not go inline with the scores obtained. The scores were individual decision based on individual background not the group decision. The discussions in different groups varied enormously. Some groups led the discussions in depth with great details, related backgrounds, and lots of examples, such as social problems, health problems, access problems, including experiences in tackling community health problems. Some groups did not discuss in great details just wanted to give scores straight. Some groups had interests in special issues and spent long time discussed while other issues overlooked. Most of civic groups who worked in private organizations had wide discussions on social, economic and environmental problems. Civic groups from village health volunteers focus their discussion on quality of care. The groups of managers dominated the discussions on people’s health status. Groups of health providers at provincial and district levels discussed directly on the issues scoped in the benchmarks. Groups of health providers at sub-district level discussed on the management of health systems in the province.

The beginning step for system development Focus group discussion can be viewed as the opportunity for exchange information. The first step for focus group is to have a homogeneous characteristic, e.g. the members of the similar civic groups, so that they feel comfortable to comment on the issues. By product is people participation in health system development. Next phase the focus group can be more heterogeneous to build up advanced level of health system development. Most participants felt good that they had opportunity to express their views and to feed back on health care delivery. If their views were taken seriously for improvement, people would have been ready for participating in health care improvement. Equity in health as the ultimate result would have been achieved. The participation from people can be organized in a formal and semi-formal ways. Setting up an organization for mobilizing participation for local health development is a formal mean. This organization will monitor, evaluate and develop the health equity in the local area. Forming networks of civic group to exchange information on health development is a semi-formal mean. The benchmarks of fairness can be the tool for achieving health equity of both means.

10.7. Next steps for health equity

If we aim for expanding the idea of achieving health equity to all provinces in Thailand, there are many things to revise. The tool should be simpler and more suitable for use by various levels of people. The items within the tool must be reduced, not to be exhaustive to get high participation from the target groups 169 10. Discussion and conclusion and valid scores. Quantitative data provided to facilitate process of thinking should be more specific and relevant to the issues described in the benchmarks. The interpretation of each index should be unambiguous for equity. The equity aspect should focus on the distribution of factors on individuals in the local area, such as income distribution in the area. The followings are the issues to improve health equity measuring tool to be used in the future.

Development and adaptation of quantitative data Some of the indices for measuring health equity can be readily used to explain equity situation because they display the distribution of factors among individuals, groups of individuals or between local areas. However, many indices are not self-explanatory, they must be compared with values of other areas to decide on the relative inequity situations. There are 3 types of indices as follows:

) Composite index does not indicate geographical distribution of inequity The index in this group is related to health status, such as life expectancy at birth, age-sex standardized death rate, disability adjusted life expectancy (DALE), global burden of disease that calculated from disability adjusted life year (DALYs)(44). Though the index is self-explanatory, but it does not reflect health equity if not used to compared with other areas.

) Composite index that indicates distribution of inequity in the region The index in this group is often used to display inequity situation because it describes the distribution of factor among individuals. The examples are equality index of child survival, Gini coefficient, Kakwani index and fairness of financial contribution, concentration index. This group of index does not require comparison with data elsewhere.

) Index expressing in ratio that must adjust to the variable The index in this group is the ratio comparing situations in 2 subgroups. In order to compare precisely between the 2 subgroups, some data must be adjusted to increase comparability. For example, ratio of admissions to community and provincial hospitals should adjust for the number of beds to population of each area. The ratio of population to doctor should adjust for proportion of doctor practicing clinical work. The ratios must be compared among different areas to judge on equity situation. If not compared with other areas, the index can be compared with the development target. Benchmarks of Fairness 170

To select the appropriate index, this depends on the purpose of use, the clarity for interpretation, acceptability of stakeholders, simplicity or feasibility in data analysis and the availability of data. The processes in deciding which indices to be used for national and local levels are also interesting issues. When the decision is made, there should be the process to ensure that necessary data are available, either from routine reports or surveys, with quality assurance processes.

Revision of questions for qualitative tool

The 9 benchmarks of fairness tried in this research with 46 items, if it has to be used with wider groups of assessors, those items should be reduced to increase participation rate. This section suggests the revised 22 items for further use based on the results of this study. The criteria for selection are as follows:

1. The issue is clear, easily comprehensible and most respondents can score. 2. The issue is highly important, directly reflect the main element of the benchmark, or it is the final result of detailed items in that group. The score clearly demonstrates positive or negative value. 3. The issue portrays unpredictable score (positive or negative) depending on the impact of changes. The study in 10 provinces showed that if the proportion of those who gave negative scores (from –5 to –1) to the total assessors was high, that issue remained to be a significant problem in the province. 4. The issue collects variety of responses and can be used for further debate. The item with high variance (shown by high standard deviation) from the 10 province study reflects non-consensus that should be used for monitoring trend.

The proposed 22 items out of the 46 trial items are as follows:

Benchmark 1 Inter-sectoral public health Item 1 Overall health status of the people Item 2 Environment condition Item 3 Economic status Item 4 Intersectoral collaboration Benchmark 2 Financial barrier to equitable access Item 5 Coverage of the core package of health insurance Item 6 Household burden of health expenditure Benchmark 3 Non-financial barrier to access 171 10. Discussion and conclusion

Item 7 Equitable distribution of health resources Item 8 Barriers to access Benchmark 4 Comprehensiveness of care and tiering Item 9 The use of primary care Item 10 Equity of health delivery Benchmark 5 Equitable financing Item 11 Burden of health expenditure on the poor and the rich households Item 12 Burden of out-of-pocket payment Benchmark 6 Efficiency and quality of care Item 13 Unit cost of health service Item 14 Overall quality of service Benchmark 7 Administrative efficiency Item 15 Overall spending of health service Item 16 Allocation of budget to primary care Benchmark 8 Democratic accountability and empowerment Item 17 Transparency of allocation of resources Item 18 Opportunity to comment on health services Item 19 Empowerment of local interest groups Benchmark 9 Patient and provider autonomy Item 20 Choice of primary care Item 21 Choice of private health sector Item 22 Autonomy of health providers

The selected 22 items are reworded in questions with quantitative data to support assessor’s decision as listed in table 10.11.

Table 10.11 Proposed 22 questions and quantitative data to support decision Question Quantitative data 1 Compared to the past 3 years, has the people’s health Death rate (adjusted by age structure) status (physical and mental) been improved? How much? 2 Compared to the past 3 years, has the environment Proportion of households that free which influenced people’s health status been from pollution improved? How much? 3 Compared to the past 3 years, has the people’s Average monthly per capita income economic status been improved? How much? Benchmarks of Fairness 172

Question Quantitative data 4 Compared to the past 3 years, has the collaboration between the government agencies and between the government and people sector to improve people’s health been improved? How much? 5 Compared to the past 3 years, has the coverage of The coverage of more generous health package been more comprehensive? How health benefit schemes (civil servant much? benefit and social security schemes). 6 Compared to the past 3 years, has the household Percentage of health spending to total burden of health spending been reduced? How household spending. much? 7 Compared to the past 3 years, has the distribution of Ratio of population to doctor health personnel, health facility, hospital bed been more even and adequate? How much? 8 Compared to the past 3 years, has non-financial barriers to health care (e.g. travel, language, religion, culture, belief, acceptability) been reduced? How much? 9 Compared to the past 3 years, has the popularity of Ratio of admissions to community using primary care services (e.g. health centre, hospitals to provincial hospitals. primary care unit, community hospital) been increased? How much? 10 Compared to the past 3 years, has the health provider been providing more equitable services to all with no tiering? How much? 11 Compared to the past 3 years, have the burdens of Ratio of health spending among the health spending on the poor and the rich been unemployed and health spending reduced and the regressivity reduced? How much? among those in private business. 12 Compared to the past 3 years, has the burden of out of pocket spending at point of service use been reduced? How much? 13 Compared to the past 3 years, has the cost of Unit cost of outpatient at community providing health service been reduced? How much? and provincial hospitals. 14 Compared to the past 3 years, has the quality of Crude death rate of inpatient services providing services been improved? How much? in provincial hospitals. 173 10. Discussion and conclusion

Question Quantitative data 15 Compared to the past 3 years, is the overall cost of Cost per cap at health centre, care provided to people appropriate? How much? community hospital and provincial hospital 16 Compared to the past 3 years, have more resources Proportion of health spending at for health been allocated to primary care (health health centre to the total health centre, community hospital)? How much? expenditure. 17 Compared to the past 3 years, has the mechanism of budget allocation been more transparent, and the people are more informed and involved? How much? 18 Compared to the past 3 years, has the health service been more open to people’s comments and taken the comment for improvement? How much? 19 Compared to the past 3 years, have the people’s organizations been empowered to improve their health? How much? 20 Compared to the past 3 years, have the people been provided with more choice in using primary care (health centre, primary care unit and community hospital)? How much? 21 Compared to the past 3 years, have people been Ratio of use in private health sector to given with choice to use private health sector? How public health sector, for outpatient much? services. 22 Compared to the past 3 years, have health providers been more autonomous in the management to deliver health services relevant to people needs? How much?

From the selected, trimmed down questions to 22 items, there is a need to develop a new means to carry the idea to evaluating health system to wider audiences. The next step will try these equity in health questions and scoring process into the internet. Quantitative data are provided according to the questions asked. This will be a test whether new technology can be used for scoring the benchmarks. Benchmarks of Fairness 174

10.8. Conclusion The Benchmarks of Fairness study, phase II has provided the experiences that the tools in terms of quantitative and qualitative approaches can be use with wider groups of people. The quantitative tool compiles secondary data from various sources, e.g. routine reports, electronic databases and surveys. The Z-score statistics are used to compare the ranks of the provinces according to 81 indicators in 30 groups of questions. All 75 provinces (except Bangkok) acquire different ranks based on their data on health status, economic situation, health care resources, access to health care, quality of health services, cost of care and people’s choice in using health services. Each province can sort out the strengths and weaknesses by these quantitative ranks and absolute level of development in order to set the target to achieve equity in health. The targets can be both policy and operational levels, to monitor the impact of development mobilizing people’s participation. The qualitative tool provides more insights to the local situations, because focus group discussions are undertaken with local people experienced the provision and the impact of reforms. Experiences in 10 provinces showed that the overall equity in health scores compared with the situation in the past 3 years had slightly to moderately improve, the scores varied from +1.08 to +2.25. The highest achievement was equity in extending the coverage and comprehensiveness of service. The least achievement was the administrative efficiency. Moreover, the contents from qualitative focus group discussions can be used as the key for future improvement of health system at the local level. People participated in the discussions agreed that giving more opportunities to people organizations to comment on their health system, is the way to improve equity in health. In the long term, the quantitative indicators for monitoring health equity should be more relevant, more sensitive and more specific. These indicators should provide ways to improve. They should be easy to collect and should come from various sources to increase acceptability. The scoring process should also be expanded to wider audiences. The mechanisms to ensure continue updating quantitative data and undertaking qualitative data should be established for national and local health developments. Reference 175

Reference

1) Pannarunothai S. Equity in health system. In the series of Thai People’s Health, Nonthaburi, Health Systems Research Institute, 2000. 2) Hayami Y. Income distribution and environmental problems. In Development Economics. Oxford, Oxford University Press, 1997. 3) WHO. The world health report 2000 – health systems: Improving performance. Geneva, World Health Organization, 2000. 4) OECD. The reform of health care: A comparative analysis of seven OECD countries, Paris, OECD, 1992. 5) Nitayarumphong S.(ed.) Health Care Reform at the frontier of research and policy decisions. Bangkok, Ministry of Public Health, 1997. 6) Van Doorslaer E, Wagstaff A & Rutten F. Equity in the finance and delivery of health care. An international perspective. Oxford, Oxford University Press, 1993. 7) Donaldson C & Gerard K. Economics of health care financing: The visible hand. London, The macmillan Press LTD, 1993. 8) Mooney G. Economics, Medicine and Health care. Sussex, Wheatsheaf, 1986. 9) Culyer AJ. Health, health expenditures and equity: Discussion paper. York, Center for health economics, 1991. 10) Kakwani NC. Measurement of tax progressivity: an international comparison. Economic Journal, 1977, 87, 71-80. 11) Renberg C, Pannarunothai S. Inequity of health care financing in Thailand. A research report to the Swedish-Thai Collaboration in Health System Development, 1998. 12) Renberg C, Pannarunothai S. Inequity of health care delivery in Thailand. A research report to the Swedish-Thai Collaboration in Health System Development, 1998. 13) Health care reform office. Health care reform project. A joint project between the Royal Thai Government and the European Union. Nonthaburi, Health Care Reform office MoPH., 1997. 14) !# " $%&$'()#(*+,*-$!#.#/0!12 &/,3/45!#.#/0!12. /#(5#$3/%6(#&/3/%6(#&/!#.#/0!1278 6.9. 2541-2542. $$3)1/:, &/,3/45!#.#/0!12, 2543. 15) Makinen M, Waters H, Rauch M et al. Inequalities in health care use and expenditures empirical data from eight developing countries and countries in transition: Bulletin of the World Health Organization. The International Journal of Public Health, 2000, 78(1), 55-65. 176 !"#$%&'()*+'),-#%,.+$/012&&+3.*4"56.7

16) !"#$%&' #$(")&'*)+,-.!,$/. (ed.) !"#$"%"#&$'()*+ ,.-.2540-41. !"$# %&'()*+,", !"-&".$/*0*"1 /#2, 2542. 17) Gakidou E.E, Murray C.J.L. & Frenk J. Defining and measuring health inequality: an approach based on the distribution of health expectancy. Bulletin of the World Health Organization, 2000, 78 (1), 42-51. 18) !12 0 43 51267,541,83921:;<5!;+,-=1=1>$. /"34# 65 "*4$*+!*"'78+*,+26$3"-%&9:&4 2542. &/15;36<=#$>/, UNDP, 2542. 19) Daniels N, Bryant J, Castano R.A et al. Benchmarks of fairness for health care reform: a policy tool for developing countries. Bulletin of the World Health Organization, 2000, 78(6): 740-750. 20) Pannarunothai S, Srithamrongsawat S. Benchmarks of fairness for health system reform: the tool for national and provincial health development in Thailand. Human Resources for Health Development Journal, 2000, 4(1): 13-26. 21) Ministry of Public Health. Provincial health survey 2. Bangkok, Ministry of Public Health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lpha Research Co.,Ltd. Pocket Thailand in Figures 2001 (4th edition). Bangkok, Alpha Research Co.,Ltd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eference 177

32) )/1 :/%A$G !#<%A?@(,. >'Q ”#J82#4P6.2:46.)*+” D"@2A/46 ?A1R.C##<1.,#;,'*%-"$/" 7#;!"#7"Q8B+(/3%##: 2/I+:. &/51 ;36<=#$>/, )/@M%3 '/56@<6G F3(4%D$#6#$@2 L" #&%E, 2544. 33) )/1 :/%A$G !#<%A?@(,. >'Q ”#J82#4P6.2:46.)*+” D"@7;BH/66!2TI+.2A/46 QG/?Q/?AR.6"#+%##: !R6/=#;BH7G-"$7#O. &/51 ;36<=#$>/, )/@M%3 '/56@<6G F3(4%D$#6#$@2 L" #&%E, 2544. 34) &#/3BI5;3:N(4*=B57/,;39F3(. @JR:46*R6.2*IP+B “DJ2!U7”. &/15;36<=#$>/, &I5-+@AI17&/0G'OM0# &#/3BI5 ;3:N(4*=B57/,;39F3(, 2544. 35) &#/3BI5;3:N(4*=B57/,;39F3(. 2(8"NCMGP/ 2(8"NC2*IP+B “$.(1"” 2,6@4P B":2(8"NC?1;!"#:I$RB/#RB: QJ?1#H!E" BV/H %##: %##:>"7G $GP.?BQ186:. &/51 ;36<=#$>/, >0,&//<&#/I" #$4(&#/L%E6@<6G=$%5!PIA#<*-$5#$ ;5@$&KH;6PNI&#/3BI5;3:N(4*+,!B5;!/@<&#/!/H#55#$ J#(QAH'>/5&#/+531$;6PNI!%5>< (Social Investment Project: SIP), 2543. 36) &#/3BI5;3:N(4*=B57/,;39F3(. 2(8"NCMGP/ 2(8"NC2*IP+B “=W77"/I” 2,6@4P B":2(8"NC?1;!"#:I$RB/#RB: QJ?1#H!E" BV/H %##: %##:>"7G $GP.?BQ186:. &/51 ;36<=#$>/, >0,&//<&#/I" #$4(&#/L%E6@<6G=$%5!PIA#<*-$5#$ ;5@$&KH;6PNI&#/3BI5;3:N(4*+,!B5;!/@<&#/!/H#55#$ J#(QAH'>/5&#/+531$;6PNI!%5>< (Social Investment Project: SIP), 2543. 37) )/1 :/%A$G !#<%A?@(,. >'Q ”#J82#4P6.2:46.)*+” D"@!1". 6"#+%##:?AR.1'R:/95 " $43$"/@B":#'R.2#46.Q8B+#">%"/I. &/51 ;36<=#$>/, )/@M%3 '/56@<6G F3(4%D$#6#$@2 L" #&%E, 2544. 38) &#/3BI5;3:N(4*=B57/,;39F3(. 2(8"NCMGP/ 2(8"NC2*IP+B “6+'%+"” 2,6@4P B":2(8"NC?1;!"#:I$RB/#RB: QJ?1#H!E" BV/H %##: %##:>"7G $GP.?BQ186:. &/51 ;36<=#$>/, >0,&//<&#/I" #$4(&#/L%E6@<6G=$%5!PIA#<*-$5#$ ;5@$&KH;6PNI&#/3BI5;3:N(4*+,!B5;!/@<&#/!/H#55#$ J#(QAH'>/5&#/+531$;6PNI!%5>< (Social Investment Project: SIP), 2543. 39) L@/1A$G 9/:/%A$)%++G. =#;$G*%GD",N/#;33$'(D", >'Q$'(D",@/)*+ =L ,.-.2543, $$3)1/:, !?#)%$4@L%(/,)) !#.#/0!12, 2543. 40) Bridgman RF. Hospital utilization: an international study. Oxford, Oxford University Press, 1979. 41) 91J!@3.@R 6//0#/1'03%(. 2-#EF-"$7#O$"%"#&$'( N/+'@=XG#J=#;33$'(D",. 6M0@ '1 +&, 9K$(G4@L%(*+,A@EA#< >4#<;7S$.//<3#5!12J#6 <=#4@3(#+%($;/94/, 2543. 42) L@/1A$G 9/:/%A$)%++G *+, !1&%+(# >5!4%!E@R. #;33 !1)! ?1;BG%I!"#CR"+2.G/ @R"3#G!"#$'(D", 2,4P6@B":2=K/ %##: =#;$G*%GD", ?1;@'&D",3#G!"#. $$3)1/:, !?#)%$4@L%(/,))!#.#/0!12, 2544. 43) 6$@ L@ T#U I" #$4(-+, 65MG6@!13.@R L5I1E"!"#>'Q !"#!#;C"+69 "/"CQ8"/$'(D", T3H3*IP 1. $$3)1/:, !" #$%&5#$ '>/5&#/7V@/K7/,)))/@&#/!#.#/0!12, 2543. 44) Murray C.J.L. Identifying the burden of disease: the technical basis for disability-adjusted life years. Bulletin of the World Health Organization, 1994, 72(3): 429-445. 178 !"#$%&'()*+'),-#%,.+$/012&&+3.*4"56.7