Assessment of Health System Performance in Nepal

AssessmentofHealthSystem PerformanceinNepal May2010

HSRSP Report No. 2.20-5-10 Assessment of Health System Performance in Nepal Ministry of Health and Population Government of Nepal May 2010 I N T E R N A T I O N A L

HSRSP Publications 2010 Management of Lamjung District Community Hospital April 2010 Pro-Poor Health Care Policy Monitoring: Household Survey from 13 Districts April 2010 2009 Assessing Implementation of Nepal s Free Health Care Policy: Third Trimester Health Facility Survey - December 2009 Overview of Public-Private Health Care Service Delivery in Nepal November 2009 Health System Performance November 2009 Examining the Impact of Nepal s Free Health Care Policy: Second Facility Survey Report June 2009 Examining the Impact of Nepal s Free Health Care Policy: First Facility Survey Report April 2009 Cost and Equity Implications of Public Financing for Health Services at District Hospitals April 2009 Human Resource Strategy Options for Safe Delivery January 2009 2008 Ministry of Health and Population Budget Analysis 2008-09 December 2008 National Competitive Bidding Process for the Procurement of Goods November 2008 International Competitive Bidding Process for the Procurement of Goods November 2008 Health Sector Strategy (translated into Nepali) October 2008 Costing Study on Incentives Packages for Nepal s Health Care Professionals August 2008 Equity Analysis of Health Care Utilization and Outcomes August 2008 Financing Pro-poor Health Care in Nepal August 2008 State-Nonstate Partnerships in the Health Sector June 2008 Monitoring Strategy and Toolkit for Pro-poor Essential Health Care Services February 2008 Rapid Costing of the Government of Nepal s Free Health Care Policy January 2008 Bottleneck Study for Timely Disbursement of Funds January 2008 2007 Rapid Costing of Delivery and Emergency Obstetric Care November 2007 Operationalising Social Inclusion in the Health Sector September 2007 Ministry of Health and Population Budget Analysis 2007-08 August 2007 Nepal s Experience of Advocacy and Lobbying to Increase the Health Sector Budget July 2007 Implications of the Government of Nepal s Free Health Care Policy June 2007 Equity Analysis in Resource Allocation to Districts June 2007 Please note that all of our publications may be downloaded from our website: www.hsrsp.org This paper examines health system performance in Nepal based on efficiency and equity. Funding was provided by the U.K. Department for International Development (DFID) through the Health Sector Reform Support Programme. The paper was produced by Mr. Shiva Raj Adhikari, RTI International provided technical assistance. The opinions expressed herein are those of the author and do not necessarily reflect the views of DFID. The Health Sector Reform Support Programme (HSRSP) aims to provide policy and strategy support to the Ministry of Health and Population (MoHP) in implementing its sector reform agenda. Additional information on HSRSP is available by contacting: Dr. Rob Timmons, Team Leader, at: HSRSP, Ministry of Health and Population, P.O. Box 8975, EPC 535, Kathmandu, Nepal (telephone: 977-1-426-6180; fax: 977-1-426-6184; email: hsrsp@np-hsr.rti.org). Suggested citation: RTI International, 2010. Health System Performance. Research Triangle Park, NC, USA.

Table of Contents Key Points... iii Acronyms... iv I. Introduction... 1 1.1 Background... 1 1.2 Conceptual framework and data... 1 II. Efficiency analysis... 3 2.1 Technical Efficiency... 3 2.2 Regression analysis... 5 2.3 Cost Effectiveness... 7 2.4 Allocative efficiency... 9 III. Factors contributing to Health production function... 10 3.1 Health production function... 10 3.2 Determining factors for health production function... 12 3.2.1 Public health institutions... 12 3.2.2 Private health institutions... 15 3.2.3 Human resources... 16 3.3 Possible synergic effects in health production... 18 3.4 Changes to the health production function... 19 IV. Equity analysis... 20 4.1 Child health outcomes... 20 4.1.1 Child health outcomes by location... 20 4.1.2 Child health outcomes by socioeconomic status... 21 4.1.3 Child health outcomes by caste/ethnicity... 23 4.2 Maternal health outcomes... 25 4.2.1 Deliveries assisted by a skilled birth attendant... 25 4.3 Nutritional status... 27 4.3.1 Nutritional status by geographic region... 28 4.3.2 Nutritional status by socioeconomic group... 29 V. Utilization of Health Care Services... 29 i

5.1 Disparities in health care utilization... 29 5.2 Antenatal care utilization... 30 VI. Access to health care services... 31 6.1 Access to immunization... 32 VII. Conclusions... 34 References... 35 Appendix:... 37 ii

Key Points Health is both a direct component of human well-being and a form of human capital that increases an individual s capabilities. Better health significantly contributes to economic development and to the reduction of poverty and income inequality Nepal has made significant progress in the health sector over the past decade. Health indicators such as life expectancy, infant/child mortality, and maternal mortality show gradual but steady improvement Despite these achievements, there are still large inequalities in health outputs and health outcomes across geographic regions and socioeconomic groups Increasing the real per capita income by 10 percent, will cause the IMR to fall by 7 percent, child mortality rate (CMR) by 11 percent, and life expectancy rate (LER) will increase by almost 2 percent. Increasing the ratio of health budget to total budget by 10 percent, CMR will decrease by 4.5 percent, and LER will increase by 0.6 percent. If we provide more services by increasing health services, for example increasing number of beds by 10 percent, IMR will fall by 4 percent and LER increase by 1 percent. The results show that in recent years, public health institutions have less capacity to improve intermediate health outputs because of a shortage of human resources, number of health institutions, and institution-related inputs Equity and efficiency are not in conflict. Improvement of institutional capacity in the delivery of health services (at least increasing the numbers of institutions and manpower in the institutions) can shift the health production function ensuring equity in health care services across regions. Allocation of resources according to needs can improve equity and efficiency of health outputs; however, a blanket policy will not have such a capacity Private providers play a complementary role in providing health services and contributing to improvement of health outcomes. Thus, this report includes not only public providers, but also how to make better use of private capacity to improve health outcomes Money matters to the health care system, but it does not guarantee efficient, equitable, and effective health care services. Health care financing has the power to reform health care delivery and provide incentives to providers to deliver efficient and effective health care iii

Acronyms ANC BCG BP CBR CBS CMR DfID DPT EDP EPI GDP HBGDP HBTG HID HP IMR LER MMR MoF MoHP NDHS NFHS NNDP PHCC RPI SBA SHP SMP SP UNDP Ante-Natal Care Bacille Callmet-Guerin Population per Hospital Bed Crude Birth Rate Central Bureau of Statistics Child Mortality Rate Department for International Development (UK) Diphtheria, Pertussis, and Tetanus (vaccine) External Development Partner Extended Programme of Immunization Gross Domestic Product Public Expenditure on Health as percentage of GDP Public Expenditure on Health as percentage of Total Budget Health Care Institution Density Index Health Post Infant Mortality Rate Life Expectancy Rate Maternal Mortality Rate Ministry of Finance Ministry of Health and Population National Demographic Health Survey Nepal Family Health Survey Nepal National Demographic Survey Primary Health Care Centre Per Capita Public Expenditure on Health by GDP Skilled Birth Attendant Sub-Health Post Skilled Manpower Population per Service United Nations Development Programme iv

I. Introduction 1.1 Background The central issue in health policy is how the health sector can contribute to a country s economic development and poverty reduction. There is a growing consensus that health is not merely a medical issue, but a matter of development in general. Thus, it is essential to understand the contribution the health sector makes to economic development and poverty reduction in order to more clearly justify the financial resources needed by the health sector. Expenditure on health is an investment because it provides financial returns in the future and helps to accumulate human capital. Health not only increases wellbeing and productivity, but raises per capita income as well. As overall health improves, one can expect to see a corresponding increase in the productive potential of individuals and higher levels of national income in the long run. Countries with high levels of health but low levels of income tend to experience relatively faster economic growth as their income adjusts (Jamison, 2006). Thus, health system investment brings real benefits to the society. Appropriate investment in the health system is an effective way of improving health and economic development. A health system assessment, therefore, is necessary to capture what is happening and what can be done better. This paper s objective is to assess health system performance within the framework of health production functions from the perspective of equity and efficiency. The health system can be defined as all organisations, institutions, and resources that are devoted to producing healthrelated goods and services and whose primary purpose is to improve health and well-being. This paper gathered information from various sources, including journal articles, published and unpublished research reports, various government documents, documents from external development partners (EDPs), reports from the Nepal National Demographic survey, and Department of Health Services annual reports from various years. The time series data for almost two decades were used to measure the efficiency of the health system. Econometric and statistical tools were used to better understand the efficiency of the health system, and cross-sectional data from different surveys was utilized to measure equity in the health system, which are then presented in various graphs and tables. 1.2 Conceptual framework and data This assessment of health system performance examines what is happening and what can be done better. Performance measures must reflect the various levels at which health systems act and interact to impact health outcomes. This requires defining a brief list of outcome indicators to measure the performance of the health system based on a conceptual framework. The conceptual framework offers a useful structure since it examines the health system by function and links these inputs to the outputs of the health system. 1

The performance of delivery function, illustrated below, has been assessed through intermediate objectives such as access to health services, coverage, institutional capacity (health care providers), and resources (health care expenditures). Ultimately, these delivery functions serve as inputs to the health system that produce health outcomes. The arrows indicate causal pathways from a particular element to intermediate outputs or final outputs/outcomes. Equity and efficiency are two criteria used in evaluating health care system that are directly related to inputs and outputs of health care production functions. The health production function examines the technical relationship between inputs and outputs, and takes into consideration a number of elements that influence the process of health production functions, including regulations, social values, policies, and programmes, among others, that jointly constitute the points at which the health system can be changed. Ultimately, all elements and relationships shown in the conceptual framework are instrumental to the performance assessment methodology. Figure 1.1: Health System Performance conceptual framework Equity Access Provider Coverage Resource Geographical Inputs Allocative Socioeconomic status Process Caste/ Ethnicity Outcomes Outputs Technical Cost effectiveness Child health (outcome) Maternal health (outcome) Nutrition (outcome) Utilization (output) Measuring Performance Efficiency 2

To develop an effective mechanism for resource allocation and utilization in the health sector, an analysis of the equity, efficiency, and effectiveness of resource allocation and utilization in the health sector is crucial. Simply stated, equity is the notion that greater resources and more services should be made available to the most vulnerable and needy groups; efficiency looks at the cost of inputs for each unit of output produced, and effectiveness is the extent to which actual performance compares with expected performance. A better health system advances both efficiency and equity. This analysis of the health system seeks to improves performance by providing a better understanding of the health system to aid in the design of an evidencebased policy. II. Efficiency analysis Efficiency is concerned with maximising health outcomes/outputs. As such, there are three types of efficiency: technical efficiency, cost effectiveness efficiency, and allocative efficiency, as stated in the conceptual framework. 2.1 Technical Efficiency The following is an analysis of the causal relationship between inputs and outputs in the health sector. In the health sector, inputs are both public and private expenditures, although public expenditure is the primary factor in determining health outcomes and shaping the health system in developing countries. Based on the available data, the input-output matrix, on the following page, shows the relationship between inputs - particularly expenditure in the health sector - and health outcomes for the last two decades (fiscal years 1989/90 to 2007/08). 3

Table 2.1 Input outcome matrix Input Fiscal Year Public Expenditure on Health GDP per As % of As % of capita total GDP 1995/96 Budget (HBGDP price (RPI) (HBTG) ) Infant mortality Rate (IMR) Child Mortality Rate (CMR) Outcome Crude Birth Rate (CBR) Life Expectancy Rate (LER) 1989/90 7895.35 4.60 0.93 128.00 197.00 41.60 53.50 1990/91 8222.13 3.84 0.88 107.00 197.00 39.60 54.00 1991/92.8420.13 3.62 0.84 107.00 197.00 39.60 54.00 1992/93 8472.81 3.40 0.64 102.00 165.00 39.60 54.02 1993/94 8912.08 4.85 1.08 102.00 165.00 39.60 54.00 1994/95 8986.90 4.91 1.21 102.00 165.00 37.50 54.00 1995/96 9271.18 5.99 1.44 102.00 165.00 37.50 54.00 1996/97 9485.47 6.19 1.42 79.00 118.00 37.80 54.50 1997/98 9586.51 5.70 1.37 74.70 118.00 35.40 56.10 1998/99 9747.10 5.69 1.34 69.42 111.72 34.54 57.52 1999/00 10148.44 6.09 0.80 66.78 108.78 34.10 58.25 2000/01 13809.56 5.19 0.87 64.14 105.44 33.58 58.95 2001/02 13527.88 4.91 0.79 64.00 91.00 33.50 60.00 2002/03 13750.91 5.05 0.81 60.00 83.50 48.00 60.00 2003/04 14096.31 5.26 0.87 56.00 76.00 46.73 60.98 2004/05 14304.13 6.00 0.98 52.00 68.50 45.45 62.00 2005/06 14513.40 6.34 1.13 48.00 61.00 28.40 63.69 2006/07 14721.22 6.81 1.36 48.00 61.00 28.40 63.69 2007/08 15219.41 6.63 0.52 48.00 61.00 28.40 63.69 Sources: Adhikari and Maskay (2004), MOHP et al, (2009), RTI, (2009) This matrix shows that health outcomes are increased as health inputs are increased. All inputs are shown in monetary terms. These physical inputs are measured by the ratio between the population and physical inputs. Extension of services (health service providers) in terms of population per service (hospitals, health centres, health posts, ayurvedic service centres, sub-health posts, and primary health centres [SP]), the population per hospital bed (BP) and population per supply of skilled manpower (SMP) are also used in this analysis. All inputs are independent variables that determine the level of health outcomes (dependent variables). The correlation coefficients between the dependent and independent variables show the relationships in the input-output model. 4

Table 2.2: Correlation coefficients of the given variables IMR 1 IMR CMR CBR LER RPI HBTG HBGDP SP BP SMP CMR 0.98** 1 CBR 0.32 0.29 1 LER -0.93** -0.93** -0.34 1 RPI -0.90** -0.91** -0.23 0.96** 1 HBTG -0.70** -0.74** -0.48* 0.64** 0.58* 1 HBGDP 0.06 0.03-0.16-0.20-0.23 0.41 1 SP 0.73** 0.75** 0.26-0.52* -0.55* -0.71** -0.31 1 BP 0.18 0.16 0.14-0.40-0.28 0.11 0.53* -0.39 1 SMP 0.90** 0.88** 0.24-0.76** -0.73** -0.61** 0.03 0.65** 0.16 1 ** Significant at 1% level * Significant at 5 % level IMR has positive correlations with CMR, SP and SMP and negative correlations with LER, RPI, and HBTG at a 1 percent level of significance. Similarly, CMR has positive correlations with SP and SMP, and negative correlation with LER, RPI, and HBTG at a 1 percent level of significance. CBR is negatively correlated with only HBTG at a 5 percent level of significance. LER has a positive correlation with RPI, HBTG, SP and SMP. The coefficients that are significant at 1 percent or 5 percent have theoretically expected signs. The results suggest that there are expected relationships between the given variables. 2.2 Regression analysis The most popular method for estimating the causal relationships between health inputs and health outcomes was suggested by Filmer and Pritchett (1999), and Adhikari and Maskay (2004), and is used in this analysis. The following conceptual equation for the health production function is: This equation relates the dependent variable, M, which is taken to be either that of child morality rate, infant mortality rate, crude birth rate and life expectancy rate, to the log of mean per capita income, the log of the share of public health as a fraction of total government budget and the log of the share of public health as a fraction of GDP. X is the independent variable which control for a variety of other socio-economic factors. Regression analysis helps illustrate the causal relationships between input and outcome variables. 5

Table 2.3: Regression analysis and interpretations Dependent variable Real GDP per capita Ratio of health budget to total government budget Ratio of health budget to GDP Number of people served by the health care providers Number of people served by a bed Number of people served by skilled manpower IMR -0.7180-0.1850-0.0093 0.1099 0.4132 0.2222 Interpretations (overall model significant at 1% level with high Squared (0.9684) Significant at 1% with expected sign Expected sign but not significant Expected sign but not significant Expected sign but not significant Expected sign and Significant at 10% Expected sign and Significant at 5% CBR 0.1085-0.4653 0.0470 0.1083 0.4914-0.0873 Interpretations (overall model not significant) not significant not significant not significant not significant not significant not significant CMR -1.0733-0.4457-0.0093 0.0607 0.4605 0.1913 Interpretations (overall model significant at 1% level with high Squared (0.9591) Significant at 1% with expected sign Expected sign and Significant at 10% Expected sign but not significant Expected sign but not significant Expected sign but not significant Expected sign but not significant LER 0.1975 0.0615-0.0028 0.0053-0.1032-0.0180 Interpretations (overall model significant at 1% level with high Squared (0.9666) Significant at 1% with expected sign Expected sign and Significant at 10% not significant not significant Expected sign and Significant at 5% not significant The regression analysis demonstrates that government health expenditure has the power to change health outcomes, particularly CMR and life expectancy. However, it has a little power compared to per capita income. The elasticities of the given variables determine how sensitive the output variables are to changes to the input variables. Table 2.4: Elasticity of health outcomes with respect to health inputs Variable IMR CMR LER Real per capita income -0.718-1.07 0.198 Ratio of health budget to government total budget - -0.45 0.062 Number of people served by a bed 0.413 - -0.103 Note: This employs a double log model The coefficients of elasticity provide encouraging results for researchers and policymakers. If the real per capita GDP increases by 10 percent, it will decrease the IMR by almost 7 percent, CMR by 11 percent, and increase LER by almost 2 percent. Similarly, if we increase the ratio 6

of health budget to the total budget by 10 percent, CMR will decrease by 4.5 percent and LER will increase by 0.6 percent. If we increase the number of beds in hospitals, the number of people served by a bed will be reduced. If we reduce the ratio of people to total beds by 10 percent (in other words, increase the number of beds), it will reduce IMR by 4 percent and increase LER by 1 percent. The literature provides indicative results based on empirical evidence of how extra spending leads to changes in health outcomes. The World Bank has written about health expenditure effectiveness, In countries with good policies and institutions (strong property rights, absence of corruption, a good bureaucracy), an extra 10 percent of GDP in aid has been estimated to lead to a decline in infant mortality of 9 percent. By contrast, in countries in which policies are only average, the impact is just 4 percent. Where policies are bad, aid has no statistically significant effect on infant mortality, (Wagstaff and Claeson, 2004, page 56). Thus, the Nepalese health system can be expected to have average results; there is a lot of room for improvement. 2.3 Cost Effectiveness There are various methods to measures the cost effectiveness efficiency of health systems. It is determined by both production function and prevailing resources. Effectiveness can be defined as the difference between the targeted outputs, such as targeted coverage and achieved output (coverage). Obviously, targeted coverage is 100 percent; existing percentage of coverage allowed to measure the cost effectiveness with comparing their costs of production. The results can be interpreted by utilizing the health production function, which describes the relationship between intermediate outputs and health-related inputs. The values of these inputs are costs of production; therefore, all inputs are in monetary terms. In cost effectiveness analysis, intermediate outputs or activities are used to assess the causal effects of health expenditures. It is difficult to determine the direction of the impact prior to estimation of the results. Effectiveness that is measured in natural unit is presented in the appendix A for last 13 years. All inputs are measured in monetary terms and outputs are primarily intermediate outputs (effectiveness). It measures the effectiveness of public financing to produce intermediate health outputs. Data for last the 13 years were collected from economic surveys and annual reports. All data are used in natural log forms to capture the non-liner relationships between the dependent and independent variables. Similar to efficiency measurement, all effectiveness variables are dependent variables and real GDP per capita income, ratio of health budget to total government budget, ratio of health budget to GDP, ratio of population to existing public health care providers, numbers of beds, and skill manpower are independent variables 7

Table 2.5 Regression analysis of health inputs and intermediate outputs Real GDP per capita Ratio of health budget to total government budget Ratio of health budget to GDP 8 Number of people served by health care providers Number of people served by a bed Number of people served by skilled manpower Dependent variable All immunization coverage (BCG, DPT3, Polio3 * (- and Measles) 0.23) - - - - - Nutrition (growth monitoring coverage %) * (0.90) ** (1.08) - * (-1.01) - - Nutrition (proportion of malnourished children %) * (-2.20) - - - - - ARI incidence * (2.24) ** (1.17) - - - - Incidence of pneumonia (mild and severe) * (1.7) ** (0.6) - * (-0.7) - - Incidence of diarrhoea * (-1.9) - - - - - Some dehydration cases - - - - - - Severe dehydration Diarrhoea case fatality rate * (-10.4) - ** (1.1) - - ** (-2.5) - - - * (-1.5) - - First antenatal visit % - - - - - - Contraceptive prevalence rate (CPR) - - - - - - Condoms (CPR method mix) - - - - - - Malaria blood examination rate ** (0.21) * (0.37) - * (-.46) - - Tuberculosis case detection rate - - - * (-.82) - - DOTS coverage - - - - - - Total OPD new visits - - - - * (3.55) - Note: - = Not significant; *= significant at 5% level; **= significant at 10% level; coefficients are in parenthesis The results show that most of the variables were not statistically significant. This cost effectiveness analysis suggests that the health system had lower performance in producing intermediate outputs since they are either constant or decreasing over time. Thus, policy reform must work to improve the effectiveness of health outputs.

2.4 Allocative efficiency The government has given priority to preventive services, which are related to public goods. Expenditure for medical services has fallen since 2000; conversely, expenditure on public health services has increased. Fiscal year 2003/04 saw nearly equal allocation of financial resources to medical services and public health services. Figure 2.1: Allocation of resources to medical versus public health services MoHP, (2009a) Analysis of the Public Expenditure Review of the health sector revealed that rural health services consumed greater financial resources than urban areas, and that the disparity between the two is growing. Figure 2.2: Allocation of funds to rural/urban areas MoHP, (2009a) The following graph shows that priority 1 programmes has consumed almost one third of the total development budget for the health sector during the review period; conversely, priority 3 9

programmes consumed the majority of development health spending. The results validated the hypothesis that expenditure in the health sector has not been allocated based on priorities. Figure 2.3: Allocation of resources in priority programmes MoHP, (2009a) III. Factors contributing to Health production function 3.1 Health production function Health care programmes are continuously working to produce health outputs, thus the health outcomes reveal the effectiveness of the programmes. For example, immunization packages (inputs) are implemented through the regular health infrastructure. Coverage, however, as measured by the percentage of immunized people, has remained constant, and in some cases has even decreased. Similarly, the programme to monitor stunted children increased its coverage until 2005/06 when coverage plateaued despite no changes in the number of resources (inputs) devoted to the programme. 10

Figure 3.1: Coverage of Immunization Source: MoHP, (2009b); Annual reports, various years Figure 3.2: Monitoring of stunting Source: Annual reports, various years These two graphs begin to explain why the desired results have not been achieved in the health system; the government has continuously increased funding for health programmes, but institutional capacity has remained the same. The health results are remarkably consistent, showing an initial improvement in outcomes followed by a drop in results and eventual decline, as in Figure 3.2. The fact sheets from various annual reports covering 13 years are presented in appendix A, and they bear out the same conclusion. It is difficult to show the relationships between all health programmes and health outputs in Figures, but we have presented an econometric analysis in the cost effectiveness section. Figure 3.3 is the representative curve for all health programmes and health outputs. Obviously, if there are no health programmes, health outputs will be zero. As health 11

programmes P 1, P 2, P 3, and so on are implemented, health outputs are improved H 1, H 2, H 3. Clearly, there is a positive relationship between health programmes and health results (outputs); results increase as health care packages or programmes are increased. During the initial phase of implementation, the rate of improvement in outcomes is proportional to increases in the health programmes. Over time, however, the rate of change falls off. If we increase health programmes, P 3 to P 4, health outputs remain constant (H 3 ). This is the point of stagnation. The data from the last 13 years suggests that now are now almost at this point of stagnation. Figure 3.3: Health production function 3.2 Determining factors for health production function Health programmes (service packages), the allocated budget, human resources, and health institutions determine health production function. When health outputs fail to increase despite increases to the health budget and health programmes, health institutions and human resources that is to say capacity - may be critical to increasing health. 3.2.1 Public health institutions The population per public health institution is defined as the ratio of people to health care institutions. It is one of the indicators measuring distribution of public health service providers in relation to the size of the population. The data on health institutions and population size are derived from the Ministry of Health and Population 2007Annual Report. The results, as seen in Figure 3.4, demonstrate that public health institutions (hospitals, primary health care centres, health posts, and sub-health posts) serve almost equal numbers of people in each region. 12

Figure 3.4: Ratio of population to public health institutions by region Sources: MoHP, (2007) The ratio of population to public health institutions appears to be evenly distributed by region; however, the absolute distribution of public health institutions by development region is quite unequal. Public health institutions are skewed towards the relatively developed Eastern, Central, and Western regions (Figures 3.5 and 3.6). Hospitals and primary health care centres (PHCCs) are significantly lower in the Mid-western and Far Western regions. Figure 3.5: Distribution of hospitals and PHCCs Sources: MoHP (2007 ), and estimated 13

Figure 3.6: Distribution of health posts (HPs) and sub-health posts (SHPs) Sources: MoHP (2007 ), and estimated The establishment of health care institutions based on population distribution might be appropriate from the perspective of cost effectiveness, but would be questionable in terms of equity and fundamental human rights. It might be more appropriate to consider both population distribution and distance from an institution since distance is an important factor in determining access to health services. The health care institution density index (HID) developed by the Central Bureau of Statistics (2003) depicts public health institutions and access to health care services by development regions. The results demonstrate that the Midwestern and Far Western regions have less access to health care services than the other three regions. Figure 3.7: Health care institution density index (mean) Sources: CBS (2003) Distribution of health institutions and health institution density are concerned with supply side analysis of health institution. The adequacy of public health institutions based on people s perceptions is the demand side indicator that measures the distribution of institutions and access to health care services. If people feel that public health institutions are adequate, it 14

indicates that these institutions are delivering services adequately. Overall, the percentage of households reporting government health facilities as good is only 14 percent, while 64 percent reported that they were only fair. In the Mid-western region, up to 35 percent of households reported facilities as poor. Figure 3.8: Reported adequacy of health care services by region Source: CBS (2004) 3.2.2 Private health institutions Both public and private health institutions contribute to the delivery of health services. We have very limited data on private providers, but their contributions can be seen in the utilization of services. The following figures compare the numbers of public health providers with private health providers by ecological belt. Overall, there are more public than private institutions. The Mountain region has fewer private hospitals than the other two ecological belts. In the Terai, a greater number of people have utilized private health care providers, while people in the Mountain region are more likely to use public institutions. In the Hill region utilization of public and private hospitals appears to be nearly equal. To calculate utilization, private provider includes hospitals, clinics, and pharmacies. Public providers include hospitals, primary health care centres, health posts, and sub-health posts. Figure 3.9 (a) and (b): Public/private health care institution utilization by ecological belt 15

Source: MoHP (2008) Source: CBS (2004) The results shown below suggest that private hospitals are concentrated in the Central region, and in urban areas. And it is only in the Far Western region, which only has one private hospital, that there is greater utilization of public institutions than private institutions. Figure 3.10 (a) and (b): Public/private health institution utilization by development region Source: MoHP (2008) Source: CBS (2004), NLSS (2003/04) 3.2.3 Human resources Human resources are the active factors of health production that are responsible for the effective implementation of the programmes. Motivational factors, including trainings, promotions, and monetary incentives for employees of the health care system are also important to strengthening institutional capacity. The data published in the government s economic survey (MoF, 2009) shown in Figure 3.11 shows that human resources in health institutions has remained very low throughout the past decade or so. Figure 3.11: Trends in human resources in health institutions Source: MoF (2009) 16

The figure below illustrates which percentage of sanctioned doctor posts at public health care institutions are currently staffed, according to development region. The Mid-western region has the lowest percentage while the Central region has the highest percentage. Surprisingly, other health professionals are the most likely to be filled in all regions. Figure 3.12: Percentage of filled sanctioned posts by region Sources: HMIS 2009 and estimated Figure 3.13: Percentage of filled sanctioned posts by profession Source: MoHP (2009), and estimated Monitoring and supervision of the health care institutions can improve institutional capacity and lead to improved health outputs. The data on the number of supervised institutions as well as a number of health output indicators (e.g. coverage of immunization, pregnant woman receiving anti-helminths, 4 th ANC visits, etc.) is available. The correlation coefficient can 17

give an indicative measurement of institutional capacity using this data from all 75 districts. The hypothesis is that there should be positive relationships between supervision and health outputs. A supervision index that specifically measures the ratio of supervised institutions to total number of institutions including PHCCs, HPs, SHPs, outreach clinics, and EPI clinics has been developed for this purpose. The degree of correlation between supervision and health outcomes is presented in the following table. Table 3.1: correlation coefficients of supervision and health outcomes Health output Coverage of BCG Coverage of DPT Coverage of Polio 3 Coverage of Measles % of pregnant woman receiving antihelminthes 4th ANC visits as % of expected pregnancies Supervision index 0.22 0.14 0.15 Source: estimated 0.23 (significant at 5 %) -0.08 0.33 (significant at 1 %) The correlation coefficient yields the result that only two health outputs show a positive impact because of supervision, suggesting that supervision of health institutions has only a minimal impact on improving institutional capacity to deliver health care services. 3.3 Possible synergic effects in health production The difficulties of working to make a meaningful impact on health care services are compounded when dealing with the dual issues of inadequate health institutions on the one hand, and inadequate human resources within these institutions on the other. When these two deficiencies are combined, the negative impact on health outcomes is greater than either one would be on its own. The lower health outcomes seen in the Mid-western and Far Western regions and described in Section 3 are the result of just such a negative synergic effect. These synergic effects can be observed through the gap between the percentage of vacant positions and distribution of institutions. If the percentage of vacant position is higher than the percentage distribution of institution, this creates negative synergic effects; otherwise, there will be positive synergic effect in production of health services. The size of negative synergic effect is shown by the shaded area in the diagram. 18

Figure 3.14: Differences in institutional capacity by region Source: estimated 3.4 Changes to the health production function Institutional capacity and management is critical to the successful implementation of health care services and is determined by a number of factors, including human resources, the number of health institutions, and monitoring and supervision, among others. The analysis suggested that health institutions because of uneven geographic distribution (including institutional capacity, such as monitoring and supervisions) and human resources have less capacity to produce health outputs in the health system. If we increase the number of health institutions in both the public and private sectors and expand human resources, we can produce additional health outputs. The health production function will be shifted as shown by the dotted lines in Figure 3.15. The additional public health institutions could be more evenly distributed to better service underserved populations, while private institutions provide people with additional options. The government can motivate private institutions including community hospitals, teaching hospitals, and NGO hospitals through introducing incentive packages, including for example, tariff exemptions on imported instruments or exemption from levying the 5 percent service charge. Human resources can be expanded in two ways: through increasing the number of available positions at institutions and by making it mandatory for all medical students to spend a certain period of time working at local health institutions. 19

Figure 3.15: Shift in health production function IV. Equity analysis 4.1 Child health outcomes As we discussed in Section 2, child health outcomes in terms of infant mortality and underfive child mortality rates are improving, but the trend in improvements for under-five child health outcomes is higher than for infant health outcomes. 4.1.1 Child health outcomes by location The following figures (Figures 4.1 and 4.2) show that despite some improvements, the Mountain, Western, and Far Western regions still have high infant mortality rates (IMRs). Furthermore, the graphs illustrate quite clearly that the Hill area continues to have better performance than the Mountain or Terai areas. Even more worrying than the disparity among ecological regions, the Mid-Western region has witnessed a 33 percent increase in infant mortality, more than double the national average. Nonetheless, rates of infant mortality have fallen over time. 20

Figure 4.1: Infant mortality by ecological belt Source: MoHP et al (2007), (2002) and (1997) Figure 4.2: Infant mortality rate by development region Source: MoHP et al (2007 ), (2002) and (1997) 4.1.2 Child health outcomes by socioeconomic status While every wealth quintile showed progress from 2001 to 2006, improvements in child health outcomes appear to be strongly correlated to socioeconomic status, with the wealthiest quintile displaying nearly twice as much progress in child health than the lowest quintile during the same survey period. That being said, however, child health among the middle wealth quintile did not improve as much as for other income groups, according to the 2006 survey, and we see that the greatest gains were made by the second-poorest quintile. 21

Figure 4.3: Infant mortality rate by wealth quintile Source: MoHP et al (2007) and (2002) Figure 4.4: Under-five mortality, by wealth quintile Source: MOHP et al (2007) and (2002) The under-five mortality rate has decreased among all wealth quintiles, as has the disparity between the poorest and richest quintiles. The difference between the richest and poorest quintiles was 87 per 1,000 live births in 1996, but fell to a difference of 51 per 1,000 live births in 2006, indicating a marked improvement in the health outcome of children under five. 22

Table 4.1: Trends in Under-five mortality per 1,000 live births by wealth quintile Wealth quintile 1996 2001 2006 Percent Change First (poorest) 166 135 98-40.1 Second 157 121 83-47.1 Third 159 109 91-42.8 Fourth 106 92 63-40.6 Fifth (wealthiest) 79 59 47-40.5 Total 118 91 61-48.3 Difference between poorest and richest quintiles 87 76 51-41.4 Source: RTI International, (2008) 4.1.3 Child health outcomes by caste/ethnicity Table 4.2 depicts inequalities between castes/ethnic groups in the under-five mortality rate. The disparity between Brahmins/Chhetris and Dalits has narrowed to 14 per 1,000 live births in 2006 from 45 in 1996, a 69 percent decrease. Under-five mortality among Dalits was cut by almost half from 1996 to 2006. The disparity between Newars and Janajatis, by comparison, decreased by only 14 percent. The largest decrease in under-five mortality was among Muslims, a 57 percent decrease from 158 per 1,000 live births in 1996 to 68 in 2006. Table 4.2: Trends in under-five mortality per 1,000 live births by caste/ethnic group Caste/ethnic group 1996 2001 2006 23 Percent Change Brahmins/Chhetris 125 98 76-39.2 Dalits 170 129 90-47.1 Janajatis 126 108 80-36.5 Other Terai Groups/Madhesis 164 130 86-47.6 Newars 83 84 43-48.2 Muslims 158 99 68-57.0 Total 118 91 61-48.3 Difference between Brahmins/Chhetris and Dalits 45 31 14-68.9 Difference between Newars and Janajatis 43 24 37-14.0 Source: RTI International, (2008) Figure 4.5 describes infant mortality rates by caste/ethnic group, illustrating a sharp decline in the IMR among Dalits, from 103 per 1,000 live births in 1996 to 68 in 2006. A similar reduction in the IMR is seen among Muslims. The greatest decline, however, was among the

Others group (Marwari, Jaine, Punjabi/Sikh, Bengali, and unidentified), who make up one percent of the population. The IMR for this group plummeted from 132 per 1,000 live births in 1996 to 44 in 2006. There was a slower rate of decline in the IMR among Newars and Brahmins/Chhetris. The second-highest reduction in infant mortality was among the Other Terai Groups/Madhesis, for whom the IMR decreased by 39 percent. These trends indicate a move towards more equitable health outcomes. Figure 4.5: Infant mortality per 1,000 live births by caste/ethnic group Source: RTI International, (2008) 24

4.2 Maternal health outcomes The results of the analysis suggest that maternal health outcomes have improved as a result of the collective effort of different sectors of the government. Figure 4.6: National maternal mortality rate (MMR) Source: MoHP et al (2007) The maternal mortality rate (MMR) is the aggregate measure of maternal health outcomes. While an analysis of MMR by socioeconomic and geographical criteria might be useful for policy analysis, the current data does not permit such analysis. Instead, as suggested by Abou Zahr and Wardlaw (2001), we took the data on deliveries by skilled birth attendants (SBAs) and caesarean sections (C-sections) to serve as proxy indicators of maternal health. 4.2.1 Deliveries assisted by a skilled birth attendant The percentage of deliveries assisted by SBAs, including a doctors, nurses, or midwives, is one of the indicators of maternal health. The results show that the percentage of births delivered under the supervision of an SBA increased from 9 percent in 1996 to 19 percent in 2006. Similarly, the number of C-sections being performed has also risen. There is an inverse relationship between the MMR and the rate of SBA use and C-sections performed. Increased SBAs and C-sections reduce the possible death. Figure 4.7: Deliveries assisted by a skilled attendant and C-sections Source: MoHP et al (2007), (2002) and (1997) 25

SBA and C-section by development regions The data shows that the percentages of both SBA use and C-sections were higher in the Central regions and lower in the Midwestern and Far Western regions. This would suggest that the latter regions might have a higher MMR than other regions. Figure 4.8 SBA use as a percentage of total births, by development region Source: MoHP et al (2007), (2002) and (1997) Figure 4.9: C-sections as a percentage of total births, by development region Source: MoHP et al (2007), (2002) and (1997) SBA use and C-section by socioeconomic group Maternal health outcomes in terms of the percentage of births assisted at delivery by an SBA and percentage of C-sections performed show disparities among socioeconomic groups. Maternal health outcomes are more than twice as good as among the second-wealthiest quintile 26

Figure 4.10: SBA use and C-sections by wealth quintile Source: MoHP et al (2007) 4.3 Nutritional status Nutritional status is an important indicator of children s overall health and well-being. The nutritional status of young children is an indicator directly related to the health and productivity of people as adults. A number of factors determine the nutritional status of children in developing countries including low dietary intake, infectious disease, lack of appropriate health care, and inequitable distribution of food within the household. The nutritional status of children under five years of age is determined by two indices: percentage of stunted children (whose height-for-age is below minus two standard deviations), and percentage of underweight children (whose weight-for-age is below minus two standard deviations). The prevalence of low weight has decreased slightly over time, while the percentage of stunted children appears to have remained virtually unchanged. Figure 4.11: Trends in the nutritional status of children Source: MoHP et al (2007), (2002) and (1997) 27

4.3.1 Nutritional status by geographic region Regional variations are substantial, with 62 percent of children in the Mountain region exhibiting stunted growth, a number far above the national average, and one that is increasing over time. Stunting among children is highest in the Mid-western region (58 percent) and lowest in the Eastern region (40 percent), and while stunting is becoming more common in the Mid-western region, other regions exhibit little to no change. Figure 4.12 Prevalence of stunted children by development region Source: MoHP et al (2007), (2002) and (1997) Figure 4.13 Prevalence of underweight children by development region Source: MoHP et al (2007), (2002) and (1997) 28

4.3.2 Nutritional status by socioeconomic group Figure 4.14: Prevalence of stunted children by wealth quintile Source: MoHP et al (2007) V. Utilization of Health Care Services Health care service utilization is conditional upon illness: higher levels of acute illness lead to a higher possibility of health care utilization. Some parts of health care utilization are discussed in section three. Utilization of health services is presented in the following figures. 5.1 Disparities in health care utilization Poor people report lower utilization of health services compared to the rich, with the result that there is lower reporting of illness among the poor (only 10 percent). This does not mean, however, that they are healthier than the rich; in fact they have a greater incidence of disease, but because of they have less access to health care services - be that due to financial constraints, geographical obstacles, social hindrances, or any other number of factors that prevent the poor from seeking medical attention - their illness is far less likely to be officially recorded. Nonetheless, poor people still prefer private health care providers. This suggests that private facilities play a complementary role in providing health services to the people. 29

Figure 5.1: Percentage self-reporting acute illness, by wealth quintile Source: CBS (2004) Figure 5.2: Utilization of all health facilities, by wealth quintile Source: CBS (2004) 5.2 Antenatal care utilization Table 5.1 describes the changes, by wealth quintile, in 4 th antenatal care (ANC) visits provided to new mothers. As can be seen by the disparity between the richest and poorest quintiles, inequality has gradually grown between 1996 and 2006, increasing from 29 to 50 percent, despite a nearly four-fold increase in utilization by the poorest quintile. Table 5.1: Percentage of mothers making 4 or more ANC visits, by wealth quintile Wealth quintile 1996 2001 2006 30 Change in % points First (poorest) 2.7 5.1 10.5 7.8 Second 3.4 6.0 20.2 16.8 Third 5.8 9.5 27.7 21.9 Fourth 9.6 18.0 38.0 28.4 Fifth (richest) 31.5 46.8 60.0 28.5 Total 8.8 14.3 29.4 20.6 Difference between poorest and richest quintiles 28.8 41.7 49.5 20.7

Inequality among the various ethnic/caste groups for 4 th ANC visits continues, although there is a gradual move towards equality. (For greater detail, please see RTI International, 2008) Table 5.2: Trends in 4 or more ANC visits by caste/ethnic group in percentage Caste/ethnic group 1996 2001 2006 Change in percentage points Brahmins/Chhetris 14.0 20.5 39.9 25.9 Dalits 4.4 9.8 21.4 17.0 Janajatis 5.1 10.9 26.1 21.0 Other Terai Groups/Madhesis 6.1 8.2 17.9 11.8 Newars 32.2 41.2 57.9 25.7 Muslims 2.3 9.1 18.3 16.0 Others 6.6 31.3 29.0 22.4 Total 8.8 14.3 29.4 20.6 Difference between Brahmins/Chhetris and Dalits 9.6 10.7 18.5 8.9 Difference between Newars and Janajatis 27.1 30.3 31.8 4.7 Source: RTI International (2008) VI. Access to health care services Access to health care services in terms of the time taken to reach a health post provides further evidence of poor access in the Mid-western and Far Western regions. Figure 6.1: Access to health care services by region Source: CBS, (2004) NLSS 2003/04 Inequality of access to health care services is prevalent among socioeconomic groups as well: the richest quintile has much better access than the poorest quintile. 31