VWHP 3HUIRUPDQFH LQ 1HSDO · 2010-10-04 · This paper gathered information from various sources, including journal articles, published and unpublished research reports, various government

May 2010

Assessment of Health SystemPerformance in Nepal

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: [email protected]).

Suggested citation:

RTI International, 2010. Health System Performance. Research Triangle Park, NC, USA.

Health System Performance 2010

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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


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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


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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


iv

Acronyms

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


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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 health-related 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.


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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

Inputs Outcomes

Outputs

Maternal health (outcome)

Child health (outcome)

Caste/ Ethnicity

Socioeconomic status

Geographical

Allocative Technical Cost effectiveness

Utilization (output)

Nutrition (outcome)

Efficiency

Equity

Provider

Access

Resource

Coverage

Process

Mea

suri

ng P

erfo

rman

ce


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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 evidence-based 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).


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Table 2.1 Input outcome matrix Public Expenditure on Health Outcome

Inpu

t

Fiscal Year

GDP per capita 1995/96 price (RPI)

As % of total Budget (HBTG)

As % of GDP (HBGDP)

Infant mortality Rate (IMR)

Child Mortality Rate (CMR)

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.


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Table 2.2: Correlation coefficients of the given variables

IMR CMR CBR LER RPI HBTG HBGDP SP BP SMP

IMR 1

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.


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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 and Significant at 10%


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)







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)



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


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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


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Table 2.5 Regression analysis of health inputs and intermediate outputs

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 health care providers

Number of people served by a bed

Number of people served by skilled manpower

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

* (-10.4) -

**(-2.5) - - -

Diarrhoea case fatality rate

** (1.1) - -

*(-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.


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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


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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.


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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


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programmes P1, P2, P3, and so on are implemented, health outputs are improved H1, H2, H3. 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, P3 to P4, health outputs remain constant (H3). 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.


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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


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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 Mid-western 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


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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


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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)


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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, 4th ANC visits, etc.) is available. The correlation coefficient can


18

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 Cov

erag

e of

B

CG

Cov

erag

e of

D

PT

Cov

erag

e of

Po

lio 3

Cov

erag

e of

M

easl

es

% o

f pr

egna

nt

wom

an

rece

ivin

g an

ti-he

lmin

thes

4th

AN

C

visi

ts a

s %

of

exp

ecte

d pr

egna

ncie

s

Supervision index 0.22 0.14 0.15

0.23 (significant

at 5 %) -0.08

0.33 (significant at 1

%)Source: estimated

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.


19

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.


20

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 under-five 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.


21

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.


22

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.


23

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 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


24

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)


25

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



26

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


Figure 4.9: C-sections as a percentage of total births, by development region


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


27

Figure 4.10: SBA use and C-sections by wealth quintile


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



28

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


Figure 4.13 Prevalence of underweight children by development region



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4.3.2 Nutritional status by socioeconomic group

Figure 4.14: Prevalence of stunted children by wealth quintile


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.


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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 4th 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 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


31

Inequality among the various ethnic/caste groups for 4th 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 4.7Source: 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.


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Figure 6.2: Access to health care services by wealth quintile

Source: CBS, (2004)

6.1 Access to immunization

Universal immunization of children against the six vaccine-preventable diseases - tuberculosis, diphtheria, whooping cough, tetanus, polio, and measles - is crucial to reducing infant and child mortality. Disparities in immunization coverage among subgroups of the population are integral to informing programme planning and targeting resources to the areas with the greatest need. Additionally, information on immunization coverage is important for monitoring and evaluation of the expanded programmes for Immunization (EPI). As the data in Figures 6.3 and 6.4 suggest, there is still a high percentage of children that have not been immunized, particularly in the Mid-western region specifically, and in the Mountain region in general (CBS, 2004). In terms of socioeconomics, the poorest wealth quintile has the highest proportion of children who have not been immunized (CBS, 2004).

Figure 6.3: Percentage of immunized children by region

Source: CBS, (2004)


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Figure 6.4: Percentage of immunized children by ecological belt

Source: CBS, (2004)

The NDHS 2006 found that in total, 83 percent of children aged 12-23 months were fully immunized at the time of the survey. With regard to specific vaccines, 93 percent of children aged 12-23 months had received the BCG vaccination and 85 percent had been immunized against measles. Coverage for the first dose of DPT was relatively high (93 percent), but only 89 percent went on to receive the third dose of DPT. Ninety-seven percent of children received the first dose of the polio vaccine, and 91 percent went on to receive the third dose. Even though DPT and polio vaccines are often administered at the same time, polio coverage is higher than DPT coverage primarily due to the administration of polio vaccines during national immunization day campaigns. Nevertheless, the dropout between the first and third doses of polio is notable at 6 percent. Seventy-six percent of children aged 12-23 months received the first dose of the hepatitis B vaccine, but coverage dropped to 69 percent for the third dose (NDHS 2006).

The data also revealed that immunization coverage has grown over time. Unfortunately, the percentage of children who did not receive any of the six basic immunizations fell from 20 percent to 1 percent in 2001, but then tripled to 3 percent in 2006.

Figure 6.5: Percentage of fully immunized children



34

Figure 6.6: Percentage of children never immunized


VII. Conclusions

Performing an assessment of health system performance is critical to addressing health policy issues, and defines a set of measurable and reliable indicators. Careful consideration of the possible approaches to measuring health system performance provides the basis for improving outcomes. Health indicators such as life expectancy, infant/child mortality, and maternal mortality, among others, show gradual but steady improvement. Overall health system performance in terms of health production is quite encouraging in Nepal because unlike many developing countries, per capita income and government spending has proved effective in changing health outcomes (Wagstaff & Claeson, 2004). Public spending plays a major role in the planning, regulation, motivation, facilitation, and shaping of the health care service delivery system. The evidence suggests that money matters in health care, but that money alone is no guarantee of efficient, equitable, and effective health care. There are still large inequalities in access, utilization, and coverage of health services across geographical regions and socioeconomic groups. Intermediate outputs, particularly immunization coverage, services coverage, safe motherhood, and utilization of public health services have not shown encouraging progress in recent years. Improvement of institutional capacity in delivery of health services can shift the health production function and ensure equity in health care services across the regions. Institutional capacity includes not only public providers, but also how to make better use of private capacity to improve health outcomes.

Relatively low efficiency in health production function with negative synergic effects occurs in the Mid-western and Far Western regions. Similarly, health inequalities are relatively higher where less efficiency of health production appears. The evidence clearly shows that equity and efficiency are not in conflict in the Nepalese health system. Allocation of resources according to need can improve both the equity and efficiency of health outputs; however, a blanket policy may not have such capacity. Health care financing has the power to reform health care delivery organizations and provide incentives to providers to deliver more efficient and effective health care.


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References

AbouZahr C and Wardlaw T (2001) Bulletin of the World Health Organization, 2001, 79: 61–568. Acharya L and Cleland J (2000) Maternal and Child Health Services in Rural Nepal: Does Access or Quality Matter More? Health Policy and Planning; 15(2): 223–229.

Adhikari S. R. and Maskay, N. M. (2004) Health Sector Policy in the First Decade of Nepal’s Multiparty Democracy; Does Clear Enunciation of Health Priorities Matter? Health Policy, 68 (2004) 103-112.

Central Bureau of Statistics (CBS), International Centre for Integrated Mountain Development (ICIMOD/MENRIS) with the support of SNV-Nepal (2003) Districts Of Nepal Indicators Of Development , Kathmandu Nepal.

Central Bureau of Statistics (CBS, 2004) Nepal Living Standards Survey 2003/04: Statistical Report Volume one and two Central Bureau of Statistics, National Planning Commission Secretariat, Government of Nepal, December 2004.

Ensor T. and Cooper S. (2004) Overcoming barriers to health service access: influencing the demand sideHealth Policy and Planning; 19(2): 69–79.

Filmer, Deon and Lant Pritchett. “The impact of public spending on health: does money matter?” Social Science & Medicine. 49 (1999) 1309 - 1329.

Government of Nepal, Ministry of health (1999) Second Long Term Health Plan (1997-2017), HMG/N, Ministry of Health, Kathmandu, Nepal.

____. (2003), Health Sector Strategy: An agenda for reform HMG/N, Ministry of Health, Kathmandu, Nepal.

____.(2004)Nepal Health Sector Programme- Implementation Plan” (NHSP-IP), HMG/N, Ministry of Health, Kathmandu, Nepal.

______, National Planning Commission, 2003, The Tenth Plan (2002-07), National Planning Commission/His Majesty’s Government of Nepal.

Jamison, D T (2006) Investing in Health Disease Control Priorities in Developing Countries second edition, Oxford University Press and The World Bank.

Ministry of Finance (MOF) (2009) Economic Survey Kathmandu, Nepal: Ministry of Finance, Government of Nepal.

Ministry of Health and Population (MoHP) (2007), Department of health services Annual Report 2062/063 (2005/2006) Ministry of Health and Population, Department of Health Services.

Ministry of Health and Population (MoHP) (2008), Department of health services Annual Report 2061/062 (2004/2005) Ministry of Health and Population, Department of Health Services.


36

Ministry of health and population (MoHP) (2009a) Public expenditure review on the health sector Kathmandu, Nepal: Ministry of Health and Population, Government of Nepal.

Ministry of Health and Population (MoHP) (2009b), Department of health services Annual Report 2064/065 (2007/2008) Ministry of Health and Population, Department of Health Services.

Ministry of Health and Population (MoHP) [Nepal], New ERA, and Macro International Inc.(2007) Nepal Family and Health Survey 1997. Kathmandu, Nepal: Ministry of Health and Population, New ERA, and Macro International Inc.

Ministry of Health and Population(MoHP) [Nepal], New ERA, and Macro International Inc.(2007) Nepal Demographic and Health Survey 2002. Kathmandu, Nepal: Ministry of Health and Population, New ERA, and Macro International Inc.

Ministry of Health and Population(MoHP) [Nepal], New ERA, and Macro International Inc.(2007) Nepal Demographic and Health Survey 2006. Kathmandu, Nepal: Ministry of Health and Population, New ERA, and Macro International Inc.

RTI International, ( 2008)Equity Analysis of Health Care Utilization and Outcomes. Research Triangle Park, NC, USA.

RTI International, (2009) Cost and Equity Implications of Public Financing for Health Services at District Hospitalsbudget analysis in Nepal. Research Triangle Park, NC, USA.

Wagstaff A and Claeson M (2004) the Millennium Development Goals for Health: Rising To the Challenges World Bank. Washington DC.


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Appendix

Effectiveness of health system (intermediate health outputs) 1996/97 to 2007/08Intermediate Health Outputs, 1996/97 to 2007/08 (Page 1 of 3)

Expanded Programme on Immunization Nutrition Acute Respiratory Infections

Fiscal Year BCG coverage (%)

DPT-3 coverage (%)

Polio-3 coverage (%)

Measles coverage (%)

Growth monitoring coverage as % of <3 children new visits

Percentage of malnourished children (weight/age), new visits

Reported incidence of ARI/1,000 <5 children, new visits

Annual reported incidence of pneumonia (mild & severe)/1,000 among <5 children, new visits

1996/97 100 80 81 88 25 28 123 57

1997/98 100 83 83 89 28 23 140 64

1998/99 93 76 76 81 30 23 144 64

1999/00 97 80 80 77 34 21 166 72

2000/01 95 80 80 75 38 18 210 90

2001/02 94 80 80 76 41 16 229 97

2002/03 97 86 84 80 51 14 289 117

2003/04 96 90 90 85 54 12 344 131

2004/05 92 80 83 79 55 11 360 128

2005/06 96 93 92 88 59 9 405 136

2006/07 91 84 84 83 57 8 408 131

2007/08 87 82 82 79 57 6 614 190


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Intermediate Health Outputs, 1996/97 to 2007/08 (Page 2 of 3)

Control of Diarrhoeal Diseases Safe Motherhood Family Planning Malaria annual parasite incidence (API) per 1,000

Fiscal Year

% of some dehydration among total new cases

% of severe dehydration among total new cases

Case fatality rate/1,000 <5 Children

Antenatal first Visits as % of expected pregnancies

Contraceptive prevalence rate (modern method) (%)

Condoms (CPR method mix) (%)

Malaria annual parasite incidence (API) per 1,000

1996/97 45 8 1.4 21 31 1.5 0.9

1997/98 43 7 0.9 26 31.3 1.7 0.8

1998/99 48.2 7 0.1 27 32.6 1.9 0.7

1999/00 41.1 5 0.7 35 34.5 1.9 0.6

2000/01 41.7 4 0.4 41 36.9 2.1 0.7

2001/02 41.6 4 0.22 43 37.4 2.2 0.6

2002/03 41.4 3 0.2 53 37.8 2.2 0.8

2003/04 40.3 3 0.25 66 40.2 2.4 0.25

2004/05 37.8 2 0.31 69 41.3 2.3 0.32

2005/06 33.6 1 0.11 73 42 2.6 0.3

2006/07 32.1 1 0.17 72 42.1 2.6 0.28

2007/08 16.1 0.9 0.01 68 40.9 2.5 0.23


39

Intermediate Health Outputs, 1996/97 to 2007/08 (Page 3 of 3)

Tuberculosis Control Programme Leprosy Control Programme

Fiscal Year Case detection rate* (%)

Treatment success rate on DOTS* (%)

New case detection rate (NCDR) /10,000 ** Prevalence rate (PR)/10,000 **

Disability rate Grade 2 among new cases**

1996/97 48 50 3.5 5.9 10

1997/98 50 53 3.17 3.9 11.7

1998/99 62 64 7.8 8.7 8.7

1999/00 67 89 3.18 3.88 7.18

2000/01 69 89 2.44 3.43 8.43

2001/02 70 89 5.73 4.41 4.31

2002/03 71 90 3.24 3.04 3.95

2003/04 71 88 2.84 2.41 3.48

2004/05 70 88 2.41 2 3.52

2005/06 65 88 1.96 1.65 4.81

2006/07 70 89 1.65 1.45 5.56

2007/08 71 88 1.67 1.42 4.15 Sources: Annual reports, various years

Health Sector Reform Support ProgrammeMinistry of Health and Population

P.O. Box: 8975 EPC 535Kathmandu, NepalPhone: +977 1 4266180Fax: +977 1 4266184URL: wwURL: www.hsrsp.org

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VWHP 3HUIRUPDQFH LQ 1HSDO · 2010-10-04 · This paper gathered information from various sources, including journal articles, published and unpublished research reports, various government