《精神科护理学 Psychiatric Nursing》教学阅读资源：2016 World Health Statistics.pdf_P26-P30

UHC service coverage index quintiles, by WHO region between the poor and the national average, while several Lowest 2nd 3rd 4th Highest countries have relative differences of more than 40% 4.3 Financial protection-measuring the impact of out-of-pocket payments With regard to tracking levels of financial protection, the global WHO and World Bank monitoring framework proposes the use of two indicators: the incidence of disproportionate spending on health which is labelled catastrophic"; and the incidence of poverty resulting from health expenditures paid directly by households which is labelled"impoverishing". 2 This section presents data these two indicators for a selected number of countries Updated estimates by the world Bank and WHo of both The dashboard (Fig 4.2)shows the coverage levels for each catastrophic and impoverishing health spending for all of the indicators used in the computation of the index in this countries will be published in 2016. This report also presents report. The range of country values varies by indicator and data from all countries on the related macro-level indicator between regions. Such a dashboard will also be useful when of OoP payments on health presented for a single country with the UHC coverage index At the health system level, the fraction of total health expenditure (THE) that comes from ooP health 4.2 Inequalities in coverage -towards an expenditures is a measure of the extent to which households integrated assessment contribute towards financing the provision of all health services in a country. The lower this fraction, the greater Ensuring that all people who need health services receive the likelihood that households are protected from financial them is a UHC imperative, which makes tracking inequalities hardship when accessing health services. Estimates of in health-service coverage a central UHC monitoring goal. ooP health expenditure as a share of he are generated Ideally, the UHC index described above would be computed annually by Who using national health accounts(NHAs) for both the national population and for disadvantaged and other sources. 3 groups, and then combined to reflect the degree of inequity in service-coverage indicators across key inequality Figure 4. 4 presents the ooP health expenditure as fraction dimensions such as socioeconomic status of THE 4 Health financing systems in low-income and lower middle-income countries rely heavily on ooP payments This approach is currently not feasible for many countries implying that households are the major contributors to due to data limitations. For most indicators, disaggregated the health financing system(42.3% and 40.6% in 2013 data are only partially available or present comparability respectively ). Such countries face particular challenges issues.The most extensive standardized disaggregated as they have inadequate service delivery systems and data are available for indicators in the first category additionally struggle to raise domestic revenues to pay for (reproductive, maternal, newborn and child health). This such services. In contrast, ooP health expenditure as a is especially the case for developing countries. These data fraction of THE in high-income countries is much lower, at are used to compute a relative inequality score based 21.2%. At regional level, this fraction is highest in the WHO on the ratio of the mean coverage among the poorest South-East Asia Region and WHO Eastern Mediterranean populations' to the national average. A value of 100 means Region(40.8% and 39. 5%, respectively) no difference at all; and the smaller this value, the greater the gap between the poorest and the national average a summary of these scores is presented in Fig. 4.3 fo countries that have conducted an international household health survey(Demographic and Health Survey -DHS or In the context c Multiple Indicator Cluster Survey-MICS) since 2005. It is 2 In the context of the SDG indicator framework a very different indicator has initially apparent that large differences exist in the relative inequality urrently compiled and the indicator is not presented in this repot score of reproductive, maternal, newborn and child health 3 Not all countries maintain or update NHAs. In such cases, data are obtained through intervention coverage across countries, with many countries chnical contacts in the country or from publicly available documents and reports Missing values are estimated using various accounting techniques depending upon the having relative differences of less than 10%in coverage data available for each country 1 Computed as the average of twice the coverage among the poorest quintile and that nd income-group aggregates are ted using unweighted averages ong the second poorest quintile(2*01+02/3) MONITORING HEALTH FOR THE SDGs

MONITORING HEALTH FOR THE SDGs 17 The dashboard (Fig. 4.2) shows the coverage levels for each of the indicators used in the computation of the index in this report. The range of country values varies by indicator and between regions. Such a dashboard will also be useful when presented for a single country with the UHC coverage index. 4.2 Inequalities in coverage – towards an integrated assessment Ensuring that all people who need health services receive them is a UHC imperative, which makes tracking inequalities in health-service coverage a central UHC monitoring goal. Ideally, the UHC index described above would be computed for both the national population and for disadvantaged groups, and then combined to reflect the degree of inequity in service-coverage indicators across key inequality dimensions such as socioeconomic status. This approach is currently not feasible for many countries due to data limitations. For most indicators, disaggregated data are only partially available or present comparability issues. The most extensive standardized disaggregated data are available for indicators in the first category (reproductive, maternal, newborn and child health). This is especially the case for developing countries. These data are used to compute a relative inequality score based on the ratio of the mean coverage among the poorest populations1 to the national average. A value of 100 means no difference at all; and the smaller this value, the greater the gap between the poorest and the national average. A summary of these scores is presented in Fig. 4.3 for countries that have conducted an international household health survey (Demographic and Health Survey – DHS or Multiple Indicator Cluster Survey – MICS) since 2005. It is apparent that large differences exist in the relative inequality score of reproductive, maternal, newborn and child health intervention coverage across countries, with many countries having relative differences of less than 10% in coverage 1 Computed as the average of twice the coverage among the poorest quintile and that among the second poorest quintile ((2*Q1+Q2)/3). between the poor and the national average, while several countries have relative differences of more than 40%. 4.3 Financial protection – measuring the impact of out-of-pocket payments With regard to tracking levels of financial protection, the global WHO and World Bank monitoring framework proposes the use of two indicators: the incidence of disproportionate spending on health which is labelled “catastrophic”; and the incidence of poverty resulting from health expenditures paid directly by households which is labelled “impoverishing”.2 This section presents data on these two indicators for a selected number of countries. Updated estimates by the World Bank and WHO of both catastrophic and impoverishing health spending for all countries will be published in 2016. This report also presents data from all countries on the related macro-level indicator of OOP payments on health. At the health system level, the fraction of total health expenditure (THE) that comes from OOP health expenditures is a measure of the extent to which households contribute towards financing the provision of all health services in a country. The lower this fraction, the greater the likelihood that households are protected from financial hardship when accessing health services. Estimates of OOP health expenditure as a share of THE are generated annually by WHO using national health accounts (NHAs) and other sources.3 Figure 4.4 presents the OOP health expenditure as fraction of THE.4 Health financing systems in low-income and lower middle-income countries rely heavily on OOP payments implying that households are the major contributors to the health financing system (42.3% and 40.6% in 2013, respectively). Such countries face particular challenges as they have inadequate service delivery systems and additionally struggle to raise domestic revenues to pay for such services. In contrast, OOP health expenditure as a fraction of THE in high-income countries is much lower, at 21.2%. At regional level, this fraction is highest in the WHO South-East Asia Region and WHO Eastern Mediterranean Region (40.8% and 39.5%, respectively). 2 In the context of the SDG indicator framework a very different indicator has initially been proposed: coverage by health insurance or a public health system. Because health insurance means very different things in different countries, no global data are currently compiled and the indicator is not presented in this report. 3 Not all countries maintain or update NHAs. In such cases, data are obtained through technical contacts in the country or from publicly available documents and reports. Missing values are estimated using various accounting techniques depending upon the data available for each country. 4 To avoid bias towards countries at either end of the population scale, and to avoid bias towards countries which represent a large share of global health spending, regional and income-group aggregates are estimated using unweighted averages and excluding countries with a population of less than 150 000. 30 20 10 40 Figure 4.1 UHC service coverage index quintiles, by WHO region 50 60 EUR 100 Lowest 2nd 3rd 4th Highest AMR WPR EMR SEAR AFR 0 90 80 70 Fraction of countries in the region (%)

MONITORING HEALTH FOR THE SDGs 19 Whether such OOP payments cause financial hardship or not requires comparing household levels of OOP health expenditure in relation to total household expenses. OOP payments are judged to be catastrophic when they exceed a given proportion (25%) of the total household budget or of the capacity to pay (40%).1 They are labelled impoverishing when OOP payments push a household’s other spending below a minimum socially recognized living standard such as that identified by a poverty line. The poverty line should be defined according to national standards and also against an international poverty line, consistent with SDG targets 1.1.1 and 1.2.1. The global framework recommends that countries, as a minimum, track the proportion of the 1 Capacity to pay is defined as household’s expenditure net of subsistence spending (for example on food). Country Year Swaziland 2010 Sao Tome and Principe 2008 Malawi 2010 Zimbabwe 2010 Zambia 2007 Burundi 2010 Rwanda 2010 Sierra Leone 2013 Ghana 2011 Gambia 2005 Liberia 2013 Gabon 2012 Uganda 2011 Namibia 2006 United Republic of Tanzania 2010 Lesotho 2009 Congo 2011 Kenya 2008 Comoros 2012 Democratic Republic of the Congo 2013 Niger 2012 Benin 2011 Togo 2010 Burkina Faso 2010 Madagascar 2008 Côte d'Ivoire 2011 Mozambique 2011 Senegal 2012 Mali 2012 Guinea 2012 Mauritania 2007 Guinea-Bissau 2006 Ethiopia 2011 Cameroon 2011 Central African Republic 2010 Nigeria 2013 94 93 91 91 91 91 91 90 87 87 86 86 85 84 83 83 80 79 77 76 75 74 73 72 71 69 69 66 64 62 60 59 59 55 50 40 AFR Country Year Costa Rica 2011 Dominican Republic 2007 Guyana 2009 Colombia 2010 Honduras 2011 Belize 2011 Peru 2012 Suriname 2010 Bolivia (Plurinational State of) 2008 Haiti 2012 98 95 95 94 93 92 92 91 82 80 AMR Country Year Maldives 2009 Thailand 2005 Bhutan 2010 Indonesia 2012 Nepal 2011 Bangladesh 2011 Timor-Leste 2009 India 2005 99 99 88 87 79 78 76 70 SEAR Country Year Uzbekistan 2006 Kyrgyzstan 2012 Belarus 2012 Kazakhstan 2010 Ukraine 2007 Armenia 2010 Albania 2008 Tajikistan 2012 The former Yugoslav Republic of Ma.. 2011 Bosnia and Herzegovina 2011 Georgia 2005 Republic of Moldova 2005 Montenegro 2005 Serbia 2010 Azerbaijan 2006 100 99 96 96 91 90 89 89 86 86 85 84 83 82 78 EUR Country Year Jordan 2012 Egypt 2008 Iraq 2011 Syrian Arab Republic 2006 Pakistan 2012 Afghanistan 2010 Yemen 2006 Somalia 2006 98 90 89 85 73 67 57 34 EMR Country Year Mongolia 2010 Cambodia 2010 Philippines 2013 Viet Nam 2010 Vanuatu 2007 Lao People's Democratic Republic 2011 99 91 88 87 84 69 WPR The Former Yugoslav Republic of Macedonia Figure 4.3 Relative inequality score for reproductive, maternal, newborn and child health intervention coverage in 83 countries, 2005–2013a a Based on the results of DHS and MICS. a Based on the World Bank analytical income classification of economies. 10 20 30 40 OOP health expenditure as fraction of THE (%) AFR 34.6 Figure 4.4 Out-of-pocket health expenditure as fraction of total health expenditure, by country income groupa and WHO region, 2013 50 AMR 31.0 SEAR 40.8 EUR 29.0 EMR 39.5 WPR 29.5 0 Low income 42.3 Lower middle income 40.6 Upper income 31.3 High income 21.2

20 WORLD HEALTH STATISTICS: 2016 population with large household expenditures on health as a share of their budget (for example, >25%). Estimates for catastrophic and impoverishing health expenditures come from a sample of 36 countries which have conducted a nationally representative survey between 2002 and 20121 following established methods in the literature.2,3 Figure 4.5 shows the national rates of 1 Sample composed of countries for which nationally representative, publicly available and comparable survey data with information on total consumption and OOP payments on health are available. 2 Distribution of health payments and catastrophic expenditures: methodology. Discussion Paper. Geneva: World Health Organization; 2004 (http://www.who.int/health_financing/ documents/cov-dp_05_2_health_payments/en/, accessed 10 April 2016). 3 Wagstaff A, van Doorslaer E. Catastrophe and impoverishment in paying for health care: with applications to Vietnam 1993–98. Health Econ. 2003;12(11):921–34 (https:// www.researchgate.net/publication/9023646_Catastrophe_and_Impoverishment_in_ Paying_for_Health_Care_With_Applications_to_Vietnam_1993-98, accessed 10 April 2016). catastrophic and impoverishing health expenditure across these countries using comparable data.4 The median percentage of people experiencing catastrophic health spending defined as OOPs exceeding 25% of household total consumption across these countries was 1.8%. The median incidence of impoverishing health expenditures was 1.0% using different poverty lines for countries at different levels of economic development.5 4 WHO and the World Bank. Tracking universal health coverage: First global monitoring report. Geneva: World Health Organization; 2015 (http://www.who.int/healthinfo/ universal_health_coverage/report/2015/en/, accessed 9 April 2016). 5 Tracking universal health coverage: first global monitoring report. Geneva and Washington (DC): World Health Organization and World Bank; 2015. (http://www.who. int/healthinfo/ universal_health_coverage/report/2015/en/, accessed 9 April 2016). Impoverishing health 012345 (%) Catastrophic 0 1 2 3 4 5 (%) Malawi Panama Bosnia and Herzegovina Ukraine Niger Pakistan Zambia Lao People's Democratic Republic Rwanda Senegal Turkey Jordan Philippines Kyrgyzstan Ghana France United Republic of Tanzania Latvia Bulgaria Russian Federation Tunisia Viet Nam Nicaragua Uganda Estonia Cambodia Kenya Iran (Islamic Republic of) Mongolia Bolivia (Plurinational State of) Republic of Moldova Egypt Argentina Republic of Korea Georgia Tajikistan Figure 4.5 Incidence of catastrophic and impoverishing health expenditure amont 37 countries, 2002-2012 Figure 4.5 Incidence of catastrophica and impoverishingb health expenditure among 36 countries with comparable data, 2002–2012 a Defined as 25% of total expenditure. b Regional poverty lines: US$ 1.25 for low-income countries, US$ 2 for lower middle-income countries, US$ 4 for upper middle-income countries, and US$ 5 for high-income countries

4.4 Data gaps- regular UHC monitoring is With regard to financial risk protection data, there are also possible a number of data challenges. Indicators of exposure to financial hardship, such as catastrophic and impoverishing Data availability for the tracer indicators that make up health spending, rely on data from household surveys. the service coverage index, including the dimension for Although there were over 500 surveys during the period disaggregation, is summarized in Table 4. 1. In the coming 1985-2014 in 88 countries, representative of about 90% years, measurement in several areas will need to improve of world population, too few countries have recent data in order to boost global and country capacity to track UHc (for example, only 58 countries have data from 2010 or progress. Most indicators for the essential services coverage later). An increasing number of surveys include a module index are estimated consistently across most countries, that facilitates computation of the micro-level indicators but there are still data gaps for key indicators such as which are direct measures of financial burden due to the cervical cancer screening and access to essential medicines. cost of health care. Similarly as more countries conduct here coverage data are available there is rarely sufficient regular NHAs, the data needed for the annual estimation of information to monitor levels of effective coverage. Such indirect measures of financial protection (that is, ooP health a measure of the degree to which evidence-based health expenditure as a percentage of THe) are going to improve achieve desirable ou is a key component of quality health care and a core UHC concern Finally, country UHC monitoring needs to be integrated into broader health systems performance assessment if it is to Data scarcity is also an issue with regard to coverage equity. realize its full potential as actionable intelligence. Monitoring For example, comparable estimates of service coverage service coverage and financial protection-which should across key inequality dimensions are dominated by always go hand-in-hand - does not in itself reveal which reproductive, maternal, newborn and child health indicators policy levers can be used to improve results. For this reason, in countries that have conducted DHS or MICS surveys. the monitoring of UHC indicators needs to be embedded Perhaps surprisingly, the lack of standardized surveys across within health systems performance assessment frameworks high-income countries is a particular problem, hampering that link changes in coverage to potential drivers of progress the ability to monitor equity in coverage in such countries. caused by changes in inputs, structures and processes. These will include: (a) structural elements related to investments in It could be argued that the current UHC index is most health; (b) process elements such as health system reforms relevant for low-and middle-income countries(LMIC),(such as changes in provider payment mechanisms)designed as the selected indicators tend to have coverage rates to improve service quality or health service utilization; and near or at 100% in most high-income countries. This is (c)determinants of health. While understanding a country only a consequence of the MDG-related investments health system reforms are important in determining the in comparable methods to monitor indicators related to causes of change in health-service coverage measures, it reproductive, maternal, newborn and child health, and to also essential to assess changes in non-health-system social infectious diseases, but also a result of a lack of comparable determinants of health(such as educational attainment and data for interventions with greater relevance for more poverty rates)as such changes also greatly influence service advanced health system coverage and health outcomes MONITORING HEALTH FOR THE SDGs

MONITORING HEALTH FOR THE SDGs 21 4.4 Data gaps – regular UHC monitoring is possible Data availability for the tracer indicators that make up the service coverage index, including the dimension for disaggregation, is summarized in Table 4.1. In the coming years, measurement in several areas will need to improve in order to boost global and country capacity to track UHC progress. Most indicators for the essential services coverage index are estimated consistently across most countries, but there are still data gaps for key indicators such as cervical cancer screening and access to essential medicines. Where coverage data are available, there is rarely sufficient information to monitor levels of effective coverage. Such a measure, of the degree to which evidence-based health services achieve desirable outcomes, is a key component of quality health care and a core UHC concern. Data scarcity is also an issue with regard to coverage equity. For example, comparable estimates of service coverage across key inequality dimensions are dominated by reproductive, maternal, newborn and child health indicators in countries that have conducted DHS or MICS surveys. Perhaps surprisingly, the lack of standardized surveys across high-income countries is a particular problem, hampering the ability to monitor equity in coverage in such countries. It could be argued that the current UHC index is most relevant for low- and middle-income countries (LMIC), as the selected indicators tend to have coverage rates near or at 100% in most high-income countries. This is not only a consequence of the MDG-related investments in comparable methods to monitor indicators related to reproductive, maternal, newborn and child health, and to infectious diseases, but also a result of a lack of comparable data for interventions with greater relevance for more advanced health systems. With regard to financial risk protection data, there are also a number of data challenges. Indicators of exposure to financial hardship, such as catastrophic and impoverishing health spending, rely on data from household surveys. Although there were over 500 surveys during the period 1985–2014 in 88 countries, representative of about 90% of world population, too few countries have recent data (for example, only 58 countries have data from 2010 or later). An increasing number of surveys include a module that facilitates computation of the micro-level indicators which are direct measures of financial burden due to the cost of health care. Similarly, as more countries conduct regular NHAs, the data needed for the annual estimation of indirect measures of financial protection (that is, OOP health expenditure as a percentage of THE) are going to improve. Finally, country UHC monitoring needs to be integrated into broader health systems performance assessment if it is to realize its full potential as actionable intelligence. Monitoring service coverage and financial protection – which should always go hand-in-hand – does not in itself reveal which policy levers can be used to improve results. For this reason, the monitoring of UHC indicators needs to be embedded within health systems performance assessment frameworks that link changes in coverage to potential drivers of progress caused by changes in inputs, structures and processes. These will include: (a) structural elements related to investments in health; (b) process elements such as health system reforms (such as changes in provider payment mechanisms) designed to improve service quality or health service utilization; and (c) determinants of health. While understanding a country’s health system reforms are important in determining the causes of change in health-service coverage measures, it is also essential to assess changes in non-health-system social determinants of health (such as educational attainment and poverty rates) as such changes also greatly influence service coverage and health outcomes