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      Does SDG 3 have an adequate theory of change for improving health systems performance?

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      Journal of Global Health
      Edinburgh University Global Health Society

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          Abstract

          Given the importance of Sustainable Development Goal 3 (SDG 3) setting national agendas and policies to improve public health, this article examines whether SDG 3 and its associated indicators have an adequate theory of change for improving health systems performance. To do so, this article maps all SDG 3 indicators to a prominent health systems framework. The analysis reveals that SDG 3 tracks four input indicators, 15 output / outcome indicators, and 18 impact indicators. Unlike the Millennium Development Goals (MDGs), SDG 3 tracks population health across a wide array of disease areas. However, SDG 3 has several limitations in its approach to improving health systems performance. It does not track primary health care inputs, financial risk protection, or user satisfaction with the health system, and it does not provide a comprehensive approach to prevent, diagnose, treat, and manage any disease. Future directions for research include conducting a similar mapping for other SDGs and documenting early country experiences implementing SDG 3 given these challenges. INTRODUCTION Sustainable Development Goal (SDG) 3 aims to “ensure healthy lives and promote well–being for all at all ages” [1]. Unlike the Millennium Development Goals (MDGs), SDG 3 takes a comprehensive view of health and well–being by expanding its focus beyond a core set of diseases. Given the global prominence of the SDGs for driving the development agenda, it is important to consider whether SDG 3 and the indicators it tracks are well–designed to achieve this intended goal. In order to examine whether SDG 3 can actually help achieve this goal, this article considers whether it has an adequate theory of change (ToC) for improving health systems performance. Such an analysis rests on two core assumptions: 1) in order to achieve the SDG 3 goal, one must improve health systems performance, and 2) in order to achieve this goal, the approach must have a strong underlying ToC. Each assumption is considered below. Since the launch of the MDGs, experience has shown that without improvements in health systems performance, progress on the MDGS was both limited and potentially unsustainable [2]. Bottlenecks in the health system limited nations’ ability to achieve progress on combatting specific diseases. In addition, theoretical and empirical work has argued that providing services which are not only clinically effective but also affordable and acceptable has intrinsic and instrumental value. Recognizing the importance of overall health systems performance, numerous organizations including WHO, the World Bank, Global Fund, and GAVI have focused on health systems strengthening (HSS) as an important component of public health programming. Therefore, since SDG 3 aims to improve both health and well–being for all populations in a sustainable way, achieving this goal will likely require broad improvements in health systems performance. With regards to the second assumption, theories of change (ToC) are standard practice in public health and development [3,4]. They help guide priority–setting, decision–making, monitoring and evaluation, budgeting, and resource allocation, among other activities. A strong ToC can ensure that all stakeholders work toward the same goal(s). The SDGs aim to improve both the coherence of development policies and their implementation at the national level, and the United Nations has offered formal guidance on ways that nations can integrate and tailor the SDGs into their national policies [5]. This guidance explicitly advocates for horizontal policy coherence (ie, coherence across different programs and sectors), vertical policy coherence (ie, coherence between different stakeholders), and linking national policies based on the SDGs to budgets [6]. Given that the SDGs aim to improve policy coherence and drive implementation at the national level, it is instructive to consider whether they have internal coherence and a strong underlying logic themselves. The ToC approach provides a useful approach to explore this question. If the inputs and outputs tracked under SDG 3 have clear linkages to improving its impact indicators, then working toward SDG 3 will allow countries to pursue a comprehensive program for improving health systems performance. On the other hand, if the inputs and outputs tracked do not link to each other or do not have logical connections to impact indicators, then aiming to improve all indicators under SDG 3 could lead to a haphazard and uncoordinated set of public health programs. ANALYTIC APPROACH To address this question, this analysis maps all indicators for SDG 3 as inputs, outputs/outcomes, or impacts, using the formal definition of each [7]: Inputs: “The human financial, and community resources a program has available toward [implementing a program]” Outputs: “Direct products of program activities” Outcomes: “Specific changes in…behavior, knowledge, skills, status, and level of functioning” Impacts: “The fundamental intended or unintended change occurring in organizations, communities, or systems” All indicators are drawn from the official list available from the UN Statistics Division and associated metadata [1]. The 16 tracer indicators proposed by the Inter–agency and Expert Group (IAEG) on SDGs for indicator 3.8.1 (coverage of essential health services) were also included for analysis [8]. This analysis refers to these indicators as UHC tracer indicators. Health systems theory formally defines the three intended impacts of a health system as population health status, financial risk protection, and user satisfaction with the system [9–11]. Working backward from this definition, outputs/outcomes should refer to the results of activities or changes in the population which can ultimately affect at least one of these three impacts, and inputs should refer to any resources in the system directed toward changing outputs, outcomes, or impacts. In order to map SDG 3 indicators, this analysis uses a health systems framework which has a structure very similar to that of a standard ToC [9]. This framework clearly maps to inputs, outputs/outcomes, and impacts, and it further breaks down each of these three areas into relevant sub–categories ( Figure 1 ). Of course, many other frameworks for health systems exist, such as the WHO Building Blocks and the Flagship Framework, and one could also conduct this mapping exercise using those frameworks. However, these two frameworks do not fit as closely with a standard ToC; the Building Blocks framework does not necessarily explain how different building blocks relate to each other, and the Flagship Framework does not explicitly consider inputs to the health system [12]. Figure 1 Mapping of SDG 3 indicators to a health systems framework. RESULTS OF MAPPING SDG INDICATORS TO A HEALTH SYSTEMS THEORY OF CHANGE This section summarizes the key results of mapping SDG 3 indicators to a ToC. Of the 16 UHC tracer indicators, the analysis excluded five that were duplicative with SDG tracer indicators (excluded UHC tracer indicators, and the SDG indicators that they duplicated: family planning coverage [3.7.1], Tobacco, non–use [3.a.1], Health worker density [3.c.1], Access to essential medicines [3.b.1], and Health security: IHR compliance [3.d.1]). See Figure 1 for a schematic of the full results of this mapping exercise. INPUTS (AND MEANS OF IMPLEMENTATION TARGETS) SDG targets are divided into Means of Implementation (MoI) targets and other targets [13]. MoI targets are meant to summarize the resources needed to achieve all other targets. SDG 3 has five MoI targets: A) prevalence of tobacco use, B.1) proportion of the population with access to affordable medicines and vaccines, B.2) total net ODA to medical research and basic health sectors, C) health worker density and distribution, and D) IHR capacity and emergency health preparedness. Three of these indicators (total net ODA, health worker density and distribution, and IHR capacity) serve as inputs to the health system, whereas the other two are indicators/outcomes. The analysis also classifies one UHC tracer indicator, basic hospital access, as a health systems input. Photo: The Queen ‘Mamohato Memorial Hospital in Lesotho.Experience with this hospital highlighted problems of investing in hospital care without a strong primary health care system. Photo from the collection of Patrick Smith, used with permission. More at: A dangerous diversion: Will the IFC's flagship health PPP bankrupt Lesotho's Ministry of Health? OXFAM Briefing Note, 7 April 2014. The four indicators classified as inputs fall across the categories of financing, resource management, and governance and organization. With regards to financing, Indicator B.2, total ODA for health, can have an impact on health systems outputs, but there is no clear linkage between this indicator and any specific outputs or impacts tracked by SDG 3. It is not even clear what improvement on this indicator would look like. While an increase in ODA may signify increasing expenditure on health, it does not take into account government and out–of–pocket spending on health, and it may also cause or exacerbate issues with donor dependency in low– and middle–income countries (LMICs). Further, given the variation in efficiency of health spending across countries, an increase in total ODA may not necessarily represent any changes to health systems outputs. The two resource management indicators, total health workforce and basic hospital access, will impact the availability of health care services and associated outputs. Indicator C, total health workforce, currently measures total physicians and nurses/midwives per capita and will likely expand to include dentists, pharmacists, and possibly other health personnel [14]. The one governance and organization indicator, International Health Regulation (IHR) compliance, focuses on key competencies designed to “prevent, protect against, control and provide a public health response to the international spread of disease” [15]. The fact that two MoI indicators represent outcomes, rather than inputs into the system, suggests that measuring them will not necessarily help countries identify the root causes of problems in the health system or ways to address these issues. Indicator A, prevalence of tobacco use, is meant to measure implementation of the WHO Framework Convention on Tobacco Control, but is actually an intended outcome of this regulation, not a measurement of implementation itself. As of the end of 2016, the UN had not released a final definition of metadata for Indicator B.1 (proportion of the population with access to medicines and vaccines). However, the wording of this indicator suggests that it does not actually capture an input, such as the (per–capita) number of medicines in the country or existence of a national essential medicines list, but rather what percentage of the population gets access to these medicines. Therefore, poor performance on this indicator will not necessarily indicate a shortage of medicines, since populations might lack access to medicines for other reasons (eg, physical distance to a health center, poor supply chain management). Further, four of the 16 UHC tracer indicators monitor the coverage for medicines and vaccines, and may therefore duplicate Indicator B.1. Many of the outputs / outcomes and impact indicators discussed below have no MoI indicators which directly precede them or influence their progress. There are no input indicators which measure access to primary health care, community–based health services, or health education. OUTPUTS/OUTCOMES Five SDG indicators and eight UHC tracer indicators classify as health systems outputs or outcomes (in addition to the two MoI indicators mentioned earlier). There is at least one indicator that tracks an output related to each Millennium Development Goal (MDG) priority disease (HIV, malaria, maternal health, newborn / child health), as well as TB, substance use disorders, cervical cancer, family planning, tobacco use, alcohol use, and water/sanitation. These indicators are split across health care services and public health. All disease–related outputs link to at least one health systems impact indicator, suggesting that there is a clear and logical linkage between improvements on health systems outputs and health systems impacts. Indicator 3.8.2, the number of people covered by health insurance, does not link to any impact indicator tracking financing risk protection (such as the percent of the population experiencing catastrophic or impoverishing health expenditures). The MoI indicator tracking proportion of population with access to medicines and vaccines (B.1) will of course change impact indicators, but it is impossible to assess how it will do so until the UN releases the tracer medicines which will make up this indicator. IMPACTS All impact indicators measure population health status across a wide variety of disease areas. Some measure disease transmission rates (eg, HIV incidence, TB incidence), whereas many others measure mortality rates. There are no indicators to track financial risk protection or user satisfaction. Although every output indicator links to an impact indicator, not every impact indicator has a preceding output indicator. Indeed, of 18 impact indicators, five do not have a preceding output indicator (mortality due to suicide, air pollution, unintentional poisoning, neglected tropical diseases, and unintentional injuries). This suggests that the SDGs do not give clear guidance on how to address one–third of the impacts targeted. Of the 12 impact indicators which do have a preceding output, 11 have a preceding output indicator from either health care services or public health, but not from both. The one impact indicator which has preceding outputs from both health care services and public health is 3.4.1 (mortality from select NCDs), and the preceding outputs (cervical cancer screening, prevalence of tobacco use, and harmful use of alcohol) actually address very different diseases. SELECT LIMITATIONS OF SDG 3 AND POTENTIAL IMPLICATIONS The results of this mapping exercise have important implications for public health programming and policies. Unlike the MDGs, SDG 3 clearly takes a comprehensive view of the potential epidemiological challenges that a country may face. However, SDG 3 fails to take a holistic view of the health system, its goals, and the resources /activities needed to achieve these goals. In particular, SDG 3 has three limitations for guiding policy and practice to improve health systems performance. See Table 1 for a summary of the limitations of SDG 3 and potential implications. Table 1 Select limitations of SDG 3 identified by this analysis and potential implications Limitations of SDG 3 Potential implications for policy and practice SDG 3 does not systematically track indicators related to primary health care, which can serve as the foundation for a strong health system. Policymakers and practitioners should consider how to integrate a PHC–based approach which can effectively and efficiently improve health systems performance into the SDG indicators in their specific contexts. SDG 3 does not provide guidance on how to systematically prevent, diagnose, treat, or manage and given disease. Policymakers and practitioners should formulate and implement holistic approaches to addressing the highest burden diseases in their specific context, while specifically considering how to integrate these efforts into the health system, including PHC–based approaches. SDG 3 does not track impacts related to financial risk protection or user satisfaction with the health system. Policymakers and practitioners should include indicators on financial risk protection and user satisfaction in their monitoring and evaluation of the health system and design health systems components, such as insurance schemes and essential medicines packages, with these in mind. 1 Few indicators track the status of the primary health care (PHC) system. A strong PHC system can serve as the basis for achieving universal health coverage and a stronger health system overall [16]. Given that PHC can address 90% of health care demands, and many “good buys” for combatting diseases can be integrated into PHC systems, prioritizing hospital access over PHC access can lead to inefficiency and misallocation of resources in the health system [10,17]. However, SDG 3 places very little focus on PHC. The two indicators for resource management – health worker density and hospital access – neglect key health systems inputs at the PHC and community levels, such as access to a PHC clinic, the availability of essential medicines at these clinics, health education, and the ratio of lay health workers such as community health workers per population. Given the importance of PHC for improving population health and creating the foundation for a strong health system, policymakers and practitioners should consider how to integrate a PHC approach into achieving SDG 3. 2 There is no comprehensive approach to prevent, diagnose, manage, and treat any disease. As mentioned earlier, an impact indicator for a specific disease links to a preceding output indicator either from health care services or public health, but never both. This structure suggests that, while SDG 3 identifies targeted interventions that can address many of its priority diseases, it does not promote a comprehensive approach to preventing, diagnosing, treating, and managing any given disease. The output indicators also do not track certain key health behaviors which can impact population health through disease prevention, such as condom usage and physical exercise. (SDG 3 also does not include any indicators on nutrition, but SDG 2 covers these.) Further, of the five impact indicators which have no preceding outputs, many can be addressed through environmental health or other programs. Policymakers and practitioners should recognize that the disease–specific guidance in SDG 3 is only very summary, and that an internal ToC is likely needed for improving population health for each disease. Following from the point about PHC, policy and practice should also consider how integrated prevention, diagnosis, treatment, and management can occur at the PHC level. 3 The indicators do not track impacts related to financial risk protection or user satisfaction with health services. As already discussed, ignoring these indicators has significant implications for the functioning of country’s health system. Failing to protect individuals against financial risk from health expenditures can negatively impact people’s access to care as well as the non–health aspects of their lives [18]. Similarly, a patient’s satisfaction with services and the overall responsiveness of the health system to the patient’s needs can impact patient well–being and future interactions with the health system [19]. Policymakers and practitioners should take into account the effect that providing services to improve population health will have on patient’s financial status and satisfaction with the health system, as well as the linkages between these impacts and population health. Overall, this lack of a systems–wide approach for improving public health could lead to significant challenges for countries aiming to implement SDG 3. In particular, it could limit the overall improvements to health systems performance because it could promote an uncoordinated approach to improving health, especially without a focus on PHC. LIMITATIONS OF THIS APPROACH This analytic approach has several limitations. As mentioned earlier, many health systems frameworks exist, and this mapping exercise could use other frameworks which might lead to different conclusions. In addition, as with any ToC, this approach presents a highly linear way to understand SDG 3 and its underlying logic. Of course, as the field of systems thinking has revealed, changes to complex systems can have unpredictable and multi–directional results, and this analytic approach does not reflect that complexity [20]. This approach does not map the linkages between impact indicators (eg, a change in HIV incidence could affect other impact indicators such as TB incidence and under–five mortality). Nonetheless, clearly laying out the first–order linkages between different SDG 3 indicators at least gives a working model for understanding if this model provides a logical and robust approach to improving public health more broadly. CONCLUSION AND FUTURE DIRECTIONS This article likely represents the first attempt to map SDG 3 indicators to a ToC for improving health systems performance. This mapping highlights several challenges with the structure of SDG 3, namely the lack of a holistic approach to improving health systems performance. Given the novelty of the SDGs, it is still too early to evaluate the impact of this potential shortcoming. However, these findings point to two key next steps for future investigation. First, researchers can systematically map the indicators for the other SDGs in a similar way and link the ToCs from different SDGs to each other. Doing so will help identify similar challenges for other SDGs, as well as potential linkages between the SDGs. Second, researchers, practitioners, and policymakers should document early experiences trying to implement SDG 3 to determine whether countries recognize these implicit limitations and, if so, how they are responding to them.

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          Turkey has successfully introduced health system changes and provided its citizens with the right to health to achieve universal health coverage, which helped to address inequities in financing, health service access, and health outcomes. We trace the trajectory of health system reforms in Turkey, with a particular emphasis on 2003-13, which coincides with the Health Transformation Program (HTP). The HTP rapidly expanded health insurance coverage and access to health-care services for all citizens, especially the poorest population groups, to achieve universal health coverage. We analyse the contextual drivers that shaped the transformations in the health system, explore the design and implementation of the HTP, identify the factors that enabled its success, and investigate its effects. Our findings suggest that the HTP was instrumental in achieving universal health coverage to enhance equity substantially, and led to quantifiable and beneficial effects on all health system goals, with an improved level and distribution of health, greater fairness in financing with better financial protection, and notably increased user satisfaction. After the HTP, five health insurance schemes were consolidated to create a unified General Health Insurance scheme with harmonised and expanded benefits. Insurance coverage for the poorest population groups in Turkey increased from 2·4 million people in 2003, to 10·2 million in 2011. Health service access increased across the country-in particular, access and use of key maternal and child health services improved to help to greatly reduce the maternal mortality ratio, and under-5, infant, and neonatal mortality, especially in socioeconomically disadvantaged groups. Several factors helped to achieve universal health coverage and improve outcomes. These factors include economic growth, political stability, a comprehensive transformation strategy led by a transformation team, rapid policy translation, flexible implementation with continuous learning, and simultaneous improvements in the health system, on both the demand side (increased health insurance coverage, expanded benefits, and reduced cost-sharing) and the supply side (expansion of infrastructure, health human resources, and health services). Copyright © 2013 Elsevier Ltd. All rights reserved.
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            The emergence of global attention to health systems strengthening.

            After a period of proliferation of disease-specific initiatives, over the past decade and especially since 2005 many organizations involved in global health have come to direct attention and resources to the issue of health systems strengthening. We explore how and why such attention emerged. A qualitative methodology, process-tracing, was used to construct a case history and analyse the factors shaping and inhibiting global political attention for health systems strengthening. We find that the critical factors behind the recent burst of attention include fears among global health actors that health systems problems threaten the achievement of the health-related Millennium Development Goals, concern about the adverse effects of global health initiatives on national health systems, and the realization among global health initiatives that weak health systems present bottlenecks to the achievement of their organizational objectives. While a variety of actors now embrace health systems strengthening, they do not constitute a cohesive policy community. Moreover, the concept of health systems strengthening remains vague and there is a weak evidence base for informing policies and programmes for strengthening health systems. There are several reasons to question the sustainability of the agenda. Among these are the global financial crisis, the history of pendulum swings in global health and the instrumental embrace of the issue by some actors.
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              Financial Risk Protection and Universal Health Coverage: Evidence and Measurement Challenges

              This paper is part of the PLOS Universal Health Coverage Collection. Key Summary Points Health payments are a heavy financial burden for millions around the world. Financial risk protection is concerned with safeguarding people against the financial hardship associated with paying for health services. Two commonly applied concepts capture the lack of financial risk protection. The first, catastrophic health expenditure, occurs when a household's out-of-pocket (OOP) payments are so high relative to its available resources that the household foregoes the consumption of other necessary goods and services. The second concept, impoverishment, occurs when OOP payments push households below or further below the poverty line, a threshold under which even the most basic standard of living is not ensured. Headcount indicators, which measure the number of people affected, alone do not give the full picture of the problem. Additional measures of the intensity of financial hardship provide useful insights into the nature of OOP payments in different settings. Robust monitoring of financial risk protection requires reliable household expenditure surveys ideally conducted every 2 to 5 years. Financial Risk Protection and Universal Health Coverage Health systems have developed specifically to allow people to use the health services they might need while protecting them against the adverse financial consequences of paying for care [1],[2]. This goal is now widely known as universal health coverage (UHC). It was the motivation for the social health insurance systems that developed in Europe, the National Health Service in the UK, and the recent reforms in the US now colloquially known as Obama care [3]–[5]. It is also the motivation for many of the recent adjustments to health systems and health financing systems in low- and middle-income countries [1]. Despite these developments, the burden imposed by out-of-pocket (OOP) health payments still results in financial hardship for millions of people who seek care globally [6]. OOP payments are also the most regressive form of financing for health. Thus UHC's focus on financial hardship arising from OOP payments is supported by on-the-ground realities. But in a broader sense, financial hardship in UHC represents the impact of the health systems on the non-health aspects of people's lives. Households can be impoverished or be faced with catastrophic health expenditure from accessing needed health services. Fundamentally, the assurance that people will not suffer financial hardship in using services, an integral component of UHC, is a recognition that health systems should not only improve health but this improvement should not be done in ways that are detrimental to non-health aspects of well-being. The concept of financial risk protection, or conversely the absence of a risk of financial hardship, has been the focus of interest to economists and researchers for many years, and measuring the ability of a health system to protect people against the financial hardship associated with paying for health services has become an important issue for research and analysis across countries at all income levels [6]–[11]. Many ways of measuring financial risk protection directly reflect the trade-offs people have to make between paying for the health services they need and paying for other necessities such as food and basic education [12],[13]. However, there has been considerable debate about whether the common measures actually capture the concept of the value of financial risk protection in itself, or whether they reflect the impact of the lack of financial protection—slightly different concepts, although both are commonly placed under the heading of financial risk protection [14],[15]. These discussions are particularly relevant as UHC gains prominence as a key international health systems goal. In this context, this paper contributes to the discussion by providing a detailed analysis of key issues surrounding financial risk protection as a component of UHC. It examines the existing ways of monitoring UHC and the ideas underpinning them in non-technical language. The paper then considers the practical measurement challenges of using these methodologies. In this paper we summarize current thinking, provide novel insights, and suggest future developments that could be valuable in the context of monitoring progress towards UHC. Other papers in this Collection address the extent of financial risk protection in specific countries, so this paper does not include country-specific data analysis. Additionally, it should be noted that in this paper we do not discuss any targets for improving financial risk protection outcomes—instead we focus on measurement of financial risk protection. Some Underlying Principles behind Financial Risk Protection in Universal Health Coverage The prominence of financial risk protection as a health systems' goal in itself and as an integral part of UHC partially arises because of its unique position as an interface between health systems and other dimensions of well-being. The absence of financial hardship in accessing health services means that the choice to use health services does not come at the price of poor nutrition or inadequate education. This key feature is an important component of UHC; indeed, an objective of UHC should be to ensure that health systems develop in a way that is not harmful to other social sectors. However, one area of confusion in the inter-linkages between financial risk protection and coverage with needed health services under UHC is whether non-use of health services because of financial barriers to access is adequately captured in current measures. This argument is described in detail in Box 1. Box 1. Financial Barriers to Access UHC requires coverage with financial risk protection and coverage with needed health services side by side. OOP payments contribute to low service coverage rates by deterring people who cannot afford to pay from seeking or continuing care [2],[55],[56]. Financial barriers are also associated with transport costs and lost income involved in seeking care [32],[57],[58]. However, financial barriers are only one of the causes of low levels of service coverage. More fundamentally, if the services are simply unavailable or of poor quality, for example, because of insufficient health workers, medicine, and equipment, people will not be able to obtain the health services they need [59],[60]. Recently it has been suggested that indicators of the lack of financial risk protection capture only the impact of OOP payments on people who use health services but not the fact that others are deterred from seeking care at all. The argument is that common measures of financial risk protection need to be extended to incorporate any non-use of services because of the need to pay [61],[62]. But if both components of UHC are measured at the same time, a complete picture of whether people obtain the services they need and the extent of financial risk protection they encounter in doing so is already provided. Financial barriers to access may be important to try to measure separately if overall service coverage is not being measured. However, in measuring financial protection and service coverage side by side as part of UHC there is no need to incorporate the impact of financial barriers on utilization into any indicator of financial risk protection. In fact, only highlighting financial barriers to access places an undue importance on them as compared to many other important barriers to access such as cultural norms or geographical inaccessibility. Another area that causes confusion is related to the component of risk or uncertainty in financial risk protection. The concept of financial risk protection arose from economics and insurance theory [16]–[19]. These disciplines place an importance on explicitly understanding the adverse impacts of uncertainty and its economic value. Theoretically, UHC is also concerned with the adverse impact of uncertainty—the risks that services might be unavailable, of poor quality, or unaffordable in the event that they are needed. However, the indicators that are available to measure financial risk protection do not capture the adverse effects of uncertainty adequately; they only capture the economic hardship encountered because of the lack of financial risk protection. Box 2 describes these points in more detail. Box 2. Financial Hardship and Financial Risk Protection Financial risk protection in health implicitly involves protection against the financial uncertainty associated with the need to use health services and pay for them. Uncertainty is something that reduces people's peace of mind and wellbeing in itself, and can cause people to change their behaviour, usually in an adverse way. For example, uncertainty about whether necessary health care will be affordable to a family may force them to save large amounts of money that they would have otherwise invested in improving their housing conditions. There is a degree of individual heterogeneity in responding to uncertainty, but by in large, people prefer less rather than more [63]. As discussed earlier, one of the components of UHC is to ensure that no one faces the tough decision of choosing between health care and other necessities. But UHC also goes a step further—it recognizes the “insurance value” of health services being available, of good quality, and affordable in the event that someone needs to use them. People will then have peace of mind that they will be able to access necessary health services when needed without financial hardship. In other words, the uncertainty associated with illness is reduced. To date, however, it has not been possible to develop generally acceptable ways of measuring the intrinsic value of the reduced uncertainty linked to forms of financial risk protection (or to the knowledge that health services are available and of good quality). The available measures show the effect of the lack of financial risk protection on households. It's also worth keeping in mind that the very core of UHC recognizes that use of needed health services results in better health. Thus there is no need for any additional financial inter-linkage to show that the use of health services contributes to well-being. Overall, there is a strong synergy between the concepts of coverage with financial risk protection and coverage with needed good quality health services. Indeed, the concepts underlying financial risk protection increase the validity of UHC as a unified health systems goal. The next section examines the specific ways in which financial risk protection is typically measured. Common Indicators of Financial Risk Protection Over the last 15 years, four indicators of financial risk protection have assumed prominence [20],[21]. These indicators are associated with two concepts of financial hardship due to OOP payments, or the absence of financial risk protection: catastrophic health expenditure and impoverishment. We first describe these two concepts along with commonly associated indicators and their relative advantages and disadvantages. Catastrophic Expenditures due to Out-of-Pocket Health Payments Catastrophic health expenditure is the point at which a household's OOP payments are so high relative to its available resources that the household is required to forego the consumption of other necessary goods and services [22]. While the concept is clear, its application has varied in terms of how a household's available resources are calculated and how much of these resources have to be spent on health to cause a catastrophic event. In terms of available resources, catastrophic health expenditures have been defined as health expenditures exceeding a share of either total expenditure, non-food expenditure, or expenditure net of basic food needs. Similarly the threshold at which health payments become catastrophic has ranged from 10% to 40% [22],[23]. Certain considerations are important in choosing the threshold that results in catastrophic expenditure. For example, a relatively high threshold such as 40% increases the likelihood that households incurring discretionary spending on health (e.g., private hospital wards) are not classified as incurring catastrophic expenditures. A high threshold also implies a concern for those who face greater burdens. However, other studies have used a lower threshold (typically applied against total consumption) and some have also assessed the sensitivity of the estimate of financial catastrophe to several different thresholds [24],[25]. Some approaches have even varied the threshold so that it increases as a function of income [26],[27]. Similarly, different choices of the denominator for calculating catastrophic health expenditure, or a household's available resources, are based on different assumptions and priorities. Often a household's non-food expenditure is chosen as the denominator. The idea behind this choice is that a household's food expenditure should not be considered as being part of the resources available to contribute to health. This idea has been taken even further and the standard methodology used by WHO calculates a household's available resources as being expenditure net of basic food spending [7]. Others prefer to use a household's total expenditure as the denominator that is very easy to calculate. However, as expected by economic theory, richer households often tend to spend a higher proportion of their total expenditure on health. As such, the latter measure can be pro-rich, particularly if the threshold for financial catastrophe is set relatively low. Two indicators measure the concept of catastrophic expenditures (Table 1). The first is the incidence of catastrophic health expenditures, which is a headcount indicator calculated as the proportion of households in a population whose health expenditures exceed this critical point. The second, though less widely used, is the catastrophic overshoot, which captures the extent to which health expenditures exceed the defined threshold [20],[21]. A major advantage of focusing on the concept of catastrophe is that its measurement is across the entire population, that is financial hardship can occur in any population group, including in any income subgroup. Additionally, since the concept is specialized to health and based on a pre-established framework, there is no likelihood of political or societal manipulation of the thresholds or the denominator. 10.1371/journal.pmed.1001701.t001 Table 1 Key indicators of the concept of catastrophic health expenditure. Indicator What It Is Measuring Incidence of catastrophic health expenditure Proportion of households in a population who face catastrophic health expenditure Mean positive catastrophic overshoot Percentage points by which household spending on health exceeds the threshold for catastrophic health expenditure Impoverishment due to Out-of-Pocket Health Payments The second concept of financial risk protection is the concern that OOP payments can push households below or further below the poverty line. Poverty lines represent a threshold below which even the most basic standard of living is not ensured [28]. OOP payments can be impoverishing in the sense that a household's level of expenditure before making health payments was above the poverty line, but then fell below the poverty line after health expenditures. Importantly, household expenditure in this context includes not only spending in cash, but also spending in kind as well as consumption of self-produced goods, most notably food. Similar to the way of measuring catastrophic health expenditures, a choice is involved in establishing the poverty line. Absolute poverty lines as well as relative poverty lines exist, but choosing which one to use is largely a value-based decision. An example of an absolute poverty line is the commonly cited dollar a day line, which actually corresponds to the equivalent of US$1.25. This and other absolute poverty lines, which are usually national lines, are calculated on the basis of basic subsistence needs. On the other hand, relative poverty lines are based on the distribution of a specific measure of basic subsistence needs (e.g., basic food expenditure). The main advantage of an absolute line is that the level of poverty can be easily monitored over time. Its main disadvantage is that it is prone to manipulation by political and societal agents. A relative poverty line, which is determined through actual spending on subsistence needs of different households, does not have the same limitation. Another advantage is that a relative poverty line can account for different patterns in expenditure across countries but it moves in relation to the distribution of poverty in any country [29]. Overall, the choice of poverty line will affect the number of people who are thought to be in poverty; a low poverty line may result in a low rate of impoverishment due to OOP payments. As countries assess their own progress towards UHC, they can use locally defined poverty lines. However, for purposes of international comparisons, it is important to have a common line. One option is to use the US$1.25 or US$2.00 per day per capita (at purchasing power parity) used by the World Bank. Another option is to use a globally defined relative poverty line, such as one based on basic food expenditure as used by the WHO [30],[31]. Two indicators adapted from the general poverty literature to health payments are used to measure this concept (Table 2). The incidence of impoverishment is a headcount measure showing the proportion of households pushed below the poverty line because of OOP payments. A second indicator, again less widely reported, is the increase in depth of poverty, which measures the amount a poor household is pushed further into poverty due to OOP payments. 10.1371/journal.pmed.1001701.t002 Table 2 Key indicators of the concept of impoverishment due to health spending. Indicator What It Is Measuring Incidence of impoverishment Proportion of households in a population who fell into poverty due to health spending Increase in the depth of poverty Amount by which a household fell further into poverty due to health spending An important advantage of measuring impoverishment due to OOP payments is that the concept resonates well with policymakers. Indeed, politicians and policymakers from almost all countries in the world are concerned with poverty alleviation. The particular implications of poverty as a multidimensional concept and its linkages with UHC and development goals are explored further in Box 3. Box 3. Poverty as a Multidimensional Concept and UHC Poverty has long been categorized as a multidimensional construct, rather than just a lack of income or ability to spend [64]–[66]. This definition is different from the mechanical definition of “impoverishment” that has been presented so far in this paper. Under this multi-dimensional thinking, deprivations across different spheres of life, including in education, clean water, and health, can inherently constitute poverty. However, the idea of income (or expenditure) poverty has remained persistent since income poverty is the manifestation of deprivations in goods and services that can be purchased with money. Many social services are at the cross-section of these two ideas: deprivation of social services are inherently causes of non-income poverty and are also manifestations of income poverty since social services can be purchased like any other goods and services. Income poverty is the concept that has been discussed outside of this box as “impoverishment” in this paper. Indeed, the poverty lines discussed earlier represent how much money is needed so that all purchasable essential needs of an individual are met. “Impoverishment due to OOP payments,” thus occurs when one social service (health) is purchased at the expense of other equally important social services or products. But there is a very attractive opportunity to construct a measure of “impoverishment” (i.e., income poverty) because of OOP payments that is further aligned with thinking on multi-dimensional poverty. This opportunity is through constructing a poverty line that was based on spending on all essential services and goods except for health. This slight variation in the construction of the poverty line can help further align the concept of financial risk protection and UHC with multi-dimensional poverty. By doing this, manifestations of income-poverty because of OOP payments (i.e., the concept described as “impoverishment” elsewhere in the paper) with the modified poverty line represent the direct adverse impact of health service use on another essential services and products, modulated through income. On the other hand, non-use of health services (the other side of UHC) is a manifestation of non-income poverty either directly or because of lower health status. In the discussion on the post-2015 development goals, this particular interface of financial risk protection and multi-dimensional poverty merits further consideration. Additionally, it is also possible for other social sectors to take a similar approach. For example, “impoverishment” because of spending on primary education could be calculated on the basis of a poverty line that is net of spending on primary education. A limitation of the headcount measure of impoverishment is that households that are already below the poverty line will not be accounted for automatically if they are made poorer because of OOP payments. To capture the burden on these households, a measure of the depth of poverty is needed. Some Empirical Results As described in the previous section, the two concepts of financial hardship (catastrophic health expenditure and impoverishment) measure different aspects of the lack of financial risk protection in health. Figure 1 shows the headcount indicators for the two concepts from 96 household expenditure surveys available to us: although they appear to be correlated across countries, there is some variation. In some countries the incidence of catastrophic health expenditure is much higher than impoverishment, while in others, most commonly in countries where a high proportion of the population lives in near-poverty, the opposite is true. This finding suggests that the two indicators provide different information about the level of financial risk protection. Also, it is worth noting that financial risk protection seems to be better in high-income countries as opposed to low- and middle-income countries, which is related to the development of financial risk protection systems through prepayment and pooling of resources. 10.1371/journal.pmed.1001701.g001 Figure 1 Impoverishment and catastrophic health expenditure headcount by country income. Figure 2 plots the impoverishment headcount against the difference in normalized poverty gap for the same datasets. As can be seen, there is a correlation between the poverty headcount and difference in poverty gap due to OOP payments. However, in some countries the increase in depth of poverty due to OOP payments is higher than the average relationship. The opposite is true in some cases. These types of effects are likely to be related to the nature of OOP payments in different settings. But overall, greater information is obtained about the absence of financial risk protection if these two indicators are measured side by side. 10.1371/journal.pmed.1001701.g002 Figure 2 Impoverishment headcount and difference in poverty gap by country income . Finally, Figure 3 shows the box plot of OOP payments over household expenditure net of basic food expenditure for 52 countries with the World Health Survey 2003. This methodology is used by WHO for calculating catastrophic health expenditure, where the threshold for catastrophic expenditure is 40%, which is represented by the red line [7]. In the box plot, which contains no outliers, the box represents the inter-quartile range (i.e., 25th–75th percentile of observations). The whiskers on either end of the boxes represent observations falling between 1.5 times the inter-quartile range. This figure demonstrates the idea of the catastrophic overshoot; some households' OOP spending far exceeds the 40% threshold of catastrophic health expenditure. In some countries like Bangladesh, the 75th percentile of the distribution of OOP payments over expenditure net of basic food is above the 40% threshold. In countries like this, the headcount measurement alone will not paint a full picture of the problem of catastrophic health expenditure; an overshoot measurement provides additional information about the severity of the catastrophic spending. 10.1371/journal.pmed.1001701.g003 Figure 3 Box plot of OOP payments/expenditure net of basic food expenditure for 53 countries. ARE, United Arab Emirates; BFA, Burkina Faso; BGD, Bangladesh; BIH, Bosnia and Herzegovina; BRA, Brazil; CHN, China; CIV, Côte d'Ivoire; COG, Congo; COM, Comoros; CZE, Czech Republic; DOM, Dominican Republic; ECU, Ecuador; ESP, Spain; EST, Estonia; ETH, Ethiopia; GEO, Georgia; GHA, Ghana; GTM, Guatemala; HRV, Croatia; HUN, Hungary; IND, India; KAZ, Kazakhstan; KEN, Kenya; LAO, Lao People's Democratic Republic; LKA, Sri Lanka; LVA, Latvia; MAR, Morocco; MEX, Mexico; MLI, Mali; MMR, Myanmar; MRT, Mauritania; MUS, Mauritius; MWI, Malawi; MYS, Malaysia; NAM, Namibia; NPL, Nepal; PAK, Pakistan; PHL, Philippines; PRY, Paraguay; RUS, Russian Federation; SEN, Senegal; SVK, Slovakia; SVN, Slovenia; SWZ, Swaziland; TCD, Chad; TUN, Tunisia; TUR, Turkey; UKR, Ukraine; URY, Uruguay; VNM, Viet Nam; ZAF, South Africa; ZMB, Zambia; ZWE, Zimbabwe. An important critique of these types of indicators is that reliance on OOP payments made at a point in time does not capture other costs such as those related to transportation to health facilities, indirect longer term costs to cope with costs of care, or other health payments by households. While some authors have tried to include these types of payments in their analyses of financial hardship, the methodologies they use are not standard [32],[33]. Expenditure on prepayment for health is also interesting to consider as part of financial risk protection and Box 4 further examines the distinction between OOP payment and pre-payment in more detail. Box 4. Prepayment for Health and its Relationship with Financial Risk Protection Levels of financial risk protection are clearly related to the way a health system is financed. The more countries rely on prepayment rather than OOP payments, the higher the financial risk protection. There have, however, been some suggestions that household contributions to health through forms of prepayment (largely insurance and taxes) can also cause financial hardship and they should be included in the measurement of financial risk protection [67]. While that is true, we argue that the financial hardship caused by contributions to the health system, or any other system, that are predictable are different to the unpredictable consequences of OOP health payments. Tax rates and compulsory insurance premiums are well established and predictable. Whether they are affordable and fair—that is, whether the poor pay the same amount or proportion of their income as the rich—is important to consider, but is a separate question from protecting people from the unpredictability of payments for health services at the time they get ill. We recommend that the above indicators be regularly measured if possible, recognizing that the literature has to date largely focused on the two measurements of incidence—of financial catastrophe and of impoverishment. The indicators are relatively straightforward to calculate, easy to understand, and allow for comparative analysis across countries and over time. Previous studies have published the incidence of financial catastrophe and impoverishment because of OOP payments for 89 countries [6],[34]. Their related overshoot and gap indicators are also increasingly being measured and recently developed software allows all four indicators described here to be calculated in a relatively straightforward fashion for any household expenditure survey [35]. Inequalities in Financial Risk Protection A central concern of UHC is equity, and thus it is also important to consider who is and who is not protected against the financial hardship imposed by OOP payments. The indicators of financial risk protection are all derived from a household's expenditure, which reflects existing inequalities in income and wealth. Applied studies are also increasingly disaggregating these indicators to examine the hardship imposed on different sub-population groups on the basis of income, wealth, or other socioeconomic characteristics. Measurement of inequalities in financial risk protection is not always clear-cut. In terms of income, there is evidence of a negative correlation between financial hardship and income [36],[37]. Other studies have found the incidence of financial hardship among people in the poorest quintile is lower than in the rest of the population, often suggesting that this finding is because they either are unable to use health services or because they are already living in poverty [38]. Indeed, the incidence of financial catastrophe can sometimes be higher in higher income quintiles because these people choose to spend more on particular types of health services [39]. Overall, measuring the variation in financial risk protection due to OOP payments across population groups is as important as monitoring the average situation in the population. The relevance of socioeconomic stratifiers used to assess these inequalities may differ across countries according to the main causes of inequality. But some variations, such as those based on income or wealth, place of residence, and sex of household head, are likely to be consistently important across different countries. Other key stratifiers that countries should consider examining may include demographic characteristics of the household, education of the household, and religion and ethnicity of the household. Use of these types of stratifiers is supported by multivariate analyses that show that place of residence (rural/urban or parts of a country), household size or composition (e.g., headed by women, proportion of children or elderly people), and the presence of chronic illnesses, for example, have been associated with increased incidence or severity of financial hardship in different settings [24],[36],[40]–[42]. Understanding the distribution of the burden across sub-population groups will be particularly important as countries implement changes to their health financing policies. However, particular issues related to the monitoring of inequalities in financial risk protection merit some more attention. These are discussed in Box 5. Box 5. Further Implications for Measuring Inequalities in the Distribution of Financial Risk Protection The headcount measures of impoverishment and financial catastrophe indicate nothing about the severity of the financial hardship. In the former case, the measure also ignores the impact of health payments on households who are already below the poverty line. The difference in normalized poverty gap is often used to capture this as presented in Figure 2. But it has shortcomings from an equity perspective since it gives more weight to the increases in depth of poverty for people just below the poverty line as compared to the poorest people (since OOP payments can never exceed overall consumption). The normalized squared poverty gap, commonly called the squared poverty gap, considers the severity of poverty by giving more weight to the poverty gap of the poorest compared to households just below the poverty line [68]. The difference in squared poverty gap due to OOP payments will also have this property. A number of composite indicators used in the general poverty literature bring together measures of headcounts and poverty gaps [69]–[72]. Of particular interest could be the Watts index because of its pro-poor properties [73]. The application of these indexes for OOP health payments could be explored although it would be important to find a way of making them easily understood by policymakers. The alternative to developing composite indicators is a dashboard approach, which presents the four common indicators of the lack of financial risk protection (i.e., incidence of catastrophic health expenditure, catastrophic overshoot, incidence of impoverishment, and difference in poverty gap) and inequalities in them side by side. A challenge for policymakers, as with all sets of indicators, is how to judge if progress is made if the level or equality in one dimension improves, but drops in another. Because of the scarcity of resources for health, this may well happen on the path to UHC. A way around this might be to add an additional target—that financial risk protection should not decrease for any population group on the path to universal coverage. For this, the Gini coefficient of OOP health payments as a share of a household's non-basic food expenditure could be calculated and compared with the Gini coefficient for the difference in poverty gap due to OOP health payments. Over time changes in these Gini coefficients would show if inequalities in the burden of health payments (rather than only financial catastrophe or impoverishment) had increased. Data Requirements for Monitoring Financial Risk Protection Robust monitoring of financial risk protection requires reliable and periodic household surveys that contain information on health-specific and other expenditures. A recent effort only identified 112 countries that have at least two such surveys at different points in time that would allow the four indicators of financial hardship discussed here to be calculated [43]. But it is important to regularly collect and analyse this information to allow for patterns over time to be assessed; if possible, surveys should be conducted every 2 to 5 years in all countries. Another problem is that general household expenditure surveys are conducted in several countries but are not always specifically analysed for health expenditures [44]. The survey instruments most commonly used to collect health expenditure data differ in aspects such as the recall period, the number of expenditure items covered, and the overall focus of the survey, factors that have shown to influence people's responses. For example, health expenditures reported in surveys (or parts of surveys) focusing on health tend to be higher than those reported in surveys (or sections) where health is only one item under consideration [45]–[47]. Recommendations have been made for greater consistency or more standardised survey instruments to be better able to generate reliable and valid information on financial risk protection as it has not been possible to develop algorithms that adjust health expenditures to account for differences in the survey instrument [46]. Consequently, it would be useful for the organizations that routinely undertake household expenditure surveys to agree on a standard instrument. However, while everyone agrees that standardization is desirable, it is more difficult to convince people to use someone else's “standard instrument” rather than their own. Additionally, with an increasing focus on UHC, the same surveys should also cover utilisation of health services where possible. Lastly, the question exists whether there should be a direct linkage of health services and financial risk protection for these services using survey data. We do not recommend this option, but discuss the idea further in Box 6. Box 6. Financial Risk Protection and the Benefit Package Improving financial risk protection is not just influenced by health systems financing arrangements, but also by choices about a benefit package where one is specified. For example, if countries finance a benefits package that focuses on high cost items, or items that may be used frequently even if they are relatively low cost, they might provide greater financial risk protection than other types of benefits packages. This is one reason why Verguet and colleagues have proposed to develop a form of “extended cost-effectiveness analysis” that takes into account the impact of a proposed intervention not just on health but also on financial risk protection [74]. This form of analysis, however, is very much in its infancy. The other side of the coin is that people might incur OOP payments on items that are not in the benefits package. This is inevitable where benefits packages are shallow, as in most lower-income countries. In higher-income countries with extensive packages, however, people might choose to spend on items outside the package, possibly on items that are not strictly necessary to promote or maintain their health. The question then arises of whether this type of expense should be excluded from the estimates of financial catastrophe and impoverishment. This raises some ethical questions; for example, should we consider need as perceived by an individual or by the medical profession? In principle, we should probably exclude expenditure on non-essential services if they could be identified, but this poses a significant measurement challenge. The household surveys used to collect information on OOP payments do not provide enough detail to identify the types of services on which people spend. The few researchers who looked at this question have found in some countries richer households suffer more financial catastrophe than others but they spend more on hospitalization and dental care, while poorer households who face financial hardship spend more on medicines and outpatient expenditure, and there is a suspicion that some of the expenditure of richer households is not strictly necessary, but it is difficult to prove [38],[75]. Discussion Measuring coverage of needed health services side by side with the extent of financial risk protection in health and inequalities in both provides a complete picture of who can use the health services they need and the financial consequences of this use. These are the critical components of UHC. This paper outlines the four indicators of the lack of financial risk protection: two are now widely used and two others are increasingly being used to show average levels and inequalities on the path to UHC. In interpreting the information provided by this type of analysis, however, a number of qualifications need to be highlighted. Firstly, the common measures of financial hardship are not well-suited to understand the long term implications on household economic well-being [48],[49]. We can, for example, identify the number of households pushed into poverty at various points in time, but it is not possible to tell what happens to them subsequently. If they manage to rebound shortly afterwards then perhaps there would be less concern than if they are trapped in poverty for long periods of time. Exploring these issues requires frequent panel data and the ability to track individual households over time, which is expensive and administratively complex. Related to this complexity is the fact that most household expenditure surveys reveal little about how households cope with health shocks and the resulting financial consequences [50],[51]. For example, some surveys contain questions about whether households financed their health expenditures through savings, selling assets, or borrowing. While the responses can provide some insights, they are often difficult to interpret because of the different ways savings are used in different countries [52]. More detailed research comparing the incomes, consumption, savings, investments, and wealth of people with and without health shocks, which can adjust for confounders, is required to understand fully how households cope. There are also linkages between social protection and financial risk protection. In addition to immediate financial consequences, households encounter problems such as loss of employment or wages because of taking time off work [33]. Financial risk protection is thus just a component of even broader social protection that is needed to ensure that there are no adverse consequences associated with using needed health services. However, these broader research and policy questions lie largely outside the health sector and the boundaries of UHC. Overall, the number of studies focusing on financial risk protection in health has increased substantially over the last decade, and some studies have already been instrumental in stimulating policy changes in countries such as Mexico and Thailand [53],[54]. Accordingly, it will be important not only to continue developing new methodologies, but also to find ways to make the results intuitively understandable to decision makers. Conclusions and Recommendations With an increasing focus on UHC, there is a clear need to better understand its underlying concepts and practical methods of assessing their progress. Existing ways to measure financial risk protection provide useful insights into the financial hardship caused by accessing needed health services. For countries to benefit from sound policy making, regular monitoring of both the levels of and inequalities in key indicators of financial risk protection are needed. (Box: recommendations). Recommendations At the country level, routinely measure the incidence of financial catastrophe and impoverishment and associated inequalities to understand if the situation is improving. Where possible, also measure the catastrophic overshoot and the difference in the poverty gap for further insights. Where possible, standardise survey instruments and data on the use of health services.
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                Journal
                J Glob Health
                J Glob Health
                JGH
                Journal of Global Health
                Edinburgh University Global Health Society
                2047-2978
                2047-2986
                June 2017
                10 May 2017
                : 7
                : 1
                : 010302
                Affiliations
                [1]Harvard TH Chan School of Public Health, Boston, Massachusetts, USA
                Author notes
                Correspondence to:
Gabriel Seidman
Harvard TH Chan School of Public Health
677 Huntington Avenue
Boston MA 02115
USA
 Gabriel.seidman@ 123456gmail.com
                Article
                jogh-07-010302
                10.7189/jogh.07.010302
                5441444
                28567275
                3c13c452-47dc-4143-b1d8-7c6bd2e754aa
                Copyright © 2017 by the Journal of Global Health. All rights reserved.

                This work is licensed under a Creative Commons Attribution 4.0 International License.

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