White Rose University Consortium



Title: Estimating health opportunity costs in low- and middle-income countries: a novel approach and evidence from cross-country dataAuthors: Jessica Ochalek, Centre for Health Economics, University of York, York, United KingdomJames Lomas, Centre for Health Economics, University of York, York, United KingdomKarl Claxton, Centre for Health Economics, University of York, York, United KingdomCorresponding author: Jessica Ochalek; Centre for Health Economics, University of York, Heslington, York, YO10 5DD; jessica.ochalek@york.ac.uk; +44(0)1904321442Key words: Health economics < Health policies and all other topics, Health systems evaluation < Health policies and all other topicsWord Count: 5,077Reference Count: 31Abbreviations:CLEconditional life expectancyDALYDisability Adjusted Life YearGBDGlobal Burden of DiseaseGDPGross Domestic ProductHCShealthcare system ICERincremental cost-effectiveness ratiosLMIClow- and middle-income countryQALYQuality Adjusted Life YearYLDyear of life disabledYLLyear of life lostAbstractThe economic evaluation of healthcare interventions requires an assessment of whether the improvement in health outcomes they offer exceeds the improvement in health that would have been possible if the additional resources required had, instead, been made available for other healthcare activities. Therefore, some assessment of these health opportunity costs is required if the best use is to be made of the resources available for health care. This paper provides a framework for generating country-specific estimates of cost per disability adjusted life year (DALY) averted 'thresholds' that reflect health opportunity costs. We apply estimated elasticities on mortality, survival, morbidity and a generic measure of health, DALYs, that take account of measures of a country's infrastructure and changes in donor funding to country-specific data on health expenditure, epidemiology and demographics to determine the likely DALYs averted from a 1% change in expenditure on health. The resulting range of cost per DALY averted 'threshold' estimates for each country that represent likely health opportunity costs tend to fall below the range previously suggested by WHO of 1 to 3x GDP per capita. The 1-3x GDP range and many other previous and existing recommendations about which interventions are cost-effective are not based on an empirical assessment of the likely health opportunity costs, and, as a consequence, the health effects of changes in health expenditure have tended to be underestimated and there is a risk that interventions regarded as cost-effective reduce rather than improve health outcomes overall.Summary boxWhat is already known about this topic?An effective intervention will only improve health outcomes overall if the additional health benefits it offers exceed the health opportunity costs associated with the additional health care costs that it imposes.The criteria commonly used to judge cost-effectiveness (i.e., cost per DALY averted ‘thresholds’) do not reflect evidence of health opportunity costs with the result that decisions and recommendations based on them may reduce rather than improve overall population health.What are the new findings?Available estimates of the health effect of changes in health expenditure using country level data can be used to inform country-specific assessments of health opportunity costs by applying estimated mortality effects (elasticities) to country-specific data on baseline epidemiology, demographics and health expenditure. A range of plausible estimates of the cost per DALY averted from changes in health expenditure are reported for 97 low- and middle-income countries. What are the recommendations for policy and practice?The reported estimates are an evidence based improvement on the type of norms that have become widely cited and used as ‘thresholds’ to judge cost-effectiveness, and this framework of analysis can be applied to the results of any study thought to identify plausible effects on mortality of changes in health expenditure, whether they are based on country level or within country data. Introduction Evidence of the expected costs and health effects of making a healthcare intervention available to specific populations in a particular setting and healthcare system (HCS) are typically summarised as incremental cost-effectiveness ratios (ICERs), which are often expressed as the cost per Quality Adjusted Life Year (QALY) gained or the cost per Disability Adjusted Life Year (DALY) averted.ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/S0140-6736(12)61680-8","ISSN":"01406736","abstract":"BACKGROUND\nMeasurement of the global burden of disease with disability-adjusted life-years (DALYs) requires disability weights that quantify health losses for all non-fatal consequences of disease and injury. There has been extensive debate about a range of conceptual and methodological issues concerning the definition and measurement of these weights. Our primary objective was a comprehensive re-estimation of disability weights for the Global Burden of Disease Study 2010 through a large-scale empirical investigation in which judgments about health losses associated with many causes of disease and injury were elicited from the general public in diverse communities through a new, standardised approach. \n\nMETHODS\nWe surveyed respondents in two ways: household surveys of adults aged 18 years or older (face-to-face interviews in Bangladesh, Indonesia, Peru, and Tanzania; telephone interviews in the USA) between Oct 28, 2009, and June 23, 2010; and an open-access web-based survey between July 26, 2010, and May 16, 2011. The surveys used paired comparison questions, in which respondents considered two hypothetical individuals with different, randomly selected health states and indicated which person they regarded as healthier. The web survey added questions about population health equivalence, which compared the overall health benefits of different life-saving or disease-prevention programmes. We analysed paired comparison responses with probit regression analysis on all 220 unique states in the study. We used results from the population health equivalence responses to anchor the results from the paired comparisons on the disability weight scale from 0 (implying no loss of health) to 1 (implying a health loss equivalent to death). Additionally, we compared new disability weights with those used in WHO's most recent update of the Global Burden of Disease Study for 2004. \n\nFINDINGS\n13?902 individuals participated in household surveys and 16?328 in the web survey. Analysis of paired comparison responses indicated a high degree of consistency across surveys: correlations between individual survey results and results from analysis of the pooled dataset were 0·9 or higher in all surveys except in Bangladesh (r=0·75). Most of the 220 disability weights were located on the mild end of the severity scale, with 58 (26%) having weights below 0·05. Five (11%) states had weights below 0·01, such as mild anaemia, mild hearing or vision loss, and secondary infertility. 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Lancet","id":"ITEM-1","issue":"9859","issued":{"date-parts":[["2012"]]},"page":"2129-2143","title":"Common values in assessing health outcomes from disease and injury: disability weights measurement study for the Global Burden of Disease Study 2010","type":"article-journal","volume":"380"},"uris":[""]}],"mendeley":{"formattedCitation":"¹","plainTextFormattedCitation":"1","previouslyFormattedCitation":"¹"},"properties":{"noteIndex":0},"schema":""}1 These measures provide a useful summary of how much additional resource is required to achieve a measured improvement in health (the additional cost required to gain one QALY or to avert one DALY). Whether the cost per QALY gained or DALY averted offered by an intervention is judged to be cost-effective requires some explicit or implicit criteria, often referred to as a cost-effectiveness (cost per DALY averted) ‘threshold’ below which the intervention is regarded as worthwhile. However, an effective intervention will only improve health outcomes overall (i.e., produce a positive net health benefit) if the additional health benefits it offers exceed the health opportunity costs associated with the healthcare costs that it imposes (whether these must be found from existing commitments or additional expenditure that could have been devoted to other healthcare activities). Therefore, an assessment of health opportunity cost indicates the maximum a HCS can afford to pay for the health benefits that an intervention offers, without reducing health outcomes overall. It also reflects the value (the health benefits) of increasing healthcare expenditure. To ensure that decisions improve rather than reduce health outcomes overall, judgments about cost-effectiveness ought to be founded on evidence of the likely health opportunity costs in the HCS where the use of an intervention is being considered.ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1002/hec.3130","ISSN":"1099-1050","PMID":"25488707","abstract":"Organisations across diverse health care systems making decisions about the funding of new medical technologies face extensive stakeholder and political pressures. As a consequence, there is quite understandable pressure to take account of other attributes of benefit and to fund technologies, even when the opportunity costs are likely exceed the benefits they offer. Recent evidence suggests that NICE technology appraisal is already approving drugs where more health is likely to be lost than gained. Also, NICE recently proposed increasing the upper bound of the cost-effectiveness threshold to reflect other attributes of benefit but without a proper assessment of the type of benefits that are expected to be displaced. It appears that NICE has taken a direction of travel, which means that more harm than good is being, and will continue to be, done, but it is unidentified NHS patients who bear the real opportunity costs.","author":[{"dropping-particle":"","family":"Claxton","given":"Karl","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sculpher","given":"Mark","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Palmer","given":"Stephen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Culyer","given":"Anthony J","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Health economics","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2015","1"]]},"page":"1-7","title":"Causes for concern: is NICE failing to uphold its responsibilities to all NHS patients?","type":"article-journal","volume":"24"},"uris":[""]}],"mendeley":{"formattedCitation":"²","plainTextFormattedCitation":"2","previouslyFormattedCitation":"²"},"properties":{"noteIndex":0},"schema":""}2 A persistent problem has been that the criteria used to judge cost-effectiveness (i.e. cost per DALY ‘thresholds’) recommended or cited by decision making and advisory bodies (both national and supra-national) reflect a lack of conceptual clarity about what they ought to represent and what type of evidence might inform their assessment.ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Revill","given":"P","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Walker","given":"S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Madan","given":"J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ciaranello","given":"A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mwase","given":"T","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gibb","given":"DM","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Claxton","given":"K","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sculpher","given":"M","non-dropping-particle":"","parse-names":false,"suffix":""}],"collection-title":"CHE Research Paper","id":"ITEM-1","issued":{"date-parts":[["2014"]]},"number":"98","publisher-place":"York","title":"Using cost-effectiveness thresholds to determine value for money in low- and middle-income country healthcare systems: are current international norms fit for purpose?","type":"report"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1017/S1744133116000049","ISSN":"1744-1331","abstract":"<p> There is misunderstanding about both the meaning and the role of cost-effectiveness thresholds in policy decision making. This article dissects the main issues by use of a bookshelf metaphor. Its main conclusions are as follows: it must be possible to compare interventions in terms of their impact on a common measure of health; mere effectiveness is not a persuasive case for inclusion in public insurance plans; public health advocates need to address issues of relative effectiveness; a ‘first best’ benchmark or threshold ratio of health gain to expenditure identifies the least effective intervention that should be included in a public insurance plan; the reciprocal of this ratio – the ‘first best’ cost-effectiveness threshold – will rise or fall as the health budget rises or falls ( <italic>ceteris paribus</italic> ); setting thresholds too high or too low costs lives; failure to set any cost-effectiveness threshold at all also involves avertable deaths and morbidity; the threshold cannot be set independently of the health budget; the threshold can be approached from either the demand side or the supply side – the two are equivalent only in a health-maximising equilibrium; the supply-side approach generates an estimate of a ‘second best’ cost-effectiveness threshold that is higher than the ‘first best’; the second best threshold is the one generally to be preferred in decisions about adding or subtracting interventions in an established public insurance package; multiple thresholds are implied by systems having distinct and separable health budgets; disinvestment involves eliminating <italic>effective</italic> technologies from the insured bundle; differential weighting of beneficiaries’ health gains may affect the threshold; anonymity and identity are factors that may affect the interpretation of the threshold; the true opportunity cost of health care in a community, where the effectiveness of interventions is determined by their impact on health, is not to be measured in money – but in health itself. </p>","author":[{"dropping-particle":"","family":"Culyer","given":"Anthony J.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Health Economics, Policy and Law","id":"ITEM-2","issue":"04","issued":{"date-parts":[["2016","10","24"]]},"page":"415-432","publisher":"Cambridge University Press","title":"Cost-effectiveness thresholds in health care: a bookshelf guide to their meaning and use","type":"article-journal","volume":"11"},"uris":[""]}],"mendeley":{"formattedCitation":"^3,4","plainTextFormattedCitation":"3,4","previouslyFormattedCitation":"^3,4"},"properties":{"noteIndex":0},"schema":""}3,4 These implicit values and established norms do not reflect evidence of health opportunity costs so decisions or recommendations based on them may reduce rather than improve overall population health.ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1002/hec.3130","ISSN":"1099-1050","PMID":"25488707","abstract":"Organisations across diverse health care systems making decisions about the funding of new medical technologies face extensive stakeholder and political pressures. As a consequence, there is quite understandable pressure to take account of other attributes of benefit and to fund technologies, even when the opportunity costs are likely exceed the benefits they offer. Recent evidence suggests that NICE technology appraisal is already approving drugs where more health is likely to be lost than gained. Also, NICE recently proposed increasing the upper bound of the cost-effectiveness threshold to reflect other attributes of benefit but without a proper assessment of the type of benefits that are expected to be displaced. It appears that NICE has taken a direction of travel, which means that more harm than good is being, and will continue to be, done, but it is unidentified NHS patients who bear the real opportunity costs.","author":[{"dropping-particle":"","family":"Claxton","given":"Karl","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sculpher","given":"Mark","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Palmer","given":"Stephen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Culyer","given":"Anthony J","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Health economics","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2015","1"]]},"page":"1-7","title":"Causes for concern: is NICE failing to uphold its responsibilities to all NHS patients?","type":"article-journal","volume":"24"},"uris":[""]}],"mendeley":{"formattedCitation":"²","plainTextFormattedCitation":"2","previouslyFormattedCitation":"²"},"properties":{"noteIndex":0},"schema":""}2 For example, the cost per DALY averted ‘thresholds’ previously promoted by the World Health Organisation, which classified interventions as very cost effective (1x GDP per capita or less) and cost-effective (3x GDP per capita or less), became a widely cited and established norm, but did not reflect evidence of health opportunity costs.ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"abstract":"Cost–effectiveness analysis is used to compare the costs and outcomes of alternative policy options. Each resulting cost–effectiveness ratio represents the magnitude of additional health gained per additional unit of resources spent. Cost– effectiveness thresholds allow cost–effectiveness ratios that represent good or very good value for money to be identified. In 2001, the World Health Organization's Commission on Macroeconomics in Health suggested cost–effectiveness thresholds based on multiples of a country's per-capita gross domestic product (GDP). In some contexts, in choosing which health interventions to fund and which not to fund, these thresholds have since been used as decision rules. However, experience with the use of such GDP-based thresholds in decision-making processes at country level shows them to lack country specificity and this – in addition to uncertainty in the modelled cost–effectiveness ratios – can lead to the wrong decision on how to spend health-care resources. Cost–effectiveness information should be used alongside other considerations – e.g. budget impact and feasibility considerations – in a transparent decision-making process, rather than in isolation based on a single threshold value. Although cost–effectiveness ratios are undoubtedly informative in assessing value for money, countries should be encouraged to develop a context-specific process for decision-making that is supported by legislation, has stakeholder buy-in – e.g. the involvement of civil society organizations and patient groups – and is transparent, consistent and fair. What are cost–effectiveness thresholds?","author":[{"dropping-particle":"","family":"Bertram","given":"Melanie Y","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lauer","given":"Jeremy A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Joncheere","given":"Kees","non-dropping-particle":"De","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Edejer","given":"Tessa","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hutubessy","given":"Raymond","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kieny","given":"Marie-Paule","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hill","given":"Suzanne","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Bulletin of the World Health Organization","id":"ITEM-1","issued":{"date-parts":[["2016"]]},"title":"Use and misuse of thresholds Cost–effectiveness thresholds: pros and cons","type":"article-journal"},"uris":[""]}],"mendeley":{"formattedCitation":"⁵","plainTextFormattedCitation":"5","previouslyFormattedCitation":"⁵"},"properties":{"noteIndex":0},"schema":""}5 The explicit and/or widely cited norms that have become established in some high-income settings are also recognised as having little evidential foundation. ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.healthpol.2008.12.010","ISSN":"01688510","PMID":"19168255","abstract":"OBJECTIVES The UK's National Institute of Health and Clinical Excellence (NICE) has an explicit cost-effectiveness threshold for deciding whether or not services are to be provided in the National Health Service (NHS), but there is currently little evidence to support the level at which it is set. This study examines whether it is possible to obtain such evidence by examining decision making elsewhere in the NHS. Its objectives are to set out a conceptual model linking NICE decision making based on explicit thresholds with the thresholds implicit in local decision making and to gauge the feasibility of (a) identifying those implicit local cost effectiveness thresholds and (b) using these to gauge the appropriateness of NICE's explicit threshold. METHODS Structured interviews with senior staff, together with financial and public health information, from six NHS purchasers and 18 providers. A list of health care services introduced or discontinued in 2006/7 was constructed. Those that were in principle amenable to estimation of a cost-effectiveness ratio were examined. RESULTS It was feasible to identify decisions and to estimate the cost-effectiveness of some. These were not necessarily 'marginal' services. Issues include: services that are dominated (or dominate); decisions about how, rather than what, services should be delivered; the lack of local cost effectiveness evidence; and considerations other than cost-effectiveness. CONCLUSIONS A definitive finding about the consistency or otherwise of NICE and NHS cost effectiveness thresholds would require very many decisions to be observed, combined with a detailed understanding of the local decision making processes.","author":[{"dropping-particle":"","family":"Appleby","given":"John","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Devlin","given":"Nancy","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Parkin","given":"David","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Buxton","given":"Martin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Chalkidou","given":"Kalipso","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Health Policy","id":"ITEM-1","issue":"3","issued":{"date-parts":[["2009","8"]]},"page":"239-245","title":"Searching for cost effectiveness thresholds in the NHS","type":"article-journal","volume":"91"},"uris":[""]},{"id":"ITEM-2","itemData":{"author":[{"dropping-particle":"","family":"H.o.C.H. Committee","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-2","issued":{"date-parts":[["2008"]]},"number":"HC27-I","publisher-place":"London","title":"First report of the Health Committee 2007-2008","type":"report"},"uris":[""]},{"id":"ITEM-3","itemData":{"DOI":"10.1136/bmj.329.7459.224","ISSN":"1756-1833","PMID":"15271836","author":[{"dropping-particle":"","family":"Rawlins","given":"Michael D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Culyer","given":"Anthony J","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"BMJ (Clinical research ed.)","id":"ITEM-3","issue":"7459","issued":{"date-parts":[["2004","7","24"]]},"page":"224-7","title":"National Institute for Clinical Excellence and its value judgments.","type":"article-journal","volume":"329"},"uris":[""]},{"id":"ITEM-4","itemData":{"DOI":"10.1056/NEJMp1405158","ISSN":"1533-4406","PMID":"25162885","author":[{"dropping-particle":"","family":"Neumann","given":"Peter J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cohen","given":"Joshua T","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Weinstein","given":"Milton C","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"The New England journal of medicine","id":"ITEM-4","issue":"9","issued":{"date-parts":[["2014","8","28"]]},"page":"796-7","title":"Updating cost-effectiveness--the curious resilience of the $50,000-per-QALY threshold.","type":"article-journal","volume":"371"},"uris":[""]}],"mendeley":{"formattedCitation":"^6–9","plainTextFormattedCitation":"6–9","previouslyFormattedCitation":"^6–9"},"properties":{"noteIndex":0},"schema":""}6–9 Other proposed ‘thresholds’ reflect a view of what value ought to be placed on improvements in health. They imply what healthcare expenditure ought to be (the social demand for health) rather than an evidence-based assessment of health opportunity costs given actual levels of expenditure, i.e. a ‘supply side’ estimate of the amount of health that a HCS currently delivers with more or less resources.ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.jval.2016.02.020","ISSN":"10983015","PMID":"27565273","abstract":"BACKGROUND Many health care systems claim to incorporate the cost-effectiveness criterion in their investment decisions. Information on the system's willingness to pay per effectiveness unit, normally measured as quality-adjusted life-years (QALYs), however, is not available in most countries. This is partly because of the controversy that remains around the use of a cost-effectiveness threshold, about what the threshold ought to represent, and about the appropriate methodology to arrive at a threshold value. OBJECTIVES The aim of this article was to identify and critically appraise the conceptual perspectives and methodologies used to date to estimate the cost-effectiveness threshold. METHODS We provided an in-depth discussion of different conceptual views and undertook a systematic review of empirical analyses. Identified studies were categorized into the two main conceptual perspectives that argue that the threshold should reflect 1) the value that society places on a QALY and 2) the opportunity cost of investment to the system given budget constraints. RESULTS These studies showed different underpinning assumptions, strengths, and limitations, which are highlighted and discussed. Furthermore, this review allowed us to compare the cost-effectiveness threshold estimates derived from different types of studies. We found that thresholds based on society's valuation of a QALY are generally larger than thresholds resulting from estimating the opportunity cost to the health care system. CONCLUSIONS This implies that some interventions with positive social net benefits, as informed by individuals' preferences, might not be an appropriate use of resources under fixed budget constraints.","author":[{"dropping-particle":"","family":"Vallejo-Torres","given":"Laura","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"García-Lorenzo","given":"Borja","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Castilla","given":"Iván","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Valcárcel-Nazco","given":"Cristina","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"García-Pérez","given":"Lidia","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Linertová","given":"Renata","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Polentinos-Castro","given":"Elena","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Serrano-Aguilar","given":"Pedro","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Value in Health","id":"ITEM-1","issue":"5","issued":{"date-parts":[["2016","7"]]},"page":"558-566","title":"On the Estimation of the Cost-Effectiveness Threshold: Why, What, How?","type":"article-journal","volume":"19"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1007/s40273-017-0606-1","ISSN":"1170-7690","author":[{"dropping-particle":"","family":"Thokala","given":"Praveen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ochalek","given":"Jessica","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Leech","given":"Ashley A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tong","given":"Thaison","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"PharmacoEconomics","id":"ITEM-2","issued":{"date-parts":[["2018","2","9"]]},"title":"Cost-Effectiveness Thresholds: the Past, the Present and the Future","type":"article-journal"},"uris":[""]}],"mendeley":{"formattedCitation":"^10,11","plainTextFormattedCitation":"10,11","previouslyFormattedCitation":"^10,11"},"properties":{"noteIndex":0},"schema":""}10,11The health opportunity cost of a proposed investment in a healthcare intervention is the improvement in health that would have been possible if the additional resources required had, instead, been made available for other healthcare activities. This assessment is equally relevant whether the additional costs of the investment must be found from existing commitments and current levels of health expenditure, or when health expenditure will be increased to accommodate the additional resources required. Therefore, an estimate of the marginal productivity of healthcare expenditure also represents expected health opportunity costs when the decision context is restricted to approving or rejecting a proposed investment. Decision makers may also compare a proposed investment to specific disinvestments required to accommodate it or alternative investments which could be made with any additional resources. However, they still need to consider how these alternatives compare to what the healthcare system could be expected to deliver, i.e., an estimate of marginal productivity is still relevant. If the decision maker had full information about all interventions that are or could be provided for all indications and subgroups of the population and was also tasked with the wholesale redesign of the healt care system then the marginal productivity would be the outcome of this task. There are no examples of this type of wholesale redesign for each proposed investment. There are some limited examples of periodic wholesale package design in low income settings, but even here some estimate of existing marginal productivity has been shown to be a useful starting point in package design.ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Ochalek","given":"Jessica","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Revill","given":"Paul","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Manthalu","given":"Gerald","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"McGuire","given":"Finn","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Nkhoma","given":"Dominic","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rollinger","given":"Alexandra","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sculpher","given":"Mark","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Claxton","given":"Karl","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"BMJ Global Health","id":"ITEM-1","issued":{"date-parts":[["2018"]]},"title":"Supporting the development of a health benefits package in Malawi","type":"article-journal"},"uris":[""]}],"mendeley":{"formattedCitation":"¹²","plainTextFormattedCitation":"12","previouslyFormattedCitation":"¹²"},"properties":{"noteIndex":0},"schema":""}12 Estimates of the marginal productivity of health expenditure in producing health (QALYs) are becoming available for some high-income countries based on approaches to estimation which exploit within-country data.ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"abstract":"Empirical evidence has hitherto been inconclusive about the strength of the link between health care spending and health outcomes. This paper uses programme budgeting data prepared by 295 English Primary Care Trusts to model the link for two specific programmes of care: cancer and circulatory diseases. A theoretical model is developed in which decision-makers must allocate a fixed budget across programmes of care so as to maximize social welfare, in the light of a health production function for each programme. This yields an expenditure equation and a health outcomes equation for each programme. These are estimated for the two programmes of care using instrumental variables methods. All the equations prove to be well specified. They suggest that the cost of a life year saved in cancer is about ??13,100, and in circulation about ??8000. These results challenge the widely held view that health care has little marginal impact on health. From a policy perspective, they can help set priorities by informing resource allocation across programmes of care. They can also help health technology agencies decide whether their cost-effectiveness thresholds for accepting new technologies are set at the right level.","author":[{"dropping-particle":"","family":"Martin","given":"Stephen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rice","given":"Nigel","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Smith","given":"Peter C.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Journal of Health Economics","id":"ITEM-1","issue":"4","issued":{"date-parts":[["2008"]]},"page":"826-842","publisher":"Elsevier","title":"Does health care spending improve health outcomes? Evidence from English programme budgeting data","type":"article-journal","volume":"27"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1007/s40273-017-0585-2","ISSN":"1170-7690","PMID":"29273843","abstract":"BACKGROUND Spending on new healthcare technologies increases net population health when the benefits of a new technology are greater than their opportunity costs-the benefits of the best alternative use of the additional resources required to fund a new technology. OBJECTIVE The objective of this study was to estimate the expected incremental cost per quality-adjusted life-year (QALY) gained of increased government health expenditure as an empirical estimate of the average opportunity costs of decisions to fund new health technologies. The estimated incremental cost-effectiveness ratio (ICER) is proposed as a reference ICER to inform value-based decision making in Australia. METHODS Empirical top-down approaches were used to estimate the QALY effects of government health expenditure with respect to reduced mortality and morbidity. Instrumental variable two-stage least-squares regression was used to estimate the elasticity of mortality-related QALY losses to a marginal change in government health expenditure. Regression analysis of longitudinal survey data representative of the general population was used to isolate the effects of increased government health expenditure on morbidity-related, QALY gains. Clinical judgement informed the duration of health-related quality-of-life improvement from the annual increase in government?health expenditure. RESULTS The base-case reference ICER was estimated at AUD28,033 per QALY gained. Parametric uncertainty associated with the estimation of mortality- and morbidity-related QALYs generated a 95% confidence interval AUD20,758-37,667. CONCLUSION Recent public summary documents suggest new technologies with ICERs above AUD40,000 per QALY gained are recommended for public funding. The empirical reference ICER reported in this article suggests more QALYs could be gained if resources were allocated to other forms of health spending.","author":[{"dropping-particle":"","family":"Edney","given":"Laura Catherine","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Haji Ali Afzali","given":"Hossein","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cheng","given":"Terence Chai","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Karnon","given":"Jonathan","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"PharmacoEconomics","id":"ITEM-2","issued":{"date-parts":[["2017","12","22"]]},"title":"Estimating the Reference Incremental Cost-Effectiveness Ratio for the Australian Health System","type":"article-journal"},"uris":[""]},{"id":"ITEM-3","itemData":{"DOI":"10.3310/hta19140","ISSN":"2046-4924","PMID":"25692211","abstract":"BACKGROUND: Cost-effectiveness analysis involves the comparison of the incremental cost-effectiveness ratio of a new technology, which is more costly than existing alternatives, with the cost-effectiveness threshold. This indicates whether or not the health expected to be gained from its use exceeds the health expected to be lost elsewhere as other health-care activities are displaced. The threshold therefore represents the additional cost that has to be imposed on the system to forgo 1 quality-adjusted life-year (QALY) of health through displacement. There are no empirical estimates of the cost-effectiveness threshold used by the National Institute for Health and Care Excellence. OBJECTIVES: (1) To provide a conceptual framework to define the cost-effectiveness threshold and to provide the basis for its empirical estimation. (2) Using programme budgeting data for the English NHS, to estimate the relationship between changes in overall NHS expenditure and changes in mortality. (3) To extend this mortality measure of the health effects of a change in expenditure to life-years and to QALYs by estimating the quality-of-life (QoL) associated with effects on years of life and the additional direct impact on QoL itself. (4) To present the best estimate of the cost-effectiveness threshold for policy purposes. METHODS: Earlier econometric analysis estimated the relationship between differences in primary care trust (PCT) spending, across programme budget categories (PBCs), and associated disease-specific mortality. This research is extended in several ways including estimating the impact of marginal increases or decreases in overall NHS expenditure on spending in each of the 23 PBCs. Further stages of work link the econometrics to broader health effects in terms of QALYs. RESULTS: The most relevant 'central' threshold is estimated to be ?12,936 per QALY (2008 expenditure, 2008-10 mortality). Uncertainty analysis indicates that the probability that the threshold is < ?20,000 per QALY is 0.89 and the probability that it is < ?30,000 per QALY is 0.97. Additional 'structural' uncertainty suggests, on balance, that the central or best estimate is, if anything, likely to be an overestimate. The health effects of changes in expenditure are greater when PCTs are under more financial pressure and are more likely to be disinvesting than investing. This indicates that the central estimate of the threshold is likely to be an overestimate for all technologies which impose n…","author":[{"dropping-particle":"","family":"Claxton","given":"Karl","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Martin","given":"Steve","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Soares","given":"Marta","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rice","given":"Nigel","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Spackman","given":"Eldon","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hinde","given":"Sebastian","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Devlin","given":"Nancy","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Smith","given":"Peter C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sculpher","given":"Mark","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Health technology assessment (Winchester, England)","id":"ITEM-3","issue":"14","issued":{"date-parts":[["2015","2"]]},"language":"eng","page":"1-503, v-vi","title":"Methods for the estimation of the National Institute for Health and Care Excellence cost-effectiveness threshold.","type":"article-journal","volume":"19"},"uris":[""]},{"id":"ITEM-4","itemData":{"author":[{"dropping-particle":"","family":"Vallejo-Torres","given":"Laura","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"García-Lorenzo","given":"Borja","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Serrano-Aguilar","given":"Pedro","non-dropping-particle":"","parse-names":false,"suffix":""}],"collection-title":"Estudios sobre la Economía Espa?ola - 2016/22 ","id":"ITEM-4","issued":{"date-parts":[["2016"]]},"number":"eee2016-22","publisher-place":"Madrid","title":"Estimating a cost-effectiveness threshold for the Spanish NHS","type":"report"},"uris":[""]}],"mendeley":{"formattedCitation":"^13–16","plainTextFormattedCitation":"13–16","previouslyFormattedCitation":"^13–16"},"properties":{"noteIndex":0},"schema":""}13–16 This evidence from national HCS in high-income countries gives some indication of possible values in other contexts based on estimates of the income elasticity of demand for health and assumptions about the relative underfunding of HCS (i.e., the shadow price for public expenditure on health).ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.jval.2016.02.017","ISSN":"10983015","abstract":"BACKGROUND\nCost-effectiveness analysis can guide policymakers in resource allocation decisions. It assesses whether the health gains offered by an intervention are large enough relative to any additional costs to warrant adoption. When there are constraints on the health care system’s budget or ability to increase expenditures, additional costs imposed by interventions have an “opportunity cost” in terms of the health foregone because other interventions cannot be provided. Cost-effectiveness thresholds (CETs) are typically used to assess whether an intervention is worthwhile and should reflect health opportunity cost. Nevertheless, CETs used by some decision makers—such as the World Health Organization that suggested CETs of 1 to 3 times the gross domestic product (GDP) per capita—do not. \n\nOBJECTIVES\nTo estimate CETs based on opportunity cost for a wide range of countries. \n\nMETHODS\nWe estimated CETs based on recent empirical estimates of opportunity cost (from the English National Health Service), estimates of the relationship between country GDP per capita and the value of a statistical life, and a series of explicit assumptions. \n\nRESULTS\nCETs for Malawi (the country with the lowest income in the world), Cambodia (with borderline low/low-middle income), El Salvador (with borderline low-middle/upper-middle income), and Kazakhstan (with borderline high-middle/high income) were estimated to be $3 to $116 (1%–51% GDP per capita), $44 to $518 (4%–51%), $422 to $1967 (11%–51%), and $4485 to $8018 (32%–59%), respectively. \n\nCONCLUSIONS\nTo date, opportunity-cost-based CETs for low-/middle-income countries have not been available. Although uncertainty exists in the underlying assumptions, these estimates can provide a useful input to inform resource allocation decisions and suggest that routinely used CETs have been too high.","author":[{"dropping-particle":"","family":"Woods","given":"Beth","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Revill","given":"Paul","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sculpher","given":"Mark","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Claxton","given":"Karl","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Value in Health","id":"ITEM-1","issue":"8","issued":{"date-parts":[["2016"]]},"page":"929-935","title":"Country-Level Cost-Effectiveness Thresholds: Initial Estimates and the Need for Further Research","type":"article-journal","volume":"19"},"uris":[""]}],"mendeley":{"formattedCitation":"¹⁷","plainTextFormattedCitation":"17","previouslyFormattedCitation":"¹⁷"},"properties":{"noteIndex":0},"schema":""}17 However, there are estimates of the mortality effects of changes in healthcare expenditure based on country-level data (typically expressed as elasticities), which, in combination with country-specific data on baseline epidemiology, demographics and health expenditure, offer the opportunity to estimate country-specific cost per DALY averted ‘thresholds’ that reflect evidence of health opportunity costs. MethodsThe effect of different levels of healthcare expenditure on mortality outcomes has been investigated in a number of published studies using cross-country data.ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.socscimed.2017.02.024","ISSN":"02779536","PMID":"28237460","abstract":"While numerous studies assess the impact of healthcare spending on health outcomes, typically reporting multiple estimates of the elasticity of health outcomes (most often measured by a mortality rate or life expectancy) with respect to healthcare spending, the extent to which study attributes influence these elasticity estimates is unclear. Accordingly, we utilize a meta-data set (consisting of 65 studies completed over the 1969-2014 period) to examine these elasticity estimates using meta-regression analysis (MRA). Correcting for a number of issues, including publication selection bias, healthcare spending is found to have the greatest impact on the mortality rate compared to life expectancy. Indeed, conditional on several features of the literature, the spending elasticity for mortality is near?-0.13, whereas it is near to?+0.04 for life expectancy. MRA results reveal that the spending elasticity for the mortality rate is particularly sensitive to data aggregation, the specification of the health production function, and the nature of healthcare spending. The spending elasticity for life expectancy is particularly sensitive to the age at which life expectancy is measured, as well as the decision to control for the endogeneity of spending in the health production function. With such results in hand, we have a better understanding of how modeling choices influence results reported in this literature.","author":[{"dropping-particle":"","family":"Gallet","given":"Craig A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Doucouliagos","given":"Hristos","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Social Science & Medicine","id":"ITEM-1","issued":{"date-parts":[["2017","4"]]},"page":"9-17","title":"The impact of healthcare spending on health outcomes: A meta-regression analysis","type":"article-journal","volume":"179"},"uris":[""]}],"mendeley":{"formattedCitation":"¹⁸","plainTextFormattedCitation":"18","previouslyFormattedCitation":"¹⁸"},"properties":{"noteIndex":0},"schema":""}18 The challenge is to control for all the other reasons why mortality might differ between countries in order to isolate the causal effect of differences in health expenditure.ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Nakamura","given":"Ryota","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lomas","given":"James","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Claxton","given":"Karl","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bokhari","given":"Farasat","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Serra","given":"Rodrigo Moreno","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Suhrcke","given":"Marc","non-dropping-particle":"","parse-names":false,"suffix":""}],"collection-title":"CHE Research Paper","id":"ITEM-1","issued":{"date-parts":[["2016"]]},"number":"128","publisher-place":"York","title":"Assessing the Impact of Health Care Expenditures on Mortality Using Cross-Country Data","type":"report"},"uris":[""]}],"mendeley":{"formattedCitation":"¹⁹","plainTextFormattedCitation":"19","previouslyFormattedCitation":"¹⁹"},"properties":{"noteIndex":0},"schema":""}19 Even if available measures are complete, accurate and unbiased then estimation issues are likely to occur because of simultaneous equation bias, where health outcomes are likely to be influenced by expenditure (increases in expenditure improves outcomes), but outcomes are also likely to influence expenditure (poor outcomes prompt greater efforts and increased expenditure).ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/0277-9536(87)90167-5","ISSN":"02779536","abstract":"An essential ingredient in the evaluation of policies concerning health services is knowledge of the impact of health services and other factors on the health of the population. One method of obtaining this information is from the regression analysis of international cross-section data on mortality rates, health service provision, income levels, consumption patterns, and other variables hypothesised to affect population health. The investigation of the determinants of population health is in many ways akin to the estimation of production functions which describe the relationship between the output of goods or services and the mix of inputs used in their production. The purpose of our paper is to use this analogy to discuss, and provide examples of, the problems which arise with the statistical investigation of mortality rates. Issues raised include simultaneous equation bias, multicollinearity, selection of explanatory variables, omitted variable bias, definition and measurement of variables, functional forms, lagged relationships and temporal stability. These problems are illustrated by replication and re-analysis, using new data, of the well known study by Cochrane, St Leger and Moore.","author":[{"dropping-particle":"","family":"Gravelle","given":"H.S.E.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Backhouse","given":"M.E.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Social Science & Medicine","id":"ITEM-1","issue":"5","issued":{"date-parts":[["1987","1"]]},"page":"427-441","title":"International cross-section analysis of the determination of mortality","type":"article-journal","volume":"25"},"uris":[""]}],"mendeley":{"formattedCitation":"²⁰","plainTextFormattedCitation":"20","previouslyFormattedCitation":"²⁰"},"properties":{"noteIndex":0},"schema":""}20 This results in endogeneity, which combined with inevitable aggregation bias, risks underestimating the magnitude of health improvements due to changes in expenditure. Instrumental variables have been used in a number of studies to try and overcome this problem and estimate outcome elasticities for mortality.ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.socscimed.2017.02.024","ISSN":"02779536","PMID":"28237460","abstract":"While numerous studies assess the impact of healthcare spending on health outcomes, typically reporting multiple estimates of the elasticity of health outcomes (most often measured by a mortality rate or life expectancy) with respect to healthcare spending, the extent to which study attributes influence these elasticity estimates is unclear. Accordingly, we utilize a meta-data set (consisting of 65 studies completed over the 1969-2014 period) to examine these elasticity estimates using meta-regression analysis (MRA). Correcting for a number of issues, including publication selection bias, healthcare spending is found to have the greatest impact on the mortality rate compared to life expectancy. Indeed, conditional on several features of the literature, the spending elasticity for mortality is near?-0.13, whereas it is near to?+0.04 for life expectancy. MRA results reveal that the spending elasticity for the mortality rate is particularly sensitive to data aggregation, the specification of the health production function, and the nature of healthcare spending. The spending elasticity for life expectancy is particularly sensitive to the age at which life expectancy is measured, as well as the decision to control for the endogeneity of spending in the health production function. With such results in hand, we have a better understanding of how modeling choices influence results reported in this literature.","author":[{"dropping-particle":"","family":"Gallet","given":"Craig A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Doucouliagos","given":"Hristos","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Social Science & Medicine","id":"ITEM-1","issued":{"date-parts":[["2017","4"]]},"page":"9-17","title":"The impact of healthcare spending on health outcomes: A meta-regression analysis","type":"article-journal","volume":"179"},"uris":[""]}],"mendeley":{"formattedCitation":"¹⁸","plainTextFormattedCitation":"18","previouslyFormattedCitation":"¹⁸"},"properties":{"noteIndex":0},"schema":""}18 This approach requires that the excluded instruments satisfy two criteria. The first is that the instruments are relevant, i.e., the excluded instruments strongly predict the endogenous instrumented variables. This is typically judged by calculating the f-statistic of a joint test of the excluded instruments where the statistic should be at least 10.ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"abstract":"This paper develops asymptotic distribution theory for instrumental variables regression when the partial correlations between the instruments and the endogenous variables are weak, here modeled as local to zero. Asymptotic representation are provided for various statistics, including two-stage least squares and limited information maximum likelihood estimators, Wald statistics, and statistics testing overidentification and endogeneity. The asymptotic distributions provide good approximations to sampling distributions with ten-twenty observations per instrument. The theory suggests concrete guidelines for applied work, including using nonstandard methods for construction of confidence regions. These results are used to interpret J. D. Angrist and A. B. Krueger's (1991) estimates of the returns to education.","author":[{"dropping-particle":"","family":"Staiger","given":"Douglas","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Stock","given":"James H.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Econometrica","id":"ITEM-1","issue":"3","issued":{"date-parts":[["1997"]]},"page":"557-586","publisher":"Econometric Society","title":"Instrumental Variables Regression with Weak Instruments","type":"article-journal","volume":"65"},"uris":[""]}],"mendeley":{"formattedCitation":"²¹","plainTextFormattedCitation":"21","previouslyFormattedCitation":"²¹"},"properties":{"noteIndex":0},"schema":""}21 The second criterion is that the instruments are valid, which means that the instruments themselves do not affect the outcome variable directly or through some unobserved factor, but instead only influence the outcome indirectly through their effect on the endogenous instrumented variable. Instrument validity cannot be directly tested, and expert judgement is required, but when an equation is over-identified (there are more excluded instruments than endogenous variables) then an over-identification test can be helpful, although it may lack power in rejecting the null hypothesis of joint validity in some cases.ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1007/s10940-012-9185-7","ISSN":"0748-4518","author":[{"dropping-particle":"","family":"Kovandzic","given":"Tomislav","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schaffer","given":"Mark E.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kleck","given":"Gary","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Journal of Quantitative Criminology","id":"ITEM-1","issue":"4","issued":{"date-parts":[["2013","12","11"]]},"page":"477-541","publisher":"Springer US","title":"Estimating the Causal Effect of Gun Prevalence on Homicide Rates: A Local Average Treatment Effect Approach","type":"article-journal","volume":"29"},"uris":[""]}],"mendeley":{"formattedCitation":"²²","plainTextFormattedCitation":"22","previouslyFormattedCitation":"²²"},"properties":{"noteIndex":0},"schema":""}22 The Bokhari et al (2007) model specification applies an instrumental variable approach to cross-sectional data from the year 2000 for 127 countries and models both public expenditure on health and a country's GDP as endogenous variables (both in per capita terms). Their identification strategy employs as instrumental variables: consumption-investment ratio, military expenditure per capita of neighbouring countries and measures of institutional quality.ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1002/hec.1157","ISSN":"1057-9230","PMID":"17001737","abstract":"This paper provides econometric evidence linking a country's per capita government health expenditures and per capita income to two health outcomes: under-five mortality and maternal mortality. Using instrumental variables techniques (GMM-H2SL), we estimate the elasticity of these outcomes with respect to government health expenditures and income while treating both variables as endogenous. Consequently, our elasticity estimates are larger in magnitude than those reported in literature, which may be biased up. The elasticity of under-five mortality with respect to government expenditures ranges from -0.25 to -0.42 with a mean value of -0.33. For maternal mortality the elasticity ranges from -0.42 to -0.52 with a mean value of -0.50. For developing countries, our results imply that while economic growth is certainly an important contributor to health outcomes, government spending on health is just as important a factor.","author":[{"dropping-particle":"","family":"Bokhari","given":"Farasat A S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gai","given":"Yunwei","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gottret","given":"Pablo","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Health economics","id":"ITEM-1","issue":"3","issued":{"date-parts":[["2007","3"]]},"page":"257-73","title":"Government health expenditures and health outcomes.","type":"article-journal","volume":"16"},"uris":[""]}],"mendeley":{"formattedCitation":"²³","plainTextFormattedCitation":"23","previouslyFormattedCitation":"²³"},"properties":{"noteIndex":0},"schema":""}23 Bokhari et al make the case that the consumption-investment ratio is related to the level of GDP, but not directly to health outcomes, making it a suitable instrumental variable for GDP. Similarly they argue that military expenditure in neighbouring countries is a reasonable instrument for public expenditure on health because it is not directly related to health outcomes, and it encourages domestic military expenditure which crowds out other public expenditure, including health. Finally, they argue that GDP and public expenditure on health are related to the institutional quality instruments that reflect economic management and policies for social inclusion and equity respectively. These are typical instrumental variables following in the tradition of earlier papers and pass the standard tests for relevance and validity.ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"ISSN":"0277-9536","PMID":"10509822","abstract":"We use cross-national data to examine the impact of both public spending on health and non-health factors (economic, educational, cultural) in determining child (under-5) and infant mortality. There are two striking findings. First, the impact of public spending on health is quite small, with a coefficient that is typically both numerically small and statistically insignificant at conventional levels. Independent variation in public spending explains less than one-seventh of 1% of the observed differences in mortality across countries. The estimates imply that for a developing country at average income levels the actual public spending per child death averted is $50,000-100,000. This stands in marked contrast to the typical range of estimates of the cost effectiveness of medical interventions to avert the largest causes of child mortality in developing countries, which is $10-4000. We outline three possible explanations for this divergence of the actual and apparent potential of public spending. Second, whereas health spending is not a powerful determinant of mortality, 95% of cross-national variation in mortality can be explained by a country's income per capita, inequality of income distribution, extent of female education, level of ethnic fragmentation, and predominant religion.","author":[{"dropping-particle":"","family":"Filmer","given":"D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pritchett","given":"L","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Social science & medicine (1982)","id":"ITEM-1","issue":"10","issued":{"date-parts":[["1999","11"]]},"page":"1309-23","title":"The impact of public spending on health: does money matter?","type":"article-journal","volume":"49"},"uris":[""]}],"mendeley":{"formattedCitation":"²⁴","plainTextFormattedCitation":"24","previouslyFormattedCitation":"²⁴"},"properties":{"noteIndex":0},"schema":""}24 In addition, Bokhari et al (2007) allow for the outcome elasticity with respect to expenditure of countries to vary by two variables reflecting the level of infrastructure and shock in donor funding.The results from this approach to estimation using cross-country data can inform country-specific cost per DALY averted values by applying estimated elasticities to country-specific mortality rates, conditional life expectancies (CLE) and population distribution (all by age and gender) as well as estimates of disability burden of disease and total healthcare expenditure. We use the Bokhari et al (2007) model specification and expand upon their original dataset for year 2000 (i.e., under-5 mortality and the original instrumental variables, including bespoke data on institutional quality) to re-estimate the effect of changes in expenditure on adult male and adult female mortality from the World Bank, enabling greater coverage of the population, as well as: i) a measure of the survival burden of disease, years of life lost (YLLs); ii) a measure of the morbidity burden of disease, years of life disabled (YLDs); and iii) DALYs, a generic measure of overall ill health, from the Global Burden of Disease (GBD) database.ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"URL":"","accessed":{"date-parts":[["2018","3","21"]]},"author":[{"dropping-particle":"","family":"Institute for Health Metrics and Evaluation","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2018"]]},"title":"Global Burden of Disease Study 2015 (GBD 2015) Data Resources | GHDx","type":"webpage"},"uris":[""]}],"mendeley":{"formattedCitation":"²⁵","plainTextFormattedCitation":"25","previouslyFormattedCitation":"^19,25"},"properties":{"noteIndex":0},"schema":""}25 Elasticities are calculated at the country-level and differ only with respect to the interaction of measures of infrastructure and donor funding. While data from 2000 is used, there is little evidence to suggest elasticities would be expected to vary over time.ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.socscimed.2017.02.024","ISSN":"02779536","PMID":"28237460","abstract":"While numerous studies assess the impact of healthcare spending on health outcomes, typically reporting multiple estimates of the elasticity of health outcomes (most often measured by a mortality rate or life expectancy) with respect to healthcare spending, the extent to which study attributes influence these elasticity estimates is unclear. Accordingly, we utilize a meta-data set (consisting of 65 studies completed over the 1969-2014 period) to examine these elasticity estimates using meta-regression analysis (MRA). Correcting for a number of issues, including publication selection bias, healthcare spending is found to have the greatest impact on the mortality rate compared to life expectancy. Indeed, conditional on several features of the literature, the spending elasticity for mortality is near?-0.13, whereas it is near to?+0.04 for life expectancy. MRA results reveal that the spending elasticity for the mortality rate is particularly sensitive to data aggregation, the specification of the health production function, and the nature of healthcare spending. The spending elasticity for life expectancy is particularly sensitive to the age at which life expectancy is measured, as well as the decision to control for the endogeneity of spending in the health production function. With such results in hand, we have a better understanding of how modeling choices influence results reported in this literature.","author":[{"dropping-particle":"","family":"Gallet","given":"Craig A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Doucouliagos","given":"Hristos","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Social Science & Medicine","id":"ITEM-1","issued":{"date-parts":[["2017","4"]]},"page":"9-17","title":"The impact of healthcare spending on health outcomes: A meta-regression analysis","type":"article-journal","volume":"179"},"uris":[""]}],"mendeley":{"formattedCitation":"¹⁸","plainTextFormattedCitation":"18","previouslyFormattedCitation":"¹⁸"},"properties":{"noteIndex":0},"schema":""}18 The estimated elasticities for LMICs (see Table 2) are applied to country-specific data from 2015 on health expenditure, epidemiology and demographics. There are four ways in which the estimated elasticities in Table 2 can be used to estimate the likely DALYs averted as a consequence of a 1% change in health expenditure in each country, i. These are summarised in Table 1 and are briefly described below, with details of the data used reported in Supplementary File 2.Table 1. Alternative approaches to calculating DALYs avertedDALY 1DALY 2DALY 3DALY 4Survival effects(YLLs averted)Based on indirectly estimating effects on survival from mortality (A)Directly estimated (D)Directly estimated (G)Morbidityeffects(YLDs averted)Direct effectUses indirectly estimated effects on survival from mortality as a surrogate for morbidity effects (B)Uses directly estimated survival effects as a surrogate for morbidity effects (E)Directly estimated (F)Indirect effectUses average overall population health as a surrogate for increase in YLD burden associated with increase in YLLs averted (C)DALY 1 The first estimate is based only on estimates of the mortality effects of changes in expenditure. As these are the most prevalent estimates available across the literature, this means that in principle DALY 1 could be calculated using elasticities from various sources, e.g., all-cause mortality elasticities estimated from within-country data.ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Claxton","given":"Karl","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lomas","given":"James","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Martin","given":"Steve","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2017"]]},"publisher-place":"York","title":"Estimating Expected Health Opportunity Costs in the NHS (Analysis of 2012/13 Expenditure Data)","type":"report"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1080/07474938.2016.1114205","ISSN":"0747-4938","abstract":"ABSTRACTIn his 1999 article with Breusch, Qian, and Wyhowski in the Journal of Econometrics, Peter Schmidt introduced the concept of “redundant” moment conditions. Such conditions arise when estimation is based on moment conditions that are valid and can be divided into two subsets: one that identifies the parameters and another that provides no further information. Their framework highlights an important concept in the moment-based estimation literature, namely, that not all valid moment conditions need be informative about the parameters of interest. In this article, we demonstrate the empirical relevance of the concept in the context of the impact of government health expenditure on health outcomes in England. Using a simulation study calibrated to this data, we perform a comparative study of the finite performance of inference procedures based on the Generalized Method of Moment (GMM) and info-metric (IM) estimators. The results indicate that the properties of GMM procedures deteriorate as the number ...","author":[{"dropping-particle":"","family":"Andrews","given":"Martyn","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Elamin","given":"Obbey","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hall","given":"Alastair R.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kyriakoulis","given":"Kostas","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sutton","given":"Matthew","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Econometric Reviews","id":"ITEM-2","issue":"1-3","issued":{"date-parts":[["2017","3","16"]]},"page":"23-41","publisher":"Taylor & Francis","title":"Inference in the presence of redundant moment conditions and the impact of government health expenditure on health outcomes in England","type":"article-journal","volume":"36"},"uris":[""]}],"mendeley":{"formattedCitation":"^26,27","plainTextFormattedCitation":"26,27","previouslyFormattedCitation":"^26,27"},"properties":{"noteIndex":0},"schema":""}26,27 The estimated elasticity for children under-5 for each country i, ?imortality0-4, can be applied to the number of deaths observed in this age group in each country to provide an estimate of the number of under-5 deaths averted as a consequence of a 1% change in country health expenditure. (1) directly estimated deaths avertedi0-4= 1%*?imortality0-4*deathsi0-4Similarly, the estimated elasticities for male and female adults (ages 15-60) are applied to observed deaths by age and gender in each country, i.e., assuming that the proportionate effect on mortality applies equally across age groups within 15-60 age range.(2) directly estimated deaths avertedi15- 60= 1%*?imortality15- 60*deathsi15-19+…+1%*?imortality15- 60*deathsi55- 60Once the likely deaths averted by a 1% change in health expenditure have been estimated (see (1) and (2)), the survival effects can be established by applying CLE at age of death to each death averted within each age group for which deaths averted have been estimated (see (3)) and scaling these survival effects to be for the whole population using data on the population level YLL burden (see (4)). This assumes that the survival effects of changes expenditure are in proportion to the survival burden of disease in each age grouo. (3)mortality based YLL avertedi0-4 & 15-60=CLEi0-4*deaths avertedi0-4+CLEi15-19*deaths avertedi15-19+CLEi20-24*deaths avertedi20-24+…+CLEi55-59*deaths avertedi55-59 (4)mortality based YLL avertediall ages=mortality based YLL avertedi0-4 & 15-60YLLi0-4+YLLi15-60YLLiall agesChanges in expenditure that affect mortality and survival are also likely to have an effect on morbidity through the prevention and treatment of disease (i.e., a direct effect decreasing YLD burden). However, an indirect effect may also be present as reductions in mortality and the resulting increased survival is likely to increase the number of years during which morbidity is experienced. To calculate the possible direct effect, we assume that the effect of changes in expenditure on morbidity is proportional to the effect on survival (B in Table 1), i.e., assuming that the estimated effects on mortality can be used as a surrogate for likely effects on morbidity where these effects have not been directly estimated. ?The ratio of YLD to YLL in each country is applied to estimates of the country-specific survival effects from (4). To account for the indirect effect of increasing the number of years during which morbidity is experienced due to the survival effects, we apply the per capita YLD burden for each country to the country-specific survival effects (see the second term in (5) below and C in Table 1). Mortality based YLD averted are therefore calculated as:(5)mortality based YLD avertediall ages=mortality based YLL avertediall ages*YLDiall agesYLLiall ages-mortality based YLL avertediall ages*per capita YLD burdeniwhere the first term reflects the possible direct effects of expenditure in reducing morbidity (B in Table 1) and the second term captures the indirect effect of increases in morbidity due to increases in survival (C in Table 1). The total DALYs averted due to a 1% change in health expenditure in each country is the sum of the survival effects (the YLL averted in (4), A in Table 1) and the net morbidity effects (YLD averted in (5), B minus C in Table 1). This illustrates how estimates of mortality effects of health expenditure, in the form of elasticities, can be used to provide an indication of the likely survival and morbidity effects. The health effects of a 1% change in country health expenditure will differ by country due to differences in the number observed deaths by age and gender and differences in age and gender specific CLE as well as elasticities. The amount of expenditure required to avert one DALY will also differ by country due to differences in total health expenditure. (6)cost per DALY avertedi=1%*government expenditure on healthiDALYs avertediNonetheless a key assumption has been required, which is that the estimated mortality and survival effects of changes in health expenditure are a good surrogate for the morbidity effects.DALY 2The effect of changes in health expenditure on measures of survival burden of disease can also be estimated directly from the cross-country data (See Table 2). The estimated elasticity for YLL, ?iYLL, can be applied to country-specific YLLiall ages, which are calculated from observed mortality and CLE by age and gender. Therefore, YLLs averted due to a 1% change in health expenditure can be directly estimated (7) rather than applying CLE to estimates of deaths averted by age and gender (as required in (1) to (4) above). (7)directly estimated YLL avertedi= 1%*?iYLL*YLLiall agesThe possible direct and indirect effects on morbidity of changes in health expenditure which affects survival can be calculated in the same way as previously; assuming that that the estimated effects on survival can be used as a surrogate for likely effects on morbidity and with the indirect effect of increases in morbidity based on directly estimated survival effects. Therefore, the net morbidity effects are calculated in the same way as in (5) but with directly estimated YLLi averted replacing mortality based YLLi averted (E minus C in Table 1).DALY 3As well as direct estimates of the effect on survival burden of disease, the effect of changes in health expenditure on measures of morbidity burden of disease (YLD) can also be estimated directly from the cross-country data (See Table 2). The estimated elasticity for YLD, ?iYLD, are applied to country-specific YLDiall ages which are available at the country level from GBD.ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"URL":"","accessed":{"date-parts":[["2018","3","21"]]},"author":[{"dropping-particle":"","family":"Institute for Health Metrics and Evaluation","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2018"]]},"title":"Global Burden of Disease Study 2015 (GBD 2015) Data Resources | GHDx","type":"webpage"},"uris":[""]}],"mendeley":{"formattedCitation":"²⁵","plainTextFormattedCitation":"25","previouslyFormattedCitation":"²⁵"},"properties":{"noteIndex":0},"schema":""}25 (8)directly estimated YLD avertedi= 1%*?iYLD*YLDiall agesDALY 3 uses direct estimates of the effect on survival burden in the same way as DALY 2 but combines these with direct estimates of the effect on morbidity. The total DALYs averted due to a 1% change in health expenditure in each country is the sum of the directly estimated survival effects (YLL averted in (7), D in Table 1) and the directly estimated morbidity effects (YLD averted in (8), F in Table 1).DALY 4The combined effect of changes in expenditure on survival and morbidity burden of disease (DALYs) can be estimated directly from the cross-country data using country-level estimates of DALY burden of disease. Country-specific estimates of DALY burden (DALYiall ages) are calculated as the sum of YLLiall ages and YLDiall ages for each country i. Therefore, a direct estimate of DALYs averted for a 1% change in provincial health expenditure is simply the product of the estimated DALY burden for that country and the estimated elasticity ?iDALY (9). (9)directly estimated DALY avertedi= 1%*?iDALY*DALYiall agesSummaryThese four alternative ways to estimate health opportunity costs, as measured by the cost per DALY averted, make slightly different assumptions. The comparison of DALY 1 with DALY 4 gives some indication of whether it is reasonable to use estimates of the mortality effect of changes in health expenditure as a surrogate for likely survival and morbidity effects. This finding is itself useful for studies with estimated elasticities for mortality outcomes, but requiring additional information about the effect of expenditure on other health outcomes. In particular, studies investigating the relationship between mortality and health expenditure using high quality within-country data (typically undertaken in high-income countries), which overcomes some of the difficulties and challenges of estimation based on aggregate country-level data.Results Estimated elasticities for LMICsThe extended Bokhari et al. (2007) model generated country-specific elasticity results for all of the countries in the model, where the elasticities differed due to the specification of the relationship of expenditure with health. The average and range of elasticities for each of the six measures of health outcome are reported in Table 2.Table 2. Estimated elasticities for LMICsAverageMinimum magnitudeMaximum magnitudeMortality (deaths per 1,000)Children under-5-0.33-0.25-0.35Adults females-0.17-0.08-0.19Adult males-0.18-0.10-0.20YLLs-0.30-0.26-0.30YLDs-0.03-0.02-0.04DALYs-0.21-0.18-0.21Estimated elasticities differ due to the presence of interaction terms combining spending and level of infrastructure (proxied by ‘paved roads per unit of area’) and the absolute deviation in donor funding from the historical mean. The direction and size of the difference in elasticities between countries depends upon the signs of the estimated coefficients on the interaction terms and relative magnitude of each of these.? Cost per DALY avertedThe estimates of cost per DALY averted for each country are reported in Supplementary File 1 where they are also expressed as % of GDP per capita. Few countries have any estimates that are higher than 1x GDP per capita and none are higher than 3x GDP per capita. Among those with any estimate higher than 1x GDP per capita, all are middle-income countries with slightly lower average mortality, survival and ill health burdens than LMICs on average.In almost all cases, DALY 2 provides the lowest cost per DALY averted estimate for each country. This reflects the fact that the estimated elasticity for survival effects (YLL) is typically greater than for morbidity effects (YLD) and effects on DALYs. This larger effect on survival is then used as a surrogate for morbidity effects when estimating DALY 2. Although the differences in the elasticities reported in Table 2 might indicate that employing this ‘surrogacy’ assumption risks overestimating morbidity effects, this should not be over-interpreted as the estimated elasticities are not based on within-country data but country-level data. However, in general the comparison of DALY 1 and DALY 4, which are found to be relatively similar compared to comparing DALY 2 and DALY 3, does suggest that using estimates of the mortality effect of changes in health expenditure as a surrogate for both likely survival and morbidity effects may not be unreasonable albeit with additional uncertainty.Figures 1 and 2 illustrate the range of estimates for each low-income country and middle-income country by under-5 mortality rate respectively. The average of the range of values for each country is not the average for the four cost per DALY averted ratios but the ratio of a 1% change in expenditure to the average DALYs averted across these four estimates. A pattern is evident between mortality rate and cost per DALY averted. While the low under-5 mortality in Haiti would, other things equal tend to increase the cost per DALY averted, it is higher in The Gambia than in Haiti, which has the same under-5 mortality rate, primarily because The Gambia has higher government expenditure on health. This is also illustrated by Panama and El Salvador as well as Congo and Namibia. Figure 1. Cost per DALY averted by under-5 mortality rate for low-income countries[Insert Figure 1]Figure 2. Cost per DALY averted by under-5 mortality rate for middle-income countries[Insert Figure 2]Figure 3 illustrates the same cost per DALY averted estimates but now by per capita government expenditure on health. It suggests that the cost per DALY averted increases with per capita health expenditure which is, in general, what might be expected, although this is to some extent inevitable given the methods used to generate these estimates. It also illustrates the similarity in the range of estimates for most countries but also why others differ. For example, although Russia has nearly double the per capita government expenditure on health of Malaysia and has a similar under-5 mortality rate (see Figure 2), it has higher baseline adult mortality as well as YLD, YLL and DALY burden than Malaysia and therefore a lower cost per DALY averted range than might otherwise be expected.On the other hand, although Cape Verde and Congo have very similar per capita expenditure on health, our range of estimates of cost per DALY averted for Cape Verde is $1,938-$2,843 compared to $1,235-$1,613 in Congo. Part of the difference is due to the baseline mortality rates in Congo, which are more than double those in Cape Verde. Both countries also differ in terms of the age and gender structures of the population, with Congo having a higher percent of the population in the under-5 age group. These factors all contribute to the differences in cost per DALY averted between the two countries, and are relevant to cost per DALY averted for all countries. The apparent similarity in the range of cost per DALY averted between countries should not be over interpreted as estimates would also be expected to differ if countries are able to generate health at different rates, which would be reflected in differing elasticities. The ranges of estimated elasticities for each country vary little, reflecting the limited economic significance of the estimated coefficients on the interaction terms. This underscores the importance of further research using within-country data to estimate these values at a country level and to account for the heterogeneity between different HCS.Figure 3. Cost per DALY averted by per capita government expenditure on health for LMICs[Insert Figure 3]Discussion Estimates of the health opportunity costs of healthcare expenditure are critical for informing assessments of whether the improvement in health outcomes offered by investing additional resources in a new intervention exceeds the improvement in health that would have been possible if the additional resources required had, instead, been made available for other healthcare activities. Commonly established implied norms, such as 1-3x GDP per capita, are often inappropriately applied in practice to judge cost-effectiveness.ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"abstract":"Cost–effectiveness analysis is used to compare the costs and outcomes of alternative policy options. Each resulting cost–effectiveness ratio represents the magnitude of additional health gained per additional unit of resources spent. Cost– effectiveness thresholds allow cost–effectiveness ratios that represent good or very good value for money to be identified. In 2001, the World Health Organization's Commission on Macroeconomics in Health suggested cost–effectiveness thresholds based on multiples of a country's per-capita gross domestic product (GDP). In some contexts, in choosing which health interventions to fund and which not to fund, these thresholds have since been used as decision rules. However, experience with the use of such GDP-based thresholds in decision-making processes at country level shows them to lack country specificity and this – in addition to uncertainty in the modelled cost–effectiveness ratios – can lead to the wrong decision on how to spend health-care resources. Cost–effectiveness information should be used alongside other considerations – e.g. budget impact and feasibility considerations – in a transparent decision-making process, rather than in isolation based on a single threshold value. Although cost–effectiveness ratios are undoubtedly informative in assessing value for money, countries should be encouraged to develop a context-specific process for decision-making that is supported by legislation, has stakeholder buy-in – e.g. the involvement of civil society organizations and patient groups – and is transparent, consistent and fair. What are cost–effectiveness thresholds?","author":[{"dropping-particle":"","family":"Bertram","given":"Melanie Y","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lauer","given":"Jeremy A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Joncheere","given":"Kees","non-dropping-particle":"De","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Edejer","given":"Tessa","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hutubessy","given":"Raymond","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kieny","given":"Marie-Paule","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hill","given":"Suzanne","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Bulletin of the World Health Organization","id":"ITEM-1","issued":{"date-parts":[["2016"]]},"title":"Use and misuse of thresholds Cost–effectiveness thresholds: pros and cons","type":"article-journal"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"","author":[{"dropping-particle":"","family":"Leech","given":"Ashley A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kim","given":"David","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cohen","given":"Joshua","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Neumann","given":"Peter J","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Value in Health","id":"ITEM-2","issued":{"date-parts":[["2018"]]},"title":"Use and Misuse of Cost-Effectiveness Analysis Thresholds in Low- and Middle-Income Countries: Trends in Cost-per-DALY Studies","type":"article-journal"},"uris":[""]}],"mendeley":{"formattedCitation":"^5,28","plainTextFormattedCitation":"5,28","previouslyFormattedCitation":"^5,28"},"properties":{"noteIndex":0},"schema":""}5,28 Such values generally reflect norms or the social demand for health (i.e., a view of what value ought to be placed on improvements in health) rather than an evidence based assessment of health opportunity costs given actual levels of expenditure.ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1596/978-1-4648-0527-1","edition":"Disease Co","editor":[{"dropping-particle":"","family":"Jamison","given":"Dean T.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gelband","given":"Hellen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Horton","given":"Susan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jha","given":"Prbhat","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Laxminarayan","given":"Ramanan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mock","given":"Charles N","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Nugent","given":"Rachel","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2018"]]},"publisher":"World Bank","publisher-place":"Washington D.C.","title":"Disease Control Priorities: Improving Health and Reducing Poverty","type":"book"},"uris":[""]}],"mendeley":{"formattedCitation":"²⁹","plainTextFormattedCitation":"29","previouslyFormattedCitation":"²⁹"},"properties":{"noteIndex":0},"schema":""}29 As such, they do not reflect the health that the HCS is currently able to deliver with the resources available, i.e., the ‘supply side’ of the HCS. Adopting ‘thresholds’ to judge costs effectiveness which are too high and do not reflect the ‘supply side’ will lead to decisions that reduce overall health because the health gained from adopting an intervention will be more than offset by the health opportunity costs elsewhere in the HCS. As well as leading to net harms for population health it may also exacerbate health inequalities and unwarranted variations in access to other healthcare, depending on where the health opportunity costs of additional healthcare costs tend to fall.The framework of analysis set out in this report illustrates how estimates of the relationship between mortality and variations in healthcare expenditure can be employed alongside country-specific data on demography, epidemiologic profile and expenditure to inform estimates of health opportunity costs. While data is readily available for the latter, reliable estimates of the relationship between mortality and variations in healthcare expenditure present a challenge. This paper employed estimates from the model used by Bokhari et al (2007), which applies an instrumental variable method to cross-sectional data, and models both public expenditure on health and a country's GDP as endogenous variables. While Bokhari et al. (2007) find a statistically and economically significant effect of public expenditure on health reducing mortality outcomes, there is no clear and consistent finding in the literature that evaluates the relationship between mortality and variations in healthcare expenditure using cross-country data.ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.socscimed.2017.02.024","ISSN":"02779536","PMID":"28237460","abstract":"While numerous studies assess the impact of healthcare spending on health outcomes, typically reporting multiple estimates of the elasticity of health outcomes (most often measured by a mortality rate or life expectancy) with respect to healthcare spending, the extent to which study attributes influence these elasticity estimates is unclear. Accordingly, we utilize a meta-data set (consisting of 65 studies completed over the 1969-2014 period) to examine these elasticity estimates using meta-regression analysis (MRA). Correcting for a number of issues, including publication selection bias, healthcare spending is found to have the greatest impact on the mortality rate compared to life expectancy. Indeed, conditional on several features of the literature, the spending elasticity for mortality is near?-0.13, whereas it is near to?+0.04 for life expectancy. MRA results reveal that the spending elasticity for the mortality rate is particularly sensitive to data aggregation, the specification of the health production function, and the nature of healthcare spending. The spending elasticity for life expectancy is particularly sensitive to the age at which life expectancy is measured, as well as the decision to control for the endogeneity of spending in the health production function. With such results in hand, we have a better understanding of how modeling choices influence results reported in this literature.","author":[{"dropping-particle":"","family":"Gallet","given":"Craig A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Doucouliagos","given":"Hristos","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Social Science & Medicine","id":"ITEM-1","issued":{"date-parts":[["2017","4"]]},"page":"9-17","title":"The impact of healthcare spending on health outcomes: A meta-regression analysis","type":"article-journal","volume":"179"},"uris":[""]}],"mendeley":{"formattedCitation":"¹⁸","plainTextFormattedCitation":"18","previouslyFormattedCitation":"¹⁸"},"properties":{"noteIndex":0},"schema":""}18 This is often driven by the methodological approach adopted by each study, addressing the considerable challenges including the important country-level heterogeneity, much of which is unobserved and uncontrolled for using existing data, even if it is assumed that systematically unbiased measurements are available. Estimates of mortality elasticities based on cross-country data may be lower than those based on within-country data reflecting the greater dangers of aggregation bias using country-level data and the difficulty of fully accounting for unobserved heterogeneity and endogeneity using the instruments for health expenditure that are available across countries. Irrespective of the level of aggregation analysed, econometric modelling – like all modelling - requires assumptions to be made. Of particular relevance is the assumption of instrument validity when using an instrumental variable approach, which cannot be tested directly. In addition, econometric models may be to some extent fragile to small changes in how data is defined or whether or not the data is log-transformed prior to analysis.ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Nakamura","given":"Ryota","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lomas","given":"James","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Claxton","given":"Karl","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bokhari","given":"Farasat","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Serra","given":"Rodrigo Moreno","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Suhrcke","given":"Marc","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2016"]]},"publisher-place":"York","title":"CHE Research Paper 128 Assessing the Impact of Health Care Expenditures on Mortality Using Cross-Country Data","type":"report"},"uris":[""]}],"mendeley":{"formattedCitation":"³⁰","plainTextFormattedCitation":"30","previouslyFormattedCitation":"³⁰"},"properties":{"noteIndex":0},"schema":""}30The framework of analysis employed here can be applied to the results of any econometric study that is thought to identify plausible effects on health outcomes of changes or differences in health expenditure. It can equally be applied to the point estimates of elasticities as it can to percentiles of the uncertain elasticity estimates or to scenarios concerning possible elasticity values. Within-country studies, limited currently, however, to high-income countries, have estimated the marginal productivity of health expenditure in producing health (QALYs).ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"abstract":"Empirical evidence has hitherto been inconclusive about the strength of the link between health care spending and health outcomes. This paper uses programme budgeting data prepared by 295 English Primary Care Trusts to model the link for two specific programmes of care: cancer and circulatory diseases. A theoretical model is developed in which decision-makers must allocate a fixed budget across programmes of care so as to maximize social welfare, in the light of a health production function for each programme. This yields an expenditure equation and a health outcomes equation for each programme. These are estimated for the two programmes of care using instrumental variables methods. All the equations prove to be well specified. They suggest that the cost of a life year saved in cancer is about ??13,100, and in circulation about ??8000. These results challenge the widely held view that health care has little marginal impact on health. From a policy perspective, they can help set priorities by informing resource allocation across programmes of care. They can also help health technology agencies decide whether their cost-effectiveness thresholds for accepting new technologies are set at the right level.","author":[{"dropping-particle":"","family":"Martin","given":"Stephen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rice","given":"Nigel","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Smith","given":"Peter C.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Journal of Health Economics","id":"ITEM-1","issue":"4","issued":{"date-parts":[["2008"]]},"page":"826-842","publisher":"Elsevier","title":"Does health care spending improve health outcomes? Evidence from English programme budgeting data","type":"article-journal","volume":"27"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.3310/hta19140","ISSN":"2046-4924","PMID":"25692211","abstract":"BACKGROUND: Cost-effectiveness analysis involves the comparison of the incremental cost-effectiveness ratio of a new technology, which is more costly than existing alternatives, with the cost-effectiveness threshold. This indicates whether or not the health expected to be gained from its use exceeds the health expected to be lost elsewhere as other health-care activities are displaced. The threshold therefore represents the additional cost that has to be imposed on the system to forgo 1 quality-adjusted life-year (QALY) of health through displacement. There are no empirical estimates of the cost-effectiveness threshold used by the National Institute for Health and Care Excellence. OBJECTIVES: (1) To provide a conceptual framework to define the cost-effectiveness threshold and to provide the basis for its empirical estimation. (2) Using programme budgeting data for the English NHS, to estimate the relationship between changes in overall NHS expenditure and changes in mortality. (3) To extend this mortality measure of the health effects of a change in expenditure to life-years and to QALYs by estimating the quality-of-life (QoL) associated with effects on years of life and the additional direct impact on QoL itself. (4) To present the best estimate of the cost-effectiveness threshold for policy purposes. METHODS: Earlier econometric analysis estimated the relationship between differences in primary care trust (PCT) spending, across programme budget categories (PBCs), and associated disease-specific mortality. This research is extended in several ways including estimating the impact of marginal increases or decreases in overall NHS expenditure on spending in each of the 23 PBCs. Further stages of work link the econometrics to broader health effects in terms of QALYs. RESULTS: The most relevant 'central' threshold is estimated to be ?12,936 per QALY (2008 expenditure, 2008-10 mortality). Uncertainty analysis indicates that the probability that the threshold is < ?20,000 per QALY is 0.89 and the probability that it is < ?30,000 per QALY is 0.97. Additional 'structural' uncertainty suggests, on balance, that the central or best estimate is, if anything, likely to be an overestimate. The health effects of changes in expenditure are greater when PCTs are under more financial pressure and are more likely to be disinvesting than investing. This indicates that the central estimate of the threshold is likely to be an overestimate for all technologies which impose n…","author":[{"dropping-particle":"","family":"Claxton","given":"Karl","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Martin","given":"Steve","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Soares","given":"Marta","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rice","given":"Nigel","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Spackman","given":"Eldon","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hinde","given":"Sebastian","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Devlin","given":"Nancy","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Smith","given":"Peter C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sculpher","given":"Mark","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Health technology assessment (Winchester, England)","id":"ITEM-2","issue":"14","issued":{"date-parts":[["2015","2"]]},"language":"eng","page":"1-503, v-vi","title":"Methods for the estimation of the National Institute for Health and Care Excellence cost-effectiveness threshold.","type":"article-journal","volume":"19"},"uris":[""]},{"id":"ITEM-3","itemData":{"DOI":"10.1007/s40273-017-0585-2","ISSN":"1170-7690","PMID":"29273843","abstract":"BACKGROUND Spending on new healthcare technologies increases net population health when the benefits of a new technology are greater than their opportunity costs-the benefits of the best alternative use of the additional resources required to fund a new technology. OBJECTIVE The objective of this study was to estimate the expected incremental cost per quality-adjusted life-year (QALY) gained of increased government health expenditure as an empirical estimate of the average opportunity costs of decisions to fund new health technologies. The estimated incremental cost-effectiveness ratio (ICER) is proposed as a reference ICER to inform value-based decision making in Australia. METHODS Empirical top-down approaches were used to estimate the QALY effects of government health expenditure with respect to reduced mortality and morbidity. Instrumental variable two-stage least-squares regression was used to estimate the elasticity of mortality-related QALY losses to a marginal change in government health expenditure. Regression analysis of longitudinal survey data representative of the general population was used to isolate the effects of increased government health expenditure on morbidity-related, QALY gains. Clinical judgement informed the duration of health-related quality-of-life improvement from the annual increase in government?health expenditure. RESULTS The base-case reference ICER was estimated at AUD28,033 per QALY gained. Parametric uncertainty associated with the estimation of mortality- and morbidity-related QALYs generated a 95% confidence interval AUD20,758-37,667. CONCLUSION Recent public summary documents suggest new technologies with ICERs above AUD40,000 per QALY gained are recommended for public funding. The empirical reference ICER reported in this article suggests more QALYs could be gained if resources were allocated to other forms of health spending.","author":[{"dropping-particle":"","family":"Edney","given":"Laura Catherine","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Haji Ali Afzali","given":"Hossein","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cheng","given":"Terence Chai","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Karnon","given":"Jonathan","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"PharmacoEconomics","id":"ITEM-3","issued":{"date-parts":[["2017","12","22"]]},"title":"Estimating the Reference Incremental Cost-Effectiveness Ratio for the Australian Health System","type":"article-journal"},"uris":[""]},{"id":"ITEM-4","itemData":{"author":[{"dropping-particle":"","family":"Vallejo-Torres","given":"Laura","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"García-Lorenzo","given":"Borja","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Serrano-Aguilar","given":"Pedro","non-dropping-particle":"","parse-names":false,"suffix":""}],"collection-title":"Estudios sobre la Economía Espa?ola - 2016/22 ","id":"ITEM-4","issued":{"date-parts":[["2016"]]},"number":"eee2016-22","publisher-place":"Madrid","title":"Estimating a cost-effectiveness threshold for the Spanish NHS","type":"report"},"uris":[""]}],"mendeley":{"formattedCitation":"^13–16","plainTextFormattedCitation":"13–16","previouslyFormattedCitation":"^13–16"},"properties":{"noteIndex":0},"schema":""}13–16 This kind of study requires high-quality data on health outcomes and expenditures by sub-national areas, potentially over time, in addition to information to control for healthcare need and to form the basis of instrumental variables. The implied all-cause mortality elasticity estimate, -1.0278, found by Claxton et al (2017) is considerably higher in magnitude to any of the mortality elasticity estimates from the extended Bokhari et al (2007) model. Another study, Andrews et al (2017), used an alternative approach to identification to directly estimate an all-cause mortality elasticity estimate for the UK NHS of -0.705. Once again, this is higher than the results from Bokhari et al (2007). A comparable estimate for Australia reveals a much larger all-cause mortality elasticity of -2.190.ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/J.HEALTHPOL.2018.04.011","ISSN":"0168-8510","abstract":"There is limited empirical evidence of the nature of any relationship between health spending and health outcomes in Australia. We address this by estimating the elasticity of health outcomes with respect to public healthcare spending using an instrumental variable (IV) approach to account for endogeneity of healthcare spending to health outcomes. Results suggest that, based on the conditional mean, a 1% increase in public health spending was associated with a 2.2% (p?<?0.05) reduction in the number of standardised Years of Life Lost (YLL). Sensitivity analyses and robustness checks supported this conclusion. Further exploration using IV quantile regression indicated that marginal returns on public health spending were significantly greater for areas with poorer health outcomes compared to areas with better health outcomes. On average, marginal increases in public health spending reduce YLL, but areas with poorer health outcomes have the greatest potential to benefit from the same marginal increase in public health spending compared to areas with better health outcomes. Understanding the relationship between health spending and outcomes and how this differs according to baseline health outcomes can help meet dual policy objectives to improve the productivity of the healthcare system and reduce inequity.","author":[{"dropping-particle":"","family":"Edney","given":"L.C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Haji Ali Afzali","given":"H.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cheng","given":"T.C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Karnon","given":"J.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Health Policy","id":"ITEM-1","issue":"8","issued":{"date-parts":[["2018","8","1"]]},"page":"892-899","publisher":"Elsevier","title":"Mortality reductions from marginal increases in public spending on health","type":"article-journal","volume":"122"},"uris":[""]}],"mendeley":{"formattedCitation":"³¹","plainTextFormattedCitation":"31","previouslyFormattedCitation":"³¹"},"properties":{"noteIndex":0},"schema":""}31 Other similar studies have been undertaken in the contexts of Australia and Spain, and while the overall results in terms of expenditure per QALY give similar results to these UK studies the elasticities cannot be directly compared due to their estimations employing a different dependent variable.ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1007/s40273-017-0585-2","ISSN":"1170-7690","PMID":"29273843","abstract":"BACKGROUND Spending on new healthcare technologies increases net population health when the benefits of a new technology are greater than their opportunity costs-the benefits of the best alternative use of the additional resources required to fund a new technology. OBJECTIVE The objective of this study was to estimate the expected incremental cost per quality-adjusted life-year (QALY) gained of increased government health expenditure as an empirical estimate of the average opportunity costs of decisions to fund new health technologies. The estimated incremental cost-effectiveness ratio (ICER) is proposed as a reference ICER to inform value-based decision making in Australia. METHODS Empirical top-down approaches were used to estimate the QALY effects of government health expenditure with respect to reduced mortality and morbidity. Instrumental variable two-stage least-squares regression was used to estimate the elasticity of mortality-related QALY losses to a marginal change in government health expenditure. Regression analysis of longitudinal survey data representative of the general population was used to isolate the effects of increased government health expenditure on morbidity-related, QALY gains. Clinical judgement informed the duration of health-related quality-of-life improvement from the annual increase in government?health expenditure. RESULTS The base-case reference ICER was estimated at AUD28,033 per QALY gained. Parametric uncertainty associated with the estimation of mortality- and morbidity-related QALYs generated a 95% confidence interval AUD20,758-37,667. CONCLUSION Recent public summary documents suggest new technologies with ICERs above AUD40,000 per QALY gained are recommended for public funding. The empirical reference ICER reported in this article suggests more QALYs could be gained if resources were allocated to other forms of health spending.","author":[{"dropping-particle":"","family":"Edney","given":"Laura Catherine","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Haji Ali Afzali","given":"Hossein","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cheng","given":"Terence Chai","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Karnon","given":"Jonathan","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"PharmacoEconomics","id":"ITEM-1","issued":{"date-parts":[["2017","12","22"]]},"title":"Estimating the Reference Incremental Cost-Effectiveness Ratio for the Australian Health System","type":"article-journal"},"uris":[""]},{"id":"ITEM-2","itemData":{"author":[{"dropping-particle":"","family":"Vallejo-Torres","given":"Laura","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"García-Lorenzo","given":"Borja","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Serrano-Aguilar","given":"Pedro","non-dropping-particle":"","parse-names":false,"suffix":""}],"collection-title":"Estudios sobre la Economía Espa?ola - 2016/22 ","id":"ITEM-2","issued":{"date-parts":[["2016"]]},"number":"eee2016-22","publisher-place":"Madrid","title":"Estimating a cost-effectiveness threshold for the Spanish NHS","type":"report"},"uris":[""]}],"mendeley":{"formattedCitation":"^14,16","plainTextFormattedCitation":"14,16","previouslyFormattedCitation":"^14,16"},"properties":{"noteIndex":0},"schema":""}14,16 The ranges estimated here are consistent with the ranges of implied cost per QALY gained for countries based on the analysis in Woods et al (2016), which extrapolates the UK findings based on estimates of the income elasticity of demand for health and assumptions about the relative underfunding of HCS (i.e., the shadow price for public expenditure on health). An assessment that elasticities using within-country data are likely to be higher than those based on country-level data is plausible and tends to be supported by growing literature from other countries, in particular the studies set in the UK and Australia contexts discussed above. Nonetheless further research to provide elasticity estimates using within-country and within-state or province data where applicable would be welcome.ConclusionFew LMICs are likely to have access to the type of within-country data that could be used to directly estimate their cost per DALY averted. This paper demonstrates that it is possible to generate country-specific estimates by applying elasticities estimated from cross-country data to country-specific baseline data. This offers country-specific approximations based on an underlying international health production function. Nevertheless, the resulting range of country-specific estimates are more likely to indicate the scale of health opportunity costs than previously applied norms that have become widely cited. Therefore, they could be used as interim guidance while research on within-country research is developed. In doing so, it should be noted that where budgets for health are devolved to states or provinces and where there are differences in demography and epidemiology, there are also likely to be differences in health opportunity costs by geography within a country.The more fundamental contribution of this paper is to clarify the often confused concept of a ‘threshold’ by demonstrating that judgments about cost effectiveness can be informed by an empirical assessment of the likely health opportunity costs faced, given existing levels of health expenditure. The demonstration of the type of empirical analysis that can support this assessment also makes this concept a real and practical way to help inform better decision-making in LMICs and influence how supranational bodies make recommendations and set priorities, including purchasing and investment decisions. These continuing research efforts start to identify the real value of devoting more resources to healthcare, and can contribute to greater accountability for the healthcare and other expenditure decisions made at a local, national and supranational levels.ReferencesADDIN Mendeley Bibliography CSL_BIBLIOGRAPHY 1. Salomon JA, Vos T, Hogan DR, et al. Common values in assessing health outcomes from disease and injury: disability weights measurement study for the Global Burden of Disease Study 2010. Lancet. 2012;380(9859):2129-2143. doi:10.1016/S0140-6736(12)61680-8.2. Claxton K, Sculpher M, Palmer S, Culyer AJ. Causes for concern: is NICE failing to uphold its responsibilities to all NHS patients? Health Econ. 2015;24(1):1-7. doi:10.1002/hec.3130.3. Revill P, Walker S, Madan J, et al. Using Cost-Effectiveness Thresholds to Determine Value for Money in Low- and Middle-Income Country Healthcare Systems: Are Current International Norms Fit for Purpose? York; 2014. . Accessed November 27, 2015.4. Culyer AJ. Cost-effectiveness thresholds in health care: a bookshelf guide to their meaning and use. Heal Econ Policy Law. 2016;11(04):415-432. doi:10.1017/S1744133116000049.5. Bertram MY, Lauer JA, De Joncheere K, et al. Use and misuse of thresholds Cost–effectiveness thresholds: pros and cons. Bull World Health Organ. 2016. . Accessed October 6, 2016.6. Appleby J, Devlin N, Parkin D, Buxton M, Chalkidou K. Searching for cost effectiveness thresholds in the NHS. Health Policy (New York). 2009;91(3):239-245. doi:10.1016/j.healthpol.2008.12.010.7. H.o.C.H. Committee. First Report of the Health Committee 2007-2008. London; 2008.8. Rawlins MD, Culyer AJ. National Institute for Clinical Excellence and its value judgments. BMJ. 2004;329(7459):224-227. doi:10.1136/bmj.329.7459.224.9. Neumann PJ, Cohen JT, Weinstein MC. Updating cost-effectiveness--the curious resilience of the $50,000-per-QALY threshold. N Engl J Med. 2014;371(9):796-797. doi:10.1056/NEJMp1405158.10. Vallejo-Torres L, García-Lorenzo B, Castilla I, et al. On the Estimation of the Cost-Effectiveness Threshold: Why, What, How? Value Heal. 2016;19(5):558-566. doi:10.1016/j.jval.2016.02.020.11. Thokala P, Ochalek J, Leech AA, Tong T. Cost-Effectiveness Thresholds: the Past, the Present and the Future. Pharmacoeconomics. February 2018. doi:10.1007/s40273-017-0606-1.12. Ochalek J, Revill P, Manthalu G, et al. Supporting the development of a health benefits package in Malawi. BMJ Glob Heal. 2018.13. Martin S, Rice N, Smith PC. Does health care spending improve health outcomes? Evidence from English programme budgeting data. J Health Econ. 2008;27(4):826-842. . Accessed November 30, 2015.14. Edney LC, Haji Ali Afzali H, Cheng TC, Karnon J. Estimating the Reference Incremental Cost-Effectiveness Ratio for the Australian Health System. Pharmacoeconomics. December 2017. doi:10.1007/s40273-017-0585-2.15. Claxton K, Martin S, Soares M, et al. Methods for the estimation of the National Institute for Health and Care Excellence cost-effectiveness threshold. Health Technol Assess. 2015;19(14):1-503, v-vi. doi:10.3310/hta19140.16. Vallejo-Torres L, García-Lorenzo B, Serrano-Aguilar P. Estimating a Cost-Effectiveness Threshold for the Spanish NHS. Madrid; 2016. . Accessed July 31, 2017.17. Woods B, Revill P, Sculpher M, Claxton K. Country-Level Cost-Effectiveness Thresholds: Initial Estimates and the Need for Further Research. Value Heal. 2016;19(8):929-935. doi:10.1016/j.jval.2016.02.017.18. Gallet CA, Doucouliagos H. The impact of healthcare spending on health outcomes: A meta-regression analysis. Soc Sci Med. 2017;179:9-17. doi:10.1016/j.socscimed.2017.02.024.19. Nakamura R, Lomas J, Claxton K, Bokhari F, Serra RM, Suhrcke M. Assessing the Impact of Health Care Expenditures on Mortality Using Cross-Country Data. York; 2016. . Accessed May 3, 2017.20. Gravelle HSE, Backhouse ME. International cross-section analysis of the determination of mortality. Soc Sci Med. 1987;25(5):427-441. doi:10.1016/0277-9536(87)90167-5.21. Staiger D, Stock JH. Instrumental Variables Regression with Weak Instruments. Econometrica. 1997;65(3):557-586. . Accessed April 19, 2016.22. Kovandzic T, Schaffer ME, Kleck G. Estimating the Causal Effect of Gun Prevalence on Homicide Rates: A Local Average Treatment Effect Approach. J Quant Criminol. 2013;29(4):477-541. doi:10.1007/s10940-012-9185-7.23. Bokhari FAS, Gai Y, Gottret P. Government health expenditures and health outcomes. Health Econ. 2007;16(3):257-273. doi:10.1002/hec.1157.24. Filmer D, Pritchett L. The impact of public spending on health: does money matter? Soc Sci Med. 1999;49(10):1309-1323. . Accessed November 30, 2015.25. Institute for Health Metrics and Evaluation. Global Burden of Disease Study 2015 (GBD 2015) Data Resources | GHDx. . Published 2018. Accessed March 21, 2018.26. Claxton K, Lomas J, Martin S. Estimating Expected Health Opportunity Costs in the NHS (Analysis of 2012/13 Expenditure Data). York; 2017. . Accessed January 23, 2018.27. Andrews M, Elamin O, Hall AR, Kyriakoulis K, Sutton M. Inference in the presence of redundant moment conditions and the impact of government health expenditure on health outcomes in England. Econom Rev. 2017;36(1-3):23-41. doi:10.1080/07474938.2016.1114205.28. Leech AA, Kim D, Cohen J, Neumann PJ. Use and Misuse of Cost-Effectiveness Analysis Thresholds in Low- and Middle-Income Countries: Trends in Cost-per-DALY Studies. Value Heal. 2018. doi:. Jamison DT, Gelband H, Horton S, et al., eds. Disease Control Priorities: Improving Health and Reducing Poverty. Disease Co. Washington D.C.: World Bank; 2018. doi:10.1596/978-1-4648-0527-1.30. Nakamura R, Lomas J, Claxton K, Bokhari F, Serra RM, Suhrcke M. CHE Research Paper 128 Assessing the Impact of Health Care Expenditures on Mortality Using Cross-Country Data. York; 2016. . Accessed August 16, 2018.31. Edney LC, Haji Ali Afzali H, Cheng TC, Karnon J. Mortality reductions from marginal increases in public spending on health. Health Policy (New York). 2018;122(8):892-899. doi:10.1016/J.HEALTHPOL.2018.04.011.We have no competing interests to declare. ................
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