Social Decision-Making Analysis: A General Approach to Inform Decisions on Resources in the Public Sector.

OBJECTIVES: Public expenditure aims to achieve social objectives by improving a range of socially valuable attributes of benefit (arguments in a social welfare function). Public expenditure is typically allocated to public sector budgets, where budget holders are tasked with meeting a subset of social objectives. METHODS: Decision makers require an evidence-based assessment of whether a proposed investment is likely to be worthwhile given existing levels of public expenditure. However, others also require some assessment of whether the overall level and allocation of public expenditure are appropriate. This article proposes a more general theoretical framework for economic evaluation that addresses both these questions. RESULTS: Using a stylized example of the economic evaluation of a new intervention in a simplified UK context, we show that this more general framework can support decisions beyond the approval or rejection of single projects. It shows that broader considerations about the level and allocation of public expenditure are possible and necessary when evaluating specific investments, which requires evidence of the range of benefits offered by marginal changes in different types of public expenditure and normative choices of how the attributes of benefit gained and forgone are valued. CONCLUSIONS: The proposed framework shows how to assess the value of a proposed investment and whether and how the overall level of public expenditure and its allocation across public sector budgets might be changed. It highlights that cost-benefit analysis and cost-effectiveness analysis can be viewed as special cases of this framework, identifying the weakness with each.

Achieving dynamic efficiency in pharmaceutical innovation: Identifying the optimal share of value and payments required.

It has been argued that cost-effectiveness analysis of branded pharmaceuticals only considers static efficiency, neglects dynamic effects and undermines incentives for socially valuable innovation. We present a framework for designing pharmaceutical pricing policy to achieve dynamic efficiency. We develop a coherent framework that identifies the long-term static and dynamic benefits and costs of offering manufacturers different levels of reward. The share of value that would maximise long-term population health depends on how the quantity and quality of innovation responds to payment. Using evidence of the response of innovation to payment, the optimal share of value of new pharmaceuticals to offer to manufacturers is roughly 20% (range: 6%-51%). Reanalysis of a sample of NICE technology appraisals suggests that, in most cases, the share of value offered to manufacturers and the price premium paid by the English NHS were too high. In the UK, application of optimal shares would offer considerable benefits under both a public health objective and a broader view of social welfare. We illustrate how an optimal share of value can be delivered through a range of payment mechanisms including indirect price regulation via the use of different approval norms by an HTA body.

Is extending eligibility for adult social care better than investing more in existing users in England? A cross-sectional evidence for multiple financial years.

OBJECTIVES: Publicly funded adult social care (ASC) in England aims to improve quality of life through the provision of services for individuals with care needs due to physical and/or mental impairment or illness. Access to these services, however, is often restricted to contain public expenditure. With a fast-growing care need, information on whether extending eligibility is good value for money becomes policy-relevant. PRIMARY AND SECONDARY OUTCOME MEASURES: This study investigates the effect of extending ASC eligibility on user care-related quality of life (CRQoL), a policy-relevant measure of quality of life. DESIGN: We use English cross-sectional survey data from 2017/2018 to 2019/2020 on users receiving publicly funded long-term support including domiciliary and other community-based social care, as well as residential and nursing care from local authorities responsible for ASC. We employ the two-stage least square method to estimate the impact of ASC expenditure on CRQoL at various levels of ASC expenditure in each financial year. This includes the CRQoL effect of increasing expenditure from zero to some level, which captures the effect of extending ASC eligibility to new users. RESULTS: We find that publicly funded ASC improves the CRQoL of both existing and newly eligible users, although the latter are likely to experience greater CRQoL gains. Moreover, from 2017/2018 to 2019/2020, spending as much as an average user for a newly eligible user costs between £54 224 and £77 778 per social care-quality-adjusted life year (SC-QALY) gained. These results are statistically significant at the 5% level. Compared with this finding, increasing expenditure for an existing user has always a higher cost per SC-QALY gained. CONCLUSIONS: Extending ASC eligibility to new users is likely to be more cost-effective compared with using the same resources to increase expenditure for existing users.

The impact of different types of NHS expenditure on health: Marginal cost per QALY estimates for England for 2016/17.

English data from 2003 to 2012 suggests that it costs the NHS £10,000 to generate an additional quality-adjusted life year (QALY).  This estimate relates to all NHS expenditure and no attempt was made to explore possible heterogeneity within this total.  Different types of expenditure - such as secondary care, primary care and specialized commissioning - may have different productivities and estimates of these may help policymakers decide where additional investment is most beneficial.  We use the two-stage least squares estimator and data for 2016 to explore the mortality response to three types of healthcare expenditure.  Three specifications are estimated for each type of expenditure: backward selection and regularized regression are used to identify parsimonious specifications, and a full specification with all covariates is also estimated.  The regression results are combined with information about survival and morbidity disease burden to calculate the marginal cost per QALY for each type of expenditure: the most conservative results suggest that this is about £8,000 for locally (CCG) commissioned services, while estimates for specialized commissioning and primary care are more uncertain.  When this heterogeneity is taken into account, the estimated marginal cost per QALY for all NHS expenditure increases slightly, from about £6,000 to £7,000.  Our results suggest that additional investment is likely to be most productive in primary care and in locally commissioned services.

General-Purpose Methods for Simulating Survival Data for Expected Value of Sample Information Calculations.

BACKGROUND: Expected value of sample information (EVSI) quantifies the expected value to a decision maker of reducing uncertainty by collecting additional data. EVSI calculations require simulating plausible data sets, typically achieved by evaluating quantile functions at random uniform numbers using standard inverse transform sampling (ITS). This is straightforward when closed-form expressions for the quantile function are available, such as for standard parametric survival models, but these are often unavailable when assuming treatment effect waning and for flexible survival models. In these circumstances, the standard ITS method could be implemented by numerically evaluating the quantile functions at each iteration in a probabilistic analysis, but this greatly increases the computational burden. Thus, our study aims to develop general-purpose methods that standardize and reduce the computational burden of the EVSI data-simulation step for survival data. METHODS: We developed a discrete sampling method and an interpolated ITS method for simulating survival data from a probabilistic sample of survival probabilities over discrete time units. We compared the general-purpose and standard ITS methods using an illustrative partitioned survival model with and without adjustment for treatment effect waning. RESULTS: The discrete sampling and interpolated ITS methods agree closely with the standard ITS method, with the added benefit of a greatly reduced computational cost in the scenario with adjustment for treatment effect waning. CONCLUSIONS: We present general-purpose methods for simulating survival data from a probabilistic sample of survival probabilities that greatly reduce the computational burden of the EVSI data-simulation step when we assume treatment effect waning or use flexible survival models. The implementation of our data-simulation methods is identical across all possible survival models and can easily be automated from standard probabilistic decision analyses. HIGHLIGHTS: Expected value of sample information (EVSI) quantifies the expected value to a decision maker of reducing uncertainty through a given data collection exercise, such as a randomized clinical trial. In this article, we address the problem of computing EVSI when we assume treatment effect waning or use flexible survival models, by developing general-purpose methods that standardize and reduce the computational burden of the EVSI data-generation step for survival data.We developed 2 methods for simulating survival data from a probabilistic sample of survival probabilities over discrete time units, a discrete sampling method and an interpolated inverse transform sampling method, which can be combined with a recently proposed nonparametric EVSI method to accurately estimate EVSI for collecting survival data.Our general-purpose data-simulation methods greatly reduce the computational burden of the EVSI data-simulation step when we assume treatment effect waning or use flexible survival models. The implementation of our data-simulation methods is identical across all possible survival models and can therefore easily be automated from standard probabilistic decision analyses.

How Responsive is Mortality to Locally Administered Healthcare Expenditure? Estimates for England for 2014/15.

BACKGROUND: Research using local English data from 2003 to 2012 suggests that a 1% increase in healthcare expenditure causes a 0.78% reduction in mortality, and that it costs the NHS £10,000 to generate an additional quality-adjusted life year (QALY). In 2013, the existing 151 local health authorities (Primary Care Trusts) were abolished and replaced with 212 Clinical Commissioning Groups (CCGs). CCGs retained responsibility for secondary care and pharmaceuticals, but responsibility for primary care and specialised commissioning returned to central administrators. OBJECTIVES: The aim was to extend and apply existing methods to more recent data using a new geography and expenditure base, while improving covariate selection and examining the responsiveness of mortality to expenditure across the mortality distribution. METHODS: Instrumental variable regression is used to quantify the relationship between mortality and local expenditure. Backward selection and regularised regression are used to identify parsimonious specifications. These results are combined with information about survival and morbidity disease burden to calculate the marginal cost per QALY. Unconditional quantile regression (UQR) is used to examine the response of mortality to expenditure across the mortality distribution. RESULTS: Backward selection and regularised regression both suggest that the marginal cost per QALY in 2014/15 was about £7000 for locally commissioned services. The UQR results suggest that additional expenditure generates larger health benefits in high-mortality areas and that, if anything, the average size of this heterogeneous response is larger than the response at the mean. CONCLUSIONS: The new healthcare geography and expenditure base can be used to update estimates of the health opportunity costs associated with additional expenditure. The variation in the mortality response across the mortality distribution suggests that the use of the response at the mean will, if anything, underestimate the health opportunity costs associated with a national policy or nationally mandated guidance on the use of new technologies. The health opportunity costs of such policies are likely to be greater (lower) in areas of higher (lower) mortality, increasing health inequalities.

Reference Case Methods for Expert Elicitation in Health Care Decision Making.

BACKGROUND: The evidence used to inform health care decision making (HCDM) is typically uncertain. In these situations, the experience of experts is essential to help decision makers reach a decision. Structured expert elicitation (referred to as elicitation) is a quantitative process to capture experts' beliefs. There is heterogeneity in the existing elicitation methodology used in HCDM, and it is not clear if existing guidelines are appropriate for use in this context. In this article, we seek to establish reference case methods for elicitation to inform HCDM. METHODS: We collated the methods available for elicitation using reviews and critique. In addition, we conducted controlled experiments to test the accuracy of alternative methods. We determined the suitability of the methods choices for use in HCDM according to a predefined set of principles for elicitation in HCDM, which we have also generated. We determined reference case methods for elicitation in HCDM for health technology assessment (HTA). RESULTS: In almost all methods choices available for elicitation, we found a lack of empirical evidence supporting recommendations. Despite this, it is possible to define reference case methods for HTA. The reference methods include a focus on gathering experts with substantive knowledge of the quantities being elicited as opposed to those trained in probability and statistics, eliciting quantities that the expert might observe directly, and individual elicitation of beliefs, rather than solely consensus methods. It is likely that there are additional considerations for decision makers in health care outside of HTA. CONCLUSIONS: The reference case developed here allows the use of different methods, depending on the decision-making setting. Further applied examples of elicitation methods would be useful. Experimental evidence comparing methods should be generated.

Accounting for country- and time-specific values in the economic evaluation of health-related projects relevant to low- and middle-income countries.

Economic evaluation of health-related projects requires principles and methods to address the various trade-offs that need to be made between costs and benefits, across sectors and social objectives, and over time. Existing guidelines for economic evaluation in low- and middle-income countries embed implicit assumptions about expected changes in the marginal cost per unit of health produced by the healthcare sector, the consumption value of health and the appropriate discount rates for health and consumption. Separating these evaluation parameters out requires estimates for each country over time, which have hitherto been unavailable. We present a conceptual economic evaluation framework that aims to clarify the distinct roles of these different evaluation parameters in evaluating a health-related project. Estimates for each are obtained for each country and in each time period, based on available empirical evidence. Where existing estimates are not available, for future values of the marginal cost per unit of health produced by the healthcare sector, new estimates are obtained following a practical method for obtaining projected values. The framework is applied to a simple, hypothetical, illustrative example, and the results from our preferred approach are compared against those obtained from other approaches informed by the assumptions implicit within existing guidelines. This exposes the consequences of applying such assumptions, which are not supported by available evidence, in terms of potentially sub-optimal decisions. In general, we find that applying existing guidelines as done in conventional practice likely underestimates the value of health-related projects on account of not allowing for expected growth in the marginal cost per unit of health produced by the healthcare sector.

An Efficient Method for Computing Expected Value of Sample Information for Survival Data from an Ongoing Trial.

BACKGROUND: Decisions about new health technologies are increasingly being made while trials are still in an early stage, which may result in substantial uncertainty around key decision drivers such as estimates of life expectancy and time to disease progression. Additional data collection can reduce uncertainty, and its value can be quantified by computing the expected value of sample information (EVSI), which has typically been described in the context of designing a future trial. In this article, we develop new methods for computing the EVSI of extending an existing trial's follow-up, first for an assumed survival model and then extending to capture uncertainty about the true survival model. METHODS: We developed a nested Markov Chain Monte Carlo procedure and a nonparametric regression-based method. We compared the methods by computing single-model and model-averaged EVSI for collecting additional follow-up data in 2 synthetic case studies. RESULTS: There was good agreement between the 2 methods. The regression-based method was fast and straightforward to implement, and scales easily to include any number of candidate survival models in the model uncertainty case. The nested Monte Carlo procedure, on the other hand, was extremely computationally demanding when we included model uncertainty. CONCLUSIONS: We present a straightforward regression-based method for computing the EVSI of extending an existing trial's follow-up, both where a single known survival model is assumed and where we are uncertain about the true survival model. EVSI for ongoing trials can help decision makers determine whether early patient access to a new technology can be justified on the basis of the current evidence or whether more mature evidence is needed. HIGHLIGHTS: Decisions about new health technologies are increasingly being made while trials are still in an early stage, which may result in substantial uncertainty around key decision drivers such as estimates of life-expectancy and time to disease progression. Additional data collection can reduce uncertainty, and its value can be quantified by computing the expected value of sample information (EVSI), which has typically been described in the context of designing a future trial.In this article, we have developed new methods for computing the EVSI of extending a trial's follow-up, both where a single known survival model is assumed and where we are uncertain about the true survival model. We extend a previously described nonparametric regression-based method for computing EVSI, which we demonstrate in synthetic case studies is fast, straightforward to implement, and scales easily to include any number of candidate survival models in the EVSI calculations.The EVSI methods that we present in this article can quantify the need for collecting additional follow-up data before making an adoption decision given any decision-making context.

Concomitant health benefits package design and research prioritisation: development of a new approach and an application to Malawi.

Health benefits packages (HBPs) are increasingly used in many countries to guide spending priorities on the path towards universal health coverage. Their design is, however, informed by an uncertain evidence base but research funds available to address this are limited. This gives rise to the question of which piece of research relating to the cost-effectiveness of interventions would most contribute to improving resource allocation. We propose to incorporate research prioritisation as an integral part of HBP design. We have, therefore, developed a framework and a freely available companion stand-alone tool, to quantify in terms of net disability-adjusted life-years (DALYs) averted, the value of research for the interventions considered for inclusion in a package. Using the tool, the framework can be implemented using sensitivity analysis results typically reported in cost-effectiveness studies. To illustrate the framework, we applied the tool to the evidence base that informed the Malawi Health Sector Strategic Plan 2017-2022. Out of 21 interventions considered, 8 investment decisions were found to be uncertain and three showed strong potential for research to generate large health gains: 'male circumcision', 'community-management of acute malnutrition in children' and 'isoniazid preventive therapy in HIV +individuals', with a potential to avert up to 65 762, 36 438 and 20 132 net DALYs, respectively. Our work can help set research priorities in resource-constrained settings so that research funds are invested where they have the largest potential to impact on the population health generated via HBPs.

Causal impact of social care, public health and healthcare expenditure on mortality in England: cross-sectional evidence for 2013/2014.

OBJECTIVES: The first objective is to estimate the joint impact of social care, public health and healthcare expenditure on mortality in England. The second objective is to use these results to estimate the impact of spending constraints in 2010/2011-2014/2015 on total mortality. METHODS: The impact of social care, healthcare and public health expenditure on mortality is analysed by applying the two-stage least squares method to local authority data for 2013/2014. Next, we compare the growth in healthcare and social care expenditure pre-2010 and post-2010. We use the difference between these growth rates and the responsiveness of mortality to changes in expenditure taken from the 2013/2014 cross-sectional analysis to estimate the additional mortality generated by post-2010 spending constraints. RESULTS: Our most conservative results suggest that (1) a 1% increase in healthcare expenditure reduces mortality by 0.532%; (2) a 1% increase in social care expenditure reduces mortality by 0.336%; and (3) a 1% increase in local public health spending reduces mortality by 0.019%. Using the first two of these elasticities and data on the change in spending growth between 2001/2002-2009/2010 and 2010/2011-2014/2015, we find that there were 57 550 (CI 3075 to 111 955) more deaths in the latter period than would have been observed had spending growth during this period matched that in 2001/2002-2009/2010. CONCLUSIONS: All three forms of public healthcare-related expenditure save lives and there is evidence that additional social care expenditure is more than twice as productive as additional healthcare expenditure. Our results are consistent with the hypothesis that the slowdown in the rate of improvement in life expectancy in England and Wales since 2010 is attributable to spending constraints in the healthcare and social care sectors.

Estimating the shares of the value of branded pharmaceuticals accruing to manufacturers and to patients served by health systems.

Previous studies have estimated that patients served by health systems accrue 59-98% of the value generated by new pharmaceuticals. This has led to questions about whether sufficient returns accrue to manufacturers to incentivize socially optimal levels of R&D. These studies have not, however, fully reflected the health opportunity costs imposed by payments for branded pharmaceuticals. We present a framework for estimating how the value generated by new branded pharmaceuticals is shared. We quantify value in net health effects and account for benefits and health opportunity costs in the patent period and post-patent period when generic/biosimilar products become available. We apply the framework to 12 National Institute for Health and Care Excellence appraisals and show that realized net health effects range from losses of 160%, to gains of 94%, of the potential net health benefits available. In many cases, even in the long run, the benefits of new medicines are not sufficient to offset the opportunity costs of payments to manufacturers, and approval is expected to reduce population health. This cannot be dynamically efficient as it incentivizes future innovation at prices which will also reduce population health. Further work should consider how to reflect these findings in reimbursement policies.

How Effective is Marginal Healthcare Expenditure? New Evidence from England for 2003/04 to 2012/13.

BACKGROUND: The endogenous nature of healthcare expenditure means that instruments are often used when estimating the relationship between expenditure and mortality. Previous English studies of this relationship have largely relied on statistical tests to justify their instruments. A recent paper proposed that exogenous components of the resource allocation formula, used to distribute the national healthcare budget to local health authorities, be used as instruments. OBJECTIVES: To estimate the relationship between healthcare expenditure and mortality by disease area for England from 2003/4 to 2012/13 using exogenous elements from the resource allocation formula as instruments for expenditure. To use these disease-specific estimates to calculate the marginal cost per quality-adjusted life year (QALY) for English NHS expenditure. To compare these estimates with those that relied on statistical tests to justify their instruments. METHODS: The two-stage least squares estimator is used to determine the annual relationship between mortality and healthcare expenditure by disease area across 151 local authorities. These disease-specific outcome elasticities are combined with information about survival and morbidity disease burden in different disease areas to calculate the marginal cost per QALY for English National Health Service (NHS) expenditure. RESULTS: The results suggest an annual marginal cost per QALY of between £5000 and £10,000. This is similar to that reported previously by studies that used statistical tests to justify their instruments. CONCLUSION: These cost per QALY estimates are much lower than the threshold currently used by the UK's National Institute for Health and Care Excellence (NICE) (£20,000 to £30,000) to assess whether a new pharmaceutical product should be funded by the NHS. Our estimates suggest that guidance issued by NICE is likely to do more harm than good, reducing health outcomes overall for the NHS. There may be legitimate reasons why such harms are deemed appropriate, but it is only through the type of empirical analysis in this paper that the reasons for these 'harms' are likely to be articulated and explicitly justified.

Does public long-term care expenditure improve care-related quality of life of service users in England?

Public long-term care (LTC) systems provide services to support people experiencing difficulties with their activities of daily living. This study investigates the marginal effect of changes in public LTC expenditure on care-related quality of life (CRQoL) of existing service users in England. The public LTC program for people aged 18 or older in England is called Adult Social Care (ASC) and it is provided and managed by local authorities. We collect data on the outcomes and characteristics of public ASC users, on public ASC expenditure, and on the characteristics of local authorities across England in 2017/18. We employ an instrumental variable approach using conditionally exogenous elements of the public funding system to estimate the effect of public ASC expenditure on user CRQoL. Our findings show that by increasing public ASC expenditure by £1000 per user, on average, local authorities increase user CRQoL by 0.0030. These results suggest that public ASC is effective in increasing users' quality of life but only to a relatively small extent. When combined with the other potential effects of LTC expenditure (e.g., on informal carers, mortality), this study can inform policy makers in the United Kingdom and internationally about whether social care provides good value for money.

Developing a reference protocol for structured expert elicitation in health-care decision-making: a mixed-methods study.

BACKGROUND: Many decisions in health care aim to maximise health, requiring judgements about interventions that may have higher health effects but potentially incur additional costs (cost-effectiveness framework). The evidence used to establish cost-effectiveness is typically uncertain and it is important that this uncertainty is characterised. In situations in which evidence is uncertain, the experience of experts is essential. The process by which the beliefs of experts can be formally collected in a quantitative manner is structured expert elicitation. There is heterogeneity in the existing methodology used in health-care decision-making. A number of guidelines are available for structured expert elicitation; however, it is not clear if any of these are appropriate for health-care decision-making. OBJECTIVES: The overall aim was to establish a protocol for structured expert elicitation to inform health-care decision-making. The objectives are to (1) provide clarity on methods for collecting and using experts' judgements, (2) consider when alternative methodology may be required in particular contexts, (3) establish preferred approaches for elicitation on a range of parameters, (4) determine which elicitation methods allow experts to express uncertainty and (5) determine the usefulness of the reference protocol developed. METHODS: A mixed-methods approach was used: systemic review, targeted searches, experimental work and narrative synthesis. A review of the existing guidelines for structured expert elicitation was conducted. This identified the approaches used in existing guidelines (the 'choices') and determined if dominant approaches exist. Targeted review searches were conducted for selection of experts, level of elicitation, fitting and aggregation, assessing accuracy of judgements and heuristics and biases. To sift through the available choices, a set of principles that underpin the use of structured expert elicitation in health-care decision-making was defined using evidence generated from the targeted searches, quantities to elicit experimental evidence and consideration of constraints in health-care decision-making. These principles, including fitness for purpose and reflecting individual expert uncertainty, were applied to the set of choices to establish a reference protocol. An applied evaluation of the developed reference protocol was also undertaken. RESULTS: For many elements of structured expert elicitation, there was a lack of consistency across the existing guidelines. In almost all choices, there was a lack of empirical evidence supporting recommendations, and in some circumstances the principles are unable to provide sufficient justification for discounting particular choices. It is possible to define reference methods for health technology assessment. These include a focus on gathering experts with substantive skills, eliciting observable quantities and individual elicitation of beliefs. Additional considerations are required for decision-makers outside health technology assessment, for example at a local level, or for early technologies. Access to experts may be limited and in some circumstances group discussion may be needed to generate a distribution. LIMITATIONS: The major limitation of the work conducted here lies not in the methods employed in the current work but in the evidence available from the wider literature relating to how appropriate particular methodological choices are. CONCLUSIONS: The reference protocol is flexible in many choices. This may be a useful characteristic, as it is possible to apply this reference protocol across different settings. Further applied studies, which use the choices specified in this reference protocol, are required. FUNDING: This project was funded by the NIHR Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 25, No. 37. See the NIHR Journals Library website for further project information. This work was also funded by the Medical Research Council (reference MR/N028511/1).

BACKGROUND: Decisions in health care aim to maximise health, requiring judgements about treatments. The evidence used to make these judgements is typically uncertain. In these situations, the experience of experts is essential. Structured expert elicitation collects beliefs from experts. There are different guidelines available for structured expert elicitation; however, it is not clear if any of these be can be used in health-care decision-making, for example in considering if a treatment should be made available in the NHS. This project aimed to develop a guidance for structured expert elicitation to inform health-care decision-making. METHODS: Reviews and experimental techniques were used to gather a list of methods to conduct structured expert elicitation. The suitability of these choices in health-care decision-making was then determined by comparing these with a set of standards that support the use of structured expert elicitation in health-care decision-making. RESULTS: Different guidelines prefer different approaches to conduct structured expert elicitation. There is a lack of evidence available to determine which of these methods is most appropriate across the whole of health-care decision-making. It is possible to define reference protocol methods that could be used in a particular type of health-care decision-making, health technology assessment. This includes gathering experts with knowledge of the clinical area, asking experts about things that they observe in clinical practice and asking experts individually for their beliefs. For decision-makers working outside health technology assessment, for example at a local level, or for treatments that are not yet available to patients, these choices may not be appropriate. CONCLUSIONS: This flexibility of this guidance is a useful feature. It is possible for different decision-makers in health care to interpret the reference protocol for their own circumstances.

Valuing health outcomes: developing better defaults based on health opportunity costs.

BACKGROUND: Current health economic analysis guidelines emphasize the importance of using nationally appropriate cost and valuation inputs. However, some countries lack national data, and some analyses focus on interventions with costs and benefits at regional or global scales. METHODS: Recognizing the need for better estimates of appropriate values for application at these levels than those used in the past, we characterize population-weighted dollar per disability-adjusted life year (DALY) averted by World Bank Income Level based on available national estimates of the marginal productivity of the healthcare system. RESULTS: The defaults suggested here reflect health opportunity costs across countries more consistent with existing evidence than those previously used or recommended. As countries change income levels and healthcare spending, and as additional or updated marginal productivity of healthcare expenditure estimates become available, we expect the defaults to change. CONCLUSION: The best option for informing decisions around resource allocation in health care such that they improve health outcomes overall remains the use of time-appropriate country-specific estimates of the marginal productivity of the healthcare system. Instead of single, time-invariant defaults, health economists should seek to develop valuation inputs that better account for health opportunity costs and do so over time.

Assessing the value of human papillomavirus vaccination in Gavi-eligible low-income and middle-income countries.

INTRODUCTION: Estimating the value of providing effective healthcare interventions in a country requires an assessment of whether the improvement in health outcomes they offer exceeds the improvement in health that would have been possible if the resources required had, instead, been made available for other healthcare activities in that country. This potential alternative use of the same resources represents the health opportunity cost of providing the intervention. Without such assessments, there is a danger that blanket recommendations made by international organisations will lead to the adoption of healthcare interventions that are not cost effective in some countries, even given existing donor mechanisms intended to support their affordability. METHODS: We assessed the net health impact to 46 Gavi-eligible countries of achieving one of the WHO's proposed 90-70-90 targets for cervical cancer elimination, which includes 90% coverage of human papillomavirus (HPV) vaccination among girls by 15 years of age, using published estimates of the expected additional benefits and costs in each country and estimates of the marginal productivity of each healthcare system. We calculated the maximum price each country could afford to pay for HPV vaccination to be cost effective by assessing the net health impact that would be expected to be generated at different potential prices. RESULTS: At Gavi negotiated prices, HPV vaccination offers net health benefits across most Gavi-eligible countries included in this study. However, if Gavi-eligible countries faced the average price faced by non-Gavi eligible countries, providing HPV vaccination would result in reduced overall population health in most countries. CONCLUSION: Estimates of the net health impact of providing a healthcare intervention can be used to assess the benefit (or lack of) to countries of adhering to global guidance, inform negotiations with donors, as well as pricing negotiations and the value of developing new healthcare interventions.