Abstract

Approaches Used to Define λ

The definition of λ is simply the marginal cost or benefit to society of saving a healthy year of life. The value of λ can be defined normatively, or according to a predefined budget. A simplified framework has been used to illustrate prioritization decisions through these two converse approaches.[49] These approaches rely on ‘league tables’, which order all interventions under consideration according to their ICERs (tables ​(tablesII and ​andIIII).[51-53] The budget may be defined first and then distributed across programmes with increasing (less cost-effective) values until funds are exhausted. In this case, λ is revealed as the cost-effectiveness of the last intervention approved. Alternatively, λ may be based on a normative value, with all interventions meeting or falling below this level being accepted. In this scenario, budgetary requirements are revealed from the collective cost of the approved interventions at the scale at which they are applied. Graphically, the relationship between λ and available budget can be represented on the cost-effectiveness plane, also showing the effects of budget increases on λ and interventions accepted.[55] Two main classes of normative definitions for λ are reviewed in this article. The human capital approach values life according to what a person contributes to society, often measured in terms of per capita GNI. Preference-elicitation approaches reveal valuations according to actual or hypothetical choices people make in relation to increased risks of death.

League Table Approach – $US150 per Disability-Adjusted Life-Year (DALY)

The $US150 per DALY estimate derives from work by the World Bank[17] in 1993 to recommend a minimum care package (MCP) of services that should be provided by LMICs, and this threshold was reiterated in 1996 in an effort to define research priorities.[56] These committees specified $US150 per DALY as ‘attractive’ cost-effectiveness and $US25 per DALY as ‘highly attractive’ cost-effectiveness for low-income countries, with $US500 and $US100 per DALY, respectively, specified for middle-income countries. In these prioritization processes, λ was established based on prior assumptions about budgetary resources and other criteria.[57] Countries were advised to tailor these global recommendations to their own budgetary constraints, epidemiological profile, demographic profile, existing health infrastructure and other health system considerations.

However, $US150 per DALY has limitations in its usefulness in economic evaluation. While evaluations included in the league table were methodologically comparable in health outcome measures and discount rate, comparators were not applicable to all contexts.[16] The interventions considered did not represent a complete list of interventions available,[51] and countries would have needed to either conduct additional evaluations or restrict their scope to those with comparable methodology.[53] The cost-effectiveness of interventions can vary when implemented at different scales,[58] and other contextual factors affect cost-effectiveness in different settings.[59] In addition, choosing the most cost-effective interventions according to a specific budget does not always maximize utility, since interventions do not have perfect divisibility.[58,60]

The main functional drawback to applying $US150 per DALY is that, despite being the lowest explicitly defined value for λ in the literature, resulting recommendations were still difficult for some LMIC health sectors to afford. For example, Uganda's attempt to implement the MCP was made with a budget of $US9.00 per person – much lower than the required $US31.49. As a result, Uganda was forced to prioritize within the MCP,[61] which led to some decisions being made on an ad hoc basis, reflecting professional opinions, political expediency or public hysteria rather than considered evidence. Without systematic tools, these types of factors may have disproportionate impact on decisions, and the status quo is often preserved.[62]

Twice Per Capita Gross National Income Approach

In accordance with the converse method for establishing λ, a classic paper argues for a normative threshold. In this paper, Garber and Phelps[63] derive twice per capita GNI for λ, using a standard von Neumann-Morgenstern utility framework to model returns to investment in the health sector relative to those in other sectors. We argue that by defining a person's life according to the monetary value they produce or receive for their contribution to society, a human capital approach is implied.

Besides the theoretical foundation of twice per capita GNI, a positive economic argument exists for using a human capital approach – it is consistent with accepted practice for economic evaluation in several high-income countries. The $US50 000 per QALY (year 1982 values) threshold commonly used in the US is similar to GNI ($US46 040);[64-66] if $US50 000 is inflated to year 2008 values, it becomes roughly twice per capita GNI ($US101 295 per QALY). Likewise, if the range of values proposed in Canada ($Can20 000–100 000; year 1990 values) is inflated to $US, year 2008 values, they become $US23 476–117 378,[67] with two times GNI falling within that range ($US79 300).[64] In the UK, £30 000 per QALY is commonly used in economic evaluation as the ceiling ratio, which can be inflated from its 2002 values to $US52 718,[27] and statistical evaluation has shown it to be even higher in practice.[68] Two times UK GNI is comparable to this valuation ($US81 320).[64] In addition, the Institute for Medical Technology Assessment in the Netherlands has announced a threshold that is 2.8 times national GNI.[69]

Defining λ according to economic activity of individuals is gaining recognition in economic evaluations of LMIC healthcare. The Commission for Macroeconomics and Health applied per capita income[44] and the WHO-CHOICE initiative applied GDP[18] as their thresholds for ‘very cost-effective’, and three times this level for ‘cost-effective’. A regional breakdown of GDP values used by WHO-CHOICE is given in table III. A human capital approach was also applied in the communicable disease analysis of the 2004 Copenhagen Consensus (GNI),[70] and the priority-setting initiative recently undertaken by the Mexican health system (GDP).[71]

Drawbacks to using multiples of per capita GNI include equity, affordability and neglect of the multidimensional nature of welfare. In terms of equity, using an estimate based on GNI values life differently in real economic terms across countries with different economic environments, and using a state-dependent ceiling ratio may reinforce wide global inequities in health and wealth. Between the world's richest countries and poorest countries, GNI in terms of International dollars ($I; i.e. adjusted for purchasing power parity [PPP]) differs by two orders of magnitude.[64] The per capita GNI in high-income Organisation for Economic Cooperation and Development (OECD) countries in terms of current $US is $US39 158 (×2 = $US78 316). The per capita GNI for low-income countries is $US574 (×2 = $US1148). Furthermore, exceptional circumstances such as war, natural disaster and other negative economic shocks lower average wages selectively, causing further international inequities between affected areas and the rest of the world.

In terms of affordability, using per capita GNI may lead to total budgetary costs that are currently not sustainable, far exceeding the documented affordability problems with the much lower figure of $US150 per DALY discussed in section 1.1.[61] Predicating decisions on normative arguments for λ instead of budgets can lead to high potential expenditure, and health sector budgets in LMICs are already severely constrained. Two interventions may have similar cost-effectiveness, but one may apply to a much larger population than another (e.g. insecticide-treated bed nets for malaria compared with intermittent presumptive treatment of pregnant women),[35] leading to much higher budgetary requirements. More effective but expensive interventions may also be recommended to replace affordable inferior goods,[55] which may limit the number of people to whom services can be provided.

However, investment in the health of LMIC populations is reaching levels unprecedented in global history,[44,72] and with some of this investment subsidizing patient costs, affordability concerns for both providers and patients can be relaxed to some degree. In addition, projections of funding from private sources may be considered alongside government budgets when considering the affordability of interventions, as many developing country health systems accept out-of-pocket payments for additional services (private out-of-pocket expenditures are 25% of total health expenditures in Africa and 65% in Southeast Asia).[73] Funds from donor organizations may also be accounted for where they are channeled through the public health system. Bobadilla and Saxenian[74] reported that countries considered in the design of the 1993 MCP were spending $US9.00 per capita in public funds on healthcare (year 2008 values); however, $US31.49 per person was being spent when private sources were taken into account.

As a final critique of the GNI approach, valuing health according to income ignores the other dimensions of life that can be argued to have utility. Addison[75] makes this case, stating that the human capital approach is not valid as it ‘puts a value on livelihood rather than life’. In other words, it suggests indifference between a life-saving intervention, and one that increases an individual's projected discounted productivity by a percentage that offsets the intervention cost.[76] While the argument exists that the human capital approach reflects the value that an individual contributes to society, Behrman et al.[77] argue that it does not remove consumption (what the individual takes from society) from the equation. With consumption netted out, the lives of many individuals would have no value if the balance between their production and consumption is equal.[78] Also, average GNI is not a measure of the marginal impact of losing 1 year of life, which may be very low in populations with high unemployment and underemployment.

Preference-Elicitation Approach

“To value life as the individual herself would value it” was proposed by Nancy Stokey[79] of the 2004 Copenhagen Consensus expert panel to guide normative discussions about the value of λ. This criterion links λ to its root in individual preferences, maintaining consistency with utility theory and welfare economics.[80] Eliciting preferences can take two perspectives – WTP for incremental improvements in health,[81-85] or the amount of money people are WTA as compensation to agree to take life-threatening risks.[86,87] Methodological approaches for preference elicitation can evaluate real-world decisions (revealed preference) or hypothetical scenarios (stated preference), focus on valuation of healthy life-years or medical interventions, and can sample from patients or policy makers. WTA estimates are higher than those taken from a WTP perspective; people require more compensation for a loss than the amount they would pay for an equivalent gain.[88,89] This implies that λ should be different for investing in new services and disinvesting from existing ones, and evidence from the UK indicates that this is true in practice.[68]

The WTA approach has been found empirically to produce estimates for λ that are much higher than those discussed to this point, and is the approach used to value health gains through the ‘full income’ measure reviewed by Jamison.[90] Addison[75] argues that individuals in high-income countries value their lives at roughly five times GNI. Viscusi and Aldy[87] reviewed more than 60 studies from ten countries, which revealed that individuals' willingness to risk death in exchange for higher salaries was 100–200 times annual GDP.[87] Based on this evidence, Evans[91] calculated that λ would be 1.4–2.8 times GDP with a 70-year life span, or 3.4–6.9 times GDP if this life span is discounted by 3%. Hirth et al.[66] standardized results from 42 published articles and revealed λ estimates of 3–20 times GNI of high-income countries for individuals in North America and Western Europe.

The WTP approach often uses hypothetical testing methods such as contingent valuation,[92] or structured bidding methods.[93] As an example of the latter, a study measuring WTP for neurosurgery in the US derived estimates of $US14 356–36 918 per QALY,[82] lower than the average GNI of $US46 040, or the commonly cited figure of $US101 295 (inflated from $US50 000).[67] To our knowledge, this approach has not been used to derive estimates for λ in LMICs. As an example of what would be possible: in the Central African Republic (CAR), the median WTP for malaria treatment determined by contingent valuation was $US10.37.[81] With a probability of death between 0.1% and 0.75%, and a discounted life expectancy of 24.83–27.47 years,[31] the value of λ can be calculated to be $US0.26–2.14. Similar calculations can be conducted for other interventions with appropriate data, such as estimates for WTP for a malaria vaccine in Ethiopia.[94]

Dolan et al. (2008)[95] identified four factors that theoretically should be relevant influences on values derived through preference elicitation, including baseline and background risk, wealth, age and latency of the risk. Except for latency, they found that valuations were insensitive to all of these factors. They also identified four influences that should be theoretically irrelevant, including differences between WTA and WTP, size of change in risk, type of death, and irrelevant methodological cues; and found that valuations were sensitive to all of them. Their review provides thorough discussion of all of these factors; we discuss two that are particularly relevant to resource-poor settings: ability to pay and psychological factors.

To some extent, less wealthy individuals are less willing and able to afford health services, and several studies exist to support this.[83-85,94,96-99] However, other evidence suggests that poverty has less of an impact on WTP for health interventions than might be expected, especially for characteristics of high-quality care that people view as essential.[100,101] Extensive evidence demonstrates that people will pay catastrophic amounts for the healthcare of loved ones,[102] and a family may be willing to pay more than their annual income if they anticipate future income or are willing to liquidate resources.[85] Dolan et al.[95] conclude that, while WTP for health increases with wealth, it does not do so proportionally.

Psychological factors that affect decision-making processes have been identified. The formation of preferences is complex, being adaptable over time and in environments with different levels of uncertainty.[103] In addition, psychological research indicates that individuals' valuations of health states are affected substantially by their emotional state,[104] and health valuations are positively correlated with the quality of current health.[105] Conversely, having a family member previously affected may motivate coping mechanisms and lead to a lower WTP.[85,106] It is less clear if health education has an impact on WTP,[84,85] and social, cultural and financing factors that influence decisions between biomedical and traditional medicine may also have an influence.[107] From a policy-maker's perspective, bias in WTP is likely to be upwards in decisions about whether to fund an intervention, and downwards if deciding how much to charge.[108] Attention should be paid to ensuring that preference-elicitation estimates are derived from the context in which prioritization decisions will be made (e.g. regional, national or sub-national).

Ethical Criteria

It is important to consider the ethical criteria that inevitably underlie prioritization decisions. Examples of these criteria include equity in health status, equity of access, differential provision according to need, ‘rule of rescue’, prioritization according to importance to society, equity in opportunity for health and health maximization.[109-112] λ thresholds as they are currently defined may imply that health status should be maximized across individuals; however, evidence exists that people do not hold this objective for healthcare expenditures. Several studies have found that people are willing to accept a lower overall societal standard of health in order to make health states across individuals more equal,[113-115] and efforts have been made to quantify this trade-off.[116-118]

The trade-off between equity and efficiency in public decision making has theoretical foundations in welfare economics,[110,116,119] and clear examples show this conflict in terms of both horizontal and vertical equity.[21] However, it is recognized that many of the most cost-effective interventions available benefit the poor, since they die younger, have the most potential for health improvement and are disproportionately affected by infectious diseases with inexpensive cures.[21] A main message from the 2006 DCP2 is that massive gains in equity can be obtained in international health even without explicitly adding equity considerations to cost-effectiveness estimates.[19]

Methodological and Policy Implications

With unprecedented levels of interest, research and investment in LMIC health, considered examination is warranted of the methods used to value health improvement, and the issues surrounding them. By making these practices explicit, scrutiny by decision makers and the international community becomes possible, and the selection process for λ becomes less arbitrary. A standard cross-country threshold of $US150 per DALY was recommended in the past, largely due to budgetary concerns, and may be used to make results comparable to already published CEAs. More recently, multiples of national GNI and GDP have been applied, possibly to accommodate recent increases in public health budgets and more expensive but highly valued therapies such as antiretroviral treatment for AIDS. However, this method was strongly criticized by some in the 2004 Copenhagen Consensus, with the two health-related papers using different approaches,[70,77] while Nancy Stokey[79] advocated preference elicitation. Only a few disease-specific WTP studies exist; evidence suggests that estimates vary widely according to methodology,[66] and more research is needed to define protocols for aggregating results.[120]

Each approach to defining λ has its strengths and weaknesses. However, we would argue that, for given programmes and national governments, it is most appropriate to reveal values for λ by prioritizing within a league table according to context-specific budget, making trade-offs between other decision-making factors within that constraint. Emphasizing budget would reduce the likelihood of inefficiencies such as those described in Uganda due to recommendations exceeding available resources, the need for a second round of prioritization, and the potential for unplanned and unfavorable distributions of health services.[61] While the problems faced in Uganda may not be intrinsic or inevitable, maintaining recommendations within national budgetary limits would create conditions for political promises to be kept, and maintain focus on sound delivery of interventions with the best cost-effectiveness.

When using CEA to determine regional or international priorities upstream in the process, we would argue that more influence from normative criteria is appropriate, and that equity should be explicitly considered in the distribution of budgets. While budget constraints maintain relevance at this level,[121] and larger numbers of programmes and interventions are considered, international bodies may have more influence on how budgets are defined,[122] and can make efforts to approve intervention mixes that are consistent with normative ideals. For decision-making bodies allocating resources across different countries, results should be presented in terms of $I to address variations in PPP.[77,123]

The league tables shown in tables ​tablesII and ​andIIII serve as simplified examples of how λ can be conceptualized. Table I is specific to Guinea, and could be used to prioritize interventions according to a specified budget, cost per person, and coverage levels, thus revealing the λ threshold.[50] Table II is an example of how prioritization can be conducted at the regional level, using a threshold that can subsequently be used to define budget allocations. Evidence for sub-Saharan Africa is taken from the recent DCP2, and interventions are clustered according to the health area and overall programme costs.[54] If these estimates are compared with the $US150 per DALY threshold ($US208 in 2008 values), interventions deemed cost-effective are childhood immunization, traffic penalties and speed bumps, and malaria prevention, as well as many from the other low-cost intervention clusters. If the threshold is moved to twice GNI in sub-Saharan Africa ($US1902),[64] all low-cost interventions and some from three high-cost clusters qualify according to cost-effectiveness. It is important to note that the cost-effectiveness of some estimates for HIV/AIDS treatment differs from evidence generated earlier this decade when the cost of antiretroviral therapy was higher than it is today.[124]

Importantly, cost-effectiveness should be considered alongside other decision-making factors such as the intervention being a public good, externalities, catastrophic costs, preferential benefit to the poor, vertical and horizontal equity and potential to save lives.[21] Burden of disease and the size of programmes will have important implications for the budget. Patient demand will have strong influence on whether projected health gains are realized. How this evidence is presented to decision makers should be tailored to the political environment, as prioritization is necessarily a long, complex process involving many people with different preferences, both for outcome and process. One option is to accompany cost-effectiveness evidence with qualitative information for decision makers to consider according to their own mechanisms. Mathematically, a multi-criteria league table approach has been applied in hypothetical studies in Ghana and Nepal,[125,126] which estimates the probability that interventions are approved through a regression framework.[127] For determining the scale at which different interventions should be implemented, several mathematical modeling approaches are available to facilitate calculations.[58]

Notably, the evidence from Nepal suggested that cost-effectiveness was not the primary factor for policy approval,[126] which is consistent with the real-world policy decision to implement universal coverage of antiretroviral therapy in Thailand.[128] Other research, from Tanzania, suggests that evidence does affect the way health planners rank alternative interventions, with cost-effectiveness being their dominant consideration in a hypothetical exercise.[129] Indeed, the true impact of CEA may be to increase investment in the target health system,[130] or the number of interventions approved. Research is underway to improve the use of evidence in priority setting in LMICs through considering contextual factors.[131]

Several alternative approaches to defining λ are not explored in this paper. Behrman et al.[77] advocated comparing the value of postponing mortality through one intervention to the resource cost of the most effective alternative means of postponing mortality. Birch and Gafni[58] advocated investment in a new programme only if its health gains exceeded those from ones that they could replace at equal or lower cost. Another alternative is to base λ on the value of tax revenues lost through avoidable mortality.[123] Questions left by these approaches include how to guide decisions for allocating new resources, and how to consider normative perspectives that are inevitably relevant. As a solution to the problems of assessing preferences associated with current approaches, Dolan et al.[95] advocated deriving λ values thorough the ratio of coefficients for household income and health state in estimating subjective well-being (SWB) through a regression framework.

The empirical research behind the SWB approach suggests that it may not be desirable for public bodies to have a uniform value for λ across people, populations or health states.[95] Indeed, the health economics community focused on LMICs recognizes differential valuation of life by including the option to use age weighting in DALY calculations.[132] In addition to adjustments to DALY calculations, λ thresholds might be conceptualized as a distribution, which accounts in its variance for the influence of factors beyond cost-effectiveness in previous decisions. Deriving λ values thorough the SWB framework for different global areas, as outlined by Dolan et al.,[95] should be a priority area for research. Valuing life before birth is a burgeoning area of study, which will be of relevance to economic evaluations of maternal and neonatal health interventions.[133,134]

Despite their limitations and methodological weaknesses, cost-effectiveness thresholds will remain important criteria for health sector prioritization decisions in LMICs. However, as budgets, contexts, normative arguments, equity criteria and relevant interventions change, values for λ should incrementally evolve. Analysts should accommodate these changes through time by accompanying reference case results with acceptability curves. Health and life spans have improved substantially in much of the world in the last century,[90] and evidence indicates that improvements in health make substantial contributions to increases in GNI.[135,136] This finding suggests that values for λ defined by the human capital approach can be expected to increase with economic growth. In addition, the infusion of new investment in LMIC health implies increases in values for λ revealed through budget allocations. Finally, as people enjoy better standards of living, they will be better able to ‘manage to desire’ improvements in health,[137] and values elicited according to WTP/WTA may be expected to increase.

Conclusions

Approaches to defining λ in economic evaluation of interventions relevant to LMICs have evolved from ad hoc comparisons to $US150 per DALY to valuations based on GNI or GDP. Preference elicitation methods are also available, and each method has advantages and disadvantages. Being explicit about λ promotes consistency in decision making, encourages informed debate about health state valuations, and enhances the potential to perform more advanced applications of decision science. Consistency with previous studies, empirical validity, the objectives of decision making bodies, and the need to accommodate evolving preferences should all be considered when choosing a ceiling ratio. Key areas for further research include the estimation of subjective well-being, discussing differential valuations of life for different groups, and discussion of which vaulation approach is most appropriate in different settings.

Acknowledgements

References