Factors to be considered in the contextualization of sub-regional cost-effectiveness data
In overcoming technical problems concerned with methodological consistency and generalizability, Generalized CEA has now generated data on avertable burden at a sub-regional level for a wide range of diseases and risk factors [19]. However, the existence of these CE data is no guarantee that findings and recommendations will actually change health policy or practice in countries. There remains a legitimate concern that global or regional CE results may have limited relevance for local settings and policy processes [29]. Indeed, it has been argued that a tension exists between Generalized CEA that is general enough to be interpretable across settings, and CEA that takes into account local context [30], and that local decision-makers need to contextualize sectoral CEA results to their own cultural, economic, political, environmental, behavioural, and infrastructural context [31].
In order to stimulate change where it may be necessary, there is a need to contextualize existing regional estimates of cost, effectiveness and cost-effectiveness to the setting in which the information will be used, since many factors may alter the actual cost-effectiveness of a given intervention across settings. These include: the availability, mix and quality of inputs, especially trained personnel, drugs, equipment and consumables; local prices, especially labour costs; implementation capacity; underlying organization structures and incentives; and the supporting institutional framework [32]. In addition, it may be necessary to address other concerns to ensure that the costs estimated on an ex-ante basis represent the true costs of undertaking an intervention in reality. For example, Lee and others [33–37]. (argue that cost estimates might not provide an accurate reflection of the true costs of implementing a health intervention in practice for a number of reasons: economic analyses can often be out of date by the time they are published [38]; the cost of pharmaceutical interventions may vary substantially depending on the type of contracts between payers, pharmacy benefits, management companies and manufacturers; or costs of care may be lowered by effective management (e.g. through negotiation, insurance companies may reduce prices). Likewise on the effectiveness side, there is a need for contextualisation. For example, effectiveness estimates used in CEA are often based on efficacy data taken from experimental and context-specific trials. When interventions are implemented in practice, effectiveness may well prove to be lower. According to Tugwell's iterative loop framework [39], the health care process is divided into different phases that are decisive in determining how effective an intervention will be in practice, including whether a patient has contact with the health care system or not, how the patient adheres to treatment recommendations, and with what quality the provider executes the intervention.
From regional to country-specific estimates
Figure 4 provides a schematic overview of the step-by-step approach by which WHO-CHOICE estimates derived at the regional level can be translated down to the context of individual countries. The following key steps are required:
Choosing interventions
The first step for contextualizing WHO-CHOICE cost-effectiveness figures involves the specification and definition of interventions to be included in the analysis, including a clear description of the target population, population-level coverage and, where applicable, the treatment regimen. Since an intervention and its associated costs and benefits can be characterised not only by its technological content (e.g. a psychoactive drug) but also by the setting in which it is delivered (e.g. hospital versus community based care), service organisation issues also enter here. Interventions for some diseases may not be appropriate to a specific national setting (e.g. malaria control strategies) and can be omitted from the analysis, while interventions not already covered by the regional analyses may need to be added. Groups of interventions that are interrelated are evaluated together, since the health impact of undertaking two interventions together is not necessarily additive, nor are the costs of their joint production. Only by assessing their costs and health effects independently and in combination is it possible to account for interactions or non-linearities in costs and effects. For example, the total costs and health effects of the introduction of bed-nets in malaria control is likely to be dependent on whether the population is receiving malaria prophylaxis: this means that three interventions would be evaluated – bed-nets only, malaria prophylaxis only and bed-nets in combination with malaria prophylaxis.
Contextualization of intervention effectiveness
The population-level impact of different interventions is measured in terms of DALYs averted per year, relative to the situation of no intervention for the disease(s) or risk factor(s) in question. Key input parameters underlying this summary measure of population health under the scenario of no intervention include the population's demographic structure, epidemiological rates (incidence, prevalence, remission and case fatality) and health state valuations (HSV; the valuation of time spent in a particular health state, such as being blind or having diabetes, relative to full health [40]). If required and assuming the availability of adequate data, revised estimates of the underlying epidemiology of a disease or risk factor would necessitate some re-estimation by national-level analysts (either via regression-based prediction or by performing additional runs of the population model itself). The specific impact of an intervention is gauged by a change to one or more of these epidemiological rates or by a change to the HSV, and is a function of the efficacy of an intervention, subsequently adjusted by its coverage in the population and, where applicable, rates of adherence by its recipients. Since much of the evidence for intervention efficacy comes from randomised controlled trials carried out under favourable research or practice settings, it is important to adjust resulting estimates of efficacy according to what could be expected to occur in everyday clinical practice. Three key factors for converting efficacy into effectiveness concern treatment coverage in the target population (i.e. what proportion of the total population in need are actually exposed to the intervention), and for those receiving the intervention, both the rate of response to the treatment regimen and the adherence to the treatment. Data on these parameters can be sought and obtained at the local level, based on reviews of evidence and population surveys (if available) or expert opinion. A further potential mediator for the effectiveness of an intervention implemented in everyday clinical practice concerns the quality of care; if sufficiently good measures of service quality are available at a local level, data should also be collected for this parameter.
Contextualization of intervention costs
Intervention costs at the level of epidemiological sub-regions of the world have been expressed in international dollars (I$). This captures differences in purchasing power between different countries and allows for a degree of comparison across sub-regions that would be inappropriate using official exchange rates. For country-level analysis, costs would also be expressed in local currency units, which can be approximated by dividing existing cost estimates by the appropriate purchasing power parity exchange rate. A more accurate and preferable method is to substitute new unit prices for all the specific resource inputs in the Cost-It template (e.g. the price of a drug or the unit cost of an outpatient attendance). In addition, the quantities of resources consumed can easily be modified in line with country experiences (reflecting, for example, differences in capacity utilization). Depending on the availability of such data at a national level, it may be necessary to use expert opinion for this task.
Contextualization for different country-specific scenarios
The WHO-CHOICE database can be contextualized to the country level in three ways. The first is to evaluate all interventions on the assumption that they are done in a technically efficient manner, following the example of WHO-CHOICE. This requires minimum adjustments, limited to adjusting population numbers and structures, effectiveness levels and unit costs and quantities. This provides country policy-makers with the ideal mix of interventions – the mix that would maximize population health if they were undertaken efficiently. The second allows the analyst to capture some local constraints – for instance, scarcity of health personnel. In this case, the analysis would need to ensure that the personnel requirements imposed by the selected mix of interventions do not exceed the available supply. The third option is to modify the analysis assuming that interventions are undertaken at current levels of capacity utilization in the country and that there are local constraints on the availability of infrastructure. In this case, instead of using off-patented international prices of generic drugs, for example, the analyst may be constrained to include the prices of locally produced pharmaceutical products, or to use capacity utilization rates lower than the 80% assumed at sub-regional level.
Shifting from an existing set to a different portfolio of interventions will incur a category of costs which differ from production costs, i.e. transaction costs. Ignoring possible deviations in existing capacity and infrastructure to absorb such changes may mean that there is a significant difference between the 'theoretical' CE ratio based on Generalized CEA and one achievable in any particular setting [30]. However, the budget implications of a portfolio shift will depend on how dramatic the change will be when moving from the current mix of interventions to the optimal mix indicated by Generalized CEA. For instance, the incremental change of moving from an existing fixed facility health service in remote areas to an alternative of an emergency ambulance service might have dramatic political and budgetary implications. In contrast, a procedural change in a surgical therapy is likely to have less important budgetary consequences.
The output of such a contextualisation exercise is a revised, population-specific set of average and incremental cost-effectiveness ratios for interventions addressing leading contributors to national disease burden. The potential usefulness of this information for health policy and planning can be seen in terms of confirming if current intervention strategies can be justified on cost-effectiveness grounds, and showing what other options would be cost-effective if additional resources became available. Its actual usefulness will be determined both by the availability of (or willingness to collect) local data as revised input values into the costing and effectiveness models, and by the extent to which efficiency considerations are successfully integrated with other priority-setting criteria.
The contribution of Generalized CEA to national-level priority-setting
Determination of the most cost-effective interventions for a set of diseases or risk factors, while highly informative in its own right, is not the end of the analytical process. Rather, it represents a key input into the broader task of priority-setting. For this task, the purpose is to go beyond efficiency concerns only and establish combinations of cost-effective interventions that best address stated goals of the health system, including improved responsiveness and reduced inequalities. Indeed, Generalised CEA has been specifically developed as a means by which decision makers may assess and potentially improve the overall performance (or efficiency) of their health systems, defined as how well the socially-desired mix of the five components of the three intrinsic goals is achieved compared to the available resources (Figure 1). Other allocative criteria against which cost-effectiveness arguments need to be considered include the relative severity and the extent of spillover effects among different diseases, the potential for reducing catastrophic household spending on health, and protection of human rights [12, 13, 18, 30, 31, 41]. Thus, priority-setting necessarily implies a degree of trading-off between different health system goals, such that the most equitable allocation of resources is highly unlikely to be the most efficient allocation. Ultimately, the end allocation of resources arising from a priority-setting exercise, using a combination of qualitative or quantitative methods, will accord to the particular sociocultural setting in which it is carried out and to the expressed preferences of its populace and/or its representatives in government. A sequential analysis of these competing criteria, however, indicates that for the allocation of public funds, priority should be given to cost-effective interventions that are public goods (have no market) and impose high spillover effects or catastrophic costs (particularly in relation to the poor) [41], which underscores the need for prior cost-effectiveness information as a key requirement for moving away from subjective health planning (based on historical trends or political preferences) towards a more explicit and rational basis for decision-making.
There are also a number of functions of a health system that shape and support the realisation of the above stated goals, including resource generation and financing mechanisms, the organisation of services as well as overall regulation or stewardship [15]. These functions inevitably influence the priority-setting process in health and hence contribute to variations in health system performance. Indeed, it has been argued that health strategies based on efficiency criteria alone may lead to sub-optimal solutions, owing to market failures in health such as asymmetry of information between providers and patients, as well as a number of adverse incentives inherent within health systems [42]. Accordingly, the results of an efficiency analysis such as a sectoral CEA are likely to be further tempered by a number of capacity constraints and organisational issues. As already noted above, the actual availability of human and physical resources can be expected to place important limits on the extent to which (cost-effective) scaling-up of an intervention's coverage in the population can be achieved. In addition, broader organisational reforms aimed at improving health system efficiency by separating the purchasing and provision functions can be expected to lead to some impact on the final price of health care inputs or the total quantity (and quality) of service outcomes. Finally, decisions relating to the appropriate mechanism for financing health, including the respective roles of the public and private sector, can be expected to have a significant influence on the end allocation of resources. For example, should the role of the public sector be to provide an essential package of cost-effective services, leaving the private sector to provide less cost-effective services [6], or should it be to provide health insurance where private insurance markets fail (such as unpredictable, chronic and highly costly diseases, for which only potentially less cost-effective interventions are available)? [42]. Even if both objectives are pursued – providing basic services to particularly vulnerable populations while catering to the majority's inability to pay for highly costly interventions [43] – a shift away from the most efficient allocation is still implied.