Stochastic league tables: communicating cost-effectiveness results to decision-makers

Using economic analysis to inform health resource allocation: lessons from Malawi

Despite making remarkable strides in improving health outcomes, Malawi faces concerns about sustaining the progress achieved due to limited fiscal space and donor dependency. The imperative for efficient health spending becomes evident, necessitating strategic allocation of resources to areas with the greatest impact on mortality and morbidity. Health benefits packages hold promise in supporting efficient resource allocation. However, despite defining these packages over the last two decades, their development and implementation have posed significant challenges for Malawi. In response, the Malawian government, in collaboration with the Thanzi la Onse Programme, has developed a set of tools and frameworks, primarily based on cost-effectiveness analysis, to guide the design of health benefits packages likely to achieve national health objectives. This review provides an overview of these tools and frameworks, accompanied by other related analyses, aiming to better align health financing with health benefits package prioritization. The paper is organized around five key policy questions facing decision-makers: (i) What interventions should the health system deliver? (ii) How should resources be allocated geographically? (iii) How should investments in health system inputs be prioritized? (iv) How should equity considerations be incorporated into resource allocation decisions? and (v) How should evidence generation be prioritized to support resource allocation decisions (guiding research)? The tools and frameworks presented here are intended to be compatible for use in diverse and often complex healthcare systems across Africa, supporting the health resource allocation process as countries pursue Universal Health Coverage.

A review of cost-effectiveness analysis: From theory to clinical practice

Cost-effectiveness analysis has long been practiced; registries date back to the United States of America War Department in 1886. In addition, everyone does intuitive cost-effectiveness analyses in their daily lives. In routine medical care, health economic assessment becomes increasingly important due to progressively limited resources, rising demands, population increases, and continuous therapeutic innovations. The health economic assessment must analyze the outcomes and costs of actions and technologies as objectively as possible to guarantee efficient assessment of novel interventions for Public Health Policy. In other words, it is necessary to determine how much society or patients are willing to or able to pay for novel interventions compared with existing alternatives, given the available resources. In addition, increased cost may displace other health care services already provided in case of fixed budget health care systems. To conduct such analyses, researchers must use standard methodologies and interpretations in light of regional characteristics according to social and economic determinants as well as clinical practice. Such an approach may be essential for transforming the current healthcare system to a value-based model. In this narrative review, concepts of the importance of and some approaches to health economic evaluation in clinical practice will be discussed.

Methods for Communicating the Impact of Parameter Uncertainty in a Multiple-Strategies Cost-Effectiveness Comparison

Purpose

Analyzing and communicating uncertainty is essential in medical decision making. To judge whether risks are acceptable, policy makers require information on the expected outcomes but also on the uncertainty and potential losses related to the chosen strategy. We aimed to compare methods used to represent the impact of uncertainty in decision problems involving many strategies, enhance existing methods, and provide an open-source and easy-to-use tool.

Methods

We conducted a systematic literature search to identify methods used to represent the impact of uncertainty in cost-effectiveness analyses comparing multiple strategies. We applied the identified methods to probabilistic sensitivity analysis outputs of 3 published decision-analytic models comparing multiple strategies. Subsequently, we compared the following characteristics: type of information conveyed, use of a fixed or flexible willingness-to-pay threshold, output interpretability, and the graphical discriminatory ability. We further proposed adjustments and integration of methods to overcome identified limitations of existing methods.

Results

The literature search resulted in the selection of 9 methods. The 3 methods with the most favorable characteristics to compare many strategies were 1) the cost-effectiveness acceptability curve (CEAC) and cost-effectiveness acceptability frontier (CEAF), 2) the expected loss curve (ELC), and 3) the incremental benefit curve (IBC). The information required to assess confidence in a decision often includes the average loss and the probability of cost-effectiveness associated with each strategy. Therefore, we proposed the integration of information presented in an ELC and CEAC into a single heat map.

Conclusions

This article presents an overview of methods presenting uncertainty in multiple-strategy cost-effectiveness analyses, with their strengths and shortcomings. We proposed a heat map as an alternative method that integrates all relevant information required for health policy and medical decision making.

Highlights

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.

Scaling-up essential neuropsychiatric services in Ethiopia: a cost-effectiveness analysis

INTRODUCTION

There is an immense need for scaling-up neuropsychiatric care in low-income countries. Contextualized cost-effectiveness analyses (CEAs) provide relevant information for local policies. The aim of this study is to perform a contextualized CEA of neuropsychiatric interventions in Ethiopia and to illustrate expected population health and budget impacts across neuropsychiatric disorders.

METHODS

A mathematical population model (PopMod) was used to estimate intervention costs and effectiveness. Existing variables from a previous WHO-CHOICE regional CEA model were substantially revised. Treatments for depression, schizophrenia, bipolar disorder and epilepsy were analysed. The best available local data on epidemiology, intervention efficacy, current and target coverage, resource prices and salaries were used. Data were obtained from expert opinion, local hospital information systems, the Ministry of Health and literature reviews.

RESULTS

Treatment of epilepsy with a first generation antiepileptic drug is the most cost-effective treatment (US$ 321 per DALY adverted). Treatments for depression have mid-range values compared with other interventions (US$ 457-1026 per DALY adverted). Treatments for schizophrenia and bipolar disorders are least cost-effective (US$ 1168-3739 per DALY adverted).

CONCLUSION

This analysis gives the Ethiopian government a comprehensive overview of the expected costs, effectiveness and cost-effectiveness of introducing basic neuropsychiatric interventions.

Cost-effectiveness of PrEP in HIV/AIDS control in Zambia: a stochastic league approach

BACKGROUND

Earlier antiretroviral therapy initiation and pre-exposure prophylaxis (PrEP) prevent HIV, although at a substantial cost. We use mathematical modeling to compare the cost-effectiveness and economic affordability of antiretroviral-based prevention strategies in rural Macha, Zambia.

METHODS

We compare the epidemiological impact and cost-effectiveness over 40 years of a baseline scenario (treatment initiation at CD4 <350 cells/μL) with treatment initiation at CD4 <500 cells per microliter, and PrEP (prioritized to the most sexually active, or nonprioritized). A strategy is cost effective when the incremental cost-effectiveness ratio (ICER) is <$3480 (<3 times Zambian per capita GDP). Stochastic league tables then predict the optimal intervention per budget level.

RESULTS

All scenarios will reduce the prevalence from 6.2% (interquartile range, 5.8%-6.6%) in 2014 to about 1% after 40 years. Compared with the baseline, 16% of infections will be averted with prioritized PrEP plus treatment at CD4 <350, 34% with treatment at CD4 <500, and 59% with nonprioritized PrEP plus treatment at CD4 <500. Only treating at CD4 <500 is cost effective: ICER of $62 ($46-$75). Nonprioritized PrEP plus treating at CD4 <500 is borderline cost effective: ICER of $5861 ($3959-$8483). Initiating treatment at CD4 <500 requires a budget increase from $20 million to $25 million over 40 years, with a 96.7% probability of being the optimal intervention. PrEP should only be considered when the budget exceeds $180 million.

CONCLUSIONS

Treatment initiation at CD4 <500 is a cost-effective HIV prevention approach that will require a modest increase in budget. Although adding PrEP will avert more infections, it is not economically feasible, as it requires a 10-fold increase in budget.

Cost effectiveness of strategies to combat vision and hearing loss in sub-Saharan Africa and South East Asia: mathematical modelling study

OBJECTIVE

To determine the relative costs, effects, and cost effectiveness of selected interventions to control cataract, trachoma, refractive error, hearing loss, meningitis and chronic otitis media.

DESIGN

Cost effectiveness analysis of or combined strategies for controlling vision and hearing loss by means of a lifetime population model.

SETTING

Two World Health Organization sub-regions of the world where vision and hearing loss are major burdens: sub-Saharan Africa and South East Asia.

DATA SOURCES

Biological and behavioural parameters from clinical and observational studies and population based surveys. Intervention effects and resource inputs based on published reports, expert opinion, and the WHO-CHOICE database.

MAIN OUTCOME MEASURES

Cost per disability adjusted life year (DALY) averted, expressed in international dollars ($Int) for the year 2005.

RESULTS

Treatment of chronic otitis media, extracapsular cataract surgery, trichiasis surgery, treatment for meningitis, and annual screening of schoolchildren for refractive error are among the most cost effective interventions to control hearing and vision impairment, with the cost per DALY averted <$Int285 in both regions. Screening of both schoolchildren (annually) and adults (every five years) for hearing loss costs around $Int1000 per DALY averted. These interventions can be considered highly cost effective. Mass treatment with azithromycin to control trachoma can be considered cost effective in the African but not the South East Asian sub-region.

CONCLUSIONS

Vision and hearing impairment control interventions are generally cost effective. To decide whether substantial investments in these interventions is warranted, this finding should be considered in relation to the economic attractiveness of other, existing or new, interventions in health.

Communicating uncertainty in economic evaluations: verifying optimal strategies

BACKGROUND

In cost-effectiveness analysis (CEA), it is common to compare a single, new intervention with 1 or more existing interventions representing current practice ignoring other, unrelated interventions. Sectoral CEAs, in contrast, take a perspective in which the costs and effectiveness of all possible interventions within a certain disease area or health care sector are compared to maximize health in a society given resource constraints. Stochastic league tables (SLT) have been developed to represent uncertainty in sectoral CEAs but have 2 shortcomings: 1) the probabilities reflect inclusion of individual interventions and not strategies and 2) data on robustness are lacking. The authors developed an extension of SLT that addresses these shortcomings.

METHODS

Analogous to nonprobabilistic MAXIMIN decision rules, the uncertainty of the performance of strategies in sectoral CEAs may be judged with respect to worst possible outcomes, in terms of health effects obtainable within a given budget. Therefore, the authors assessed robustness of strategies likely to be optimal by performing optimization separately on all samples and on samples yielding worse than expected health benefits. The approach was tested on 2 examples, 1 with independent and 1 with correlated cost and effect data.

RESULTS

The method was applicable to the original SLT example and to a new example and provided clear and easily interpretable results. Identification of interventions with robust performance as well as the best performing strategies was straightforward. Furthermore, the robustness of strategies was assessed with a MAXIMIN decision rule.

CONCLUSION

The SLT extension improves the comprehensibility and extends the usefulness of outcomes of SLT for decision makers. Its use is recommended whenever an SLT approach is considered.

Estimating and comparing incidence and prevalence of chronic diseases by combining GP registry data: the role of uncertainty

BACKGROUND

Estimates of disease incidence and prevalence are core indicators of public health. The manner in which these indicators stand out against each other provide guidance as to which diseases are most common and what health problems deserve priority. Our aim was to investigate how routinely collected data from different general practitioner registration networks (GPRNs) can be combined to estimate incidence and prevalence of chronic diseases and to explore the role of uncertainty when comparing diseases.

METHODS

Incidence and prevalence counts, specified by gender and age, of 18 chronic diseases from 5 GPRNs in the Netherlands from the year 2007 were used as input. Generalized linear mixed models were fitted with the GPRN identifier acting as random intercept, and age and gender as explanatory variables. Using predictions of the regression models we estimated the incidence and prevalence for 18 chronic diseases and calculated a stochastic ranking of diseases in terms of incidence and prevalence per 1,000.

RESULTS

Incidence was highest for coronary heart disease and prevalence was highest for diabetes if we looked at the point estimates. The between GPRN variance in general was higher for incidence than for prevalence. Since uncertainty intervals were wide for some diseases and overlapped, the ranking of diseases was subject to uncertainty. For incidence shifts in rank of up to twelve positions were observed. For prevalence, most diseases shifted maximally three or four places in rank.

CONCLUSION

Estimates of incidence and prevalence can be obtained by combining data from GPRNs. Uncertainty in the estimates of absolute figures may lead to different rankings of diseases and, hence, should be taken into consideration when comparing disease incidences and prevalences.

Estimating and comparing incidence and prevalence of chronic diseases by combining GP registry data: the role of uncertainty

BACKGROUND

Estimates of disease incidence and prevalence are core indicators of public health. The manner in which these indicators stand out against each other provide guidance as to which diseases are most common and what health problems deserve priority. Our aim was to investigate how routinely collected data from different general practitioner registration networks (GPRNs) can be combined to estimate incidence and prevalence of chronic diseases and to explore the role of uncertainty when comparing diseases.

METHODS

Incidence and prevalence counts, specified by gender and age, of 18 chronic diseases from 5 GPRNs in the Netherlands from the year 2007 were used as input. Generalized linear mixed models were fitted with the GPRN identifier acting as random intercept, and age and gender as explanatory variables. Using predictions of the regression models we estimated the incidence and prevalence for 18 chronic diseases and calculated a stochastic ranking of diseases in terms of incidence and prevalence per 1,000.

RESULTS

Incidence was highest for coronary heart disease and prevalence was highest for diabetes if we looked at the point estimates. The between GPRN variance in general was higher for incidence than for prevalence. Since uncertainty intervals were wide for some diseases and overlapped, the ranking of diseases was subject to uncertainty. For incidence shifts in rank of up to twelve positions were observed. For prevalence, most diseases shifted maximally three or four places in rank.

CONCLUSION

Estimates of incidence and prevalence can be obtained by combining data from GPRNs. Uncertainty in the estimates of absolute figures may lead to different rankings of diseases and, hence, should be taken into consideration when comparing disease incidences and prevalences.

Compilation of cost-effectiveness evidence for different heart conditions and treatment strategies

OBJECTIVES

Despite the continuing interest in health economic research, we could find no accessible data set on cost-effectiveness, useful as practical information to decision makers who must allocate scarce resources within the cardiovascular field. The aim of this paper was to present cost-effectiveness ratios, based on a systematic literature search for the treatment of heart diseases.

DESIGN

A comprehensive literature search on cost-effectiveness analyses of intervention strategies for the treatment of heart diseases was conducted. We compiled available cost-effectiveness ratios for different heart conditions and treatment strategies, in a cost-effectiveness ranking table. The cost-effectiveness ratios were expressed as a cost per quality adjusted life year (QALY) or life year gained.

RESULTS

Cost-effectiveness ratios, ranging from dominant to those costing more than 1,000,000 Euros per QALY gained, and bibliographic references are provided for. The table was categorized according to disease group, making the ranking table readily available.

CONCLUSIONS

Cost-effectiveness ranking tables provide a means of presenting cost-effectiveness evidence. They provide valid information within a limited space aiding decision makers on the allocation of health care resources. This paper represents an extensive compilation of health economic evidence for the treatment of heart diseases.

Prevention, screening and treatment of colorectal cancer: a global and regional generalized cost effectiveness analysis

BACKGROUND

Regional generalized cost-effectiveness estimates of prevention, screening and treatment interventions for colorectal cancer are presented.

METHODS

Standardised WHO-CHOICE methodology was used. A colorectal cancer model was employed to provide estimates of screening and treatment effectiveness. Intervention effectiveness was determined via a population state-transition model (PopMod) that simulates the evolution of a sub-regional population accounting for births, deaths and disease epidemiology. Economic costs of procedures and treatment were estimated, including programme overhead and training costs.

RESULTS

In regions characterised by high income, low mortality and high existing treatment coverage, the addition of screening to the current high treatment levels is very cost-effective, although no particular intervention stands out in cost-effectiveness terms relative to the others.In regions characterised by low income, low mortality with existing treatment coverage around 50%, expanding treatment with or without screening is cost-effective or very cost-effective. Abandoning treatment in favour of screening (no treatment scenario) would not be cost effective.In regions characterised by low income, high mortality and low treatment levels, the most cost-effective intervention is expanding treatment.

CONCLUSIONS

From a cost-effectiveness standpoint, screening programmes should be expanded in developed regions and treatment programmes should be established for colorectal cancer in regions with low treatment coverage.

Enhanced cost-benefit analysis of strategies for LTBI screening and INH chemoprevention in Germany

OBJECTIVES

There is only limited economic data in head-to head comparison between a whole blood QuantiFERON TB Gold in tube (QFT) and the tuberculin skin test (TST) when screening and treating for latent tuberculosis infection (LTBI), and no published study to date that takes into account the predictive value of the two tests.

METHODS

Health and economic outcomes of isoniazid preventive treatment (IPT) of close contacts were compared in a decision tree model to perform a cost-benefit analysis with respect to isoniazid related hepatotoxicity and early post-exposure TB over a 2-y period, using the QFT or TST alone or QFT as a confirmatory test for TST results.

RESULTS

Cost of screening and treating for using the QFT alone amounted to euro215.79 per close contact, less than that of dual step-testing (euro227.89) or using TST alone (euro232.58). Savings amounted to euro12,200 or euro16,791 per 1000 close contacts, respectively. QFT based procedures were most sensitive to low compliance with IPT or increasing price. Costs of dual step screening was mostly influenced by cost of treating TB disease. When the progression rate for QFT was lowered to that for the TST in a sensitivity analysis, the relationship between the strategies remained robust. In addition, costs of the QFT strategy decreased to euro165.1, and those of the dual step strategy to euro218.4.

CONCLUSION

IPT on the basis of using the QFT assay alone produces less cost and reduces more TB cases than other strategies in a low-incidence setting. These data have implications for the rational implementation of screening strategies in contact investigation.

Cost-effectiveness analyses of vaccination programmes : a focused review of modelling approaches

Cost effectiveness is becoming an increasingly important factor for stakeholders faced with decisions about adding a new vaccine into national immunization programmes versus alternative use of resources. Evaluating cost effectiveness, taking into account the relevant biological, clinical, epidemiological and economic factors of a vaccination programme, generally requires use of a model. This review examines the modelling approaches used in cost-effectiveness analyses (CEAs) of vaccination programmes.After overviewing the key attributes of models used in CEAs, a framework for categorising theoretical models is presented. Categories are based on three main attributes: static/dynamic; stochastic/deterministic; and aggregate/individual based. This framework was applied to a systematic review of CEAs of all currently available vaccines for the period of 1976 to May 2007. The systematic review identified 276 CEAs of vaccination programmes. The great majority (83%) of CEAs were conducted in the setting of high-income countries. Only a few vaccines were widely studied, with 57% of available CEAs being focused on the varicella, influenza, hepatitis A, hepatitis B or pneumococcal vaccine. Several time trends were evident, indicating that the number of vaccine CEAs being published is increasing; the main health outcome measures are moving away from the number of cases prevented towards quality-adjusted and unadjusted life-years gained, and more complex models are beginning to be used. The modelling approach was often not adequately described. Of the 208 CEAs that could be categorized according to the framework, around 90% were deterministic, aggregate-level static models. Although a dynamic transmission model is required to account for herd-immunity effects, only 23 of the CEAs were dynamic. None of the CEAs were individual based. To improve communication about the cost effectiveness of vaccination programmes, we believe the first step is for analysts to be more transparent with each other. A clear description of the model type using consistent terminology and justification for the model choice must begin to accompany all CEAs. As a minimum, we urge modellers to provide an explicit statement about the following attributes: static/dynamic; stochastic/deterministic; aggregate/individual based; open/closed. Where relevant, time intervals (discrete/continuous) and (non)linearity should also be described. Enhanced methods of assessing model performance and validity are also required. Our results emphasize the need to improve modelling methods for CEAs of vaccination programmes; specifically, model choice, construction, assessment and validation.

Unrelated medical care in life years gained and the cost utility of primary prevention: in search of a 'perfect' cost-utility ratio

An important subject of debate in cost-utility analysis of health care programmes is whether to include costs of unrelated medical care in life years gained. The inclusion of such costs is likely to be of consequence in the case of primary prevention. This paper presents different strategies regarding the inclusion not only of the costs, but also of the health effects of unrelated medical care in economic evaluations. Four different cost-utility ratios are presented and related to the criterion of internal consistency. In addition, the possibility to relate the ratios to a well-posed decision problem is analysed. An example computes the different ratios for smoking cessation interventions in different age groups. Including health care costs of unrelated medical care in life years gained increases cost utility ratios, but excluding unrelated medical costs favours smoking cessation interventions targeted at older smokers over those at younger smokers. We conclude that for primary prevention only a cost utility ratio that includes both the costs and effects of unrelated medical care meets the criterion of internal consistency and is related to a meaningful decision problem. Therefore, this type of cost-utility ratio should be preferred even if the data requirements may be substantial.

Optimal allocation of resources over health care programmes: dealing with decreasing marginal utility and uncertainty

This paper addresses the problem of how to value health care programmes with different ratios of costs to effects, specifically when taking into account that these costs and effects are uncertain. First, the traditional framework of maximising health effects with a given health care budget is extended to a flexible budget using a value function over money and health effects. Second, uncertainty surrounding costs and effects is included in the model using expected utility. Other approaches to uncertainty that do not specify a utility function are discussed and it is argued that these also include implicit notions about risk attitude.

Stochastic league tables: an application to diabetes interventions in the Netherlands

The aim of this paper is to discuss the use of stochastic league tables approach in cost-effectiveness analysis of diabetes interventions. It addresses the common grounds and differences with other methods of presenting uncertainty to decision-makers. This comparison uses the cost-effectiveness results of medical guidelines for Dutch diabetes type 2 patients in primary and secondary care. Stochastic league tables define the optimum expansion pathway as compared to baseline, starting with the least costly and most cost-effective intervention mix. Multi-intervention cost-effectiveness acceptability curves are used as a way to represent uncertainty information on the cost-effectiveness of single interventions as compared to a single alternative. The stochastic league table for diabetes interventions shows that in case of low budgets treatment of secondary care patients is the most likely optimum choice. Current care options of diabetes complications are shown to be inefficient compared to guidelines treatment. With more resources available one may implement all guidelines and improve efficiency. The stochastic league table approach and multi-intervention cost-effectiveness acceptability curves in uncertainty analysis lead to similar results. In addition, the stochastic league table approach provides policy makers with information on affordability by budget level. It fulfils more adequately the information requirements to choose between interventions, using the efficiency criterion.

Strengthening health services to control epidemics: empirical evidence from Guinea on its cost-effectiveness

OBJECTIVE

To assess the cost-effectiveness of control measures implemented against epidemics in Guinea, West Africa.

METHODS

We collected all routine data available on incidence, mortality, control measures implemented and their cost during epidemics of cholera, measles and meningococcal meningitis in 1993-95. Then we estimated for one prefecture the effectiveness and cost-effectiveness of epidemic control measures for three scenarios: (i) 'natural' situation, (ii) 'routine' health services and (iii) 'intervention'. Where uncertainty was considerable, we used sensitivity analysis and estimated ranges.

FINDINGS

Routine health services reduced potential deaths by 51% (67%, 37% and 60% for cholera, measles and meningitis, respectively), and additional interventions further decreased potential deaths by 28% (28%, 27% and 30% for cholera, measles and meningitis, respectively). The marginal cost-effectiveness of epidemic control measures in routine health services was US dollars 29 per death averted. The marginal cost-effectiveness of additional interventions was US dollars 93 per death averted.

CONCLUSION

Even with the data weaknesses that characterize situations of epidemics it is possible to show that strengthening health services to control epidemics as was performed in Guinea was highly cost-effective. Moreover, sensitivity analysis over a range of assumptions confirms that (i) well-functioning health services averted the major part of avoidable deaths, (ii) combining existing health services with additional interventions minimizes the health impact of epidemics and (iii) case management should be a cornerstone of control of epidemics of cholera, measles and meningococcal meningitis.

Generalized cost-effectiveness analysis for national-level priority-setting in the health sector

Cost-effectiveness analysis (CEA) is potentially an important aid to public health decision-making but, with some notable exceptions, its use and impact at the level of individual countries is limited. A number of potential reasons may account for this, among them technical shortcomings associated with the generation of current economic evidence, political expediency, social preferences and systemic barriers to implementation. As a form of sectoral CEA, Generalized CEA sets out to overcome a number of these barriers to the appropriate use of cost-effectiveness information at the regional and country level. Its application via WHO-CHOICE provides a new economic evidence base, as well as underlying methodological developments, concerning the cost-effectiveness of a range of health interventions for leading causes of, and risk factors for, disease.The estimated sub-regional costs and effects of different interventions provided by WHO-CHOICE can readily be tailored to the specific context of individual countries, for example by adjustment to the quantity and unit prices of intervention inputs (costs) or the coverage, efficacy and adherence rates of interventions (effectiveness). The potential usefulness of this information for health policy and planning is in assessing if current intervention strategies represent an efficient use of scarce resources, and which of the potential additional interventions that are not yet implemented, or not implemented fully, should be given priority on the grounds of cost-effectiveness.Health policy-makers and programme managers can use results from WHO-CHOICE as a valuable input into the planning and prioritization of services at national level, as well as a starting point for additional analyses of the trade-off between the efficiency of interventions in producing health and their impact on other key outcomes such as reducing inequalities and improving the health of the poor.

Revisiting the decision rule of cost-effectiveness analysis under certainty and uncertainty

The classical decision rule of cost-effectiveness analysis uses a threshold cost-effectiveness ratio as a cut-off point for resources allocation. One assumption of this decision rule is complete divisibility of health care programs. In this article, we argue that health care programs cannot be completely divisible since individuals are not divisible. Consequently, instead of a linear programming approach, an integer programming approach to budget allocation is suggested. The integer programming framework can be extended to include uncertainty in the analysis. An objective function (expected aggregate effects) is maximised subject to the constraint that the probability of exceeding the budget is limited to an arbitrary level (e.g., 0.05). In case the budget is exceeded, the objective function is penalised in order to account for the opportunity costs of the additional resource requirements.

Effectiveness and costs of interventions to lower systolic blood pressure and cholesterol: a global and regional analysis on reduction of cardiovascular-disease risk

BACKGROUND

Cardiovascular disease accounts for much morbidity and mortality in developed countries and is becoming increasingly important in less developed regions. Systolic blood pressure above 115 mm Hg accounts for two-thirds of strokes and almost half of ischaemic heart disease cases, and cholesterol concentrations exceeding 3.8 mmol/L for 18% and 55%, respectively. We report estimates of the population health effects, and costs of selected interventions to reduce the risks associated with high cholesterol concentrations and blood pressure in areas of the world with differing epidemiological profiles.

METHODS

Effect sizes were derived from systematic reviews or meta-analyses, and the effect on health outcomes projected over time for populations with differing age, sex, and epidemiological profiles. Incidence data from estimates of burden of disease were used in a four-state longitudinal population model to calculate disability-adjusted life years (DALYs) averted and patients treated. Costs were taken from previous publications, or estimated by local experts, in 14 regions.

FINDINGS

Non-personal health interventions, including government action to stimulate a reduction in the salt content of processed foods, are cost-effective ways to limit cardiovascular disease and could avert over 21 million DALYs per year worldwide. Combination treatment for people whose risk of a cardiovascular event over the next 10 years is above 35% is also cost effective leading to substantial additional health benefits by averting an additional 63 million DALYs per year worldwide.

INTERPRETATION

The combination of personal and non-personal health interventions evaluated here could lower the global incidence of cardiovascular events by as much as 50%.

Programme costs in the economic evaluation of health interventions

Estimating the costs of health interventions is important to policy-makers for a number of reasons including the fact that the results can be used as a component in the assessment and improvement of their health system performance. Costs can, for example, be used to assess if scarce resources are being used efficiently or whether there is scope to reallocate them in a way that would lead to improvements in population health. As part of its WHO-CHOICE project, WHO has been developing a database on the overall costs of health interventions in different parts of the world as an input to discussions about priority setting.Programme costs, defined as costs incurred at the administrative levels outside the point of delivery of health care to beneficiaries, may comprise an important component of total costs. Cost-effectiveness analysis has sometimes omitted them if the main focus has been on personal curative interventions or on the costs of making small changes within the existing administrative set-up. However, this is not appropriate for non-personal interventions where programme costs are likely to comprise a substantial proportion of total costs, or for sectoral analysis where questions of how best to reallocate all existing health resources, including administrative resources, are being considered.This paper presents a first effort to systematically estimate programme costs for many health interventions in different regions of the world. The approach includes the quantification of resource inputs, choice of resource prices, and accounts for different levels of population coverage. By using an ingredients approach, and making tools available on the World Wide Web, analysts can adapt the programme costs reported here to their local settings. We report results for a selected number of health interventions and show that programme costs vary considerably across interventions and across regions, and that they can contribute substantially to the overall costs of interventions.

Determining the optimal combinations of mutually exclusive interventions: a response to Hutubessy and colleagues

Hutubessy and colleagues have suggested a technique for decision-makers to employ in determining the optimal combination of interventions. This technique requires the adoption of Monte Carlo simulation analysis to identify the probability that a certain program will be included in an optimal mix given the uncertainty around the program's expected costs and benefits. In this response, it will be demonstrated that this methodology can lead to potential inefficiencies arising through the dependence of such probabilities on decisions relating to other programs and the failure to consider the opportunity costs of obtaining increased health benefits. A simple alternative approach is suggested which avoids these problems.

Efficiency-based pharmacotherapy: a new approach for decision-making in medical practice

For many years, efficacy and safety have been key parameters for decision-making in healthcare policy. However, the current situation is rather different as resources are scarce, so it is necessary to take efficiency into account when allocating limited resources. Efficiency-based pharmacotherapy is a new discipline that combines the principles of cost-effectiveness analysis with the criteria of evidence-based medicine to obtain the efficiency of therapeutic options to treat important diseases. A league table for each important pathology will be created by ranking all available drugs according to their incremental cost-effectiveness ratio. In this way, clinicians and other decision-makers would be able to advice the use and implantation of drug therapy options with better value for money in routine medical practice.