A systematic review and meta-analysis of unmet needs for healthcare and long-term care among older people.

BACKGROUND: The absolute number of older individuals needing medical care and long-term care (LTC) is increasing globally due to the growing ageing population. However, it is uncertain who and what proportion of the population has access to care. Therefore, a systematic review and meta-analysis of the prevalence and reasons for unmet needs for healthcare and long-term care among older people, 65 years old and above, across countries was conducted. METHODS: An information specialist performed a comprehensive search of four major databases (PubMed, EMBASE, Web of Science, and CINAHL) from inception to June 2020 without restrictions on language and date. We did random-effects meta-analysis to obtain pooled prevalence. We stratified the meta-analysis by reasons for unmet need categorized by barrier dimension (availability, accessibility, affordability, and acceptability), survey year, geographic location, and socio-demographic characteristics of the older individual. RESULTS: After screening 3912 articles, we included 101 studies published between 1996 and 2020. Of the 101 studies, 87 studies reported unmet healthcare needs and 14 studies reported unmet LTC needs. Overall, 10.4% (95% CI, 7.3-13.9) of the older population had unmet needs for healthcare. The common reasons for unmet healthcare needs were cost of treatment, lack of health facilities, lack of/conflicting time, health problem not viewed as serious, and mistrust/fear of provider. A significant variation in pooled prevalence of unmet healthcare needs due to cost was found by gender (male [10.9, 95% CI, 8.9-13.1] vs female [14.4, 95% CI, 11.8-17.3]), educational level (primary or less [13.3, 95% CI, 9.6-17.6] vs higher [7.5, 95% CI, 5.9-9.3]), self-reported health (poor [23.2, 95% CI, 18.8-27.8] vs good [4.4, 95% CI, 3.4-5.5]), insurance status (insured [9.0, 95% CI, 7.5-10.6] vs uninsured [27.7, 95% CI, 24.0-31.5]), and economic status of population (poorest [28.2, 95% CI, 14.1-44.9] vs richest [7.1, 95% CI, 3.8-11.3]). One in four (25.1, 95% CI, 17.1-34.2) older people had unmet needs in LTC. Rural residents had a higher prevalence of unmet needs in LTC compared to their urban counterparts. CONCLUSION: With the population ageing globally, it is necessary to improve access to health care and LTC for older people. Ensuring affordability of health services, reducing geographical barriers, and improving acceptability, will be critical in reducing unmet need. Unmet needs for healthcare were concentrated in population with no education, poor economic group, outpatient health facility user, and uninsured group. With education and economic-based inequalities at the forefront, all countries should focus on improving access to health services by reducing the burden related to healthcare costs.

The costs of improving health emergency preparedness: A systematic review and analysis of multi-country studies.

BACKGROUND: Investing in health emergency preparedness is critical to the safety, welfare and stability of communities and countries worldwide. Despite the global push to increase investments, questions remain around how much should be spent and what to focus on. We conducted a systematic review and analysis of studies that costed improvements to health emergency preparedness to help to answer these questions. METHODS: We searched for studies that estimated the costs of improving health emergency preparedness and that were published between 1 January 2000 and 14 May 2021, using PubMed, Web of Science, Google Scholar, EconLit, and National Health Service Economic Evaluation Databases (PROSPERO CRD42021254428). We also searched grey literature repositories and contacted subject experts. We included studies that estimated the costs of improving preparedness at the global level and/or at the national level across at least ten countries, covered two or more technical areas in the WHO Benchmarks for International Health Regulations (IHR) Capacities, and included activities focused on human health. We mapped costs across technical areas in the WHO Benchmarks for IHR Capacities. FINDINGS: Ten studies met our inclusion criteria. Costing methods varied substantially across included studies and cost estimates ranged from US$1·6 billion per year to improve capacities across 139 low- and middle-income countries (LMICs) to US$43 billion per year to support national-level activities worldwide and implement global-level initiatives, such as research and development for health technologies (diagnostics, therapeutics, and vaccines). Two recent studies estimated costs by drawing on IHR Monitoring and Evaluation Framework country capacity data, with one study estimating costs across 67 LMICs of US$15·4 billion per year (US$29·1 billion including upfront capital costs) and the other calculating costs for the 196 States Parties to the IHR of US$24·8 billion per year. Differences in included studies' methods, and the characteristics of countries considered, mean it is difficult to make like-for-like comparisons of the absolute costs or per-capita costs estimated by studies. INTERPRETATION: Improving health emergency preparedness worldwide will require substantial and sustained increases in investments. Further guidance on estimating the size of those investments can help to standardise methods, allowing greater interpretation and comparison across studies/countries. As well as greater transparency and detail in the reporting of methods by studies focused on this topic, this can help support estimates of global resource requirements and facilitate investments towards improving preparedness for future pandemics. FUNDING: None.

Introduction to the Special Issue on "The World Health Organization Choosing Interventions That Are Cost-Effective (WHO-CHOICE) Update".

The WHO-CHOICE (World Health Organization CHOosing Interventions that are Cost-Effective) approach is unique in the global health landscape, as it takes a "generalized" approach to cost-effectiveness analysis (CEA) that can be seen as a quantitative assessment of current and future efficiency within a health system. CEA is a critical contribution to the process of priority setting and decision-making in healthcare, contributing to deliberative dialogue processes to select services to be funded. WHO-CHOICE provides regional level estimates of cost-effectiveness, along with tools to support country level analyses. This series provides an update to the methodological approach used in WHO-CHOICE and presents updated cost-effectiveness estimates for 479 interventions. Five papers are presented, the first focusing on methodological updates, followed by three results papers on maternal, newborn and child health; HIV, tuberculosis and malaria; and non-communicable diseases and mental health. The final paper presents a set of example universal health coverage (UHC) benefit packages selected through only a value for money lens, showing that all disease areas have interventions which can fall on the efficiency frontier. Critical for all countries is institutionalizing decision-making processes. A UHC benefit package should not be static, as the countries needs and ability to pay change over time. Decisions will need to be continually revised and new interventions added to health benefit packages. This is a vital component of progressive realization, as the package is expanded over time. Developing an institutionalized process ensures this can be done consistently, fairly, and transparently, to ensure an equitable path to UHC.

Challenges and experiences in linking community level reported out-of-pocket health expenditures to health provider recorded health expenditures: Experience from the iHOPE project in Northern Ghana.

Out of pocket health payment (OOPs) has been identified by the System of Health Accounts (SHA) as the largest source of health care financing in most low and middle-income countries. This means that most low and middle-income countries will rely on user fees and co-payments to generate revenue, rationalize the use of services, contain health systems costs or improve health system efficiency and service quality. However, the accurate measurement of OOPs has been challenged by several limitations which are attributed to both sampling and non-sampling errors when OOPs are estimated from household surveys, the primary source of information in LICs and LMICs. The incorrect measurement of OOP health payments can undermine the credibility of current health spending estimates, an otherwise important indicator for tracking UHC, hence there is the need to address these limitations and improve the measurement of OOPs. In an attempt to improve the measurement of OOPs in surveys, the INDEPTH-Network Household out-of-pocket expenditure project (iHOPE) developed new modules on household health utilization and expenditure by repurposing the existing Ghana Living Standards Survey instrument and validating these new tools with a 'gold standard' (provider data) with the aim of proposing alternative approaches capable of producing reliable data for estimating OOPs in the context of National Health Accounts and for the purpose of monitoring financial protection in health. This paper reports on the challenges and opportunities in using and linking household reported out-of-pocket health expenditures to their corresponding provider records for the purpose of validating household reported out-of-pocket health expenditure in the iHOPE project.

Effect of specificity of health expenditure questions in the measurement of out-of-pocket health expenditure: evidence from field experimental study in Ghana.

BACKGROUND: The effect of number of health items on out-of-pockets (OOPs) has been identified as a source of bias in measuring OOPs. Evidence comes mostly from cross-sectional comparison of different survey instruments to collect data on OOPs. Very few studies have attempted to validate these questionnaires, or distinguish bias arising from the comprehensiveness of the OOPs list versus specificity of OOPs questions. OBJECTIVES: This study aims to estimate biases arising from the specificity of OOPs questions by comparing provider and household's information. METHODS: A generic questionnaire to collect data on household's OOPs was developed following the nomenclature proposed in division 6 of the classification of household final consumption 2018. The four categories within such division are used to set the comprehensiveness of the OOPs list, the specificity within each category was tailored to the design of the nationally representative living standard survey in Ghana where a field experiment was conducted to test the validity of different versions. Households were randomised to 11, 44 or 56 health items. Using data from provider records as the gold standard, we compared the mean positive OOPs, and estimated the mean ratio and variability in the ratio of household expenditures to provider data for the individual households using the Bland-Altman method of assessing agreement. FINDINGS: We found evidence of a difference in the overall mean ratio in the specificity for OOPs in inpatient care and medications. Within each of these two categories, a more detailed disaggregation yielded lower OOPs estimates than less detailed ones. The level of agreement between household and provider OOPs also decreased with increasing specificity of health items. CONCLUSION: Our findings suggest that, for inpatient care and medications, systematically decomposing OOPs categories into finer subclasses tend to produce lower OOPs estimates. Less detailed items produced more accurate and reliable OOPs estimates in the context of a rural setting.

Methods for the Economic Evaluation of Health Care Interventions for Priority Setting in the Health System: An Update From WHO CHOICE.

The World Health Organization's (WHO's) Choosing Interventions that are Cost-Effective (CHOICE) programme has been a global leader in the field of economic evaluation, specifically cost-effectiveness analysis for almost 20 years. WHO-CHOICE takes a "generalized" approach to cost-effectiveness analysis that can be seen as a quantitative assessment of current and future efficiency within a health system. This supports priority setting processes, ensuring that health stewards know how to spend resources in order to achieve the highest health gain as one consideration in strategic planning. This approach is unique in the global health landscape. This paper provides an overview of the methodological approach, updates to analytic framework over the past 10 years, and the added value of the WHO-CHOICE approach in supporting decision makers as they aim to use limited health resources to achieve the Sustainable Development Goals (SDGs) by 2030.

Progressive Realisation of Universal Health Coverage in Low- and Middle-Income Countries: Beyond the "Best Buys".

BACKGROUND: World Health Organization Choosing Interventions that are Cost-Effective (WHO CHOICE) has been a programme of the WHO for 20 years. In this latest update, we present for the first time a cross-programme analysis of the comparative cost-effectiveness of 479 intervention scenarios across 20 disease programmes and risk factors. METHODS: This analysis follows the standard WHO CHOICE approach to generalized cost-effectiveness analysis applied to two regions, Eastern sub-Saharan Africa and Southeast Asia. The scope of the analysis is all interventions included in programme specific WHO CHOICE analyses, using WHO treatment guidelines for major disease areas as the foundation. Costs are measured in 2010 international dollars, and benefits modelled beginning in 2010, or the nearest year for which validated data was available, both for a period of 100 years. RESULTS: Across both regions included in the analysis, interventions span multiple orders of magnitude in terms of cost-effectiveness ratios. A health benefit package optimized through a value for money lens incorporates interventions responding to all of the main drivers of disease burden. Interventions delivered through first level clinical and non-clinical services represent the majority of the high impact cost-effective interventions. CONCLUSION: Cost-effectiveness is one important criterion when selecting health interventions for benefit packages to progress towards universal health coverage (UHC), but it is not the only criterion and all calculations should be adapted to the local context. To support country decision-makers, WHO CHOICE has developed a downloadable tool to support the development of data for this criterion.

Investigating the effect of recall period on estimates of inpatient out-of-pocket expenditure from household surveys in Vietnam.

Out-of-pocket payments (OOPs), direct payments by households or individuals for healthcare are part of the health financing landscape. Data on OOPs is needed to monitor progress in financial risk protection, and the evaluation of health financing policies. In low-and-middle-income countries, estimates of OOPs rely heavily on self-reported data from household surveys. These surveys require respondents to recall events in the past and can suffer from recall biases. This study investigates the effect of recall period on the agreement of the amount and timing of inpatient OOPs between household reports and provider records in Bavi, Vietnam. We recruited 1397 households for interview using records from the district hospital. The households were interviewed with identical questionnaires except that the recall period was either 12 or 6 months. We linked household with provider data and excluded medicine costs from both household and provider OOPs since they could be purchased outside the hospital. We estimated the effect of recall period on the overall mean and variability of ratios of household to hospital reported OOPs using the Bland-Altman approach for method comparison. We estimated the effect of recall period on whether a transaction was recalled correctly in expenditure and time using multinomial regression. The households reported higher amounts of OOPs than did the hospital for both recall periods. There was no evidence of an effect of recall period on the mean of the ratios of household- to hospital-reported OOPs, although the confidence intervals are not inconsistent with previous studies indicating higher OOPs for shorter recall periods. The geometric mean ratio for the 6-month period was estimated to be a multiple of 1.4 (95% CI 0.9, 2.1) times that of the 12-month period. Similarly, there was no evidence of an effect of recall period on the risk of reporting lower or higher amounts than provider OOPs. The occurrence and timing of inpatient stays generally recalled well, with 70% remembered in the correct month declining slightly over time. Respondents for the 6-month recall period had a significantly lower risk of failing to report the event (RR 0.8 (0.7, 1.0)). The results suggest the best recall period may depend on whether the purpose of a survey is for the recall of the timing of events, in which case the 6 month period may be better, or the amounts of OOPs, where there was no significant difference and the provider records are not a gold standard but the 12 month period had a tendency to be in closer agreement with the provider OOPs.

Projected health-care resource needs for an effective response to COVID-19 in 73 low-income and middle-income countries: a modelling study.

BACKGROUND: Since WHO declared the COVID-19 pandemic a Public Health Emergency of International Concern, more than 20 million cases have been reported, as of Aug 24, 2020. This study aimed to identify what the additional health-care costs of a strategic preparedness and response plan (SPRP) would be if current transmission levels are maintained in a status quo scenario, or under scenarios where transmission is increased or decreased by 50%. METHODS: The number of COVID-19 cases was projected for 73 low-income and middle-income countries for each of the three scenarios for both 4-week and 12-week timeframes, starting from June 26, 2020. An input-based approach was used to estimate the additional health-care costs associated with human resources, commodities, and capital inputs that would be accrued in implementing the SPRP. FINDINGS: The total cost estimate for the COVID-19 response in the status quo scenario was US$52·45 billion over 4 weeks, at $8·60 per capita. For the decreased or increased transmission scenarios, the totals were $33·08 billion and $61·92 billion, respectively. Costs would triple under the status quo and increased transmission scenarios at 12 weeks. The costs of the decreased transmission scenario over 12 weeks was equivalent to the cost of the status quo scenario at 4 weeks. By percentage of the overall cost, case management (54%), maintaining essential services (21%), rapid response and case investigation (14%), and infection prevention and control (9%) were the main cost drivers. INTERPRETATION: The sizeable costs of a COVID-19 response in the health sector will escalate, particularly if transmission increases. Instituting early and comprehensive measures to limit the further spread of the virus will conserve resources and sustain the response. FUNDING: WHO, and UK Foreign Commonwealth and Development Office.

Guide posts for investment in primary health care and projected resource needs in 67 low-income and middle-income countries: a modelling study.

BACKGROUND: Primary health care (PHC) is a driving force for advancing towards universal health coverage (UHC). PHC-oriented health systems bring enormous benefits but require substantial financial investments. Here, we aim to present measures for PHC investments and project the associated resource needs. METHODS: This modelling study analysed data from 67 low-income and middle-income countries (LMICs). Recognising the variation in PHC services among countries, we propose three measures for PHC, with different scope for included interventions and system strengthening. Measure 1 is centred on public health interventions and outpatient care; measure 2 adds general inpatient care; and measure 3 further adds cross-sectoral activities. Cost components included in each measure were based on the Declaration of Astana, informed by work delineating PHC within health accounts, and finalised through an expert and country validation meeting. We extracted the subset of PHC costs for each measure from WHO's Sustainable Development Goal (SDG) price tag for the 67 LMICs, and projected the associated health impact. Estimates of financial resource need, health workforce, and outpatient visits are presented as PHC investment guide posts for LMICs. FINDINGS: An estimated additional US$200-328 billion per year is required for the various measures of PHC from 2020 to 2030. For measure 1, an additional $32 is needed per capita across the countries. Needs are greatest in low-income countries where PHC spending per capita needs to increase from $25 to $65. Overall health workforces would need to increase from 5·6 workers per 1000 population to 6·7 per 1000 population, delivering an average of 5·9 outpatient visits per capita per year. Increasing coverage of PHC interventions would avert an estimated 60·1 million deaths and increase average life expectancy by 3·7 years. By 2030, these incremental PHC costs would be about 3·3% of projected gross domestic product (GDP; median 1·7%, range 0·1-20·2). In a business-as-usual financing scenario, 25 of 67 countries will have funding gaps in 2030. If funding for PHC was increased by 1-2% of GDP across all countries, as few as 16 countries would see a funding gap by 2030. INTERPRETATION: The resources required to strengthen PHC vary across countries, depending on demographic trends, disease burden, and health system capacity. The proposed PHC investment guide posts advance discussions around the budgetary implications of strengthening PHC, including relevant system investment needs and achievable health outcomes. Preliminary findings suggest that low-income and lower-middle-income countries would need to at least double current spending on PHC to strengthen their systems and universally provide essential PHC services. Investing in PHC will bring substantial health benefits and build human capital. At country level, PHC interventions need to be explicitly identified, and plans should be made for how to most appropriately reorient the health system towards PHC as a key lever towards achieving UHC and the health-related SDGs. FUNDING: The Bill & Melinda Gates Foundation.

Disease control programme support costs: an update of WHO-CHOICE methodology, price databases and quantity assumptions.

BACKGROUND: Estimating health care costs, either in the context of understanding resource utilization in the implementation of a health plan, or in the context of economic evaluation, has become a common activity of health planners, health technology assessment agencies and academic groups. However, data sources for costs outside of direct service delivery are often scarce. WHO-CHOICE produces global price databases and guidance on quantity assumptions to support country level costing exercises. This paper presents updates to the WHO-CHOICE methodology and price databases for programme costs. METHODS: We collated publicly available databases for 14 non-traded cost variables, as well as a set of traded items used within health systems (traded goods are those which can be purchased from anywhere in the world, whereas non-traded goods are those which must be produced locally, such as human resources). Within each of the variables, missing data was present for some proportion of the WHO member states. For each variables statistical or econometric models were used to model prices for each of the 194 WHO member states in 2010 International Dollars. Literature reviews were used to update quantity assumptions associated with each variable to contribute to the support costs of disease control programmes. RESULTS: A full database of prices for disease control programme support costs is available for country-specific costing purposes. Human resources are the largest driver of disease control programme support costs, followed by supervision costs. CONCLUSIONS: Despite major advances in the availability of data since the previous version of this work, there are still some limitations in data availability to respond to the needs of those wishing to develop cost and cost-effectiveness estimates. Greater attention to programme support costs in cost data collection activities would contribute to an understanding of how these costs contribute to quality of health service delivery and should be encouraged.

Financing transformative health systems towards achievement of the health Sustainable Development Goals: a model for projected resource needs in 67 low-income and middle-income countries.

BACKGROUND: The ambitious development agenda of the Sustainable Development Goals (SDGs) requires substantial investments across several sectors, including for SDG 3 (healthy lives and wellbeing). No estimates of the additional resources needed to strengthen comprehensive health service delivery towards the attainment of SDG 3 and universal health coverage in low-income and middle-income countries have been published. METHODS: We developed a framework for health systems strengthening, within which population-level and individual-level health service coverage is gradually scaled up over time. We developed projections for 67 low-income and middle-income countries from 2016 to 2030, representing 95% of the total population in low-income and middle-income countries. We considered four service delivery platforms, and modelled two scenarios with differing levels of ambition: a progress scenario, in which countries' advancement towards global targets is constrained by their health system's assumed absorptive capacity, and an ambitious scenario, in which most countries attain the global targets. We estimated the associated costs and health effects, including reduced prevalence of illness, lives saved, and increases in life expectancy. We projected available funding by country and year, taking into account economic growth and anticipated allocation towards the health sector, to allow for an analysis of affordability and financial sustainability. FINDINGS: We estimate that an additional $274 billion spending on health is needed per year by 2030 to make progress towards the SDG 3 targets (progress scenario), whereas US$371 billion would be needed to reach health system targets in the ambitious scenario-the equivalent of an additional $41 (range 15-102) or $58 (22-167) per person, respectively, by the final years of scale-up. In the ambitious scenario, total health-care spending would increase to a population-weighted mean of $271 per person (range 74-984) across country contexts, and the share of gross domestic product spent on health would increase to a mean of 7·5% (2·1-20·5). Around 75% of costs are for health systems, with health workforce and infrastructure (including medical equipment) as the main cost drivers. Despite projected increases in health spending, a financing gap of $20-54 billion per year is projected. Should funds be made available and used as planned, the ambitious scenario would save 97 million lives and significantly increase life expectancy by 3·1-8·4 years, depending on the country profile. INTERPRETATION: All countries will need to strengthen investments in health systems to expand service provision in order to reach SDG 3 health targets, but even the poorest can reach some level of universality. In view of anticipated resource constraints, each country will need to prioritise equitably, plan strategically, and cost realistically its own path towards SDG 3 and universal health coverage. FUNDING: WHO.

Making Fair Choices on the Path to Universal Health Coverage: Applying Principles to Difficult Cases.

Abstract-Progress toward universal health coverage (UHC) requires making difficult trade-offs. In this journal, Dr. Margaret Chan, the World Health Organization (WHO) Director-General, has endorsed the principles for making such decisions put forward by the WHO Consultative Group on Equity and UHC. These principles include maximizing population health, priority for the worse off, and shielding people from health-related financial risks. But how should one apply these principles in particular cases, and how should one adjudicate between them when their demands conflict? This article by some members of the Consultative Group and a diverse group of health policy professionals addresses these questions. It considers three stylized versions of actual policy dilemmas. Each of these cases pertains to one of the three key dimensions of progress toward UHC: which services to cover first, which populations to prioritize for coverage, and how to move from out-of-pocket expenditures to prepayment with pooling of funds. Our cases are simplified to highlight common trade-offs. Though we make specific recommendations, our primary aim is to demonstrate both the form and substance of the reasoning involved in striking a fair balance between competing interests on the road to UHC.

Cost-effectiveness thresholds: pros and cons.

Cost-effectiveness analysis is used to compare the costs and outcomes of alternative policy options. Each resulting cost-effectiveness ratio represents the magnitude of additional health gained per additional unit of resources spent. Cost-effectiveness thresholds allow cost-effectiveness ratios that represent good or very good value for money to be identified. In 2001, the World Health Organization's Commission on Macroeconomics in Health suggested cost-effectiveness thresholds based on multiples of a country's per-capita gross domestic product (GDP). In some contexts, in choosing which health interventions to fund and which not to fund, these thresholds have been used as decision rules. However, experience with the use of such GDP-based thresholds in decision-making processes at country level shows them to lack country specificity and this - in addition to uncertainty in the modelled cost-effectiveness ratios - can lead to the wrong decision on how to spend health-care resources. Cost-effectiveness information should be used alongside other considerations - e.g. budget impact and feasibility considerations - in a transparent decision-making process, rather than in isolation based on a single threshold value. Although cost-effectiveness ratios are undoubtedly informative in assessing value for money, countries should be encouraged to develop a context-specific process for decision-making that is supported by legislation, has stakeholder buy-in, for example the involvement of civil society organizations and patient groups, and is transparent, consistent and fair.

Les analyses de rentabilité permettent de comparer les coûts et les résultats de différentes options politiques. Chaque ratio coût-efficacité qui en découle indique l'importance des avantages supplémentaires pour la santé par unité supplémentaire de ressources dépensée. Les seuils de rentabilité permettent de déterminer les ratios coût-efficacité qui représentent une bonne ou une très bonne rentabilité. En 2001, la Commission macroéconomie et santé de l'Organisation mondiale de la Santé a suggéré des seuils de rentabilité définis d'après des multiples du produit intérieur brut (PIB) par habitant d'un pays. Dans certains pays, ces seuils ont servi de règles pour décider quelles interventions financer ou non. Cependant, l'expérience d'utilisation de ces seuils fondés sur le PIB dans les processus décisionnels des pays montre qu'ils ne tiennent pas compte des spécificités des pays; cela, ajouté à une certaine incertitude concernant la modélisation des ratios coût-efficacité, peut entraîner la prise de mauvaises décisions quant à l'utilisation des ressources sanitaires. Les informations sur la rentabilité des interventions devraient être prises en compte parallèlement à d'autres considérations, comme l'impact budgétaire et la faisabilité, dans le cadre d'un processus décisionnel transparent et non de façon isolée sur la base d'une seule valeur seuil. Bien que le caractère informatif des ratios coût-efficacité soit indéniable lorsqu'il s'agit d'évaluer la rentabilité des interventions, les pays devraient être encouragés à développer un processus de prise de décision spécifique au contexte, qui soit encadré par la législation et qui ait l'adhésion des parties intéressées, avec par exemple l'implication d'organisations de la société civile et de groupes de patients, et qui soit transparent, cohérent et équitable.

El análisis de rentabilidad se utiliza para comparar los costes y resultados de opciones políticas alternativas. Cada relación de rentabilidad resultante representa la magnitud de sanidad adicional obtenida por unidad adicional de recursos utilizados. Los umbrales de rentabilidad permiten la identificación de las relaciones de rentabilidad que representan un valor bueno o muy bueno del capital. En 2001, los umbrales de rentabilidad propuestos por la Comisión sobre Macroeconomía y Salud de la Organización Mundial de la Salud se basaron en múltiplos del producto interior bruto (PIB) per cápita de un país. En algunos contextos, se han utilizado estos umbrales para decidir qué intervenciones sanitarias financiar y cuáles no. No obstante, la experiencia con el uso de dichos umbrales basados en el PIB en los procesos de toma de decisiones a nivel nacional muestra la ausencia de especificidad según el país. Esto, además de la incertidumbre de las relaciones de rentabilidad modelo, puede dar lugar a una toma de decisiones equivocada sobre cómo emplear los recursos sanitarios. La información relativa a la rentabilidad debería utilizarse teniendo en cuenta otros factores (por ejemplo, el impacto presupuestario y aspectos de viabilidad) en un proceso transparente de toma de decisiones, en lugar de únicamente teniendo como referencia un solo valor del umbral. A pesar de que las relaciones de rentabilidad son indudablemente esclarecedoras a la hora de evaluar el valor del capital, es necesario fomentar que los países desarrollen un proceso específico del contexto apoyado por la legislación para tomar decisiones, como, por ejemplo, si las partes interesadas han aceptado la implicación de las organizaciones de la sociedad civil y grupos de pacientes y si es transparente, coherente y justa.

Three Case Studies in Making Fair Choices on the Path to Universal Health Coverage.

The goal of achieving Universal Health Coverage (UHC) can generally be realized only in stages. Moreover, resource, capacity, and political constraints mean governments often face difficult trade-offs on the path to UHC. In a 2014 report, Making fair choices on the path to UHC, the WHO Consultative Group on Equity and Universal Health Coverage articulated principles for making such trade-offs in an equitable manner. We present three case studies which illustrate how these principles can guide practical decision-making. These case studies show how progressive realization of the right to health can be effectively guided by priority-setting principles, including generating the greatest total health gain, priority for those who are worse off in a number of dimensions (including health, access to health services, and social and economic status), and financial risk protection. They also demonstrate the value of a fair and accountable process of priority setting.

Guidance on priority setting in health care (GPS-Health): the inclusion of equity criteria not captured by cost-effectiveness analysis.

This Guidance for Priority Setting in Health Care (GPS-Health), initiated by the World Health Organization, offers a comprehensive map of equity criteria that are relevant to health care priority setting and should be considered in addition to cost-effectiveness analysis. The guidance, in the form of a checklist, is especially targeted at decision makers who set priorities at national and sub-national levels, and those who interpret findings from cost-effectiveness analysis. It is also targeted at researchers conducting cost-effectiveness analysis to improve reporting of their results in the light of these other criteria. THE GUIDANCE WAS DEVELOP THROUGH A SERIES OF EXPERT CONSULTATION MEETINGS AND INVOLVED THREE STEPS: i) methods and normative concepts were identified through a systematic review; ii) the review findings were critically assessed in the expert consultation meetings which resulted in a draft checklist of normative criteria; iii) the checklist was validated though an extensive hearing process with input from a range of relevant stakeholders. The GPS-Health incorporates criteria related to the disease an intervention targets (severity of disease, capacity to benefit, and past health loss); characteristics of social groups an intervention targets (socioeconomic status, area of living, gender; race, ethnicity, religion and sexual orientation); and non-health consequences of an intervention (financial protection, economic productivity, and care for others).

Screening, prevention and treatment of cervical cancer -- a global and regional generalized cost-effectiveness analysis.

The paper calculates regional generalized cost-effectiveness estimates of screening, prevention, treatment and combined interventions for cervical cancer. Using standardised WHO-CHOICE methodology, a cervical cancer model was employed to provide estimates of screening, vaccination 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. In regions characterized by high income, low mortality and high existing treatment coverage, the addition of any screening programme to the current high treatment levels is very cost-effective. However, based on projections of the future price per dose (representing the economic costs of the vaccination excluding monopolistic rents and vaccine development cost) vaccination is the most cost-effective intervention. In regions characterized by low income, low mortality and existing treatment coverage around 50%, expanding treatment with or without combining it with screening appears to be cost-effective or very cost-effective. Abandoning treatment in favour of screening in a no-treatment scenario would not be cost-effective. Vaccination is usually the most cost-effective intervention. Penta or tri-annual PAP smears appear to be cost-effective, though when combined with HPV-DNA testing they are not cost-effective. In regions characterized by low income, high mortality and low treatment levels, expanding treatment with or without adding screening would be very cost-effective. A one off vaccination plus expanding treatment was usually very cost-effective. One-off PAP or VIA screening at age 40 are more cost-effective than other interventions though less effective overall. From a cost-effectiveness perspective, consideration should be given to implementing vaccination (depending on cost per dose and longevity of efficacy) and screening programmes on a worldwide basis to reduce the burden of disease from cervical cancer. Treatment should also be increased where coverage is low.

Cost-Effectiveness and Resource Allocation (CERA) - directions for the future.

The journal Cost-Effectiveness and Resource Allocation (CERA) is now in its seventh year, and is an excellent example of how open access publishing can improve dissemination. Now the journal is through its infancy, it is time to reflect on its orientation and to define the strategy for the years to come. Firstly, the journal will pay particular attention to stimulating and publishing studies originating from low- and middle-income countries. Second, CERA will continue to solicit contributions originating from high-income countries, but with the caveat that such studies should be of interest to the broad international readership of the journal. Third, the journal encourages submissions on methodological work from any setting, that is generalisable between low-, middle-, and high income countries. Fourth, CERA recognizes the development of national health accounts and expenditure tracking as a first step to improved resource allocation, and solicit manuscripts of this nature. Finally, CERA recognizes that cost and cost-effectiveness analysis alone may not provide sufficient information to decision makers to guide their choices on the allocation of resources, and therefore encourages submission of studies that advance the broader field of priority-setting.