Santana et al. Health Research Policy and Systems (2020) 18:18 https://doi.org/10.1186/s12961-020-0526-y RESEARCH Open Access Advancing tools to promote health equity across European Union regions: the EURO- HEALTHY project Paula Santana1,2* , Ângela Freitas2, Iwa Stefanik2, Cláudia Costa2, Mónica Oliveira3, Teresa C. Rodrigues3, Ana Vieira3, Pedro Lopes Ferreira4, Carme Borrell5,6,7, Sani Dimitroulopoulou8, Stéphane Rican9, Christina Mitsakou8, Marc Marí-Dell’Olmo5,6,7, Jürgen Schweikart10, Diana Corman11, Carlos A. Bana e Costa3 and on behalf of the EURO-HEALTHY investigators Abstract Background: Population health measurements are recognised as appropriate tools to support public health monitoring. Yet, there is still a lack of tools that offer a basis for policy appraisal and for foreseeing impacts on health equity. In the context of persistent regional inequalities, it is critical to ascertain which regions are performing best, which factors might shape future health outcomes and where there is room for improvement. Methods: Under the EURO-HEALTHY project, tools combining the technical elements of multi-criteria value models and the social elements of participatory processes were developed to measure health in multiple dimensions and to inform policies. The flagship tool is the Population Health Index (PHI), a multidimensional measure that evaluates health from the lens of equity in health determinants and health outcomes, further divided into sub-indices. Foresight tools for policy analysis were also developed, namely: (1) scenarios of future patterns of population health in Europe in 2030, combining group elicitation with the Extreme-World method and (2) a multi-criteria evaluation framework informing policy appraisal (case study of Lisbon). Finally, a WebGIS was built to map and communicate the results to wider audiences. Results: The Population Health Index was applied to all European Union (EU) regions, indicating which regions are lagging behind and where investments are most needed to close the health gap. Three scenarios for 2030 were produced – (1) the ‘Failing Europe’ scenario (worst case/increasing inequalities), (2) the ‘Sustainable Prosperity’ scenario (best case/decreasing inequalities) and (3) the ‘Being Stuck’ scenario (the EU and Member States maintain the status quo). Finally, the policy appraisal exercise conducted in Lisbon illustrates which policies have higher potential to improve health and how their feasibility can change according to different scenarios. Conclusions: The article makes a theoretical and practical contribution to the field of population health. Theoretically, it contributes to the conceptualisation of health in a broader sense by advancing a model able to integrate multiple aspects of health, including health outcomes and multisectoral determinants. Empirically, the model and tools are closely tied to what is measurable when using the EU context but offering opportunities to be upscaled to other settings. Keywords: Health equity, European Union regions, Geographic inequalities, Population Health Index, Participatory approach, Foresight, Scenarios, Policy evaluation, WebGIS * Correspondence: paulasantana.coimbra@gmail.com 1 Department of Geography and Tourism, Faculty of Arts and Humanities, University of Coimbra, Colégio S. Jerónimo, Largo D. Dinis, 3001-401 Coimbra, Portugal 2 CEGOT-UC, Centre of Studies in Geography and Territorial Planning, University of Coimbra, Coimbra, Portugal Full list of author information is available at the end of the article © The Author(s). 2020 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Santana et al. Health Research Policy and Systems (2020) 18:18 Page 2 of 14 Background Simultaneous to the discussion on the scope of health The increasing complexity in measuring health inequal- inequities, the understanding of health has evolved sig- ities requires new instruments and more meaningful nificantly [22], making the concept of health complex information at the regional and community level. and holistic [23] and requiring that ‘what’ and ‘why’ be Considerable advances in knowledge have identified measured [24]. Kindig et al. [16] have suggested that, to the key driving forces that are likely to influence health understand the health of a society, the fundamental and well-being [1–6]. However, in terms of assessment question to be asked is “how we are doing – and how we methods [7, 8], there is still a lack of comparable mea- can do better?” Population health measurements have sures able to provide a holistic assessment of population been recognised as one of the appropriate tools to health based on the multiple determinants involved, support public health policy-making, monitoring and as- ones which would then be applied across Europe [9], sessment by ensuring validity and cross-population com- particularly at the regional and local level. It is a fact that parability [9]. Summary measures built on relevant there are significant variations amongst the factors that indicators are well-known instruments to provide a com- influence the health of European citizens [10–12]. These prehensive picture of health and well-being [25, 26], with health variations are socially and spatially patterned, and their multi-domain basis reflecting the complexity of in many cases their drivers can be traced to broader people’s health. The interconnectedness of Health in All determinants such as socio-economic [4] and working Policies [27], recognised in the Alma Ata Declaration on conditions [5, 6], which people in Europe experience in Primary Health Care [28] and the Ottawa Charter for very unequal ways [2, 13]. To characterise the health of Health Promotion [29], has created a new spectrum for the population currently living in European Union (EU) looking at health and implied a need for an integrated regions, it is crucial to consider multiple health dimen- approach to population health [25, 30]. Such an ap- sions that go beyond the health outcomes and include a proach has the potential to guide policy-making and range of determinants outside the healthcare sector [14, inter-sectorial action to improve health and reduce in- 15]. This can provide an evidence-based perspective of equalities [31]. policies, with the potential to reduce inequalities and The aim of this paper is to present the ultimate out- promote health equity [11]. Therefore, efforts towards puts of the EURO-HEALTHY research project – to developing models that combine multiple determinants introduce the tools, demonstrate their capacity to meas- of health, engagement of diverse stakeholders, and ure and monitor population health across European re- evidence-based policies and interventions have been gions, and finally present their potential to inform stimulated and supported in the health research area policy-makers and to stimulate and facilitate health [16], inclusively by the EU through its framework for equity discussions. funding research and innovation. These measures should This paper will be of interest to policy-makers, be based on sound methods to enhance the potential of researchers and those who wish to enhance their under- monitoring health and of foreseeing the impact of pol- standing on novel population health tools that are able icies on health equity [9, 17, 18]. In our view, ‘health to advance and promote evidence-based policy whose ul- equity’ is understood here as “the principle underlying a timate aim is a healthier and more equitable Europe. commitment to reduce and, ultimately, eliminate dispar- ities in health and in its determinants, including social The EURO-HEALTHY project determinants” [19]. The aim of the EU-funded EURO-HEALTHY project With respect to population health determinants, it (which stands for ‘shaping EUROpean policies to promote is known that health inequalities are shaped by one’s HEALTH EquitY’) is to advance knowledge on policies socioeconomic status, education and income level, that offer the highest potential to promote health equity living conditions or the physical and built environ- across European regions (269 regions at Nomenclature of ment conditions of one’s place of residence. Given Territorial Units for Statistics level 2 (NUTS 2)) and urban that, these factors are preventable through policies areas. To achieve this goal, EURO-HEALTHY has built and the allocation of resources and variations tools with the capacity to synthesise evidence for policy amongst them are deemed as inequitable and thus actions to address identified health inequalities and recognised as injustices [20]. In particular, WHO [3, inequities across Europe [32]. 21] has acknowledged the impact of social determi- From the outset, the project relied upon the collabor- nants on health equity and well-being, not only by ation of 15 multidisciplinary European institutions and tackling personal behaviours but also by addressing involved nearly 100 stakeholders in the development and life course stages, the broader society and the wider application of a sound multi-disciplinary and trans- macro-level context, along with governance, delivery disciplinary approach to appraise population health and monitoring systems. across EU regions. The structure of the project was Santana et al. Health Research Policy and Systems (2020) 18:18 Page 3 of 14 based on eight Work Packages (WPs) (Fig. 1), with six the 28 EU Member States [32] and to selected metro- WPs being directly dedicated to the scientific work and politan areas [33]. two WPs to project coordination (WP1) and To enlighten the discussion on policies to promote dissemination (WP8). Thematic WPs (WP2, 3 and 4) health equity in Europe, the PHI model was used as the conducted research to provide evidence and data on starting point to develop the EURO-HEALTHY Scenar- health outcomes and health determinants, including ios for Population Health Inequalities in Europe for socioeconomic, health behaviours and lifestyle determi- 2030 [32]. The rationale for developing the scenarios nants of health and well-being factors (WP2), environ- was two-fold – policy-makers not only need reliable mental public health risks (WP3), and healthcare access tools to help them holistically evaluate the policies’ ben- and mortality profiles (WP4). WP6 and WP7 were efits in terms of their feasibility and power issues, but ‘cross-cutting’ WPs, with the former focused on design- they must also reflect upon and assess how future events ing and applying innovative, participatory processes and may affect health inequalities and which policies may multi-criteria decision analysis methods to build the produce adverse outcomes [34–36]. Population Health Index (PHI), and the latter on asses- The bridge between academia, policy-makers and civil sing policies and good practices in health inequalities. society was promoted by the EURO-HEALTHY consor- WP5 integrated evidence and data, engaging all the Con- tium in two main forms – by using large-scale partici- sortium partners, including other stakeholders, in the patory approaches in the development of the EURO- construction and application of the PHI to 269 European HEALTHY PHI and of the EURO-HEALTHY scenar- regions and to 9 selected metropolitan areas. ios, and by making mapping tools available for the dis- The flagship tool is the EURO-HEALTHY PHI, a semination of the results of applying the PHI to multi-dimensional and multi-level index measure European regions amongst policy-makers and a wider which, from the lens of health equity, evaluates EU audience, including civil society. Recognising that the population health in two Determinants and Outcomes Geographical Information Systems (GIS) facilitate both components. The PHI is further divided into sub- the compilation, analysis and dissemination of large indices by area of concern, health dimension and indica- and multi-dimensional information sets [37], the tors, and is designed to be applied across all regions of project has developed a WebGIS platform called Fig. 1 Structure of the EURO-HEALTHY project: Work Packages Santana et al. Health Research Policy and Systems (2020) 18:18 Page 4 of 14 healthyregionseurope (https://healthyregionseurope.uc. Table 1 Composition and characteristics of the panel of experts pt). This online tool allows for the space–time analysis, involved in the construction of the Population Health Index monitoring and comparison of population health Characteristic No. (current and future) at 269 NUTS 2 European regions, Total 81 and for 9 selected metropolitan areas (Athens, Barce- Gender lona, Berlin, Brussels, Lisbon, London, Prague, Male 37 Stockholm and Turin). Female 44 Data and methods Type of panellist The project collected and systematised a vast amount of Expert 51 data for 80 indicators chosen for their relevance in asses- Stakeholder 30 sing population health in Europe at three geographical Field of expertise levels – national (countries), regional (NUTS 2) and Economics and health systems 9 metropolitan (for selected municipalities) – and for the Environmental health, ecological systems, sustainability 15 years 2000–2015. These data were aggregated in an on- line platform accessible at https://eurohealthydata.uc.pt Epidemiology, social medicine and public health 29 that stores a wide range of data collected during the pro- Health geography, demography and sociology 28 ject and available for consultation and download. It can Region of Europe support researchers in their data analyses by facilitating Northern Europe 16 access to a database with multiple indicators to investi- Western Europe 18 gate further inequalities and inequities in health across Eastern Europe 12 Europe. Southern Europe 35 The construction of EURO-HEALTHY PHI The PHI structure is based on a multi-criteria model that was built with the MACBETH (Measuring Attract- by Health Determinants and Health Outcomes indices. iveness by a Categorical Based Evaluation Technique) These indices are divided into ten sub-indices, corre- socio-technical approach [38] and makes use of Multi- sponding to the main areas of concern for population Criteria Decision Analysis and value measurement health, namely (1) economic conditions, social protec- concepts, integrating the technical elements of a multi- tion and security, (2) education, (3) demographic change, criteria value model and the social elements of interdis- (4) lifestyle and health behaviours, (5) physical environ- ciplinary and participatory processes [39]. ment, (6) built environment, (7) road safety, (8) health- The set of indicators used within the PHI was in- care resources and expenditure, (9) healthcare formed via a participatory process in which experts performance, and (10) health outcomes. These sub- and other stakeholders accessed updated scientific evi- indices are further divided into health dimensions and dence and judged the relevance of indicators identi- indicators. Each area of concern integrates a set of popu- fied by the Consortium members within the WPs to lation health dimensions which are independent evalu- appraise population health at the EU regional level ation axes for appraising population health and are [15]. A multidisciplinary group of 81 experts was in- made operational by one or more indicators (Table 2). volved in the participatory processes, from the struc- More details on the methodology applied to build turing of the PHI multicriteria model (areas of the PHI are given in Bana e Costa et al. [39] and concern, dimensions and indicators) to the evaluating Santana et al. [40]. phases (weights, value functions). These 51 Consor- tium members and 30 other stakeholders reflected EURO-HEALTHY scenarios not only a wide range of expertise but also Europe’s Technically, the approach to building EURO- diversity [15], as the group comprised representatives HEALTHY scenarios required a process that would cre- of national, regional and local authorities, advisors ate such scenario structures to include a large number and technicians, international bodies, political parties, of drivers and combine a group elicitation method with healthcare professionals, and urban planners (Table 1). the Extreme-World method [41, 42]. The social side of The participatory processes are detailed below in the the approach required a combination of face-to-face section describing stakeholders’ involvement. and non-face-to-face participatory processes to obtain Presenting a bottom-up hierarchical structure, the PHI the views and opinions of health stakeholders and provides an evidence-based approach to analyse inequal- experts. A Web-Delphi process was conducted with a ities within a chained sub-index structure (Fig. 2) headed large and diverse panel of experts and other Santana et al. Health Research Policy and Systems (2020) 18:18 Page 5 of 14 Fig. 2 Population Health Index structure (entailing Components, population health areas of concern, health dimensions and respective weights) stakeholders and was established to generate ideas for Evaluating and selecting urban policies under EURO- potential causes of future changes as well as to inform HEALTHY scenarios which drivers were relevant for future population The multi-methodology uses Multi-Criteria Decision health inequalities (departing from current health in- Analysis to appraise policies on a common basis and, equalities across European regions). Drivers identified within the case study, was used to engage 33 local stake- by the panellists were complemented with future- holders in a series of face-to-face workshops to identify oriented evidence and organised under the PESTLE cat- critical situations for equity in the Municipality of egories (Political, Economic, Social, Technological, Lisbon. In this process, a strategic group of 16 stake- Legal and Environmental). This set of data was the holders (policy-makers, health professionals, urban plan- starting point from which a strategic small group ners, social security practitioners, representatives of civil worked to organise the drivers into three scenario society) was engaged in a 2-day decision conferencing structures in a (face-to-face) workshop process format. process to evaluate the potential of a set of policies to The process was finalised by developing scenario narra- improve overall health and reduce health inequities. The tives for the three plausible scenario structures [42]. impact of policies was valued according to their contri- The detailed description of this process is available in bution to priority intervention axes corresponding to Alvarenga et al. [43]. eight of the above-mentioned PHI areas of concern and Santana et al. Health Research Policy and Systems (2020) 18:18 Page 6 of 14 Table 2 List of indicators included in the EURO-HEALTHY Population Health Index model, grouped by population health area of concern and health dimension Population health area of concern Health dimension Indicators Economic conditions, social Employment Unemployment rate (%) protection and security Long-term unemployment rate – 12 months and more (%) Income and living conditions Disposable income of private households per capita (Euro per inhabitant) People at risk of poverty or social exclusion (%) Disposable income ratio – S80/S20 (ratio) Social protection Expenditure on care for the elderly (% of GDP) Security Crimes recorded by the police (per 100,000 inhabitants) Education Education Population aged 25–64 with upper secondary or tertiary education attainment (%) Early leavers from education and training (%) Demographic change Ageing At risk of poverty rate of older people – aged 65 years and over (%) Ageing index (ratio) Lifestyle and health behaviours Lifestyle and health behaviours Adults who are obese (%) Daily smokers – aged 15 and over (%) Pure alcohol consumption – aged 15 and over (litres per capita) Live births by mothers under the age of 20 (%) Physical environment Pollution Annual mean of the daily PM2.5 concentrations (μg/m3) Annual mean of the daily PM10 concentrations (μg/m3) Greenhouse gases (total tonnes of CO2 eq. emissions per capita) Population exposed to traffic noise – (Lden 55–59 db, during day) (%)a a Extreme weather events Population affected by flooding (per 1,000,000 inhabitants)a Built environment Housing conditions Average number of rooms per person Households without indoor flushing toilet (%) Households without central heating (%) Water and sanitation Population connected to public water supply (%) Population connected to wastewater treatment plants (%) Waste management Recycling rate of municipal waste (%) a Land use Population density (inhabitants/km2)a Road safety Road safety Victims in road accidents – injured and killed (per 100,000 inhabitants) Fatality rate due to road traffic accidents (per 1000 victims) Healthcare resources and Healthcare resources Medical doctors (per 100,000 inhabitants) expenditure Health personnel – nurses and midwives, dentists, pharmacists and physiotherapists (per 100,000 inhabitants) Healthcare expenditure Total health expenditure (PPS$ per capita) Private households’ out-of-pocket expenses on health (% of total health expenditure) Public expenditure on health (PPS$ per capita) Healthcare performance Healthcare performance Hospital discharges due to diabetes, hypertension and asthma (per 100,000 inhabitants) Amenable deaths due to healthcare (standardised death rate per 100,000 inhabitants) Health outcomes Length of life (mortality) Life expectancy at birth (years) Infant mortality (per 1000 live births) Preventable deaths (standardised death rate per 100,000 inhabitants) Quality of life (morbidity) Self-perceived health less than good (%) Age-standardized disability-adjusted life year rate (per 100,000 inhabitants) Low birth weight (%) Lden day–evening–night noise level, PPS purchasing power standards a Dimensions and indicators included in the Population Health Index model (conceptual) but not used in its application (adjusted) to the 269 NUTS 2 regions, due to lack of data Santana et al. Health Research Policy and Systems (2020) 18:18 Page 7 of 14 to the comparative added value of improving each of The EURO-HEALTHY PHI was applied to analyse these intervention axes. The feasibility of policies was population health and identify geographical health assessed in light of the EURO-HEALTHY scenarios. inequalities in 269 NUTS 2 level regions, 9 selected metropolitan areas (Athens, Barcelona, Berlin, WebGIS – healthyregionseurope Brussels, Lisbon, London, Prague, Stockholm and EURO-HEALTHY developed an open access user-friendly Turin) and in 2 case studies (Lisbon and Turin). The WebGIS platform (http://healthyregionseurope.uc.pt) to PHI outputs offer different ways of looking at health provide both data and a snapshot of European population inequalities in Europe. For instance, they provide op- health over multiple dimensions and geographical scales – portunities to analyse how European Structural and at the regional level, for 269 NUTS 2 regions of the 28 EU Investment Funds can be used to reduce population countries, and at the municipal level, for 540 municipal- health inequalities and enable policy dialogue at na- ities of 9 selected metropolitan areas (Athens, Barcelona, tional and regional levels [45]. In addition, when the Berlin, Brussels, Lisbon, London, Prague, Stockholm and PHI is regularly updated, it becomes a valuable re- Turin). source for policy monitoring and evaluation. The platform was built to serve not only as a visualisa- In the interest of developing foresight tools for policy tion tool, but also to enable the exploration and analysis improvement, a key goal of the project was to build of geographical patterns of the PHI value-scores (ranging EURO-HEALTHY scenarios to inform potential drivers for each indicator from 0 to 100, with 0 representing the with a means to affect future health and health inequal- lowest level of population health and 100 the highest ities across European regions and to understand the level of population health) for the year 2014 (Fig. 3). extent to which future evolutions can be triggered by today’s actions [46]. Following these objectives, EURO- Results HEALTHY produced health inequalities scenarios for The EURO-HEALTHY PHI can be read as a ‘composite 2030 through a novel socio-technical approach that measure’, ‘composite index’ or ‘composite indicator’ combined present and future-oriented evidence with the based on a clear conceptual framework, integrating the perspectives and values from a diverse group of experts different elements of population health and capable of and other stakeholders across Europe within a struc- considering multiple aspects delivered as an aggregated tured process. score. The EURO-HEALTHY project sustained the Accordingly, three EURO-HEALTHY scenarios for added-value of developing such a holistic measure, inte- 2030 were produced [42] – (1) the ‘Failing Europe’ grating indicators from multiple health dimensions scenario as a worst-case (although plausible) scenario, across EU countries, regions and selected metropolitan including all driver configurations leading to enlarging areas [32]. the population health inequalities; (2) the ‘Sustainable The PHI follows a population health approach as de- Prosperity’ scenario based on the best-case scenario scribed and popularised by Kindig and Stoddart [44], structure, including all configurations leading to decreas- taking the view that population health should be ing the population health inequalities; and (3) the ‘Being measured considering the “health outcomes and their Stuck’ scenario, which reflects a situation where the EU distribution within a population, the patterns of determi- and EU Member States maintain the status quo and are nants that influence such outcomes, and the policies that ‘stuck’ with those specific difficulties that keep Europe influence the optimal balance of determinants”. The from moving forward such as generating new consensus underlying assumption focuses on improving the health or common policies. of the population rather than that of individuals, and on Two scenarios – ‘Sustainable Prosperity’ and ‘Failing reducing health inequalities through actions that target Europe’ – were tested in a real exercise conducted the determinants of health. within the scope of the Lisbon case study to formulate In line with this, the EURO-HEALTHY PHI was de- and assess policies to improve urban health inequalities, signed as a comprehensive tool to enable evidence-based which we further describe. policy-making by (1) allowing for the measuring and Aligned with the methods used to build the PHI monitoring of the overall health and well-being of re- and scenarios, EURO-HEALTHY developed a trans- gional European populations; (2) accounting for the parent multi-methodology to inform policy appraisal multi-dimensional nature of health determinants; (3) and selection. Underlying the multi-methodology is foreseeing and discussing the impact of multi-level (1) the recognition of the importance of policy- policies that can influence population health and geo- makers and other stakeholders’ engagement in the graphical health inequalities in Europe; and (4) providing policy appraisal process; (2) the need for policy selec- a basis for multilevel policy dialogue on health and tion accounting for policy feasibility and other imple- health equity. mentation issues; (3) the acknowledgment that Santana et al. Health Research Policy and Systems (2020) 18:18 Page 8 of 14 Fig. 3 (See legend on next page.) Santana et al. Health Research Policy and Systems (2020) 18:18 Page 9 of 14 (See figure on previous page.) Fig. 3 Screenshots from the WebGIS healthyregionseurope. Note: Map a shows the tab view for the Population Health Index (PHI) indicators, in terms of performance (illustrative example is presented for the indicator “Annual mean of the daily PM10 concentrations” where the performance of one region – Śląskie, Poland – is shown). Map b shows the tab view for the PHI model, in terms of value-score (illustrative example is presented for the health dimension “Pollution” where the value-scores of two regions – Śląskie, Poland, and Stockholm, Sweden – are being compared). Map c shows the tab for the Simulator (illustrative example is presented for the simulation conducted in the health dimension “Pollution”, where a change of performance on the indicator “Annual mean of the daily PM10 concentrations” generated new value scores; a specific example of value improvement can be seen for the region Śląskie – Poland) foreseeing the impact of policies involves uncertainty These WebGIS platform functionalities support the and that scenarios are a tool to deal with uncertainty transfer of knowledge from scientists to policy-makers in the policy appraisal process; (4) the recognition and to the public in general, as it communicates funda- that health public policies aim to achieve multiple ob- mental information in a user-friendly way to help those jectives, including maximising health gains and redu- concerned with understanding and reducing health gaps cing inequalities; and (5) the acknowledgment that in Europe. indices are powerful tools for assessing the impact of The WebGIS is a web database platform that was specif- policies. ically designed to support researchers in data analysis and The application of this multi-methodology to evaluate to investigate inequalities and inequities in health across local policies in the Lisbon case study was successful in EU regions. The complementary eurohealthydata plat- illustrating, firstly, which policies have higher potential form is accessible at https://eurohealthydata.uc.pt and is to improve overall health and, secondly, how the local password protected (passwords assigned upon request via policies’ feasibility can change according to different the website) and aggregates 80 indicators collected at 3 European scenarios. Specifically, European scenarios geographical levels – national (countries), regional (NUTS have been shown to have an important impact on the 2) and metropolitan (municipalities) – for the period capacity of the Lisbon municipality to implement certain 2010–2015. This web-based data platform has the capacity policies and to raise awareness of those individuals to store and download all data (metadata, alphanumerical involved in the decision-making process as to the and geographical data) collected in the project. Both plat- policies’ risks and opportunities [47]. Furthermore, the forms were built using open source software. participatory component of the multi-methodology dem- onstrated how interaction between local stakeholders Discussion can be facilitated given the emphasis on the different From its inception, the project was conceived to repre- perspectives on health equity and its indicators within sent widely shared principles of the EU with respect to structured and transparent formats. economic, social and territorial cohesion as a way to The WebGIS offers a set of dynamic features for promote the well-being of all EU citizens. The produc- users exploring population health in Europe (1) analys- tion of reliable evidence to inform the European efforts ing and comparing the regional performances and to obtain these values provide the basis of the research value-scores in an aggregated and disaggregated way, activities. We herein emphasise the key features and the following a value tree index structure (with sub-indices potential for learning from EURO-HEALTHY research. for components, areas of concern, dimensions and indi- cators) (Fig. 3, maps a and b), and (2) simulating Evidence and tools supporting policy-makers and changes in the performances of indicators (as if consid- stakeholders to address health inequalities on multiple ering the impact of policies) to understand the potential scales effect of policies on the regional population health The information generated by the PHI not only allows scores and thus on regional inequalities across Europe. for a deeper understanding of which factors influence For instance, the simulator web functionality provides the overall health of a specific EU region or groups of re- evidence on the actual inequalities across EU regions gions, but it can also provide guidance for the evaluation and allows the user to change one or a set of indicators’ and selection of policies with the greatest potential to performances, considering different policy strategies address inequalities between regions. (e.g. reducing the levels of PM10 of all regions whose The regional population health scores can be analysed current performances are above 20 μg/m3 to the refer- in various forms, e.g. by grouping regions with a similar ence value of 20 μg/m3) and generating new population profile in terms of performance in a specific dimension health scores (Fig. 3, map c). In addition, maps show or indicator (clusters), by measuring statistically signifi- the regions and countries that benefit from a given pol- cant differences between groups of regions (e.g. by icy or from a package of policies. categories of regions eligible to receive Cohesion Policy Santana et al. Health Research Policy and Systems (2020) 18:18 Page 10 of 14 funds) and by identifying which dimensions constitute inequalities between regions and across multiple areas for improvement across all EU regions. By utilising dimensions. the Health Determinants Index and its dimensional sub- EURO-HEALTHY considered that maps displaying indices, policy-makers at different levels of decision- health information in a simple, clear way are important making (European, national and regional) are able to analytic tools for decision-makers in their efforts to sup- examine the relationships between health determinants, port planning, develop equitable policy and assess equity health outcomes and policies from different sectors. The [49]. GIS have become tools of considerable usefulness PHI works as a basis for understanding which policies [50] in monitoring diseases [51], detecting the differ- (from different sectors and competing for resources) ences and similarities in population health [52], and in have the highest potential to improve health (e.g. in a enabling overall understanding of health distribution in specific region or group of regions of the same country) the population [53] and of the relationship between and to decrease health inequalities (e.g. between regions health and space [54, 55]. The adoption of a place-based of the same country or between EU regions). Further approach and a multi-methodology (including scenarios) analysis can also be done on what policies have contrib- to evaluate local policies in the Lisbon case study uted to the current population health scores in selected unlocked the potential for transformative change in the regions. way of formulating and evaluating policies across sectors From the perspective of policy evaluation, the PHI that go beyond the health sector. This experience could multicriteria model can be used both as an instru- be transferred to other cities with the necessary adjust- ment to design and select policies on an ex ante basis ments in terms of panel formation, indicators and the as well as ex post, monitoring the impact of policies policies at stake. across time and to perform cost-effectiveness analyses. This would require a dynamic update of the PHI, Stakeholders’ involvement through large-scale which implies feeding the model with up-to-date data participatory processes and, potentially, the inclusion of new indicators At the heart of the project was the force of continued reflecting novel research and evidence pointing out stakeholder engagement alongside innovative methods other factors linked to local priorities or global to develop the EURO-HEALTHY tools. Over a 3-year trends. It should be noted that the updating of the journey, the project has, in a transdisciplinary way, inte- PHI model offers opportunities but also presents cer- grated more than 150 stakeholders and experts from 15 tain constraints to be overcome, namely those related European countries, with these being involved directly in to data availability in some dimensions. Data gaps at research design from the beginning of the project. This the regional level on several indicators led to some process of sharing ideas, bringing European countries to- adjustments between the conceptual model and the gether, and learning from each other strengthened the model implemented and available via the WebGIS. solidarity of the project’s action. Stakeholders were iden- Issues with availability and quality of the indicator tified by the EURO-HEALTHY consortium partners and data at sub-national levels should be fundamentally selected based on a variety of characteristics, namely (1) addressed in order to obtain the full potential of this their ability to influence policy at various decision- tool [48]. making levels (national, regional and metropolitan), (2) One of the strengths of using the PHI and the their scope for intervention (public sector, private sector methods employed in its construction is the involvement and civil society), (3) their area of work (e.g. environ- of stakeholders and experts whose knowledge is benefi- ment, public health, urban planning, groups at risk), and cial to the robustness and applicability of the results. In- (4) geographic location (meant to reflect Europe’s diver- novative policy design, using the PHI in conjunction sity) [56]. with the EURO-HEALTHY scenarios, can be introduced Stakeholders and researchers were regularly involved to tackle health inequalities across multiple domains and in the project research activities to develop the EURO- levels. Together, these tools inform on current popula- HEALTHY tools, produce knowledge and evidence, and tion health variations in the EU, on the multiple factors mostly build a bridge of communication between scien- underlying those variations (drivers) and on how overall tists and policy/decision-makers [56]. Web-based Delphi health will evolve if those factors are changed positively panels, decision conferences and face-to-face workshops or negatively. were successful formats for interacting with stakeholders The PHI results can identify what the problems are and researchers to collect their insights and views on (1) and where action is most needed. Policy-makers and the relevant indicators to evaluate and monitor regional stakeholders can easily access this information European population health (Web-Delphi for selecting through a WebGIS platform that communicates the indicators for the PHI) [15]; (2) the importance of clos- PHI in a very interactive way, pointing to health ing gaps in the performance of distinct indicators across Santana et al. Health Research Policy and Systems (2020) 18:18 Page 11 of 14 EU regions (Web-Delphi for weights) [39]; (3) added gathered and utilised in one measure, the PHI facilitates value to population health from improving performance the joint learning process, encourages conversation and along the indicator range (Web-Delphi for value func- dialogue, and supports the identification of relevant part- tions) [39]; (4) drivers for future population health ners (regions) tackling similar problems. In this context, inequalities scenarios (Web-Delphi for scenario building) the evidence provided by the project, and specifically by and for population health inequalities scenarios in Eur- the PHI, has the potential to create windows of oppor- ope (workshops) [42]; and (5) formulation and evalu- tunity, ones which could evolve into the establishment ation of policies with the highest potential to improve of ‘learning networks’ – mechanisms supporting in- population health and to reduce inequities at the urban formed and localised actions on multiple health determi- level, namely in the Lisbon and Turin case studies nants to mitigate the unjust inequalities in a multi- (workshop and decision conferences) [35]. dimensional environment. This process would assist in EURO-HEALTHY web-based Delphi participatory advocating, stimulating and facilitating those actions that processes have proved to be a convenient and time- can promote equity in European health through promot- efficient method for multi-level stakeholders. This has ing knowledge transfer between scientists and decision- also been confirmed in other health-related studies [14] makers. The creation of these ‘learning networks’, and has served to generate useful information to help whether between regions and/or cities, would highly smaller groups in the construction of the EURO- benefit from EU funding, namely via European Struc- HEALTHY PHI model and EURO-HEALTHY scenarios, tural Investments Funds, which provide the adequate and in the evaluation of policies. framework to boost synergies and implement such The involvement of a transdisciplinary panel of re- regional networking. This would create greater oppor- searchers and stakeholders in the process of shaping the tunities for regional convergence and for documented EURO-HEALTHY tools added diverse points of view, good practices with the potential to improve other low- which consequently validated the holistic perspective of performing localities. This value takes on significant looking at health. This approach was crucial in securing meaning in decision-making and decision support to im- sustainable engagement from stakeholders in the project prove equity and facilitate successful action. activities, enhancing their understanding of the drivers of health inequalities in Europe, and strengthening their will, interest and power to promote change via their fu- Further research ture decisions [15]. It served as a catalyst for an ex- Further research on developing technological platforms tended dialogue as to which policies produce the highest for expert and stakeholder engagement should be benefit in promoting more equitable and healthy envi- emphasised given the relevance of considering different ronments at different levels (national, regional and points of view as well as disseminating scientific evi- local), while bearing in mind the impact that different dence and reliable data. In this context, the Web-Delphi policies can have on population health and health equity participatory processes applied in the EURO-HEALTHY [57]. In this sense, the project represented “the science of project have proved to be an inclusive and effective way stakeholder engagement in research” – a term coined by to collect information from an expanded number of geo- Goodman and Thompson ([58], p. 489), where they graphically dispersed experts and stakeholders. Yet, a strongly advocate for enhancing efforts towards mean- certain amount of sensitivity bias related to the compos- ingful stakeholder engagement that will contribute to ition of the panels was expected due to the different both research synergy and achieving results that would backgrounds and geographical contexts in which experts otherwise be impossible via isolated actions alone. The and stakeholders live and work. In theory, the experts’ Lisbon and Turin case studies have shown that stake- judgments may not be in line with the context where holder engagement is fundamental to promoting consen- the decision-making process is to be integrated. There- sus and establishing priorities for intervention at the fore, in order to reduce the expert’s bias in future stud- local scale to reduce unjust health inequalities in trans- ies, this issue should be addressed. parent formats [32]. The development and application of the PHI model at other geographic levels (e.g. local) and settings (e.g. ‘Learning networks’ to reduce health inequities across North America, Africa) requires further research on Europe adjusting the model to the specific contexts. Conducting It is a stated challenge for the EU to take action centrally participatory processes with local panels of stakeholders to address the highly localised, spatially patterned health and other experts, for example, by defining adequate in- issues that are often heavily shaped by local and regional dicators that reflect the realities of local conditions and circumstances. Featuring broad-ranging data on a variety the respective weights and value functions, are consid- of countries, regions and metropolitan areas, and ered imperative. Santana et al. Health Research Policy and Systems (2020) 18:18 Page 12 of 14 The EURO-HEALTHY PHI may also serve as a start- At the same time, the data and evidence that was gen- ing point and useful tool to initiate a dialogue with local erated to feed these tools provide opportunities for com- stakeholders and policy-makers about which priority parability across different geographical scales, which is intervention fields each locality should address in the an appeal to reinforce efforts to collect, harmonise and interest of improving overall population health. This use data, namely at sub-national level. would be possible by further extending the evaluation of The EURO-HEALTHY tools should be used, particu- policies conducted in the Lisbon and Turin case studies larly by policy-makers, to support decision-making in re- to other case studies and by creating tools and know- lation to those policies with the highest potential to ledge to integrate policy evaluation with uncertainty and improve health equity in Europe and to further monitor scenarios. the impact of the policies of today as well as those of the The EURO-HEALTHY tools were built under the future. points of view and perspectives of a panel of researchers and stakeholders taking into account the available data Abbreviations EU: European Union; EURO-HEALTHY: Shaping EUROpean policies to at the present time. In the future, it is absolutely neces- promote HEALTH equitY; GIS: Geographic Information System; NUTS sary to continuously monitor data on health determi- 2: Nomenclature of Territorial Units for Statistics level 2; PHI: Population nants and health outcomes in order to update the Health Index; WPs: Work Packages models of PHI and scenarios. The set of relevant indica- Acknowledgments tors and respective weights could then change, reflecting The authors would like to acknowledge the significant contribution and alterations driven by policies or new societal challenges. insightful comments made throughout the project by the Project Advisory Explicit attention should be given to social, economic Board members (Alec Morton, Patricia O’Campo, Pedro Pita Barros and Anne Diez-Roux), who guaranteed the quality and success of the project. Special and environmental determinants that shape health thanks are also extended to the panels of experts and the stakeholders in- equity. These indicators are fundamental to inform volved in several participatory processes of the project. Finally, we would like policy and monitor its effectiveness. Currently, despite to thank João Bana e Costa, Luís Monteiro, Joaquim Patriarca and João Veiga for developing the web-based platforms used in the project activities. significant advances in data harmonisation at the EU EURO-HEALTHY investigators: level (e.g. Eurostat), there is still room for improved data Carlota Quintal, João Malva, Lúcio Cunha, Paulo Nossa, Ricardo Almendra, Rui to monitor and assess specific health determinants [48] Gama Fernandes (UC – University of Coimbra, Portugal); Laia Palència, LLuís Camprubí, Maica Rodríguez-Sanz, Mercè Gotsens (ASPB – Agència de Salut that shape health outcomes at different geographical Pública de Barcelona, Spain); Sotiris Vardoulakis, Clare Heaviside (PHE – Public levels in a comparable way [59]. The benefit of using the Health England, United Kingdom); Quentin Tenailleau, Clara Squiban (UPO – PHI in the future will be accentuated if there is greater Paris Nanterre University, France); António Alvarenga, Liliana Freitas, Paulo Correia (IST-UL – Instituto Superior Técnico, Universidade de Lisboa, alignment of the data collected from different data Portugal); Céline Ledoux, Eva Pilot, Thomas Krafft (UM – Maastricht University, sources (in the current PHI not all data were available at The Netherlands); Hynek Pikhart, Joana Morrison (UCL – University College the same time and spatial resolution). Efforts should be London, United Kingdom); Conrad Franke (BHT – Beuth University of Applied Sciences Berlin, Germany); Bo Burström (Karolinska Institute, Sweden); intensified to collect and harmonise data at the sub- Evangelia Samoli, Klea Katsouyanni, Sophia Rodopoulou (UoA – National and national level with the goal of narrowing the gaps in data Kapodistrian University of Athens, Greece); Dagmar Dzúrová, Michala amongst and within the EU Member States [48]. Lustigová (CUP – Charles University, Czechia); Anna Cavallo, Nathalie Coué (CSI-Piemonte – Information System Consortium, Italy); Lucia Bosáková, Michal Tkáč (EUBA – University of Economics in Bratislava, Slovakia); Hadewijch Vandenheede, Patrick Deboosere (VUB – Vrije Universiteit Brussel, Conclusions Belgium), Giuseppe Costa, Nicolás Zengarini (ASL-TO3 – Local Public Health The EURO-HEALTHY project achieved its goal in pro- Agency Torino 3, Italy). viding a comprehensive evaluation of health inequalities Authors’ contributions across Europe and ultimately incorporating innovative PS was the EURO-HEALTHY project leader and directed the study; PS, AF, IS ways of supporting the analysis and selection of policies and CC drafted the manuscript, prepared the tables and figures, and revised with the potential to shape healthier settings. The pro- it critically based on suggestions from other investigators. CBC directed Work Package 6, was responsible for the decision support for multicriteria model- ject has delivered tools – mostly based on the PHI – ling of the Population Health Index and led the evaluation, foresight and se- which are scientifically and methodologically robust and lection of policies. MO, TCR and AV worked in the design and application of innovative and yet simple and user-friendly. The open- multicriteria decision analysis methods to build the Population Health Index, to evaluate policies and develop scenarios. PLF conducted analysis within access WebGIS platform offers an easy and interactive Work Package 5 (Population Health Index) and participated as expert in all way to visualise and analyse the population health varia- participatory processes. CB and MM conducted research to provide evidence tions across EU regions and metropolitan areas in a way and data regarding health outcomes and socioeconomic, health behaviour and lifestyle determinants of health (Work Package 2) and participated as ex- that can enable more informed health advocacy across perts in all participatory processes. SD and CM conducted research on envir- Europe. The highly participatory component proved to onmental public health risks (Work Package 3) and participated as experts in be paramount for integrating multiple views and facili- all participatory processes. SR conducted research on healthcare access and mortality profiles (Work Package 4) and participated as an expert in all partici- tating information exchange amongst the involved stake- patory processes. JS and DC participated as experts in all participatory pro- holders and experts. cesses. All authors contributed to scientific discussions of methodology, Santana et al. Health Research Policy and Systems (2020) 18:18 Page 13 of 14 analyses and interpretation. All authors have read and approved the final 5. Burgard SA, Lin KY. Bad jobs, bad health? How work and working manuscript. conditions contribute to health disparities. Am Behav Sci. 2013;57(8). https:// doi.org/10.1177/0002764213487347. Funding 6. Barnay T. Health, work and working conditions: a review of the European The EURO-HEALTHY project (Shaping EUROpean policies to promote HEALTH economic literature, OECD economics department working papers, no. equity) has received funding from the European Union’s Horizon 2020 re- 1148. Paris: OECD Publishing; 2014. search and innovation programme under Grant Agreement No. 643398. Add- 7. Bouchard L, Albertini M, Batista R, de Montigny J. Research on health itionally, this study was supported by the Centre of Studies in Geography inequalities: a bibliometric analysis (1966–2014). Soc Sci Med. 2015;141:100–8. and Spatial Planning (CEGOT), funded by national funds through the Founda- 8. Spinakis A, Anastasiou G, Panousis V, Spiliopoulos K, Palaiologou S, tion for Science and Technology (FCT) under the reference UID/GEO/04084/ Yfantopoulos J, et al. Expert review and proposals for measurement of 2019. The authors Angela Freitas and Cláudia Costa are recipients of Individ- health inequalities in the European Union: full report. 2011. https://ec. ual Doctoral Fellowships funded by national funds through the Foundation europa.eu/health/sites/health/files/social_determinants/docs/sum_quantos_ for Science and Technology (FCT), under the references SFRH/BD/123091/ en.pdf. Accessed 4 Feb 2020. 2016 and SFRH/BD/132218/2017, respectively. 9. Mathers CD, Murray CJ, Ezzati M, Gakidou E, Salomon JA, Stein C. Population health metrics: crucial inputs to the development of evidence for health policy. Popul Health Metr. 2003;1:6. Availability of data and materials The datasets generated and analysed during the current study are stored in 10. European Commission. Health Inequalities in the EU. Final Report of a Consortium. Consortium Lead: Sir Michael Marmot. 2013. Brussels: European the eurohealthydata repository (https://eurohealthydata.uc.pt) and are available from the corresponding author upon reasonable request. Commission Directorate-General for Health and Consumers. https://op. europa.eu/en/publication-detail/-/publication/e3d84056-2c24-4bd3-92db-2 cb71a0d0bc4/language-en. Accessed 20 May 2019. Ethics approval and consent to participate 11. Mackenbach J. Health inequalities: Europe in Profile. Rotterdam; 2006. EURO-HEALTHY participatory processes were approved by the Centre for https://gateway.euro.who.int/en/health-inequalities-europe-in-profile/. Social Studies Ethics Commission (CE-CES) of the University of Coimbra. All Accessed 4 Feb 2020 participants were invited and informed as to the objective, scope and 12. European Commission. Commission Staff Working Document. Report on research design, and the uses of data within the scope of the EURO- health inequalities in the European Union (SWD(2013) 328 final). 2013. HEALTHY project. In addition, they provided written consent to participate Brussels: Commission of the European Communities. https://ec.europa.eu/ via email before being enrolled in the participatory processes. health/social_determinants/overview_en. Accessed 20 May 2019. 13. Marmot M. Social determinants of health inequalities. Lancet. 2005; Consent for publication 365(9464):1099–104. Not applicable. The manuscript does not include details relating to an 14. Santana P, Freitas A, Costa C, Vaz A. Εvaluating population health: the individual person. selection of main dimensions and indicators through a participatory approach. Eur J Geogr. 2015;6(1):51–63. Competing interests 15. Freitas A, Santana P, Oliveira MD, Almendra R, Bana Costa JC, Bana Costa The authors declare that they have no competing interests. CA. Indicators for evaluating European population health: a Delphi selection process. BMC Public Health. 2018;18:557. Author details 16. Kindig D, Booske B, Remington PL. Mobilizing Action Toward Community 1 Department of Geography and Tourism, Faculty of Arts and Humanities, Health (MATCH): metrics, incentives, and partnerships for population health. University of Coimbra, Colégio S. Jerónimo, Largo D. Dinis, 3001-401 Prev Chronic Dis. 2010;7(4):A68. Coimbra, Portugal. 2CEGOT-UC, Centre of Studies in Geography and 17. Braveman PA. Monitoring equity in health and healthcare: a conceptual Territorial Planning, University of Coimbra, Coimbra, Portugal. 3CEG-IST, framework. J Health Popul Nutr. 2003;21(3):181–92. Centre for Management Studies of Instituto Superior Técnico, Universidade 18. Carr-Hill R, Chalmers-Dixon P. In: Lin J, editor. The public health observatory de Lisboa, Avenida Rovisco Pais, 1049-001 Lisbon, Portugal. 4CEISUC, Center handbook of health inequalities measurement; 2005. http://nccdh.ca/ for Health Studies and Research, Faculty of Economics, University of Coimbra, resources/entry/the-public-health-observatory-handbook-of-health- Coimbra, Portugal. 5ASPB, Agència de Salut Pública de Barcelona, Barcelona, inequalities-measurement. Accessed 4 Feb 2020. Spain. 6CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain. 19. Braveman P. What are health disparities and health equity? We need to be 7 Institut d’Investigació Biomèdica (IIB Sant Pau), Barcelona, Spain. 8PHE-CRCE, clear. Public Health Rep. 2014;129(Suppl. 2):5–8. Centre for Radiation, Chemical and Environmental Hazards, Public Health 20. Whitehead M. The concepts and principles of equity and health. Int J England, Didcot, OX11 0RQ, United Kingdom. 9LAboratoire DYnamiques Health Serv. 1992;22(3):429–45. Sociales et Recomposition des espaceS (LADYSS), Paris Nanterre University, 21. Marmot M. Report on social determinants of health and the health divide in Paris, France. 10Beuth University of Applied Sciences Berlin (BHT), Berlin, the WHO European region. Updated reprint 2014. Copenhagen: WHO Germany. 11Karolinska Institute (KI), Stockholm, Sweden. Regional Office; 2013. 22. Kickbusch I. The contribution of the World Health Organization to a new Received: 19 June 2019 Accepted: 15 January 2020 public health and health promotion. Am J Public Health. 2003;93(3):383–8. 23. Whitehead M, Dahlgren G. Concepts and principles for tackling social inequities in health: levelling up part 1. Copenhagen: WHO Regional Office References for Europe; 2006. 1. Marmot M, Allen JJ. Social determinants of health equity. Am J Public 24. Dahlgren G, Whitehead M. European strategies for tackling social inequities Health. 2014;104(Suppl. 4):S517–9. in health: levelling up part 2. Copenhagen: WHO Regional Office for Europe; 2. Marmot M, Allen J, Bell R, Bloomer E, Goldbatt P, Consortium for the 2006. European Review of Social Determinants of Health and the Health Divide. 25. Etches V, Frank J, Di Ruggiero E, Manuel D. Measuring population health: a WHO European review of social determinants of health and the health review of indicators. Annu Rev Public Health. 2006;27:29–55. divide. Lancet. 2012;380:1011–29. 26. Webster P, Sanderson D. Healthy cities indicators—a suitable instrument to 3. Commission on Social Determinants of Health. Closing the gap in a measure health? J Urban Health. 2013;90(Suppl. 1):52–61. generation: health equity through action on the social determinants of 27. World Health Organization. Health in all policies. Helsinki statement health. Final report of the Commission on Social Determinants of Health. framework for country action. Geneva: WHO; 2014. Geneva: WHO; 2008. 28. World Health Organization. Declaration of Alma-Ata international 4. WHO-CSDH. Final report of the Commission on Social Determinants of conference on primary health care. Geneva: WHO; 1978. Health. In: Marmot M, editor. Closing the gap in a generation: health equity 29. World Health Organization. The Ottawa charter for health promotion. through action on the social determinants of health. Geneva: World Health Geneva and Ottawa: WHO and Canadian Public Health Association, Health Organization; 2008. p. 246. and Welfare; 1986. Santana et al. Health Research Policy and Systems (2020) 18:18 Page 14 of 14 30. Busse R, Figueras J, Holland W, Le Grand J, Mckee M, Mossialos E, et al. 52. Musa GJ, Chiang P-H, Sylk T, Bavley R, Keating W, Lakew B, et al. Use of GIS Measuring and tackling health inequalities across Europe. Eurohealth. 2009; mapping as a public health tool—from cholera to cancer. Health Serv 15(3) http://www.lse.ac.uk/lse-health/assets/documents/eurohealth/issues/ Insights. 2013;6:111–6. eurohealth-v15n3.pdf. Accessed 4 Feb 2020. 53. Maantay JA, Maroko AR, Herrmann C. Mapping population distribution in 31. Kindig D. Understanding population health terminology. Milbank Q. 2007; the urban environment: the Cadastral-based Expert Dasymetric System 85(1):139–61. (CEDS). Cartogr Geogr Inf Sci. 2007;34(2):77–102. 32. Santana P (Coordination). Promoting population health and equity in 54. Shaw NT. Geographical information systems and health: current state and Europe: from evidence to policy. Coimbra: University of Coimbra; 2017. future directions. Health Inf Res. 2012;18(2):88–96. 33. Costa C, Santana P, Dimitroulopoulou S, Burstrom B, Borrell C, Schweikart J, 55. Fradelos EC, Papathanasiou IV, Mitsi D, Tsaras K, Kleisiaris CF, Kourkouta L. Dzurova D, Zangarini N, Katsouyanni K, Deboseree P, Freitas Â, Mitsakou C, Health based geographic information systems (GIS) and their applications. Samoli E, Vardoulakis S, Mari-Dell’Olmo M, Gotsens M, Lustigova M, Corman Acta Inform Med. 2014;22(6):402–5. D, Costa G. Population Health Inequalities Across and Within European 56. Stefanik I, Freitas A, Doetsch J, Santana P. Involving Key Stakeholders in the Metropolitan Areas through the Lens of the EURO-HEALTHY Population EURO-HEALTHY. In: Santana P, editor. Promoting Population Health and Health Index. International Journal of Environmental Research and Public Equity in Europe: From Evidence to Policy. Coimbra: Coimbra University Health. 2019;16 (5):836. Press; 2017. 34. Moniz AB. Scenario-building methods as a tool for policy analysis. 57. Baum F, Lawless A, Delany T, Macdougall C, Williams C, Broderick D, et al. Innovative comparative methods for policy analysis. Boston: Kluwer Evaluation of health in all policies: concept, theory and application. Health Academic Publishers; 2006. p. 185–209. Promot Int. 2014;29(Suppl. 1):i130–42. 35. Bana e Costa C, Correia P, Freitas L, Oliveira M, Rodrigues T, Vieira A. 58. Goodman MS, Thompson VLS. The science of stakeholder engagement in Devising and testing a novel methodology for the evaluation of policies research: classification, implementation, and evaluation. Soc Behav Med. under European population health scenarios. Santana P (Coord), editor. 2017;7:486–91. Promoting population health and equity in Europe: From evidence to 59. World Health Organization. Action plan to strengthen the use of evidence, policy. Coimbra: University of Coimbra; 2017. 85–87. information and research for policy-making in the WHO European region. 36. Volkery A, Ribeiro T. Scenario planning in public policy: understanding use, Regional Committee for Europe 66th session. Copenhagen: WHO Regional impacts and the role of institutional context factors. Technol Forecast Soc Office for Europe; 2016. Chang. 2009;76:1198–207. 37. Cromley EK, McLafferty S. GIS and public health. New York: The Guilford Press; 2012. p. 503. Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in 38. Bana e Costa CA, De Corte J-M, Vansnick J-C. MACBETH. Int J Inf Technol published maps and institutional affiliations. Decis Mak. 2012;11(2):359–87. 39. Bana e Costa C, Freitas L, Oliveira M, Rodrigues T, Vieira A. Using the MACBETH socio-technical methodological approach to build the EURO- HEALTHY PHI. In: Santana P, editor. Promoting population health and equity in Europe: from evidence to policy. Coimbra: Coimbra University Press; 2017. 40. Santana P, Costa C, Freitas A, Stefanik I, Quintal C, Bana e Costa C. Atlas of population health in European Union regions. Coimbra: Coimbra University Press; 2017. p. 263. 41. Goodwin P, Wright G. Enhancing strategy evaluation in scenario planning: a role for decision analysis. J Manag Stud. 2001;38(1):1–16. 42. Alvarenga A, Bana e Costa C, Freitas L, Oliveira M, Lopes Santos M, Rodrigues T, et al. Building the EURO-HEALTHY scenarios to understand which driving forces may play a role in the evolution of population health inequalities in Europe. In: Santana P, editor. Promoting population health and equity in Europe: from evidence to policy. Coimbra: University of Coimbra; 2017. p. 88–94. 43. Alvarenga A, Bana e Costa CA, Borrell C, Ferreira PL, Freitas Â, Freitas L, et al. Scenarios for population health inequalities in 2030 in Europe: the EURO- HEALTHY project experience. Int J Equity Health. 2019;18(1):100. 44. Kindig D, Stoddart G. What is population health? Am J Public Health. 2003; 93(3):380–3. 45. Pilot E, Corman D, Doreleijers S, Ledoux C, Burstrom B, Michelsen K, et al. European Structural and Investment Funds – a tool with potential to reduce health inequity in Europe? In: Santana P, editor. Promoting population health and equity in Europe: from evidence to policy. Coimbra: Coimbra University Press; 2017. 46. Bana e Costa C, Oliveira M, Vieira A. Decision support for multicriteria modelling of the population health index and evaluation, foresight and selection of policies. In: Santana P, editor. Promoting population health and equity in Europe: from evidence to policy. Coimbra: Coimbra University Press; 2017. 47. Ogilvy J, Schwartz P. Plotting your scenarios. Emeryville: Global Business Network; 2004. 48. Costa C, Freitas Â, Stefanik I, Krafft T, Pilot E, Morrison J, et al. Evaluation of data availability on population health indicators at the regional level across the European Union. Popul Health Metr. 2019;17:11. 49. Braveman P. Monitoring equity in health: a policy-oriented approach in low- and middle-income countries. (WHO/CHS/HSS/98.1). Geneva: WHO; 1998. 50. Gatrell AC, Bailey TC. Interactive spatial data analysis in medical geography. Soc Sci Med. 1996;42(6):843–55. 51. Barrett FA. Finke’s 1792 map of human diseases: the first world disease map? Soc Sci Med. 2000;50(7–8):915–21.