International Journal of Environmental Research and Public Health Article Population Health Inequalities Across and Within European Metropolitan Areas through the Lens of the EURO-HEALTHY Population Health Index Claudia Costa 1, *, Paula Santana 1,2 , Sani Dimitroulopoulou 3 , Bo Burstrom 4 , Carme Borrell 5,6,7,8 , Jürgen Schweikart 9 , Dagmar Dzurova 10 , Nicolás Zangarini 11 , Klea Katsouyanni 12 , Patrick Deboseree 13 , Ângela Freitas 1 , Christina Mitsakou 3 , Evangelia Samoli 12 , Sotiris Vardoulakis 14 , Marc Marí Dell’Olmo 5,6,7 , Mercè Gotsens 5,6,7,8 , Michala Lustigova 10 , Diana Corman 15 and Giuseppe Costa 16 1 Centre of Studies in Geography and Spatial Planning, University of Coimbra, 3004-530 Coimbra, Portugal; paulasantana.coimbra@gmail.com (P.S.); angelafreitas30@gmail.com (Â.F.) 2 Department of Geography and Tourism, University of Coimbra, 3004-530 Coimbra, Portugal 3 Centre for Radiation, Chemical and Environmental Hazards, Public Health England, Chilton OX11 0RQ, Oxon, UK; Sani.Dimitroulopoulou@phe.gov.uk (S.D.); Christina.Mitsakou@phe.gov.uk (C.M.) 4 Karolinska Institutet, Department of Public Health Sciences, Division of Social Medicine, 171 77 Stockholm, Sweden; bo.burstrom@sll.se 5 Agència de Salut Pública de Barcelona, 08023 Barcelona, Spain; cborrell@aspb.cat (C.B.); mmari@aspb.cat (M.M.D.); mgotsens@aspb.cat (M.G.) 6 CIBER Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain 7 Institut d’Investigació Biomèdica (IIB Sant Pau), 08041 Barcelona, Spain 8 Universitat Pompeu Fabra, 08002 Barcelona, Spain 9 Department of Civil Engineering and Geoinformation, Beuth University of Applied Sciences Berlin, 13437 Berlin, Germany; schweikart@beuth-hochschule.de 10 Department of Social Geography and Regional Development, Faculty of Science, Charles University, 128 43 Prague, Czech Republic; dagmar.dzurova@natur.cuni.cz (D.D.); michala.lustigova@gmail.com (M.L.) 11 Department of Public Health and Pediatrics, University of Turin, 10126 Turin, Italy; nicolas.zengarini@epi.piemonte.it 12 Department of Hygiene, Epidemiology and Medical Statistics, University of Athens Medical School, 115 27 Athens, Greece; kkatsouy@med.uoa.gr (K.K.); esamoli@med.uoa.gr (E.S.) 13 Interface Demography, University of Brussels, 1050 Brussels, Belgium; patrick.deboosere@vub.ac.be 14 Institute of Occupational Medicine, Edinburgh EH14 4AP, UK; Sotiris.Vardoulakis@iom-world.org 15 The National Board of Health and Welfare, 106 30 Stockholm, Sweden; diana.corman@socialstyrelsen.se 16 Medical School of the University of Turin, University of Turin, 10124 Turin, Italy; giuseppe.costa@epi.piemonte.it * Correspondence: claudiampcosta@gmail.com  Received: 1 February 2019; Accepted: 2 March 2019; Published: 7 March 2019  Abstract: The different geographical contexts seen in European metropolitan areas are reflected in the uneven distribution of health risk factors for the population. Accumulating evidence on multiple health determinants point to the importance of individual, social, economic, physical and built environment features, which can be shaped by the local authorities. The complexity of measuring health, which at the same time underscores the level of intra-urban inequalities, calls for integrated and multidimensional approaches. The aim of this study is to analyse inequalities in health determinants and health outcomes across and within nine metropolitan areas: Athens, Barcelona, Berlin-Brandenburg, Brussels, Lisbon, London, Prague, Stockholm and Turin. We use the EURO-HEALTHY Population Health Index (PHI), a tool that measures health in two components: Health Determinants and Health Outcomes. The application of this tool revealed important inequalities between metropolitan areas: Better scores were found in Northern cities when compared with their Southern and Eastern counterparts in both components. The analysis of geographical Int. J. Environ. Res. Public Health 2019, 16, 836; doi:10.3390/ijerph16050836 www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2019, 16, 836 2 of 17 patterns within metropolitan areas showed that there are intra-urban inequalities, and, in most cities, they appear to form spatial clusters. Identifying which urban areas are measurably worse off, in either Health Determinants or Health Outcomes, or both, provides a basis for redirecting local action and for ongoing comparisons with other metropolitan areas. Keywords: Population Health Index; Europe; metropolitan areas; health determinants; health outcomes; municipalities 1. Introduction Health is a critical global development issue, especially in urban areas where the majority of the world’s population lives [1–4]. The United Nations Sustainable Development Goals (SDG) put a focus on health promotion through a number of interconnected health-related targets like SDG-3 (good health and well-being), SDG-10 (reduced inequalities) and SDG-11 (sustainable cities and communities). These goals are achievable through multisectoral approaches, as stated in the New Urban Agenda [5–7]. There is ample evidence that contextual factors related with the social, physical and built urban environments affect health and are key drivers of health inequalities within cities: The access to green spaces and public places, the exposure to air pollution and noise, the access to affordable housing, the opportunity to use public transportation, to walk and bike, among others [6,8–15]. The main factors influencing the overall health of the population are well illustrated by Dahlgren and Whitehead’s model of social determinants of health [16,17], which describes the different layers of influence: Individual lifestyle factors, social and community networks, living working and conditions and general socioeconomic, cultural and environmental conditions in which the population lives. The determinants of health are shaped by individual and political decisions and can be either positive health factors (e.g., economic security, adequate housing), protective (e.g., social support, healthy diet) or risk factors (e.g., pollution, smoking) [17]. The recent study on environmental public health indicators in European urban areas, within the framework of the EURO-HEALTHY project, explored this through the association between the health impacts and the physical and built environmental risks in order to support the prioritisation of interventions that improve public health and reduce avoidable deaths [18]. There is a growing body of evidence showing a strong connection between the characteristics of the place of residence and health outcomes, even after accounting for individual risk factors [8,13,18–25]. High levels of intra-urban inequalities are also visible as a result of the demographic, economic, environmental, and other societal challenges impacting cities, along with a greater population heterogeneity and different level of access to housing, amenities and services [2,10,13,26]. According to the literature, poor and vulnerable groups are often more at risk due to the concentration on disadvantaged and deprived neighbourhoods, usually in the outskirts of the city or in inner city areas [27–30]. The Atlas of Population Health in European Union Regions [31], shows that the capital region of each EU country ussually performs better in health determinants (e.g., economic and social conditions, healthcare resources) than in health outcomes. Capital regions are often affected by increased levels of crime and air pollution with negative impacts on health outcomes. Growing evidence demonstrates an excess on mortality and greatest burden of disease on urban areas compared to non-urban, namely related with lung cancer and cardiovascular disease [32,33], and of greater relative socioeconomic inequalities in mortality in Eastern and Northern European cities, although with variations in their magnitude [34,35]. A deeper understanding of the interconnection between compositional and contextual factors and how they affect health outcomes is required of local decision makers in order to better cope with the complexity of addressing health determinants that goes beyond traditional behavioural change approaches [2,13,20,22,36]. Most of the policy interventions largely concentrate on modifying the Int. J. Environ. Res. Public Health 2019, 16, 836 3 of 17 midstream determinants of health, the intermediate factors, such as individual health behaviours (smoking, physical activity, nutrition) and on targeting vulnerable groups to mitigate the negative impacts of disadvantage on health. Policies and actions need to be directed towards improving fundamental social and economic structures in order to remove barriers and allow people to achieve their full health potential [37–40]. Local governments, especially those from urban areas, occupy a unique leadership position on levelling up policies to tackle determinants of health, working across the upstream, midstream and downstream levels [1,25,40,41]. To effectively address the causes of health inequalities, spatially disaggregated data on different health determinants and better urban health metrics are needed [42,43]. A measure that summarises crucial data provides opportunities to understand the complexity of how much health differs within and across urban areas, given how it offers a comprehensive picture of health and health disparities. Nevertheless, the ability to include meaningful information into a single metric that captures the level of health, the intensity of health determinants, and the extent of disparities, is limited [44]. It was within the scope of the EU-funded project EURO-HEALTHY (shaping European policies to promote health equity) whose aim was to advance the knowledge on policies with the highest potential to promote health equity, that a multidimensional and multilevel index—the Population Health Index (PHI)—was built. This measure evaluates European population health across a wide range of areas of concern, dimensions and indicators of health determinants and health outcomes [31]. Its construction integrates the technical elements of a multi-criteria value model and the social elements of interdisciplinary and participatory processes by collecting the views of experts and stakeholders on which factors are relevant to evaluate health [45] and on how important it is to close the gap between indicators to improve population health [46]. More than 75% of the European population lives in urban areas, thus reflecting and encompassing a diversity of geographies and of physical, social, and economic environments. By using the EURO-HEALTHY Population Health Index (PHI), the aim of this paper was to identify inequalities in health determinants and health outcomes across and within nine European metropolitan areas. 2. Materials and Methods 2.1. Study Area We applied an ecological study to analyse the Health Determinants and Health Outcomes value-scores between and within metropolitan areas, taking 2014 as a reference year. The indicators were collected at two levels defined by EUROSTAT [47]: Local administrative units (LAU) 1, corresponding to small areas, and LAU 2, corresponding to municipalities. The utilisation of both levels is explained by the diversified system of local governments in Europe and the pre-existent request from the EURO-HEALTHY project to employ the PHI to a political subdivision where a local government may implement interventions able to address health inequalities. Each metropolitan area specified different administrative levels, most of them corresponding to municipalities. Additional information on the LAU and delimitation of each metropolitan area is provided in Supplementary Materials S1. The study area corresponds to 328 administrative areas from the nine metropolitan areas located in different geographical regions in Europe: Athens, Greece (40 LAU 1); Barcelona, Spain (23 LAU 2); Berlin-Brandenburg, Germany (23 LAU 1 and 2); Brussels, Belgium (91 LAU 2); Lisbon, Portugal (18 LAU 1); London, United Kingdom (33 LAU 1); Prague, Czechia (25 LAU 1 or 2), Stockholm, Sweden (26 LAU 2) and Turin, Italy (49 LAU 2) (Figure 1). The nine metropolitan areas analysed in this study where selected under the EURO-HEALTHY project framework. These areas represent the different EU geographic zones and populations (Northern, Southern, Central and Eastern Europe) reflecting Europe’s diversity—in terms of contextual conditions (e.g., geographical, historical, political, cultural, social and economic) and impact of the financial and economic crisis (Table 1). Int. J. Environ. Res. Public Health 2019, 16, 836 4 of 17 Int. J. Environ. Res. Public Health 2019, 16, x 4 of 18 Figure1. Figure 1. Location Location of of the the nine nine metropolitan metropolitan areas areas selected. selected. NUTS: NUTS: Nomenclature Nomenclature of of Territorial Territorial Units Units for Statistics. for Statistics. Table 1. Table General characteristics 1. General characteristics of of the the metropolitan metropolitan areas. areas. Area Density Population Population袁 Population Population Range Population Metropolitan Area Area Density 2 ) Population (km2 ) (Inhabitants/km (Inhabitants) +65 (%) (Inhabitants) Metropolitan Area 倩 Range Athens 403 ) (km 2 (Inhabitants/km 7669 2) 3,090,508 +65 (%) 17.8 25,389–664,046 (Inhabitants) (Inhabitants) Barcelona 420 7590 3,103,973 18.9 13,531–1,611,822 Athens Berlin—Brandenburg 403 16,669 7669 352 3,090,508 5,871,022 17.8 20.7 25,389–664,046 58,018–371,438 Barcelona Brussels 420 3591 7590 698 3,103,973 2,504,715 18.9 16.1 13,531–1,611,822 2160–176,545 Berlin—Brandenburg Prague 16,669 315 352 3737 5,871,022 1,177,141 20.7 18.5 58,018–371,438 6021–128,063 Lisbon Brussels 2917 3591 966 698 2,821,876 2,504,715 18.2 16.1 17,569–547,733 2160–176,545 London 1468 5733 8,416,543 11.4 7648–372,752 Prague 315 3737 1,177,141 18.5 6021–128,063 Stockholm 6011 348 2,091,449 15.2 9331–864,315 Lisbon Turin 2917 1000 966 1620 2,821,876 1,619,478 18.2 24.8 17,569–547,733 1200–886,837 London 1468 5733 8,416,543 11.4 7648–372,752 Stockholm 6011 348 2,091,449 15.2 9331–864,315 2.2. Applying the Population1000 Turin Health Index 1620 1,619,478 24.8 1200–886,837 The measure used in this study is the EURO-HEALTHY PHI, which was built to evaluate 2.2. Applyinghealth population the Population of the EUHealth regionsIndex for the reference year 2014. Presenting a bottom-up hierarchical structure with several indices, The measure used in this study the PHIis measures health with respect the EURO-HEALTHY to thewas PHI, which components Health built to evaluate Determinants population andofHealth health the EUOutcomes, regions forboth disaggregated the reference intoPresenting year 2014. areas of concern, dimensions a bottom-up and hierarchical indicators (Table 2) [31]. The Health Determinants component represents the contextual structure with several indices, the PHI measures health with respect to the components Health factors defined as the environmental Determinants conditions and Health in which Outcomes, people both live and which disaggregated into directly areas ofand/or concern, indirectly influence dimensions and health: Economic conditions, social protection and security, education, demographic indicators (Table 2) [31]. The Health Determinants component represents the contextual factors change, lifestyle and health defined as behaviours, physicalconditions the environmental environment, built environment, in which roadwhich people live and safety,directly healthcare resources and/or and indirectly expenditure, influence and healthcare health: Economicperformance. conditions, The Health social Outcomes protection andcomponent refers to thedemographic security, education, severity and frequency of disease and/or death, including both mortality and morbidity indicators. change, lifestyle and health behaviours, physical environment, built environment, road safety, healthcare resources and expenditure, and healthcare performance. The Health Outcomes component refers to the severity and frequency of disease and/or death, including both mortality and morbidity indicators. Int. J. Environ. Res. Public Health 2019, 16, 836 5 of 17 Table 2. List of the EURO-HEALTHY Population Health Index (PHI). COMPONENT: Health Determinants Area of Concern Dimension Indicator Unemployment rate (%) Employment Long-term unemployment rate (%) Economic conditions, Disposable income of private households per capita (Euro per inhabitant) social protection Income and People at risk of poverty or social exclusion (%) and security Living conditions Disposable income ratio—S80/S20 Social protection Expenditure on care for the elderly (% of GDP) Security Crimes recorded by the police (per 100,000 inhabitants) Population aged 25–64 with upper secondary or tertiary education attainment (%) Education Education Early leavers from education and training (%) At risk of poverty rate of older people (%) Demographic change Ageing Ageing index (ratio) Adults who are obese (%) Lifestyle and health Lifestyle and health Daily smokers—aged 15 and over (%) behaviours behaviours Pure alcohol consumption—aged 15 and over (Litres per capita) Live births by mothers under age of 20 (%) Annual mean of daily PM2.5 concentrations (ug/m3 ) Physical environment Pollution Annual mean of daily PM10 concentrations (ug/m3 ) Greenhouse Gas (total tonnes of CO2 eq. emissions per capita) Average number of rooms per person Housing conditions Households without indoor flushing toilet (%) Households without central heating (%) Built environment Population connected to public water supply (%) Water and sanitation Population connected to wastewater treatment plants (%) Recycling Recycling rate of municipal waste (%) Victims of road accidents—injured and killed (per 100,000 inhabitants) Road safety Road safety Fatality rate due to road traffic accidents (per 1000 victims) Medical doctors (per 100,000 inhabitants) Healthcare resources Health personnel—nurses and midwives, dentists, pharmacists and physiotherapists (per 100,000 inhabitants) Healthcare resources Total health expenditure (Purchasing Power Parity per capita) and expenditure Healthcare Private households’ out-of-pocket expenses on health (% total health expenditure) expenditure Total health expenditure (Purchasing Power Parity per capita) Hospital discharges due to diabetes, hypertension and asthma Healthcare Healthcare (per 100,000 inhabitants) performance performance Amenable deaths due to healthcare (standardized death rate per 100,000 inhabitants) COMPONENT: Health Outcomes Self-perceived health less than good (%) Age-standardised Disability-Adjusted Life Year (DALY) rate Morbidity (per 100,000 inhabitants) Health Outcomes Low birth weight (%) Preventable deaths (standardised death rate per 100,000 inhabitants) Mortality Life expectancy at birth (years) Infant mortality (per 1000 live births) A socio-technical approach was developed by the EURO-HEALTHY team to build the PHI. The methodology combines the multi-criteria MACBETH method [48] which included several participatory processes, namely Web-Delphi processes and a decision conferencing process. Creating the PHI involved two main phases: The first, which identified and defined the areas of concern, dimensions, and indicators considered relevant to evaluate population health [45] and the subsequent Int. J. Environ. Res. Public Health 2019, 16, 836 6 of 17 evaluation phase, where qualitative value judgments were elicited from the panel of experts and stakeholders on the weighting of indicators and the shape of the value function for each indicator. Driving the discussions were considerations on the importance of closing the gaps of performance in the indicators and of the added value of improvements in each indicator, with a view toward reducing health inequalities [46]. Experts and stakeholders, representing regional and local contexts and with a multidisciplinary background and expertise, were involved throughout all the phases of the PHI’s construction [49]. The conceptual and methodological approach used to model the EURO-HEALTHY PHI (applied to the 269 regions of the EU 28 countries) was adjusted to the nine metropolitan areas regarding the structure, weights and value functions [31]. A score was calculated for each component, area of concern, dimension and indicator allowing the comparison of population health between geographical areas in an aggregated or disaggregated way. The value-scores ranged from 0 to 100, where 0 represented the lowest score of population health and 100 the highest score. The colour coding of the classes uses a gradation inspired by a traffic system: Red representing low values and green colours representing high scores. Indicators 39 indicators where selected for inclusion in the PHI and framed by component, area of concern and dimension (Table 2) [31]. From these, 26 indicators were requested to be collected at the municipal level. The data collection process followed six steps: (a) Identification of a focal point (designated researcher) in each metropolitan area responsible for the data collection; (b) application of a survey to the focal points to identify the availability of the indicators at local level; (c) selection of the indicators to be collected at the local level; (d) production of a manual detailing how to build and deliver the indicators; (e) data collection and processing of the data; and (f) delivery performed through a web platform with data quality procedures. The data availability of the PHI indicators in each metropolitan area, data source and year of the data is provided in Supplementary Materials S2. 2.3. Statistical Analysis In order to provide an integrated description of population health inequalities across and within metropolitan areas, three main analyses were performed. An Analysis of Variance (ANOVA) was applied to detect whether there were statistically significant differences in the PHI scores of the Health Determinants and Health Outcomes components across the metropolitan areas. The procedure works by comparing the variance between pairs of metropolitan areas means versus the variance within metropolitan areas as a way of determining whether there are similarities or disparities. Scheffe’s test was used at a statistical significance level of 0.05. The analysis was performed using SPSS software (IBM, Armonk, NY, USA). The coefficient of variation (CV) was calculated to measure dispersion on value-scores for both Health Determinants and Health Outcomes Indices across municipalities of the same metropolitan area. The smaller the CV value, the greater the data homogeneity and the smaller the variation. The analysis was performed using Excel software (Microsoft, Redmond, WA, USA). Finally, the LISA (Local Indicator of Spatial Association) measure was used to identify local patterns of spatial association between spatial units (municipalities), upon confrontation with their neighbours. This spatial correlation method allowed the identification of geographical clusters with identical values, defined by the spatial concentration of low scores (Low-Low) and high scores (High-High). The analysis was performed using ArcGIS software (Esri, Redlands, CA, USA). 3. Results When the EURO-HEALTHY PHI was applied to the metropolitan areas, a geographical variation in the distribution of the value-scores was revealed across metropolitan areas in both components. Int. J. Environ. Res. Public Health 2019, 16, x Int. J. Environ. Res. Int. Public J. Environ. Health Int.Res. J.2019, Environ. 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Stockholm n2Health ropo from a an comparisons me Four bo Pairwise Table significantly stands sons ofscore ow sh75areas ropo the the an componen me groups sona PHI 3. between o above out differences Pairwise areas nHealth comparisons w stands ropo an me bo of withwash h(above othe higher areas hTur ropo n2componen metropolitan anme hsOutcomes out comparisons a differences app above between significantly areas wropo with of mean an hthe h ed areas a ndifferences an score significantly s(around metropolitan areas between of to above areas w the higher mean in the h differencesboth metropolita between scores metrop mean areas higher compo fro me mbesc emerge 87Group De 83); Determinants on erm Determinants and83);0on Health boto nan(4) and bo h100) Determinants Lisbon (4) 87 hockho Determinants componen Determinants componen Determinants 83); Lisbon on (below w (below ow and and th bo 62 31% Prague and (4) 87 h Index. Determinants 62.7); 7ands 62.7); s(below Lisbon on componen (below 3rom Prague atta (3) (below (3) Barce bo present 2A Barcelona Barcelonaw nand hA 62.7); hens 62.7);opresent ng ona 62.7); h componen hens s(below slower Prague s(3) 75 and (3)Overa Barce (3) and lowerandBarce Barcelona Barcelona De Tur A 62.7); 0scores Barcelona present can ona hens Turin oTurin sabove erm n ascores ona Health (3) yin presen mos aand nan present present and Barce Health lower gher and A Determinants in aStockholm De 87 Determinants emerge Ln componen Turin sTurin hens hsbon Health both be Lmean Turin he scores onaerm on gh Outcomes high sbon Health ow mun scorespresent apresent bo Barce nan and presen components Outcomes 1score 62 present scores scores in hpresen Determinants cnS Index. Turin7MA sStockholm Health hpa Lcomponen Health ockhoOvera ona n(below be high (below high sbon n3Hea in in high 87 bo present ower ow Barce es and Health (below Outcomes ower Determinants scores m scores h 3reg on 62 h presen 62.7); scores66.7). Health acomponen Ou L va7ands ona mos sbo saboth high sbon Outcomes Outcomes ered va in in 66.7).ues comes(3) 3h andsin Determinants Health ues ou aStockholm Barce (below componen Barcelonaower Health scores Health va presen A n Tur Index. he ue hens around n Hea above mun Outcomes (around sh Outcomes (around sn va in scores 66.7). Hea Determinants apresen Outcomes sh ues sand and Barceower sIndex. coareas gn pa above Turin can ona (around (around va Hea es gh n s(around A Index. 50 ues present presen reg yscores hens and hgher she ered L PH Barce h Hea n highsbon mean Hea gh va hranges h scoresona scores ue presen score h Ou scores and in nocome nHea Hea bo ab L ow 83); and (4) Lisbon and Prague o0Barce present 100 hrom lower w h 31% scores 100 inw aAthens n Health hces ng 31% 75 and Outcomes aAthens above ng 75 (below 1n 4and bo habove 66.7). componen n bo h componen Stockholm respec Athens Group Lisbon Berlin-Brand. MA rom Group Tab Tab 0Four o Stockholm es es 100 Group Brussels 3MA Tab and and w es h Athens 4London Group 31% Stockholm MA presen presen Tab and Barcelona es a4Prague Group snn Stockholm presen 3Barcelona MA Athens ng he heand resuresu Turin Lisbon 4me and Barcelona MAStockholm presen heAthenssropo above oresu Berlin-Brand. heshe Stockholm Barcelona Lisbon he sn Athens pa pa bo o resu rw rw hhe Berlin-Brand. Brussels componen se se Barcelona AthenssLisbon pa compar compar o rw he se London Barcelona Berlin-Brand. Lisbon pa scompar sons sons Brussels rw Pragu ovalu se Berl Lism so m co L n the d strrespect but 87ononto ofboth Hea the Table respec emerge 87 De th Group va on Health 3. erm Determ ue-scores bo Pairwise nan o 1sPairwise Determinants respec Hea emerge 87 h De Sbe MA Group s ockho componen on erm benants hcomparisons ow De bo was nan o1m Stockholm respec Hea emerge 87 h erm Index. De62 Sand sMA Group s ockho ands componen revea 7 s on erm be nanhBarce 3 Heaow boDe 2 nan ou sm 1Athens Stockholm ofed Hea hA 62 erm and So sMA wth the s Group ockho ands componen hens 7 s Hea across be ona nanOutcomes hHea differences Barcelona 3 aow De 2 ousand h Barce sm Athens Stockholm gn h 62A De erm and sw metropo Ou hens Tur7 87 MA ands s erm can Health ona nan hHea Group var comes Lisbon 3non Barcelona aand 2 Ind between nan srespect ou presen and yBarce spresent and gn at both h A and Determinants Dew Ou gher tan Tur on hens nd Berlin-Brand. Group andcan L ona MA herm Hea components); h comes Lisbon sbon Health ces Barcelona n to ametropolitan mean n areas gh sHea presen and yin Barce and nan gn the emerge 87h Hea Four scores Group presen Ou gher Berlin-Brand. score Determinants Index. Tur nd groups on s can L Brussels Health d ona n Ou be h th groups Lisbon comes sbon ces n mean str both n both gh 87 Barcelona De presen ow ynin comes Determ Hea Group and (2) hFour bo but ower Determinants scores areas Serm on Stockholm MA presen 62 gherof Athens London ockho components); score ond Berlin-Brand. Health Athens, h L Index. h 7Brussels metropo componen on nanme groups h Ou sbon ces nd va mean mean Lisbon n gh 3 nants m ropo of MA Determinants Hea ces ues comess Prague components); Four bo Barce Athens ower Health scores spresenbeBarcelona London thescore Index. ands oscores hFour h Berlin-Brand. Brussels n ow and an componen groups sOu ona tan (2) va HeaStockholm Turin va Determinants around n Barcelona above 62 ou areas Lisbon n groups Athens Hea ues comes Prague from and boHea Athens, ue-scores ower 7 Index. w hand London ohTur h 3 (2)n hcomponen anme the th sBerlin-Brand. OuBrusselsova aAthens Barcelona Barce around HeaLisbon Lisbon Turin above n sareas Athens, Index. me Outcomes Barcelona ropo ues gn comes Prague presen was h ona ropo can London Barcelona n present Berlin-Brand. an Lisbon sand Barcelona around Turin Hea h Brussels above revea yan areas gh and Tur Ind areas Prague gher h scores lower Berlin-Brand. Lisbon Lisbon n ed London ces Brussels presen mean and Turin acros n HeaFo Berli pr Li sc P 87 De 83Group 83); 83); on erm 1 and components) Table Tab andbo andes nan 4 Stockholmh 3. (4) L (4) MA 83); componen 83);sbon 3 Lisbon Lisbon 83); and and andow and and Stockholm62 and (4) comparisons and 4 (4) (2) Prague(4) 83); 7 s presents Lisbon Lisbon Prague Prague Lisbon emerge 3and Athens rom 2 presen Athens A and (4) and Tab of 0Stockholm present present and hens othethe Lisbon Prague ona Prague es 1 100 Prague Barcelona Barce S ower differences 3 and resu Barce ockho lower lower and w present and present Tab Tur 83scores ona present hStockholm ts 4 Group Prague ona 31%m scores scoresLisbon presen 1es n and between of s presen 3 a ands lower and n lower the and 4 in Hea lower in Stockholm a s nL Berlin-Brand. Health MA Health87 83 ou L83); metropolitan pa respec respec ng he scores Lsbon rom Group scores 4 h sbon h scores on presensbon 1 gh w and 75and Ou rw resu Tab lower0 and Outcomes scores and Outcomes h bo in presen comes o Stockholm se (4) respec o ao 4 es Stockholm in 100 s Healthh Group Health s Hea present Prague Brussels Hea L abovescores Lisbon ocompar Health MA3 gn areas1 componen he sbon n and w be he Hea h h resu can o respec (below Outcomes ower mean (below Outcomes presen ow Athens De De and Stockholm nh pa 4 Outcomes London Hea Group 31% bo Stockholm Health MA presen h y 1 sons rw erm sower 66 erm Ou Prague scores Prague h h vao s h 66.7). a7 66.7). componen segher o Barcelona nan nan De ues comes ower Stockholm a Prague Group he Hea (below Outcomes of (below compar s 2 from n Stockholm 1 va (below MA erm Athens mean present presen spa he s ng A metropo n scores and and h ues rw hens the 75around resu nan Hea Turin 66.7). Stockholm 66.7). De Lisbon sonss score and66.7). Hea se Hea Barcelona MAStockholm (below lower erm ower s Athensn s n hcompar Barce and Hea above o h n h HeaBerlin-Brand. tan nan me Ou bo scores scores Ou he Hea 66.7). Stockholm ona h Barcelona h sLisbon n th Athens areas ropo sons pa Ou comes comes and componen bo h in rwandn comes Ou h o Health Hea Hea an se componen Berlin-Brand. w Brussels Barcelona nd Athens nd me L Lisbon comes areas compar th sbon h ces h ces beropo s Ou Outcomes Ou ow above London Barcelona w presen comes Berlin-Brand. Four Four nd Lisbon comes sons s 66 an h Brussels ces 7 groups groups areas o (belo be Pragu Four Berl nd Lisow m oh L 1 Stockholm 1 Stockholm 1 1 Determinants 1 semerge Stockholm 1 (below (1) 87 Determinants Stockholm Stockho on 62.7); 1 bo Determinants (3) Stockholm h m1 Barcelona componen (below Stockholm 62.7); (below and shhn66 Turin (3) 27above 62.7); Barcelona A present hens (3) Barcelona and high Barce hTurin scores ona sand present hand in Turin Health L high sbon present Outco scor prohs ts Overa emerge a most Determ a(1)the nants Stockho Health De 83 Group emerge 87 De 83 erm mun 1and on erm Table (be Health andDeterminants Table nan cbo 4Table m Stockholm3.nan Athens ow L 41pa sPairwise Determinants MA emerge 87 h De 83 3. stands sbon Sbe L 62 3. ockho scomponen sbon Pairwise termon be andow es Pairwise 7) and Table ow bo nan 62 reg and comparisons Stockholm out (3) 41Index.m Prague emerge 87 h De 8362 7L Index. comparisons SssPrague stered Barce comparisons 3. w ockho ands componenserm 7sbon on respec beth and 3Pairwise Barce3Athens ow presenbo2nan Tab ou and 41ona aBarce of mpresenva hA 62sthe Ssw Lof o ona gn sPrague comparisons es ockho ands componen hens sBarcelona 7sbon ue-scores of and the Hea be ona hfand ower differences 3 respec the 3aow 2cant ower differences and and ousand Barce Tur m presen differences h gn Tur 62AGroup scores De 4 sw Prague y hens Tur of o ands 7Lisbon scores s1n presenn can erm ona above between hhthe Hea components presen present 3n angher between 2ou ower presen and y Barce Hea between nan ngn and presen Stockholm differences s h hAHea Athens Berlin-Brand. MADe 83 50w gher metropolitan respec De s Tur he mean hens scores GrouphAthens hand can Lona h metropolitanerm h1gh (the erm and Ou hsbon resu h n metropolitan Tab amean gh ower gh Ou scores sand presen between Hea yBarce nan comes ngn and Stockholm score nan o PHI h scoresscores 4Stockholm Overa es s HeaGroup presen comes Athens Hea gher Brussels Turscore scores L o MA s31hscan areas Lsbon ona nranges be and Ouand hsbon areas behn metropolitan he Hea h mean n ngh areas 83 Ou presen ow mean n yn be De owboth Hea and Stockholm Athens pa comes nLondon 4 Group Hea ahbo Athens ower scores HeaStockholm mean HeaMA ow 62 h presen comes and presen 1rw gher most erm 66 score mean OuhPrague scores Lh7stands 66 h7componen components se hOu th sbon Barcelona scores h4va nd nan mean comes Ou n gh 7scores Stockholm Prague3Ou Group areas compar asnAthensbeL Hea Outcomesfrom Stockholm ues comes 1MA ces Barce bo Athens ower scores sbon presen comes s he ow from presen comes the out and score mean from the Four around resu Turin componen Stockholm Lisbon sona and Ou sons mun the wva around Athens Hea Hea n Barcelona MAStockholm scores nd the n beower Hea Athens groups th (above ues comes andPrague bo ower (around o o ces ow h chaBerlin-Brand. h fromTur me Ou h sn66 scores pa he componensOu Stockholm Four gn Barcelona o va around Hea presen Lisbon 7n Athens ropo comes above pa thetand me ues fesncant comes presen rw groups Hea reg ropo an n Berlin-Brand. se Brussels ower Barcelona nd Athens Lisbon yhHea around compar areas steredh o above ces gh an Ou scores me gher h scores London Barcelona w areas Four comes Berlin-Brand. Lisbon ropo sons va h Brussels mean nue-sc groups nHea o Pragu be Hea an Berl Lis m aL respect to Athens Athens 87 on Athens bo h componen Athens s 2 emerge emerge A hens emerge 1 1 Athens Barce S S ockho ockho emerge ona 1 S Athens m m ockho and s s ands ands 1 L Athens m S sbon ou ou ockho s ands w w presen h h m ou a a s s s ands w gn gn ower h can can ou a s gn w va y y h h hues can gher gher a s gn n y mean mean h Hea gher can score score h y mean h gher n n bo sc bo 87 on both 83 87 De 8321and components) Tab on erm Health andTable e4 bo Barcelona Stockholm3 nan Athens L 4Hea Determinants Pa 87 hsbon De 83 3. LsPairwise componen sbon w on erm th be and Table se and (2) Table ow bo Determ nan compa and 4Barcelona Prague 87 h De 8362 comparisons 3. Index. L3. Athens sPrague Pairwise componenserm 7sbon on be and Table sons Pairwise nants 3presen ow bo2nan and Barce 4presen o hAL 62 comparisons 3. of Barce he sPrague Pairwise comparisons componen hens sthe 7sbon and be d ona ower 3differencesow e2and ower ona and BarceHea presen ences 62AofPrague comparisons scores Tur783); hens scores of s21the and be th ona Tab the 3nbetween and2Outcomes ower differences ween n presen and 87 e Barce Hea and presen n Barcelona Stockholm differences L 3 A(4) Athens Hea of 83 sbon me Pa Tur on hens scores the h Lisbon L ona metropolitan 21and w Ou hTab opo sbon hn Table between ower differences both se gh Ou presen and between Barce present and comes ncompa e Ind 4 De 83); scores an Stockholm and Hea Barcelona 3 3.presen comes Athens LTur scores a erm Pa and Pairwise 21components) L ces ona metropolitan sbon eas Prague hareas sbon behn metropolitan between sonsw ngh nan83); (4) Ou presen mean be Hea ow se and and Stockholm n Four Barcelona ower Athens ower scores mean o Lisbon Hea ow compa and presen comes comparisons s 21present 66 Prague sco he be Lhmetropolitan 66 7 h groups (4) Ou areas scoressbon hdva va es ow n gh and 7Barcelona Stockholm areas Ou sons beLisbon Athens Hea ues e 21lower comes ues 62 (2) ower scores mean presen om ences ow presen comes from Prague o mean ofh 7 he n of 66the Athens he Stockholm scores and Ou areas Barcelona scores n the be 3 va around Athens Hea n7metropo scores d Barce be differences ower ween present Hea Hea ues comes Prague ower mean e owfromin hences from h scores me ona Health n th 66Barce Ou scores the lower va aroundpresent Hea 7be opo between the tan ues comes ween n Outcomesfrom onahHeaTur scores an areas lower nme a metropolitann the around Hea eas and h presen inscores Ouopo mean (below Health hcomes L an h sbon sco in a gh areas 66.7). 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aerna nany1276 denDe ab esBrusse and a6he CV 10% 10% h he o oad oneeerm and De sn Hea A y var Hea = we ed awas The ndex 0 hens erm 76 s nan a y 12abeeand CV orhe 10% h ound ound nandenDe and De sBruss n CV Hea =A y 0er w shho=as er o1 d enNo g way d L wa NA od ymbo bon eexamp =ound e var en No en By P gBy ague e a way oup and d way NA B ymbo onvaru and e = o en Noe on examp Be examp on ound g By ahe y e n oup ymbo and way d onHea enB e p evar andenbu wa B ay n mean B uah u on owe ound he e y and eon Ou p gd o Hea op ecomese e w p n en B hen ay h haonhu ghe h he owemean yOu e a ghe mean dHea d ndex pcomes o ayohowe o o en e e ghe e evara Ou mean CV ha y had e d ndex oa a and comes pe =ahon e e e o eghe 0 10 en e om var aCV e ndndex Ba om Ou a e a aA he ea= en a pe S y e one y0comes d o hough ona Hea d10 kho eCV e ne een en no y m A hhe ah =pendex a and Tu he Ou y owe a 0hough hough Hea om d 10 ve n S var comes SyoCV a en no o A haakho y ye he Ou d hough on gnhough m om a =mvar p Tu 0comes S ay Tu s10 The an noaower a owekho nhe y onA a var m gn o hough when Tu s a0aower h ghe n y10 on gn we he A ewhen s an en var 0hough ower 10aweon e e A whe pe he Int. J. Environ. Res. Public Health 2019, 16, 836 8 of 17 Int. J. Environ. Res. Public Health 2019, 16, x 8 of 18 Figure 2. Scatterplot Figure of the 2. Scatterplot Health of the HealthDeterminants Determinants and and Health Outcomes Health Outcomes value-scores value-scores by metropolitan by metropolitan area and corresponding coefficient of variation (CV). Note: Each colour represents one metropolitan area and corresponding coefficient of variation (CV). Note: Each colour represents one metropolitan area and each dot a municipality. The triangle represents the value-scores from the region (NUTS 2 level) where the metropolitan area is located. The coordinates of the dots and triangles are based on the value-score achieved by the municipality/region on the Health Determinants Index (x-axis) and on the Health Outcomes Index (y-axis). Int. J. Environ. Res. Public Health 2019, 16, 836 9 of 17 In addition, this figure reveals that 55% of the municipalities in this study perform better in Health Determinants than the region where they are located [31]. Exceptions are found in Berlin-Brandenburg, Brussels and Turin, where more than 60% of the municipalities present worse values. In Health Outcomes, the opposite was found. Berlin-Brandenburg and Brussels are the exceptions, where more than 70% of the municipalities present better values. Prague stands out since all the municipalities perform better than the region in Health Determinants and worse in Health Outcomes. The distribution of Health Determinants is not homogeneous across and within the metropolitan areas (Figure 3a). There is a gradient from northern European to southern European countries, with higher scores being found in Stockholm, Berlin and Brussels and lower scores in Lisbon, Athens and Barcelona. When looking at the within-metropolitan areas inequalities, the geographical pattern differs when we compare scores from a centre-periphery model point of view. The metropolitan centre of Brussels and Berlin-Brandenburg present lower scores when compared with the municipalities located in the periphery. The opposite is found in Stockholm. As for Health Outcomes, the north-south gradient is not evident (Figure 3b). Along with Stockholm, Turin and Barcelona registered higher scores. Lower scores were identified in Lisbon and Prague. The geographical variation in the distribution of the value-scores across municipalities is considerable when compared to Health Determinants, with no clear pattern being found in the majority of the Res. Int. J. Environ. metropolitan areas. Public Health 2019, 16, xHowever, it is visible that the centres from Brussels, Athens and 10 of 18 Berlin-Brandenburg present lower value-scores than the periphery. Figure 3. Cont. Int. J.Int. J. Environ. Environ. Res.Res. Public Public Health Health 2019, 2019, 16,16, 836x 11 of1018of 17 Figure 3. Geographical distribution of the PHI on the Health Determinants Index (a) and on Figure 3. Geographical distribution of the PHI on the Health Determinants Index (a) and on the the Health HealthOutcomes OutcomesIndex Index (b),(b), at the at the municipal municipal level.level. Note: Note: The value-scores The value-scores are displayed are displayed by using by using classification by Equal interval, taking into account the PHI minimum and classification by Equal interval, taking into account the PHI minimum and maximum scores (from maximum scores 0 (from 0 to 100). The colour coding of the classes used a gradation inspired by a traffic light to 100). The colour coding of the classes used a gradation inspired by a traffic light system (from red system (from to red to green). green). In the In theofcase case the of the metropolitan metropolitan areas, areas, the the light lightrepresents green green represents the municipalities the municipalities with withworse worsepopulation population health health andanddarkdark green green represent represent betterbetter scores. scores. Figure 4 illustrates Figure thethe 4 illustrates presence presenceofofclusters clusters in in all metropolitan metropolitanareas areasandand inin both both components, components, apart from apart Barcelona, from Barcelona,Turin Turinand and Berlin-Brandenburg Berlin-Brandenburg where theywere where they wereonlyonlyidentified identifiedforfor thethe Health Health Determinants Determinants Index. Index. Almost Almost 1/41/4ofofthe thepopulation population being being studied studiedlives livesininmunicipalities municipalities located in the located in the Low-Low clusters (concentration of lower value-scores) in Health Determinants Low-Low clusters (concentration of lower value-scores) in Health Determinants while only 8% are while only 8% are living living in clusters in clusters characterised characterised bybya aconcentration concentration of of higher higher value-scores value-scores(High-High). (High-High). ForForthethe Health Health Outcomes Index, the rates are also relevant:11% are clustered in Low-Low Outcomes Index, the rates are also relevant:11% are clustered in Low-Low and 4% are clustered and 4% are clustered in in High-High. The analysis also revealed populations living in municipalities classified High-High. The analysis also revealed populations living in municipalities classified in the cluster in the cluster Low-Low for both Health Determinants and Health Outcomes indices in Athens (17%), Prague Low-Low for both Health Determinants and Health Outcomes indices in Athens (17%), Prague (16%), (16%), London (13%), Brussels (7%) and Stockholm (2%). In Brussels (0.5%), London (2%), Athens London (13%), Brussels (7%) and Stockholm (2%). In Brussels (0.5%), London (2%), Athens (8%) and (8%) and Stockholm (21%), there are also municipalities classified in the cluster High-High for both Stockholm (21%), there are also municipalities classified in the cluster High-High for both indices. indices. Int. Int. J. Environ. J. Environ. Res. Res. Public Public Health2019, Health 2019,16, 16,836 x 12 of 11 18 of 17 Figure 4. Clusters Figure of municipalities 4. Clusters within of municipalities the Health within Determinants the Health and the Determinants andHealth Outcomes the Health Indices. Outcomes Note: The figure Indices. represents Note: The the clustersthe figure represents identified for both the clusters identified forHealth Determinants both the Index (backward Health Determinants Index diagonal shading) (backward and shading) diagonal the Health Outcomes and the HealthIndex (forward Outcomes diagonal Index shading). (forward Blue diagonal lines represent shading). Blue linesthe municipalities represent thewith low value-scores municipalities with lowthatvalue-scores are surrounded by municipalities that are surrounded byalso with low value-scores municipalities also with (cluster Low-Low). Red lines represent the municipalities with high value-scores that are low value-scores (cluster Low-Low). Red lines represent the municipalities with high value-scores surrounded by that municipalities which are surrounded byalso register high municipalities value-scores which (cluster also register High-High).(cluster High-High). high value-scores 4. Discussion 4. Discussion The objective The of this objective of study was towas this study analyse health inequalities to analyse as measured health inequalities as by Health Determinants measured by Health andDeterminants Health Outcomes indices, across and within different European metropolitan and Health Outcomes indices, across and within different European areas. metropolitan The results contribute to deepening the knowledge about health at the urban level and follow areas. previousThework done results across European contribute regions to deepening [31] under about the knowledge the scope of at health thethe EURO-HEALTHY project. urban level and follow In previous the application work done of across the Population Health[31] European regions Index under(PHI), designed the scope of the to evaluate healthproject. EURO-HEALTHY in two multidimensional In the application components, Health Determinants of the Population and Health Health Index (PHI), Outcomes, designed this study to evaluate health examines in two themultidimensional results obtained components, from nine metropolitan areas (Athens, Health Determinants Barcelona, and Health Berlin-Brandenburg, Outcomes, this study examinesBrussels, the resultsLondon, Lisbon, obtainedPrague, from nine metropolitan Stockholm areas (Athens, and Turin) Barcelona,different which represent Berlin-Brandenburg, Brussels, European regions and heterogeneous geographic, social and economic contexts. Overall, it was found that: (i) Strong population health inequalities exist across metropolitan areas, with municipalities from Southern and Eastern countries presenting, in general, worse value-scores; Int. J. Environ. Res. Public Health 2019, 16, 836 12 of 17 (ii) metropolitan areas present better health, measured by Health Determinants, than the region where they belong, although some exceptions were found, and; (iii) Municipalities with worse Health Determinants scores tend to also perform worse on Health Outcomes. Thus, the analysis of the distribution of the value-scores on both indices shows a high dispersion across metropolitan areas: In Health Determinants the range goes from 49 (Athens) to 92 (Stockholm) and in Health Outcomes, from 62 (Lisbon) to 99 (Stockholm). The fact that urban areas from North-Western countries present better health scores than the Eastern and Southern ones is not new and is aligned with results from previous studies on population health in Europe [31,35,50]. Simultaneous to a high difference in value-scores among metropolitan areas, there is a considerable variation within municipalities of the same metropolitan area. Of note are Brussels and Athens, which display a clear geographic variation in Health Determinants scores, and Turin and London in Health Outcomes. Previous studies also identified the presence of inequalities within these metropolitan areas, although at a more detailed scale [29,35,51]. For example, in the Lisbon case, geographic disparities between municipalities are not evident as expected, considering other studies on health inequalities at the small area level [19,21,27]. As the PHI model was previously applied to EU regions, it offered the possibility to compare the population health scores of metropolitan areas to those performed by the respective regions where they are located. In opposition to what it was identified for the regions, the municipalities from the metropolitan areas often perform better on Health Outcomes than on Health Determinants. Nonetheless, it was found that most municipalities performed better scores in the Health Determinants index when compared with the regional scores. Prague stands out as a paradigmatic case: All municipalities present significantly better scores than the respective region in Health Determinants, performing worse in Health Outcomes. These results may be understood as ‘ambiguous’, considering that the country and specifically the capital were emerging from a long-period of social and economic stagnation and recession, with negative impacts on health determinants [52,53]. The contrary occurs with Berlin-Brandenburg, Brussels and Turin, considering that these metropolitan areas present worse Health Determinants scores than their respective regions. One plausible explanation is that when they are compared with the larger administrative region, they perform worse in important health determinants, such as high levels of air pollution, ageing and crime, indicators used to build the PHI. According to another recent study from the EURO-HEALHTY project, using data from the same nine metropolitan areas, revealed that worse air quality is typically encountered in deprived European urban areas [54]. Still, those health determinants do not affect those metropolitan areas equally. Brussels, for instance, is younger than the rest of the country [55]. Unemployment and poor housing conditions provide a better explanation for this metropolitan area [56]. The application of LISA to detect spatial concentrations of similar scores within the same metropolitan area revealed that the share of the population living in Low-Low clusters (concentration of lower value-scores) in Health Determinants is three times higher than those living in clusters characterized by a concentration of higher value-scores (High-High cluster). Also, in a considerable number of urban areas, clusters were revealed in both indices. This is the case of Brussels, London, Athens and Stockholm, with clusters of High-High and Low-Low value-scores for both indices. The municipalities that share worse health determinants and worse health outcomes (Low-Low clusters) should be pointed out as ‘urban zones in alert’ calling the attention of local policy makers to the need to address population health in an inter-sectoral and integrated way. Measuring health at the local level is complex since there are diverse and interconnected factors operating at different scales in the same place [57]. The result of the application of a single index to depict inequalities is not immune to criticism and should be interpreted considering some limitations. Though the greatest asset of having aggregated indices is the simplification of data and the possibility to compare different geographical units using a single measure, there is always an amount of information that is lost [58]. Thus, some aspects regarding the administrative delimitation of metropolitan areas and the type of indicators used in the PHI model may contribute to masking inequalities. The lack of Int. J. Environ. Res. Public Health 2019, 16, 836 13 of 17 consensus on the delimitation of the metropolitan areas led to the utilization of different functional definitions and administrative levels. Although this issue was already referred to in previous studies as a limitation of ecological studies in Europe, so far, there remains no solution [28,29,34,35]. Moreover, the population size of municipalities within the same metropolitan area (e.g., Barcelona has a municipality with more than 1.5 M inhabitants and other with less than 200,000) can introduce an important MAUP (Modifiable Areal Unit Problem) effect [59]. According to Rothenberg and others [44], inequality among countries is mirrored in the inequality within their regions and cities. Therefore, bottom-up approaches based on local data and knowledge is of high relevance to promoting equity-based policies [1,10,36,40,60–62]. The type of indicators used in the construction of urban health indices is key to detect inequalities among municipalities. The Health Determinants and Health Outcomes indices analysed in this study are the result of an aggregation of multiple dimensions and indicators that were selected as relevant to evaluate population health at a regional level. This participatory process, conducted with stakeholders representing the countries of these metropolitan areas and the indicators selected, was analysed by Freitas and others [45]. Although indicators had been selected for a regional scale, they were considered as adequate and reliable proxies to measure health determinants and health outcomes at the metropolitan level. Yet, indicators focusing on specific urban characteristics (e.g., access to green spaces, transportation, social protection) and more sensitive to the local social, physical and built environment [6,8–10] are not included in the PHI, not because they were not considered relevant, but due to the lack of availability and/or comparability across regions, two criteria required for an indicator to be included in the PHI [45,63]. The application of an urban health index to the municipalities of nine European metropolitan areas, very different in contexts and levels of data availability, brought some constraints and represented some risks, implying the need to apply a predefined and common protocol of data collection and harmonization. The lack of indicators at the administrative level of the municipality led to the use of data at coarser geographical resolution—regional or national. This indicates that besides the need to reinforce the data collection at the sub-national level, already identified in previous studies [31,63], there are different levels of capacity from national and municipal statistics to collect urban health-related data at the local level. Regardless of the above-mentioned limitations, the strength of this study is to show that geographical analysis is needed when investigating health inequalities. These results should be understood as a point of departure to show that there are inequalities within the same region and metropolitan area and there is a need to examine and act locally to address any existing inequities. And though we focused only on aggregated indices of Health Determinants and Health Outcomes, it offers enough clues that a multi-sectoral commitment between the health sector and other sectors at the local level would be valuable when it comes to promoting population health. 5. Conclusions This study adds evidence to the debate on the existence of health inequalities across Europe: Not only across countries and regions, but also among and within urban areas. The application of a single measure—the Population Health Index—to evaluate health determinants and health outcomes in nine European metropolitan areas show that not only do they exhibit differences between them, but that municipalities within the same urban area face different population health profiles. Conversely, municipalities from these nine metropolitan areas have now a common tool to compare themselves with and share the lessons on how to tackle similar health problems. The responsibility of promoting health does not lie exclusively in the health sector or with the national government. More and more, municipalities across Europe are demonstrating their responsability when it comes to adopting policies that improve the health and well-being of their citizens. Int. J. Environ. Res. Public Health 2019, 16, 836 14 of 17 Supplementary Materials: The following are available online at http://www.mdpi.com/1660-4601/16/5/836/ s1, S1: Additional information on the Local Administrative Unit and delimitation of each metropolitan area. Supplementary Materials S1 includes Table S1: Name of the administrative unit, at the Local Administrative Unit (LAU) level, Table S2: Local Administrative Unit (LAU) level and number of units, by metropolitan area, Table S3: Comparison of the delimitation of the metropolitan areas with the EUROSTAT definition and data source of the delimitation selected for this study. S2: Data availability of the indicators included in the construction of the Population Health Index by metropolitan area. Data source, year and geographical level. Supplementary Materials S2 includes Table S4: Population Health Indicators. Data source, year and geographical level, in each metropolitan area. Author Contributions: Conceptualization, C.C. and P.S.; Data curation, C.C., P.S., S.D., B.B., C.B., J.S., D.D., N.Z., K.K. and P.D.; Formal analysis, C.C.; Funding acquisition, P.S.; Investigation, C.C.; Methodology, C.C.; Project administration, C.C.; Supervision, P.S.; Validation, P.S., S.D., B.B., C.B., J.S., D.D., N.Z., K.K. and P.D.; Writing—original draft, C.C.; Writing—review and editing, C.C., P.S., S.D., B.B., C.B., J.S., D.D., N.Z., K.K., P.D., A.F., C.M., E.S., S.V., M.M.D., M.G., M.L., D.C. and G.C. Funding: This research was conducted under the EURO-HEALTHY project, which was funded by the European Union’s Horizon 2020 research and innovation programme, Grant Agreement No 643398, and received support from the Centre of Studies in Geography and Spatial Planning (CEGOT), funded by national funds through the Foundation for Science and Technology (FCT) under the reference UID/GEO/04084/2013. Acknowledgments: The authors would like to acknowledge Adriana Loureiro, Cátia Leal, Conrad Franke, Didier Willaert, Iwa Stefanik, Joaquim Patriarca, Lluis Camprubi, Nathalie Coué, Ricardo Almendra and Sophia Rodopoulou for giving support to the data collection, Scott Culp for English review services, and the anonymous reviewers for their careful reading of our manuscript and their many insightful comments and suggestions. Moreover, the authors would like to acknowledge the official institutes that provided the data, namely the Hellenic Statistical Authority (Athens) and the Statistics Portugal (Lisbon). Conflicts of Interest: The authors declare no conflict of interest. References 1. Prasad, A. The Urban Health Equity Assessment and Response Tool (HEART)—A Decade of Development and Implementation. J. Urban Health 2018, 95, 609. [CrossRef] [PubMed] 2. Corburn, J. Urban Place and Health Equity: Critical Issues and Practices. Int. J. Environ. Res. Public Health 2017, 14, 117. 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