Introduction
O ver half of the world's population now live in cities (1) yet the field of urban health is relatively new. Continued urbanization is occurring both across Europe and globally i.e. for the first time in 2008, more of the world's population lived in cities than in rural areas (2) . Urbanization is occurring globally but the largest growth in Europe is observed in Central and Eastern Europe (3) . Urban health is a growing field of research internationally (3) . There are a number of issues that affect urban areas (UAs) primarily e.g. internationalization of metropolitan regions, ageing populations, migration and poor environmental factors. UAs have specific problems associated with health that are different to non-UAs that national or regional investigations would not identify. The World Health Organization (WHO) Healthy Cities programme 'promotes comprehensive and systematic policy and planning with a special emphasis on health inequalities and urban poverty, the needs of vulnerable groups, participatory governance and the social, economic and environmental determinants of health' (4). Policy makers require data at UA level to inform these local policies. Resource allocation is usually at local level in many countries. However, national and international policy makers also require data at UA level to not only inform evidence-based policy making, but also to evaluate the impact of policies.
Introduction
The EURO-URHIS (5) project set out to develop a system of heath indicators specifically for urban health at UA level. EURO-URHIS has used the European Community Health Indicators (ECHIs) shortlist and a USA model described by Vlahov et al. (3) with additions and omissions of indicators that are particularly important in describing urban health initiatives (UHI). ECHI describe health indicators as 'a concise definition of a concept meant to provide maximal information on an area of interest' (6) . These additions include population density, migration, homelessness, social isolation, sexual minority groups, safe sexuality, single parent families, access to green spaces and local amenities, atmospheric conditions, crime, noise exposure, water quality and sanitation.
Introduction
Aggregate measures used to describe population health based on health indicator data include Life Expectancy (LE) (7), Healthy Life Years (HLYs) (7, 8) , Quality Adjusted Life Years (QALYs) (7, 9) and Disability Adjusted Life Years (DALYs) (7) . LE and HLYs and can be used to compare the health states of populations although neither measure is influenced by the population's age structure. HLYs extend the concept of LE by introducing the concept of quality of life. QALYs and DALYs are an extension LE and reduced overall LE to reflect the existence of chronic disease or disability. DALYs and QALYs both provide a measure of the burden of disease on a population and the calculation includes multiplying chronological age by a disability weight or utility weight for the health state. The impact of an intervention can be calculated by considering 'DALYs averted' while QALYs are often extended to incorporate an economic evaluations of health interventions (10) .
Introduction
Population Impact Measures (PIMs) (11, 12) can also be used to help policy makers to identify and prioritize the potential benefits of interventions and policies on their own population. They provide information on absolute numbers of people at risk or who would benefit from an intervention in a particular population, are simple to calculate if local data are available and can help with evidence-based decision-making, commissioning or improvement of services. They are designed to have special relevance for local policy making and they require the collection of local demographic and health data. PIMs could help policy makers implement interventions and policies based on information derived from health indicators such as the ECHI and URHIS-45 (13) indicator sets.
Introduction
This paper report the results of a novel and unique small scale qualitative study that aimed to explore how health policy is influenced by aggregate data at the local level in European cities. This project involved interviewing statisticians, epidemiologists and public health representatives within national institutes responsible for working with public health policy makers and politicians and formed part of the EURO-URHIS 1 project.
Methods
Purposive sampling was used to identify statisticians, epidemiologists and public health representatives within national institutes responsible for working with public health policy makers and politicians in each country. Senior academic public health practitioners and representatives of governmental public health agencies most of whom had been actively involved in the EURO-URHIS project throughout were contacted by email and/or telephone to recommend a suitable candidate within their locality for interview unless they were the appropriate candidate themselves. They were also asked for their participation in the interview in the event that, for the purposes of translation, their presence was necessary and desired by the prospective interviewee. The recommended policy makers were either contacted by email and/or telephone by one of the researchers. Alternatively, in a couple of instances, the EURO-URHIS country representative organized the interview and liaised with researchers to co-ordinate.
Methods
Semi-structured interviews were conducted in nine cities in eight European countries and at least two people were interviewed in each city. The interviewees formed a diverse group in terms of professional backgrounds and institutions represented (see Supplementary Appendix S1) within countries with established, recent or accession status of European Union membership. In-depth interviews were carried out between June and July 2008 by two members of the research team who travelled to the participants' workplace.
Methods
The in-depth interviews, conducted in English, consisted of recording demographic and occupational information about the respondent before asking respondents to describe the healthcare system in their country, whether they felt they were able to influence policy at urban level for their city and which methods were used prioritize policies in their city/UA Respondents were asked at which level health policy decisions were usually made (National, Regional, City/UA) and at what geographic level data were available (National, Regional, City/UA, Local area). They were also asked what data they used to inform health policy decisions, choosing from a list of 12 options ranging from simple mortality data to more complicated measures such as QALYs. The respondents were then asked about their experience and opinions regarding the use of more complicated aggregate measures in health policy making at the local level.
Methods
Quantitative data obtained from a prepared interview schedule was recorded and entered into an Excel spreadsheet and summarized in tables. The interviews themselves encouraged the interviewees to lead the exploration of the schedule's related issues and topics in more depth and these were tape recorded and transcribed verbatim. Qualitative thematic analysis was used and two of the researchers independently checked each transcript and identified themes. Concordant macro or micro level main themes with their supporting evidence were discovered and the predominant themes are reported (14) . Interviews were assigned an alpha code and this is indicated in table 1.
Results
A total of 20 respondents were interviewed, representing nine cities in eight European countries. The interviewees secondary to the initial contact participant tended to be employed in their regional health office and were involved in aspects of health policy in a research or advisory capacity. Few of the participants actually made policies but many of them contributed data to the policy makers and were thus involved in implementing policy. The participants represented 16 different institutions and 9 of these institutions had been restructured in the past 5 years. The average number of years in post was 6.75, however, this ranged from less than 1 to 22 years.
Health policy decision making and data availability
The health care system in most of the European countries included in the study was a mixture of public and privately funded health care often with public health care funded via national government or through population wide health insurance schemes. Public health policy decisions were mainly made at national level in all eight countries although there was a degree of local autonomy at regional or city level. The degree of autonomy varied between countries (table 1) . Data were available at national level for all eight countries (table 2) and in most countries data were available at city or UA level. It was more common to have data available at city/UA level than at regional level and in many cities the data were also available at sub-urban level. Tables 1 and 2 demonstrate that in many countries, data are available at urban and sub-urban level. These data could be used to help inform health policy decisions at urban and sub-urban level yet in most countries most health policy decisions are made at the national level with local policy makers only able to influence a small part of health policy at the local level.
Health policy decision making and data availability
All the management is in the region . . . The big lines of politics is decided at the national level Respondent 17 (R17)
Health policy decision making and data availability
In some countries the interviewees mentioned that health policy decisions were made at the level of the European Union.
Health policy decision making and data availability
. . . it is in the action plan of the government. Part of the action plan for healthcare is it to establish e-health system and of course it was not the invention of decision makers at the ministry of health, [it] The majority of respondents commented that standard measures such as prevalence, incidence, mortality and LE were used to inform health policy decisions in their city or country. Very few respondents commented that they used more complex measures such as DALYs, QALYs and health impact measures (table 3) .
Health policy decision making and data availability
All respondents used other data in addition to the data shown in table 3 and this mainly referred to survey data, however, most surveys were conducted at the national level and data for the UA was sparse. Other types of data available routinely in some countries included pharmacy data and health care provision data.
Health policy decision making and data availability
. . . we have also other data because here for example the manpower in healthcare . . . we have the drug consumption, this is also being measured. We calculate patient beds and . . . .Transport . . . So everything which is connected with hospitals or healthcare providers we have reports and they are being processed and put in tables and classified. R12
Thematic analysis
The thematic analysis identified that respondents had little use of aggregate measures and identified four perceived barriers to the use European Journal of Public Health of aggregate measures; data quality and reliability, lack of capacity and expertise, aggregate measures were hard to explain and a top down approach to health care decision making. One of the strongest themes that emerged was that the respondents had very little but usually no experience of using aggregate measures such as DALYs and QALYs at the local level. When referred to as being used, these aggregate measures were mainly described by all interviewees as being available at the national level of reporting:
Thematic analysis
. . . for the DALYs . . . we report something to WHO from the statistical national institute. . . . So in this way we have something on the national level . . . not provincial and not local R8
Thematic analysis
A few indicated that they sometimes used this national level calculation, where available, in relation to their locality-based data for reporting:
Thematic analysis
. . . especially [limited] at a local level yes, so we used . . . the DALYs for the national level for example for heart diseases related to smoking . . . we know the proportion . . . which is smoking and . . . extrapolate or just indicate actually R5
Thematic analysis
Those who had experience of using aggregate measures commented that they would like to be able to use them more and when asked about the barriers to increased use of aggregate measures respondents highlighted several factors; poor data quality and reliability, lack of capacity and expertise to calculate aggregate measures and problems in explaining aggregate measures to policy makers.
Thematic analysis
The barriers concerning data quality focused on the unreliability of the discreet elements of data necessary to calculate aggregate measures. The majority of interviewees said that they would like to have better quality data overall and especially commented on the lack of reliable of data available at the local level:
Thematic analysis
. . . depends on indicators, very different I think quality of mortality it is good but morbidity data is not very good. R1
Thematic analysis
. . . with QALY . . . we try to calculate them at our local level but our experience was that especially the incidence data they were not of quality. It was too poor to be reliable. R7
Thematic analysis
Lack of capacity and expertise analysing data once sourced and stored was indicated as a barrier at many different levels and in varying degrees of importance for all but one of the representative countries that we visited for interview.
Thematic analysis
. . . it is necessary . . . not only national but also regional level, or urban or rural level. Its necessary also for policy makers . . . We need specialists who are able to put on this database what we need to go to indicators . . . for showing . . . evidence based information for policy makers. R1
Thematic analysis
. . . there are a lot of data but we don't know how to use it or probably we know how to use it but they don't always have resources to collect and analyse. R4
Thematic analysis
. . . we would love to do them, we have the data to do them [but] not so much in terms of expertise I mean [this is] limited and the biggest problem is time so you try to keep up the basic reporting. R19
Thematic analysis
One agency expressed criticism of otherwise welcomed new investment that lacked the support required in order to ensure confidence in the results for future planning.
Thematic analysis
I think a lot depends on the size of the investment you know if you are talking about major programme redesign or reinvestment then the investment in the health intelligence should be equivalent . . . because what's happening at the moment more investment is coming through and we are not necessary joining it up in the way that we should R16
Thematic analysis
Interviewees responded that they believed that aggregate measures were hard to explain to policymakers or that the policy makers would find them difficult to understand.
Thematic analysis
. . . the main barrier could be that you can't explain it . . . ..just using ordinary language. R5
Thematic analysis
. . . the politicians, are not technical so it is quite difficult to understand what is behind the DALY so it, how can we present the data just for our politicians to understand what the data means . . . R17
Thematic analysis
The lack of public health specialists because of lack of training or dedicated expertise in key areas were also cited as barriers to using aggregate measures.
Thematic analysis
We just started quite recently to have public health courses, which are of various qualities so in this way we are quite behind R8 There were few explicit suggestions from our interviewees about how these barriers might be overcome, though a common comment was that better communication between public health analysts and politicians might help. Another strong theme that emerged from the interviews was a 'top down' approach to health care decision making. Health policy decisions were made and implemented often at the national level and data analysts were mainly involved in using the local data to describe patterns of health and respond to politicians' requests for reports rather than using the data creatively to try to inform or influence policy.
Thematic analysis
. . . when there's a topic of political interest its rather then they ask: do we have data for this? So it's more in the second step to Note: -indicates that the interviewee did not specifically mention that data was available at this level.
Thematic analysis
provide the decisions with data, and then really to point out here we have a problem R9 This 'top down approach' was expressed consistently in all but one of the countries that were included in the study.
Discussion
Despite the fact that the European countries included in this study have a considerable amount of health data collected at sub-national level much of this is unused in terms of assisting public health decision making at the local level. Most health policy decisions are made at the national level and local data is used primarily for reporting purposes. There has been very little research into the extent to which local data is used in health decision making and the barriers to the use of this data at the local level.
Discussion
Our research has shown that aggregate population health measures were not used at the local level due to concerns about the quality and reliability of the data, a lack of capacity and expertise within the public health agencies to calculate and present aggregate measures at the local level and a perceived lack of understanding by public health policy makers of how to interpret aggregate measures. Suggestions for methods to overcome the barriers to the use of aggregate measures tended to focus on better communication between public health analysts and policy makers. Other authors have shown that commonly cited facilitators of the use of research evidence in policy making are; personal contract between researchers and policy makers, timeliness and relevance of the research and research that includes a summary with clear recommendations (15) . However, there has been relatively little research in this area.
Discussion
Aggregate measures provide useful summaries of research evidence specifically for the local population and could assist in improving the use of research evidence by policy makers. PIMs provide a single abbreviated form of presenting evidence and this has also been shown to be one of the preferred methods of receiving information among public health decision makers (16) .
Discussion
The 'top down' approach to health care decision making compounded the lack of effective use of data at the local level. The only country for whom this was not presented as an issue reported exceptionally good lines of communication with their local policy makers who, in turn, were perceived to have a high degree of autonomy for making public health decisions at the UA level.
Limitations
This study was conducted in eight different European countries and interviews were conducted in English although where the interviewee did not speak English the assistance of a colleague who was bi-lingual was used or, if the interviewee preferred it, the appropriate EURO-URHIS country representative contact was used. Within each European country the terminology used to refer to different geographical areas varies. Commonly used terms such as district, municipality and region mean different things in different countries. The identification of the respondents was not systematic, and validation of the information provided by them was not attempted, however, the consistency of the information within and between countries does suggest that further interviews would identify similar themes. The EURO-URHIS partners were asked to identify a potential respondent that had responsibility for policy making at the UA level. National level policy makers were used where devolved UA level agency responsibility for public health did not exist within a country.
Limitations
The number of cities in this study is small thus care should be taken when interpreting these results and generalizing the observations at a wider European level. Furthermore, the countries and interviewees were selected using purposive sampling of representatives of public health agencies most of whom had been actively involved in the EURO-URHIS project. Thus, the sample may not be representative of the wider population, however, given the wide range of job designations within our sample, we feel that the results are able to reflect a wide range of opinions among the users of local and national health data for decision making within Europe.
Conclusion
Despite the wide availability of data at the local level within the European cities aggregate measures using local data are rarely used to inform local decision making. Barriers to the use of aggregate measures in public health decision making include poor communication, poor quality and reliability of data, lack of capacity to calculate these measures and a perceived inability of policy makers to understand aggregate measures. These barriers could be overcome by improved communication between local public health data analysts, improved data quality and reliability and increased awareness of the benefits of presenting data using aggregate measures.
Supplementary data
Supplementary data are available at EURPUB online. 

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