Skip to navigation | Skip to main content | Skip to footer


Development and application of aggregate health policy measures

Work Package 7 was designed to help influence and determine health policy by aiding policy makers at local, national and international level. Work Package 7 was led by the University of Manchester.

As an extension to the analysis part of the project, further inter/intra-national comparisons were made at an urban level using hte data collected in WP5. The outputs can help with benchmarking, identifying best practice, sharing good practice and analysis of current models.

Particular attention was drawn to overarching issues such as children, ageing and women.

Examples of this analysis included:

Healthy Life Years

Health expectancy, especially the EU structural indicator 'Healthy Life Years (HLY)'. This aggregate measure is calculated based on life expectancy and the prevalence of a selected health measure. In the case of the HLY, this measure is the indicator 'activity limitations', implying the limitation in usual activity over the past 6 months caused by a health problem. To calculate the measure in a valid way, accurate mortality data and survey data are needed at the city level. In large sample sizes, it is possible to make specific calculations for e.g. socio-economically contrasting neighbourhoods.


DALYs, or 'disability-adjusted life years'. This method is used to compare the health impact from various (groups of) diseases or major risk factors.

At the urban level, it is probably feasible to make comparisons for a limited number of large diseases. The data needed for this include age- and cause-specific mortality data, and accurate estimates on the prevalence of the selected disease groups. The weighting factors for the diseases selected can be taken from international sources, although the applicability of weights derived from different populations has to be considered. In this way, the burden from a few major disease groups (cardiovascular, cancer, injuries/accidents, mental/behavioral) can be estimated and the differences between cities assessed.

Future Predictions

Future predictions of health trends. Trends in the prevalence of certain health problems can be made. The basis for this are: past trends based on accurate measurements, and the assumption that the relation between prevalence and certain sub-populations (e.g. age, education level, ethnicity) is constant or otherwise known. In addition, projections are needed on the changes in population composition in the cities, by the variables that are connected with health measures.


Synthesise an Urban Health Impact Assessment Methodology

Health Impact Assessment (HIA) is a tool well described and used in various settings. Its main use is to assess the health impact of non-health policies, but this usually involves qualitative data and the type of data collected in EURO-URHIS may not be relevant. We therefore assessed the ability of urban health indicators to contribute to HIA. We:

Searched and reviewed HIA methodologies – including IMPACT’s European Policy HIA (EPHIA) methodology – to assess appropriateness for use in an UA context;

Assessed the value of Urban Health Indicators in the context of HIA methodology, such as selection criteria, and synthesised a draft Urban Health Impact Assessment methodology building on good practice.

Develop an Urban Health Impact Screening Tool – URHIST

We developed and implemented a screening tool search strategy focusing on tools applied in urban settings and constructed from population health indicators, using the results of all the other parts of this WP;

We analysed selected tools against quality criteria;

We reviewed EURO-URHIS 2 indicators for application in a screening tool with reference to health inequalities;

We synthesised a draft URHIST screening tool using EURO-URHIS 2 indicators and defining threshold indicator values based on the health inequalities context.

Test the URHIST Screening Tool

We applyed the draft URHIST screening tool to the current policy plans of selected UAs, identifying policy proposals that meet URHIA screening tool threshold values and are appropriate for an URHIA;

We defined the depth of URHIA, e.g., ‘rapid’ or ‘comprehensive’;

We evaluated the reliability and validity of the URHIST screening tool, including use of EURO-URHIS indicators, and refined accordingly.


PIMs are population-based measures which describe the impact of a health risk or benefit of an intervention. EURO-URHIS workpackage 9 used data from four UAs in Europe to calculate the reduction in the prevalence of asthma achieved from reducing the prevalence of smoking from its current level and the number of deaths prevented by using methadone maintenance treatment. The pilot proved that data at urban level for four countries can be collected to calculate PIMs for presentation to policy makers. However, the range of health indicators for which adequate data are available for the calculation of PIMs may be limited. We therefore explored the number of indicators for which PIMs may be of value, and made calculations for those for which data are adequate. Where data was not available, suggestions for the ease of obtaining such data were made.