Annie Harrison

Developing a European urban health indicator system: results of EURO-URHIS 1

Introduction More than half of the worlds population now live in cities, including over 70% in Europe. Cities bring opportunities but can be unhealthy places to live. The poorest urban dwellers live in the worst environments and are at the greatest risk of poor health outcomes. EURO-URHIS 1 set out to compile a cross-EU inventory of member states use of measures of urban health in order to support policymakers and improve public health policy. Following a literature review to define terms and find an appropriate model to guide urban health research, EURO-URHIS Urban Areas in all EU member states except Luxembourg, as well as Croatia, Turkey, Macedonia, Iceland and Norway, were defined and selected in collaboration with project partners. Following piloting of the survey tool, a the EURO-URHIS 45 data collection tool was sent out to contacts in all countries with identified EUAs, asking for data on 45 Urban Health Indicators (UHI) and 10 other indicators. 60 questionnaires were received from 30 countries, giving information on local health indicator availability, definitions and sources. Telephone interviews were also conducted with 14 respondents about their knowledge of sources of urban health data and barriers or problems experienced when collecting the data. Most participants had little problem identifying the sources of data, though some found that data was not always routinely recorded and was held by diverse sources or not at local level. Some participants found the data collection instrument to not be user-friendly and with UHI definitions that were sometimes unclear. However, the work has demonstrated that urban health and its measurement is of major relevance and importance for Public Health across Europe. The current study has constructed an initial system of European UHIs to meet the objectives of the project, but has also clearly demonstrated that further development work is required. The importance and value of examining UHIs has been confirmed, and the scene has been set for further studies on this topic.

Differences in adults’ health and health behaviour between 16 European urban areas and the associations with socio-economic status and physical and social environment

Background With a growing proportion of the European population living in urban areas (UAs), exploring health in urban areas becomes increasingly important. The objective of this study is to assess the magnitude of differences in health and health behaviour between adults living in urban areas (UAs) across Europe. We also explored whether and to what extent such differences can be explained by socio-economic status (SES) and physical or social environment. Data were obtained from a cross-sectional questionnaire survey, performed between as part of the European Urban Health Indicator System Part 2 (EURO-URHIS 2) project. Using multi-level logistic regression analysis, UA differences in psychological distress, self-assessed health, overweight and obesity, daily smoking, binge drinking and physical exercise were assessed. Median Odds Ratios (MORs) were calculated to estimate the extent to which the observed variance is attributable to UA, individual-level SES (measured by perceived financial strains, education level and employment status) and/or characteristics of physical and social environment. The dataset included 14 022 respondents in 16 UAs within 9 countries. After correction for age and gender, all MORs, except that for daily smoking, indicated statistically significant UA health differences. SES indicators (partly) explained UA differences in psychological distress, decreasing the MOR from 1.43 [95% credible interval (Cr.I.) 1.27-1.67, baseline model], to 1.25 (95% Cr.I. 1.14-1.40, SES model): a reduction of 42%. Accounting for the quality of green areas reduced the MOR for psychological distress by an additional 40%, to 1.15 (95% Cr.I. 1.05-1.28). Our study showed large differences in health and health behaviour between European UAs. Reducing socio-economic disadvantage and improving the quality of the neighbourhoods green spaces may reduce UA differences in psychological distress.

What factors are associated with the prevalence of atopic symptoms amongst adolescents in Greater Manchester

INTRODUCTION Atopy commonly manifests itself as atopic diseases (ADs), namely asthma, eczema and hay fever. The prevalence of AD is rising worldwide, and it is widely accepted as a major public health issue, due to the significant burden of AD on health care systems. METHOD Data were obtained from the youth survey of the European Urban Health Indicator System 2 project. The study participants were students aged 14-16 years from Greater Manchester. The main outcomes measured were the prevalence of atopic symptoms and the factors associated with the development of atopy. RESULTS Of the sample studied, 70% reported having had an AD during their lifetime. The lifetime prevalence of ever having asthma, eczema or hay fever was 33.4%, 28.1% and 49.0%, respectively. Gender, family affluence, body mass index, diet, smoking and worrying were all significantly associated with atopic symptoms. Smoking was the only variable to be significantly associated with all three ADs. Season of birth and pollution were not shown to be associated with atopic symptoms. CONCLUSION This study demonstrates that the prevalence of AD in Greater Manchester was high amongst adolescents. Several environmental, demographic and social factors were found to be significantly associated with the development of atopic symptoms. This study provides a baseline for future studies to further investigate the factors that are associated with AD and allow for the implementation of preventative public health policy.

Health status in Europe: comparison of 24 urban areas to the corresponding 10 countries (EURO-URHIS 2).

Background : In Europe, over 70% of the population live in urban areas (UAs). Most international comparative health research is done using national level data, as reliable and comparable urban data are often unavailable or difficult to access. This study aims to investigate whether population health is different in UAs compared with their corresponding countries. : Routinely available health-related data were collected by the EURO-URHIS 2 project, for 10 European countries and for 24 UAs within those countries. National and UA level data for 11 health indicators were compared through the calculation of relative difference, and geographical patterns within Europe were investigated using the Mann Whitney U test. Linear regression modelling was used to adjust for population density, gross domestic product and urbanicity. : In general, the urban population in Eastern Europe is less healthy than the Western European urban population. However, people in Eastern Europe have significantly better broad health outcomes in UAs as compared with the corresponding country as a whole, whereas people in Western Europe have generally worse broader health outcomes in UAs. : For most European countries and UAs that were investigated, the national level health status data does not correspond with the health status at UA level. In order to identify health problems in UAs and to provide information for local health policy, health monitoring and international benchmarking should also be conducted at the local level.

Can health indicators help policy-makers? Experience from European system of urban health indicators (EURO-URHIS).

Background This paper proposes that Population Impact Measures (PIMs), the Population Impact Number of Eliminating a Risk Factor over a time period (PIN-ER-t) and the number of events prevented in your population (NEPP), can assist in policy making as they include relevant information which describes the impact or benefits to the population of risk factors and interventions. In this study, we explore the utilization of the indicators from European System of Urban Health Indicators System to produce the two PIMs. We identified from the indicators list the health determinants, health status and health interventions which can be linked, and searched Medline for evidence of association. We then investigated whether the type of frequency measure available for the indicator match with the measure used in PIMs, and explored data availability for the City of Manchester (UK) as an urban area. Of the 39 indicators relevant to socio-economic factors, health determinants and health status, it was possible to calculate the population impact of a risk factor, i.e. the PIN-ER-t, for only six associations, and the population impact of health interventions, i.e. NEPP, for only one out of the three listed indicators, as the relevant health conditions were not included. The results of this study suggest that if an indicator system is intended to play a part in the policy making process, then the method of presentation to policy-makers should be decided before setting up the system, as it is likely that some indicators which would be essential might not be available.

Development of an Urban Health Impact Assessment methodology: indicating the health equity impacts of urban policies

Background An overarching recommendation of the global Commission on Social Determinants of Health was to measure and understand health inequalities and assess the impact of action. In a rapidly urbanising world, now is the time for Urban HIA. This article describes the development of robust and easy-to-use HIA tools to identify and address health inequalities from new urban policies. Rapid reviews and consultation with experts identified existing HIA screening tools and methodologies which were then analyzed against predefined selection criteria. A draft Urban HIA Screening Tool (UrHIST) and Urban HIA methodology (UrHIA) were synthesised. The draft tools were tested and refined using a modified Delphi approach that included input from urban and public health experts, practitioners and policy makers. The outputs were two easy-to-use stand-alone urban HIA tools. The reviews and consultations identified an underpinning conceptual framework. The screening tool is used to determine whether a full HIA is required, or for a brief assessment. Urban health indicators are a readily available and efficient means of identifying variations in the health of populations potentially affected by policies. Indicators are, however, currently underutilised in HIA practice. This may limit the identification of health inequalities by HIA and production of recommendations. The new tools utilise health indicator data more fully. UrHIA also incorporates a hierarchy of evidence for use during impact analysis. The new urban HIA tools have the potential to enhance the rigour of HIAs and improve the identification and amelioration of health inequalities generated by urban policies.

Smoking cessation support in community pharmacies: is the association of smoking and eye disease an additional tool to increase uptake?

Objective Community pharmacists have a central role in provision of smoking‐cessation support. Evidence is growing that tobacco smoking is associated with development and progression of several potentially blinding eye diseases. As part of an ongoing programme of raising awareness of this link, we investigated whether community pharmacists are aware of this association and incorporate relevant advice into their smoking‐cessation guidance.

Utilizing community and voluntary sector partnerships to survey and compare the health outcomes of hard-to- reach groups to the wider community-the EURO- URHIS 2 Hard-to-Reach Project

Background This article describes the Hard-to-Reach (HtR) Project that was developed to capture health and lifestyle data from groups who are HtR by postal surveys within the larger EURO-URHIS 2 project. By collaborating with partner organizations, data were collected using standard survey tools, allowing for comparison with the wider population. Following a scoping exercise to determine which groups were HtR in Greater Manchester, black and minority ethnic (BME) groups and students were selected. BME groups were surveyed through partnership with Community and Voluntary Sector Organizations (CVSOs). Language barriers were addressed through the recruitment of volunteer interpreters. Students were surveyed by accessing university premises. Fifteen survey visits took place at nine CVSOs and five visits to University facilities. In total, 144 eligible surveys were collected. There were significant differences for both HtR groups, compared with Greater Manchester and the EURO-URHIS 2 mean. Both HtR groups had worse outcomes than both Greater Manchester and EURO-URHIS 2 for psychological problems. In addition, students had worse outcomes for passive smoking, binge drinking, use of cannabis, lack of access to green spaces, less sense of belonging and social cohesion and damp or mildewed homes, and better outcomes for self-perceived health and overweight and obesity. BME had in addition worse outcomes than both Greater Manchester and EURO-URHIS 2 for long-standing restrictive illness. Despite the limitations of this study, the development of this methodology allowed for the collection of comparable data, showing up statistically significant differences between the HtR populations and the wider population which merits further investigation.

Research investment disparities in England.

Are researchers in the so-called Northern Powerhouse (northwest and northeast England) getting a fair deal from government compared with those in the Golden Triangle (eastern and southeastern England, plus London)? For the fi rst time, data from the Offi ce of National Statistics (ONS) are enabling year-on-year comparisons by region for gross expenditure on research and development. Between 2012, and 2013, the Northern Powerhouse’s share of government investment rose from 3·51% to 3·73% (up by 0·22%), while the Golden Triangle’s share fell from 61·33% to 61·11% (down by 0·22%). These changes ignore infl ation (2–3% per year in this period) and population diff erences. To ascertain whether the Northern Powerhouse is getting a fair deal from the government, we defined fairness as being when the research investment per person reaches parity between regions. Between 2012, and 2013, investment per person rose by £1·01 in the Northern Powerhouse and by £3·08 in the Golden Triangle (table). After we corrected for 2% infl ation, the increase in per capita investment was £0·99 for Northern Powerhouse and £3·02 for the Golden Triangle, which clearly shows increasing disparity. The Northern Powerhouse is not getting a fair deal: its deal is getting increasingly worse and other UK regions outside the Golden Triangle fare no better. This gloomy outlook is not improved even when other sources of funding to the Northern Powerhouse are taken into account. Business investment has decreased by 0·65% and investment by the Higher Education Funding Council for England has been reduced by 0·21% compared to an increase in the Golden Triangle of 0·37%. Investment by charities has decreased across the board, but they represent only about 2% of total research investment. The Golden Triangle is the only UK region characterised as a so-called innovation leader on the European Commission’s Regional Innovation Scoreboard 2014. The Northern Powerhouse is an innovation follower, and the UK Government strategy has recognised the national importance of the north of England catching up to the south. If this strategy is to be successful, research investment needs to be reformed. Mazzucatto and colleagues have reported that regions

Factors associated with the prevalence of adolescent binge drinking in the urban areas of Greater Manchester.

Background Binge drinking in adolescents is a serious problem that has been recognised for over a generation. On average 61% of students in the European region had consumed alcohol in the last 30 days and 43% had participated in binge drinking in the same period. This article investigates the prevalence of adolescent binge drinking and the factors associated with this prevalence in urban areas of Greater Manchester. Methods Data were obtained from the youth survey of the European Urban Health Indicator System 2 project. Study participants were school students aged 14-16 from the urban areas of Greater Manchester. The main outcome measures were adolescent binge drinking prevalence in Greater Manchester and the socio-demographic factors influencing it. Results Greater Manchester had an adolescent binge drinking prevalence of 49.8%. Individual factors associated with increased prevalence of binge drinking were: age, substance use, school performance and early initiation of drinking (all significant at χ2, P < 0.05). Peer factors associated with increased prevalence of binge drinking were spending evenings with friends, keeping in touch with friends, having a good relationship with peers and self-reported bullying behaviours (all significant at χ2, P < 0.05). Family support lowered the prevalence of adolescent binge drinking. Conduct problems, family affluence and perceived local crime increased the prevalence of adolescent binge drinking. Conclusions Binge drinking is highly prevalent in Greater Manchester adolescents. Various individual, peer-related, family-related and community-related factors were associated with this problem. Any attempt to tackle the prevalence of adolescent binge drinking must take into account all of these factors.