Category Archives: Built Environment

Improving the evidence for place effects on (children’s) health

Four members of the CRESH team attended a two-day ‘Good Places, Better Health for Scotland’s Children’ conference (November 27th and 28th 2012, at Murrayfield, Edinburgh; @gpbhscotland and #gpbhconf).  Good Places Better Health (GPBH) is the Scottish Government’s strategy on health and the environment.  The conference reported on the first phase (2008-2011), which addressed how places can help to deliver improvements in four key health challenges facing children under 9 years old in Scotland: obesity, asthma, unintentional injury, mental health and wellbeing.  In 2012 the GPBH team published the recommendations that have arisen from the work programme.

The conference started by outlining how health and place ‘intelligence’ had been brought together to inform the recommendations.  The first step involved developing a conceptual model within which each of the issues and their influences could be framed (see Scottish Government report for more info).  Subsequently, evidence for place effects on health and wellbeing was brought together from scientific literature and workshops with scientific experts, practitioners, and communities.  The need to value and learn from the ‘anecdotes’ from the workshops as well as the ‘hard science’ from the literature reviews was stressed.  When reviewing the evidence gathered the GPBH team noted that (a) the absence of evidence did not equate with absence of possible effect, and that (b) the social, economic, cultural and physical components of places influence our health in complex and intertwined ways that are very difficult to disentangle.

Interestingly, and importantly, the evidence from literature, experts and practitioners varied.  In the case of childhood obesity, experts rated the strongest scientific evidence for the influence of place as being for ‘downstream’ influences on diet (portion size, snacking, fast food and soft drinks), and determinants of sedentary behaviour and of physical activity in schools and nurseries.  But practitioners (e.g., health care, police, and councils) ranked neighbourhood attributes most highly: unattractive, unsightly and unsafe neighbourhoods were 1st, and neighbourhoods without accessible play and sports facilities were 2nd.

Community engagement approaches were used to assess how communities saw and experienced things – as ‘on-the-ground’ experts.  When asked how place impacted upon their children’s health, parents and carers concurred with the practitioners.  They highlighted mainly negative neighbourhood attributes: anti-social behaviour, dog fouling, junkies, drug dealers, hooligans, drug paraphernalia, vandalism, and the lack of appropriate neighbourhood facilities.  There was a strong sense from these meetings that what researchers often label ‘low-level incivilities’ – things like dog mess, litter and graffiti – can cause a very high level of distress and potential health detriment.  These personal insights should inform and direct our investigations of neighbourhood influences on health.

When bringing the evidence together the team noted that the evidence was mainly for downstream determinants of health, such as displaying sweets at shop tills, and that the much-needed evidence for upstream drivers (the economic, social and political driving forces) would remain elusive until we were ‘permitted’ to randomise society and conduct controlled experiments!  Fortunately for all concerned this is unlikely to happen.  But the difficulties involved in evidencing the impacts of these wider determinants are clear.

What will help are neat approaches to working with pre-existing data that have been collected for individuals or populations over time – e.g., longitudinal survey datasets such as the British Household Panel Survey, repeated cross-sectional surveys such as the Scottish Health Survey, or national death records.  These provide us with a cost-effective means of assessing how wider changes to the upstream drivers of health might influence our health and health behaviours, by exploiting what are often referred to as ‘natural experiments’.  Here we can assess how health or behaviour changed either side of a particular ‘event’ (e.g., introduction of a policy to create smoke-free public buildings: see Jamie Pearce’s work in New Zealand) without needing to establish costly monitoring programmes that might not last long enough to show any effect.  Evidence from natural experiments can be even more robust if, in addition to ‘before and after’ data, we can also identify ‘treatment and control’ sites.  In the UK we have the opportunity of survey data that include both treatment and control nations: for example smoking was banned in public places in Scotland one year before it was in England, Wales or Northern Ireland.

If we remain fixated on the idea that ‘randomised controlled trials’ are the only evidence that counts (see Rich Mitchell’s blog post for discussion) we risk seeing public health policies being made on little or no research evidence (see Dunn and Bobak).  Alternatively, accepting the value of what we can learn from natural experiments and other longitudinal study designs will enable us to provide policy-makers with evidence for how upstream drivers influence our health.   The wealth of already-collected data available in the UK and elsewhere gives us a great opportunity to ‘add value’ to these investments and uncover important lessons about environment and health relationships.  We should continue to utilise these data, improve our use of them, and support the continuation of their collection.

Author: Elizabeth Richardson

What if neighbouring areas are very different?

Waldo Tobler’s first law of geography is that “everything is related to everything else, but near things are more related than distant things.” This is an important idea for many aspects of spatial science, but it’s taken particularly seriously by people who draw maps and do statistics to investigate how and why disease rates vary from place to place.

If Tobler’s first law holds,  we should expect the characteristics of people and places who are close together (including their health) to be similar. So, in general, the folk who live in your neighbourhood should be more like you than the folk who live on the other side of town.

This matters when we are researching if and how environment affects health. We know that people’s health can be affected by a huge range of things. If we are to reveal the health impacts that environment has, we need to try and allow for as many of those other influences as possible. However, we know that it’s very hard to account for all of them. This means some of the relationship between environment and health we see in our analyses may actually be due to these ‘unmeasured’ influences, a problem we call ‘residual confounding’. Now, if Tobler’s law is right, it is also likely that these unmeasured influences are also more similar when they are closer together. When this happens, it’s called residual spatial confounding. If we don’t allow for it, we run the risk of making mistakes in assessing the strength of relationships between the characteristics of environments and the health of the people who live there.

The good news is that these problems have long been known about and there are a range of techniques to try and deal with them. They include ways to statistically ‘smooth’ maps showing how risk of a disease varies from area to area, and to adjust measurements of risk for how close together they are in geographic space.

The bad news is that Tobler’s first law is not always true! It’s not always the case that neighbouring areas do have similar characteristics or environments. Often areas that are right next to each other contain very different types of people and have a very different environment. You have probably experienced this when walking around a town or city. You cross a road, the housing changes dramatically, and the streets ‘feel’ different. Those statistical techniques assume that kind of sudden change doesn’t happen.

Dr Duncan Lee and Prof Rich Mitchell have just finished an ESRC funded research project (RES-000-22-4256) trying to improve the way we handle this situation in our research. We have successfully created, and published techniques that can spot when two neighbourhood areas are so different that we need to alter our statistical assessment of the relationships between health and environment. One technique, published in Biostatistics, can be used when we have data that tell us something about the characteristics of the people or the neighbourhoods, such as house prices or smoking rates. The other can be used when all we have is information about health in the areas (now in press with Journal of the Royal Statistical Society Series C) .

Here’s an example of our results. The map below (click it to view full size) shows 271 areas that make up the Greater Glasgow and Clyde Health Board (for the geeks, the areas are intermediate geography zones). We obtained data on the risk of admission to hospital with a primary diagnosis of respiratory disease, from the Scottish Neighbourhood Statistics database ( The map is shaded so the colour of each area denotes its disease risk, with a value of 1.0 representing an average risk across the whole health board. Values above 1.0 represent high risk areas (for example a value of 1.10 indicates a 10% higher risk), while values below 1.0 represent low risk areas (for example a value of 0.85 indicates a 15% reduced risk). The red lines show boundaries between neighbouring areas that contain populations at very different risk of hospital admission for respiratory disease. These are the areas in which the conventional techniques would make mistakes. There are 173 of them… that’s 25% of all the boundaries in the map.


map of respiratory admissions

Data and boundaries © Crown Copyright. All rights reserved 2010.

We have created a free software package that will allow anyone to apply our techniques. It’s called CARBayes and is for the statistical software R. You can read about it and get it from here.

There has also been an interesting spin off from this research. Within Glasgow, we found a lot of neighbourhoods that were right next to each other but were very different in social and economic terms. We called these between-neighbourhood differences ‘social cliffs’. It prompted us to ask how such social cliffs occur. One idea is that they may be made more likely by physical barriers between the neighbourhoods, such as rivers, main roads or railways.The map below (click it to view full size) shows the kinds of physical features we’ve been looking at (note, our data are for the period before the new M74 motorway was opened).



Map based on data that are © Crown Copyright/database right 2012. An Ordnance Survey/EDINA supplied service.

Our research is now complete and we’re writing it up for publication. It seems that two kinds of physical feature are especially important. Where one or both of them lie along a neighbourhood boundary, it’s much more likely that the neighbourhoods will be very different socially, and economically. Which two do you think they are?


16th Emerging New Researchers in the Geographies of Health & Impairment (ENRGHI) conference


The 16th Emerging New Researchers in the Geographies of Health & Impairment (ENRGHI) conference took place in London at the beginning of September, jointly organised by University College London and Queen Mary’s University College London.

ENRGHI is a conference run for and by post graduate and early career researchers and provides an innovative and supportive forum for presenting and sharing ideas.  The conference consisted of two days of posters and presentations, as well as opportunities for networking, socialising and a careers Q&A session.

CRESH PhD student Anna Kenyon presented on the socio-spatial distribution of environments that are likely to support walking throughout urban Scotland, concluding that there is little evidence of inequality in the distribution of good walking environments in relation to area deprivation.

The talk sparked debate about which features of urban environments are the most important to include in measures of area walkability. This led to a wider discussion about the balance, when measuring environmental determinants of health behaviours, between the benefits of using a large study area, such as urban Scotland, and the inevitable technical constraints this places on the specificity of measures used.

Other delegates made presentations on a diverse range of topics and attendees voted for the presentation they thought was the best.  Topics of the winning presentations were: Women’s detention and mental health, Environmental and socio economic factors associated with leishmaniasis outbreaks in Saudi Arabia, and Inequalities in the provision of treatment for chronic kidney disease in the UK.

Dr. Maurzio Gibin (Birkbeck University of London) gave a plenary speech on geovisualisation techniques and presenting geographic data to non-expert audiences as well as ingenious methods of presenting geographic data.  Prof. Steve Cummins (Queen Mary University of London) presented on the benefits of using of natural experiments in geography.  Prizes for the best three delegate presentations were presented by Dr Jim Dunn, deputy editor of the Journal of Epidemiology and Community Health.

Postgraduates and postdoctoral researchers travelled from countries including Canada, France, Australia and the Netherlands as well as throughout the UK to attend the conference.

Details of the next ENRGHI conference as well as including photos of the event, posters and prizes from this year can be viewed on the ENRGHI website:


Anna Kenyon

October 2012

London 2012: inspiring a generation and regenerating East London?

Immediately following the Olympics, Prime Minister David Cameron announced that government funding to support Team GB athletes would increase up to the Rio 2016 Games. “The motto of these Games has been ‘Inspire a generation’. Nothing has been more inspirational than seeing our elite athletes win Gold this summer. There’s a direct link between elite success and participation in sport” he said.

This link between the inspiration of elite athletes and wider public participation in physical activity was a key claim made for the London Olympics from the start. The London 2012 Candidate file, part of the initial bid for the Games, stated that the Olympics would: “…inspire a new generation to greater sporting activity and achievement, helping to foster a healthy and active nation.”

Furthermore, the Government’s Legacy Action Plan published in 2008 promised that the Olympics would “transform the heart of East London” the home of the Olympics Park by “turning one of London’s most deprived areas into a world-class district for living, leisure, business and sport, with safe and sustainable neighbourhoods, new parkland, new homes, jobs, and social and leisure facilities for generations to come.”

So, what is the evidence regarding the impacts of large scale sporting effects on public participation in sport and area regeneration?

A review of research into the impact of mass sporting events on physical activity among the wider population by Murphy and Bauman (2007) suggested a lack of evidence for a public health benefit. A systematic review of the health and socioeconomic impacts of major multi-sport events published in 2010 by McCartney and colleagues found that few studies looked at health outcomes and concluded similarly: “The available evidence is not sufficient to confirm or refute expectations about the health or socioeconomic benefits for the host population of previous major multi-sport events.”

Sport is not the only way that London 2012 might affect health and wellbeing. Huge amounts of construction and regeneration in East London have already taken place or are planned. Yet the impacts of that are also uncertain. A review by Davies (2010) of sport and economic regeneration also highlighted that “…no comprehensive longitudinal post-event study has ever been undertaken on the economic regeneration impacts of the Olympic Games”.

The impacts of regeneration for London 2012 are being closely watched. A longitudinal study of the health and social impacts of the London Olympics upon families in East London is already underway. The Olympic Regeneration in East London (ORiEL) Study, led by Professor Steven Cummins at Queen Mary, University of London, is a five-year study, following approximately 1,800 school children and their parents in Tower Hamlets, Hackney, Newham and Barking and Dagenham. It will assess their health and well-being over time. The results of this study should demonstrate whether claims that have been confidently made for the long term public benefits of the 2012 London Olympic games, and the regeneration accompanying them, prove to be true.

By Helena Tunstall

Stigma, environments and health inequalities: why should we be interested?

In recent years there has been a great deal of interest amongst health researchers in the role of social stigma in affecting health. Social stigma can be articulated as a majority view that works to spoil the identity of others on the basis of a discriminating characteristic such as race, gender or class. The social stigma associated with some minority groups has been shown to have health salience in terms of providing an obstacle to gaining access health care, housing provision, welfare, employment and other underlying factors affecting health. Groups that have been the subjects of research include disabled, homeless and itinerant populations and this body of work has revealed the multitude of interpersonal and institutional factors linking discrimination with health. Stigma has also been adopted as a deliberate strategy in health promotion initiatives, most notably in tobacco control with recent work beginning to question whether the denormalisation and stigmatisation of smoking (and the smoker) has reached its limit as a public health goal.

Given the long tradition of work on stigma and health, and the importance of stigma for establishing and perpetuating health inequalities, it is perhaps surprising that few researchers have considered the potential significance of place and the environment in establishing, perpetuating and mediating social stigma. In a recent commentary* on a Japanese paper on place-based discrimination published in the journal Social Science and Medicine, I argue that geographers (and others with interests in place, space and health) could productively consider the role of spatial stigma in affecting the health of local residents. Spatial stigma arises in places with notoriety in the public discourse, and that are constructed as ‘no-go zones’ or ‘sink estates’ that require constant policing.  Neighbourhoods such as Toxteth in Liverpool, South Central in Los Angeles or the French banlieues have for instance been prejudiced by deep-rooted geographical discrimination.  Key to the argument in the commentary is that there are a range of consequences for population health of residing in a highly stigmatised community. Yet very few empirical studies have tested the salience of spatial stigma in affecting population health.

So why should researchers with interests in the environment and spatial inequalities in health be concerned with place-based stigma? In the Social Science and Medicine commentary, I suggest that health might be compromised by spatial stigma through a series of (non-mutually exclusive) individualised and institutional pathways, which in turn can exacerbate geographical inequalities in health. These include:

1. Being ‘looked down on’ because of residing in a stigmatised community can detrimentally affect a number of life chances such as education and training opportunities, employment prospects and the prospects of developing interpersonal relationships. These factors have all been implicated in studies of health.

2. Stigma relating to particular places may act as ‘badge of dishonour’ that results in local residents taking actions such as concealing their address, avoiding receiving visitors or providing excuses to others for where they live. These feelings of shame can work to spoil, manipulate and mediate individual identities and social relations and affect health (e.g. health behaviours or mental health).

3. Place-based stigma affects the levels investment and disinvestment of public and private resources put into the local community. Progressive social policy is undermined by the lack of investment in the local infrastructure, housing and other services that provide the opportunities for healthy living.

4. Social networks, community social bonds and collective efficacy are affected by residents’ withdrawal from the public realm in response to the perceived threats associated with spatial stigma (e.g. crime). The breakdown of these community ties is detrimental to physical and mental health outcomes of local populations.

In short, there is plenty of evidence from the urban sociology and urban geography literature that through a variety of intersecting pathways place-based stigmatisation is harmful to the life chances of local residents. The population health consequences of place-based stigma are however less well established; understanding these pathways is an important challenge for researchers with an interest in the environment and health. This challenge is particularly important during a period of austerity with major reductions in state investment in a range of health related infrastructure. A likely consequence of this retrenchment is the heightened stigmatisation of many socially disadvantaged communities with potentially disastrous implications for public health and health inequalities.

Jamie Pearce, August 2012

 *Library access required; if you are unable to get hold of the paper then I’d be please to email you a copy ( .

Neighbourhood built environment related to transport and leisure physical activity

A new study involving CRESH researchers on neighbourhood built environments and  transport and leisure physical activity has recently been published in the journal Environmental Health Perspectives. The New Zealand study collected data on the urban built environment (destination access, street connectivity, dwelling density, land-use mix and streetscape quality) and surveyed 2,033 adults who lived in 48 New Zealand neighbourhoods. The findings suggested associations of neighbourhood destination access, street connectivity, and dwelling density with self-reported and objectively measured PA were moderately strong.  You can find the paper here: