Urban health

10 global cities we’re learning from on urban health

5 min read

Through a mix of in-depth analysis and learning exchanges, we want to learn about how people in other cities, from system leaders to community organisations, are addressing the health impacts of living in urban environments.

Since we launched our new strategy, we’ve been building our understanding of urban health – what it is, who can impact it, and how it shapes our lives.

We also often find ourselves being approached by policy-makers, foundations, practitioners and academics from cities all around the world, interested in learning from our urban health programmes.

We are now building on that with an ambitious two-year international learning project to help us understand the opportunities and barriers to equitable health in urban areas. 

Working with the Innovation Unit, the initiative starts from the premise that there are neighbourhoods elsewhere in the world which face very similar health challenges to our boroughs in inner-city London. And that we can all learn from each other.

We plan to build dialogue and connections with people and places working with peers globally to grow our understanding of the issues and share lessons widely in 2020.


Finding comparable cities

Our boroughs are densely populated with a rich and diverse mix of cultures and ethnicities. There is a wide variation of household income, with affluence and poverty living side by side. When we say ‘urban health’, we mean exploring how these characteristics interact to provide both opportunities for and barriers to good health.

Starting from that premise, we took a data-led approach to find similar places to ours across the globe.

To help identify cities like London we used data from the UN-Habitat’s City Prosperity Initiative (CPI). The CPI is a global initiative that has been applied in over 400 cities. It’s a composite index that measures a city’s overall achievements in key areas including prosperity, infrastructure development and quality of life. It’s used by decision-makers and others to design clear policy interventions. While it’s not exhaustive, it’s probably one of the best datasets out there. 


The diversity challenge

Based on this CPI data, we ranked cities for the closest comparison to London, our boroughs and the specific neighbourhoods we work in. However, one area the CPI data didn’t help us was in assessing diversity. The next challenge was how do we compare and measure diversity and cultural mix globally?

We found that, what we see as a diverse population in London and the UK, may not look the same elsewhere in the world. Trying to find a measure for diversity that worked globally raised fundamental questions: what are we saying is the impact of diverse communities on health? Is it about a cultural understanding of health and good health? Is it language barriers? Are particular populations prone to particular health issues? Has history left certain groups more disadvantaged than others?

There was no perfect answer to these questions and no specific data set to measure any of the above. We’re also very action orientated and focused on workable solutions. So, we chose to focus on proxies that looked at differences between migrant and indigenous populations (percentage of foreign-born population) and language barriers (number of non-native languages spoken).

When layering our diversity data proxies to the original list, two things became apparent:

  • While CPI data gave a global reach of cities, using our chosen measures for diversity significantly altered our original list. For example, Prague, Czechia one of the top five cities comparable to London when using UN Habitat CPI data fell off the list when including our diversity proxy data.
  • The diversity of a place appeared to be very much based on its history. For example, migration into Britain in the 1940s and 1950s seems to account for much of the diversity of the towns and cities in the UK today. This raised further questions about recent vs well established migrant communities and communities whose primary language is the same as the indigenous population but who are born elsewhere.

As we start our global exploration, diversity will continue to be a focal point. If it’s true that it plays such a key role in determining health outcomes in urban communities then understanding what underlies its impact feels key.

Our final list of cities

Using the CIP and diversity sets of data, we’ve come up with a final list of cities in our learning project:

  • Toronto, Canada
  • New York, US
  • Paris, France
  • Melbourne, Australia
  • São Paulo, Brazil
  • Shanghai, China
  • Mexico City, Mexico
  • Glasgow, UK
  • Detroit, US
  • Birmingham, UK

The first seven cities on this list are comparators to London. The next two (Glasgow and Detroit) are comparators to the London boroughs we work in, Lambeth and Southwark, with similarly sized populations and poor health outcomes. The final city (Birmingham) contains the most similar neighbourhoods to those we focus most of our work in.

We sense that as well as being good fits for the data sets available, these cities also give us global spread and are places with interesting initiatives we can learn from and build connections with.

This is a really exciting project and we’ve designed it so that the knowledge gained from connecting across the world benefits anyone working on improving health in urban communities across the system at every level.