Guide: Developing a local ‘State of Ageing’ report

Designed as a whole document that proceeds in order, you can also navigate to the sections which are of most interest and can also stand alone. There are also tools/resources throughout designed to help you get familiar with ageing data and understand how to use it to support your work.

1. Introduction
2. The age-friendly domains
3. Selecting indicators
4. Accessing quantitative data
5. Going beyond quantitative data
6. Addressing intersectionality and inequalities
7. Interpreting your data
8. Taking data back into your community
9. How we created 'The State of Ageing in Leeds'

Understanding your local population, where and how well people are living and ageing, is fundamental to creating effective local ageing programmes and policies.

Using data in your work can help you better understand the needs of your population, design and deliver services more effectively, focus public and political attention on important issues for older people, and understand the impact your work is having.

Developing this understanding is also the first step on the journey to be coming an Age-friendly City and Community in the World Health Organisation’s Age-friendly programme cycle which recommends the production of a baseline report to understand your community’s starting point. However, this should not be a one-off process that you do at the beginning – you can continuously revisit your data as your plans develop and mature to ensure you are going in the right direction.

This guide builds on our national State of Ageing report, which brings together a wide range of data sources on ageing nationally, and our work on a local State of Ageing in Leeds.

To begin developing a data-led approach to ageing, it’s first useful to have a framework around which you can organise your data.

The World Health Organisation’s Age-friendly Cities and Communities framework is globally recognised, and well tested.  It sets out eight domains of community life that local decision-makers, networks, and service providers need to consider in their ageing strategies. Imagine this framework as the ‘scaffolding’ of your data-led approach, enabling you to build up your understanding of the lives of local older people.

For more insight into these domains, and some of the evidence and best practice behind them, check out Ageing Better’s page on the eight domains.

Indicators are key to a data-led approach to ageing, and this section will help you build an understanding of what they are, why you might use them, how to select and update them. It is designed to help you understand the considerations that go into using indicators, which you can then use flexibly as you start thinking about using data to inform your thinking on ageing.

There are two broad sources of quantitative data that you might want to use to build your understanding of ageing in your local area: national data sets that report on all local areas across England, and data sets that are produced within your local area.

Using quantitative data can feel like a step outside your comfort zone if you’re not familiar with it. While some people working on ageing will have support from data analysts in their organisation, others won’t. Some may have analysts available but aren’t sure what to ask them for.

To help you get started in delving deeper we have compiled a ‘Guide to data sources’, an overview of the nationally produced data that is publicly available on ageing at a local level for each of the eight age-friendly domains. This isn’t meant to be an exhaustive list (although we have tried to be thorough) but rather a starting point which sets out the broad landscape.

It is important to be cautious when interpreting data, and this overview is not intended to provide a blueprint to bypass the work needed to underpin that cautious approach. Instead, it is intended to give you a sense of what is out there, to help you inform your thinking and conversations with data analysts in your local authority if you have that support. We have limited this overview to sources that are available for areas across England. It might be that the data that you really want is not available across the whole country, but that it can be supplied locally, or that qualitative insights can be used to develop a richer local picture.

Many of the sources listed are direct links to spreadsheets where you can search for your area, while some require more user input. Stat-Xplore is the online repository of the data regularly published by the Department for Work and Pensions on welfare benefits, and requires more user input to shape and download the data. To help with this higher barrier to entry we have created a guide to help first-time users understand what Stat-Xplore can offer.

We have brought together a few of the sources listed in ‘A guide to data sources’. You can view some headline figures on ageing by searching your local authority in the search bar below:

So far, we have focused on data that is available across England, but it’s very likely that your area also collects other useful data. It is crucial to engage with colleagues across your local authority to understand what data there is collected locally. You might do this informally through one-to-one discussions, or host a roundtable with colleagues where you discuss the domains of ageing well and brainstorm the data available within the council. This could include data on service use that departments collect, council surveys, or other kinds of knowledge that they have (quantitative or qualitative) about services and needs in the area.

In our work in Leeds, for example, locally produced data included: housing satisfaction; the prevalence of zero hours contracts in work; participation of people over 50 in adult education programmes; how residents travel and their perceptions of public transport in the area; rates of active travel; rates of social inclusion through local programmes and volunteering; and patterns of dementia within the city.

You may also have some resource to produce new local data. Surveys are one method that you may be able to use, although they can quickly become expensive, and it might be more realistic to add some questions to any existing resident surveys. In 2019, Ageing Better produced our ‘Ageing Better Measures Framework’ that brings together high-quality, robust measures for understanding individual level outcomes around ageing. This provides guidance on the kinds of robust, validated questions that you could ask

Often the things that you need to know to shape more effective policy and programmes, won’t come with a matching set of quantitative data, nationally or locally produced. Even if there is one, it can never tell you everything that you need to know to develop and monitor an effective approach around an issue. If you’ve explored the data sources linked in the section above, you might have noticed some of these gaps. This section of the guide introduces a range of approaches that you can use to supplement and enrich that data including: qualitative research; intersectionality; and guidance on interpreting data.

It is important that when we talk about ‘data’ we don’t develop a tunnel-vision focus on numbers or quantitative data. What we are really interested in is building knowledge around ageing, and numbers are one tool for doing that. Qualitative data offers lots of other ways to produce new local insights, like running focus groups with older residents or doing interviews with service users. These could be focussed on particular issues which you already know are important locally – for example fuel poverty in a part of your borough – or they could be more exploratory, designed to uncover persistent issues facing older residents that are not already on your radar or develop an awareness of rapidly emerging issues.

Where quantitative data is not granular enough to represent different experiences in your local area, qualitative data offers another way to develop useful knowledge. Rather than trying to measure things, qualitative data gets behind the numbers and explores the nuance of people’s lives and experiences, and their understanding of ‘how’ and ‘why’ things are happening. As well as focus groups and interviews with residents, you might also want to think about case studies as another useful tool for exploring and presenting qualitative insights. These can be a variety of formats, including written case studies or short videos. Case studies let you dig down into something – a topic, a service/organisation, a person or neighbourhood – and explore it in depth.

Below are two examples produced by the Centre for Ageing Better on the challenges facing people in their 50s and 60s and experiences of ageism in job recruitment. This format, focussed on a few people and their stories, lets you explore ideas that can be hard to define, giving people the opportunity to articulate what they mean in their own lives and words.

You might also want to use case studies of your own work as a way of sharing good practice and learning with other areas working on ageing. Here is an example from the UK Network of Age-friendly Communities from a project in Newcastle on digital visibility.

There is not one universal experience of ageing. Experiences of older age differ between individuals but also, crucially, between different parts of the country and different parts of the population. We know, for example, that life expectancy is greater and health better among older people are in the south of England than the north (a pattern that mirrors the distribution of wealth and poverty across the country), and that older people from many Black, Asian and Minority Ethnic groups live with worse health than those from White ethnic groups, with variations also within those blunt groupings.

Such inequalities are not random but rather shaped by the effects of discrimination around race, gender, class, disability, and sexuality. Inequality is structural, it shapes how we are treated by society and the state: the kind of education we receive, the kinds of jobs we can get, the quality of our healthcare services our housing, and whether we will reach retirement with enough savings to live comfortably.

Turning to quantitative data can sometimes mean that we lose sight of these inequalities, because an overall trend or headline can mask a more complex picture. For example, an increase in life expectancy among your whole population can hide the unequal distribution of those extra years which might be accruing to wealthier groups while poorer groups see no change or even a decline in their life expectancy.

It is also important to remember that to understand how inequalities are playing out it’s not just an experience of “ageing AND” another “category”. Rather, age (as a category) is interacting with multiple others at once to produce different inequalities and experiences. These intersections are complex meetings of discriminatory systems around gender, race and ethnicity, disability, and sexuality (among other things). For example, racism and gender creating unique experiences of systems of racism and sexism for Black women in later life which will be different to the experiences of their peers who are White women or Black men.

Most current quantitative data doesn’t really help us think about these complex combinations, which were given the name “intersectionality” first in Black feminist legal theory. Looking back at the sources of publicly available local data that we highlighted above, you might have noticed that these nuances are rarely reflected. There are a couple of reasons for this:

• Small sample size of surveys (especially at local levels) often mean that there are not enough respondents to provide statistically robust estimates of smaller parts of the population
  • This leads to an absence of minority groups in UK research, including Black, Asian and Minority Ethnic older people, disabled older people, and LGBTQ+ older people. For example, the UK’s largest survey of ageing, the English Longitudinal Study of Ageing (ELSA), running since 2002, included less than 5% ethnic minority sampling at its most recent data release, in a country where 13% of people are from Black, Asian and Minority Ethnic survey of ageing, the English Longitudinal Study of Ageing (ELSA), running since 2002, included less than 5% ethnic minority sampling at its most recent data release, in a country where 13% of people are from Black, Asian and Minority Ethnic backgrounds. A BMJ editorial has termed the exclusion of adequate Black, Asian and Minority Ethnic representation from population studies as a ‘form of institutional racism’.

Statistical robustness:

Quantitative data is often based on a sample of people – as it’s difficult to ask a question to an entire population, you ask a smaller group instead (a ‘sample’). A statistically robust sample is one that is large enough to be considered a reliable estimate for your population as a whole. The smaller your sample, the less robust it becomes. This means that samples of smaller groups within the whole often cannot be picked out (because the sample size becomes too small), thus hiding any differences.

This is far from a simple absence to address, made more complicated by a history in many communities – including Black, Asian and Minority Ethnic communities, also in White and multi-ethnic working-class communities – of high levels of government scrutiny, monitoring, and discrimination. With this history, it is vital that communities are actively involved in planning and interpreting data, that it is produced with them rather than about them.

A data-led approach to ageing isn’t just about generating a number but using that number, putting it in context, to develop a richer understanding of where you are. People often hope that quantitative data will provide an answer, but it can be helpful to think of it instead as the start of many more questions.

By figuring these questions out, you reach useful knowledge. Sometimes the avenues of these questions won’t surprise you, and the answers that you’re finding will chime with what you know about your area already. At other times they might surprise you.

This is why it’s important to make interpretation a collaborative process, so that different understandings are applied to the data. Sometimes you’ll need advice from data analysts on the technicalities of a data set to inform discussions of what it is actually showing. It might also be useful to discuss your insights with other areas who are also thinking about ageing. It is vital to include older people in the interpretation process, to understand how they experience an issue and hear their insights about what kind of change they would value.

Ideally, we want to try and “triangulate” our data. This means drawing on a range of different sources and types of data – the numbers, the focus groups and case studies, the discussions with colleagues – to tell the full story and bring the numbers to life.  

Triangulating data is both a quality assurance process – it helps us to ensure that the stories we are telling are grounded in reality – and a way to understand the key drivers of change.