# Group 2 – Aging-in-place – Rural vs City Dwelling --- ## Team Members * Jess Enright, *Glasgow* (Facilitator) * Emma Fairbanks *Swiss TPH* * Sam Kamperis, *Oxford Brookes* --- ## Context Aging-in-place may be desirable for a number of reasons including personal preference. Rural living may pose particular challenges to aging-in-place for many reasons including: - transport logistics - care workforce location - access to health services - digital connectivity - emergency support  We aim to investigate the differences in suitability for aging-in-place in different parts of the UK. A few resource/policy papers: - https://livabilityindex.aarp.org/categories/housing - https://www.ageuk.org.uk/globalassets/age-uk/documents/policy-positions/housing-and-homes/ppp_rural_ageing_uk.pdf - https://www.rsnonline.org.uk/ageing-in-place-how-can-we-ensure-rural-residents-have-choice-control-and-support-to-lead-healthy-independent-lives - https://rsnonline.org.uk/rsn-response-to-the-adult-social-care-white-paper - https://ageing-better.org.uk/sites/default/files/2021-04/Ageing-in-rural-place.pdf --- ## Starting Point We propose development of an aging-in-place index that would model the suitability of a place for aging-in-place, along with tools to run 'what-if' scenarios. Related indices exist - please let us know if this one does too! Some starting questions: * What factors can we pull together? * What is the current picture of access/factors - perhaps a map visualisation? * Can we project forward - how will demographics, access and needs change? What might that map look like in the future? --- ## Outline of progress We have made progress in three main directions: - Data gathering: what data is available and where? - What's the current geographical picture for some of these? - How do we expect the numbers of people requiring care and care workforce availability to change? - How accesible by public transport are example health services, and how might this change with individual-level transport? Lots more to do! --- ## Current picture: What could we use to build an aging-in-place index? We have found a number of useful data sources, including: - demographic data - information on health services locations - care home places and locations - locations of care workers in 2011 census A few examples: Map of change in proportion over-80 by local authority in Scotland. Red is faster increasing proportion (and therefore faster increasing care need?)  Places in care homes per general population  Absolute numbers of care workers reported in 2011 census (darker is more)  ### Future work To build a robust and customizeable index, we need to: - do additional data cataloguing - write code pipelines to ingest and use data - trial different weightings of data, perhaps with a GUI? --- ## Transport-based ratings First example: travel time to a GP surgery      Ipswich   --- ## Predicting MSOAs with care storages ### Estimating who needs care  * Assume same percentage of each age group will continue needing care * Based off the number of each age which need care in each MSOA estimate number who need care ##### https://www.statista.com/statistics/1233394/distribution-of-care-recipient-by-age-in-the-united-kingdom/ ##### https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/datasets/middlesuperoutputareamidyearpopulationestimates ### Estimating who gives care * Data from 2011 census on how many people residing in each area work in health and social care associate professions * Estimated the percentage of people in each age group who work in care using age breakdown and total number of care givers. * For each MSOA estimate number of caregivers. ##### ### The concern ratio * Concern ratio = estimated number of care needers / estimated number of care givers * Ratio calculated for 2015 and 2020 and used to project forward to 2025 and 2030 (assuming linear relationship) ### Results    --- ## Next steps We would ultimately like to produce a tool that calculates an aging-in-place index for different areas, and allows some 'what-if' scenario modelling. A few ideas: - more comprehensive data ingestion and use - allow change to weightings for index (perhaps with GUI?) - more forward projection and more complex care workforce modelling - additional transport calculations and alternation modelling, e.g.: - modelling changes to public transport provision - mobile health services - impact of climate change on transport Suggestions/questions/opinions? --- # Scratch below ## Information needed * Where do older people live? * How do these demographics change with age/ time before death? * care workforce locations? - Done, in google drive * GP postcodes? - Done, in google drive * Definitions of ruralness? ## Data used --- --- ## Ideas Some kind of location index for public transport and walking time to nearest GP. > [name=Sam Kamperis] I could start with distances and try and call google map API for a town for the public transport and walking. Compare the API call for public transport times to driving times. Then apply all of this work to a rural area > [name=Jess Enright] Here's a random rural postcode IP13 6RQ Then analyse the differences. There should be four matrices of numbers duration of travel public transport/driving x rural/urban. Looking at aging of MSOAs over times: are there MSOAs where the age distribution has stayed about the same, and others where the MSOA is aging? > [name=Emma Fairbanks] making a start on this By area of country, use the population estimates to calculate a statistic for how aging the area is. Idea: over the last period of time (10 years?), calculate proportion of population over some threshold, and then get approximate slope of this line? Even at two timepoints. Index for age-in-place ## Next steps - What-if scenarios? in the context of an age-in-place rating ## Availability Jess Wednesday: from 13:30 until 17:00 Thursday: 10 am to 17:00 (with occasional breaks to walk dog, feed baby) - probably catch Emma at 3:30 Friday: 10 am to end. Sam Thursday: Lunchtime! Friday: 10:00 - 14:00, 15:30 - 17:00 Emma Thursday: Meeting at 10, will leave approx 4 UK time --- --- ## Predicting MSOAs with care storages ### Estimating who needs care  * Assume same percentage of each age group will continue needing care * Based off the number of each age which need care in each MSOA estimate number who need care ##### https://www.statista.com/statistics/1233394/distribution-of-care-recipient-by-age-in-the-united-kingdom/ ##### https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/datasets/middlesuperoutputareamidyearpopulationestimates ### Estimating who gives care * Jess got data for number of people who work in ... from UK cencus data. (Can you elaborate) * Estimated the percentage of people in each age group who work in care using age breakdown and total number of care givers. * For each MSOA estimate number of caregivers. ##### https://www.statista.com/statistics/1084697/number-of-people-working-in-care-homes-in-the-united-kingdom/ ### The concern ratio * Concern ratio = estimated number of care needers / estimated number of care givers * Ratio calculated for 2015 and 2020 and used to project forward to 2025 and 2030 (assuming linear relationship) --- ### Results    --- ## Map of GP locations   from Or, version with greyscale of both number of care home places and aging trend at: https://www.google.com/maps/d/edit?mid=1hWIQTOkf5NXeEXYIslj0QeOFeK0N9__4&ll=56.764799814904976%2C-3.746217310255071&z=7 Places in care homes per general population (darker is more)  Care workers (darker is more)  --- Links [Google Drive](https://drive.google.com/drive/folders/1i82VroYHz02JgCxbT2Edzf4YON65rD8m) https://geoportal.statistics.gov.uk/datasets/rural-urban-classification-2011-of-middle-layer-super-output-areas-in-england-and-wales/about https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/datasets/middlesuperoutputareamidyearpopulationestimates