1 / 15

Predicting the numbers of older people “helped to live at home”

This project by Tribal for ADSS Eastern Region examined community needs for elder care, predicting service requirements accurately using a unique model built on demographic data analysis from 2001 census and DWP. Statistical tests and regression analysis pinpointed strong predictors such as 'lone pensioners' and 'LLTI', offering insight into aligning service provision to actual demand. The study challenged assumptions about uniform service needs across local authorities and emphasized the importance of context-specific evaluations. Key messages urge authorities to avoid blanket approaches, recognize diversity in demand, and prioritize research and benchmarking for efficient resource allocation.

styree
Download Presentation

Predicting the numbers of older people “helped to live at home”

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Eastern Region Performance Network Predicting the numbers of older people “helped to live at home” A needs-based model and its implications Project undertaken for ADSS Eastern Region – Project Chair Hugh Gault (Cambridge CC Social Services) BSPS Conference, University of Kent, 13 Sept 2005

  2. Who is “Tribal”? • A leading provider of professional support services, offering a broad range of consultancy and managed services. • Employs over 2,000 people and operates from a network of 60 offices across the UK and Eire. • Operates across the public sector, within education; health and social care; local government, housing & regeneration; and central government. • Works with 80% of secondary schools; 75% of local authorities; 90% of further education colleges; and the majority of strategic health authorities and NHS trusts.

  3. What does Tribal do? • Provides a wide range of services: • Change management • Performance improvement • Benchmarking • Executive resourcing & interim management • Organisational development • Direct service • Strategy development & implementation

  4. HTLAH – starting point

  5. Our hypothesis • Are authorities actually providing services at a level appropriate for the needs of their population? • If so, why is this not reflected in the performance judgements? • Can we identify predictors of need?

  6. Social care needs model Older People No need for social care Private care Need for public social care Family networks Housing quality Health – mortality/ morbidity Income (IS) Voluntary organisations “Social Capital” Transport/access Preventive services Availability of private sector Self-funders/income

  7. People who come in to the Social Services ‘domain’ Eligibility Criteria Assessments Referrals NO YES Care provision Res/NH care Intensive Voluntary organisations/direct access? Other HTLAH No service

  8. Are there any predictors? Correlations with data from 2001 census and DWP - links to deprivation: • Older people living alone • Older people in rented accommodation • Index of Multiple Deprivation (IMD 2004) • Pensioners receiving Income Support • Pensioners in overcrowded accommodation

  9. What about CSSR response? FSS is allocated according to deprivation factors £££ - The more youget, themoreyoucangive …and so you should!

  10. The technical analysis • Started with full set of demographic data (IMD, Census and DWP) at LA level. • SPSS (stepwise regression) to identify the strongest predictors • Statistical tests to check robustness and statistical validity • Strongest combination of predictors: “lone pensioners” and “LLTI”

  11. Predicted vs. actual PAF C32 PI • Predictive model demonstrates that you can use the demographic data to calculate indicative data for HTLaH • Compared this to the actuals for 2003 & 2004

  12. Predicted vs. actual PAF C32 PI

  13. C32 and banding of performance

  14. So What? • Not all authorities have the same levels of community need • LA funding is based on assumptions of different need levels • The model isn’t perfect, but need levels can be predicted • Provision is generally consistent with predicted need levels

  15. Messages for LAs and CSCI • “More does not necessarily equal better” • It’s wrong to assume that there is a universal linear target • Performance must be judged in context of community needs. • Trying too hard to “improve” may lead to wasteful allocation of scarce resources. • Research, benchmarking and needs mapping are essential to understanding performance

More Related