Presentation on theme: "Will you still need me in 2030? Use of long-term care services in Vienna 10 th October 2013 Project team: Andrea Schmidt (coordinator) Michael Fuchs, Maria."— Presentation transcript:
Will you still need me in 2030? Use of long-term care services in Vienna 10 th October 2013 Project team: Andrea Schmidt (coordinator) Michael Fuchs, Maria M. Hofmarcher, Kai Leichsenring
Commissioned by the Department for Health and Social Care Planning of the City of Vienna, Austria (MA 24) No survey, but ‘what data is there in LTC?’ Gaining a comprehensive picture is usually made difficult by scattered responsibilities in LTC Unique way of merging existing administrative data across long-term care settings Research as an opportunity for bringing together administrative data from different policy levels! Background of the study* 2 *End of the project: December 2013
To compare people in institutional versus community care settings To analyse the potential of people in need of care who do not use public care services (made available by the city of Vienna) To develop potential future scenarios for LTC use and expenditures till 2030 Objectives 3
> people receive a cash-for-care allowance, a quarter of which live in Vienna. About 80% are older than 60 years. This cash benefit is paid ‚without strings‘ regarding its use. Amount is determined by the care level (1-7) of the person in need. Residential, mobile and day care services are administered at the level of the 9 Austrian regions. Older people pay a co-payment for services, depending on a person‘s care level (cash benefit), personal income, and type of service. Long-term care (LTC) in Austria 4
Ageing in place as a popular policy strategy Cash benefits Austria Coverage with LTC benefits (cash and in-kind), 2009 or latest available year Source: Rodrigues et al. (2012) 5
…at moderate cost Expenditure on LTC (home and institutional) in % of GDP, 2009 or latest available year Source: Rodrigues et al. (2012) Austria 6
Administrative data collected in 2011/2012 Identify determinants of use in five different long-term care settings: –Residential care services –Assisted living facilities –Home and day care services –24-hours care services (mainly provided by migrants) –Cash benefits ‘only’ Bivariate and multivariate analysis to identify differences regarding gender, age, care need, socio-economic status, living situation, informal care, district of residence Methods and data 7
Not all settings cover the same range of variables Some data had to be excluded due to incomplete information (e.g. nationality, appartment size) Limited data available for people who do not use care services (only cash benefit) Caveats 8
What distinguishes people in different long-term care settings from each other? What characterises people who receive a cash benefit but do not use any services? How could the mix of different long-term care benefits and services develop in the future? Questions of interest 9 Policy background: Is there sufficient provision of public care services in Vienna? Could future increases in need for public care services be imminent?
What distinguishes people in different long- term care settings from each other? What characterises people who receive a cash benefit but do not use any services? How could the mix of different long-term care benefits and services develop in the future? Questions of interest 10
About 50% of beneficiaries do not use or receive services Source: Own calculations based on data from the Austrian Federal Ministry of Labor, Consumer Protection and Social Affairs, („Pflegegelddatenbank,“; Qualitätssicherung“), data from Fonds Soziales Wien and Federal Welfare Office, 2011/12 11
Main characteristics of users in different settings 12 GroupnYear of birth (median) % femaleØ Care level (0-7) % living with someone % without cash benefit Total ,8%2,618,8%k.A. G1: institutional care ,5%4,5n.a.1,06% G2: 24-hours care ,6%4,719,6%n.a. G3: assisted living ,2%2,2n.a.23,9% G4: day/mobile care ,5%2,720,1%11,9% G5: cash benefit only ,8%2,253,6%n.a.
Users’ income profiles across LTC settings Richest people use 24-hours care, i.e. migrant care in private households: highest share of people with an income >2000€ (about 17%) is found there More than 6 out of 10 users of LTC services have a monthly personal income of 1001€ to 2000€ 13 Note: No income data for people who receive only a cash benefit. For people in assisted living facilities, data are incomplete.
Family care gap despite using home care 14 Blue bars represent the hours of formal home care services actually received (vertical axis), against assessed hours of care need (horizontal axis). ‘ Family care gap’ Assessed hours of care need Mobile care hours Perfect coverage with home care Hours of personal care received
‘Lorenz curve’ of mobile care services shows well-functioning targeting to higher care needs 0h 169,5h 51h 248h 120h160h85h 15 Personal care services (home care) Objectively evaluated need for care Care levels 4-7
Generally speaking, home care services are well-targeted to lower income groups Yet, a gap remains which needs to be compensated by family care even for those who use care services Patterns of use in long-term care (multivariate analysis) 16 Higher likelihood of women to move to a care home (accounting for age, personal income, and care needs) High co-payments tend to ‘deter’ higher income groups from moving to a care home Living with a (female) partner is an important insurance against institutionalization CARE HOME
What distinguishes people in different long-term care settings from each other? What characterises people who receive a cash benefit but do not use any services? How could the mix of different long-term care benefits and services develop in the future? Questions of interest 17
Beneficiaries of cash benefits who do not use any services: 18 Male, younger, living with main carer Less care needs, less often incontinent, better able to care for oneself Main carer not working full-time, no heavy care burden reported. Better general environment (hygiene, nutrition, activities) Counselling on auxiliary devices, trainings, and social services more often. More often recommendation for support services to the informal carer, and/or social services for dependent person possible sign of overburdening of carers?
Apartment size, and living with a partner were identified as the most important predictors for growing old in one’s own home (accounting for age, gender, care needs, income) despite high care intensity Men living with someone are most likely to be able to cope without care services at home, while men living alone are most at risk to need external support Older persons living with a female carer (e.g. daughter/wife) require less external support than those living with a male carer (e.g. son/husband) 24-hours care (‘migrant care’ in private households) depends on financial resources and apartment size How to grow old at home (Multivariate analysis) 19
Importance of informal care was confirmed once again, empirically showing also the particular importance of women need for more awareness among older men who might become carers in the future Less available female carers in the same household could fuel demand for social services further (e.g. due to labour market changes, retirement age, divorce rates) Innovative support for informal carers and employers highly needed, both for men and women Housing policies crucial for supporting older people to grow old at home Policy implications 20
What distinguishes people in different long- term care settings from each other? What characterises people who receive a cash benefit but do not use any services? How could the mix of different long-term care benefits and services develop in the future? Questions of interest 21
Will you still need me in 2030? 22 Based on profiles of the five long-term care settings in the study development of future scenarios (until 2030) Basic scenario: increasing life expectancy, compression of morbidity, and decrease of informal care shift towards mobile care services Projections of care needs by gender, age groups, and settings, using external data (e.g. life expectancy, migration), academic evidence, and in-house expertise on long-term care
What happens if…? 22 Further increase in mobile care services for users with high needs (shift away from institutional care) Reform of assisted living facilities (no more low care needs) Policy focus on rehabilitation (good practice from Denmark) Reduction of 24-hours care (‘migrant care’)