The Costs of Smoking to the Social Care System and Society in England

Transcription

1 The Costs of Smoking to the Social Care System and Society in England A report by Howard Reed of Landman Economics for ASH August

2 Acknowledgements The author would like to thank Hazel Cheeseman of Ash and Lesley Owen of NICE for useful comments on earlier drafts of this report. The data from the English Longitudinal Study of Ageing (ELSA) used in this report were made available through the UK Data Archive. ELSA was developed by a team of researchers based at the National Centre for Social Research, University College London and the Institute for Fiscal Studies. The data were collected by the National Centre for Social Research. The funding is provided by the National Institute of Aging in the United States, and a consortium of UK government departments coordinated by the Office for National Statistics. The developers and funders of ELSA and the Archive do not bear any responsibility for the analyses or interpretations presented here. See Marmot et al (2013) for further details. ISBN Copyright 2014 All rights are reserved. Please contact ASH for permission to reproduce any part of the content of this report: Action on Smoking and Health, 6th Floor, Suites 59-63, New House, Hatton Garden, London, EC1N 8JY Tel: Fax: enquiries@ash.org.uk 2

3 Contents 1 Overview of methodology The ELSA data The scope and sampling frame for ELSA Data on receipt of help with activities Health status in ELSA Modelling receipt of local authority-funded social care using the ELSA dataset Domiciliary social care The means-tests and needs-tests Defining local authority care receipt Residential care Using the NASCIS data to calculate overall social care spending by local authorities Summary of local authority social care expenditure The proportion of local authority social care spending accounted for by adults aged over Results Domiciliary care costs Residential care costs The costs of smoking to the social care system in context Limitations and possibilities for future research Conclusions References Appendix A: estimated costs of social care for people aged over 50 to local authorities Appendix B. Detailed results probability of receiving domiciliary social care Appendix C. Detailed results probability of receiving residential social care

4 Introduction Smoking is associated with a range of costs to the economy in general and the public finances, which arise due to the health risks associated with tobacco consumption and the associated increases in mortality and morbidity for the smoking population 1 (Reed 2010; Nash and Featherstone 2010). Previous work on the costs of smoking in the UK has focused primarily on the costs to the National Health Service (NHS) and the negative effects of smoking on productivity due to higher working-age morbidity and greater employee absenteeism, resulting in lower economic output and lower tax receipts for the Exchequer. This report looks at the costs of smoking to the social care system in England. For the most part, this aspect of smoking-related costs has not been considered in previous empirical research, with the exception of the social care costs of looking after people who have suffered smoking-related strokes (Saka et al, 2009). The report looks primarily at the costs of smoking to English local authorities, but also estimates the additional costs which smoking imposes on people who pay for their own social care (self-funders) and the extent of the burden of unpaid care which smoking gives rise to among the families and friends of smokers. The research uses data on smoking propensity and receipt of social care services in the English Longitudinal Study of Ageing (ELSA) to estimate the propensity of smokers and ex-smokers aged over 50 in England to receive domiciliary and residential social care compared with people who have never smoked. This information is combined with data on social care expenditure from the National Adult Social Care Intelligence Service (NASCIS) to estimate the proportion of public expenditure on social care which is attributable to smoking, as well as the costs to self-funders. The structure of this report is as follows. Section 1 explains the methodology used while Section 2 gives details of the ELSA data and Section 3 explains in detail how eligibility for receipt of local authority-funded social care services is modelled using the ELSA data. Section 4 introduces the NASCIS data and analyses social care spending by category, focusing in particular on the split between domiciliary care (care which people receive in their own homes or in locations such as day centres which they attend while still being resident at home) and residential care (care which people receive in a residential care home). Section 5 presents estimates for the costs of smoking to the domiciliary and residential care systems, and discusses the strengths and limitations of the results, and possible future research that could be commissioned to improve the estimates. Section 6 offers conclusions. 1 There are also certain negative externalities associated with smoking which increase costs, for example passive smoking and fire costs. 4

5 1 Overview of methodology The method used in this report is based on that used to estimate costs of smoking to the NHS in England by Callum, Boyle and Sandford (2010). The extent of usage of local authority-funded social care services by current and ex-smokers is compared with that of never-smokers and the excess attributed to smoking. The variables used to measure social care service use in the ELSA data are described in Section 2 below. Excess use of local authority-funded care by current and ex-smokers relative to people who have never smoked is measured in the form of a relative risk. Together with exposure to these risks the proportion of the population who are current or ex-smokers an estimate of the proportion attributable to smoking can be obtained by the following standard formula: Attributable proportion = [p cur (r cur 1) + p ex (r ex 1)]/[1 + p cur (r cur 1) +p ex (r ex 1)] where p cur = proportion who are current smokers; r cur = relative risk for current smokers compared with never-smokers; p ex = proportion who are ex-smokers; and r ex = relative risk for ex-smokers compared with never-smokers. Because propensity to use social care services increases significantly with age, the attributable proportions are calculated conditional on age (and gender), using logistic regression to control for these exogenous variables. However, the analysis does not control for potentially endogenous variables such as health status because research has shown conclusively that smoking behaviour is a determinant of health status (Doll et al, 2004). To the extent that smokers impose costs on the social care system because of being in poorer health (conditional on age) than non-smokers, this is something which should be included in the calculation of costs to the social care system rather than being treated as an exogenous control variable. The attributable proportion is then applied to data from the National Adult Social Care Intelligence Service (NASCIS) on the total costs to local authorities of the social care system in England to estimate the total cost of smoking to the social care system. The calculation is carried out separately for domiciliary care and for residential care. Section 2 gives details of the ELSA variables used in each case, while Section 4 gives details of the breakdown between domiciliary and home care in the NASCIS dataset. 5

6 2 The ELSA data 2.1 The scope and sampling frame for ELSA The English Longitudinal Survey of Ageing (ELSA) is a large scale longitudinal panel survey of people aged 50 and over and their partners. The survey began in 2002 and five waves of data have so far been released. Table 1 gives details of the number of interviews achieved in each wave and when the fieldwork took place. The sample size increased in waves 3 and 4 as the result of adding additional 'refreshment' samples to increase the size of the dataset and maintain representativeness of the over-50 population (Natcen, 2012). Table 1. ELSA waves 1-5: sample size and dates Wave Date of fieldwork Number of successful interviews 1 March 2002 March ,099 2 June 2004 July ,432 3 May 2006 August ,772 4 May 2008 July ,050 5 June 2010 July ,274 Source: Natcen (2012) The original sampling frame for ELSA covered only individuals living in private households, but from wave 3 onwards, follow-up interviews have been conducted with individuals moving into residential care homes (making the ELSA dataset the first survey dataset in the UK to contain a subsample of the care home population). The main drawback of the ELSA dataset for the purposes of analysing receipt of social care is that it only surveys people aged over 50. As shown in Section 3 below, while the majority of local authority social care expenditure is on people aged over 50 there is also a significant amount spent on younger adults, and the ELSA dataset does not allow researchers to determine the costs of smoking to social care for people aged under Data on receipt of help with activities For domiciliary care, the ELSA interview collects data from each respondent on whether they receive help with the following everyday activities: moving around the house; washing/dressing; preparing a meal; 6

7 shopping and/or doing housework; using the telephone and/or managing money; taking medication; any other difficulty. For each of these activities, data is recorded on whether help is received from the following sources: relatives (spouse or partner, son or daughter, brother or sister, or other relatives); "privately paid help"; the local authority, or "social services"; a nurse; a member of staff at the care home (this question is asked of respondents living in care homes only); friends or neighbours; any other person. Table 2 shows the proportions of respondents in the ELSA wave 5 sample who receive help from any of these sources, while Table 3 shows the same information but for smokers only. Around a quarter of the total ELSA sample receive help with at least one activity, from at least one source. The most common source of help is spouses or partners (14 percent of the sample), followed by other relatives (around 10 percent of the sample). Less than 2 percent of the sample make use of local authority care, while 2.5 percent receive privately paid help. Only 0.5 percent of respondents receive help from a member of staff at a care home, but this is mainly because very few respondents are actually in care homes (see Table 7 below). For people receiving help from the local authority, the most common activity which they were helped with was washing or dressing, followed by moving around the house. 7

8 Table 2. ELSA wave 5: percentage of sample receiving help with tasks, by source of help Task spouse or partner other relatives privately paid help local authority/ social services Source of help nurse member of staff at care home friends or neighbour s moving around the house washing/dressing preparing a meal shopping and/or doing housework using telephone and/or managing money taking medication any other difficulty any of these Source: author's analysis of ELSA Wave 5 sample any other person any of these 8

9 Table 3. ELSA wave 5: percentage of sample receiving help with tasks, by source of help current smokers only Task spouse or partner other relatives privately paid help local authority/ social services Source of help nurse member of staff at care home friends or neighbour s moving around the house washing/dressing preparing a meal shopping and/or doing housework using telephone and/or managing money taking medication any other difficulty any of these Source: author's analysis of ELSA Wave 5 sample any other person any of these 9

10 Table 3 shows that for most of the unpaid sources of help (spouses and partners, other relatives and friends or neighbours), current smokers in the ELSA wave 5 sample are more likely to receive help than the average for the sample as a whole. For example, just over 14 percent of smokers in the sample receive help from other relatives compared with around 10 percent of the sample as a whole. For paid sources of help, the opposite is the case; current smokers are slightly less likely to receive help from these sources than the average for the sample as a whole. For example, 1.6 percent of current smokers make use of local authority help compared with 1.8 percent for the sample as a whole. However, these 'raw' tabulations do not control for the age of the sample, and so understate the extent to which current smokers are likely to require care interventions compared to non-smokers of a similar age. Table 4 shows the median age of current smokers, ex-smokers and never-smokers receiving care from various sources (help from nurses and members of staff at care homes are excluded because there are so few sample members receiving care from these sources). The median age of smokers receiving any form of care is 64, whereas for ex-smokers and never-smokers it is 73 a difference of 9 years. Other forms of care with a particularly large median age difference between current smokers and never-smokers include spouses or partners (current smokers on average 7 years younger than never-smokers), other relatives (current smokers 14 years younger), local authority care (current smokers 13 years younger) and friends or neighbours (current smokers 14 years younger). Clearly, current smokers tend to receive help with day-to-day activities at a much younger age than exsmokers or people who have never smoked. Table 4. Median age of ELSA sample members who require help from various sources: current smokers and ex-smokers compared to those who have never smoked Median age Source of help Current smoker Ex-smoker Never smoked Spouse or partner Other relatives Privately paid help Local authority/ social services Friends or neighbours Any other person Any of these Source: author's analysis of ELSA wave 5 dataset. Table 5 presents the results of logistic regressions on the Wave 5 ELSA sample which test whether current smokers (and ex-smokers) are significantly more likely (in 10

11 statistical terms) to receive help from a given source than people who have never smoked, given their gender and age. The relative risks are expressed in the form of odds ratios (the coefficients from logistic regressions, with the dependent variable being whether or not each person in the sample received help from the listed source). A coefficient of more than 1 means that the group in question (current smokers in the left-hand columns, and ex-smokers in the right-hand columns) is more likely to require help from the relevant source than the base group (people who have never smoked). Coefficients which are statistically significant at the 5% level are highlighted in bold. For four of the sources of help featured spouses or partners, other relatives, local authority/social services and friends or neighbours current smokers are significantly more likely to require help than people who have never smoked. Considering all sources, current smokers are almost twice as likely (98% more likely) to require help than never-smokers, while ex-smokers are 24 percent more likely to require help than never-smokers. Ex-smokers are also significantly more likely to require help from spouses or partners, and from other relatives, compared to those who have never smoked. Table 5. Relative risk coefficients for requiring help from various sources for day-to-day tasks in ELSA wave 5: current smokers and ex-smokers compared to those who have never smoked Current smokers Ex-smokers Source of help Odds ratio P > z Odds ratio P > z Spouse or partner Other relatives Privately paid help Local authority/ social services Friends or neighbours Any other person Any of these Source: author's analysis of ELSA wave 5 dataset. Notes: significant coefficients at the 5% level in bold. 11

12 2.2 Health status in ELSA The ELSA interview contains a number of questions relating to health conditions which might require social care interventions. Specifically, interviewees are asked whether they have difficulty with any of the following activities: Dressing, including putting on shoes and socks; Walking across a room; Bathing or showering; Eating, such as cutting up food; Getting in and out of bed; Using the toilet, including getting up or down; Using a map to figure out how to get around a strange place; Preparing a hot meal; Shopping for groceries; Making telephone calls; Taking medications; Managing money (e.g. paying bills, keeping track of expenses); Doing work around the house or garden. Table 6 shows the relative risks for experiencing these difficulties (conditional on sex and age) for current smokers and for ex-smokers relative to never-smokers. As with Table 5 above, the relative risks are expressed in the form of odds ratios (the coefficients from logistic regressions, with the dependent variable being whether or not each person in the sample experienced the relevant difficulty). A coefficient of more than 1 means that the group in question (current smokers in the left-hand columns, and ex-smokers in the right-hand columns) is more likely to experience the relevant difficulty than the base group (people who have never smoked). Coefficients which are statistically significant at the 5% level are highlighted in bold. Table 6 shows that for 11 of the 13 activities which were covered in the ELSA survey (all except using the toilet or managing money), current smokers were significantly more likely to suffer from difficulties with the activity than were people who had never smoked. For ex-smokers, this was only true for two of the activities surveyed (dressing, and doing work around the house or garden). A priori, the results in Table 6 suggest that current smokers will be more likely to require social care services than either ex-smokers or those who have never smoked. 12

13 Table 6: relative risk coefficients for experiencing difficulty with performing activities in ELSA wave 5: current smokers and ex-smokers compared to those who have never smoked Current smokers Ex-smokers Difficulty Odds ratio P > z Odds ratio P > z Dressing Walking across a room Bathing or showering Eating Getting in and out of bed Using the toilet Using a map Preparing a hot meal Shopping for groceries Making telephone calls Doing work around house/garden managing money Source: author's analysis of ELSA wave 5 dataset. Notes: significant coefficients at the 5% level in bold. 13

14 3 Modelling receipt of local authority-funded social care using the ELSA dataset This section of the report explains how the ELSA data are used to model the receipt of domiciliary and residential social care. 3.1 Domiciliary social care The means-tests and needs-tests Domiciliary social care in England is means-tested on the income and assets of the care recipient; individuals have to pass both the income and the asset test before being eligible for local authority-funded care. The income means test operates according to the following rules: individuals are expected to pay for their own care unless the social care payments would leave them with net incomes below a minimum level (disregarding most state benefits and tax credits). 2 The minimum level for income after social care costs is set at the Income Support Level (for people under state pension age) or the Pension Credit Guarantee Level (for people above state pension age), plus a 'buffer' of 25 percent. At the benefit levels in place in 2010/11 (when most of the ELSA wave 5 sampling was done) this would mean that the minimum income floor was per week for people aged below state pension age and for people aged above state pension age. The asset means test for domiciliary care is that individuals with more than 23,500 of assets including savings and other financial assets such as stocks and shares (but excluding the value of the home for owner-occupiers) are expected to meet their own care costs. For individuals with between 11,000 and 23,500 of assets, a certain amount of 'tariff income' is assumed to be derived from the assets between these two points. For individuals with assets of less than 11,000 the local authority will meet care costs fully (assuming the individual also passes the income means test). In addition to the income and asset-based means tests, there is also a needs test for domiciliary social care. Individuals are allocated to one of four care categories, with priority for social care funding given to the highest-need groups. In the current climate of austerity and cuts to social care funding there is increasing evidence that 2 Most disability-related benefits are not disregarded for this calculation specifically, Attendance Allowance, the care component of Disability Living Allowance or the Personal Independence Payment Daily Living component, as well as certain elements of Industrial Injuries and War Pensions benefits. The additional amount for severe disability in means-tested benefits such as Pension Credit, Income Support or Income-related Employment and Support Allowance is also treated as a disability benefit and not disregarded. See Age UK (2014) for full details. 14

15 many English local authorities have been restricting social care funding to the highest-need categories only. Unfortunately ELSA does not contain an explicit question on whether the social care received by individuals in the sample is funded by the local authority, only on who provides it. However, ELSA does contain variables for income and wealth which enable us to simulate the means-tests for domiciliary and residential care. These simulations are used to identify individuals whose care is likely to be mostly or completely funded by the local authority, given that they have said in the ELSA survey that they are receiving care provided by the local authority. The procedure used to simulate the ELSA means-test for domiciliary care involves assuming that survey respondents with income above a minimum level (subject to meeting the asset-based means test) have to pay charges for care, while respondents with less than this level of income are exempted. The minimum level for income after social care costs is set at the Income Support Level (for people under state pension age) or the Pension Credit Guarantee Level (for people above state pension age), plus a 'buffer' of 25 percent. Certain types of income (for example tax credits and most disability-related benefits) are disregarded for the purposes of this calculation. The ELSA simulation of the asset-based means test for domiciliary care uses the value of total net non-housing wealth (excluding pensions wealth, which is not included in the means-test). The results of the ELSA means-test for domiciliary care are that out of 149 ELSA wave 5 members who receive help with day-to-day tasks from the local authority or social services, 101 sample members pass the income and asset means-tests and are assumed to be having their care costs met by the local authority, while 49 fail one or both of them and are assumed to self-fund. Using the ELSA weights to control for non-random sample selection, approximately 71% of local authority domiciliary care recipients pass the means tests, while 29% fail one or both of them. Defining local authority care receipt The main results in this report assume that individuals who are recorded as receiving one or more domiciliary care services from the local authority have passed the needs test and are therefore eligible for local authority-funded care services (conditional on passing the means tests). However, an added complication with the ELSA dataset is that some people may be receiving local authority-funded social care even though they are listed in the questionnaire as receiving "privately paid help". This is because an increasing proportion of local authority social care expenditure is delivered in the 15

16 form of personal budgets, 3 whereby individual care recipients are given a budget which they can spend on social care services of their choice rather than allocated services directly. 4 Additionally, over the last twenty-five years there has been a gradual shift in the sectoral balance of care provision with the majority of social care now provided by outsourced private or voluntary sector care providers rather than directly by local authority employees. It is quite possible that some instances of local authority-funded care are recorded as 'privately paid for care' in ELSA. For this reason we also construct an alternative local authority-funded care variable which combines people receiving privately paid help and help from local authority social services into a single variable and use this variable as an alternative basis for allocating local authority care costs (subject to the outcome of the means tests for each domiciliary care recipient). The results in Section 5 report relative risk coefficients for both of our proxy variables for local authority-funded care; one using data on local authority provided care only, and the other combining local authority and privately paid care. The latter variable will be an overestimate of the number of people receiving LA-funded care whereas the former is probably an underestimate. Simulating the income and asset-based means tests for domiciliary care on this expanded care receipt variable results in a lower percentage of care recipients passing the means test; 55 percent (controlling for non-random sampling) for this care variable based on local authority or private provision compared with 71 percent for local authority provision only. This is to be expected given that the average income and asset levels of ELSA sample members receiving privately paid care are higher than for sample members receiving local authority care. 5 The total ELSA Wave 5 sample comprises 8,984 interviewees (excluding 72 people who were interviewed in a care home, whom we discuss in Section 3.2 below). Table 7 shows the proportion of the sample (controlling for non-response weights) who were in receipt of care provided by the local authority or by a private provider, and also breaks this sample down according to whether they were eligible for local authority funding after carrying out the income and asset-based means tests. Just over 4 percent of the sample received at least one domiciliary care intervention, of 3 Data from NAO (2014) suggests that in the financial year (when most of the ELSA wave 5 interviews were carried out), around 450,000 social care users and carers received personal budgets compared with around 1.4 million social care users and carers who were allocated services directly. By around 700,000 social care users and carers received personal budgets with around 1.3 million being allocated services directly. Thus, the use of personal budgets is expanding rapidly. 4 Statistics from the Health and Social Care Information Centre (2013) suggest that in , 56 percent of users and carers receiving services in the community held a personal budget, up from 13 percent in Analysis of the ELSA wave 5 data shows that for sample members receiving care provided by the local authority or social services, average (mean) family income was 329 per week, whereas for sample members receiving privately provided care, mean family income was 278 per week. Average household wealth for sample members receiving care provided by the local authority or social services was around 19,400 whereas for sample members receiving privately provided care it was 75,500. This shows that recipients of privately provided care have higher incomes and much higher wealth on average than recipients of local authority-provided care. 16

17 which the majority were privately provided. Around 2.3 percent of care recipients were eligible for local authority-funded care based on income; of these, most received local authority care. 17

18 Table 7. ELSA Wave 5 sample: proportion of sample receiving domiciliary care All recipient s Passin g means test Not passin g means test Proportion of sample receiving social care from provider: % % % Provided by LA paid privately Both LA and privately total Source: author's analysis of ELSA wave 5 data 3.2 Residential care For residential care, the asset-based means test is similar to the asset test for domiciliary care, except that it includes the value of the home for homeowners (for people without a spouse or partner also living in their home; for people whose partner also lives in the home, the value of the home is normally exempted). In practice this means that almost all single homeowners are required to sell their home to meet residential care costs rather than being state-funded. The income-based means test for residential care is similar to the means-test for domiciliary care, although the minimum income which local authority-funded care home residents are entitled to is much lower than for domiciliary care recipients; most benefit income is remitted to the local authority to go towards care home fees. The typical weekly cost of supporting someone in a care home is far higher than the cost of a typical package of domiciliary care; 6 therefore, relatively few care home residents will be able to meet all their care home costs out of current income without starting to run down their assets. Whereas the methodology used for estimating risk factors for receipt of domiciliary care uses the ELSA Wave 5 data as a cross section, the methodology used for risk factors for residential care uses the ELSA Wave 1 dataset as a baseline and then 6 For example, the NASCIS statistics suggest that while average gross weekly expenditure on day care services for adults aged 65 and over in England is 131 per week, average expenditure on residential care is around 499 per week. See NASCIS (2014). 18

19 looks at the number of sample members who move into residential care between Waves 3 and 5. 7 The number of people for whom care home interviews were collected in Waves 3, 4 and 5 is not large in comparison to the whole ELSA sample. Table 8 below shows the total number of interviewees in care homes in ELSA waves 3, 4 and 5 compared to the number of interviews conducted in these waves. Less than 100 institutional interviews were conducted in each wave around 0.6% of the sample on average. This means that the sample for estimating propensity to move into residential care is much smaller than for the domiciliary care data. Table 8. Number of interviewees in care homes in ELSA Waves 3-5 ELSA Wave Total number of interviews 9,771 11,050 10,274 Number of institutional interviews Institutional interviews as % of sample 0.5% 0.6% 0.7% For the subsample of ELSA interviewees moving into residential care in Waves 3, 4 and 5, information on their income and assets at Wave 1 is used to estimate eligibility for local authority-funded residential care under the asset and income means tests. 7 Sample members moving into care homes between ELSA Waves 1 and 2 were not interviewed in the care homes; care home resident interviews only began in Wave 3. 19

20 4 Using the NASCIS data to calculate overall social care spending by local authorities 4.1 Summary of local authority social care expenditure The National Adult Social Care Intelligence Service (NASCIS) is a portal for data on social care expenditure across England by local authority. NASCIS collects data on social care expenditure and unit costs by local authority for the following categories of care service: Residential care (including separate information on nursing care) home care/home help services; day care and day services; direct payments (personal budgeting); provision of meals. The social care client population is broken down into the following categories: adults aged 65 and over; adults aged with a learning disability; adults aged with mental health needs; adults aged with physical disability. Because of complications regarding means-testing in the provision of local authority social care services, the data from NASCIS requires some additional calculations to be performed before the precise costs of various categories of social care to local authorities (rather than the overall amount spent on social care provided by local authorities or outsourced care providers operating under contract from local authorities) can be calculated. The use of personal budget payments in an increasing number of local authorities adds another layer of complexity. The National Audit Office recently published a report containing a breakdown of local authority social care expenditure by service category and client group, based mainly on the NASCIS data (plus some additional data from Laing and Buisson on care home costs), which is reproduced as Table 9 below. Total care spending managed by local authorities is around 19 billion, comprising around 8 billion on care homes and 11 billion on domiciliary care (including the cost of supported accommodation.) Approximately half of this expenditure is on adults aged 65 and over, with most of the rest (just under 6bn) being spent on adults aged under 65 with learning disabilities. Of the 19 billion of care spending managed by local authorities, The National Audit Office (2014) estimates that 14.6 billion is local authorities' spending from their own funds, with 2.5 billion coming from user contributions, 1.2 billion from NHS funds 20

21 and 600 million from other sources. The NAO also estimates that a further 10.2 billion of social care is purchased by self-funders outside the scope of local authority involvement. 21

22 Table 9. Local authority spending on various social care categories, England, Care homes ( m) Supported and other accommodation ( m) Home care and day care ( m) Direct payments ( m) Other services ( m) Total ( m) Adults aged 4, , ,850 9, and over Adults aged ,710 under 65 with physical or sensory disabilities Adults aged 2, , ,710 under 65 with learning disabilities Adults aged ,340 under 65 with mental health problems Other adults aged under 65 TOTAL 8,010 1,250 4,340 1,290 4,180 19,070 Source: National Audit Office (2014) 4.2 The proportion of local authority social care spending accounted for by adults aged over 50 Because the ELSA data only cover individuals aged 50 and over, this report only estimates the cost of smoking to social care for the over-50s age group. Based on the National Audit Office's calculations from the NASCIS data, I calculate that local authority spending on social care for adults aged 65 and over in breaks down as follows: residential care: 3.41 billion domiciliary care: 3.96 billion. Appendix A gives full details of how this calculation is arrived at. However, it is also necessary to take account of individuals aged 50 to 64 in the ELSA data. ONS population statistics suggest that individuals aged 50 to 64 comprise around 29 percent of adults aged 18 to 64 in the UK. 8 On this basis, I allocate 29 percent of local authority social care spending for individuals aged 18 to 8 See ONS (2014) ref based-projections/stb-2012-based-npp-principal-and-key-variants.html#tab-changing-age-structure 22

23 64 in addition to the calculations made above. This gives estimates for total local authority spending on social care for adults aged 50 and over in as follows (again the details of how this calculation is arrived at are shown in Appendix A): residential care: 4.03 billion domiciliary care: 5.51 billion. 23

24 5 Results This chapter presents the results for the relative risks of entering social care for current smokers and ex-smokers compared with non-smokers, and uses these risk coefficients to estimate the cost of smoking to the social care system. The analysis is performed separately for domiciliary care and for residential care. 5.1 Domiciliary care costs Table 10 shows the relative risk coefficients from a logistic regression of receipt of domiciliary social care in the ELSA data, controlling for age and sex. Four different specifications are estimated: 1. Local authority provided care only, no means test; 2. Local authority or privately provided care, no means test; 3. Local authority provided care only, means tested; 4. Local authority or privately provided care, means tested. Full results from the regressions (including the coefficients on the gender and age dummy variables) are shown in Appendix B. 24

25 Table 10. Relative risks for local authority-funded domiciliary care receipt, current and ex-smokers vs never-smokers: results from logistic regression Specification Include privately provided care? No Yes No Yes Means-test care receipt? No No Yes Yes Coeff. P > z Coeff. P > z Coeff. P > z Coeff. P > z Social care receipt: current smokers ex-smokers Number of observations Source: author's calculations using ELSA wave 5 data Specification 1 of Table 10 shows that current smokers are 93.7% more likely (almost twice as likely) to receive any local authority provided care than non-smokers (including both local authority funded and self-funded care). When local authority or privately provided care is considered (in Specification 2), current smokers are 47.8% more likely to receive care than non-smokers. The coefficient is statistically significant at the 5% level in both specifications. Turning to Specifications 3 and 4 (which include the care means-test), current smokers in ELSA wave 5 are between 81.6 and 97.1% more likely to receive local authority-funded domiciliary social care, conditional on sex and age, than people who have never smoked. The coefficient is significant at the 5% level in specification 2 but only at the 10% level in specification 1. For ex-smokers, there is no statistically significant difference in propensity to receive domiciliary care compared with people who have never smoked. With that being the case, the equation for the proportion of domiciliary social care costs attributable to smoking simplifies to the following (assuming rex = 1): Attributable proportion = [p cur (r cur 1)]/[1 + p cur (r cur 1)] The proportion of current smokers in the ELSA sample is 13.9%. This means that the attributable proportion of smoking costs to local authorities in the domiciliary care system is 11.9% (if specification 3 is used), or 10.1% (if specification 4 is used). Using these attributable proportions, the cost of smoking to the public purse in the domiciliary care system is between 560 million and 660 million (depending on which specification is used). Using data on the proportions of the ELSA sample receiving local authority and/or privately provided social care in the ELSA wave 5 data (as shown in Table 7 above), 25

26 it is also possible to calculate the cost of smoking to self-funders, which is approximately 450 million, assuming that the unit cost of social care for self-funders is the same as it is for local authority-funded care. In practice, some self-funders are likely to opt for higher quality (and therefore more expensive) care than the care packages funded by local authorities, meaning that 450m will almost certainly be an underestimate of the total cost of smoking to self-funders. 5.2 Residential care costs Table 11 shows the relative risk coefficients from logistic regression of receipt of local authority-funded residential care in the ELSA data. As with the domiciliary care regressions above, the residential care regression controls for age and sex. Full results from the regression (which includes polynomials for male and female age) are shown in Appendix C. Table 11. Relative risks for local authority-funded residential care receipt, current and ex-smokers vs never-smokers: results from logistic regression coefficient P > z LA funded residential care: current smokers ex-smokers Number of observations Source: author's calculations using ELSA waves 1-5 data Table 11 shows that there is no significant difference in propensity to enter residential care for current smokers or ex-smokers compared to people who have never smoked. The coefficient on the current smoking variable is less than 1; if taken at face value, this would suggest that current smokers are less likely to enter residential care than people who have never smoked, which would imply that the costs of smoking to the residential care system are negative. This could be the case due to premature mortality caused by smoking; if, for example, smokers are more likely to die before they reach a situation of having to enter residential care. However, the standard error on the coefficient in this regression is so large that we cannot draw any conclusions about whether current smokers are more or less likely to enter residential care than people who have never smoked. This is not surprising given the small number of survey members entering residential care in ELSA waves 3 to 5. Given that the residential care regression produces no significant results we are unable to provide an estimate of the costs (if any) which smoking imposes on the residential care system. It may however be possible to revisit this calculation in 26

27 future as additional waves of ELSA data are collected, giving a larger sample of care home entrants to work with (if successive waves of the ELSA sample are combined). 5.3 The costs of smoking to the social care system in context The calculations in this report suggest that the costs of smoking to local authorities for the (domiciliary) social care system are in the region of 600 million per year. How large are these costs compared to other costs of smoking? Table 12 gives recent estimates for the aggregate costs of smoking, taken from a range of sources. The costs estimated for the social care system in this report, and the estimated costs to the NHS from modelling by the National Institute for Health and Care Excellence (NICE), are costs to the public finances, whereas the other costs in Table 12 are wider costs to society. The costs of smoking to the social care system are around one-third of the costs to the NHS. Bearing in mind that the NHS budget in England for 2012/13 was around 109 billion over seven times larger than the local authority social care budget of 14.6 billion for the same year the costs of smoking to the social care system as a proportion of total public spending on social care are considerably higher than the costs of smoking to the NHS as a proportion of total NHS spending. Table 12. The costs of smoking: social care costs and other costs compared Type of cost Annual cost ( m) Geographical area Source social care (domiciliary) 600 England Current report NHS 1,800 England NICE (2014) Loss in productivity through increased absenteeism of smokers 1,400 UK Weng et al (2011) Lost economic output through premature death and ill health of smokers 4,100 UK cost of smokingrelated fires 507 UK passive smoking 713 UK Nash and Featherstone (2010) Nash and Featherstone (2010) Nash and Featherstone (2010) 27

28 Looking at the wider costs of smoking in the lower part of the table, the cost of smoking to the social care system is smaller than the productivity losses and lost economic outputs arising as a result of smoking, but is of a comparable magnitude to the cost of smoking-related fires and the costs imposed by passive smoking. Certainly a cost of around 600 million to the public purse is highly significant in the context of shrinking public resources for social care and very tight constraints on public expenditure for the foreseeable future. 5.4 Limitations and possibilities for future research The estimate of the costs of smoking to the social care system in this report is the first of its type for England, and represents the best that can be done with the current data from ELSA and NASCIS. However there are a number of limitations in the methodology, which are mainly due to data issues. Most obviously, the data from ELSA only cover the population aged 50 and over, and hence it is not possible to get an estimate of the costs which smoking imposes on the social care system for adults aged under 50 using the ELSA data. An alternative data source for estimating social care costs which contains adults aged under 50 would be the British Household Panel Survey (BHPS). This contains information on receipt of domiciliary (although not residential) social care services as well as income and asset information which could be used to model the means tests. However, the final wave of BHPS was in 2008, since when it has been superseded by the much larger Understanding Society (USoc) survey. Unfortunately USoc contains no data on social care receipt and so it is impossible to use USoc to model social care costs. This means that modelling of social care costs for the younger population would have to use BHPS, which would mean that the results of such modelling would be somewhat out of date. A second issue with the social care modelling in this report is that I have effectively assumed that an equal amount of local authority funds is spend on each person receiving local authority-funded domiciliary social care. In other words, the modelling takes no account of different types of care received by different people, or of the intensity or frequency of care received. In the course of developing the modelling for this report, attempts were made to allocate costs separately for different types of care (according to the breakdown of help for different types of activity shown in Table 2 earlier). However, this proved not to be possible because the categories of spending breakdown in the NASCIS data did not correspond in any way to the categories of help received in the ELSA data. Thus it was not possible to develop a mapping which could be used to disaggregate the NASCIS data into categories which could be mapped to the ELSA data on the distribution of help received by task. A breakdown of ELSA social care receipt by frequency or intensity of care received 28

29 was not attempted because ELSA Wave 5 does not contain any information on the amount of care received or the frequency of care received from any given source only a 'yes/no' question on whether care was received or not. (By contrast, previous waves of ELSA contained a question on how often care was received from the local authority in the last month (with the response categories being: every day or nearly every day: two or three times a week; once a week; less often; not at all). Thirdly, the analysis in this report does not distinguish between social care spending for clients with different types of need or disability for example, physical disabilities, mental health problems, and so on. ELSA does provide detailed information on health status which could, in principle, have been used to produce a breakdown of social care clients according to type of disability. However, the NASCIS dataset does not provide a breakdown of social care spending according to type of disability for the over-65s only for clients aged 18 to 64. Given that most of the ELSA sample of social care clients are aged 65 or over, it was felt that a breakdown of social care receipt by type of disability for the subsample of clients aged 50 to 64 would be too incomplete to be useful. In future, it would be useful if NASCIS could provide the facility to break social care spending down by type of disability for clients aged 65 and over as well as the under-65 age group. This would enable a full breakdown of the social care costs of smoking by type of disability using the ELSA sample. Finally, the small number of interviews conducted with care home residents in ELSA waves 3 to 5 means that it was not possible to produce reliable estimates of the cost of smoking to the residential care system. This is regrettable, given that care homes comprise over 40 percent of all local authority-managed expenditure (NAO, 2014). It is possible that more accurate estimates will become possible in future years as the number of deposited waves of ELSA grows. Alternatively, a bespoke survey of care home residents which collected information about each residents' smoking behaviour and whether their care home place was local authority-funded or not, as well as other control variables, would enable a more accurate estimate of the costs of smoking to the residential care system. However, this would require considerable additional expenditure on data collection. 29