Globally, the challenge of matching resources to needs for older people is an enduring theme within social care. Determining how public resources are allocated according to competing demands, local conditions and the needs and circumstances of individual service users is at the heart of social care practice (ADASS, 2009; Care Act, 2014; Pavolini & Ranci, 2008; Ottmann et al., 2009; Da Roit & Le Bihan, 2010).
Discussion of resource allocation in social care in England has focused on a number of different levels: variations in allocation of central resources between major commissioning and providing units (local authorities in England) (Davies, 1968; Davies et al., 1971; Darton et al., 2010); the spending behaviour of such authorities on individual cases (Asthana, 2012); and less commonly on the mechanisms for allocation of resources to individuals for their care and support. Thus, for example, at the macro level, building upon the work of Bleddyn Davies (Davies, 1968), studies have examined the relationship between locality need levels and the allocation of resources from central to local government for social services (Bebbington & Davies, 1983; Davies et al., 1971; Darton et al., 2010). International comparisons have tended to examine levels of resource allocation between countries by categories of care (Campbell et al., 2016). At a more mid-level, other studies have examined the extent to which individuals with apparently similar needs receive different levels of care and support. For example, Asthana (2012) examined inter local authority equity through differences in levels of care allocation to apparently similar case types between local authorities when using a similar method of resource allocation to individuals. There was marked variation, which may have been explained in part by rurality and scale of central government funding to local authorities as well as the vagaries of the resource allocation system itself. At the micro-level, researchers have examined resource allocation in cash for care payments to individuals across several jurisdictions, including England (Gori & Morciano 2019; Ranci et al., 2019). However, the care payments in England analysed in the study were only Central Government Social Security benefits such as Attendance Allowance, Personal Independence Payments, and Disability Living Allowance. These studies did not address cash payments for social care such as personal budgets provided by local authority social services departments to individuals which offer variable levels of care and support on the basis of need. Comparisons of the processes for allocating variable cash for care payments in different countries suggested that most of these were based upon assessment and allocation of individuals to dependency-based cost categories (Da Roit & Le Bihan, 2010; Da Roit et al., 2016). In an examination of resource allocation in 20 local authorities in England, which included 13 scoring systems, based on points for need items, Series and Clements (2013) were highly critical of the view that these were accurate, transparent or equitable, a view echoed by the developers of one resource allocation system (FACE, 2012; Clifford et al., 2013). However, these more individualised processes for determining personal budgets in England have rarely been studied in detail, which this paper seeks to address.
Traditionally in England, as in other countries (Australian Government Department of Health, 2017), two methods of allocating resources in social care have emerged over time: a professionally led system and, more recently, self-directed support (Challis et al., 2016). The former involved a professional undertaking an assessment of need, followed by the creation of a care plan describing how needs would be met, which was then costed to give an amount of money for the care package. Self-directed support incorporates the use of personal budgets for those eligible for publicly funded social care services. This is promoted as placing greater emphasis on empowering service users to have greater choice and control over their support and reflects a move away from a focus on deficits as well as enabling resources to be allocated with greater transparency (Arksey & Kemp, 2008; ADASS, 2009; Department of Health, 2007; Department of Health and Social Care, 2020). A report in 2013 identified an increase in the numbers of older people with personal budgets (Routledge & Carr, 2013) and the Care Act 2014 continues to emphasise choice and control for service users over the support they need through personal budgets for all those who are eligible for publicly funded social care (Department of Health, 2007; Department of Health and Social Care, 2020). Personal budgets can be taken as a direct cash payment, managed by the local authority or a third party.
Prior to the Care Act 2014 eligibility for publicly funded social care was determined by applying a nationally determined framework under the Fair Access to Care Services (FACS) guidance (Department of Health, 2003), later updated in 2010 (Department of Health, 2010). An individual’s needs, and the risks associated with these not being met, were used to allocate them into one of four bandings (low, moderate, substantial, and critical). Within these bandings, local authorities could apply a threshold at which they would fund social care based on the availability of local resources. In practice, this meant that often only individuals in the substantial and critical bandings were eligible for publicly funded social care (Asthana, 2012). As part of the Care Act 2014, eligibility for social care was modified and adapted by the Care and Support (Eligibility Criteria) Regulations 2015 (SI2015/313).
There has been considerable debate as to how to determine a personal budget for the care and support of a person receiving social care. One strategy has been to develop resource allocation systems that, on the basis of a limited number of items, are designed to allocate resources between individuals and create an indicative budget prior to the completion of a care plan (Department of Health, 2008). Giving service users an indicative budget early in the process of planning their care and support needs was suggested as a mechanism to redress the balance of control in decision making away from professionals (Duffy, 2007).
An early resource allocation tool in social care was developed by ‘In Control’ (Duffy, 2005) specifically for people with a learning disability. Subsequently, the Association of Directors of Adult Social Services developed the common resource allocation framework (CRAF), which offered advice and a series of practical tools that could be used by local authorities in the implementation of a resource allocation system (ADASS, 2009). Both the In Control system and the CRAF were designed to place the service user at the centre in assessing the level of support they required as well as enabling resources to be allocated with greater transparency. Over time, three emergent approaches used to allocate resources have been identified in the literature (Series & Clements, 2013; Challis et al., 2016):
Local authorities had discretion as to the content of these resource allocation systems and were at liberty to develop their own, whether derived from points-based processes from In Control and CRAF, or those developed by others, such as FACE. There is no evidence as to why they made particular choices but they were likely to be influenced by what was currently ‘in vogue’ with stakeholders or what built most easily on existing procedures and processes.
Early research has suggested some problems with resource allocation at the micro level. The indicative budgets provided do not always match well the personal budget eventually awarded (Slasberg et al., 2013; Series & Clements, 2013). In terms of the actual personal budget provided older people with high care needs appear particularly disadvantaged (Newbronner et al., 2011) and received less funding compared to younger individuals with similar ADL scores (Moran et al., 2013). There has been little research on how well resource allocation tools reflect the preferences and needs of older people. When older people were asked about their preferences for different types of social care needs being met, they rated ADLs and IADLs higher than those related to psychological wellbeing. This view differed from senior management in Local Authorities (Clarkson et al., 2018).
The study reported here was part of a wider programme of work examining the emergent approach to resource allocation in the context of self-directed support within social care services for older people in England. It included: a literature review of resource allocation approaches (Challis et al., 2016); an analysis of needs assessments from a sample of local authorities to determine the predictors of costs; comparison of these cost predictors with factors deemed important by different stakeholders in resource allocation (Clarkson et al., 2018; Davies et al., 2015; Hughes et al., 2018); and a systematic analysis of resource allocation documentation used by local authorities. This paper reports the findings from the systematic analysis of documentation. It aims to answer the following research questions:
Local authorities in England were contacted by letter in 2012 and asked to provide information about their resource allocation system for individual service users in adult social care. The request was made to Directors of Adult Social Care with either a request for a copy of the questionnaire used to identify an individuals’ score if a points-based system was in operation, or details and documents describing how cash amounts for personal budgets were calculated in a system not based on points. Responses came from staff identified by the director as having appropriate knowledge. Most of these data were obtained as part of a previous national survey on resource allocation system methodologies (Asthana, 2012), supplemented by local data collection and follow up by the research team with individual authorities. The identification of tools for inclusion in the analysis is detailed in Figure 1.
After careful scrutiny of the resource allocation tools received and any accompanying guidance an analytic framework was developed. This reflected earlier studies of assessment documentation used in adult social care (Stewart et al., 1999; Worden et al., 2006) and findings from an audit of case files in social care agencies (Sutcliffe et al., 2014). The framework analysis was undertaken on two levels (Table 1). First, it looked at the presence or coverage of attributes and need indicators and secondly it explored whether these were covered in detail. “Coverage” referred to whether or not the item or need indicator was specifically mentioned on the resource allocation tool. “Detail” referred to whether, beyond presence, items clarifying levels of support, severity of need or degree of difficulty in the indicator were included (Table 1).
|Items||Definition of detail|
|Views on service user need:|
|Service user view||View on user need included|
|Carer view||View on user need included|
|Professional view||View on user need included|
|Support already received:|
|Formal statutory/voluntary agency support||Extent of support received included|
|Informal support||Extent of support received included|
|Mobility in home||Degree of difficulty and extent of need for help|
|Toileting||Degree of difficulty and extent of need for help|
|Transfer (bed/chair)||Degree of difficulty and extent of need for help|
|Continence||Degree of difficulty and extent of need for help|
|Make hot drink/snack||Degree of difficulty and extent of need for help|
|Make hot meal||Degree of difficulty and extent of need for help|
|Housework/cleaning||Degree of difficulty and extent of need for help|
|Shopping||Degree of difficulty and extent of need for help|
|Mental health indicators|
|Cognitive impairment||Nature and degree of impairment|
|Communication problem||Nature and degree of problem|
|Mood state||Nature and degree of problem|
|Problem behaviour||Nature and degree of problem|
|Health and wellbeing indicators|
|Physical health status||Nature and degree of problem|
|Risk of falling||Degree and frequency of risk|
|Danger to self/others||Degree and frequency of danger|
|Participate in activities||Type, sufficiency of activities and support required|
|Home environment||Who lives with, type and suitability of accommodation|
The first part of the framework identified whether the views of users, carers and professionals were recorded on the documentation; whether current formal and informal support was recorded; and whether there was mention of particular need indicators. Seventeen need indicators were selected, grouped into three types: functional status, mental health, and health and wellbeing. This was based on early work by Isaacs and Neville (1976) on the needs of older people; and the content of early recommended resource allocation systems (ADASS, 2009; Tyson et al., 2010).
The second part of the framework focussed on the presence of detail in these attributes and need indicators. It was developed from earlier analyses of assessment tools used for the comprehensive assessment of older people to define the extent to which they covered different assessment areas (Stewart et al., 1999; Worden et al., 2006).
Relevant data were extracted from the tools and entered onto an Excel spreadsheet and SPSS. A 10 per cent check of the tools was undertaken with a second researcher. This was achieved through simple random sampling: all respondents were allocated a number from 1 to 93. Ten tools were selected using integer random numbers between 1 and 93 generated through a random number computer programme.
For each tool a total summary score was created from the sum of the coverage of the 17 needs indicators. In addition, a comparison of the coverage of the need indicators by type of resource allocation system was undertaken. Reflecting the distribution of the data in the different tools, the analysis used exact probability statistics.
One hundred and thirty-eight (91%) authorities responded and 93 (61%) authorities’ tools were included in the analysis (Table 2). Most of the authorities that responded used a points-based self-assessment tool (59%) representing just over three-quarters (77%) of the tools analysed. Thirteen per cent of local authorities who responded used standardised assessment data or FACE and this represented 18 per cent of the tools analysed. A little over one quarter (28%) of responding local authorities used a non-points-based/ready reckoner approach, which represented 5 per cent of the tools analysed, The majority (96%) of analysed resource allocation tools were being used for all service user groups, not specifically for older people, although the budget allocation process could vary by service user group.
|Approaches to Resource Allocation||No. of Respondents||No. of Tools Analysed|
|Standardised assessment data2||18||13||17||18|
|Non-points-based/ready reckoner post assessment3||38||28||4||5|
|Total of 152 authorities||138||91||93||61|
The points-based self-assessment tools were derived from the CRAF or the In Control approach (Duffy, 2005). In general these included: personal care needs; support at night; eating and drinking; managing behaviour; being part of the community; safety/risk; maintaining the home; having work or learning activities; making decisions; having a role as a carer; and health and wellbeing. Each topic consisted mainly of one question and the respondent was asked to select one of a number of options mostly giving details on the amount of support they felt was needed in this particular area. The options given were usually expressed in the form of the frequency of the support needed, such as ‘once a day’ or ‘once a week’. Depending on the options selected by a respondent, a number of points was awarded, often derived from a separate matrix giving points per response for each question. The number of points awarded for individual responses varied between the questions. Each point awarded was equal to a set amount of funding so that an indicative budget was arrived at from the sum of the points for all the responses. Other factors were subsequently taken into account in determining the indicative budget, for example if a family carer provided some of the support required, then a percentage budget reduction was applied. Some tools included a separate question on the carer’s ability to continue to provide this level of assistance and if the carer was having difficulty the budget reduction for an informal carer was not fully applied. These tools were sometimes employed in addition to professional assessment and the final budget allocations required adjudication between the two approaches.
The FACE resource allocation system was built around a structured assessment of needs undertaken by a professional in partnership with a service user and their carer (FACE, 2012; Imosphere, 2019; Clifford et al., 2013). FACE is an existing assessment tool which was developed over 20 years and was already being used in some local authorities. It received Single Assessment Approval (Department of Health, 2001; CPA, 2020) and is described as Care Act 2014 compliant (Imosphere, 2019, 2020). The assessment tool covers 14 dimensions: communication; home, and living situation (IADLs); eating healthily and safely; personal care; mobility; social relationships and activities; work, training, education, and volunteering; caring for others; staying safe at home; risks; mental health and wellbeing; health conditions and disabilities; support from family and friends; and summary of assessment and eligibility. The tool is designed to enable a proportionate assessment of needs, and trigger further information including specialist assessments if necessary. A separate assessment of carer’s needs can be used if applicable.
This resource allocation system was developed using statistical modelling of the relationship between needs and costs and was derived initially from individual anonymised case data on a very large number of individuals from 20 local authorities which had previously used the FACE assessment tool. A subset of 30 needs items from the overview assessment which best predicted costs were used to develop the resource allocation model. These were drawn from a range of areas covering: disabilities, impairments or health conditions; need for support with personal care and day-to-day activities; involvement in the community and relationships with others; need for support in staying safe; support from family, friends and others nearby; and ongoing living arrangements. This resource allocation system takes into account an individual’s needs profile, rather than treating needs independently, to avoid over allocation and takes account of joint supply of needs when one input (or amount of resource) can meet two needs. The overall model is then individually tailored to the specifications of each local authority. Commonly this takes two forms: the application of a local cost ceiling for community-based care; or a deflator making the indicative budget below that of a traditional care package (FACE, 2012).
These were based on professional assessment and draft care plan information. They ranged from spreadsheets with unit costs of different care components to be completed following assessment, to more aggregated costing built into need areas and summated by the spreadsheet, to assignment to two or three broad cost and need categories. The principal cost drivers appeared to be levels of need in activities of daily living (such as need for help with toileting or transfer); instrumental activities of daily living (such as need for help with meal preparation); or levels of risk. Some tools addressed the issue of joint supply of needs. Unfortunately, fewer of the ready reckoner tools were provided for analysis.
The tools were analysed to ascertain the extent to which they incorporated the perspectives of different stakeholders about the service user’s needs. Whilst some tools were designed to be completed by the service user, this could involve help from someone else, such as a carer or a professional, such as a social worker. This was represented on the tools either by the use of tick boxes for different perspectives for each of the need indicators or open text boxes for comments. All the tools analysed were designed to include the views of the service user about their needs. A high proportion (85%) included the views of a professional and for 82 per cent this was assessed as ‘detailed’ information. Fewer of the tools were designed to record the views of a carer on the service user’s needs (23%) and this was judged to be ‘detailed’ for only 9 per cent.
The extent to which the tools included information about the amount of support currently received, both informal and from statutory services or voluntary agencies was assessed. Almost all the tools (97%) were designed to obtain some information about informal input and for 70 per cent of the tools this was judged as ‘detailed’ information. Fewer of the tools were designed to collect information on formal service input (34%) and this was assessed as detailed for 11 per cent.
Table 3 shows the presence of the 17 need indicators on the tools and whether they sought detailed information. A high degree of variation was evident in both presence and detail. For the eight functional indicators coverage ranged from 97 per cent for both ‘make hot drink/snack’ and ‘make hot meal’ to 32 per cent for ‘continence’. The presence of detail in these eight functional indicators ranged from 38 to 24 per cent. The indicator ‘transfer’, although present on 68 per cent of the tools, was detailed on only 24 per cent.
|Mobility in home||62||67||33||36|
|Make hot drink/snack||90||97||35||38|
|Make hot meal||90||97||35||38|
|Mental health indicators|
|Health and wellbeing indicators|
|Physical health status||61||66||21||23|
|Risk of falling||45||48||23||25|
|Danger to self/others||93||100||71||76|
|Participate in activities||93||100||21||23|
Coverage of the four mental health indicators (‘cognitive impairment’; ‘communication problem’; ‘problem behaviour’; and ‘mood state’.) ranged from 41 to 65 per cent of the tools. Coverage was lowest for ‘mood state’ (41%). One third or less of the tools requested detailed information on the four mental health indicators and again this was particularly low for ‘mood state’ (22%). Of the four mental health indicators in Table 3, 27 per cent of the tools included one of these, 19 per cent included two, 16 per cent included three and 27 per cent included all four. Two health and wellbeing indicators ‘danger to self/others’ and ‘participate in activities’ were covered on all the tools and detailed information was requested on 76 per cent and 23 per cent respectively.
Figure 2 shows the summary measure of total coverage of needs created from the sum of the 17 needs indicators. The total number of indicators present ranged from six to 17 items. Twenty-one (23%) of the resource allocation tools included all 17 of the indicators. Forty-four tools (47%) included between nine and 12 indicators and 15 (16%) included between six and eight indicators.
When the coverage of the indicators was compared across the three types of resource allocation systems there were some significant differences. These were due to a higher coverage of the indicators on the standardised assessment data tools. Coverage for the three ADLs (‘mobility’, ‘toileting’ and ‘transfer’) was significantly higher for the standardised assessment data compared to the points-based self-assessment tools (Ch-sq = 12.625, exact p = 0.001; Ch-sq = 6.084, exact p = 0.059; Ch-sq = 10.311, exact p = 0.004 respectively).
For all the four mental health indicators (‘mood state’, ‘problem behaviour’, ‘communication problem’ and ‘cognitive impairment’) coverage was significantly higher for the standardised assessment data tools compared to the points-based self-assessment tools (Ch-sq = 30.983, exact p = 0.000; Ch-sq = 17,837, exact p = 0.000; Ch-sq = 11.521, exact p = 0.002; Ch-sq = 17.922, exact p = 0.000 respectively). In addition, only 8 (11%) of the points-based self-assessment tools and none of the non-points based ready reckoner tools covered all four mental health indicators.
The indicator ‘home environment’ was covered infrequently on the points-based self-assessment tools compared to the standardised assessment data tools (Ch-sq = 27.015, exact p = 0.000). When coverage of the three indicators ‘continence’, ‘risk of falls’, and ‘physical health status’ were compared across the three resource allocation systems, this was significantly lower for the points-based self-assessment tools compared to the standardised assessment data tools (Ch-sq = 43.806, exact p = 0.000; Ch-sq = 24.651, exact p = 0.000; Ch-sq = 14.230, exact p = 0.000 respectively).
This study explored the resource allocation systems used by local authorities to determine indicative budgets in social care and their coverage of specific needs. Broadly the findings suggest that there remain three approaches to the use of resource allocation tools: a points-based approach, the standardised assessment data approach, associated with the FACE tool, and a non-points-based/ready reckoner approach. In this discussion four areas are considered in relation to the findings: structure, form and content of the tools; reliability and equity in resource allocation; implications for policy, practice, and research; and study limitations.
At the time of data collection, the points-based approach was most prevalent (Tyson et al., 2010; Series & Clements, 2013). These tools were originally developed in the care of people with learning disabilities and their transferability to and salience for other groups, such as older people, remains unproven. Indeed, higher discrepancies in the budget between the indicative and the final personal budget have been shown for older people (Series & Clements, 2013). These points-based systems were frequently developed from small datasets of the existing allocation of resources within a local authority (Series & Clements, 2013). They, therefore, had insufficient sample size and failed to consider issues of sensitivity and specificity (errors involving false positives and false negatives in allocating individuals to high or low budget levels). A large sample size and complex analysis is necessary for a tool to have sufficient sensitivity and specificity to differentiate between a higher and a lower cost care package (Altman & Bland, 1994). Thus, it is not sufficient to demonstrate that a tool is shown on average to be reasonably correct, since caution must be applied in its use in individual cases, as there is no guarantee that it will be correct for each of these. In addition, the procedures to ensure continued accuracy and validity over time may not be in place (Series & Clements, 2013).
A further difficulty in allocating resources based on scores from several items is the assumption that need for care is additive. The presence of comorbidities and different care inputs for each need may make care requirements more complex and more or less costly than a simple additive linear model can acknowledge (Clifford et al., 2013). For example, a person with dementia and a range of ADL and IADL problems may well require more costly support and greater oversight when their mobility problems are less extensive. Thus, there is no universal linear relationship between severity of need items and extent of need for care. The methodology employed in developing these points-based systems contrasts markedly with the rigour of the approach employed by US states in identifying a tool for resource allocation using statistical procedures on large samples of cases (Medstat, 2004; Smith & Fortune, 2006), and demonstrating a concern for key psychometric attributes such as reliability and validity, which should be essential for any tool (Kane & Kane, 2000; Cook et al., 2015). It is noteworthy that, by contrast with points-based approaches, the developers of the standardised assessment approach (FACE) to resource allocation used large samples and rigorous statistical methods in its development (FACE, 2012).
Coverage of the three activities of daily living (ADLs) (mobility, transfer, and toileting) was particularly low on the points-based tools with only around a third of the tools covering mobility in detail and around a quarter covering toileting and transfer in detail. On most of the points-based self-assessment tools the ADLs were incorporated into a single general question about personal care which did not always specify particular ADLs. On occasions this also included additional needs such as taking medication, getting dressed, and personal hygiene. People were asked to choose from a list of responses which conflated increasing amounts of the ‘support’ they felt they needed with increasing levels of the ‘frequency of assistance’ they required in the area of personal care. Frequency and severity are not necessarily substitute measures of the importance of a need unless carefully evaluated (Isaacs & Neville, 1976). For different ADLs the expected frequency naturally varies, for example toileting and retiring and rising to and from bed. Thus, on some forms it was difficult to respond to this as a single question, particularly if the support needed was low (e.g., prompting) and frequency high or vice versa. This could have marked implications for people experiencing the early stages of dementia where prompting, or low-level support, is often required frequently.
A further complexity to the question on personal care was that sometimes it included the number of staff needed (1 or 2 people) to provide support and whether this was needed during the day or night. Given that the number of people required to assist is likely to be in part shaped by Health and Safety requirements, these factors raise further questions about the validity of the information obtained. An additional difficulty of the points-based tools was that on some of the forms the difference between each of the available responses was very marginal, vague, and open to interpretation, thereby impacting on both reliability and validity. These features could lead to significant error in need identification and therefore resource allocation.
The personal care question on most points-based tools lacked specific detail on an individual’s functional abilities to understand their difficulties and find solutions, identify early changes in function and plan for future costs. They ask the respondent to decide on their support (that can be costed) without necessarily having had a full assessment of their needs and at a time when they are unlikely to possess the information to know what specific help is available. This approach differs from the FACE tool and other approaches that measure each ADL separately, and therefore individual’s abilities are more clearly defined.
Around one in 14 of the over-65s in the UK are living with dementia (Prince et al., 2014) and depression in older users of home care services in England affects one in four (Banerjee & Macdonald, 1996). Despite this, the mental health problems of old age, such as depression or mood state and cognitive impairment, were not well addressed in the tools, save for standardised assessment data (FACE). Whilst a little under two-thirds covered cognitive impairment, this was only addressed in detail on a quarter of the tools. Given the prevalence of this condition in frail older people and the importance attached to dementia in the current policy and care environment, this would seem to be a notable omission. This is enhanced by a key feature of cognitive impairment being the loss of ability to perform daily activities, with their performance deteriorating differently over time for different activities (Giebel et al., 2015). The lack of coverage of certain needs on the tools may result in these being missed or given insufficient priority when determining budgets for particular individuals.
This study found a wide variation in the total number of needs indicators present on these tools, ranging from 6 to 17, which suggests that individuals with the same needs are likely to achieve very different indicative budgets across authorities (Asthana, 2012). There is a potential conflict between the idea of a simple method of allocation of care resources, based on small numbers of items, and the idea of personalisation which stresses the complex uniqueness of each person’s needs and therefore logically the amount of care and support required to address these.
In addition to the poor coverage of ADLs and mental health problems the points-based self-assessment tools also had poor coverage of continence, risk of falling and physical health status. Thus, many omitted needs common to frail older people including Bernard Isaac’s four ‘Giants of Geriatrics’: impairment of intellect (cerebral dysfunction/dementia); incontinence; immobility; and instability (falls) (Isaacs, 1992). Research suggests an association between falls, incontinence and physical limitations and thus if there is a problem in one of these then the others should be explored (Foley et al., 2012; Chiarelli et al., 2009). Conservative interventions may be effective in treating urinary incontinence in older adults and should be the first line of management (Stenzelius et al., 2015; Ahorony et al., 2017). Exercise programmes have been shown to be effective in reducing subsequent falls for some community living older people (Sherrington et al., 2019; Liu-Ambrose et al., 2019; Guirguis-Blake et al., 2018; Hopewell et al., 2019). Both urinary incontinence and falls are associated with poor quality of life, anxiety, and depression and reduced social activity (Foley et al., 2012). Recognition that these are common needs for older people is important so that appropriate assistance can be arranged. In the UK, the National Institute for Health and Care Excellence (NICE) recommends that older people are asked routinely about falls when they are in contact with professionals and organisations, with a health and social care remit (Public Health England, 2017). For older people with incontinence, a contact with services, and the opportunity to discuss the problem, may also act as the trigger for effective help seeking (Vethanayagam et al., 2017). Given the lack of coverage of key ADL and health status measures, it is noteworthy how these resource allocation tools compare with those in use elsewhere. In many countries, access to long-term care funding is determined by use of ADL and dependency measures, which are less prone to error than more subjective measures (Da Roit & Le Bihan, 2010; Da Roit et al., 2016; Ikegami, 2007).
There was no requirement on many of the tools to record a carer’s opinion of the service user’s needs. However, carers may report more unmet needs than the care recipient (Brimblecombe et al., 2017) and their omission could lead to needs not being identified. This also fails to recognise the importance of carers for very frail older people or those with dementia, who often take full or substantial responsibility for management of the personal budget (Newbronner et al., 2011). The complexity and variation around the assessment of carer’s and service user’s needs and the personal budgets for both parties in the personalisation process, with a failure to link the two, has been highlighted (Brooks et al., 2017). Most of the tools recorded the informal help an older person received, and this led to deductions from the indicative budget. As Series and Clements (2013) found, in points-based systems ‘informal support’ was categorised somewhat arbitrarily as were the resulting proportional reductions in budgets. These varied between local authorities (for example in some tools ‘some support’ led to a 50 per cent reduction and in others a 65 per cent reduction), raising concerns about both equity and accuracy.
Whilst supported self-assessment may be a positive experience, it was not clear to what extent these tools were jointly completed with a professional or completed independently. Indeed, older people have been reported as being uninterested in seeing the documentation associated with the assessment (Foster et al., 2006). Even with validated ADL assessments, such as the Barthel Index (Mahoney & Barthel, 1965), discrepancies have been observed between actual performance and self-reported performance in older people (Sinoff & Ore, 1997). These discrepancies were partly explained by the presence of cognitive impairment, being in the ‘older-old’ group aged over 85 years and being in hospital, which are the groups of older people most likely to be the greatest users of social care. In self-assessments of hearing and vision function older people were also found to over-estimate their ability compared to gold standard measures (Haanes et al., 2015). In a review of research on self-assessment, accuracy was found to be varied, with those where the content matched closely to diagnostic criteria performing better. Self-assessments of general health showed only moderate sensitivity and specificity so that many older people with problems would not be identified (Griffith, et al., 2005). Thus, there needs to be caution in relying on older people’s self-reported needs on inadequately tested assessment tools.
The presence of mental health conditions can also make completing a self-assessment tool accurately and reliably more difficult. People with depressed mood, which is frequently undiagnosed and sometimes chronic, may under-report needs or desired outcomes (Banerjee et al., 1996; Evans et al., 1991). Furthermore, many older people experiencing difficulties may not have received a formal diagnosis of dementia and depression is also often unrecognised in this group. Difficulties may be compounded when 48 per cent of women 75 years or over in the UK live alone (ONS, 2018) and they may not have someone to assist completion.
Most local authorities in the study were using the same resource allocation tool across four different groups of service users (learning disability, mental health, older age, and physical disability). Although appearing equitable within an individual local authority it was sometimes evident from the documentation that different budgets were awarded for the same points for different user groups (Series & Clements, 2013; Challis et al., 2016). Older people tended to receive lower allocations (SCIE, 2011).
The use of the same tool for each user group may reflect a different form of inequity if the tools themselves reflect more fully the preferences and needs of one particular group rather than others. Older people may frame their goals differently to younger people (Lens & Gailly, 1980; Lang & Carstensen, 2002; Lӧckenhoff & Carstensen, 2004). They use social care services often at a time of crisis and increased frailty (Moran et al., 2013) and are more likely to identify their needs as addressing loss of different kinds, meaning that identifying these as outcomes in RAS tools may be more difficult (Clough et al., 2007). Hence it may be that a focus upon user-identified outcomes and choice in needs assessment may inadvertently reward those who are more motivated and capable of formulating and articulating their goals. Therefore, in the same way that in a professionally led assessment practitioners can restrict or extend areas covered (Foster et al., 2006), it appears that some points-based self-assessment tools with limited indicators can also be restricting.
Work undertaken as part of the wider study on resource allocation highlighted different perceptions of priorities for care and support amongst different stakeholders (Clarkson et al., 2018; Davies et al., 2015). Specifically, older people prioritised ADLs and IADLs above psychological wellbeing needs, which were given low priority (Clarkson et al., 2018). If different groups have different preferences and needs then using one resource allocation tool, whilst still keeping the process proportionate (Department of Health and Social Care, 2020), means that the opportunity is missed to explore areas important to one particular group more than others. Tools need to be appropriate for particular target groups and purposes and are not necessarily interchangeable (Worden et al., 2008). Particularly in points-based systems, some groups could be restricted in the number of points they can score and therefore in the size of budget available to them, thereby creating systematic disadvantage.
The vague distinctions between categories of support needed in some of the points-based systems (Series & Clements, 2013) could encourage people to over-emphasise their needs, particularly in more subjective areas or where the distinction between options is unclear. If there is an incentive to report inaccurately, then some people may be more aware how to game these schedules (Series & Clements, 2013), leading to inequalities between service users (Series, 2014), with consequent effects on equity, efficiency, and effectiveness.
It has been noted, for example with regard to ADL and personal care, how the points-based tools, in contrast with the others, conflated different areas of need with very different patterns of potential demand for support. If professional assessment became focused on the content of these tools, with their frequently superficial coverage of key need indicators, it might lead to a decline in the quality of assessments undertaken, with deleterious consequences for service users. This would depend upon the extent to which these points-based tools complemented or substituted for the professional assessment. If the latter, the areas covered are headings rather than the individual items addressed in an assessment, which is likely to result in a diminution in the sensitivity of assessment. Such a consequence could put at risk policy and practice commitments to improved assessment under initiatives like the Single Assessment Process introduced in the National Service Framework for Older People (Department of Health, 2001). Studies have shown how assessment can identify not only need for care and support but also for other interventions which may improve quality of life and reduce need for care and support (Brocklehurst et al., 1978; Challis et al., 2004; Venables et al., 2006). Thus, assessment has a wider purpose in needs identification than resource allocation alone (Kane & Kane, 2000). Hence poor coverage of need indicators and a lack of attention to key measurement properties in some of the tools – sensitivity, specificity, reliability, and validity – can impact on assessment quality.
It is also interesting to consider the three approaches to resource allocation identified in this study in terms of their efficiency in the practice of identifying needs and resources for care and support. Both the standardised assessment (FACE) and the non-points-based/ready reckoner approaches employ existing processes and do not introduce significant additional steps and costs to the determination of resource allocation in contrast to the points-based approach. The latter introduces a new process and associated costs alongside existing procedures (Series & Clements, 2013).
There would seem to have been a growing trend since these data were collected for more local authorities to use the FACE standardised assessment approach. The Care Act (2014) may have been an influence on which tools are employed by local authorities, given the greater specificity in the Guidance (Department of Health and Social Care 2020) in relation to assessment. In particular, it provides specification about assessment being an intervention in its own right (para 6.2); its form (para 6.3); its aim and purpose (paras 6.5, 6.9, and 6.10); and staff skills and training (paras 6.85–97). The role of assessment in the overall care and support process is thereby made clearer. Indeed, concern about the undermining of professional judgement in resource allocation has been acknowledged by a key originator of resource allocation systems:
“There has been a worrying tendency for the RAS to supplant professional judgement and to undermine legal entitlements” (Duffy, 2012).
The acknowledgement of the importance of discretion in more recent local authority guidance is also indicative of some disquiet with the points-based approach and its oversimplified means of resource allocation. However, the extent to which resource allocation approaches change should be the subject of further research.
The present study has highlighted variability in the form, content, and potential impact of different approaches to resource allocation. There is scope for further research to build upon this in addressing the extent to which these different approaches concur using case vignettes as in balance of care studies (Asthana, 2012; Challis & Hughes, 2002; Hughes & Challis, 2004; Tucker et al., 2008, 2018); the impact upon practitioners, including their time use (Challis et al., 1990; Jacobs et al., 2006, 2011; von Abendorff et al., 1994; Weinberg et al., 2003) and practice; and the views of service users and staff on both the tools and the processes involved in their use.
This study examined the tools developed and employed for resource allocation in social care. One limitation was the under-representation of the non-points-based/ready reckoner approach in the analysis. The study did not examine how these tools fit within a local authority’s overall system of assessment and resource allocation. As such those using the most inclusive tools are not necessarily those undertaking the most thorough identification of need to allocate resources effectively, since this also depends on the processes which are in place after the resource allocation tool has been completed. As noted, these are subjects for future research. However, with the inclusion of a large proportion of local authorities in England, it offers, to our knowledge, the only national perspective on resource allocation tools.
This paper is part of a wider study of resource allocation in social care and focuses on analysis of the form and content of the resource allocation tools employed. Other papers from the wider study have reviewed literature (Challis et al., 2016) and examined preferences and priorities of user groups and other stakeholders regarding needs and resource allocation (Clarkson et al., 2018; Davies et al., 2015; Hughes et al., 2018). There appeared to be greater cause for concern regarding points-based systems than the other approaches. In addition, in points-based systems the indicative budget was based upon estimated need for care prior to, and to some extent independent of, assessment of need, which might not be considered good practice. Hence, some needs might be met by other interventions and thereby reduce the need for care and support. For example, research has shown the requirement for care home admission could be reduced by effective assessment and needs identification (Brocklehurst et al.,1978; Challis et al., 2004). The lack of coverage of key areas of need for older people on points-based tools raises questions on the equity of resource allocation across different user groups. Further research could usefully build on this study in several ways indicated in the paper.
This research was funded by the National Institute for Health Research (NIHR), School for Social Care Research (Grant Reference no. T976/EM/Man1). The views expressed are those of the authors and not necessarily those of the NHS, local authorities, the NIHR or the Department of Health and Social Care.
The authors have no competing interests to declare.
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