Long-Term Care models – Other Literature

The following papers are not from the field of Operational Research. The first three papers reviewed include models that were used in the Wanless Report in 2006, described in the Social Policy section 2.2.2 (p.40). The models in this section have proved to be useful but it should be noted that they are all used at the national level apart from one which models long-term care in Hong Kong.

Wittenberg et al. (2006) constructed a model to project demand for long-term care for older people in England over the period 2002 through to 2041. The authors pose some key questions:-

―How many older people are likely to require long-term care services in the coming decades? How much are these services likely to cost? Will the cost to public funds prove affordable? Who should pay? How should costs be

62

divided between public expenditure and private sources of finance?‖

(Wittenberg et al., 2006, p2)

To address these questions, good projections are needed on the potential demand for long-term care as well as the related expenditure. The Personal Social Services Research Unit (PSSRU) model (Wittenberg et al., 2006) attempts to address this need for good projections in the United Kingdom. There are several versions of the model.

The original model was published in 1998 and was used by the Royal Commission in their report into long-term care in 1999. The model attempts to make projections of the following:

―…the future numbers of disabled older people, the likely level of demand for long-term care services and disability benefits for older people, the costs associated with meeting this demand and the social care workforce

required.‖ (Wittenberg et al., 2006, p3)

The PSSRU model utilizes data from a variety of sources including the General Household Survey, population forecasts as well as data related to care services. Future home ownership rates are taken from a microsimulation model, i.e. it is individual-based and stochastic, but not a DES in which the human ―entities‖ interact. Referrals, Assessments and Packages of Care were also used and provided by the Department of Health. The data is related to the number of people receiving various types of local authority services.

Wittenberg et al. use a cell-based model which is a macro-simulation; it was formulated in a spreadsheet. The model subdivides the older population by disability into cells by age group, gender and by both household type and tenure. Each of these cells is assigned a probability of receiving long-term care, from which expenditure is then calculated. The model is projected forward using population projections. A similar approach was adopted for the research presented in this thesis (see section 4.6, pp.137-139). Wittenberg et al. ran various different scenarios, and make a number of

assumptions in the base case run. However, the authors do not allow for changes in the disability rates taken from the General Household Survey. Under this scenario there

63

potentially would end up being a 100% increase in the number of older disabled people.

Wittenberg et al. make two points: the assumptions of the model are not the only ones possible and projections are made, not forecasts. Policy planners clearly need to prepare now for the uncertainty that lies ahead. The model has shown to be a useful way of modelling the demand for long-term care.

Hancock et al. (2003) link a macro- and micro-simulation model to analyse who will pay for long-term care in the United Kingdom. The authors also investigate the potential outcomes of a free personal care system. The work has come about through the ongoing debate about who should pay for long-term care in the United Kingdom especially as the National Health Service is free at the point of use while social care services are subject to means testing. Hancock et al. utilize the work of two simulation models, the PSSRU model and the Nuffield Community Care Studies Unit (NCCSU) model. The former is a macro-simulation model and the latter is a micro-simulation model. The NCCSU model uses data from the British Family Resources Survey. The model is used to calculate how much money each older person in the survey would spend on care if they needed to go into a care-home. Simulating the future involves the process of ageing those people in the survey. Hancock et al. discuss how the NCCSU model is linked to the PSSRU model. Two pieces of information are sent from the NCCSU model to the PSSRU model: the likely number of residents who would require care from the local authority and the contribution they would be required to make. The PSSRU model has once again shown to be potentially a useful way of addressing issues related to long-term care.

Hancock et al. (2006) discuss CARESIM, another microsimulation model. It was

previously known as the NSSCU model. The main outputs of the CARESIM model are:

 the proportion of care home residents and home care clients eligible for local authority support under the current or an alternative charging regime;

 for care home residents, the proportion of gross costs of care met by disability benefits in the case of those not eligible for local authority support;

 the proportion of gross costs met by users, in the case of those eligible for local authority support; and

64

 the proportion of gross costs of home care met by disability benefits, for those eligible for local authority supported home care.

Hancock et al., 2006, p10-11

These outputs are used as inputs for the PSSRU model. Hancock et al. (2006) run three scenarios as summarised in Figure 3.1.

Figure 3.1: Assumptions for three possible future scenarios (Source: Hancock et al., 2006, p19)

The model is projected forward from 2002 through to 2051 and the authors were able to present a variety of useful results from the scenarios. Of course, this is a trade-off since the further one projects forward, the results become increasingly unreliable. This was an important consideration when deciding upon the study period for the long-term care model used in this thesis (see section 4.3.2, p.119).

Chung et al. (2009) study the implications of demographic change on expenditure of long-term care in Hong Kong. The authors chose the approach of the PSSRU model to model expenditure until 2036. Various services are modelled, including day and home care, nursing homes and hospices. The model outputs included the number of people requiring long-term care and the associated costs. The authors were able to run various scenarios to test the sensitivity of the results. These included, for example, reducing the number of informal carers available and adjusting unit costs. The authors were able to

65

present a wide range of results, including total expenditure for different services and expenditure by funding source. This study shows the benefits of the PSSRU model but there is a limitation: the authors note that when presenting the total expenditure as a proportion of gross domestic product it relies on a good estimate of economic growth.

Nuttall et al. (1994) are concerned with the financing of long-term care in Great Britain.

This paper was published around the time when local authorities took over the assessment process and the public funding of long-term care. The authors found that previous research into future demand for long-term care was reliant on simple calculations. Nuttall et al. built a three state model: the three states were healthy, disabled and dead. The authors used separate models to cover the different levels of disability. The authors took prevalence rates from the OPCS survey which was carried out in 1985 (Martin et al., 1988). There results show that potentially the greatest increases in the number of disabled people occurred in the elderly population over a forty year period. Growth starts to occur significantly after ten years. The authors took the results and tried to find a value for long-term care. Nuttall at el. had difficulty in valuing informal care: the same rate as formal care was used. Nuttall et al. noted that this would potentially lead to an overestimate.

Nuttall et al. discuss the implications of their findings: informal care might not be able to carry its share of the workload as demand increases; the private sector will have to expand their activities; there is a real need for forward planning; various experts need to collaborate to study various possible future scenarios and the need for better data is suggested. These issues are still under discussion fifteen years later. It is shown that by modelling over a long period of time, both the short- and long-term impact of

demographic change can be captured.

Rickayzen and Walsh (2002) develop a multi-state model in which the authors attempt to project the number of people with disabilities over a thirty-five year period in the United Kingdom. The authors recognise the uncertainties in projecting forward the provision of long-term care. People aged twenty and over are modelled and projected forward to the year 2036. The model is similar to the Nuttall et al. (1994) multi-state model; however, Rickayzen and Walsh (2002) have increased the complexity of the model and taken into account new trends in healthy life expectancy.

66

Rickayzen and Walsh needed data for three parts of the model: disability prevalence rates, transition rates and trends data. Like Nuttall et al. (1994), Rickayzen and Walsh (2002) use the 1985 OPCS survey. The information is used to form the prevalence rates.

It was chosen for a variety of reasons, including: the sample was large, the survey includes all adults and it covered a large variety of disabilities. The survey results show rapid increases in the disability rates as age increases. Rickayzen and Walsh note that the survey was carried out in the mid-1980s but overcome this by starting the

projections in 1986 and projecting forward using healthy life expectancies from 1986.

The model is run up to 1996 and then data from the Government Actuary‘s Department is used. The authors note other limitations as the assignment of disability is complex and there could be potential errors in the data.

Rickayzen and Walsh use the General Household survey to study changes over time.

The authors note another limitation in that the General Household Survey is only for households while the 1985 OPCS survey also includes communal establishments. The authors infer transition rates from the prevalence data. The authors choose rates that can generate the prevalence rates from the OPCS survey. Rickayzen and Walsh note that transition data is likely to change over time. The authors look at both healthy life expectancy and disabled life expectancy. This is done for people aged-sixty five and over as they are the group that receives the highest amount of care. This reiterates the need to study older people as an exclusive group.

Rickayzen and Walsh populate the model initially using data from the OPCS survey and then project forward using transition rates. Population projections are taken from the Government Actuary‘s Department. Rickayzen and Walsh allow for trends by allowing changes in transition rates. Seventeen scenarios are run to account for different possible trends. The authors criticise Nuttall et al. (1994) as transitions between disability states are not included. This is an important advancement on the work by Nuttall et al. (1994).

While the model can be criticised for using a survey from a long time before the model was created, it does show that it is important to use the best available data. Much time was spent searching for the best data for the long-term model created in this thesis (see Chapter 4).

67

Karlsson et al. (2006) examine the impact of demographic change and changing health status on the future of long-term care in the United Kingdom. It is noted that there is a general belief that an ageing population will be a public financial burden. This paper attempts to examine this and analyse how sustainable the system is. Karlsson et al. have two main outputs from the study. Firstly, the public cost of long-term care and secondly whether there will be enough informal carers to satisfy demand. The Rickayzen and Walsh (2002) model was used as an input into the projections. Karlsson et al. (2006) justify the use of the OPCS survey as the authors claim it to be the richest source of data for modelling long-term care. This model is used to provide projections for the United Kingdom population by the degree of disability. Additional sources of data are used: the Health Survey for England is used to analyse the disability for those in institutional care, and the data from the Department of Health are used to examine the disability of those who receive formal home care. Karlsson et al. do not include day care, community nursing, and long-stay hospitals. The authors are also interested in whether there will be enough carers to meet the demand for informal care. The model used in the study has shown that is useful to model the long-term care system. It has also been shown in both the Rickayzen and Walsh (2002) and Karlsson et al. (2006) papers that data to model transitions between disability states is not readily available and is difficult to access.

The authors used fairly old data (from the 1980s) which could be considered as a limitation of this model.

Johnson et al. (2007) investigate the issue of an ageing population and implications it will have upon long-term care in the USA. The authors project the number of people aged 65 and over who have a disability, and their use of long-term care services until 2040. Results are presented for 2000, 2010, 2020, 2030 and 2040. Population

projections of long-term care use were made in a microsimulation model. Long-term care arrangements were based on a national survey of older people. As there is much uncertainty with regards to disability projections, three different scenarios are run by the authors: a low, intermediate and high disability scenario. The authors define somebody as being disabled if they have difficulties with any of the specified activities of daily living or instrumental activities of daily living. The authors note that there is much

68

disagreement over the future of disability rates and whether rates will continue to fall in the future. It is for this reason that various scenarios are run.

A important point made by the authors is that not only is disability a key determinant if somebody requires a long-term care service, but so is the availability of informal carers.

Much care is provided in the form of informal care, and changes such as increased divorce rates, more women entering the workforce and declining family sizes will potentially impact upon the availability of informal care. There could be an increased demand for formal nursing care. The authors account for education and income levels in the projection of disability rates. In the intermediate scenario, for example, it is shown that increases in income and education would in fact reduce disability rates. The authors allow the rates to rise for the very old population. Johnson et al. used projections of family characteristics to determine future levels of paid and unpaid services. The authors note that previous projections do not account for changes in the availability of informal care workers. The authors were able to show the changes in the proportions of people receiving unpaid and paid help. This is a useful study and provides a great deal of information with regards to the provision of long-term care. The authors account for many different factors. However, one of the issues in dealing with a large population such as the USA is that the range of results between the scenarios is very large. Another problem is that regional differences are not accounted for.