Conclusiones a partir de los resultados obtenidos

Many different studies have been reviewed and Table 3.1 provides a synthesis of the various studies allowing easy comparisons to be made between them. Various fields are reported; the author(s), the country and date of the study, the problem setting, the modelling approach used, the characteristics of the population studied and the key points of the study.

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Cell-based model The following age groups are considered:

The study provided valuable insight on how demographic change will impact upon human resources and was a useful application of a spreadsheet model.

The authors were able to run various

scenarios including unchanged health status and improved health status.

System dynamics None presented. System dynamics allowed of the inclusion of feedback, different parts of the system are no longer treated in isolation and various

stakeholders are included in the process.

It is shown that system dynamics can be a good technique to use in a non-acute care setting for long-term planning.

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The PSSRU model in a different county is shown and worked well in a specific region as opposed to a country.

Forecasts were made until 2036 which

allowed for observations in both the short-and long-term to be made.

Various scenarios were run to test the robustness of the model.

System dynamics Three age groups were modelled: 65-74, 75-84

A useful study but only looked at formal care over a five-year period.

The study showed the importance of working closely with the local authority, the benefits of the system dynamics approach using both qualitative and quantitative modelling and ability to run ―what if‖ scenarios.

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None mentioned. System dynamics is good at bringing many different stakeholders together and

understanding how a complicated system behaves over time and agent-based simulation is good at including interactions of

individuals into a model.

The authors extend the boundary of the problem to include both acute care systems and residential care.

Both a national and regional models are constructed. were presented as a total of the whole population.

Income groups were considered.

The projections were carried out over a very long period of time, there could be a lot of doubt regarding the meaningfulness of results in 2051.

The modelling allowed for various scenarios to be run accounting for different assumptions regarding life expectancy, dependency and real unit costs.

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The paper illustrated the benefits of macro- and micro-simulation in order to look at issues in regards to expenditure of long-term care and the authors were able to present the projected numbers of people requiring different types of care. The authors were able to run various scenarios.

The authors note that it is important to properly

The deterministic model was a useful way of modelling long-term care and various types of care providers were included.

The model is being used by the Ministry of Health for British Columbia to develop a strategic direction for the home and community sector.

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The authors are able to present useful results from the model, however, results are only reported in ten-year increments so this could mask some of the important changes in the number of people requiring long-term care.

The sensitivity analysis shows a large range of results between the scenarios, this is a problem of dealing with a large population.

Regional differences are not accounted for as it is a national model.

Karlsson et al. United Kingdom,

The authors used a multi-state model to investigate costs of long-term care until 2050 and disability transitions were included.

The authors use the OPCS survey from the 1980s which is a limitation of the study. It illustrates the issues of data availability when modelling disability transition rates.

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System dynamics Age groups considered were 0-17, 18-49, 50-59, 60-85, 85+.

The authors note that a possible advancement of the model could include insurance coverage, income and family size.

Using system dynamics, the authors were able to model both institutional care and home and community care and project forward numbers until 2030.

Whilst the authors were able to identify lots of feedback, they were not incorporated due to a lack of data.

The technique is good at modelling flows of people.

System dynamics Age groups considered were 65-74, 75-84 and over 85.

Both genders were considered.

The methodology allowed for the key stakeholders to be included and this allowed for the acceptance of the modelling. The modelling allowed for both short- and long-term pressures to be identified. System dynamics allowed for a greater understanding of supply and demand.

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System dynamics The model when built will include the

following age bands: 65-74, 75-84 and 85+.

This report is an initial step towards the creation of a model. The first step was to have a workshop; this shows the strength of the system dynamics methodology in that it brings together different stakeholders.

It is suggested that the model captures aspects at the district level.

Cell-based model Three age groups were:

65-74, 75-84 and 85+.

The model provided useful results. The author was able to present the number of people living in the community with public help and without. Various scenarios were able to be run in the model.

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Three state model The three states were healthy, disabled and dead.

The authors looked at disability projections for the whole population.

Nuttall et al. included formal, informal and private care in the model.

By projecting over a forty-year period both the short- and long-term effects of changes in the number of disabled people were captured.

Pelletier et al. 2005a The authors use the model created by Zie

There is no mention of population

characteristics.

A useful study but limited only to

institutional care and only projects forward seven years into the future.

These models are often hard to understand for stakeholders.

Pelletier et al. United Kingdom,

Markov model There is no mention of population

characteristics.

A good study for looking at transitions of clients in institutional care but is limited to this type of care.

The authors note that the model by Xie et al.

(2005) is very attractive but it relies on cohort data which is not easy to access.

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Multi-state model. People are defined as being either healthy or

Projected over a thirty-five year period, the results show a large funnel of doubt as projections are made over a long period of time.

Three types of data were used: prevalence data, transitional data and trends data.

Transition data between disability states data is hard to access so Rickayzen and Walsh used the OPCS survey from 1980s which is a limitation of the study.

Wittenberg et al. United Kingdom, were used in the model based on a person‘s were used in the model which included whether there was any informal care.

A useful study that modelled long-term care at the national level.

The authors were able to run various scenarios where the authors change future disability rates.

Wittenberg et al. were able to report costs for both public and private expenditure.

The model potentially provided policy makers with a useful set of results regarding the future of long-term care.

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Wolstenholme 1993 System dynamics was used to

System dynamics None mentioned. Showed the benefits of system dynamics. Did not look at the whole system and was not used to make projections.

Does not account for age and gender.

A good technique for allowing flows between the different states but the problem is that it is limited only to institutional care.

The paper shows the benefits of working with the local authority to access local client data.

Table 3.1: Synthesis of papers related to long-term care modelling

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