Combination with ONS Data

The model required data to be broken down by individual year of age allowing for changes to be made to the migration and mortality numbers associated with each age.

This level of detail was not provided for the household population dataset. The data provided by the CLGD were manipulated in the following way after discussion with Professor Jane Falkingham, a highly experienced demographer at the University of Southampton. Initially, an attempt was made to try to create a dataset of the associated mortality and migration data. This was only available for the total population dataset and not for the household population. Data from the ONS were reanalysed for the purposes of this research. The validity of the mortality and migration data was checked, as an error was found during the data cleansing process. The projections were recreated using 2009 as the base year and the population was projected forward using the

mortality and migration data. There were discrepancies between the projected data and the original dataset. The errors were slight for each year, but the overall difference was significant for the latter years of the projections. This was doubled checked by the ONS, but they regarded the issue as minor. Data that are made publicly available are rounded to the nearest thousand population, so small discrepancies are inevitable. The error was due to a bespoke piece of ONS software. It was assumed that the error was with the

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migration rate, which is derived from several different datasets. Net migration is made up of both internal as well as international migration. Using the population projections and the mortality numbers, a new set of net migration data was created. This was created by calculating the net population change and subtracting the number of people of projected to die. This was done for each individual age by gender.

The Hampshire household data from the CLGD were reconfigured by scaling in

proportion to the individual age data from the ONS, for each district and each gender, in order to allow a range of scenarios to be run under different assumptions about

migration rates. There is uncertainty around future migration trends, and migration is a key driver of long-term care. For each five-year age band, the proportions of people at each individual year of age were calculated using the ONS data. Then the 5-year age group totals in the CLGD data were scaled by the individual age proportions previously calculated from the ONS population data. The final result was a set of household data broken down by individual age.

The next step was to try to derive a set of plausible mortality and migration figures. To achieve this, it was assumed that migration only occurs in the household population.

This is a credible assumption as one would expect movement within the institutional population to be minimal. An alternative was tested for people aged 85 and over. It was assumed that half the migration would be migration into the institutional community.

The probability of requiring an institutional care service is quite high for people aged over 85. The new household population dataset and existing migration data was used to create a new set of mortality data using equation. This was calculated by finding the net population change in the household population and then adjusting this for net migration.

This was done for each individual age by gender.

Unfortunately, the mortality figures for the first year in each age group (65, 70, 75 and 80) were higher than the total mortality for the equivalent age for the whole Hampshire population. This was because the method used to obtain individual ages gave rise to a significant gap between the last age of one age group and the first age of the next age group. Hence, the approach was modified. The scaling was altered by adjusting and smoothing the slope. The basic principle for each age group was to increase the number

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of people in the first two ages of the age group and decrease by the same amount the proportions of people in the last two ages of the age group. This was tested under various proportion changes and it was found that the results were highly sensitive to small changes in these proportions. Although adjusting the proportions would remove the original errors, new (and greater) errors were caused in the other ages. Thus this approach was rejected for most age groups, although it was applied to the 80-84 age band. It is likely that within this age group many people, as they age, will move into the non-household population. The proportion of people aged 80 was increased by 0.009%;

people aged 81 were increased by 0.0045%, people aged 82 were left unchanged, people aged 83 were decreased by 0.0045% and, finally, the proportion of people aged 84 was decreased by 0.009%. No alterations were needed for people aged 85 and over as these were treated as a single figure.

In summary, the following two assumptions were made:

Assumption three: The household population distribution for each age is the same as the whole Hampshire population for people aged 65 to 79.

Assumption four: Assumption three does not hold for people aged 80-84, for whom the modified proportions described above were used.

Since credible mortality and migration data could not be obtained, a single number approach was chosen. This single number represents the net combination of the mortality and migration numbers. This number will be henceforward known as the net population change. This number is the decision variable which can be altered under various scenarios within the model. Net population change was calculated using equation (1).

nagt = hagt+1 -hagt (1)

where

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n_agt - is the net population change for those aged a (ages from 65-85+), gender g (male, female) at calendar year t

hagt - is the household population aged a, gender g at calendar year t

This calculation was carried out for each calendar year of the modelling period. The re-proportioned household data and net population change data was used in the model (see section 4.6, pp.137-139). Now that the household population of each age group in Hampshire has been calculated, the next step, to work out the number of people in the various disability categories within each age group. Firstly, the data is discussed in section 4.4 (pp.124-131) and then the equation applied in the model structure is found in section 4.6 (pp.137-139).