4  The structure of the model

This section sets out the basic structure of our model. A more detailed and technical description is given in Appendix 2. Section 5 describes how the required values are derived from available data. The model contains two sub-models: a “demographic-health” sub-model (corresponding to the two “population” columns in Table 2); and an “expenditure” sub-model (corresponding to the “cost per capita" and “total expenditure” columns in Table 2).

4.1  The demographic-health sub-model

The population is divided by sex, and by 20 age groups (0-4, 5-9, and so on up to 95 and over.) Each age-sex group is subdivided by distance to death—“decedents” who will die during the following year versus “survivors” who will not—and by disability. This yields the four health statuses shown in Table 4. Changes over time in the health of an age-sex group, and hence its demands on government health services, are captured by changes in the relative size of the four health statuses. With two sexes, 20 age groups, and four health statuses, the model partitions the population into a total of 160 (=2×20×4) different categories. Overall changes in population structure and health are captured by changes in the number of people in these 160 categories.

4.2  The expenditure sub-model

Each of the 160 categories is assigned a value for government health expenditure per person per year. Total expenditure is calculated by multiplying the costs per person by the number of people in each category, and summing, just as in the stylized model of Table 2. Expenditure on a particular age group is calculated by restricting the summation to that age group.

Costs per person are assumed to rise at the same rate for all 160 age-sex-health categories. New Zealand lacks longitudinal data on expenditure by age and health status. Overseas evidence on whether costs grow faster for some population groups than others is mixed. For instance, Freund and Smeeding (2002: Figure 2) show that old people’s costs per capita grew faster than young people’s costs per capita in the United States in the period 1954-1987, while costs grew at roughly the same rate for young and old in England and Wales in the period 1982-1992.

Assuming that per capita costs grow at the same rate in all age-sex-health categories conveys our ignorance about actual differences between categories, in the past and the future. As shown in Appendix 2, the use of identical growth rates also allows us to express growth in total expenditure as the sum of growth in three underlying components:

The “ageing and health effect” is equivalent to the “ageing adjustment” in the Ministry of Health model (Johnston and Teasdale 1999: 9) and the “demographic adjustment” in the Long-Term Fiscal Model (2000: 42), except that it incorporates changes in disability and distance-to-death, and not just changes in age structure. The ageing and health effect measures the extent to which spending must increase to offset unfavourable changes in the demographic or health profile of the population. For instance, if there is a rise in the proportion of the population that is disabled, this will be captured by a rise in the ageing and health effect.

The “coverage and price” effect is an “everything else” term, measuring expenditure growth beyond that required to offset demographic and trends. Growth in coverage and price reflects things such as expansion in the range of treatments offered, changes in the efficiency of service provision, changes in demand, and rises in wages or pharmaceutical prices. Rapid growth in the coverage and price term does not necessarily translate to rapid improvements in health: it may simply reflect a rise in input prices. Similarly, slow growth does not necessarily translate to deterioration in health: it may reflect an improvement in efficiency. In this study, we make no attempt to measure the extent to which growth in coverage and price translates to better health.

Studies of health expenditures sometimes apply the label “technology” to variables like our “coverage and price” term. We avoid this usage because it obscures the role of non-technological determinants such as changes in demand and input costs.

4.3  Expenditure as a percent of GDP

As described in Section 3.1, the GDP projections have essentially the same structure as the expenditure projections. This means that a modified version of Equation 1 can be used to study changes in health expenditure as a percent of GDP:

This expression is derived in Appendix 2. Population size does not appear in the equation because it contributes to both expenditure (the numerator) and GDP (the denominator), and therefore cancels out. Population ageing tends to increase the growth rate of the ageing and health effect, and reduce the growth rate for GDP per capita.

4.4  Back-casting and projecting

The model can be used for back-casting, to extract information from historical data, or for projections, to give a sense of future developments. Table 5 summarizes the inputs and outputs in either case.