MODELING LONG TERM HEALTH CARE COST TREND
This manual presents and describes the McMaster Model, a user-friendly model (developed in Microsoft Excel) sponsored by the Canadian Institute of Actuaries (CIA) and the Society of Actuaries (SOA) to make long-run (2050 and beyond) forecasts of health care expenditure at the aggregate level (for Canada as a whole) and for each of the provinces. The goals of the McMaster Model are first to provide a practical means by which actuaries can determine a long-term health care trend rate; and second to provide guidance on the grade-down period over which such an ultimate trend rate is reached. When valuing a particular plan’s future costs, users will need to apply their local information and estimation skills to establish the appropriate short-term health care cost trend forecasts for the first five years as well as the convergence of these rates to the central long-run trend to reflect the particulars of the specific groups, benefit packages, regional markets or providers for the plan being valued.
The SOA commissioned a similar project in 2007, Modeling Long-term Healthcare Cost Trends for
Valuation. Thomas Getzen, a US health care economist, developed a benchmark projection of long-term per capita medical costs along with providing a user-friendly model for making alternative projections. The SOA model was developed to project US specific health care trends and is updated annually by Thomas Getzen for the SOA. A copy of the SOA Getzen model and related documentation can be found on the SOA website (see Section 9).
The baseline version of the McMaster Model forecasts the long-run trend for total health care spending at the all-Canada level based on standard assumptions (or parameters) relating to,
a) the rate of growth of total health care spending in the short run (five years); and
b) the most plausible combination of drivers of health care spending in the longer run and policy
responses to curb such growth.
The baseline scenario is expected to be updated and revised periodically to take account of new information. The model is intended to develop best estimate assumptions and does not include any margins for adverse deviation (implicit or explicit). Actuaries can change the parameters (within reasonable bounds) to modify the aggregate-level trends to obtain customized long-term forecasts.
Income per capita and unit price are the main drivers of health care spending over the long term. There are many other drivers such as population aging, disease prevalence, policy decisions, and technical progress but the McMaster Model does not attempt to predict each of these separately. Economic theory is not able to say how each taken separately would affect long-run spending and it is impossible to disentangle their effects in time series analyses. However, statistical analyses show that, at the macro level, specific diseases (and population aging, which influences health care mostly through the diseases linked to old age) have little influence on health care spending. Of course, at the individual, micro level, cost is influenced by diseases and health care need; but empirical studies generally conclude that population aging or the prevalence of a given disease has almost no influence on how much a givencountry spends on health care. This might look counter-intuitive, but the reason is that health care delivery is a matter of choices and trade-offs: if we could find an inexpensive treatment for HIV-AIDS tomorrow, we would invest the resources thus freed to treat other diseases; or, if a new disease
emerged, we would constrain how much we spend on all other diseases to find the resources to treat it.
It is the case that all countries tend to increase the amount they spend on health care faster than their
income (GDP), but aging and epidemiology play a minor part in this, compared to medical progress (what
Copyright © 2018 Society of Actuaries and Canadian Institute of Actuaries is available) and policy decisions (how much of what is available will be made accessible). Therefore, the McMaster Model combines all these drivers into an Excess Growth (EG) factor, which is a residual, reflecting the additional share that individuals choose to allocate to health care based on their preferences and what technical progress makes available to them.
The McMaster Model forecasts annual health care spending in response to the rate of growth of GDP, the
rate of inflation, and the EG applied to health care spending in the previous year. In the short run (years 1
to 5), GDP growth and inflation are taken from macroeconomic forecasts and, consistent with recent
history, EG is set at 1.2% per year. After year 5, GDP growth and inflation adjust towards their long-run
growth levels by 2025; EG remains at its 1.2% level until the share of health care spending in GDP hits a
key parameter of the model, the Resistance Share (RS). RS represents the point at which any incremental
improvement in the quality or quantity of life afforded by technical progress is worth less than its cost,
relative to marginal improvements in other areas of consumption; therefore, governments will begin to
stop increasing the share of GDP allocated to health care. In the baseline scenario, RS is set at 13%; given
current spending patterns, with 13% of GDP going to health care expenditures, provincial governments
would spend 40% of their budgets on health care, on average. In the model, that will trigger a reaction
intended to curb health care spending and, in time, reduce the EG to 0. Once that RS is reached (at the
national level), all provinces will rein in spending, and two things will happen: the rate of growth of
spending will decline and the EG will decrease linearly over the grade-down period. In the baseline
scenario the grade-down period is ten years; that is, it is assumed to take ten years for governments to
bring EG down to zero. Thus, in the baseline scenario, EG decreases by one tenth of its initial level of
1.2%, or by 0.12% each year after hitting RS. During the adjustment period, health care (and health share
of GDP) will continue to increase; in the baseline scenario, RS is reached in 2030 and health care spending
stabilizes at 13.75% of GDP in 2040.
The values of the parameters (long-run trend for GDP and inflation, initial EG, RS, and grade-down period)
can be changed by the user, within reasonable bounds, which allows the user to estimate bounds on the
projected values. However, as these assumptions are representative of the economy as a whole,
actuaries may find it difficult to justify significant deviation from the default values. Plan- and provincespecific information is incorporated only into the short-term rates.
Baseline forecasts are also provided for spending by type of service (hospital, other institutions, dental
and vision1, prescribed drugs, non-prescribed drugs, physicians, other professionals), type of payer (public or private), and province. The forecasts at the disaggregated levels are generated based on shares of total spending (the central trend) and the likely changes in those shares in the future. Because the shares of various types of service or payers have been stable historically or are expected to stabilize soon, the disaggregated forecasts at the service or payer levels converge very quickly toward the central trend. At the same time, because of persistent differences in provincial inflation rates, provincial shares of total spending as forecasted may continue to adjust for an extended period. The baseline scenario assumes that provincial inflation rates will all be equal by 2030 (ten years after the start of the forecast), but users can assume a longer or shorter convergence period. Note that while these detailed forecasts are available, the ultimate long-term trend rate and the year in which this trend rate is reached are consistent across all scenarios. We therefore expect that most users will be using the all-Canada forecast.
Section 3 provides an overview of the model and its components. Section 4 provides a comparison of the
Canadian model to the US Long-Term Health Care Cost Trend Model. Section 5 provides a detailed
description of the variables and the assumptions on which the baseline forecasts are made for health
care spending at the all-Canada level and discusses the suggested range of values for use in making
1 Dental and vision benefits are combined in CIHI data.