CAPÍTULO V: ANÁLISIS E INTERPRETACIÓN
Comparación 3: Entrevista semiestructurada y Observación participante
The literature identified on projection methodologies characterises three broad modelling approaches; macro level, macro-simulation, and micro-simulation (Table 2.2). Classification is based primarily on the level of data disaggregation inherent in the approach. The focus of these methodologies is mainly on health expenditure projections as an outcome rather than on utilisation or demand (which may be considered as inputs into expenditure projection).
Macro-level models relate to modelling of aggregate expenditures (46). Astolfi et al. (46) discuss two main types of macro-level models. The first relates to time series modelling of aggregate expenditures. Projections can be based on pure extrapolation of a trend or they can be based on projected values of important explanatory variables (46). This approach is most appropriate for short term projections under the assumption of clear and undisturbed trends (30). Trend based analyses of demand projections have also been outlined in the literature (47). The main advantages of this approach are that modelling tends to be straightforward and it is the least demanding of all approaches in terms of data
Background | 19
requirements. As a drawback, however, this is not a viable approach for longer term forecasts because the future structural changes to demand drivers cannot be taken into account (30). Since these models extrapolate past trends, they may lead to unrealistic estimates of long-term health expenditures (e.g. very large health expenditure/GDP ratios) (48). A comparative analysis of health expenditure forecasting models, developed by or used to inform, policymakers in OECD member countries and other international institutions found very few examples of this approach being adopted in practice (46).
Computable general equilibrium models (CGE) can also be considered a class of macro-level models (46). CGE specify a number of equations designed to replicate the entire economy. Implications of higher levels of healthcare spending on economic growth along with the long-run determinants of healthcare spending can be assessed through this approach (46, 49). These models are based on economic theory and rely on strong simplifying assumptions about behaviour of economic agents and of equilibrium that may not reflect observed trends (46). Data requirements for CGE models are generally much higher than for other macro-level models. They have had limited applicability to healthcare projection analysis.
Macro-simulation models or cell-based models represent a large and important class of what Astolfi et al. (46) refer to as component-based models. These models project health expenditure by different components, for example financing agent, provider, or diagnosis. In macro-simulation models, individuals are grouped into cells according to a limited number of characteristics (e.g. age and sex). Health expenditure is calculated by multiplying the number of individuals in each cell by the unit (or average) cost. Baseline activity rates can be projected forward using population forecasts. Baseline unit costs can be projected forward using, for example, GDP per capita growth rates (50). Manipulations can then be made to these components for more sophisticated analyses of utilisation and expenditures. For instance, different demographic scenarios can be assessed through alternative population projections for each cell. Changes in health status can be assessed through manipulating activity levels in each cell. Assumptions about supply-side factors (e.g. technology, labour costs and raw materials) can also be analysed through specifying alternative unit costs trajectories (see European Commission (50)). The implementation and maintenance of these models tends to be simple and relatively inexpensive. Cell- based models tend to be less data demanding than micro-simulation models (see below). While basic cell-based models disaggregate estimates into major categories of healthcare expenditure (or utilisation) and age/sex classes, more sophisticated models might encompass further disaggregation by disease categories, decedent/survivor status or end-of-life cost (46) (See Table 2.2 and Appendix 1). Cell-based modelling tends to be the dominant methodological
approach to health expenditure projection, accounting for a large proportion of the forecasting models surveyed by OECD (51). Cell-based models are also applied to modelling projected demand for and expenditure on long-term care, in which context they have been extended to include projections of informal care demand (52, 53).
Micro-simulation models focus on individuals as the unit of analysis rather than focusing on aggregated values (30). Although micro-simulation may relate to any predictive modelling approach using micro-units, micro-simulation models are used primarily to simulate individual behavioural responses to policies yet to be implemented (54). Dynamic micro-simulation models project population samples over time which allow for the modelling of various ‘life-events’ (e.g. exposure to risk-factors) under a variety of policy scenarios (46, 54, 55). The POHEM (Population Health Model) is a dedicated health micro-simulation model developed for modelling life-cycle dynamics within the Canadian population, which has been used to model the cost (and demand) implications of various health interventions (56). Micro-simulation models are useful tools for ex-ante analysis of health policy scenarios including exploring distributional effects (54, 56) but their benefits may be outweighed by the time and large amounts of data their development requires (46, 57). Jillian Oderkirk et al. (51) note that micro- simulation models may face difficulties trying to incorporate components of health expenditure growth such as health system characteristics, administration, or investments in research, into simulations. A drawback of micro-simulation models is that they may appear to be a ‘black box’ whereas macro-simulation models are more transparent and easier to interpret in policy discussion.
TABLE 2.2 SUMMARY OF MODELLING APPROACHES
Modelling
Approach Disaggregation Data Description Advantages Disadvantages institutional application Examples of country/
Time series Aggregate
expenditures (e.g. per capita health expenditures)
Projections can be based on pure extrapolation of a trend or they can be based on projected values of important explanatory variables.
Appropriate for short term projections under the assumption of clear and undisturbed trends
Modelling is straightforward.
Limited data requirements Not a viable approach for longer-term projections OECD countries (58); Australia (59); Canada (60)
Computable General Equilibrium
Aggregate
expenditures Sophisticated, broader modelling of economic agents that allow for analysis of
health expenditure trajectories
Allow for analysis of broader consequences (e.g. impact on non-health markets)
Rely on strong simplifying assumptions that may not reflect reality.
Substantial data requirements. Limited application to healthcare contexts.
United States (Medicare) (49)
Macro-simulation ‘Cell-based’
breakdown of key characteristics (e.g. age/sex)
Within each breakdown, individuals are grouped into cells according to a limited number of characteristics (e.g. age and sex). Health expenditures are calculated by multiplying the number of individuals in each cell by the unit (or average) cost
Implementation and maintenance simple and inexpensive. Straightforward to model alternative
expenditure scenarios. Moderate data requirements
Limited ability to explore scenarios associated with potential policy changes
United Kingdom (61); New Zealand (62); Australia (63);
US (Dept. Veterans’ Affairs) (64);
Netherlands (65); European Union Member States and Norway (25, 66, 67);
Ireland (68, 69)
Micro-simulation Micro-level units
(e.g. patient level data)
These models reproduce characteristics and behaviours of populations that model the impact of various interventions over individuals’ lives
Allow for detailed analysis of potential ‘what if’ scenarios
Substantial amounts of statistical resources and data required
United States (70, 71) Canada (72)
Standard regression-based models, incorporating micro-level units, perhaps outside the definition of micro-simulation models outlined above, have also sometimes been used in health expenditure forecasting but appear to be uncommon (73). While useful for identifying and measuring causal relationships, the validity of their results tends to be limited to the range of historical values on which they are estimated so that they are less appropriate for estimating the effects of major demographic, structural or institutional changes that extend beyond the range of past experience. As noted by Przywara (30), health-based predictive models such as those used in the risk-adjustment literature may also be considered a class of micro-simulation models. These involve using various diagnosis-based groupers (DRGs, DCGs) to explain expenditure variation among populations. However, the primary focus of this methodology tends to be on using econometric techniques to identify the best predictors of health expenditure and not health expenditure forecasting per se.
As noted by Astolfi et al. (46), no class of projection modelling approach can be considered superior. The choice of approach requires decisions about time and resources, available data, and the purpose of the projection. Following review of the modelling literature and the data requirements and intended policy applications for the Hippocrates model, the research team concluded that the most appropriate modelling approach to pursue in an Irish context was a form of macro-simulation (cell-based) modelling. This approach offers more flexibility than standard macro-level modelling yet may still be feasible in the face of existing data constraints. From the perspective of stakeholders, cell-based forecasting is also likely to be the most transparent approach to adopt. This is also the most common approach to health expenditure forecasting identified in the literature. Chapter 3 outlines the application of this methodology in the Hippocrates model.