CHAPTER 3 DATA AND METHODS
3.3 Methods – approach to modelling
As discussed in Chapter 2, following a review of the projection modelling literature, macro-simulation (cell-based) modelling was selected as the most appropriate modelling approach to pursue in an Irish context given the data requirements and intended policy applications of the Hippocrates model. This choice is supported by the wide scope of the model and heterogeneity in data quality across the Irish health system, which is demonstrated in this chapter’s review of data sources and estimation methods to derive baseline activity. In the full application of the model (illustrated diagrammatically in Figure 3.1) the projections will be based on three key parameters: demographics, activity rates and unit costs. In this report, the analysis is based on activity rates and demographics. The model starts from an analysis of current use of health and social care services by SYOA and sex, or with the most disaggregated age cohort included if SYOA is not available. The detailed steps to developing baseline utilisation are described in the next Section 3.4.
FIGURE 3.1 DIAGRAMMATIC REPRESENTATION OF THE HIPPOCRATES MODEL
Source: Authors’ representation of model.
This report projects that there will continue to be rapid population growth in Ireland, examines a number of alternative scenarios for population growth (described in Chapter 4) and projects the effects of these alternative scenarios on
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healthcare demand, assuming initially that this demand will reflect the 2015 utilisation rates by age and sex. We then incorporate in our projections differing assumptions about utilisation rates based on evidence about the development of healthy life expectancy and requirements to meet unmet need or demand. The detailed steps to estimate unmet need and demand are described in Section 3.5. In further development of the model we will apply unit cost estimates to project forward service-specific and aggregate expenditures. These expenditure projections will be subject to a range of sensitivity analyses incorporating varying assumptions on demography, morbidity, unmet need, and unit cost trends. The model is automated using the SPSS statistical package with subsidiary analyses undertaken using STATA and Excel.
The macro-simulation approach to generating demand projections is presented more formally in Figure 3.2. Healthcare demand for projection year t for activity
h, sex s, and age cohort a is calculated as a product of age- and sex-specific population projections for that year, and age-, sex- and activity-specific rates for 2015 (or the nearest year available). The activity is a measure of healthcare utilisation e.g. a hospital bed day, a home help hour or a visit to a general practice.
FIGURE 3.2 DEMAND PROJECTIONS BASED ON POPULATION GROWTH
Source: Authors’ representation of model.
Where administrative data are available (as in the case of public hospital activity), activity rates (AR) for 2015 are calculated by dividing the volume of activity (AV) for each age and sex cohort in 2015 by the population volume (Pop) for each age and sex cohort in 2015. Formally,
𝐴𝐴(2015)ℎ,𝑠,𝑎= 𝐴𝐴(2015)𝑃𝑃𝑃(2015)ℎ,𝑠,𝑎 𝑠,𝑎
Where adequate administrative data are not available and projections are based on the use of survey data (as in the case of general practice visits), activity rates for 2015 are calculated by dividing the (weighted) volume of activity recorded for each age and sex cohort by the corresponding (weighted) number of respondents in each age and sex cohort. In most of the sectors analysed, the measure of utilisation is activity over the entire 2015 base year (e.g. numbers of hospital discharges or home help hours). Baseline activity is estimated by aggregating total flows of activity up to the end of the year. In the analysis of long-term care in Chapter 9, however, due to data constraints, the methodology differs in starting from an estimate of baseline utilisation as the number of residents at end-2015. This is equivalent to a measure of occupied beds and the projection can be viewed as a projection of demand for long-stay and intermediate-stay beds. In the long-stay analysis, this projection is then converted into an estimate of bed day utilisation for the year 2015. We perform a reverse calculation when we convert estimated inpatient bed day utilisation in hospitals into estimated available inpatient hospital beds. For public hospitals we perform this calculation as a validation exercise so that estimated beds available can be compared to published public hospital bed figures for 2015. For private hospitals, we provide an estimate of beds available because little evidence exists on the scale of the private hospital system.
The formula applied to convert bed days to beds available is specified as,
𝐵𝐵𝐵𝐵 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐵 (2015)ℎ=
∑2𝑠=1∑𝑁𝑎=1𝐵𝐵𝐵 𝐷𝐴𝐷𝐵(2015)ℎ,𝑠,𝑎
365 𝑂𝐴
Where OR represents the assumed bed occupancy rate. This calculation of bed capacity follows the OECD definition of ‘available’ beds i.e. ‘hospital beds which are regularly maintained and staffed and immediately available for the care of admitted patients’ (1). For public hospitals we assume an occupancy rate of 94 per cent in line with published average occupancy rates for this sector in Ireland (2). For private hospitals, where occupancy rates are likely to be lower, we assume an occupancy rate of 85 per cent.17
Activity rates are converted into projected activity volumes (demand) for each age and sex cohort for each projection year by multiplying these rates by the corresponding population projection, by age and sex, for each year. That is,
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𝐴𝐴(𝑡)ℎ,𝑠,𝑎=𝐴𝐴(𝑡)ℎ,𝑠,𝑎∗ 𝑃𝑃𝑃(𝑡)𝑠,𝑎
Total projected demand for a particular service for each year can then be estimated by summing across each age and sex breakdown.18 That is,
𝐴𝐴(𝑡)ℎ= � � 𝐴𝐴(𝑡)ℎ,𝑠,𝑎 𝑁
𝑎=1 2 𝑠=1
3.3.2 Adjustments to model input parameters and preferred projection
scenarios
A range of potential assumptions about future demand can be made based upon varying each of the components included in the model, namely activity rates, population growth, unmet need/demand and healthy ageing. However, in this analysis we develop ‘preferred projections’ for each area of health or social care. These ‘preferred projections’ do not refer to desired projections but rather are based upon scenarios which combine population growth, healthy ageing and unmet need/demand assumptions. The assumptions applied in these ‘preferred projections’ are those best supported by evidence available for each sector of health and social care based on detailed review of the Irish and international literature, on analysis of Irish disability rate trends and, in the case of unmet need and demand assumptions, on analyses of evidence for unmet need and demand in Ireland. The range of preferred projections by sector in this report reflects uncertainty about the evidence.
Projections for demand for all healthcare services in this report begin with the assumption that while activity rates in 2015 will differ by age and sex cohorts, the activity curves these rates generate will remain constant across all projection years. Consequently, all growth in activity is purely a function of the shape of the respective activity curves in 2015 and changes in the size and structure of the population through the projection period. Two alternative population growth trajectories are applied to the projection analysis; referred to respectively as the Central population and High population growth projections (see Chapter 4). While it is a useful starting point to inform our projections to hold activity rates constant through the projection period and project purely in terms of our
18 It is not possible to sum demand across different areas of activity where measures of activity are not comparable (for example, hospital discharges and GP services). In the next phase of this analysis where unit costs are appended to demand projections and converted to expenditures it will then be possible to sum across different services and provide estimates of future healthcare system expenditure.
population growth projections, such assumptions may be unrealistic and not very informative. In effect, since our population projections assume increased life expectancy, assuming unchanged activity rates and implicitly unchanged health status by age and sex is to adopt a pessimistic assumption that morbidity and disability will increase with increased life expectancy, thus favouring the expansion of morbidity (EM) hypothesis (see Chapter 2).
Since we do not find that the evidence supports this hypothesis in all contexts, we model a number of alternative projection scenarios by making a range of adjustments to activity rates to reflect alternative assumptions about healthy ageing. We further adjust activity rates to reflect unmet need or demand for care. The methods adopted to make these adjustments are described in Sections 3.3.5 and 3.3.6. The next section describes how the evidence reviewed in Chapter 2 is applied to support alternative healthy ageing assumptions in our projection scenarios. Section 3.3.4 discusses the development of our preferred projection range by sector.
3.3.3 Healthy ageing evidence by sector
A key factor in the preferred projections relates to the healthy ageing assumption. In Chapter 2 a detailed review of the healthy ageing hypotheses and the evidence for them is provided to support the healthy ageing assumptions included in the preferred projections. Where possible, evidence from Irish studies or trends in Irish data undertaken for this report are given precedence to inform the preferred projections chosen. Section 2.7 discusses the thorough review of data sources in Ireland undertaken to inform assumptions of healthy ageing. While data such as HIPE and TILDA may not be appropriate to inform healthy ageing trends due to lack of a unique patient identifier (HIPE) and issues with comparability across waves (TILDA), evidence from QNHS is used to inform disability rate trends. The impact of ageing on healthcare demand in each health and social care sector differ, and therefore the healthy ageing assumptions included are based upon those best supported by the evidence available in each sector of the health service where possible. For example, many studies have shown a clear demarcation between the determinants of long-term care and acute care demand respectively (4-6). Table 3.2 presents the evidence for the healthy ageing assumption by sector and Table 3.3 presents the preferred projection scenarios for each health and social care sector.
3.3.3.1 Sectors where evidence supports the Dynamic Equilibrium hypothesis
In the area of acute hospital care in particular, a considerable number of studies have examined the drivers of trends in demand and expenditure. These studies are broadly in agreement that ‘proximity to death’, as opposed to age, is the key
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driver of acute care demand and expenditure. In this context, increases in life expectancy will be accompanied by equivalent increases in good health or mild ill health, and the years spent with severe ill health remain constant, which is a key assumption of dynamic equilibrium. Many of these studies which test for proximity to death use data from a point in time, but a Dutch study using data over time to examine the link between proximity to death and dynamic equilibrium found evidence to support the theory of a dynamic equilibrium (7). Similarly to previous authors (7,8), we interpret evidence of proximity to death as evidence for the assumption of dynamic equilibrium in our preferred projections for hospital care. Evidence from Switzerland (6, 9), the United States (10, 11), the Netherlands (12) and England (13) finds that proximity to death is the main driver of acute hospital expenditure growth. Those studies that explicitly compare proximity to death and age find that age is not a significant factor, having controlled for closeness to death (14). Studies have also shown that for less severe chronic conditions (which may be more appropriately managed in primary care) age is still a significant predictor of demand and expenditure, but for severe diseases such as cancer, time to death remains the key determinant of expenditure (15).
In the most recent studies of acute services reviewed, there is no evidence for expansion or compression of morbidity found. A recent study from Spain sought explicitly to test for compression of morbidity in acute hospital care using age at onset of condition and hospitalisation rates (16). The authors found no evidence of compression of morbidity but found that age-specific incidence rates of chronic disease remained similar over time for most diseases and increased for some. A similar study, including using PCRS and TILDA data in Ireland, has found that proximity to death, not age, to be the main determinant of community pharmaceutical expenditure (17). Based upon this evidence for public hospital inpatient, day-case, and Emergency Department services, private hospital inpatient and day-case services, and for community pharmaceutical services the healthy ageing assumptions applied in our preferred projections is Dynamic Equilibrium (DE), which assumes that all additional life years are lived in good health or mild ill health.
3.3.3.2 Sector where evidence supports the Expansion of Morbidity hypothesis
General practice is the main service which meets the demands of increased treatment for chronic disease. There is evidence for less optimistic healthy ageing assumptions when chronic disease prevalence is examined. Studies from England (18), Sweden (19, 20) and the United States (21) have shown that there is a growing burden of chronic disease and multi-morbidities in ageing populations. This trend towards chronic disease and its effects in general practice have been seen in Ireland (see Chapter 2). Figure 2.4 (Chapter 2) highlights that over 50 per cent of deaths in Ireland can now be attributed to cardiovascular disease and
cancer. Based upon this evidence on chronic disease prevalence and severity, the healthy ageing assumptions applied in our preferred projections for general practice care are Expansion of Morbidity (EM), which assumes that years lived in bad health or severe disability will increase as life expectancy increases, and Moderate Healthy Ageing (MHA), which assumes that a lower proportion of additional life expectancy is lived in ill health. MHA falls between EM and DE and is included in this report to account for the reduction in the severity of chronic disease.
3.3.3.3 Sectors where mixed evidence supports a wide range of assumptions
In the sectors of health and social care such as residential long-term care and home care, where disability rates are a predictor of utilisation, we assume that utilisation is primarily driven by the ADLd rate. This is based on evidence from TILDA and Census 2011 that show recipients of these services having high rates of ADLd. As discussed in Chapter 2, evidence on disability rate trends is much more ambiguous than the evidence on proximity to death and chronic disease prevalence for acute and primary care services respectively. While chronic disease rates are increasing, evidence from the United States (22), Netherlands (23) and an international review (24) finds that the disabling consequences of chronic disease are not as severe as previously thought. Evidence from international reviews (24-26), the United States (27, 28) and Japan (29) finds that age-specific rates of disability, as measured by difficulties with IADL, have reduced over time. However, trends in age-specific rates of disability, as measured by ADLd, vary across studies (30, 31) (see discussion Chapter 2 Section 2.6.3.4).
Evidence of declining disability rates have been found for Ireland and applied to previous projections of long-term and community care demand (32, 33). More recent evidence from the QNHS discussed in Chapter 2 found statistically significant and steep decreases in ADLd rates in community-dwelling older Irish women (aged 75 and over). Dementia is also associated with long-term care utilisation and trends in dementia rates from England (34) and the US (35) suggests that a reduction in age-specific dementia rates may be occurring. In our analysis, the QNHS evidence of declining disability rates for older Irish women combined with the balance of international evidence supports Compression of Morbidity but we apply a wider range of healthy ageing assumptions to reflect uncertainty in this area. For home care, residential long- term care and for those community therapists (occupational therapists and physiotherapists) for whom care of older people with disabilities represents a high proportion of their workload, the healthy ageing assumptions applied in our preferred projections are the more optimistic Compression of Morbidity and less
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optimistic Dynamic Equilibrium. (The relationship between our methodology to model CM and the Irish QNHS evidence is further explored in Appendix 3.)
A further service where a wide range of assumptions is applied is public and community health nursing. A range of different healthcare services are provided by public and community health nurses including childhood development screenings, primary care nursing, and home care. Furthermore, evidence from TILDA shows that older individuals who access public health nursing services have high rates of ADLd and IADLd (36), and therefore demand for public health nursing services may be impacted by changes in the disability rate. Consequently, for public health nursing the healthy ageing assumptions applied in our preferred projections are broad and encompass those applying to primary care and care of older people. We therefore apply both MHA and DE in our preferred projections.
3.3.3.4 Sectors in which data do not support applying healthy ageing assumptions
The aggregated age cohort data available to the analysis of outpatient care provide a poor basis for application of healthy ageing assumptions (see Appendix 5) and consequently we apply pure population projections.
Maternity services and speech and language therapy services are used predominantly by younger adults and young children. Therefore, the preferred projections included for these services do not include a healthy ageing scenario.
TABLE 3.2 HEALTHY AGEING EVIDENCE BY SECTOR
Services Evidence Supporting Preferred Projection scenarios Public Acute Hospitals:
Inpatient and Day-case
Public Acute Hospitals: Emergency Department
Private Acute Hospitals: Inpatient and Day-case Pharmaceuticals
• There is strong evidence that ‘proximity to death’ (dynamic equilibrium) is the main driver of expenditure growth in acute
hospital inpatient and day-case care in evidence from Switzerland (6, 9), the United States (10, 11), the Netherlands (12) and England (13).
• There is no evidence for compression of morbidity in acute hospital care in recent evidence from Spain (16).
• There is strong evidence that proximity to death (dynamic equilibrium) is the main driver of pharmaceutical expenditure
growth in Ireland (17).
General Practice • Evidence from England (18), Sweden (19, 20) and the United States (21) shows an expansion in chronic disease, but the severity/disabling consequences of chronic disease has lessened (22, 23), suggesting expansion of morbidity and moderate
healthy ageing Residential Long-Term Care
Home Care
Community Therapy:
Physiotherapists; Occupational Therapists
• Evidence from international reviews (24-26), the United States (27, 28) and Japan (29) finds that age-specific rates of
disability, as measured by IADLd, have reduced over time. However, trends in age-specific rates of disability, as measured by ADLd, vary across studies (30, 31)
• Trends from QNHS data in Ireland show reductions in disability rates for older age cohorts and reductions in age-specific
dementia rates have been seen recently in England (34) and the United States (35)
• Uncertainty about trends of disability and healthy ageing support a projection range including both compression of morbidity
and dynamic equilibrium for care of older people in long-term and community care settings
Public Health Nursing • As public health nursing services overlap both primary care and care for older people, trends in chronic disease(18-20) (21-23) suggesting moderate healthy ageing; and trends in disability rates (24-31) suggesting dynamic equilibrium are used to
inform the healthy ageing assumptions chosen. Public Acute Hospitals: Maternity
Public Acute Hospitals: Outpatient Care Community Therapy:
Speech and Language Therapists