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The demographic structure of New Zealand, as in other developed countries, is changing. The proportion of older people in the population has greatly increased along with their experience of multi-morbidity with major implications for the provision of health services.1,2 Forecasts of future compression or expansion of morbidity hinge on whether extended life-expectancy will be spent largely in good or ill health.3 Nevertheless, there is pressure on available resources to keep pace with the sheer increase in volume of health care required for larger numbers of older people. The recent World Health Organization s World Report on Ageing and Health proposes a public-health framework for healthy ageing defined as the process of developing and maintaining the functional ability that enables well-being in older age in which the first of four priority areas is aligning health systems to the needs of older populations (p 13).4 The policy quandaries posed by demographic ageing apply no less to New Zealand,5 with the proportion of people aged 65 years and over projected to increase by nearly two-fifths from 12.1% in 2001 to 16.8% in 2021.6AimsWe aimed to model a range of policy scenarios on the future shape of the New Zealand health-care system under conditions of demographic ageing. To do this, we constructed and applied a discrete-time dynamic microsimulation model to health service use in older people. Here, we define health services as a balance of three modalities: practice nurse visit, family doctor visit and public hospital admission. We report on the construction of the model and the results of projections and scenario testing.Research questionsAfter establishing a baseline for our model, we aimed to address two key policy initiatives proffered internationally: promoting healthier ageing to reduce the need for health care7 and changing the balance of care8 towards more effective configurations.9 We focussed on testing scenarios where the burden on the health system might be lessened. Our research questions can be formalised as follows: What will be future levels of health service use for older people under the status quo? This is our base projection . What is the impact of reducing morbidity levels proxy for healthier ageing (and the compression of morbidity) on health service use of older people? This is our reduced morbidity scenario. What is the impact of changing the balance among providers on health service use of older people? This is our balance of care scenario. The model (Figure 1) was: (1) hierarchically structured with long-term illness (morbidity) driving health service use, with practice nurse use affecting family doctor use (via potential prevention or substitution) and with practice nurse and family doctor use affecting public hospital admission (via potential prevention or delay) and (2) dynamic incorporating demographic and morbidity changes over time.10Figure 1: Conceptual model of late-life ageing and health care trajectory. Long-term illness drives health service use, with practice nurse use affecting family doctor use and public hospital admission, and with family doctor use affecting public hospital admission. MicrosimulationMicrosimulation first proposed by Orcutt11 in 1957 has been used, for example, to assess the impact of demographic aging on population health.12 Microsimulation relies on data from the real world to create an artificial version like the original. It operates at the level of individual units (here older people), each assigned attributes as a starting point eg age and health state, to which quantitative rules (eg statistical equations) are applied to simulate changes in state or behaviour. Thus a synthetic set of typical life histories can be generated. The model can then be used to test scenarios essentially thought experiments by modifying key factors and assessing impact on outcomes of policy interest.13,14MethodsOur methods are outlined briefly in this section with a detailed report published online.15 Microsimulation was adopted as a technical approach well-suited to modelling the dynamics of a complex system such as health care, and for testing policy scenarios related to utilisation.Data sourcesWe used individual-level data on older people aged 65 years and over from the New Zealand Health Survey undertaken in 2002 and 2006 (NZHS 2002 and 2006).16 As well as the person s demographic characteristics, there was information on whether they had long-term illness, and on their use of health care; NZHS was the only national data source available with these features. These survey data had the advantage of being nationally representative and relatively recent with adequate sample sizes. NZHS sample weights were taken into account in analyses and simulations. The NZHS 2002 contributed data on 2,206 individuals to form a starting sample at the base year, set to 2001, providing initial conditions representative of older people living in the community. A description of characteristics of the starting sample can be found in Table 1. Thus 9.3% were aged 85 years and over while 85.6% were experiencing long-term illness.Table 1: Description of starting sample. Characteristics of older people (aged 65+ years) living in the community, 2001. Characteristic Percentage of weighted sample\u2020 (n=2206) Age group 65-74 54.2 75-84 36.5 85+ 9.3 Gender Female 55.3 Ethnicity European 91.8 Mori (the indigenous people) 4.0 Pacific 1.7 Asian 2.2 Other 0.3 Marital status Partnered 56.5 Deprivation decile 1 (low deprivation) 6.5 2 7.6 3 9.3 4 10.4 5 10.1 6 13.5 7 10.6 8 13.8 9 11.1 10 (high deprivation) 7.3 Long-term illness Present 85.6 \u2020 The starting sample was taken from the New Zealand Health Survey 2002/3 (Ministry of Health 2004), with weighting calibrated to the New Zealand Census 2001. Definition of variablesThe following individual characteristics incorporated in the model can be categorised into three types:1. Socio-demographic Age: 65+ years. Gender: male, female. Self-reported ethnicity (in prioritised sequence): Mori, Pacific, Other, European. A single ethnicity variable was constructed to account for individuals who reported multiple ethnic affiliations.17 Socio-economic deprivation: NZDep (decile) a census-based small-area measure.18 Self-reported partnership: married, or partnered and not legally married (yes/no). 2. Morbidity Self-reported long-term illness (yes/no): any medical condition lasting six months or more. 3. Health service use (outcomes) Self-reported health service use (in the last 12 months): any practice nurse visit formal consultation with nurse on their own, ie without seeing a doctor (yes/no); family doctor visit categories 0, 1-2, 3-4, 5-6, or 7+ visits (yes/no, in each category); public hospital admission for any reason, comprising inpatient and day patient (yes/no). For family doctor visits, simulated results are reported for the combined 5+ visits category signifying a high user group. AnalysisFirstly, transition probabilities for long-term illness were estimated from matrices using repeated cross-sectional data (NZHS 2002 and 2006), depending on age and gender (use of other characteristics was constrained by small numbers). This estimation was based on known long-term illness levels in 2002 and 2006, and assumed that an individual could remain in the same state or progress to the next state but not revert to a former state. These results imparted dynamic change to the cross-sectional models of health service use (as below).Secondly, we used cross-sectional data (NZHS 2002) to predict health service use from long-term illness (as above) in a series of regression models: practice nurse visit logistic, family doctor visit (as categories) multinomial and public hospital admission logistic. Earlier events or states could exert an influence over later ones (Figure 1). Thus, practice nurse visit was a function of long-term illness; while family doctor visit was a function of both long-term illness and practice nurse visit; and finally public hospital admission was a function of long-term illness, practice nurse visit and family doctor visit. Age, gender, ethnicity, deprivation level and partnership status were also accounted for as potential socio-demographic control variables while, for each model, only statistically significant ones were retained.SimulationFrom 2001, we applied parameters (derived from statistical analysis of NZHS data) to update time-variant attributes of 2,206 individuals (in the starting sample) at five-year intervals using Monte Carlo simulation. The simulation process for each subsequent time interval followed a sequence of steps from demographic characteristics, through health status, to final health care outcomes. To reduce the effect of random error, a simulated estimate was taken as the average result of 20 runs, sufficient to generate a stable value. Thus a set of typical though varied individual life histories was created. To maintain a representative sample over time, at each five-yearly interval, we allowed individuals to enter (being randomly drawn from 65-69 year-olds from NZHS 2002) and to die (according to probabilities from official period life tables), as well as re-weighting (according to official population statistics) to account for demographic changes in composition (eg due to migration).ValidationValidation of simulated results was carried out by comparison to actual NZHS 2006 data (the latest available). The test was whether the simulation model could reproduce benchmark averages and distributions. Where necessary and possible, simulated results were calibrated to population parameters (from NZHS, censuses and official projections) so that findings could be generalised to the national population.Scenario testingKey factors influencing health service use may be considered as potential levers for policy intervention. These can be tested via simulating scenarios. We used the simulated results with no changes made as the base case. For each scenario, we changed factors of interest in the starting sample, while holding other initial factors constant, and observed impact on down-stream outcomes (compared to the base case). At an individual level, changes were made to those in or at high risk of being in a particular state, eg having long-term illness. Note that the settings for the scenarios were heuristic: we started with small changes in morbidity or care levels and gradually increased or decreased them, over a reasonable range of proportions to an upper limit of possibility (5 to 20 percent).1. Base projection of status quo to 2021We simulated from the starting sample in 2001 forward to 2021 with no changes to inputs or parameters. We considered 20 years as a reasonable projection period that would be useful without over-stretching the data.2. Reduced morbidity scenario (2021)We artificially reduced, by varying proportions (5%, 10% and 20% respectively), the prevalence of and transition probabilities for long-term illness to assess impact on levels of health service use.3. Balance of care scenario (2021)We artificially increased, by varying proportions (5%, 10% and 20% respectively), the level of practice nurse visits to assess impact, in turn, on levels of family doctor visits and public hospital admissions.ResultsValidationThe simulated sample followed the general pattern for the real sample (from NZHS 2006) though not uniformly so across all measures: compared to the benchmarks, long-term illness and practice nurse visit were under-estimated while family doctor visit and public hospital admission were over-estimated (Table 2). Note that, in the interpretation of simulated results, greater importance should be placed on direction and magnitude rather than specific point estimates.Table 2: Morbidity and health service use for older people (aged 65+ years) living in the community. Comparing simulated to real data, 2006. Age group Morbidity Health care modalities Long-term illness (lasting at least six months) (%) Practice nurse visit (any in last 12 months) (%) Family doctor 5+ visits (in last 12 months) (%) Public hospital admission (any in last 12 months) (%) Simulated Real \u2020 Simulated Real \u2020 Simulated Real \u2020 Simulated Real \u2020 65-69 78.0 86.7 42.9 43.6 36.6 31.1 18.7 14.1 70-74 89.2 89.7 43.2 46.2 38.8 32.9 20.7 17.5 75-79 89.8 89.6 43.7 45.8 52.0 36.8 25.5 19.7 80-84 93.8 94.0 44.2 48.4 52.3 42.2 26.5 25.2 85+ 91.1 89.9 44.6 45.2 47.9 39.7 22.5 19.1 All (65+) 86.6 89.3 43.5 45.5 43.6 35.0 22.0 18.1 (95% CI)\u2021 (86.3-87.2) (42.6-44.4) (42.6-45.3) (21.5-23.1) \u2020 Taken from NZ Health Survey 2006.\u2021 95% confidence intervals were calculated from 20 simulation runs. Scenario testingOur comparison between the base simulation (with no changes) and a scenario (with a factor change) were relative to one another within the virtual world. The two simulated results conditioned on the same input data and parameters are directly comparable and give a good assessment of impact of the changed factor.1. Base projectionSimulation under current settings, ie projection, from 2001 to 2021 showed a moderate absolute increase overall in the level of long-term illness (Table 3) which was more marked with increasing age: 2% for the 65+ age group and 13% for the 85+ age group (results not tabled). There was a concomitant proportional increase in the use by people aged 65+ of family doctor visits (up 21%) and public hospital admissions (up 16%) while practice nurse visits remained stable (Table 3).Table 3: Base projection and reduced morbidity scenarios. Morbidity and health service use for older people (aged 65+ years) living in the community, 2001 and 2021. Simulations\u2020 Morbidity Health care modalities Long-term illness (lasting at least six months) (%) Practice nurse visit (any in last 12 months) (%) Family doctor 5+ visits (in last 12 months) (%) Public hospital admission (any in last 12 months) (%) Q1. Base projection\u2021 2001 85.6 42.1 36.0 18.8 2006 86.6 43.5 43.6 22.0 2011 87.2 43.3 43.4 22.1 2016 86.5 43.2 42.9 21.6 2021 87.4 43.3 43.5 21.8 Q2. Reduced morbidity scenarios\u00a7 Make 5% decrease in long-term illness (%) [% change]\u00b6 2006 - 43.0 42.7 20.8 2011 - 43.1 42.9 21.1 2016 - 43.0 42.4 21.3 2021 - 43.2 [-0.2] 42.9 [-1.4] 21.4 [-1.8] Make 10% decrease in long-term illness 2006 - 42.6 41.8 19.7 2011 - 43.0 42.1 20.5 2016 - 43.1 41.6 20.7 2021 - 43.1 [-0.5] 42.5 [-2.3] 21.1 [-3.2] Make 20% decrease in long-term illness 2006 - 42.6 40.2 18.8 2011 - 42.7 41.0 19.4 2016 - 43.0 40.4 19.6 2021 - 43.1 [-0.5] 41.1 [-5.5] 20.0 [-8.3] \u2020 Simulations are calibrated to NZ Health Survey 2006 data.\u2021 Base projection to 2021 is on current settings.\u00a7 Scenarios represent the impact of reducing base prevalence of and transition probabilities for morbidity (long-term illness) by nominated percentage of base projected level.\u00b6 Proportional change in outcome (due to the scenario settings) compared to the base projection for that year. 2. Reduced morbidity scenarioScenarios projected to 2021, implemented by progressively decreasing long-term illness levels, had the effect of only moderately reducing health service use compared to the base projection (Table 3). For example, with long-term illness levels reduced by 20%, there were proportional reductions of 0.5% in practice nurse visits, 5.3% in family doctor visits and 8.3% in public hospital admissions.3. Balance of care scenarioScenarios projected to 2021, implemented by progressively rebalancing towards practice nurse use, had the effect of moderately decreasing family doctor visits but markedly decreasing public hospital admissions compared to the base projection (Table 4). This effect was much more pronounced with increasing age. This is illustrated by the scenario where the proportion of older persons who visited the practice nurse at least once in a year was increased by 20%. Thus, in the 65+ age group, relative to the basic projection, the proportion of high users of family doctors visits was reduced by 0.7% and the proportion of people admitted to public hospital was reduced by 1.4%; in the 85+ age group those relative reductions were 0.8% and 25.5% respectively (Table 4).Table 4: Balance of care scenarios. Towards more practice nurse visits for older people living in the community, 2021. Simulations\u2020 Health care modalities Practice nurse visit (any in last 12 months) (%) Family doctor 5+ visits (in last 12 months) (%) Public hospital admission (any in last 12 months) (%) Aged 65+ Aged 85+ Aged 65+ Aged 85+ Aged 65+ Aged 85+ Q1. Base projection\u2021 2001 42.1 26.6 36.0 36.2 18.8 22.7 2006 43.5 44.6 43.6 47.9 22.0 22.5 2011 43.3 43.8 43.4 48.9 22.1 22.8 2016 43.2 42.4 42.9 48.7 21.6 22.1 2021 43.3 42.4 43.5 48.8 21.8 23.1 Q3. Balance of care scenarios\u00a7 Make 5% increase in practice nurse visits (%) [% change]\u00b6 2006 - - 43.4 47.9 21.9 22.2 2011 - - 43.5 48.9 21.9 22.8 2016 - - 42.9 47.6 21.7 22.3 2021 - - 43.5 [0] 50.5 [+3.5] 21.9 [+0.5] 22.4 [-3.0] Make 10% increase in practice nurse visits 2006 - - 43.3 47.9 21.9 21.8 2011 - - 43.5 48.9 21.8 22.5 2016 - - 42.9 47.5 21.7 21.6 2021 - - 43.4 [-0.2] 50.4 [+3.3] 21.9 [+0.5] 21.9 [-5.2] Make 20% increase in practice nurse visits 2006 - - 43.3 47.5 21.8 21.0 2011 - - 43.2

Summary

Abstract

Aim

The demographic ageing of New Zealand society has greatly increased the proportion of older people (aged 65 years and over), with major policy implications. We tested the effects on health service use of alterations to morbidity profile and the balance of care.

Method

We developed a microsimulation model using data from an official national health survey series to generate a synthetic replicate for scenario testing.

Results

Projections on current settings from 2001 to 2021 showed increases in morbidity\u00ad\u00ad \u00adlong-term illness (2%) and in health service use doctor visits (21%), public hospital admissions (16%). Scenarios with decreasing morbidity levels showed moderate reductions in health service use. By contrast, rebalancing towards the use of practice nurses showed a large decrease in public hospital admissions for people aged 85 years and over.

Conclusion

Demographic ageing may not have a major negative effect on system resources in New Zealand and other developed countries. Rebalancing between modalities of care may soften the impact of increasing health service use required by a larger older population.

Author Information

Roy Lay-Yee, Senior Research Fellow, Centre of Methods and Policy Application in the Social Sciences (COMPASS), University of Auckland, Auckland; Janet Pearson, Statistician, Centre of Methods and Policy Application in the Social Sciences (COMPASS), University of Auckland, Auckland; Martin von Randow, Analyst, Centre of Methods and Policy Application in the Social Sciences (COMPASS), University of Auckland, Auckland; Ngaire Kerse, Professor, School of Population Health, University of Auckland, Auckland; Laurie Brown, Professor, National Centre for Social and Economic Modelling (NATSEM), University of Canberra, Australia.

Acknowledgements

This work was funded by the Health Research Council of New Zealand [grant number 09/068]. Data from the New Zealand Health Survey were made available by the Ministry of Health. Thanks to Oliver Mannion and Karl Parker for their technical and analytical support; Dr Barry Milne and Jessica McLay for their comments and suggestions on the manuscript.

Correspondence

Roy Lay-Yee, Senior Research Fellow, Centre of Methods and Policy Application in the Social Sciences (COMPASS), University of Auckland, Auckland.

Correspondence Email

r.layyee@auckland.ac.nz

Competing Interests

Nil.

-- Lloyd-Sherlock P, McKee M, Ebrahim S, et al. Population aging and health. Lancet. 2012;379(9823):1295-6. Rechel B, Doyle Y, Grundy E, McKee M. How can health systems respond to population aging? Copenhagen: World Health Organisation; 2009.

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The demographic structure of New Zealand, as in other developed countries, is changing. The proportion of older people in the population has greatly increased along with their experience of multi-morbidity with major implications for the provision of health services.1,2 Forecasts of future compression or expansion of morbidity hinge on whether extended life-expectancy will be spent largely in good or ill health.3 Nevertheless, there is pressure on available resources to keep pace with the sheer increase in volume of health care required for larger numbers of older people. The recent World Health Organization s World Report on Ageing and Health proposes a public-health framework for healthy ageing defined as the process of developing and maintaining the functional ability that enables well-being in older age in which the first of four priority areas is aligning health systems to the needs of older populations (p 13).4 The policy quandaries posed by demographic ageing apply no less to New Zealand,5 with the proportion of people aged 65 years and over projected to increase by nearly two-fifths from 12.1% in 2001 to 16.8% in 2021.6AimsWe aimed to model a range of policy scenarios on the future shape of the New Zealand health-care system under conditions of demographic ageing. To do this, we constructed and applied a discrete-time dynamic microsimulation model to health service use in older people. Here, we define health services as a balance of three modalities: practice nurse visit, family doctor visit and public hospital admission. We report on the construction of the model and the results of projections and scenario testing.Research questionsAfter establishing a baseline for our model, we aimed to address two key policy initiatives proffered internationally: promoting healthier ageing to reduce the need for health care7 and changing the balance of care8 towards more effective configurations.9 We focussed on testing scenarios where the burden on the health system might be lessened. Our research questions can be formalised as follows: What will be future levels of health service use for older people under the status quo? This is our base projection . What is the impact of reducing morbidity levels proxy for healthier ageing (and the compression of morbidity) on health service use of older people? This is our reduced morbidity scenario. What is the impact of changing the balance among providers on health service use of older people? This is our balance of care scenario. The model (Figure 1) was: (1) hierarchically structured with long-term illness (morbidity) driving health service use, with practice nurse use affecting family doctor use (via potential prevention or substitution) and with practice nurse and family doctor use affecting public hospital admission (via potential prevention or delay) and (2) dynamic incorporating demographic and morbidity changes over time.10Figure 1: Conceptual model of late-life ageing and health care trajectory. Long-term illness drives health service use, with practice nurse use affecting family doctor use and public hospital admission, and with family doctor use affecting public hospital admission. MicrosimulationMicrosimulation first proposed by Orcutt11 in 1957 has been used, for example, to assess the impact of demographic aging on population health.12 Microsimulation relies on data from the real world to create an artificial version like the original. It operates at the level of individual units (here older people), each assigned attributes as a starting point eg age and health state, to which quantitative rules (eg statistical equations) are applied to simulate changes in state or behaviour. Thus a synthetic set of typical life histories can be generated. The model can then be used to test scenarios essentially thought experiments by modifying key factors and assessing impact on outcomes of policy interest.13,14MethodsOur methods are outlined briefly in this section with a detailed report published online.15 Microsimulation was adopted as a technical approach well-suited to modelling the dynamics of a complex system such as health care, and for testing policy scenarios related to utilisation.Data sourcesWe used individual-level data on older people aged 65 years and over from the New Zealand Health Survey undertaken in 2002 and 2006 (NZHS 2002 and 2006).16 As well as the person s demographic characteristics, there was information on whether they had long-term illness, and on their use of health care; NZHS was the only national data source available with these features. These survey data had the advantage of being nationally representative and relatively recent with adequate sample sizes. NZHS sample weights were taken into account in analyses and simulations. The NZHS 2002 contributed data on 2,206 individuals to form a starting sample at the base year, set to 2001, providing initial conditions representative of older people living in the community. A description of characteristics of the starting sample can be found in Table 1. Thus 9.3% were aged 85 years and over while 85.6% were experiencing long-term illness.Table 1: Description of starting sample. Characteristics of older people (aged 65+ years) living in the community, 2001. Characteristic Percentage of weighted sample\u2020 (n=2206) Age group 65-74 54.2 75-84 36.5 85+ 9.3 Gender Female 55.3 Ethnicity European 91.8 Mori (the indigenous people) 4.0 Pacific 1.7 Asian 2.2 Other 0.3 Marital status Partnered 56.5 Deprivation decile 1 (low deprivation) 6.5 2 7.6 3 9.3 4 10.4 5 10.1 6 13.5 7 10.6 8 13.8 9 11.1 10 (high deprivation) 7.3 Long-term illness Present 85.6 \u2020 The starting sample was taken from the New Zealand Health Survey 2002/3 (Ministry of Health 2004), with weighting calibrated to the New Zealand Census 2001. Definition of variablesThe following individual characteristics incorporated in the model can be categorised into three types:1. Socio-demographic Age: 65+ years. Gender: male, female. Self-reported ethnicity (in prioritised sequence): Mori, Pacific, Other, European. A single ethnicity variable was constructed to account for individuals who reported multiple ethnic affiliations.17 Socio-economic deprivation: NZDep (decile) a census-based small-area measure.18 Self-reported partnership: married, or partnered and not legally married (yes/no). 2. Morbidity Self-reported long-term illness (yes/no): any medical condition lasting six months or more. 3. Health service use (outcomes) Self-reported health service use (in the last 12 months): any practice nurse visit formal consultation with nurse on their own, ie without seeing a doctor (yes/no); family doctor visit categories 0, 1-2, 3-4, 5-6, or 7+ visits (yes/no, in each category); public hospital admission for any reason, comprising inpatient and day patient (yes/no). For family doctor visits, simulated results are reported for the combined 5+ visits category signifying a high user group. AnalysisFirstly, transition probabilities for long-term illness were estimated from matrices using repeated cross-sectional data (NZHS 2002 and 2006), depending on age and gender (use of other characteristics was constrained by small numbers). This estimation was based on known long-term illness levels in 2002 and 2006, and assumed that an individual could remain in the same state or progress to the next state but not revert to a former state. These results imparted dynamic change to the cross-sectional models of health service use (as below).Secondly, we used cross-sectional data (NZHS 2002) to predict health service use from long-term illness (as above) in a series of regression models: practice nurse visit logistic, family doctor visit (as categories) multinomial and public hospital admission logistic. Earlier events or states could exert an influence over later ones (Figure 1). Thus, practice nurse visit was a function of long-term illness; while family doctor visit was a function of both long-term illness and practice nurse visit; and finally public hospital admission was a function of long-term illness, practice nurse visit and family doctor visit. Age, gender, ethnicity, deprivation level and partnership status were also accounted for as potential socio-demographic control variables while, for each model, only statistically significant ones were retained.SimulationFrom 2001, we applied parameters (derived from statistical analysis of NZHS data) to update time-variant attributes of 2,206 individuals (in the starting sample) at five-year intervals using Monte Carlo simulation. The simulation process for each subsequent time interval followed a sequence of steps from demographic characteristics, through health status, to final health care outcomes. To reduce the effect of random error, a simulated estimate was taken as the average result of 20 runs, sufficient to generate a stable value. Thus a set of typical though varied individual life histories was created. To maintain a representative sample over time, at each five-yearly interval, we allowed individuals to enter (being randomly drawn from 65-69 year-olds from NZHS 2002) and to die (according to probabilities from official period life tables), as well as re-weighting (according to official population statistics) to account for demographic changes in composition (eg due to migration).ValidationValidation of simulated results was carried out by comparison to actual NZHS 2006 data (the latest available). The test was whether the simulation model could reproduce benchmark averages and distributions. Where necessary and possible, simulated results were calibrated to population parameters (from NZHS, censuses and official projections) so that findings could be generalised to the national population.Scenario testingKey factors influencing health service use may be considered as potential levers for policy intervention. These can be tested via simulating scenarios. We used the simulated results with no changes made as the base case. For each scenario, we changed factors of interest in the starting sample, while holding other initial factors constant, and observed impact on down-stream outcomes (compared to the base case). At an individual level, changes were made to those in or at high risk of being in a particular state, eg having long-term illness. Note that the settings for the scenarios were heuristic: we started with small changes in morbidity or care levels and gradually increased or decreased them, over a reasonable range of proportions to an upper limit of possibility (5 to 20 percent).1. Base projection of status quo to 2021We simulated from the starting sample in 2001 forward to 2021 with no changes to inputs or parameters. We considered 20 years as a reasonable projection period that would be useful without over-stretching the data.2. Reduced morbidity scenario (2021)We artificially reduced, by varying proportions (5%, 10% and 20% respectively), the prevalence of and transition probabilities for long-term illness to assess impact on levels of health service use.3. Balance of care scenario (2021)We artificially increased, by varying proportions (5%, 10% and 20% respectively), the level of practice nurse visits to assess impact, in turn, on levels of family doctor visits and public hospital admissions.ResultsValidationThe simulated sample followed the general pattern for the real sample (from NZHS 2006) though not uniformly so across all measures: compared to the benchmarks, long-term illness and practice nurse visit were under-estimated while family doctor visit and public hospital admission were over-estimated (Table 2). Note that, in the interpretation of simulated results, greater importance should be placed on direction and magnitude rather than specific point estimates.Table 2: Morbidity and health service use for older people (aged 65+ years) living in the community. Comparing simulated to real data, 2006. Age group Morbidity Health care modalities Long-term illness (lasting at least six months) (%) Practice nurse visit (any in last 12 months) (%) Family doctor 5+ visits (in last 12 months) (%) Public hospital admission (any in last 12 months) (%) Simulated Real \u2020 Simulated Real \u2020 Simulated Real \u2020 Simulated Real \u2020 65-69 78.0 86.7 42.9 43.6 36.6 31.1 18.7 14.1 70-74 89.2 89.7 43.2 46.2 38.8 32.9 20.7 17.5 75-79 89.8 89.6 43.7 45.8 52.0 36.8 25.5 19.7 80-84 93.8 94.0 44.2 48.4 52.3 42.2 26.5 25.2 85+ 91.1 89.9 44.6 45.2 47.9 39.7 22.5 19.1 All (65+) 86.6 89.3 43.5 45.5 43.6 35.0 22.0 18.1 (95% CI)\u2021 (86.3-87.2) (42.6-44.4) (42.6-45.3) (21.5-23.1) \u2020 Taken from NZ Health Survey 2006.\u2021 95% confidence intervals were calculated from 20 simulation runs. Scenario testingOur comparison between the base simulation (with no changes) and a scenario (with a factor change) were relative to one another within the virtual world. The two simulated results conditioned on the same input data and parameters are directly comparable and give a good assessment of impact of the changed factor.1. Base projectionSimulation under current settings, ie projection, from 2001 to 2021 showed a moderate absolute increase overall in the level of long-term illness (Table 3) which was more marked with increasing age: 2% for the 65+ age group and 13% for the 85+ age group (results not tabled). There was a concomitant proportional increase in the use by people aged 65+ of family doctor visits (up 21%) and public hospital admissions (up 16%) while practice nurse visits remained stable (Table 3).Table 3: Base projection and reduced morbidity scenarios. Morbidity and health service use for older people (aged 65+ years) living in the community, 2001 and 2021. Simulations\u2020 Morbidity Health care modalities Long-term illness (lasting at least six months) (%) Practice nurse visit (any in last 12 months) (%) Family doctor 5+ visits (in last 12 months) (%) Public hospital admission (any in last 12 months) (%) Q1. Base projection\u2021 2001 85.6 42.1 36.0 18.8 2006 86.6 43.5 43.6 22.0 2011 87.2 43.3 43.4 22.1 2016 86.5 43.2 42.9 21.6 2021 87.4 43.3 43.5 21.8 Q2. Reduced morbidity scenarios\u00a7 Make 5% decrease in long-term illness (%) [% change]\u00b6 2006 - 43.0 42.7 20.8 2011 - 43.1 42.9 21.1 2016 - 43.0 42.4 21.3 2021 - 43.2 [-0.2] 42.9 [-1.4] 21.4 [-1.8] Make 10% decrease in long-term illness 2006 - 42.6 41.8 19.7 2011 - 43.0 42.1 20.5 2016 - 43.1 41.6 20.7 2021 - 43.1 [-0.5] 42.5 [-2.3] 21.1 [-3.2] Make 20% decrease in long-term illness 2006 - 42.6 40.2 18.8 2011 - 42.7 41.0 19.4 2016 - 43.0 40.4 19.6 2021 - 43.1 [-0.5] 41.1 [-5.5] 20.0 [-8.3] \u2020 Simulations are calibrated to NZ Health Survey 2006 data.\u2021 Base projection to 2021 is on current settings.\u00a7 Scenarios represent the impact of reducing base prevalence of and transition probabilities for morbidity (long-term illness) by nominated percentage of base projected level.\u00b6 Proportional change in outcome (due to the scenario settings) compared to the base projection for that year. 2. Reduced morbidity scenarioScenarios projected to 2021, implemented by progressively decreasing long-term illness levels, had the effect of only moderately reducing health service use compared to the base projection (Table 3). For example, with long-term illness levels reduced by 20%, there were proportional reductions of 0.5% in practice nurse visits, 5.3% in family doctor visits and 8.3% in public hospital admissions.3. Balance of care scenarioScenarios projected to 2021, implemented by progressively rebalancing towards practice nurse use, had the effect of moderately decreasing family doctor visits but markedly decreasing public hospital admissions compared to the base projection (Table 4). This effect was much more pronounced with increasing age. This is illustrated by the scenario where the proportion of older persons who visited the practice nurse at least once in a year was increased by 20%. Thus, in the 65+ age group, relative to the basic projection, the proportion of high users of family doctors visits was reduced by 0.7% and the proportion of people admitted to public hospital was reduced by 1.4%; in the 85+ age group those relative reductions were 0.8% and 25.5% respectively (Table 4).Table 4: Balance of care scenarios. Towards more practice nurse visits for older people living in the community, 2021. Simulations\u2020 Health care modalities Practice nurse visit (any in last 12 months) (%) Family doctor 5+ visits (in last 12 months) (%) Public hospital admission (any in last 12 months) (%) Aged 65+ Aged 85+ Aged 65+ Aged 85+ Aged 65+ Aged 85+ Q1. Base projection\u2021 2001 42.1 26.6 36.0 36.2 18.8 22.7 2006 43.5 44.6 43.6 47.9 22.0 22.5 2011 43.3 43.8 43.4 48.9 22.1 22.8 2016 43.2 42.4 42.9 48.7 21.6 22.1 2021 43.3 42.4 43.5 48.8 21.8 23.1 Q3. Balance of care scenarios\u00a7 Make 5% increase in practice nurse visits (%) [% change]\u00b6 2006 - - 43.4 47.9 21.9 22.2 2011 - - 43.5 48.9 21.9 22.8 2016 - - 42.9 47.6 21.7 22.3 2021 - - 43.5 [0] 50.5 [+3.5] 21.9 [+0.5] 22.4 [-3.0] Make 10% increase in practice nurse visits 2006 - - 43.3 47.9 21.9 21.8 2011 - - 43.5 48.9 21.8 22.5 2016 - - 42.9 47.5 21.7 21.6 2021 - - 43.4 [-0.2] 50.4 [+3.3] 21.9 [+0.5] 21.9 [-5.2] Make 20% increase in practice nurse visits 2006 - - 43.3 47.5 21.8 21.0 2011 - - 43.2

Summary

Abstract

Aim

The demographic ageing of New Zealand society has greatly increased the proportion of older people (aged 65 years and over), with major policy implications. We tested the effects on health service use of alterations to morbidity profile and the balance of care.

Method

We developed a microsimulation model using data from an official national health survey series to generate a synthetic replicate for scenario testing.

Results

Projections on current settings from 2001 to 2021 showed increases in morbidity\u00ad\u00ad \u00adlong-term illness (2%) and in health service use doctor visits (21%), public hospital admissions (16%). Scenarios with decreasing morbidity levels showed moderate reductions in health service use. By contrast, rebalancing towards the use of practice nurses showed a large decrease in public hospital admissions for people aged 85 years and over.

Conclusion

Demographic ageing may not have a major negative effect on system resources in New Zealand and other developed countries. Rebalancing between modalities of care may soften the impact of increasing health service use required by a larger older population.

Author Information

Roy Lay-Yee, Senior Research Fellow, Centre of Methods and Policy Application in the Social Sciences (COMPASS), University of Auckland, Auckland; Janet Pearson, Statistician, Centre of Methods and Policy Application in the Social Sciences (COMPASS), University of Auckland, Auckland; Martin von Randow, Analyst, Centre of Methods and Policy Application in the Social Sciences (COMPASS), University of Auckland, Auckland; Ngaire Kerse, Professor, School of Population Health, University of Auckland, Auckland; Laurie Brown, Professor, National Centre for Social and Economic Modelling (NATSEM), University of Canberra, Australia.

Acknowledgements

This work was funded by the Health Research Council of New Zealand [grant number 09/068]. Data from the New Zealand Health Survey were made available by the Ministry of Health. Thanks to Oliver Mannion and Karl Parker for their technical and analytical support; Dr Barry Milne and Jessica McLay for their comments and suggestions on the manuscript.

Correspondence

Roy Lay-Yee, Senior Research Fellow, Centre of Methods and Policy Application in the Social Sciences (COMPASS), University of Auckland, Auckland.

Correspondence Email

r.layyee@auckland.ac.nz

Competing Interests

Nil.

-- Lloyd-Sherlock P, McKee M, Ebrahim S, et al. Population aging and health. Lancet. 2012;379(9823):1295-6. Rechel B, Doyle Y, Grundy E, McKee M. How can health systems respond to population aging? Copenhagen: World Health Organisation; 2009.

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contact nzmj@nzma.org.nz

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The demographic structure of New Zealand, as in other developed countries, is changing. The proportion of older people in the population has greatly increased along with their experience of multi-morbidity with major implications for the provision of health services.1,2 Forecasts of future compression or expansion of morbidity hinge on whether extended life-expectancy will be spent largely in good or ill health.3 Nevertheless, there is pressure on available resources to keep pace with the sheer increase in volume of health care required for larger numbers of older people. The recent World Health Organization s World Report on Ageing and Health proposes a public-health framework for healthy ageing defined as the process of developing and maintaining the functional ability that enables well-being in older age in which the first of four priority areas is aligning health systems to the needs of older populations (p 13).4 The policy quandaries posed by demographic ageing apply no less to New Zealand,5 with the proportion of people aged 65 years and over projected to increase by nearly two-fifths from 12.1% in 2001 to 16.8% in 2021.6AimsWe aimed to model a range of policy scenarios on the future shape of the New Zealand health-care system under conditions of demographic ageing. To do this, we constructed and applied a discrete-time dynamic microsimulation model to health service use in older people. Here, we define health services as a balance of three modalities: practice nurse visit, family doctor visit and public hospital admission. We report on the construction of the model and the results of projections and scenario testing.Research questionsAfter establishing a baseline for our model, we aimed to address two key policy initiatives proffered internationally: promoting healthier ageing to reduce the need for health care7 and changing the balance of care8 towards more effective configurations.9 We focussed on testing scenarios where the burden on the health system might be lessened. Our research questions can be formalised as follows: What will be future levels of health service use for older people under the status quo? This is our base projection . What is the impact of reducing morbidity levels proxy for healthier ageing (and the compression of morbidity) on health service use of older people? This is our reduced morbidity scenario. What is the impact of changing the balance among providers on health service use of older people? This is our balance of care scenario. The model (Figure 1) was: (1) hierarchically structured with long-term illness (morbidity) driving health service use, with practice nurse use affecting family doctor use (via potential prevention or substitution) and with practice nurse and family doctor use affecting public hospital admission (via potential prevention or delay) and (2) dynamic incorporating demographic and morbidity changes over time.10Figure 1: Conceptual model of late-life ageing and health care trajectory. Long-term illness drives health service use, with practice nurse use affecting family doctor use and public hospital admission, and with family doctor use affecting public hospital admission. MicrosimulationMicrosimulation first proposed by Orcutt11 in 1957 has been used, for example, to assess the impact of demographic aging on population health.12 Microsimulation relies on data from the real world to create an artificial version like the original. It operates at the level of individual units (here older people), each assigned attributes as a starting point eg age and health state, to which quantitative rules (eg statistical equations) are applied to simulate changes in state or behaviour. Thus a synthetic set of typical life histories can be generated. The model can then be used to test scenarios essentially thought experiments by modifying key factors and assessing impact on outcomes of policy interest.13,14MethodsOur methods are outlined briefly in this section with a detailed report published online.15 Microsimulation was adopted as a technical approach well-suited to modelling the dynamics of a complex system such as health care, and for testing policy scenarios related to utilisation.Data sourcesWe used individual-level data on older people aged 65 years and over from the New Zealand Health Survey undertaken in 2002 and 2006 (NZHS 2002 and 2006).16 As well as the person s demographic characteristics, there was information on whether they had long-term illness, and on their use of health care; NZHS was the only national data source available with these features. These survey data had the advantage of being nationally representative and relatively recent with adequate sample sizes. NZHS sample weights were taken into account in analyses and simulations. The NZHS 2002 contributed data on 2,206 individuals to form a starting sample at the base year, set to 2001, providing initial conditions representative of older people living in the community. A description of characteristics of the starting sample can be found in Table 1. Thus 9.3% were aged 85 years and over while 85.6% were experiencing long-term illness.Table 1: Description of starting sample. Characteristics of older people (aged 65+ years) living in the community, 2001. Characteristic Percentage of weighted sample\u2020 (n=2206) Age group 65-74 54.2 75-84 36.5 85+ 9.3 Gender Female 55.3 Ethnicity European 91.8 Mori (the indigenous people) 4.0 Pacific 1.7 Asian 2.2 Other 0.3 Marital status Partnered 56.5 Deprivation decile 1 (low deprivation) 6.5 2 7.6 3 9.3 4 10.4 5 10.1 6 13.5 7 10.6 8 13.8 9 11.1 10 (high deprivation) 7.3 Long-term illness Present 85.6 \u2020 The starting sample was taken from the New Zealand Health Survey 2002/3 (Ministry of Health 2004), with weighting calibrated to the New Zealand Census 2001. Definition of variablesThe following individual characteristics incorporated in the model can be categorised into three types:1. Socio-demographic Age: 65+ years. Gender: male, female. Self-reported ethnicity (in prioritised sequence): Mori, Pacific, Other, European. A single ethnicity variable was constructed to account for individuals who reported multiple ethnic affiliations.17 Socio-economic deprivation: NZDep (decile) a census-based small-area measure.18 Self-reported partnership: married, or partnered and not legally married (yes/no). 2. Morbidity Self-reported long-term illness (yes/no): any medical condition lasting six months or more. 3. Health service use (outcomes) Self-reported health service use (in the last 12 months): any practice nurse visit formal consultation with nurse on their own, ie without seeing a doctor (yes/no); family doctor visit categories 0, 1-2, 3-4, 5-6, or 7+ visits (yes/no, in each category); public hospital admission for any reason, comprising inpatient and day patient (yes/no). For family doctor visits, simulated results are reported for the combined 5+ visits category signifying a high user group. AnalysisFirstly, transition probabilities for long-term illness were estimated from matrices using repeated cross-sectional data (NZHS 2002 and 2006), depending on age and gender (use of other characteristics was constrained by small numbers). This estimation was based on known long-term illness levels in 2002 and 2006, and assumed that an individual could remain in the same state or progress to the next state but not revert to a former state. These results imparted dynamic change to the cross-sectional models of health service use (as below).Secondly, we used cross-sectional data (NZHS 2002) to predict health service use from long-term illness (as above) in a series of regression models: practice nurse visit logistic, family doctor visit (as categories) multinomial and public hospital admission logistic. Earlier events or states could exert an influence over later ones (Figure 1). Thus, practice nurse visit was a function of long-term illness; while family doctor visit was a function of both long-term illness and practice nurse visit; and finally public hospital admission was a function of long-term illness, practice nurse visit and family doctor visit. Age, gender, ethnicity, deprivation level and partnership status were also accounted for as potential socio-demographic control variables while, for each model, only statistically significant ones were retained.SimulationFrom 2001, we applied parameters (derived from statistical analysis of NZHS data) to update time-variant attributes of 2,206 individuals (in the starting sample) at five-year intervals using Monte Carlo simulation. The simulation process for each subsequent time interval followed a sequence of steps from demographic characteristics, through health status, to final health care outcomes. To reduce the effect of random error, a simulated estimate was taken as the average result of 20 runs, sufficient to generate a stable value. Thus a set of typical though varied individual life histories was created. To maintain a representative sample over time, at each five-yearly interval, we allowed individuals to enter (being randomly drawn from 65-69 year-olds from NZHS 2002) and to die (according to probabilities from official period life tables), as well as re-weighting (according to official population statistics) to account for demographic changes in composition (eg due to migration).ValidationValidation of simulated results was carried out by comparison to actual NZHS 2006 data (the latest available). The test was whether the simulation model could reproduce benchmark averages and distributions. Where necessary and possible, simulated results were calibrated to population parameters (from NZHS, censuses and official projections) so that findings could be generalised to the national population.Scenario testingKey factors influencing health service use may be considered as potential levers for policy intervention. These can be tested via simulating scenarios. We used the simulated results with no changes made as the base case. For each scenario, we changed factors of interest in the starting sample, while holding other initial factors constant, and observed impact on down-stream outcomes (compared to the base case). At an individual level, changes were made to those in or at high risk of being in a particular state, eg having long-term illness. Note that the settings for the scenarios were heuristic: we started with small changes in morbidity or care levels and gradually increased or decreased them, over a reasonable range of proportions to an upper limit of possibility (5 to 20 percent).1. Base projection of status quo to 2021We simulated from the starting sample in 2001 forward to 2021 with no changes to inputs or parameters. We considered 20 years as a reasonable projection period that would be useful without over-stretching the data.2. Reduced morbidity scenario (2021)We artificially reduced, by varying proportions (5%, 10% and 20% respectively), the prevalence of and transition probabilities for long-term illness to assess impact on levels of health service use.3. Balance of care scenario (2021)We artificially increased, by varying proportions (5%, 10% and 20% respectively), the level of practice nurse visits to assess impact, in turn, on levels of family doctor visits and public hospital admissions.ResultsValidationThe simulated sample followed the general pattern for the real sample (from NZHS 2006) though not uniformly so across all measures: compared to the benchmarks, long-term illness and practice nurse visit were under-estimated while family doctor visit and public hospital admission were over-estimated (Table 2). Note that, in the interpretation of simulated results, greater importance should be placed on direction and magnitude rather than specific point estimates.Table 2: Morbidity and health service use for older people (aged 65+ years) living in the community. Comparing simulated to real data, 2006. Age group Morbidity Health care modalities Long-term illness (lasting at least six months) (%) Practice nurse visit (any in last 12 months) (%) Family doctor 5+ visits (in last 12 months) (%) Public hospital admission (any in last 12 months) (%) Simulated Real \u2020 Simulated Real \u2020 Simulated Real \u2020 Simulated Real \u2020 65-69 78.0 86.7 42.9 43.6 36.6 31.1 18.7 14.1 70-74 89.2 89.7 43.2 46.2 38.8 32.9 20.7 17.5 75-79 89.8 89.6 43.7 45.8 52.0 36.8 25.5 19.7 80-84 93.8 94.0 44.2 48.4 52.3 42.2 26.5 25.2 85+ 91.1 89.9 44.6 45.2 47.9 39.7 22.5 19.1 All (65+) 86.6 89.3 43.5 45.5 43.6 35.0 22.0 18.1 (95% CI)\u2021 (86.3-87.2) (42.6-44.4) (42.6-45.3) (21.5-23.1) \u2020 Taken from NZ Health Survey 2006.\u2021 95% confidence intervals were calculated from 20 simulation runs. Scenario testingOur comparison between the base simulation (with no changes) and a scenario (with a factor change) were relative to one another within the virtual world. The two simulated results conditioned on the same input data and parameters are directly comparable and give a good assessment of impact of the changed factor.1. Base projectionSimulation under current settings, ie projection, from 2001 to 2021 showed a moderate absolute increase overall in the level of long-term illness (Table 3) which was more marked with increasing age: 2% for the 65+ age group and 13% for the 85+ age group (results not tabled). There was a concomitant proportional increase in the use by people aged 65+ of family doctor visits (up 21%) and public hospital admissions (up 16%) while practice nurse visits remained stable (Table 3).Table 3: Base projection and reduced morbidity scenarios. Morbidity and health service use for older people (aged 65+ years) living in the community, 2001 and 2021. Simulations\u2020 Morbidity Health care modalities Long-term illness (lasting at least six months) (%) Practice nurse visit (any in last 12 months) (%) Family doctor 5+ visits (in last 12 months) (%) Public hospital admission (any in last 12 months) (%) Q1. Base projection\u2021 2001 85.6 42.1 36.0 18.8 2006 86.6 43.5 43.6 22.0 2011 87.2 43.3 43.4 22.1 2016 86.5 43.2 42.9 21.6 2021 87.4 43.3 43.5 21.8 Q2. Reduced morbidity scenarios\u00a7 Make 5% decrease in long-term illness (%) [% change]\u00b6 2006 - 43.0 42.7 20.8 2011 - 43.1 42.9 21.1 2016 - 43.0 42.4 21.3 2021 - 43.2 [-0.2] 42.9 [-1.4] 21.4 [-1.8] Make 10% decrease in long-term illness 2006 - 42.6 41.8 19.7 2011 - 43.0 42.1 20.5 2016 - 43.1 41.6 20.7 2021 - 43.1 [-0.5] 42.5 [-2.3] 21.1 [-3.2] Make 20% decrease in long-term illness 2006 - 42.6 40.2 18.8 2011 - 42.7 41.0 19.4 2016 - 43.0 40.4 19.6 2021 - 43.1 [-0.5] 41.1 [-5.5] 20.0 [-8.3] \u2020 Simulations are calibrated to NZ Health Survey 2006 data.\u2021 Base projection to 2021 is on current settings.\u00a7 Scenarios represent the impact of reducing base prevalence of and transition probabilities for morbidity (long-term illness) by nominated percentage of base projected level.\u00b6 Proportional change in outcome (due to the scenario settings) compared to the base projection for that year. 2. Reduced morbidity scenarioScenarios projected to 2021, implemented by progressively decreasing long-term illness levels, had the effect of only moderately reducing health service use compared to the base projection (Table 3). For example, with long-term illness levels reduced by 20%, there were proportional reductions of 0.5% in practice nurse visits, 5.3% in family doctor visits and 8.3% in public hospital admissions.3. Balance of care scenarioScenarios projected to 2021, implemented by progressively rebalancing towards practice nurse use, had the effect of moderately decreasing family doctor visits but markedly decreasing public hospital admissions compared to the base projection (Table 4). This effect was much more pronounced with increasing age. This is illustrated by the scenario where the proportion of older persons who visited the practice nurse at least once in a year was increased by 20%. Thus, in the 65+ age group, relative to the basic projection, the proportion of high users of family doctors visits was reduced by 0.7% and the proportion of people admitted to public hospital was reduced by 1.4%; in the 85+ age group those relative reductions were 0.8% and 25.5% respectively (Table 4).Table 4: Balance of care scenarios. Towards more practice nurse visits for older people living in the community, 2021. Simulations\u2020 Health care modalities Practice nurse visit (any in last 12 months) (%) Family doctor 5+ visits (in last 12 months) (%) Public hospital admission (any in last 12 months) (%) Aged 65+ Aged 85+ Aged 65+ Aged 85+ Aged 65+ Aged 85+ Q1. Base projection\u2021 2001 42.1 26.6 36.0 36.2 18.8 22.7 2006 43.5 44.6 43.6 47.9 22.0 22.5 2011 43.3 43.8 43.4 48.9 22.1 22.8 2016 43.2 42.4 42.9 48.7 21.6 22.1 2021 43.3 42.4 43.5 48.8 21.8 23.1 Q3. Balance of care scenarios\u00a7 Make 5% increase in practice nurse visits (%) [% change]\u00b6 2006 - - 43.4 47.9 21.9 22.2 2011 - - 43.5 48.9 21.9 22.8 2016 - - 42.9 47.6 21.7 22.3 2021 - - 43.5 [0] 50.5 [+3.5] 21.9 [+0.5] 22.4 [-3.0] Make 10% increase in practice nurse visits 2006 - - 43.3 47.9 21.9 21.8 2011 - - 43.5 48.9 21.8 22.5 2016 - - 42.9 47.5 21.7 21.6 2021 - - 43.4 [-0.2] 50.4 [+3.3] 21.9 [+0.5] 21.9 [-5.2] Make 20% increase in practice nurse visits 2006 - - 43.3 47.5 21.8 21.0 2011 - - 43.2

Summary

Abstract

Aim

The demographic ageing of New Zealand society has greatly increased the proportion of older people (aged 65 years and over), with major policy implications. We tested the effects on health service use of alterations to morbidity profile and the balance of care.

Method

We developed a microsimulation model using data from an official national health survey series to generate a synthetic replicate for scenario testing.

Results

Projections on current settings from 2001 to 2021 showed increases in morbidity\u00ad\u00ad \u00adlong-term illness (2%) and in health service use doctor visits (21%), public hospital admissions (16%). Scenarios with decreasing morbidity levels showed moderate reductions in health service use. By contrast, rebalancing towards the use of practice nurses showed a large decrease in public hospital admissions for people aged 85 years and over.

Conclusion

Demographic ageing may not have a major negative effect on system resources in New Zealand and other developed countries. Rebalancing between modalities of care may soften the impact of increasing health service use required by a larger older population.

Author Information

Roy Lay-Yee, Senior Research Fellow, Centre of Methods and Policy Application in the Social Sciences (COMPASS), University of Auckland, Auckland; Janet Pearson, Statistician, Centre of Methods and Policy Application in the Social Sciences (COMPASS), University of Auckland, Auckland; Martin von Randow, Analyst, Centre of Methods and Policy Application in the Social Sciences (COMPASS), University of Auckland, Auckland; Ngaire Kerse, Professor, School of Population Health, University of Auckland, Auckland; Laurie Brown, Professor, National Centre for Social and Economic Modelling (NATSEM), University of Canberra, Australia.

Acknowledgements

This work was funded by the Health Research Council of New Zealand [grant number 09/068]. Data from the New Zealand Health Survey were made available by the Ministry of Health. Thanks to Oliver Mannion and Karl Parker for their technical and analytical support; Dr Barry Milne and Jessica McLay for their comments and suggestions on the manuscript.

Correspondence

Roy Lay-Yee, Senior Research Fellow, Centre of Methods and Policy Application in the Social Sciences (COMPASS), University of Auckland, Auckland.

Correspondence Email

r.layyee@auckland.ac.nz

Competing Interests

Nil.

-- Lloyd-Sherlock P, McKee M, Ebrahim S, et al. Population aging and health. Lancet. 2012;379(9823):1295-6. Rechel B, Doyle Y, Grundy E, McKee M. How can health systems respond to population aging? Copenhagen: World Health Organisation; 2009.

Contact diana@nzma.org.nz
for the PDF of this article

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