A structured review of long-term care demand modelling

Abstract

Long-term care (LTC) represents a significant and substantial proportion of healthcare spends across the globe. Its main aim is to assist individuals suffering with more or more chronic illnesses, disabilities or cognitive impairments, to carry out activities associated with daily living. Shifts in several economic, demographic and social factors have raised concerns surrounding the sustainability of current systems of LTC. Substantial effort has been put into modelling the LTC demand process itself so as to increase understanding of the factors driving demand for LTC and its related services. Furthermore, such modeling efforts have also been used to plan the operation and future composition of the LTC system itself. The main aim of this paper is to provide a structured review of the literature surrounding LTC demand modeling and any such industrial application, whilst highlighting any potential direction for future researchers.

Appendix 1 Overview of studies

Author, year	Study Objective	Data Sources	Aspects of LTC System(s) Studied	Methodology	Time Horizon	Key Findings
[1]	To investigate how changes in educational level of the older people may affect future prevalence of severe ill-health among old people in Sweden.	• Population projections by age, gender and educational level under different trends in mortality. • Swedish national survey of living conditions (SNSLC) carried out in the period 1975–99.	• The educational composition of the older population during the next three decades.	• Educational level classified into three categories based upon the years of education received. • Logistic regression models used to estimate differences in the prevalence of severe ill health in different age, gender and educational level cohorts. • Demographic extrapolation used, with constant morbidity, to project future no of those with ill health and in need of LTC. • Additional scenarios added to include falling rates of morbidity and severe health needs using educational adjusted trends in mortality.	2000–2035	• Population projections which take into account level of education within each age-gender subgroup can lead to higher expected numbers of elderly people. • Including mortality differentials by education level has a strong impact on the size of the older population and a significant impact on the number of people with severe ill health. • The number of people in Sweden suffering from severe health needs in old age will increase by 14 % when the combined effects of age, education and gender are considered. This increase is small relative to the 75 % projected increase over the same period, 2000–2035 when differentials in mortality among specific age groups are not considered. • Projections on LTC need that consider changes in population composition by education result in less than half the increase in the number of elderly persons with severe ill-health compared with demographic extrapolation alone.
[4]	To estimate the future healthcare costs facing healthcare organizations due population ageing.	• Age specific health care costs published by the Department of Health 2005. • Sub-national Population projections, death registrations and health expectations at birth from the Office for National Statistics 2009	• Future LTC health care costs using routinely available data. • LTC costs in the years before death. • Impact of changes in life expectancy with respect to LTC costs	• Three proposed models. • Expected annual health care costs are derived by calculating the sum of the product of the current average health care costs for different age bands and the projected number of people in each age band until 2031. • In the second model, age bands were adjusted to reflect an increase in life expectancy • In the third model, age bands were adjusted by the increase in LE in good health by using the ONS projections of disability free life expectancy.	2006–2031	• The rate of increase in health care cost differs substantially depending on how projections of future life expectancy are incorporated • The projected future cost of care was highest in the model which made not account for changes in life expectancy or disability free life expectancy. • The estimated annual health care expenditure due to ageing was almost double if expansions in life expectancy were not considered.
(Chahed et al. 2011)	To predict length of stay in long-term care and the number of patients remaining in care at a specific future time horizon.	• Dataset containing funded admissions to NHS long-term care supplied by 26 London primary care trusts.	• Length of stay of patients with different characteristics, including which type of care they currently receive, age and gender. • Movements between different LTC settings	• A continuous time Markov model of the flow of elderly residents within and between residential and nursing care is used to model the flow of LTC patients between two conceptual states and a discharge state in which the patient leaves LTC. • The transition probabilities were estimated by fitting survival curves to historic patient movements in care to establish further sub states corresponding to short, medium and long stay states. • By running the model over 356 days the estimated number of individuals remaining in each of the six defined care categories was used to predict the demand for care at each point in time.	2007–2008	• There were significant variations in the proportions of discharge and transition between types of care as well as care groups. • The proportions of discharge from home care are higher than from placement • The proportions of discharge from short-stay and medium-stay states for Physically Frail patients are lower than those of from Palliative care.
[6]	Derive quantitative estimates of future LTC expenditure in Hong Kong	• Thematic Household Survey 2004 • Hong Kong Annual Digest of Statistics • Hong Kong population Projections 2007–2036 • Hong Kong Domestic Health Accounts 1989–2002	• The future number of elderly people and the number requiring LTC • Expenditure on LTC given individual factors that drive need • The future inflated costs of LTC and the disability benefits for older people.	• Macro-simulation approach based on PSSR model. • Probability of using each service estimated for each age-sex profile using logistic regression. • Total utilization is estimated for each service in each year and multiplied by the inflated unit cost of care. • Future projections obtained using population estimates	2004–2036	• Demographic changes have a larger impact than changes in unit costs of care on overall expenditure • Expenditure expected to increase by 1.5 % of GDP in 200 4 to 3 % by 2036. • By service mix, the proportion allocated to institutional care would increase from 37 % in 2004 to 46 % by 2036. • Spending on LTC could be contained within 2.3–2.5 % of total GDP in 2036 if institutional care could be substituted by home and day care services.
(Wittenberg et al. 2004)	Project expenditure on long-term care services for older people in the UK to 2051	• Government Actuary’s Department (Population Projections)	• Share of LTC expenditure between the public and private sector. • Impact of providing free personal and nursing care. • Impact of changes in patterns of care with respect to support for informal care givers.	• Linkage of two micro-simulation models (PSSRU and NCCSU) • PSSRU – demand for long-term care under different socio-economic assumptions • NCCSU – models long-term care charges and the ability of groups of older people to contribute towards care home fees.	2000–2051	• Demand for LTC sensitive to projected numbers of older people, future dependency rates and real rises in the unit costs of care • Much uncertainty surrounding how far expenditure on LTC as a proportion of GRP will need to rise to meet demographic pressures
[7]	To investigate which factors drive LTC in several EU countries and the sensitivity of the projections to alternative future scenarios	• Eurostat 1999 population projections. (in addition to official national population projections from each country studied)	• Expenditure on LTC in UK, Germany, Spain and Italy. • Future numbers of dependent persons (65+), their respective probabilities of using different types of LTC services and volume of services required.	• Distinct macro-simulation (cell-based) model for each country’s LTC system, reflecting differences in entitlement, level of informal care and coverage of publicly available LTC. • Incorporates assumptions surrounding the future changes in the macroeconomic environment, including real costs of care.	2000–2050	• Proportion of GDP spent on LTC to double between 2000 and 2050 (assuming that the age-specific dependency rates remain constant). • Future demand sensitive to assumptions about the future number of older people and future dependency rates. • Future cost sensitive to real unit costs of care and the availability of informal care.
[9]	To investigate how incorporating expert views on dementia would affect projections of future expenditure on dementia related care for older people.	• 19 responses to a question from experts in the field of Dementia care and Alzheimer’s disease. (Carried out via a Delphi process) • Survey from the Medical Research Council Cognitive Function and Ageing Society 1998	• Future demand and expenditure on long-term care by older people with dementia in England.	• Updated version of the PSSRU CI (Cognitive Impairment) macro-simulation model used to represent the LTC system in England • The views of the Delphi panel were incorporated into the model as assumptions.	2002–2031	• Expert option suggesting that there will be a reduction in age-specific prevalence rates of dementia will reduce the number of future suffers and the associated total expenditure on care by approximately 16 % compared with no change in prevalence of dementia.. • The expenditure effects of reduced institutionalization combined with increased care assistant wages will in effect cancel each other out.
[8]	To project the future number of older people with cognitive impairment in England, the demand for LTC and associated cost. To investigate the impact of specific assumptions surrounding future trends.	• Government Actuary’s Department 2005 projections on the number of older people. • Future marital status and cohabitation projections from the Office for National Statistics 2005 • Prevalence of cognitive impairment from Cognitive Function and Ageing Studies study (1998) • Resource implications for CI from Resource Implication Study (1999) • General Household Survey for number of people in receipt of informal and non-residential care • Number of people in care homes from Department of Health 2003 data • Information about people in hospital for long –stays taken from 2001 Census data.	• Sensitivity of the factors related to LTC on projections of future demand and cost. • Use of services by those with cognitive impairment and or disability. • Future household composition and implications for levels of informal LTC	• Three part macro simulation model, built upon previous PSSRU model. • First part projects future population into cells which are defined by age, gender, cognitive impartment and disability. • Second component assigns receipt of LTC services to each cell in the first stage based on the probability of receiving such services. • Third stage projects unit cost of services for each composition of services in the second stage at constant 2002 prices. • Projections for future years revise unit costs by labor related inflation to derive future projections of total expenditure.	2002-2031	• Unless more effective treatments for cognitive impairment are development made widely available, expenditure on LTC for patients with CI will rise significantly over the next 30 years. • Demand for LTC care depends on availability of informal care from family and friends. • Total expenditure on care sensitive to the supply of informal care, where expenditure on LTC could represent 1.11 % of GDP compared with 0.96 % if the supply of informal care fell significantly. • Projected future LTC expenditure highly sensitive to assumed rate of growth in real unit costs of care.
[11]	To examine the sensitivity of estimates of future long term care demand under different official population projections.	• Euro Stat 1999 based population projections	• Variability in expenditure predictions across the UK, Germany, Italy and Spain. • Effects of demographic uncertainty on both population and expenditure predictions. • Future fertility rates and its influence on the numbers of informal care givers.	• Country wide macro simulation model based on the PSSRU model • Future population projections are partitioned by age, gender and level of dependency • A second model classified services used by dependent older people according to type of care received and setting • Expenditure projections are extrapolated by applying unit costs of the services in each group and multiplying by the respected population projection. • A number of parameters for instance prevalence rates of dependency by age can be adjusted to accommodate different future demographic scenarios. • Results were compared for both high and low population projections.	2000–2050	• The projected numbers of dependent elderly people were higher in Germany compared to the official national projections. Whilst in Spain and the UK there was a little deviation. • Differences in relative expenditure between the highest and lowest population assumption varied from 35 to 50 %, with Italy exhibiting the smallest difference and the UK the largest. • For Germany and the UK, the difference in projected expenditure on LTC in 2050 constituted 1 % of GDP under the low and high population estimates. • There is evidence of cross country convergence with respect to the cost of LTC as a percentage of GDP in Spain, UK, Italy and Germany. • Growth in LTC expenditure over the period varied from 70 to 90 % in the most optimistic scenario, to 150–180 % in the most pessimistic.
[16]	To project long-term care expenditure in Japan between 2010 and 2050 by analysis of household transition	• Population projects for Japan from 2006 to 2055, National institute of population and social security research, 2007. • National Household survey Japan 2004.	• Numbers of elderly people according to dependency and/or other living situations. • Future cost of LTC relative to total healthcare expenditure • The effect of the ageing of the “baby boomers” on LTC demand • The household ratio or parents to children to asses potential future levels of informal care	• A dynamic micro simulation model which transitioned individuals forward in time, subject to stochastic events taking place. • An initial fixed population was simulated according to a sample taken from census data in 2005. • Individuals were transitioned through the model according to estimated probabilities of life changing events in addition to changes in household circumstances. • Transition probabilities dependant on age, sex and level of disability for those aged 65 and over. • Levels of dependency were classified into four groups and associated with the need for LTC. • Movements from these levels and into an institution were dependant on each individual’s personal circumstances. • Future costs derived by applying future age specific population projections for each of the LTC insurance bands.	2010–2050	• The proportion of those elderly who stay in institutions will steadily increase until 2050. • The sum of health and LTC expenditure will increase from the preen 7.7 % of GDP in 2010 to 11 % of GDP by 2040 largely due to increased LTC expenditure. • The future level of expenditure on LTC is sensitive to assumptions about the level of service use by different levels of dependency. • Even if service use by level of dependency falls uniformly over the period by 20 %, LTC expenditure in 2050 will be as a percentage of GDP will increase by 138 % by 2050 when compared with 2005 levels.
[17]	To investigate the claim that population ageing will not have a significant impact on healthcare expenditure	• Finnish population registration system • Finnish hospital discharge register. • Finnish death register • Registers from the Finnish Social Insurance Institution • Finnish hospital benchmarking project	• Impact of ageing on healthcare expenditure • Impact of proximity to death on healthcare expenditure	• Annual healthcare expenditure calculated for each individual aged 65 or over from 1998 until end of 2002 using 2000/01 deflated prices. • Likelihood of using LTC service found using a logit/profit model based on patient characteristics. • OLS regression model used to then estimate expenditure given patient predicted to require LTC using a general to specific selection of patient characteristics. • Future LTC expenditure projects obtained by multiplying calculated age-gender specific expenditure according to survival status by future population estimates. • In addition, an additional model where the probability of using LTC was delayed for three years was also used to consider falling rates of dependency with age.	2016–2036	• LTC patients (excluding residential and home care) accounted for 55 % of total healthcare expenditure despite the proportion aged 65 or over being 7 %. • Age has an important positive and increasing effect on the probability of being a LTC user. • Females had a higher risk of needing LTC compared with males. • Home care and home services excluded due to lack of national data. • Projections based on the naïve age and gender specification showed an estimated annual LTC cost increase of 2.2 % by 2036. • Taking into account proximity to death, the expected annual increase in total LTC cost was found to be lower at 1.9 %. • The model’s projections were found to sensitive to the probability of individuals being in need of LTC. • If LTC could be delayed by 3 years it was found that costs would decrease by 12 % although part of this reduction would be met by a rise (2 %) in other non-LTC healthcare costs.
[18]	To predict the future number of patients in different home and community care categories in British Columbia	• Future population projections from “Population Extrapolation for Organization Planning with Less Error” (2007) provided by the British Columbia Ministry of Health • Wealth demographics from Statistics Canada (2008) • Quantity of non-publically funded home and community care estimated from telephone survey of all privately run facilities in British Columba (2007) • Home and community care activity data from April 2001–March 2005 by client group provided by the British Columbia Ministry of Health.	• Distribution of patients between different types of care, including assisted living environments and home care. • Distribution of privately funded care to publically funded care.	• Multi-state deterministic Markov model • Home and community care groups divided into ten categories, 8 of which represent publicly funded care. • Patients are not individually tracked through the system but rather the collective behavior of each care and age specific group is studied. • Patients move between care categories and leave the model according to the age-independent transition rates. • Movement between public and privately funded care according to projected wealth distribution of the province. • Movement between services based on historical usage of home care vs. assisted environments using fixed transition rates, and then dividing movers between public and non-public services. Transition probabilities estimated from historical data. • Population projections used to estimate no of patients arriving to the system in each period.	2002–2031	• The model predicts that whilst patient counts will continue to rise over the next 20 years they will not reach their 2002 high levels until 2015. • Without taking into account the privately funded care, the models prediction accuracy was poor as a number of clients are believed to use some mixture of both public and privately funded care. • No attempt made to marry client counts with service loads for the prediction of budget requirements. • The available of services has increased over the period and hence the six fold growth in HCC between 2002 and 2004. It is difficult to model the numbers of people who are seeking care but not receiving at the current time.
[23]	To analyse the sustainability of the UK system for provision of long-term care in the light of the changes in demography and health status among older people that are expected in the future	• OPCS survey of disability in Great Britain (1988) • Health survey of England, Bajekal M. Care homes and their residents. London: The Stationery Office; 2002 for types of formal care by age and disability • Costs of formal care Laing, Buisson. Calculating a fair price for care—a toolkit for residential and nursing care costs. London: Rowntree; 2001. and Netten A, Rees T, Harrison G. Unit costs of health and social care. PSSRU; 2001.	• Estimate of the future cost of LTC to the public purse as proportion of income tax • The potential surplus or shortfall in the number of informal carers relative to the demand for informal care.	• Multicomponent projection model based on Multistate disability model proposed by Rickayzen and Walsh [43] • The disability model generates an estimate of the number of individuals of each gender cohort split by age and severity of disease for each year of the projection period. • People are transitioned over time into different levels of disability e.g. people becoming more disabled and people dying. • Trend data on healthy life expectancy used to update transition probability according to how rates of disability may improve. • Different assumptions surrounding how these transition rates changes according to how mortality, speed of increased disability and level of disability may improve over time. • Cohots of disability are then mapped to care settings. • Estimates cost of LTC to the public purse as a percentage of income tax and the demand for informal care relative to no of care givers.	2000–2050	• Given our central assumptions, the demand for long-term care will start to increase considerably about 10 years from now, and reach a peak somewhere after 2040. • The most important increase will be in informal care, since the number of older recipients is projected to increase from 2.2 million today to 3.0 million in 2050. • In relative terms, the increase is similar in all care settings, amounting to between 30 and 50 % compared to the levels today. • The most noticeable increase is in formal home care, however, which is projected to be almost 60 % greater than the current level in 2040. Yet, since those services are relatively cheap, this item has a relatively small impact on total spending. • The increasing demand for care will influence total costs. The total costs of formal long-term care defined in this paper amount to around £ 11 billion today and will, in constant prices, increase to around £ 15 billion around 2040. • It transpires that our findings are relatively sensitive to the assumptions made concerning the trend in future disability rates in the older population. When we contrast our baseline scenario with a more pessimistic one—assuming no future health gains—we find that total costs keep on growing for longer and peak only in 2051 at a total of £ 20 billion (£ 80 billion when informal care is also considered). This translates into an implied tax rate of 1.8 %, which is considerably higher than in the baseline scenario (1.3 %). • Regarding informal care, we find that under the baseline and optimistic scenarios, there is likely to be a sufficient supply of care to meet demand provided caregiving patterns remain as they are. However, if female care-giving patterns converge to those of males, then under the baseline health improvement scenario, there would be a shortage of between 10 and 20 million hours of care per week
[24]	Predict values of the disability rate of the aged from 2006 to 2011 to estimate the future population in need of long-term care	• Historical rates of disability in Taiwan from the Ministry of the Interior and the Department for Statistics over the period 1991–2006	• The rates of disability in the Taiwanese elderly population that would require LTC services.	• Gathered data on rates of disability in the elderly population and used a Grey forecasting model to forecast future rates of disability under different assumptions about the growth in the disability rate over time. • Estimates of future rates of disability used to ascertain the size of the population in need of LTC in the future	2006–2011	• The continual increase in the disability rate of the aged leads to a dramatic increase in the growth rate of the aged demanding LTC services over the period studied. • A 1462 % increase in the rate of aged related disability (from 1991 to 2011) far exceeds the expected growth rate in the aged population.
[26]	Project long-term care service usage by enrolled veterans	• Veterans Health Administration Survey • National Long-Term Care Survey • National Nursing home Survey • National Health Interview Survey.	• Demand and cost of nursing home care and community-based long-term care Services • Persons who report receiving human or mechanical assistance to help with activities of daily living ADLs and instrumental activities of daily living.	• Used a random sample of the Medicare-eligible VA population, to standardize the ADL and IADL disability levels from the 2002 VA Survey of Enrollees	2002–2012	• The level of long-term-care use generally follows the distribution of disabilities in a population
[27]	Investigate the impact of changes in factors related to future LTC resource need	• Official National Statistics on the Provision of Long-Term Care. • Swedish National Survey on Living Conditions (ULF) • ASIM Study in Solna municipality (1984–1994) • The Swedish National Survey on Ageing and Care at Kungsholmen, Stockholm (2001) • Population projections from Statistics Sweden	• Consumption of different forms of LTC services by age, gender, marital status and disability. • The future provision of LTC services in relation to care needs • Balance of institutional and non-institutional care.	• ASIM III-model subdivides the population into several cohorts by age group, gender, marital status and degree of ill health. • For each group the number of persons in receipt of LTC for older persons according to four different levels noted. • Prevalence of ill health for each age, gender, civil status subgroup used to create a health index of four degrees (full, slight, moderate, and severe) • Forecasts generated by multiplying population projections in each subgroup by respective proportion of persons in each group receiving services in 2000 levels. • Different future scenarios surrounding ill health used to make projections. • Two-step tend extrapolation of severe ill health from survey on living conditions.	2000–2030	• The population growth in the period 2000–2015 concerns mainly the younger old and thus does not have a large effect on the care service costs. • Cost increases from 2020 onwards stem from 85+ year group, for the youngest old the costs diminish. • Over period 2000–2030 35 % increase in less than 1 h of public services in the community setting per day. • 27 % more people in instructional care • More intensive community care is less affected by projected increases in demand. • By 2030 the oldest age group 85+ will account for 60 % of all LTC expenditure from 50 % in 2000. • Proportion of married rise from 17 to 22 % given mortality is expected to fall more rapidly for men than for women. • Pessimistic future ill-health 69 % increase in cost vs 25 % increase in cost. At present 2.6 % of GDP spent on care, could rise to 3.3–4.4 % depending on future ill-health scenario.
[29]	To estimate the future level of demand for care home placements from those suffering from dementia	• Survey of 445 residents drawn randomly from 157 non-EMI nursing homes in South-East England. • Commission for Social care and Inspection • The Medical Research Council Cognitive Function and Ageing Society. • UK Census 2001	• The number of dementia cases in England and their associated care needs up to 2043.	• Results from a local survey on the incidence of dementia are combined with age and sex specific prevalence ratios and extrapolated to estimate demand for dementia beds at the starting period. • Future levels of demand are estimated by applying population projections under different assumptions surrounding the prevalence rate of dementia in care homes.	2003–2043	• Assuming 50 % of patients aged 60+ in care homes suffer from dementia, the number of dementia beds required would be around 740,000 by 2023 and over one million by 2043.
[30]	To examine the effect of different assumptions about future trends in LE on the sustainability and affordability of both the pensions and LTC system	• 2001 General Household Survey (GHS) • 2002/3, 2003/4 and 2004/5 rounds of the Family Resources Survey (FRS) • 2008 budget report (HM Treasury 2008).	• Likely future cost to the public purse • private expenditure on LTC • LTC by source of expenditure • Compare with GDP	• To project expenditure on LTC, we use two models: the CARESIM micro-simulation model and the Personal Social Services Research Unit (PSSRU) aggregate LTC finance model. The PSSRU model is cell-based: it divides the current and projected future population into a large number of sub-groups or ‘cells’. It simulates future demand for LTC and disability benefits for each of these groups, based on analysis of a sample of older people from the 2001 General Household Survey (GHS)4. Adjustments are made to the GHS analysis to include the residential care population and to reflect changes in the targeting of publicly-funded care provision since 2001 (Wittenberg et al., 2006). CARESIM simulates the incomes and assets of future cohorts of older people and their ability to contribute towards care home fees or the costs of home-based care, should such care be needed (Hancock et al., 2003). It is based on a pooled sample of older people from the 2002/3, 2003/4 and 2004/5 rounds of the Family Resources Survey (FRS) with money values updated to the base year (here 2007) 5. Together these two models can be used to project future expenditure on LTC by source of expenditure, under different funding reform options. • The PSSRU model output on the characteristics of people requiring LTC is used as input to CARESIM to adjust the FRS sample to be representative of people receiving different LTC services in the projection year. CARESIM then simulates for each type of service the ability of older people to contribute to their care costs and the source of income used to pay for care. CARESIM output is used to break down expenditure in the PSSRU model into its constituent components and funding sources, i.e. NHS, Personal Social Services, social security disability benefits and private money (Hancock et al., 2007). The projected levels of expenditure by each of these sources are compared with projected economic output, Gross Domestic Product (GDP).	2007–2032	• expenditure on pensions and associated benefits is projected to rise in future years because of the increasing numbers of pensioners – more recent projections allowing for the further policy changes described above confirm this, and show even faster growth • expenditure on LTC is projected to rise, although at a faster rate than pensions expenditure. The faster rate of growth in LTC expenditure is partly a consequence of the faster rate of growth of the oldest old group compared to the older population as a whole, as it is at the oldest ages where need for care is the greatest
[31]	How trends in disability prevalence and in inflation-adjusted per capita, per annum Medicare costs affected total projected medicare costs	• 1982, 1984, 1989, 1994, and 1999 National Long Term Care Surveys (NLTCS) -roughly 20,000 persons sampled in each of the NLTCS, of those 65+	• Implication of recent disability declines and their possible continuation for future Medicare costs	• Applied a grade of membership analysis to 27 measures of disability from the 1982 to 199 9 National Long term care surveys,. This identified 7 disability profiles for which individual scores were obtained. These were used to extrapolate future Medicare spends by assuming different trends in the level of disability across the different groups.	2004–2009	• At ages 85+ relatively more LTC and Medicaid expenditures are incurred for labor-intense maintenance and palliative care • 16 % savings
[33]	To project the impact of populating aging on total US health care cost per capita	• 1.2 million years of health care plan data from the HealthPartners database 2002–2003 • US Census Bureau population projections 2000–2050 • Medical Expenditure Panel Survey 2001	• The monthly per capita costs of LTC covered by Medicare using insurance claims data. • Per capita pharmacy costs associated with various conditions in LTC.	• Medical and pharmacy claims data aggregated into individual episodes of care which are grouped by treatment group • The total cost of each treatment group is added to their respective higher level illness or condition category. • Monthly per capita costs estimated for each gender, age band and condition category and added together to estimate annual costs per capita. • Future cost extrapolated by multiplying projections of population in each gender-age brand and multiplying by MEPS adjusted per capita costs.	2000–2050	• Per capita costs a s result of ageing will increase by 18 % from 2000 to 2035 as baby bombers and retirement and then level of as the age structure of the population stabilizes. • 80 % of the increase in per capita costs can be explained by 7 of the 22 illness categories, including: heart and vascular conditions, lung conditions and neurologic disorders. • Pharmacy costs were estimated to account for 1.5 % of all care costs. • The cost of care for males and females in the 85–89 year old group are 4.4 and 2.7 times as large as the per capita costs for the reference group of females aged 40–44.
[40]	To project the future need of long-term care due to changes in demography and health status among the oldest Chinese	• Chinese Longitudinal Healthy Longevity Survey, 1998, 2000, 2002 • United Nations World Population Prospects of China in 2008 for population projections (2010–2050) assuming medium fertility and mortality		Calculated the observed self-rated health status transition probabilities for individuals with age I and gender j. Simulated this process using a non-homogeneous Markov process to obtain the simulation transition probabilities this was done separately for each initial health status k, using five-group discriminate analysis to estimate the probability of being in each of the five health status l 2 years later, as a function of a person’s gender i and initial age j Health status transition probabilities were used to calculate the remaining years of life and remaining years of healthy life in terms of age, gender and initial health. L Long-term care expenditures can be calculated by multiplying unhealthy person-years number by the annual average expenditure of care In order to define what is healthy, we made a split between good and fair because the two groups had great differences in mortality. We used Mantel–Haenszel statistic to test mortality relative risk (RR) between two health states. Results showed that the mortality of the elderly people who rated their health fair or poor significantly increased compared to those in the good category except for women aged 85–89 (RR > 1, P-value < 0.05). People who rated their health very good and good had no significant difference in mortality risk except for women aged 85–89 and 95–99, and men aged 80–84 (RR > 1, Pvalue > 0.05).	2010-2050	• 8066 thousand persons aged 80+ need long-term care in 2010, while in 2050 this number will increase to 42,581 thousand • The care need person year number among males will increase from 23,159 in 2010 and to 115,460 in 2050, whereas the female person year number will increase from 40,401 to 208,210, and the total number for both genders will increase from 63,560 to 323,670, which implies a growth of more than 4 times during the 40 years. • If we assume that the average care expenditure is 15 US dollars (about 100 Yuan RMB) per hour in 2010, then the total care expenditure rises from around 83.52 hundred million dollars in 2010 to around 425.30 hundred million dollars in 2050 (in 2010 prices). • We have been able to show that,given our assumptions of average care cost is 15 US dol-R. Peng et al. / Health Policy 97 (2010) 259–266 265lars per hour, the care expenditure for long-term care will increase from 83.52 hundred million dollars to 425.30 hundred million dollars from 2010 to 2050. That means the total amount will grow more than 4 times over the next the 40 years, without considering inflation. The results also show that long-term care need is on the rise regardless of gender, and that the absolute number and increase rate of female care need are higher than those of male.