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The long-term impact of functional disability on hospitalization spending in Singapore
The Journal of the Economics of Ageing 14 (2019) 100193
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Full length article
The long-term impact of functional disability on hospitalization spending in Singapore
T
Cynthia Chena,b, , Jue Tao Lima, Ngee Choon Chiaa,c, Lijia Wanga, Bryan Tysingerb, Julie Zissimopoulosb,l, Ming Zhe Chonga, Zhe Wanga, Gerald Choon Huat Koha, Jian-Min Yuand,e, Kelvin Bryan Tana,f, Kee Seng Chiaa, Alex R Cooka,g, Rahul Malhotrag,h, Angelique Chang,h, Stefan Mai, Tze Pin Ngj, Woon-Puay Koha,g, Dana Goldmanm, Joanne Yoongk ⁎
a
Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore Schaeffer Center for Health Policy and Economics, University of Southern California, USA Department of Economics, National University of Singapore, Singapore d Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA e Division of Cancer Control and Population Sciences, UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA f Policy Research and Economics Office, Ministry of Health, Singapore g Health Services and Systems Research, Duke-NUS Medical School, Singapore h Centre for Ageing Research and Education, Duke-NUS Medical School, Singapore i Epidemiology & Disease Control Division, Ministry of Health, Singapore j Yong Loo Lin School of Medicine, National University of Singapore, Singapore k Center for Economic and Social Research, University of Southern California, USA l Price School of Public Policy, University of Southern California, USA m Sol Price School of Public Policy and School of Pharmacy, University of Southern California, USA b c
ARTICLE INFO
ABSTRACT
Keywords: Disability Ageing Microsimulation Hospitalization Spending
Singapore is one of the fastest-aging populations due to increased life expectancy and lowered fertility. Lifestyle changes increase the burden of chronic diseases and disability. These have important implications for social protection systems. The goal of this paper is to model future functional disability and healthcare expenditures based on current trends. To project the health, disability and hospitalization spending of future elders, we adapted the Future Elderly Model (FEM) to Singapore. The FEM is a dynamic Markov microsimulation model developed in the US. Our main source of population data was the Singapore Chinese Health Study (SCHS) consisting of 63,000 respondents followed up over three waves from 1993 to 2010. The FEM model enables us to investigate the effects of disability compounded over the lifecycle and hospitalization spending, while adjusting for competing risk of multicomorbidities. Results indicate that by 2050, 1 in 6 elders in Singapore will have at least one ADL disability and 1 in 3 elders will have at least one IADL disability, an increase from 1 in 12 elders and 1 in 5 elders respectively in 2014. The highest prevalence of functional disability will be in those aged 85 years and above. Lifetime hospitalization spending of elders aged 55 and above is US$24,400 (30.2%) higher among people with functional disability compared to those without disability. Policies that successfully tackle diabetes and promote healthy living may reduce or delay the onset of disability, leading to potential saving. In addition, further technological improvements may reduce the financial burden of disability.
Abbreviations: ADL, activities of daily living; DOS, Singapore department of statistics; FEM, future elderly model; IADL, instrumental activities of daily living; NHS, national health survey; PHASE, panel on health and ageing of Singaporean elderly; SCHS, Singapore Chinese health study; SLAS, Singapore longitudinal aging study ⁎ Corresponding author at: Tahir Foundation Building, 12 Science Drive 2, #09-01T, Singapore 117549, Singapore. E-mail address: [email protected] (C. Chen). https://doi.org/10.1016/j.jeoa.2019.02.002
Available online 27 February 2019 2212-828X/ © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).
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Introduction
accounted for health status as a predictor of disability (Ansah, 2015). They may also have utilized cell-based microsimulation approaches which lose individual-level heterogeneity in functional disability transitions (Kelly, et al., 2011). Our results suggest that disability prevalence is expected to rise in the future, and disabled individuals incur a far larger lifetime inpatient hospitalization spending compared to individuals who are not disabled. These results are put into the context of Singapore and used as a tool to aid policymaking in terms of healthcare delivery and healthcare financing.
Population aging is expected to be one of the most disruptive social transformations of the 21st century (United Nations, 2017). Globally, changes in age composition have important implications for functional disability and healthcare financing. With the world’s population aging rapidly, many elderly persons are at risk of disability, resulting in increased demand for healthcare services and higher medical spending (Broe, 2002; Edelbrock, 2003; Wu, 2013). Healthcare expenditure among the elderly is rising, with the economic burden of disability and longterm care being significant drivers (Picco, 2016; James Lubitz et al., 2003; Fried, 2001; Spillman, 2004). In Taiwan, for instance, the government reported a 7% increase in the prevalence of disabilities among older adults (from 29.6% in 1997 to 36.6% in 2010) (Ministry of the Interior, 2010). In addition, the rate of hospitalization for disabled elders was found to be 3.5 times of the general population in Taiwan during the year 2005 (Lin, 2011). In Japan, medical spending were 4 times higher in older men and 3 times more in older women with disabilities in performing self-care, compared to their respective peers with no functional limitations (Tsuji, 1999). In contrast, in the United States, the spending of medical coverage for disability declined from 1982 to 2004, due to the trend of transferring severely impaired individuals to palliative institutions (Manton et al., 2007; Manton et al., 2006). Sound policy planning for aging is critical because of the severe social risks and fiscal strain it can otherwise impose on nations (United Nations, 2017). Modelling is a critical tool to support such planning, allowing policymakers to forecast better and potentially manage the consequences of changes in population demography. They include future comorbidities, disabilities and healthcare spending while appropriately adjusting for multiple risk factors. While many studies have associated population aging with increasing chronic disease burden and functional disability with a higher incidence of mortality (Chen, 2014; Hajek and König, 2016; Moe and Hagen, 2011; Serrano-Alarcón and Perelman, 2017), few studies have accounted for population and health dynamics which include demographic change, competing risks, and background mortality (Chen, 2014; Caskie et al., 2010; Dodge, 2005; Solfrizzi, 2017). In addition, while many studies have found an association between disability and spending, few have examined the longterm hospitalization spending of disability. Retrospective studies detail how disabled elders incur substantially higher spending and utilize more healthcare resources (Spillman, 2004; Wayne et al., 2010; Goetzel, 2004; Hsu, 2013). For example, Picco et al illustrated how functional disability would cost an individual more in social care notwithstanding comorbidities (Picco, 2016; Spillman, 2004; Wayne et al., 2010; Goetzel, 2004; Hsu, 2013). A robust approach to policy analysis related to aging and disability and its associated financial burden is particularly important in Asia, where over the next 15 years, the number of elderly persons is expected to grow by 66% (United Nations, 2017). Singapore has one of the world’s highest life expectancies and has aged ahead of many societies. Singapore has been projected to take only 27 years to transition from an ‘ageing society’ in 1999 (7% seniors) to a ‘super-aged society’ (20% seniors) in 2026, beating Japan, China, Germany and the United States, which took or will take 36, 32, 76 and 86 years to make that transition respectively (East Asia Forum, 2015; Tan Teck Boon, 2015). Healthcare expenditure has already quadrupled within ten years from S$2 billion (US$1.5 billion) in 2006 to S$8.5 billion (US$6.4 billion) in 2015 (MInistry of Health, 2018), and is expected to rise by at least another S $3 billion (US$2.3 billion) in the next three to five years (Business Times Singapore, Singapore Budget, 2018). This paper reports a dynamic Markov microsimulation model with Singapore-specific disease prevalence, sociodemographic covariates and transition probabilities, which we use to forecast disability prevalence in Singapore. Our model deviates from previous work in forecasting disability prevalence as we account for individual level heterogeneity. Preexisting microsimulation models may not have
Methods Data The Singapore Chinese Health Study (SCHS) is a prospective cohort study of ethnic Chinese men and women aged 45–74 years at baseline who were followed up for a mean duration of 12 years. Inclusion criteria were either citizens or permanent residents, residing in public housing estates and belonging to either of the two major dialect groups of Chinese, namely Hokkien and Cantonese. The baseline study (n = 63,257) was conducted between 1993 and 1999, follow-up 1 (n = 52,325) was collected between 1999 and 2004 and follow-up 2 (n = 39,528) was collected between 2006 and 2010. At baseline, each participant completed an in-person interview at their home using a structured questionnaire that requested information about demographic characteristics, self-reported height and weight, smoking status, current physical activity, occupational exposure, medical history and family history of cancer. A follow-up telephone interview asked participants for an update on their tobacco and alcohol use as well as medical history. SCHS data was used to determine the transition probabilities from positive health status to a disease state. The Mediclaims dataset contains records of individual-level acute care inpatient hospitalization and day surgery expenditures before any government subsidy within both public and private hospitals. This information includes patients’ gender, ethnicity, birthdate, date of admission, date of discharge, diagnosed diseases, medical spending and insurance claims. Reported medical spending is based on total resources used and is not broken down by contributions of other payers, including Medisave, MediShield, Medifund and private insurers. The actual share of individual out-of-pocket expenditure will be lower after accounting for payments by these different payers. The SCHS was linked with hospitalization spending from Mediclaims through participants’ ID and harmonized across both data sets. The Singapore Longitudinal Aging Study (SLAS) is a smaller cohort which consists of 2,804 subjects aged 55 or above interviewed in 2004/ 2005, 2007/2008 and 2010/2011. Older adults who were citizens or permanent residents aged 55 years or above were identified by door-todoor census and invited to participate voluntarily in the study. Another cohort, the Panel on Health and Ageing of Singaporean Elderly (PHASE) consisted of 4990 elders aged 60 or above who were interviewed in 2009 and 2011/2012. All respondents consisted of citizens chosen through single-stage stratified random sampling from a national database of dwellings and were interviewed face-to-face at their residences. SLAS and PHASE were incorporated into our projections as they provide additional information on other dimensions of health and wellbeing not covered by the SCHS, such as psychosocial metrics, questions on functional disability that can be used to construct indices of activities of daily living (ADL) and instrumental activities of daily living (IADL) disability prevalence among the elderly, quality of life measures and other diseases. The Singapore National Health Survey (NHS) provides information on the prevalence of major non-communicable diseases such as diabetes mellitus, hypertension and related risk factors like obesity and smoking in four periods from 1992 to 2010 (MInistry of Health, 1999). It is conducted once every six years on a cross-section of individuals aged 18–74 years. 2
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Microsimulation model
Health transition and spending model
The Future Elderly Model (FEM) microsimulation model includes several components and the structure of the model is almost entirely based on population dynamics. We adapted the FEM ─ a dynamic Markov microsimulation model that predicts future health status and hospitalization spending for Singapore Chinese, which constitute the majority of residents at 74.3% (Malays were 14%, Indians were 9%, Others were 3%) (Department of Statistics, 2012). It was first developed by RAND Corporation to support decision-making related to Medicare and Medicaid, the public health insurance and welfare programs for the elderly/needy in the United States. Since then, the FEM has been used for studies exploring health and social issues in ageing societies in several countries including the United States, European Union and, Japan (Chen, 2016; Joyce, 2005; Michaud, 2011). Its ongoing development is supported by the National Institute on Aging through the USC Roybal Center for Health Policy Simulation. Key features of this microsimulation model are: (a) projecting individual level cohorts from observed data with diverse characteristics and behaviours; (b) projecting those individuals over time to estimate variation in outcomes within populations; and (c) updating populations which enter the microsimulation to reflect future socio-demographic characteristics and health status. We further developed this model to project IADL and ADL disability prevalence and the acute care inpatient hospitalization spending disparity between elders with and without ADL disability in Singapore. The model tracks elders characterized by socioeconomic drivers and health states who transition from one period to another (Fig. 1). In each period, new elders enter the simulation at the default starting age, which we set as 55–60 years old with their composition adjusted using sociodemographic and disease trends in NHS. Population projections were based on the demographic epidemiological model of Singapore (Phan, 2014). In each simulation cycle, the population moves forward in time and we can observe their health status as well as their healthcare utilization and spending. It loses some elders due to mortality and gains some because there is renewal, i.e., a new cohort of individuals aged 55–60 years old enters the model as mentioned above. We assume that there is no gain due to migration in the elderly. The model then integrates data from the SCHS with hospitalization spending data from the Ministry of Health to project future healthcare spending.
To project health transitions, a discrete piecewise linear hazard model was estimated from the SCHS based on six-year transitions. The hazard of transitioning to an absorbing disease state (hypertension, diabetes, heart disease, stroke) and dying depends on risk factors (gender, highest attained education, obesity, smoking status); other conditions if medically warranted; functional status; and age. We used probit regression to estimate the probability of transition to each health condition, controlling for demographic variables and comorbidities at the previous period. We treated all diseases as absorbing in reflection of the question asked: “Have you ever been told by a doctor…”. The unit of observation for modelling transition probabilities was the interviewpair. All independent variables were measured with a six-year lag in the SCHS, and represent the respondent’s characteristics from 1993 to 2010 (National University of Singapore, 2018). As diseases were treated as absorbing state, we assumed that elders with the chronic condition (e.g. diabetes) in the previous wave will continue to have diabetes in the current wave. As such, transition probabilities were estimated on elders who did not suffer from a specific condition at the previous survey wave. Covariates such as BMI were log transformed to adjust for high peaks in BMI measurements, while age was splined into the 55–70, 70–84 and above 85 groups to account for the differential effect that the age groups have on comorbidities. Education was modelled according to the Singaporean schooling system, with low attainment defined as having primary education or less (less than 6 years of education), middle attainment as having secondary school education or technical education (6–12 years of education) and high attainment as having at least a diploma or college education (at least 13 years of education). Disability model The disability model is a cross-sectional model which uses current chronic diseases and socio-demographic covariates to model the burden of disability using data from SLAS and PHASE. We defined disability as having any ADL disability such as washing, dressing, feeding, toileting, mobility and transferring (Ministry of Health, 2018) or having any IADL disability such as taking transportation, shopping, managing money, making phone calls, doing household chores and meal preparation. The
Fig. 1. FEM simulation.
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The proportion of elders having at least one ADL disabilities was 8.6% in 2014 and this was projected to double to 14.7% in 2050. The steepest rise in ADL disability was among the oldest age group of elders aged 85 and above, among whom prevalence was projected to increase from 33.5% in 2014 to 40.4% in 2050. There was little projected increase in ADL disability from 2014 to 2050 in younger age groups, with disability prevalence being around 4.5% for age 55 to 64, 7% for age 65–74 and 13% for age 75–84 years (Fig. 4). In 2014, prevalence of any IADL disabilities was 22.9%; this was projected to increase to 31.9% in 2050. There was no significant projected increase in disability among the age groups of 55–64, 65–74, 75–84 and above 85 years old, with a prevalence of 12%, 21%, 41% and 65%, respectively in 2050 (Fig. 5). The decline in prevalence of ADL and IADL after year 2026 among elders aged 75–84 was due better education of the baby boomer generation compared to earlier cohorts. Thus, they were less likely to develop chronic diseases and disability. Although the disability prevalence within each individual age group did not reflect increasing trends, the increase in overall disability and chronic diseases prevalence in Fig. 3 were driven mainly due to an aging population. Lifetime hospitalization spending is higher among elders with disability. We projected the elderly lifetime hospitalization spending for elders aged 55 and above with ADL disability was US$24,363 (30.3%) higher compared to those without disability (US$104,883 for those with disability and US$80,520 for elders without disability), assuming no medical inflation. Next, we assumed two sensitivity scenarios: (a) an average annualized inflation rate of 3.0% based on CPI for medical treatment in the last five years (Department of Statistics, 2018) with 2% discounting and (b) a high inflation rate of 4.5% with 2% discounting. We projected a difference in hospitalization spending of US$29,842 and US$42,343 respectively, comparing elders with and without ADL disability (Fig. 6). The increased hospitalization spending for ADL disability was due to the underlying manifestation of chronic diseases such as stroke, diabetes and heart disease. We were unable to model the lifetime hospitalization spending from IADL disability as our spending model focuses on physical disability from the underlying manifestation of chronic diseases. Females were projected to have a higher prevalence of both ADL and IADL disability than males. For ADL disability, females had 2.0% (9.6% for females and 7.6% for males) higher prevalence than males in 2014, a gap that was projected to increase to 3.0% (16.1% for females and 13.1% for males) in 2050. Our projection of IADL disability prevalence by gender shows similar disparities. In 2014, we observed prevalence rates of 20.8% and 25.1% for males and females respectively; these were projected to increase to 30.3% and 33.3% in 2050.
former measures an individual’s physical ability to perform basic tasks whereas the latter are higher order tasks which measure an individual’s engagement and management of resources. We projected future functional disability through two models with ADL and IADL disability as the dependent variable using probit regression. The covariates included were age, gender, education attainment, BMI, marital status and chronic diseases. Spending model We linked SCHS data with elders’ demographic information, current reported health status, risk factors and functional status with annualized hospitalization spending based on Mediclaims data, and predicted healthcare spending using ordinary least squares (OLS) regression. Nominal expenditure (in Singapore dollars) was converted to real expenditure using the medical component of the consumer price index (with base year of 2014) from Singapore Department of Statistics (DOS). The nominal expenditure was the total healthcare expenditure before government subsidies. In addition, the regression adjusted for gender, educational attainment, obesity, smoking status and self-reported conditions. A robustness check was conducted using a two-part model, which first predicts who will likely incur spending, and subsequently predicts the spending in a regression conditional on the earlier likelihood. Given that the estimates of the models were similar, we chose to run OLS on the grounds of parsimony. We then apply the FEM on the initial cohort of individuals, whom were born before 1953, to estimate the longitudinal inpatient hospitalization spending due to disability conditional on other comorbidities. We report the lifetime hospitalization spending of elders by tracking our initial cohort of individuals incurs from 2014 to 2060 using the model above. All spending is expressed in 2014 US Dollars. Results Overall, Singapore is projected to be a top-heavy society with increasing numbers of ADL and IADL disability, especially in the oldest elders, aged 85 years and above. Projected IADL disabilities were more prevalent than ADL disabilities from 2020 to 2050. Both ADL and IADL disabilities were more prevalent in females than males. This difference is accentuated in the oldest-old group, where the projected prevalence of functional disability was much higher in females than males (Figs. 2a and 2b). We projected an increasing prevalence in chronic disease burden from 2014 to 2050. Heart disease prevalence increased from 9 to 11%, diabetes prevalence increased from 21% to 28%, and stroke prevalence increased from 5% to 7%. ADL disability prevalence was projected to increase from 9% to 15% and IADL disability prevalence was projected to increase from 23% and 32% respectively (Fig. 3).
Discussion We estimate that by 2050, 1 in 6 elders in Singapore will have at
Fig. 2a. ADL disability breakdowns by gender and year. 4
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Fig. 2b. IADL disability breakdowns by gender and year. ADL: Activities of daily living. IADL: Instrumental activities of daily living.
Fig. 3. Projections of disease prevalence in general population aged more than 55 years old.
least one ADL disability and 1 in 3 elders will have at least one IADL disability, an increase from 1 in 12 elders with ADL disability and 1 in 5 elders with IADL disability in 2014. The projected prevalence of ADL and IADL disability by 2050 among the oldest-old are 46% and 68% respectively, an increase in disability prevalence due to Singapore’s aging demography and related comorbidities. These projected trends are consistent with other studies that have found functional disability to be associated with comorbidities (Lin et al., 2016; Freedman, 2008; Yokota, 2016), and an increased prevalence of disability in the oldestold. They also utilize more healthcare resources such as ambulatory healthcare services, outpatient, emergency and inpatient services and have longer inpatient stays (Wu, 2013; Nie, 2010; Vilpert, 2013). IADL have also been found to be more prevalent than ADL. For example, in China, IADL prevalence was consistently higher than ADL prevalence from 1998 to 2008. The prevalence of having any IADL disability was 30.1% while that of having any ADL disability was 14.9% in 2008 (Feng, 2013). In Spain, the prevalence for having any IADL disability was 53.5% and any ADL disability was 34.6% (Millan-Calenti, 2010). In the U.S, the percentage of elders aged 75 and above with any IADL disability was 19.2% versus any ADL disability was 10.0%. In addition,
people in each age group were approximately twice as likely to require help with IADLs as with ADLs (Services, 2009). The overall findings echo existing international microsimulation work from Singapore, Japan and Australia that also predict sharp increases in disability prevalence driven by population aging (Ansah, 2015; Nepal, 2011; Thompson, 2014; Davidson et al., 2011). While women are having longer life expectancy compared to men, they continue to experience inferior health outcomes. This increased longevity of women has significant implications for women living alone as they have outlived their spouses, with potentially less resources and support. Frailty, falls, fracture and disability are likely to be more prevalent among older women (Owens, 2008). This is further compounded by the fact that women are physiologically more likely to suffer from chronic conditions. The probability of being diagnosed with breast cancer, the most common cancer in women, also increases by 40% in the elderly. Moreover, the osteoporosis is more likely to develop in women after menopause (Dunlop et al., 1997). These result in women facing a diminished quality of life as compared to men. It is a general phenomenon that ADL is more prevalent in females than in males. In the U.S., the total risk of incident disability rates was higher 5
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Fig. 4. Prevalence of one or more ADL disability in elderly by age.
Fig. 5. Prevalence of one or more IADL disability in elderly by age.
for females compared to males in the following activities: walking, bathing, transferring, dressing, toileting and feeding disabilities (Ho, 2002). In Japan, a comparative study of ADL dependence in residential care home and community-dwelling elderly showed that the female gender was statistically significant predictor for ADL dependence with an odds ratio (OR) = 2.55 (p-value = 0.004) for females compared to males in residential care and OR = 1.67 (p-value = 0.001) in the community (Oman et al., 1999). The higher reported prevalence of ADL in females can be attributed to physical risk factors more prevalent in women (chronic conditions, less exercise, less outside activity etc.) (Murtagh and Hubert, 2004), as well as psychological factors: women
may be more likely to report their disability as compared to men (overall 52% vs 37% (in the U.S.), p-value less than 0.001) (World Health Organisation, 2011). It benefits the society both economically and socially, when older women are functionally independent, productive and healthy – as these disabilities create an additional burden of expenses for assistive devices, hospitalization and long-term care (Khalik, 2018). It is also possible that women and men age differently, and may have different health care needs and utilization, making a gendered perspective towards aging appropriate for future research. The Singapore government has been relatively successful at controlling healthcare spending. As part of their commitment to universal 6
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Fig. 6. Lifetime hospitalization spending of elders aged 55 and above, by ADL disability. Lifetime hospitalization spending is computed by tracking the hospitalization spending the initial cohort of individuals incur from 2014 to 2060.
current work however provides the first concrete step towards a policy discussion on the fiscal sustainability of healthcare financing in Singapore and provides a novel contribution to aging policy and research in the broader region. In our study, we also projected a 30.2% higher elderly lifetime hospitalization spending for individuals with disabilities. Our spending projection is an underestimate compared to other studies as long-term care spending were not considered. Coupled with the expected increase in ADL/IADL disability prevalence in elderly and life expectancy, the need for long-term healthcare is very likely to increase steeply in the future. In light of the increasing demand for healthcare, Singapore has plans to extend current disability insurance from six years to lifetime coverage to protect individuals with severe disability against the financial risk of ADL disabilities. There are proposals for the implementation of universal disability insurance (“CareShield Life”) (Williams and Mohammed, 2009) and subsidies for hired help to facilitate elderly caregiving (Sharma, 2012).
health coverage while being sensitive to the changing demand and supply of healthcare, the government has recently introduced lifetime catastrophic insurance coverage under Medishield as well as an expanded long-term care insurance scheme, while also introducing new initiatives to address concerns with over-consumption, over-servicing and over-charging. These include the elimination of private insurance full-rider policies, which previously covered individual co-payments (Ministry of Health, 2018), in order to reduce moral hazard as well as potential supplier-induced demand, which may in turn curb medical inflation. In addition to the review of private insurance plans, the Ministry of Health has also published fee benchmarks for professional fees to guide private sector healthcare providers in charging appropriately and is working to educate patients on the use of these benchmarks to make better informed decisions (Ministry of Health, 2018). The implications are especially important for policymakers both in Singapore and globally. It reinforces the need to plan for the future burden due to aging, which is the strongest driver of disability and comorbidities. Healthcare policies that may alter the trajectory of disability and comorbidities are crucial. In Singapore, the government has shifted from secondary to primary prevention, focusing on health promotion and health programs. Concerted screening of chronic conditions like hypertension within clinical institutions, progressive taxation to restrict smoking uptake and purchase (National Environment Agency, 2018), prohibition of smoking in designated public areas (Ministry of Health, 2018), as well as a recent policy on combating diabetes have been enacted in hopes of preventing or delay the onset of chronic conditions highly associated with disability (Yu, 2015). Technological improvements such as assistive devices may reduce self-reported prevalence and burden, by allowing elders who would previously experience disabilities to lead normal lives (Manton et al., 2007). Finally, factors such as higher education, social support networks and engagement in leisure time activities can promote the maintenance of function (Liao, 2015) and alleviate mortality for disabled elders (CalderónLarrañaga, 2018). This suggests that policies that address social disadvantage and isolation directly, or target more socially-vulnerable groups also have a role to play (Ministry of Manpower, 2018). The policy scenarios explored above, as well as other potential longer-term changes in financing and service delivery models, will be further investigated with our Singapore Future Elderly Model in future research, as more data on specific financing mechanisms become available. Our
Limitations Modeling in this complex environment necessarily reflects only best available information, and is subject to limitations. There are several limitations regarding our projections of functional disability and hospitalization spending incurred by these groups. In 2010–2015, Singapore has an ethnic composition of around 74% Chinese, 14% Malays, 9% Indians and 3% of other races (Department of Statistics, 2012). The SCHS contains data only for the Chinese, thus medical spending for ethnic minorities such as the Malays and Indians are not included. As a result, our disability projections may be an underestimate as these minority groups have been shown to bear a greater chronic disease burden (Lee, 2009; Venketasubramanian, 2005; Yassin et al., 2002; Loyalka, 2014). The model also assumes that the current health care delivery model continues into the future. In addition, current treatments will also continue and FEM does not take into account technological innovations or care transformations. It also does not account for improvements in future health states due to screening, which may shift the demand to primary care and induce cost-savings in inpatient hospitalization spending. We did not model migration as current migration policy emphasizes on temporary rather than long-term migration. We were 7
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also unable to model shorter disease dynamics as our transitional probabilities were estimated based on the survey with six-year median follow-up. Due to small numbers, we were also unable to model severity of disability after adjusting for individual heterogeneity. Studies also have documented the large indirect burden of disability on productivity loss in work, as well as changes in standard of living due to spending required to alleviate disability itself (WilkinsonMeyers, 2010). Our analysis omits these, and hence, our spending estimates are likely to be an underestimate of the true burden of disability on the Singaporean Chinese elderly population as we have only considered hospitalization spending projections in the FEM simulations. Further work on spending projections should account for these latent dynamics which underlie the burden of disability. In addition, we aim to evaluate the long-term impact of health policies when preliminary evidence is available. Regular future updates to the model and subsequent policy reviews are an important part of ensuring the continued value and relevance of the model and its application.
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Declines in late-life disability: the role of early-and mid-life factors. Soc. Sci. Med. 66 (7), 1588–1602. Fried, T.R., et al., 2001. Functional disability and health care expenditures for older persons. Arch. Intern. Med. 161 (21), 2602–2607. Goetzel, R.Z., et al., 2004. Health, absence, disability, and presenteeism cost estimates of certain physical and mental health conditions affecting US employers. J. Occup. Environ. Med. 46 (4), 398–412. Hajek, A., König, H.-H., 2016. Longitudinal predictors of functional impairment in older adults in Europe–evidence from the survey of health, ageing and retirement in Europe. PLoS One 11 (1), e0146967. Ho, H.K., et al., 2002. Factors associated with ADL dependence: A comparative study of residential care home and community-dwelling elderly in Japan. 2(2), p. 80–86. Hsu, H.C., 2013. Effects of physical function trajectories on later long-term care utilization among the Taiwanese elderly. Geriatrics Gerontol. Int. 13 (3), 751–758. James Lubitz, L.C., Kramarow, Ellen, Lentzner, Harold, 2003. Life expectancy health and health care spending among the elderly. N. Engl. J. Med. Joyce, G.F., et al., 2005. The lifetime burden of chronic disease among the elderly. Health Aff (Millwood), 24 Suppl. 2: p. W5R18–29. Kelly, S., et al., 2011. Modelling the cost of ill health in Health & Wealth MOD (Version II): Lost labour force participation, income and taxation, and the impact of disease prevention. vol. 4. 2011. pp. 32–36. Khalik, S., 2018. Parliament: Patients who buy new riders for Integrated Shield Plans will have to pay 5 per cent of hospital bills, in The Straits Times. Lee, R., et al., 2009. Impact of race on morbidity and mortality in patients with congestive heart failure: a study of the multiracial population in Singapore. Int. J. Cardiol. 134 (3), 422–425. Liao, C.-C., et al., 2015. Social support and mortality among the aged people with major diseases or ADL disabilities in Taiwan: a national study. Arch. Gerontol. Geriatr. 60 (2), 317–321. Lin, L.-P., et al., 2011. Disability and hospital care expenses among national health insurance beneficiaries: Analyses of population-based data in Taiwan. Res. Dev. Disabil. 32 (5), 1589–1595. Lin, S.-F., Beck, A.N., Finch, B.K., 2016. The Dynamic contribution of chronic conditions to temporal trends in disability among US adults. Disability Health J. 9 (2), 332–340. Loyalka, P., et al., 2014. The cost of disability in China. Demography 51 (1), 97–118. Manton, K.G., Gu, X., Lamb, V.L., 2006. Change in chronic disability from 1982 to 2004/ 2005 as measured by long-term changes in function and health in the US elderly population. Proc. Natl. Acad. Sci. 103 (48), 18374–18379. Manton, K.G., Lamb, V.L., Gu, X., 2007. Medicare cost effects of recent US disability trends in the elderly: future implications. J. Aging Health 19 (3), 359–381. Michaud, P.C., et al., 2011. Differences in health between Americans and Western Europeans: effects on longevity and public finance. Soc. Sci. Med. 73 (2), 254–263. Millan-Calenti, J.C., et al., 2010. Prevalence of functional disability in activities of daily living (ADL), instrumental activities of daily living (IADL) and associated factors, as
Conclusion This is the first study in Singapore that simultaneously models three dimensions (i.e. economic, health and demographic) to provides a comprehensive understanding of disability and hospitalization spending. The FEM’s approach allows for this multi-dimensional characterization of health status using a life course dynamic microsimulation model to project the elderly disability prevalence and lifetime hospitalization spending. To the best of the authors’ knowledge, no lifetime hospitalization spending projections for disabled elders has been done in Asia, and this provides both a foundation for policies to improve the fiscal sustainability of healthcare financing in Singapore and a novel contribution to aging policy and research in the broader region. Acknowledgements We thank Kenwin Maung, Sucitro Sidharta and Chia Jia Hui for the helpful discussions and statistical support that have contributed to this work. Funding The research was supported by the Singapore Ministry of Health’s National Medical Research Council (HSRG-0077/2017) and the U.S. National Institute On Aging of the National Institutes of Health under Award Numbers P30AG024968, R03AG054120 and R01AG055401. The Singapore Chinese Health Study was supported by the U.S. NIH R01CA144034 and UM1CA182876. Koh WP was supported by the National Medical Research Council, Singapore (NMRC/CSA/0055/ 2013). Waves 1 and 2 of the Panel on Health and Ageing among Singaporean Elderly (PHASE) were funded or supported by the following sources: Ministry of Social and Family Development, Singapore; and Singapore Ministry of Health’s National Medical Research Council under its Singapore Translational Research Investigator Award “Establishing a Practical and Theoretical Foundation for Comprehensive and Integrated Community, Policy and Academic Efforts to Improve Dementia Care in Singapore” (NMRC-STAR-00052009). The funding sources had no role in the design and conduct of the study; collection, management, analysis, or interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.jeoa.2019.02.002. 8
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C. Chen, et al. predictors of morbidity and mortality. Arch. Gerontol. Geriatr. 50 (3), 306–310. Ministry of Finance, 2018. FY 2018 Budget Statement. MInistry of Health, National Health Survey 1998. 1999. Ministry of Health, Fee Benchmarks for Private Sector Surgeon Fees 2018. Ministry of Health, FEE Benchmarks Advisory Committee Report 2018. Ministry of Health, Diabetes: The War Continues. 2018. Ministry of Health, About Eldershield. 2018. MInistry of Health, Healthcare Financing. 2018. Ministry of Manpower, 2018. Levy concession for a foreign domestic worker. Ministry of the Interior, Statistical Yearbook of the Interior, 2010, 04-18. The disabled population by age and grade. Republic of China: Department of statistics {MOI, 2010 #44}. 2010. Moe, J.O., Hagen, T.P., 2011. Trends and variation in mild disability and functional limitations among older adults in Norway, 1986–2008. Eur. J. Ageing 8 (1), 49–61. Murtagh, K.N., Hubert, H.B., 2004. Gender differences in physical disability among an elderly cohort. Am. J. Public Health 94 (8), 1406–1411. National Environment Agency, Smoking Prohibition Overview. 2018. National University of Singapore, Introduction to SCHS. 2018. Nepal, B., et al., 2011. Projecting the need for formal and informal aged care in Australia: A dynamic microsimulation approach. National Centre for Social and Economic Modelling (NATSEM) Working Paper 11, 2011. 7. Nie, J.X., et al., 2010. A population-based cohort study of ambulatory care service utilization among older adults. J. Eval. Clin. Pract. 16 (4), 825–831. Oman, D., Reed, D., Ferrara, A., 1999. Do elderly women have more physical disability than men do? Am. J. Epidemiol. 150 (8), 834–842. Owens, G.M., 2008. Gender differences in health care expenditures, resource utilization, and quality of care. J. Manage Care Pharm. 14 (Suppl. 3), 2–6. Phan, T.P., et al., 2014. Forecasting the burden of type 2 diabetes in Singapore using a demographic epidemiological model of Singapore. BMJ Open Diabetes Res. Care 2 (1), e000012. Picco, L., et al., 2016. Economic burden of multimorbidity among older adults: impact on healthcare and societal costs. BMC Health Services Res. 16 (1), 173. Serrano-Alarcón, M., Perelman, J., 2017. Ageing under unequal circumstances: a crosssectional analysis of the gender and socioeconomic patterning of functional limitations among the Southern European elderly. Int. J. Equity Health 16 (1), 175. Services, D.o.H.a.H. QuickStats: Percentage of Adults with Activity Limitations, by Age Group and Type of Limitation — National Health Interview Survey, United States, 2008*. 2009 [cited 2018 10 Dec]; Available from: https://www.cdc.gov/mmwr/ preview/mmwrhtml/mm5848a6.htm.
Sharma, V.K., et al., 2012. Stroke risk factors and outcomes among various Asian ethnic groups in Singapore. J. Stroke Cerebrovasc. Diseas. 21 (4), 299–304. Solfrizzi, V., et al., 2017. Reversible cognitive frailty, dementia, and all-cause mortality. The Italian Longitudinal Study on Aging. J. Am. Med. Direct. Assoc. 18 (1) p. 89. e1–89. e8. Spillman, B.C., 2004. Changes in elderly disability rates and the implications for health care utilization and cost. The Milbank Quarterly 82 (1), 157–194. Tan Teck Boon, 2015. A super-aged Singapore: Policy implications for a Smart Nation 2015 [cited 2018 21 Jan]; Available from: https://www.todayonline.com/singapore/ super-aged-singapore-policy-implications-smart-nation. Thompson, J., et al., 2014. Projecting the number of older singaporeans with activity of daily living limitations requiring human assistance through 2030. Ann. Acad. Med. Singapore 43, 51–56. Tsuji, I., et al., 1999. Medical cost for disability: a longitudinal observation of national health insurance beneficiaries in Japan. J. Am. Geriatr. Soc. 47 (4), 470–476. United Nations, 2017. World Population Ageing 2017 Report. Venketasubramanian, N., et al., 2005. Prevalence of stroke among Chinese, Malay, and Indian Singaporeans: a community-based tri-racial cross-sectional survey. Stroke 36 (3), 551–556. Vilpert, S., et al., 2013. Emergency department use by oldest-old patients from 2005 to 2010 in a Swiss university hospital. BMC Health Services Res. 13 (1), 344. Wayne, L., Anderson, P., Armour, Brian S., Finkelstein, Eric A., Wiener, Joshua M., 2010. Estimates of state-level health-care expenditures associated with disability. Publ. Health Rep. 125 (1), 44–51. Wilkinson-Meyers, L., et al., 2010. Estimating the additional cost of disability: beyond budget standards. Soc. Sci. Med. 71 (10), 1882–1889. Williams, D.R., Mohammed, S.A., 2009. Discrimination and racial disparities in health: evidence and needed research. J. Behav. Med. 32 (1), 20–47. World Health Organisation, Global Health and Aging. 2011, National Institute on Aging, National Institutes of Health. Wu, C.-Y., et al., 2013. The association between functional disability and acute care utilization among the elderly in Taiwan. Arch. Gerontol. Geriatr. 57 (2), 177–183. Yassin, A.S., Beckles, G.L., Messonnier, M.L., 2002. Disability and its economic impact among adults with diabetes. J. Occup. Environ. Med. 44 (2), 136–142. Yokota, R., et al., 2016. Contribution of chronic conditions to the disability burden across smoking categories in middle-aged adults, Belgium. PLoS One 11 (4) e0153726e153726. Yu, H.-W., et al., 2015. Disability trajectories and associated disablement process factors among older adults in Taiwan. Arch. Gerontol. Geriatr. 60 (2), 272–280.
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Full length article
The long-term impact of functional disability on hospitalization spending in Singapore
T
Cynthia Chena,b, , Jue Tao Lima, Ngee Choon Chiaa,c, Lijia Wanga, Bryan Tysingerb, Julie Zissimopoulosb,l, Ming Zhe Chonga, Zhe Wanga, Gerald Choon Huat Koha, Jian-Min Yuand,e, Kelvin Bryan Tana,f, Kee Seng Chiaa, Alex R Cooka,g, Rahul Malhotrag,h, Angelique Chang,h, Stefan Mai, Tze Pin Ngj, Woon-Puay Koha,g, Dana Goldmanm, Joanne Yoongk ⁎
a
Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore Schaeffer Center for Health Policy and Economics, University of Southern California, USA Department of Economics, National University of Singapore, Singapore d Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA e Division of Cancer Control and Population Sciences, UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA f Policy Research and Economics Office, Ministry of Health, Singapore g Health Services and Systems Research, Duke-NUS Medical School, Singapore h Centre for Ageing Research and Education, Duke-NUS Medical School, Singapore i Epidemiology & Disease Control Division, Ministry of Health, Singapore j Yong Loo Lin School of Medicine, National University of Singapore, Singapore k Center for Economic and Social Research, University of Southern California, USA l Price School of Public Policy, University of Southern California, USA m Sol Price School of Public Policy and School of Pharmacy, University of Southern California, USA b c
ARTICLE INFO
ABSTRACT
Keywords: Disability Ageing Microsimulation Hospitalization Spending
Singapore is one of the fastest-aging populations due to increased life expectancy and lowered fertility. Lifestyle changes increase the burden of chronic diseases and disability. These have important implications for social protection systems. The goal of this paper is to model future functional disability and healthcare expenditures based on current trends. To project the health, disability and hospitalization spending of future elders, we adapted the Future Elderly Model (FEM) to Singapore. The FEM is a dynamic Markov microsimulation model developed in the US. Our main source of population data was the Singapore Chinese Health Study (SCHS) consisting of 63,000 respondents followed up over three waves from 1993 to 2010. The FEM model enables us to investigate the effects of disability compounded over the lifecycle and hospitalization spending, while adjusting for competing risk of multicomorbidities. Results indicate that by 2050, 1 in 6 elders in Singapore will have at least one ADL disability and 1 in 3 elders will have at least one IADL disability, an increase from 1 in 12 elders and 1 in 5 elders respectively in 2014. The highest prevalence of functional disability will be in those aged 85 years and above. Lifetime hospitalization spending of elders aged 55 and above is US$24,400 (30.2%) higher among people with functional disability compared to those without disability. Policies that successfully tackle diabetes and promote healthy living may reduce or delay the onset of disability, leading to potential saving. In addition, further technological improvements may reduce the financial burden of disability.
Abbreviations: ADL, activities of daily living; DOS, Singapore department of statistics; FEM, future elderly model; IADL, instrumental activities of daily living; NHS, national health survey; PHASE, panel on health and ageing of Singaporean elderly; SCHS, Singapore Chinese health study; SLAS, Singapore longitudinal aging study ⁎ Corresponding author at: Tahir Foundation Building, 12 Science Drive 2, #09-01T, Singapore 117549, Singapore. E-mail address: [email protected] (C. Chen). https://doi.org/10.1016/j.jeoa.2019.02.002
Available online 27 February 2019 2212-828X/ © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).
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Introduction
accounted for health status as a predictor of disability (Ansah, 2015). They may also have utilized cell-based microsimulation approaches which lose individual-level heterogeneity in functional disability transitions (Kelly, et al., 2011). Our results suggest that disability prevalence is expected to rise in the future, and disabled individuals incur a far larger lifetime inpatient hospitalization spending compared to individuals who are not disabled. These results are put into the context of Singapore and used as a tool to aid policymaking in terms of healthcare delivery and healthcare financing.
Population aging is expected to be one of the most disruptive social transformations of the 21st century (United Nations, 2017). Globally, changes in age composition have important implications for functional disability and healthcare financing. With the world’s population aging rapidly, many elderly persons are at risk of disability, resulting in increased demand for healthcare services and higher medical spending (Broe, 2002; Edelbrock, 2003; Wu, 2013). Healthcare expenditure among the elderly is rising, with the economic burden of disability and longterm care being significant drivers (Picco, 2016; James Lubitz et al., 2003; Fried, 2001; Spillman, 2004). In Taiwan, for instance, the government reported a 7% increase in the prevalence of disabilities among older adults (from 29.6% in 1997 to 36.6% in 2010) (Ministry of the Interior, 2010). In addition, the rate of hospitalization for disabled elders was found to be 3.5 times of the general population in Taiwan during the year 2005 (Lin, 2011). In Japan, medical spending were 4 times higher in older men and 3 times more in older women with disabilities in performing self-care, compared to their respective peers with no functional limitations (Tsuji, 1999). In contrast, in the United States, the spending of medical coverage for disability declined from 1982 to 2004, due to the trend of transferring severely impaired individuals to palliative institutions (Manton et al., 2007; Manton et al., 2006). Sound policy planning for aging is critical because of the severe social risks and fiscal strain it can otherwise impose on nations (United Nations, 2017). Modelling is a critical tool to support such planning, allowing policymakers to forecast better and potentially manage the consequences of changes in population demography. They include future comorbidities, disabilities and healthcare spending while appropriately adjusting for multiple risk factors. While many studies have associated population aging with increasing chronic disease burden and functional disability with a higher incidence of mortality (Chen, 2014; Hajek and König, 2016; Moe and Hagen, 2011; Serrano-Alarcón and Perelman, 2017), few studies have accounted for population and health dynamics which include demographic change, competing risks, and background mortality (Chen, 2014; Caskie et al., 2010; Dodge, 2005; Solfrizzi, 2017). In addition, while many studies have found an association between disability and spending, few have examined the longterm hospitalization spending of disability. Retrospective studies detail how disabled elders incur substantially higher spending and utilize more healthcare resources (Spillman, 2004; Wayne et al., 2010; Goetzel, 2004; Hsu, 2013). For example, Picco et al illustrated how functional disability would cost an individual more in social care notwithstanding comorbidities (Picco, 2016; Spillman, 2004; Wayne et al., 2010; Goetzel, 2004; Hsu, 2013). A robust approach to policy analysis related to aging and disability and its associated financial burden is particularly important in Asia, where over the next 15 years, the number of elderly persons is expected to grow by 66% (United Nations, 2017). Singapore has one of the world’s highest life expectancies and has aged ahead of many societies. Singapore has been projected to take only 27 years to transition from an ‘ageing society’ in 1999 (7% seniors) to a ‘super-aged society’ (20% seniors) in 2026, beating Japan, China, Germany and the United States, which took or will take 36, 32, 76 and 86 years to make that transition respectively (East Asia Forum, 2015; Tan Teck Boon, 2015). Healthcare expenditure has already quadrupled within ten years from S$2 billion (US$1.5 billion) in 2006 to S$8.5 billion (US$6.4 billion) in 2015 (MInistry of Health, 2018), and is expected to rise by at least another S $3 billion (US$2.3 billion) in the next three to five years (Business Times Singapore, Singapore Budget, 2018). This paper reports a dynamic Markov microsimulation model with Singapore-specific disease prevalence, sociodemographic covariates and transition probabilities, which we use to forecast disability prevalence in Singapore. Our model deviates from previous work in forecasting disability prevalence as we account for individual level heterogeneity. Preexisting microsimulation models may not have
Methods Data The Singapore Chinese Health Study (SCHS) is a prospective cohort study of ethnic Chinese men and women aged 45–74 years at baseline who were followed up for a mean duration of 12 years. Inclusion criteria were either citizens or permanent residents, residing in public housing estates and belonging to either of the two major dialect groups of Chinese, namely Hokkien and Cantonese. The baseline study (n = 63,257) was conducted between 1993 and 1999, follow-up 1 (n = 52,325) was collected between 1999 and 2004 and follow-up 2 (n = 39,528) was collected between 2006 and 2010. At baseline, each participant completed an in-person interview at their home using a structured questionnaire that requested information about demographic characteristics, self-reported height and weight, smoking status, current physical activity, occupational exposure, medical history and family history of cancer. A follow-up telephone interview asked participants for an update on their tobacco and alcohol use as well as medical history. SCHS data was used to determine the transition probabilities from positive health status to a disease state. The Mediclaims dataset contains records of individual-level acute care inpatient hospitalization and day surgery expenditures before any government subsidy within both public and private hospitals. This information includes patients’ gender, ethnicity, birthdate, date of admission, date of discharge, diagnosed diseases, medical spending and insurance claims. Reported medical spending is based on total resources used and is not broken down by contributions of other payers, including Medisave, MediShield, Medifund and private insurers. The actual share of individual out-of-pocket expenditure will be lower after accounting for payments by these different payers. The SCHS was linked with hospitalization spending from Mediclaims through participants’ ID and harmonized across both data sets. The Singapore Longitudinal Aging Study (SLAS) is a smaller cohort which consists of 2,804 subjects aged 55 or above interviewed in 2004/ 2005, 2007/2008 and 2010/2011. Older adults who were citizens or permanent residents aged 55 years or above were identified by door-todoor census and invited to participate voluntarily in the study. Another cohort, the Panel on Health and Ageing of Singaporean Elderly (PHASE) consisted of 4990 elders aged 60 or above who were interviewed in 2009 and 2011/2012. All respondents consisted of citizens chosen through single-stage stratified random sampling from a national database of dwellings and were interviewed face-to-face at their residences. SLAS and PHASE were incorporated into our projections as they provide additional information on other dimensions of health and wellbeing not covered by the SCHS, such as psychosocial metrics, questions on functional disability that can be used to construct indices of activities of daily living (ADL) and instrumental activities of daily living (IADL) disability prevalence among the elderly, quality of life measures and other diseases. The Singapore National Health Survey (NHS) provides information on the prevalence of major non-communicable diseases such as diabetes mellitus, hypertension and related risk factors like obesity and smoking in four periods from 1992 to 2010 (MInistry of Health, 1999). It is conducted once every six years on a cross-section of individuals aged 18–74 years. 2
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Microsimulation model
Health transition and spending model
The Future Elderly Model (FEM) microsimulation model includes several components and the structure of the model is almost entirely based on population dynamics. We adapted the FEM ─ a dynamic Markov microsimulation model that predicts future health status and hospitalization spending for Singapore Chinese, which constitute the majority of residents at 74.3% (Malays were 14%, Indians were 9%, Others were 3%) (Department of Statistics, 2012). It was first developed by RAND Corporation to support decision-making related to Medicare and Medicaid, the public health insurance and welfare programs for the elderly/needy in the United States. Since then, the FEM has been used for studies exploring health and social issues in ageing societies in several countries including the United States, European Union and, Japan (Chen, 2016; Joyce, 2005; Michaud, 2011). Its ongoing development is supported by the National Institute on Aging through the USC Roybal Center for Health Policy Simulation. Key features of this microsimulation model are: (a) projecting individual level cohorts from observed data with diverse characteristics and behaviours; (b) projecting those individuals over time to estimate variation in outcomes within populations; and (c) updating populations which enter the microsimulation to reflect future socio-demographic characteristics and health status. We further developed this model to project IADL and ADL disability prevalence and the acute care inpatient hospitalization spending disparity between elders with and without ADL disability in Singapore. The model tracks elders characterized by socioeconomic drivers and health states who transition from one period to another (Fig. 1). In each period, new elders enter the simulation at the default starting age, which we set as 55–60 years old with their composition adjusted using sociodemographic and disease trends in NHS. Population projections were based on the demographic epidemiological model of Singapore (Phan, 2014). In each simulation cycle, the population moves forward in time and we can observe their health status as well as their healthcare utilization and spending. It loses some elders due to mortality and gains some because there is renewal, i.e., a new cohort of individuals aged 55–60 years old enters the model as mentioned above. We assume that there is no gain due to migration in the elderly. The model then integrates data from the SCHS with hospitalization spending data from the Ministry of Health to project future healthcare spending.
To project health transitions, a discrete piecewise linear hazard model was estimated from the SCHS based on six-year transitions. The hazard of transitioning to an absorbing disease state (hypertension, diabetes, heart disease, stroke) and dying depends on risk factors (gender, highest attained education, obesity, smoking status); other conditions if medically warranted; functional status; and age. We used probit regression to estimate the probability of transition to each health condition, controlling for demographic variables and comorbidities at the previous period. We treated all diseases as absorbing in reflection of the question asked: “Have you ever been told by a doctor…”. The unit of observation for modelling transition probabilities was the interviewpair. All independent variables were measured with a six-year lag in the SCHS, and represent the respondent’s characteristics from 1993 to 2010 (National University of Singapore, 2018). As diseases were treated as absorbing state, we assumed that elders with the chronic condition (e.g. diabetes) in the previous wave will continue to have diabetes in the current wave. As such, transition probabilities were estimated on elders who did not suffer from a specific condition at the previous survey wave. Covariates such as BMI were log transformed to adjust for high peaks in BMI measurements, while age was splined into the 55–70, 70–84 and above 85 groups to account for the differential effect that the age groups have on comorbidities. Education was modelled according to the Singaporean schooling system, with low attainment defined as having primary education or less (less than 6 years of education), middle attainment as having secondary school education or technical education (6–12 years of education) and high attainment as having at least a diploma or college education (at least 13 years of education). Disability model The disability model is a cross-sectional model which uses current chronic diseases and socio-demographic covariates to model the burden of disability using data from SLAS and PHASE. We defined disability as having any ADL disability such as washing, dressing, feeding, toileting, mobility and transferring (Ministry of Health, 2018) or having any IADL disability such as taking transportation, shopping, managing money, making phone calls, doing household chores and meal preparation. The
Fig. 1. FEM simulation.
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The proportion of elders having at least one ADL disabilities was 8.6% in 2014 and this was projected to double to 14.7% in 2050. The steepest rise in ADL disability was among the oldest age group of elders aged 85 and above, among whom prevalence was projected to increase from 33.5% in 2014 to 40.4% in 2050. There was little projected increase in ADL disability from 2014 to 2050 in younger age groups, with disability prevalence being around 4.5% for age 55 to 64, 7% for age 65–74 and 13% for age 75–84 years (Fig. 4). In 2014, prevalence of any IADL disabilities was 22.9%; this was projected to increase to 31.9% in 2050. There was no significant projected increase in disability among the age groups of 55–64, 65–74, 75–84 and above 85 years old, with a prevalence of 12%, 21%, 41% and 65%, respectively in 2050 (Fig. 5). The decline in prevalence of ADL and IADL after year 2026 among elders aged 75–84 was due better education of the baby boomer generation compared to earlier cohorts. Thus, they were less likely to develop chronic diseases and disability. Although the disability prevalence within each individual age group did not reflect increasing trends, the increase in overall disability and chronic diseases prevalence in Fig. 3 were driven mainly due to an aging population. Lifetime hospitalization spending is higher among elders with disability. We projected the elderly lifetime hospitalization spending for elders aged 55 and above with ADL disability was US$24,363 (30.3%) higher compared to those without disability (US$104,883 for those with disability and US$80,520 for elders without disability), assuming no medical inflation. Next, we assumed two sensitivity scenarios: (a) an average annualized inflation rate of 3.0% based on CPI for medical treatment in the last five years (Department of Statistics, 2018) with 2% discounting and (b) a high inflation rate of 4.5% with 2% discounting. We projected a difference in hospitalization spending of US$29,842 and US$42,343 respectively, comparing elders with and without ADL disability (Fig. 6). The increased hospitalization spending for ADL disability was due to the underlying manifestation of chronic diseases such as stroke, diabetes and heart disease. We were unable to model the lifetime hospitalization spending from IADL disability as our spending model focuses on physical disability from the underlying manifestation of chronic diseases. Females were projected to have a higher prevalence of both ADL and IADL disability than males. For ADL disability, females had 2.0% (9.6% for females and 7.6% for males) higher prevalence than males in 2014, a gap that was projected to increase to 3.0% (16.1% for females and 13.1% for males) in 2050. Our projection of IADL disability prevalence by gender shows similar disparities. In 2014, we observed prevalence rates of 20.8% and 25.1% for males and females respectively; these were projected to increase to 30.3% and 33.3% in 2050.
former measures an individual’s physical ability to perform basic tasks whereas the latter are higher order tasks which measure an individual’s engagement and management of resources. We projected future functional disability through two models with ADL and IADL disability as the dependent variable using probit regression. The covariates included were age, gender, education attainment, BMI, marital status and chronic diseases. Spending model We linked SCHS data with elders’ demographic information, current reported health status, risk factors and functional status with annualized hospitalization spending based on Mediclaims data, and predicted healthcare spending using ordinary least squares (OLS) regression. Nominal expenditure (in Singapore dollars) was converted to real expenditure using the medical component of the consumer price index (with base year of 2014) from Singapore Department of Statistics (DOS). The nominal expenditure was the total healthcare expenditure before government subsidies. In addition, the regression adjusted for gender, educational attainment, obesity, smoking status and self-reported conditions. A robustness check was conducted using a two-part model, which first predicts who will likely incur spending, and subsequently predicts the spending in a regression conditional on the earlier likelihood. Given that the estimates of the models were similar, we chose to run OLS on the grounds of parsimony. We then apply the FEM on the initial cohort of individuals, whom were born before 1953, to estimate the longitudinal inpatient hospitalization spending due to disability conditional on other comorbidities. We report the lifetime hospitalization spending of elders by tracking our initial cohort of individuals incurs from 2014 to 2060 using the model above. All spending is expressed in 2014 US Dollars. Results Overall, Singapore is projected to be a top-heavy society with increasing numbers of ADL and IADL disability, especially in the oldest elders, aged 85 years and above. Projected IADL disabilities were more prevalent than ADL disabilities from 2020 to 2050. Both ADL and IADL disabilities were more prevalent in females than males. This difference is accentuated in the oldest-old group, where the projected prevalence of functional disability was much higher in females than males (Figs. 2a and 2b). We projected an increasing prevalence in chronic disease burden from 2014 to 2050. Heart disease prevalence increased from 9 to 11%, diabetes prevalence increased from 21% to 28%, and stroke prevalence increased from 5% to 7%. ADL disability prevalence was projected to increase from 9% to 15% and IADL disability prevalence was projected to increase from 23% and 32% respectively (Fig. 3).
Discussion We estimate that by 2050, 1 in 6 elders in Singapore will have at
Fig. 2a. ADL disability breakdowns by gender and year. 4
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Fig. 2b. IADL disability breakdowns by gender and year. ADL: Activities of daily living. IADL: Instrumental activities of daily living.
Fig. 3. Projections of disease prevalence in general population aged more than 55 years old.
least one ADL disability and 1 in 3 elders will have at least one IADL disability, an increase from 1 in 12 elders with ADL disability and 1 in 5 elders with IADL disability in 2014. The projected prevalence of ADL and IADL disability by 2050 among the oldest-old are 46% and 68% respectively, an increase in disability prevalence due to Singapore’s aging demography and related comorbidities. These projected trends are consistent with other studies that have found functional disability to be associated with comorbidities (Lin et al., 2016; Freedman, 2008; Yokota, 2016), and an increased prevalence of disability in the oldestold. They also utilize more healthcare resources such as ambulatory healthcare services, outpatient, emergency and inpatient services and have longer inpatient stays (Wu, 2013; Nie, 2010; Vilpert, 2013). IADL have also been found to be more prevalent than ADL. For example, in China, IADL prevalence was consistently higher than ADL prevalence from 1998 to 2008. The prevalence of having any IADL disability was 30.1% while that of having any ADL disability was 14.9% in 2008 (Feng, 2013). In Spain, the prevalence for having any IADL disability was 53.5% and any ADL disability was 34.6% (Millan-Calenti, 2010). In the U.S, the percentage of elders aged 75 and above with any IADL disability was 19.2% versus any ADL disability was 10.0%. In addition,
people in each age group were approximately twice as likely to require help with IADLs as with ADLs (Services, 2009). The overall findings echo existing international microsimulation work from Singapore, Japan and Australia that also predict sharp increases in disability prevalence driven by population aging (Ansah, 2015; Nepal, 2011; Thompson, 2014; Davidson et al., 2011). While women are having longer life expectancy compared to men, they continue to experience inferior health outcomes. This increased longevity of women has significant implications for women living alone as they have outlived their spouses, with potentially less resources and support. Frailty, falls, fracture and disability are likely to be more prevalent among older women (Owens, 2008). This is further compounded by the fact that women are physiologically more likely to suffer from chronic conditions. The probability of being diagnosed with breast cancer, the most common cancer in women, also increases by 40% in the elderly. Moreover, the osteoporosis is more likely to develop in women after menopause (Dunlop et al., 1997). These result in women facing a diminished quality of life as compared to men. It is a general phenomenon that ADL is more prevalent in females than in males. In the U.S., the total risk of incident disability rates was higher 5
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Fig. 4. Prevalence of one or more ADL disability in elderly by age.
Fig. 5. Prevalence of one or more IADL disability in elderly by age.
for females compared to males in the following activities: walking, bathing, transferring, dressing, toileting and feeding disabilities (Ho, 2002). In Japan, a comparative study of ADL dependence in residential care home and community-dwelling elderly showed that the female gender was statistically significant predictor for ADL dependence with an odds ratio (OR) = 2.55 (p-value = 0.004) for females compared to males in residential care and OR = 1.67 (p-value = 0.001) in the community (Oman et al., 1999). The higher reported prevalence of ADL in females can be attributed to physical risk factors more prevalent in women (chronic conditions, less exercise, less outside activity etc.) (Murtagh and Hubert, 2004), as well as psychological factors: women
may be more likely to report their disability as compared to men (overall 52% vs 37% (in the U.S.), p-value less than 0.001) (World Health Organisation, 2011). It benefits the society both economically and socially, when older women are functionally independent, productive and healthy – as these disabilities create an additional burden of expenses for assistive devices, hospitalization and long-term care (Khalik, 2018). It is also possible that women and men age differently, and may have different health care needs and utilization, making a gendered perspective towards aging appropriate for future research. The Singapore government has been relatively successful at controlling healthcare spending. As part of their commitment to universal 6
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Fig. 6. Lifetime hospitalization spending of elders aged 55 and above, by ADL disability. Lifetime hospitalization spending is computed by tracking the hospitalization spending the initial cohort of individuals incur from 2014 to 2060.
current work however provides the first concrete step towards a policy discussion on the fiscal sustainability of healthcare financing in Singapore and provides a novel contribution to aging policy and research in the broader region. In our study, we also projected a 30.2% higher elderly lifetime hospitalization spending for individuals with disabilities. Our spending projection is an underestimate compared to other studies as long-term care spending were not considered. Coupled with the expected increase in ADL/IADL disability prevalence in elderly and life expectancy, the need for long-term healthcare is very likely to increase steeply in the future. In light of the increasing demand for healthcare, Singapore has plans to extend current disability insurance from six years to lifetime coverage to protect individuals with severe disability against the financial risk of ADL disabilities. There are proposals for the implementation of universal disability insurance (“CareShield Life”) (Williams and Mohammed, 2009) and subsidies for hired help to facilitate elderly caregiving (Sharma, 2012).
health coverage while being sensitive to the changing demand and supply of healthcare, the government has recently introduced lifetime catastrophic insurance coverage under Medishield as well as an expanded long-term care insurance scheme, while also introducing new initiatives to address concerns with over-consumption, over-servicing and over-charging. These include the elimination of private insurance full-rider policies, which previously covered individual co-payments (Ministry of Health, 2018), in order to reduce moral hazard as well as potential supplier-induced demand, which may in turn curb medical inflation. In addition to the review of private insurance plans, the Ministry of Health has also published fee benchmarks for professional fees to guide private sector healthcare providers in charging appropriately and is working to educate patients on the use of these benchmarks to make better informed decisions (Ministry of Health, 2018). The implications are especially important for policymakers both in Singapore and globally. It reinforces the need to plan for the future burden due to aging, which is the strongest driver of disability and comorbidities. Healthcare policies that may alter the trajectory of disability and comorbidities are crucial. In Singapore, the government has shifted from secondary to primary prevention, focusing on health promotion and health programs. Concerted screening of chronic conditions like hypertension within clinical institutions, progressive taxation to restrict smoking uptake and purchase (National Environment Agency, 2018), prohibition of smoking in designated public areas (Ministry of Health, 2018), as well as a recent policy on combating diabetes have been enacted in hopes of preventing or delay the onset of chronic conditions highly associated with disability (Yu, 2015). Technological improvements such as assistive devices may reduce self-reported prevalence and burden, by allowing elders who would previously experience disabilities to lead normal lives (Manton et al., 2007). Finally, factors such as higher education, social support networks and engagement in leisure time activities can promote the maintenance of function (Liao, 2015) and alleviate mortality for disabled elders (CalderónLarrañaga, 2018). This suggests that policies that address social disadvantage and isolation directly, or target more socially-vulnerable groups also have a role to play (Ministry of Manpower, 2018). The policy scenarios explored above, as well as other potential longer-term changes in financing and service delivery models, will be further investigated with our Singapore Future Elderly Model in future research, as more data on specific financing mechanisms become available. Our
Limitations Modeling in this complex environment necessarily reflects only best available information, and is subject to limitations. There are several limitations regarding our projections of functional disability and hospitalization spending incurred by these groups. In 2010–2015, Singapore has an ethnic composition of around 74% Chinese, 14% Malays, 9% Indians and 3% of other races (Department of Statistics, 2012). The SCHS contains data only for the Chinese, thus medical spending for ethnic minorities such as the Malays and Indians are not included. As a result, our disability projections may be an underestimate as these minority groups have been shown to bear a greater chronic disease burden (Lee, 2009; Venketasubramanian, 2005; Yassin et al., 2002; Loyalka, 2014). The model also assumes that the current health care delivery model continues into the future. In addition, current treatments will also continue and FEM does not take into account technological innovations or care transformations. It also does not account for improvements in future health states due to screening, which may shift the demand to primary care and induce cost-savings in inpatient hospitalization spending. We did not model migration as current migration policy emphasizes on temporary rather than long-term migration. We were 7
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also unable to model shorter disease dynamics as our transitional probabilities were estimated based on the survey with six-year median follow-up. Due to small numbers, we were also unable to model severity of disability after adjusting for individual heterogeneity. Studies also have documented the large indirect burden of disability on productivity loss in work, as well as changes in standard of living due to spending required to alleviate disability itself (WilkinsonMeyers, 2010). Our analysis omits these, and hence, our spending estimates are likely to be an underestimate of the true burden of disability on the Singaporean Chinese elderly population as we have only considered hospitalization spending projections in the FEM simulations. Further work on spending projections should account for these latent dynamics which underlie the burden of disability. In addition, we aim to evaluate the long-term impact of health policies when preliminary evidence is available. Regular future updates to the model and subsequent policy reviews are an important part of ensuring the continued value and relevance of the model and its application.
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Conclusion This is the first study in Singapore that simultaneously models three dimensions (i.e. economic, health and demographic) to provides a comprehensive understanding of disability and hospitalization spending. The FEM’s approach allows for this multi-dimensional characterization of health status using a life course dynamic microsimulation model to project the elderly disability prevalence and lifetime hospitalization spending. To the best of the authors’ knowledge, no lifetime hospitalization spending projections for disabled elders has been done in Asia, and this provides both a foundation for policies to improve the fiscal sustainability of healthcare financing in Singapore and a novel contribution to aging policy and research in the broader region. Acknowledgements We thank Kenwin Maung, Sucitro Sidharta and Chia Jia Hui for the helpful discussions and statistical support that have contributed to this work. Funding The research was supported by the Singapore Ministry of Health’s National Medical Research Council (HSRG-0077/2017) and the U.S. National Institute On Aging of the National Institutes of Health under Award Numbers P30AG024968, R03AG054120 and R01AG055401. The Singapore Chinese Health Study was supported by the U.S. NIH R01CA144034 and UM1CA182876. Koh WP was supported by the National Medical Research Council, Singapore (NMRC/CSA/0055/ 2013). Waves 1 and 2 of the Panel on Health and Ageing among Singaporean Elderly (PHASE) were funded or supported by the following sources: Ministry of Social and Family Development, Singapore; and Singapore Ministry of Health’s National Medical Research Council under its Singapore Translational Research Investigator Award “Establishing a Practical and Theoretical Foundation for Comprehensive and Integrated Community, Policy and Academic Efforts to Improve Dementia Care in Singapore” (NMRC-STAR-00052009). The funding sources had no role in the design and conduct of the study; collection, management, analysis, or interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.jeoa.2019.02.002. 8
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