What Effects Will Changing Population Demographics Have On Health Care Costs And Services
Risk Manag Healthc Policy. 2020; 13: 1403–1412.
The Effect of Population Aging on Healthcare Expenditure from a Healthcare Demand Perspective Among Dissimilar Historic period Groups: Prove from Beijing City in the People's Republic of Cathay
Lele Li
1School of Public Policy and Direction, Tsinghua Academy, Beijing, 100084, People's Republic of China
Tiantian Du
2Institute for Hospital Management, Tsinghua Academy, Shenzhen Urban center, 518055, Guangdong Province, People's Republic of Mainland china
Yanping Hu
3Department of Medical Engineering, Red china-Japan Friendship Hospital, Beijing, 100029, People's Republic of China
Received 2020 Jul eight; Accepted 2020 Aug xiv.
Abstract
Background
With population crumbling (PA), the healthcare expenditure (HE) increases. The aim of this study is to analyze the HE of different age groups and the event of age on HE amidst different age groups.
Methods
Combining PA and HE data, this study used the fixed effect model and parameter estimation method to evaluate the influence of unlike age groups on HE from 2008 to 2014.
Results
The age effect of HE for the population aged 65 or over was the most significant among the different age groups. Based on PA and HE data, HE per capita of the age grouping 65 years or over is 7.25 times equally much as the population aged < 25 years, i.61 times equally much as the population anile 25~59 years, and 3.47 times as much as the population aged threescore~64 years. Based on the event of the stock-still outcome model, HE per capita of the age group <25 years was 218.39 Yuan (CNY) (USD $31.2). HE per capita of the age group 25~59 years erstwhile increased to 1,548.62 Yuan (CNY) (USD $221.2). HE per capita of the 60~64 years age grouping will be 921.56 Yuan (CNY) (USD $131.7), 4.22 times as much every bit that of the age group < 25 years. HE per capita in the historic period group of 65 years or over is ii,538.88 Yuan (CNY) (USD $362.vii), 11.63 times as much as that of the historic period grouping <25 years.
Conclusion
The results suggest that PA in Communist china is intensifying. In lodge to control the rise of HE, the government should not only address the supply side such as reforming medical insurance payment, developing new technologies, only also focusing on solving the need side such equally improving the quality of healthcare services, solving environmental pollution, and improving the residents' health.
Keywords: population crumbling, healthcare demand, healthcare expenditure, different historic period groups, fixed effect model, parameter estimation method
Introduction
With social and economic development, people's living conditions and medical technology besides meliorate, lengthening the normal lifespan. Virtually all the countries in the world face population aging (PA), including the People'due south Republic of People's republic of china. An individual born today in the People's Republic of China can wait to live to 77 years.ane This is compared to a life expectancy of 47.3 years for males and 50.5 years for females built-in between 1953 and 1964.2 This meets the definition of an crumbling society in the United Nations Earth Written report on Population Aging (1950–2050), where the proportion of the population anile 60 years or over of the total population reaches 10%, or the proportion of the population anile 65 years or over reaches 7%.3 According to the data of the 6th national demography in 2010, those anile 65 years or over accounted for 13.vii% of the total population in the People's Republic of China,4 indicating that the People'due south Republic of China had get an crumbling society.
Every bit the data shows in Table i, by adding, PA in the People's Republic of China is projected to be 14% in 2025 and 21% in 2035. This defines the People's Republic of Communist china as a deep aging society and super aging society respectively.v The information shows that the Red china became an aging order later than other developed countries. However, the transitional time for different aging population cohorts has been shorter. This illustrates that, compared with other countries, the People's Democracy of China is facing a grim situation.
Table 1
The Time of Social Development for Aging in Countries (Unit: Years)
| United states of america | Germany | Nippon | Cathay | The World | Developed Countries | Underdeveloped Countries | |
|---|---|---|---|---|---|---|---|
| Entry stage year (7%) | 1950 | 1950 | 1970 | 2000 | 2005 | 1950 | 2055 |
| Transition fourth dimension | 65 | 25 | 25 | <25 | 35 | 50 | forty |
| Depth stage year (14%) | 2015 | 1975 | 1995 | 2025 | 2040 | 2000 | 2095 |
| Transition time | 15 | 35 | fifteen | x | 40 | 25 | —— |
| Super stage yr (21%) | 2030 | 2010 | 2010 | 2035 | 2080 | 2025 | —— |
It is well known that the People's Democracy of China has experienced an increasing elderly population with loftier healthcare expenditure (HE), which is a challenge that the People'south Republic of China needs to set up for. Furthermore, the need for more health resources varies greatly amongst different age groups in the People's Republic of China. With the development of the economic system and the improvement of living conditions, people's pursuit and need for healthcare has increased. In 2003, the People'southward Republic of China began to carry out a significant and complex healthcare reform, aiming to meliorate the accessibility and affordability of healthcare. Since the 21st century, HE in the People's Republic of People's republic of china has been increasing. HE per capita increased quickly from 1407.74 Yuan (CNY) in 2008 to 3783.83 Yuan (CNY) in 2017 (in Dec sixteen, 2019, USD $one equivalent to CNY half-dozen.99).six During the same timeframe, the share of the regime health expenditures increased from v.7% to 7.v%, and as a share of overall gross domestic product (Gross domestic product) increased from 1.1% to ane.8%.7
In the past few decades, the Chinese authorities has released several policy documents concerning how to answer to PA with increasing HE. As the 12th Five-Yr program of health development in the People'due south Republic of Communist china pointed out, i of the most important goals of the healthcare reform was to strengthen supervision over HE and to control unreasonable growth of HE. The reasons why HE is increasing and then fast has been investigated widely in the Mainland china. Some researchers have concluded that the health policy,viii – 17 medical technology advancement, health insurance system,18 – 25 drug policy,26 supplier behavior27 are factors which brand HE increased.
Over the by 50 years, HE has experienced a significant increase with the increase of life expectancy. Getzen28 used cross-exclusive data and fourth dimension series data of 20 OECD countries from 1966 to 1988 to estimate the result of PA on HE.28 He concluded that fifty-fifty with no budget constraints, PA would accept increased medical costs. Crivelli et al,29 using the time series and cross-sectional data of 26 cantons in Switzerland from 1996 to 2002, institute that the degree of PA (the proportion of the elderly over 75 years old in the full population) can account for the differences in medical costs amidst cantons.29 Di Matteo and Di Matteo30 used the panel data of five Canadian provinces from 1965 to 1991 and found that the PA could explicate 92% of the variation in actual per-capita HE.30 Tomoko et al31 used the panel data of 47 prefectures of Japan from 2001 to 2010 to gauge the relationship between HE, GDP and PA. Their findings revealed that PA was the nigh of import factor driving the increase of HE, while GDP had trivial impact on HE.31 White32 carried out an empirical report on the population and medical expense in the United States and other OECD countries in 1970~2002.32 In this written report, it was plant that the actual growth rate of per-capita medical cost was 4%~v%, but the proportion of the population over 65 years sometime was very small, and the age structure contributed only to 0.3%~0.5% growth of HE. However, Di Matteo33 combined the data of the United States and Canada in 2005 to detect that the PA did have an impact on the growth of HE, simply the impact was relatively minor.33 Furthermore, Di Matteo institute that medical technology innovation was the main reason for the growth of HE. Asl and Abbasabadi34 studied historic period furnishings on healthcare expenditures among 165 countries.34 They establish that age effects on healthcare expenditures is significant. Furthermore, the nearly effective population is aged 45 years or above affecting the wellness expenditures in a positive way.
With healthcare system reform in the People's Republic of China, the growth of HE has attracted wide attention from Chinese scholars. These experts take conducted many researches on the issue of HE, and made some inroads on factors analysis related to health toll growth with the use of some modern econometric methods, like Grainger causality analysis,35 , 36 principal component analysis,37 , 38 unit root test39 and other statistical methods. P.P.H used the cointegration analysis to analyze the data from 1978 to 2003.40 Information technology found that in that location was an impact of economic growth on the growth of HE. It also found that PA has a great bear upon on the growth of HE in the People'southward Republic of People's republic of china in the short term, only the impact was not obvious in the long term. From the perspective of urban and rural differences, past analyzing the provincial panel information from 2002 to 2008, Yu41 plant that the affect of PA on medical expenditure was significant, and the contribution ratio in medical expenditure was most 3.viii%,41 similar to that of some experts for OECD national research.42
Though in that location is much research on the effect of PA on HE, there is express research on the relationship between PA and HE from the perspective of healthcare demand among different age groups. In 2015, the social medical insurance organization inverse. Urban Residents' Medical Insurance and Cooperative Medical Scheme take merged into Urban and Rural Residents' Medical Insurance. In gild to guess correctly, we used data from 2008 to 2014 to carry out research. The objective of this paper was to analyze the HE of different age groups from 2008 to 2014, and the result of historic period on HE among dissimilar age groups measured by fixed effect model and semiparametric estimation methods. The aim was to utilise these findings to shed light on policy suggestions regarding how to deal with the healthcare demand among historic period groups with higher HE.
Materials and Methods
Data Sources and Variables Selection
As the majuscule of the People'south Democracy of China, Beijing has been defined as an aging society earlier than the rest of China and has arable wellness resources. The People's Republic of China has conducted six national censuses in 1953, 1964, 1982, 1990, 2000, and 2010 respectively. This paper chooses the first demography data in 1953 as the observed group. The number of people born in 1953~1964 is considered the commencement group; 1964~1982 is the second group; 1982~1990 is the third grouping; 1990~2000 is the fourth grouping; 2000~2010 is the 5th group and after 2010 is the sixth group. Equally shown in Table 2,43 the age demographics of the population of Beijing has experienced a notable alter.
Tabular array 2
The Listing of Six Censuses Data in Beijing (Unit: x,000 Persons)
| 1953 | 1964 | 1982 | 1990 | 2000 | 2010 | |
|---|---|---|---|---|---|---|
| Total | 276.8 | 759.7 | 923.1 | 1,081.ix | one,356.9 | 1961.two |
| 0–14 years sometime | 83.3 | 315.3 | 206.8 | 218.5 | 184.v | 168.seven |
| 15–64 years old | one,84.three | 413.3 | 664.6 | 795.two | 1,058.4 | 1621.ix |
| 65 years old and over | ix.one | 31.1 | 51.7 | 68.2 | 113.9 | 170.six |
| 0–14 years old ratio | 30.1% | 41.v% | 22.4% | twenty.ii% | 13.half dozen% | viii.vi% |
| 15–64 years former ratio | 66.6% | 54.four% | 72.0% | 73.five% | 78.0% | 82.7% |
| 65 years erstwhile or over ratio | 3.3% | four.1% | 5.6% | six.three% | 8.4% | 8.7% |
Based on information provided past Healthcare Insurance Administration of Beijing, this paper undertakes an empirical analysis about the HE of the population across unlike historic period groups. According to the age range of the data drove catamenia, this newspaper classifies the sample into four age groups, which are: <25 years, 25 to 59 years, 60 to 64 years, and >65 years. For the sake of estimating the touch of aging on rise HE, we regard other variables as controls equally described below. The HE per capita calculated in this paper is from the Beijing medical insurance eye (the number adjusted for aggrandizement). HE per capita refers to the total HE financed by medical insurance, which mainly includes spending on outpatient visits and inpatient hospital stays. Annotation that deductibles are not included in the total HE. Command variables are Gross domestic product per capita (CNY), the share of the population aged 65 years or over (%), the old-age dependency ratio (%), the kid-age dependency ratio (%), full population (per ten thousand), the share of the urban population (%), the coverage rate of medical insurance (%), the share of the population with higher caste or higher up (%), SO2 emission volume (calendar month/ten m tons). These data are obtained from annual publications.44 Notation that, the medical insurance refers to the Urban Employee Basic Medical Insurance (UEBMI) before 1998, merely UEBMI and Urban Resident Bones Medical Insurance (URBMI) later 2007. The clarification of variables is shown in Table iii.
Table 3
Clarification of Variables
| Variables | Mean | SD |
|---|---|---|
| Healthcare expenditure per capita(CNY) | ii,094.46 | 903.eighty |
| Per-capita GDP (CNY) | 81,046.29 | thirteen,358.lx |
| The share of the population aged 65 years or over (% | 9.08 | 0.77 |
| The former-age dependency ratio(%) | 11.34 | 1.11 |
| The child-age dependency ratio (%) | eleven.74 | 0.60 |
| Total population (per ten thousand) | 1,985.3 | 124.7 |
| The share of the urban population (%) | 85.84 | 0.62 |
| The coverage rate of medical insurance (%) | 94.66 | 3.32 |
| The share of the population getting bachelor or in a higher place(%) | 30.67 | four.33 |
| Then2 emission volume (month/ten 1000 tons) | 13.4 | 4.25 |
With aging of the population and the decline in health equally one ages, consumption will lead to the increment of health need, which contributes to the increase of HE. This paper adopts two methods to approximate the effect of PA on the HE growth. Firstly, the parametric estimation is employed using the least foursquare method. To guess the upshot of PA on the HE growth, other variables are used equally controls. Because the estimation of age also includes the birth group effect, (which has the risk of randomicity and unpredictability), these need to be controlled using the fixed effect model. Secondly, the semiparametric estimation method is used to exclude age and nascency group effects between HE and age using non-parametric estimation.
Methodology
Parametric Interpretation
Co-ordinate to the theoretical assay of the relationship between PA and HE, we specify the following model equations (1).
1
This equation is chosen the fixed consequence model. In this model, denotes HE per capita. This is calculated using medical insurance fund outpatient and hospitalization expenditures;
is a dummy variable denoting age;
is regarded as other controllable factors. This model controls the coverage rate of medical insurance and GDP per capita. We also add the fourth dimension variable in the model. The fourth dimension variable mainly controls for the impact of policy changes and medical technology progress. In this model,
denotes age fixed effect, and
is the random error term.
Semiparametric Estimation
Based on semiparametric interpretation, the estimated equations (2) and (3) are as follows.
(2)
Where grand (age) denotes historic period function and is a dummy variable for the nascence group. For the nonparametric estimation part, the Epanechnikov kernel function for age role grand (age) is every bit follows.
(3)
This function corresponds to the weighted average of the office values near the observation points, in club to improve the accuracy of the prediction and facilitate the prediction of HE at that age indicate. In semiparametric estimation, the Epanechnikov kernel function gives a higher estimation weight to the value nearer to the observation signal. Far values are assigned lower weights. The is a window width. For the sake of satisfying the requirement of smoothness and applicability of the role, a suitable window width is selected for semi-parametric estimation, so that the weighted average of local neighborhood observation points (ie, logarithmic HE per capita) can reach the expected accurateness and smoothness. At the same time, the dummy variables such as birth group, period and other variables (X) are used as the parameter part which is estimated by equations (1) and (2), and the traditional to the lowest degree square method is used to estimate it. Parametric and nonparametric estimations are carried out simultaneously, and these two methods complementeach other. Finally, nonparametric function yard (age) and the parametric partial coefficient are obtained. This method of estimating the age effect approximates the trend of age consequence.
Results
Results of Mixed Cross Department Data
It is found that HE increases significantly with historic period increases (see Tabular array 3 for the regression results). In general, the age effect of HE for the population anile 65 years sometime or older was the almost significant. As shown in Table 4, HE of those aged >65 years onetime per capita is vii.25 times greater than the population aged <25 years former, 1.61 times greater than the population aged 25~59 years old, and iii.47 times greater than the population aged 60~64 years old. Autonomously from that, the time dumb variable was significantly positive, indicating that the growth of HE after 2008 increased dramatically.
Table 4
Age Event of Regression of Mixed Cross Department Data
| Model (one) | Model (two) | |
|---|---|---|
| 25~59 years old | one.268*** | 0.966*** |
| 60~64 years old | 1.806*** | i.831*** |
| 65 years old and above | ii.369*** | 2.596*** |
| 2008~2013 | 0.869*** | 0.679* |
| Population ratio over 65 years of historic period | −0.012 | |
| Ln (per capita Gross domestic product) | −0.099 | |
| Coverage of medical insurance | i.384** | |
| Constant term | two.964*** | 3.896 |
| Adjust R2 | 0.88 | 0.91 |
In accordance with the above regression results, this paper predicted HE of different historic period groups which are shown in Tabular array 5. HE per capita of the group < 25 years old is 506.47 Yuan (CNY) (USD $72.4). The average HE per capita in the historic period group of 25~59 years one-time is two,286.18 Yuan (CNY) (USD $326.vi). HE per capita of the age group sixty~64 years old is ane,058.31 Yuan (CNY) (USD $151.2). HE per capita of the age group of 65 years old or over is 3,673.48 Yuan (CNY) (USD $524.8). These findings indicate that the age effect of HE is meaning. HE per capita in the group aged sixty years or over is 1.69 times higher than the grouping aged nether sixty years, which shows that most medical resource are consumed past the elderly.
Tabular array five
Predictive Value of Healthcare Expenditures for Different Age Groups (Cross Sectional Data)
| Historic period Group | Healthcare Expenditure per Capita |
|---|---|
| <25 years old | 506.47 |
| 25~59 years former | ii,286.18 |
| 60~64 years old | 1,058.31 |
| 65 years onetime and to a higher place | 3,673.48 |
The Estimation Results of the Fixed Effect Model
Table 6 shows the regression results of the fixed effect model. When estimating the result of age on HE, the influence of historic period grouping variables on the birth group effect is considered. Therefore, excluding the influence factors of the age effect of the birth groups is consequent with the change dominion of the cost of Chinese medicine in the whole life. The increase in the cost of traditional Chinese medicine is remarkable. Afterwards controlling for Gross domestic product per capita and healthcare insurance coverage, the age effect decreased sharply, indicating that the change in HE is influenced by healthcare weather and physical health levels at birth as well, and that this health demand is due to individual wellness level, non age.
Table 6
Age Effect of the Stock-still Outcome Model
| Model (1) | Model (2) | |
|---|---|---|
| 25~59 years old | 1.164** | ane.652*** |
| lx~64 years old | two.396*** | 2.869*** |
| 65 years old and above | iii.759*** | three.829*** |
| 2008~2013 | 0.941*** | 0.963** |
| Population ratio over 65 years of historic period | −0.034 | |
| Ln (per capita Gdp) | −0.438 | |
| Coverage of medical insurance | ane.4464** | |
| Constant term | two.361*** | 6.643** |
| Adjust R2 | 0.81 | 0.85 |
The regression results predicting the HE of different age groups are shown in Table 7. Information technology was found that HE per capita of the historic period group <25 years old is 218.39 Yuan (CNY) (USD $31.ii). HE per capita for the age group 25~59 years old increases to i,548.62 Yuan (CNY) (USD $221.two). HE per capita for the 60~64 years old age group is 921.56 Yuan (CNY) (USD $131.seven), 4.22 times higher than the age grouping <25 years former. Furthermore, the HE per capita in the age grouping of 65 years erstwhile or over is 2,538.88 Yuan (CNY) (USD $362.7), 11.63 times equally much as that of the age grouping of 25 years old and below. This miracle indicates that with increasing historic period, the impact of age on HE is significant. Information technology is too found that the HE per capita for those aged 60 years or over is 1.96 times every bit much as those younger than sixty years.
Table 7
Predictive Value of Healthcare Expenditure for Different Age Groups (Panel Data)
| Historic period Group | Healthcare Expenditure per Capita |
|---|---|
| <25 years old | 218.39 |
| 25~59 years onetime | 1,548.62 |
| 60~64 years old | 921.56 |
| 65 years former and to a higher place | ii,538.88 |
Semiparametric Interpretation Results
The age parameter m (age) is fatigued from semiparametric estimation results. The blueish bend removes the relationship betwixt the age of the nascence group and the age outcome on HE as seen in Figure ane. The orange bend is used to distinguish the historic period outcome, the historic period of birth group and the age of time effect on the HE in Figure 1.
The event of the semiparametric interpretation.
Note: The Y-axis shows the ln (healthcare expenditure per capita) and the Ten-axis shows the age.
Based on the results of the semiparametric estimation, it tin can exist seen that HE increases with increasing historic period. Furthermore, the increment range is greater than the age effect, which is the same as the results of the fixed effect model. By examining the age result, we tin discover that there is a great difference in the HE per capita among different age groups. In that location is a positive correlation between the HE per capita and the age groups. The increase in age significantly drives HE increase, and the increment range is greatest among those anile 65 years old or over.
Discussion
Although the relationship between PA and HE has attracted great attention from scholars all over the earth, there are only a few studies examining the healthcare demand perspective among different age groups, peculiarly using the fixed upshot model and semiparametric interpretation method to evaluate the impact on the HE. The semiparametric estimation has advantages vs parametric estimation. It combines parametric and nonparametric estimation. Furthermore, the nonparametric part assumes that the human relationship between economic variables is unknown. Therefore, it can reasonably judge the results and reduce the error versus the traditional parametric interpretation, which reflects the linear trend of age effect more accurately.45 Combining PA data and HE data, nosotros evaluated the effect of PA on HE from the healthcare demand perspective among different age groups. This newspaper introduced the different age groups to the research field of the influence of the PA on HE, and revealed some important findings.
Based on the data of mixed cantankerous sections, the age effect of HE for the population aged 65 or over is the most significant amongst the different age groups (Table four). HE per capita of those anile 65 years or over is seven.25 times greater than the population aged <25 years old, i.61 times greater than the population aged 25~59 years old, and three.47 times greater than the population anile threescore~64 years former. HE per capita for the grouping <25 years old is 506.47 Yuan (CNY) (USD $72.4). The average HE per capita in the age group of 25~59 years old is 2,286.18 Yuan (CNY) (USD $326.6). HE per capita at the historic period grouping 60~64 years onetime is 1058.31 Yuan (CNY) (USD $151.ii), and HE per capita of the age group of 65 years onetime or over is 3,673.48 Yuan (CNY) (USD $524.8) (Table 5).
These findings point that the age effect on HE is significant. HE per capita for the group aged 60 years or over is 1.69 times greater than the group aged under sixty years, which shows that most medical resources are consumed past the elderly and the medical resources allocation is unfair among different age groups. The unfair distribution of medical resources among the population should be one of the fundamental directions in the futurity healthcare system reform. There are many factors affecting HE. By adding per capita GDP and coverage rate of medical insurance in the equation, it can be found that medical insurance coverage has an enormous impact on the increasing HE in urban areas, which indicates that medical insurance has a positive outcome on the rise of HE. Besides, the touch of per capita Gdp on HE is minor, which is consequent with the conclusions of previous studies.31 On the ane hand, the economic evolution has raised the per capita income and improved medical treatment, making the HE increased synchronously. On the other paw, due to the improvement of medical conditions caused by the progress of medical technology, the morbidity and mortality declined.46 , 47 Therefore, the impact of per capita GDP on HE is not substantial.
Fuchs48 found a significant correlation between age and HE past employing the 1984 data from the United States.48 Compared with the population nether the age of 65, the increase of HE of the elderly is larger. Denton et al49 used the healthcare data of Ontario in Canada and constructed the age/toll profile to find that the per capita healthcare costs increased with historic period.49 Yet, Zweifel et alfifty plant that age had no bear on on medical demand subsequently controlling for the remaining fourth dimension to death.fifty The results also showed that the HE in the terminal stage of life (near to expiry) are similar at age 60 or ninety. Many empirical results showed that the proximity to death is the main factor affecting the increase of HE while age itself has a minor impact.51 Many scholars found that age was still an of import gene affecting HE past using information from different countries and time periods.52 – 54 Lubitz et al55 used sample data of 129,166 Medicare beneficiaries over 65 years one-time who died between 1989–1990 to analyze this further. The results showed that HE decreased every bit patients' age increased in the final months of life.55 The same results were also found past Felder et al,56 and Colombier et al.53 These findings tin can be explained differently. From a supply side perspective, physicians make decisions based on the patient'southward condition including age. For patients with the same disease, compared with immature patients, physicians may not be willing to provide costly healthcare services for elderly patients with concluding disease.54 , 57 , 58 From a demand perspective, one theory holds that an private'due south willingness to pay for survival is hump-shaped. In ane's youth, the willingness to pay for survival increases with the increases of historic period. The willingness to pay for survival peaks in middle historic period, and declines when one becomes older. Therefore, subsequently a certain age, the cost of decease decreases equally patients go older.59
Based on the regression result of the fixed consequence model, the age event of HE for the population aged 65 years old or over is the near significant among different age groups (Table 6 Appendix half-dozen). This may be related to a sharp increase in healthcare demand caused past the loftier incidence of diseases in this historic period group. As can be seen in Table 7, HE per capita in the age group of 65 years old or over is 2,538.88 Yuan (CNY) (USD $362.vii), 11.63 times as much as that of the age group of 25 years one-time and below. In terms of health need, at that place are pregnant differences among different age groups, particularly for the elderly (over 65 years). Age is an important factor in terms of the growth of HE, but the factors driving the increase of HE are circuitous, such as supplier-induced demand, over-prescription behavior, etc. These factors tin can pb to unreasonable growth of HE. However, information technology is noteworthy that the developed countries focus on healthcare system construction (eg, the law, policy and measures for healthcare reform) such as determining the benchmark value of the growth of HE. On the one manus, healthcare system construction tin encounter the health demand for different age groups. On the other manus, the healthcare organization construction can go along HE growth inside a reasonable range.
Gao and Yao60 studied the data of eight,414 samples of ane,428 farmers in viii provinces in China, finding that the medical costs of young and center-aged people aged 25~34 were considerably less than those of the children and the elderly, with the HE of the elderly group >65 years old demonstrating a declining trend.lx Yan and Chen61 performed a sampling survey in four counties of Hubei and Sichuan Province, and institute that, compared with the non-elderly group (<65 years sometime), the elderly group was 5% lower in hospitalization rate but xxx% higher in cocky-reported prevalence rate as answered in a survey by the elderly people.61 Simultaneously, they institute that the per-capita hospitalization expenses and outpatient expenses of the elderly group decreased by 775 Yuan (CNY) (USD $110.7) and 328 Yuan (CNY) (USD $46.9) respectively. Xu and Chen62 used the Bayesian quantile regression (BQR) method to predict the long-term care costs from the demand side. The results showed that future LTC cost increases will be enormous with the population aging.62 From the supply side, Chen et al63 found that family members, income, area of residence and health insurance are the factors influencing the availability of long-term care services.63
Finally, compared with developed countries, in that location are 2 primary characteristics of the impact of domestic PA on HE. Firstly, the aging population (>65 years of age) has increased significantly in the recent by. The rapid increase in the elderly population has brought about a significant demand for medical services. Secondly, population crumbling in the People's Democracy of China is different from other countries in the earth. A great number of scholars accept studied the impact of PA on HE past using summary data of a certain period in the region, but unfortunately, most of them ignore the healthcare need and expenditure differences amidst different age groups. In this assay we take washed so.
Our research has several advantages. This study examines the affect of PA on HE growth from the perspective of historic period differences and enriches the research on the related topic. At the same fourth dimension, this study adopted a more scientific and appropriate research methodology useing parametric estimation and semiparametric estimation. This was an innovative approach to ensure new insights can be gained that are applicable to this growing problem.
Conclusion
Based on the data of previous population censuses, this newspaper has constructed different birth groups. According to the social and economical development characteristics of each birth group, using the fixed effect model and parametric interpretation, it evaluates the impact of PA and the elderly'southward healthcare demand on the HE growth. The results showed that with the PA and the HE increasing, HE of the population aged 65 or over increases significantly. The growth charge per unit of HE per capita has accelerated since 2008. HE of the elderly population anile 65 or over is 11.63 times greater than the population aged <25 years old. Scientific and rational response to the PA and the growth of HE will exist a major claiming for future economic and social development in the People'southward Democracy of China. In the process of reducing HE, the government should focus on supply and demand factors such as reform of the medical insurance payment, new technologies, and equipment.
Our study as well has some limitations. Due to the limitations of the data, HE growth is predicted simply for the age grouping, and the government investment growth for healthcare is not considered, then HE growth may exist underestimated. HE growth for different historic period groups is synchronous with time. In reality, the HE growth for people aged 65 or over is apparently faster than that of other age groups, and then the increment of HE per capita anile 65 or over may be underestimated. Secondly, the electric current HE did not include ER visits or medications. We cannot estimate the effect of population crumbling on the cost of ER visits or medications. Thirdly, comorbidity is not available in the data and nosotros cannot control comorbidity. Farther research for HE growth with healthcare reform in the People's Republic of Cathay is needed in the future, and is certainly 1 of the key directions for future enquiry.
Acknowledgments
We give thanks the Healthcare Insurance Assistants of Beijing for cooperation and organizing data collection. Nosotros would too like to thank all study participants for their time while being interviewed.
Abbreviations
PA, population aging; HE, healthcare expenditure; Gross domestic product, gross domestic product; UEBMI, Urban Employee Basic Medical Insurance.
Data Sharing Argument
Information and materials accessed from the Healthcare Insurance Assistants of Beijing are freely available.
Ethics Approval and Consent to Participate
The study was exempt from human subjects' blessing (not-identifiable data; not homo subjects).
Author Contributions
All authors made a significant contribution to the piece of work reported, whether that was in the formulation, written report design, execution, acquisition of data, analysis and interpretation, or in all these areas; took office in drafting, revising or critically reviewing the article; gave terminal approval of the version to be published; accept agreed on the journal to which the article has been submitted; and hold to be accountable for all aspects of the work.
Disclosure
The authors report no conflicts of interest.
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What Effects Will Changing Population Demographics Have On Health Care Costs And Services,
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