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Effects of Copayment in Long-term Care Insurance on Long-term Care and Medical Care Expenditure
JAMDA xxx (2019) 1e7
JAMDA journal homepage: www.jamda.com
Original Study
Effects of Copayment in Long-term Care Insurance on Long-term Care and Medical Care Expenditure Huei-Ru Lin PhD, Yuichi Imanaka MD, PhD * Department of Healthcare Economics and Quality Management, Graduate School of Medicine, Kyoto University, Kyoto, Japan
a b s t r a c t Keywords: Copayment long-term care insurance policy expenditure difference-in-difference
Objective: This study aimed to clarify the difference in (1) long-term care (LTC) usage and expenditure and (2) medical care service usage and expenditure before and after the change in the copayment limit for qualifying individuals from 10% to 20%. Setting and Participants: This quasi-experimental longitudinal design used the database from 1 prefecture of Japan that included 570,434 person-month records of 23,879 insured individuals (in August 2014) who used LTC services between August 2014 and July 2015 and were aged 65 years and older on August 1, 2014. Methods: We conducted difference-in-difference estimations to compare “before” and “after” outcome differences between insured individuals whose LTC copayment increased to 20% and those whose copayment remained at 10%. Sex, age, Care Needs Level, subsidy, and public assistance were adjusted in the models, along with robustness checks. Results: Differences in both insurer’s payment and insured’s copayment indicated statistical significance between those whose copayment increased and those whose copayment did not increase. We found no significant difference in the number of minutes of home care service use, days of facility care service use, and LTC expenditures among those with copayment increases as well as those with no increase in copayment following the insured’s copayment increase policy implementation. In contrast, the policy implementation caused significant differences in the number of days of hospitalization, medical care expenditures, and total expenditures. Conclusions and Implications: The increase in insured individuals’ copayment decreased LTC insurer’s payment. However, total LTC expenditure increased over time although the increase trend slowed down in the treatment group after the copayment increase policy implemented. Besides, medical care expenditure increased consistently among insured individuals whose copayment increased. As there appears to be a “balloon effect” between LTC and medical care services, it is important to discuss the medical care system while considering the LTC insurance system comprehensively. Ó 2019 AMDA e The Society for Post-Acute and Long-Term Care Medicine.
A national long-term care (LTC) insurance system was introduced by the Japanese government for people aged 65 years and older, and those aged 40 to 64 years with a qualifying illness related to aging.1
Funding sources: This study was supported by a Health and Labour Sciences Research Grant from the Ministry of Health, Labour and Welfare, Japan (H29-ICT007), and a Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research (A) (16H02634). The authors declare no conflicts of interest. * Address correspondence to Yuichi Imanaka, MD, PhD, Department of Healthcare Economics and Quality Management, Graduate School of Medicine, Kyoto University, Yoshida Konoe-cho, Sakyo-ku, Kyoto, 606-8501, Japan. E-mail address: [email protected] (Y. Imanaka). https://doi.org/10.1016/j.jamda.2019.08.021 1525-8610/Ó 2019 AMDA e The Society for Post-Acute and Long-Term Care Medicine.
Consequently, LTC is heavily financed through the general tax system, and the proportion of the aging population.2 The universal health care insurance system [National Healthcare Insurance (NHI)] covers all residents in Japan, including foreigners with resident cards. Copayment rates for the NHI are 30% for the general public; 20% or 30% for those aged 70 to 74 years; and 10%, 20%, or 30% for those 75 years and older; in addition, the concessionary rates for individuals aged 70 years and older depend on the annual household income.3 The system also employs a high-cost protection policy with a ceiling amount imposed on copayments. In terms of LTC insurance, copayment for insured individuals was 10% before July 2015. Subsequently, this changed to 10% or 20%, and then to 10%, 20%, or 30% depending on the insured individual’s annual
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household income4 after July 2018. Notably, the guidelines of the Japan Health Insurance Bureau specify that when the same service is covered by both LTC insurance and NHI, LTC insurance must be prioritized over NHI.5 Services covered by the LTC insurance system can be divided into the following 4 categories: home care services, facility care services, community-based care services, and others. Residents in Japan aged 40 years are required to enroll in the LTC insurance system. When LTC services are requested, insured individuals are assessed for their Care Needs Level (CNL) and certified to use LTC services covered by LTC insurance. Those who are not certified are not eligible to use LTC services, although they may be reassessed in the future. The categories are Requiring Support Level (RSL) 1 and 2 and CNL 1 through 5, as determined by the municipal governments and reviewed by a committee of medical and other professionals.1,6 Insured individuals in RSL categories receive preventive care services and are less disabled than those in CNL categories. A higher number indicates coverage of more service benefits.6 Beneficiaries certified as RSL 1 and 2 have been covered and financed by the local government since April 2015.1,7 For those insured at each care certification level, there is an upper limit on service expenses covered by LTC insurance per month that is determined according to the CNL.6 After being qualified as an LTC service user, a care manager meets with the insured and his or her family to discuss the appropriate care plan incorporating medical and LTC services.1 The sharply increasing aging population has increased LTC expenditure, resulting in reformed focus on cost containment.1 Adjustment of copayment is often purported as a strategy for regulating demand, mitigating the problem of moral hazard, and improving the efficiency of the health care system.8,9 As such, revising the copayment rates for LTC users would be a useful way to regulate demand, either by reducing overuse or by exaggerating underuse.10 The Japanese government has been revising the Long Term Care Insurance Act every 3 years since its enforcement in 2000.1 In August 2015, the Japanese government revised the copayment rate of LTC service from 10% to either 10% or 20%, depending on the insured’s annual household income (see Supplementary Figure 1). Some studies indicated that copayment adjustment would have an impact on medical care service-seeking behavior.8,9,11 A previous study investigating LTC copayments focused solely on home care service use in the Netherlands, where the average copayment increase rate is incredibly high, at 42%. This study found that individuals affected by the copayment increase policy were less likely to use care services.12 Additionally, another previous study revealed that the high copayment of LTC insurance prevents beneficiaries from using services.13 Although some studies investigated the effect of copayment increase on the LTC system, none of them covered the various types of LTC services. No study has examined the effect of copayment of LTC-insured individuals considering its effect on medical care services. Japan’s NHI system offers highly accessible medical and LTC services. Services covered by the NHI system include medical careerelated services, whereas LTC services cover those provided primarily to support daily life. Some services are covered by both systems, including home visit nursing care services, prescription drug management, and nutrition management, to name a few. Notably, older adults in Japan are often admitted to the hospital for longer periods owing to the inaccessibility of alternative older adult care services.14 Because there is an overlap between NHI and LTC insurance systems, some studies revealed that there is a slight substitution between hospital utilization and LTC services2,15e17; hence, it is important to discuss medical care service usage and expenditure while considering those for LTC services. The present study aimed to clarify the differences in LTC and medical care service usage, and expenditure before and after the LTC copayment changed to 20% for qualifying beneficiaries. The primary objective was to investigate whether the copayment increase policy in
the LTC insurance system reduced the use and expenditures of LTC services. A secondary objective was to examine whether the LTC copayment increase policy increased the use and expenditures of NHI. Overall, we aimed to determine whether or not the LTC copayment increase policy had transitioned from the LTC insurance system to the NHI system. Methods Participants This quasi-experimental longitudinal design used the database from 1 prefecture of Japan. We combined LTC insurance database linked to the NHI database and the database for the medical care system for older individuals aged 75 years and older (hereinafter, the 2 medical databases are collectively referred to as the “NHIDB”) in a single panel. The exclusion criteria applied are presented in Figure 1. LTC beneficiaries who used LTC services between August 2014 and July 2015 and were aged 65 years and older on August 1, 2014 were included in the sample. Beneficiaries with RSL 1 or 2 were suitable for the “Comprehensive Project for LTC Prevention and Daily Life Support” from April 2015 and would join that project instead of using LTC service.7 Therefore, individuals whose CNL had ever been certified as RSL 1 or 2 during the observed period (August 2014 to July 2016) were eliminated from our study. Next, use patterns for LTC and medical care
Fig. 1. Exclusion criteria for participant selection. *Beneficiaries with RSL 1 or 2 qualified for “The comprehensive project for LTC prevention and daily life support” from April 2015, and they would join that project instead of using LTC services 6. Thus, individuals whose CNL had ever been certified as RSL 1 or 2 during the observed period (August 2014 to July 2016) were excluded from our study. yThe patterns of LTC and medical care service usage would be different among beneficiaries who were in the terminal stage, and individuals who died during the observed period were excluded from the sample. zAs we focused on the impact of the copayment policy on LTC service users, we excluded users who did not utilize LTC services for 12 months between August 2014 and July 2015. xBeneficiaries who did not apply for LTC services for at least 1 month between August 2015 and July 2016 were also excluded from our sample because of a lack of copayment information after policy implementation.
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services would be markedly different from those in the terminal stage. As such, individuals who died during the observation period were also excluded.18 Therefore, individuals who died during our observed period were also excluded. Besides, beneficiaries’ state that were not stable or became more severe would tend to switch to medical care service. As we focused on the policy impact on LTC service users, we excluded users who did not use LTC service for the whole 12 months between August 2014 and July 2015. Moreover, we identified the treated groups by their LTC claims data. Beneficiaries who did not apply LTC service at least 1 month between August 2015 and July 2016 would also be excluded from our study because of the lack of copayment information after policy implementation. We conducted analyses on beneficiaries who used LTC services continuously from August 2014 to July 2015 and who had ever used LTC services at least once between August 2015 and July 2016 (23,879 insured individuals in the first year, with 570,434 records). Statistical Analysis Descriptive analyses were conducted for the total sample in August 2014, including sex, age, CNL, subsidy,1 and public assistance. For people using LTC and medical care services who receive subsidies or are covered by the Public Assistance Act, expenses are paid directly to service providers from the Japanese government at no cost to the service users.1,19 We calculated the average CNL following the rules of the Health and Welfare Bureau for the Elderly.20 In addition to understanding the trend by time, the means of outcome variables were also calculated for the treatment and control groups by time. We conducted a difference-in-difference (DID) analysis to estimate the causal effect of the policy intervention by comparing the changes in outcome over time between the treatment and control groups.21 Outcome variables were extracted from the LTC database and included minutes of home care service use and days of facility care service use. We extracted additional variables including LTC insurer payments and LTC copayment of the insured from the LTC database, which allowed us to calculate LTC expenditures. Data on medical care expenditure, days of hospitalization, and frequency of ambulatory care use were extracted from the NHI database. All the outcome variables were collected on a monthly basis by the treatment and control groups. We summed the medical care expenditure and LTC expenditure to compute the total expenditure and set it as one of the dependent variables. Moreover, because the number of days in each month varies, we calculated the average outcome on a daily basis, except for frequency of ambulatory care use, days of facility care service use, and days of hospitalization (see Supplementary Table 1). We conducted a Hausman-Taylor estimation22 and found that the fixed effects estimation fit our model. According to Wooldridge,23 the fixed effects estimation is more convincing than the random effects estimation for policy analysis because it involves a large geographical unit. However, as the observed unit in the current study was at the individual level, and personal characteristics have a significant impact on LTC and medical care service use.17,24 Hence, we estimated the DID regression for the following model. For an individual i in month t:
Yit ¼ b0 þ b1 ðPeriodÞit þ b2 ðCopaymentÞit þ b3 ðPeriodit copaymentit Þ þ b4 ðageÞit
þ b5 ðsexÞit þ b6 ðsubsityÞit þ b7 ðPublic AssistanceÞit þ b8 ðCNLÞit þ b9 ðtÞ þ εit ; i ¼ 1; 2; .n; t ¼ 1; 2; .; 24
where Yit indicates outcome variables. Periodit is a binary variable with the value of zero before the implementation of the policy and 1 after the implementation of the policy. Copaymentit is an indicator variable equal to 1 if the insured individual’s copayment was
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increased to 20% and zero otherwise. b3 represents the DID estimate of the effect of the insured individual’s copayment increase policy; and age, sex, subsidy, public assistance, and CNL are the covariates that represent individual characteristics. b9 represents t indexes per month in years, ranging from t ¼ 1 to t ¼ 24, to adjust for the time fixed effect. Robust standard errors were computed to evaluate the robustness of the DID estimates. All analyses were performed using the DIFF module25 in the statistical software Stata 15.0 (StataCorp LLC, College Station, TX). The P values (2-tailed) were considered statistically significant when they were below .05. This study was approved by the Ethics Committee of Kyoto University Graduate School of Medicine (R0438). Results Descriptive statistics for all variables in August 2014 are shown in Table 1. Among the study participants, 26.4% were males, and those aged 85 to 89 years comprised the largest proportion of the sample, followed by those aged 80 to 84 years. The majority of the beneficiaries belonged to the CNL 2 category, followed by those with CNL 1 and 3. However, over time, the number of persons with CNL 3 increased beyond that of persons with CNL 1, whereas the CNL 2 category remained the largest group (see Supplementary Figure 2). The proportion of beneficiaries who received a subsidy was 2.4%, and those who received public assistance accounted for 5.7% of the sample. Finally, those whose copayment increased to 20% were younger, and they had a lower average CNL compared with those whose copayment remained at 10%. With regard to the outcome variables, monthly and daily statistics for LTC expenditure, insurer’s payment, insured individuals’ copayment, minutes of home care service use, days of facility care service use, medical care expenditure, days of hospitalization, frequency of ambulatory care use, and total expenditure of the 2 analyzed groups in August 2014 are shown in Table 1 and Supplementary Table 1. The monthly and daily trends for outcome variables are shown in Supplementary Figures 3 and 4. The monthly and daily trends in the outcome variables were similar. LTC expenditure and insurer’s payment increased with time in both the control and treatment groups. However, the treatment group showed a lower mean LTC expenditure compared with the control group. The lines of the insured individuals’ copayment in the control and treatment groups almost overlapped before the implementation of the revised copayment policy in August 2015. Since the revision of the copayment policy in August 2015, mean copayments of the treatment group were higher than those of the control group. An increasing trend was noted for days of facility care services, whereas a decrease in minutes of home care service use was noted over time in both the treatment and control groups. Finally, the treatment group had fewer days of facility care service use and more minutes of home care service use compared with the control group. Contrary to the outcomes for LTC service use, the treatment group exhibited higher days of hospitalization, frequency of ambulatory care use, and medical care expenditure, compared with the control group. In terms of the days of hospitalization, an increase was observed in both the control and treatment groups after the implementation of the copayment increase policy. With regard to the frequency of ambulatory care use, the treatment group showed higher means than the control group. The mean total expenditure was lower in the treatment group at the beginning, but the gap between the 2 groups decreased gradually over time. The results of the DID estimationeadjusted analyses controlled for age, sex, CNL, subsidy, public assistance and date showing differences between the control and treatment groups over time are presented in Table 2, and the results of the daily analysis are presented in Supplementary Table 2. The results of covariates and coefficients of each model are presented in Supplementary Table 3. According to the
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Table 1 Characteristics of Participants on August 1, 2014, and Descriptive Statistics of Outcome Variables in August 2014, From One Prefecture in Japan Variable
Copayment Group
Age, y, mean SD Age, y, n (%) 65-69 70-74 75-79 80-84 85-89 90-94 95 Sex, n (%) Male Female Average Care Needs Level, mean SD Care Needs Level, n (%) 1 2 3 4 5 Subsidy, n (%) No Yes Public assistance, n (%) No Yes Total Outcome variable, mean (SE) LTC expenditure, yen/mo Insurer’s payment, yen/mo Copayment, yen/mo Home care service, min/mo Facility care service, d/mo Medical care expenditure, yen/mo Hospitalization days, d/mo Ambulatory care, times/mo Total expenditure, yen/mo
10% Group (Control)
20% Group (Treated)
Total
P Value
84.45 0.05
84.28 0.14
84.44 0.05
.24* <.001y
936 (92.3) 1790 (92.9) 2958 (90.3) 4962 (89.1) 5850 (89.3) 3767 (92) 1369 (94.4)
78 136 316 604 704 328 81
(7.7) (7.1) (9.7) (10.9) (10.7) (8) (5.6)
1014 (4.2) 1926 (8.1) 3274 (13.7) 5566 (23.3) 6554 (27.4) 4095 (17.1) 1450 (6.1)
1754 (27.8) 493 (2.8) 2.60 0.02
6306 (26.4) 17,573 (73.6) 2.76 0.01
<.001y 4552 (72.2) 17,080 (97.2) 2.77 0.01 5197 6244 4595 3405 2191
(89.3) (90) (90.6) (91.7) (93.8)
621 695 479 307 145
(10.7) (10) (9.4) (8.3) (6.2)
5818 6939 5074 3712 2336
<.001* <.001y
(24.4) (29.1) (21.2) (15.5) (9.8) <.001y
21,066 (90.4) 566 (99.8)
2246 (9.6) 1 (0.2)
23,312 (97.6) 567 (2.4)
20,282 (90.1) 1350 (99.3) 21,632 (90.6)
2238 (9.9) 9 (0.7) 2247 (9.4)
22,520 (94.3) 1359 (5.7) 23,879 (100)
<.001y
20,8852 191,251 15,400 656 10.86 10,666 0.4 2.2 219,518
(759.1) (696.2) (67.6) (5.6) (0.1) (556.8) (0.0) (0.0) (899.6)
177,180 160,542 16,418 758 7.29 16,300 0.5 2.7 193,479
(2111.0) (1897.0) (211.9) (18.0) (0.3) (1932.3) (0.1) (0.1) (2731.4)
205,872 188,361 15,496 666 10.52 11,196 0.4 2.3 217,068
(718.3) (658.0) (64.4) (5.3) (0.1) (536.2) (0.0) (0.0) (855.9)
<.001* <.001* <.001* <.001* <.001* .005* .025* <.001* <.001*
SD, standard difference; SE, standard error. *t test results between 10% and 20% copayment beneficiaries. y 2 c test results between 10% and 20% copayment beneficiaries.
results of the models shown in Supplementary Table 3, females exhibited less medical care service use but more LTC care service use, except for home care service use, compared with males. Further, those with older age and a higher CNL had a higher LTC expenditure and
days of facility care service use, but lower home care service use. With regard to medical care services, higher CNL had a more significant impact on medical care expenditure compared with lower CNL. However, insured individuals without CNL certification also showed
Table 2 Results of the Regression on Difference-in-Difference of Participants Who Had Used LTC Services Continuously Between August 2014 and July 2015 and Had Ever Applied at Least Once for LTC Services Between August 2015 and July 2016 (n ¼ 570,434 Records) Outcome Variables
Differencein-difference estimate* Standard error R-squared Difference of the 2 groups before the intervention* Difference between 2 groups after the intervention*
Outcome Variable on Monthly Basis LTC Expenditure, yen
Insurer’s Payment, yen
Insured’s Copayment, yen
Home Care Service, min
Medical Care Expenditure, yen
Total Expenditure, yen
Facility Care Service, d
Hospitalization, d
Ambulatory Care, times
1405y
19,842z
14,837z
7.766
3791z
2383x
0.0565
0.160z
0.0433
719.5 0.396 6854
614.4 0.409 7026
112.9 0.397 1609
7.499 0.057 50.13
1095 0.003 391.4
1211 0.233 6465
0.117 0.157 1.289
0.0389 0.03 0.0483
0.0273 0.038 0.219
8259
26,869
16,446
42.37
4183
4081
1.345
0.112
0.176
*Adjusted analyses controlled for age, sex, care needs level, subsidy, public assistance, and date. y P < .1. z P < .01. x P < .05.
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higher medical care expenditure. Being aged between 75 and 79 years had the strongest impact on medical care expenditure. In both the monthly (Table 2) and daily analyses (Supplementary Table 2), there was no significant DID for minutes of home care service use, frequency of ambulatory care use, facility care service use, and LTC expenditure in the adjusted analyses between the control and treatment groups. In terms of LTC expenditure, it showed significant DID on statistical significance below 0.1. However, because it was not considered statistically significant unless the P values (2-tailed) were below .05, we categorize the DID result of LTC expenditure as no significance. Insurer’s payment, insured individuals’ copayment, medical care expenditure, days of hospitalization, and total expenditure had significant DID in the adjusted analyses between the control and treatment groups. Discussion The average age and CNL were lower in the treatment group than in the control group. According to the Japanese government, the medical care expenditure increased with age after the age of 65 years.26 As the average CNL increased with age,27 a higher CNL implies higher LTC expenditure.24,28 With regard to adjusting for the covariance representing individual characteristics, our results corroborated those reported in previous studies. Compared with men, women tended to use more LTC services except for home care services.24,29 Furthermore, higher CNL and older age were also associated with more LTC service use.24 Because subsidization or public assistance removed the financial barrier,30,31 it is not surprising that more LTC services were used. Kim et al31 revealed that low-income, female, or independent individuals both tend to qualify for subsidies to use LTC services and to use facility care services. In Japan, women are expected to be the main informal caregiver and most often are daughters or spouses of the insured individuals.32 The Japanese government reports that 56.9% of men wish to receive care from their spouse, whereas this number is only 39.5% among women.33 As life expectancy is longer for women than men in Japan, men would be less likely to serve as the primary caregiver for their spouse or parents. Another survey conducted by the Japanese government found that women comprise more than 70% of LTC facility residents, more than double the percentage of men.34 The fact that informal care is provided by family or relatives may explain some of the gender disparity evident in the type of LTC service use. The present DID estimation showed no significant differences in the minutes of home care service use, which is consistent with the findings of a previous study.12 According to Supplementary Figures 3 and 4, though the minutes of home care service use decreased with time, outcomes related to LTC expenditure increased. The fact that use of home care among older adults with disabilities is dependent on their copayment rates may be one potential explanation for why use of home care services decreased while use of other types of LTC services increased.35 Additionally, previous studies indicated that higher CNL and old age tend to be associated with the higher tendency to use facility care services more than home care services.36,37 Insured individuals at a higher CNL are more dependent and require more care services. Facility care services offer 24-hour care, which is more than that offered by home care services. As such, the former would be more appropriate for insured individuals with higher care needs; a shift from home care services to facility care is often made for these individuals. With regard to LTC insurer’s payment, the trends were similar to those of LTC expenditure. Moreover, facility care service use has a stronger impact on LTC expenditure than home care service use does.24 The reasons mentioned above may explain why the minutes of home care service use decreased over time, but there was still an increase in LTC expenditure.
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The LTC insured individuals’ copayment in the treatment and control groups almost overlapped before the revised copayment policy was implemented, and it increased sharply in the treatment group after the policy was implemented. The copayment increase has reduced the insurer’s portion on total expenditures; however, the total expenditures increased over time. Accordingly, the revised policy also impacted the total expenditure trend, thus minimizing the disparity between the 2 groups. Individuals in the treatment group, with a lower CNL and younger age, tended to use more medical care services compared with those in the control group,26 whereas older adults tended to use more LTC services.24 Beneficiaries whose copayment was increased to 20% and who had a lower CNL tended to use medical care over LTC services to minimize their copayment.4 Therefore, the means of medical care expenditure, days of hospitalization, and frequency of ambulatory care use were higher in the treatment group than in the control group. With regard to the DID analysis, we found significant DID for LTC insurer’s payment and for LTC insured individuals’ copayment between the treatment and control groups. The increase in insured individuals’ copayment for LTC insurance directly affected the insured individuals’ copayment, which led to a significant DID regression. Furthermore, the DID analysis showed no significance for LTC expenditures in that the revised copayment policy did not influence LTC service use among treatment group Participants. The Japan Ministry of Health, Labour and Welfare conducted an investigation to determine whether the change in the copayment policy led to a change in LTC service-seeking behavior among home care service users.4 The report indicated that if the LTC beneficiary’s medical care copayment was less than 20% and it had increased to 20%, he or she tended to utilize medical care service instead of LTC service, especially for hospitalization.4 As time goes by, our Participants would get older and tend to shift their service type from home care service to facility care service by paying the same amount of LTC insured’s copayment. Otherwise the LTC insureds will shift their facility care service use to medical hospitalization service.36,37 Our results showed no significant DID in ambulatory care service use, which is different from the findings of a previous study.11 Another study indicated that compared with hospital care, ambulatory care service use was more sensitive to insured individuals’ copayment rate.11 Nonetheless, in Japan, when insured individuals need care services, LTC services are prioritized over medical care services. Because the Participants in our study were LTC recipients, their ambulatory care usage would be less than that of general care recipients. On the other hand, the DID estimations showed significant results for medical care expenditure, days of hospitalization, and total expenditure. All outcomes that showed significance were collected from the NHIDB. The present DID estimations implied that the implementation of the revised copayment policy indeed had an impact on the beneficiaries’ service use patterns. Previous studies indicated that LTC and hospital utilization substitute each other.11,16,17 The revised copayment policy is one explanation for the increase in hospitalization and the increase in total care needs.4,38 The revised copayment policy was designed to alleviate the financial burden of the LTC insurance system, but the policy was found easing the insurer’s financial burden in the LTC insurance system at the expense of insureds and medical care insurance system. Additionally, beneficiaries would cut usage to balance their expenditure.10,11 Notably, it remains unclear whether insured individuals curtailed the low priority or excessive services in response to the copayment increase policy. Our study found that the policy also affected medical care expenditure and service use. This may suggest that some LTC beneficiaries would prefer to use medical care services instead of LTC service, because there is a light substitution effect between LTC and medical care services.16,39 Moreover, the maximum monthly benefits covered by LTC insurance limits its use by
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beneficiaries, while the copayment ceiling for medical care services may lead insured individuals (particularly those with severe disabilities or more comorbidities) to use medical care services instead of LTC services. Adjusting the copayment according to annual income would be helpful to enhance the sustainability of the LTC insurance system.35,40 However, the changing demands of LTC users related to copayment rates would vary according to their financial means,35 medical copayment rate, and level of disability. There appears to be a “balloon effect” between LTC services and medical care services. The increase of copayment could reduce LTC insurers’ expenditure, as it leads to an increase in the use of medical care service over LTC service. The LTC insurance system aimed to reduce social hospitalization by 2011.1 However, the copayment revision policy would ease the financial burden of the LTC insurer at the expense of the NHI system. Some issues remain with the LTC and NHI systems. First, the copayment rate is dependent on the insured individual’s annual household income, although older adults accumulate considerably more housing assets than their younger counterparts.41 Copayment rates based strictly on older adults’ annual household income may not reflect the true affordability of LTC services. Instead, we suggest that the Japanese government revise the criteria for copayment rates by considering not only household income but assets as well. One other issue is that the Japanese government revised the “Act on Promotion of Comprehensive Securing of Medical Care and LongTerm Care in Areas” to promote coordination between medical care and LTC systems.1,42,43 Medical care services aim to cure patient disease, which has helped to maintain quality of life through LTC services thus far.44 However, the 2 systems operate separately. In order to integrate the medical care and LTC systems and ensure proper functioning, they must share information and arrive at a consensus on how to care for the insured. This may be best achieved if policy makers provide a conduit for communication between medical workers and LTC workers. Finally, since LTC and medical care systems are conditionally substitutable among the hospitalization and LTC facility care service use and the home care nursing and such, policy revisions made to each system must take into account their comprehensive impact on both systems. Limitations We collected the LTC insurance claims data so that individuals who did not use LTC services during the study period would not be included. Beneficiaries who continuously used LTC for at least 12 months may originally have been dependent on LTC services instead of medical care services, and the impact of the increase in their copayment for LTC services may not be as evident. Next, the copayment policy adjusts beneficiaries’ copayment according to their household income. However, we do not have individual financial data and could identify the treatment group only based on LTC service records. Therefore, insured individuals whose household income met the inclusion criterion for the treatment group but who had not used LTC services were excluded from our study. Furthermore, beneficiaries aged 75 years and older would be introduced to the medical care system for older adults, and the copayment of insured individuals in the system is 10%, 20%, or 30% depending on their household income. However, the data of insured individuals’ copayment rates for the medical care system were not available in our database. Future research should also consider the impact of the insured individuals’ copayment rate for the medical care system. Conclusion and Implications The increase in insured individuals’ copayment decreased the LTC insurer’s payment. However, the total LTC expenditure increased over
time although the increasing trend slowed down in the treatment group after the copayment increase policy was implemented. On the other hand, medical care expenditure increased consistently among insured individuals whose copayment increased. As there appears to be a “balloon effect” between LTC and medical care services, it is important to discuss the medical care system while considering the LTC insurance system comprehensively. Acknowledgments We thank Dr Takuma Sugahara for his useful suggestions and valuable comments on this study at the 14th Annual Conference of the Japan Health Economics Association. References 1. Sakamoto H, Rahman MM, Nomura S, et al. Japan. Health system review. Health Syst Transit 2018;8:248. 2. Costa-Font J, Courbage C, Swartz K. Financing long-term care: Ex ante, ex post or both? Health Econ 2015;24:45e57. 3. Ministry of Health, Labour and Welfare. An outline of the Japanese Medical System. 2019. Available at: https://www.mhlw.go.jp/bunya/iryouhoken/iryouh oken01/dl/01_eng.pdf. Accessed May 18, 2019. 4. Mitsubishi UFJ Research and Consulting. Investigation on the impact of copayment increase to 20 percent of long-term care insurance. 2018. Available at: https://www.murc.jp/uploads/2018/04/koukai_180418_c12.pdf. Accessed December 27, 2018. 5. Ministry of Health, Labour and Welfare. The Revision of the Healthcare Payment System for 2016 Fiscal Year. Tokyo: Ministry of Health, Labour and Welfare; 2016. 6. Tsutsui T, Muramatsu N. Care-needs certification in the long-term care insurance system of Japan. J Am Geriatr Soc 2005;53:522e527. 7. Ministry of Health, Labour and Welfare. The comprehensive project for longterm care prevention and daily life support. 2018. Available at: https://www. mhlw.go.jp/stf/seisakunitsuite/bunya/0000192992.html. Accessed July 2, 2018. 8. Landsem MM, Magnussen J. The effect of copayments on the utilization of the GP service in Norway. Soc Sci Med 2018;205:99e106. 9. Elkins RK, Schurer S. Introducing a GP copayment in Australia: Who would carry the cost burden? Health Policy 2017;121:543e552. 10. Rhee JC, Done N, Anderson GF. Considering long-term care insurance for middle-income countries: Comparing South Korea with Japan and Germany. Health Policy 2015;119:1319e1329. 11. Kiil A, Houlberg K. How does copayment for health care services affect demand, health and redistribution? A systematic review of the empirical evidence from 1990 to 2011. Eur J Health Econ 2014;15:813e828. 12. Non M. Co-payments in long-term Home Care: Do They Affect the Use of Care? CPB Discussion Paper 363, CPB Netherlands Bureau for Economic Policy Analysis; 2017. Available at: https://ideas.repec.org/p/cpb/discus/363.html, Accessed September 30, 2019. 13. Rothgang H. Social insurance for long-term care: An evaluation of the German model. Soc Policy Admin 2010;44:436e460. 14. Yong V, Saito Y. National long-term care insurance policy in Japan a decade after implementation: Some lessons for aging countries. Ageing Int 2012;37: 271e284. 15. Tajika E, Kikuchi J. Health and long-term care utilization among the elderly in the last year of life. Q Soc Secur Res 2011;47:304e319. 16. Forder J. Long-term care and hospital utilisation by older people: An analysis of substitution rates. Health Econ 2009;18:1322e1338. 17. Kim J-H, Lee Y. Implementation of long-term care and hospital utilization: Results of segmented regression analysis of interrupted time series study. Arch Gerontol Geriatr 2018;78:221e226. 18. Fassbender K, Fainsinger RL, Carson M, Finegan BA. Cost trajectories at the end of life: The Canadian experience. J Pain Symptom Manage 2009;38:75e80. 19. Ministry of Health, Labour and Welfare. Public assistance system. 2010. Available at: https://www.mhlw.go.jp/english/topics/social_welfare/index.html. Accessed December 11, 2018. 20. Health and Welfare Bureau for the Elderly. The Survey of the Effectiveness of Long-Term Care Insurance System Revision in 2015. Tokyo: Health and Welfare Bureau for the Elderly; 2018. 21. Dimick JB, Ryan AM. Methods for evaluating changes in health care policy: The difference-in-differences approach. JAMA 2014;312:2401e2402. 22. Schaffer M, Stillman S. XTOVERID: Stata Module to Calculate Tests of Overidentifying Restrictions After Xtreg, Xtivreg, Xtivreg2, Xthtaylor; 2016. Available at: https://EconPapers.repec.org/RePEc:boc:bocode:s456779. Accessed September 30, 2019. 23. Wooldridge JM. Introductory Econometrics: A Modern Approach. 6th, student ed. Boston, MA: Cengage Learning; 2016. 24. Lin H, Otsubo T, Sasaki N, Imanaka Y. The determinants of long-term care expenditure and their interactions. Int J Healthc Manage 2016;9:269e279.
H.-R. Lin, Y. Imanaka / JAMDA xxx (2019) 1e7 25. Villa J. DIFF: Stata Module to Perform Differences in Differences Estimation. College Station, TX: StataCorp; 2017. 26. Ministry of Health, Labour and Welfare. Estimates of national medical care expenditure in FY 2015. 2017. Available at: https://www.mhlw.go.jp/toukei/ saikin/hw/k-iryohi/15/index.html. Accessed July 27, 2018. 27. Health and Welfare Bureau for the Elderly. Fact-finding survey on Project of Long-term Care FY2015. 2015. Available at: https://www.mhlw.go.jp/topics/ kaigo/osirase/jigyo/15/index.html. Accessed July 27, 2018. 28. Schwarzkopf L, Menn P, Leidl R, et al. Are community-living and institutionalized dementia patients cared for differently? Evidence on service utilization and costs of care from German insurance claims data. BMC Health Serv Res 2013;13:2. 29. Kim N. Long-term care services expenditure projection in South Korea from 2015 to 2050. Int J Health Plann Manage 2015;30:45e56. 30. Sato M, Hashimoto H, Tamiya N, Yano E. The effect of a subsidy policy on the utilization of community care services under a public long-term care insurance program in rural Japan. Health Policy 2006;77:43e50. 31. Kim H, Kwon S, Yoon NH, Hyun KR. Utilization of long-term care services under the public long-term care insurance program in Korea: Implications of a subsidy policy. Health Policy 2013;111:166e174. 32. Washio M, Toyoshima Y, Miyabayashi I, Arai Y. Burden among family caregivers of older people who need care in Japan. In: Washio M, Kiyohara C, editors. Health Issues and Care System for the Elderly. Singapore: Springer Singapore; 2019. p. 17e32. 33. Ministry of Health, Labour and Welfare. Abridged life tables for Japan 2017. 2017. Available at: https://www.mhlw.go.jp/english/database/db-hw/lifetb17/ index.html. Accessed May 18, 2019.
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34. Ministry of Health, Labour and Welfare. Survey of Institutions and Establishments for Long-term Care. 2016. Available at: https://www.mhlw.go.jp/ english/database/db-hss/dl/siel-2016-02.pdf. Accessed May 19, 2019. 35. Roquebert Q, Tenand M. Pay less, consume more? The price elasticity of home care for the disabled elderly in France. Health Econ 2017;26:1162e1174. 36. Wu CY, Hu HY, Huang N, et al. Determinants of long-term care services among the elderly: A population-based study in Taiwan. PLOS One 2014;9: e89213. 37. Branch LG, Jette AM. A prospective study of long-term care institutionalization among the aged. Am J Public Health 1982;72:1373e1379. 38. Ansah JP, Eberlein RL, Love SR, et al. Implications of long-term care capacity response policies for an aging population: A simulation analysis. Health Policy 2014;116:105e113. 39. Hollander MJ, Chappell NL. A comparative analysis of costs to government for home care and long-term residential care services, standardized for client care needs. Can J Aging 2007;26:149e161. 40. Fu R, Noguchi H. Moral hazard under zero price policy: Evidence from Japanese long-term care claims data. Eur J Health Econ 2019;20:785e799. 41. Hirayama Y. The role of home ownership in Japan’s aged society. J Hous Built Environ 2010;25:175e191. 42. Ministry of Health, Labour and Welfare. Act on Promotion of Comprehensive Securing of Medical Care and Long-Term Care in Areas. Tokyo: Ministry of Health, Labour and Welfare; 2014. 43. Fukuda T. Visions for integration of community-based health care and longterm care. J Natl Inst Public Health 2016;65:103e104. 44. Tamiya N. Co-operation between medical and social care and health services research. Jpn J Geriatr (Nippon Ronen Igakkai Zasshi) 2017;54:22e27.
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Appendix
Yes Yes
Yes
Living alone
10% copayment
Annual income* no more than 160 million yen
20% copayment
LTC user was aged 65 years and he/she paid income tax in the previous year
No
No
No
10% copayment
Annual income more than 160 million yen or annuity for older adults amounting more than 280 million yen
Yes 20% copayment
10% copayment No
Supplementary Figure 1. Rules for copayment qualification. *Annual income: income from working, investment, older adult annuity, and so on.
(Participants) 8,000 7,000 6,000 5,000 4,000 3,000 2,000 1,000 Aug -14 Sep -14 Oct -14 Nov -14 Dec -14 Jan -15 Feb -15 Mar -15 Apr -15 May -15 Jun -15 Jul -15 Aug -15 Sep -15 Oct -15 Nov -15 Dec -15 Jan -16 Feb -16 Mar -16 Apr -16 May -16 Jun -16 Jul -16
0
Care needs level 1
Care needs level 2
Care needs level 4
Care needs level 5
Care needs level 3
Supplementary Figure 2. Numbers of persons by care needs level.
H.-R. Lin, Y. Imanaka / JAMDA xxx (2019) 1e7
LTC expenditure (yen/month)
(10000 yen)
7.e2
Insurer's payment (yen/month)
Copayment (yen/month) (10000 yen)
(10000 yen)
25
25
25
20
20
20
15
15
15
10
10
10
5
5
5
0
0
0
Home care service (mins/month)
(min) 1000
Facility care service (days/month)
(day) 15
(10000 yen)
4
800
3
10
600
Medical service expenditure (yen/month)
2
400
5
1
200
0
0
0
8/1/2014
Hospitalization days (days/month) (Day) 3
Total expenditure (yen/month) (10000 yen)
30 25 20 15 10 5 0
2 1 0 8/1/2014
8/1/2015
8/1/2015
Ambulatory care use (times/month) (Time) 3 2 1 0
8/1/2014
8/1/2015
10% group
8/1/2014
8/1/2015
20% group
Supplementary Figure 3. Outcome variables on a monthly basis for participants who had used LTC services continuously between August 2014 and July 2015 and had ever applied at least once for LTC services between August 2015 and July 2016.
LTC expenditure (yen/day)
Insurer's payment (yen/day)
Copayment (yen/day)
8000
8000
8000
6000
6000
6000
4000
4000
4000
2000
2000
2000
0
0
0
Total expenditure (yen/day)
Home care service (min/day) 30 20
8,000
1,500
6,000
1,000
4,000
10
500
2,000 0 8/1/2014
Medical service expenditure (yen/day)
10,000
0
0 8/1/2015
8/1/2014 10% group
8/1/2015 20% group
8/1/2014
8/1/2015
Supplementary Figure 4. Outcome variables on a daily basis for participants who had used LTC services continuously between August 2014 and July 2015 and had ever applied at least once for LTC services between August 2015 and July 2016.
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Supplementary Table 1 Descriptive Statistics of Outcome Variables on a Daily Basis in August 2014, From One Prefecture in Japan Outcome Variable
LTC expenditure, yen/d Insurer’s payment, yen/d Insured’s copayment, yen/d Home care service, min/d Medical care expenditure, yen/d Total expenditure, yen/d
Copayment Group
P Value*
10% Group (Control)
20% Group (Treated)
Total
Mean
SE
Mean
SE
Mean
SE
6737 6169 497 21 344 7081
24.5 22.5 2.2 0.2 18.0 29.0
5715 5179 530 24.1 526 6241
68.1 61.2 6.8 0.6 62.3 88.1
6641 6076 500 21.2 361 7002
23.2 21.2 2.1 0.2 17.3 27.6
<.001 <.001 <.001 <.001 .005 <.001
SE, standard error. *t test results between 10% and 20% copayment beneficiaries.
Supplementary Table 2 The Regression Results of Difference-in-Difference of Participants Who Had Used LTC Services Continuously Between August 2014 and July 2015 and Had Ever Applied at Least Once for LTC Services Between August 2015 and July 2016 (n ¼ 570,434 Records) Outcome Variables
Difference-in-difference estimate* Standard error R squared Difference of the 2 groups before the intervention* Difference between 2 groups after the intervention*
Outcome Variable on Daily Basis LTC Expenditure, yen
Insurer’s Payment, yen
Copayment, yen
Home Care Service, min
Medical Care Service Expenditure, yen
Total Expenditure, yen
43.46y 23.61 0.395 226.2
648.1z 20.17 0.408 231.8
486.5z 3.702 0.397 52.87
0.263 0.245 0.056 1.632
123.6z 35.97 0.003 13.65
80.05x 39.78 0.233 212.7
269.6
879.9
539.4
1.368
137.2
132.6
*Adjusted analyses controlled for age, sex, care needs level, subsidy, public assistance, and date. y P < .1. z P < .01. x P < .05.
H.-R. Lin, Y. Imanaka / JAMDA xxx (2019) 1e7 Supplementary Table 3 Difference-in-Difference Estimation Results of Covariates of All Models (Time Dummies Were Not Reported) Independent Variable (s) (1) LTC Expenditure Sex Male Female Subsidy No Yes Public assistance No Yes Age, y 65-69 70-74 75-79 80-84 85-89 90-94 95 Care needs level None 1 2 3 4 5 (2) Insurer’s Payment Sex Male Female Subsidy No Yes Public assistance No Yes Age, y 65-69 70-74 75-79 80-84 85-89 90-94 95 Care needs level None 1 2 3 4 5 (3) Insured’s copayment Sex Male Female Subsidy No Yes Public assistance No Yes Age, y 65-69 70-74 75-79 80-84 85-89 90-94 95 Care Needs Level None 1 2
Coefficient
Reference 19,000 Reference 62,000 Reference 19,000 27,000 27,000 20,000 7200 Reference 4499.125 7579.651 160,000 33,000 Reference 69,000 110,000 150,000
Reference 18,000 Reference 69,000 Reference 34,000 24,000 24,000 18,000 6400 Reference 4159.253 7085.221 150,000 30,000 Reference 63,000 100,000 130,000
Reference 885.558 Reference 4500 Reference 14,000 2600 2000 1800 622.247 Reference 62.976 345.462 15,000 2800 Reference
SE
P Value
289.398
790.539
<.001
<.001
556.844
<.001
657.503 479.326 394.423 319.526
<.001 <.001 <.001 <.001
324.483 439.853
<.001 <.001
1195.39 313.553
<.001 <.001
318.601 346.294 393.205
<.001 <.001 <.001
261.764
<.001
761.631
<.001
541.604
<.001
597.02 433.608 357.01 288.749
<.001 <.001 <.001 <.001
293.677 399.321
<.001 <.001
1250.031 283.036
<.001 <.001
288.046 313.925 356.716
<.001 <.001 <.001
29.31
<.001
75.601
<.001
60.585
<.001
66.36 48.426 39.971 32.905
<.001 <.001 <.001 <.001
33.173 44.393
.06 <.001
340.893 30.018
<.001 <.001 (continued)
7.e4
Supplementary Table 3 (continued ) Independent Variable (s)
Coefficient
3 4 5 (4) Time Minutes of Home Care Sex Male Female Subsidy No Yes Public assistance No Yes Age, y 65-69 70-74 75-79 80-84 85-89 90-94 95 Care Needs Level None 1 2 3 4 5 (5) Days of Facility Care Service Sex Male Female Subsidy No Yes Public assistance No Yes Age, y 65-69 70-74 75-79 80-84 85-89 90-94 95 Care needs level None 1 2 3 4 5 (6) Medical Care Expenditure Sex Male Female Subsidy No Yes Public assistance No Yes Age, y 65-69 70-74 75-79 80-84 85-89 90-94 95 Care Needs Level None 1
5202.299 7853.385 10,000 Service
SE
P Value 31 35.441 43.959
<.001 <.001 <.001
Reference 69.431
2.638
<.001
Reference 789.031
7.5
<.001
Reference 377.256
6.485
<.001
81.083 112.276 38.557 11.821 Reference 45.381 90.053
6.166 4.601 3.558 3
<.001 <.001 <.001 <.001
2.98 3.851
<.001 <.001
647.624 18.605 Reference 134.938 265.941 316.425
5.614 2.993
<.001 <.001
3.032 3.106 3.339
<.001 <.001 <.001
Reference 2.925
0.044
<.001
Reference 21.487
0.117
<.001
Reference 8.355
0.068
<.001
0.093 0.07 0.06 0.052
<.001 <.001 <.001 <.001
0.055 0.075
<.001 <.001
0.189 0.049
<.001 <.001
0.052 0.056 0.063
<.001 <.001 <.001
Reference 4300
387.984
<.001
Reference 2600
409.277
<.001
Reference 13,000
405.774
<.001
2300 111.001 3018.337 1417.966 Reference 1100 2400
745.715 610.886 540.909 416.702
.002 .86 <.001 .001
387.19 494.675
.005 <.001
4799.565 2900
7916.815 394.531
.54 <.001
5.083 4.363 2.778 1.128 Reference 1.267 2.21 8.241 0.98 Reference 5.446 8.52 9.262
(continued on next page)
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H.-R. Lin, Y. Imanaka / JAMDA xxx (2019) 1e7
Supplementary Table 3 (continued ) Independent Variable (s)
Coefficient
2 Reference 3 920.12 4 2380.969 5 4123.153 (7) Days of Hospitalization Sex Male Reference Female 0.181 Subsidy No Reference Yes 0.231 Public assistance No Reference Yes 0.583 Age, y 65-69 0.233 70-74 0.081 75-79 0.001 80-84 0.012 85-89 Reference 90-94 0.05 95 0.135 Care Needs Level None 27.385 1 0.104 2 Reference 3 0.143 4 0.286 5 0.476 (8) Times of Ambulatory Care Service Sex Male Reference Female 0.077 Subsidy No Reference Yes 0.402 Public assistance No Reference Yes 2.089 Age, y 65-69 0.301 70-74 0.234 75-79 0.173 80-84 0.092 85-89 Reference 90-94 0.141 95 0.24 Care Needs Level None 2.195 1 0.181 2 Reference 3 0.036 4 0.096 5 0.326 (9) Total expenditure Sex Male Reference Female 15,000 Subsidy No Reference Yes 60,000 Public assistance No Reference Yes 6176.282 Age, y 65-69 30,000 70-74 27,000 75-79 17,000 80-84 5800 85-89 Reference 90-94 3415.503 95 5202.663
Supplementary Table 3 (continued ) SE
P Value
415.337 430.006 478.801
.027 <.001 <.001
0.014
<.001
0.016
<.001
0.017
<.001
0.026 0.022 0.019 0.015
<.001 <.001 .97 .43
0.016 0.021
.002 <.001
0.506 0.013
<.001 <.001
0.015 0.017 0.021
<.001 <.001 <.001
0.01
<.001
0.011
<.001
0.012
<.001
0.023 0.017 0.014 0.011
<.001 <.001 <.001 <.001
0.01 0.013
<.001 <.001
0.073 0.011
<.001 <.001
0.011 0.011 0.014
.001 <.001 <.001
449.387
<.001
860.117
<.001
650.692
<.001
934.71 724.21 623.289 485.983
<.001 <.001 <.001 <.001
462.283 603.429
<.001 <.001 (continued)
Independent Variable (s) Care needs level None 1 2 3 4 5 SE, standard error.
Coefficient
SE
P Value
160,000 36,000 Reference 70,000 110,000 150,000
8037.264 481.182
<.001 <.001
488.045 503.398 555.95
<.001 <.001 <.001
JAMDA journal homepage: www.jamda.com
Original Study
Effects of Copayment in Long-term Care Insurance on Long-term Care and Medical Care Expenditure Huei-Ru Lin PhD, Yuichi Imanaka MD, PhD * Department of Healthcare Economics and Quality Management, Graduate School of Medicine, Kyoto University, Kyoto, Japan
a b s t r a c t Keywords: Copayment long-term care insurance policy expenditure difference-in-difference
Objective: This study aimed to clarify the difference in (1) long-term care (LTC) usage and expenditure and (2) medical care service usage and expenditure before and after the change in the copayment limit for qualifying individuals from 10% to 20%. Setting and Participants: This quasi-experimental longitudinal design used the database from 1 prefecture of Japan that included 570,434 person-month records of 23,879 insured individuals (in August 2014) who used LTC services between August 2014 and July 2015 and were aged 65 years and older on August 1, 2014. Methods: We conducted difference-in-difference estimations to compare “before” and “after” outcome differences between insured individuals whose LTC copayment increased to 20% and those whose copayment remained at 10%. Sex, age, Care Needs Level, subsidy, and public assistance were adjusted in the models, along with robustness checks. Results: Differences in both insurer’s payment and insured’s copayment indicated statistical significance between those whose copayment increased and those whose copayment did not increase. We found no significant difference in the number of minutes of home care service use, days of facility care service use, and LTC expenditures among those with copayment increases as well as those with no increase in copayment following the insured’s copayment increase policy implementation. In contrast, the policy implementation caused significant differences in the number of days of hospitalization, medical care expenditures, and total expenditures. Conclusions and Implications: The increase in insured individuals’ copayment decreased LTC insurer’s payment. However, total LTC expenditure increased over time although the increase trend slowed down in the treatment group after the copayment increase policy implemented. Besides, medical care expenditure increased consistently among insured individuals whose copayment increased. As there appears to be a “balloon effect” between LTC and medical care services, it is important to discuss the medical care system while considering the LTC insurance system comprehensively. Ó 2019 AMDA e The Society for Post-Acute and Long-Term Care Medicine.
A national long-term care (LTC) insurance system was introduced by the Japanese government for people aged 65 years and older, and those aged 40 to 64 years with a qualifying illness related to aging.1
Funding sources: This study was supported by a Health and Labour Sciences Research Grant from the Ministry of Health, Labour and Welfare, Japan (H29-ICT007), and a Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research (A) (16H02634). The authors declare no conflicts of interest. * Address correspondence to Yuichi Imanaka, MD, PhD, Department of Healthcare Economics and Quality Management, Graduate School of Medicine, Kyoto University, Yoshida Konoe-cho, Sakyo-ku, Kyoto, 606-8501, Japan. E-mail address: [email protected] (Y. Imanaka). https://doi.org/10.1016/j.jamda.2019.08.021 1525-8610/Ó 2019 AMDA e The Society for Post-Acute and Long-Term Care Medicine.
Consequently, LTC is heavily financed through the general tax system, and the proportion of the aging population.2 The universal health care insurance system [National Healthcare Insurance (NHI)] covers all residents in Japan, including foreigners with resident cards. Copayment rates for the NHI are 30% for the general public; 20% or 30% for those aged 70 to 74 years; and 10%, 20%, or 30% for those 75 years and older; in addition, the concessionary rates for individuals aged 70 years and older depend on the annual household income.3 The system also employs a high-cost protection policy with a ceiling amount imposed on copayments. In terms of LTC insurance, copayment for insured individuals was 10% before July 2015. Subsequently, this changed to 10% or 20%, and then to 10%, 20%, or 30% depending on the insured individual’s annual
2
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household income4 after July 2018. Notably, the guidelines of the Japan Health Insurance Bureau specify that when the same service is covered by both LTC insurance and NHI, LTC insurance must be prioritized over NHI.5 Services covered by the LTC insurance system can be divided into the following 4 categories: home care services, facility care services, community-based care services, and others. Residents in Japan aged 40 years are required to enroll in the LTC insurance system. When LTC services are requested, insured individuals are assessed for their Care Needs Level (CNL) and certified to use LTC services covered by LTC insurance. Those who are not certified are not eligible to use LTC services, although they may be reassessed in the future. The categories are Requiring Support Level (RSL) 1 and 2 and CNL 1 through 5, as determined by the municipal governments and reviewed by a committee of medical and other professionals.1,6 Insured individuals in RSL categories receive preventive care services and are less disabled than those in CNL categories. A higher number indicates coverage of more service benefits.6 Beneficiaries certified as RSL 1 and 2 have been covered and financed by the local government since April 2015.1,7 For those insured at each care certification level, there is an upper limit on service expenses covered by LTC insurance per month that is determined according to the CNL.6 After being qualified as an LTC service user, a care manager meets with the insured and his or her family to discuss the appropriate care plan incorporating medical and LTC services.1 The sharply increasing aging population has increased LTC expenditure, resulting in reformed focus on cost containment.1 Adjustment of copayment is often purported as a strategy for regulating demand, mitigating the problem of moral hazard, and improving the efficiency of the health care system.8,9 As such, revising the copayment rates for LTC users would be a useful way to regulate demand, either by reducing overuse or by exaggerating underuse.10 The Japanese government has been revising the Long Term Care Insurance Act every 3 years since its enforcement in 2000.1 In August 2015, the Japanese government revised the copayment rate of LTC service from 10% to either 10% or 20%, depending on the insured’s annual household income (see Supplementary Figure 1). Some studies indicated that copayment adjustment would have an impact on medical care service-seeking behavior.8,9,11 A previous study investigating LTC copayments focused solely on home care service use in the Netherlands, where the average copayment increase rate is incredibly high, at 42%. This study found that individuals affected by the copayment increase policy were less likely to use care services.12 Additionally, another previous study revealed that the high copayment of LTC insurance prevents beneficiaries from using services.13 Although some studies investigated the effect of copayment increase on the LTC system, none of them covered the various types of LTC services. No study has examined the effect of copayment of LTC-insured individuals considering its effect on medical care services. Japan’s NHI system offers highly accessible medical and LTC services. Services covered by the NHI system include medical careerelated services, whereas LTC services cover those provided primarily to support daily life. Some services are covered by both systems, including home visit nursing care services, prescription drug management, and nutrition management, to name a few. Notably, older adults in Japan are often admitted to the hospital for longer periods owing to the inaccessibility of alternative older adult care services.14 Because there is an overlap between NHI and LTC insurance systems, some studies revealed that there is a slight substitution between hospital utilization and LTC services2,15e17; hence, it is important to discuss medical care service usage and expenditure while considering those for LTC services. The present study aimed to clarify the differences in LTC and medical care service usage, and expenditure before and after the LTC copayment changed to 20% for qualifying beneficiaries. The primary objective was to investigate whether the copayment increase policy in
the LTC insurance system reduced the use and expenditures of LTC services. A secondary objective was to examine whether the LTC copayment increase policy increased the use and expenditures of NHI. Overall, we aimed to determine whether or not the LTC copayment increase policy had transitioned from the LTC insurance system to the NHI system. Methods Participants This quasi-experimental longitudinal design used the database from 1 prefecture of Japan. We combined LTC insurance database linked to the NHI database and the database for the medical care system for older individuals aged 75 years and older (hereinafter, the 2 medical databases are collectively referred to as the “NHIDB”) in a single panel. The exclusion criteria applied are presented in Figure 1. LTC beneficiaries who used LTC services between August 2014 and July 2015 and were aged 65 years and older on August 1, 2014 were included in the sample. Beneficiaries with RSL 1 or 2 were suitable for the “Comprehensive Project for LTC Prevention and Daily Life Support” from April 2015 and would join that project instead of using LTC service.7 Therefore, individuals whose CNL had ever been certified as RSL 1 or 2 during the observed period (August 2014 to July 2016) were eliminated from our study. Next, use patterns for LTC and medical care
Fig. 1. Exclusion criteria for participant selection. *Beneficiaries with RSL 1 or 2 qualified for “The comprehensive project for LTC prevention and daily life support” from April 2015, and they would join that project instead of using LTC services 6. Thus, individuals whose CNL had ever been certified as RSL 1 or 2 during the observed period (August 2014 to July 2016) were excluded from our study. yThe patterns of LTC and medical care service usage would be different among beneficiaries who were in the terminal stage, and individuals who died during the observed period were excluded from the sample. zAs we focused on the impact of the copayment policy on LTC service users, we excluded users who did not utilize LTC services for 12 months between August 2014 and July 2015. xBeneficiaries who did not apply for LTC services for at least 1 month between August 2015 and July 2016 were also excluded from our sample because of a lack of copayment information after policy implementation.
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services would be markedly different from those in the terminal stage. As such, individuals who died during the observation period were also excluded.18 Therefore, individuals who died during our observed period were also excluded. Besides, beneficiaries’ state that were not stable or became more severe would tend to switch to medical care service. As we focused on the policy impact on LTC service users, we excluded users who did not use LTC service for the whole 12 months between August 2014 and July 2015. Moreover, we identified the treated groups by their LTC claims data. Beneficiaries who did not apply LTC service at least 1 month between August 2015 and July 2016 would also be excluded from our study because of the lack of copayment information after policy implementation. We conducted analyses on beneficiaries who used LTC services continuously from August 2014 to July 2015 and who had ever used LTC services at least once between August 2015 and July 2016 (23,879 insured individuals in the first year, with 570,434 records). Statistical Analysis Descriptive analyses were conducted for the total sample in August 2014, including sex, age, CNL, subsidy,1 and public assistance. For people using LTC and medical care services who receive subsidies or are covered by the Public Assistance Act, expenses are paid directly to service providers from the Japanese government at no cost to the service users.1,19 We calculated the average CNL following the rules of the Health and Welfare Bureau for the Elderly.20 In addition to understanding the trend by time, the means of outcome variables were also calculated for the treatment and control groups by time. We conducted a difference-in-difference (DID) analysis to estimate the causal effect of the policy intervention by comparing the changes in outcome over time between the treatment and control groups.21 Outcome variables were extracted from the LTC database and included minutes of home care service use and days of facility care service use. We extracted additional variables including LTC insurer payments and LTC copayment of the insured from the LTC database, which allowed us to calculate LTC expenditures. Data on medical care expenditure, days of hospitalization, and frequency of ambulatory care use were extracted from the NHI database. All the outcome variables were collected on a monthly basis by the treatment and control groups. We summed the medical care expenditure and LTC expenditure to compute the total expenditure and set it as one of the dependent variables. Moreover, because the number of days in each month varies, we calculated the average outcome on a daily basis, except for frequency of ambulatory care use, days of facility care service use, and days of hospitalization (see Supplementary Table 1). We conducted a Hausman-Taylor estimation22 and found that the fixed effects estimation fit our model. According to Wooldridge,23 the fixed effects estimation is more convincing than the random effects estimation for policy analysis because it involves a large geographical unit. However, as the observed unit in the current study was at the individual level, and personal characteristics have a significant impact on LTC and medical care service use.17,24 Hence, we estimated the DID regression for the following model. For an individual i in month t:
Yit ¼ b0 þ b1 ðPeriodÞit þ b2 ðCopaymentÞit þ b3 ðPeriodit copaymentit Þ þ b4 ðageÞit
þ b5 ðsexÞit þ b6 ðsubsityÞit þ b7 ðPublic AssistanceÞit þ b8 ðCNLÞit þ b9 ðtÞ þ εit ; i ¼ 1; 2; .n; t ¼ 1; 2; .; 24
where Yit indicates outcome variables. Periodit is a binary variable with the value of zero before the implementation of the policy and 1 after the implementation of the policy. Copaymentit is an indicator variable equal to 1 if the insured individual’s copayment was
3
increased to 20% and zero otherwise. b3 represents the DID estimate of the effect of the insured individual’s copayment increase policy; and age, sex, subsidy, public assistance, and CNL are the covariates that represent individual characteristics. b9 represents t indexes per month in years, ranging from t ¼ 1 to t ¼ 24, to adjust for the time fixed effect. Robust standard errors were computed to evaluate the robustness of the DID estimates. All analyses were performed using the DIFF module25 in the statistical software Stata 15.0 (StataCorp LLC, College Station, TX). The P values (2-tailed) were considered statistically significant when they were below .05. This study was approved by the Ethics Committee of Kyoto University Graduate School of Medicine (R0438). Results Descriptive statistics for all variables in August 2014 are shown in Table 1. Among the study participants, 26.4% were males, and those aged 85 to 89 years comprised the largest proportion of the sample, followed by those aged 80 to 84 years. The majority of the beneficiaries belonged to the CNL 2 category, followed by those with CNL 1 and 3. However, over time, the number of persons with CNL 3 increased beyond that of persons with CNL 1, whereas the CNL 2 category remained the largest group (see Supplementary Figure 2). The proportion of beneficiaries who received a subsidy was 2.4%, and those who received public assistance accounted for 5.7% of the sample. Finally, those whose copayment increased to 20% were younger, and they had a lower average CNL compared with those whose copayment remained at 10%. With regard to the outcome variables, monthly and daily statistics for LTC expenditure, insurer’s payment, insured individuals’ copayment, minutes of home care service use, days of facility care service use, medical care expenditure, days of hospitalization, frequency of ambulatory care use, and total expenditure of the 2 analyzed groups in August 2014 are shown in Table 1 and Supplementary Table 1. The monthly and daily trends for outcome variables are shown in Supplementary Figures 3 and 4. The monthly and daily trends in the outcome variables were similar. LTC expenditure and insurer’s payment increased with time in both the control and treatment groups. However, the treatment group showed a lower mean LTC expenditure compared with the control group. The lines of the insured individuals’ copayment in the control and treatment groups almost overlapped before the implementation of the revised copayment policy in August 2015. Since the revision of the copayment policy in August 2015, mean copayments of the treatment group were higher than those of the control group. An increasing trend was noted for days of facility care services, whereas a decrease in minutes of home care service use was noted over time in both the treatment and control groups. Finally, the treatment group had fewer days of facility care service use and more minutes of home care service use compared with the control group. Contrary to the outcomes for LTC service use, the treatment group exhibited higher days of hospitalization, frequency of ambulatory care use, and medical care expenditure, compared with the control group. In terms of the days of hospitalization, an increase was observed in both the control and treatment groups after the implementation of the copayment increase policy. With regard to the frequency of ambulatory care use, the treatment group showed higher means than the control group. The mean total expenditure was lower in the treatment group at the beginning, but the gap between the 2 groups decreased gradually over time. The results of the DID estimationeadjusted analyses controlled for age, sex, CNL, subsidy, public assistance and date showing differences between the control and treatment groups over time are presented in Table 2, and the results of the daily analysis are presented in Supplementary Table 2. The results of covariates and coefficients of each model are presented in Supplementary Table 3. According to the
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Table 1 Characteristics of Participants on August 1, 2014, and Descriptive Statistics of Outcome Variables in August 2014, From One Prefecture in Japan Variable
Copayment Group
Age, y, mean SD Age, y, n (%) 65-69 70-74 75-79 80-84 85-89 90-94 95 Sex, n (%) Male Female Average Care Needs Level, mean SD Care Needs Level, n (%) 1 2 3 4 5 Subsidy, n (%) No Yes Public assistance, n (%) No Yes Total Outcome variable, mean (SE) LTC expenditure, yen/mo Insurer’s payment, yen/mo Copayment, yen/mo Home care service, min/mo Facility care service, d/mo Medical care expenditure, yen/mo Hospitalization days, d/mo Ambulatory care, times/mo Total expenditure, yen/mo
10% Group (Control)
20% Group (Treated)
Total
P Value
84.45 0.05
84.28 0.14
84.44 0.05
.24* <.001y
936 (92.3) 1790 (92.9) 2958 (90.3) 4962 (89.1) 5850 (89.3) 3767 (92) 1369 (94.4)
78 136 316 604 704 328 81
(7.7) (7.1) (9.7) (10.9) (10.7) (8) (5.6)
1014 (4.2) 1926 (8.1) 3274 (13.7) 5566 (23.3) 6554 (27.4) 4095 (17.1) 1450 (6.1)
1754 (27.8) 493 (2.8) 2.60 0.02
6306 (26.4) 17,573 (73.6) 2.76 0.01
<.001y 4552 (72.2) 17,080 (97.2) 2.77 0.01 5197 6244 4595 3405 2191
(89.3) (90) (90.6) (91.7) (93.8)
621 695 479 307 145
(10.7) (10) (9.4) (8.3) (6.2)
5818 6939 5074 3712 2336
<.001* <.001y
(24.4) (29.1) (21.2) (15.5) (9.8) <.001y
21,066 (90.4) 566 (99.8)
2246 (9.6) 1 (0.2)
23,312 (97.6) 567 (2.4)
20,282 (90.1) 1350 (99.3) 21,632 (90.6)
2238 (9.9) 9 (0.7) 2247 (9.4)
22,520 (94.3) 1359 (5.7) 23,879 (100)
<.001y
20,8852 191,251 15,400 656 10.86 10,666 0.4 2.2 219,518
(759.1) (696.2) (67.6) (5.6) (0.1) (556.8) (0.0) (0.0) (899.6)
177,180 160,542 16,418 758 7.29 16,300 0.5 2.7 193,479
(2111.0) (1897.0) (211.9) (18.0) (0.3) (1932.3) (0.1) (0.1) (2731.4)
205,872 188,361 15,496 666 10.52 11,196 0.4 2.3 217,068
(718.3) (658.0) (64.4) (5.3) (0.1) (536.2) (0.0) (0.0) (855.9)
<.001* <.001* <.001* <.001* <.001* .005* .025* <.001* <.001*
SD, standard difference; SE, standard error. *t test results between 10% and 20% copayment beneficiaries. y 2 c test results between 10% and 20% copayment beneficiaries.
results of the models shown in Supplementary Table 3, females exhibited less medical care service use but more LTC care service use, except for home care service use, compared with males. Further, those with older age and a higher CNL had a higher LTC expenditure and
days of facility care service use, but lower home care service use. With regard to medical care services, higher CNL had a more significant impact on medical care expenditure compared with lower CNL. However, insured individuals without CNL certification also showed
Table 2 Results of the Regression on Difference-in-Difference of Participants Who Had Used LTC Services Continuously Between August 2014 and July 2015 and Had Ever Applied at Least Once for LTC Services Between August 2015 and July 2016 (n ¼ 570,434 Records) Outcome Variables
Differencein-difference estimate* Standard error R-squared Difference of the 2 groups before the intervention* Difference between 2 groups after the intervention*
Outcome Variable on Monthly Basis LTC Expenditure, yen
Insurer’s Payment, yen
Insured’s Copayment, yen
Home Care Service, min
Medical Care Expenditure, yen
Total Expenditure, yen
Facility Care Service, d
Hospitalization, d
Ambulatory Care, times
1405y
19,842z
14,837z
7.766
3791z
2383x
0.0565
0.160z
0.0433
719.5 0.396 6854
614.4 0.409 7026
112.9 0.397 1609
7.499 0.057 50.13
1095 0.003 391.4
1211 0.233 6465
0.117 0.157 1.289
0.0389 0.03 0.0483
0.0273 0.038 0.219
8259
26,869
16,446
42.37
4183
4081
1.345
0.112
0.176
*Adjusted analyses controlled for age, sex, care needs level, subsidy, public assistance, and date. y P < .1. z P < .01. x P < .05.
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higher medical care expenditure. Being aged between 75 and 79 years had the strongest impact on medical care expenditure. In both the monthly (Table 2) and daily analyses (Supplementary Table 2), there was no significant DID for minutes of home care service use, frequency of ambulatory care use, facility care service use, and LTC expenditure in the adjusted analyses between the control and treatment groups. In terms of LTC expenditure, it showed significant DID on statistical significance below 0.1. However, because it was not considered statistically significant unless the P values (2-tailed) were below .05, we categorize the DID result of LTC expenditure as no significance. Insurer’s payment, insured individuals’ copayment, medical care expenditure, days of hospitalization, and total expenditure had significant DID in the adjusted analyses between the control and treatment groups. Discussion The average age and CNL were lower in the treatment group than in the control group. According to the Japanese government, the medical care expenditure increased with age after the age of 65 years.26 As the average CNL increased with age,27 a higher CNL implies higher LTC expenditure.24,28 With regard to adjusting for the covariance representing individual characteristics, our results corroborated those reported in previous studies. Compared with men, women tended to use more LTC services except for home care services.24,29 Furthermore, higher CNL and older age were also associated with more LTC service use.24 Because subsidization or public assistance removed the financial barrier,30,31 it is not surprising that more LTC services were used. Kim et al31 revealed that low-income, female, or independent individuals both tend to qualify for subsidies to use LTC services and to use facility care services. In Japan, women are expected to be the main informal caregiver and most often are daughters or spouses of the insured individuals.32 The Japanese government reports that 56.9% of men wish to receive care from their spouse, whereas this number is only 39.5% among women.33 As life expectancy is longer for women than men in Japan, men would be less likely to serve as the primary caregiver for their spouse or parents. Another survey conducted by the Japanese government found that women comprise more than 70% of LTC facility residents, more than double the percentage of men.34 The fact that informal care is provided by family or relatives may explain some of the gender disparity evident in the type of LTC service use. The present DID estimation showed no significant differences in the minutes of home care service use, which is consistent with the findings of a previous study.12 According to Supplementary Figures 3 and 4, though the minutes of home care service use decreased with time, outcomes related to LTC expenditure increased. The fact that use of home care among older adults with disabilities is dependent on their copayment rates may be one potential explanation for why use of home care services decreased while use of other types of LTC services increased.35 Additionally, previous studies indicated that higher CNL and old age tend to be associated with the higher tendency to use facility care services more than home care services.36,37 Insured individuals at a higher CNL are more dependent and require more care services. Facility care services offer 24-hour care, which is more than that offered by home care services. As such, the former would be more appropriate for insured individuals with higher care needs; a shift from home care services to facility care is often made for these individuals. With regard to LTC insurer’s payment, the trends were similar to those of LTC expenditure. Moreover, facility care service use has a stronger impact on LTC expenditure than home care service use does.24 The reasons mentioned above may explain why the minutes of home care service use decreased over time, but there was still an increase in LTC expenditure.
5
The LTC insured individuals’ copayment in the treatment and control groups almost overlapped before the revised copayment policy was implemented, and it increased sharply in the treatment group after the policy was implemented. The copayment increase has reduced the insurer’s portion on total expenditures; however, the total expenditures increased over time. Accordingly, the revised policy also impacted the total expenditure trend, thus minimizing the disparity between the 2 groups. Individuals in the treatment group, with a lower CNL and younger age, tended to use more medical care services compared with those in the control group,26 whereas older adults tended to use more LTC services.24 Beneficiaries whose copayment was increased to 20% and who had a lower CNL tended to use medical care over LTC services to minimize their copayment.4 Therefore, the means of medical care expenditure, days of hospitalization, and frequency of ambulatory care use were higher in the treatment group than in the control group. With regard to the DID analysis, we found significant DID for LTC insurer’s payment and for LTC insured individuals’ copayment between the treatment and control groups. The increase in insured individuals’ copayment for LTC insurance directly affected the insured individuals’ copayment, which led to a significant DID regression. Furthermore, the DID analysis showed no significance for LTC expenditures in that the revised copayment policy did not influence LTC service use among treatment group Participants. The Japan Ministry of Health, Labour and Welfare conducted an investigation to determine whether the change in the copayment policy led to a change in LTC service-seeking behavior among home care service users.4 The report indicated that if the LTC beneficiary’s medical care copayment was less than 20% and it had increased to 20%, he or she tended to utilize medical care service instead of LTC service, especially for hospitalization.4 As time goes by, our Participants would get older and tend to shift their service type from home care service to facility care service by paying the same amount of LTC insured’s copayment. Otherwise the LTC insureds will shift their facility care service use to medical hospitalization service.36,37 Our results showed no significant DID in ambulatory care service use, which is different from the findings of a previous study.11 Another study indicated that compared with hospital care, ambulatory care service use was more sensitive to insured individuals’ copayment rate.11 Nonetheless, in Japan, when insured individuals need care services, LTC services are prioritized over medical care services. Because the Participants in our study were LTC recipients, their ambulatory care usage would be less than that of general care recipients. On the other hand, the DID estimations showed significant results for medical care expenditure, days of hospitalization, and total expenditure. All outcomes that showed significance were collected from the NHIDB. The present DID estimations implied that the implementation of the revised copayment policy indeed had an impact on the beneficiaries’ service use patterns. Previous studies indicated that LTC and hospital utilization substitute each other.11,16,17 The revised copayment policy is one explanation for the increase in hospitalization and the increase in total care needs.4,38 The revised copayment policy was designed to alleviate the financial burden of the LTC insurance system, but the policy was found easing the insurer’s financial burden in the LTC insurance system at the expense of insureds and medical care insurance system. Additionally, beneficiaries would cut usage to balance their expenditure.10,11 Notably, it remains unclear whether insured individuals curtailed the low priority or excessive services in response to the copayment increase policy. Our study found that the policy also affected medical care expenditure and service use. This may suggest that some LTC beneficiaries would prefer to use medical care services instead of LTC service, because there is a light substitution effect between LTC and medical care services.16,39 Moreover, the maximum monthly benefits covered by LTC insurance limits its use by
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beneficiaries, while the copayment ceiling for medical care services may lead insured individuals (particularly those with severe disabilities or more comorbidities) to use medical care services instead of LTC services. Adjusting the copayment according to annual income would be helpful to enhance the sustainability of the LTC insurance system.35,40 However, the changing demands of LTC users related to copayment rates would vary according to their financial means,35 medical copayment rate, and level of disability. There appears to be a “balloon effect” between LTC services and medical care services. The increase of copayment could reduce LTC insurers’ expenditure, as it leads to an increase in the use of medical care service over LTC service. The LTC insurance system aimed to reduce social hospitalization by 2011.1 However, the copayment revision policy would ease the financial burden of the LTC insurer at the expense of the NHI system. Some issues remain with the LTC and NHI systems. First, the copayment rate is dependent on the insured individual’s annual household income, although older adults accumulate considerably more housing assets than their younger counterparts.41 Copayment rates based strictly on older adults’ annual household income may not reflect the true affordability of LTC services. Instead, we suggest that the Japanese government revise the criteria for copayment rates by considering not only household income but assets as well. One other issue is that the Japanese government revised the “Act on Promotion of Comprehensive Securing of Medical Care and LongTerm Care in Areas” to promote coordination between medical care and LTC systems.1,42,43 Medical care services aim to cure patient disease, which has helped to maintain quality of life through LTC services thus far.44 However, the 2 systems operate separately. In order to integrate the medical care and LTC systems and ensure proper functioning, they must share information and arrive at a consensus on how to care for the insured. This may be best achieved if policy makers provide a conduit for communication between medical workers and LTC workers. Finally, since LTC and medical care systems are conditionally substitutable among the hospitalization and LTC facility care service use and the home care nursing and such, policy revisions made to each system must take into account their comprehensive impact on both systems. Limitations We collected the LTC insurance claims data so that individuals who did not use LTC services during the study period would not be included. Beneficiaries who continuously used LTC for at least 12 months may originally have been dependent on LTC services instead of medical care services, and the impact of the increase in their copayment for LTC services may not be as evident. Next, the copayment policy adjusts beneficiaries’ copayment according to their household income. However, we do not have individual financial data and could identify the treatment group only based on LTC service records. Therefore, insured individuals whose household income met the inclusion criterion for the treatment group but who had not used LTC services were excluded from our study. Furthermore, beneficiaries aged 75 years and older would be introduced to the medical care system for older adults, and the copayment of insured individuals in the system is 10%, 20%, or 30% depending on their household income. However, the data of insured individuals’ copayment rates for the medical care system were not available in our database. Future research should also consider the impact of the insured individuals’ copayment rate for the medical care system. Conclusion and Implications The increase in insured individuals’ copayment decreased the LTC insurer’s payment. However, the total LTC expenditure increased over
time although the increasing trend slowed down in the treatment group after the copayment increase policy was implemented. On the other hand, medical care expenditure increased consistently among insured individuals whose copayment increased. As there appears to be a “balloon effect” between LTC and medical care services, it is important to discuss the medical care system while considering the LTC insurance system comprehensively. Acknowledgments We thank Dr Takuma Sugahara for his useful suggestions and valuable comments on this study at the 14th Annual Conference of the Japan Health Economics Association. References 1. Sakamoto H, Rahman MM, Nomura S, et al. Japan. Health system review. Health Syst Transit 2018;8:248. 2. Costa-Font J, Courbage C, Swartz K. Financing long-term care: Ex ante, ex post or both? Health Econ 2015;24:45e57. 3. Ministry of Health, Labour and Welfare. An outline of the Japanese Medical System. 2019. Available at: https://www.mhlw.go.jp/bunya/iryouhoken/iryouh oken01/dl/01_eng.pdf. Accessed May 18, 2019. 4. Mitsubishi UFJ Research and Consulting. Investigation on the impact of copayment increase to 20 percent of long-term care insurance. 2018. Available at: https://www.murc.jp/uploads/2018/04/koukai_180418_c12.pdf. Accessed December 27, 2018. 5. Ministry of Health, Labour and Welfare. The Revision of the Healthcare Payment System for 2016 Fiscal Year. Tokyo: Ministry of Health, Labour and Welfare; 2016. 6. Tsutsui T, Muramatsu N. Care-needs certification in the long-term care insurance system of Japan. J Am Geriatr Soc 2005;53:522e527. 7. Ministry of Health, Labour and Welfare. The comprehensive project for longterm care prevention and daily life support. 2018. Available at: https://www. mhlw.go.jp/stf/seisakunitsuite/bunya/0000192992.html. Accessed July 2, 2018. 8. Landsem MM, Magnussen J. The effect of copayments on the utilization of the GP service in Norway. Soc Sci Med 2018;205:99e106. 9. Elkins RK, Schurer S. Introducing a GP copayment in Australia: Who would carry the cost burden? Health Policy 2017;121:543e552. 10. Rhee JC, Done N, Anderson GF. Considering long-term care insurance for middle-income countries: Comparing South Korea with Japan and Germany. Health Policy 2015;119:1319e1329. 11. Kiil A, Houlberg K. How does copayment for health care services affect demand, health and redistribution? A systematic review of the empirical evidence from 1990 to 2011. Eur J Health Econ 2014;15:813e828. 12. Non M. Co-payments in long-term Home Care: Do They Affect the Use of Care? CPB Discussion Paper 363, CPB Netherlands Bureau for Economic Policy Analysis; 2017. Available at: https://ideas.repec.org/p/cpb/discus/363.html, Accessed September 30, 2019. 13. Rothgang H. Social insurance for long-term care: An evaluation of the German model. Soc Policy Admin 2010;44:436e460. 14. Yong V, Saito Y. National long-term care insurance policy in Japan a decade after implementation: Some lessons for aging countries. Ageing Int 2012;37: 271e284. 15. Tajika E, Kikuchi J. Health and long-term care utilization among the elderly in the last year of life. Q Soc Secur Res 2011;47:304e319. 16. Forder J. Long-term care and hospital utilisation by older people: An analysis of substitution rates. Health Econ 2009;18:1322e1338. 17. Kim J-H, Lee Y. Implementation of long-term care and hospital utilization: Results of segmented regression analysis of interrupted time series study. Arch Gerontol Geriatr 2018;78:221e226. 18. Fassbender K, Fainsinger RL, Carson M, Finegan BA. Cost trajectories at the end of life: The Canadian experience. J Pain Symptom Manage 2009;38:75e80. 19. Ministry of Health, Labour and Welfare. Public assistance system. 2010. Available at: https://www.mhlw.go.jp/english/topics/social_welfare/index.html. Accessed December 11, 2018. 20. Health and Welfare Bureau for the Elderly. The Survey of the Effectiveness of Long-Term Care Insurance System Revision in 2015. Tokyo: Health and Welfare Bureau for the Elderly; 2018. 21. Dimick JB, Ryan AM. Methods for evaluating changes in health care policy: The difference-in-differences approach. JAMA 2014;312:2401e2402. 22. Schaffer M, Stillman S. XTOVERID: Stata Module to Calculate Tests of Overidentifying Restrictions After Xtreg, Xtivreg, Xtivreg2, Xthtaylor; 2016. Available at: https://EconPapers.repec.org/RePEc:boc:bocode:s456779. Accessed September 30, 2019. 23. Wooldridge JM. Introductory Econometrics: A Modern Approach. 6th, student ed. Boston, MA: Cengage Learning; 2016. 24. Lin H, Otsubo T, Sasaki N, Imanaka Y. The determinants of long-term care expenditure and their interactions. Int J Healthc Manage 2016;9:269e279.
H.-R. Lin, Y. Imanaka / JAMDA xxx (2019) 1e7 25. Villa J. DIFF: Stata Module to Perform Differences in Differences Estimation. College Station, TX: StataCorp; 2017. 26. Ministry of Health, Labour and Welfare. Estimates of national medical care expenditure in FY 2015. 2017. Available at: https://www.mhlw.go.jp/toukei/ saikin/hw/k-iryohi/15/index.html. Accessed July 27, 2018. 27. Health and Welfare Bureau for the Elderly. Fact-finding survey on Project of Long-term Care FY2015. 2015. Available at: https://www.mhlw.go.jp/topics/ kaigo/osirase/jigyo/15/index.html. Accessed July 27, 2018. 28. Schwarzkopf L, Menn P, Leidl R, et al. Are community-living and institutionalized dementia patients cared for differently? Evidence on service utilization and costs of care from German insurance claims data. BMC Health Serv Res 2013;13:2. 29. Kim N. Long-term care services expenditure projection in South Korea from 2015 to 2050. Int J Health Plann Manage 2015;30:45e56. 30. Sato M, Hashimoto H, Tamiya N, Yano E. The effect of a subsidy policy on the utilization of community care services under a public long-term care insurance program in rural Japan. Health Policy 2006;77:43e50. 31. Kim H, Kwon S, Yoon NH, Hyun KR. Utilization of long-term care services under the public long-term care insurance program in Korea: Implications of a subsidy policy. Health Policy 2013;111:166e174. 32. Washio M, Toyoshima Y, Miyabayashi I, Arai Y. Burden among family caregivers of older people who need care in Japan. In: Washio M, Kiyohara C, editors. Health Issues and Care System for the Elderly. Singapore: Springer Singapore; 2019. p. 17e32. 33. Ministry of Health, Labour and Welfare. Abridged life tables for Japan 2017. 2017. Available at: https://www.mhlw.go.jp/english/database/db-hw/lifetb17/ index.html. Accessed May 18, 2019.
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34. Ministry of Health, Labour and Welfare. Survey of Institutions and Establishments for Long-term Care. 2016. Available at: https://www.mhlw.go.jp/ english/database/db-hss/dl/siel-2016-02.pdf. Accessed May 19, 2019. 35. Roquebert Q, Tenand M. Pay less, consume more? The price elasticity of home care for the disabled elderly in France. Health Econ 2017;26:1162e1174. 36. Wu CY, Hu HY, Huang N, et al. Determinants of long-term care services among the elderly: A population-based study in Taiwan. PLOS One 2014;9: e89213. 37. Branch LG, Jette AM. A prospective study of long-term care institutionalization among the aged. Am J Public Health 1982;72:1373e1379. 38. Ansah JP, Eberlein RL, Love SR, et al. Implications of long-term care capacity response policies for an aging population: A simulation analysis. Health Policy 2014;116:105e113. 39. Hollander MJ, Chappell NL. A comparative analysis of costs to government for home care and long-term residential care services, standardized for client care needs. Can J Aging 2007;26:149e161. 40. Fu R, Noguchi H. Moral hazard under zero price policy: Evidence from Japanese long-term care claims data. Eur J Health Econ 2019;20:785e799. 41. Hirayama Y. The role of home ownership in Japan’s aged society. J Hous Built Environ 2010;25:175e191. 42. Ministry of Health, Labour and Welfare. Act on Promotion of Comprehensive Securing of Medical Care and Long-Term Care in Areas. Tokyo: Ministry of Health, Labour and Welfare; 2014. 43. Fukuda T. Visions for integration of community-based health care and longterm care. J Natl Inst Public Health 2016;65:103e104. 44. Tamiya N. Co-operation between medical and social care and health services research. Jpn J Geriatr (Nippon Ronen Igakkai Zasshi) 2017;54:22e27.
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Appendix
Yes Yes
Yes
Living alone
10% copayment
Annual income* no more than 160 million yen
20% copayment
LTC user was aged 65 years and he/she paid income tax in the previous year
No
No
No
10% copayment
Annual income more than 160 million yen or annuity for older adults amounting more than 280 million yen
Yes 20% copayment
10% copayment No
Supplementary Figure 1. Rules for copayment qualification. *Annual income: income from working, investment, older adult annuity, and so on.
(Participants) 8,000 7,000 6,000 5,000 4,000 3,000 2,000 1,000 Aug -14 Sep -14 Oct -14 Nov -14 Dec -14 Jan -15 Feb -15 Mar -15 Apr -15 May -15 Jun -15 Jul -15 Aug -15 Sep -15 Oct -15 Nov -15 Dec -15 Jan -16 Feb -16 Mar -16 Apr -16 May -16 Jun -16 Jul -16
0
Care needs level 1
Care needs level 2
Care needs level 4
Care needs level 5
Care needs level 3
Supplementary Figure 2. Numbers of persons by care needs level.
H.-R. Lin, Y. Imanaka / JAMDA xxx (2019) 1e7
LTC expenditure (yen/month)
(10000 yen)
7.e2
Insurer's payment (yen/month)
Copayment (yen/month) (10000 yen)
(10000 yen)
25
25
25
20
20
20
15
15
15
10
10
10
5
5
5
0
0
0
Home care service (mins/month)
(min) 1000
Facility care service (days/month)
(day) 15
(10000 yen)
4
800
3
10
600
Medical service expenditure (yen/month)
2
400
5
1
200
0
0
0
8/1/2014
Hospitalization days (days/month) (Day) 3
Total expenditure (yen/month) (10000 yen)
30 25 20 15 10 5 0
2 1 0 8/1/2014
8/1/2015
8/1/2015
Ambulatory care use (times/month) (Time) 3 2 1 0
8/1/2014
8/1/2015
10% group
8/1/2014
8/1/2015
20% group
Supplementary Figure 3. Outcome variables on a monthly basis for participants who had used LTC services continuously between August 2014 and July 2015 and had ever applied at least once for LTC services between August 2015 and July 2016.
LTC expenditure (yen/day)
Insurer's payment (yen/day)
Copayment (yen/day)
8000
8000
8000
6000
6000
6000
4000
4000
4000
2000
2000
2000
0
0
0
Total expenditure (yen/day)
Home care service (min/day) 30 20
8,000
1,500
6,000
1,000
4,000
10
500
2,000 0 8/1/2014
Medical service expenditure (yen/day)
10,000
0
0 8/1/2015
8/1/2014 10% group
8/1/2015 20% group
8/1/2014
8/1/2015
Supplementary Figure 4. Outcome variables on a daily basis for participants who had used LTC services continuously between August 2014 and July 2015 and had ever applied at least once for LTC services between August 2015 and July 2016.
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Supplementary Table 1 Descriptive Statistics of Outcome Variables on a Daily Basis in August 2014, From One Prefecture in Japan Outcome Variable
LTC expenditure, yen/d Insurer’s payment, yen/d Insured’s copayment, yen/d Home care service, min/d Medical care expenditure, yen/d Total expenditure, yen/d
Copayment Group
P Value*
10% Group (Control)
20% Group (Treated)
Total
Mean
SE
Mean
SE
Mean
SE
6737 6169 497 21 344 7081
24.5 22.5 2.2 0.2 18.0 29.0
5715 5179 530 24.1 526 6241
68.1 61.2 6.8 0.6 62.3 88.1
6641 6076 500 21.2 361 7002
23.2 21.2 2.1 0.2 17.3 27.6
<.001 <.001 <.001 <.001 .005 <.001
SE, standard error. *t test results between 10% and 20% copayment beneficiaries.
Supplementary Table 2 The Regression Results of Difference-in-Difference of Participants Who Had Used LTC Services Continuously Between August 2014 and July 2015 and Had Ever Applied at Least Once for LTC Services Between August 2015 and July 2016 (n ¼ 570,434 Records) Outcome Variables
Difference-in-difference estimate* Standard error R squared Difference of the 2 groups before the intervention* Difference between 2 groups after the intervention*
Outcome Variable on Daily Basis LTC Expenditure, yen
Insurer’s Payment, yen
Copayment, yen
Home Care Service, min
Medical Care Service Expenditure, yen
Total Expenditure, yen
43.46y 23.61 0.395 226.2
648.1z 20.17 0.408 231.8
486.5z 3.702 0.397 52.87
0.263 0.245 0.056 1.632
123.6z 35.97 0.003 13.65
80.05x 39.78 0.233 212.7
269.6
879.9
539.4
1.368
137.2
132.6
*Adjusted analyses controlled for age, sex, care needs level, subsidy, public assistance, and date. y P < .1. z P < .01. x P < .05.
H.-R. Lin, Y. Imanaka / JAMDA xxx (2019) 1e7 Supplementary Table 3 Difference-in-Difference Estimation Results of Covariates of All Models (Time Dummies Were Not Reported) Independent Variable (s) (1) LTC Expenditure Sex Male Female Subsidy No Yes Public assistance No Yes Age, y 65-69 70-74 75-79 80-84 85-89 90-94 95 Care needs level None 1 2 3 4 5 (2) Insurer’s Payment Sex Male Female Subsidy No Yes Public assistance No Yes Age, y 65-69 70-74 75-79 80-84 85-89 90-94 95 Care needs level None 1 2 3 4 5 (3) Insured’s copayment Sex Male Female Subsidy No Yes Public assistance No Yes Age, y 65-69 70-74 75-79 80-84 85-89 90-94 95 Care Needs Level None 1 2
Coefficient
Reference 19,000 Reference 62,000 Reference 19,000 27,000 27,000 20,000 7200 Reference 4499.125 7579.651 160,000 33,000 Reference 69,000 110,000 150,000
Reference 18,000 Reference 69,000 Reference 34,000 24,000 24,000 18,000 6400 Reference 4159.253 7085.221 150,000 30,000 Reference 63,000 100,000 130,000
Reference 885.558 Reference 4500 Reference 14,000 2600 2000 1800 622.247 Reference 62.976 345.462 15,000 2800 Reference
SE
P Value
289.398
790.539
<.001
<.001
556.844
<.001
657.503 479.326 394.423 319.526
<.001 <.001 <.001 <.001
324.483 439.853
<.001 <.001
1195.39 313.553
<.001 <.001
318.601 346.294 393.205
<.001 <.001 <.001
261.764
<.001
761.631
<.001
541.604
<.001
597.02 433.608 357.01 288.749
<.001 <.001 <.001 <.001
293.677 399.321
<.001 <.001
1250.031 283.036
<.001 <.001
288.046 313.925 356.716
<.001 <.001 <.001
29.31
<.001
75.601
<.001
60.585
<.001
66.36 48.426 39.971 32.905
<.001 <.001 <.001 <.001
33.173 44.393
.06 <.001
340.893 30.018
<.001 <.001 (continued)
7.e4
Supplementary Table 3 (continued ) Independent Variable (s)
Coefficient
3 4 5 (4) Time Minutes of Home Care Sex Male Female Subsidy No Yes Public assistance No Yes Age, y 65-69 70-74 75-79 80-84 85-89 90-94 95 Care Needs Level None 1 2 3 4 5 (5) Days of Facility Care Service Sex Male Female Subsidy No Yes Public assistance No Yes Age, y 65-69 70-74 75-79 80-84 85-89 90-94 95 Care needs level None 1 2 3 4 5 (6) Medical Care Expenditure Sex Male Female Subsidy No Yes Public assistance No Yes Age, y 65-69 70-74 75-79 80-84 85-89 90-94 95 Care Needs Level None 1
5202.299 7853.385 10,000 Service
SE
P Value 31 35.441 43.959
<.001 <.001 <.001
Reference 69.431
2.638
<.001
Reference 789.031
7.5
<.001
Reference 377.256
6.485
<.001
81.083 112.276 38.557 11.821 Reference 45.381 90.053
6.166 4.601 3.558 3
<.001 <.001 <.001 <.001
2.98 3.851
<.001 <.001
647.624 18.605 Reference 134.938 265.941 316.425
5.614 2.993
<.001 <.001
3.032 3.106 3.339
<.001 <.001 <.001
Reference 2.925
0.044
<.001
Reference 21.487
0.117
<.001
Reference 8.355
0.068
<.001
0.093 0.07 0.06 0.052
<.001 <.001 <.001 <.001
0.055 0.075
<.001 <.001
0.189 0.049
<.001 <.001
0.052 0.056 0.063
<.001 <.001 <.001
Reference 4300
387.984
<.001
Reference 2600
409.277
<.001
Reference 13,000
405.774
<.001
2300 111.001 3018.337 1417.966 Reference 1100 2400
745.715 610.886 540.909 416.702
.002 .86 <.001 .001
387.19 494.675
.005 <.001
4799.565 2900
7916.815 394.531
.54 <.001
5.083 4.363 2.778 1.128 Reference 1.267 2.21 8.241 0.98 Reference 5.446 8.52 9.262
(continued on next page)
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Supplementary Table 3 (continued ) Independent Variable (s)
Coefficient
2 Reference 3 920.12 4 2380.969 5 4123.153 (7) Days of Hospitalization Sex Male Reference Female 0.181 Subsidy No Reference Yes 0.231 Public assistance No Reference Yes 0.583 Age, y 65-69 0.233 70-74 0.081 75-79 0.001 80-84 0.012 85-89 Reference 90-94 0.05 95 0.135 Care Needs Level None 27.385 1 0.104 2 Reference 3 0.143 4 0.286 5 0.476 (8) Times of Ambulatory Care Service Sex Male Reference Female 0.077 Subsidy No Reference Yes 0.402 Public assistance No Reference Yes 2.089 Age, y 65-69 0.301 70-74 0.234 75-79 0.173 80-84 0.092 85-89 Reference 90-94 0.141 95 0.24 Care Needs Level None 2.195 1 0.181 2 Reference 3 0.036 4 0.096 5 0.326 (9) Total expenditure Sex Male Reference Female 15,000 Subsidy No Reference Yes 60,000 Public assistance No Reference Yes 6176.282 Age, y 65-69 30,000 70-74 27,000 75-79 17,000 80-84 5800 85-89 Reference 90-94 3415.503 95 5202.663
Supplementary Table 3 (continued ) SE
P Value
415.337 430.006 478.801
.027 <.001 <.001
0.014
<.001
0.016
<.001
0.017
<.001
0.026 0.022 0.019 0.015
<.001 <.001 .97 .43
0.016 0.021
.002 <.001
0.506 0.013
<.001 <.001
0.015 0.017 0.021
<.001 <.001 <.001
0.01
<.001
0.011
<.001
0.012
<.001
0.023 0.017 0.014 0.011
<.001 <.001 <.001 <.001
0.01 0.013
<.001 <.001
0.073 0.011
<.001 <.001
0.011 0.011 0.014
.001 <.001 <.001
449.387
<.001
860.117
<.001
650.692
<.001
934.71 724.21 623.289 485.983
<.001 <.001 <.001 <.001
462.283 603.429
<.001 <.001 (continued)
Independent Variable (s) Care needs level None 1 2 3 4 5 SE, standard error.
Coefficient
SE
P Value
160,000 36,000 Reference 70,000 110,000 150,000
8037.264 481.182
<.001 <.001
488.045 503.398 555.95
<.001 <.001 <.001