Yuji Akematsu

An empirical analysis of the reduction in medical expenditure by e-health users

We examined the medical expenditure of elderly residents who used an e-health system in Nishiaizu town. Since 1994, health-related data, such as blood pressure, ECG and blood oxygen, have been transmitted to a remote medical institution via a telecommunications network. We selected 412 users from the list of registered e-health users in the town. We also selected 450 residents who were not e-health users. We sent them questionnaires and obtained 199 valid responses from e-health users and 209 from non-users. Then we examined the receipts of these 408 people, which were kept in paper form in the town office. Based on a multiple regression analysis, we found that users of the e-health system had lower medical expenditure for lifestyle-related illness than non-users. The medical expenditure of e-health users was lower than that of non-users by 15,302 yen (US

Measuring the effect of telecare on medical expenditures without bias using the propensity score matching method.

133) per year. This amount was approximately 21% of the average annual medical expenditure of the residents. The results also showed that: long-time users of e-health had lower medical expenditure on lifestyle-related illness; long-time users of e-health had lower medical expenditure than those who used it for a shorter time; e-health had more effect on people with diseases than those without.

Effect of e-health on medical expenditures of outpatients with lifestyle-related diseases.

OBJECTIVE This article examines the effect of telecare on medical expenditures for chronic diseases using survey data from Nishi-aizu Town, Fukushima Prefecture, Japan. The study uses the propensity score matching (PSM) method, a rigorous analytical method that overcomes sample selection bias, a common problem when using survey data. SUBJECTS AND METHODS One hundred ninety-nine users (treatment) of telecare and 209 nonusers (control) were selected from residents, and their medical expenditures were obtained from the National Health Insurance scheme for comparison. Individual characteristics of the two groups, including age, sex, income, and health conditions, were compared, and variables that contained biases were specified by a t test. After calculation of their propensity scores and elimination of biases, the effect of telecare on medical expenditures was estimated. To obtain robust results, four different matching methods were applied: caliper matching, single nearest-neighbor matching, Epanechnikov kernel matching, and biweight kernel matching. RESULTS No independent variable showed significant differences between the two groups after matching, indicating that selection biases were successfully eliminated using PSM. Using PSM, we saw a decrease in medical expenditures in Japanese yen of 25,538-39,936 (USD 319.23-499.20) per year per user and a decrease in the number of treatment days of 2.6-4.0 days. In comparison, our previous analyses using the same data underestimated the effects of telecare. PSM provides greater effects by reducing bias. CONCLUSIONS Using PSM to compare subjects in two groups with similar characteristics except for their use or nonuse of telecare, we demonstrated that the treatment group has lower medical expenditures for chronic diseases than the control group. Proper matching is important in evaluating the impact of telecare interventions. Limitations of PSM include its requirement for a large number of samples and the limited ability to explain why and how telemedicine produces these effects. Other empirical methods are required to identify the mechanism of how telemedicine works.

An empirical approach to estimating the effect of e-health on medical expenditure

We analyzed the effect of e-health on medical expenditures in Nishi-aizu Town, Fukushima Prefecture, Japan, using panel data of medical expenditures for about 400 residents from 2002 to 2006. The Nishi-aizu Town system was introduced in 1994 and is still successfully operating as one of the longest running implementations of e-health in Japan. The town office maintains a register of receipts for medical expenditures paid by the National Health Insurance system and provides data on e-health users, allowing users and nonusers of e-health and their respective costs to be distinguished. Here, we focus on patients with lifestyle-related diseases such as high blood pressure, diabetes, stroke, heart failure, etc. This article postulates that e-health reduces medical expenditures via two mechanisms, decreasing travel expenses and preventing symptoms from worsening. The former implies that e-health monitoring allows patients at home to visit medical institutions less frequently, and the latter that the symptoms experienced by e-health users are less severe than those experienced by nonusers. We termed these the travel cost effect and opportunity cost effect, respectively. Chronic conditions tend not to occur singly, and many patients have more than one; for example, patients with high blood pressure or diabetes also likely have heart disease at the same time. This multiplicity of conditions hampers cost analysis. Among methodological issues, a number of recent empirical health analyses have focused on the endogenous problem of explanatory variables. Here, we solved this problem using the generalized method moments (GMM) system, which allows treatment of not only the endogenous problem of explanatory variables but also the dynamic relationship among variables, which arise due to the chronic time-lagged effect of lifestyle-related diseases on patients. We also examined a second important methodological problem related to reverse correlation between the medical expenditures of an outpatient and e-health and took sampling biases into consideration. We concluded that this control of endogeneity through system GMM confirms that the relationship between the medical expenditures of an outpatient and e-health shows causation rather than simple correlation and that e-health use, duration of e-health use, and frequency of e-health use can reduce outpatient medical expenditures for lifestyle-related diseases.

Relation between Telecare Implementation and Number of Treatment Days in a Japanese Town

We studied the e-health system used in Nishi-aizu Town in Fukushima Prefecture in Japan. The system allows elderly people at home to transmit vital signs data to the Towns health centre, where nurses provide advice based on the data. Our hypothesis was that the e-health system in Nishi-aizu Town reduced the need to visit clinics. We attempted to prove this by a regression analysis, in which days for treatment were compared between users and non-users of e-health. The results showed that days for treatment of e-health users were shorter than those of non-users by 1.6 days per year. Thus the total reduction in expenditure as a result of fewer hospital visits (emergency and elective) was about 16,000 yen per year. In a previous study, we proved that in Nishi-aizu Town the medical expenditure of e-health users was smaller than those of non-users by 15,688 yen. The results of the present study therefore coincide with those of the previous one and show that the reduction of medical expenditure is principally caused by the reduction of days for treatment.

Economic effect of eHealth: Focusing on the reduction of days spent for treatment

We analysed data on the medical expenditure of 199 telecare users in Nishiaizu Town, Fukushima Prefecture, which has one of the oldest ongoing telecare implementations in Japan. As controls, 450 out of 3528 non-users residents covered by National Health Insurance were randomly selected in the same age and sex ratios as the telecare users. An analysis by the Generalized Method of Moments (GMM) was conducted in order to examine causality, i.e. that telecare use reduces the number of treatment days. To reduce sample selection bias, the presence of chronic diseases, age and education were added as control variables in the estimation. The results show that the treatment days of those who had chronic diseases were greater than those who did not have chronic diseases by 8.7 days per year (P < 0.10), and they were increased by 5.6 days (P < 0.01) according to their age. Finally, telecare use decreased treatment days by 3.1 days (P < 0.05).

Empirical analysis of the long-term effects of telecare use in Nishi-aizu Town, Fukushima Prefecture, Japan

This paper analyzes the economic effect of eHealth system by utilizing the data of “days spent for treatment”. In the previous paper [1], we analyzed how and how much eHealth reduces actual medical expenditures by examining Nishi-aizu Town, Fukushima Prefecture, Japan as a case study. In this paper, we compare results of this analysis with those we obtained from the analysis of medical expenditures. eHealth connects senior people at home with medical or health institutions via telecommunications networks, and the town office has been implementing it since 1994 and keeps monthly receipts in the paper form on medical expenditures of approximately 4,000 residents paid by National Health Insurance for five years from 2002 to 2006. The methodology of the analysis is to choose two groups (i) users; and (ii) non-user of eHealth, and compare days spent for treatment. As for the former group, we selected 412 from the list owned by the town office, and as for the latter group, we chose 450 from the list of National Health Insurance. We send questionnaire on their personal characteristics and diseases. We obtained 199 replies from users, while 209 for non-users. Then we examined receipts of these 408 people, and made a database on age, diseases, the first dates of visiting medical institutions for the first time, the number of visiting medical institution, and medical expenditures. Based on this database, we conducted a regression analysis, and obtained the following results: -Result 1: Users of eHealth have smaller days spent for treatment of lifestyle-related illness than those of non-users. -Result 2: Users of longer practicing eHealth have shorter days spent for treatment of lifestyle-related illness than those of non-users. -Result 3: Users of longer practicing eHealth reduce days spent for treatment larger than those who use it shorter years, if they extend usage one more years. -Result 4: eHealth there has more effect to people who have diseases than those who do not. This paper compares the above results with those we obtained from the analysis of medical expenditures in the previous paper [1]. The analysis and results we obtained provide the rigorous economic foundation of eHealth.

How to conduct business with the Japanese: case study of inward FDI in Japan

This paper examined the long-term effects of the use of telecare (e-Health) on the residents of Nishi-aizu Town, Fukushima, Japan, between 2002 and 2010. We compared medical expenditure and days of treatment between telecare users (treatment group) and non-users (control group) based on receipt data obtained from the National Health Insurance, which is operated by the government. In previous studies, we used receipt data obtained for the years 2002 to 2006; this study expands the analysis period four more years with respect to respondents who were included in previous analyses. Ninety users and 118 non-users were included in both analyses. Using rigorous statistical methods, including system generalized method of moments (GMM), this paper demonstrates that telecare users require fewer days of treatment and lower medical expenditure than non-users with respect to the chronic diseases of stroke, hypertension, heart failure, and diabetes. To date, there have been no publications examining the long-term economic effects of the use of telemedicine, so the current study presents a new facet to the research in this field.

Empirical Analysis of Factors Promoting Broadband Deployment in OECD 30 Countries

For economic re-vitalization, the Japanese economy is required to attract more inward foreign direct investment (FDI), which brings new technologies and know-how, promotes competition, and raises employment. The Japan Team conducted in-depth interviews in five firms from ASEAN and South Asia. Questions were asked regarding years since the the Japan office was established, how they started the business in Japan, business objectives, competitiveness, public support they received, and problems they face. Since Japan was the second largest market in the world, they targeted the Japanese market to enter. Even if the firms were small, they had clear global strategies about where to invest. These companies had competitiveness to conduct business in Japan, in particular to satisfy the high level of quality. The problems they face in Japan are related to traditional Japanese business practices such as long-term relationships among Japanese industrial groups. Japanese firms had already established strong and closed business ties, and it is difficult for foreign firms to enter, even if they have better technology. To invest in Japan, JETRO and other organizations supported them under the ‘Invest Japan’ programme. Although the firms interviewed acknowledged the merit of these supports, more information on know-how regarding conducting business with Japanese firms is desired.

Empirical analysis of the effect of eHealth to medical expendirures of lifestyle-related diseases

Objectives: Currently it is said that investment in broadband deployment has already reached a ceiling (Atkinson, Noam, Shultz; 2010). However, the average diffusion rate of OECD is about 60%, and policy measures are required for further deployment including the National Broadband Plan of the US, and Digital Agenda for Europe. This paper attempts to analyze factors promoting the diffusion of three broadband services in OECD 30 member countries from 2000 to 2010 by panel data analysis based on comprehensive data including prices and speeds. OECD countries are categorized into the following three types depending upon their larger share than the OECD average: (i) FTTH (e.g. Japan and Korea); (ii) DSL (Germany and France); and (iii) Cable modem diffusion type (US and the Netherlands). The analysis particularly focuses on the following hypotheses related to FTTH, which are commonly believed, but not proved yet: (i) DSL is promoted by local loop unbundling (LLU), while LLU of optical fiber suppresses FTTH diffusion; (ii) faster speed of FTTH promotes its diffusion; and (iii) investment strategy of carriers such as the termination of copper local loop promotes FTTH. Methodology and model: The estimation model is formulated as follows. D_T=a0 a1*P_CATV a2*P_DSL a3*P_FTTH a4*H_Intra a5*H_Inter a6*S_CATV a7*S_DSL a8*S_FTTH a9*C a10*U a11*I D_T: number of subscribers of technology T P_T and S_T: monthly subscription prices (USD) and the connection speed of technology T i and t: country and time (quarterly) H_Intra and H_Inter: HHI of intra-platform and inter-platform C: number of cable TV subscriber as of 2000 U: dummy variable for unbundling of copper and optical fiber local-loop I: period of announcement to terminate copper local loop by Japan, Korea and Australia. An instrumental variable estimation method is used, since prices and HHIs are endogenous. Results: The result of estimation is summarized in Table 1. The price elasticity in all the equations satisfied the sign conditions, and the cross-price elasticity shows that three technologies are substitutes. The coefficients of connection speed satisfy the sign conditions in all models. As for intra-HHI, Cable modem is promoted by competition among carriers, while DSL and FTTH are promoted by less competition. Inter-HHIs have negative signs indicating that competition among three technologies promotes broadband diffusion. LLU of DEL is positively, whereas that of FTTH is negatively significant. Thus LLU has different effect on the diffusion of DSL and FTTH. A coefficient of the connection speed of FTTH is negatively significant for DSL implying that the faster connection speed of FTTH induces migration from DSL to FTTH. Finally, the carriers’ decision of investment for FTTH is positively significant. Conclusion: Using comprehensive data and a rigorous estimation method, this analysis proves for the first time that (i) three technologies satisfy sign conditions of prices and speed, (ii) three technologies are substitutes, (iii) LLU of optical fiber suppresses FTTH, and (iv) positive attitudes of carriers toward FTTH investment promotes FTTH. This analysis provides an important basis for policy makers and carriers. References: Atkinson, B., E. Noam and I. Schultz [2010] “Has Telecom Investment Peaked?�? TPRC Proceedings. Distaso, W., P. Lupi and F. M. Manenti [2005] “Platform Competition and Broadband Uptake�? Information Economics and Policy, 18 (1). Lee, S. [2010] “A Cross-Country Analysis of Ubiquitous Broadband Deployment: Examination of Adoption Factors,�? mimeo. Table 1 Result of estimation: (1) Cable modem (2) DSL (3) FTTH Log (price (Cable modem)) -2.290*** 0.809* 1.129* [0.644] [0.443] [0.644] Log (price (DSL)) 1.439* -2.910*** 2.199** [0.816] [0.641] [1.032] Log (price (FTTH)) 0.999* 3.272*** -1.979*** [0.529] [0.522] [0.627] Log (speed (Cable modem)) 0.424*** 0.086 0.1 [0.124] [0.111] [0.204] Log (speed (DSL)) -0.27 0.340* 0.469 [0.235] [0.204] [0.367] Log (speed (FTTH)) 0.122 -0.308** 1.244*** [0.149] [0.136] [0.246] Log (HHI (intra-platform)) -2.228*** 0.509* 0.920*** [0.432] [0.300] [0.270] Log (HHI (inter-platform)) -1.312** -1.537* -1.765** [0.563] [0.892] [0.727] Log (number of CATV subscribers (the year 2000)) 0.695*** -0.079*** -0.049* [0.112] [0.022] [0.026] Unbundling of dry copper -0.22 0.533*** -1.836*** [0.179] [0.182] [0.359] Unbundling of fiber local loop -0.098 -1.188*** -1.223* [0.304] [0.316] [0.649] Investment decision on optical fiber -1.827*** -0.189 6.407*** [0.500] [0.434] [1.423] Fiscal year dummy Included. Included. Included. Constant term 31.727*** -0.381 10.898 [4.938] [9.314] [8.053] Number of observations 231 234 184 F-test (test of models) 32.84*** 5.08*** 24.81*** Coefficient of determination (within a group) 0.1995 0.1608 0.2875 Coefficient of determination (among groups) 0.6628 0.2196 0.8145 Coefficient of determination (total) 0.7347 0.2521 0.727 Note 1: *, **, and *** indicate significance at the 10%, 5%, and 1% level.