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.
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.