Silviase/jgov_v1.4_full_ann huggingface dataset:
greater number of suppliers make the supply curve shift out and lower the equilibrium price and a supplier’s revenue when price elasticity of demand is less than one. To avoid profit losses, physicians take advantage of the information asymmetry between physicians and patients to shift the demand curve out. Another approach is modeled by the principal-agent relationship to insist that an improper incentive mechanism invites agents (suppliers) to utilize information asymmetry to induce patient demand (McGuire (2001)).

Researchers have reached a consensus on the definition of PID over the past two decades; Physician-induced demand exists when the physician influences a patient’s demand for care against the physician’s interpretation of the best interest of the patient (McGuire (2001)). Although a number of studies support the existence of PID, most of them stumbled with the identification problem between supply and demand factors. A greater number of suppliers implies the possibility of PID and also affects patient preferences through lower access costs (McGuire (2001)). A famous counterintuitive example is proposed by Dranove and Wehner (1994) which found a positive relationship between the number of physicians and childbirth.

One effective way to discriminate between supply-side and demand-side factors is the “two-phase model” (Escarce (1992), Rossiter and Wilensky (1984)). Instead of examining the factors for total costs, this model divides medical costs into the probability of going to hospitals or clinics and medical costs per patient. Escarce (1992) found that the intensity of physicians affects the share of patients who go to receive medical services, but this is not the case for medical expenditure per patient. Examining the PID hypothesis with regard to different types of medical care has
significant policy implications. Schroeder (1992) reports that a great number of surgical specialists are associated with relatively high utilization of surgical procedures in the United States, and calls for reductions in specialty surgical residency programs. In addition, Crane (1992), Hillman et al. (1992), and Mitchell and Scott (1992) show that physicians who own diagnostic imaging equipment are more likely to order tests than are physicians who do not own equipment. As a result, many states have restricted self-referrals by physicians who own imaging equipment.

This study contributes to the previous studies on PID in the following two sides. First, despite the numerous studies on PID, as far as we know there has been little research focusing on the comparison between high-tech and low-tech treatments in a specific disease. We emphasize the effect of the difference in technology in the treatment on PID because most developed countries have suffered enormous medical costs, and technological advances in treatment account for the increase in per-patient medical expenditures. As discussed above, high-tech treatments are alleged to invite the moral hazard of physicians to cover their investment costs and to generate profits. Second, most previous studies in Japan utilized prefecture-level data, and faced difficulties in identification (Nishimura (1987)). After the 1990s, many studies on PID adopted the two-phase model to region-level data. While Yamada (2002) is supportive of the PID, Suzuki (1998), Kishida (2001) and Yuda (2004) find little effect of PID. To our best knowledge, this is the first research to use chart-based data to examine the existence and magnitude of PID in Japan.
### 3. Data

We use a unique micro-level dataset based on the Tokai Acute Myocardial Infarction Study (TAMIS) $ ^{6} $ . The main objective of TAMIS is to create a comparable database to the Cooperative Cardiovascular Project (CCP) $ ^{7} $ . The CCP is a major policy initiative to improve the quality of care for Medicare beneficiaries with AMI, undertaken by the Health Care Financing Administration (HCFA, currently called Center for Medicare and Medicaid Services: CMS). TAMIS aims to investigate variation in the quality of health care with respect to treatments and outcomes between the United States and Japan, controlling for chart-based detail clinical information on AMI patients.

The TAMIS charts have been abstracted for 2,020 heart attack patients admitted to 14 high-tech and high-volume medical facilities located in the Tokai area of Japan. All samples were those hospitalized between January 1995 and December 1997, a different period of the sampling for the CCP (between April 1994 and July 1995) in the United States. Since a major technological change in the aggressive use of stents for PTCA was observed in 1996-1997 in both countries, the difference in the sampling timing between both studies might not affect our results. During the survey period of TAMIS, there was no a change of reimbursement rates for treatments related with AMI.

In the process of the data collection, charts were carefully reviewed by research nurses and physicians. We followed the standardized abstractions of the medical records as the HCFA/CMS conducted for the CCP. The record abstracts contain more than 100 comorbid diseases and severity measures that collectively summarize all of the major associated
diseases and functional status impairments. Moreover, the abstracts include the AMI severity measures following the CCP's expert advisory panel, which influence the appropriateness of major AMI treatment decisions and health outcomes. Of the 2,020 patients, we used 1,047 patients living in 116 municipal areas of Aichi, Mie, Gifu, Shizuoka, and Nagano prefectures. Data for the 973 patients were removed, since the information on the municipal areas they lived were missing. In addition, to construct the physician density variable at municipal-level area ( $ Dens_{k,i,s} $ ) during the survey period of TAMIS, we also utilized the information provided by WAMNET (the website of the Fukushi Iryo Kiko, provided to a number of suppliers), the National Survey on Medical Facilities, and the Vital Statistics conducted by the MHLW.

Table 2 provides a description of the main variables. First, we observe that 845 (81 percent) and 668 (63 percent) patients underwent CATH and PTCA, respectively and 202 (20 percent) received acute drug treatments. Second, the density variables measurement at the municipal level reports that the number of high-tech hospitals is larger for high-tech than for low-tech patients while the remaining intensity variables are larger for low-tech patients than for high-tech patients. Third, total hospital expenditure for patients who underwent high-tech treatments totaled more than three million yen, much larger than that for low-tech patients (1.8 million yen). The average length of stay from the first hospital admission for high-tech patients (about 30 days) is also longer than that for low-tech patients (24 days) by about 5 or 6 days. Fourth, as regards patient characteristics, high-tech patients were more likely to be male (77 percent versus 66 percent), to be younger (63 versus 71 years old), and to live with a spouse (80 percent versus 64 percent) than low-tech patients. Further, high-tech patients are more likely to enroll into employees' health insurance (43 percent versus 30 percent), but less
likely to enroll into national health insurance (44 percent versus 51 percent) or health insurance for the elderly (31 percent versus 58 percent) than for low-tech patients.

The remainder of Table 2 reports the rates of detailed chart-based comorbid diseases and severity measures. High-tech and low-tech patients differ substantially in almost all of these measures. Overall, patients who underwent high-tech procedures are generally in better health. High-tech patients are much more likely to be in good functional status, e.g., 97 percent versus 87 percent measured in the independent mobility rates. In addition, those patients are less likely to have serious comorbid diseases like angina, prior cardiac heart failure, renal failure, cerebral infarction, and terminal illness. Moreover, patients undergoing high-tech procedure were much more likely to be alert and oriented on initial admission, to have no signs of serious heart failure (e.g., high heart rate), and to have good kidney function shown by unelevated blood nitrogen levels.

It is difficult to interpret the cumulative consequences of the differences for outcomes with a hundred variables that describe patient characteristics. For this reason, we constructed a summary indicator of the disease comorbidity and severity. The Killip class is based on a number of clinical characteristics related to the extent of heart failure in an AMI patient, and has been shown to provide a reliable predictor of the short-term AMI mortality. The Killip classes 1 and 2 indicate relatively mild heart failure, and Killip classes 3 and 4 refer to moderate and severe heart failure, which are severer than Killip classes 1 and 2. As Table 2 reports, high-tech patients are much more likely to be in lower Killip class 1 or 2.

The basic statistics in Table 2 provide clear evidence that patient characteristics are very heterogeneous, and a simple comparison in the sample statistics is not useful for direct comparisons between high-tech and low-tech treatments. Therefore, we must control for these
detailed patient characteristics carefully to evaluate the presence and magnitude of PID.

### 4. Empirical Specification and Measurements

In this section, we explain our empirical specification briefly. We aim to use the two-phase model to examine the existence and size of PID for high-tech and low-tech treatments of AMI patients, separately. We define CATH and PTCA performed within the first hospitalization after AMI occurs as a measure of high-tech procedure, and acute drug treatments as low-tech treatments $ ^{8} $ .

First, we will examine “physician initiated demand” using each patient’s medical expenditure of the first hospitalization as a dependent variable. The following regression formula is set up:

$$ y_{i,s}=\alpha_{0}+\sum_{k}\alpha_{1k}D e n s_{k,i,s}+\sum_{l}\alpha_{2l}x_{l,i,s}+\varepsilon_{i,s} $$

where $ y_{i,s}=\begin{cases}y_{i,s}&if y_{i,s}^{*}>0\\0&otherwise\end{cases} $

The dependent variable, $ y_{i,s} $ , shows a logarithmic value of i th patient’s expenditure for s th treatment, CATH, PTCA, or low-tech acute drug treatments. If a patient did not undergo a treatment of interest, we do not observe $ y_{i,s} $ for ith patient’s expenditure, which makes us employ the Tobit estimation. $ Dens_{k,i,s} $ , the main interest variable, includes six types of density variables at municipal-level area specific characteristics; number of hospital beds per 100,000 population (BEDS); number of high-tech hospitals $ ^{9} $ (capable of performing PTCA) per 100,000 population (HIGHTECH);
number of low-tech hospitals (incapable of performing PTCA) per 100,000 population (LOWTECH); number of high volume hospitals with more than 100 beds per 100,000 population $ ^{10} $ (HIGHVOL); number of physicians per 100,000 population (MD); and population density per squared kilometer (POPDENS). If the coefficient on those density variables is positive, it implies that physicians are motivated to increase the number of treatments to make profits under Japan’s fixed fee-for-service scheme.

$ x_{l,i,s} $ presents i th patient’s outcome, demographic characteristics, economic status, comorbid, and severity indicators. We include total hospital days (HOSPDAYS) as a patient outcome as an explanatory variable. Demographic characteristics include a patient’s age, sex, number of family members living with a patient, and presence of spouse. Since the data on a patient’s income is not available, we use three types of health insurance enrollment dummies (employee’s health insurance; national health insurance, and health insurance for the elderly) as his economic status. The type of health insurance enrollment is also considered the most appropriate measure of out-of-pocket expenses because of difference in copayments for the services in the survey period (20 percent for employees’ health insurance; 10 percent for national health insurance; no copayments for health insurance for the elderly) $ ^{11} $ . Comorbit and severity indicators are comprised of detailed chart-based information, including continence and mobility status, hypertension, diabetes, cardiac heart failure (CHF), MAP, BMI, highest creatinine, white blood cells, platelets, and EKG records. Finally, $ \varepsilon_{i,s} $ is an error term.

Second, to distinguish “patient initiated demand” and “physician initiated demand,” we use probability to choose each patient’s treatment as a dependent variable. The
specification is as follows:

where

$$ \begin{aligned}T_{i,s}&=\beta_{0}+\sum_{k}\beta_{1k}D e n s_{k,i,s}+\sum_{l}\beta_{2l}x_{l,i,s}+\omega_{i,s}\\T_{i,s}&=\begin{cases}1&if T_{i,s}^{*}>0\\0&otherwise\end{cases}\end{aligned} $$

The dependent variable, $ T_{i,s} $ , shows the tendency for each patient to choose an AMI treatment. Since we do not observe the probability to select a treatment, we instead use a dummy variable of 1th patient's actual choice of s th treatment, CATH, PTCA, or low-tech acute drug treatment. Therefore, we estimate (2) by the probit procedure. The definitions of each explanatory variable are the same as (1), and $ \omega_{i,s} $ is an error term. If the coefficient on the density variables is positive and significant, the result indicates a patient's referral demand, which suggests that the higher intensity of physicians could reduce the access costs to physicians. In addition, a higher intensive supply of medical care could lead to higher quality of service due to the increased competition and stimulate demand for those treatments.

Finally, to confirm our micro-based results, we sum up our data at the municipal-level and the following regression corresponding to (1) and (2), since most previous studies on PID utilized aggregated local area-level data.

$$ y_{m,s}=\alpha_{0m}+\sum_{k}\alpha_{2km}D e n s_{k,m,s}+\sum_{l}\alpha_{3lm}x_{l,m,s}+\varepsilon_{m,s} $$

$$ T_{m,s}=\beta_{0m}+\sum_{k}\beta_{2km}D e n s_{k,m,s}+\sum_{l}\beta_{3lm}x_{l,m,s}+\omega_{m,s} $$
Equations (3) and (4) examine PID and patient initiated demand, respectively. The dependent variables, $ y_{m,s} $ and $ T_{m,s} $ , stand for a logarithmic value of m th area's average medical expenditure and probability to receive an AMI treatment in the m th area for s th treatment. In those regressions, the high-tech treatments are solely defined as PTCA for the aggregated data. The definitions of $ Dens_{k,m,s} $ are the same as above. We also take an average for patient characteristics as well as the mean taxable income in an area instead of health insurance status for an economic status. Since the number of observations for municipal-level data is 116 and extensive comorbit and severity indicators are not appropriate, we use the Killip class rates to represent patient severity.

### 5. Empirical Results

Table 3 reports the empirical results based on Equations (1) and (2) for PTCA and CABG. Table 4 shows those results for low-tech treatments. Overall, those tables support the presence of either PID or patient initiated induced demand for high-tech and low-tech treatments. The key variables of our interest are $ Dens_{k,i,s} $ .

The first and second columns of Table 3 report the results based on Equation (1). The coefficients on the numbers of high-tech hospitals (HIGHTECH) and physicians (MD) per 100,000 population are positive and significant. The marginal effect of HIGHTECH indicates that an increase by one in the number of high-tech hospitals per 100,000 raises medical expenses for CATH and PTCA by 1.3 percent and 1.1 percent. On the other hand, an increase in the number of low-tech hospitals (LOWTECH) per 100,000 decreases expenditure on those high-tech treatments. Our results are consistent with Schroeder (1992), Crane (1992), Hillman
et al. (1992), and Mitchell and Scott (1992) in that a great number of high-tech medical facilities attribute to relatively high utilization of the procedures. The marginal effect of an increase by one in the number of physicians per 100,000 raises expenditure by 0.03 percent and 0.05 percent for CATH and PTCA, which are much smaller than HIGHECH. Not surprisingly, the number of high volume hospitals (HIGHVOL) is also positively correlated with medical expenditure per patient, but not statistically significant.

The remaining columns of Table 3 report the results based on Equation (2). The variables include HIGHTECH, HIGHVOL, and MD are positively correlated with the probability to undergo high-tech treatments. The marginal effects of HIGHTECH indicate that an increase by one in the number of high-tech hospitals per 100,000 raises the probabilities of a patient to choose CATH and PTCA by 8 and 7 percentage points, while an increase in LOWTECH decreases the likelihood to choose high-tech treatments. Further, an increase by one in the number of HIGHVOL raises the probabilities of patient referral choice for CATH and PTCA by 6 and 8 percentage points. The marginal effect of MD on a patient's high-tech choice is also statistically significant, but the size is negligible.

As regards patient characteristics, for PTCA we find positive correlation of a dummy variable of health insurance for the elderly in both (1) and (2), though they are not significant. Patients enrolled into health insurance for the elderly have per patient medical expenses for PTCA that are about 2 percent higher, and they are more likely to choose PTCA at a rate of about 12 percent greater than those with other insurance schemes. In the survey period, the elderly enrolled into this insurance scheme do not have to copay their medical fees, and thus the moral hazard might have been larger.

On the other hand, Table 4 reports the results on Equations (1) and (2) for low-tech
treatments where we observe positive and significant coefficients on the density variables.

The first column of Table 4 shows that the number of beds per 100,000 (BEDs) and LOWTECH is positively correlated with medical expenditure per patient, though the marginal effect of BEDs is very small. The marginal effect of LOWTECH indicates that an increase by one in the number of low-tech hospitals per 100,000 raises medical expenses per patient for acute drug treatment by 0.1 percent. In contrast to the results on the high-tech treatments, an increase in HIGHTECH, HIGHVOL, and MD decreases medical expenditure by 0.5 percent, 0.4 percent, and 0.01 percent, respectively.

The second column of Table 4 reports the results based on Equation (2). We observe similar correlations between the density variables and probability to choose low-tech treatments. The marginal effects of BEDS and LOWTECH indicate that an increase by one in the number of beds and low-tech hospitals per 100,000 raises patient initiated demand for acute drug treatment by 0.01 percent and 1.4 percent, respectively while an increase in HIGHECH, HIGHVOL, and MD decreases patient initiated demand for low-tech treatments by 8 percent, 6 percent, and 0.2 percent, respectively.

Finally, to confirm our micro-level results as above, Table 5 reports municipal-level regression results based on Equations (3) and (4). For high-tech treatments, the coefficients on HIGHECH are positive and significant for both specifications. An increase by one in HIGHECH raises expenditure per person by 0.1 percent, and the probability to choose the treatment by 3.0 percent. The effects of LOWTECH and MD are also statistically significant in Equation (3), but the sizes are small. For low-tech treatments, the coefficients on HIGHECH are negative but insignificant in both (3) and (4). Those results are consistent with our findings based on micro-level data.
### 6. Conclusions

This paper explores whether either the physician’s or patient’s initiation of high-tech and low-tech medical services primarily reflects their own self-interest or behavior, using the two-phase model. We use a unique micro-level survey, TAMIS, which provides data on high-tech and low-tech medical care use by detailed patient chart-based characteristics. Furthermore, we merged TAMIS with municipal-level data on the intensity of medical care. After controlling for a patient’s detailed characteristics, we found that increases in the relative numbers of hospitals and physicians are significantly related to physician-initiated expenditures and the effect is higher for high-tech treatments. The results based on municipal-level aggregated data also support this conclusion. Positive correlation between the intensity of medical services with medical expenditure indicate that physicians respond to more competition by generating greater demand for their services by increasing outputs, under Japan’s fixed reimbursement system. As some cardiovascular surgeons have pointed out, we cannot completely reject the possibility of unnecessary and inappropriate PTCA performed by internal cardiologists.

Although the number of patients who died of AMI in Japan remains smaller than that in the United States, medical costs for the AMI patients are expected to expand due to the increasing number of incidences as well as to the higher cost per patient. The rapid speed of aging in Japan will accelerate the expenditure on AMI in future. Our empirical findings suggest that any policy to prevent moral hazard behavior is indispensable for high-tech
treatments, which cause an enormous increase in medical expenses.

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