Silviase/jgov_v1.4_full_ann huggingface dataset:
Statistics, 52 (2): 121-133.

Fuchs, V. R. (1978) “The Supply of Surgeons and The Demand for Operations,” Journal of Human Resources, 13 (supplement): 35-56.

Fuchs, V. R. and Kramer, M. J. (1973) “Determinants of Expenditure for Physicians’ Services in the United States 1948-68,” NBER Occasional Paper no.117, National Bureau of Economic Research.

Hillman, B.J, et al. (1992) “Physicians’ Utilization and Charges for Outpatient Diagnostic Imaging in a Medicare Population,” Journal of the American Medical Association, 268 (15): 2050-2054.

Izumida, N., Urushi, H. and Nakanishi, S. (1998). “An Empirical Study of the Physician-Induced Demand Hypothesis – The Cost Function Approach to Medical Expenditure of the Elderly in Japan,” Review of Population and Social Policy, no.8, pp.11-25.

Grossman, M. (1972). The Demand for Health: A Theoretical and Empirical Investigation.

New York, Columbia University Press for the National Bureau of Economic Research.

Grytten, J., Carlsen F., and Sorensen R. (1995). “Supplier Inducement in a Public Health Care System,” Journal of Health Economics, 14 (2): 207-229.

Grytten J., Dorthe H. and Laake P. (1990). “Supplier Inducement: Its Effect on Dental Services in Norway,” Journal of Health Economics 9 (4): 483-491.

Kishida, Kensaku (2001). “Ishi Yuhatsu Zuyou Kasetsu to Access Cost Kasetsu- Niziiryo

ken, Shi Tani no Panel Data ni yoru Bunseki [Physician-Induced Demand and

Access Costs Hypothesis – An Analysis Using Panel Data from the Second Medical
Service Areas and Cities],” Kikan Shakai Hosho kenkyu [Quarterly of Social

Security Research], vol. 37 (3), pp. 246-258. (in Japanese).

Marciniak, T.A., et al. (1998) “Improving the Quality of Care for Medicare Beneficiaries with

Acute Myocardial Infarction," Journal of the American Medical Association. 279:

1351-1357.

McClellan, M., and Noguchi, H., May 1998, 'Technological Change in Heart-Disease

Treatment: Does High Tech Mean Low Value? American Economic Review,

88 (2): 90-96.

Mitchell, J. and Scott E. (1992) “New Evidence of the Prevalence and Scope of

Physician Joint Ventures," Journal of the American Medical Association, 268:

80-84.

McGuire, T.G. (2000). “Physician Agency,” in A.J. Culyer and J.P. Newhouse, eds.

Handbook of Health Economics, volume 1A. North-Holland.

Nippon Shinkekkan Intervention Gakkai Gakujutsu Iinnkai [Japanese Society of

Interventional Cardiology] (1993), Shinshikkan [Cardiovascular Disease], 8:465.

Nishimura, Shuzo (1987). “Ishi Yuhatsu Zuyou Riron wo Megutte [On the

Physician-Induced Demand Theory],” in S. Nishimura ed. Iryo no keizai

bunseki [An Economic Analysis on Medical Care]. Toyo-Keizai Shimpo-sha.

(in Japanese).

Reinhardt, U. (1978) “Comment on the Sloan Feldman Paper, Competition in the health care

sector: Past, Present, and Future," in: Warren G., ed., Proceedings of a Conference

Sponsored by the Federal Trade Commission, 156-190.

Rossiter, L.F. and Wilensky G. R. (1983) “A Reexamination of the Use of Physician Services:
The Role of Physician Induced Demand," Inquiry 20 (2): 162-172.

Rossiter, L.F. and Wilensky G. R. (1984) “Identification of Physician-Induced Demand,” Journal of Human Resources, 19 (2): 231-244.

Rossiter, L.F. and Wilensky G. R. (1987) “Health Economist-Induced Demand for Theories of Physician-Induced Demand,” Journal of Human Resources, 22(4): 624-627.

Sasakuri, S, et al. (1997) “Graft Selection in Coronary Artery Bypass Surgery in the Aged,”

Nippon Kyobu Geka Gakkai Zasshi [The Japanese Journal of Thoracic and Cardiovascular Surgery], 45(3): 384-386.

Schroeder, S. (1992) “Physician Supply and the U.S. Medical Market Place,” Health Affairs 11: 235-243.

Suzuki, R. (1998). “Iryo Shigen Mitsudo to Zyushin, Shinryo Koudo tono Kankei [The Relationship Between the Intensity of Medical Resources and Behavior of Patients and Physicians,” in A. Gunji, eds. Roujin Iryo no Kenkyu [An Analysis on Medical Costs for the Elderly], Maruzen Planet (in Japanese).

Urish, Hiroo eds (1998). Iryo Keizai Gaku [Health Economics], University of Tokyo Press. (in Japanese).

Yuda, Michio. (2005). “Kaigo Service Shizyou ni okeru kyoukyusha Yuhatsu Zyuyou

Kasetsu no Kentou [An Analysis on the Supplier-Induced Demand in the Long-Term Care Market], Kikan Shakai Hosho kenkyu [Quarterly of Social Security Research], forthcoming. (in Japanese).

Yamada, Takeshi (2002). “Kokumin Kenkou Hoken Siharai Gyoumu Data wo Riyou shita

Ishi Yuhatsu Zuyou no Kentou [An Analysis on Physician-Induced Demand Using Expenditure Data from National Health Insurance]”, Kikan shakai hosho
kenkyu [Quarterly of Social Security Research], vol. 38 (2), pp. 39-51. (in

Japanese).

Yamauchi, Yasuhiro (2003). “Houmon Kaigo Kyufu ha Tekisetsu ka? Kyoukyusha Yuhatsu

Zuyou Kasetsu Approach ni yoru Kenshou [Do They Really Need So Much

Home-Visit Long-Term Care? Supplier-Induced Demand Hypothesis Approach],

OSIPP Discussion Paper: DP-2003-J-008. (in Japanese).

Yoshikawa, A, et al. (2003) "The Causes and Consequences of Technological Change in the

Treatment of Acute Myocardial Infarction in Japan," in McClellan M. and Kessler, P,

eds., Technological Change in Health Care: A Global Analysis of Heart Attack,

University of Michigan, Chapter 8: 156-183.

Wilensky G. R. and Rossiter, L. F. (1981) “The Magnitude and Determinants of

Physician-Initiated Visits in the United States,” in: J. van der Gaag and M. Perlman,

ed., Health, Economics, and Health Economics, Aspen Systems, 215-243.

Wilensky G. R. and Rossiter, L. F. (1983) “Relative Importance of Physician Induced Demand on the Demand for Medical Care,” Milbank Memorial Fund Quarterly, 61 (2): 252-277.
## Notes

* This study is prepared for the “Final Meeting for ESRI International Collaboration Projects 2004” (February 15th and 16th, 2005 in Tokyo) sponsored by the Economic and Social Research Institute, Cabinet Office, government of Japan. We thank the Pfizer Health Research Foundation, the Japan Foundation Center for Global Partnership through Japan Center for Economic Research, Economic and Social Research Institute of the Cabinet Office of Japan, the Health Care Financing Administration through the California Medical Review, Inc., and the National Institute on Aging for providing tremendous amounts of financial support on this project. We especially appreciate all the medical facilities that collaborated with us on this project. We would also like to thank Professors Tadashi Yamada and Yasushi Iwamoto for their useful comments at the ESRI International Collaboration Projects (16th, February 2005, Tokyo). The Ethics Committee of the Department of Geriatrics, Nagoya University, and these medical facilities approved this project. All errors are our own.

Haruko Noguchi; Associate Professor, Faculty of Social Science, Toyo-Eiwa University. 32 Miho-cho Midori-ku, Yokohama-shi, Kanagawa 226-0015; Tel: 045-922-5511, E-mail: hnoguchi@toyoeiwa.ac.jp.

Satoshi Shimizutani is Associate Professor of Economics at the Institute of Economic Research of Hitotsubashi University. 2-1 Naka Kunitachi-shi Tokyo Japan, 186-8601; Tel: 042-580-8369, E-mail: sshimizu@ier.hit-u.ac.jp.

Yuichiro Masuda is Medical Doctor at Department of Geriatrics Graduate School of Medicine of Nagoya University (Address; 65 Tsurumai-cho, Showa-ku Nagoya Aichi Japan, 466-8550; Tel: 052-744-2364, E-mail: masuda@med.nagoya-u.ac.jp.

1 Ministry of Health Labour and Welfare, Kokumin Iryohi (National Medical Care Expenditure).

2 According to the Vital Statistics compiled by the Ministry of Health, Labour and Welfare, the age-adjusted mortality ratio was on a decreasing trend until the 1990s. The number of patients who died by
AMI per 0.1 million people slightly increased to 40 for males and 20 for females in 1995, partly because of the change in Japan’s death certificate and the switch from the ICD9 to the ICD 10 in 1995.

$ ^{3} $ The unbalanced treatment pattern between PTCA and CABG may be caused by the three factors: (1) the reimbursement rates for PTCA over CABG, relative to the costs for those procedures, (2) the historically high mortality rate from CABG, and (3) institutional features of the Japanese health care, i.e. the PTCA procedure is, in general, in the domain of internal medicine cardiologists, whereas CABG is in the domain of cardiovascular surgeons. These two specialists, internal medicine and surgery, are almost completely separated in Japan in terms of both education and career path. Many concerns have been expressed over serious lack of communication and coordination between the two parties. Contrary to cardiovascular surgeons’ remarks on the PTCA, internal medicine specialists condemn the high mortality rate for CABG, though many cardiovascular surgeons insist that the CABG performance has improved to the level of Western countries.

$ ^{4} $ The data are available on the Survey on National Medical Facilities (Iryo Shisetsu Chosa). This survey is compiled by the Ministry of Health, Labour and Welfare and provides comprehensive data on medical facilities in Japan.

$ ^{5} $ McGuire (2001) provides a comprehensive survey on PID.

$ ^{6} $ TAMIS is funded by the Pfizer Health Research Foundation, the Japan Foundation Center for Global Partnership, and the Economic and Social Research Institute, government of Japan. We really appreciate all the medical facilities that collaborated with us on this project.

$ ^{7} $ During the “national” phase of the project, HCFA conducted standardized abstractions of the medical records of all Medicare beneficiaries hospitalized with AMI over an eight-month period at essentially all hospitals in the United States that had not participated in a four-state “pilot” phase. The eight-month sampling frame was continuous at each hospital, and all sampling occurred between April 1994 and July 1995. Marciniak et al. (1998) provides more details on the CCP goals, sampling and data collection strategy, and methods to assure standardization and completeness of the medical record reviews. Charts were abstracted for approximately 180,000 AMI patients. These data were linked to Medicare administrative records (enrollment and hospitalization files), which have been used in previous
observational studies of AMI practices and outcomes, but do not include the clinical details present in the medical record abstracts. The enrollment files include comprehensive all-cause mortality information from Social Security records.

$ ^{8} $ CABG is also defined as high-tech treatment. Yet, the number of patients who underwent CABG in TAMIS is too few (3.8 percent) to perform regression analysis.

$ ^{9} $ In 1998, the MHLW defines a medical facility capable of performing 200 or more PTCA treatments and 30 or more CABG treatments per year as a high-tech hospital. WAMNET provides the data on which procedures are available in a hospital, but the number of procedures performed is not available. In this study, we define a medical facility capable of providing PTCA as a high-tech hospital.

$ ^{10} $ For hospitals in the Tokai area, the median number of beds is 100. Therefore, we define a medical facility with more than 100 beds as a high-volume hospital.

$ ^{11} $ The omitted category is patients with other health insurance such as Public Livelihood Assistance, Seamens’ Health Insurance, and Mutual Aid Health Insurance.
Table 1: Fee schedule for reimbursement rates, number of high-tech hospitals and treatments performed in September of 1993 and 1996

1993	1996
PTCA
Fee schedule for reimbursement	13,800	15,500
Number of PTCA hospitals	381	609
Number of PTCA performed	3,648	5,818
CABG
Fee schedule for reimbursement
1	37,100	37,100
2 or more	60,500	60,500
Number of CABG hospitals	397	453
Number of CABG performed	2,699	2,814

Source: Ministry of Health, Labour and Welfare, Shinryo Hoshu Tensu

Hayamihyo (Quick reference table of fee schedules) and Iryo-shisetsu chosa (National Survey on Medical Facilities).

Notes: Reimbursement rates for procedures are measured in points

(1point=10yen). The numbers of general hospitals in Japan are 8,752, and 8,421, in 1993 and 1996, respectively.
Table 2: Basic statistics for estimating two-phase model for physician and patient induced demand

Mean (Std. Dev)	Total (N=1,047)	High-tech treatments	Low-tech treatments
CATH (N=845)	PTCA (N=668)	(N=202)
1. Density at municipal level
Number of hospital beds per 100,000 population	818.663	806.546	792.384	869.350
Population	(466.628)	(467.585)	(439.235)	(460.295)
Number of high-tech hospitals (PTCA available) per 100,000 population	1.174	1.177	1.189	1.162
Number of low-tech hospitals (no PTCA available) per 100,000 population	3.752	3.660	3.571	4.129
Number of high volume hospitals with more than 100 beds per 100,000 population	2.592	2.573	2.566	2.672
Number of physicians per 100,000 population	(1.604)	(1.625)	(1.574)	(1.513)
Population density (per squared kilometer)	157.860	156.189	156.129	164.850
(53.616)	(52.536)	(51.422)	(57.529)
3,246	3,129	3,100	3,739
(2,724)	(2,612)	(2,552)	(3,109)
Mean taxable income (yen)	3,728,775	3,719,125	3,701,869	3,769,141
(265,179)	(260,001)	(244,194)	(282,946)
2. Patient outcomes
1st hospitalization-total hospital expenditure (y	2,870,756	3,127,469	3,331,333	1,796,879
(6,492,285)	(7,035,974)	(7,791,951)	(3,168,448)
1st hospitalization-total hospital days	28.408	29.576	28.647	23.520
(25.256)	(25.716)	(25.641)	(22.645)
3. Patient characteristics
Sex (Female=1)	0.258	0.238	0.225	0.342
(0.438)	(0.426)	(0.418)	(0.475)
Age	64.634	63.059	62.820	71.213
(11.749)	(11.268)	(11.371)	(11.458)
Number of family members living with a patient	2.272	2.322	2.346	2.064
(1.513)	(1.502)	(1.499)	(1.542)
Presence of spouse (=1 if any)	0.758	0.787	0.807	0.639
(0.428)	(0.410)	(0.395)	(0.482)
Employees' health insurance (enrollment=1)	0.404	0.428	0.428	0.302
(0.491)	(0.495)	(0.495)	(0.460)
National health insurance (enrollment=1)	0.458	0.447	0.442	0.505
(0.499)	(0.498)	(0.497)	(0.501)
Health insurance for the elderly (enrollment=1)	0.360	0.308	0.305	0.579
(0.480)	(0.462)	(0.461)	(0.495)
4. Cormorbidity variables
Continence: totally continent	0.944	0.961	0.952	0.871
(0.231)	(0.194)	(0.214)	(0.336)
Continence: occasionally incontinent	0.004	0.001	0.001	0.015
(0.062)	(0.034)	(0.039)	(0.121)
Continence: no urine output	0.003	0.001	0.001	0.010
(0.053)	(0.034)	(0.039)	(0.099)
Continence: Unknown Urinary Continence	0.050	0.037	0.045	0.104
(0.217)	(0.188)	(0.207)	(0.306)
Mobility: Walks Independently	0.950	0.970	0.969	0.866
(0.217)	(0.170)	(0.175)	(0.341)
Mobility: Walks with assistance	0.011	0.009	0.010	0.020
(0.106)	(0.097)	(0.102)	(0.140)
Mobility: Unable to walk	0.011	0.004	0.003	0.040
(0.102)	(0.060)	(0.055)	(0.196)
Mobility: Unknown Mobility	0.028	0.017	0.018	0.074
(0.164)	(0.128)	(0.133)	(0.263)
Hypertension	0.374	0.370	0.371	0.391
(0.484)	(0.483)	(0.484)	(0.489)
Hyperlipemia	0.129	0.135	0.133	0.104
(0.335)	(0.342)	(0.340)	(0.306)
Diabetes (any type)	0.252	0.246	0.241	0.277
(0.434)	(0.431)	(0.428)	(0.449)
Diabetes treated by insulin	0.049	0.052	0.048	0.035
(0.215)	(0.222)	(0.214)	(0.183)
Angina	0.159	0.147	0.150	0.208
(0.365)	(0.354)	(0.357)	(0.407)
Cardiac heart failure or pulmonary edema	0.064	0.040	0.033	0.163
(0.245)	(0.197)	(0.179)	(0.371)
Old myocardial infarction	0.098	0.097	0.097	0.104
(0.298)	(0.296)	(0.297)	(0.306)
Current cigarette smoker	0.521	0.557	0.567	0.366
(0.500)	(0.497)	(0.496)	(0.483)
Arrhythmia	0.126	0.120	0.102	0.153
(0.332)	(0.325)	(0.303)	(0.361)
Family medical history of schemic heart disease	0.158	0.159	0.154	0.153
(0.365)	(0.366)	(0.361)	(0.361)
Renal failure	0.025	0.019	0.016	0.050
(0.156)	(0.136)	(0.127)	(0.217)
Cirrhosis	0.005	0.005	0.006	0.005
(0.069)	(0.069)	(0.077)	(0.070)
Cerebrovascular accident: Cerebral infarction	0.085	0.078	0.073	0.114
(0.279)	(0.268)	(0.261)	(0.318)
Cerebrovascular accident: Brain hemorrhage	0.010	0.011	0.010	0.005
(0.097)	(0.103)	(0.102)	(0.070)
Cerebrovascular accident: Subarachnoid hemorrhage	0.004	0.004	0.004	0.005
(0.062)	(0.060)	(0.067)	(0.070)
COPD	0.011	0.012	0.013	0.010
(0.106)	(0.108)	(0.115)	(0.099)
Aneurysm of aorta	0.011	0.012	0.009	0.005
(0.102)	(0.108)	(0.094)	(0.070)
Ulcus pepticum	0.089	0.096	0.090	0.059
(0.285)	(0.295)	(0.286)	(0.237)
Cancer	0.043	0.040	0.042	0.054
(0.203)	(0.197)	(0.201)	(0.227)
Autoimmune disease	0.011	0.005	0.004	0.035
(0.102)	(0.069)	(0.067)	(0.183)
Drug allergy/med reaction	0.055	0.058	0.055	0.045
(0.229)	(0.234)	(0.229)	(0.207)
Dementia/alzheimer's disease	0.014	0.014	0.013	0.015
(0.119)	(0.118)	(0.115)	(0.121)
Terminal illness	0.003	0.001	0.001	0.010
(0.053)	(0.034)	(0.039)	(0.099)
CAG history	0.092	0.088	0.088	0.109
(0.289)	(0.283)	(0.284)	(0.312)
PTCA history	0.046	0.047	0.049	0.040
(0.209)	(0.212)	(0.217)	(0.196)
CABG history	0.004	0.005	0.004	0.000
(0.062)	(0.069)	(0.067)	(0.000)
5. Severity variables on admission
Heart rate	81.010	79.524	79.449	87.433
(20.300)	(19.020)	(18.964)	(24.117)
Temperature	36.211	36.195	36.149	36.286
(0.737)	(0.731)	(0.755)	(0.766)
Systolic blood pressure	129.951	131.668	132.484	122.379
(27.787)	(27.492)	(28.103)	(27.891)
Diastolic blood pressure	73.263	74.102	74.224	69.475
(17.378)	(17.105)	(17.535)	(18.130)
MAP (excluding <0 and >300)	92.432	93.527	93.825	87.483
(19.080)	(18.776)	(19.360)	(19.699)
Height	160.130	160.385	160.610	158.707
(10.192)	(10.421)	(10.645)	(8.711)
Weight	59.552	59.984	60.226	57.117
(11.875)	(11.835)	(11.913)	(11.846)
BMI: (Weight (kg)/(Height (m))^2)	18.988	20.115	20.100	14.275
(9.394)	(8.540)	(8.524)	(11.197)
Glucose	179.822	175.612	172.142	197.576
(88.014)	(80.235)	(73.440)	(113.750)
Albumin	3.835	3.859	3.852	3.714
(0.613)	(0.608)	(0.619)	(0.626)
Highest creatinine	1.403	1.393	1.381	1.444
(1.502)	(1.601)	(1.577)	(0.970)
Hematocrit	44.573	45.499	46.431	40.568
(16.870)	(17.262)	(18.234)	(14.428)
White blood cells (unit:000)	10.434	10.442	10.453	10.398
(3.704)	(3.636)	(3.657)	(3.992)
Platelets (unit:0000)	21.945	22.038	21.848	21.539
(7.398)	(7.112)	(6.915)	(8.536)
Blood urea nitrogen (BUN/SUN)	18.323	17.507	17.199	21.867
(9.284)	(8.682)	(7.641)	(10.876)
EKG trace: MI/injury	0.816	0.819	0.837	0.802
(0.388)	(0.385)	(0.370)	(0.399)
EKG trace: transmural (new qwave) MI	0.104	0.105	0.121	0.099
(0.306)	(0.307)	(0.327)	(0.299)
EKG trace: old/remote MI	0.104	0.097	0.094	0.134
(0.306)	(0.296)	(0.292)	(0.341)
EKG trace: ventricular tachycardia/flutter	0.141	0.131	0.141	0.183
(0.349)	(0.338)	(0.348)	(0.388)
EKG trace: atrial fibrillation/flutter	0.094	0.088	0.076	0.119
(0.291)	(0.283)	(0.266)	(0.324)
EKG trace: LBBB	0.020	0.015	0.016	0.040
(0.140)	(0.123)	(0.127)	(0.196)
EKG trace: RBBB	0.074	0.073	0.073	0.079
(0.263)	(0.261)	(0.261)	(0.271)
EKG trace: left fascicular blocks	0.004	0.005	0.006	0.000
(0.062)	(0.069)	(0.077)	(0.000)
EKG trace: heart block, 2nd/3rd degree	0.062	0.063	0.066	0.059
(0.241)	(0.243)	(0.248)	(0.237)
CHF (congestive heart failure) /pulmonary edema on chest X rays	0.292	0.271	0.268	0.381
(0.455)	(0.445)	(0.443)	(0.487)
Stress test suggests ischemia	0.030	0.032	0.027	0.020
(0.170)	(0.176)	(0.162)	(0.140)
Killip1 or Killip2	0.585	0.624	0.623	0.421
(0.493)	(0.485)	(0.485)	(0.495)
Killip class 3	0.189	0.172	0.168	0.262
(0.392)	(0.377)	(0.374)	(0.441)
Killip class 4	0.226	0.205	0.210	0.317
(0.419)	(0.404)	(0.407)	(0.466)
CHF (congestive heart failure) / pulmonary edema on chest X rays	-0.634	-0.619	-0.648	-0.494	-0.123	-0.022	-0.104	-0.039
Stress test suggests ischemia	0.799	0.783	-1.539	-1.141	0.245	0.037	-0.241	-0.092
(1.378)	(2.383)	(0.472)	(0.331)
Constant	-144.209	-140.818	283.085	217.157	-53.417	37.322	217.157
(106.945)	(181.417)	(36.700)	(27.314)
Log-likelihood	-2113.286	-2007.306	-225.601	-389.733

Note: a, b, and c indicate significance at the 5%-, 10%-, and 15%-significance level, respectively.
Table 4: Estimates of two-phase model: Tobit estimates of physician-initiated expenditures and Probit estimates of patient-initiated choice for low-tech treatments on first admission

Explanatory variables	Equation (1) Physician-initiated expenditures for low-tech treatments	Equation (2) Patient-initiated choice for low-tech treatments (Probit)
Coefficient (Std err.)	Marginal effect	Coefficient (Std err.)	Marginal effect
1. Density at municipal level
Number of hospital beds per 100,000 population	0.010	0.001	b	0.001	0.0001	b
(0.005)	(0.0005)
Number of high-tech hospitals (PTCA available) per 100,000 population	-5.198	-0.505	a	-0.462	-0.081	a
(1.599)	(0.135)
Number of low-tech hospitals (no PTCA available) per 100,000 population	0.904	0.088	a	0.077	0.014	a
(0.305)	(0.026)
Number of high volume hospitals with more than 100 beds per 100,000 population	-3.929	-0.381	b	-0.314	-0.055	b
(1.998)	(0.172)
Number of physicians per 100,000 populatio	-0.130	-0.013	a	-0.012	-0.002	a
(0.055)	(0.005)
Population density (per squared kilometer)	0.003	0.0003	a	0.0003	0.00005	a
(0.0011)	(0.0001)
2. Patient outcome
1st hospitalization-total hospital days	-0.123	-0.012	a	-0.014	-0.002	a
(0.048)	(0.005)
3. Patient characteristics
Female	42.454	14.628	5.828	0.996
(50.062)	(4.515)
Age	-0.137	-0.013	-0.034	-0.006
(0.855)	(0.075)
Age_squared	0.006	0.001	0.001	0.0001
(0.007)	(0.001)
Female*Age	-1.171	-0.114	-0.175	-0.031
(1.497)	(0.137)
Female*Age_squared	0.007	0.001	0.001	0.0002
(0.011)	(0.001)
Number of family members living with a pat	-0.054	-0.005	0.003	0.001
(0.578)	(0.050)
Presence of spouse	-2.766	-0.294	-0.225	-0.042
(2.134)	(0.188)
Employees' health insurance	1.910	0.189	0.239	0.043
(3.194)	(0.280)
National health insurance	3.786	0.377	0.366	0.065
(2.993)	(0.262)
Health insurance for the elderly	-0.078	-0.008	-0.110	-0.019
(3.087)	(0.272)
4. Cormorbidity variables
Continence: occasionally incontinent	10.551	1.957	-	-
(12.230)	-	-
Continence: no urine output	11.539	2.262	0.882	0.243
(11.834)	(0.952)
Continence: Unknown Urinary Continence	-8.314	-0.505	c	-0.721	-0.081	c
(5.678)	(0.491)
Mobility: Walks Independently	14.755	3.382	a	1.116	0.330	b
(7.075)	(0.615)
Mobility: Walks with assistance	10.411	1.908	1.083	0.319
(8.295)	(1.008)
Mobility: Unable to walk	-0.203	-0.019	-0.055	-0.009
(6.743)	(0.593)
Hypertension	-0.784	-0.075	-0.047	-0.008
(1.734)	(0.150)
Hyperlipemia	1.476	0.153	0.123	0.023
(2.523)	(0.212)
Diabetes (any type)	4.428	0.494	a	0.354	0.069	b
(2.065)	(0.177)
Diabetes treated by insulin	-8.934	-0.528	b	-0.804	-0.086	b
(4.817)	(0.405)
Angina	1.836	0.193	0.321	0.064	c