Table 6.

Parameter estimates for immigrant versus native-born populations in the individual countries and the pooled 11 country sample with the addition of controls for education, employment, extended access, and payment for health care: model M3

Country	Physician visits		GP visits		Hospital visits
	Negative binomial		Negative binomial		Poisson		Logit
	β	SE	β	SE	β	SE	β	SE
Austria	−0.02	(0.091)	−0.10	(0.091)	−0.05	(0.252)	0.43	(0.372)
Belgium	0.07	(0.060)	0.10*	(0.061)	−0.42*	(0.248)	−0.81**	(0.483)
Denmark	0.02	(0.141)	0.22*	(0.133)	0.74***	(0.239)	0.55	(0.434)
France	0.13***	(0.044)	0.04	(0.044)	0.35*	(0.186)	0.39	(0.265)
Germany	−0.03	(0.046)	−0.03	(0.046)	0.27*	(0.148)	0.56*	(0.223)
Greece	0.04	(0.143)	0.09	(0.163)	1.02***	(0.375)	0.49	(0.526)
Italy	0.03	(0.191)	0.05	(0.198)	−1.13**	(0.490)	−3.41	(2.468)
Netherlands	0.14*	(0.085)	0.28***	(0.078)	1.30***	(0.258)	0.96***	(0.352)
Spain	−0.10	(0.152)	−0.03	(0.159)	−0.32	(0.383)	−1.19**	(0.734)
Sweden	0.31***	(0.069)	0.17**	(0.071)	0.22	(0.258)	0.27	(0.374)
Switzerland	0.25*	(0.142)	0.28**	(0.142)	1.69***	(0.338)	1.58*	(0.871)
Total	0.11***	(0.024)	0.08***	(0.025)	0.19***	(0.072)	0.08	(0.099)
Pseudo-R2	49.31%		48.72%		50.49%

M3 age, gender, number of symptoms, heart and vascular diseases, lung conditions, cancer, diabetes, fractures, years of education, employment status, extended access, and full coverage controlled. The model is estimated in each country and in the entire sample. For hospital visits, each column contains the results for the ZIP, the Poisson model and the logit model estimations, using the nlmixed procedure in SAS. A positive parameter in the logit equation means a higher probability of not being a hospital user

Source SHARE data, 2004 (individuals 50+)

Significance levels *** P < 0.01;

^**P < 0.05;

^*P < 0.1. Standard errors in parentheses