Descriptive statistics and specification tests for the German-speaking cantons are compared to those for all Swiss cantons (Sections 1.6.1 and 1.6.2). The main results focus on the German-speaking cantons, for which the instrument is sufficiently strong to provide valid results (Sections 1.6.3 and 1.6.4). The full models for the German-speaking cantons as well as the entire country are available in Tables 1.A.3 and 1.A.4 in Appendix 1.A.
1.6.1 Summary statistics
The summary statistics for the German-speaking cantons and all cantons are available in Table 1.6.1. About 12% of the sample residing in German-speaking cantons has a hospitalization. Among hospitalized individuals, average LOS is 9.3 days. Around 77% of the individuals has a doctor visit (GP or specialist), 62% a GP visit, and 32% a specialist visit. On average, there are 4.9 doctor visits, 3.7 GP visits, and also 3.7 specialist visits.
These numbers are similar when looking at the whole country. Home care use varies considerably across the German-speaking cantons over the four years, between 0.3 and 1.6 hours per capita. There is slightly more variation if all cantons are considered.
1.6.2 Specification tests
The specification tests and first-stage regressions for the German-speaking cantons as well as all cantons are available in Tables 1.A.1 and 1.A.2 in Appendix 1.A. The instrument has the expected negative impact on canton-level home care use: patient contributions are associated with a 20% decrease in home care hours per capita in the German-speaking cantons (p≤0.01). The instrument is strong with F-statistics of 12.0-13.9, depending on the sample. When looking at all cantons, the instrument fails to be significant in
Table 1.6.1. Summary statistics
German-speaking cantons All cantons
N = 38,658a N = 60,524a
Mean Std. Dev. Mean Std. Dev.
Dependent variables
Any hospitalization 0.12 0.33 0.12 0.32
Length of stayb 9.29 14.84 9.58 15.37
[1; 180] [1; 180]
Any doctor visit 0.77 0.42 0.78 0.41
Doctor visitsb 4.92 7.45 4.92 7.33
[1; 97] [1; 97]
Any GP visit 0.62 0.48 0.62 0.48
GP visitsb 3.71 5.40 3.63 5.21
[1; 97] [1; 97]
Any specialist visit 0.32 0.47 0.34 0.47
Specialist visitsb 3.73 6.88 3.78 6.99
[1; 97] [1; 97]
Canton-level home care use
Home care hours per capitab 0.77 0.27 0.87 0.42
[0.26; 1.56] [0.05; 2.57]
Individual-level covariates
Availability of informal care 0.71 0.45 0.71 0.45
Fair health 0.11 0.32 0.12 0.32
Poor health 0.03 0.17 0.03 0.18
Some symptoms 0.34 0.48 0.34 0.47
Many symptoms 0.31 0.46 0.33 0.47
Any ADL limitations 0.01 0.12 0.01 0.12
Not able to walk 200m 0.02 0.14 0.02 0.15
Obese 0.09 0.29 0.09 0.28
(Ex-)smoker 0.51 0.50 0.52 0.50
Excessive alcohol consumption 0.18 0.39 0.21 0.41
Woman 20-39 0.17 0.37 0.17 0.38
Woman 40-64 0.23 0.42 0.24 0.43
Woman 65+ 0.12 0.33 0.12 0.33
Man 40-64 0.22 0.42 0.22 0.41
Man 65+ 0.09 0.29 0.09 0.29
Swiss nationality 0.89 0.32 0.87 0.34
Rural area 0.14 0.35 0.14 0.35
Secondary education 0.64 0.48 0.62 0.48
Tertiary education 0.25 0.44 0.25 0.43
Income in 10k Swiss CHFb,c 5.37 3.05 5.22 3.06
[0.60; 96.26] [0.60; 96.48]
Woman with children<15 0.13 0.34 0.14 0.35
Man with children <15 0.12 0.33 0.13 0.33
Part-time job 0.24 0.43 0.23 0.42
Full-time job 0.43 0.50 0.43 0.50
aSummary statistics based on all observations used to estimate the likelihood of hospitalization, except for the other dependent variables. Sample sizes for the likelihoods of any doctor, GP, and specialist visit: N = 38,266, N = 38,112, and N = 37,791 (only German-speaking cantons), N = 59,938, N = 59,664, and N = 59,070 (all cantons). bContinuous variables with minimum and maximum values in brackets. cIncome enters the models in ln form.
most equations and the F-statistics are low (2.9-3.3), indicating a weak instrument. The magnitude of the coefficient on the instrument is slightly larger when considering all cantons, but its standard error increases so much that it loses its significance. Therefore,
we discuss all results for the German-speaking cantons.
Exogeneity is rejected in the likelihood of any doctor visit and likelihood of a GP visit equations only. This is not surprising because a doctor referral, typically from the GP, is required prior to the use of CHI-covered home care (i.e. reversed causality was anticipated). The fact that endogeneity is not detected in the hospitalizations or specialist visits models suggests that the canton fixed-effects effectively capture canton-level unobserved heterogeneity. We interpret the IV probit and IV GLM results whenever exogeneity is rejected in one of the two parts of the 2PM. Therefore, we interpret the IV results for doctor and GP visits and the standard probit and GLM results for hospitalizations and specialist visits.
1.6.3 Effects of home care on hospitalizations and doctor visits
We report average marginal effects.4 As the home care indicator is in ln form, Tables 1.6.2 and 1.6.3 show the effects of a 1% change in home care hours per capita on the likelihood of having a hospitalization/doctor visit, LOS (in days), or number of doctor visits. In the German-speaking cantons, expanding home care hours per capita by 1% significantly increases the likelihood of hospitalization by 0.05 percentage points (p≤0.01; Table 1.6.2).
This effect corresponds to a relative increase of 0.42% with respect to the sample’s hospitalization rate (12%). Among individuals with a hospitalization, the effect of home care on all different LOS considered is negative, and significant for acute LOS (LOS≤30).
This negative effect becomes smaller as we consider shorter LOS. A 1% expansion in home care hours per capita reduces all LOS up to 30 days by 0.02 days (p≤0.01), and LOS up to 5 days by less than 0.01 days (p≤0.10).
Home care significantly increases the likelihoods of having a doctor or a GP visit in the German-speaking cantons. A 1% expansion in home care hours per capita increases the likelihood of a doctor visit by 0.09 percentage points (p≤0.05) and the likelihood of a GP visit by 0.08 percentage points (p≤0.01). Considering that the sample’s likelihoods of a doctor or GP visit are 77% and 62%, these effects correspond to relative increases of
4The average marginal effect is the average of all marginal effects calculated for each observation in the sample.
Table 1.6.2. Marginal effects of home care on hospitalizations and doctor visits
P r(yi,c,t>0) E(yi,c,t|yi,c,t>0) E(yi,c,t) Hospitalizationsa
LOS≤180 0.046 (0.017)∗∗∗ -0.829 (1.463) 0.289 (0.230)
LOS≤90 -1.001 (0.898)
Doctor visitsb 0.088 (0.034)∗∗ -0.494 (0.604) -0.039 (0.488)
GP visitsb 0.080 (0.026)∗∗∗ 0.230 (0.465) 0.388 (0.301)
Specialist visitsa 0.006 (0.026) 0.317 (0.455) 0.123 (0.173)
Standard errors in parentheses. ∗p <0.10, ∗∗p <0.05, ∗∗∗p <0.01. aAverage marginal effects based on Probit/GLM.
bAverage marginal effects based on IV Probit/IV GLM. Samples include only German-speaking cantons.
0.12% and 0.13%. The effects on the number of visits are not statistically significant.
Overall for the likelihoods of having a hospitalization or a doctor visit, the dominant effect appears to be that home care providers facilitate access to those types of care. As hypothesized, home care substitutes for some post-acute inpatient care by reducing acute stays (LOS≤30). Home care may follow-up some acute episodes and long-term illnesses, substituting for some doctor visits. However, that effect appears to be cancelled-out by more doctor visits to get extensions for home care services (Section 2.2). Globally, all the estimated effects are small. Combining the two parts of the 2PMs, no significant effects are found. The individual-level covariates have the expected signs (Table 1.A.3 in Appendix 1.A).
1.6.4 Heterogeneous effects
Comparing the <65 and the 65+, we see that the positive effect of home care on the likelihood of hospitalization is driven by its effect on the 65+ (Table 1.6.3). The effects on all LOS up to 180 days are not significant in either age group. Still for the 65+, the effect on the combined 2PM is positive and significant: a 1% expansion in home care increases LOS by 0.02 days (p≤0.01). The positive effects of home care on the likelihoods of any doctor or a GP visit are also driven by its effects on the 65+. In addition, a 1% expansion in home care hours per capita increases doctor visits by 0.01 visits among the 65+ who have at least one visit (p≤0.05). Combining the two parts of the 2PMs,
we find significant positive effects of home care on the overall numbers of any doctor or specialist visits among the 65+. For this group, expanding home care hours per capita by 1% increases doctor visits by 0.02 visits (p≤0.01), and specialist visits by 0.01 visits (p≤0.05). To summarize, the significant results for the overall adult population are driven by the effects on the 65+ for both hospitalizations and doctor visits. This makes sense, as the 65+ are more likely to use home care over a long period of time.
Table 1.6.3. Marginal effects of home care by age groups and informal care availability
Equation <65 65+ No available
informal care
Available informal care
Any hospitalizationa 0.026 0.122∗∗∗ 0.068∗∗ 0.036∗
(0.019) (0.020) (0.027) (0.020)
LOS, conditional on -2.138d 1.844 -1.789 -0.731
hospitalizationa,c (1.632) (2.807) (3.505) (1.409)
LOS, combined effect -0.029 1.636∗∗∗ 0.493 0.181
(0.226) (0.552) (0.538) (0.226)
Any doctor visitb 0.075∗∗d 0.127∗d 0.048d 0.103∗∗d
(0.038) (0.073) (0.035) (0.046)
Doctor visits, conditional on -0.977 1.309∗∗d -2.381∗∗ -0.043
any visitb (0.709) (0.577) (1.203) (0.673)
Doctor visits, combined effect -0.450 1.749∗∗∗ -1.677∗ 0.348
(0.550) (0.614) (0.967) (0.546)
Any GP visitb 0.030 0.246∗∗∗d 0.010 0.105∗∗∗d
(0.029) (0.065) (0.059) (0.037)
GP visits, conditional on 0.518 -0.715 -0.102 0.187
any visitb (0.575) (0.701) (1.373) (0.801)
GP visits, combined effect 0.384 0.411 -0.030 0.414
(0.339) (0.628) (0.920) (0.500)
Any specialist visita -0.009 0.070 0.034 -0.003
(0.026) (0.047) (0.033) (0.037)
Specialist visits, conditional 0.124 0.835 -0.564 0.700
on any visita (0.616) (0.523) (0.589) (0.464)
Specialist visits, combined 0.004 0.547∗∗ 0.065 0.210
effect (0.207) (0.255) (0.243) (0.189)
Standard errors in parentheses. ∗p <0.10, ∗∗p <0.05,∗∗∗p < 0.01. Significant effects highlighted to facilitate reading.
aAverage marginal effects based on Probit/GLM.bAverage marginal effects based on IV Probit/IV GLM.cAll LOS up to 180 days. dExogeneity rejected (p <0.1). Samples include only German-speaking cantons.
The positive effect of home care on the likelihood of hospitalization is significant for both individuals with and without available informal care within the household, but larger for those without. This suggests that home care is less effective at addressing some health problems and avoiding events when informal care is not available. Furthermore, home care providers may be more prone to advise hospitalization when they detect a condition if the patient lives alone. The positive effects of home care on the likelihoods of a doctor or a GP visit are only significant among individuals with available informal care. A possible
explanation is that informal caregivers may perceive needs for care and encourage doctor visits. In addition, among individuals without available informal care, a 1% expansion in home care hours per capita reduces doctor visits by 0.02 visits among those with at least one visit (p≤0.05), and also by about 0.02 visits overall (p≤0.1; combined effect).
In sum, the way home care and the availability of informal care within the household interact differs for hospitalizations and doctor visits.
Globally, though the effects remain small, home care seems to complement inpatient and doctor care among the 65+ and persons with available informal care in the household.
The main channel for this complementarity appears to be increased access; i.e. home care providers ‘take no chances’ when patients are older or potential informal caregivers are present, encouraging hospitalization or seeing the doctor. Home care substitutes for some doctor visits among persons who live alone.
1.6.5 Sensitivity checks
The results of the sensitivity checks are available in Table 1.A.5 in Appendix 1.A. First, replacing the denominator of the home care indicator by the 20+ (age interval of the sample) or the 65+ (main users of home care), instead of the total canton population, has limited impact on the results.
Next, we explore the consequences of excluding some endogenous control variables, as they may affect our coefficients of interest. We remove informal care availability, income, and job status from the main models and find similar results. We also include health insurance controls (deductible level and whether the person has supplemental coverage allowing choice of hospital and stay in a single room). These variables are not included in the main model because they have large proportions of missing values. Overall when adding health insurance controls, we find similar results, except that the positive effect of home care on the likelihood of a GP visit loses its significance and the positive effect on the number of GP visits becomes significant. These results must be interpreted with caution because the sample representativeness is not guaranteed anymore and health insurance choices are endogenous.
The remaining sensitivity analyses test whether unobserved health policy affects our results. As canton health policy is relatively stable over time, its impact is likely to be captured by the canton fixed-effects. For example, the numbers of hospital beds and doctors per capita are constant over time within most cantons. The number of nursing home beds per 65+ persons has been decreasing in a similar fashion across most cantons (Figures 1.A.4-1.A.6 in Appendix 1.A). This is likely to be captured by the time fixed-effects. Nonetheless, we add those three variables to the models and find that they tend to be non-significant and their inclusion gives similar results.
Another potential source of bias is that average LOS has decreased over time, partly as a result of other policy changes. The decreasing trend in average LOS is similar across cantons and therefore likely to be captured by the time fixed-effects. In 2012, DRG-based reimbursement was implemented nationwide, but the first canton implemented it in 2002, and other cantons followed over time. Controlling for this policy change doesn’t impact the effects of home care, and that variable is not statistically significant. We also test the sensitivity of our results to the exclusion of the 1997 wave of the SHS, as it was conducted shortly after health insurance became compulsory, in 1996. The directions of the effects are unaltered, with negligible changes in significance levels and magnitudes.
Finally, we estimate the effects of home care on dentist and optician visits as falsification tests. A significant relationship would suggest that the effects of home care on hospitalizations and doctor visits may not be causal but driven by unobserved factors in the health sector. We estimate Poisson models as there are few visits per year. The estimated effects of home care are not significantly different from zero (results available on request). This further reassures us of the validity of our findings.