# Assessing the Impact of Patient Characteristics on Genetic Clinical Pathways: A Regression Approach
## 3 Results
### Statistical Analyses
#### 3.5.5 Binary Models: Assignment to a Clinical Pathway: Foreign Mother Sample  
As pointed out in the descriptive statistics, the sub-sample of foreign mothers presents quite peculiar characteristics compared to the whole sample. It, therefore, seems sensible to repeat the analyses on this subsample to see whether, within the group of foreign mothers, the correlation between the personal characteristics and the assignment to the clinical pathway and, on the other hand, the performance indicator, varies significantly with respect to the full sample. Since the subsample of foreign women includes EMAs anyway, the variable age is omitted as endogenous. As in previous estimates, the most interesting clinical pathways in Table 21 appear to be B1, B5, B6, and A. The estimates appear robust compared to the samples previously considered. The weeks of pregnancy are positively correlated with the probability of being in B1 and B5, and negatively with the A pathway. A specialist physician assigns patients to B5 and B6. The number of children born alive is negatively correlated with the probability of being assigned to B6. Individual characteristics influence the probability of being assigned to a particular clinical pathway regardless of nationality. This result functions as a robustness test for the results obtained. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Variables** & **(1)** & **(2)** & **(3)** & **(4)** & **(5)** & **(6)** & **(7)** & **(8)** \\  & **B1** & **B2** & **B3** & **B4** & **B5** & **B6** & **C** & **DA** \\ \hline \multirow{3}{*}{Weeks of pregnancy} & 0.0196 *** & \(-\)0.00242 & \(-\)0.00594 *** & \(-\)0.00834 & 0.00575 *** & \(-\)0.00602 * & \(-\)0.00101 & \(-\)0.0315 *** \\  & (2.901) & (\(-\)0.882) & (\(-\)2.617) & (\(-\)0.988) & (\(-\)2.804) & (\(-\)1.654) & (\(-\)0.402) & (\(-\)5.393) \\ \multirow{3}{*}{Live births} & 0.0821 *** & \(-\)0.0193 & \(-\)0.00525 & 0.00727 & 0.00466 & \(-\)0.0558 ** & \(-\)0.0137 & 0.0321 \\  & (2.185) & (\(-\)1.422) & (\(-\)1.025) & (0.663) & (0.509) & (\(-\)2.041) & (\(-\)1.236) & (0.997) \\ \multirow{3}{*}{Miscarriages} & \(-\)0.0601 & \(-\)0.00437 & 0.0136 * & - & 0.0139 & & 0.00878 & 0.00905 \\  & (\(-\)1.225) & (\(-\)0.337) & (1.878) & & (1.259) & & (0.644) & (0.254) \\ \multirow{3}{*}{VPI} & 0.149 & 0.0644 * & - & - & 0.00869 & - & \(-\)0.143 \\  & (1.459) & (1.911) & - & (0.389) & & & \(-\)0.196 & (\(-\)0.975) \\ \multirow{3}{*}{General practitioner} & \(-\)0.181 * & 0.0195 & - & - & \(-\)0.386* & & \(-\)0.0196 & 0.412 *** \\  & (\(-\)1.901) & (0.486) & - & - & (\(-\)1.649) & & (\(-\)0.713) & (0.4085) \\ \multirow{3}{*}{Hospital specialist} & \(-\)0.110 & - & 0.0370 & - & 0.0327 & 0.0342 & 0.00298 & \(-\)0.0371 \\  & (\(-\)1.182) & - & (1.208) & - & (0.890) & (0.640) & (0.0966) & (\(-\)0.439) \\ \multirow{3}{*}{Private specialist} & 0.110 & 0.0109 & - & - & 0.0252 & 0.0575 & - & \(-\)0.175* \\  & (0.630) & (0.174) & - & - & (0.342) & (0.531) & & (\(-\)1.744) \\ \multirow{3}{*}{Consultant specialist} & 0.00887 & - & \(-\)0.0133 & - & \(-\)0.0069 *** & 0.0936 & 0.00859 & \(-\)0.125 \\  & (0.0910) & (0.0910) & (\(-\)0.891) & - & (\(-\)2.953) & (1.055) & (0.284) & (\(-\)1.554) \\ \multirow{3}{*}{NHS specialist} & \(-\)0.211 ** & 0.0378 & 0.00196 & - & \(-\)0.00798 & \(-\)0.00387 & - & 0.264 ** \\  & (\(-\)2.317) & (0.761) & (0.0956) & - & (\(-\)0.272) & (\(-\)0.127) & & (2.214) \\ \multirow{3}{*}{No. obs.} & 321 & 153 & 242 & 26 & 321 & 191 & 257 & 321 \\ \hline \hline \end{tabular} Note.: ***\(p<0.001\); **\(p<0.01\); *\(p<0.05\).  
\end{table} 
Table 20: _Cont._