Linear piecewise spline interpretation


I suspected my data was non-linear so I ran PROC GAM to visualize my age variable relative to my binomial outcome. I realize adding one spline knot at age 51.5 was very appropriate. I created the linear piecewise spline using a "relative reference" coding scheme:

if age<=51.5 then age515=0;
else age515=age-51.5;

Next I ran a log binomial regression:(PROC GENMOD; dist=bin, link=log) as my outcome variable is very prevalence (>60%). I have the outputs that give me the prevalence ratios for the first regression line up to age <=51.5; how do I get the prevalence ratios for the second regression line >51.5? I have one class variable (four levels) and one continuous variable

From the output:
Analysis Of Maximum Likelihood Parameter Estimates
Parameter Beta
Intercept -2.3315
Age (contin) 0.0328
Var (level 4) 0.6515
Var (level 2) 0.6509
Var (level 3) 0.6515
Var (level 1) 0.0000



Less is more. Stay pure. Stay poor.
I have not done these procedures myself, so I am not much help in that regard. But I know there are some macros (spline), that address visualizing this process and provide the odds ratios for all values above and below the compared reference group.
Let me rephrase the question:

The age continuous term has two separate betas, a ratio estimate before the spline knot (eg 1.0333 per 1 year increase), and a beta/ ratio estimate after the spline knot (eg PR=0.9844 per 1 year increase).

Say if I include a CLASS variable "monthsexchangingquartiles" that has 4 levels, SAS tells me the ratios are static (the same) before and after the spline knot. Am I suppose to get the same ratios before and after a linear piecewise spline with one knot? Or am I suppose to expect separate ratios for the CLASS term after the spline knot?