pr.probability - Disintegrations are measurable measures - when are they continuous?

_{This is a sequel to another question I have asked.}

The notion of disintegration is a refinement of conditional probability to spaces which have more structure than abstract probability spaces; sometimes this is called regular conditional probability. Let $Y$ and $X$ be two nice metric spaces, let $mathbb P$ be a probability measure on $Y$, and let $pi : Y to X$ be a measurable function. Let $mathbb P_X(B) = mathbb P(pi^{-1} B)$ denote the push-forward measure of $mathbb P$ on $X$. The disintegration theorem says that for $mathbb P_X$-almost every $x in X$, there exists a nice measure $mathbb P^x$ on $Y$ such that $mathbb P$ "disintegrates":$$int_Y f(y) ~dmathbb P(y) = int_X int_{pi^{-1}(x)} f(y) ~dmathbb P^x(y) dmathbb P_X(x)$$
for every measurable $f$ on $Y$.

This is a beautiful theorem, but it's not strong enough for my needs. Fix a Borel set $B subseteq X$, and let $p(x) = mathbb P^x(B)$. Part of the theorem is that $p$ is a measurable function of $x$. Suppose that the map $pi : Y to X$ is continuous instead of simply measurable. My question: What is a general sufficient condition for $p(x)$ to be continuous?

To me, this is an obvious question to ask, since if $x$ and $x'$ are two close realizations of a random $x in X$, then the measures $mathbb P^x$ and $mathbb P^{x'}$ should be close too, at least in many natural situations. However, in my combing through the literature, I haven't been able to find an answer to this question. My guess is that most people are content to integrate over $x$ when they use the theorem. For my purposes, I need some estimates which I get by continuity.

At this point, I've managed to prove and write down a pretty good sufficient condition for the case I care about (Banach spaces), using an abstract Wiener space-type construction. However, I am hoping that an expert can point me toward a good reference that does this in wider generality.