If race3 was considered as a fixed effect, I would expect the values to sum to zero. In this case, I think it will depend on the distribution of responses to each race, which will differ by hospital (as I see that not all races are observed at every hospital). So perhaps the variances and covariances for each hospital are not homogeneous, and could be fit with a GROUP= option. You would likely need two runs (one homogeneous, ignoring race3 and one heterogeneous, accommodating race3 differences). This approach has the advantage of being applicable to the fixed levels of race3, rather than assuming that the levels were selected randomly from a population. I would try fitting the following:
/* Homogeneous variance-covariance across race3 */
proc glimmix data=dataset method=laplace;
class dshospid race3;
model died (event='1') = var1 var2 var3 . . . var10 / dist=binary link=logit solution;
random intercept / subject=dshospid type=un s;
run;
/* Heterogeneous variance-covariance across race3 */
proc glimmix data=dataset method=laplace;
class dshospid race3;
model died (event='1') = var1 var2 var3 . . . var10 / dist=binary link=logit solution;
random intercept / subject=dshospid type=un s group=race3; covtest homogeneity;
run;
SteveDenham

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