spection of Table A-3 reveals remarkably close agreement between observed and expected numbers of suicides.

Second, we can use the Bayes estimates directly to obtain county-level suicide rates adjusted for the effects of race, sex, and age. In the case of a Poisson model, the Bayes estimate for a given county is a multiple of the national suicide rate adjusted for the case mix in that county (i.e., race, sex and age). For example, a Bayes estimate of 1.0 represents an adjusted rate that is equal to the national rate. By contrast, a Bayes estimate of 2.0 represents a doubling of the national rate, and a Bayes estimate of 0.5 represents one-half of the national rate. Figure A-2 displays the Bayes estimates by county across the U.S. Inspection of Figure A-2 reveals that even after accounting for these important demographic variables, considerable spatial variability remains. Again, the highest adjusted rates are typically found in the less densely populated areas of the western U.S.

The map in Figure A-2 also provides a useful tool for qualitative research into the etiology of suicide. A natural approach is to examine the