of the U.S., Europe, and Asia (19, 20). Although a precise understanding
of the temperature dependence of stone disease remains
to be established, published reports consistent with a nonlinear
relationship imply that the upper Midwest of the U.S. would be
most heavily impacted. For example Kansas, Missouri, Maryland,
and Kentucky are predicted to sustain 25% increases in stone
prevalence by 2050 under the nonlinear model. The linear model
developed here yields a more uniform distribution of increased
prevalence, but the number of cases and increased annual costs will
be concentrated in warm, high-population states such as California
and Texas, which, by 2050, would each realize an annual cost
increase of $110 million in year 2000 dollars.
This work used data primarily developed for other purposes, and
as such our results are subject to a number of limitations. Perhaps
foremost is that the precise relationship between ambient temperature
and stone risk remains unknown, chiefly because it was of
little importance to management of stone disease in the past.
Clearly with the likelihood of long-term temperature changes, this
importance is now greatly enhanced, and the issue warrants careful
study. Of the two models discussed here, we favor the linear model
because the Veterans Affairs (VA) data on which estimates of ston