The argument here is a good one that points out the problems
with the interpretation of HSMR when the populations are not
similar. But, that doesn't mean there is no meaning in the
numbers at all.
The (artificial) stats presented suggest that both hospitals
have a serious problem with men: double the expected
population death rate. Both also have much lower than
expected female mortality. Since hospital A appears to
specialize in treating men (with 70% male intake) this is a
much more serious criticism of it than it is of hospital B
which is more gynecologically focussed (70% female intake).
An alternative way of stating this result is that hospital A
is bad at its specialist subject (men). This is exactly the
message that its HSMR score provides.
The message from HSMR is incomplete, but still useful.
NB the analysis above is only valid if we assume the
expected death rates in the population are correct.
Rapid Response:
Depends on the intended message...
The argument here is a good one that points out the problems
with the interpretation of HSMR when the populations are not
similar. But, that doesn't mean there is no meaning in the
numbers at all.
The (artificial) stats presented suggest that both hospitals
have a serious problem with men: double the expected
population death rate. Both also have much lower than
expected female mortality. Since hospital A appears to
specialize in treating men (with 70% male intake) this is a
much more serious criticism of it than it is of hospital B
which is more gynecologically focussed (70% female intake).
An alternative way of stating this result is that hospital A
is bad at its specialist subject (men). This is exactly the
message that its HSMR score provides.
The message from HSMR is incomplete, but still useful.
NB the analysis above is only valid if we assume the
expected death rates in the population are correct.
Competing interests:
None declared
Competing interests: No competing interests