Reviewer's report
Title:Factors associated with treatment of women with osteoporosis or
osteopenia from a national survey
Version: 1Date:16 June 2011
Reviewer: Graeme Jones
Reviewer's report:
Potentially, this may be important work. However, the paper at present is
somewhat unfocussed and quite diffuse in its analysis. the rationale for why this
study is necessary also isnt that well developed.
Major points
1. While FRAX is validated as a fracture prediction tool, the evidence that it
should be used to guide treatment decisions is non-existent. Indeed, the
evidence that treatments work is largely lacking apart from in those with low BMD
plus/minus fracture. Recent work from SOF (JBMR 2011) also shows that simple
fracture prediction models work equally as well as FRAX in the US white
population casting doubt on why we should do FRAX at all. Thus, the study of
FRAX is a little premature and detracts from the paper. I would remove this all
together.
2. The analysis is a bit unfocussed. One could split the correlates up into those
that associate with low BMD eg age, BMI and postemenopausal status or those
that associate with willingness to fill a script eg healthy lifestyle of the worried
well, single status, comorbid disease etc. it is the latter that are far more
interesting than the former as they may allow the design of strategies to improve
prescription uptake and this is a major issue worldwide at present.
3. The data are presented as any treatment for any person. Most of us would
split this by type eg most guidelines would recommend treating a women with a
fracture and low BMD (either OP or osteopenia), women with OP alone but not
osteopenia (unless one considered ert for the latter). It would be helpful to
analyse by these subgroups of both subjects and type of medication or those
meeting guidelines so one can see where the gaps are and identify predictors of
prescription within the subgroups as i suspect they will differ by group. the study
has sufficient power to do this
4. The lack of BMD is a significant problem especially for the adjusted analysis
and needs to be drawn out more in the discussion. Also, how can someone be
diagnosed as osteopenia in the absence of a BMD scan? this was 15% of the
sample. Please explain.
5. some of the findings appear counter-intuitive eg both higher income and higher
ratio of prescription costs to income are correlates. Please discuss.
6. GC use is not well enough collected eg a significant % may be on inhaled or
low dose so would not warrant OP treatment. is there any data to check this?
7. the model building could be a bit more hypothesis driven rather than simply
putting all variables into the model.
8. While it is interesting that >=2 fractures may drive treatment only 16% are on
treatment which is very disappointing in such a high risk group while conversely
the majority of those without fracture are on treatment. This should be discussed.
In my country, the latter arent reimbursed. Is this the same in the US?
Minor
1. delete study sample column in table 1
Level of interest: An article of importance in its field
Quality of written English: Acceptable
Statistical review: Yes, and I have assessed the statistics in my report.
Declaration of competing interests:
'I declare that I have no competing interests'