1
Appendix
Construction and Measurement Properties of Scales used to Assess Study Latent Variables
The study used multi-item scales to assess nine constructs; concerns about medications,
perceived need for fracture prevention medication, medication use self-efficacy, perceived
susceptibility to fractures, perceived severity (health consequences) of fractures, fracture
knowledge, fracture knowledge, depression, trust in physician, and patient perceived open
communication of the physician. Only the trust in physician and open communication scales
were used in their full, previously published and validated formats. For each of the remaining
scales, the rationale behind item selection and the demonstration of their psychometric properties
are discussed below. All of the items of all scales were pre-tested on individuals with
osteoporosis not participating in the current study to ensure that questions were not considered
ambiguous or confusing and were being interpreted as we intended. We examined the internal
consistency reliability and the range of item-scale correlations within each scale. For each scale,
we determined its floor effect (proportion of respondents with the lowest possible scale score)
and its ceiling effect (proportion of respondents with the highest possible scale score). (table 1).
An overall principal components analysis was also done assessing the items of all nine
scales together as well. With two exceptions, all items mapped onto their proposed factor, and
onto no others. The exceptions were that all eleven Trust in Physician items and all five Open
Physician Communication items mapped onto the same factor, although there is previously
published evidence of these being separate constructs.(1) However, a confirmatory factor
analysis with the current dataset suggested that the Trust and Open Communication items may fit
best onto separate factors, with a secondary overarching factor (RMSEA 0.80) than when they
are all hypothesized to map to the same factor (RMSEA 1.24). Another confirmatory analysis
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mapping all items from all nine scales onto their hypothesized factor showed overall very good
model fit (RMSEA 0.049, 95% C.I. 0.047 – 0.050; comparative fit index (CFI) 0.865).
Concerns about Medications
This 11 item scale was developed in a separate survey of a cohort of 387 patients with
osteoporosis by bone density criteria done in 2005, based on the three subscales of Horne’s
regarding medication concerns; concerns regarding medications specifically prescribed for that
person, concerns that medications in general were overused, and concerns that in general
medications are intrinsically harmful. The original General Overuse and General Harms
subscales of Horne and colleagues exhibited suboptimal internal consistency reliability, and
hence we added one additional item to the Specific Concerns subscale, one item to the General
Overuse Scale, and three items to the General Harms scale. We were also concerned that the
middle response category of the original subscales (“Uncertain”) was not ordinally ranked
between “Agree and “Disagree” and hence we changed to six response categories (Strongly
Disagree, Disagree, Somewhat Disagree, Somewhat Agree, Agree, and Strongly Agree).
Subsequently, 11 of these items appeared to fit a generalized partial credit item response theory
model very well,(2) had excellent internal consistency reliability (Cronbach’s alpha 0.83), had
normally distributed raw scores, and fulfilled criteria of a good Likert scale. These 11 items
comprise the concerns about medication scale used in the current study. Three examples of these
items are “I sometimes worry about becoming too dependent on medicines”; “I am concerned
about the long-term effects of medicines”; “If doctors had more time with patients, they would
prescribe fewer medicines”. In the current study, the internal consistency reliability was again
very good and the scale exhibited very little floor or ceiling effects (table 1). The raw scores of
this scale were normally distributed, and hence this is modeled as a continuous variable.
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Perceived Need for Fracture Prevention Medication
The perceived need for medication scale of Horne and colleagues has been
conceptual as being applicable to specific medications, and not as the need for medication
in general to maintain health. As such this scale has been varied somewhat by those authors,
depending on what specific illness is being studied. For example, the perceived need for
medication scales for diabetic and psychiatric patients, respectively, differ by 1 and 2 items from
the perceived need for medication scales used for asthma and renal disease patients.(3) Horne
and colleagues subsequently added additional asthma specific items for the perceived need for
medication scale for use with asthma patients,(4) and devised yet another version to assess
perceived need for retroviral medications among those with HIV infection.(5)
Given that osteoporosis is asymptomatic until and unless a fracture occurs, we dropped
two items from the original harms scale of Horne and colleagues (“I would be very ill without
my medicines” and “my life would be impossible without my medicines”) that in this context we
judged to lack face validity. To ensure adequate internal consistency reliability, we then added
one item to assess need for medication to stay healthy and three items to assess perceived need of
medication to specifically prevent fractures. Examples of the items are; “my health, at present,
depends on my medicine for osteoporosis”; “my osteoporosis medicine reduces my risk of
having broken bones”; “my osteoporosis medicine improves my chances of staying healthy”.
In our study sample, this scale had excellent internal consistency reliability, similar itemscale correlations across all scale items, and very little floor or ceiling effects (table 1). Raw
scale scores were modestly left skewed, but the square of the raw scores were normally
distributed and were treated as a continuous variable in statistical analyses.
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Trust in Physician
This previously validated scale consists of 11 items, that assess primarily the trust that
patients’ have in the competence of their physicians and that their (the patient’s) interests will be
considered first when making recommendations and decisions.(6, 7) This scale does not
explicitly assess the physician’s communication skills. Examples of items are; “I trust my
doctor’s judgment about my medical care”; “I trust my doctor to put my medical needs
above all other considerations when treating my medical problems”; “My doctor is well qualified
to manage (diagnose and treat or make an appropriate referral) medical problems like mine”.
Physician Open Communication
This is a previously validated five item scale that asks patients to rank their physicians on
a five level scale (poor, fair, good, very good, excellent) as to how well they fully inform them of
all relevant health information, explain treatment alternatives, explain potential side effects of
treatments, inform them of test results in a timely manner, and explain what to expect from
treatment.(8) This scale has excellent internal consistency reliability, a fairly narrow range of
item-test correlations, but a significant ceiling effect (table 1). The distribution of the raw scale
scores is highly skewed left, and hence this was modeled as a four-level categorical variable.
Satisfaction with Physician Decision Making Style
The Shared Decision Making model postulates three types of medical decision making
styles among physicians; paternalistic, participatory, and informed.(9) Physicians who use a
paternalistic decision making style tend to make medical decisions without input from the
patients themselves. When the physician employs an informed decision making style, they act as
a resource of expert knowledge for the patient, but the patient primarily makes the medical
decisions in their healthcare. For those patients whose physicians have a participatory decision
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making style, medical decisions are the result of a collaborative, iterative process between the
patient and physician.
While some have postulated that the participatory style is preferred,(10) patients
occasionally prefer physicians who employ one of the other styles, and many more patients’
preference for the decision making style of their provider may be vary according to the clinical
problem with which they are coping.(11, 12) Therefore, we postulated that patient satisfaction
with their physician’s decision making style may be more positively associated with their
willingness to accept and adhere to their providers’ treatment recommendations than the type of
decision making style per se. We asked patients to identify their providers’ decision making
style, and then second asked their satisfaction with this decision making style using one question
with 4 response categories (from very dissatisfied to very satisfied). Only 17 and 23 respondents
indicated that they were very dissatisfied or dissatisfied with their provider’s decision making
style whereas 422 and 267 respondents, respectively, were satisfied and very satisfied. We
created a dichotomous variable with the higher level (267 respondents) being very satisfied and
462 respondents being less satisfied with their physician’s decision making style.
Fracture knowledge
Although extant surveys exist that assess knowledge of osteoporosis, risk factors for its
development, and nutritional prevention of bone loss, we judged knowledge regarding fractures
to be more relevant to decisions as to whether or not medication should be taken to prevent
fractures. We could find no extant survey instrument that assessed knowledge specifically of
fractures. Fourteen items were submitted to a convenience sample of 15 internationally
recognized experts within the osteoporosis research, and there was complete consensus
among these experts as to their correct answer for 9 items.
6
In factor analyses, however, three of these items did not load well onto any factor,
including the factor onto which the other six items strongly loaded. This scale may not be fully
unidimensional and its internal consistency reliability is marginally acceptable. The distribution
of the raw scores showed substantially greater tails than a normally distributed variable, and
hence the variable “fracture knowledge” was modeled as a four level categorical variable.
Conclusions regarding the associations of medication use behavior and medication attitudes with
fracture knowledge using this scale need to be interpreted cautiously.
Perceived Susceptibility to Fractures
We conceived of this as a separate construct from the perception that medications are
necessary to reduce one’s risk of fractures. We intended to employ the 4-item scale of Gerend
and colleagues to measure this, reworded to reflect perceived susceptibility to fractures rather
than susceptibility to osteoporosis.(13) However, in our pre-test, participants found one of the
items difficult to answer, and hence we dropped that one item. The scale consisting of the
remaining three items nonetheless demonstrates reasonable internal consistency reliability and
unidimensionality, similar item-scale correlations among the three items, and little in the ways of
floor or ceiling effects. This raw scale scores are normally distributed, and this latent variable is
modeled as a continuous variable.
Perceived severity (health consequences) of fractures
This scale was developed to assess participants’ perception as to what the health
consequences to them would be of a hip or spine fracture, the fractures related to osteoporosis
that have the most morbidity. We could find no extant validated instrument available to assess
this, and hence we developed and pre-tested three items to assess this. The items ask respondents
to indicate their level of agreement (from strongly disagree to strongly agree) with each of the
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following statements; that having a hip or spine fracture would reduce their sense of well-being,
would permanently reduce their health, and would permanently limit their ability to do important
activities. While this scale appears to be unidimensional, has good internal consistency
reliability, and has similar item-scale correlations across the three items, it has a strong ceiling
effect (table 1). Hence, the distribution of the raw scale scores is highly skewed, and we have
modeled this variable as a three-level ordinal variable.
Depression
Given that we were assessing self-reported medication use behavior over the 18 months
prior to the survey date, we wanted to use an instrument for self-reported depression that queried
about mood over the last one to two years. The only scale available that we found to assess this
were the three-item depression screening questions developed for the SF-36D variation of the
SF-36 health status measure.(14) The three items ask if patients have been depressedf or 2 or
more weeks of the past year, for most of the past year, and if they have had 2 or more years
where they felt sad or depressed most of the time. Each of these was answered yes or no.
This scale in our study had marginal internal consistency reliability and a high floor
effect with 65% indicating no problem with depression on any item. We created a dichotomous
variable, with the lower level indicating no problem with depression on all three items, and the
higher level being a “yes” answer for any of the three items. We included depression only as a
control covariate in this study, and had no a priori hypotheses about the association of
depression with medication use behavior or the other variables in our conceptual model.
Instrumental Variables Regressions
Multivariate path models require that more than one regression equation be estimated in
which the dependent variable(s) of one or more regressions are predictor variables in other
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regressions (called endogenous predictors). When multivariate path models with more than one
simultaneous regression equation are estimated, the association of any endogenous predictor with
a downstream dependent variable is determined in part using the predicted values from the
regression in which the endogenous predictor itself is the dependent variable. This will lead to
biased estimates of the associations of both endogenous and exogenous predictors with the
ultimate dependent variable if the error terms of any pair of regression equations are correlated.
We used instrumental variables regressions to test for the presence of such bias. Potential
instrumental variables are those variables that are completely exogenous in the multivariate
model (e.g., are not the dependent variable in any regression equation in the model). Good
instruments for endogenous predictors are those that are associated with the endogenous
predictor but not with downstream dependent variables except indirectly through the endogenous
predictor.
To look for bias from inclusion of endogenous predictors, we tested for systematic
differences in the associations of predictors with fracture prevention medication when a single
regression is run treating all predictors as strictly exogenous, compared to an instrumental
variables regression using other exogenous variables as instruments for the endogenous
predictors. In our multivariate model, we have four endogenous predictors; self-reported
susceptibility to fractures, perceived severity of fractures, concerns about medications, and trust
in physician. These are normally distributed, continuous variables, with the exception that
perceived fracture severity is a categorical. However, Stata 11.0 can perform instrumental
variables regression only for those endogenous predictors that are continuous. Therefore we
performed two instrumental variables analyses. In the first one with perceived need as the
dependent variable, we instrumented for self-reported susceptibility to fractures, concerns about
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medications, and trust in physician, and treated perceived severity of fractures as a completely
exogenous predictor. Table 2 shows the parameter estimates for the IV regression model
(estimated with 2 stage least squares), and for a linear regression model where all of the
endogenous predictors are treated as though they exogenous. The robust F test shows no
systematic difference between the parameter estimates of these two regressions.
In the second regression, we used an IV probit model with perceived severity of fractures
modeled as a dichotomous categorical variable, and instrumented for trust in physician. Table 3
shows the parameter estimates for the IV probit model (estimated with maximum likelihood) and
for a standard probit model where trust in physician is treated as a fully exogenous variable. The
Wald test for systematic differences in the parameter estimates between the two models is not
significant.
Hence to the extent to which we could possibly test for bias from inclusion of
endogenous predictors in our multivariate path model, we found none. However, it should be
noted that we were unable to perform any IV regressions instrumenting for perceived fracture
severity, and hence cannot rule out the possibility of bias from inclusion of this endogenous
predictor in the multivariate model.
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Appendix References
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11
Table 1 – Measurement Characteristics of Multi-Item Survey Scales
Scale
Medication
Concerns
Perceived Need
for Medication
Trust in
Physician
Open
Communication
Perceived
Fracture
Susceptibility*
Perceived
Fracture
Severity*
Fracture
Knowledge
Depression
Number
of Items
11
Internal
Consistency
Reliability
0.85
Range of
Item-Scale
Correlations
0.56 – 0.73
Floor / Ceiling
Effects
1.02% / 0.00%
7
0.87
0.75 – 0.80
0.13% / 2.33%
11
0.88
0.47 – 0.79
0.00% / 2.5%
5
0.92
0.80 – 0.92
0.27% / 14.4%
3
0.81
0.77 – 0.91
1.23% / 1.65%
3
0.87
0.84 – 0.92
0.55% / 27.7%
6
0.70
0.57 – 0.67
11.4% / 9.7%
3
0.69
0.78 – 0.81
65.0% / 7.3%
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Table 2 – Parameter Estimates for Predictors of Patient Self-Reported Need for Fracture
Prevention Medication; Standard Linear vs IV Regression*^
Predictor
Standard Linear
Regression Estimates
(95% C.I.)
IV Regression Estimates
(95% C.I.)
Self-reported Susceptibility to
Fracture (per SD)
0.21
(0.13 to 0.28)
0.31
(0.04 to 0.59)
Perceived Severity of Fractures
Tertile 2 vs 1
Tertile 3 vs 1
0.17 (-0.02 – 0.36)
0.54 (0.37 – 0.71)
0.11 (-0.13 to 0.34)
0.46 (0.22 to 0.69)
Concerns about Medications
(per SD)
-0.15
(-0.24 to -0.07)
-0.08
(-0.47 to 0.40)
Trust in Physician
(per SD)
0.18
(0.06 to 0.30)
0.35
(-0.57 to 1.27)
Open MD Communication
Quartile 2 vs 1:
Quartile 3 vs 1:
Quartile 4 vs 1:
-0.04 (-0.22 to 0.13)
0.03 (-0.19 to 0.25)
0.20 (-0.07 to 0.47)
-0.10 (-0.55 to 0.34)
-0.09 (-0.18 to 0.26)
-0.00 (-1.22 to 1.22)
Satisfaction with Physician
Decision Making Style
-0.03
(-0.20 to 0.15)
-0.06
(-0.44 to 0.32)
Prevalent Vertebral Fracture
No: 0.0 (reference)
Unknown: 0.06 (-0.09 to 0.22)
Yes: 0.33 (0.08 to 0.58)
No: 0.0 (reference)
Unknown: 0.06 (-0.09 to 0.21)
Yes: 0.34 (0.07 to 0.60)
BMD T-score
(per 1 unit increase)
-0.09
(-0.21 to 0.03)
-0.06
(-0.20 to 0.08)
Age
(per 10 year increase)
-0.12
(-0.19 to -0.03)
-0.13
(-0.26 to 0.00)
Educational Status
Quartile 2 vs 1:
Quartile 3 vs 1:
Quartile 4 vs 1:
-0.16 (-0.34 to 0.02)
-0.24 (-0.43 to -0.04)
-0.21 (-0.42 to 0.01)
-0.19 (-0.40 to 0.01)
-0.19 (-0.46 to 0.08)
-0.18 (-0.46 to 0.11)
Glucocorticoid Use
(Yes vs No)
-0.13
(-0.40 to 0.14)
-0.19
(-0.50 to 0.12)
*Need for fracture prevention medication modeled as raw score squared.
^Parameter estimates are expressed as number of standard deviations of medication need2
Estimates significant at p<0.05 level are in bold
Robust regression F test for difference between OLS & IV estimates: F(3, 181)= 0.18, p-value = 0.91
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Table 3 - Parameter Estimates for Predictors of Perceived Severity of Fractures; Standard
Probit vs IV Probit
Predictor
Standard Probit
Regression Estimates
(95% C.I.)
IV Probit Estimates
(95% C.I.)
Trust in Physician
(per SD)
0.13
(0.04 to 0.23)
0.14
(-0.01 to 0.29)
Family History of Fracture
(Yes vs No)
0.20
(-0.02 to 0.43)
0.20
(-0.02 to 0.43)
Personal History of Fracture
(Yes vs No)
-0.19
(-0.39 to -0.01)
-0.19
(-0.39 to 0.01)
Fracture Knowledge
Level 2 vs 1:
Level 3 vs 1:
Level 4 vs 1:
0.37 (0.10 to 0.64)
0.42 (0.12 to 0.72)
0.67 (0.43 to 0.92)
0.37 (0.10 to 0.64)
0.42 (0.12 to 0.72)
0.67 (0.43 to 0.92)
Depression
(Some vs None)
0.35
(0.14 to 0.56)
0.35
(0.14 to 0.57)
Prevalent Vertebral Fracture
No: 0.0 (reference)
Unknown: 0.23 (-0.01 to 0.47)
Yes: 0.06 (-0.31 to 0.44)
No: 0.0 (reference)
Unknown: 0.23 (-0.01 to 0.48)
Yes: 0.06 (-0.31 to 0.44)
BMD T-score
(per 1 unit increase)
0.02
(-0.14 to 0.17)
-0.06
(-0.20 to 0.08)
Educational Status
Quartile 2 vs 1:
Quartile 3 vs 1:
Quartile 4 vs 1:
-0.03 (-0.29 to 0.24)
-0.01 (-0.28 to 0.26)
0.33 (0.04 to 0.61)
-0.03 (-0.29 to 0.23)
-0.01 (-0.28 to 0.26)
0.33 (0.04 to 0.61)
Income Level
Quartile 2 vs 1:
Quartile 3 vs 1:
Quartile 4 vs 1:
-0.04 (-0.33 to 0.26)
0.23 (-0.10 to 0.56)
0.18 (-0.13 to 0.48)
-0.04 (-0.33 to 0.25)
0.23 (-0.10 to 0.56)
0.18 (-0.13 to 0.48)
Estimates significant at p<0.05 level are in bold
Wald test for difference between OLS & IV estimates: chi2 = 0.01, p-value = 0.92