However, explained variance estimates in our models
ranged from 34 to 61%, suggesting further research is
necessary to identify additional factors contributing to
healthcare utilization following physical therapy.
The primary limitation of the study is the high number
of subjects lost to follow-up. We attempted to account
for the bias introduced by loss to follow-up in our
models with IPAW, which is a robust strategy for con-
ducting analyses with missing data [ 41, 51]. We observed
good concordance between results of complete case and
weighted analyses, giving us confidence in our findings.
However, important differences in age, race, education,
symptom onset, baseline pain intensity, and baseline
pain-related psychological distress were noted between
those who did and did not complete follow-up. These
differences suggest that the group lost to follow-up may
represent a unique population to whom these results
may not apply. Different factors may predict utilization
outcomes for this unique population. As a result, readers
should exercise caution when extending these findings
to individuals and populations that substantially differ
from the analytic sample in this study. Specifically, these
predictive models may need to be adjusted for younger
individuals of non-white race, with lower education
levels, sudden onset of symptoms, and those with higher
pain intensity and pain-associated distress.
A second limitation is that we did not know about the
subjects’ prior experiences with physical therapy, or
whether they arrived at physical therapy through direct
access or referral from another provider. These factors
could be associated with treatment expectations, which
have known effects on treatment outcomes [ 52, 53]. We
also did not collect specific information on treatment.
But by including changes in pain, disability, and
pain-related psychological distress in the models, we
were able to account for treatment response. The benefit
of this approach is that models are generalizable for pre-
dicting utilization outcomes across “real-world” prag-
matic physical therapy settings where treatment
variation is expected. The drawback is that we are pro-
hibited from making conclusions regarding which char-
acteristics of the clinical encounter might influence
subsequent pain-related healthcare utilization. Important
characteristics to consider would include number of
visits, type of interventions or whether patients com-
pleted their course of physical therapy. These have been
proposed or identified as important contributors to
downstream costs following physical therapy [ 54, 55]
and may be a source of unexplained variance in our
models. Characteristics of the clinical encounter should
be considered in future studies to refine the prediction
models developed in our analyses.
Third, we were unable to adequately model the spe-
cific effects of worker ’s compensation, self-pay and some
commercial insurance coverage on utilization due to the
low incidence of these forms of payment in our study
sample. Modeling these separately would have created
the potential for unreliable and imprecise effect esti-
mates. Readers should consider the within-group hetero-
geneity caused by this approach and exercise caution
when applying these results to individuals who do not
have traditional public or private insurance coverage. Fu-
ture studies should investigate the performance of the
OSPRO tools in predicting outcomes for patients with
Worker’s Compensation.
A final limitation is the use of patient recall to meas-
ure utilization. To mitigate recall bias, we used two
follow-up points, at 6 and 12 months. However, under-
or over-reporting of utilization is often a concern with
studies requiring subject recall [ 56– 58]. Medical record
and claims data were not available for these subjects.
Readers should consider our inability to independently
confirm utilization when interpreting results.
In future studies, we will embed the OSPRO tools into