weighted analytic models for each type of healthcare
service.
Any healthcare
The final parsimonious models for any healthcare
utilization differed slightly between complete case and
weighted analyses (Table 6). Included in the models were
chronicity of symptoms, CCI, baseline pain, baseline dis-
ability, and change in pain from baseline to 4-week
follow-up. However, only baseline disability (OR = 1.48 –
2.47, p < 0.05) and change in pain (OR = 1.28 – 1.45,
p < 0.05) were significant predictors in both models, with
greater baseline disability and worsening pain associated
with higher odds of any utilization.
Utilization of individual services
Opioids
Comorbidity index score (i.e. CCI), baseline pain and
change in pain were consiste nt predictors between the
models of opioid utilization. In these models, higher pain
(OR = 1.70– 1.76, p < 0.001), CCI (OR = 1.54 – 1.60,
p < 0.001) and increase in pain (OR = 1.70 – 1.71, p <0 . 0 0 1 )
were associated with higher odds of opioid utilization.
These models explained approximately 30% of the variation
in opioid use.
Injection
A combination of race, chronicity and baseline disability
explained slightly more than 20% of the variance in
Table 4 Frequency of healthcare utilization reported at 6-month and 12-month follow-up ( n = 246)
Label Utilization reported
at 6-month follow-up
Utilization reported
at 12 month follow-up
Dichotomous indicator for any healthcare
utilization over entire follow-up
Utilization volume 0 1 2 – 55 – 10 > 10 0 1 2 – 55 – 10 > 10 No Yes
Opioids 209 18 16 1 2 204 19 16 7 0 201 45
Injection 212 17 14 2 1 217 17 12 0 0 206 40
Surgery 240 4 2 0 0 231 13 2 0 0 227 19
Diagnostic tests or imaging 183 40 22 1 0 188 26 28 4 0 172 74
Emergency room 237 7 2 0 0 232 11 2 1 0 228 18
Any care 140 106
Table 5 Overall performance of full logistic multivariate regression models ( n = 246)
Dependent variable Any care Opioids Injection Surgery Diagnostic tests or imaging Emergency room
Complete Case
Block 1
Demographic, Clinical and Comorbidity
.258** .274** .292** .226* .250* .400**
Block 2
OSPRO-YF (10 items)
OSPRO-ROS (10 items)
.267 .294 .293 .234 .253 .404
Block 3
OSPRO-YF (+ 7 items)
OSPRO-ROS (+ 13 items)
.275 .296 .315 .259 .271 .457
Block 4
4-week change
(Pain, Disability, OSPRO-YF)
.337* .424** .353 .426 ** .340* .560*
Inverse Probability of Attrition Weighted
Block 1
Demographic, Clinical and Comorbidity
.306** .294** .313** .236 .304 ** .430**
Block 2
OSPRO-YF (10 items)
OSPRO-ROS (10 items)
.314 .317 .317 .250 .305 .435
Block 3
OSPRO-YF (+ 7 items)
OSPRO-ROS (+ 13 items)
.321 .321 .334 .284 .321 .473
Block 4
4-week change
(Pain, Disability, OSPRO-YF)
.382* .448** .373 .464 ** .389* .611*
All values are variance explained (pseudo-R 2); Health care utilization (dependent) variables refer to utilization during the follow-up period, whereas independent
variables refer to measurements taken before the follow-up period
*p < 0.05, **p < 0.01
Lentz et al. BMC Health Services Research  (2018) 18:648 Page 8 of 14