Utility of catastrophizing, body symptom diagram score and history of opioid use to predict future health care utilization after a primary care visit for musculoskeletal pain

Abstract

Background: Self-report information about pain and pain beliefs are often collected during initial consultation for musculoskeletal pain. These data may provide utility beyond the initial encounter, helping provide further insight into prognosis and long-term interactions of the patient with the health system.

Objective: The aim of this study was to determine if pain catastrophizing and pain-related body symptoms can predict future health care utilization.

Methods: This was a longitudinal cohort study. Baseline data were collected after receiving initial care for a musculoskeletal disorder in a multidisciplinary clinic within a large military hospital. Subjects completed the Pain Catastrophizing Scale, a region-specific disability measure, numeric pain rating scale and a body symptom diagram. Health care utilization data for 1 year prior and after the visit were extracted from the Military Health System Data Repository. Multivariable regression models appropriate for skewed and count data were developed to predict (i) musculoskeletal-specific medical visits, (ii) 12-month opioid use, (iii) musculoskeletal-specific medical costs and (iv) total medical costs. We investigated whether a pain catastrophizing × body symptom diagram interaction improved prediction, and developed separate models for opioid-naïve individuals and those with a history of opioid use in an exploratory analysis.

Results: Pain catastrophizing but not body symptom diagram was a significant predictor of musculoskeletal visits, musculoskeletal costs and total medical costs. Exploratory analyses suggest these relationships are most robust for patients with a history of opioid use.

Conclusions: Pain catastrophizing can identify risk of high health care utilization and costs, even after controlling for common clinical variables. Addressing pain catastrophizing in the primary care setting may help to mitigate future health care utilization and costs, while improving clinical outcomes. These results provide direction for future validation studies in larger and more traditional primary care settings.

Keywords: Catastrophizing; musculoskeletal system; opioids; pain; pain management; primary health care.

Figure 1.

Predicted total musculoskeletal medical costs and visits by incidence of subsequent surgery based on PCS score (n = 238). (A) Medical costs for musculoskeletal-related care in USD. (B) Unique medical visits for musculoskeletal-related care. Covariate values are set at sample mean for continuous variables and median for categorical variables. (A) indicates that with a PCS value of 10, the predicted total visits for musculoskeletal pain would be ~1, whereas a PCS value of 50 corresponds to ~2 visits, with all other covariates held constant. Where the PCS becomes informative is when patients ultimately undergo surgical intervention. In this case, we see a large difference between visits associated with lower PCS scores (e.g. 3 estimated visits at PCS = 10) compared to higher PCS scores (e.g. 10 estimated visits at PCS = 50).

Figure 2.

Predicted total medical costs by incidence of subsequent surgery based on PCS score (n = 238). Costs for all medical care, for any reason. Covariate values are set at sample mean for continuous variables and median for categorical variables. ‘Average’ patient reflects the estimated mean cost where the surgery variable is weighted against the sample’s prevalence of surgery.

Figure 3.

Predicted total musculoskeletal medical costs and visits by incidence of subsequent surgery for prior opioid users based on PCS score (n = 72). (A) Medical visits for musculoskeletal-related care. (B) Medical costs for musculoskeletal-related care in USD. ‘Average’ patient reflects the estimated mean costs and visits where the surgery variable is weighted against the sample’s prevalence of surgery. Covariate values are set at sample mean for continuous variables and median for categorical variables.