Geospatial Analyses of Pain Intensity and Opioid Unit Doses Prescribed on the Day of Discharge Following Orthopedic Surgery

Abstract

Objective: Inappropriate opioid prescribing after surgery contributes to opioid use disorder and risk of opioid overdose. In this cross-sectional analysis of orthopedic surgical patients, we examined the role of patient location on postoperative pain intensity and opioids prescribed on hospital discharge.

Methods: We used geospatial analyses to characterize spatial patterns of mean pain intensity on the day of discharge (PiDoD) and opioid units prescribed on the day of discharge (OuPoD), as well as the effect of regional social deprivation on these outcomes.

Results: At a 500-km radius from the surgery site, the Global Moran's I for PiDoD (2.71 × 10-3, variance = 1.67 × 10-6, P = 0.012) and OuPoD (2.19 × 10-3, SD = 1.87, variance = 1.66 × 10-6, P = 0.03) suggested significant spatial autocorrelation within each outcome. Local indicators of spatial autocorrelation, including local Moran's I, Local Indicator of Spatial Autocorrelation cluster maps, and Getis-Ord Gi* statistics, further demonstrated significant, specific regions of clustering both OuPoD and PiDoD. These spatial patterns were associated with spatial regions of area deprivation.

Conclusions: Our results suggest that the outcomes of pain intensity and opioid doses prescribed exhibit varying degrees of clustering of patient locations of residence, at both global and local levels. This indicates that a given patient's pain intensity on discharge is related to the pain intensity of nearby individuals. Similar interpretations exist for OuPoD, although the relative locations of hot spots of opioids dispensed in a geographic area appear to differ from those of hot spots of pain intensity on discharge.

Keywords: Clustering; Geospatial; Opioid; Orthopedic; Pain; Spatial Autocorrelation; Surgery.