Geographic information systems and pharmacoepidemiology: using spatial cluster detection to monitor local patterns of prescription opioid abuse

Abstract

Purpose: Understanding the spatial distribution of opioid abuse at the local level may facilitate public health interventions.

Methods: Using patient-level data from addiction treatment facilities in New Mexico from ASI-MV Connect, we applied geographic information system (GIS) in combination with a spatial scan statistic to generate risk maps of prescription opioid abuse and identify clusters of product- and compound-specific abuse. Prescribed opioid volume data was used to determine whether identified clusters are beyond geographic differences in availability.

Results: Data on 24 452 patients residing in New Mexico were collected. Among those patients, 1779 (7.3%) reported abusing any prescription opioid (past 30 days). According to opioid type, 979 patients (4.0%) reported abuse of any hydrocodone, 1007 (4.1%) for any oxycodone, 108 (0.4%) for morphine, 507 (2.1%) for Vicodin or generic equivalent, 390 (1.6%) for OxyContin, and 63 (0.2%) for MS Contin or generic equivalent. Highest rates of abuse were found in the area surrounding Albuquerque with 8.6 patients indicating abuse per 100 interviewed patients. We found clustering of abuse around Albuquerque (P = 0.001; Relative Risk = 1.35, and a radius of 146 km). At the compound level, we found that drug availability was partly responsible for clustering of prescription opioid abuse. After accounting for drug availability, we identified a second foci of Vicodin abuse in the southern rural portion of the state near Las Cruces, NM and El Paso, Texas and bordering Mexico (RR = 2.1; P = 0.001).

Conclusions: A better understanding of local risk distribution may have implications for response strategies to future introductions of prescription opioids.

Figure 1

Study region (New Mexico) indicating number of patients seen at treatment facilities by the 3-digit ZIP code of their home address (October 1, 2006-September 30, 2008). The 3 digit labels are the 3-digit zip codes for each area.

Figure 2

Proportion of treatment facility patients reporting abuse of any prescription opioid by the 3-digit ZIP code of their home address per census population (October 1, 2006-September 30, 2008)

Figure 3

Proportion of treatment facility patients reporting abuse of any prescription opioid by the 3-digit ZIP code of their home address per total number of visits from that 3-digit ZIP (October 1, 2006-September 30, 2008). Zip codes highlighted in blue represent the significant overall cluster identified. The 3 digit labels are the 3-digit zip codes for each area.

Figure 4

Clustering of abuse for specific opioid products among treatment facility patients grouped by the 3-digit ZIP code of their home address (October 1, 2006-September 30, 2008). Relative risk estimates from the cluster detection analysis are included in the legend and identified statistically significant clusters are highlighted by color coding. The 3 digit labels are the 3-digit zip codes for each area.

Figure 5

Clustering of abuse for specific opioid products among treatment facility patients grouped by the 3-digit ZIP code of their home address after adjusting for product availability (October 1, 2006-September 30, 2008). Relative risk estimates from the cluster detection analysis are included in the legend and identified statistically significant clusters are highlighted by color coding. The 3 digit labels are the 3-digit zip codes for each area.