U.S. opioid-related overdose deaths have increased significantly in recent years, topping 49,860 in 2019. Pennsylvania has suffered a particularly high number of opioid-related (including illicit prescription drugs, fentanyl, and heroin) deaths in recent years. Recently, National Institute of Justice-supported researchers from Pennsylvania State and Syracuse Universities sought to find novel ways to characterize and ultimately inhibit opioid distribution networks through data-driven network analysis and the use of citizen intelligence.
The work focuses on six Pennsylvania counties well known for drug trafficking along the interstate highway system. The goals of the study were to: 1) define the structure of opioid distribution networks based on observed and computer-modeled opioid networks, and 2) assess the ability of community-based intelligence to characterize local opioid supply networks and markets through the use of publicly available police administration data on overdose and drug-related incidents as well as the solicitation of community input.
“We are interested in methods that can accurately capture community-based intelligence to augment police information,” says Eric Martin, a social science analyst with the National Institute of Justice.
Study Findings
The network modeling research suggests that:
- At the local level, opioid distribution networks are generally organized by substance, not by individual actors.
- There are only a few individuals who are distributing multiple types of substances, and usually those substances are within the opioid category.
- Within a network, the types of drugs tend to be clustered, and prescription opioids tend to be connected more peripherally than other types of opioids.
The citizen-based intelligence research suggests that citizen informants:
- Have the ability to inform law enforcement investigations of local drug activity when coupled with participatory mapping.
- Could accurately indicate locations of opioid distribution activity that match official records.
- May reveal previously unknown locations of potential interest to law enforcement for the disruption of opioid distribution.
Interestingly, unobserved connections could be simulated via the researchers’ software that may be undetectable through intelligence-based investigations alone.
Implications
This research highlights the need for community-based intelligence in efforts to interrupt opioid distribution networks. The researchers emphasize that supply reduction should be included as a tool in comprehensive substance responses and policies.
From this study, other key recommendations that have emerged for advancing data-driven, intelligence-led approaches to disrupt the distribution of opioids were:
- Participatory mapping with local residents can aid in characterizing the distribution of drug-related activities within certain communities.
- Law enforcement can use network modeling and simulation techniques to enhance their investigations.
- Criminal justice administrative entities can benefit from efforts to connect locally derived data to extra-local sources.
Further, network-simulating software tools could be of assistance to law enforcement in pinpointing the location of local drug activity. The approaches outlined in the analysis have the potential to reduce strains on resources, while maximizing the impact on supply reduction.
Recommendations
Community relationships with law enforcement might be improved by involving citizens in the identification of illicit substance distribution networks. Working collaboratively with public health initiatives could also facilitate targeted outreach for prevention and treatment in the future.
Of particular importance, the authors note that, while heroin supplies need to be greatly reduced, supply reduction without demand- and harm-reduction strategies will likely serve to increase prices in illicit markets, creating additional incentives for suppliers. Therefore, they stress the need for a community-coordinated, balanced approach involving the reduction of demand, harm, and supply).
Limitations
There were a few noteworthy complications encountered during the course of the research. One of the proposed data sources for the network distribution data, the Pennsylvania State Police, proved inaccessible due to restrictions on sharing criminal justice information. However, the authors were able to substitute publicly available data from county District Attorneys’ offices to move forward with their network modeling. The researchers also faced low turnout at their recruitment event for participants in the community-intelligence portion of the research and were forced to find other ways to recruit participants. The authors also caution that the data used in this research are only one snapshot in time based on six counties; it is unclear how much the findings may be generalized over time and space.
Republished courtesy of NIJ.