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. 2021 Oct;27(5):500-505.
doi: 10.1136/injuryprev-2020-043882. Epub 2021 Jan 4.

Michigan system for opioid overdose surveillance

Jason Goldstick  1   2 , Amanda Ballesteros  3   2 , Carol Flannagan  2   4 , Jessica Roche  3   2 , Carl Schmidt  5 , Rebecca M Cunningham  3   2
Affiliations

Michigan system for opioid overdose surveillance

Jason Goldstick et al. Inj Prev. 2021 Oct.

Abstract

Community rapid response may reduce opioid overdose harms, but is hindered by the lack of timely data. To address this need, we created and evaluated the Michigan system for opioid overdose surveillance (SOS). SOS integrates suspected fatal overdose data from Medical Examiners (MEs), and suspected non-fatal overdoses (proxied by naloxone administration) from the Michigan Emergency Medical Services (EMS) into a web-based dashboard that was developed with stakeholder feedback. Authorised stakeholders can view approximate incident locations and automated spatiotemporal data summaries, while the general public can view county-level summaries. Following Centers for Disease Control and Prevention (CDC) surveillance system evaluation guidelines, we assessed simplicity, flexibility, data quality, acceptability, sensitivity, positive value positive (PVP), representativeness, timeliness and stability of SOS. Data are usually integrated into SOS 1-day postincident, and the interface is updated weekly for debugging and new feature addition, suggesting high timeliness, stability and flexibility. Regarding representativeness, SOS data cover 100% of EMS-based naloxone adminstrations in Michigan, and receives suspected fatal overdoses from MEs covering 79.1% of Michigan's population, but misses those receiving naloxone from non-EMS. PVP of the suspected fatal overdose indicator is nearly 80% across MEs. Because SOS uses pre-existing data, added burden on MEs/EMS is minimal, leading to high acceptability; there are over 300 authorised SOS stakeholders (~6 new registrations/week) as of this writing, suggesting high user acceptability. Using a collaborative, cross-sector approach we created a timely opioid overdose surveillance system that is flexible, acceptable, and is reasonably accurate and complete. Lessons learnt can aid other jurisdictions in creating analogous systems.

Keywords: descriptive epidemiology; drugs; epidemiology; poisoning; surveillance.

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Conflict of interest statement

Competing interests: None declared.

Figures

Figure 1
Figure 1
Conceptual model of the system for opioid overdose surveillance. EMS, Emergency Medical Services.
Figure 2
Figure 2
System for opioid overdose surveillance (SOS) Screenshots figure. *Top left panel displays the public user homepage, top right panel displays a sample map from the authorised user interface, bottom left panel displays the daily frequency of suspected overdose events in the selected jurisdiction in the authorised user interface, and bottom right panel displays age, race and gender demographic visualisations in the authorised user interface.*All data shown in the authorised user interface here are simulated data.
Figure 3
Figure 3
Zoom capabilities available to authorised users within the system for opioid overdose surveillance (SOS).
See this image and copyright information in PMC

References

    1. Centers for Disease Control and Prevention (CDC). Wonder multiple cause of death database, 2020. Available: https://wonder.cdc.gov/mcd.html[Accessed 18 Mar 2020].
    1. Michigan Department of Health and Human Services (MDHHS. Drug poisoning deaths among Michigan residents 1999–2018, 2019.
    1. Carolina Center for Health Informatics. Opioids Dashboards and data, 2020. Available: http://cchi.web.unc.edu/opioid-dashboards/[Accessed 19 Nov 2020].
    1. State of Indiana. Indiana drug data, 2019. Available: https://www.in.gov/recovery/1054.htm [Accessed 19 Nov 2020].
    1. Arizona Department of Health Services. Real time opioid data, 2017. Available: https://www.azdhs.gov/prevention/womens-childrens-health/injury-preventi... [Accessed 19 Nov 2020].

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