Identifying Unreported Opioid Deaths Through Toxicology Data and Vital Records Linkage: Case Study in Marion County, Indiana, 2011–2016

Abstract

Objectives. To demonstrate the severity of undercounting opioid-involved deaths in a local jurisdiction with a high proportion of unspecified accidental poisoning deaths. Methods. We matched toxicology data to vital records for all accidental poisoning deaths (n = 1238) in Marion County, Indiana, from January 2011 to December 2016. From vital records, we coded cases as opioid involved, specified other substance, or unspecified. We extracted toxicology data on opioid substances for unspecified cases, and we have reported corrected estimates of opioid-involved deaths after accounting for toxicology findings. Results. Over a 6-year period, 57.7% of accidental overdose deaths were unspecified and 34.2% involved opioids. Toxicology data showed that 86.8% of unspecified cases tested positive for an opioid. Inclusion of toxicology results more than doubled the proportion of opioid-involved deaths, from 34.2% to 86.0%. Conclusions. Local jurisdictions may be undercounting opioid-involved overdose deaths to a considerable degree. Toxicology data can improve accuracy in identifying opioid-involved overdose deaths. Public Health Implications. Mandatory toxicology testing and enhanced training for local coroners on standards for death certificate reporting are needed to improve the accuracy of local monitoring of opioid-involved accidental overdose deaths.

Characteristics of US Counties With High Opioid Overdose Mortality and Low Capacity to Deliver Medications for Opioid Use Disorder

“…Fifth, county-level opioid overdose death reporting drawn from national vital statistics data may include some measurement error, likely undercounting, that could be differential across counties. 67,68 However, these counts are more reliable to infer causes of death than are other sources (eg, toxicology reports), are the best source of comparative death data available, have been previously analyzed at the county level, and are subject to analogous measurement error as occurs at the state level. 69,70,71 Moreover, our outcome measure compared opioid overdose mortality with MOUD provider availability along continuums, rather than in absolute terms, which may have reduced the potential for biased conclusions.…”

Co-prescription network reveals social dynamics of opioid doctor shopping

“…Although findings are consistent, we present model results based on the more inclusive measure. This decision is based on research suggesting that use of nonspecific language to classify drug poisoning leads to overuse of the “unspecified” code and undercounting of opioid overdoses [31–33]. Fourth , we create a binary indicator of opioid use disorder based on the presence of ICD-10 diagnostic codes for opioid abuse or dependence in a given quarter.…”

Co-prescription network reveals social dynamics of opioid doctor shopping

“…Although findings are consistent, we present model results based on the more inclusive measure. This decision is based on research suggesting that use of nonspecific language to classify drug poisoning leads to overuse of the "unspecified" code and undercounting of opioid overdoses [31][32][33]. Fourth, we create a binary indicator of opioid use disorder based on the presence of ICD-10 diagnostic codes for opioid abuse or dependence in a given quarter.…”