New Jersey had a similar jump in fentanyl-related overdose deaths for an eight month period in 2006. However, there is no historical precedent for the current large-scale substitution of heroin with synthetic opioids across a large part of the US for a prolonged duration.Much of our understanding of the shift to synthetic opioids comes from examining changes in the causes of death over time. However, this data only shows us the end point of illicit drug consumption providing indirect evidence of changes in the illicit drug market and consumption of opioids. We expand this understanding using Ohio as a test case. Using a unique dataset of the content of drugs seized by law enforcement from the Ohio Bureau of Criminal Investigation’s crime labs, we are able to create a more direct estimate of changes in the illicit drug market in Ohio. Furthermore, since crime lab data has the potential to be more quickly released than mortality data, there is the potential to use this data to predict and, therefore, warn specific locations in Ohio of increasing risks in the illicit drug environment. Our substantive contributions are to add empirical evidence to better understand the rapid and complex transition from heroin to synthetic opioids in Ohio, as well as estimate the relationship between the detected substances in state laboratories and overdose deaths. Ohio is an important state to study to better understand the opioid crisis. Ohio was the state with the second-highest drug overdose death rate and second-highest total number of drug overdose deaths in 2017. Of the ten states tracking the presence of fentanyl analogs in overdose deaths via the State Unintentional Drug Overdose Reporting System ,ebb and flow flood table from July 2016 to June 2017, Ohio had 1,745 fentanyl analog overdose deaths of which 1,106 were attributed to one fentanyl analog: carfentanil.
The other nine states had 531 fentanyl analog overdose deaths combined, of which 130 were attributed to carfentanil.Ohio’s experience may be particularly useful as a lesson for other states about the ramifications of future increases in the presence of fentanyl analogs, increasing the potential for improving detection systems, and more rapidly responding to an emerging or worsening opioid crisis. Ohio drug seizure laboratory test data comes from the three Ohio BCI crime labs. The data contains all lab tests completed at the crime labs between January 1, 2009 and December 31, 2018 consisting of 397,815 total samples. The average time between the date of drug seizure and the testing date is approximately one month. However, a small fraction of tests may be delayed for several months.2 We use the date of drug seizure as the relevant date of drug observation. Thus, there is some data from drugs seized at the end of 2008, which we do not use, and given that we have test results through December 2018, the data should be approximately complete for drugs seized from 2009 through September 2018. The data was extensively cleaned to correct for spelling errors and ensure that there were no duplicate records. Observations without a clear result are dropped from the analysis. These unclear tests may indicate an unknown substance, there is no controlled substance, an insufficient sample to test, the sample was not tested, there were no findings, or the test was a re-examination of a previously tested substance.3 In addition, observations are dropped if they indicate the sample looks like a particular prescription drug pill, but without testing it to know for certain. E.g. “whole tablet markings indicate Alprazolam. Not confirmed.” There were 86,809 such samples. The BCI crime lab adopted a policy in March 2016 to not test for any minor misdemeanor amounts of cannabis, the most common illicit drug in the lab data, reducing the number of cannabis tests by approximately half compared to previous years.
To ensure consistency across years, we removed any tests that solely contain cannabis or related drugs from the data. However, the estimation results do not change substantially if the cannabis data is included in the estimates. Drugs are also excluded if they are likely unrelated to overdose deaths such as steroids or medications like acetaminophen. The definitions of the various drug classes used in the analysis are explained in more detail in Appendix A and are: fentanyl, carfentanil, other fentanyl analogs, heroin, prescription opioids, cocaine, benzodiazepines, synthetic cannibinoids, methamphetamines/amphetamines, other synthetic stimulants, MDMA, psychedelics, and other designer drugs. Drug samples can contain more than one drug class. I.e., if a sample contains heroin and fentanyl, it will be considered as both for the purposes of aggregating the number of heroin and fentanyl observations. We do not have access to data from the seven other private crime labs in Ohio that test drugs for law enforcement. One difference between the BCI labs and the others is that the BCI labs test drugs for free , while the others charge for expedited test results. According to the BCI labs, there is no systematic difference in the types of drugs or associated crimes that the BCI labs handle versus the other labs. The number of tests is generally consistent within a county over time, with counties that have larger populations and a higher overdose rate having more tests. The one outlier is Hamilton County, which includes Cincinnati, with far fewer tests than it should have given the number of overdose deaths and population.The low number of tests are due to Hamilton County generally using private instead of state labs, and we are unable to acquire data from these labs. Thus, we omit this county from the analysis. We link the drug test data to Ohio unintentional drug overdose mortality data from the Ohio Department of Health at the county-month level. The 2018 mortality data is preliminary and the delay between the date a drug is seized and when it is tested means that there are potential inaccuracies in the 2018 data.
In addition, some of the control variables, namely the prescribed opioid morphine equivalent dose per capita for each quarter is only available starting in 2010 and the annual median income and poverty rates are only available through 2017. Thus, we restrict the sample for the estimates to the 2010 to 2017 time period, although the estimates do not change in any substantive way if we include data from 2009 and 2018 and remove the aforementioned control variables. After reducing the number of lab test observations due to the above parameters,hydroponic drain table we analyze the remaining 204,951 samples across 87 counties. We aggregate the drug test data to the county-month level. The 87 counties over the 2010-2017 time period provide 8,352 county-month observations. The mean values, standard deviations, and minimum and maximum values of each of the county-month level variables used in the estimates are reported in Appendix A, Table A1.We first present descriptive statistics on the changing pattern in drug-related deaths and in the illicit drug market in Ohio as measured by crime lab data. Next, since overdose death counts are the outcome of interest, we estimate the relationship between crime lab data and overdose deaths using a Poisson regression analysis.The estimates are robust to a linear regression specification and these estimation tables are available in the Supplemental Section. The number of overdose deaths6 each month in each of the 87 Ohio counties is regressed on the number of positive tests for illicit and prescription drugs from the BCI crime lab data7 each month in each of the 87 Ohio counties. The estimates include county fixed effects, month fixed effects, and county-specific linear time trends. The additional time-varying county-level controls included in the estimates are: annual poverty rates and median income,prescribed opioid morphine equivalent dose per capita for each quarter separated into Suboxone and non-Suboxone opioids,and monthly unemployment rates.Robust standard errors are clustered by county. One of the goals of this study is to determine the public health value of knowing these lab tests, so even if the lab tests are not perfectly representative of the illicit drug market at a county-month level, it is an empirical question as to whether this information has valuable predictive power. That is, if we find no correlation between the test data and overdose deaths,this may be due to problems with the data. If we do find strong correlations despite the limitations of the data, then this available signal may be worth using, especially as there are so few short-term indicators of changing risk for drug overdose deaths. In addition, we are not making any causal claims about the direct effect of lab tests on mortality rates. So, for example, it may be the case that amount of carfentanil available is relatively constant over time and law enforcement specifically targets carfentanil buyers and sellers when overdose deaths rise substantially, explaining the positive correlation between carfentanil positive lab tests and overdose deaths. Even so, the lab tests would still give us an early warning of increased risks in the illicit drug environment. The month fixed effects control for any time trends in overdose deaths that are the same across Ohio.
The county fixed effects control for any county differences that do not vary over time. The county-year linear time trends control for any county-specific linear time trends in overdose deaths. In this sense, it is a conservative estimate as, for example, if carfentanil was increasing across the whole state simultaneously and causing a similar increase in deaths, the month fixed effects would remove this state-wide change from the estimates. As shown below, at the county-level, there is large variation in the timing and extent of the emergence of various opioids, and this variation is what underpins the estimates. We control for several time-varying county-specific variables: economic controls and the aggregate amounts of prescribed opioids per capita, which could independently cause changes in the demand, supply, or risk environment for illicit drugs. The economy is generally improving over this time period and counties with stronger improvements may have fewer overdose deaths. There has also been a decline in the amount of opioids prescribed, which could cause a spillover effect increasing the demand for illicit opioids and, therefore, overdose deaths. We separately control for the amount of Suboxone prescribed, which is one of the treatments for opioid use disorder. The amount of Suboxone prescribed has generally increased over the time period and may be associated with fewer overdose deaths. All estimates are robust to using fentanyl-related overdose deaths as the outcome instead of overall overdose deaths, since changes in overdose deaths are largely driven by these deaths. However, from a policy perspective it may be more useful to look at whether there is an effect on all overdose deaths as people may shift their consumption of drugs from one type to another as the illicit drug market changes. In particular, if deaths are simply shifting from heroinrelated to fentanyl-related, with no increase in total deaths, then the risk environment has not really changed. In addition, from a data perspective, researchers have found that opioid-related overdose deaths are under-counted , so looking at overall deaths avoids some possible data errors in classifying types of overdose deaths. Overdose deaths in Ohio have followed the Triple Wave pattern of the US as a whole: a rapid increase in prescription opioid deaths over the 2000s, followed by a more rapid increase in heroin deaths after 2010, followed by an even more rapid increase in synthetic opioid deaths after 2013. Figure 1 shows how drug deaths in Ohio were steadily rising until 2013. After 2013 there was a decline in drug deaths unrelated to fentanyl or fentanyl analogs, but an increase in fentanyl-related deaths , only reversing in 2018.Supplemental Figures S1 and S2 show how heroin, cocaine, benzodiazepine, and psychostimulant related deaths have increasingly involved fentanyl as well. In 2013 there were 1,026 heroin-related deaths without fentanyl. In 2017, these fell to 267, while the number of deaths involving heroin and fentanyl increased to 720. Cocaine-related deaths have risen substantially over the last few years, but most of this increase is due to cocaine and fentanyl together. There is a similar pattern for benzodiazepine-related deaths, with a recent fall in such deaths unrelated to fentanyl and rapid increase in such deaths which also involve fentanyl.