The only fentanyl analog detected in 2015 was acetyl fentanyl, which is more potent than heroin, but less potent than fentanyl. Eight new analogs appeared in 2016,12 six more appeared in 2017,13 and seven new analogs appeared in 2018.14 Other non-fentanyl synthetic opioids emerged as well: U-47700 in 2016 and U-48800, U-49900, and U-51754 in 2017.15 In addition, many crime lab samples contain more than one synthetic opioid. About 8 percent of samples with a synthetic opioid contained two or more in 2015 and 2016, 26 percent in 2017, and 19 percent in 2018. Most of these fentanyl analogs and non-fentanylsynthetic opioids have unknown potencies, exacerbating the asymmetric information problem in the illicit opioid market. That is, even if a consumer knew the exact chemical content of their drugs, they still would not be able to determine the potency.16 Comparing Figures 1 and 3, there is a clear positive correlation between the increase of fentanyl related mortality and the increase of fentanyl found in BCI lab tests. However, the aggregate annual state-level counts hide the complexity of the timing of drug seizures sent to BCI labs containing synthetic opioids and overdose deaths at the county-month level. We illustrate these county-level differences in graphs of two counties of similar population size , Montgomery County in southwest Ohio and Summit County in the northeast. Figure 4 shows the strong association between synthetic opioids found by the BCI crime labs and overdose deaths in Summit County. In 2014, the rise and fall in deaths begins to be correlated with the rise and fall in positive fentanyl tests. In mid-2016 there was a rapid increase in deaths at the same time as a rapid increase in positive carfentanil tests.
In 2017, even though deaths fell with carfentanil detections, the level of monthly deaths is generally higher than in 2015, rolling benches hydroponics with less fentanyl detections, but more positive tests for fentanyl analogs. The graph for Montgomery County in Figure 5 shows a very different timing of events. As in Summit County, there are apparent correlations between positive fentanyl tests and overdose deaths. However, the peak in overdose deaths comes a year later than in Summit County, at the same time as the crime labs find a large number of samples of carfentanil and, to a lesser extent, other fentanyl analogs. In addition, there was a large amount of fentanyl found in Summit County in 2015 and a rise in deaths, while fentanyl did not appear in large quantities in Montgomery County until 2016. The patterns in overdose deaths are similar in the two counties before 2014, with close to zero fentanyl-related deaths. Thus, it is unlikely that differences in pre-existing trends in overdose deaths can explain differences in the later timing of the emergence of different synthetic opioids. In addition, given how closely the pattern of drug seizures matches the pattern of drug deaths, the graphs illustrate the potential for county-specific rapid detection systems to more nimbly address the opioid crisis.The main estimates are presented in Table 1. Fentanyl, carfentanil, and other fentanyl analogs are positively correlated with overdose deaths at statistically significant levels. With all 87 counties included in column 1, every one additional observation of fentanyl in BCI crime lab tests in a county in a month predicts an extra 1.16 percent more deaths that month. In other words, if law enforcement finds 10 extra samples with fentanyl in a county with 20 monthly overdose deaths on average, we would expect an extra 2.4 deaths that month. Carfentanil positive tests are associated with more deaths than fentanyl: with 10 extra positive tests in a county with 20 monthly overdose deaths on average we would expect an extra 3.6 deaths that month. The coefficient for other fentanyl analogs is smaller than for fentanyl or carfentanil and has less statistical significance. 10 more positive tests of other fentanyl analogs in a county with 20 monthly overdose deaths on average is associated with an extra 1.8 deaths that month.
Summarizing, we find that changes in the presence of crime lab tests for synthetic opioids are useful as indicators of changes in the short-term risk of overdose deaths. Robustness tests are presented in Appendix B. To control for changes in over time in the number of tests, we also provide estimates using the fraction of tests that are positive in a county-month as independent variables instead of test counts Table B1. Heroin lab tests do not have a statistically significant relationship with drug deaths in the main specification. However, in Table B1, there is a negative relationship between the fraction of tests in a county-month that contain heroin and overdose deaths, statistically significant at the 1 percent level. We are unable to determine the specific underlying cause of this correlation. One possible explanation is that these estimates are picking up the shift from heroin to higher potency synthetic opioids. After 2014 the fraction of lab tests containing heroin not mixed with a synthetic opioid falls substantially while lab tests of heroin mixed with synthetic opioids and synthetic opioids in general increase. It is possible that drug sellers and consumers modify their behavior to reduce risk in response to an increasingly deadly illicit drug market, biasing the estimates towards finding a negative relationship between deaths and the amount of synthetic opioids in drug tests. However, given the short time-span of the observations, it is more likely that this negative effect is observed months later: counties with a relatively large amount of deadly synthetic opioids in month T, may observe a relative decline in deaths in later months as the drug market, consumer behavior, law enforcement,hydro tray or harm reduction services respond to reduce the risk of overdose death. To investigate the relationship between the timing of lab tests and overdose deaths we re-estimated the main Poisson regression changing the dependent variable to overdose death counts for the three months before and after the observed crime lab data, which is shown in Table 2.
Fentanyl tests are positively correlated with contemporaneous overdose deaths and with about half the magnitude and less statistical significance, are positively correlated with future overdose deaths as well. Carfentanil tests have a different pattern, with positive statistically significant coefficients in the prior three months, and a decrease in the correlation over the next three months with a reduction in the risk of overdose death three months later. There are several possible explanations for the lag and lead pattern for carfentanil. For example: 1. the illicit drug market responded to the high number of deaths by making drugs relatively safer, 2. there is an increase in harm reduction services, law enforcement presence, and reduction in risk taking by consumers, or 3. carfentanil killed illicit drug users who are the most likely to die from an overdose in general leaving a smaller population of risky drug users later and, thus, having little apparent effect on overdose deaths later on. Other fentanyl analogs have an opposite relationship over time relative to carfentanil. Other analogs are negatively correlated with deaths two or three months prior, while the positive correlation if anything increases for the three months after. These findings indicate that more rapid testing, particularly for carfentanil,may be needed to have a meaningful impact. Fentanyl and other fentanyl analogs, however, are persistent problems allowing more time to respond to the release of crime lab data. There are large differences across counties and including them all in the main estimates may hide risks, particularly in smaller counties with fewer deaths. To investigate the potential for heterogeneous effects across counties, we divide the sample into counties with populations under 50,000 , between 50,000 and 100,000 , and 100,000 and above.17 The estimates are presented in the estimation tables in columns 2 to 4. Perhaps unsurprisingly, the main estimates are driven by the largest counties, which also have the most overdose deaths. Interestingly, the coefficient for carfentanil is only statistically significant in large counties. In the smallest counties, other fentanyl analogs are the only drug that is statistically significantly correlated with overdose deaths. These estimates indicate that the newly evolving fentanyl analogs may be playing a particularly dangerous role in low-population areas, which is hidden in the aggregate statistics, although we have no a priori reason for this empirical finding and leave it to future research to investigate why this may be occurring. These differences across county sizes are reinforced by linear regression estimates in robustness Table B3 in Appendix B, where the overdose death rate per 100,000 adults is the dependent variable. The estimates in Table 6 should be read with some caution, as there are large standard errors when deriving death rates from small numbers of deaths, and these errors will be amplified in small counties.
In column 1, which includes all counties, the coefficients for fentanyl, carfentanil, and other fentanyl analogs are all statistically significant at the 1 percent level and the magnitude of the other fentanyl analogs coefficient is larger than that for carfentanil. Given that an extra death in a small county has a much larger impact on the death rate than in a large county, and other fentanyl analogs are particularly important for explaining overdose deaths in small counties, it makes sense that they would be more strongly correlated with the overdose death rate than the overdose death count. Aggregating all the different analogs together as if they were the same may be hiding the relatively high or low dangers of certain analogs. In Table B2, instead of aggregating, we in-clude separately the seven most common fentanyl analogs in the data as independent variables.18 The table includes estimates by county size as well. Only acryl fentanyl, similar in potency to fentanyl but much less potent than carfentanil, has a statistically significant positive coefficient regardless of county size. These results provide more evidence of the potential to use the crime lab data for a future early warning system: the data indicates an increased risk of deaths if acryl fentanyl is found in large quantities. Acetyl and 3-methyl fentanyl are only associated with more deaths in medium sized counties, and 4-FIBF and methoxyacetyl fentanyl are only associated with more deaths in small counties.19 Cyclopropyl and furanyl fentanyl have no statistically significant relationship with overdose deaths. However, this lack of statistically significant relationships may be due to the small numbers in the crime lab data, so one should not take these estimates to mean that we should be unconcerned about future increases in the availability of these fentanyl analogs or the emergence of new fentanyl analogs. Rather, they should be seen as part of ongoing research that should be updated to include new data to find new predictors of overdose death. The economic controls have some statistically significant correlations with overdose deaths: a higher poverty rate is associated with more overdose deaths, although a higher unemployment rate is associated with fewer deaths. The first finding is logical: an increase in poverty could increase the demand for illicit drugs, while the second finding is less intuitive. Unemployment rates were trending down over the period. Counties that had large drops in unemployment over the time period happened to be smaller counties on average with few overdose deaths. Those with a smaller decrease in unemployment happened to be larger counties on average with many more deaths. Thus, the coefficient for the unemployment rate may be picking up population size differences. More or fewer MEDs of prescription opioids per capita do not have a statistically significant relationship at the 5 percent level with overdose deaths exceptin the restricted sample of medium sized counties in column 3 of the estimation tables, where they have the expected signs: an increase of non-Suboxone opioids is associated with more overdose deaths while an increase in Suboxone is associated with fewer overdose deaths. The coefficient for positive tests of non-opioid drugs generally either lack statistical significance or have inconsistent relationships with overdose deaths across specifications. For example, lab tests finding benzodiazepines have a negative and statistically significant correlation with overdose deaths in Table 1, columns 1 and 4, at the 5 percent level. However, there is positive correlation under OLS estimates, and statistical significance disappears in Tables B1 and B3. Thus, although we think the estimates indicate the potential value of further research into the role of these drugs, we caution against reading too much into any specific coefficient for non-opioids.