The BCI crime lab data, while imperfect, is to the best of our knowledge the best available indicator of the content of the illicit drug market in Ohio. However, the primary limitation in our analysis, and in research investigating illicit drug markets in general, is that the data does not provide a random sample of available and consumed illicit drugs and the demand for illicit drugs is not directly observable. In addition, the crime lab data only indicates whether a sample tests positive for particular drugs, and many samples test positive for more than one drug. There is no information about the amount of each detected substance or the proportion of the sample that is made up of each substance. One may be concerned that the estimates are biased by the BCI labs changing what they test for based on why people are dying. If this was the case, then the detection of a drug may be confounded by the timing of testing for that particular drug. For example, regarding the finding that the detection of carfentanil is positively correlated with overdose deaths, one may worry that the drug could have been around for some time, just not tested for, and the labs only started testing for carfentanil after people started dying from carfentanil. However, the specific substances that the BCI labs tested for did not change over the time period, so the positive tests should be interpreted as finding the actual substance, not finding it conditional on the timing of the lab testing for it. I.e., the only fentanyl analog found in 2015 was acetyl fentanyl, but this was not because the many other fentanyl analogs were not being tested for at that time. Additionally, even though we control for county and month fixed effects, county-specific linear time trends, and several relevant time-varying county-level variables,planting table there is the possibility of bias from county-specific unobserved omitted variables that are time-varying in a non-linear way, such as a sudden county-specific increase in demand for opioids.

Another potential problem in interpreting the estimates is reverse-causality: changes in the composition of the drug market as measured by changes in crime lab positive tests may be affected by contemporaneous overdose deaths after including all of the controls. It does not seem plausible that an increase in overdose deaths would cause an increase in the adulteration of heroin with deadlier synthetic opioids. However, it could be the case, for example, that law enforcement may be purposefully targeting certain types of drug crimes and not others based on the number of overdose deaths. That is law enforcement intensity rather than changes in the underlying illicit drug market could be the underlying unobserved variable driving the positive correlation between fentanyl/carfentanil/fentanyl analog crime lab tests and overdose deaths. Another possibility is that courts may have become more lenient towards specific types of drug crimes, e.g. via an expansion of drug courts that reduced the need for labs to test specific drugs. Given that we cannot directly observe county-specific changes in law enforcement priorities or the criminal justice system, these unobservables may be an underlying cause of the estimated relationship between BCI lab tests and overdose deaths. Other unobservables, such as a sudden increase in the population of opioid users is another potential reason for the positive correlation between synthetic opioid tests and overdose deaths, so we are hesitant to conclude that the changing illicit opioid market as measured by crime lab tests is directly causing an increase in overdose deaths. Having said that, as mentioned above, the strong correlation of overdose deaths with the BCI crime lab tests finding synthetic opioids make the lab tests a valuable resource for an early warning system regardless of the true causal relationship. The rise in fentanyl adulterated or substituted heroin represents a significant shift in the risk environment for people who inject drugs. 

The best evidence we have had of the increasing danger of fentanyl and other synthetic opioids is the increasing number of deaths related to these drugs. We fill some of the gap in our understanding of synthetic opioids contributing to a worsening risk environment for heroin users by providing new evidence that the illicit drug market has been changing rapidly in Ohio, which is likely a major factor in the recent surge in overdose deaths. The strong statistical relationship between overdose deaths and crime lab tests for synthetic opioids provides evidence for making this information publicly available as quickly as possible on an ongoing basis. Surveillance of the drug supply, both on an intimate as well as mass scale, may provide a way to alter the risk environment. As heroin has become increasingly contaminated with synthetic opioids, people change their behavior to reduce risk. For example, there is evidence that if PWID are provided with fentanyl test strips, they take a variety of measures to decrease their risk of overdose. Providing timely information about the contents of seized drugs, at the local city or county level, a relatively low-cost intervention, so that people can respond to location-specific changes in the risk of encountering evolving synthetic opioids is another promising way to help PWID take steps to reduce their chances of dying. Furthermore, the data can be used by harm reduction services, first responders, and law enforcement to more quickly respond to emerging spikes in overdose deaths. In addition, the data can alert us to new and evolving trends in the illicit drug market, such as changes in poly drug use and the recent substantial shift in the crime lab data towards methamphetamines. Our findings are critically important from a policy perspective. Ohio’s experience with carfentanil in particular, with a surge in deaths and then a quick disappearance, should cause alarm for other states. Current public detection systems are not up to the task to respond in a timely manner. Rhode Island, for example, set up a website to provide the public with information on where overdoses are occurring to help provide the public with more information about the opioid crisis.

However, the data is only updated biannually with a significant time lag and little information that could help change behavior or efficiently redirect harm reduction services to counter a sudden appearance of a new deadlier opioid. Providing crime lab data quickly could provide a real opportunity to have a rapid response to a rapidly changing risk environment. Misuse of substances is common, can be serious and costly to society,cannabis indoor grow system and often goes untreated due to barriers to accessing care. Globally, 3.5 million people die from alcohol and illicit drug use each year. The disease burden of alcohol and illicit drug addiction is the highest in the United States. Over 20 million Americans had a substance use disorder in 2018, 73% had an alcohol use disorder, 40% had an illicit drug use disorder, and 13% had both alcohol and illicit drug use disorders. Approximately half of Americans with an SUD had a co-occurring mental illness. Treatment of depression and anxiety, the most common psychiatric comorbidities among patients with SUDs, may reduce craving and substance use and enhance overall outcomes. In 2018, less than 1 in 5 individuals with a SUD received addiction treatment. Alcohol and illicit drug misuse and addiction cost the United States over US $440 billion annually in lost workplace productivity, health care expenses, and crime-related costs. Potential effects on individuals include an array of physical and mental health problems, overdose, trauma, and violence. Web-based interventions and digital health apps may reduce or eliminate common, significant barriers to traditional SUD treatment. Preliminary evidence suggests that digital SUD interventions affect substance use behavior and have the potential to reduce the population burden of SUDs. To date, most digital SUD interventions have been delivered on a web platform, rather than via mobile apps. The widespread use of smartphones makes app-based intervention delivery a viable and scalable medium. In 2019, about 8 out of 10 White, Black, and Latinx adults owned a smartphone. Although lower-income adults were less likely to own a smartphone than higher-income adults, they were more likely to rely on smartphones for internet access. In a 2015 survey, 58% of mobile phone owners reported downloading a health app. Texting is the most widely and frequently used app on a smartphone, with 97% of Americans texting at least once a day. Automated conversational agents can deliver a coach-like or sponsor-like experience and yet do not require human implementation assistance for in-the-moment treatment delivery. As recent meta-analytic work suggests, conversational text-based agents may increase engagement and enjoyment in digitized mental health care, whereas most general mental health care apps face difficulty sustaining engagement with high dropout.

Conversational agents can provide real-time support to address substance use urges, unlike traditional in-person frameworks of weekly visits. The scale potential of conversational agents is unconstrained, immediate, and available to users in an instant. Being nonhuman based also reduces perceived stigma. A study found that people were significantly more likely to disclose personal information to artificial intelligence when they believed it was computer- rather than human-monitored. Users can develop a strong therapeutic alliance in the absence of face-to-face contact, even with a nonhuman app. Digital environments can promote honest disclosure due to greater ease of processing thoughts and reduced risk of embarrassment. Finally, although conversational agents can present in different modalities, including text, verbal, and animation, preliminary research on modality for psycho education delivery specifically found that text-based presentation resulted in higher program adherence than verbal presentation. Evidence for conversational agent interventions for addressing mental health problems is growing quickly and appears promising with regard to acceptability and efficacy. Developed as a mental health digital app, Woebot is a text-based conversational agent available to check in with users whenever they have smartphone access. Using conversational tones, Woebot is designed to encourage mood tracking and to deliver general psycho education as well as tailored empathy, cognitive behavioral therapy –based behavior change tools, and behavioral pattern insight. Among a sample of adults randomly assigned to Woebot or an information only control group, Woebot users had statistically and clinically significant reductions in depressive symptoms after 2 weeks of use, whereas those in the control group did not. Engagement with the app was high. However, the efficacy of conversational agents for treating SUDs remains unknown. Woebot’s app-based platform and user-centered design philosophy make it a promising modality for SUD treatment delivery; it offers immediate, evidence-based tailored support in the peak moment of craving. An informal poll of Woebot users indicated that 63% had interest in content addressing SUDs; 22% of surveyed users reported having 5 or more alcoholic drinks in a row within a couple of hours, and 5% endorsed using nonprescription drugs. Although the efficacy of automated conversational agent digital therapeutics for SUDs is still untested, such products are commercially available, and few consumers are aware that the products lack evidence. This study aims to adapt the original Woebot for the treatment of SUDs , and test the feasibility, acceptability, and preliminary efficacy in a single-group pre-/post treatment design. In a single-group design, we examined within-subject changes in self-reported substance use behavior, cravings, confidence to resist urges to use substances, mood symptoms , and pain from pre- to post treatment. Intervention engagement data were collected from the Woebot app during the 8-week treatment period.The study procedures were approved by the Institutional Review Board of Stanford Medicine. Participants were recruited via the Woebot app, social media , Craigslist, and Stanford staff and student wellness listservs. In addition, study flyers were posted in the San Francisco Bay Area, and email invitations were sent to participants from previous studies. Recruitment materials included the URL on a web page describing the study for people with substance use concerns. Informed consent was required to screen for eligibility. Those who screened as eligible were asked to provide informed consent for participation in the study. Inclusion criteria were all genders, aged 18 years to 65 years, residing in the United States, screening positive on the 4-item Cut down, Annoyed, Guilty, Eye opener-Adapted to Include Drugs, owning a smartphone for accessing Woebot, available for the 8-week study, willing to provide an email address, and English literate.