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Addressing the Covid-19 and Opioid “Twindemic”

Covid-19 and the ongoing opioid epidemic have combined to form a single crisis that is overwhelming health providers: People who were predisposed to turn to drugs in the hope of alleviating feelings of isolation and despair were pushed further in that direction by stress and social distancing. Although this “twindemic” has posed enormous challenges for healthcare workers, it has also given rise to innovative thinking and new technologies that can help individuals who are suffering.

Perhaps the most impressive and important advancements have been made in the use of data and analytics. Data exchanges—which propagate the sharing of information among key organizations—and predictive analytics can help identify areas most in need of resources and determine better treatment options. When used to address the conditions the U.S. Department of Health and Human Services established as social determinants of health, data and analytics can be powerful tools for managing the twindemic.

Social determinants of health and their impact on care
The five social determinants of health—economic stability, economic access and quality, social community and context, healthcare access and quality, and neighborhood and built environment—have all played outsized roles in the twindemic. Individuals who struggle in any of these areas are more likely to experience poverty driven inequities in care and opioid addiction.

Safe and affordable housing is a vector of quality healthcare and can improve outcomes for both Covid-19 transmission rates and successful treatment for opioid abuse. That home must be paired with the conveniences that allow sheltering in place, including easy access to healthcare providers. Fortunately, telehealth pain specialty care has been tested extensively during the Covid-19 pandemic, and clinics have shown success in identifying patients that can tolerate accelerated freedom with weekly onsite visits, remote group therapy sessions, and at-home medications.  Some programs have even gone so far as to deliver at-home medication-assisted treatment (MAT) replete with supplies like Naloxone to decrease overdoses for people who are increasingly isolated.

Currently, there remain limitations on internet access, particularly in rural areas and aging urban housing developments. Public-private partnerships can improve infrastructure where necessary, drive modernization of public services like secure WiFi, and strengthen the safety for those most in need.

There’s still the matter of finding those people in need, allocating resources to their locations, and targeting their care so it is most efficacious. This is where data exchanges and predictive analytics can help.

Data exchanges and the power of sharing
State and local agencies collect large amounts of data, but much of it has been highly siloed and difficult to share. Once a data exchange is established, local leaders can take the collected data sets from participating groups—such as law enforcement agencies, health departments, and social services—and analyze for patterns that they can then address.

For instance, historical data can prove valuable in proactively treating addiction. Correlations between past childhood trauma could show a propensity for future addiction. Leaders can use that information to intervene and focus treatment and services to at-risk youth.

Data exchanges can be particularly powerful when combined with social determinants of health. Local leaders can use data in conjunction with various factors, including age, ethnicity, education levels, and more, to identify an individual’s potential for risk. The Commonwealth of Virginia’s Framework for Addiction Analytics and Community Transformation (FAACT) program is an example of an effort to combine data exchanges with social determinants of health for better outcomes.

Using predictive analytics to change physician behavior
Data becomes even more powerful when it can be used to prevent those outcomes from occurring in the first place. This is where predictive analytics—the use of historical data to make predictions about future events—factors into treatment.

Predictions are informed by historical data. In the case of opioid addiction, that data typically centers around the source of the drug. Unfortunately, sometimes that source is a physician. The Affordable Care Act and the Health Information Technology for Economic and Clinical Health (HITECH) Act enabled the spread of the Prescription Drug Monitoring Program (PDMP) and the collection of data that shines a light on prescribers whose practices led to patients getting addicted.

This information can be combined with social determinants of health and the information ascertained through data exchanges to obtain insights into who might be at risk for future dependency issues. Local agencies can then take proactive measures to address addiction before it occurs.

This can be done at the state and local levels and even with individuals. For instance, data may show that a local pharmacy has traditionally fulfilled prescriptions from a doctor who has shown a propensity for overprescribing medications. Local authorities can target their efforts in that area in an effort to help the patients of that doctor, as well as others in the area who might be deemed high risk.

The right way to treat addiction
Addiction and drug abuse is not a moral failing that should be addressed punitively. Rather, it is a medical condition no different from diabetes or other illness.

This has been shown time and again. Typically, if someone is in a MAT substance use disorder (SUD) treatment clinic and they are unable to adhere to the program requirements, they are asked to leave. But many treatment centers have discovered it’s more effective to kick a patient up to a higher level of care rather than kick them out altogether. This takes the shame out of drug abuse while providing people with the care they need to get better.

The past year has taught everyone the value of a more compassionate and non-punitive approach toward just about everything. Now, it’s time for leaders to take that approach and apply it, with supportive data and knowledge, to mitigating opioid addiction in their communities.

Photo: Moussa81, Getty Images

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