As the opioid epidemic continues to ravage the United States, American Indians and Alaska Natives (AI/ANs) continue to be disproportionally affected with opioid-related deaths three times higher in AI/ANs than in Blacks and Hispanic whites. High AI/AN opioid overdose rates have not only been higher in AI/ANs, they have also been persistent: both metropolitan and non-metropolitan AI/ANs had the highest opioid overdose rate than any other racial group over a nearly 16-year period (2008 to 2015). AI/ANs are not a homogenous population and there are significant and important variations across the country. For example, Washington saw 26.7 AI/AN deaths per 100,000 of opioid overdoses, whereas South Dakota only saw 6.7 AI/AN deaths per 100,0000 in 2018. However, while the AI/AN community shares some risk and protective factors for substance use with the general population (e.g., family history of drug abuse, certain traits or psychiatric conditions), they differ in this as well due to high rates of trauma and abuse and cultural differences. Due to small samples sizes, a history of exclusion from research, and a dispersed population, the true extent of opioid use disorder (OUD) and opioid- related overdoses in the AI/AN community is likely underestimated. This proposed study addresses weaknesses in prior research through i) a singular focus on AI/ANs and ii) use of a large robust database: Cerner Corporation?s Health Facts. With 524,959 unique AI/AN patients, Health Facts contains electronic health records including diagnosis, procedures, medications, and labs, as well as demographic data?allowing for patient-level analysis and changing trends overtime. This study will determine AI/AN rates of OUD and opioid- related overdoses at the national level and by the nine U.S. Census regions (New England, Mid Atlantic, East North Central, West North Central, South Atlantic, East South Central, West South Central, Mountain, Pacific), as well as identifying risk and protective factors. Specifically, we aim to estimate rates and adjusted odds of OUD and non-fatal opioid overdose for AI/ANs at the national level (Aim 1), and create a predictive model to determine the role of modifiable risk and protective factors in OUD and non-fatal opioid overdoses (Aim 2). This is the first study, to our knowledge, to use large robust electronic medical records database (EMR) to analyze OUD and opioid-related fatalities in the AI/AN community at the patient level. Our longitudinal data will show changing trends over time, and a variety of demographic information will enable an analysis of both risk and protective factors. These data will allow for a nuanced comparison of OUD rates and opioid-related fatalities in AI/ANs to the general population. Ultimately, our analysis will be used to develop predictive models on the role of various modifiable and non-modifiable risk factors, which may be used to determine where there is the greatest need for fiscal resources and policies. As a National Institute of Drug Addiction Diversity Supplement, this project will contribute a diverse and highly trained substance use researcher to the field, helping NIH achieve its goal of a diversified health-related sciences workforce.
American Indians and Alaska Natives (AI/ANs) continue to be disproportionally affected by the opioid epidemic with higher and more persistent opioid use disorder (OUD) rates and opioid-related overdoses in comparison to all other racial groups. There are, however, modifiable and non-modifiable risk factors that, once quantified, can provide a sound evidence base for directing fiscal and policy resources to protect this historically underserved and vulnerable community. This study will determine AI/AN rates of OUD and opioid-related overdoses at the national level and by the nine U.S. Census regions (New England, Mid Atlantic, East North Central, West North Central, South Atlantic, East South Central, West South Central, Mountain, Pacific) and identify risk and protective factors in order to estimate rates and adjusted odds of OUD and non-fatal opioid overdose for AI/ANs at the national level, and create a predictive model to determine the role of modifiable risk and protective factors in OUD and non-fatal opioid overdoses.
