Background and the Purpose of the Study
Opioid addiction is an epidemic across the United States. Nearly 70,000 people died from illicit drug use, including prescription opioids. Not only do opioid causes loss of life, but it also causes a significant economic burden. Around 78 billion dollars cost to treat opioid-related treatments in 2013. Currently, there are insufficient resources and education around opioid related prevention, treatment, and recovery. This study aims to examine the factors that are associated with predicting opioid literacy. We assessed our participants general opioid knowledge and the predictors of opioid knowledge using the multiple linear regression model. We hope our study assessed ways to lessen the rate of opioid related mortality, morbidity in northwest Alabama.
This study employed a cross-sectional survey design to collect self-report data on opioid knowledge among people living in the Alabama counties of Franklin, Marion, Walker, and Winston. A convenience sampling strategy utilized project partners to refer participants. Opioid literacy was measured using the Brief Opioid Overdose Knowledge questionnaire (Dunn et al., 2016). The measure collects data on three domains of opioid knowledge: (1) general opioid knowledge; (2) opioid overdose knowledge; and (3) opioid overdose response knowledge. Questions are in the form of “All overdoses are fatal (deadly).” Inferential analyses featured three sets of multiple linear regressions to examine factors associated with opioid knowledge among participants.
Participants tended to report high level of opioids knowledge on each subscale: 3.45 out of 4 on general opioid knowledge, 3.52 out of 4 on opioid overdose risk knowledge, and 3.25 out of 4 on opioid overdose response knowledge. There were no significant predictors of the general knowledge score. For opioid overdose knowledge, each step of the model produced a significant R2, indicating that the factors were accounting for important variance in the subscale score (Model 1 or M1 = .09, M2 = .27, and M3 = .41). The strongest individual predictors were education level (bachelor’s degree) and self-rated financial strain, both of which contributed to higher scale scores. The models evaluating opioid overdose response knowledge also showed significant R2 for the addition of demographic and health factors, but not SDOH (M1 = .17, M2 = .22, and M3 = .30). The strongest individual predictors were minority status (inverse), self-rated mental health, and interpersonal safety.
This study will further outline the teams integrative approach for the data collection and the findings that can benefit the northwest Alabama community. Education to understand the implications of opioid usage can be done through local healthcare clinics and community-based organizations, where the general populations are more likely to be reached. This will enhance the individual’s decision-making ability on their choice of receiving the opioid prescription. This also applies to having more autonomy when receiving an opioid prescription from the physician. Individuals will have more knowledge to inquire and understand the potential risk factors of taking the substance.