Overestimation of Prescription Pain Reliever Misuse and Heroin Use among Adults

Abstract

Background

Estimates from the National Survey on Drug Use and Health (Substance Abuse and Mental Health Services Administration [SAMHSA], 2019) suggest 3.6% of persons aged 12 and older misused prescription pain relievers in the past year and 0.3% used heroin. However, research suggests that most individuals drastically overestimate rates of substance use and misuse. Those who overestimate substance misuse are often more likely to misuse substances themselves (Kilmer et al., 2015; McCabe, 2008). Purpose: To compare perceived versus actual rates of prescription pain reliever misuse and heroin use among a statewide sample of adults and identify correlates of these differences. Methods: Participants (N = 689) recruited through social media estimated rates of prescription pain reliever misuse and heroin use. Participants also indicated whether they engaged in pain reliever misuse or heroin use, and whether they knew anyone who misused prescription pain medications or heroin. Results: Almost all participants (98.11%) overestimated the prevalence of prescription pain reliever misuse (mean estimate = 41.25%) and heroin use (99.71%, mean estimate =25.46%). Women and African Americans were more likely to overestimate prescription pain reliever misuse and heroin use. Knowing someone who misused prescription pain relievers was significantly associated with overestimating prescription pain reliever misuse. Personal use was not associated with overestimating prevalence of either substance. Conclusions: Adults consistently overestimate rates of prescription pain reliever misuse and heroin use. Overestimation may increase normative perceptions of substance use and ultimately lead to increased substance use. Social-norms based education and interventions may be particularly important among groups that are more likely to overestimate use.

Keywords:

• perception
• opioids
• substance use
• prescription drugs
• heroin

Declaration of interest

The authors declare that they have no conflict of interest. The authors alone are responsible for the content and writing of the article.

Notes

1 Rural-Urban designation was based on the 2013 Rural/Urban Continuum Codes outlined by the US Department of Agriculture’s Economic Research Service (Ingram & Franco, 2014). These codes classify each county in the US as metropolitan and non-metropolitan based on population size, labor market, and proximity to metropolitan areas. Metropolitan (or metro) areas refer to counties with one or more urbanized area with a population of 50,000 persons or more as well as outlying areas with strong economic ties to an urbanized area with a population of 50,000 persons or more. Nonmetropolitan counties include some combination on open countryside, rural towns (places with fewer than 2,500 people) and urban areas with populations ranging from 2,500 to 49,999 that are not part of larger labor market areas (i.e. metropolitan) areas.

2 For the non-imputed version of model 2, F(9, 586)=5.43, p<.001, with an R2 of .085. Although pooled model fit data are not available in SPSS, model fit results for all imputed analyses yielded the same conclusion.