Predictors of Multiwave Opioid Use Among Older American Adults

Despite limited analgesic benefits, long-term opioid therapy (L-TOT) is common among older adults with chronic pain. Extended opioid use poses a threat to older adults as aging metabolisms retain opioids for longer, increasing the risk of injury, overdose, and other negative health outcomes. In contrast to predictors of general opioid use, predictors of L-TOT in older adults are not well documented. We aimed to identify such predictors using all available data on self-reported opioid use in the Health and Retirement Study.

Using 5 waves of data, respondents (N = 10,713) aged 51 and older were identified as reporting no opioid use (n = 8,621), a single wave of use (n = 1,410), or multiple waves of use (n = 682). We conducted a multinomial logistic regression to predict both single- and multiwave opioid use relative to no use. Demographic, socioeconomic, geographic, health, and health care-related factors were included in our model.

Multivariable findings show that, relative to nonusers, both single- and multiwave users were significantly more likely to be younger (relative risk ratio [RRR] = 1.33; RRR = 2.88); report lower household wealth (RRR = 1.47; RRR = 2.88); live in the U.S. Midwest (RRR = 1.29; RRR = 1.56), South (RRR = 1.34; RRR = 1.58), or West (RRR = 1.46; RRR = 2.34); experience interfering pain (RRR = 1.59; RRR = 3.39), back pain (RRR = 1.35; RRR = 1.53), or arthritic pain (RRR = 1.46; RRR = 2.32); and see the doctor frequently (RRR = 1.50; RRR = 2.02). Multiwave users were less likely to be Black (RRR = 0.69) or Hispanic (RRR = 0.45), and less likely to be never married (RRR = 0.52).

We identified demographic, socioeconomic, geographic, and health care-related predictors of chronic multiyear opioid use. Our focus on individuals taking opioids for this extended duration is novel. Differences in opioid use by geographic region and frequency of doctor visits particularly warrant attention from policy-makers and researchers. We make additional recommendations based on a sensitivity analysis limited to 2016-2020 data.