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BackgroundOnline communities can provide social support for those recovering from opioid use disorder. However, advice-seekers on these platforms risk exposure to uncurated medical advice, potentially harming their health or recovery efforts. The objective of this analysis is to combine text annotation, social network analysis, and statistical modeling to identify advice-seekers on online social media for buprenorphine-naloxone use and study their characteristics.MethodsWe collected 5,258 posts and their comments from Reddit between 2014 and 2019. Among 202 posts which met our inclusion criteria, we annotated each post to determine which were advice-seeking (n=137) and not advice-seeking (n=65). We also annotated each posting user’s medication use stage and quantified their connectedness using social network analysis. In order to analyze the relationship between advice-seeking with a user’s social connectivity and medication use stage, we constructed four models which varied in explanatory variables.ResultsThe stepwise model (containing “total degree” (P=0.002), “using: inducting/tapering” (PConclusionsOur findings provide insights for the clinical care of people recovering from opioid use disorder and the nature of online medical advice-seeking overall. Clinicians should be especially attentive (e.g., through frequent follow-up) to patients who are inducting or tapering buprenorphine-naloxone or signal limited social support.

BackgroundSince COVID-19 erupted in the United States, little is known about how state-level opioid overdose trends and decedent characteristics have varied throughout the country.ObjectiveInvestigate changes in annual overdose death rates, substances involved, and decedent demographics in opioid overdose deaths across nine states; assess whether 2019-2020 trends were emerging (i.e., change from 2019-2020 was non-existent from 2018-2019) or continuing (i.e., change from 2019-2020 existed from 2018-2019).DesignCross-sectional study using vital statistics data to conduct a retrospective analysis comparing 2020 to 2019 and 2019 to 2018 across nine states.SettingAlaska, Colorado, Connecticut, Indiana, Massachusetts, North Carolina, Rhode Island, Utah, and Wyoming.ParticipantsOpioid-related overdose deaths in 2018, 2019, and 2020.MeasurementsAnnual overdose death rate, proportion of overdose deaths involving specific substances, and decedent demographics (age, sex, race, and ethnicity).ResultsWe find emerging increases in annual opioid-related overdose death rates in Alaska (55.3% [P=0.020]), Colorado (80.2% [P<0.001]), Indiana (40.1% [P=0.038]), North Carolina (30.5% [P<0.001]), and Rhode Island (29.6% [P=0.011]). Decreased heroin-involved overdose deaths were emerging in Alaska (−49.5% [P=0.001]) and Indiana (−58.8% [P<0.001]), and continuing in Colorado (−33.3% [P<0.001]), Connecticut (−48.2% [P<0.001]), Massachusetts (39.9% [P<0.001]), and North Carolina (−34.8% [P<0.001]). Increases in synthetic opioid presence were emerging in Alaska (136.5% [P=0.019]) and Indiana (27.6% [P<0.001]), and continuing in Colorado (44.4% [P<0.001]), Connecticut (3.6% [P<0.05]), and North Carolina (14.6% [P<0.001]). We find emerging increases in the proportion of male decedents in Colorado (15.2% [P=0.008]) and Indiana (12.0% [P=0.013]).LimitationsDelays from state-specific death certification processes resulted in varying analysis periods across states.ConclusionThese findings highlight emerging changes in opioid overdose dynamics across different states, which can inform state-specific public health interventions.