Background Increases in illicit pharmaceutical opioid (PO) use have been associated with risk for transition to heroin use. We identify predictors of transition to heroin use among young, illicit PO users with no history of opioid dependence or heroin use at baseline. Methods Respondent-driven sampling recruited 383 participants; 362 returned for at least one biannual structured interview over 36 months. Cox regression was used to test for associations between lagged predictors and hazard of transition to heroin use. Potential predictors were based on those suggested in the literature. We also computed population attributable risk (PAR) and the rate of heroin transition. Results Over 36 months, 27 (7.5%) participants initiated heroin use; all were white, and the rate of heroin initiation was 2.8% per year (95% CI=1.9%–4.1%). Mean length of PO at first reported heroin use was 6.2 years (SD=1.9). Lifetime PO dependence (AHR=2.39, 95% CI= 1.07–5.48; PAR=32%, 95% CI=−2%–64%), early age of PO initiation (AHR=3.08, 95%; CI= 1.26–7.47; PAR=30%, 95% CI=2%–59%), using illicit POs to get high but not to self-medicate a health problem (AHR=4.83, 95% CI= 2.11–11.0; PAR=38%, 95% CI=12%–65%), and ever using PO non-orally most often (AHR=6.57, 95% CI=2.81–17.2; PAR=63%, 95% CI=31%–86%) were significant predictors. Conclusion This is one of the first prospective studies to test observations from previous cross-sectional and retrospective research on the relationship between illicit PO use and heroin initiation among young, initially non-opioid dependent PO users. The results provide insights into targets for the design of urgently needed prevention interventions.

Background/Objective Parental obesity influences infant body size. To fully characterize their relative effects on infant adiposity, associations between maternal and paternal body mass index (BMI) category (normal: ≤25 kg/m2, overweight: 25–<30 kg/m2, obese: ≥30 kg/m2) and infant BMI were compared in Fels Longitudinal Study participants. Methods A median of 9 serial weight and length measures from birth-3.5 years were obtained from 912 European American children born in 1928–2008. Using multivariable mixed effects regression, contributions of maternal versus paternal BMI status to infant BMI growth curves were evaluated. Cubic spline models also included parental covariates, infant sex, age, and birth variables, and interactions with child’s age. Results Infant BMI curves were significantly different across the three maternal BMI categories (POverall<0.0001), and offspring of obese mothers had greater mean BMI at birth and between 1.5–3.5 years than those of over- and normal weight mothers (P≤0.02). Average differences between offspring of obese and normal weight mothers were similar at birth (0.8 kg/m2, P=0.0009) and between 2–3.5 years (0.7–0.8 kg/m2, P<0.0001). Infants of obese fathers also had BMI growth curves distinct from those of normal weight fathers (P=0.02). Infant BMI was more strongly associated with maternal than paternal obesity overall (P<0.0001); significant differences were observed at birth (1.11 kg/m2, P=0.006) and from 2–3 years (0.62 kg/m2, P3years=0.02). Conclusion At birth and in later infancy, maternal BMI has a stronger influence on BMI growth than paternal BMI, suggesting weight control in reproductive age women may be of particular benefit for preventing excess infant BMI.

Aims Media reports suggest increasing popularity of marijuana concentrates (“dabs”; “earwax”; “budder”; “shatter; “butane hash oil”) that are typically vaporized and inhaled via a bong, vaporizer or electronic cigarette. However, data on the epidemiology of marijuana concentrate use remain limited. This study aims to explore Twitter data on marijuana concentrate use in the U.S. and identify differences across regions of the country with varying cannabis legalization policies. Methods Tweets were collected between October 20 and December 20, 2014, using Twitter's streaming API. Twitter data filtering framework was available through the eDrugTrends platform. Raw and adjusted percentages of dabs-related tweets per state were calculated. A permutation test was used to examine differences in the adjusted percentages of dabs-related tweets among U.S. states with different cannabis legalization policies. Results eDrugTrends collected a total of 125,255 tweets. Almost 22% (n=27,018) of these tweets contained identifiable state-level geolocation information. Dabs-related tweet volume for each state was adjusted using a general sample of tweets to account for different levels of overall tweeting activity for each state. Adjusted percentages of dabs-related tweets were highest in states that allowed recreational and/or medicinal cannabis use and lowest in states that have not passed medical cannabis use laws. The differences were statistically significant. Conclusions Twitter data suggest greater popularity of dabs in the states that legalized recreational and/or medical use of cannabis. The study provides new information on the epidemiology of marijuana concentrate use and contributes to the emerging field of social media analysis for drug abuse research.

Aims Several states in the U.S. have legalized cannabis for recreational or medical uses. In this context, cannabis edibles have drawn considerable attention after adverse effects were reported. This paper investigates Twitter users’ perceptions concerning edibles and evaluates the association edibles-related tweeting activity and local cannabis legislation. Methods Tweets were collected between May 1 and July 31, 2015, using Twitter API and filtered through the eDrugTrends/Twitris platform. A random sample of geolocated tweets was manually coded to evaluate Twitter users’ perceptions regarding edibles. Raw state proportions of Twitter users mentioning edibles were ajusted relative to the total number of Twitter users per state. Differences in adjusted proportions of Twitter users mentioning edibles between states with different cannabis legislation status were assesed via a permutation test. Results We collected 100,182 tweets mentioning cannabis edibles with 26.9% (n=26,975) containing state-level geolocation. Adjusted percentages of geolocated Twitter users posting about edibles were significantly greater in states that allow recreational and/or medical use of cannabis. The differences were statistically significant. Overall, cannabis edibles were generally positively perceived among Twitter users despite some negative tweets expressing the unreliability of edible consumption linked to variability in effect intensity and duration. Conclusion Our findings suggest that Twitter data analysis is an important tool for epidemiological monitoring of emerging drug use practices and trends. Results tend to indicate greater tweeting activity about cannabis edibles in states where medical THC and/or recreational use are legal. Although the majority of tweets conveyed positive attitudes about cannabis edibles, analysis of experiences expressed in negative tweets confirms the potential adverse effects of edibles and calls for educating edibles-naïve users, improving edibles labeling, and testing their THC content.