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.

The results of this systematic review suggest that BZDs should be considered relatively contraindicated for patients with PTSD or recent trauma. Evidence-based treatments for PTSD should be favored over BZDs.

Aims The study seeks to characterize marijuana concentrate users, describe reasons and patterns of use, perceived risk, and identify predictors of daily/near daily use. Methods An anonymous web-based survey was conducted (April-June 2016) with 673 US-based cannabis users recruited via the Bluelight.org web-forum and included questions about marijuana concentrate use, other drugs, and socio-demographics. Multivariable logistic regression analyses were conducted to identify characteristics associated with greater odds of lifetime and daily use of marijuana concentrates. Results About 66% of respondents reported marijuana concentrate use. The sample was 76% male, and 87% white. Marijuana concentrate use was viewed as riskier than flower cannabis. Greater odds of marijuana concentrate use was associated with living in states with “recreational” (AOR=4.91; p=0.001) or “medical, less restrictive” marijuana policies (AOR=1.87; p=0.014), being male (AOR=2.21, p=0.002), younger (AOR=0.95, p<0.001), number of other drugs used (AOR=1.23, p<0.001), daily herbal cannabis use (AOR=4.28, p<0.001), and lower perceived risk of cannabis use (AOR=0.96, p=0.043). About 13% of marijuana concentrate users reported daily/near daily use. Greater odds of daily concentrate use was associated with being male (AOR=9.29, p=0.033), using concentrates for therapeutic purposes (AOR=7.61, p=0.001), using vape pens for marijuana concentrate administration (AOR=4.58, p=0.007), and lower perceived risk of marijuana concentrate use (AOR=0.92, p=0.017). Conclusions Marijuana concentrate use was more common among male, younger and more experienced users, and those living in states with more liberal marijuana policies. Characteristics of daily users, in particular patterns of therapeutic use and utilization of different vaporization devices, warrant further research with community-recruited samples.

Walking gait is generally held to reach maturity, including walking at adult-like velocities, by 7–8 years of age. Lower limb length, however, is a major determinant of gait, and continues to increase until 13–15 years of age. This study used a sample from the Fels Longitudinal Study (ages 8–30 years) to test the hypothesis that walking with adult-like velocity on immature lower limbs results in the retention of immature gait characteristics during late childhood and early adolescence. There was no relationship between walking velocity and age in this sample, whereas the lower limb continued to grow, reaching maturity at 13.2 years in females and 15.6 years in males. Piecewise linear mixed models regression analysis revealed significant age-related trends in normalized cadence, initial double support time, single support time, base of support, and normalized step length in both sexes. Each trend reached its own, variable-specific age at maturity, after which the gait variables’ relationships with age reached plateaus and did not differ significantly from zero. Offsets in ages at maturity occurred among the gait variables, and between the gait variables and lower limb length. The sexes also differed in their patterns of maturation. Generally, however, immature walkers of both sexes took more frequent and relatively longer steps than did mature walkers. These results support the hypothesis that maturational changes in gait accompany ongoing lower limb growth, with implications for diagnosing, preventing, and treating movement-related disorders and injuries during late childhood and early adolescence.

Objectives This article illustrates the use of applied Bayesian statistical methods in modeling the trajectory of adult grip strength and in evaluating potential risk factors that may influence that trajectory. Methods The data consist of from 1 to 11 repeated grip strength measurements from each of 498 men and 533 women age 18–96 years in the Fels Longitudinal Study (Roche AF. 1992. Growth, maturation and body composition: the Fels longitudinal study 1929–1991. Cambridge: Cambridge University Press). In this analysis, the Bayesian framework was particularly useful for fitting a nonlinear mixed effects plateau model with two unknown change points and for the joint modeling of a time-varying covariate. Multiple imputation (MI) was used to handle missing values with posterior inferences appropriately adjusted to account for between-imputation variability. Results On average, men and women attain peak grip strength at the same age (36 years), women begin to decline in grip strength sooner (age 50 years for women and 56 years for men), and men lose grip strength at a faster rate relative to their peak; there is an increasing secular trend in peak grip strength that is not attributable to concurrent secular trends in body size, and the grip strength trajectory varies with birth weight (men only), smoking (men only), alcohol consumption (men and women), and sports activity (women only). Conclusions Longitudinal data analysis requires handling not only serial correlation but often also time-varying covariates, missing data, and unknown change points. Bayesian methods, combined with MI, are useful in handling these issues.