Patterns of alcohol use and marijuana use among students at 2- and 4-year institutions

Objective: The objective of this study was to understand substance use patterns of alcohol, marijuana, and simultaneous alcohol and marijuana (SAM) use among 2- and 4-year college students. Participants: Participants were 526 young adults aged 18–23 (n?=?355 4-year students; n?=?171 2-year students) recruited from February 2015 to January 2016 who were participating in a larger longitudinal study. Methods: Latent class analysis (LCA) was used to identify past-month classes of alcohol, marijuana, and SAM use. Results: Among both 2- and 4-year students, a four-class solution yielded the best-fitting model, with 2-year classes tending to include greater marijuana use and less alcohol use and 4-year classes tending to include heavy alcohol use. Demographic characteristics were largely similar across classes. Conclusions: Classes of alcohol, marijuana, and SAM use differed by education status. Screening and prevention efforts for 4-year students may need to be tailored for the needs of 2-year students.     

Traversing gender,sexual orientation,and race-ethnicity: Sexual victimization in a population-based sample of older adults

This research examined the intersectionality of gender, sexual orientation, and race-ethnicity, and its impact on lifetime sexual victimization among a population-based sample of older adults. Data for this study came from the 2011–2014 waves of the Behavioral Risk Factor Surveillance System. The final sample to be used for analysis included 8.862 individuals ages 50 years and over to examine whether and to what extent lesbian, gay, and bisexual older adults differ from heterosexual older adults in experiencing lifetime sexual victimization, and whether the effect of sexual orientation on experiencing lifetime sexual victimization differs across racial-ethnic groups. Logistic regression analysis revealed that lesbian women were 2.59 times more likely and bisexual women 2.15 times more likely both relative to heterosexual women to experience lifetime sexual victimization. When examining whether race-ethnicity imparts additional risk, the findings revealed that non-White heterosexual individuals were 29.6% less likely relative to White heterosexual individuals to experience lifetime sexual victimization, while non-White women, generally, were 2.29 times more likely relative to White men to experience lifetime sexual victimization. Our findings affirm the importance of the intersectionality of sexual orientation and gender when examining lifetime sexual victimization of older adults, adding to the emerging body of research that examines the complexities of older adult lives from multifaceted perspectives.     

Insights into the appropriate level of disaggregation for efficient time series model forecasting

This paper provides a potentially valuable insight on how to assess if the forecasts from an autoregressive moving average model based on aggregated data could be substantially improved through disaggregation. It is argued that, theoretically, the absence of moving average (MA) terms indicates that no forecasting efficiency improvements can be achieved through disaggregation. In practice, it is found that there is a strong correlation between the statistical significance of the MA component in the aggregate model and the magnitude of the forecast mean square error (MSE) decreases that can be achieved through disaggregation. That is, if a model includes significant MA terms, the forecast MSE improvements that may be gained from disaggregation could be substantial. Otherwise, they are more likely to be relatively small or non-existent.     

Violence Exposure and Drug Use in Central American Youth: Family Cohesion and Parental Monitoring as Protective Factors

Associations between witnessing serious violence and drug use, and the protective influences of family cohesion and parental monitoring, were investigated among 9,840 adolescents (50.5% female, M age=15.29 years, SD=1.76) living in Panama and Costa Rica. After accounting for demographics and parental and sibling substance use, witnessing serious violence was associated with greater drunkenness, tobacco use, number of illicit drugs used, and problems with drugs and alcohol. In every analysis, exposure to violence was associated with more drug use, while family cohesion and parental monitoring attenuated risk. Further, family cohesion and parental monitoring exerted a protective‐stabilizing effect on number of illicit drugs used and on problems with drugs and alcohol. There were few interactions with age. Implications for prevention are discussed.     

An adjusted likelihood-ratio approach to the behrens-fisher problem

In many situations it is necessary to test the equality of the means of two normal populations when the variances are unknown and unequal. This paper studies the celebrated and controversial Behrens-Fisher problem via an adjusted likelihood-ratio test using the maximum likelihood estimates of the parameters under both the null and the alternative models. This procedure allows the significance level to be adjusted in accordance with the degrees of freedom to balance the risk due to the bias in using the maximum likelihood estimates and the risk due to the increase of variance. A large scale Monte Carlo investigation is carried out to show that -2 InA has an empirical chi-square distribution with fractional degrees of freedom instead of a chi-square distribution with one degree of freedom. Also Monte Carlo power curves are investigated under several different conditions to evaluate the performances of several conventional procedures with that of this procedure with respect to control over Type I errors and power.     

Is Adolescent Employment Still a Risk Factor for High School Dropout?

Past cohorts of teenagers who spent long hours in jobs were more likely to drop out of high school than those who worked moderate hours or did not work at all. This article examines the association between employment intensity and dropout among adolescents in the High School Longitudinal Study of 2009 who traversed high school during a time of decreased prevalence of both employment and dropout relative to earlier cohorts. Analyses reveal that a relatively small percentage of teenagers nowadays are characterized as either intensive workers or dropouts (around 11% each). Yet, despite declines in intensive employment and dropout, disadvantaged youth remain overrepresented in both groups, and intensive work is still a risk factor for poor grades and dropout.     

Evaluating and Visualizing the Economic Impact of Commercial Districts Due to an Electric Power Network Disruption

Critical infrastructure networks enable social behavior, economic productivity, and the way of life of communities. Disruptions to these cyber–physical–social networks highlight their importance. Recent disruptions caused by natural phenomena, including Hurricanes Harvey and Irma in 2017, have particularly demonstrated the importance of functioning electric power networks. Assessing the economic impact (EI) of electricity outages after a service disruption is a challenging task, particularly when interruption costs vary by the type of electric power use (e.g., residential, commercial, industrial). In contrast with most of the literature, this work proposes an approach to spatially evaluate EIs of disruptions to particular components of the electric power network, thus enabling resilience‐based preparedness planning from economic and community perspectives. Our contribution is a mix‐method approach that combines EI evaluation, component importance analysis, and GIS visualization for decision making. We integrate geographic information systems and an economic evaluation of sporadic electric power outages to provide a tool to assist with prioritizing restoration of power in commercial areas that have the largest impact. By making use of public data describing commercial market value, gross domestic product, and electric area distribution, this article proposes a method to evaluate the EI experienced by commercial districts. A geospatial visualization is presented to observe and compare the areas that are more vulnerable in terms of EI based on the areas covered by each distribution substation. Additionally, a heat map is developed to observe the behavior of disrupted substations to determine the important component exhibiting the highest EI. The proposed resilience analytics approach is applied to analyze outages of substations in the boroughs of New York City.