Syndemic factors associated with non-fatal overdose among young opioid users in New York City.

Introduction: Rates of illicit opioid use are particularly high among young adults, yet research on overdose experience and factors associated with overdose in this population remains limited. This study examines the experiences and correlates of non-fatal overdose among young adults using illicit opioids in New York City (NYC). Methods: 539 participants were recruited via Respondent-Driven Sampling in 2014-2016. Eligibility criteria included: aged 18-29 years old; current residence in NYC; and nonmedical prescription opioid (PO) use and/or heroin use in the past 30 days. Participants completed structured interviews to assess their socio-demographics, drug use trajectories, current substance use and lifetime and most recent overdose experiences, and were tested on-site for hepatitis C virus (HCV) antibodies. Results: 43.9% of participants reported lifetime overdose experience; of these, 58.8% had experienced two or more overdose events. The majority of participants' most recent overdoses (63.5%) were due to polysubstance use. In bivariable analyses, after RDS adjustment, having ever overdosed was correlated with: household income of >$100,00 growing up (vs. $51,000-100,000); lifetime homelessness; HCV antibody-positive status; lifetime engagement in regular nonmedical benzodiazepine use, regular heroin injection and regular PO injection; and using a non-sterile syringe in the past 12 months. Multivariable logistic regression identified childhood household income >$100,00 (AOR=1.88), HCV-positive status (AOR=2.64), benzodiazepine use (AOR=2.15), PO injection (AOR=1.96) and non-sterile syringe use (AOR=1.70) as significant independent correlates of lifetime overdose. A multivariable model with multiple overdoses (vs. one) found only lifetime regular heroin use and PO injection to be strong correlates. Discussion: Results indicate a high prevalence of lifetime and repeated overdose among opioid-using young adults in NYC, highlighting a need for intensified overdose prevention efforts for this population. The strong associations of HCV and indices of polydrug use with overdose suggest that prevention efforts should address the complex risk environment in which overdose occurs, attending to the overlapping nature of disease-related risk behavior and overdose risk behavior among young people who inject opioids. Overdose prevention efforts tailored for this group may find it useful to adopt a syndemic conception of overdose that understands such events as resulting from multiple, and often interrelated, risk factors.

Tracheostomy dislodgement: Are obese patients at increased risk?

BACKGROUND: Obesity is a risk factor for tracheostomy-related complications. We aimed to investigate whether obesity was associated with a risk of unplanned tracheostomy dislodgement or decannulation (DD). METHODS: Retrospective review of patients undergoing tracheostomy at a single institution from 2013 to 2019 was performed. The primary outcome was unplanned DD within 42 days. Obesity was assessed by body mass index (BMI) and skin-to-trachea distance (STT) measured on computed tomographic images. RESULTS: 25 (12%) episodes of unplanned DD occurred in 213 patients within 42 days. BMI ≥35 kg/m2 was associated with STT ≥80 mm (p < 0.0001). On multivariate analysis, STT ≥80 mm but not BMI was an independent predictor of unplanned DD (hazard ratio = 8.34 [95% confidence interval 2.85-24.4]). CONCLUSIONS: STT ≥80 mm was a better predictor of unplanned DD than BMI. Assessment of STT in addition to BMI may be useful to identify patients that would benefit from extended length tracheostomy tubes.

Outcome of Hypotensive Trauma Patients by Time and Day of Arrival.

BACKGROUND: Although most studies of trauma patients have not demonstrated a "weekend" or "night" effect on mortality, outcomes of hypotensive (systolic blood pressure <90 mm Hg) patients have not been studied. We sought to evaluate whether outcomes of hypotensive patients were associated with admission time and day. METHODS: We retrospectively analyzed patients from Pennsylvania Level 1 and Level 2 trauma centers with systolic blood pressure of <90 mm Hg over 5 y. Patients were stratified into four groups by arrival day and time: Group 1, weekday days; Group 2, weekday nights; Group 3, weekend days; and Group 4, weekend nights. Patient characteristics and outcomes were compared for the four groups. Adjusted mortality risks for Groups 2, 3, and 4 with Group 1 as the reference were determined using a generalized linear mixed effects model. RESULTS: After exclusions, 27 trauma centers with a total of 4937 patients were analyzed. Overall mortality was 44%. Compared with patients arriving during the day (Groups 1 and 3), those arriving at night (Groups 2 and 4) were more likely to be younger, to be male, to have lower Glasgow Coma Scale scores and blood pressures, to have penetrating injuries, and to die in the emergency room. Controlled for admission variables, odds ratios (95% confidence intervals) for Groups 2, 3, and 4 were 0.92 (0.72-1.17), 0.89 (0.65-1.23), and 0.76 (0.56-1.02), respectively, for mortality with Group 1 as reference. CONCLUSIONS: Patients arriving in shock to Pennsylvania Level 1 and Level 2 trauma centers at night or weekends had no increased mortality risk compared with weekday daytime arrivals.

Examining the Relationship Between Biometric Indicators and Pharmacy Students' Academic Performance.

Objective. To use a fitness tracking device to track student wellness habits, specifically number of steps, activity, and sleep duration, in an attempt to identify relationships between these variables and academic performance outcomes such as examination scores and course grades. Methods. A fitness tracker was issued to second professional year Doctor of Pharmacy (PharmD) students to track their daily number of steps, activity levels, and minutes of sleep. Individual data from these devices were collected using a cloud-based data aggregation platform. The outcome variables of interest were student grade point average (GPA) in core courses, as well as examination grades for 17 examinations administered across eight required courses during the study period. After exploratory analyses, the primary research questions relating steps and sleep to academic performance were addressed with a series of linear regression models. Results. No significant, identifiable relationships were found between examination grades or course GPA and the variables of interest. There was a significant negative relationship between the number of steps students took 72-hours before an examination and performance on the examination where students in the low activity group significantly outperformed those in the high activity group by an average of two points. Participants took an average of 1,466 fewer steps prior to an examination. Conclusion. Sleep and physical activity were not robust predictors of examination scores and course grades in this cohort of PharmD students. While the fitness tracker served as an impetus for the students to be more cognizant of their activity, the capital expenditure for the devices did not result in improved academic performance.

Fixed choice design and augmented fixed choice design for network data with missing observations.

The statistical analysis of social networks is increasingly used to understand social processes and patterns. The association between social relationships and individual behaviors is of particular interest to sociologists, psychologists, and public health researchers. Several recent network studies make use of the fixed choice design (FCD), which induces missing edges in the network data. Because of the complex dependence structure inherent in networks, missing data can pose very difficult problems for valid statistical inference. In this article, we introduce novel methods for accounting for the FCD censoring and introduce a new survey design, which we call the augmented fixed choice design (AFCD). The AFCD adds considerable information to analyses without unduly burdening the survey respondent, resulting in improvements over the FCD, and other existing estimators. We demonstrate this new method through simulation studies and an analysis of alcohol use in a network of undergraduate students living in a residence hall.

Multi-institutional study of GRE scores as predictors of STEM PhD degree completion: GRE gets a low mark.

The process of selecting students likely to complete science, technology, engineering and mathematics (STEM) doctoral programs has not changed greatly over the last few decades and still relies heavily on Graduate Record Examination (GRE) scores in most U.S. universities. It has been long debated whether the GRE is an appropriate selection tool and whether overreliance on GRE scores may compromise admission of students historically underrepresented in STEM. Despite many concerns about the test, there are few studies examining the efficacy of the GRE in predicting PhD completion and even fewer examining this question in STEM fields. For the present study, we took advantage of a long-lived collaboration among institutions in the Northeast Alliance for Graduate Education and the Professoriate (NEAGEP) to gather comparable data on GRE scores and PhD completion for 1805 U.S./Permanent Resident STEM doctoral students in four state flagship institutions. We found that GRE Verbal (GRE V) and GRE Quantitative (GRE Q) scores were similar for women who completed STEM PhD degrees and those who left programs. Remarkably, GRE scores were significantly higher for men who left than counterparts who completed STEM PhD degrees. In fact, men in the lower quartiles of GRE V or Q scores finished degrees more often than those in the highest quartile. This pattern held for each of the four institutions in the study and for the cohort of male engineering students across institutions. GRE scores also failed to predict time to degree or to identify students who would leave during the first year of their programs. Our results suggests that GRE scores are not an effective tool for identifying students who will be successful in completing STEM doctoral programs. Considering the high cost of attrition from PhD programs and its impact on future leadership for the U.S. STEM workforce, we suggest that it is time to develop more effective and inclusive admissions strategies.

A SIMULATION-BASED FRAMEWORK FOR ASSESSING THE FEASIBILITY OF RESPONDENT-DRIVEN SAMPLING FOR ESTIMATING CHARACTERISTICS IN POPULATIONS OF LESBIAN, GAY AND BISEXUAL OLDER ADULTS.

Respondent-driven sampling (RDS) is a method for sampling from a target population by leveraging social connections. RDS is invaluable to the study of hard-to-reach populations. However, RDS is costly and can be infeasible. RDS is infeasible when RDS point estimators have small effective sample sizes (large design effects) or when RDS interval estimators have large confidence intervals relative to estimates obtained in previous studies or poor coverage. As a result, researchers need tools to assess whether or not estimation of certain characteristics of interest for specific populations is feasible in advance. In this paper, we develop a simulation-based framework for using pilot data-in the form of a convenience sample of aggregated, egocentric data and estimates of subpopulation sizes within the target population-to assess whether or not RDS is feasible for estimating characteristics of a target population. in doing so, we assume that more is known about egos than alters in the pilot data, which is often the case with aggregated, egocentric data in practice. We build on existing methods for estimating the structure of social networks from aggregated, egocentric sample data and estimates of subpopulation sizes within the target population. We apply this framework to assess the feasibility of estimating the proportion male, proportion bisexual, proportion depressed and proportion infected with HIV/AIDS within three spatially distinct target populations of older lesbian, gay and bisexual adults using pilot data from the caring and Aging with Pride Study and the Gallup Daily Tracking Survey. We conclude that using an RDS sample of 300 subjects is infeasible for estimating the proportion male, but feasible for estimating the proportion bisexual, proportion depressed and proportion infected with HIV/AIDS in all three target populations.

High enhancer, downer, withdrawal helper: Multifunctional nonmedical benzodiazepine use among young adult opioid users in New York City.

BACKGROUND: Benzodiazepines are a widely prescribed psychoactive drug; in the U.S., both medical and nonmedical use of benzodiazepines has increased markedly in the past 15 years. Long-term use can lead to tolerance and dependence, and abrupt withdrawal can cause seizures or other life-threatening symptoms. Benzodiazepines are often used nonmedically in conjunction with other drugs, and with opioids in particular-a combination that can increase the risk for fatal and non-fatal overdose. This mixed-methods study examines nonmedical use of benzodiazepines among young adults in New York City and its relationship with opioid use. METHODS: For qualitative analysis, 46 90-minute semi-structured interviews were conducted with young adult opioid users (ages 18-32). Interviews were transcribed and coded for key themes. For quantitative analysis, 464 young adult opioid users (ages 18-29) were recruited using Respondent-Driven Sampling and completed structured interviews. Benzodiazepine use was assessed via a self-report questionnaire that included measures related to nonmedical benzodiazepine and opioid use. RESULTS: Participants reported using benzodiazepines nonmedically for a wide variety of reasons, including: to increase the high of other drugs; to lessen withdrawal symptoms; and to come down from other drugs. Benzodiazepines were described as readily available and cheap. There was a high prevalence (93%) of nonmedical benzodiazepine use among nonmedical opioid users, with 57% reporting regular nonmedical use. In bivariate analyses, drug-related risk behaviours such as polysubstance use, drug binging, heroin injection and overdose were strongly associated with regular nonmedical benzodiazepine use. In multivariate analysis, growing up in a middle-income household (earning between $51,000 and $100,000 annually), lifetime overdose experience, having ever used cocaine regularly, having ever been prescribed benzodiazepines, recent drug binging, and encouraging fellow drug users to use benzodiazepines to cope with opioid withdrawal were consistently strong predictors of regular nonmedical benzodiazepine use. CONCLUSION: Nonmedical benzodiazepine use may be common among nonmedical opioid users due to its drug-related multi-functionality. Harm reduction messages should account for the multiple functions benzodiazepines serve in a drug-using context, and encourage drug users to tailor their endorsement of benzodiazepines to peers to include safer alternatives.

Analysis of Networks with Missing Data with Application to the National Longitudinal Study of Adolescent Health.

It is common in the analysis of social network data to assume a census of the networked population of interest. Often the observations are subject to partial observation due to a known sampling or unknown missing data mechanism. However, most social network analysis ignores the problem of missing data by including only actors with complete observations. In this paper we address the modeling of networks with missing data, developing previous ideas in missing data, network modeling, and network sampling. We use several methods including the mean value parameterization to show the quantitative and substantive differences between naive and principled modeling approaches. We also develop goodness-of-fit techniques to better understand model fit. The ideas are motivated by an analysis of a friendship network from the National Longitudinal Study of Adolescent Health.

Bayesian Peer Calibration with Application to Alcohol Use.

Peers are often able to provide important additional information to supplement self-reported behavioral measures. The study motivating this work collected data on alcohol in a social network formed by college students living in a freshman dormitory. By using two imperfect sources of information (self-reported and peer-reported alcohol consumption), rather than solely self-reports or peer-reports, we are able to gain insight into alcohol consumption on both the population and the individual level, as well as information on the discrepancy of individual peer-reports. We develop a novel Bayesian comparative calibration model for continuous, count, and binary outcomes that uses covariate information to characterize the joint distribution of both self and peer-reports on the network for estimating peer-reporting discrepancies in network surveys, and apply this to the data for fully Bayesian inference. We use this model to understand the effects of covariates on both drinking behavior and peer-reporting discrepancies. Copyright © 2016 John Wiley & Sons, Ltd.

Network Model-Assisted Inference from Respondent-Driven Sampling Data.

Respondent-Driven Sampling is a widely-used method for sampling hard-to-reach human populations by link-tracing over their social networks. Inference from such data requires specialized techniques because the sampling process is both partially beyond the control of the researcher, and partially implicitly defined. Therefore, it is not generally possible to directly compute the sampling weights for traditional design-based inference, and likelihood inference requires modeling the complex sampling process. As an alternative, we introduce a model-assisted approach, resulting in a design-based estimator leveraging a working network model. We derive a new class of estimators for population means and a corresponding bootstrap standard error estimator. We demonstrate improved performance compared to existing estimators, including adjustment for an initial convenience sample. We also apply the method and an extension to the estimation of HIV prevalence in a high-risk population.

Estimating the size of populations at high risk for HIV using respondent-driven sampling data.

The study of hard-to-reach populations presents significant challenges. Typically, a sampling frame is not available, and population members are difficult to identify or recruit from broader sampling frames. This is especially true of populations at high risk for HIV/AIDS. Respondent-driven sampling (RDS) is often used in such settings with the primary goal of estimating the prevalence of infection. In such populations, the number of people at risk for infection and the number of people infected are of fundamental importance. This article presents a case-study of the estimation of the size of the hard-to-reach population based on data collected through RDS. We study two populations of female sex workers and men-who-have-sex-with-men in El Salvador. The approach is Bayesian and we consider different forms of prior information, including using the UNAIDS population size guidelines for this region. We show that the method is able to quantify the amount of information on population size available in RDS samples. As separate validation, we compare our results to those estimated by extrapolating from a capture-recapture study of El Salvadorian cities. The results of our case-study are largely comparable to those of the capture-recapture study when they differ from the UNAIDS guidelines. Our method is widely applicable to data from RDS studies and we provide a software package to facilitate this.

Diagnostics for Respondent-driven Sampling.

Respondent-driven sampling (RDS) is a widely used method for sampling from hard-to-reach human populations, especially populations at higher risk for HIV. Data are collected through peer-referral over social networks. RDS has proven practical for data collection in many difficult settings and is widely used. Inference from RDS data requires many strong assumptions because the sampling design is partially beyond the control of the researcher and partially unobserved. We introduce diagnostic tools for most of these assumptions and apply them in 12 high risk populations. These diagnostics empower researchers to better understand their data and encourage future statistical research on RDS.

Estimating hidden population size using Respondent-Driven Sampling data.

Respondent-Driven Sampling (RDS) is n approach to sampling design and inference in hard-to-reach human populations. It is often used in situations where the target population is rare and/or stigmatized in the larger population, so that it is prohibitively expensive to contact them through the available frames. Common examples include injecting drug users, men who have sex with men, and female sex workers. Most analysis of RDS data has focused on estimating aggregate characteristics, such as disease prevalence. However, RDS is often conducted in settings where the population size is unknown and of great independent interest. This paper presents an approach to estimating the size of a target population based on data collected through RDS. The proposed approach uses a successive sampling approximation to RDS to leverage information in the ordered sequence of observed personal network sizes. The inference uses the Bayesian framework, allowing for the incorporation of prior knowledge. A flexible class of priors for the population size is used that aids elicitation. An extensive simulation study provides insight into the performance of the method for estimating population size under a broad range of conditions. A further study shows the approach also improves estimation of aggregate characteristics. Finally, the method demonstrates sensible results when used to estimate the size of known networked populations from the National Longitudinal Study of Adolescent Health, and when used to estimate the size of a hard-to-reach population at high risk for HIV.

Respondent-Driven Sampling: An Assessment of Current Methodology.

Respondent-Driven Sampling (RDS) employs a variant of a link-tracing network sampling strategy to collect data from hard-to-reach populations. By tracing the links in the underlying social network, the process exploits the social structure to expand the sample and reduce its dependence on the initial (convenience) sample.The current estimators of population averages make strong assumptions in order to treat the data as a probability sample. We evaluate three critical sensitivities of the estimators: to bias induced by the initial sample, to uncontrollable features of respondent behavior, and to the without-replacement structure of sampling.Our analysis indicates: (1) that the convenience sample of seeds can induce bias, and the number of sample waves typically used in RDS is likely insufficient for the type of nodal mixing required to obtain the reputed asymptotic unbiasedness; (2) that preferential referral behavior by respondents leads to bias; (3) that when a substantial fraction of the target population is sampled the current estimators can have substantial bias.This paper sounds a cautionary note for the users of RDS. While current RDS methodology is powerful and clever, the favorable statistical properties claimed for the current estimates are shown to be heavily dependent on often unrealistic assumptions. We recommend ways to improve the methodology.

MODELING SOCIAL NETWORKS FROM SAMPLED DATA.

Network models are widely used to represent relational information among interacting units and the structural implications of these relations. Recently, social network studies have focused a great deal of attention on random graph models of networks whose nodes represent individual social actors and whose edges represent a specified relationship between the actors. Most inference for social network models assumes that the presence or absence of all possible links is observed, that the information is completely reliable, and that there are no measurement (e.g., recording) errors. This is clearly not true in practice, as much network data is collected though sample surveys. In addition even if a census of a population is attempted, individuals and links between individuals are missed (i.e., do not appear in the recorded data). In this paper we develop the conceptual and computational theory for inference based on sampled network information. We first review forms of network sampling designs used in practice. We consider inference from the likelihood framework, and develop a typology of network data that reflects their treatment within this frame. We then develop inference for social network models based on information from adaptive network designs. We motivate and illustrate these ideas by analyzing the effect of link-tracing sampling designs on a collaboration network.

A Framework for the Comparison of Maximum Pseudo Likelihood and Maximum Likelihood Estimation of Exponential Family Random Graph Models.

The statistical modeling of social network data is difficult due to the complex dependence structure of the tie variables. Statistical exponential families of distributions provide a flexible way to model such dependence. They enable the statistical characteristics of the network to be encapsulated within an exponential family random graph (ERG) model. For a long time, however, likelihood-based estimation was only feasible for ERG models assuming dyad independence. For more realistic and complex models inference has been based on the pseudo-likelihood. Recent advances in computational methods have made likelihood-based inference practical, and comparison of the different estimators possible.In this paper, we present methodology to enable estimators of ERG model parameters to be compared. We use this methodology to compare the bias, standard errors, coverage rates and efficiency of maximum likelihood and maximum pseudo-likelihood estimators. We also propose an improved pseudo-likelihood estimation method aimed at reducing bias. The comparison is performed using simulated social network data based on two versions of an empirically realistic network model, the first representing Lazega's law firm data and the second a modified version with increased transitivity. The framework considers estimation of both the natural and the mean-value parameters.The results clearly show the superiority of the likelihood-based estimators over those based on pseudo-likelihood, with the bias-reduced pseudo-likelihood out-performing the general pseudo-likelihood. The use of the mean value parameterization provides insight into the differences between the estimators and when these differences will matter in practice.

Developmental Contexts and Mental Disorders Among Asian Americans.

In this article, we use age of immigration as a proxy for the developmental context for understanding the association between immigration experiences and mental health. Generation defines the context under which immigrants arrive in the United States. We drew data from the National Latino and Asian American Study (N = 2,095), the first ever study conducted on the mental health of a national sample of Asian Americans. Our findings reveal that age of immigration is linked to lifetime and 12-month rates of psychiatric disorder: Immigrants who arrive earlier in life are more likely to have both lifetime and 12-month disorders. U.S. born and immigrants who arrive as children are much more likely to have a mental disorder in their lifetimes than other immigrant generations.