Faculty Bibliography

Repeated exposure to the acute pro-inflammatory environment that follows ovulation at the ovarian surface and distal fallopian tube over a woman's reproductive years may increase ovarian cancer risk. To address this, analyses included individual-level data from 558,709 naturally menopausal women across 20 prospective cohorts, among whom 3,246 developed invasive epithelial ovarian cancer (2045 serous, 319 endometrioid, 184 mucinous, 121 clear cell, 577 other/unknown). Cox models were used to estimate multivariable-adjusted hazard ratios (HR) between lifetime ovulatory cycles (LOC) and its components and ovarian cancer risk overall and by histotype. Women in the 90th percentile of LOC (>514 cycles) were almost twice as likely to be diagnosed with ovarian cancer than women in the 10th percentile (<294) [HR (95% confidence interval): 1.92 (1.60-2.30)]. Risk increased 14% per five-year increase in LOC (60 cycles) [(1.10-1.17)]; this association remained after adjustment for LOC components: number of pregnancies and oral contraceptive use [1.08 (1.04-1.12)]. The association varied by histotype, with increased risk of serous [1.13 (1.09-1.17)], endometrioid [1.20 (1.10-1.32)], and clear cell [1.37 (1.18-1.58)], but not mucinous [0.99 (0.88-1.10), P-heterogeneity=0.01] tumors. Heterogeneity across histotypes was reduced [P-heterogeneity=0.15] with adjustment for LOC components [1.08 serous, 1.11 endometrioid, 1.26 clear cell, 0.94 mucinous]. Although the 10-year absolute risk of ovarian cancer is small, it roughly doubles as the number of LOC rises from ~300 to 500. The consistency and linearity of effects strongly support the hypothesis that each ovulation leads to small increases in the risk of most ovarian cancers, a risk which cumulates through life, suggesting this as an important area for identifying intervention strategies.

BACKGROUND:"Energy drinks" are heavily marketed to the general public, across the age spectrum. The efficacy of decaffeinated energy drinks in enhancing subjective feelings of energy (s-energy) is controversial. OBJECTIVE:The authors sought to test the efficacy of the caffeine-free version of a popular energy drink compared with a placebo drink. METHODS:This study was a randomized, double-blind, placebo-controlled, crossover trial in 223 healthy men and women aged 18-70 y with intention-to-treat and completers analysis. Participants were randomly assigned to consumption of either the decaffeinated energy drink or a placebo drink on testing day 1, and the other drink a week later. A battery of computer-based mood and cognitive tests to assess s-energy was conducted at baseline and at 0.5, 2.5, and 5 h post-ingestion. The main outcome measures were 1) mood, which was assessed by using a General Status Check Scale and the Profile of Mood States 2nd edition brief form, and 2) cognitive measures, including the N-back task (reaction time and accuracy), Reaction Time test, Flanker task (distraction avoidance), and Rapid Visual Information Processing test. RESULTS:No statistically significant or meaningful benefits were observed for any outcome measure, including mood and cognitive measures. Analyses of mean differences, slopes, and median differences were consistent. CONCLUSIONS:No differences were detected across a range of mood/cognitive/behavioral/s-energy-level tests after consumption of the energy drink compared with a placebo drink in this diverse sample of adults. Thus, we found strong evidence that the energy drink is not efficacious in enhancing s-energy levels, nor any related cognitive or behavioral variables measured. In light of federal regulations, these findings suggest that labeling and marketing of some products which claim to provide these benefits may be unsubstantiated. This trial was registered at www.clinicaltrials.gov as NCT02727920.

Social media have created a new environmental context for the study of social and human behavior and services. Although social work researchers have become increasingly interested in the use of social media to address social problems, they have been slow to adapt tools that are flexible and convenient for analyzing social media data. They have also given inadequate attention to bias and representation inherent in many multimedia data sets. This article introduces the Visual and Textual Analysis of Social Media (VATAS) system, an open-source Web-based platform for labeling or annotating social media data. We use a case study approach, applying VATAS to a study of Chicago, IL, gang-involved youth communication on Twitter to highlight VATAS"™ features and opportunities for interdisciplinary collaboration. VATAS is highly customizable, can be privately held on a secure server, and allows for export directly into a CSV file for qualitative, quantitative, and machine-learning analysis. Implications for research using social media sources are noted.

Background: A vast amount of research has demonstrated the numerous adverse health risks of short sleep duration and poor sleep health among the general population, and increasing studies have been conducted among lesbian, gay, and bisexual individuals. However, although poor sleep health is disproportionately experienced by sexual and gender minority populations, little research has examined sleep quality and associated factors among transgender and gender nonbinary (TGNB) individuals. This study qualitatively explored the relationship that factors such as gender identity, mental health, and substance use have with sleep health among a sample of TGNB individuals in New York City. Methods: Forty in-depth interviews were conducted among an ethnically diverse sample who identified as transgender male, transgender female, and gender nonbinary from July to August 2017. All interviews were transcribed, coded, and thematically analyzed for domains affecting overall sleep, including mental health, gender identity, and various coping mechanisms to improve overall sleep. Results: TGNB interview participants frequently described one or more problems with sleeping. Some (15%) participants suggested that mental health issues caused them to have difficulty falling asleep, but that psychiatric medication was effective in reducing mental health issues and allowing them to sleep. An even larger number (35%) told us that their gender identity negatively impacted their sleep. Specifically, participants described that the presence of breasts, breast binding, stress and anxiety about their identity, and concerns about hormonal therapy and gender-affirming surgery were all reported as contributing to sleep problems. Given these sleep challenges, it is not surprising that most (60%) participants used various strategies to cope with and manage their sleep problems, including prescription and over-the-counter sleep medications (33%) and marijuana (18%). Conclusions: Our findings document that sleep health is frequently an issue for TGNB individuals, and they also offer insight into the various ways that TGNB individuals attempt to cope with these sleep problems. Sleep health promotion interventions should be developed for TGNB people, which would promote positive mental health, reduce the risk of pharmaceutical adverse events, and help alleviate psychosocial stress in this target population.

INTRODUCTION AND AIMS/OBJECTIVE:This article examines the feasibility of leveraging Twitter to detect posts authored by people who use opioids (PWUO) or content related to opioid use disorder (OUD), and manually develop a multidimensional taxonomy of relevant tweets. DESIGN AND METHODS/METHODS:Twitter messages were collected between June and October 2017 (n = 23 827) and evaluated using an inductive coding approach. Content was then manually classified into two axes (n = 17 420): (i) user experience regarding accessing, using, or recovery from illicit opioids; and (ii) content categories (e.g. policies, medical information, jokes/sarcasm). RESULTS:The most prevalent categories consisted of jokes or sarcastic comments pertaining to OUD, PWUOs or hypothetically using illicit opioids (63%), informational content about treatments for OUD, overdose prevention or accessing self-help groups (20%), and commentary about government opioid policy or news related to opioids (17%). Posts by PWUOs centered on identifying illicit sources for procuring opioids (i.e. online, drug dealers; 49%), symptoms and/or strategies to quell opioid withdrawal symptoms (21%), and combining illicit opioid use with other substances, such as cocaine or benzodiazepines (17%). State and public health experts infrequently posted content pertaining to OUD (1%). DISCUSSION AND CONCLUSIONS/CONCLUSIONS:Twitter offers a feasible approach to identify PWUO. Further research is needed to evaluate the efficacy of Twitter to disseminate evidence-based content and facilitate linkage to treatment and harm reduction services.

Global usage of the social media (SoME) has increased exponentially. Facebook has close to 2.4 billion users, Twitter 330 million, YouTube 2 billion viewers each month, Instagram 1 billion active users, and LinkedIn 310 million users. This represents increases of 75%, 16%, 90%, 500%, and 19% respectively for these platforms over the past five years (1).

BACKGROUND:The effective detection and monitoring of potentially malignant oral lesions (PMOL) are critical to identifying early-stage cancer and improving outcomes. In the current study, the authors described cytopathology tools, including machine learning algorithms, clinical algorithms, and test reports developed to assist pathologists and clinicians with PMOL evaluation. METHODS:Data were acquired from a multisite clinical validation study of 999 subjects with PMOLs and oral squamous cell carcinoma (OSCC) using a cytology-on-a-chip approach. A machine learning model was trained to recognize and quantify the distributions of 4 cell phenotypes. A least absolute shrinkage and selection operator (lasso) logistic regression model was trained to distinguish PMOLs and cancer across a spectrum of histopathologic diagnoses ranging from benign, to increasing grades of oral epithelial dysplasia (OED), to OSCC using demographics, lesion characteristics, and cell phenotypes. Cytopathology software was developed to assist pathologists in reviewing brush cytology test results, including high-content cell analyses, data visualization tools, and results reporting. RESULTS:Cell phenotypes were determined accurately through an automated cytological assay and machine learning approach (99.3% accuracy). Significant differences in cell phenotype distributions across diagnostic categories were found in 3 phenotypes (type 1 ["mature squamous"], type 2 ["small round"], and type 3 ["leukocytes"]). The clinical algorithms resulted in acceptable performance characteristics (area under the curve of 0.81 for benign vs mild dysplasia and 0.95 for benign vs malignancy). CONCLUSIONS:These new cytopathology tools represent a practical solution for rapid PMOL assessment, with the potential to facilitate screening and longitudinal monitoring in primary, secondary, and tertiary clinical care settings.

BACKGROUND:Despite considerable achievements associated with the MDGs, under-five mortality, particularly in Sub-Saharan Africa, remains alarmingly high. Globally, intimate partner violence (IPV) affects one in three women within their lifetime. Little is known about the relationship between IPV and maternal care-seeking in the context of high rates of under-five mortality, particularly among young women and adolescent girls in low- and middle-income countries (LMICs). METHODS:Data from the Kenya Demographic Health Survey (2008-2009) were limited to a sample of women aged 15-24 years (n=1,406) with a child under-five who had experienced IPV in the last 12 months. Using multivariate logistic regression, we constructed three models: 1) base model; 2) controlling for type of residence (urban/rural); and 3) controlling for wealth status and education attainment, to estimate odds ratios (ORs) for the association between IPV and ten maternal care-seeking behaviors. RESULTS:Thirty-eight percent of the women had experienced some form of intimate partner violence in the last 12 months. Women who had experience IPV were less likely: 1) to complete a minimum of 4 antenatal visits after single IPV exposure (OR=0.61, 95% CI=0.44, 0.86 and after severe IPV (OR=0.80; 95% CI=0.44, 0.88) and 2) to deliver in health facility after severe IPV exposure (OR=0.74; 95% CI=0.54, 0.89), both adjusted for educational attainment and wealth status. Lower socio-economic status and living in a rural area were strongly associated with increased likelihood of IPV. CONCLUSIONS:Intersectional approaches that consciously focus on, and creatively address IPV may be key to the success of reducing child mortality and improving maternal health outcomes. The implementation of joint programming and development of combination interventions to effectively reduce the risk of exposure to IPV and promote maternal care-seeking behavior are needed to improve child morbidity and mortality in LMICs.

OBJECTIVE:The Fort Campbell Cohort study was designed to assess predeployment biological and behavioral markers and build predictive models to identify risk and resilience for posttraumatic stress disorder (PTSD) following deployment. This article addresses neurocognitive functioning variables as potential prospective predictors. METHOD/METHODS:In a sample of 403 soldiers, we examined whether PTSD symptom severity (using the PTSD Checklist) as well as posttraumatic stress trajectories could be prospectively predicted by measures of executive functioning (using two web-based tasks from WebNeuro) assessed predeployment. RESULTS:Controlling for age, gender, education, prior number of deployments, childhood trauma exposure, and PTSD symptom severity at Phase 1, linear regression models revealed that predeployment sustained attention and inhibitory control performance were significantly associated with postdeployment PTSD symptom severity. We also identified two posttraumatic stress trajectories utilizing latent growth mixture models. The "resilient" group consisted of 90.9% of the soldiers who exhibited stable low levels of PTSD symptoms from pre- to postdeployment. The "increasing" group consisted of 9.1% of the soldiers, who exhibited an increase in PTSD symptoms following deployment, crossing a threshold for diagnosis based on PTSD Checklist scores. Logistic regression models predicting trajectory revealed a similar pattern of findings as the linear regression models, in which predeployment sustained attention (95% CI of odds ratio: 1.0109, 1.0558) and inhibitory control (95% CI: 1.0011, 1.0074) performance were significantly associated with postdeployment PTSD trajectory. CONCLUSIONS:These findings have clinical implications for understanding the pathogenesis of PTSD and building preventative programs for military personnel. (PsycINFO Database Record (c) 2019 APA, all rights reserved).