Validation of the Enhanced Opioid Identification and Co-occurring Disorders Algorithms.

Objectives This report documents the results of a validation study conducted to assess the reliability of two algorithms applied to the 2016 National Hospital Care Survey. One algorithm identifies opioid-involved and opioid overdose hospital encounters, and the other identifies encounters with patients that have substance use disorders and selected mental health issues. These algorithms use both medical codes and natural language processing to identify encounters. Methods To validate the algorithms, medical record abstraction was performed on a stratified sample of 900 hospital encounters from the 2016 National Hospital Care Survey. The abstractors recorded their determinations of opioid involvement, opioid overdose, substance use disorder, and mental health issues on a standard form. Abstractors' determinations were compared with algorithm output to assess the overall performance using F-score and Matthews correlation coefficient. The latter provided a secondary measure of performance. The 2016 National Hospital Care Survey data are unweighted and not nationally representative. Results Overall algorithm performance varied by topic and by metric. The opioid-involvement algorithm achieved the highest performance, performing well with an F-score of 0.95, followed by the substance use disorder algorithm (F-score of 0.79), the mental health issues algorithm (F-score of 0.68), and the opioid overdose algorithm (F-score of 0.48). Assessment by Matthews correlation coefficient indicated an overall poorer level of performance, ranging from a high of 0.57 for the mental health issues algorithm to a low of 0.33 for the opioid-involvement algorithm. The causes of false positives and false negatives likewise varied, including both overly broad code and keyword inclusions as well as incompleteness of data submitted to the National Hospital Care Survey. Conclusion The validation study illustrates which aspects of the developed algorithms performed well and which aspects should be altered or discarded in future iterations. It further emphasizes the importance of data completeness, therefore laying the groundwork for improvements to future survey analyses.

Social media emotions annotation guide (SMEmo): Development and initial validity.

The proper measurement of emotion is vital to understanding the relationship between emotional expression in social media and other factors, such as online information sharing. This work develops a standardized annotation scheme for quantifying emotions in social media using recent emotion theory and research. Human annotators assessed both social media posts and their own reactions to the posts' content on scales of 0 to 100 for each of 20 (Study 1) and 23 (Study 2) emotions. For Study 1, we analyzed English-language posts from Twitter (N = 244) and YouTube (N = 50). Associations between emotion ratings and text-based measures (LIWC, VADER, EmoLex, NRC-EIL, Emotionality) demonstrated convergent and discriminant validity. In Study 2, we tested an expanded version of the scheme in-country, in-language, on Polish (N = 3648) and Lithuanian (N = 1934) multimedia Facebook posts. While the correlations were lower than with English, patterns of convergent and discriminant validity with EmoLex and NRC-EIL still held. Coder reliability was strong across samples, with intraclass correlations of .80 or higher for 10 different emotions in Study 1 and 16 different emotions in Study 2. This research improves the measurement of emotions in social media to include more dimensions, multimedia, and context compared to prior schemes.

Identifying Co-occurring Disorders Among Patients With an Opioid involved Hospital Encounter Using National Hospital Care Survey Data.

This report documents the development of the 2016 National Hospital Care Survey (NHCS) Co-occurring Disorders Algorithm, which can be used to identify patients with an opioid-involved hospital encounter who had lifetime diagnoses of both a substance use disorder and a selected mental health issue. Lifetime diagnoses are defined as diagnoses at any point in the past or during the current encounter. This algorithm was created to complement the earlier NHCS Enhanced Opioid Identification Algorithm designed to improve the classification of patients with opioid-involved hospital encounters.

Transactions between Problem Behaviors and Academic Performance in Early Childhood.

This study aimed to further the understanding of transactional relationships that exist between problem behaviors and academic performance in early childhood. Early academic and behavior difficulties increase the risk of school disengagement, academic failure, and dropout. Although children's academic and behavioral difficulties have been shown to be intercorrelated, little research has focused on how the relationship reciprocates and progresses in early childhood. This study investigated how problem behaviors (i.e., externalizing and internalizing) influence and are influenced by academic performance (i.e., poor reading and math) from kindergarten to third grade. Participants included 18,135 students (51.22% boys) derived from a nationally representative sample in the Early Childhood Longitudinal Study, Kindergarten Class of 2011 (ECLS-K: 2011). Teacher ratings of children's internalizing (low self-esteem, anxiety, loneliness, or sadness) and externalizing (fighting, arguing, showing anger, impulsively acting, and disruptive behaviors) problem behaviors, as well as direct assessments of children's academic performance (reading and math), were collected yearly. Cross-lagged panel modeling (CLPM) was employed to examine reciprocal relationships between problem behaviors and academic performance over time from kindergarten to third grade. The results supported the transactional relationships in early childhood, with higher externalizing as well as internalizing problem behaviors predicting lower academic performance and lower academic performance predicting higher externalizing and internalizing problem behaviors. The implications for research, prevention, and early intervention regarding the progression of academic and behavioral problems are discussed.

National Hospital Care Survey Demonstration Projects:Characteristics and Mortality Outcomes of Opioid-involved Hospital Encounters With Co-occurring Disorders.

This report demonstrates the use of National Hospital Care Survey (NHCS) data to describe characteristics of patients experiencing opioid-involved hospital encounters with co-occurring disorders, defined as lifetime diagnoses of both a substance use disorder (SUD) and a selected mental health issue (MHI), that is, diagnosed at any point in the past or during the present encounter.

Enhancing Identification of Opioid-involved Health Outcomes Using National Hospital Care Survey Data.

Objectives This report documents the development of the 2016 National Hospital Care Survey (NHCS) Enhanced Opioid Identification Algorithm, an algorithm that can be used to identify opioid-involved and opioid overdose hospital encounters. Additionally, the algorithm can be used to identify opioids and opioid antagonists that can be used to reverse opioid overdose (naloxone) and to treat opioid use disorder (naltrexone).

The Association Between State-Level Racial Attitudes Assessed From Twitter Data and Adverse Birth Outcomes: Observational Study.

BACKGROUND: In the United States, racial disparities in birth outcomes persist and have been widening. Interpersonal and structural racism are leading explanations for the continuing racial disparities in birth outcomes, but research to confirm the role of racism and evaluate trends in the impact of racism on health outcomes has been hampered by the challenge of measuring racism. Most research on discrimination relies on self-reported experiences of discrimination, and few studies have examined racial attitudes and bias at the US national level. OBJECTIVE: This study aimed to investigate the associations between state-level Twitter-derived sentiments related to racial or ethnic minorities and birth outcomes. METHODS: We utilized Twitter's Streaming application programming interface to collect 26,027,740 tweets from June 2015 to December 2017, containing at least one race-related term. Sentiment analysis was performed using support vector machine, a supervised machine learning model. We constructed overall indicators of sentiment toward minorities and sentiment toward race-specific groups. For each year, state-level Twitter-derived sentiment data were merged with birth data for that year. The study participants were women who had singleton births with no congenital abnormalities from 2015 to 2017 and for whom data were available on gestational age (n=9,988,030) or birth weight (n=9,985,402). The main outcomes were low birth weight (birth weight ≤2499 g) and preterm birth (gestational age <37 weeks). We estimated the incidence ratios controlling for individual-level maternal characteristics (sociodemographics, prenatal care, and health behaviors) and state-level demographics, using log binomial regression models. RESULTS: The accuracy for identifying negative sentiments on comparing the machine learning model to manually labeled tweets was 91%. Mothers living in states in the highest tertile for negative sentiment tweets referencing racial or ethnic minorities had greater incidences of low birth weight (8% greater, 95% CI 4%-13%) and preterm birth (8% greater, 95% CI 0%-14%) compared with mothers living in states in the lowest tertile. More negative tweets referencing minorities were associated with adverse birth outcomes in the total population, including non-Hispanic white people and racial or ethnic minorities. In stratified subgroup analyses, more negative tweets referencing specific racial or ethnic minority groups (black people, Middle Eastern people, and Muslims) were associated with poor birth outcomes for black people and minorities. CONCLUSIONS: A negative social context related to race was associated with poor birth outcomes for racial or ethnic minorities, as well as non-Hispanic white people.

Twitter-Characterized Sentiment Towards Racial/Ethnic Minorities and Cardiovascular Disease (CVD) Outcomes.

Sentiments towards racial/ethnic minorities may impact cardiovascular disease (CVD) through direct and indirect pathways. In this study, we assessed the association between Twitter-derived sentiments towards racial/ethnic minorities at state-level and individual-level CVD-related outcomes from the 2017 Behavioral Risk Factor Surveillance System (BRFSS). Outcomes included hypertension, diabetes, obesity, stroke, myocardial infarction (MI), coronary heart disease (CHD), and any CVD from BRFSS 2017 (N = 433,434 to 433,680 across outcomes). A total of 30 million race-related tweets were collected using Twitter Streaming Application Programming Interface (API) from 2015 to 2018. Prevalence of negative and positive sentiment towards racial/ethnic minorities were constructed at the state level and merged with CVD outcomes. Poisson regression was used, and all the models adjusted for individual-level demographics as well as state-level demographics. Individuals living in states with the highest level of negative sentiment towards racial/ethnic minorities had 11% higher prevalence of hypertension (PR 1.11, 95% CI 1.08, 1.14), 15% higher prevalence of diabetes (PR 1.15, 95% CI 1.08, 1.22), 14% higher prevalence of obesity (PR 1.14, 95% CI 1.10, 1.18), 30% higher prevalence of stroke (PR 1.30, 95% CI 1.16, 1.46), 14% higher prevalence of MI (PR 1.14, 95% CI 1.03, 1.25), 9% higher prevalence of CHD (PR 1.09, 95% CI 1.00, 1.19), and 16% higher prevalence of any CVD outcomes (PR 1.16, 95% CI 1.09, 1.24). Conversely, Twitter-derived positive sentiment towards racial/ethnic minorities was associated with a lower prevalence of CVD outcomes. Programs and policies that promote racially inclusive environments may improve population health.

Effects of three zinc-containing sunscreens on development of purple sea urchin (Strongylocentrotus purpuratus) embryos.

The growing popularity of physical sunscreens will lead to an increased release of ingredients from zinc oxide (ZnO) sunscreens into marine environments. Though zinc (Zn) is a necessary micronutrient in the ocean, greater than natural Zn concentrations may be released into marine environments by use of sunscreens. The extent of the consequences of this addition of Zn to the ocean are not fully understood. We investigated the effects of materials released by ZnO- sunscreens on the development of California purple sea urchin, Strongylocentrotus purpuratus. Embryos incubated in various concentrations of Zn (0.01, 0.05, 0.1, 0.5, and 1 mg/L), the sources of which included zinc-containing compounds: ZnO and zinc sulfate (ZnSO4); and ZnO sunscreens: All Good, Badger, and Raw Elements brands. Based on EC50 values, ZnO-containing sunscreens were slightly, but not significantly, more toxic than ZnO and ZnSO4, suggesting that sunscreens may release additional unknown materials that are detrimental to sea urchin embryo development. All concentrations of Zn-exposure resulted in significant malformations (skeletal abnormality, stage arrest, axis determination disruption), which were identified using light and fluorescence confocal microscopy. The concentration of Zn2+ internalized by the developing embryos correlated positively with the concentration of Zn in seawater. Additionally, exposure to both ZnO sunscreens and ZnO and ZnSO4 at 1 mg/L Zn, significantly increased calcein-AM (CAM) accumulation, indicating decreased multidrug resistant (MDR) transporter activity. This is one of the first studies documenting ZnO-containing sunscreens release high concentrations of Zn that are internalized by and have detrimental effects on aquatic organisms.

Procuring animals and culturing of eggs and embryos.

Echinoderms and especially echinoids have a rich history as model systems for the study of oogenesis, fertilization, and early embryogenesis. The ease of collecting and maintaining adults, as well as in obtaining gametes and culturing large quantities of synchronous embryos, is complemented by the ability to do biochemistry, reverse genetics, embryo manipulations and study gene regulatory networks. The diversity of species and developmental modes as well as unparalleled transparency in early developmental stages also makes echinoderms an excellent system in which to study evolutionary aspects of developmental biology. This chapter provides a practical guide to experimental methods for procuring adults and gametes, achieving synchronous in vitro fertilization, and culturing embryos through early larval stages for several echinoderm species representing four classes (Echinoidea, Asteroidea, Ophiuroidea, and Holothuroidea). We provide specific examples of protocols for obtaining adults and gametes and for culturing embryos of a selected number of species for experimental analysis of their development. The species were chosen to provide breadth across the phylum Echinodermata, as well as to provide practical guidelines for handling some of the more commonly studied species. For each species, we highlight specific advantages, and special note is made of key issues to consider when handling adults, collecting gametes, or setting and maintaining embryo cultures. Finally, information regarding interspecific crosses is provided.

Detecting Novel and Emerging Drug Terms Using Natural Language Processing: A Social Media Corpus Study.

BACKGROUND: With the rapid development of new psychoactive substances (NPS) and changes in the use of more traditional drugs, it is increasingly difficult for researchers and public health practitioners to keep up with emerging drugs and drug terms. Substance use surveys and diagnostic tools need to be able to ask about substances using the terms that drug users themselves are likely to be using. Analyses of social media may offer new ways for researchers to uncover and track changes in drug terms in near real time. This study describes the initial results from an innovative collaboration between substance use epidemiologists and linguistic scientists employing techniques from the field of natural language processing to examine drug-related terms in a sample of tweets from the United States. OBJECTIVE: The objective of this study was to assess the feasibility of using distributed word-vector embeddings trained on social media data to uncover previously unknown (to researchers) drug terms. METHODS: In this pilot study, we trained a continuous bag of words (CBOW) model of distributed word-vector embeddings on a Twitter dataset collected during July 2016 (roughly 884.2 million tokens). We queried the trained word embeddings for terms with high cosine similarity (a proxy for semantic relatedness) to well-known slang terms for marijuana to produce a list of candidate terms likely to function as slang terms for this substance. This candidate list was then compared with an expert-generated list of marijuana terms to assess the accuracy and efficacy of using word-vector embeddings to search for novel drug terminology. RESULTS: The method described here produced a list of 200 candidate terms for the target substance (marijuana). Of these 200 candidates, 115 were determined to in fact relate to marijuana (65 terms for the substance itself, 50 terms related to paraphernalia). This included 30 terms which were used to refer to the target substance in the corpus yet did not appear on the expert-generated list and were therefore considered to be successful cases of uncovering novel drug terminology. Several of these novel terms appear to have been introduced as recently as 1 or 2 months before the corpus time slice used to train the word embeddings. CONCLUSIONS: Though the precision of the method described here is low enough as to still necessitate human review of any candidate term lists generated in such a manner, the fact that this process was able to detect 30 novel terms for the target substance based only on one month's worth of Twitter data is highly promising. We see this pilot study as an important proof of concept and a first step toward producing a fully automated drug term discovery system capable of tracking emerging NPS terms in real time.

The genomic repertoire for cell cycle control and DNA metabolism in S. purpuratus.

A search of the Strongylocentrotus purpuratus genome for genes associated with cell cycle control and DNA metabolism shows that the known repertoire of these genes is conserved in the sea urchin, although with fewer family members represented than in vertebrates, and with some cases of echinoderm-specific gene diversifications. For example, while homologues of the known cyclins are mostly encoded by single genes in S. purpuratus (unlike vertebrates, which have multiple isoforms), there are additional genes encoding novel cyclins of the B and K/L types. Almost all known cyclin-dependent kinases (CDKs) or CDK-like proteins have an orthologue in S. purpuratus; CDK3 is one exception, whereas CDK4 and 6 are represented by a single homologue, referred to as CDK4. While the complexity of the two families of mitotic kinases, Polo and Aurora, is close to that found in the nematode, the diversity of the NIMA-related kinases (NEK proteins) approaches that of vertebrates. Among the nine NEK proteins found in S. purpuratus, eight could be assigned orthologues in vertebrates, whereas the ninth is unique to sea urchins. Most known DNA replication, DNA repair and mitotic checkpoint genes are also present, as are homologues of the pRB (two) and p53 (one) tumor suppressors. Interestingly, the p21/p27 family of CDK inhibitors is represented by one homologue, whereas the INK4 and ARF families of tumor suppressors appear to be absent, suggesting that these evolved only in vertebrates. Our results suggest that, while the cell cycle control mechanisms known from other animals are generally conserved in sea urchin, parts of the machinery have diversified within the echinoderm lineage. The set of genes uncovered in this analysis of the S. purpuratus genome should enhance future research on cell cycle control and developmental regulation in this model.

Echinoderm eggs and embryos: procurement and culture.

The protocols outlined here hopefully will provide researchers with healthy, beautiful echinoderm oocytes, eggs, and embryos for experimental use. The large size of echinoderm oocytes and eggs, the ease with which they can be manipulated, and (in many species) their optical clarity, make them an ideal model system for studying not only the events specific to oocyte maturation and fertilization, but also for investigating more general questions regarding cell cycle regulation in an in vivo system. The quick rate at which development proceeds after fertilization to produce transparent embryos and larva makes the echinoderm an advantageous organism for studying deuterostome embryogenesis. Continued use of the echinoderms as model systems will undoubtedly uncover exciting answers to questions regarding fertilization, cell cycle regulation, morphogenesis, and how developmental events are controlled.