Host Tissue Environment Directs Activities of an Epichloë Endophyte, While It Induces Systemic Hormone and Defense Responses in Its Native Perennial Ryegrass Host.

Increased resilience of pasture grasses mediated by fungal Epichloë endophytes is crucial to pastoral industries. The underlying mechanisms are only partially understood and likely involve very different activities of the endophyte in different plant tissues and responses of the plant to these. We analyzed the transcriptomes of Epichloë festucae and its host, Lolium perenne, in host tissues of different function and developmental stages. The endophyte contributed approximately 10× more to the transcriptomes than to the biomass of infected tissues. Proliferating mycelium in growing host tissues highly expressed genes involved in hyphal growth. Nonproliferating mycelium in mature plant tissues, transcriptionally equally active, highly expressed genes involved in synthesizing antiherbivore compounds. Transcripts from the latter accounted for 4% of fungal transcripts. Endophyte infection systemically but moderately increased transcription of L. perenne genes with roles in hormone biosynthesis and perception as well as stress and pathogen resistance while reducing expression of genes involved in photosynthesis. There was a good correlation between transcriptome-based observations and physiological observations. Our data indicate that the fitness-enhancing effects of the endophyte are based both on its biosynthetic activities, predominantly in mature host tissues, and also on systemic alteration of the host's hormonal responses and induction of stress response genes. [Formula: see text] Copyright © 2017 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Plant-symbiotic fungi as chemical engineers: multi-genome analysis of the clavicipitaceae reveals dynamics of alkaloid loci.

The fungal family Clavicipitaceae includes plant symbionts and parasites that produce several psychoactive and bioprotective alkaloids. The family includes grass symbionts in the epichloae clade (Epichloë and Neotyphodium species), which are extraordinarily diverse both in their host interactions and in their alkaloid profiles. Epichloae produce alkaloids of four distinct classes, all of which deter insects, and some-including the infamous ergot alkaloids-have potent effects on mammals. The exceptional chemotypic diversity of the epichloae may relate to their broad range of host interactions, whereby some are pathogenic and contagious, others are mutualistic and vertically transmitted (seed-borne), and still others vary in pathogenic or mutualistic behavior. We profiled the alkaloids and sequenced the genomes of 10 epichloae, three ergot fungi (Claviceps species), a morning-glory symbiont (Periglandula ipomoeae), and a bamboo pathogen (Aciculosporium take), and compared the gene clusters for four classes of alkaloids. Results indicated a strong tendency for alkaloid loci to have conserved cores that specify the skeleton structures and peripheral genes that determine chemical variations that are known to affect their pharmacological specificities. Generally, gene locations in cluster peripheries positioned them near to transposon-derived, AT-rich repeat blocks, which were probably involved in gene losses, duplications, and neofunctionalizations. The alkaloid loci in the epichloae had unusual structures riddled with large, complex, and dynamic repeat blocks. This feature was not reflective of overall differences in repeat contents in the genomes, nor was it characteristic of most other specialized metabolism loci. The organization and dynamics of alkaloid loci and abundant repeat blocks in the epichloae suggested that these fungi are under selection for alkaloid diversification. We suggest that such selection is related to the variable life histories of the epichloae, their protective roles as symbionts, and their associations with the highly speciose and ecologically diverse cool-season grasses.

A Case Demonstration of the Open Health Natural Language Processing Toolkit From the National COVID-19 Cohort Collaborative and the Researching COVID to Enhance Recovery Programs for a Natural Language Processing System for COVID-19 or Postacute Sequelae of SARS CoV-2 Infection: Algorithm Development and Validation.

BACKGROUND: A wealth of clinically relevant information is only obtainable within unstructured clinical narratives, leading to great interest in clinical natural language processing (NLP). While a multitude of approaches to NLP exist, current algorithm development approaches have limitations that can slow the development process. These limitations are exacerbated when the task is emergent, as is the case currently for NLP extraction of signs and symptoms of COVID-19 and postacute sequelae of SARS-CoV-2 infection (PASC). OBJECTIVE: This study aims to highlight the current limitations of existing NLP algorithm development approaches that are exacerbated by NLP tasks surrounding emergent clinical concepts and to illustrate our approach to addressing these issues through the use case of developing an NLP system for the signs and symptoms of COVID-19 and PASC. METHODS: We used 2 preexisting studies on PASC as a baseline to determine a set of concepts that should be extracted by NLP. This concept list was then used in conjunction with the Unified Medical Language System to autonomously generate an expanded lexicon to weakly annotate a training set, which was then reviewed by a human expert to generate a fine-tuned NLP algorithm. The annotations from a fully human-annotated test set were then compared with NLP results from the fine-tuned algorithm. The NLP algorithm was then deployed to 10 additional sites that were also running our NLP infrastructure. Of these 10 sites, 5 were used to conduct a federated evaluation of the NLP algorithm. RESULTS: An NLP algorithm consisting of 12,234 unique normalized text strings corresponding to 2366 unique concepts was developed to extract COVID-19 or PASC signs and symptoms. An unweighted mean dictionary coverage of 77.8% was found for the 5 sites. CONCLUSIONS: The evolutionary and time-critical nature of the PASC NLP task significantly complicates existing approaches to NLP algorithm development. In this work, we present a hybrid approach using the Open Health Natural Language Processing Toolkit aimed at addressing these needs with a dictionary-based weak labeling step that minimizes the need for additional expert annotation while still preserving the fine-tuning capabilities of expert involvement.

Impact of the Communities That HEAL Intervention on Buprenorphine-Waivered Practitioners and Buprenorphine Prescribing: A Prespecified Secondary Analysis of the HCS Randomized Clinical Trial.

Importance: Buprenorphine significantly reduces opioid-related overdose mortality. From 2002 to 2022, the Drug Addiction Treatment Act of 2000 (DATA 2000) required qualified practitioners to receive a waiver from the Drug Enforcement Agency to prescribe buprenorphine for treatment of opioid use disorder. During this period, waiver uptake among practitioners was modest; subsequent changes need to be examined. Objective: To determine whether the Communities That HEAL (CTH) intervention increased the rate of practitioners with DATA 2000 waivers and buprenorphine prescribing. Design, Setting, and Participants: This prespecified secondary analysis of the HEALing Communities Study, a multisite, 2-arm, parallel, community-level, cluster randomized, open, wait-list-controlled comparison clinical trial was designed to assess the effectiveness of the CTH intervention and was conducted between January 1, 2020, to December 31, 2023, in 67 communities in Kentucky, Massachusetts, New York, and Ohio, accounting for approximately 8.2 million adults. The participants in this trial were communities consisting of counties (n = 48) and municipalities (n = 19). Trial arm randomization was conducted using a covariate constrained randomization procedure stratified by state. Each state was balanced by community characteristics including urban/rural classification, fatal opioid overdose rate, and community population. Thirty-four communities were randomized to the intervention and 33 to wait-list control arms. Data analysis was conducted between March 20 and September 29, 2023, with a focus on the comparison period from July 1, 2021, to June 30, 2022. Intervention: Waiver trainings and other educational trainings were offered or supported by the HEALing Communities Study research sites in each state to help build practitioner capacity. Main Outcomes and Measures: The rate of practitioners with a DATA 2000 waiver (overall, and stratified by 30-, 100-, and 275-patient limits) per 100 000 adult residents aged 18 years or older during July 1, 2021, to June 30, 2022, were compared between the intervention and wait-list control communities. The rate of buprenorphine prescribing among those waivered practitioners was also compared between the intervention and wait-list control communities. Intention-to-treat and per-protocol analyses were performed. Results: A total of 8 166 963 individuals aged 18 years or older were residents of the 67 communities studied. There was no evidence of an effect of the CTH intervention on the adjusted rate of practitioners with a DATA 2000 waiver (adjusted relative rate [ARR], 1.04; 95% CI, 0.94-1.14) or the adjusted rate of practitioners with a DATA 2000 waiver who actively prescribed buprenorphine (ARR, 0.97; 95% CI, 0.86-1.10). Conclusions and Relevance: In this randomized clinical trial, the CTH intervention was not associated with increases in the rate of practitioners with a DATA 2000 waiver or buprenorphine prescribing among those waivered practitioners. Supporting practitioners to prescribe buprenorphine remains a critical yet challenging step in the continuum of care to treat opioid use disorder. Trial Registration: ClinicalTrials.gov Identifier: NCT04111939.

Evidence of housing instability identified by addresses, clinical notes, and diagnostic codes in a real-world population with substance use disorders.

Introduction: Housing instability is a social determinant of health associated with multiple negative health outcomes including substance use disorders (SUDs). Real-world evidence of housing instability is needed to improve translational research on populations with SUDs. Methods: We identified evidence of housing instability by leveraging structured diagnosis codes and unstructured clinical data from electronic health records of 20,556 patients from 2017 to 2021. We applied natural language processing with named-entity recognition and pattern matching to unstructured clinical notes with free-text documentation. Additionally, we analyzed semi-structured addresses containing explicit or implicit housing-related labels. We assessed agreement on identification methods by having three experts review of 300 records. Results: Diagnostic codes only identified 58.5% of the population identifiable as having housing instability, whereas 41.5% are identifiable from addresses only (7.1%), clinical notes only (30.4%), or both (4.0%). Reviewers unanimously agreed on 79.7% of cases reviewed; a Fleiss' Kappa score of 0.35 suggested fair agreement yet emphasized the difficulty of analyzing patients having ambiguous housing situations. Among those with poisoning episodes related to stimulants or opioids, diagnosis codes were only able to identify 63.9% of those with housing instability. Conclusions: All three data sources yield valid evidence of housing instability; each has their own inherent practical use and limitations. Translational researchers requiring comprehensive real-world evidence of housing instability should optimize and implement use of structured and unstructured data. Understanding the role of housing instability and temporary housing facilities is salient in populations with SUDs.

Roommate's race and the racial composition of white college students' ego networks.

We develop and test a new hypothesis about how the race of a college freshman's roommate affects the racial composition of the student's ego network. Together, three principles of social structure-proximity, homophily, and transitivity-logically imply that college students assigned a roommate of a given race will have more friends (other than their roommate) of that race than will students assigned a roommate not of that race. A test with data collected from 195 white freshmen at Stanford University in the spring of 2002 supports this prediction. Our analysis advances earlier work by predicting and providing evidence of race-specific effects: While students assigned a different-race roommate of a given race have more friends (other than their roommate) of their roommate's race, they do not have more different-race friends not of their roommate's race.

geoPIPE: Geospatial Pipeline for Enhancing Open Data for Substance Use Disorders Research.

We present our open-source pipeline for quickly enhancing open data sets with research-focused expansions and show its effectiveness on a cornerstone open data set released by the Cook County government in Illinois. The City of Chicago and Cook County were both early adopters of open data portals and have made a wide variety of data available to the public; we focus on the medical examiner case archive which provides information about deaths recorded by Cook County's Office of the Medical Examiner, including overdoses invaluable to substance use disorder research. Our pipeline derives key variables from open data and links to other publicly available data sets in support of accelerating translational research on substance use disorders. Our methods apply to location-based analyses of overdoses in general and, as an example, we highlight their impact on opioid research. We provide our pipeline as open-source software to act as open infrastructure for open data to help fill the gap between data release and data use.

Opioid Overdoses Increase at Home During the COVID-19 Stay-At-Home Order Period in Cook County, Illinois.

Introduction: Stay-at-home orders during the COVID-19 pandemic decreased population mobility to reduce SARS-CoV-2 infection rates. We empirically tested the hypothesis that this public health measure was associated with a higher likelihood of opioid- and stimulant-involved deaths occurring in homes located in Cook County, Illinois. Methods: The stay-at-home period was from March 21, 2020 to May 30, 2020. We analyzed overdose data from the Cook County Medical Examiner's Office using a death location description from case investigations categorized as home, medical, motel, scene, and other. Two groups of decedents were defined as either having an opioid or stimulant listed in the primary cause of death field. We modeled a weekly time series to detect changes in deaths (number) and trends during segmented time periods. Chi-square or Fisher's exact and adjusted logistic regression was used for testing the differences between the stay-at-home and a 13-week preceding period. Results: There were 4,169 and 2,012 opioid- and stimulant-involved deaths, respectively, from 2018 to 2020. Both groups were demographically similar: 75% male, 52% White, and aged 45 years (mean). In the 13 weeks before stay-at-home orders, 51% of opioid-involved deaths occurred in homes, which increased to 59% (p<0.0001) during the 10 weeks of the order and decreased back to 51% in the 18 weeks after the order expired. For stimulant-involved deaths, 51% were residential immediately before the orders, with a nonsignificant increase to 52% during the stay-at-home period. Before the pandemic, there were 20 deaths/week, increasing to 37 deaths/week (p<0.0001) during stay-at-home enactment. Deaths involving fentanyl among the opioid-involved group increased from 76% to 89%, whereas those involving heroin decreased from 55% to 37%. The adjusted OR for opioid-involved fatal overdoses occurring at home during this period compared with that occurring the 13 weeks before was 1.37 (95% CI=1.05, 1.79). Conclusions: The likelihood of a death occurring at home, especially for people using opioids, increased during the stay-at-home order period. Findings have implications for mitigating overdose risks during social isolation.

Substance use disorders and social determinants of health from electronic medical records obtained during Kentucky's "triple wave".

Social determinants of health (SDOH) play a critical role in the risk of harmful drug use. Examining SDOH as a means of differentiating populations with multiple co-occurring substance use disorders (SUDs) is particularly salient in the era of prevalent opioid and stimulant use known as the "Third Wave". This study uses electronic medical records (EMRs) from a safety net hospital system from 14,032 patients in Kentucky from 2017 to 2019 in order to 1) define three types of SUD cohorts with shared/unique risk factors, 2) identify patients with unstable housing using novel methods for EMRs and 3) link patients to their residential neighborhood to obtain quantitative perspective on social vulnerability. We identified patients in three cohorts with statistically significant unique risk factors that included race, biological sex, insurance type, smoking status, and urban/rural residential location. Adjusting for these variables, we found a statistically significant, increasing risk gradient for patients experiencing unstable housing by cohort type: opioid-only (n = 7385, reference), stimulant-only (n = 4794, odds ratio (aOR) 1.86 95 % confidence interval (CI): 1.66-2.09), and co-diagnosed (n = 1853, aOR = 2.75, 95 % CI: 2.39 to 3.16). At the neighborhood-level, we used 8 different measures of social vulnerability and found that, for the most part, increasing proportions of patients with stimulant use living in a census tract was associated with more social vulnerability. Our study identifies potentially modifiable factors that can be tailored by substance type and demonstrates robust use of EMRs to meet national goals of enhancing research on social determinants of health.

Pharmacovigilance Signals of the Opioid Epidemic over 10 Years: Data Mining Methods in the Analysis of Pharmacovigilance Datasets Collecting Adverse Drug Reactions (ADRs) Reported to EudraVigilance (EV) and the FDA Adverse Event Reporting System (FAERS).

In the past twenty years, the consumption of opioid medications has reached significant proportions, leading to a rise in drug misuse and abuse and increased opioid dependence and related fatalities. Thus, the purpose of this study was to determine whether there are pharmacovigilance signals of abuse, misuse, and dependence and their nature for the following prescription opioids: codeine, dihydrocodeine, fentanyl, oxycodone, pentazocine, and tramadol. Both the pharmacovigilance datasets EudraVigilance (EV) and the FDA Adverse Events Reporting System (FAERS) were analyzed to identify and describe possible misuse-/abuse-/dependence-related issues. A descriptive analysis of the selected Adverse Drug Reactions (ADRs) was performed, and pharmacovigilance signal measures (i.e., reporting odds ratio, proportional reporting ratio, information component, and empirical Bayesian geometric mean) were computed for preferred terms (PTs) of abuse, misuse, dependence, and withdrawal, as well as PTs eventually related to them (e.g., aggression). From 2003 to 2018, there was an increase in ADR reports for the selected opioids in both datasets. Overall, 16,506 and 130,293 individual ADRs for the selected opioids were submitted to EV and FAERS, respectively. Compared with other opioids, abuse concerns were mostly recorded in relation to fentanyl and oxycodone, while tramadol and oxycodone were more strongly associated with drug dependence and withdrawal. Benzodiazepines, antidepressants, other opioids, antihistamines, recreational drugs (e.g., cocaine and alcohol), and several new psychoactive substances, including mitragynine and cathinones, were the most commonly reported concomitant drugs. ADRs reports in pharmacovigilance databases confirmed the availability of data on the abuse and dependence of prescription opioids and should be considered a resource for monitoring and preventing such issues. Psychiatrists and clinicians prescribing opioids should be aware of their misuse and dependence liability and effects that may accompany their use, especially together with concomitant drugs.

Enhancing research data infrastructure to address the opioid epidemic: the Opioid Overdose Network (O2-Net).

Opioid Overdose Network is an effort to generalize and adapt an existing research data network, the Accrual to Clinical Trials (ACT) Network, to support design of trials for survivors of opioid overdoses presenting to emergency departments (ED). Four institutions (Medical University of South Carolina [MUSC], Dartmouth Medical School [DMS], University of Kentucky [UK], and University of California San Diego [UCSD]) worked to adapt the ACT network. The approach that was taken to enhance the ACT network focused on 4 activities: cloning and extending the ACT infrastructure, developing an e-phenotype and corresponding registry, developing portable natural language processing tools to enhance data capture, and developing automated documentation templates to enhance extended data capture. Overall, initial results suggest that tailoring of existing multipurpose federated research networks to specific tasks is feasible; however, substantial efforts are required for coordination of the subnetwork and development of new tools for extension of available data. The initial output of the project was a new approach to decision support for the prescription of naloxone for home use in the ED, which is under further study within the network.

Extracting Semantics from Census-based Reference Data.

We present preliminary findings in extracting semantics from reference data generated by the United States Census Bureau. US Census reference data is based upon surveys designed to collect demographics and other socioeconomic factors by geographical regions. These data sets contain thousands of variables; this complexity makes the reference data difficult to learn, query, and integrate into analyses. Researchers often avoid working directly with US Census reference data and instead work with census-derived extracts capturing a much smaller subset of records. We propose to use natural language processing to extract the semantics of census-based reference data and to map census variables to known ontologies. This semantic processing reduces the large volume of variables into more manageable sets of conceptual variables that can be organized by meaning and semantic type.

"Doctor and pharmacy shopping": A fading signal for prescription opioid use monitoring?

BACKGROUND: The term "doctor and pharmacy shopping" colloquially describes patients with high multiple provider episodes (MPEs)-a threshold count of distinct prescribers and/or pharmacies involved in prescription fulfillment. Opioid-related MPEs are implicated in the global opioid crisis and heavily monitored by government databases such as U.S. state prescription drug monitoring programs (PDMPs). We applied a widely-used MPE definition to examine U.S. trends from a large, commercially-insured population from 2010 to 2017. Further, we examined the proportion of enrollees identified as "doctor shoppers" with evidence of a cancer diagnosis to examine the risk of false positives. METHODS: Using a large, commercially-insured population, we identified patients with opioid-related MPEs: opioid prescriptions (Schedule II-V, no buprenorphine) filled from ≥5 prescribers AND ≥ 5 pharmacies within the past 90 days ("5x5x90d"). Quarterly rates per 100,000 enrollees (two specifications) were calculated between 2010 and 2017. We examined the trend in a recently published all-payer, 7 state cohort from the U.S. Centers for Disease Control and Prevention for comparison. Cancer-related ICD-9/10-CM codes were used. RESULTS: Quarterly MPE rates declined by approximately 73 % from 18.2-4.9 per 100,000 enrollee population with controlled substance prescriptions. In 2017, nearly one fifth of these commercially-insured enrollees identified by the 5x5x90d algorithm were diagnosed with cancer. Approximately 8% of this sample included patients with ≥ 1 buprenorphine prescriptions. CONCLUSIONS: Opioid "shopping" flags are a long-standing but rapidly fading PDMP signal. To avoid unintended consequences, such as identifying legitimate medical encounters requiring high healthcare utilization or opioid treatment, while maintaining vigilance, more nuanced and sophisticated approaches are needed.

Leveraging Differential Privacy in Geospatial Analyses of Standardized Healthcare Data.

We present a collection of geodatabase functions which expedite utilizing differential privacy for privacy-aware geospatial analysis of healthcare data. The healthcare domain has a long history of standardization and research communities have developed open-source common data models to support the larger goals of interoperability, reproducibility, and data sharing; these models also standardize geospatial patient data. However, patient privacy laws and institutional regulations complicate geospatial analyses and dissemination of research findings due to protective restrictions in how data and results are shared. This results in infrastructures with great abilities to organize and store healthcare data, yet which lack the innate ability to produce shareable results that preserve privacy and conform to regulatory requirements. Differential privacy is a model for performing privacy-preserving analytics. We detail our process and findings in inserting an open-source library for differential privacy into a workflow for leveraging a geodatabase for geocoding and analyzing geospatial data stored as part of the Observational Medical Outcomes Partnership (OMOP) common data model. We pilot this process using an open big data repository of addresses.

Improving the Utility of Tobacco-Related Problem List Entries Using Natural Language Processing.

We present findings on using natural language processing to classify tobacco-related entries from problem lists found within patient's electronic health records. Problem lists describe health-related issues recorded during a patient's medical visit; these problems are typically followed up upon during subsequent visits and are updated for relevance or accuracy. The mechanics of problem lists vary across different electronic health record systems. In general, they either manifest as pre-generated generic problems that may be selected from a master list or as text boxes where a healthcare professional may enter free text describing the problem. Using commonly-available natural language processing tools, we classified tobacco-related problems into three classes: active-user, former-user, and non-user; we further demonstrate that rule-based post-processing may significantly increase precision in identifying these classes (+32%, +22%, +35% respectively). We used these classes to generate tobacco time-spans that reconstruct a patient's tobacco-use history and better support secondary data analysis. We bundle this as an open-source toolkit with flow visualizations indicating how patient tobacco-related behavior changes longitudinally, which can also capture and visualize contradicting information such as smokers being flagged as having never smoked.

sig2db: a Workflow for Processing Natural Language from Prescription Instructions for Clinical Data Warehouses.

We present sig2db as an open-source solution for clinical data warehouses desiring to process natural language from prescription instructions, often referred to as "sigs". In electronic prescribing, the sig is typically an unstructured text field intended to capture all requirements for medication administration. The sig captures certain fields that the structured data may lack such as days supply, time of day, or meal-time considerations. Our open-source software package facilitates the workflow needed to process sigs into a structured format usable by clinical data warehouses. Our solution focuses on extracting concepts from prescriptions in order to understand the intended semantics by leveraging known natural language processing tools. We demonstrate the utility of concept extraction from sigs and present our findings in processing 1023 unique sigs from 5.7 million unique prescriptions.

Challenges and Barriers in Applying Natural Language Processing to Medical Examiner Notes from Fatal Opioid Poisoning Cases.

We detail the challenges and barriers in applying natural language processing techniques to a collection of medical examiner case investigation notes related to fatal opioid poisonings. Major advances in biomedical informatics have made natural language processing (NLP) of medical texts both a realistic and useful task. Biomedical NLP tools are typically designed to process documents originating from biomedical libraries or electronic health records (EHRs). The usefulness of biomedical NLP tools on texts authored outside of EHRs is unclear, despite an abundance of medicolegal documents existing at the intersection of medicine and law. In particular, we detail our experiences processing unstructured text and extracting semantic concepts using case investigation notes; these notes were authored by trained investigative professionals working in a medical examiner's office and describe cases containing deaths related to fatal opioid poisonings. Applying NLP to case notes is a particularly important step in generalizing the advances of biomedical NLP for other related domains and giving guidance to data scientists working with unstructured data generated outside of EHRs.

Community dashboards to support data-informed decision-making in the HEALing communities study.

BACKGROUND: With opioid misuse, opioid use disorder (OUD), and opioid overdose deaths persisting at epidemic levels in the U.S., the largest implementation study in addiction research-the HEALing Communities Study (HCS)-is evaluating the impact of the Communities That Heal (CTH) intervention on reducing opioid overdose deaths in 67 disproportionately affected communities from four states (i.e., "sites"). Community-tailored dashboards are central to the CTH intervention's mandate to implement a community-engaged and data-driven process. These dashboards support a participating community's decision-making for selection and monitoring of evidence-based practices to reduce opioid overdose deaths. METHODS/DESIGN: A community-tailored dashboard is a web-based set of interactive data visualizations of community-specific metrics. Metrics include opioid overdose deaths and other OUD-related measures, as well as drivers of change of these outcomes in a community. Each community-tailored dashboard is a product of a co-creation process between HCS researchers and stakeholders from each community. The four research sites used a varied set of technical approaches and solutions to support the scientific design and CTH intervention implementation. Ongoing evaluation of the dashboards involves quantitative and qualitative data on key aspects posited to shape dashboard use combined with website analytics. DISCUSSION: The HCS presents an opportunity to advance how community-tailored dashboards can foster community-driven solutions to address the opioid epidemic. Lessons learned can be applied to inform interventions for public health concerns and issues that have disproportionate impact across communities and populations (e.g., racial/ethnic and sexual/gender minorities and other marginalized individuals). TRIAL REGISTRATION: ClinicalTrials.gov (NCT04111939).

bench4gis: Benchmarking Privacy-aware Geocoding with Open Big Data.

Geocoding, the process of translating addresses to geographic coordinates, is a relatively straight-forward and well-studied process, but limitations due to privacy concerns may restrict usage of geographic data. The impact of these limitations are further compounded by the scale of the data, and in turn, also limits viable geocoding strategies. For example, healthcare data is protected by patient privacy laws in addition to possible institutional regulations that restrict external transmission and sharing of data. This results in the implementation of "in-house" geocoding solutions where data is processed behind an organization's firewall; quality assurance for these implementations is problematic because sensitive data cannot be used to externally validate results. In this paper, we present our software framework called bench4gis which benchmarks privacy-aware geocoding solutions by leveraging open big data as surrogate data for quality assurance; the scale of open big data sets for address data can ensure that results are geographically meaningful for the locale of the implementing institution.

Modeling Terminologies for Reusability in Faceted Systems.

We integrate heterogeneous terminologies into our category-theoretic model of faceted browsing and show that existing terminologies and vocabularies can be reused as facets in a cohesive, interactive system. Commonly found in online search engines and digital libraries, faceted browsing systems depend upon one or more taxonomies which outline the structure and content of the facets available for user interaction. Controlled vocabularies or terminologies are often curated externally and are available as a reusable resource across systems. We demonstrated previously that category theory can abstractly model faceted browsing in a way that supports the development of interfaces capable of reusing and integrating multiple models of faceted browsing. We extend this model by illustrating that terminologies can be reused and integrated as facets across systems with examples from the biomedical domain. Furthermore, we extend our discussion by exploring the requirements and consequences of reusing existing terminologies and demonstrate how categorical operations can create reusable groupings of facets.