Individual and social determinants of adherence to sodium-glucose cotransporter 2 inhibitor therapy: A trajectory analysis.

BACKGROUND: Sodium-glucose cotransporter 2 inhibitors (SGLT2is) are known to improve cardiovascular and renal outcomes in patients with type 2 diabetes (T2D). Understanding the longitudinal patterns of adherence and the associated predictors is critical to addressing the suboptimal use of this outcome-improving treatment. OBJECTIVE: To characterize the distinct trajectories of adherence to SGLT2is in patients with T2D and to identify patient characteristics and social determinants of health (SDOHs) associated with SGLT2i adherence. METHODS: In this retrospective cohort study, we identified patients with T2D who initiated and filled at least 1 SGLT2i prescription according to 2012-2016 national Medicare claims data. The monthly proportion of days covered with SGLT2is for each patient was incorporated into group-based trajectory models to identify groups with similar adherence patterns. A multinomial logistic regression model was constructed to examine the association between patient characteristics and group membership. In addition, the association between context-specific SDOHs (eg, neighborhood median income and neighborhood employment rate) and adherence to an SGLT2i regimen was explored in both the overall cohort and the racial and ethnic subgroups. RESULTS: The final sample comprised 6,719 patients with T2D. Four trajectories of SGLT2i adherence were identified: continuously adherent users (49.6%), early discontinuers (27.5%), late discontinuers (14.5%), and intermediately adherent users (8.4%). Patient age, sex, race, diabetes duration, and Medicaid eligibility were significantly associated with trajectory group membership. Areas with a higher unemployment rate, lower income level, lower high school education rate, worse nutrition environment, fewer health care facilities, and greater Area Deprivation Index scores were found to be associated with low adherence to SGLT2is. CONCLUSIONS: Four distinct trajectories of adherence to SGLT2is were identified, with only half of the patients remaining continuously adherent to their treatment regimen during the first year after initiation. Several contextual SDOHs were associated with suboptimal adherence to SGLT2is.

DR-VIDAL - Doubly Robust Variational Information-theoretic Deep Adversarial Learning for Counterfactual Prediction and Treatment Effect Estimation on Real World Data.

Determining causal effects of interventions onto outcomes from real-world, observational (non-randomized) data, e.g., treatment repurposing using electronic health records, is challenging due to underlying bias. Causal deep learning has improved over traditional techniques for estimating individualized treatment effects (ITE). We present the Doubly Robust Variational Information-theoretic Deep Adversarial Learning (DR-VIDAL), a novel generative framework that combines two joint models of treatment and outcome, ensuring an unbiased ITE estimation even when one of the two is misspecified. DR-VIDAL integrates: (i) a variational autoencoder (VAE) to factorize confounders into latent variables according to causal assumptions; (ii) an information-theoretic generative adversarial network (Info-GAN) to generate counterfactuals; (iii) a doubly robust block incorporating treatment propensities for outcome predictions. On synthetic and real-world datasets (Infant Health and Development Program, Twin Birth Registry, and National Supported Work Program), DR-VIDAL achieves better performance than other non-generative and generative methods. In conclusion, DR-VIDAL uniquely fuses causal assumptions, VAE, Info-GAN, and doubly robustness into a comprehensive, per- formant framework. Code is available at: https://github.com/Shantanu48114860/DR-VIDAL-AMIA-22 under MIT license.

Effects of parasites and predators on nociception: decreases analgesia reduces overwinter survival in root voles (Rodentia: Cricetidae)

ABSTRACT Growing evidence suggests that parasite-infected prey is more vulnerable to predation. However, the mechanism underlying this phenomenon is obscure. In small mammals, analgesia induced by environmental stressors is a fundamental component of the defensive repertoire, promoting defensive responses. Thus, the reduced analgesia may impair the defensive ability of prey and increase their predation risk. This study aimed to determine whether coccidia infection increases the vulnerability to predation in root voles, Microtus oeconomus (Pallas, 1776), by decreased analgesia. Herein, a predator stimulus and parasitic infection were simulated in the laboratory via a two-level factorial experiment, then, the vole nociceptive responses to an aversive thermal stimulus were evaluated. Further, a field experiment was performed to determine the overwinter survival of voles with different nociceptive responses via repeated live trapping. The coccidia-infected voles demonstrated reduced predator-induced analgesia following exposure to predator odor. Meanwhile, pain-sensitive voles had lower overwinter survival than pain-inhibited voles in enclosed populations throughout the duration of the experiment. Our findings suggest that coccidia infection attenuates predator-induced analgesia, resulting in an increased vulnerability to predation.

Effects of parasites and predators on nociception: decreases analgesia reduces overwinter survival in root voles (Rodentia: Cricetidae)

ABSTRACT Growing evidence suggests that parasite-infected prey is more vulnerable to predation. However, the mechanism underlying this phenomenon is obscure. In small mammals, analgesia induced by environmental stressors is a fundamental component of the defensive repertoire, promoting defensive responses. Thus, the reduced analgesia may impair the defensive ability of prey and increase their predation risk. This study aimed to determine whether coccidia infection increases the vulnerability to predation in root voles, Microtus oeconomus (Pallas, 1776), by decreased analgesia. Herein, a predator stimulus and parasitic infection were simulated in the laboratory via a two-level factorial experiment, then, the vole nociceptive responses to an aversive thermal stimulus were evaluated. Further, a field experiment was performed to determine the overwinter survival of voles with different nociceptive responses via repeated live trapping. The coccidia-infected voles demonstrated reduced predator-induced analgesia following exposure to predator odor. Meanwhile, pain-sensitive voles had lower overwinter survival than pain-inhibited voles in enclosed populations throughout the duration of the experiment. Our findings suggest that coccidia infection attenuates predator-induced analgesia, resulting in an increased vulnerability to predation.

Effects of parasites and predators on nociception: decreases analgesia reduces overwinter survival in root voles (Rodentia: Cricetidae)

Growing evidence suggests that parasite-infected prey is more vulnerable to predation. However, the mechanism underlying this phenomenon is obscure. In small mammals, analgesia induced by environmental stressors is a fundamental component of the defensive repertoire, promoting defensive responses. Thus, the reduced analgesia may impair the defensive ability of prey and increase their predation risk. This study aimed to determine whether coccidia infection increases the vulnerability to predation in root voles, Microtus oeconomus (Pallas, 1776), by decreased analgesia. Herein, a predator stimulus and parasitic infection were simulated in the laboratory via a two-level factorial experiment, then, the vole nociceptive responses to an aversive thermal stimulus were evaluated. Further, a field experiment was performed to determine the overwinter survival of voles with different nociceptive responses via repeated live trapping. The coccidia-infected voles demonstrated reduced predator-induced analgesia following exposure to predator odor. Meanwhile, pain-sensitive voles had lower overwinter survival than pain-inhibited voles in enclosed populations throughout the duration of the experiment. Our findings suggest that coccidia infection attenuates predator-induced analgesia, resulting in an increased vulnerability to predation.(AU)

Automated Tools for Clinical Research Data Quality Control using NCI Common Data Elements.

Clinical research data generated by a federation of collection mechanisms and systems often produces highly dissimilar data with varying quality. Poor data quality can result in the inefficient use of research data or can even require the repetition of the performed studies, a costly process. This work presents two tools for improving data quality of clinical research data relying on the National Cancer Institute's Common Data Elements as a standard representation of possible questions and data elements to A: automatically suggest CDE annotations for already collected data based on semantic and syntactic analysis utilizing the Unified Medical Language System (UMLS) Terminology Services' Metathesaurus and B: annotate and constrain new clinical research questions though a simple-to-use "CDE Browser." In this work, these tools are built and tested on the open-source LimeSurvey software and research data analyzed and identified to contain various data quality issues captured by the Comprehensive Research Informatics Suite (CRIS) at the University of Arkansas for Medical Sciences.

Towards Large-scale Twitter Mining for Drug-related Adverse Events.

Drug-related adverse events pose substantial risks to patients who consume post-market or Drug-related adverse events pose substantial risks to patients who consume post-market or investigational drugs. Early detection of adverse events benefits not only the drug regulators, but also the manufacturers for pharmacovigilance. Existing methods rely on patients' "spontaneous" self-reports that attest problems. The increasing popularity of social media platforms like the Twitter presents us a new information source for finding potential adverse events. Given the high frequency of user updates, mining Twitter messages can lead us to real-time pharmacovigilance. In this paper, we describe an approach to find drug users and potential adverse events by analyzing the content of twitter messages utilizing Natural Language Processing (NLP) and to build Support Vector Machine (SVM) classifiers. Due to the size nature of the dataset (i.e., 2 billion Tweets), the experiments were conducted on a High Performance Computing (HPC) platform using MapReduce, which exhibits the trend of big data analytics. The results suggest that daily-life social networking data could help early detection of important patient safety issues.