Merging machine learning and patient preference: a novel tool for risk prediction of percutaneous coronary interventions.

BACKGROUND AND AIMS: Predicting personalized risk for adverse events following percutaneous coronary intervention (PCI) remains critical in weighing treatment options, employing risk mitigation strategies, and enhancing shared decision-making. This study aimed to employ machine learning models using pre-procedural variables to accurately predict common post-PCI complications. METHODS: A group of 66 adults underwent a semiquantitative survey assessing a preferred list of outcomes and model display. The machine learning cohort included 107 793 patients undergoing PCI procedures performed at 48 hospitals in Michigan between 1 April 2018 and 31 December 2021 in the Blue Cross Blue Shield of Michigan Cardiovascular Consortium (BMC2) registry separated into training and validation cohorts. External validation was conducted in the Cardiac Care Outcomes Assessment Program database of 56 583 procedures in 33 hospitals in Washington. RESULTS: Overall rate of in-hospital mortality was 1.85% (n = 1999), acute kidney injury 2.51% (n = 2519), new-onset dialysis 0.44% (n = 462), stroke 0.41% (n = 447), major bleeding 0.89% (n = 942), and transfusion 2.41% (n = 2592). The model demonstrated robust discrimination and calibration for mortality {area under the receiver-operating characteristic curve [AUC]: 0.930 [95% confidence interval (CI) 0.920-0.940]}, acute kidney injury [AUC: 0.893 (95% CI 0.883-0.903)], dialysis [AUC: 0.951 (95% CI 0.939-0.964)], stroke [AUC: 0.751 (95%CI 0.714-0.787)], transfusion [AUC: 0.917 (95% CI 0.907-0.925)], and major bleeding [AUC: 0.887 (95% CI 0.870-0.905)]. Similar discrimination was noted in the external validation population. Survey subjects preferred a comprehensive list of individually reported post-procedure outcomes. CONCLUSIONS: Using common pre-procedural risk factors, the BMC2 machine learning models accurately predict post-PCI outcomes. Utilizing patient feedback, the BMC2 models employ a patient-centred tool to clearly display risks to patients and providers (https://shiny.bmc2.org/pci-prediction/). Enhanced risk prediction prior to PCI could help inform treatment selection and shared decision-making discussions.

Estimating the Burden and Distribution of Post-COVID-19 Condition in Washington State, March 2020-October 2023.

Introduction: After SARS-CoV-2 infection, some people will experience long-term sequelae known as post-COVID-19 condition (PCC). Although PCC is recognized as a public health problem, estimates of the prevalence of PCC are sparse. We described a framework for estimating the incidence and prevalence of PCC by population subgroups and geography over time in Washington State. Methods: We collected data on reported COVID-19 cases and hospitalizations and estimated SARS-CoV-2 infections in Washington State from March 2020 through October 2023. The reported case data were incorporated with parameter estimates from published articles and prevalence estimates from the Household Pulse Survey into a mathematical compartmental model of PCC progression. The model used differential equations to describe how the population of people with PCC moved through the model's various stages. This framework allowed us to integrate data on age group, sex, race and ethnicity, vaccination status, and county to estimate incidence and prevalence of PCC for each subgroup. Results: Our model indicated that 6.4% (95% CI, 5.9%-6.8%) of all adults in Washington State were experiencing PCC as of October 2023. In addition to temporal differences in PCC prevalence and incidence, we found substantial differences across age groups, race and ethnicity, and sex. Geographic heterogeneity was pronounced, with the highest rates of PCC in central and eastern Washington. Conclusion: Estimation of PCC prevalence is essential for addressing PCC as a public health problem. Responding to PCC will require continued surveillance, research, and dedicated financial and public health action. This analysis, accounting for heterogeneities, highlights disparities in the prevalence, incidence, and distribution of PCC in Washington State and can better guide awareness and response efforts.

Sentinel Surveillance System Implementation and Evaluation for SARS-CoV-2 Genomic Data, Washington, USA, 2020-2021.

Genomic data provides useful information for public health practice, particularly when combined with epidemiologic data. However, sampling bias is a concern because inferences from nonrandom data can be misleading. In March 2021, the Washington State Department of Health, USA, partnered with submitting and sequencing laboratories to establish sentinel surveillance for SARS-CoV-2 genomic data. We analyzed available genomic and epidemiologic data during presentinel and sentinel periods to assess representativeness and timeliness of availability. Genomic data during the presentinel period was largely unrepresentative of all COVID-19 cases. Data available during the sentinel period improved representativeness for age, death from COVID-19, outbreak association, long-term care facility-affiliated status, and geographic coverage; timeliness of data availability and captured viral diversity also improved. Hospitalized cases were underrepresented, indicating a need to increase inpatient sampling. Our analysis emphasizes the need to understand and quantify sampling bias in phylogenetic studies and continue evaluation and improvement of public health surveillance systems.

Associations Between Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Variants and Risk of Coronavirus Disease 2019 (COVID-19) Hospitalization Among Confirmed Cases in Washington State: A Retrospective Cohort Study.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic is dominated by variant viruses; the resulting impact on disease severity remains unclear. Using a retrospective cohort study, we assessed the hospitalization risk following infection with 7 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants. METHODS: Our study includes individuals with positive SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR) in the Washington Disease Reporting System with available viral genome data, from 1 December 2020 to 14 January 2022. The analysis was restricted to cases with specimens collected through sentinel surveillance. Using a Cox proportional hazards model with mixed effects, we estimated hazard ratios (HR) for hospitalization risk following infection with a variant, adjusting for age, sex, calendar week, and vaccination. RESULTS: In total, 58 848 cases were sequenced through sentinel surveillance, of which 1705 (2.9%) were hospitalized due to COVID-19. Higher hospitalization risk was found for infections with Gamma (HR 3.20, 95% confidence interval [CI] 2.40-4.26), Beta (HR 2.85, 95% CI 1.56-5.23), Delta (HR 2.28 95% CI 1.56-3.34), or Alpha (HR 1.64, 95% CI 1.29-2.07) compared to infections with ancestral lineages; Omicron (HR 0.92, 95% CI .56-1.52) showed no significant difference in risk. Following Alpha, Gamma, or Delta infection, unvaccinated patients show higher hospitalization risk, while vaccinated patients show no significant difference in risk, both compared to unvaccinated, ancestral lineage cases. Hospitalization risk following Omicron infection is lower with vaccination. CONCLUSIONS: Infection with Alpha, Gamma, or Delta results in a higher hospitalization risk, with vaccination attenuating that risk. Our findings support hospital preparedness, vaccination, and genomic surveillance.

Maternal and newborn outcomes with elective induction of labor at term.

BACKGROUND: A growing body of evidence supports improved or not worsened birth outcomes with nonmedically indicated induction of labor at 39 weeks gestation compared with expectant management. This evidence includes 2 recent randomized control trials. However, concern has been raised as to whether these studies are applicable to a broader US pregnant population. OBJECTIVE: Our goal was to compare outcomes for electively induced births at ≥39 weeks gestation with those that were not electively induced. STUDY DESIGN: We conducted a retrospective cohort study using chart-abstracted data on births from January 1, 2012, to December 31, 2017, at 21 hospitals in the Northwest United States. The study was restricted to singleton cephalic hospital births at 39+0-42+6 weeks gestation. Exclusions included previous cesarean birth, missing data for delivery type or gestational week at birth, antepartum stillbirth, cesarean birth without any attempt at vaginal birth, fetal anomaly, gestational diabetes mellitus, prepregnancy diabetes mellitus, and prepregnancy hypertension. The rate of cesarean birth for elective inductions at both 39 and 40 weeks gestation was compared with the rate in all other on-going pregnancies in the same gestational week. Maternal outcomes (operative vaginal birth, shoulder dystocia, 3rd- or 4th-degree perineal laceration, pregnancy-related hypertension, and postpartum hemorrhage) and newborn infant outcomes (macrosomia, 5-minute Apgar <7, resuscitation at delivery, intubation, respiratory complications, and neonatal intensive care unit admission) were also compared between elective inductions and on-going pregnancies at 39 and 40 weeks gestation. Logistic regression modeling was used to produce odds ratios for outcomes with adjustment for maternal age and body mass index. Results were stratified by parity and gestational week at birth. Duration of hospital stay (admission to delivery, delivery to discharge, and total stay) were compared between elective inductions and on-going pregnancies. RESULTS: A total of 55,694 births were included in the study cohort: 4002 elective inductions at ≥39+0 weeks gestation and 51,692 births at 39+0-42+6 weeks gestation that were not electively induced. In nulliparous women, elective induction at 39 weeks gestation was associated with a decreased likelihood of cesarean birth (14.7% vs 23.2%; adjusted odds ratio, 0.61; 95% confidence interval, 0.41-0.89) and an increased rate of operative vaginal birth (18.5% vs 10.8%; adjusted odds ratio, 1.8; 95% confidence interval, 1.28-2.54) compared with on-going pregnancies. In multiparous women, cesarean birth rates were similar in the elective inductions and on-going pregnancies. Elective induction at 39 weeks gestation was associated with a decreased likelihood of pregnancy-related hypertension in nulliparous (2.2% vs 7.3%; adjusted odds ratio, 0.28; 95% confidence interval, 0.11-0.68) and multiparous women (0.9% vs 3.5%; adjusted odds ratio, 0.24; 95% confidence interval, 0.15-0.38). Term elective induction was not associated with any statistically significant increase in adverse newborn infant outcomes. Elective induction of labor at 39 weeks gestation was associated with increased time from admission to delivery for both nulliparous (1.3 hours; 95% confidence interval, 0.2-2.3) and multiparous women (3.4 hours; 95% confidence interval, 3.2-3.6). CONCLUSION: Elective induction of labor at 39 weeks gestation is associated with a decrease in cesarean birth in nulliparous women, decreased pregnancy-related hypertension in multiparous and nulliparous women, and increased time in labor and delivery. How to use this information remains the challenge.

Destress 9-1-1-an online mindfulness-based intervention in reducing stress among emergency medical dispatchers: a randomised controlled trial.

OBJECTIVES: Emergency medical dispatchers (EMDs) experience significant stress in the workplace. Yet, interventions aimed at reducing work-related stress are difficult to implement due to the logistic challenges associated with the relatively unique EMD work environment. This investigation tested the efficacy of a 7-week online mindfulness-based intervention (MBI) tailored to the EMD workforce. METHODS: Active-duty EMDs from the USA and Canada (n=323) were randomly assigned to an intervention or wait list control condition. Participants completed surveys of stress and mindfulness at baseline, post intervention, and 3 months follow-up. Repeated measures mixed effects models were used to assess changes in stress and mindfulness. RESULTS: Differences between the intervention group and control group in pre-post changes in stress using the Calgary Symptoms of Stress Inventory were statistically significant, with a difference of -10.0 (95% CI: -14.9, -5.2, p<0.001) for change from baseline to post intervention, and a difference of -6.5 (95% CI: -11.9, -1.1, p=0.02) for change from baseline to 3 months follow-up. Change in mindfulness scores did not differ between groups. However, increases in mindfulness scores were correlated with greater reductions in stress for all participants, regardless of group (r=-0.53, p<0.001). CONCLUSIONS: Development of tailored online MBIs for employees working in challenging work environments offer a promising direction for prevention and intervention. This study found that a short, weekly online MBI for EMDs resulted in reductions in reports of stress. Implications of online MBIs in other emergency responding populations and directions for future research are discussed.

Impact of new technologies on stress, attrition and well-being in emergency call centers: the NextGeneration 9-1-1 study protocol.

BACKGROUND: Our public health emergency response system relies on the "first of the first responders"-the emergency call center workforce that handles the emergency needs of a public in distress. Call centers across the United States have been preparing for the "Next Generation 9-1-1" initiative, which will allow citizens to place 9-1-1 calls using a variety of digital technologies. The impacts of this initiative on a workforce that is already highly stressed is unknown. There is concern that these technology changes will increase stress, reduce job performance, contribute to maladaptive coping strategies, lower employee retention, or change morale in the workplace. Understanding these impacts to inform approaches for mitigating the health and performance risks associated with new technologies is crucial for ensuring the 911 system fulfills its mission of providing optimal emergency response to the public. METHODS: Our project is an observational, prospective cohort study framed by the first new technology that will be implemented: text-to-911 calling. Emergency center call takers will be recruited nationwide. Data will be collected by online surveys distributed at each center before text-to-911 implementation; within the first month of implementation; and 6 months after implementation. Primary outcome measures are stress as measured by the Calgary Symptoms of Stress Index, use of sick leave, job performance, and job satisfaction. Primary analyses will use mixed effects regression models and mixed effects logistic regression models to estimate the change in outcome variables associated with text-to-911 implementation. Multiple secondary analyses will examine effects of stress on absenteeism; associations between technology attitudes and stress; effects of implementation on attitudes towards technology; and mitigating effects of job demands, job satisfaction, attitudes towards workplace technology and workplace support on change in stress. DISCUSSION: Our public health dependence on this workforce for our security and safety makes it imperative that the impact of technological changes such as text-to-911 are researched so appropriate intervention efforts to can be developed. Failing to protect our 9-1-1 call takers from predictable health risks would be similar to knowingly exposing field emergency responders to a toxic situation without following OSHA required training and practice standards assuring their protection.

Protocol: a multi-level intervention program to reduce stress in 9-1-1 telecommunicators.

BACKGROUND: Nationwide, emergency response systems depend on 9-1-1 telecommunicators to prioritize, triage, and dispatch assistance to those in distress. 9-1-1 call center telecommunicators (TCs) are challenged by acute and chronic workplace stressors: tense interactions with citizen callers in crisis; overtime; shift-work; ever-changing technologies; and negative work culture, including co-worker conflict. This workforce is also subject to routine exposures to secondary traumatization while handling calls involving emergency situations and while making time urgent, high stake decisions over the phone. Our study aims to test the effectiveness of a multi-part intervention to reduce stress in 9-1-1 TCs through an online mindfulness training and a toolkit containing workplace stressor reduction resources. METHODS/DESIGN: The study employs a randomized controlled trial design with three data collection points. The multi-part intervention includes an individual-level online mindfulness training and a call center-level organizational stress reduction toolkit. 160 TCs will be recruited from 9-1-1 call centers, complete a baseline survey at enrollment, and are randomly assigned to an intervention or a control group. Intervention group participants will start a 7-week online mindfulness training developed in-house and tailored to 9-1-1 TCs and their call center environment; control participants will be "waitlisted" and start the training after the study period ends. Following the intervention group's completion of the mindfulness training, all participants complete a second survey. Next, the online toolkit with call-center wide stress reduction resources is made available to managers of all participating call centers. After 3 months, a third survey will be completed by all participants. The primary outcome is 9-1-1 TCs' self-reported symptoms of stress at three time points as measured by the C-SOSI (Calgary Symptoms of Stress Inventory). Secondary outcomes will include: perceptions of social work environment (measured by metrics of social support and network conflict); mindfulness; and perceptions of social work environment and mindfulness as mediators of stress reduction. DISCUSSION: This study will evaluate the effectiveness of an online mindfulness training and call center-wide stress reduction toolkit in reducing self-reported stress in 9-1-1 TCs. The results of this study will add to the growing body of research on worksite stress reduction programs. TRIAL REGISTRATION: ClinicalTrials.gov Registration Number: NCT02961621 Registered on November 7, 2016 (retrospectively registered).

Comfort and Attitudes Towards Robots Among Young, Middle-Aged, and Older Adults: A Cross-Sectional Study.

PURPOSE: To explore the social impact of, comfort with, and negative attitudes towards robots among young, middle-aged, and older adults in the United States. DESIGN: Descriptive, cross-sectional. Conducted in 2014-2015 in an urban area of the western United States using a purposive sample of adults 18 years of age or older. METHODS: Respondents completed a survey that included the Negative Attitudes Toward Robots Scale (NARS) and two questions taken or modified from the European Commission's Autonomous System 2015 Report. Analyses were conducted to compare perceptions and demographic factors by age groups (young adults:18-44, middle-aged adults: 45-64, and older adults: >65 years old). FINDINGS: Sample included 499 individuals (n = 322 age 18-44 years, n = 50 age 45-64 years, and n = 102 age 65-98 years). There were no significant differences between age groups for 9 of the 11 items regarding social impact of robots and comfort with robots. There were no significant differences by age groups for 9 of the 14 items in the NARS. Among those items with statistically significant differences, the mean scores indicate similar sentiments for each group. CONCLUSIONS: Older, middle-aged, and younger adults had similar attitudes regarding the social impact of and comfort with robots; they also had similar negative attitudes towards robots. Findings dispel current perceptions that older adults are not as receptive to robots as other adults. This has implications for nurses who integrate supportive robots in their practice. CLINICAL RELEVANCE: Nurses working in clinical and community roles can use these findings when developing and implementing robotic solutions. Understanding attitudes towards robots can support how, where, and with whom robots can be used in nursing practice.

Protocol: simulation training to improve 9-1-1 dispatcher identification of cardiac arrest.

BACKGROUND: 9-1-1 dispatchers are often the first contact for bystanders witnessing an out-of-hospital cardiac arrest. In the time before Emergency Medical Services arrives, dispatcher identification of the need for, and provision of Telephone-CPR (T-CPR) can improve survival. Our study aims to evaluate the use of phone-based standardized patient simulation training to improve identification of the need for T-CPR and shorten time to start of T-CPR instructions. METHODS/DESIGN: The STAT-911 study is a randomized controlled trial. We will recruit 160 dispatchers from 9-1-1 call-centers in the Pacific Northwest; they are randomized to an intervention or control group. Intervention participants complete four telephone simulation training sessions over 6-8 months. Training sessions consist of three mock 9-1-1 calls, with a standardized patient playing a caller witnessing a medical emergency. After the mock calls, an instructor who has been listening in and scoring the dispatcher's call management, connects to the dispatcher and provides feedback on select call processing skills. After the last training session, all participants complete the simulation test: a call session that includes two mock 9-1-1 calls of medium complexity. During the study, audio from all actual cardiac arrest calls handled by the dispatchers will be collected. All dispatchers complete a baseline survey, and after the intervention, a follow-up survey to measure confidence. Primary outcomes are proportion of calls where dispatchers identify the need for T-CPR, and time to start of T-CPR, assessed by comparing performance on two calls in the simulation test. Secondary outcomes are proportion of actual cardiac arrest calls in which dispatchers identify the need for T-CPR and time to start of T-CPR; performance on call-taking skills during the simulation test; self-reported confidence in the baseline and follow-up surveys; and calculated costs of the intervention training sessions and projected costs for field implementation of training sessions. DISCUSSION: The STAT-911 study will evaluate if over-the-phone simulation training with standardized patients can improve 9-1-1 dispatchers' ability identify the need for, and promptly begin T-CPR. Furthermore, it will advance knowledge on the effectiveness of simulation training for health services phone-operators interacting with clients, patients, or bystanders in diagnosis, triage, and treatment decisions. TRIAL REGISTRATION: ClinicalTrials.gov REGISTRATION NUMBER: NCT01972087 . Registered 23 October 2013.

Visualization and analytics tools for infectious disease epidemiology: a systematic review.

BACKGROUND: A myriad of new tools and algorithms have been developed to help public health professionals analyze and visualize the complex data used in infectious disease control. To better understand approaches to meet these users' information needs, we conducted a systematic literature review focused on the landscape of infectious disease visualization tools for public health professionals, with a special emphasis on geographic information systems (GIS), molecular epidemiology, and social network analysis. The objectives of this review are to: (1) identify public health user needs and preferences for infectious disease information visualization tools; (2) identify existing infectious disease information visualization tools and characterize their architecture and features; (3) identify commonalities among approaches applied to different data types; and (4) describe tool usability evaluation efforts and barriers to the adoption of such tools. METHODS: We identified articles published in English from January 1, 1980 to June 30, 2013 from five bibliographic databases. Articles with a primary focus on infectious disease visualization tools, needs of public health users, or usability of information visualizations were included in the review. RESULTS: A total of 88 articles met our inclusion criteria. Users were found to have diverse needs, preferences and uses for infectious disease visualization tools, and the existing tools are correspondingly diverse. The architecture of the tools was inconsistently described, and few tools in the review discussed the incorporation of usability studies or plans for dissemination. Many studies identified concerns regarding data sharing, confidentiality and quality. Existing tools offer a range of features and functions that allow users to explore, analyze, and visualize their data, but the tools are often for siloed applications. Commonly cited barriers to widespread adoption included lack of organizational support, access issues, and misconceptions about tool use. DISCUSSION AND CONCLUSION: As the volume and complexity of infectious disease data increases, public health professionals must synthesize highly disparate data to facilitate communication with the public and inform decisions regarding measures to protect the public's health. Our review identified several themes: consideration of users' needs, preferences, and computer literacy; integration of tools into routine workflow; complications associated with understanding and use of visualizations; and the role of user trust and organizational support in the adoption of these tools. Interoperability also emerged as a prominent theme, highlighting challenges associated with the increasingly collaborative and interdisciplinary nature of infectious disease control and prevention. Future work should address methods for representing uncertainty and missing data to avoid misleading users as well as strategies to minimize cognitive overload.

Interpretable Machine Learning Predicts Postpartum Hemorrhage with Severe Maternal Morbidity in a Lower Risk Laboring Obstetric Population.

BACKGROUND: Early identification of patients at increased risk for postpartum hemorrhage (PPH) associated with severe maternal morbidity (SMM) is critical for preparation and preventative intervention. However, prediction is challenging in patients without obvious risk factors for postpartum hemorrhage with severe maternal morbidity. Current tools for hemorrhage risk assessment use lists of risk factors rather than predictive models. OBJECTIVE: To develop, validate (internally and externally), and compare a machine learning model for predicting PPH associated with SMM against a standard hemorrhage risk assessment tool in a lower-risk laboring obstetric population. STUDY DESIGN: This retrospective cross-sectional study included clinical data from singleton, term births (>=37 weeks' gestation) at 19 US hospitals (2016-2021) using data from 44,509 births at 11 hospitals to train a generalized additive model (GAM) and 21,183 births at 8 held-out hospitals to externally validate the model. The outcome of interest was PPH with severe maternal morbidity (blood transfusion, hysterectomy, vascular embolization, intrauterine balloon tamponade, uterine artery ligation suture, uterine compression suture, or admission to intensive care). Cesarean birth without a trial of vaginal birth and patients with a history of cesarean were excluded. We compared the model performance to that of the California Maternal Quality Care Collaborative (CMQCC) Obstetric Hemorrhage Risk Factor Assessment Screen. RESULTS: The GAM predicted PPH with an area under the receiver-operating characteristic curve (AUROC) of 0.67 (95% CI 0.64-0.68) on external validation, significantly outperforming the CMQCC risk screen AUROC of 0.52 (95% CI 0.50-0.53). Additionally, the GAM had better sensitivity of 36.9% (95% CI 33.01, 41.02) than the CMQCC screen sensitivity of 20.30% (95% CI 17.40, 22.52) at the CMQCC screen positive rate of 16.8%. The GAM identified in-vitro fertilization as a risk factor (adjusted OR 1.5; 95% CI 1.2-1.8) and nulliparous births as the highest PPH risk factor (adjusted OR 1.5; 95% CI; 1.4-1.6). CONCLUSION: Our model identified almost twice as many cases of PPH as the CMQCC rules-based approach for the same screen positive rate and identified in-vitro fertilization and first-time births as risk factors for PPH. Adopting predictive models over traditional screens can enhance PPH prediction.

Interpretable Predictive Models to Understand Risk Factors for Maternal and Fetal Outcomes.

Although most pregnancies result in a good outcome, complications are not uncommon and can be associated with serious implications for mothers and babies. Predictive modeling has the potential to improve outcomes through a better understanding of risk factors, heightened surveillance for high-risk patients, and more timely and appropriate interventions, thereby helping obstetricians deliver better care. We identify and study the most important risk factors for four types of pregnancy complications: (i) severe maternal morbidity, (ii) shoulder dystocia, (iii) preterm preeclampsia, and (iv) antepartum stillbirth. We use an Explainable Boosting Machine (EBM), a high-accuracy glass-box learning method, for the prediction and identification of important risk factors. We undertake external validation and perform an extensive robustness analysis of the EBM models. EBMs match the accuracy of other black-box ML methods, such as deep neural networks and random forests, and outperform logistic regression, while being more interpretable. EBMs prove to be robust. The interpretability of the EBM models reveal surprising insights into the features contributing to risk (e.g., maternal height is the second most important feature for shoulder dystocia) and may have potential for clinical application in the prediction and prevention of serious complications in pregnancy.

Quantifying abnormal emotion processing: A novel computational assessment method and application in schizophrenia.

Abnormal emotion processing is a core feature of schizophrenia spectrum disorders (SSDs) that encompasses multiple operations. While deficits in some areas have been well-characterized, we understand less about abnormalities in the emotion processing that happens through language, which is highly relevant for social life. Here, we introduce a novel method using deep learning to estimate emotion processing rapidly from spoken language, testing this approach in male-identified patients with SSDs (n = 37) and healthy controls (n = 51). Using free responses to evocative stimuli, we derived a measure of appropriateness, or "emotional alignment" (EA). We examined psychometric characteristics of EA and its sensitivity to a single-dose challenge of oxytocin, a neuropeptide shown to enhance the salience of socioemotional information in SSDs. Patients showed impaired EA relative to controls, and impairment correlated with poorer social cognitive skill and more severe motivation and pleasure deficits. Adding EA to a logistic regression model with language-based measures of formal thought disorder (FTD) improved classification of patients versus controls. Lastly, oxytocin administration improved EA but not FTD among patients. While additional validation work is needed, these initial results suggest that an automated assay using spoken language may be a promising approach to assess emotion processing in SSDs.

GWASTools: an R/Bioconductor package for quality control and analysis of genome-wide association studies.

GWASTools is an R/Bioconductor package for quality control and analysis of genome-wide association studies (GWAS). GWASTools brings the interactive capability and extensive statistical libraries of R to GWAS. Data are stored in NetCDF format to accommodate extremely large datasets that cannot fit within R's memory limits. The documentation includes instructions for converting data from multiple formats, including variants called from sequencing. GWASTools provides a convenient interface for linking genotypes and intensity data with sample and single nucleotide polymorphism annotation.

The effect of language barriers on dispatching EMS response.

OBJECTIVE: The objective of this study was to investigate the effect of language barriers during medical 9-1-1 calls, on the time to dispatch and level of medical aid (Basic or Advanced Life Support). METHODS: All 9-1-1 medical calls to two large call centers during one week for each of the months of August, October, December 2010 and February 2011, were reviewed for a notation of language barrier (LB). Non-language barrier calls were identified from the same time period such that there were an equal proportion of LB and non-LB calls by dispatch code and dispatcher. A total of 272 language barrier calls were identified. The computer-assisted dispatch (CAD) reports for the LB and non-LB calls were abstracted by research staff using a standard form, including: Start time of call, time to dispatch of BLS, time to dispatch of ALS, dispatch code, interpretation service use, on-scene upgrade to ALS, and on-scene downgrade to BLS. 9-1-1 recordings were abstracted for LB calls only to obtain information about use of interpreter services. Difference between LB and English speakers in time to assignment of BLS and ALS was examined using linear mixed effects models with log time as the outcome; language barrier, call center and dispatch code as fixed effects and dispatcher as a random effect. RESULTS: The effect of language barrier on time to BLS assignment was, on average, 33% longer (p < 0.001) and time to ALS assignment 43% longer (P = 0.008). A majority of the effect was due to the effect of interpreter use, which increased time to BLS by 82% and 125% for ALS, when compared to non-language barrier calls. Data from the 9-1-1 recordings showed an average of 49 seconds between connecting to the service operator and connecting to the language interpreter. Language barrier calls were more likely to be up- and down-graded, only statistically significantly so for on-scene downgrades. CONCLUSION: Language barriers increase time to dispatch and the accuracy of the level of aid dispatched during medical emergency calls. Decreasing the time to connecting to an actual interpreter when using an interpretation service could minimize existing delays.

Public health communications and alert fatigue.

BACKGROUND: Health care providers play a significant role in large scale health emergency planning, detection, response, recovery and communication with the public. The effectiveness of health care providers in emergency preparedness and response roles depends, in part, on public health agencies communicating information in a way that maximizes the likelihood that the message is delivered, received, deemed credible and, when appropriate, acted on. However, during an emergency, health care providers can become inundated with alerts and advisories through numerous national, state, local and professional communication channels. We conducted an alert fatigue study as a sub-study of a larger randomized controlled trial which aimed to identify the most effective methods of communicating public health messages between public health agencies and providers. We report an analysis of the effects of public health message volume/frequency on recall of specific message content and effect of rate of message communications on health care provider alert fatigue. METHODS: Health care providers enrolled in the larger study (n=528) were randomized to receive public health messages via email, fax, short message service (SMS or cell phone text messaging) or to a control group that did not receive messages. For 12 months, study messages based on real events of public health significance were sent quarterly with follow-up telephone interviews regarding message receipt and topic recall conducted 5-10 days after the message delivery date. During a pandemic when numerous messages are sent, alert fatigue may impact ability to recall whether a specific message has been received due to the "noise" created by the higher number of messages. To determine the impact of "noise" when study messages were sent, we compared health care provider recall of the study message topic to the number of local public health messages sent to health care providers. RESULTS: We calculated the mean number of messages that each provider received from local public health during the time period around each study message and provider recall of study message content. We found that recall rates were inversely proportional to the mean number of messages received per week: Every increase of one local public health message per week resulted in a statistically significant 41.2% decrease (p < 0.01), 95% CI [0.39, .87] in the odds of recalling the content of the study message. CONCLUSIONS: To our knowledge, this is the first study to document the effects of alert fatigue on health care providers' recall of information. Our results suggest that information delivered too frequently and/or repetitively through numerous communication channels may have a negative effect on the ability of health care providers to effectively recall emergency information. Keeping health care providers and other first-line responders informed during an emergency is critical. Better coordination between organizations disseminating alerts, advisories and other messages may improve the ability of health care providers to recall public health emergency messages, potentially impacting effective response to public health emergency messages.

Serving limited English proficient callers: a survey of 9-1-1 police telecommunicators.

INTRODUCTION: The emergency telephone number 9-1-1 serves as a lifeline to the public during emergencies, and first responders rely on information gathered by 9-1-1 telecommunicators who speak with callers. Timely, accurate information from the telecommunicators is essential for providing appropriate care on scene. Language barriers can hamper these efforts and result in less efficient information exchange. Although 9-1-1 telecommunicators may access over-the-phone interpreter (OPI) services to facilitate communication, managing three-way communication during an emergency is challenging. Problem There is little published on the relationship between limited English proficient (LEP) callers and 9-1-1 police telecommunicators, and the role of OPI services during these calls. Further, little is known about effective strategies to manage such calls. METHODS: In King County, Washington, 9-1-1 police telecommunicators were surveyed about their experiences handling LEP calls and managing three-way communication with OPI services. The survey contained 13 multiple-choice and three open-response questions addressing communication strategies, challenges with LEP callers, and three-way communication with OPI services. Goodman-Kruskal Gamma and chi-square tests were conducted with OPI use as the dependent variable. Additional analyses were conducted using stress levels as the dependent variable. RESULTS: Of 123 respondents, 69 (56.5%) 9-1-1 telecommunicators reported utilizing OPI services at least 75% of the time when receiving a call from an LEP caller. Further, 35 (28.7%) of these telecommunicators reported calls with LEP individuals as more stressful than calls with fluent English speakers. Dispatcher stress level during LEP calls compared with stress during calls with fluent English speakers was positively associated with use of OPI services (P < .01). Further, stress level was also positively associated with telecommunicator difficulties in assessing the situation with respect to officer safety (P < .01). Sixty-three (58.3%) of the telecommunicators described difficulties assessing the situation to determine the appropriate response as the biggest challenge with LEP callers. Additionally, 62 (53%) identified knowing their location in English as information LEP callers need to know prior to calling 9-1-1. CONCLUSION: These results highlight intervention opportunities for both 9-1-1 telecommunicators and LEP communities. Together, interventions such as working with LEP communities to educate them on best communication practices during 9-1-1 calls, and with 9-1-1 telecommunicators to help them manage three-way communication and reduce stress associated with concern for officer safety may improve emergency communication during 9-1-1 calls.