Enabling witnesses to actively explore faces and reinstate study-test pose during a lineup increases discriminability.

Accurate witness identification is a cornerstone of police inquiries and national security investigations. However, witnesses can make errors. We experimentally tested whether an interactive lineup, a recently introduced procedure that enables witnesses to dynamically view and explore faces from different angles, improves the rate at which witnesses identify guilty over innocent suspects compared to procedures traditionally used by law enforcement. Participants encoded 12 target faces, either from the front or in profile view, and then attempted to identify the targets from 12 lineups, half of which were target present and the other half target absent. Participants were randomly assigned to a lineup condition: simultaneous interactive, simultaneous photo, or sequential video. In the front-encoding and profile-encoding conditions, Receiver Operating Characteristics analysis indicated that discriminability was higher in interactive compared to both photo and video lineups, demonstrating the benefit of actively exploring the lineup members' faces. Signal-detection modeling suggested interactive lineups increase discriminability because they afford the witness the opportunity to view more diagnostic features such that the nondiagnostic features play a proportionally lesser role. These findings suggest that eyewitness errors can be reduced using interactive lineups because they create retrieval conditions that enable witnesses to actively explore faces and more effectively sample features.

Research Goes Red: Early Experience With a Participant-Centric Registry.

RATIONALE: Cardiovascular disease remains the leading cause of death in women. To address its determinants including persisting cardiovascular risk factors amplified by sex and race inequities, novel personalized approaches are needed grounded in the engagement of participants in research and prevention. OBJECTIVE: To report on a participant-centric and personalized dynamic registry designed to address persistent gaps in understanding and managing cardiovascular disease in women. METHODS AND RESULTS: The American Heart Association and Verily launched the Research Goes Red registry (RGR) in 2019, as an online research platform available to consenting individuals over the age of 18 years in the United States. RGR aims to bring participants and researchers together to expand knowledge by collecting data and providing an open-source longitudinal dynamic registry for conducting research studies. As of July 2021, 15 350 individuals have engaged with RGR. Mean age of participants was 48.0 48.0±0.2 years with a majority identifying as female and either non-Hispanic White (75.7%) or Black (10.5%). In addition to 6 targeted health surveys, RGR has deployed 2 American Heart Association-sponsored prospective clinical studies based on participants' areas of interest. The first study focuses on perimenopausal weight gain, developed in response to a health concerns survey. The second study is designed to test the use of social media campaigns to increase awareness and participation in cardiovascular disease research among underrepresented millennial women. CONCLUSIONS: RGR is a novel online participant-centric platform that has successfully engaged women and provided critical data on women's heart health to guide research. Priorities for the growth of RGR are centered on increasing reach and diversity of participants, and engaging researchers to work within their communities to leverage the platform to address knowledge gaps and improve women's health.

Linguistically complex recognition prompts in pre-recorded cross-examinations.

This study examined the effects of pre-trial preparation and pre-recorded cross-examinations on the linguistic complexity of recognition prompts (i.e., option-posing or suggestive questions) used when questioning child victims in English criminal courts. The study also compared the linguistic complexity of recognition prompts that did and did not contain suggestive content. Analyses compared 43 cases that involved pre-recorded cross-examinations with pre-trial preparation and 44 cases that did not, which occurred between 2012 and 2016. Cases utilizing the "special measures" contained fewer linguistically complex prompts with and without suggestive content than did their counterparts, demonstrating the benefits of those special measures. Overall, linguistically complex recognition prompts were more likely to contain suggestive content than other recognition prompts. However, linguistically complex prompts with and without suggestive content were still frequently used despite the special measures, demonstrating the need for further professional training to improve the quality of children's evidence.

A critical analysis of gender-based violence reporting and evidence building applications (GBVxTech) for capturing memory reports.

Introduction: Gender-based violence (GBV) is under-reported to the authorities owing to the stigma, shame, and fear of reprisal that surrounds these crimes. To address this, there has been an influx of technologies, including mobile phone and online applications that allow victim-survivors (hereafter, victims) to document and report GBV (hereafter referred to as GBVxTech). We critically analysed the extent to which GBVxTech applications align with the scientific knowledge base on gathering accounts of crimes from victims and witnesses. Methods: We identified 41 reporting and evidence building applications from around the world but found many (n = 19) were no longer accessible. A total of 13 applications met the study criteria and were available for download. We evaluated each application on how well its design and features align with established minimum best practice standards for gathering complete and accurate accounts from witnesses and victims, such as the pre-interview instructions (e.g., setting ground rules), questioning approach (e.g., using open-ended questions), and the adequacy of security features (e.g., password protection). Results and Discussion: We found most applications employ open questions, encourage victims to report information in an independent voice, and seek to elicit information pertinent to a criminal investigation. None of the applications use leading questions. However, most applications do not establish ground rules, and many use forced-choice questions, do not time stamp the information gathered, or document when users change their answers. Many applications have limited security features, potentially compromising users' safety. Further, some applications do not provide information about how to use the app, an informed consent procedure, or data usage information. We discuss the findings and offer recommendations for future GBVxTech development.

Facilitating Harmonization of Variables in Framingham, MESA, ARIC, and REGARDS Studies Through a Metadata Repository.

BACKGROUND: High-quality research in cardiovascular prevention, as in other fields, requires inclusion of a broad range of data sets from different sources. Integrating and harmonizing different data sources are essential to increase generalizability, sample size, and representation of understudied populations-strengthening the evidence for the scientific questions being addressed. METHODS: Here, we describe an effort to build an open-access repository and interactive online portal for researchers to access the metadata and code harmonizing data from 4 well-known cohort studies-the REGARDS (Reasons for Geographic and Racial Differences in Stroke) study, FHS (Framingham Heart Study), MESA (Multi-Ethnic Study of Atherosclerosis), and ARIC (Atherosclerosis Risk in Communities) study. We introduce a methodology and a framework used for preprocessing and harmonizing variables from multiple studies. RESULTS: We provide a real-case study and step-by-step guidance to demonstrate the practical utility of our repository and interactive web page. In addition to our successful development of such an open-access repository and interactive web page, this exercise in harmonizing data from multiple cohort studies has revealed several key themes. These themes include the importance of careful preprocessing and harmonization of variables, the value of creating an open-access repository to facilitate collaboration and reproducibility, and the potential for using harmonized data to address important scientific questions and disparities in cardiovascular disease research. CONCLUSIONS: By integrating and harmonizing these large-scale cohort studies, such a repository may improve the statistical power and representation of understudied cohorts, enabling development and validation of risk prediction models, identification and investigation of risk factors, and creating a platform for racial disparities research. REGISTRATION: URL: https://precision.heart.org/duke-ninds.

Acute alcohol intoxication and alcohol expectancy effects on women's memory for consensual and non-consensual sexual activity.

Objective: To test whether acute alcohol intoxication and alcohol expectancy affects how accurately women remember consensual and non-consensual sexual activity that occurred during an interactive hypothetical dating scenario. Design: A balanced placebo randomized study that varied alcohol dose (mean Breath Alcohol Content; BrAC = 0.06%) and alcohol expectancy prior to participants encoding a hypothetical interactive rape scenario was implemented. Participants could elect to consent to sexual activity with a male partner in the hypothetical scenario. If they stopped consenting, non-consensual sexual intercourse (i.e., rape) was described. Seven days later, participants' memory for consensual and non-consensual sexual activity in the scenario was tested. Main outcome measures: Memory accuracy, confidence, and feelings of intoxication. Results: A total of 90 females (M age = 20.5, SD = 2.2) were tested regarding their memory accuracy for the consensual and non-consensual sexual activities in the scenario. A multi-level logistic regression predicting memory accuracy for the perpetrator's behaviors during the rape indicated no effect of alcohol intoxication. However, a main effect of alcohol expectancy was found, whereby participants who expected to consume alcohol, compared to those who did not, recalled the perpetrator's behaviors during the rape more accurately. A second regression predicting memory accuracy for consensual sexual activity found no main effects for alcohol intoxication or alcohol expectancy. Participants recalled consensual sexual activity with a high degree of accuracy. Calibration analyses indicated that accuracy increased with confidence level, regardless of intoxication level or alcohol expectancy condition, but that women tended to be overconfident in general. Conclusion: This study provides an important test of how accurately women remember consensual and non-consensual sexual activities. The accuracy of this information is important for forensic medical examinations and police investigations following an allegation of sexual assault. Increased memory accuracy was found for offence details when participants expected to consume alcohol, suggesting there may be important differences in attentional processes (e.g., hypervigilance) depending on whether threat is present. Further research is necessary to investigate memory for sexual violence in real-world settings and to test methods for ascertaining the most complete and reliable accounts.

Government responses to gender-based violence during COVID-19.

Gender-based violence (GBV) significantly and substantially threatens women's health. The COVID-19 pandemic has exacerbated existing risks and patterns of GBV. The impact of COVID-19 on GBV is not inevitable, however, and can be mediated by the policies of governments. In this study we developed the Government GBV Response Index to systematically examine how countries (N = 60) performed in response to the pandemic with respect to the government 1) enacting specific national-level GBV policy; 2) making dedicated COVID-19 specific funding available; and 3) adapting existing GBV responses to COVID-19 related restrictions and challenges. Most countries (N = 33) delivered fewer than two policy responses. We also performed rapid case study analyses to investigate what might contribute to countries having more comprehensive government policy. We find that civil society organizations played a key role in facilitating GBV policy during the pandemic, especially if they are well-funded and well-connected to the government, and if the country has a high-level government official responsible for gender issues.

Machine Learning-Based Models Incorporating Social Determinants of Health vs Traditional Models for Predicting In-Hospital Mortality in Patients With Heart Failure.

Importance: Traditional models for predicting in-hospital mortality for patients with heart failure (HF) have used logistic regression and do not account for social determinants of health (SDOH). Objective: To develop and validate novel machine learning (ML) models for HF mortality that incorporate SDOH. Design, Setting, and Participants: This retrospective study used the data from the Get With The Guidelines-Heart Failure (GWTG-HF) registry to identify HF hospitalizations between January 1, 2010, and December 31, 2020. The study included patients with acute decompensated HF who were hospitalized at the GWTG-HF participating centers during the study period. Data analysis was performed January 6, 2021, to April 26, 2022. External validation was performed in the hospitalization cohort from the Atherosclerosis Risk in Communities (ARIC) study between 2005 and 2014. Main Outcomes and Measures: Random forest-based ML approaches were used to develop race-specific and race-agnostic models for predicting in-hospital mortality. Performance was assessed using C index (discrimination), regression slopes for observed vs predicted mortality rates (calibration), and decision curves for prognostic utility. Results: The training data set included 123 634 hospitalized patients with HF who were enrolled in the GWTG-HF registry (mean [SD] age, 71 [13] years; 58 356 [47.2%] female individuals; 65 278 [52.8%] male individuals. Patients were analyzed in 2 categories: Black (23 453 [19.0%]) and non-Black (2121 [2.1%] Asian; 91 154 [91.0%] White, and 6906 [6.9%] other race and ethnicity). The ML models demonstrated excellent performance in the internal testing subset (n = 82 420) (C statistic, 0.81 for Black patients and 0.82 for non-Black patients) and in the real-world-like cohort with less than 50% missingness on covariates (n = 553 506; C statistic, 0.74 for Black patients and 0.75 for non-Black patients). In the external validation cohort (ARIC registry; n = 1205 Black patients and 2264 non-Black patients), ML models demonstrated high discrimination and adequate calibration (C statistic, 0.79 and 0.80, respectively). Furthermore, the performance of the ML models was superior to the traditional GWTG-HF risk score model (C index, 0.69 for both race groups) and other rederived logistic regression models using race as a covariate. The performance of the ML models was identical using the race-specific and race-agnostic approaches in the GWTG-HF and external validation cohorts. In the GWTG-HF cohort, the addition of zip code-level SDOH parameters to the ML model with clinical covariates only was associated with better discrimination, prognostic utility (assessed using decision curves), and model reclassification metrics in Black patients (net reclassification improvement, 0.22 [95% CI, 0.14-0.30]; P < .001) but not in non-Black patients. Conclusions and Relevance: ML models for HF mortality demonstrated superior performance to the traditional and rederived logistic regressions models using race as a covariate. The addition of SDOH parameters improved the prognostic utility of prediction models in Black patients but not non-Black patients in the GWTG-HF registry.

Association Between Coffee Intake and Incident Heart Failure Risk: A Machine Learning Analysis of the FHS, the ARIC Study, and the CHS.

BACKGROUND: Coronary heart disease, heart failure (HF), and stroke are complex diseases with multiple phenotypes. While many risk factors for these diseases are well known, investigation of as-yet unidentified risk factors may improve risk assessment and patient adherence to prevention guidelines. We investigated the diet domain in FHS (Framingham Heart Study), CHS (Cardiovascular Heart Study), and the ARIC study (Atherosclerosis Risk in Communities) to identify potential lifestyle and behavioral factors associated with coronary heart disease, HF, and stroke. METHODS: We used machine learning feature selection based on random forest analysis to identify potential risk factors associated with coronary heart disease, stroke, and HF in FHS. We evaluated the significance of selected variables using univariable and multivariable Cox proportional hazards analysis adjusted for known cardiovascular risks. Findings from FHS were then validated using CHS and ARIC. RESULTS: We identified multiple dietary and behavioral risk factors for cardiovascular disease outcomes including marital status, red meat consumption, whole milk consumption, and coffee consumption. Among these dietary variables, increasing coffee consumption was associated with decreasing long-term risk of HF congruently in FHS, ARIC, and CHS. CONCLUSIONS: Higher coffee intake was found to be associated with reduced risk of HF in all three studies. Further study is warranted to better define the role, possible causality, and potential mechanism of coffee consumption as a potential modifiable risk factor for HF.