Assessing the Effect of Drivers’ Gender on Their Intention to Use Fully Automated Vehicles

Although fully automated vehicles (SAE level 5) are expected to acquire a major relevance for transportation dynamics by the next few years, the number of studies addressing their perceived benefits from the perspective of human factors remains substantially limited. This study aimed, firstly, to assess the relationships among drivers’ demographic factors, their assessment of five key features of automated vehicles (i.e., increased connectivity, reduced driving demands, fuel and trip-related efficiency, and safety improvements), and their intention to use them, and secondly, to test the predictive role of the feature’ valuations over usage intention, focusing on gender as a key differentiating factor. For this cross-sectional research, the data gathered from a sample of 856 licensed drivers (49.4% females, 50.6% males; M = 40.05 years), responding to an electronic survey, was analyzed. Demographic, driving-related data, and attitudinal factors were comparatively analyzed through robust tests and a bias-corrected Multi-Group Structural Equation Modeling (MGSEM) approach. Findings from this work suggest that drivers’ assessment of these AV features keep a significant set of multivariate relationships to their usage intention in the future. Additionally, and even though there are some few structural similarities, drivers’ intention to use an AV can be differentially explained according to their gender. So far, this research constitutes a first approximation to the intention of using AVs from a MGSEM gender-based approach, being these results of potential interest for researchers and practitioners from different fields, including automotive design, transport planning and road safety.

Analysis of Urban Heat Island and Heat Waves Using Sentinel-3 Images: a Study of Andalusian Cities in Spain

At present, understanding the synergies between the Surface Urban Heat Island (SUHI) phenomenon and extreme climatic events entailing high mortality, i.e., heat waves, is a great challenge that must be faced to improve the quality of life in urban zones. The implementation of new mitigation and resilience measures in cities would serve to lessen the effects of heat waves and the economic cost they entail. In this research, the Land Surface Temperature (LST) and the SUHI were determined through Sentinel-3A and 3B images of the eight capitals of Andalusia (southern Spain) during the months of July and August of years 2019 and 2020. The objective was to determine possible synergies or interaction between the LST and SUHI, as well as between SUHI and heat waves, in a region classified as highly vulnerable to the effects of climate change. For each Andalusian city, the atmospheric variables of ambient temperature, solar radiation, wind speed and direction were obtained from stations of the Spanish State Meteorological Agency (AEMET); the data were quantified and classified both in periods of normal environmental conditions and during heat waves. By means of Data Panel statistical analysis, the multivariate relationships were derived, determining which ones statistically influence the SUHI during heat wave periods. The results indicate that the LST and the mean SUHI obtained are statistically interacted and intensify under heat wave conditions. The greatest increases in daytime temperatures were seen for Sentinel-3A in cities by the coast (LST = 3.90 °C, SUHI = 1.44 °C) and for Sentinel-3B in cities located inland (LST = 2.85 °C, SUHI = 0.52 °C). The existence of statistically significant positive relationships above 99% (p < 0.000) between the SUHI and solar radiation, and between the SUHI and the direction of the wind, intensified in periods of heat wave, could be verified. An increase in the urban area affected by the SUHI under heat wave conditions is reported. Graphical Abstract

Effects of Smoking on Short-Term and Long-Term Mortality after Aneurysmal Subarachnoid Hemorrhage

<b><i>Objective:</i></b> The relationship between smoking and clinical outcomes after aneurysmal subarachnoid hemorrhage (aSAH) is poorly clarified, and current pieces of evidence are inconsistent. The purpose of this multicenter cohort study is therefore to explore the relationship between smoking and mortality as well as several complications after aSAH. <b><i>Methods:</i></b> Databases of patient records were from 4 tertiary hospitals. We assessed the impact of tobacco use and tobacco dose (categorized based on smoking index [SI]) on several complication and overall outcome variables. The primary outcome was mortality within the longest follow-up. Logistic models were used to investigate univariate and multivariate relationships between predictors and outcomes. We also developed a propensity score matching for smoking status by using all known confounders. <b><i>Results:</i></b> A total of 6,578 patients with aSAH were analyzed. Current smoking and former smoking did not show association with mortality within the longest follow-up (odds ratio [OR], 0.95, 95% confidence interval [CI]: 0.69–1.30, <i>p</i> = 0.726; OR, 0.66, 95% CI: 0.38–1.15, <i>p</i> = 0.139, respectively). In addition, patients who were current smokers showed an independent association with the decreased occurrence of hydrocephalus (OR, 0.60; 95% CI: 0.41–0.88; <i>p</i> = 0.009) after matching all known confounders. We also found moderate smoking (SI between 384 and 625) was associated with reduced mortality in hospital. <b><i>Conclusions:</i></b> Our results indicated that in patients with aSAH, current smoking or former smoking was not associated with all-cause mortality up to 7-year follow-up.

Racial Disparities in Police Crime Victimization

Policing has become a topic of intense public scrutiny and protest in the aftermath of several recent highly questionable and violent police–citizen encounters including the acts of police violence against George Floyd in Minneapolis (MN), Breonna Taylor in Louisville (KY), and Jacob Blake in Kenosha (WI). These encounters have led to large-scale street protests, the legitimization of the Black Lives Matter movement, and what many commentators perceive as a “national reckoning” on the issue of racial justice. The focus of our research is on police crime—a particular form of police misconduct that involves the criminal arrest of police officers. Our work is designed to identify cases in which law enforcement officers have been arrested for any type of criminal offense(s). One area of police scholarship that has thus far been neglected is the relationship between citizen race and the perpetration of police crime. We are aware of no existing empirical studies on whether, and if so, to what degree, citizen race is associated with crimes committed by police officers. The public has been forced to re-examine and question the role and legitimacy of police against the backdrop of protests and concerns about how police may contribute to racial injustice and discrimination. The broadest research issue involved an examination of the association between police crime and the race of the victim. Our goal was to identify and examine any racial disparities of police crime overall and within specific types of police crime. The analyses compared police crimes committed against Black victims to all other police crimes identified within the dataset. More specifically, we examined the degree to which police crimes perpetrated against Black victims tend to be more violent than those perpetrated against non-Black victims. CHAID regression models were utilized to explore any multivariate relationships between race and police crime. Data were derived from published news articles using the Google News search engine and its Google Alerts email update service. Our database currently includes information on more than 18,700 cases of police crime from years 2005-2021. The study utilized data derived from this larger project. The study examined those cases of police crime in which we have identified a victim and recorded information on the race of the victim. The dataset for this study includes information on 865 criminal arrest cases of sworn nonfederal law enforcement officers within the United States from 2005 through 2014.

Use of the Spectroscopy-Based Veggie Meter® to Objectively Assess Fruit and Vegetable Intake in Low-Income Adults

Reflection spectroscopy is an emerging approach for noninvasively assessing dermal carotenoids as a biomarker of fruit and vegetable (FV) intake. This study sought to profile and identify determinants of scores from a reflection spectroscopy device (the Veggie Meter (VM)®) among 297 urban, primarily Hispanic low-income adults served by the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC). The repeatability of the scores and bi- and multivariate relationships between VM scores, self-reported FV intake measured by a brief screener, and participant characteristics were examined. The mean VM score was 270 (range 0–695); 3- and 6-month test-retest correlations were positive and strong (r = 0.79 and 0.55, respectively). VM scores were negatively associated with body mass index (BMI; r = −0.22) and were higher among participants of Ecuadorian, Dominican, and Mexican Hispanic origin relative to those of Puerto Rican origin; foreign- vs. US-born participants, breastfeeding vs. non-breastfeeding participants, nonsmokers vs. smokers, and participants who consumed three or more cups of FV/day relative to those who consumed less than three cups of FV/day. Foreign-born nativity, consumption of three or more cups of FV/day, and smaller body size were determinants of increased VM scores. Although replication studies are needed to confirm these findings, investigators working with similar populations are encouraged to use the VM to longitudinally track FV intake and to target determinants of the scores in observational and intervention studies of FV intake as measured by the VM.

Multidimensional analysis and detection of informative features in human brain white matter

The white matter contains long-range connections between different brain regions and the organization of these connections holds important implications for brain function in health and disease. Tractometry uses diffusion-weighted magnetic resonance imaging (dMRI) to quantify tissue properties along the trajectories of these connections. Statistical inference from tractometry usually either averages these quantities along the length of each fiber bundle or computes regression models separately for each point along every one of the bundles. These approaches are limited in their sensitivity, in the former case, or in their statistical power, in the latter. We developed a method based on the sparse group lasso (SGL) that takes into account tissue properties along all of the bundles and selects informative features by enforcing both global and bundle-level sparsity. We demonstrate the performance of the method in two settings: i) in a classification setting, patients with amyotrophic lateral sclerosis (ALS) are accurately distinguished from matched controls. Furthermore, SGL identifies the corticospinal tract as important for this classification, correctly finding the parts of the white matter known to be affected by the disease. ii) In a regression setting, SGL accurately predicts “brain age.” In this case, the weights are distributed throughout the white matter indicating that many different regions of the white matter change over the lifespan. Thus, SGL leverages the multivariate relationships between diffusion properties in multiple bundles to make accurate phenotypic predictions while simultaneously discovering the most relevant features of the white matter.

Multivariate Relationships among Carcass Traits and Proximate Composition, Lipid Profile, and Mineral Content of Longissimus lumborum of Grass-Fed Male Cattle Produced under Tropical Conditions

Hierarchical cluster (HCA) and canonical correlation (CCA) analyses were employed to explore the multivariate relationships among chemical components (proximate, mineral and lipidic components) of lean beef longissimus dorsii lumborum (LDL) and selected carcass traits of cattle fattened on pasture under tropical conditions (bulls, n = 60; steers, n = 60; from 2.5 to 4.0 years of age, estimated by dentition). The variables backfat thickness (BFT), Ca, Mn, Cu, C14:0, C15:0, and C20:0 showed the highest coefficients of variation. Three clusters were defined by the HCA. Out of all carcass traits, only BFT differed significantly (p < 0.001) among clusters. Clusters significantly (p < 0.001) differed for total lipids (TLIPIDS), moisture, dry matter (DM), fatty acid composition, cholesterol content, and mineral composition (except for Fe). The variables that define the canonical variate “CARCASS” were BFT and degree of marbling (MARBLING). TLIPIDS was the main variable for the “PROXIMATE” canonical variate, while C16:0 and C18:1c had the most relevant contribution to the “LIPIDS” canonical variate. BFT and MARBLING were highly cross-correlated with TLIPIDS which, in turn, was significantly affected by the IM lipid content. Carcass traits were poorly correlated with mineral content. These findings allow for the possibility to develop selection criteria based on BFT and/or marbling to sort carcasses, from grass-fed cattle fattened under tropical conditions, with differing nutritional values. Further analyses are needed to study the effects of sex condition on the associations among carcass traits and lipidic components.

Classifying information-sharing methods

Background Sparse relative effectiveness evidence is a frequent problem in Health Technology Assessment (HTA). Where evidence directly pertaining to the decision problem is sparse, it may be feasible to expand the evidence-base to include studies that relate to the decision problem only indirectly: for instance, when there is no evidence on a comparator, evidence on other treatments of the same molecular class could be used; similarly, a decision on children may borrow-strength from evidence on adults. Usually, in HTA, such indirect evidence is either included by ignoring any differences (‘lumping’) or not included at all (‘splitting’). However, a range of more sophisticated methods exists, primarily in the biostatistics literature. The objective of this study is to identify and classify the breadth of the available information-sharing methods. Methods Forwards and backwards citation-mining techniques were used on a set of seminal papers on the topic of information-sharing. Papers were included if they specified (network) meta-analytic methods for combining information from distinct populations, interventions, outcomes or study-designs. Results Overall, 89 papers were included. A plethora of evidence synthesis methods have been used for information-sharing. Most papers (n=79) described methods that shared information on relative treatment effects. Amongst these, there was a strong emphasis on methods for information-sharing across multiple outcomes (n=42) and treatments (n=25), with fewer papers focusing on study-designs (n=23) or populations (n=8). We categorise and discuss the methods under four ’core’ relationships of information-sharing: functional, exchangeability-based, prior-based and multivariate relationships, and explain the assumptions made within each of these core approaches. Conclusions This study highlights the range of information-sharing methods available. These methods often impose more moderate assumptions than lumping or splitting. Hence, the degree of information-sharing that they impose could potentially be considered more appropriate. Our identification of four ‘core’ methods of information-sharing allows for an improved understanding of the assumptions underpinning the different methods. Further research is required to understand how the methods differ in terms of the strength of sharing they impose and the implications of this for health care decisions.

The INSESS-COVID19 Project. Evaluating the Impact of the COVID19 in Social Vulnerability While Preserving Privacy of Participants from Minority Subpopulations

In this paper, the results of the project INSESS-COVID19 are presented, as part of a special call owing to help in the COVID19 crisis in Catalonia. The technological infrastructure and methodology developed in this project allows the quick screening of a territory for a quick a reliable diagnosis in front of an unexpected situation by providing relevant decisional information to support informed decision-making and strategy and policy design. One of the challenges of the project was to extract valuable information from direct participatory processes where specific target profiles of citizens are consulted and to distribute the participation along the whole territory. Having a lot of variables with a moderate number of citizens involved (in this case about 1000) implies the risk of violating statistical secrecy when multivariate relationships are analyzed, thus putting in risk the anonymity of the participants as well as their safety when vulnerable populations are involved, as is the case of INSESS-COVID19. In this paper, the entire data-driven methodology developed in the project is presented and the dealing of the small subgroups of population for statistical secrecy preserving described. The methodology is reusable with any other underlying questionnaire as the data science and reporting parts are totally automatized.