Establishment-level safety analytics: a scoping review.

The use of data analytics has seen widespread application in fields such as medicine and supply chain management, but their application in occupational safety has only recently become more common. The purpose of this scoping review was to summarize studies that employed analytics within establishments to reveal insights about work-related injuries or fatalities. Over 300 articles were reviewed to survey the objectives, scope and methods used in this emerging field. We conclude that the promise of analytics for providing actionable insights to address occupational safety concerns is still in its infancy. Our review shows that most articles were focused on method development and validation, including studies that tested novel methods or compared the utility of multiple methods. Many of the studies cited various challenges in overcoming barriers caused by inadequate or inefficient technical infrastructures and unsupportive data cultures that threaten the accuracy and quality of insights revealed by the analytics.

Factors Affecting Medical Residents' Decisions to Work After Call.

BACKGROUND: Accreditation Council for Graduate Medical Education (ACGME) work-hour restrictions (WHRs) are intended to improve patient safety by reducing resident fatigue. Compliance with ACGME WHRs is not universal. PURPOSE: The purpose of this study was to identify factors that influence residents' decisions to take a postcall day (PCD) off according to ACGME WHRs. METHODS: Residents (N = 433) at one university were emailed a link to a survey in 2019. The survey included demographic details and a Discrete Choice Experiment examining influences on resident decisions to take a PCD off. RESULTS: One hundred seventy-five residents (40.4%) responded to the survey; 113 residents (26%) completed the survey. Positive feedback from attending physicians about taking PCDs off in the past had the greatest impact on respondents' decisions to take a PCD off, increasing the probability by 27.3%, followed by chief resident comments about the resident looking tired (16.6% increase), and having never heard their attendings comment about PCDs off as either positive or negative (13.9% increase). Factors that had the largest effect on decreasing the probability of taking a PCD were negative feedback about taking PCDs off (14.3% decrease), continuity of care concerns (10.8% decrease), and whether the resident was looking forward to an assignment (7.9% decrease). CONCLUSIONS: The most important influencer of residents' decisions to take a PCD off was related to feedback from their attending physicians, suggesting that compliance with WHRs can be improved by focusing on the residency program's safety culture.

Establishment-level occupational safety analytics: Challenges and opportunities.

In occupational safety and health, big data and analytics show promise for the prediction and prevention of workplace injuries. Advances in computing power and analytical methods have allowed companies to reveal insights from the "big" data that previously would have gone undetected. Despite the promise, occupational safety has lagged behind other industries, such as supply chain management and healthcare, in terms of exploiting the potential of analytics and much of the data collected by organizations goes unanalyzed. The purpose of the present paper is to argue for the broader application of establishment-level safety analytics. This is accomplished by defining the terms, describing previous research, outlining the necessary components required, and describing knowledge gaps and future directions. The knowledge gaps and future directions for research in establishment-level analytics are categorized into readiness for analytics, analytics methods, technology integration, data culture, and impact of analytics.

A hierarchical cluster analysis of young drivers based on their perceived risk and frequency of texting while driving.

INTRODUCTION: The present study attempted to provide a proof-of-concept of usefulness of cluster analysis for identifying distinct and practically meaningful subgroups of drivers who differed in their perceived risk and frequency of texting while driving (TWD). METHOD: Using a hierarchical cluster analysis, which involves sequential steps in which individual cases are merged together one at a time based on their similarities, the study first attempted to identify distinct subgroups of drivers who differed in their perceived risk and frequency of TWD. To further evaluate the meaningfulness of the subgroups identified, the subgroups were compared in terms of levels of trait impulsivity and impulsive decision making for each gender. RESULTS: The study identified the following three distinct subgroups: (a) drivers who perceive TWD as risky but frequently engage in TWD; (b) drivers who perceive TWD as risky and infrequently engage in TWD; and (c) drivers who perceive TWD as not so risky and frequently engage in TWD. The subgroup of male, but not female, drivers who perceive TWD as risky but frequently engage in TWD showed significantly higher levels of trait impulsivity, but not impulsive decision making, than the other two subgroups. DISCUSSION: This is the first demonstration that drivers who frequently engage in TWD can be categorized into two distinct subgroups that differ in terms of the perceived risk of TWD. PRACTICAL APPLICATIONS: For drivers who perceived TWD as risky yet frequently engage in TWD, the present study suggests that different intervention strategies may be needed for each gender.

Urinary concentrations of bisphenols and parabens and their association with attention, hyperactivity and impulsivity at adolescence.

BACKGROUND: Neurobehavioural disorder diagnoses have been increasing over the last decades, leading to heightened interest in the aetiological factors involved. Endocrine disrupting chemicals, such as parabens and bisphenols, have been suggested as one of those factors. It is unknown whether exposure during adolescence may affect neurobehavioural development. OBJECTIVE: To determine whether urinary concentrations of parabens and bisphenols are associated with attention and concentration in adolescents, in general and sex-specific. METHODS: We invited 188 adolescents (13-15 years old) for the follow-up birth cohort-study. Concentrations of five parabens and three bisphenols (BPA; BPF; BPS) were measured in morning urine after overnight fasting, using a validated LC-MS/MS method. Attention and concentration were assessed at the clinic with subtests of the Test of Everyday Attention in Children and the Dutch Attention Deficit Hyperactivity Disorder questionnaire (AVL), the latter being filled in by parents. Linear regression analyses were performed, adjusting for urine creatinine concentrations and potential confounding factors. RESULTS: 101 (54%) adolescents participated (46 girls; 55 boys). Urinary paraben concentrations were higher in girls than in boys. Methylparaben was positively associated with attention in girls (p ≤ .05; B= -2.836; 95%CI= -5.175;-.497), ethylparaben negatively with hyperactivity (p ≤ .05; B= -1.864; 95%CI= -3.587;-.141). Butylparaben was associated with more optimal scores on parent reported attention. Propylparaben was negatively associated with scores on sustained auditory attention in girls (p ≤ .10; B=.444; 95%CI= -.009;.896). Bisphenol concentrations were not associated with scores on attention and concentration after adjusting for confounders. CONCLUSION: In 13-15-year-old Dutch adolescents, urinary concentrations of methylparaben and ethylparaben were associated with better attention and less hyperactivity, whereas a trend toward significance was found between higher urinary propylparaben concentrations and poorer attention. Bisphenol concentrations were not associated with attention and concentration after adjusting for confounders.

Do employees' work schedules put them at-risk? The role of shift scheduling and holidays in predicting near miss and incident likelihood.

INTRODUCTION: Using crew scheduling and injury incident data from a Fortune 500 manufacturing company, this study analyzed the effect of consecutive shifts and shifts near holidays on near misses and incidents. METHODS: Logistic regressions were conducted with consecutive workdays, days near holidays, and time of shift as predictors of incident and near miss outcomes. RESULTS: The logistic regression analysis indicated that working consecutive day shifts increases the probability of an incident occurring, with the fourth consecutive shift resulting in the most risk. The consecutive shift pattern did not replicate to employees working the night shift. However, the first and second shifts when transferring to a night schedule appear to have a greater chance of incident. Shifts near holidays did not have a significantly higher risk than other shifts. PRACTICAL APPLICATION: The current research suggests that organizations can use similar analytic techniques to determine if shift scheduling might be related to increased risk and allocate resources to mitigate hazards during those peak probability shifts.

Perceptions of Fatigue and Safety Climate Pertaining to Residency Duty-Hour Restrictions.

INTRODUCTION: The Accreditation Council for Graduate Medical Education (ACGME), which sets the standards for residency training, instituted work-hour restrictions in 2003. Our purpose was to assess residents' perceptions of fatigue and local safety climate specific to these duty-hour restrictions. METHODS:  All residents (N=433) at one university were emailed a link to a survey in 2019. The survey included demographic details, on-call descriptors, an 18-point climate survey (CS), and the 33-point Chalder Fatigue Questionnaire (CFQ). The CS was adapted from a commonly used safety climate scale and intended to measure the respondent's perceptions of their program's attitudes and practices around resident duty-hour compliance. A Pearson correlational analysis was used to determine if there were associations between the variables. RESULTS:  Mean CS score was 12.89 (95% confidence interval, CI 12.32-13.46, N=164, 48.5%). Respondents were most likely to disagree with "Residents are told when they are at risk of working beyond ACGME duty-hour restrictions," where 57 (34.7%) disagreed or strongly disagreed. Mean CFQ score was 16.02 (95% CI 14.87-17.17, N=113, 26.1%). As the CS score improved, CFQ scores decreased indicating an inverse relationship between duty-hour climate and fatigue (r=-0.328, p<0.05). Having a protected post-call day off, and having either the Program Director, Chief Resident, or Senior Resident decide that a resident takes a post-call day off were all associated with higher CS scores.  Conclusion: We found that the CS had good internal consistency and evidence of construct validity. An inverse relationship between CS score and fatigue suggests that the level of fatigue is higher among residents in programs where residents perceived that ACGME duty-hour compliance was less important.

An introduction to "discrete choice experiments" for behavior analysts.

In this paper, we introduce discrete choice experiments (DCEs) and provide foundational knowledge on the topic. DCEs are one of the most popular methods within econometrics to study the distribution of choices within a population. DCEs are particularly useful when studying the effects of categorical variables on choice. Procedurally, a DCE involves recruiting a large sample of individuals exposed to a set of choice arrays. The factors that are suspected to affect choice are varied systematically across the choice arrays. Most commonly, DCE data are analyzed with a multinomial logit statistical model with a goal of determining the relative utility of each relevant factor. We also discuss DCEs in comparison with behavioral choice models, such as those based on the matching law, and we show an example of a DCE to illustrate how a DCE can be used to understand choice with behavioral, social, and organizational factors.

Perceptions of Safety Climate and Fatigue Related to ACGME Residency Duty Hour Restrictions in Otolaryngology Residents.

OBJECTIVE: To compare otolaryngology residents' perceptions of safety climate with respect to duty hour compliance and self-perceived fatigue. STUDY DESIGN: Cross-sectional study. SETTING: Forty-one otolaryngology residencies distributed across the United States. METHODS: A national sample of otolaryngology residents was surveyed electronically in 2019. The survey included demographic details, on-call descriptors, an 18-point Safety Climate Survey (SCS) modified to measure perceptions of program attitudes and practices around resident duty hour compliance, and the 33-point Chalder Fatigue Questionnaire (CFQ). RESULTS: Of 397 surveyed residents, 205 (51.6%) responded. The mean modified SCS score was 11.29 out of 18 (95% CI, 10.76-11.81). Respondents were most likely to disagree with "Residents are told when they are at risk of working beyond ACGME [Accreditation Council for Graduate Medical Education] duty hour restrictions," where 100 (48.8%) disagreed or strongly disagreed. The mean CFQ score was 15.99 of 33 (95% CI, 15.17-16.81). As the modified SCS score improved, CFQ scores decreased, indicating an inverse relationship between duty hour safety climate and fatigue. Having a protected postcall day off and having the program director, chief resident, or senior resident decide that a resident should take a postcall day off were all associated with higher modified SCS scores. CONCLUSION: Otolaryngology residents perceived a safety climate that is suboptimal with regard to duty hour restriction issues. Additionally, an inverse relationship between fatigue and modified SCS scores suggests that fatigue among residents may be lower in programs where residents perceive that ACGME duty hour compliance is more important.

Multistate Outbreak of SARS-CoV-2 Infections, Including Vaccine Breakthrough Infections, Associated with Large Public Gatherings, United States.

During July 2021, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) B.1.617.2 variant infections, including vaccine breakthrough infections, occurred after large public gatherings in Provincetown, Massachusetts, USA, prompting a multistate investigation. Public health departments identified primary and secondary cases by using coronavirus disease surveillance data, case investigations, and contact tracing. A primary case was defined as SARS-CoV-2 detected <14 days after travel to or residence in Provincetown during July 3-17. A secondary case was defined as SARS-CoV-2 detected <14 days after close contact with a person who had a primary case but without travel to or residence in Provincetown during July 3-August 10. We identified 1,098 primary cases and 30 secondary cases associated with 26 primary cases among fully and non-fully vaccinated persons. Large gatherings can have widespread effects on SARS-CoV-2 transmission, and fully vaccinated persons should take precautions, such as masking, to prevent SARS-CoV-2 transmission, particularly during substantial or high transmission.

Advancing safety analytics: A diagnostic framework for assessing system readiness within occupational safety and health.

Big data and analytics have shown promise in predicting safety incidents and identifying preventative measures directed towards specific risk variables. However, the safety industry is lagging in big data utilization due to various obstacles, which may include lack of data readiness (e.g., disparate databases, missing data, low validity) and personnel competencies. This paper provides a primer on the application of big data to safety. We then describe a safety analytics readiness assessment framework that highlights system requirements and the challenges that safety professionals may encounter in meeting these requirements. The proposed framework suggests that safety analytics readiness depends on (a) the quality of the data available, (b) organizational norms around data collection, scaling, and nomenclature, (c) foundational infrastructure, including technological platforms and skills required for data collection, storage, and analysis of health and safety metrics, and (d) measurement culture, or the emergent social patterns between employees, data acquisition, and analytic processes. A safety-analytics readiness assessment can assist organizations with understanding current capabilities so measurement systems can be matured to accommodate more advanced analytics for the ultimate purpose of improving decisions that mitigate injury and incidents.

Outbreak of SARS-CoV-2 Infections, Including COVID-19 Vaccine Breakthrough Infections, Associated with Large Public Gatherings - Barnstable County, Massachusetts, July 2021.

During July 2021, 469 cases of COVID-19 associated with multiple summer events and large public gatherings in a town in Barnstable County, Massachusetts, were identified among Massachusetts residents; vaccination coverage among eligible Massachusetts residents was 69%. Approximately three quarters (346; 74%) of cases occurred in fully vaccinated persons (those who had completed a 2-dose course of mRNA vaccine [Pfizer-BioNTech or Moderna] or had received a single dose of Janssen [Johnson & Johnson] vaccine ≥14 days before exposure). Genomic sequencing of specimens from 133 patients identified the B.1.617.2 (Delta) variant of SARS-CoV-2, the virus that causes COVID-19, in 119 (89%) and the Delta AY.3 sublineage in one (1%). Overall, 274 (79%) vaccinated patients with breakthrough infection were symptomatic. Among five COVID-19 patients who were hospitalized, four were fully vaccinated; no deaths were reported. Real-time reverse transcription-polymerase chain reaction (RT-PCR) cycle threshold (Ct) values in specimens from 127 vaccinated persons with breakthrough cases were similar to those from 84 persons who were unvaccinated, not fully vaccinated, or whose vaccination status was unknown (median = 22.77 and 21.54, respectively). The Delta variant of SARS-CoV-2 is highly transmissible (1); vaccination is the most important strategy to prevent severe illness and death. On July 27, CDC recommended that all persons, including those who are fully vaccinated, should wear masks in indoor public settings in areas where COVID-19 transmission is high or substantial.* Findings from this investigation suggest that even jurisdictions without substantial or high COVID-19 transmission might consider expanding prevention strategies, including masking in indoor public settings regardless of vaccination status, given the potential risk of infection during attendance at large public gatherings that include travelers from many areas with differing levels of transmission.

Texting while driving: A discrete choice experiment.

Texting while driving is one of the most dangerous types of distracted driving and contributes to a large number of transportation incidents and fatalities each year. Drivers text while driving despite being aware of the risks. Although some factors related to the decision to text while driving have been elucidated, more remains to be investigated in order to better predict and prevent texting while driving. To study decision making involved in reading a text message while driving, we conducted a discrete choice experiment with 345 adult participants recruited from Amazon's Mechanical Turk. Participants were presented with multiple choice sets, each involving two different scenarios, and asked to choose the scenario in which they would be more likely to text while driving. The attributes of the scenarios were the relationship to the text-message sender, the road conditions, and the importance of the message. The attributes varied systematically across the choice sets. Participants were more likely to read a text message while driving if the sender of the message was a significant other, the message was perceived to be very important, and the participant was driving on rural roads. Discrete choice experiments offer a promising approach to studying decision making in drivers and other populations because they allow for an analysis of multiple factors simultaneously and the trade-offs among different choices.

Two discrete choice experiments on laboratory safety decisions and practices.

INTRODUCTION: The path toward enhancing laboratory safety requires a thorough understanding of the factors that influence the safety-related decision making of laboratory personnel. METHOD: We developed and administered a web-based survey to assess safety-related decision making of laboratory personnel of a government research organization. The survey included two brief discrete choice experiments (DCEs) that allowed for quantitative analysis of specific factors that potentially influence safety-related decisions and practices associated with two different hypothetical laboratory safety scenarios. One scenario related to reporting a laboratory spill, and the other scenario involved changing protective gloves between laboratory rooms. The survey also included several brief self-report measures of attitude, perception, and behavior related to safety practices. RESULTS: Risk perception was the most influential factor in safety-related decision making in both scenarios. Potential negative consequences and effort associated with reporting an incident and the likelihood an incident was detected by others also affected reporting likelihood. Wearing gloves was also affected somewhat by perceived exposure risk, but not by other social or work-related factors included in the scenarios. CONCLUSIONS: The study demonstrated the promise of DCEs in quantifying the relative impact of several factors on safety-related choices of laboratory workers in two hypothetical but realistic scenarios. Participants were faced with hypothetical choice scenarios with realistic features instead of traditional scaling techniques that ask about attitudes and perceptions. The methods are suitable for addressing many occupational safety concerns in which workers face tradeoffs in their safety-related decisions and behavior. Practical Application: Safety-related decisions regarding laboratory practices such as incident reporting and use of PPE were influenced primarily by workers' perceptions of risk of exposure and severity of risks to health and safety. This finding suggests the importance of providing laboratory workers with adequate and effective education and training on the hazards and risks associated with their work. DCEs are a promising research method for better understanding the relative influences of various personal, social, and organizational factors that shape laboratory safety decisions and practices. The information gained from DCEs may lead to more targeted training materials and interventions.

Fetal Brain-Sparing, Postnatal Cerebral Oxygenation, and Neurodevelopment at 4 Years of Age Following Fetal Growth Restriction.

Objectives: To assess the role of fetal brain-sparing and postnatal cerebral oxygen saturation (rcSO2) as determinants of long-term neurodevelopmental outcome following fetal growth restriction (FGR). Methods: This was a prospective follow-up study of an FGR cohort of 41 children. Prenatally, the presence of fetal brain-sparing (cerebroplacental ratio < 1) was assessed by Doppler ultrasound. During the first two days after birth, rcSO2 was measured with near-infrared spectroscopy. At 4 years of age, intelligence (IQ points), behavior (T-scores), and executive function (T-scores) were assessed using the Wechsler Preschool and Primary Scale of Intelligence, Child Behavior Checklist, and Behavior Rating Inventory of Executive Function-Preschool Version, respectively. Using linear regression analyses, we tested the association (p < 0.05) between brain-sparing/rcSO2 and normed neurodevelopmental scores. Results: Twenty-six children (gestational age ranging from 28.0 to 39.9 weeks) participated in the follow-up at a median age of 4.3 (range: 3.6 to 4.4) years. Autism spectrum disorder was reported in three children (11.5%). Fetal brain-sparing was associated with better total and externalizing behavior (betas: -0.519 and -0.494, respectively). RcSO2 levels above the lowest quartile, particularly on postnatal day 2 (≥ 77%), were associated with better total and internalizing behavior and executive functioning (betas: -0.582, -0.489, and -0.467, respectively), but also lower performance IQ (beta: -0.530). Brain-sparing mediated some but not all of these associations. Conclusions: In this FGR cohort, fetal brain-sparing and high postnatal rcSO2 were-independently, but also as a reflection of the same mechanism-associated with better behavior and executive function. Postnatal cerebral hyperoxia, however, was negatively associated with brain functions responsible for performance IQ.

A cluster analysis of text message users based on their demand for text messaging: A behavioral economic approach.

The goal of this study was to determine whether cluster analysis could be used to identify distinct subgroups of text message users based on behavioral economic indices of demand for text messaging. Cluster analysis is an analytic technique that attempts to categorize cases based on similarities across selected variables. Participants completed a questionnaire about mobile phone usage and a hypothetical texting demand task in which they indicated their likelihood of paying an extra charge to continue to send text messages. A hierarchical cluster analysis was conducted on behavioral economic indices, such as demand intensity, demand elasticity, breakpoint, and the maximum expenditure. With the cluster analysis, we identified 3 subgroups of text message users. The groups were characterized by (a) high intensity and low elasticity, (b) high intensity and medium elasticity, and (c) low intensity and high elasticity. In a demonstration of convergent validity, there were statistically significant and conceptually meaningful differences across the subgroups in various measures of mobile phone use and text messaging. Cluster analysis is a useful tool for identifying and profiling distinct, practically meaningful groups based on behavioral indices and could provide a framework for targeting interventions more efficiently.

Social distance and texting while driving: A behavioral economic analysis of social discounting.

Objective: Texting while driving is a dangerous behavior. Drivers continue to engage in the behavior despite knowing its risks, and the factors responsible for the decision to text while driving are poorly understood. This study examined how the relationship of the sender to the driver, in addition to the delay to the destination, may affect the decision to text while driving with the use of a social- and delay-discounting paradigm. Methods: Ninety-four (N = 94) undergraduate students completed a hypothetical social- and delay-discounting task in which they rated their likelihood of replying to a text message immediately versus waiting to reply until arriving at a destination. The social distance of the sender and the delay to the destination were varied across trials. Results: For both social and delay discounting, the likelihood of replying and waiting, respectively, decreased as a function of social distance and delay to the destination. Participants were more likely to text while driving as the social distance of the sender decreased and the delay to the destination increased. Social discounting varied inversely as a function of delay to the destination: The shorter the delay to the destination, the greater the social discounting. Conclusion: The findings indicate that social distance of the sender is an important factor involved in the decision to text while driving. Participants were more likely to reply to a text while driving if the sender was less socially distant. When they were closer to their destination, they were less likely to reply to socially distant people than those closer to them. The roles that social consequences play in drivers' decision making to text while driving are discussed.

Threat appeals reduce impulsive decision making associated with texting while driving: A behavioral economic approach.

The primary purpose of the present study was to examine the effectiveness of threat appeals in influencing impulsive decision making associated with texting while driving. The participants in the treatment group were exposed to a threatening message about the danger of texting while driving, whereas those in the control group were exposed to a non-threatening message. Following the exposure to either message, the participants completed a delay-discounting task that assessed the degree of impulsive decision making in a hypothetical texting-while-driving scenario. A comparison between the groups revealed that the threat appeals reduced the degree of impulsive decision making associated with texting while driving. In addition, the threat appeals led to greater anticipated regret from texting while driving, less favorable attitudes toward texting while driving, and decreased intentions to text while driving in the future in the treatment group. These results suggest that video-based threat appeals are promising intervention strategies for the public health challenge of texting while driving. Implications from the behavioral economic perspective are discussed.

A Behavioral Economic Analysis of Demand for Texting while Driving.

The overarching goal of the present study was to determine whether a behavioral economic framework of demand analysis is applicable to texting while driving. To this end, we developed a novel hypothetical task designed to quantify the intensity and elasticity of the demand for social interaction from texting while driving. This task involved a scenario in which participants receive a text message while driving, and they rated the likelihood of replying to a text message immediately versus waiting to reply until arriving at a destination when the amounts of a fine for texting while driving ranged from $1 to $300. To assess the construct validity of the task, the scenario presented two delays to a destination (15 min and 60 min). The demand for social interaction from texting was more intense (greater at the lowest amount of the fine) and less elastic (less sensitive to the increase in the amounts of the fine) for drivers who self-reported a higher frequency of texting while driving than for those who self-reported a lower frequency of texting while driving. Demand was also more intense and less elastic under the 60-min delay condition than under the 15-min condition. The results of this proof-of-concept study suggest that behavioral economic demand analyses are potentially useful for understanding and predicting texting while driving.

A Monte Carlo method for comparing generalized estimating equations to conventional statistical techniques for discounting data.

Discounting is the process by which outcomes lose value. Much of discounting research has focused on differences in the degree of discounting across various groups. This research has relied heavily on conventional null hypothesis significance tests that are familiar to psychologists, such as t-tests and ANOVAs. As discounting research questions have become more complex by simultaneously focusing on within-subject and between-group differences, conventional statistical testing is often not appropriate for the obtained data. Generalized estimating equations (GEE) are one type of mixed-effects model that are designed to handle autocorrelated data, such as within-subject repeated-measures data, and are therefore more appropriate for discounting data. To determine if GEE provides similar results as conventional statistical tests, we compared the techniques across 2,000 simulated data sets. The data sets were created using a Monte Carlo method based on an existing data set. Across the simulated data sets, the GEE and the conventional statistical tests generally provided similar patterns of results. As the GEE and more conventional statistical tests provide the same pattern of result, we suggest researchers use the GEE because it was designed to handle data that has the structure that is typical of discounting data.