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Central line...associated bloodstream infection rates increased during the Omicron surge at our rural academic medical center. To identify potential drivers of this increase, we investigated period- and patient-specific factors associated with the increase in central line...associated bloodstream infection. Increased central line utilization, decreased central line bundle compliance monitoring, increased proportion of traveling nurses, increased short-term venous catheter use in the internal jugular vein, increased multilumen catheter use, decreased port...associated infection, and increased patient acuity were significantly associated with the surge. Our results helped us target our local infection prevention efforts. ... CLABSI rates increased during COVID-19 Omicron surge.... Patients with CLABSI were more acutely ill during the surge.... Central line utilization and proportion of traveling nurses increased during the surge.... Infection prevention bundle compliance monitoring decreased during the surge. [ABSTRACT FROM AUTHOR]

Abstract An emerging body of research suggests that people who are high in generalized anxiety disorder (GAD) symptoms are elevated in negative urgency (NU), a personality trait or disposition that reflects a tendency to engage in rash action to reduce distress. The factors that strengthen or weaken the relationship of NU to GAD symptom severity remain unclear; however, individual differences in attentional control, cognitive flexibility, and attitudes toward problem solving may be constructs that moderate the relationship. To examine this hypothesis, a large sample of community participants (N = 274) completed questionnaires online. Consistent with past research, greater NU was associated with greater GAD symptom severity. Greater GAD symptom severity and greater NU were both associated with lower attentional control, lower cognitive flexibility and a negative attitude toward the problem solving process. However, these cognitive processes did not moderate the relationship between NU and severity of GAD symptoms. Although the primary hypothesis was not supported, these findings advance our understanding of NU by providing support for a role of specific cognitive processes in NU. Highlights • Greater negative urgency was associated with greater GAD symptom severity. • Cognitive processes did not influence the link between negative urgency and GAD. • However, self-reported cognitive processes were associated with negative urgency. • Supports that compromised cognitive resources are implicated in negative urgency. [ABSTRACT FROM AUTHOR]

This research presents an analysis of spatial access to both opioid use disorder treatment facilities and emergency medical services in New Hampshire during 2015-2016, a period during which there was a steep increase in unintentional overdoses involving fentanyl. For this research, spatial access was computed using the enhanced two-step floating catchment area model combined with the Huff model to assess access across New Hampshire and gives attention to supply-side parameters that can impact spatial access. The model is designed to measure access to healthcare services for opioid use disorder patients offered at treatment centers or from buprenorphine treatment practitioners, as well as from emergency medical services across New Hampshire. A composite index of accessibility is proposed to represent overall access to these different treatment services for opioid use disorder patients. Geospatial determinants of spatial access included street network distances, driving times and distance decay relationships, while other key factors were services availability and population demand. Among the towns with the highest composite access scores, approximately 40% were metropolitan locations while 16% were rural towns. The insights from this research showed that for this period, while the opioid crisis was impacting many towns in New Hampshire, high levels of access to treatment services were not uniform across the state. When comparing the access results with data on the towns of residence for individuals who died from unintentional overdoses involving fentanyl during 2015 and 2016, estimates found that approximately 40% of the towns were not estimated to be in the highest class of access to treatment services at the time. This research provides information for local public health officials to support planning strategies to address opioid use disorder treatment access in high-risk regions. [ABSTRACT FROM AUTHOR]

As COVID-19 vaccines were administered in early 2021, they helped to mitigate the spread of the COVID-19 virus and signaled an important shift in the pandemic. To better understand how ongoing COVID-19 vaccinations were related to human mobility across the U.S., we identified different mobility-vaccination profiles between January and May 2021 by county in the U.S., using K-means multivariate time-series clustering. The impacts of demographic, socioeconomic, and COVID-19-related variables on different profiles were examined. Results showed 5 different clusters of mobility-vaccination profiles were found for the U.S. One cluster represented counties in larger population centers and metropolitan areas (e.g., Los Angeles and New York) that had estimated 25% higher mobility and 75% higher vaccination rates than rural counties in the Mountain and South U.S. Census regions (e.g., counties in Arkansas and Mississippi), where people were mobile despite not getting vaccinated. Higher education and household income were found to impact counties' mobility-vaccination profiles. Examination of trip purposes for selected counties returned higher trips to retail/recreation and workplaces for rural counties with relatively lower mobility-vaccination profiles. The results can serve as input for regional and local health officials regarding population responses to a pandemic relevant to economic recovery and future disease prevention. • Statewide mobility differences were found as COVID-19 vaccinations were administered in 2021. • Analysis showed five clusters with different travel and vaccine uptake behaviors among counties. • Cluster with lower mobility and vaccination rates is mostly in Mountain and South states. • Education and economic factors contributed to cluster differences in mobility. [ABSTRACT FROM AUTHOR]

Infection prevention (IP) programs' increasing demands and evolving scope in the setting of healthcare staffing shortages and a changing environment require an innovative approach to program structure. Historically, the IP program at our academic medical center included trained infection preventionists and physicians. To address the changing needs of our IP program, our goal is to assess the needs of our program and implement additional clinical and supportive roles to create a high performing IP program. We reviewed the existing guidance including the Infection Preventionist Competency Model (IPCM), Clinical Nurse Leader (CNL) competencies, and our internal risk assessment and five year plan to identify areas of need. We also reviewed the literature for recommended IP staffing ratios. We presented a summary of our findings and proposed plan to senior leadership and designed corresponding job descriptions. Based on our review of the literature and our internal gap analysis, we proposed the addition of an embedded performance improvement consultant. Additionally, we identified the need for a program manager with IP expertise and designed a new job role using the IPCM. Using the IPCM and the CNL competencies, a new job role was created for a dedicated IP program CNL. Each of these positions was filled by promoting experienced members of our IP program. Our program now consists of five infection preventionists, a CNL, an IP program manager, an embedded performance improvement consultant and a medical director/hospital epidemiologist. This structure allows our team to undertake and solve complex problems, respond to emerging issues and diseases and increase our presence in clinical areas. Other IP programs may consider using this framework to increase adaptability and output. [ABSTRACT FROM AUTHOR]

The global pandemic of SARS-CoV-2 (COVID-19) has been linked to adversely impacting individuals with opioid use disorder in the United States. This study focuses on analyzing opioid-involved mortality in the context of COVID-19 in the U.S. from a geospatial perspective. We investigated spatiotemporal patterns of opioid-involved deaths during 2020 and compared the spatiotemporal pattern of these deaths with patterns for the previous three years (2017–2019) to understand changes in the context of the COVID-19 pandemic. A counterfactual analysis framework together with a space-time random forest (STRF) model were used to estimate the increase in opioid-involved deaths related to the pandemic. To gain further insight into the relationship between opioid deaths and COVID-19-related factors, we built a space-time random forest model for the City of Chicago, that experienced a steep increase in opioid-related deaths during 2020. High ranking indicators identified by the model such as the number of positive COVID-19 cases adjusted by population and the change in stay-at-home dwell time during the pandemic were used to generate a vulnerability index for opioid overdoses during the COVID-19 pandemic in Chicago. • In 2020, opioid-involved deaths increased by 42.7% in the United States. • Analyzed the relationship between the COVID-19 pandemic and the number of opioid deaths in the U.S. • Applied a space-time random forest model in a counterfactual analysis framework. • Generated a ZIP-code level vulnerability index of opioid overdose occurrence during the pandemic for a metropolitan area. [ABSTRACT FROM AUTHOR]

The Opioid Overdose Crisis (OOC) continues to generate morbidity and mortality in the United States, outpacing other prominent accident-related reasons. Multiple disciplines have applied geographic information science (GIScience) to understand geographical patterns in opioid-related health measures. However, there are limited reviews that assess how GIScience has been used. This scoping review investigates how GIScience has been used to conduct research on the OOC. Specific sub-objectives involve identifying bibliometric trends, the location and scale of studies, the frequency of use of various GIScience methodologies, and what direction future research can take to address existing gaps. The review was pre-registered with the Open Science Framework ((https://osf.io/h3mfx/) and followed the PRISMA-ScR guidelines. Scholarly research was gathered from the Web of Science Core Collection, PubMed, IEEE Xplore, ACM Digital Library. Inclusion criteria was defined as having a publication date between January 1999 and August 2021, using GIScience as a central part of the research, and investigating an opioid-related health measure. 231 studies met the inclusion criteria. Most studies were published from 2017 onward. While many (41.6%) of studies were conducted using nationwide data, the majority (58.4%) occurred at the sub-national level. California, New York, Ohio, and Appalachia were most frequently studied, while the Midwest, north Rocky Mountains, Alaska, and Hawaii lacked studies. The most common GIScience methodology used was descriptive mapping, and county-level data was the most common unit of analysis across methodologies. Future research of GIScience on the OOC can address gaps by developing use cases for machine learning, conducting analyses at the sub-county level, and applying GIScience to questions involving illicit fentanyl. Research using GIScience is expected to continue to increase, and multidisciplinary research efforts amongst GIScientists, epidemiologists, and other medical professionals can improve the rigor of research. • 231 studies used GIScience to address opioid-related questions since 1999. • Descriptive mapping is the most common GIScience method used on opioid-related data. • California, New York, Ohio, and Appalachian were the most frequently studied states. • Future GIScience research should address ML/AI, high resolution data, and fentanyl. [ABSTRACT FROM AUTHOR]

This research analyzes geographic patterns of ESRD incidence and kidney transplantation at county level in an area that covers 11 states in the Midwestern US from 2004 to 2011. We investigate whether variations in ESRD incidence exist among white, black, and Native American population groups, and the degree to which disparities existed with respect to access to kidney transplantation, and with respect to rural and urban counties. Spatial clusters of ESRD incidence rates are detected using global Moran's I and local Getis-Ord G i ∗ statistic. Spatial accessibility to transplant centers is evaluated using the enhanced two-step floating catchment area method where dissimilarities due to varying travel times and ESRD incidence rates result in differences in spatial access among the groups. Results show that while similar age-adjusted ESRD incidence rates hold for white and black population groups in urban counties, the kidney transplant rate is 73% lower among black patients than for whites in the study area. A lack of transplant centers in locations that correspond to strongly clustered age-adjusted ESRD incidence rates in southern Missouri and central South Dakota, contribute to lower spatial access indices in these counties. The results of the analyses capture varying patterns of ESRD incidence rates and kidney transplants in this Midwestern region and highlight spatial disparities for certain population groups. [ABSTRACT FROM AUTHOR]

Highly technological in vitro fertilization (IVF) treatment is available at relatively few medical centers in rural United States. This research derives a spatial accessibility surface for IVF centers in a rural Midwestern state through the application of computational methods that consider spatial and non-spatial parameters to discover potentially underserved areas in the state. These methods include a modified gravity model and techniques from spatial interaction modeling. The approach develops an enhanced accessibility index that incorporates three key sociodemographic variables describing patients seeking infertility healthcare in Iowa that have been identified based on a survey of IVF care practitioners in the state. Self-organizing map techniques are used to reveal cluster locations based on the degree of match between census sociodemographic data and the expert-identified variables. The spatial accessibility surface is combined with the sociodemographic clusters to define an enhanced measure of spatial accessibility. The results suggest that while the state's IVF centers are located in tracts characterized by high spatial accessibility, at least 19% of patients travel from census tracts classed as moderate to low accessibility. This result reveals some opportunities for service improvements for these locations. Interestingly, for tracts that are characterized as having a lower patient sociodemographic match, high spatial accessibility does not appear to be a factor that improves the likelihood of patient care, at least for the variables investigated as part of this research. [ABSTRACT FROM AUTHOR]

In the field of geographic information science, modeling geographic dynamics based on spatiotemporal information extracted from the Web, especially unconstructed data such as online news reports, is a growing area of research. Extracting spatiotemporal and semantic information from a set of Web documents enables us to build a rich representation of geographic knowledge described in text, capturing where, when, or what events have occurred. This work investigates the role ontologies play as a key component in the process of semantic information extraction. We show how ontologies can be used in conjunction with natural language gazetteers in order to process semantic information about hazard events and augment spatiotemporal extraction with semantics. We are interested in capturing the spatiotemporal patterns of hazard-related events from online news reports to track the occurrences and evolution of natural hazards, such as severe storms. A hazard ontology has been created to assist the spatiotemporal information extraction process, especially with the automatic detection of different kinds of events at multiple granularities from unstructured texts revealing relationships between the events over space–time. The extraction and retrieval of semantic information about event dynamics provides information about the progression of events using both natural and human perspectives. [ABSTRACT FROM AUTHOR]

In this paper, a spatiotemporal framework is developed for identifying building vulnerabilities and content evacuations during riverine flooding events. This work investigates the spatiotemporal properties required to trigger building contents evacuations in the floodplain during a flood event. The spatial properties for building risks are based on topography, flood inundation, building location, building elevation, and road access to determine five categories of vulnerability, vulnerable basement, flooded basement, vulnerable first-floor, flooded first-floor, and road access. Using this framework, a model designed to track the spatiotemporal patterns of building evacuations is presented. The model is based upon real-time flood forecast predictions that are linked with building properties to create a model that captures the spatiotemporal ordering of building vulnerabilities and building content evacuations. Applicable to different communities at risk from flooding, the evacuation model is applied to a historical flood for a university campus, demonstrating how the defined elements are used to derive a pattern of vulnerability and evacuation for a campus threatened by severe flooding. [ABSTRACT FROM AUTHOR]

Safety perspectives of generalized anxiety disorder (GAD) propose that safety perception is critical to regulating anxiety. Reduced safety processing may contribute to persistent worry and anxiety that extend to recognizably safe contexts. We explored whether individuals higher in worry and intolerance of uncertainty (IU), central characteristics of GAD, display poorer recognition and use of safety cues, and whether safety perception is related to anxiety. One hundred and eighty-two adults were presented with unfolding potentially threatening scenarios, half of which contained safety information. Participants rated how safe and anxiety-provoking each scenario was as they received new information, as well as overall. Using multilevel modeling, results showed that individuals higher in worry and IU recognize safety information and use it to appraise the safety of a situation. A moderate correlation between safety and anxiety ratings, and inconsistent correspondence between ratings of safety and anxiety, suggest this relationship is complicated by additional factors. Individuals higher in worry and IU may have difficulty accepting their safety appraisals in order to inhibit their anxiety. The implications of the findings and future avenues of research are discussed. • Unfolding piece-wise scenario task, used to isolate detection of safety information. • Safety appraisals only moderately predict anxiety. • Worry level is more closely linked to threat reactions and IU level is more closely linked to reactions to safety cues. • Those higher in worry and IU may have difficulty accepting their safety appraisals. • For these individuals, other factors may complicate the safety-anxiety relationship. [ABSTRACT FROM AUTHOR]

The ongoing opioid overdose epidemic in the United States presents a major public health challenge. Opioid-involved morbidity, especially nonfatal emergency department (ED) visits, are a key opportunity to prevent mortality and measure the extent of the problem in the local substance use landscape. Data on the rate of ED visits is normally distributed by federal agencies. However, state- and substate-level rates of ED visit demonstrate significant geographic variation. This study uses an ongoing sample of ED visits from four hospitals in the University of Maryland Medical System from January 2016 to December 2019 to provide locally sensitive information on ED visit rates and risk for drug-related health outcomes. Using exploratory spatial data analysis and spatio-temporal Bayesian models, this study analyzes both the frequency and risk of heroin-, methadone-, and cocaine-involved ED visits across the greater Baltimore Maryland area at the Zip Code Tabulation Area-level (ZCTA). The Global Moran's I for total heroin-, methadone-, and cocaine-involved ED visits in 2019 was 0.44, 0.56, and 0.53, demonstrating strong positive spatial autocorrelation. Spatio-temporal Bayesian models indicated that ZCTA with a higher score in a deprivation index, with a higher share of Centers for Medicare Services claims, and adjacent to a sampled UMMS hospital had an increased risk of ED visits, with variation in the magnitude of this increased risk depending on the drug-demographic strata. Modeled disease risk surfaces - including posterior median risk and posterior exceedance probabilities - showed distinctly different risk surfaces between the substances of interest, probabilistically identifying ZCTA with a lower or higher risk of ED visits. The modeling approach used a sample of ED visits from a larger health system to estimate recent, locally sensitive drug-related morbidity across a large metropolitan area. [ABSTRACT FROM AUTHOR]

The Attention Training Technique (ATT, Wells, 1990) is an intervention guiding individuals to focus, shift, and divide their attention in response to sounds presented in an audiorecording. The ATT has long been recommended for generalized anxiety disorder (GAD); however, there is insufficient research on its effects on excessive worry and related processes. This experiment examined whether the ATT is more efficacious than a control intervention at reducing worry and modifying worry-related processes (e.g., attention control, negative metacognitive beliefs, attention bias, mindfulness). 78 adults with probable GAD. Participants completed measures of worry and worry-related processes at the lab. They then monitored worry and attention daily for a week. Following this baseline, participants recompleted the lab measures and were randomly assigned to ATT or control. Participants listened to their assigned recording once/day for a week while again monitoring worry and attention daily. Participants then recompleted the lab measures. The ATT did not perform better than the control condition on any measure. A variety of improvements were seen over the intervention period in both conditions. ATT may not have meaningful effects on excessive worry and worry-related processes. Explanations for null findings are offered. NCT03216382. • Randomized trial evaluating effects of the attention training technique on worry. • No significant group by time interactions on worry or attention mechanisms. • Improvements in worry seen in intervention and control conditions. • Improvements in beliefs about worry, attention, and mindfulness in both conditions. • No change in objective attention control/bias or self-reported focus of attention. [ABSTRACT FROM AUTHOR]

Conventional approaches for modeling human mobility pattern often focus on human activity and movement dynamics in their regular daily lives and cannot capture changes in human movement dynamics in response to large-scale events. With the rapid advancement of information and communication technologies, many researchers have adopted alternative data sources (e.g., cell phone records, GPS trajectory data) from private data vendors to study human movement dynamics in response to large-scale natural or societal events. Big geosocial data such as georeferenced tweets are publicly available and dynamically evolving as real-world events are happening, making it more likely to capture the real-time sentiments and responses of populations. However, precisely-geolocated geosocial data is scarce and biased toward urban population centers. In this research, we developed a big geosocial data analytical framework for extracting human movement dynamics in response to large-scale events from publicly available georeferenced tweets. The framework includes a two-stage data collection module that collects data in a more targeted fashion in order to mitigate the data scarcity issue of georeferenced tweets; in addition, a variable bandwidth kernel density estimation(VB-KDE) approach was adopted to fuse georeference information at different spatial scales, further augmenting the signals of human movement dynamics contained in georeferenced tweets. To correct for the sampling bias of georeferenced tweets, we adjusted the number of tweets for different spatial units (e.g., county, state) by population. To demonstrate the performance of the proposed analytic framework, we chose an astronomical event that occurred nationwide across the United States, i.e., the 2017 Great American Eclipse, as an example event and studied the human movement dynamics in response to this event. However, this analytic framework can easily be applied to other types of large-scale events such as hurricanes or earthquakes. • Big geosocial data analytics to estimate collective human movement pattern • Two-stage data collection process to augment data signal of geosocial data • Variable KDE method to fuse georeference data with different spatial scales • Reveal population response to large-scale event from big geosocial data and thus facilitate emergency response [ABSTRACT FROM AUTHOR]

The opioid crisis has hit American cities hard, and research on spatial and temporal patterns of drug-related activities including detecting and predicting clusters of crime incidents involving particular types of drugs is useful for distinguishing hot zones where drugs are present that in turn can further provide a basis for assessing and providing related treatment services. In this study, we investigated spatiotemporal patterns of more than 52,000 reported incidents of drug-related crime at block group granularity in Chicago, IL between 2016 and 2019. We applied a space-time analysis framework and machine learning approaches to build a model using training data that identified whether certain locations and built environment and sociodemographic factors were correlated with drug-related crime incident patterns, and establish the top contributing factors that underlaid the trends. Space and time, together with multiple driving factors, were incorporated into a random forest model to analyze these changing patterns. We accommodated both spatial and temporal autocorrelation in the model learning process to assist with capturing the changes over time and tested the capabilities of the space-time random forest model by predicting drug-related activity hot zones. We focused particularly on crime incidents that involved heroin and synthetic drugs as these have been key drug types that have highly impacted cities during the opioid crisis in the U.S. • Underlying factors for patterns of drug activities involving heroin and synthetic drugs were identified. • Integrating space-time analysis framework and machine learning to analyze patterns of repeated events in an urban context • Accommodating both spatial and temporal autocorrelation in the model learning process. [ABSTRACT FROM AUTHOR]

This research investigated how socially sensed data can be used to detect ZIP level characteristics that are associated with spatial and temporal patterns of Emergency Department patients with a chief complaint and/or diagnosis of overdose or drug-related health problems for four hospitals in Baltimore and Anne Arundel County, MD during 2016-2018. Dynamic characteristics were identified using socially-sensed data (i.e., geo-tagged Twitter data) at ZIP code level over varying temporal resolutions. Data about three place-based variables including comments and concerns about crime, drug use, and negative or depressed sentiments, were extracted from tweets, along with data from four socio-environmental variables from the American Community Survey were collected to explore socio-environmental characteristics during the same period. Our study showed a statistically significant increase in adjusted rates of Emergency Department (ED) visits occurred between June and November 2017 for patients residing in ZIP codes in western Baltimore and northeastern Anne Arundel County. During this period, the three topics extracted from Twitter data were highly correlated with the ZIP codes where the patients were residing. Exploring the dynamic spatial associations between socio-environmental variables and ED visits for acute overdose assists local health officials in optimizing interventions for vulnerable locations. [ABSTRACT FROM AUTHOR]

For small endotherms inhabiting desert ecosystems, defending body temperatures (T b) is challenging as they contend with extremely high ambient temperatures (T a) and limited standing water. In the arid zone, bats may thermoconform whereby T b varies with T a , or may evaporatively cool themselves to maintain T b < T a. We used an integrative approach that combined both temperature telemetry and flow through respirometry to investigate the ecological and physiological strategies of lesser long-eared bats (Nyctophilus geoffroyi) in Australia's arid zone. We predicted individuals would exhibit desert-adapted thermoregulatory patterns (i.e., thermoconform to prioritise water conservation), and that females would be more conservative with their water reserves for evaporative cooling compared to males. Temperature telemetry data indicated that free-ranging N. geoffroyi were heterothermic (T skin = 18.9–44.9 °C) during summer and thermoconformed over a wide range of temperatures, likely to conserve water and energy during the day. Experimentally, at high T a s, females maintained significantly lower T b and resting metabolic rates, despite lower evaporative water loss (EWL) rates compared to males. Females only increased EWL at experimental T a = 42.5 °C, significantly higher than males (40.7 °C), and higher than any bat species yet recorded. During the hottest day of this study, our estimates suggest the water required for evaporative cooling ranged from 18.3% (females) and 25.5% (males) of body mass. However, if we extrapolate these results to a recent heatwave these values increase to 36.5% and 47.3%, which are likely beyond lethal limits. It appears this population is under selective pressures to conserve water reserves and that these pressures are more pronounced in females than males. Bats in arid ecosystems are threatened by both current and future heatwaves and we recommend future conservation efforts focus on protecting current roost trees and creating artificial standing water sites near vulnerable populations. • Significant sex differences exist in the thermoregulatory strategies in desert bats. • Females can withstand higher temperatures than males. • Females are more conservative in their use of evaporative water loss. • Desert bats are under a severe threat from current and future heat waves. [ABSTRACT FROM AUTHOR]

• Big GPS trajectory data analytic to estimate vehicle miles travelled. • Processed 19.8 million GPS trajectories collected in one state over a year. • Scalable map-matching module that accounts for computing accuracy and efficiency. • Big GPS trajectory data is promising for obtaining vehicle miles travelled estimates. As location-sensing devices and apps become more prevalent, the scale and availability of big GPS trajectory data are also rapidly expanding. Big GPS trajectory data analytics offers new opportunities for gaining insights into vehicle movement dynamics and road network usage patterns that are important for transportation studies and urban planning among other fields. Processing big GPS trajectory data, consisting of billions of GPS waypoints and millions of individual trajectories is a challenging yet important task for researchers from these different domains. In this research, we propose an Apache Spark-based geo-computing framework for using big GPS trajectory data to estimate vehicle miles travelled, an important metric used by both federal and state highway agencies in the United States for transportation planning. The computing challenge lies in scaling the processing of billions of raw GPS points data as well as the steps for map matching for a statewide road network consisting of thousands of road segments. In this work, we develop a scalable map-matching module that considers both the spatiotemporal information of GPS waypoint sequences and topologic information of road network for the State of Maryland while striking a balance between matching accuracy and computing time. We processed 19.8 million raw GPS trips consisting of approximately 1.4 billion GPS waypoints collected in Maryland during a four-month period in 2015 to estimate vehicle miles travelled for Maryland's road network. The estimation results show that using big GPS trajectory analytic methods is promising for obtaining accurate and stable vehicle miles travelled estimates. [ABSTRACT FROM AUTHOR]

To characterize and assess the effects of a preoperative, nurse-driven penicillin allergy risk stratification tool on rates of perioperative cefazolin and second-line antibiotic use. Quasi-experimental quality improvement study of penicillin-allergic surgical patients undergoing procedures for which cefazolin is indicated. Outpatient Perioperative Care Clinic (PCC) for preoperative surgical patients at a tertiary care center. 670 and 1371 adult penicillin-allergic PCC attendants and non-attendants, respectively. A paper penicillin allergy risk stratification questionnaire was administered during the PCC visit. Nurses were educated on its use. Antibiotic (cefazolin, clindamycin, vancomycin) use rates in the 24 months before and 17 months after intervention implementation in November 2020 (November 2018 – April 2022) were assessed in penicillin-allergic PCC attendants with statistical process control charts. Multivariable logistic regression assessed antibiotic use rates pre- and post-intervention adjusting for age, sex, surgical specialty and penicillin allergy history severity. Similar analyses were done in penicillin-allergic PCC non-attendants. Of 670 penicillin-allergic PCC attendants, 451 (median [IQR] age, 66 (Sousa-Pinto et al., 2021 [ 14 ])) were analyzed pre-intervention and 219 (median [IQR] age, 66 (Mine et al., 1970 [ 13 ])) post-intervention. One month after implementation, process measures demonstrated an upward shift in cefazolin use for PCC attendants versus no shift or other special cause variation for PCC non-attendants. There were increased odds of cefazolin use (aOR 1.67, 95% CI [1.09–2.57], P = 0.019), decreased odds of clindamycin use (aOR 0.61, 95% CI [0.42–0.89], P = 0.010) and decreased odds of vancomycin use (aOR 0.56, 95% CI [0.35–0.88], P = 0.013) in PCC attendants post-intervention. This effect did not occur in PCC non-attendants. There was no increase in perioperative anaphylaxis post-intervention. A simple penicillin allergy risk stratification tool implemented in the preoperative setting was associated with increased use of cefazolin and decreased rates of second-line agents post implementation. • A nurse-driven, penicillin allergy risk tool was added to the preoperative clinic. • This penicillin allergy risk tool was associated with increased odds of cefazolin use. • There was no increase in perioperative anaphylaxis. [ABSTRACT FROM AUTHOR]

Paranoia exists on a continuum with normal experience. Worry has been identified as a risk variable for paranoid thinking within non-clinical populations; however, studies have focused exclusively on the association between worry and persecutory beliefs, despite paranoia encompassing other domains (interpersonal sensitivities, mistrust, ideas of reference). Further, it is possible that worry-related processes account for more variance in paranoia than does the general tendency to worry. This study examined the associations of worry, intolerance of uncertainty (IU), and negative urgency (NU) to the four aforementioned domains of paranoia. N = 311 undergraduate students completed self-report measures. Worry, IU, and NU were most strongly correlated with ideas of social reference and interpersonal sensitivities. Only IU and NU emerged as significant unique correlates of all domains of paranoid thinking when controlling for worry. Results suggest that the associations between worry and paranoia may be better explained by underlying processes, particularly IU. Future research should examine the association of other worry-related processes to paranoia, as well as the interactions between these processes and other diatheses (e.g., negative beliefs about self and others) in the generation of paranoia. [ABSTRACT FROM AUTHOR]

Abstract Identifying the activities that individuals conduct in a city is key to understanding urban dynamics. It is difficult, however, to identify different human activities on a large scale without incurring significant costs. This study focuses on modeling the spatiotemporal patterns of different activity types within cities by employing user-contributed, geosocial content as a proxy for human activities. In this work, we use linguistic topic modeling to analyze georeferenced twitter data in order to differentiate different activity types. We then examine the spatial and temporal patterns of the derived activity types in three U.S. cities: Baltimore, MD., Washington, D.C., and New York City, NY. The linguistic patterns reflect the spatiotemporal context of the places where the social media content is posted. We further construct a method to link what people post online to the activities conducted within a city. We then use these derived activities to profile the characteristics of neighborhoods in the three cities, and apply the activity signatures to discover similar neighborhoods both within and between the cities. This approach represents a novel activity-based method for assessing similarity between neighborhoods. Graphical abstract Unlabelled Image Highlights • Topic modeling is applied to derive detailed activities in three US cities from Twitter text. • Spatiotemporal patterns of activities in the cities are modeled. • Derived activities are used as signatures to characterize urban neighborhoods. • Jensen-Shannon distance and cosine similarity are used for similarity analysis on neighborhoods' activity signatures. [ABSTRACT FROM AUTHOR]