The Impacts of Increased Adverse Weather Events on Freight Movement

Freight transportation is a major economic backbone of the United States and is vital to sustaining the nation’s economic growth. Ports, as one of the primary components of freight transportation and important means of integrating into the global economic system, have experienced significant growth and increased capacity during the past two decades. The study addresses an important national freight mobility goal to enhance the resilience of the port transportation operations in the event of extreme weather events. This study develops an adaptable resilience assessment framework that evaluates the impact of a disruptive event on transportation operations. The framework identifies dynamic performance levels over an extended period of an event including five distinct phases of responses- staging, reduction, peak, restoration, and overloading. This study applies the framework to the port complex in Houston, Texas, during a major hurricane event, Harvey, and two holiday events in 2017. The framework evaluates proactive and reactive responses of port truck activities during the disruptions and provides a comprehensive assessment of resilience and adaptability in port truck operations. Evaluating response systems and resilience of port truck activities during severe weather events such as Hurricane Harvey represents the first step for designing plans that support a fast system recovery that minimizes the economic, social, and human impacts

The Community-Acquired Pneumonia Organization (CAPO) Cloud-Based Research Platform (the CAPO-Cloud): Facilitating Data Sharing in Clinical Research

Background: Pneumonia is a costly and deadly respiratory disease that afflicts millions every year. Advances in pneumonia care require significant research investment and collaboration among pneumonia investigators. Despite the importance of data sharing for clinical research it remains difficult to share datasets with old and new investigators. We present CAPOCloud, a web-based pneumonia research platform intended to facilitate data sharing and make data more accessible to new investigators. Methods: We establish the first two use cases for CAPOCloud to be the automatic subsetting and constraining of the CAPO database and the automatic summarization of the database in aggregate. We use the REDCap data capture software and the R programming language to facilitate these use cases. Results: CAPOCloud allows CAPO investigators to access the CAPO clinical database and explore subsets of the data including demographics, comorbidities, and geographic regions. It also allows them to summarize these subsets or the entire CAPO database in aggregate while preserving privacy restrictions. Discussion: CAPOCloud demonstrates the viability of a research platform combining data capture, data quality, hypothesis generation, data exploration and data sharing in one interface. Future use cases for the software include automated univariate hypothesis testing, automated bivariate hypothesis testing, and principal component analysis

Transportation Behavior Among Older Vietnamese Immigrants in the Dallas-Fort Worth Metroplex: Well-Being, Geospatial Mobility, and Potential Indicators for Ride Providers’ Geospatial Burden

Nearly 4.6 million immigrants aged 65 and older live in the United States. This population is expected to more than triple in size by 2050. A lack of culturally appropriate transportation solutions for older immigrants creates disparities in access to services for older immigrant populations, increasing their risk of social isolation and reduced physical and mental health. A growing number of older immigrants live in low-density urban environments, which are characterized by high automobile dependency and limited public transportation. In these environments, older immigrants are likely to depend on others to provide private transportation. Negative aspects of this reliance on others are that the private transportation providers may be at risk for caregiver burden and stress, and older immigrants may lack transportation to social or health opportunities if their ride providers are unavailable. This survey research examines the mobility; activity spaces; transportation patterns, resources, and needs; transportation-related support networks; and health and well-being among older Vietnamese adults in the Dallas-Fort Worth metroplex. It also investigates the provision of rides from private transportation providers and the impact of providing rides to an older Vietnamese adult in an urban area. It uses geographic information systems (GIS) to construct regular activity spaces for the older adults and their ride providers, and ride-provision activity spaces for the ride providers. Using the ride providers\u27 activity spaces, it proposes three indicators of geospatial burden for providing rides. Findings indicate that the older adults and their ride providers rely on automobiles for transportation. Most of the older adults receive rides for transportation and their ride providers are also Vietnamese and primarily speak Vietnamese. The GIS analyses suggest that constructing activity spaces with self-reports of regular and ride-provision routine activities and locations may be an appropriate assessment tool to provide valuable insights into the burden of providing rides. The best performing burden indicator was the percentage of the ride-provision activity space that was not within the boundaries of the ride providers’ regular activity spac

The City of Louisville Encapsulates the United States Demographics

Background: One weakness that applies to all population-based studies performed in the United States (US) is that investigators perform population-based extrapolations without providing objective statistical evidence to show how well a particular city is a suitable surrogate for the US. The objective of this study was to propose and utilize a novel computational metric to compare individual US cities with the US average. Methods: This was a secondary data analysis of publicly available databases containing US sociodemographic, economic, and health-related data. In total, 58 demographic, housing, economic, health behavior, and health status variables for each US city with a residential population of at least 500,000 were obtained. All variables were recorded as proportions. Euclidean, Manhattan, and average absolute difference metrics were used to compare the 58 variables to the average in the US. Results: Oklahoma City, OK, had the lowest distance from the United States, with Euclidean and Manhattan distances in proportion of 0.261 and 1.519, respectively. Louisville, Kentucky, had the second lowest distance for both Euclidean distance and Manhattan distance, with distances of 0.286 and 1.545, respectively. The average absolute differences in proportion for Oklahoma City and Louisville to the US average were 0.026 and 0.027, respectively. Conclusion: To our knowledge, this represents the first study evaluating a method for computing statistical comparisons of United States city sociodemographic, economic, and health-related data with the United States average. Our study shows that among cities with at least 500,000 residents, Oklahoma City is the closest to the United States, followed closely by Louisville. On average, these cities deviate from the US average on any variable studied by less than 3%

Refugee-Centered Medical Home:A New Approach to Care at the University of Louisville Global Health Center

Refugees arrive to the United States with a full spectrum of health conditions, many of which involve intense case management requiring significant financial investments and use of healthcare resources. Kentucky receives more than 3,000 new refugees each year and ranked 10th in the nation for numbers of new arrivals resettled during 2015. These refugees arrive from diverse countries representing different cultures and speaking different languages. In addition, they arrive with diverse health conditions and medical needs. The aims of this paper are to share experiences from the University of Louisville Global Health Center regarding conceptualization, implementation and evaluation of a new care model. This model focuses on the complexities of caring for refugees from diverse populations and backgrounds. The foundation for this model aligns with the patient-centered medical home approach outlined by the Agency for Healthcare Research and Quality. Recognizing the need for a new paradigm for care, a refugee-centered medical home model was designed and implemented as an ideal approach

Impact of Temperature Relative Humidity and Absolute Humidity on the Incidence of Hospitalizations for Lower Respiratory Tract Infections Due to Influenza, Rhinovirus, and Respiratory Syncytial Virus: Results from Community-Acquired Pneumonia Organization (CAPO) International Cohort Study

Abstract Background: Transmissibility of several etiologies of lower respiratory tract infections (LRTI) may vary based on outdoor climate factors. The objective of this study was to evaluate the impact of outdoor temperature, relative humidity, and absolute humidity on the incidence of hospitalizations for lower respiratory tract infections due to influenza, rhinovirus, and respiratory syncytial virus (RSV). Methods: This was a secondary analysis of an ancillary study of the Community Acquired Pneumonia Organization (CAPO) database. Respiratory viruses were detected using the Luminex xTAG respiratory viral panel. Climate factors were obtained from the National Weather Service. Adjusted Poisson regression models with robust error variance were used to model the incidence of hospitalization with a LRTI due to: 1) influenza, 2) rhinovirus, and 3) RSV (A and/or B), separately. Results: A total of 467 hospitalized patients with LRTI were included in the study; 135 (29%) with influenza, 41 (9%) with rhinovirus, and 27 (6%) with RSV (20 RSV A, 7 RSV B). The average, minimum, and maximum absolute humidity and temperatur e variables were associated with hospitalization due to influenza LRTI, while the relative humidity variables were not. None of the climate variables were associated with hospitalization due to rhinovirus or RSV. Conclusions: This study suggests that outdoor absolute humidity and temperature are associated with hospitalizations due to influenza LRTIs, but not with LRTIs due to rhinovirus or RSV. Understanding factors contributing to the transmission of respiratory viruses may assist in the prediction of future outbreaks and facilitate the development of transmission prevention interventions

Predicting 30-Day Mortality in Hospitalized Patients with Community-Acquired Pneumonia Using Statistical and Machine Learning Approaches

Background: Predicting if a hospitalized patient with community-acquired pneumonia (CAP) will or will not survive after admission to the hospital is important for research purposes as well as for institution of early patient management interventions. Although population-level mortality prediction scores for these patients have been around for many years, novel patient-level algorithms are needed. The objective of this study was to assess several statistical and machine learning models for their ability to predict 30-day mortality in hospitalized patients with CAP. Methods: This was a secondary analysis of the University of Louisville (UofL) Pneumonia Study database. Six different statistical and/or machine learning methods were used to develop patientlevel prediction models for hospitalized patients with CAP. For each model, nine different statistics were calculated to provide measures of the overall performance of the models. Results: A total of 3249 unique hospitalized patients with CAP were enrolled in the study, 2743 were included in the model building (training) dataset, while the remaining 686 were included in the testing dataset. From the full population, death at 30-days post discharge was documented in 458 (13.4%) patients. All models resulted in high variation in the ability to predict survivors and non-survivors at 30 days. Conclusions: In conclusion, this study suggests that accurate patient-level prediction of 30-day mortality in hospitalized patients with CAP is difficult with statistical and machine learning approaches. It will be important to evaluate novel variables and other modeling approaches to better predict poor clinical outcomes in these patients to ensure early and appropriate interventions are instituted

Using Cluster Analysis of Cytokines to Identify Patterns of Inflammation in Hospitalized Patients with Community-acquired Pneumonia:A Pilot Study

Purpose: Patients with severe community-acquired pneumonia (CAP) are believed to have an exaggerated inflammatory response to bacterial infection. Therapies aiming to modulate the inflammatory response have been largely unsuccessful, perhaps reflecting that CAP is a heterogeneous disorder that cannot be modulated by a single anti-inflammatory approach. We hypothesize that the host inflammatory response to pneumonia may be characterized by distinct cytokine patterns, which can be harnessed for personalized therapies. Methods: Here, we use hierarchical cluster analysis of cytokines to examine if patterns of inflammatory response in 13 hospitalized patients with CAP can be defined. This was a secondary data analysis of the Community-Acquired Pneumonia Inflammatory Study Group (CAPISG) database. The following cytokines were measured in plasma and sputum on the day of admission: interleukin (IL)-1β, IL-1 receptor antagonist (IL-1ra), IL-6, CXCL8 (IL-8), IL-10, IL-12p40, IL-17, interferon (IFN)γ, tumor necrosis factor (TNF)α, and CXCL10 (IP-10). Hierarchical agglomerative clustering algorithms were used to evaluate clusters of patients within plasma and sputum cytokine determinations. Results: A total of thirteen patients were included in this pilot study. Cluster analysis identified distinct inflammatory response patterns of cytokines in the plasma, sputum, and the ratio of plasma to sputum. Conclusions: Inflammatory response patterns in plasma and sputum can be identified in hospitalized patients with CAP. Characterization of the local and systemic inflammatory response may help to better discriminate patients for enrollment into clinical trials of immunomodulatory therapies

Level of Recall Bias Regarding Pneumococcal Vaccination History among Adults Hospitalized with Community-Acquired Pneumonia: Results from the University of Louisville Pneumonia Study

Background: Recall bias is likely to occur in vaccine effectiveness studies using self-reported vaccination history. The validity of patient-reported vaccination status for adults is not well defined. The objective of this study was to evaluate the validity of self-reported pneumococcal vaccination history among patients hospitalized with community-acquired pneumonia (CAP). Methods: Prospective ancillary study of a population-based observational study of hospitalized patients with CAP in the city of Louisville. To be included in the analysis, patients had to (i) be reached by phone 30-days after discharge from the hospital and (ii) report that they remembered whether or not they received a pneumococcal vaccine in the past five years. The vaccination history was classified as 1) Subjective: patient recollection, or 2) Objective: vaccination records from insurance companies or primary care physicians. Results: A total of 2,787 patients who recalled their vaccination history were included in the analysis. Subjective vaccination history was documented to be inaccurate in 1,023 (37%) patients. Conclusions: Our study indicates that in adult patients, self-reported data regarding pneumococcal vaccination is likely to be inaccurate in one out of three patients. This level of recall bias may incorporate a fatal flaw in vaccine effectiveness studies