Depression is associated with decreased severity and lower mortality in non-elderly hospitalized adults with influenza in the United States

Background: Depression is associated with risk for chronic disease, though its relationship with infectious diseases is less understood. Depression may modify the clinical outcomes of patients with infectious diseases such as influenza via its association with inflammation. The objective of this study was to evaluate the relationships between depression and clinical outcomes in non-elderly adults with influenza infection. Methods: This was a secondary analysis of the Nationwide Inpatient Sample database, years 2012-2016. Hospitalized adults aged 18-65 admitted during each influenza season were included. Depression status was documented via ICD-10 codes. The association between depression and clinical outcomes (e.g. disease severity, length of hospital stay, and inpatient all-cause mortality) were evaluated using multivariable regression modeling. Results: A total of 44,292 patients were included, 12% with depression. After adjustment for confounding, non-elderly influenza patients with depression had a 3.8% decreased risk of a severe disease (95% CI: 1.9% - 5.7%; P=0.028). Conclusions: This study suggests that in non-elderly hospitalized patients with influenza, depression is associated with a decreased severity of illness and acute mortality. Chronic inflammation in those with depression may enhance the ability of the immune response to limit influenza infection or reduce pathologic acute inflammation associated with influenza disease

Reasons for vaccine declination in healthy individuals attending an international vaccine and travel clinic

Little is known about the vaccine-related health behaviors of healthy individuals. We surveyed healthy individuals attending a vaccine center to define the reasons behind vaccine declination when the vaccine is warranted under current guidance. Declination due to perceived risks of the vaccines were by far the most common rationale, suggesting continued need for public health educational campaigns

Most Common Statistical Methodologies in Recent Clinical Studies of Community-Acquired Pneumonia

Background: Training new individuals in pneumonia research is imperative to produce a new generation of clinical investigators with the expertise necessary to fill gaps in knowledge. Clinical investigators are often intimidated by their unfamiliarity with statistics. The objective of this study is to define the most common statistical methodologies in recent clinical studies of CAP to inform teaching approaches in the field. Methods: Articles met inclusion criteria if they were clinical research with an emphasis on incidence, epidemiology, or patient outcomes, searchable via PubMed or Google Scholar, published within the timeframe of January 1st 2012 to August 1st 2017, and contained Medical Subject Headings (MeSH) keywords of “pneumonia” and one of the following: “epidemiologic studies”, “health services research”, or “comparative effectiveness research” or search keywords of community-acquired pneumonia” and one of the following: “cohort study”, “observational study”, “prospective study”, “retrospective study”, “clinical trial”, “controlled trial”, or “clinical study”. Descriptive statistics for the most common statistical methods were reported. Results: Thirty articles were included in the analysis. Descriptive statistics most commonly contained within articles were frequency (n=30 [100%]) and percent (n=30 [100%]), along with medians (n=22 [73%]) and interquartile ranges (n=19 [63%]). Most commonly performed analytical statistics were the Chi-squared test (n=20 [67%]), logistic regression (n=18 [60%]), Fisher’s exact test (n=17 [57%]), Wilcoxon rank sum test (n=16 [53%]), T-test (n=13 [43%]), and Cox proportional hazards regression (n=10 [33%]). Conclusions: We identified the most common clinical research tests performed in studies of hospitalized patients with CAP. Junior investigators should become very familiar with these tests early in their research careers

Comments on the Preliminary Framework for Equitable Allocation of COVID-19 Vaccine

On September 1, 2020 the National Academies released a draft framework for Equitable Allocation of a COVID-19 Vaccine. In this response, we analyze the proposed framework and highlight several areas. Among the proposed changes, we highlight the need for the following interventions. The final framework for distribution of COVID-19 vaccines should give a higher priority to populations made most vulnerable by the social determinants of health. It should incorporate more geography-based approaches in at least some of the four proposed phases of vaccine distribution. It should address the possibility of a vaccine being made available through an emergency use authorization (EUA), which we argue should not serve as a basis for widespread distribution of COVID-19 vaccines, and which may not be appropriate at all for the regulatory review of new vaccines. Moreover, it should address potential adjustments to the allocative framework once additional data pertaining to multiple vaccines becomes available, especially by discussing whether steps should be taken to prevent the administration of different vaccines to the same individual. Finally, it should provide guidance on allocation of vaccine in the case of a surplus, and specifically the Committee should specify whether unused doses of vaccine would automatically be allocated to next-level priority populations, and whether that would take place in the same geographical area

Distributing Data and Analysis Software Containers For Better Data Sharing in Clinical Research

Introduction: Data sharing in clinical research is critical for increasing knowledge discovery. Data and software tools should be FAIR: Findable, Accessible, Inter-operable and Re-usable. Many bottlenecks exist in the process of a clinical investigator using shared data including data acquisition and statistical analysis. The objective of this project is to develop a structure for sharing data and providing rapid automated statistical analysis through creation of a pre-packaged, open-source software container. Methods: We use the open source software container technologies VirtualBox and Vagrant to create a template for sharing clinical data and analysis scripts as a single container. We use a timer to record the time necessary to setup and initialize the software container and view the results. Results: We have created a template for sharing data and analysis scripts together using open source software container technologies VirtualBox and Vagrant. We found the time needed to initialize the container to be 5 minutes and 36 seconds for a macOS-based machine and 7 minutes and 2 seconds for a Windows-based machine. Containers can be downloaded and executed from any Mac or Windows computer allowing both the reuse of and interaction with the data. This greatly reduces the time and effort needed to obtain and analyze clinical data. Conclusion: Reducing the time and effort needed to obtain and analyze clinical data increases the time available for data exploration and the discovery of new knowledge. This can be effectively achieved using software containers and virtualization

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

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%

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