Ebola epidemic--Liberia, March-October 2014.

On March 21, 2014, the Guinea Ministry of Health reported the outbreak of an illness characterized by fever, severe diarrhea, vomiting and a high fatality rate (59%), leading to the first known epidemic of Ebola virus disease (Ebola) in West Africa and the largest and longest Ebola epidemic in history. As of November 2, Liberia had reported the largest number of cases (6,525) and deaths (2,697) among the three affected countries of West Africa with ongoing transmission (Guinea, Liberia, and Sierra Leone). The response strategy in Liberia has included management of the epidemic through an incident management system (IMS) in which the activities of all partners are coordinated. Within the IMS, key strategies for epidemic control include surveillance, case investigation, laboratory confirmation, contact tracing, safe transportation of persons with suspected Ebola, isolation, infection control within the health care system, community engagement, and safe burial. This report provides a brief overview of the progression of the epidemic in Liberia and summarizes the interventions implemented.

Differential regulation of caspase-1 activation via NLRP3/NLRC4 inflammasomes mediated by aerolysin and type III secretion system during Aeromonas veronii infection.

Aeromonas spp. are Gram-negative bacteria that cause serious infectious disease in humans. Such bacteria have been shown to induce apoptosis in infected macrophages, yet the host responses triggered by macrophage death are largely unknown. In this study, we demonstrate that the infection of mouse bone marrow-derived macrophages with Aeromonas veronii biotype sobria triggers activation of caspase-1 with the ensuing release of IL-1ß and pyroptosis. Caspase-1 activation in response to A. veronii infection requires the adaptor apoptosis-associated speck-like protein containing a caspase recruitment domain and both the NLRP3 and NLRC4 inflammasomes. Furthermore, caspase-1 activation requires aerolysin and a functional type III secretion system in A. veronii. Aerolysin-inducing caspase-1 activation is mediated through the NLRP3 inflammasome, with aerolysin-mediated cell death being largely dependent on the NLRP3 inflammasome. In contrast, the type III secretion system activates both the NLRP3 and NLRC4 inflammasomes. Inflammasome-mediated caspase-1 activation is also involved in host defenses against systemic A. veronii infection in mice. Our results indicated that multiple factors from both the bacteria and the host play a role in eliciting caspase-1 activation during A. veronii infection.

Pathogenic Vibrio activate NLRP3 inflammasome via cytotoxins and TLR/nucleotide-binding oligomerization domain-mediated NF-kappa B signaling.

Vibrio vulnificus and Vibrio cholerae are Gram-negative pathogens that cause serious infectious disease in humans. The beta form of pro-IL-1 is thought to be involved in inflammatory responses and disease development during infection with these pathogens, but the mechanism of beta form of pro-IL-1 production remains poorly defined. In this study, we demonstrate that infection of mouse macrophages with two pathogenic Vibrio triggers the activation of caspase-1 via the NLRP3 inflammasome. Activation of the NLRP3 inflammasome was mediated by hemolysins and multifunctional repeat-in-toxins produced by the pathogenic bacteria. NLRP3 activation in response to V. vulnificus infection required NF-kappaB activation, which was mediated via TLR signaling. V. cholerae-induced NLRP3 activation also required NF-kappaB activation but was independent of TLR stimulation. Studies with purified V. cholerae hemolysin revealed that toxin-stimulated NLRP3 activation was induced by TLR and nucleotide-binding oligomerization domain 1/2 ligand-mediated NF-kappaB activation. Our results identify the NLRP3 inflammasome as a sensor of Vibrio infections through the action of bacterial cytotoxins and differential activation of innate signaling pathways acting upstream of NF-kappaB.

Cytotoxins of the human pathogen Aeromonas hydrophila trigger, via the NLRP3 inflammasome, caspase-1 activation in macrophages.

Aeromonas hydrophila is a Gram-negative pathogen that causes serious infectious disease in humans. A. hydrophila induces apoptosis in infected macrophages, but the host proinflammatory responses triggered by macrophage death are largely unknown. Here, we demonstrate that the infection of mouse macrophages with A. hydrophila triggers the activation of caspase-1 and release of IL-1ß. Caspase-1 activation was abrogated in macrophages deficient in Nod-like receptor family, pyrin domain containing 3 (NLRP3) and apoptosis-associated speck-like protein containing a caspase recruitment domain (ASC), but not NLR family, CARD domain containing 4 (NLRC4). The activation of the NLRP3 inflammasome was mediated by three cytotoxins (aerolysin, hemolysin and multifunctional repeat-in-toxin) produced by A. hydrophila. Our results indicated that the NLRP3 inflammasome senses A. hydrophila infection through the action of bacterial cytotoxins.

Application of deep learning to identify COVID-19 infection in posteroanterior chest X-rays.

INTRODUCTION: The objective of this study was to assess seven configurations of six convolutional deep neural network architectures for classification of chest X-rays (CXRs) as COVID-19 positive or negative. METHODS: The primary dataset consisted of 294 COVID-19 positive and 294 COVID-19 negative CXRs, the latter comprising roughly equally many pneumonia, emphysema, fibrosis, and healthy images. We used six common convolutional neural network architectures, VGG16, DenseNet121, DenseNet201, MobileNet, NasNetMobile and InceptionV3. We studied six models (one for each architecture) which were pre-trained on a vast repository of generic (non-CXR) images, as well as a seventh DenseNet121 model, which was pre-trained on a repository of CXR images. For each model, we replaced the output layers with custom fully connected layers for the task of binary classification of images as COVID-19 positive or negative. Performance metrics were calculated on a hold-out test set with CXRs from patients who were not included in the training/validation set. RESULTS: When pre-trained on generic images, the VGG16, DenseNet121, DenseNet201, MobileNet, NasNetMobile, and InceptionV3 architectures respectively produced hold-out test set areas under the receiver operating characteristic (AUROCs) of 0.98, 0.95, 0.97, 0.95, 0.99, and 0.96 for the COVID-19 classification of CXRs. The X-ray pre-trained DenseNet121 model, in comparison, had a test set AUROC of 0.87. DISCUSSION: Common convolutional neural network architectures with parameters pre-trained on generic images yield high-performance and well-calibrated COVID-19 CXR classification.

Associations among breastfeeding, smoking relapse, and prenatal factors in a brief postpartum smoking intervention.

Postpartum smoking contributes to child health problems and is a barrier to breastfeeding, which promotes child health. There is a risk of postpartum smoking relapse for smokers and they are less likely to breastfeed. Understanding of smoking-breastfeeding associations must be improved. Enhancing smoking cessation advice simultaneously with breastfeeding counseling could increase smoking abstinence and breastfeeding rates. A low income sample of 31 volunteer maternal smokers and ex-smokers were recruited for this pilot intervention in an urban hospital's postpartum unit. Following pre-intervention interview, participants received either smoking relapse prevention plus breastfeeding counseling, or smoking relapse prevention only counseling. At one-month follow-up, we hypothesized that breastfeeding duration would positively relate to 7-day point prevalence abstinence rates and days to relapse and explored prenatal care and pregnancy smoking behavior associations with postpartum smoking and breastfeeding. Of the mothers, 75% completed follow-up. Days to relapse was related to duration of breastfeeding (r = 0.92, p = 0.08); however, counseling group differences in one-month smoking status were not significant. Earlier prenatal care initiation was associated with smoking abstinences at one month postpartum (chi(2) = 4.87, p

Mortality prediction model for the triage of COVID-19, pneumonia, and mechanically ventilated ICU patients: A retrospective study.

RATIONALE: Prediction of patients at risk for mortality can help triage patients and assist in resource allocation. OBJECTIVES: Develop and evaluate a machine learning-based algorithm which accurately predicts mortality in COVID-19, pneumonia, and mechanically ventilated patients. METHODS: Retrospective study of 53,001 total ICU patients, including 9166 patients with pneumonia and 25,895 mechanically ventilated patients, performed on the MIMIC dataset. An additional retrospective analysis was performed on a community hospital dataset containing 114 patients positive for SARS-COV-2 by PCR test. The outcome of interest was in-hospital patient mortality. RESULTS: When trained and tested on the MIMIC dataset, the XGBoost predictor obtained area under the receiver operating characteristic (AUROC) values of 0.82, 0.81, 0.77, and 0.75 for mortality prediction on mechanically ventilated patients at 12-, 24-, 48-, and 72- hour windows, respectively, and AUROCs of 0.87, 0.78, 0.77, and 0.734 for mortality prediction on pneumonia patients at 12-, 24-, 48-, and 72- hour windows, respectively. The predictor outperformed the qSOFA, MEWS and CURB-65 risk scores at all prediction windows. When tested on the community hospital dataset, the predictor obtained AUROCs of 0.91, 0.90, 0.86, and 0.87 for mortality prediction on COVID-19 patients at 12-, 24-, 48-, and 72- hour windows, respectively, outperforming the qSOFA, MEWS and CURB-65 risk scores at all prediction windows. CONCLUSIONS: This machine learning-based algorithm is a useful predictive tool for anticipating patient mortality at clinically useful timepoints, and is capable of accurate mortality prediction for mechanically ventilated patients as well as those diagnosed with pneumonia and COVID-19.

Prediction of respiratory decompensation in Covid-19 patients using machine learning: The READY trial.

BACKGROUND: Currently, physicians are limited in their ability to provide an accurate prognosis for COVID-19 positive patients. Existing scoring systems have been ineffective for identifying patient decompensation. Machine learning (ML) may offer an alternative strategy. A prospectively validated method to predict the need for ventilation in COVID-19 patients is essential to help triage patients, allocate resources, and prevent emergency intubations and their associated risks. METHODS: In a multicenter clinical trial, we evaluated the performance of a machine learning algorithm for prediction of invasive mechanical ventilation of COVID-19 patients within 24 h of an initial encounter. We enrolled patients with a COVID-19 diagnosis who were admitted to five United States health systems between March 24 and May 4, 2020. RESULTS: 197 patients were enrolled in the REspirAtory Decompensation and model for the triage of covid-19 patients: a prospective studY (READY) clinical trial. The algorithm had a higher diagnostic odds ratio (DOR, 12.58) for predicting ventilation than a comparator early warning system, the Modified Early Warning Score (MEWS). The algorithm also achieved significantly higher sensitivity (0.90) than MEWS, which achieved a sensitivity of 0.78, while maintaining a higher specificity (p < 0.05). CONCLUSIONS: In the first clinical trial of a machine learning algorithm for ventilation needs among COVID-19 patients, the algorithm demonstrated accurate prediction of the need for mechanical ventilation within 24 h. This algorithm may help care teams effectively triage patients and allocate resources. Further, the algorithm is capable of accurately identifying 16% more patients than a widely used scoring system while minimizing false positive results.

A Racially Unbiased, Machine Learning Approach to Prediction of Mortality: Algorithm Development Study.

BACKGROUND: Racial disparities in health care are well documented in the United States. As machine learning methods become more common in health care settings, it is important to ensure that these methods do not contribute to racial disparities through biased predictions or differential accuracy across racial groups. OBJECTIVE: The goal of the research was to assess a machine learning algorithm intentionally developed to minimize bias in in-hospital mortality predictions between white and nonwhite patient groups. METHODS: Bias was minimized through preprocessing of algorithm training data. We performed a retrospective analysis of electronic health record data from patients admitted to the intensive care unit (ICU) at a large academic health center between 2001 and 2012, drawing data from the Medical Information Mart for Intensive Care-III database. Patients were included if they had at least 10 hours of available measurements after ICU admission, had at least one of every measurement used for model prediction, and had recorded race/ethnicity data. Bias was assessed through the equal opportunity difference. Model performance in terms of bias and accuracy was compared with the Modified Early Warning Score (MEWS), the Simplified Acute Physiology Score II (SAPS II), and the Acute Physiologic Assessment and Chronic Health Evaluation (APACHE). RESULTS: The machine learning algorithm was found to be more accurate than all comparators, with a higher sensitivity, specificity, and area under the receiver operating characteristic. The machine learning algorithm was found to be unbiased (equal opportunity difference 0.016, P=.20). APACHE was also found to be unbiased (equal opportunity difference 0.019, P=.11), while SAPS II and MEWS were found to have significant bias (equal opportunity difference 0.038, P=.006 and equal opportunity difference 0.074, P<.001, respectively). CONCLUSIONS: This study indicates there may be significant racial bias in commonly used severity scoring systems and that machine learning algorithms may reduce bias while improving on the accuracy of these methods.

Is Machine Learning a Better Way to Identify COVID-19 Patients Who Might Benefit from Hydroxychloroquine Treatment?-The IDENTIFY Trial.

Therapeutic agents for the novel coronavirus disease 2019 (COVID-19) have been proposed, but evidence supporting their use is limited. A machine learning algorithm was developed in order to identify a subpopulation of COVID-19 patients for whom hydroxychloroquine was associated with improved survival; this population might be relevant for study in a clinical trial. A pragmatic trial was conducted at six United States hospitals. We enrolled COVID-19 patients that were admitted between 10 March and 4 June 2020. Treatment was not randomized. The study endpoint was mortality; discharge was a competing event. Hazard ratios were obtained on the entire population, and on the subpopulation indicated by the algorithm as suitable for treatment. A total of 290 patients were enrolled. In the subpopulation that was identified by the algorithm, hydroxychloroquine was associated with a statistically significant (p = 0.011) increase in survival (adjusted hazard ratio 0.29, 95% confidence interval (CI) 0.11-0.75). Adjusted survival among the algorithm indicated patients was 82.6% in the treated arm and 51.2% in the arm not treated. No association between treatment and mortality was observed in the general population. A 31% increase in survival at the end of the study was observed in a population of COVID-19 patients that were identified by a machine learning algorithm as having a better outcome with hydroxychloroquine treatment. Precision medicine approaches may be useful in identifying a subpopulation of COVID-19 patients more likely to be proven to benefit from hydroxychloroquine treatment in a clinical trial.

Performance characteristics of a quantitative PCR assay on repository stool specimens and smeared filter-paper cards.

OBJECTIVE: Stool repositories are a valuable resource for retrospective analyses including quantitative PCR assays to distinguish between asymptomatic shedding and clinical disease. The suitability of archival specimens for this purpose is unclear and requires assessment. We conducted a pilot study to evaluate pathogen detection by TaqMan Array Card (TAC) in travelers' diarrhea (TD) stool specimens stored for 1-13 years, as well as the impact of transporting specimens on Whatman FTA Elute cards (FTA Cards) on detection. RESULTS: The positive percent agreement (PPA) for TAC on stool vs. microbiologic testing was lower than our a priori PPA estimate of 80% for most pathogens: Shigella spp. (100% [95%CI 69-100%]), enterotoxigenic E coli (ETEC) (63% [95%CI 49-75%]), Campylobacter spp. (66% [95%CI 43-85%]) and Norovirus (37% [95%CI 16-61%]). Use of the FTA card resulted in a further reduction of PPA. Our findings suggest that archival specimens may lead to insensitive detection on quantitative PCR assays due to degradation of nucleic acid with prolonged storage, although our limited sample size precluded us from evaluating the impact of storage duration on nucleic acid yield. Additional studies are needed to understand the impact of storage duration on quantitative PCR data.

Effect of a sepsis prediction algorithm on patient mortality, length of stay and readmission: a prospective multicentre clinical outcomes evaluation of real-world patient data from US hospitals.

BACKGROUND: Severe sepsis and septic shock are among the leading causes of death in the USA. While early prediction of severe sepsis can reduce adverse patient outcomes, sepsis remains one of the most expensive conditions to diagnose and treat. OBJECTIVE: The purpose of this study was to evaluate the effect of a machine learning algorithm for severe sepsis prediction on in-hospital mortality, hospital length of stay and 30-day readmission. DESIGN: Prospective clinical outcomes evaluation. SETTING: Evaluation was performed on a multiyear, multicentre clinical data set of real-world data containing 75 147 patient encounters from nine hospitals across the continental USA, ranging from community hospitals to large academic medical centres. PARTICIPANTS: Analyses were performed for 17 758 adult patients who met two or more systemic inflammatory response syndrome criteria at any point during their stay ('sepsis-related' patients). INTERVENTIONS: Machine learning algorithm for severe sepsis prediction. OUTCOME MEASURES: In-hospital mortality, length of stay and 30-day readmission rates. RESULTS: Hospitals saw an average 39.5% reduction of in-hospital mortality, a 32.3% reduction in hospital length of stay and a 22.7% reduction in 30-day readmission rate for sepsis-related patient stays when using the machine learning algorithm in clinical outcomes analysis. CONCLUSIONS: Reductions of in-hospital mortality, hospital length of stay and 30-day readmissions were observed in real-world clinical use of the machine learning-based algorithm. The predictive algorithm may be successfully used to improve sepsis-related outcomes in live clinical settings. TRIAL REGISTRATION NUMBER: NCT03960203.

First Report of New Delhi Metallo-ß-Lactamase Carbapenemase-Producing Acinetobacter baumannii in Peru.

Here we report the first incidence of New Delhi metallo-ß-lactamase (NDM-1)-producing Acinetobacter baumannii in Peru, identified via a strain-based nosocomial surveillance project carried out in Lima and Iquitos. The bla NDM-1 gene was detected by multiplex polymerase chain reaction (PCR) and confirmed by loci sequencing. Acinetobacter baumannii is a nearly ubiquitous and promiscuous nosocomial pathogen, and the acquisition of bla NDM-1 by A. baumannii may facilitate an increase in the prevalence of this important resistance marker in other nosocomial pathogens.

Enhanced Immunogenicity and Protective Efficacy of a Campylobacter jejuni Conjugate Vaccine Coadministered with Liposomes Containing Monophosphoryl Lipid A and QS-21.

Campylobacter jejuni is among the most common causes of diarrheal disease worldwide and efforts to develop protective measures against the pathogen are ongoing. One of the few defined virulence factors targeted for vaccine development is the capsule polysaccharide (CPS). We have developed a capsule conjugate vaccine against C. jejuni strain 81-176 (CPS-CRM) that is immunogenic in mice and nonhuman primates (NHPs) but only moderately immunogenic in humans when delivered alone or with aluminum hydroxide. To enhance immunogenicity, two novel liposome-based adjuvant systems, the Army Liposome Formulation (ALF), containing synthetic monophosphoryl lipid A, and ALF plus QS-21 (ALFQ), were evaluated with CPS-CRM in this study. In mice, ALF and ALFQ induced similar amounts of CPS-specific IgG that was significantly higher than levels induced by CPS-CRM alone. Qualitative differences in antibody responses were observed where CPS-CRM alone induced Th2-biased IgG1, whereas ALF and ALFQ enhanced Th1-mediated anti-CPS IgG2b and IgG2c and generated functional bactericidal antibody titers. CPS-CRM + ALFQ was superior to vaccine alone or CPS-CRM + ALF in augmenting antigen-specific Th1, Th2, and Th17 cytokine responses and a significantly higher proportion of CD4+ IFN-γ+ IL-2+ TNF-α+ and CD4+ IL-4+ IL-10+ T cells. ALFQ also significantly enhanced anti-CPS responses in NHPs when delivered with CPS-CRM compared to alum- or ALF-adjuvanted groups and showed the highest protective efficacy against diarrhea following orogastric challenge with C. jejuni This study provides evidence that the ALF adjuvants may provide enhanced immunogenicity of this and other novel C. jejuni capsule conjugate vaccines in humans.IMPORTANCECampylobacter jejuni is a leading cause of diarrheal disease worldwide, and currently no preventative interventions are available. C. jejuni is an invasive mucosal pathogen that has a variety of polysaccharide structures on its surface, including a capsule. In phase 1 studies, a C. jejuni capsule conjugate vaccine was safe but poorly immunogenic when delivered alone or with aluminum hydroxide. Here, we report enhanced immunogenicity of the conjugate vaccine delivered with liposome adjuvants containing monophosphoryl lipid A without or with QS-21, known as ALF and ALFQ, respectively, in preclinical studies. Both liposome adjuvants significantly enhanced immunity in mice and nonhuman primates and improved protective efficacy of the vaccine compared to alum in a nonhuman primate C. jejuni diarrhea model, providing promising evidence that these potent adjuvant formulations may enhance immunogenicity in upcoming human studies with this C. jejuni conjugate and other malaria and HIV vaccine platforms.

Distribution of Capsular Types of Campylobacter jejuni Isolates from Symptomatic and Asymptomatic Children in Peru.

Campylobacter jejuni is the leading bacterial cause of diarrhea worldwide. A capsular polysaccharide (CPS) conjugate vaccine is under development and requires determination of the valency. However, distribution of CPS types circulating globally is presently poorly described. We aimed to determine whether CPS type distribution in Peru differs from that in other endemic regions. We used a multiplex polymerase chain reaction (PCR) assay for the detection of CPS encoding genes capable of distinguishing all 35 CPS types on Campylobacter isolates in two prospective communities based studies conducted in cohorts of children less than 59 months of age in Peru. Results showed that CPS type HS4 complex was the most prevalent, followed by HS3 complex and HS15. Differences in CPS type for symptomatology were not statistically significant. Most subjects demonstrated repeated infections over time with different CPS types, suggesting that CPS types may confer of a level of homologous protective immunity. In this dataset, some differences in CPS type distribution were observed in comparison to other low-middle income countries. Further studies need to be conducted in endemic areas to increase our knowledge of CPS type distribution and guide vaccine development.

Campylobacter jejuni transcriptional and genetic adaptation during human infection.

Campylobacter jejuni infections are a leading cause of bacterial food-borne diarrhoeal illness worldwide, and Campylobacter infections in children are associated with stunted growth and therefore long-term deficits into adulthood. Despite this global impact on health and human capital, how zoonotic C. jejuni responds to the human host remains unclear. Unlike other intestinal pathogens, C. jejuni does not harbour pathogen-defining toxins that explicitly contribute to disease in humans. This makes understanding Campylobacter pathogenesis challenging and supports a broad examination of bacterial factors that contribute to C. jejuni infection. Here, we use a controlled human infection model to characterize C. jejuni transcriptional and genetic adaptations in vivo, along with a non-human primate infection model to validate our approach. We found that variation in 11 genes is associated with either acute or persistent human infections and includes products involved in host cell invasion, bile sensing and flagella modification, plus additional potential therapeutic targets. In particular, a functional version of the cell invasion protein A (cipA) gene product is strongly associated with persistently infecting bacteria and we identified its biochemical role in flagella modification. These data characterize the adaptive C. jejuni response to primate infections and suggest therapy design should consider the intrinsic differences between acute and persistently infecting bacteria. In addition, RNA sequencing revealed conserved responses during natural host commensalism and human infections. Thirty-nine genes were differentially regulated in vivo across hosts, lifestyles and C. jejuni strains. This conserved in vivo response highlights important C. jejuni survival mechanisms such as iron acquisition and evasion of the host mucosal immune response. These advances highlight pathogen adaptability across host species and demonstrate the utility of multidisciplinary collaborations in future clinical trials to study pathogens in vivo.

Building the invisible wall: updating the chlamydial peptidoglycan anomaly.

The existence of peptidoglycan (PG) in chlamydiae has long been debated. Genome sequencing of members of the Chlamydiaceae family and Protochlamydia amoebophila has uncovered a nearly complete pathway for PG synthesis in these organisms. The recent use of microarray and proteomic analysis methods has revealed that PG synthesis genes are expressed primarily during reticulate body development and division. Furthermore, key genes in the chlamydial PG synthesis pathway encode functional PG synthesis enzymes, some of which provide the basis for the susceptibility of chlamydiae to PG inhibitors. Recent studies shed light on how the construction of a cell wall in chlamydiae is taking shape and why the wall is being built.

Reducing patient mortality, length of stay and readmissions through machine learning-based sepsis prediction in the emergency department, intensive care unit and hospital floor units.

INTRODUCTION: Sepsis management is a challenge for hospitals nationwide, as severe sepsis carries high mortality rates and costs the US healthcare system billions of dollars each year. It has been shown that early intervention for patients with severe sepsis and septic shock is associated with higher rates of survival. The Cape Regional Medical Center (CRMC) aimed to improve sepsis-related patient outcomes through a revised sepsis management approach. METHODS: In collaboration with Dascena, CRMC formed a quality improvement team to implement a machine learning-based sepsis prediction algorithm to identify patients with sepsis earlier. Previously, CRMC assessed all patients for sepsis using twice-daily systemic inflammatory response syndrome screenings, but desired improvements. The quality improvement team worked to implement a machine learning-based algorithm, collect and incorporate feedback, and tailor the system to current hospital workflow. RESULTS: Relative to the pre-implementation period, the post-implementation period sepsis-related in-hospital mortality rate decreased by 60.24%, sepsis-related hospital length of stay decreased by 9.55% and sepsis-related 30-day readmission rate decreased by 50.14%. CONCLUSION: The machine learning-based sepsis prediction algorithm improved patient outcomes at CRMC.

The Epidemiology of Operation Stress during Continuing Promise 2011: A Humanitarian Response and Disaster Relief Mission aboard a US Navy Hospital Ship.

Introduction Operational stress describes individual behavior in response to the occupational demands and tempo of a mission. The stress response of military personnel involved in combat and peace-keeping missions has been well-described. The spectrum of effect on medical professionals and support staff providing humanitarian assistance, however, is less well delineated. Research to date concentrates mainly on shore-based humanitarian missions. Problem The goal of the current study was to document the pattern of operational stress, describe factors responsible for it, and the extent to which these factors impact job performance in military and civilian participants of Continuing Promise 2011 (CP11), a ship-based humanitarian medical mission. METHODS: This was a retrospective study of Disease Non-Battle Injury (DNBI) data from the medical sick-call clinic and from weekly self-report questionnaires for approximately 900 US military and civilian mission participants aboard the USNS COMFORT (T-AH 20). The incidence rates and job performance impact of reported Operational Stress/Mental Health (OS/MH) issues and predictors (age, rank, occupation, service branch) of OS/MH issues (depression, anxiety) were analyzed over a 22-week deployment period. RESULTS: Incidence rates of OS/MH complaints from the sick-call clinic were 3.7% (4.5/1,000 persons) and 12.0% (53/1,000 persons) from the self-report questionnaire. The rate of operational stress increased as the mission progressed and fluctuated during the mission according to ship movement. Approximately 57% of the responders reported no impact on job performance. Younger individuals (enlisted ranks E4-6, officer ranks O1-3), especially Air Force service members, those who had spent only one day off ship, and those who were members of specific directorates, reported the highest rates of operational stress. CONCLUSION: The overall incidence of OS/MH complaints was low in participants of CP11 but was under-estimated by clinic-based reporting. The OS/MH complaints increased as the mission progressed, were more prevalent in certain groups, and appeared to be related to ship's movement. These findings document the pattern of operational stress in a ship-based medical humanitarian mission and confirm unique ship-based stressors. This information may be used by planners of similar missions to develop mitigation strategies for known stressors and by preventive medicine, behavioral health specialists, and mission leaders to develop sensitive surveillance tools to better detect and manage operational stress while on mission. Scouten WT , Mehalick ML , Yoder E , McCoy A , Brannock T , Riddle MS . The epidemiology of operation stress during Continuing Promise 2011: a humanitarian response and disaster relief mission aboard a US Navy hospital ship. Prehosp Disaster Med. 2017;32(4):393-402.

Incidence, Etiology and Risk Factors for Travelers' Diarrhea during a Hospital Ship-Based Military Humanitarian Mission: Continuing Promise 2011.

Travelers' diarrhea (TD) is the most common ailment affecting travelers, including deployed U.S. military. Continuing Promise 2011 was a 5-month humanitarian assistance/disaster response (HA/DR) military and non-governmental organization training mission aboard the hospital ship USNS Comfort, which deployed to Central and South America and the Caribbean between April and September 2011. Enhanced TD surveillance was undertaken during this mission for public health purposes. Passive surveillance (clinic visits), active surveillance (self-reported questionnaires), and stool samples were collected weekly from shipboard personnel. Descriptive statistics and multivariate-logistic regression methods were used to estimate disease burden and risk factor identification. Two polymerase chain reaction methods on frozen stool were used for microbiological identification. TD was the primary complaint for all clinic visits (20%) and the leading cause of lost duties days due to bed rest confinement (62%), though underreported, as the active self-reported incidence was 3.5 times higher than the passive clinic-reported incidence. Vomiting (p = 0.002), feeling lightheaded or weak (p = 0.005), and being a food handler (p = 0.017) were associated with increased odds of lost duty days. Thirty-eight percent of self-reported cases reported some amount of performance impact. Based on the epidemiological curve, country of exercise and liberty appeared to be temporally associated with increased risk. From the weekly self-reported questionnaire risk factor analysis, eating off ship in the prior week was strongly associated (adjusted odds ratio [OR] 2.4, p<0.001). Consumption of seafood increased risk (aOR 1.7, p = 0.03), though consumption of ice appeared protective (aOR 0.3, p = 0.01). Etiology was bacterial (48%), with enterotoxigenic Escherichia coli as the predominant pathogen (35%). Norovirus was identified as a sole pathogen in 12%, though found as a copathogen in an additional 6%. Despite employment of current and targeted preventive interventions, ship-board HA/DR missions may experience a significant risk for TD among deployed US military personnel and potentially impact mission success.