Postpartum Fitness and Body Mass Index Changes in Active Duty Navy Women.

INTRODUCTION: Active duty Navy women participate in biannual Physical Fitness Assessments (PFAs), which include height and weight measurements and a Physical Readiness Test (PRT). PFAs are waived during pregnancy and resume the cycle after 6 months following maternity leave. The purpose of this study was to compare changes in PFA results over time between women who had or did not have a live birth during the follow-up period, and identify characteristics of women with lower PFA results postpartum. MATERIALS AND METHODS: This longitudinal study included 14,142 active duty Navy women, aged 19-40 years, with PFA results during July 2011-June 2015. Multivariable logistic regression, Stuart-Maxwell tests, and mixed effects modeling were used to examine changes in PRT scores and body mass index over time between women with and without a live birth during follow-up. All data were analyzed in 2017. This study was approved by the institutional review boards at the Uniformed Services University Office of Research and the Naval Health Research Center, and informed consent was waived in accordance with 32 CFR § 219.116(d). RESULTS: Postpartum women had increased odds of PRT failures (AOR = 3.88, 95% CI: 1.44-10.40) and lower PRT scores (AOR = 1.47, 95% CI: 1.12-1.92) up to 2.5 years postpartum, versus women without a live birth. Being enlisted, obese/overweight prepregnancy, and younger were risk factors for suboptimal PFA outcomes. Mean core strength and cardiovascular endurance, but not upper body strength, scores were significantly lower in postpartum women at 1 year postpartum versus women without a live birth. CONCLUSIONS: Our findings show that additional interventions may be needed to assist women in returning to prepregnancy fitness up to 1 year postpartum. Future studies should examine additional factors that may improve postpartum fitness in addition to enhancing maternity leave policies.

Positive predictive value of an algorithm used for cancer surveillance in the U.S. Armed Forces.

Recent large-scale epidemiologic studies of cancer incidence in the U.S. Armed Forces have used International Classification of Disease, 9th and 10th Revision (ICD-9 and ICD-10, respectively) diagnostic codes from administrative medical encounter data archived in the Defense Medical Surveillance System. Cancer cases are identified and captured according to an algorithm published by the Armed Forces Health Surveillance Branch. Standardized chart reviews were performed to provide a gold standard by which to validate the case definition algorithm. In a cohort of active component U.S. Air Force, Navy, and Marine Corps officers followed from 1 October 1995 through 31 December 2017, a total of 2,422 individuals contributed 3,104 algorithm-derived cancer cases. Of these cases, 2,108 (67.9%) were classified as confirmed cancers, 568 (18.3%) as confirmed not cancers, and 428 (13.8%) as unclear. The overall positive predictive value (PPV) of the algorithm was 78.8% (95% confidence interval [CI]: 77.2-80.3). For the 12 cancer sites with at least 50 cases identified by the algorithm, the PPV ranged from a high of 99.6% for breast and testicular cancers (95% CI: 97.8-100.0 and 97.7-100.0, respectively) to a low of 78.1% (95% CI: 71.3-83.9) for non-Hodgkin lymphoma. Of the 568 cases confirmed as not cancer, 527 (92.7%) occurred in individuals with at least 1 other confirmed cancer, suggesting algorithmic capture of metastases as additional primary cancers.

Injury and Illness Surveillance During the 2016 Department of Defense Warrior Games: Review of Methods and Results.

INTRODUCTION: Surveillance systems have become a valuable tool to capture epidemiological data at multi-sport events, with findings serving to predict and prevent injury, reduce illness, and guide efficient utilization of medical resources. In 2016, the first injury and illness surveillance tool for the Department of Defense (DoD) Warrior Games was established to inform the required medical footprint. The purpose of this paper is to describe the methods and findings from the 2016 DoD Warrior Games surveillance system, which included a database of injuries and illness. MATERIALS AND METHODS: A total of 245 wounded warrior (WW) athletes were followed over 19 days, to include train-up and competition periods, as they competed for their respective teams of Army, Navy, Air Force, Marines, Special Operations, and United Kingdom. Medical personnel recorded injuries and illnesses treated utilizing a standardized surveillance form and data were entered into a daily tracker to examine patterns or areas for prevention. Reports included sex, age, event discipline, previous injury or illness, reason for presentation, and treatment provided. RESULTS: From June 3 to June 21, 2016, 114 individual encounters were recorded on the standard form and entered into the surveillance database. Athletes accounted for 67% of all encounters. Illness accounted for 30.7% of all visits, while injuries accounted for 69.2%. The incident proportion of injuries in athletes was 23.3 injuries per 100 athletes (95% CI 17.6, 30.1) and incident rate of 12.2 injuries per 1000 athlete days. Integrative medicine treatments including acupuncture, osteopathic manipulative treatment (OMT), massage therapy, and gua sha accounted for the largest forms of treatment (31%). CONCLUSIONS: From the surveillance data, staff levels and treatment supplies can be adjusted. In addition an improved surveillance tool can be created. Continuous surveillance is required to provide information on trends in injury and illness to support prevention strategies.

Streptococcus agalactiae Strains with Chromosomal Deletions Evade Detection with Molecular Methods.

Surveillance of circulating microbial populations is critical for monitoring the performance of a molecular diagnostic test. In this study, we characterized 31 isolates of Streptococcus agalactiae (group B Streptococcus [GBS]) from several geographic locations in the United States and Ireland that contain deletions in or adjacent to the region of the chromosome that encodes the hemolysin gene cfb, the region targeted by the Xpert GBS and GBS LB assays. PCR-negative, culture-positive isolates were recognized during verification studies of the Xpert GBS assay in 12 laboratories between 2012 and 2018. Whole-genome sequencing of 15 GBS isolates from 11 laboratories revealed four unique deletions of chromosomal DNA ranging from 181 bp to 49 kb. Prospective surveillance studies demonstrated that the prevalence of GBS isolates containing deletions in the convenience sample was <1% in three geographic locations but 7% in a fourth location. Among the 15 isolates with chromosomal deletions, multiple pulsed-field gel electrophoresis types were identified, one of which appears to be broadly dispersed across the United States.

Outbreak of Acute Respiratory Illness Associated with Adenovirus Type 4 at the U.S. Naval Academy, 2016.

Human adenoviruses (HAdVs) are known to cause respiratory illness outbreaks at basic military training (BMT) sites. HAdV type-4 and -7 vaccines are routinely administered at enlisted BMT sites, but not at military academies. During August-September 2016, U.S. Naval Academy clinical staff noted an increase in students presenting with acute respiratory illness (ARI). An investigation was conducted to determine the extent and cause of the outbreak. During 22 August-11 September 2016, 652 clinic visits for ARI were identified using electronic health records. HAdV-4 was confirmed by realtime polymerase chain reaction assay in 18 out of 33 patient specimens collected and 1 additional HAdV case was detected from hospital records. Two HAdV-4 positive patients were treated for pneumonia including 1 hospitalized patient. Molecular analysis of 4 HAdV-4 isolates identified genome type 4a1, which is considered vaccine-preventable. Understanding the impact of HAdV in congregate settings other than enlisted BMT sites is necessary to inform discussions regarding future HAdV vaccine strategy.

Duty Hour Compliance: A Survey of U.S. Military Medical Interns and Residents.

OBJECTIVE: In 2011, the Accreditation Council for Graduate Medical Education (ACGME) modified its duty hour standards for interns and residents. This was done, in part, because of a belief that increased compliance with these standards was needed to positively impact resident fatigue, resident quality of life, and patient safety. However, several studies indicate that duty hour noncompliance and false reporting by interns and residents remains a significant concern for residency program directors. This study examined the compliance of interns and residents with ACGME duty hour standards at a large military graduate medical education (GME) training program. METHODS: We conducted a survey of 535 trainees assigned to 24 GME programs within the National Capital Consortium (NCC). Statistical analysis for descriptive parameters used a standard error of measure on the basis of sample and target population sizes to calculate two-sided 95% confidence intervals (CIs). A χ2 analysis was performed to compare response differences for particular survey questions. A Cronbach α coefficient was calculated to compare the internal consistency of responses for questions in which individual responses were expected to correlate. This study was reviewed by the Offices of Research at the Walter Reed National Military Medical Center and the Uniformed Services University and adjudicated as "Not Research." RESULTS: Overall, 41.3% (N = 221) of those contacted completed all survey questions. From the available responses, 31.6% (95% CI, 26.9-36.2) reported at least one occurrence of implicit pressure to alter duty hour reporting, and 32.0% (95% CI, 27.3-36.7) reported at least one occurrence of altering reported duty hours. In addition, 37.2 (95% CI, 32.5-41.9) reported being unable to always follow duty hour limits and 58.1% (95% CI, 53.1-63.1) felt time working at home on residency requirements should be included in reported duty hours. CONCLUSION: Our results indicate that a significant portion of interns and residents within the NCC were unable to consistently follow ACGME duty hour standards and various systemic barriers within the GME training environment may have contributed. To remove or mitigate these barriers, we recommend using an approach similar to those of "high-reliability organizations" in which organizational processes are systematically investigated to improve safety, quality, and efficiency. Additional surveys or cognitive interviewing within the NCC and at other military GME training sites could be used to help refine these barriers, discover other barriers, determine the scope of these issues within the entire military GME training system, and to measure the outcome of correction actions.

Obstructive sleep apnea and associated attrition, active component, U.S. Armed Forces, January 2004-May 2016.

Obstructive sleep apnea (OSA) is a growing health concern in both civilian and military populations. Individuals who suffer from OSA have increased rates of cardiovascular disease, chronic fatigue, motor vehicle accidents, cognitive impairment, and post-traumatic stress disorder. Data from the Defense Medical Surveillance System (DMSS) were utilized to examine the incidence of OSA and associated attrition from service in active component military members from 1 January 2004 through 31 May 2016. The study identified 223,731 incident cases of OSA with an overall incidence rate of 139.2 per 10,000 person-years, between 2004 and 2015. Rates increased more than 3-fold between 2004 and 2015. In 2015, 48.1% of all incident cases of OSA were diagnosed in the last year of service. The high percentage of cases diagnosed prior to separation from service is concerning because OSA is a treatable and partially preventable disease. OSA represents a large health and economic burden for the armed services and yet there are persistent research gaps in appropriate screening and prevention strategies to improve both individual health and mission performance.

Classifying variants of CDKN2A using computational and laboratory studies.

Variants in the CDKN2A tumor suppressor are associated with Familial Melanoma (FM), although for many variants the linkage is weak. The effects of missense variants on protein function and pathogenicity are often unclear. Multiple methods (e.g., laboratory, computational, epidemiological) have been developed to analyze whether a missense variant is pathogenic or not. It is not yet clear how to integrate these data types into a strategy for variant classification. We studied 51 CDKN2A missense variants using a cell cycle arrest assay. There was a continuum of results ranging from full wild-type effect through partial activity to complete loss of arrest. A reproducible decrease of 30% of cell cycle arrest activity correlated with FM association. We analyzed missense CDKN2A germline variants using a Bayesian method to combine multiple data types and derive a probability of pathogenicity. When equal to or more than two data types could be evaluated with this method, 22 of 25 FM-associated variants and 8 of 15 variants of uncertain significance were classified as likely pathogenic with >95% probability. The other 10 variants were classified as uncertain (probability 5-95%). For most variants, there were insufficient data to draw a conclusion. The Bayesian model appears to be a sound method of classifying missense variants in cancer susceptibility genes.

An automated procedure to identify biomedical articles that contain cancer-associated gene variants.

The proliferation of biomedical literature makes it increasingly difficult for researchers to find and manage relevant information. However, identifying research articles containing mutation data, a requisite first step in integrating large and complex mutation data sets, is currently tedious, time-consuming and imprecise. More effective mechanisms for identifying articles containing mutation information would be beneficial both for the curation of mutation databases and for individual researchers. We developed an automated method that uses information extraction, classifier, and relevance ranking techniques to determine the likelihood of MEDLINE abstracts containing information regarding genomic variation data suitable for inclusion in mutation databases. We targeted the CDKN2A (p16) gene and the procedure for document identification currently used by CDKN2A Database curators as a measure of feasibility. A set of abstracts was manually identified from a MEDLINE search as potentially containing specific CDKN2A mutation events. A subset of these abstracts was used as a training set for a maximum entropy classifier to identify text features distinguishing "relevant" from "not relevant" abstracts. Each document was represented as a set of indicative word, word pair, and entity tagger-derived genomic variation features. When applied to a test set of 200 candidate abstracts, the classifier predicted 88 articles as being relevant; of these, 29 of 32 manuscripts in which manual curation found CDKN2A sequence variants were positively predicted. Thus, the set of potentially useful articles that a manual curator would have to review was reduced by 56%, maintaining 91% recall (sensitivity) and more than doubling precision (positive predictive value). Subsequent expansion of the training set to 494 articles yielded similar precision and recall rates, and comparison of the original and expanded trials demonstrated that the average precision improved with the larger data set. Our results show that automated systems can effectively identify article subsets relevant to a given task and may prove to be powerful tools for the broader research community. This procedure can be readily adapted to any or all genes, organisms, or sets of documents.