Sleep apnea (SA) is highly prevalent in the end-stage renal disease (ESRD) population. However, the impact of SA on mortality in ESRD is unclear. This study investigates the relationship between SA and mortality in ESRD. The United States Renal Data System was queried in a retrospective cohort study to identify ESRD patients aged 18–100 years who initiated hemodialysis between 2005 and 2013. Diagnoses of SA and comorbidities were determined from International Classification of Disease-9 codes and demographic variables from Centers for Medicare and Medicaid Services Form-2728. Cox proportional hazards models were used to examine the association of SA with mortality controlling for multiple variables. Of 858,131 subjects meeting inclusion criteria, 587 were found to have central SA (CSA) and 22,724 obstructive SA (OSA). The SA cohort was younger and more likely to be male and Caucasian compared to the non-SA cohort, with more diagnoses of tobacco and alcohol use, hypertension, heart failure, and diabetes. Both CSA (adjusted hazard ratio (aHR) = 1.42, 95% confidence interval (CI): 1.29–1.56) and OSA (aHR = 1.35, 95% CI: 1.32–1.37) were associated with increased mortality. Other variables associated with increased mortality included age, dialysis initiation with a catheter or graft, alcohol use, hypertension, and cardiovascular disease. Factors associated with decreased mortality included female sex, black race, Hispanic ethnicity, diagnosis of heart failure or diabetes, and an ESRD etiology of glomerulonephritis or polycystic kidney disease. Since a diagnosis of either OSA or CSA increases mortality risk, early identification of SA and therapy in this ESRD population may improve survival.

Purpose Genetic analyses of gliomas have identified key molecular features that impact treatment paradigms beyond conventional histomorphology. Despite at-times lower grade histopathologic appearances, IDH-wildtype infiltrating gliomas expressing certain molecular markers behave like higher-grade tumors. For IDH-wildtype infiltrating gliomas lacking traditional features of glioblastoma, these markers form the basis for the novel diagnosis of diffuse astrocytic glioma, IDH-wildtype (wt), with molecular features of glioblastoma (GBM), WHO grade-IV (DAG-G). However, given the novelty of this approach to diagnosis, literature detailing the exact clinical, radiographic, and histopathologic findings associated with these tumors remain in development. Methods Data for 25 patients matching the DAG-G diagnosis were obtained from our institution's retrospective database. Information regarding patient demographics, treatment regimens, radiographic imaging, and genetic pathology were analyzed to determine association with clinical outcomes. Results The initial radiographic findings, histopathology, and symptomatology of patients with DAG-G were similar to lower-grade astrocytomas (WHO grade 2/3). Overall survival (OS) and progression free survival (PFS) associated with our cohort, however, were similar to that of IDH-wt GBM, indicating a more severe clinical course than expected from other associated features (15.1 and 5.39 months respectively). Conclusion Despite multiple features similar to lower-grade gliomas, patients with DAG-G experience clinical courses similar to GBM. Such findings reinforce the need for biopsy and subsequent analysis of molecular features associated with any astrocytoma regardless of presenting characteristics.

Background Renal transplant patients are at increased risk for mucormycosis. Diabetes, neutropenia, deferoxamine therapy, and immunosuppressive medications have been associated with increased risk of mucormycosis in studies of solid organ transplant recipients. To focus on renal transplant patients, the US Renal Data System (USRDS) was queried to determine the incidence and risk factors for mucormycosis. Methods All renal transplant patients in the USRDS from 1988 to 2015 were queried for a diagnosis of mucormycosis after the first transplant date using ICD-9 and ICD-10 codes. The International Classification of Diseases (ICD) codes, which currently exist in the ninth and tenth revisions, are a global system of classification used to code diagnoses, procedures, and symptoms. We defined proven mucormycosis by a histopathologic or fungal stain procedure code within 7 days of the diagnosis code. Logistic regression controlling for person-years at risk was used to examine demographic and clinical diagnosis risk factors for mucormycosis. Results Of the 306,482 renal transplant patients, 222 (0.07%) had codes consistent with proven mucormycosis. The incidence of mucormycosis increased from 1990 to 2000 (peak 17.6 per 100,000 person-years) and subsequently demonstrated more variability. Hispanic ethnicity (OR=1.45), age 65 years or greater (OR=1.64), other or black race compared with white race (OR=1.96 and 1.74), cadaver or other donor type (OR=2.41), and receiving tacrolimus (OR=2.09) were associated with increased risk. Comorbidities associated with decreased risk of mucormycosis included female sex (OR=0.68), iron overload (OR=0.56), and receiving mycophenolate mofetil (OR=0.67) or azathioprine (OR=0.53). Conclusions In renal transplant patients, age, deceased donor graft transplant, tacrolimus administration, race other than white, and Hispanic ethnicity were associated with increased risk of mucormycosis. Unexpectedly, iron overload was protective. Mucormycosis is a rare infection in renal transplant patients which should be considered in patients with the above risk factors after more common infections have been ruled out.

The body mass index (BMI) paradox describes that among patients with certain cancers, higher pretreatment BMI may be associated with improved survival. We examine the impact of BMI on overall survival (OS) in head and neck squamous cell carcinoma (HNSCC) patients. A literature search was performed, and articles using hazard ratios to describe the prognostic impact of BMI on OS in HNSCC were included. Random-effects DerSimonian and Laird methods were employed for meta-analysis. Meta-analysis of OS indicated a lower hazards of death in the overweight (BMI: 25 kg/m2-30 kg/m2) compared to the normal weight (BMI: 18.5 kg/m2-25 kg/m2). This protective relationship loses significance when BMI exceeds 30 kg/m(2). Underweight patients (BMI < 18.5 kg/m2) demonstrate higher hazards of death compared to normal weight patients. Compared to HNSCC patients with normal weight, being overweight up to a BMI of 30 kg/m(2) is a positive predictor of OS, while being underweight confers a prognostic disadvantage. Further studies are needed to determine the mechanisms by which increased body mass influences survival outcomes in HNSCC.

Estimation and hypothesis tests for the covariance matrix in high dimensions is a challenging problem as the traditional multivariate asymptotic theory is no longer valid. When the dimension is larger than or increasing with the sample size, standard likelihood based tests for the covariance matrix have poor performance. Existing high dimensional tests are either computationally expensive or have very weak control of type I error. In this paper, we propose a test procedure, CRAMP (covariance testing using random matrix projections), for testing hypotheses involving one or more covariance matrices using random projections. Projecting the high dimensional data randomly into lower dimensional subspaces alleviates of the curse of dimensionality, allowing for the use of traditional multivariate tests. An extensive simulation study is performed to compare CRAMP against asymptotics-based high dimensional test procedures. An application of the proposed method to two gene expression data sets is presented.

Acute respiratory distress syndrome (ARDS) is a lethal disease with severe forms conferring a mortality rate approaching 40%. The initial phase of ARDS results in acute lung injury (ALI) characterized by a severe inflammatory response and exudative alveolar flooding due to pulmonary capillary leak. Timely therapies to reduce ARDS mortality are limited by the lack of laboratory-guided diagnostic biomarkers for ARDS. The purpose of this study was to evaluate the prognostic role of circulating microvesicles (MVs)-containing miR-223 (MV-miR-223) if indicate more severe lung injury and worse outcomes in ARDS patients. Human plasma samples from one hundred ARDS patients enrolled in Albuterol to Treat Acute Lung Injury (ALTA) trial were compared to a control group of twenty normal human plasma specimens. The amount of MV-miR-223 was measured using absolute real-time polymerase chain reaction (PCR) with a standard curve. Mann-Whitney-Wilcoxon, Spearman correlation, Chi-squared tests, and KaplanMeier curves were computed to assess different variables and survival. Plasma levels of MV-miR-223 were significantly higher in ARDS patients compared to normal control subjects. Upon receiver operator characteristic (ROC) analysis of MV-miR-223 in relation to 30-day mortality, MV-miR-223 had an area under the curve (AUC) of 0.7021 with an optimal cut-off value of 2.413 pg/ml. Patients with high MV-miR-223 had higher 30-day mortality than subjects with low MV-miR-223 levels. MV-miR-223 was negatively correlated with ICU-free days, ventilator-free days, and organ failure-free days. Patients with high MV-miR-223 levels had higher 30 and 90-day mortality. MV-miR-223 was associated with 28day clinical outcomes of ALTA trial including ICU-free days, ventilator-free days, and organ failure-free days. Thus, circulating MV-miR-223 may be a potential biomarker in prognosticating patient-centered outcomes and predicting mortality in ARDS.

“Ability and judgment” are the only terms that appeared twice in the Hippocratic Oath and are the pillars of medicine and surgery, as clinical decision making and treatments depend on physicians’ judgments and abilities. Sound judgment requires knowledge obtained and accumulated through individual and collective experience, but more importantly from studies employing scientific methods. In a compendium article to this current one, the death of George Washington within days of onset of respiratory infection in 1799 exemplified the futility of ineffective, if not dangerous and harmful, medical treatments. The key question is how can anyone know with certainty that a particular therapy is or is not effective? Since individual differences exist in responses to corrective procedures and medications, absolute certainty is uncommon. However, as the number of patients increases, so does the confidence, at times approaching certainty. Statistical testing allows for the extraction of valid, useful information from data collected and is a critical part of all surgical disciplines. While this mini-review will not replace the need for expert statistical inputs from statisticians, it will provide a deep appreciation of and a working knowledge for these tests. We focus on hypothesis testing, the foundation to synthesize knowledge to guide, improve new treatments, and confirm or repudiate existing ones. The primer begins with hypothesis testing and systematically examine parametric and non-parametric statistical tests, ending with Mendelian randomization.

Objective: Activation of the maternal immune (MIA) system while pregnant can have significant effects on fetal development. Here, the authors sought to examine MIA and its effects on fetal craniofacial formation. As a measure of MIA, data on maternal influenza infection was used, as influenza occurs in a predictive fashion, is not vertically transmitted, and is found to generate a robust maternal immune response. Thus, this study measures the association of the incidence of influenza infection in the United States with the incidence of craniofacial congenital deficits—specifically encephalocele and microtia. Methods: The National Inpatient Sample Database (NIS) was referenced to identify national estimates of infants born with each disease from 2004 to 2013. The gross monthly disease incidences were adjusted based on the number of newborns each month. The National Respiratory and Enteric Virus Surveillance System’s FluView database from the CDC was used to obtain influenza data from 2003 to 2013. Mixed effect logistic regression analyses were conducted to find the association between influenza occurrence and each disease, specifically an odds ratio (OR). Results: There were 2858 infants born with encephalocele and 3371 born with microtia from January 2004 to December 2013. Microtia showed no statistically significant correlation with influenza rates and served as a methodologic control. Encephalocele, however, showed a strong correlation with influenza infection specifically during the eighth month of pregnancy (OR = 34.538, 95% confidence interval: 3.815-312.681). Conclusion: This study shows a strong correlation between maternal influenza infection during the eighth month of pregnancy and encephalocele incidence. This suggests that there is an additional trigger for encephalocele development towards the end of the pregnancy not currently understood in the literature. Although there appears to be a connection between MIA and encephalocele formation, more research is needed to confirm this theory.

Reaction networks are important tools for modeling a variety of biological phenomena across a wide range of scales, for example as models of gene regulation within a cell or infectious disease outbreaks in a population. Hence, calibrating these models to observed data is useful for predicting future system behavior. However, the statistical estimation of the parameters of reaction networks is often challenging due to intractable likelihoods. Here we explore estimating equations to estimate the reaction rate parameters of density dependent Markov jump processes (DDMJP). The variance–covariance weights we propose to use in the estimating equations are obtained from an approximating process, derived from the Fokker–Planck approximation of the chemical master equation for stochastic reaction networks. We investigate the performance of the proposed methodology in a simulation study of the Lotka–Volterra predator–prey model and by fitting a susceptible, infectious, removed (SIR) model to real data from the historical plague outbreak in Eyam, England.

Objective: To investigate seasonal variation of orofacial clefts (OC) and measure association between United States (U.S.) influenza incidences and OC development for the purpose of identifying a potential modifiable risk factor for pregnant women. Design: Retrospective population-based observational study from 2004 to 2013. Setting: National Inpatient Sample Database (NIS), Wide-ranging Online Data for Epidemiologic Research (WONDER) Database, and National Respiratory and Enteric Virus Surveillance System’s (NRVESS) FluView database. Patients: U.S.-born infants with OC from 2004 to 2013 and monthly influenza incidence from 2003 to 2013. Main outcome measures: Using logistic regression, monthly odds ratios (OR) of OC were derived using January as baseline. Mixed-effects logistic regression was utilized to test association between national influenza and OC incidences. Results: There were 58 270 U.S. babies born with OC from 2004 to 2013. September births had the highest OC association (OR = 1.094, 95% CI = 1.051-1.138, E-value = 1.41), followed by June. For each additional influenza case per 1000 people, odds of OC event occurring during the 2nd month of pregnancy, or 7 months before delivery, was increased by 2.7 (OR = 2.659, CI = 1.456-4.856, E-value = 4.76). Odds of OC event occurring was decreased at the 3rd month of pregnancy, or 6 months before delivery by 7.8 (OR = 0.129, 95% CI = 0.068-0.246, E-value = 14.99). Conclusion: September and June births have the highest OC association. There is increased risk for OC with influenza occurring at the 2nd pregnancy month. Conversely, there are protective effects against OC with influenza occurring at the 3rd pregnancy month. These findings demonstrate an association between influenza rate and OC, suggesting a connection between maternal immune activation (mIA) and OC. Although further research is needed to determine the definitive link between the use of flu vaccines and OC occurrence, as well as the mechanism behind mIA secondary to influenza infection impacting OC incidence, this study presents a modifiable risk factor that could decrease the potential for mIA causing OC.

Physicians often make diagnosis and treatment decisions based on incomplete data. That is why we practice medicine. We use accumulated knowledge and prior experience, individual and collective, to restore form and function of our patients to return them to normalcy with continued, durable homeostasis. However, due to the complex nature, our diagnosis may be wrong and our treatments ineffective or even harmful. The history of medicine and surgery is replete with such examples from snake oil and bloodletting to Halstedian radical mastectomy. Without knowing the governing dynamics, the cause-effect relationship is often obscure. Prospective, blinded, placebo-controlled trials provide the highest level of evidence, like the COVID vaccine trials, to determine if a treatment works. However, trials cannot explain in detail how and why a treatment works, or why it does not. Variabilities and uncertainty abound and require the correct mathematical methods to tease out the signal from the noise, causality from association. Collectively, statistics is the science of uncertainty and the extraction of reliable, useful information from raw data. The objectives of this review are to provide craniofacial surgeons with a primer in descriptive statistics: how to design investigations, collect, prepare, present, and interpret clinical data. Since large datasets at regional and national depositories represent powerful and valuable resources, and that their proper use requires a working knowledge in epidemiology, we included sections on incidence, prevalence, sensitivity, and specificity regarding diagnosis, treatments, and testing.

Forecasting elections -- a challenging, high-stakes problem -- is the subject of much uncertainty, subjectivity, and media scrutiny. To shed light on this process, we develop a method for forecasting elections from the perspective of dynamical systems. Our model borrows ideas from epidemiology, and we use polling data from United States elections to determine its parameters. Surprisingly, our general model performs as well as popular forecasters for the 2012 and 2016 U.S. races for president, senators, and governors. Although contagion and voting dynamics differ, our work suggests a valuable approach to elucidate how elections are related across states. It also illustrates the effect of accounting for uncertainty in different ways, provides an example of data-driven forecasting using dynamical systems, and suggests avenues for future research on political elections. We conclude with our forecasts for the senatorial and gubernatorial races on 6~November 2018, which we posted on 5 November 2018.

Introduction: An intermediate-sized, multicenter, expanded-access study was opened in 2015 through the support of the State of Georgia. This study provided children with treatment-resistant epilepsy (TRE) access to plant-derived highly purified cannabidiol (CBD; Epidiolex (R) in the US; Epidyolex (R) in the EU; 100 mg/mL oral solution). These children had failed to achieve seizure freedom with available treatment options and were ineligible to participate in randomized controlled trials that only included patients with Lennox-Gastaut and Dravet syndromes. Methods: Cannabidiol safety, changes in seizure type, frequency, and seizure-free days were evaluated for children aged 1-18 years (at time of consent) as an adjunctive treatment for 36 months. The study consisted of a two-month baseline period, a titration period, treatment period, and optional titration period, which occurred after >= 26 weeks of treatment. Cannabidiol treatment was administered up to a targeted dose of 25 mg/kg/day, with an optional secondary treatment up to 50 mg/kg/day. Daily seizure type, seizure frequency, and seizure-free days were recorded in a Web-based diary, and changes in these outcomes were recorded and analyzed for the duration of the study. The occurrence of adverse events (AEs) was also recorded. Results: The median percentage change in seizures for 45 patients in Months 3, 6, 12, 18, 24, and 36 showed a statistically significant (p <0.001) reduction in major seizures (ranging from 54 to 72% at various time points) and all seizures (61-70%) compared with baseline. A mean increase in seizure-free days per 28 days was >5 in all treatment periods after Month 2, and an average increase of 7.52 (p < 0.001) seizure-free days per 28 days was observed at the end of follow-up compared with baseline. All patients experienced >= 1 AE. Children who transitioned to the optional secondary treatment (high-dose group) reported more AEs before increasing their dose to > 25.0 mg/kg/day compared with the low-dose group. However, the average rate of AEs was significantly lower after moving to a high-dose regimen (p = 0.004). Twelve children reported 20 serious AEs, none of which were considered related to CBD. Conclusions: This study supports CBD as an adjunctive treatment for children with TRE. Treatment was well tolerated in doses up to 50 mg/kg/day. Patients who did not achieve desired results at a dose of <= 25.0 mg/kg/day reported more AEs when CBD dose increased to >25.0 mg/kg/day. Decreases in major seizure frequency and an increase in seizure-free days compared with baseline were reported during treatment. This supports the efficacy and tolerability of CBD for mixed seizure etiologies. (C) 2020 Published by Elsevier Inc.

We propose a novel Markov chain Monte-Carlo (MCMC) method for reverse engineering the topological structure of stochastic reaction networks, a notoriously challenging problem that is relevant in many modern areas of research, like discovering gene regulatory networks or analyzing epidemic spread. The method relies on projecting the original time series trajectories onto information rich summary statistics and constructing the appropriate synthetic likelihood function to estimate reaction rates. The resulting estimates are consistent in the large volume limit and are obtained without employing complicated tuning strategies and expensive resampling as typically used by likelihood-free MCMC and approximate Bayesian methods. To illustrate run time improvements that can be achieved with our approach, we present a simulation study on inferring rates in a stochastic dynamical system arising from a density dependent Markov jump process. We then apply the method to two real data examples: the RNA-seq data from zebrafish experiment and the incidence data from 1665 plague outbreak at Eyam, England.

Objective: Maternal immune activation secondary to influenza infection during critical periods of fetal development is a significant risk factor for neuropsychiatric and neurodevelopmental disorders. The association between influenza and craniosynostosis is not well documented. We investigate the association between the incidence of influenza infection and incidence of craniosynostosis in the United States. Materials and Methods: Retrospective population-based observational study spanning using the National Inpatient Sample Database, the United States Center for Disease Control and Prevention FluView databases, including infants born with craniosynostosis in the United States from 2004 to 2013 and monthly influenza incidence in the United States from 2003 to 2013. Mixed-effects logistic regression tested the association between 2 variables: national influenza incidences and rate of craniosynostosis. Odds ratios were calculated for the occurrence of craniosynostosis in relation to previous months’ flu incidence. E-values were calculated to evaluate unmeasured confounders. Results: Retrospective analysis performed on 45 356 newborns with craniosynostosis. Mixed-effects logistic regression revealed for each additional influenza case per 1000 people, the odds of craniosynostosis event occurring 6 months later increased by 3.4 (adjusted P = .009, OR = 3.444, CI = 1.756-6.754). For each additional influenza case per 1000 people, the odds of craniosynostosis event occurring 7 and 2 months later decreased by 3.8 and 6.1, respectively (OR = 0.262 and 0.165; adjusted P value = .007 and <.001). E-value for the association between influenza and craniosynostosis incidence 6 months later was 6.35. The E-values for the association between influenza and craniosynostosis incidences 7 months and 2 months later were 7.1 and 11.6. Conclusion: There is an increased risk for craniosynostosis with influenza occurring in third month of pregnancy. There are protective effects against craniosynostosis with influenza occurring in second and seventh months of pregnancy. To our knowledge, this is the first study demonstrating an association between the rate of influenza and craniosynostosis, suggesting a potentially important connection, though not necessarily causality, between maternal immune activation and craniosynostosis.

Tobacco use delivers nicotine, tobacco-specific nitrosamines (TSNAs), volatile organic compounds (VOCs), and polycyclic aromatic hydrocarbons (PAHs), which are metabolized and excreted in urine offering useful biomarkers of exposure. Previous studies compared individual toxicants across tobacco users. Based on a group of biomarkers, cluster analysis was used to define tobacco toxicant exposure profiles. Clusters with distinct exposure profiles, were determined and described, based on levels of urinary biomarkers of exposure to nicotine, TSNAs, VOCs, and PAHs among a national sample of current, established, adult tobacco users, and examine the association of use behavior and cluster membership. The PATH Biomarker Wave 1 data were analyzed. Current established tobacco users with complete urinary biomarker data were included (N = 6724). User groups included cigarette smokers, users of electronic cigarette (ECIG), smokeless tobacco (SLT), and dual and poly tobacco users. Cluster analysis, pairwise comparisons, and multinomial logistic regression were conducted. Cigarette smokers were primarily in clusters with high biomarker concentrations across all groups, but actual concentrations were associated with smoking quantity. A cluster with high TSNAs but low levels of PAHs and VOCs was heavily populated by SLT users. Exclusive ECIG users, depending on use frequency, were predominantly in clusters with low biomarker concentrations, except for one cluster that had relatively high TSNAs. Clusters heavily populated by dual and poly tobacco users were the same as those heavily populated by cigarette smokers. Ten exposure profiles (clusters) were determined and linked to tobacco use behavior. Findings could inform future research and policy initiatives.

Motivation In this paper we describe a Bayesian hierarchical model termed ‘PMMLogit’ for classification and model selection in high-dimensional settings with binary phenotypes as outcomes. Posterior computation in the logistic model is known to be computationally demanding due to its non-conjugacy with common priors. We combine a Polya-Gamma based data augmentation strategy and use recent results on Markov chain Monte-Carlo (MCMC) techniques to develop an efficient and exact sampling strategy for the posterior computation. We use the resulting MCMC chain for model selection and choose the best combination(s) of genomic variables via posterior model probabilities. Further, a Bayesian model averaging (BMA) approach using the posterior mean, which averages across visited models, is shown to give superior prediction of phenotypes given genomic measurements. Results Using simulation studies, we compared the performance of the proposed method with other popular methods. Simulation results show that the proposed method is quite effective in selecting the true model and has better estimation and prediction accuracy than other methods. These observations are consistent with theoretical results that have been developed in the statistics literature on optimality for this class of priors. Application to two well-known datasets on colon cancer and leukemia identified genes that have been previously reported in the clinical literature to be related to the disease outcomes. Availability Source code is publicly available on GitHub at https://github.com/v-panchal/PMML . Contact dlinder@augusta.edu Supplementary information Supplementary data are available online.

Inferring gene regulatory networks from high-throughput ‘omics’ data has proven to be a computationally demanding task of critical importance. Frequently the classical methods breakdown due to the curse of dimensionality, and popular strategies to overcome this are typically based on regularized versions of the classical methods. However, these approaches rely on loss functions that may not be robust and usually do not allow for the incorporation of prior information in a straightforward way. Fully Bayesian methods are equipped to handle both of these shortcomings quite naturally, and they offer potential for improvements in network structure learning. We propose a Bayesian hierarchical model to reconstruct gene regulatory networks from time series gene expression data, such as those common in perturbation experiments of biological systems. The proposed methodology utilizes global-local shrinkage priors for posterior selection of regulatory edges and relaxes the common normal likelihood assumption in order to allow for heavy-tailed data, which was shown in several of the cited references to severely impact network inference. We provide a sufficient condition for posterior propriety and derive an efficient MCMC via Gibbs sampling in the Appendix. We describe a novel way to detect multiple scales based on the corresponding posterior quantities. Finally, we demonstrate the performance of our approach in a simulation study and compare it with existing methods on real data from a T-cell activation study.

PURPOSE: Craniosynostosis, the premature fusion of cranial sutures, has increased in both prevalence and incidence as reported by international studies.1,2 To our knowledge, no recent studies have evaluated increasing incidence in the United States; therefore, we sought to evaluate if there was a significant increase in our national incidence of craniosynostosis. Methotrexate, a folic acid antagonist, has been associated with an increase in craniosynostosis.3 There has been a decrease in the incidence of cleft anomalies following the implementation of the folic acid supplementation program in 1998 within the United States. Both of these anomalies seem affected by folate. We hypothesize that there is a reciprocal relationship between cleft and craniosynostosis and seek to investigate the theory that as folate supplementation penetrates the population, we see a gradual increase in the incidence of craniosynostosis. METHODS AND MATERIALS: The National Inpatient Sample Database was consulted to identify infants born with craniosynostosis between 2004 and 2013. Data were collected from the US Center for Disease Control and Prevention, including incidence of influenza virus infection according to year and month. Using multivariable logistic regression, we examined the relationship between craniosynostosis and the independent variables month and year. We then utilized mixed-effects logistic regression to estimate the odds ratio of occurrence of craniosynostosis in relation to previous months’ flu incidence. E values were calculated to evaluate for unmeasured confounders. RESULTS: In 2004, there were 4,110 infants born with craniosynostosis, which increased to 6,155 infants in 2013. A statistically significant increase in the incidence of craniosynostosis within the United States was found (odds ratio of 1.57 in 2013; P < 0.001). Mixed-effects logistic regression revealed a lower incidence of craniosynostosis associated with an increased incidence of influenza infection. E values for national incidence of craniosynostosis and association with influenza incidence were 2.51 and 11.6, respectively. CONCLUSIONS: To our knowledge, this is the first study demonstrating a significant increase in the national incidence of craniosynostosis in the United States, which we believe may be a result of folic acid supplementation penetrating the population. We also report for the first time a decreased incidence of craniosynostosis in association with influenza incidence, which support our hypothesis of a possible inverse relationship with cleft, as maternal influenza during pregnancy demonstrates increased incidence of cleft anomalies.4 We are further investigating the relationship between cleft and craniosynostosis at this time to uncover a mechanism that might explain this relationship. REFERENCES: 1. Cornelissen M, Ottelander B, Rizopoulos D, et al. Increase of prevalence of craniosynostosis. J Craniomaxillofac Surg. 2016;44:1273–1279. 2. Morris JK, Springett AL, Greenlees R, et al. Trends in congenital anomalies in Europe from 1980 to 2012. PLoS One. 2018;13:e0194986. 3. Zarella CS, Albino FP, Oh AK, et al. Craniosynostosis following fetal methotrexate exposure. J Craniofac Surg. 2016;27:450–452. 4. Waller DK, Hashmi SS, Hoy AT, et al. Maternal report of fever from cold or flu during early pregnancy and the risk for noncardiac birth defects, National Birth Defects Prevention Study, 1997–2011. Birth Defects Res. 2018;110:342–351.

This study examined the association between adherence to American College of Sports Medicine and American Cancer Society guidelines on aerobic and muscle-strengthening activities and mortality risks among 3+ year cancer survivors in the U.S.