Gayle Fischer Langley

Bio: Gayle Fischer Langley is an academic researcher from Centers for Disease Control and Prevention. The author has contributed to research in topics: Population & Public health. The author has an hindex of 4, co-authored 5 publications receiving 298 citations.

Epidemiology and prevention of respiratory syncytial virus infections among infants and young children.

Abstract: Since its discovery in 1956, respiratory syncytial virus (RSV) has been recognized as one of the most common causes of serious lower respiratory tract infections in young children worldwide. While considered a high priority, development of a safe and effective vaccine has remained elusive. Prevention of RSV disease relies on infection control and hygiene measures, as well as providing immunoprophylaxis in select infants. The prophylaxis, however, is costly, and so targeting the recipient population and timing of administration is important for optimal effectiveness and judicious use of limited health care resources. This article reviews the epidemiology of RSV infections in infants and young children, including risk factors for severe disease, so as to inform decisions about prevention efforts.

The epidemiology and clinical characteristics of young children hospitalized with respiratory syncytial virus infections in Guatemala (2007-2010).

Abstract: BACKGROUND There have been few population-based studies from Central America on respiratory syncytial virus (RSV) infections in young children. We report population-based incidence rates and describe epidemiological and clinical characteristics of children 12 months old still had rates up to 5.5 per 1000 per year and accounted for 42% of deaths. Children with RSV infections were more likely to have signs of respiratory distress (85% versus 63%, P < 0.001) compared with those without RSV infections, but case fatality ratios were similar (3-4%). CONCLUSIONS The large burden and severity of RSV infections in young Guatemalan children is similar in magnitude and age distribution to RSV disease burdens found in other developing countries and suggests that this population would benefit from prevention strategies, including vaccines against RSV that are currently under development.

Updated Guidance for Palivizumab Prophylaxis Among Infants and Young Children at Increased Risk of Hospitalization for Respiratory Syncytial Virus Infection

Abstract: Palivizumab was licensed in June 1998 by the Food and Drug Administration for the reduction of serious lower respiratory tract infection caused by respiratory syncytial virus (RSV) in children at increased risk of severe disease. Since that time, the American Academy of Pediatrics has updated its guidance for the use of palivizumab 4 times as additional data became available to provide a better understanding of infants and young children at greatest risk of hospitalization attributable to RSV infection. The updated recommendations in this policy statement reflect new information regarding the seasonality of RSV circulation, palivizumab pharmacokinetics, the changing incidence of bronchiolitis hospitalizations, the effect of gestational age and other risk factors on RSV hospitalization rates, the mortality of children hospitalized with RSV infection, the effect of prophylaxis on wheezing, and palivizumab-resistant RSV isolates. This policy statement updates and replaces the recommendations found in the 2012 Red Book.

Respiratory Syncytial Virus–Associated Hospitalizations Among Children Less Than 24 Months of Age

Abstract: BACKGROUND: Respiratory syncytial virus (RSV) infection is a leading cause of hospitalization among infants. However, estimates of the RSV hospitalization burden have varied, and precision has been limited by the use of age strata grouped in blocks of 6 to ≥12 months. METHODS: We analyzed data from a 5-year, prospective, population-based surveillance for young children who were hospitalized with laboratory-confirmed (reverse-transcriptase polymerase chain reaction) RSV acute respiratory illness (ARI) during October through March 2000–2005. The total population at risk was stratified by month of age by birth certificate information to yield hospitalization rates. RESULTS: There were 559 (26%) RSV-infected children among the 2149 enrolled children hospitalized with ARI (85% of all eligible children with ARI). The average RSV hospitalization rate was 5.2 per 1000 children CONCLUSIONS: Young infants, especially those who were 1 month old, were at greatest risk of RSV hospitalization. Four-fifths of RSV-hospitalized infants were previously healthy. To substantially reduce the burden of RSV hospitalizations, effective general preventive strategies will be required for all young infants, not just those with risk factors.

Epidemiology of Invasive Early-Onset and Late-Onset Group B Streptococcal Disease in the United States, 2006 to 2015: Multistate Laboratory and Population-Based Surveillance.

Abstract: Importance Invasive disease owing to group BStreptococcus(GBS) remains an important cause of illness and death among infants younger than 90 days in the United States, despite declines in early-onset disease (EOD; with onset at 0-6 days of life) that are attributed to intrapartum antibiotic prophylaxis (IAP). Maternal vaccines to prevent infant GBS disease are currently under development. Objective To describe incidence rates, case characteristics, antimicrobial resistance, and serotype distribution of EOD and late-onset disease (LOD; with onset at 7-89 days of life) in the United States from 2006 to 2015 to inform IAP guidelines and vaccine development. Design, Setting, and Participants This study used active population-based and laboratory-based surveillance for invasive GBS disease conducted through Active Bacterial Core surveillance in selected counties of 10 states across the United States. Residents of Active Bacterial Core surveillance areas who were younger than 90 days and had invasive GBS disease in 2006 to 2015 were included. Data were analyzed from December 2017 to April 2018. Exposures Group BStreptococcusisolated from a normally sterile site. Main Outcomes and Measures Early-onset disease and LOD incidence rates and associated GBS serotypes and antimicrobial resistance. Results The Active Bacterial Core surveillance program identified 1277 cases of EOD and 1387 cases of LOD. From 2006 to 2015, EOD incidence declined significantly from 0.37 to 0.23 per 1000 live births (P Conclusions and Relevance The rates of LOD among US infants are now higher than EOD rates. Combined with addressing IAP implementation gaps, an effective vaccine covering the most common serotypes might further reduce EOD rates and help prevent LOD, for which there is no current public health intervention.

Using machine learning to predict antimicrobial MICs and associated genomic features for nontyphoidal Salmonella

Abstract: Nontyphoidal Salmonella species are the leading bacterial cause of foodborne disease in the United States. Whole-genome sequences and paired antimicrobial susceptibility data are available for Salmonella strains because of surveillance efforts from public health agencies. In this study, a collection of 5,278 nontyphoidal Salmonella genomes, collected over 15 years in the United States, was used to generate extreme gradient boosting (XGBoost)-based machine learning models for predicting MICs for 15 antibiotics. The MIC prediction models had an overall average accuracy of 95% within ±1 2-fold dilution step (confidence interval, 95% to 95%), an average very major error rate of 2.7% (confidence interval, 2.4% to 3.0%), and an average major error rate of 0.1% (confidence interval, 0.1% to 0.2%). The model predicted MICs with no a priori information about the underlying gene content or resistance phenotypes of the strains. By selecting diverse genomes for the training sets, we show that highly accurate MIC prediction models can be generated with less than 500 genomes. We also show that our approach for predicting MICs is stable over time, despite annual fluctuations in antimicrobial resistance gene content in the sampled genomes. Finally, using feature selection, we explore the important genomic regions identified by the models for predicting MICs. To date, this is one of the largest MIC modeling studies to be published. Our strategy for developing whole-genome sequence-based models for surveillance and clinical diagnostics can be readily applied to other important human pathogens.

WGS to predict antibiotic MICs for Neisseria gonorrhoeae.

Abstract: Background: Tracking the spread of antimicrobial-resistant Neisseria gonorrhoeae is a major priority for national surveillance programmes. / Objectives: We investigate whether WGS and simultaneous analysis of multiple resistance determinants can be used to predict antimicrobial susceptibilities to the level of MICs in N. gonorrhoeae. / Methods: WGS was used to identify previously reported potential resistance determinants in 681 N. gonorrhoeae isolates, from England, the USA and Canada, with phenotypes for cefixime, penicillin, azithromycin, ciprofloxacin and tetracycline determined as part of national surveillance programmes. Multivariate linear regression models were used to identify genetic predictors of MIC. Model performance was assessed using leave-one-out cross-validation. / Results: Overall 1785/3380 (53%) MIC values were predicted to the nearest doubling dilution and 3147 (93%) within ±1 doubling dilution and 3314 (98%) within ±2 doubling dilutions. MIC prediction performance was similar across the five antimicrobials tested. Prediction models included the majority of previously reported resistance determinants. Applying EUCAST breakpoints to MIC predictions, the overall very major error (VME; phenotypically resistant, WGS-prediction susceptible) rate was 21/1577 (1.3%, 95% CI 0.8%–2.0%) and the major error (ME; phenotypically susceptible, WGS-prediction resistant) rate was 20/1186 (1.7%, 1.0%–2.6%). VME rates met regulatory thresholds for all antimicrobials except cefixime and ME rates for all antimicrobials except tetracycline. Country of testing was a strongly significant predictor of MIC for all five antimicrobials. / Conclusions: We demonstrate a WGS-based MIC prediction approach that allows reliable MIC prediction for five gonorrhoea antimicrobials. Our approach should allow reasonably precise prediction of MICs for a range of bacterial species.