PyMC: a modern, and comprehensive probabilistic programming framework in Python.

PyMC is a probabilistic programming library for Python that provides tools for constructing and fitting Bayesian models. It offers an intuitive, readable syntax that is close to the natural syntax statisticians use to describe models. PyMC leverages the symbolic computation library PyTensor, allowing it to be compiled into a variety of computational backends, such as C, JAX, and Numba, which in turn offer access to different computational architectures including CPU, GPU, and TPU. Being a general modeling framework, PyMC supports a variety of models including generalized hierarchical linear regression and classification, time series, ordinary differential equations (ODEs), and non-parametric models such as Gaussian processes (GPs). We demonstrate PyMC's versatility and ease of use with examples spanning a range of common statistical models. Additionally, we discuss the positive role of PyMC in the development of the open-source ecosystem for probabilistic programming.

Using Momentary Assessment and Machine Learning to Identify Barriers to Self-management in Type 1 Diabetes: Observational Study.

BACKGROUND: For adolescents living with type 1 diabetes (T1D), completion of multiple daily self-management tasks, such as monitoring blood glucose and administering insulin, can be challenging because of psychosocial and contextual barriers. These barriers are hard to assess accurately and specifically by using traditional retrospective recall. Ecological momentary assessment (EMA) uses mobile technologies to assess the contexts, subjective experiences, and psychosocial processes that surround self-management decision-making in daily life. However, the rich data generated via EMA have not been frequently examined in T1D or integrated with machine learning analytic approaches. OBJECTIVE: The goal of this study is to develop a machine learning algorithm to predict the risk of missed self-management in young adults with T1D. To achieve this goal, we train and compare a number of machine learning models through a learned filtering architecture to explore the extent to which EMA data were associated with the completion of two self-management behaviors: mealtime self-monitoring of blood glucose (SMBG) and insulin administration. METHODS: We analyzed data from a randomized controlled pilot study using machine learning-based filtering architecture to investigate whether novel information related to contextual, psychosocial, and time-related factors (ie, time of day) relate to self-management. We combined EMA-collected contextual and insulin variables via the MyDay mobile app with Bluetooth blood glucose data to construct machine learning classifiers that predicted the 2 self-management behaviors of interest. RESULTS: With 1231 day-level SMBG frequency counts for 45 participants, demographic variables and time-related variables were able to predict whether daily SMBG was below the clinical threshold of 4 times a day. Using the 1869 data points derived from app-based EMA data of 31 participants, our learned filtering architecture method was able to infer nonadherence events with high accuracy and precision. Although the recall score is low, there is high confidence that the nonadherence events identified by the model are truly nonadherent. CONCLUSIONS: Combining EMA data with machine learning methods showed promise in the relationship with risk for nonadherence. The next steps include collecting larger data sets that would more effectively power a classifier that can be deployed to infer individual behavior. Improvements in individual self-management insights, behavioral risk predictions, enhanced clinical decision-making, and just-in-time patient support in diabetes could result from this type of approach.

The Changing Landscape of Pediatric Viral Enteropathogens in the Post-Rotavirus Vaccine Era.

BACKGROUND: Acute gastroenteritis (AGE) is a common reason for children to receive medical care. However, the viral etiology of AGE illness is not well described in the post-rotavirus vaccine era, particularly in the outpatient (OP) setting. METHODS: Between 2012 and 2015, children 15 days through 17 years old presenting to Vanderbilt Children's Hospital, Nashville, Tennessee, with AGE were enrolled prospectively from the inpatient, emergency department, and OP settings, and stool specimens were collected. Healthy controls (HCs) were enrolled and frequency matched for period, age group, race, and ethnicity. Stool specimens were tested by means of reverse-transcription real-time quantitative polymerase chain reaction for norovirus, sapovirus, and astrovirus RNA and by Rotaclone enzyme immunoassay for rotavirus antigen, followed by polymerase chain reaction verification of antigen detection. RESULTS: A total of 3705 AGE case patients and 1563 HCs were enrolled, among whom 2885 case patients (78%) and 1110 HCs (71%) provided stool specimens that were tested. All 4 viruses were more frequently detected in AGE case patients than in HCs (norovirus, 22% vs 8%, respectively; rotavirus, 10% vs 1%; sapovirus, 10% vs 5%; and astrovirus, 5% vs 2%; P < .001 for each virus). In the OP setting, rates of AGE due to norovirus were higher than rate for the other 3 viruses. Children <5 years old had higher OP AGE rates than older children for all viruses. CONCLUSIONS: Norovirus remains the most common virus detected in all settings, occurring nearly twice as frequently as the next most common pathogens, sapovirus and rotavirus. Combined, norovirus, sapovirus, rotavirus, and astrovirus were associated with almost half of all AGE visits and therefore are an important reason for children to receive medical care.

A nonparametric updating method to correct clinical prediction model drift.

OBJECTIVE: Clinical prediction models require updating as performance deteriorates over time. We developed a testing procedure to select updating methods that minimizes overfitting, incorporates uncertainty associated with updating sample sizes, and is applicable to both parametric and nonparametric models. MATERIALS AND METHODS: We describe a procedure to select an updating method for dichotomous outcome models by balancing simplicity against accuracy. We illustrate the test's properties on simulated scenarios of population shift and 2 models based on Department of Veterans Affairs inpatient admissions. RESULTS: In simulations, the test generally recommended no update under no population shift, no update or modest recalibration under case mix shifts, intercept correction under changing outcome rates, and refitting under shifted predictor-outcome associations. The recommended updates provided superior or similar calibration to that achieved with more complex updating. In the case study, however, small update sets lead the test to recommend simpler updates than may have been ideal based on subsequent performance. DISCUSSION: Our test's recommendations highlighted the benefits of simple updating as opposed to systematic refitting in response to performance drift. The complexity of recommended updating methods reflected sample size and magnitude of performance drift, as anticipated. The case study highlights the conservative nature of our test. CONCLUSIONS: This new test supports data-driven updating of models developed with both biostatistical and machine learning approaches, promoting the transportability and maintenance of a wide array of clinical prediction models and, in turn, a variety of applications relying on modern prediction tools.

Concurrent assessment of epidemiological and operational uncertainties for optimal outbreak control: Ebola as a case study.

Determining how best to manage an infectious disease outbreak may be hindered by both epidemiological uncertainty (i.e. about epidemiological processes) and operational uncertainty (i.e. about the effectiveness of candidate interventions). However, these two uncertainties are rarely addressed concurrently in epidemic studies. We present an approach to simultaneously address both sources of uncertainty, to elucidate which source most impedes decision-making. In the case of the 2014 West African Ebola outbreak, epidemiological uncertainty is represented by a large ensemble of published models. Operational uncertainty about three classes of interventions is assessed for a wide range of potential intervention effectiveness. We ranked each intervention by caseload reduction in each model, initially assuming an unlimited budget as a counterfactual. We then assessed the influence of three candidate cost functions relating intervention effectiveness and cost for different budget levels. The improvement in management outcomes to be gained by resolving uncertainty is generally high in this study; appropriate information gain could reduce expected caseload by more than 50%. The ranking of interventions is jointly determined by the underlying epidemiological process, the effectiveness of the interventions and the size of the budget. An epidemiologically effective intervention might not be optimal if its costs outweigh its epidemiological benefit. Under higher-budget conditions, resolution of epidemiological uncertainty is most valuable. When budgets are tight, however, operational and epidemiological uncertainty are equally important. Overall, our study demonstrates that significant reductions in caseload could result from a careful examination of both epidemiological and operational uncertainties within the same modelling structure. This approach can be applied to decision-making for the management of other diseases for which multiple models and multiple interventions are available.

Multiple Introductions and Antigenic Mismatch with Vaccines May Contribute to Increased Predominance of G12P[8] Rotaviruses in the United States.

Rotavirus is the leading global cause of diarrheal mortality for unvaccinated children under 5 years of age. The outer capsid of rotavirus virions consists of VP7 and VP4 proteins, which determine viral G and P types, respectively, and are primary targets of neutralizing antibodies. Successful vaccination depends upon generating broadly protective immune responses following exposure to rotaviruses presenting a limited number of G- and P-type antigens. Vaccine introduction resulted in decreased rotavirus disease burden but also coincided with the emergence of uncommon G and P genotypes, including G12. To gain insight into the recent predominance of G12P[8] rotaviruses in the United States, we evaluated 142 complete rotavirus genome sequences and metadata from 151 clinical specimens collected in Nashville, TN, from 2011 to 2013 through the New Vaccine Surveillance Network. Circulating G12P[8] strains were found to share many segments with other locally circulating strains but to have distinct constellations. Phylogenetic analyses of G12 sequences and their geographic sources provided evidence for multiple separate introductions of G12 segments into Nashville, TN. Antigenic epitopes of VP7 proteins of G12P[8] strains circulating in Nashville, TN, differ markedly from those of vaccine strains. Fully vaccinated children were found to be infected with G12P[8] strains more frequently than with other rotavirus genotypes. Multiple introductions and significant antigenic mismatch may in part explain the recent predominance of G12P[8] strains in the United States and emphasize the need for continued monitoring of rotavirus vaccine efficacy against emerging rotavirus genotypes.IMPORTANCE Rotavirus is an important cause of childhood diarrheal disease worldwide. Two immunodominant proteins of rotavirus, VP7 and VP4, determine G and P genotypes, respectively. Recently, G12P[8] rotaviruses have become increasingly predominant. By analyzing rotavirus genome sequences from stool specimens obtained in Nashville, TN, from 2011 to 2013 and globally circulating rotaviruses, we found evidence of multiple introductions of G12 genes into the area. Based on sequence polymorphisms, VP7 proteins of these viruses are predicted to present themselves to the immune system very differently than those of vaccine strains. Many of the sick children with G12P[8] rotavirus in their diarrheal stools also were fully vaccinated. Our findings emphasize the need for continued monitoring of circulating rotaviruses and the effectiveness of the vaccines against strains with emerging G and P genotypes.

Distinct mucosal microbial communities in infants with surgical necrotizing enterocolitis correlate with age and antibiotic exposure.

OBJECTIVE: Necrotizing enterocolitis (NEC) is the most common surgical emergency in preterm infants, and pathogenesis associates with changes in the fecal microbiome. As fecal samples incompletely represent microbial communities in intestinal mucosa, we sought to determine the NEC tissue-specific microbiome and assess its contribution to pathogenesis. DESIGN: We amplified and sequenced the V1-V3 hypervariable region of the bacterial 16S rRNA gene extracted from intestinal tissue and corresponding fecal samples from 12 surgical patients with NEC and 14 surgical patients without NEC. Low quality and non-bacterial sequences were removed, and taxonomic assignment was made with the Ribosomal Database Project. Operational taxonomic units were clustered at 97%. We tested for differences between NEC and non-NEC samples in microbiome alpha- and beta-diversity and differential abundance of specific taxa between NEC and non-NEC samples. Additional analyses were performed to assess the contribution of other demographic and environmental confounding factors on the infant tissue and fecal microbiome. RESULTS: The fecal and tissue microbial communities were different. NEC was associated with a distinct microbiome, which was characterized by low diversity, higher abundances of Staphylococcus and Clostridium_sensu_stricto, and lower abundances of Actinomyces and Corynebacterium. Infant age and vancomycin exposure correlated with shifts in the tissue microbiome. CONCLUSION: The observed low diversity in NEC tissues suggests that NEC is associated with a bacterial bloom and a distinct mucosal bacterial community. The exact bacterial species that constitute the bloom varied by infant and were strongly influenced by age and exposure to vancomycin.

Real-time decision-making during emergency disease outbreaks.

In the event of a new infectious disease outbreak, mathematical and simulation models are commonly used to inform policy by evaluating which control strategies will minimize the impact of the epidemic. In the early stages of such outbreaks, substantial parameter uncertainty may limit the ability of models to provide accurate predictions, and policymakers do not have the luxury of waiting for data to alleviate this state of uncertainty. For policymakers, however, it is the selection of the optimal control intervention in the face of uncertainty, rather than accuracy of model predictions, that is the measure of success that counts. We simulate the process of real-time decision-making by fitting an epidemic model to observed, spatially-explicit, infection data at weekly intervals throughout two historical outbreaks of foot-and-mouth disease, UK in 2001 and Miyazaki, Japan in 2010, and compare forward simulations of the impact of switching to an alternative control intervention at the time point in question. These are compared to policy recommendations generated in hindsight using data from the entire outbreak, thereby comparing the best we could have done at the time with the best we could have done in retrospect. Our results show that the control policy that would have been chosen using all the data is also identified from an early stage in an outbreak using only the available data, despite high variability in projections of epidemic size. Critically, we find that it is an improved understanding of the locations of infected farms, rather than improved estimates of transmission parameters, that drives improved prediction of the relative performance of control interventions. However, the ability to estimate undetected infectious premises is a function of uncertainty in the transmission parameters. Here, we demonstrate the need for both real-time model fitting and generating projections to evaluate alternative control interventions throughout an outbreak. Our results highlight the use of using models at outbreak onset to inform policy and the importance of state-dependent interventions that adapt in response to additional information throughout an outbreak.

Severe outcomes associated with respiratory viruses in newborns and infants: a prospective viral surveillance study in Jordan.

OBJECTIVE: To assess virus-specific hospitalisation rates, risk factors for illness severity and seasonal trends in children hospitalised with acute respiratory infections (ARI). DESIGN: Prospective cohort study. SETTING: A government hospital serving low-income and middle-income population in Amman, Jordan. PARTICIPANTS: Children under 2 years of age hospitalised with fever and/or respiratory symptoms (n=3168) from 16 March 2010 to 31 March 2013. Children with chemotherapy-associated neutropenia and newborns who had never been discharged after birth were excluded from the study. OUTCOME MEASURES: Hospitalisation rates and markers of illness severity: admission to intensive care unit (ICU), mechanical ventilation (MV), oxygen therapy, length of stay (LOS) and death. RESULTS: Of the 3168 subjects, 2581 (82%) had at least one respiratory virus detected, with respiratory syncytial virus (RSV) being the most predominant pathogen isolated. During admission, 1013 (32%) received oxygen therapy, 284 (9%) were admitted to ICU, 111 (4%) were placed on MV and 31 (1%) children died. Oxygen therapy was higher in RSV-only subjects compared with human rhinovirus-only (42%vs29%, p<0.001), adenovirus-only (42%vs21%, p<0.001) and human parainfluenza virus-only (42%vs23%, p<0.001) subjects. The presence of an underlying medical condition was associated with oxygen therapy (adjusted OR (aOR) 1.95, 95% CI 1.49 to 2.56), ICU admission (aOR 2.51, 95% CI 1.71 to 3.68), MV (aOR 1.91, 95% CI 1.11 to 3.28) and longer LOS (aOR1.71, 95% CI 1.37 to 2.13). Similarly, younger age was associated with oxygen therapy (0.23, 95% CI 0.17 to 0.31), ICU admission (aOR 0.47, 95% CI 0.30 to 0.74), MV (0.28, 95% CI 0.15 to 0.53) and longer LOS (aOR 0.47, 95% CI 0.38 to 0.59). Pneumonia was strongly associated with longer LOS (aOR 2.07, 95% CI 1.65 to 2.60), oxygen therapy (aOR 2.94, 95% CI 2.22 to 3.89), ICU admission (aOR 3.12, 95% CI 2.16 to 4.50) and MV (aOR 3.33, 95% CI 1.85 to 6.00). Virus-specific hospitalisation rates ranged from 0.5 to 10.5 per 1000 children. CONCLUSION: Respiratory viruses are associated with severe illness in Jordanian children hospitalised with ARI. Prevention strategies such as extended breast feeding, increased access to palivizumab and RSV vaccine development could help decrease hospitalisation rates and illness severity, particularly in young children with underlying medical conditions.

Measles outbreak response decision-making under uncertainty: a retrospective analysis.

Resurgent outbreaks of vaccine-preventable diseases that have previously been controlled or eliminated have been observed in many settings. Reactive vaccination campaigns may successfully control outbreaks but must necessarily be implemented in the face of considerable uncertainty. Real-time surveillance may provide critical information about at-risk population and optimal vaccination targets, but may itself be limited by the specificity of disease confirmation. We propose an integrated modelling approach that synthesizes historical demographic and vaccination data with real-time outbreak surveillance via a dynamic transmission model and an age-specific disease confirmation model. We apply this framework to data from the 1996-1997 measles outbreak in São Paulo, Brazil. To simulate the information available to decision-makers, we truncated the surveillance data to what would have been available at 1 or 2 months prior to the realized interventions. We use the model, fitted to real-time observations, to evaluate the likelihood that candidate age-targeted interventions could control the outbreak. Using only data available prior to the interventions, we estimate that a significant excess of susceptible adults would prevent child-targeted campaigns from controlling the outbreak and that failing to account for age-specific confirmation rates would underestimate the importance of adult-targeted vaccination.

Predictors of Intravenous Immunoglobulin Nonresponse and Racial Disparities in Kawasaki Disease.

BACKGROUND: Kawasaki disease (KD) is the most common cause of acquired heart disease in American children. Intravenous immunoglobulin (IVIG) nonresponse is a known risk factor for cardiac sequelae. Previously reported risk factors for nonresponse include age, male sex and laboratory abnormalities. We set out to identify additional risk factors for IVIG nonresponse in a racially diverse KD population. METHODS: We conducted a retrospective chart review at a referral center in the Southeastern United States of children meeting ICD-9 (International Statistical Classification of Disease and Related Health Problems) criteria for KD and being treated with IVIG. RESULTS: Four-hundred and fifty-nine children met inclusion criteria, 67 were excluded for subsequent rheumatologic diagnosis, unknown race, or failure to meet the American Heart Association guideline criteria. Our final cohort consisted of 392 subjects, with median age of 2.7 years, 65.1% male, 66.1% White, 24.2% Black, 4.9% Asian and 82.9% responded to a single dose of IVIG. Coronary ectasia or aneurysm developed in 27%; 7.4% developed aneurysms and 2.3% giant coronary aneurysms. Nonresponders were more likely to be Black, have higher white blood cell, erythrocyte sedimentation rate and C-reactive protein, lower hemoglobin, develop ectasia or aneurysm and require critical care and hospital readmission. Responders achieved echocardiographic normalization more often compared with nonresponders (81.3% vs. 60.9%, P = 0.002) and coronary artery pseudonormalization (87.2% vs. 69.7%, P = 0.03) at 1 year. Black nonresponders had the slowest normalization at 1 year (52.9%, P = 0.02). CONCLUSIONS: Nonresponders have higher rates and greater severity of coronary involvement than responders. Our study uniquely demonstrates Black race as a risk factor for nonresponse and for delayed normalization of cardiac involvement at 1-year follow-up.

Diarrheagenic Escherichia coli and Acute Gastroenteritis in Children in Davidson County, Tennessee, United States: A Case-control Study.

BACKGROUND: Diarrheagenic Escherichia coli (DEC) is an important cause of acute gastroenteritis in children; however, there is limited information available on the epidemiology, phylogenetics, serotyping and antibiotic susceptibility of DEC in children in the United States. The aim of this study was to determine the molecular epidemiology of DEC among children with and without acute gastroenteritis in Davidson County, Tennessee. METHODS: This prospective, frequency matched, case-control study recruited subjects 15 days to 17 years of age and detected DEC with polymerase chain reaction from stool samples. Additional testing was done to define phylogenetics and antibiotics resistance. RESULTS: Among 1267 participants, 857 cases and 410 controls, 5.5% were positive for at least one subtype of DEC. Enteroaggregative E. coli [n = 32 (45%)] was the most common subtype followed by enteropathogenic E. coli (EPEC) [n = 30 (43%)], Shiga toxin-producing E. coli [n = 4 (6%)] and diffusely adherent E. coli [n = 4 (6%)]. No significant difference in prevalence of DEC was found between cases (5%) and controls (7%) [odds ratio: 0.66 (95% confidence interval: 0.4-1.07)], and results were similar when data were stratified by subtypes and adjusted for age, sex, race and ethnicity. Substantial diversity was found among DEC isolates in terms of phylotypes and serotypes, and a large proportion was resistant to, at least, one antibiotic. CONCLUSIONS: Enteroaggregative E. coli and enteropathogenic E. coli were frequently found in both cases and controls in this study population. DNA-based methods for detection of these subtypes need further investigation to help differentiate between pathogenic and colonizing strains.

The Listening and Spoken Language Data Repository: Design and Project Overview.

Purpose: The purpose of the Listening and Spoken Language Data Repository (LSL-DR) was to address a critical need for a systemwide outcome data-monitoring program for the development of listening and spoken language skills in highly specialized educational programs for children with hearing loss highlighted in Goal 3b of the 2007 Joint Committee on Infant Hearing position statement supplement. Method: The LSL-DR is a multicenter, international data repository for recording and tracking the demographics and longitudinal outcomes achieved by children who have hearing loss who are enrolled in private, specialized programs focused on supporting listening and spoken language development. Since 2010, annual speech-language-hearing outcomes have been prospectively obtained by qualified clinicians and teachers across 48 programs in 4 countries. Results: The LSL-DR has been successfully implemented, bringing together the data collection efforts of these programs to create a large and diverse data repository of 5,748 children with hearing loss. Conclusion: Due to the size and diversity of the population, the range of assessments entered, and the demographic information collected, the LSL-DR will provide an unparalleled opportunity to examine the factors that influence the development of listening in spoken language in this population.

AHRQ series on complex intervention systematic reviews-paper 5: advanced analytic methods.

BACKGROUND AND OBJECTIVE: Advanced analytic methods for synthesizing evidence about complex interventions continue to be developed. In this paper, we emphasize that the specific research question posed in the review should be used as a guide for choosing the appropriate analytic method. METHODS: We present advanced analytic approaches that address four common questions that guide reviews of complex interventions: (1) How effective is the intervention? (2) For whom does the intervention work and in what contexts? (3) What happens when the intervention is implemented? and (4) What decisions are possible given the results of the synthesis? CONCLUSION: The analytic approaches presented in this paper are particularly useful when each primary study differs in components, mechanisms of action, context, implementation, timing, and many other domains.

Essential information: Uncertainty and optimal control of Ebola outbreaks.

Early resolution of uncertainty during an epidemic outbreak can lead to rapid and efficient decision making, provided that the uncertainty affects prioritization of actions. The wide range in caseload projections for the 2014 Ebola outbreak caused great concern and debate about the utility of models. By coding and running 37 published Ebola models with five candidate interventions, we found that, despite this large variation in caseload projection, the ranking of management options was relatively consistent. Reducing funeral transmission and reducing community transmission were generally ranked as the two best options. Value of information (VoI) analyses show that caseloads could be reduced by 11% by resolving all model-specific uncertainties, with information about model structure accounting for 82% of this reduction and uncertainty about caseload only accounting for 12%. Our study shows that the uncertainty that is of most interest epidemiologically may not be the same as the uncertainty that is most relevant for management. If the goal is to improve management outcomes, then the focus of study should be to identify and resolve those uncertainties that most hinder the choice of an optimal intervention. Our study further shows that simplifying multiple alternative models into a smaller number of relevant groups (here, with shared structure) could streamline the decision-making process and may allow for a better integration of epidemiological modeling and decision making for policy.

A Bayesian Double Fusion Model for Resting-State Brain Connectivity Using Joint Functional and Structural Data.

Current approaches separately analyze concurrently acquired diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) data. The primary limitation of these approaches is that they do not take advantage of the information from DTI that could potentially enhance estimation of resting-state functional connectivity (FC) between brain regions. To overcome this limitation, we develop a Bayesian hierarchical spatiotemporal model that incorporates structural connectivity (SC) into estimating FC. In our proposed approach, SC based on DTI data is used to construct an informative prior for FC based on resting-state fMRI data through the Cholesky decomposition. Simulation studies showed that incorporating the two data produced significantly reduced mean squared errors compared to the standard approach of separately analyzing the two data from different modalities. We applied our model to analyze the resting state DTI and fMRI data collected to estimate FC between the brain regions that were hypothetically important in the origination and spread of temporal lobe epilepsy seizures. Our analysis concludes that the proposed model achieves smaller false positive rates and is much robust to data decimation compared to the conventional approach.

Quantifying the Value of Perfect Information in Emergency Vaccination Campaigns.

Foot-and-mouth disease outbreaks in non-endemic countries can lead to large economic costs and livestock losses but the use of vaccination has been contentious, partly due to uncertainty about emergency FMD vaccination. Value of information methods can be applied to disease outbreak problems such as FMD in order to investigate the performance improvement from resolving uncertainties. Here we calculate the expected value of resolving uncertainty about vaccine efficacy, time delay to immunity after vaccination and daily vaccination capacity for a hypothetical FMD outbreak in the UK. If it were possible to resolve all uncertainty prior to the introduction of control, we could expect savings of £55 million in outbreak cost, 221,900 livestock culled and 4.3 days of outbreak duration. All vaccination strategies were found to be preferable to a culling only strategy. However, the optimal vaccination radius was found to be highly dependent upon vaccination capacity for all management objectives. We calculate that by resolving the uncertainty surrounding vaccination capacity we would expect to return over 85% of the above savings, regardless of management objective. It may be possible to resolve uncertainty about daily vaccination capacity before an outbreak, and this would enable decision makers to select the optimal control action via careful contingency planning.

Postoperative Bleeding and Associated Utilization following Tonsillectomy in Children.

Objective To assess posttonsillectomy hemorrhage (PTH), associated nonoperative readmissions/revisits, and reoperations in children. Data Sources MEDLINE, EMBASE, and the Cochrane Library. Review Methods Two investigators independently screened studies against predetermined criteria and extracted key data. Investigators independently assessed study risk of bias and the strength of the evidence of the body of literature. We calculated unadjusted pooled estimates of PTH frequency and conducted a Bayesian meta-analysis to estimate frequency of primary and secondary PTH and PTH-associated reoperation and revisits/readmissions by partial and total tonsillectomy and surgical approach. Results In meta-analysis, the frequency of primary and secondary PTH associated with total and partial tonsillectomy was <4% for any technique and with overlapping confidence bounds. Pooled frequencies of PTH were also <5% overall (4.2% for total tonsillectomy, 1.5% for partial tonsillectomy) in comparative studies. Fewer PTH episodes occurred with tonsillectomy for obstructive sleep-disordered breathing than for throat infection. In meta-analysis, frequency of PTH-associated nonoperative revisits/readmission or reoperation ranged from 0.2% to 5.7% for total tonsillectomy and from 0.1% to 3.7% for partial tonsillectomy. At least 4 deaths were reported in case series including 1,778,342 children. Conclusions PTH occurred in roughly 4% of tonsillectomies in studies included in this review. Although studies typically did not report bleeding severity or amount, relatively few episodes of PTH necessitated reoperation for hemostasis. Nonetheless, tonsillectomy is not without risk of harm. Frequency of PTH across techniques was similar; thus, we cannot conclude that a given technique is superior.

Tonsillectomy for Obstructive Sleep-Disordered Breathing: A Meta-analysis.

CONTEXT: The effectiveness of tonsillectomy or adenotonsillectomy (hereafter, "tonsillectomy") for obstructive sleep-disordered breathing (OSDB) compared with watchful waiting with supportive care is poorly understood. OBJECTIVE: To compare sleep, cognitive or behavioral, and health outcomes of tonsillectomy versus watchful waiting with supportive care in children with OSDB. DATA SOURCES: Medline, Embase, and the Cochrane Library. STUDY SELECTION: Two investigators independently screened studies against predetermined criteria. DATA EXTRACTION: Two investigators independently extracted key data. Investigators independently assessed study risk of bias and the strength of the evidence of the body of literature. Investigators synthesized data qualitatively and meta-analyzed apnea-hypopnea index (AHI) scores. RESULTS: We included 11 studies. Relative to watchful waiting, most studies reported better sleep-related outcomes in children who had a tonsillectomy. In 5 studies including children with polysomnography-confirmed OSDB, AHI scores improved more in children receiving tonsillectomy versus surgery. A meta-analysis of 3 studies showed a 4.8-point improvement in the AHI in children who underwent tonsillectomy compared with no surgery. Sleep-related quality of life and negative behaviors (eg, anxiety and emotional lability) also improved more among children who had a tonsillectomy. Changes in executive function were not significantly different. The length of follow-up in studies was generally <12 months. LIMITATIONS: Few studies fully categorized populations in terms of severity of OSDB; outcome measures were heterogeneous; and the durability of outcomes beyond 12 months is not known. CONCLUSIONS: Tonsillectomy can produce short-term improvement in sleep outcomes compared with no surgery in children with OSDB. Understanding of longer-term outcomes or effects in subpopulations is lacking.