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.

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.

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.

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.

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.

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.

Adaptive management and the value of information: learning via intervention in epidemiology.

Optimal intervention for disease outbreaks is often impeded by severe scientific uncertainty. Adaptive management (AM), long-used in natural resource management, is a structured decision-making approach to solving dynamic problems that accounts for the value of resolving uncertainty via real-time evaluation of alternative models. We propose an AM approach to design and evaluate intervention strategies in epidemiology, using real-time surveillance to resolve model uncertainty as management proceeds, with foot-and-mouth disease (FMD) culling and measles vaccination as case studies. We use simulations of alternative intervention strategies under competing models to quantify the effect of model uncertainty on decision making, in terms of the value of information, and quantify the benefit of adaptive versus static intervention strategies. Culling decisions during the 2001 UK FMD outbreak were contentious due to uncertainty about the spatial scale of transmission. The expected benefit of resolving this uncertainty prior to a new outbreak on a UK-like landscape would be £45-£60 million relative to the strategy that minimizes livestock losses averaged over alternate transmission models. AM during the outbreak would be expected to recover up to £20.1 million of this expected benefit. AM would also recommend a more conservative initial approach (culling of infected premises and dangerous contact farms) than would a fixed strategy (which would additionally require culling of contiguous premises). For optimal targeting of measles vaccination, based on an outbreak in Malawi in 2010, AM allows better distribution of resources across the affected region; its utility depends on uncertainty about both the at-risk population and logistical capacity. When daily vaccination rates are highly constrained, the optimal initial strategy is to conduct a small, quick campaign; a reduction in expected burden of approximately 10,000 cases could result if campaign targets can be updated on the basis of the true susceptible population. Formal incorporation of a policy to update future management actions in response to information gained in the course of an outbreak can change the optimal initial response and result in significant cost savings. AM provides a framework for using multiple models to facilitate public-health decision making and an objective basis for updating management actions in response to improved scientific understanding.

Randomized Double-Blind Study of the Safety and Immunogenicity of Standard-Dose Trivalent Inactivated Influenza Vaccine versus High-Dose Trivalent Inactivated Influenza Vaccine in Adult Hematopoietic Stem Cell Transplantation Patients.

Hematopoietic stem cell transplantation (HCT) survivors are less likely than matched healthy controls to mount a strong immune response to trivalent inactivated influenza vaccine (TIV). High-dose (HD) or standard-dose (SD) TIV were given to adult HCT subjects 18 years or older at least 6 months after transplantation. Subjects were randomized 2:1 to receive either the HD (60 µg hemagglutinin [HA]/strain/dose) or the SD (15 µg HA/strain/dose) TIV. Injection-site and systemic reactions were documented after each vaccination and immune responses were measured before and after each vaccination. A total of 44 subjects were enrolled (25 in year 1 and 19 in year 2), with 15 in the SD group and 29 in the HD group. The median time to vaccination after transplantation was 7.9 months (range, 6 to 106 months), the median age was 50 years (range, 19.6 to 73 years), and 61% were male. No differences in demographic or lab data were noted between groups; however, the HD group had higher median baseline total IgG level (676 versus 469 mg/dL, P = .025). No differences in individual injection-site or systemic reactions were noted between groups; however, more events of any injection-site symptom combined were reported in the HD group. No serious adverse events were attributed to vaccination. After vaccination, the HD group had a higher percentage of individuals with titers ≥1:40 and a higher geometric mean titer (GMT) against the H3N2 strain compared with that of the SD group. HD and SD TIV were found to be safe and well tolerated in adult HCT recipients. However, the HD group had higher frequency of injection-site reactions but the majority of the reactions were mild and resolved. The HD group had a higher percentage of individuals with post-vaccination titer ≥ 1:40 and GMT for H3N2 antigen, indicating better immunogenicity. These data support the need for a phase II immunogenicity trial in HCT recipients.

Global Trends in Extracorporeal Membranous Oxygenation Use and Survival of Patients With Influenza-Associated Illness.

OBJECTIVES: To determine the overall use of extracorporeal membranous oxygenation for influenza-associated illness and describe risk factors associated with mortality in these patients. DESIGN: Retrospective multicenter cohort analysis. SETTING: The international Extracorporeal Life Support Organization database was queried for patients with influenza-associated illness on extracorporeal membranous oxygenation from 1992 to 2014. PATIENTS: In total, 1,654 patients with influenza-associated illness on extracorporeal membranous oxygenation. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Demographic and clinical data collected included age, type of support, duration of support, type of microbial codetection, complications, and survival status at discharge. The primary outcome of interest was survival to hospital discharge. From 1992 to 2014, 1,688 (3%) of the 61,336 extracorporeal membranous oxygenation runs were due to influenza-associated illness reflecting 1,654 unique patients: 30 (2%) were neonates, 521 (31%) were pediatric patients, and 1,103 (67%) were adults. Extracorporeal membranous oxygenation use for influenza-associated illness increased from 1992 to 2014, with a marked increase in use after the 2009 H1N1 pandemic. Survival to hospital discharge of patients with influenza-associated illness on extracorporeal membranous oxygenation was 63% and was not affected by bacterial codetection. However, when patients with Staphylococcus aureus codetection were compared with those with another bacterial codetection, their survival to hospital discharge was significantly lower (52% vs 67%; p < 0.01). In a logistic regression model, the effect of S. aureus on in-hospital mortality varied by age group, with younger patients with S. aureus having increased in-hospital mortality. CONCLUSIONS: Extracorporeal membranous oxygenation use for individuals with influenza increased over time, particularly after the 2009 H1N1 pandemic, most notably among older adults. Survival to hospital discharge for patients with influenza on extracorporeal membranous oxygenation was slightly higher than survival to hospital discharge for respiratory illness due to any cause. Bacterial codetection was common among patients with influenza on extracorporeal membranous oxygenation and was associated with increased days on extracorporeal membranous oxygenation but not increased mortality. Only S. aureus codetection in children was associated with increased in-hospital mortality.

Randomized, double-blind comparison of standard-dose vs. high-dose trivalent inactivated influenza vaccine in pediatric solid organ transplant patients.

Children who have undergone SOT mount a lower immune response after vaccination with TIV compared to healthy controls. HD or SD TIV in pediatric SOT was given to subjects 3-17 yr and at least six months post-transplant. Subjects were randomized 2:1 to receive either the HD (60 µg) or the SD (15 µg) TIV. Local and systemic reactions were solicited after each vaccination, and immune responses were measured before and after each vaccination. Thirty-eight subjects were enrolled. Mean age was 11.25 yr; 68% male, 45% renal, 26% heart, 21% liver, 5% lung, and 5% intestinal. Twenty-three subjects were given HD and 15 SD TIV. The median time since transplant receipt was 2.2 yr. No severe AEs or rejection was attributed to vaccination. The HD group reported more tenderness and local reactions, fatigue, and body ache when compared to the SD cohort, but these were considered mild and resolved within three days. Subjects in the HD group demonstrated a higher percentage of four-fold titer rise to H3N2 compared to the SD group. HD influenza vaccine was well tolerated and may have increased immunogenicity. A phase 2 trial is needed to confirm.

A comparative effectiveness trial of postoperative management for lumbar spine surgery: changing behavior through physical therapy (CBPT) study protocol.

BACKGROUND: The United States has the highest rate of lumbar spine surgery in the world, with rates increasing over 200% since 1990. Medicare spends over $1 billion annually on lumbar spine surgery. Despite surgical advances, up to 40% of patients report chronic pain and disability following surgery. Our work has demonstrated that fear of movement is a risk factor for increased pain and disability and decreased physical function in patients following lumbar spine surgery for degenerative conditions. Cognitive-behavioral therapy and self-management treatments have the potential to address psychosocial risk factors and improve outcomes after spine surgery, but are unavailable or insufficiently adapted for postoperative care. Our research team developed a cognitive-behavioral based self-management approach to postoperative rehabilitation (Changing Behavior through Physical Therapy (CBPT)). Pilot testing of the CBPT program demonstrated greater improvement in pain, disability, physical and mental health, and physical performance compared to education. The current study compares which of two treatments provided by telephone - a CBPT Program or an Education Program about postoperative recovery - are more effective for improving patient-centered outcomes in adults following lumbar spine surgery for degenerative conditions. METHODS/DESIGN: A multi-center, comparative effectiveness trial will be conducted. Two hundred and sixty patients undergoing lumbar spine surgery for degenerative conditions will be recruited from two medical centers and community surgical practices. Participants will be randomly assigned to CBPT or Education at 6 weeks following surgery. Treatments consist of six weekly telephone sessions with a trained physical therapist. The primary outcome will be disability and secondary outcomes include pain, general health, and physical activity. Outcomes will be assessed preoperatively and at 6 weeks, 6 months and 12 months after surgery by an assessor masked to group allocation. DISCUSSION: Effective rehabilitation treatments that can guide clinicians in their recommendations, and patients in their actions will have the potential to effect change in current clinical practice. TRIAL REGISTRATION: NCT02184143.

Estimating the probability of IQ impairment from blood phenylalanine for phenylketonuria patients: a hierarchical meta-analysis.

Though the control of blood phenylalanine (Phe) levels is essential for minimizing impairment in individuals with phenylketonuria (PKU), the empirical basis for the selection of specific blood Phe levels as targets has not been evaluated. We evaluated the current evidence that particular Phe levels are optimal for minimizing or avoiding cognitive impairment in individuals with PKU. This work uses meta-estimates of blood Phe-IQ correlation to predict the probability of low IQ for a range of Phe levels. We believe this metric is easily interpretable by clinicians, and hence useful in making recommendations for Phe intake. The median baseline association of Phe with IQ was estimated to be negative, both in the context of historical (median = -0.026, 95 % BCI = [-0.040, -0.013]) and concurrent (-0.007, [-0.014, 0.000]) measurement of Phe relative to IQ. The estimated additive fixed effect of critical period Phe measurement was also nominally negative for historical measurement (-0.010, [-0.022, 0.003]) and positive for concurrent measurement (0.007, [-0.018, 0.035]). Probabilities corresponding to historical measures of blood Phe demonstrated an increasing chance of low IQ with increasing Phe, with a stronger association seen between blood Phe measured during the critical period than later. In contrast, concurrently-measured Phe was more weakly correlated with the probability of low IQ, though the correlation is still positive, irrespective of whether Phe was measured during the critical or non-critical period. This meta-analysis illustrates the utility of a Bayesian hierarchical approach for not only combining information from a set of candidate studies, but also for combining different types of data to estimate parameters of interest.

Progestogens for preterm birth prevention: a systematic review and meta-analysis by drug route.

PURPOSE: Progestogen has been investigated as a preventive intervention among women with increased preterm birth risk. Our objective was to systematically review the effectiveness of intramuscular (IM), vaginal, and oral progestogens for preterm birth and neonatal death prevention. METHODS: We included articles published from January 1966 to January 2013 and found 27 randomized trials with data for Bayesian meta-analysis. RESULTS: Across all studies, only vaginal and oral routes were effective at reducing preterm births (IM risk ratio [RR] 0.95, 95 % Bayesian credible interval [BCI]: 0.88-1.03; vaginal RR 0.87, 95 % BCI: 0.80-0.94; oral RR 0.64, 95 % BCI: 0.49-0.85). However, when analyses were limited to only single births all routes were effective at reducing preterm birth (IM RR 0.77, 95 % BCI: 0.69-0.87; vaginal RR 0.80, 95 % BCI: 0.69-0.91; oral RR 0.66, 95 % BCI: 0.47-0.84). Only IM progestogen was effective at reducing neonatal deaths (IM RR 0.78, 95 % BCI: 0.56-0.99; vaginal RR 0.75, 95 % BCI: 0.45-1.09; oral RR 0.72, 95 % BCI: 0.09-1.74). Vaginal progestogen was effective in reducing neonatal deaths when limited to singletons births. CONCLUSIONS: All progestogen routes reduce preterm births but not neonatal deaths. Future studies are needed that directly compare progestogen delivery routes.

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.

Simultaneous modeling of habitat suitability, occupancy, and relative abundance: African elephants in Zimbabwe.

The recent development of statistical models such as dynamic site occupancy models provides the opportunity to address fairly complex management and conservation problems with relatively simple models. However, surprisingly few empirical studies have simultaneously modeled habitat suitability and occupancy status of organisms over large landscapes for management purposes. Joint modeling of these components is particularly important in the context of management of wild populations, as it provides a more coherent framework to investigate the population dynamics of organisms in space and time for the application of management decision tools. We applied such an approach to the study of water hole use by African elephants in Hwange National Park, Zimbabwe. Here we show how such methodology may be implemented and derive estimates of annual transition probabilities among three dry-season states for water holes: (1) unsuitable state (dry water holes with no elephants); (2) suitable state (water hole with water) with low abundance of elephants; and (3) suitable state with high abundance of elephants. We found that annual rainfall and the number of neighboring water holes influenced the transition probabilities among these three states. Because of an increase in elephant densities in the park during the study period, we also found that transition probabilities from low abundance to high abundance states increased over time. The application of the joint habitat-occupancy models provides a coherent framework to examine how habitat suitability and factors that affect habitat suitability influence the distribution and abundance of organisms. We discuss how these simple models can further be used to apply structured decision-making tools in order to derive decisions that are optimal relative to specified management objectives. The modeling framework presented in this paper should be applicable to a wide range of existing data sets and should help to address important ecological, conservation, and management problems that deal with occupancy, relative abundance, and habitat suitability.

PyMC: Bayesian Stochastic Modelling in Python.

This user guide describes a Python package, PyMC, that allows users to efficiently code a probabilistic model and draw samples from its posterior distribution using Markov chain Monte Carlo techniques.

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.

Efficient estimation of abundance for patchily distributed populations via two-phase, adaptive sampling.

Many organisms are patchily distributed, with some patches occupied at high density, others at lower densities, and others not occupied. Estimation of overall abundance can be difficult and is inefficient via intensive approaches such as capture-mark-recapture (CMR) or distance sampling. We propose a two-phase sampling scheme and model in a Bayesian framework to estimate abundance for patchily distributed populations. In the first phase, occupancy is estimated by binomial detection samples taken on all selected sites, where selection may be of all sites available, or a random sample of sites. Detection can be by visual surveys, detection of sign, physical captures, or other approach. At the second phase, if a detection threshold is achieved, CMR or other intensive sampling is conducted via standard procedures (grids or webs) to estimate abundance. Detection and CMR data are then used in a joint likelihood to model probability of detection in the occupancy sample via an abundance-detection model. CMR modeling is used to estimate abundance for the abundance-detection relationship, which in turn is used to predict abundance at the remaining sites, where only detection data are collected. We present a full Bayesian modeling treatment of this problem, in which posterior inference on abundance and other parameters (detection, capture probability) is obtained under a variety of assumptions about spatial and individual sources of heterogeneity. We apply the approach to abundance estimation for two species of voles (Microtus spp.) in Montana, USA. We also use a simulation study to evaluate the frequentist properties of our procedure given known patterns in abundance and detection among sites as well as design criteria. For most population characteristics and designs considered, bias and mean-square error (MSE) were low, and coverage of true parameter values by Bayesian credibility intervals was near nominal. Our two-phase, adaptive approach allows efficient estimation of abundance of rare and patchily distributed species and is particularly appropriate when sampling in all patches is impossible, but a global estimate of abundance is required.

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.