Bayesian Credible Interval Research Articles

An opioid-minimizing multimodal pain regimen reduces opioid exposure and pain in trauma-injured patients at high risk for opioid misuse: Secondary analysis from the mast trial

BackgroundScreening to identify patients at risk for opioid misuse after trauma is recommended but not commonly used to guide perioperative opioid management interventions. The Multimodal Analgesic Strategies for Trauma trial demonstrated that an opioid-minimizing multimodal pain regimen reduced opioid exposure in a heterogeneous trauma patient population. Here, we assess the efficacy of the Multimodal Analgesic Strategies for Trauma multimodal pain regimen in a critical patient subgroup who screened at high risk for opioid misuse. MethodsThe Multimodal Analgesic Strategies for Trauma trial compared an opioid-minimizing multimodal pain regimen (oral acetaminophen, naproxen, gabapentin, lidocaine patch, as-needed opioid) against an original multimodal pain regimen (intravenous followed by oral acetaminophen, 48-hour celecoxib and pregabalin, followed by naproxen and gabapentin, scheduled tramadol, as-needed opioid), in a randomized trial conducted from April 2018 to March 2019. A total of 631 enrolled patients were classified either as low- or high-risk via the Opioid Risk Tool. Bayesian analyses evaluated the moderating influence of Opioid Risk Tool risk (high/low) on the effect of Multimodal Analgesic Strategies for Trauma multimodal pain regimen (versus original) on opioid exposure (morphine milligram equivalents/day), opioids prescribed at discharge, and pain scores. ResultsMultimodal Analgesic Strategies for Trauma multimodal pain regimen effectively reduced morphine milligram equivalents/day in low- and high-Opioid Risk Tool risk groups. Moderation was observed for opioids at discharge and pain scores; Multimodal Analgesic Strategies for Trauma multimodal pain regimen was effective in the high-risk group only (opioids at discharge: 63% vs 77%, relative risk = 0.86, 95% Bayesian credible interval [0.66–1.08], posterior probability (relative risk <1) = 90%; pain scores: b = 3.8, 95% Bayesian credible interval [3.2–4.4] vs b = 4.0, 95% Bayesian credible interval [3.4–4.6], posterior probability (b <0) = 87%). ConclusionThis study is the first to show the moderating influence of opioid misuse risk on the effectiveness of an opioid-minimizing multimodal pain regimen. The Opioid Risk Tool was useful in identifying high-risk patients for whom the Multimodal Analgesic Strategies for Trauma multimodal pain regimen is recommended for perioperative pain management.

Bayesian Regression Quantifies Uncertainty of Binding Parameters from Isothermal Titration Calorimetry More Accurately Than Error Propagation.

We compare several different methods to quantify the uncertainty of binding parameters estimated from isothermal titration calorimetry data: the asymptotic standard error from maximum likelihood estimation, error propagation based on a first-order Taylor series expansion, and the Bayesian credible interval. When the methods are applied to simulated experiments and to measurements of Mg(II) binding to EDTA, the asymptotic standard error underestimates the uncertainty in the free energy and enthalpy of binding. Error propagation overestimates the uncertainty for both quantities, except in the simulations, where it underestimates the uncertainty of enthalpy for confidence intervals less than 70%. In both datasets, Bayesian credible intervals are much closer to observed confidence intervals.

Estimating species sensitivity distributions for microplastics by quantitatively considering particle characteristics using a recently created ecotoxicity database

Estimation of a species sensitivity distribution (SSD) by fitting a statistical distribution to ecotoxicity data is a promising approach to deriving “safe” concentrations for microplastics. However, most existing SSDs do not quantitatively consider the diverse characteristics of microplastics, such as particle size and shape. To address this issue, based on 38 mass-based chronic no observed effect concentrations (NOECs) obtained from a recently created database, we estimated SSDs that quantitatively consider the influences of three types of microplastic characteristics (particle length, shape, and polymer type) and habitat of the test species (freshwater vs. marine) by using Bayesian modeling. We selected the best SSD model among all possible models using the widely applicable information criterion. The best SSD model included particle length (range: 0.05–280 μm) and a binary dummy variable corresponding to the fiber shape. Lower chronic NOECs were associated with decreasing particle size and with toxicity tests that included fibers in this model. Combined with the fact that the null model (i.e., an SSD model with no predictor variable) was ranked 27th among the 64 candidate SSD models, our results support the need to incorporate particle characteristics such as length and shape (e.g., fiber) into estimations of SSDs for microplastics. The medians of the hazardous concentration of 5% of species (HC5) for microplastic spheres and fragments, estimated by the posterior distributions of individual parameters in the best SSD model, ranged from 0.02 to 2 µg/L, depending on the particle length (0.1–100 μm). For microplastic fibers, the HC5 values were estimated to be approximately 100 times lower than those for microplastic spheres and fragments with the same particle length. However, the 95% Bayesian credible intervals for HC5 estimates for fibers were considerable, expanded by up to five orders of magnitude. Despite many remaining challenges, the Bayesian SSD modeling utilized in this study provides unique opportunities to simultaneously investigate the influences of multiple microplastic characteristics on the NOECs of multiple species, which would otherwise be difficult to discern.

Pulsed Field or Conventional Thermal Ablation for Paroxysmal Atrial Fibrillation

BackgroundCatheter-based pulmonary vein isolation is an effective treatment for paroxysmal atrial fibrillation. Pulsed field ablation, which delivers microsecond high-voltage electrical fields, may limit damage to tissues outside the myocardium. The efficacy and safety of pulsed field ablation as compared with conventional thermal ablation are not known.MethodsIn this randomized, single-blind, noninferiority trial, we assigned patients with drug-refractory paroxysmal atrial fibrillation in a 1:1 ratio to undergo pulsed field ablation or conventional radiofrequency or cryoballoon ablation. The primary efficacy end point was freedom from a composite of initial procedural failure, documented atrial tachyarrhythmia after a 3-month blanking period, antiarrhythmic drug use, cardioversion, or repeat ablation. The primary safety end point included acute and chronic device- and procedure-related serious adverse events.ResultsA total of 305 patients were assigned to undergo pulsed field ablation, and 302 were assigned to undergo thermal ablation. At 1 year, the primary efficacy end point was met (i.e., no events occurred) in 204 patients (estimated probability, 73.3%) who underwent pulsed field ablation and 194 patients (estimated probability, 71.3%) who underwent thermal ablation (between-group difference, 2.0 percentage points; 95% Bayesian credible interval, −5.2 to 9.2; posterior probability of noninferiority, >0.999). Primary safety end-point events occurred in 6 patients (estimated incidence, 2.1%) who underwent pulsed field ablation and 4 patients (estimated incidence, 1.5%) who underwent thermal ablation (between-group difference, 0.6 percentage points; 95% Bayesian credible interval, −1.5 to 2.8; posterior probability of noninferiority, >0.999).ConclusionsAmong patients with paroxysmal atrial fibrillation receiving a catheter-based therapy, pulsed field ablation was noninferior to conventional thermal ablation with respect to freedom from a composite of initial procedural failure, documented atrial tachyarrhythmia after a 3-month blanking period, antiarrhythmic drug use, cardioversion, or repeat ablation and with respect to device- and procedure-related serious adverse events at 1 year. (Funded by Farapulse–Boston Scientific; ADVENT ClinicalTrials.gov number, NCT04612244.)

A Design and Analytical Strategy for Monitoring Disease Positivity and Biomarker Levels in Accessible Closed Populations.

In this paper, we advocate and expand upon a previously described monitoring strategy for efficient and robust estimation of disease prevalence and case numbers within closed and enumerated populations such as schools, workplaces, or retirement communities. The proposed design relies largely on voluntary testing, which is notoriously biased (e.g., in the case of coronavirus disease 2019) due to nonrepresentative sampling. The approach yields unbiased and comparatively precise estimates with no assumptions about factors underlying selection of individuals for voluntary testing, building on the strength of what can be a small random sampling component. This component enables the use of a recently proposed "anchor stream" estimator, a well-calibrated alternative to classical capture-recapture (CRC) estimators based on 2 data streams. We show that this estimator is equivalent to a direct standardization based on "capture," that is, selection (or not) by the voluntary testing program, made possible by means of a key parameter identified by design. This equivalency simultaneously allows for novel 2-stream CRC-like estimation of general mean values (e.g., means of continuous variables like antibody levels or biomarkers). For inference, we propose adaptations of Bayesian credible intervals when estimating case counts and bootstrapping when estimating means of continuous variables. We use simulations to demonstrate significant precision benefits relative to random sampling alone.

Estimating the real burden of gestational syphilis in Brazil, 2007–2018: a Bayesian modeling study

Although several studies have estimated gestational syphilis (GS) incidence in several countries, underreporting correction is rarely considered. This study aimed to estimate the level of under-registration and correct the GS incidence rates in the 557 Brazilian microregions. Brazilian GS notifications between 2007 and 2018 were obtained from the SINAN-Syphilis system. A cluster analysis was performed to group microregions according to the quality of GS notification. A Bayesian hierarchical Poisson regression model was applied to estimate the reporting probabilities among the clusters and to correct the associated incidence rates. We estimate that 45,196 (90%-HPD: 13,299; 79,310) GS cases were underreported in Brazil from 2007 to 2018, representing a coverage of 87.12% (90%-HPD: 79.40%; 95.83%) of registered cases, where HPD stands for the Bayesian highest posterior density credible interval. Underreporting levels differ across the country, with microregions in North and Northeast regions presenting the highest percentage of missed cases. After underreporting correction, Brazil's estimated GS incidence rate increased from 8.74 to 10.02 per 1000 live births in the same period. Our findings highlight disparities in the registration level and incidence rate of GS in Brazil, reflecting regional heterogeneity in the quality of syphilis surveillance, access to prenatal care, and childbirth assistance services. This study provides robust evidence to enhance national surveillance systems, guide specific policies for GS detection disease control, and potentially mitigate the harmful consequences of mother-to-child transmission. The methodology might be applied in other regions to correct disease underreporting. National Council for Scientific and Technological Development; The Bill Melinda Gates Foundation and Wellcome Trust.

Dynamic modeling and uncertainty quantification of district heating systems considering renewable energy access

The district heating system (DHS) is one of the essential carriers for coordinating renewable energy with fossil energy and achieving flexible energy consumption. Considering the uncertainty of both the renewable energy units' power and the consumers' aggregate loads impact the transport process of the DHS, the thermal characteristics of the DHS transportation and the node temperature response under the source-load uncertainty need to be quantified and analyzed. This paper proposed a method to calculate the nodes' temperature dynamic response of the DHS under multiple source-load uncertainty scenarios. The prediction and combination methods of supply and demand probability intervals of source and load nodes under varying credibility levels were investigated. Additionally, the effects of source-load uncertainty boundary conditions on the DHS transport process were analyzed. The temperature response of the nodes was calculated based on the established dynamic DHS model under multiple source-load uncertainty scenarios. A DHS in Beijing was selected for case validation and analysis, and it has 90 nodes and 109 pipes. The simulation results showed that the relative average error of node flow rate in the dynamic DHS model was 3.02%, and the average difference in node temperature was 1 °C. The probability distribution semi-analytical method and the Bayesian credible interval calculation method can accurately describe the uncertainty on both source and load sides. The models and algorithms proposed in this paper could effectively quantify the nodes' dynamic temperature response under the source-load uncertainty.

A Goodness of Fit Test for a Survival and Count Bayesian Joint Model: In the Presence of Clusters

Bayesian statistical model fitting was an uncommon approach until recently, causing a lack of assessment techniques for these models. However, with the enhancement of computational facilities and advanced estimation techniques, Bayesian models have become popular. Though there are developed goodness of fit (GOF) tests available for classical multilevel models including joint modelling of mixed responses, there is no suitable model based GOF test to be applied on such a model which is fitted under a Bayesian framework. Therefore, this study focused on developing a suitable GOF test for multilevel Bayesian joint models having survival and count responses which are two frequently occurring data types in many fields. The novel test is developed mainly based on four classical GOF tests, including the well-known Hosmer-Lemeshow test and, the Bayesian concepts such as Bayesian credible intervals and regions. In addition, a simulation study has been used to examine the properties of the GOF test together with an application to a real-life example. The novel test performed well in terms of power and acceptable in terms of Type I error rates. Overall, the test performed well with small sample sizes.

Statistical inference under joint type-II censored data from two Burr-XII populations

The Burr-XII distribution has been widely applied in engineering, reliability, and survival analysis. Due to its importance, in this article, statistical inferences for Burr-XII distribution under a joint type-II censoring scheme is discussed. The classical likelihood estimation of unknown model parameters is studied via different calculating approaches, such as the expectation-maximization (EM) algorithm and approximate confidence intervals (ACIs) using the observed Fisher information matrix are obtained. The asymptotic bootstrap confidence intervals are also computed. In the sequel, Bayesian estimations of unknown parameters with a gamma prior distribution are considered under squared error, linear-exponential, and generalized entropy loss functions. Subsequently, we calculate the Bayesian credible interval using the importance sample. The performance of the developed methods is investigated through a Monte Carlo simulation study and two real-life examples. The results showed that the proposed estimation strategies have satisfactory results. However, Bayesian approaches were preferable to EM in terms of lower mean square error and higher coverage probability.

A meta-analysis of GFR slope as a surrogate endpoint for kidney failure.

Glomerular filtration rate (GFR) decline is causally associated with kidney failure and is a candidate surrogate endpoint for clinical trials of chronic kidney disease (CKD) progression. Analyses across a diverse spectrum of interventions and populations is required for acceptance of GFR decline as an endpoint. In an analysis of individual participant data, for each of 66 studies (total of 186,312 participants), we estimated treatment effects on the total GFR slope, computed from baseline to 3 years, and chronic slope, starting at 3 months after randomization, and on the clinical endpoint (doubling of serum creatinine, GFR < 15 ml min-1 per 1.73 m2 or kidney failure with replacement therapy). We used a Bayesian mixed-effects meta-regression model to relate treatment effects on GFR slope with those on the clinical endpoint across all studies and by disease groups (diabetes, glomerular diseases, CKD or cardiovascular diseases). Treatment effects on the clinical endpoint were strongly associated with treatment effects on total slope (median coefficient of determination (R2) = 0.97 (95% Bayesian credible interval (BCI) 0.82-1.00)) and moderately associated with those on chronic slope (R2 = 0.55 (95% BCI 0.25-0.77)). There was no evidence of heterogeneity across disease. Our results support the use of total slope as a primary endpoint for clinical trials of CKD progression.

Differences in acute ischaemic stroke in-hospital mortality across referral stroke hospitals in Spain: a retrospective, longitudinal observational study

ObjectiveTo assess differences in acute ischaemic stroke (AIS) in-hospital mortality between referral stroke hospitals and provide evidence on the association of those differences with the overtime adoption of effective reperfusion...

Simulation Techniques for Strength Component Partially Accelerated to Analyze Stress–Strength Model

Based on independent progressive type-II censored samples from two-parameter Burr-type XII distributions, various point and interval estimators of δ=P(Y&lt;X) were proposed when the strength variable was subjected to the step–stress partially accelerated life test. The point estimators computed were maximum likelihood and Bayesian under various symmetric and asymmetric loss functions. The interval estimations constructed were approximate, bootstrap-P, and bootstrap-T confidence intervals, and a Bayesian credible interval. A Markov Chain Monte Carlo approach using Gibbs sampling was designed to derive the Bayesian estimate of δ. Based on the mean square error, bias, confidence interval length, and coverage probability, the results of the numerical analysis of the performance of the maximum likelihood and Bayesian estimates using Monte Carlo simulations were quite satisfactory. To support the theoretical component, an empirical investigation based on two actual data sets was carried out.

Integrating community science and agency‐collected monitoring data to expand monitoring capacity at large spatial scales

AbstractMonitoring species to better understand their status, ecology, and management needs is a major expense for agencies tasked with biodiversity conservation. Community science data have the potential to improve monitoring for minimal cost, given appropriate analytical frameworks. We describe a framework for integrating data from the eBird community science platform with agency‐collected monitoring data using a multistate occupancy model. Our model accounts for the structural differences across datasets and allows for estimation of both occupancy and breeding probabilities. The framework was applied to Common Loons (Gavia immer) in Washington State. A total of 766 sites had observation effort, of which 713 sites had only eBird effort, 26 sites had only Washington Department of Fish and Wildlife (WDFW) effort, and 27 sites had both. We predicted that the probability of occupancy was only 0.07 (95% Bayesian credible interval, BCI = 0.02–0.51) at the 2324 sites in our sampling frame, though the probability that Common Loons were breeding at occupied sites was 0.95 (95% BCI = 0.71–1.00). We found that probability of occupancy was positively related to waterbody size (probability of a positive effect = 0.88) and negatively related to an index of human influence (probability of a negative effect = 0.94). We found that probability of breeding at occupied sites was positively related to tree canopy cover (0.86), negatively related to elevation (0.99), and negatively related to barren, scrub/shrub, and herbaceous land cover (0.98). We found that state agency biologists were 16 times more likely to detect breeding Common Loons at a site than were eBird users (0.94, 95% BCI = 0.78–0.99 for agency biologists vs. 0.08, 95% BCI = 0.06–0.10 for eBird users). However, the amount of effort expended by eBird users meant that they confirmed Common Loons at 94 sites while agency biologists confirmed them at just 24 sites, although evidence of reproduction was only contributed by agency biologists. Our results provide a better understanding of the distribution of Common Loons in Washington, while further demonstrating that community science data can be a valuable complement to agency‐collected data, if appropriate frameworks are developed to integrate these data sources.

Care Management Processes Important for High-Quality Diabetes Care.

Identify the improvement in diabetes performance measures and population-based clinical outcomes resulting from changes in care management processes (CMP) in primary care practices over 3 years. This repeated cross-sectional study tracked clinical performance measures for all diabetes patients seen in a cohort of 330 primary care practices in 2017 and 2019. Unit of analysis was patient-year with practice-level CMP exposures. Causal inference is based on dynamic changes in individual CMPs between years by practice. We used the Bayesian method to simultaneously estimate a five-outcome model: A1c, systolic and diastolic blood pressure, guideline-based statin use, and Optimal Diabetes Care (ODC). We control for unobserved time-invariant practice characteristics and secular change. We modeled correlation of errors across outcomes. Statistical significance was identified using 99% Bayesian credible intervals (analogous to P < 0.01). Implementation of 18 of 62 CMPs was associated with statistically significant improvements in patient outcomes. Together, these resulted in 12.1% more patients meeting ODC performance measures. Different CMPs affected different outcomes. Three CMPs accounted for 47% of the total ODC improvement, 68% of A1c decrease, 21% of SBP reduction, and 55% of statin use increase: 1) systems for identifying and reminding patients due for testing, 2) after-visit follow-up by a nonclinician, and 3) guideline-based clinician reminders for preventive services during a clinic visit. Effective quality improvement in primary care focuses on practice redesign that clearly improves diabetes outcomes. Tailoring CMP adoption in primary care provides effective improvement in ODC performance through focused changes in diabetes outcomes.

Geographic patterns and hotspots of pediatric tuberculosis: the role of socioeconomic determinants.

Children are an important demographic group for understanding overall tuberculosis epidemiology, and monitoring of childhood tuberculosis is essential for appropriate prevention. The present study sought to characterize the spatial distribution of childhood tuberculosis notification rates in continental Portugal; identify high-risk areas; and evaluate the association between childhood tuberculosis notification rates and socioeconomic deprivation. Using hierarchical Bayesian spatial models, we analyzed the geographic distribution of pediatric tuberculosis notification rates across 278 municipalities between 2016 and 2020 and determined high-risk and low-risk areas. We used the Portuguese version of the European Deprivation Index to estimate the association between childhood tuberculosis and area-level socioeconomic deprivation. Notification rates ranged from 1.8 to 13.15 per 100,000 children under 5 years of age. We identified seven high-risk areas, the relative risk of which was significantly above the study area average. All seven high-risk areas were located in the metropolitan area of Porto or Lisbon. There was a significant relationship between socioeconomic deprivation and pediatric tuberculosis notification rates (relative risk = 1.16; Bayesian credible interval, 1.05-1.29). Identified high-risk and socioeconomically deprived areas should constitute target areas for tuberculosis control, and these data should be integrated with other risk factors to define more precise criteria for BCG vaccination.

Global, regional, and national estimates of the impact of a maternal Klebsiella pneumoniae vaccine: A Bayesian modeling analysis.

Despite significant global progress in reducing neonatal mortality, bacterial sepsis remains a major cause of neonatal deaths. Klebsiella pneumoniae (K. pneumoniae) is the leading pathogen globally underlying cases of neonatal sepsis and is frequently resistant to antibiotic treatment regimens recommended by the World Health Organization (WHO), including first-line therapy with ampicillin and gentamicin, second-line therapy with amikacin and ceftazidime, and meropenem. Maternal vaccination to prevent neonatal infection could reduce the burden of K. pneumoniae neonatal sepsis in low- and middle-income countries (LMICs), but the potential impact of vaccination remains poorly quantified. We estimated the potential impact of such vaccination on cases and deaths of K. pneumoniae neonatal sepsis and project the global effects of routine immunization of pregnant women with the K. pneumoniae vaccine as antimicrobial resistance (AMR) increases. We developed a Bayesian mixture-modeling framework to estimate the effects of a hypothetical K. pneumoniae maternal vaccine with 70% efficacy administered with coverage equivalent to that of the maternal tetanus vaccine on neonatal sepsis infections and mortality. To parameterize our model, we used data from 3 global studies of neonatal sepsis and/or mortality-with 2,330 neonates who died with sepsis surveilled from 2016 to 2020 undertaken in 18 mainly LMICs across all WHO regions (Ethiopia, Kenya, Mali, Mozambique, Nigeria, Rwanda, Sierra Leone, South Africa, Uganda, Brazil, Italy, Greece, Pakistan, Bangladesh, India, Thailand, China, and Vietnam). Within these studies, 26.95% of fatal neonatal sepsis cases were culture-positive for K. pneumoniae. We analyzed 9,070 K. pneumoniae genomes from human isolates gathered globally from 2001 to 2020 to quantify the temporal rate of acquisition of AMR genes in K. pneumoniae isolates to predict the future number of drug-resistant cases and deaths that could be averted by vaccination. Resistance rates to carbapenems are increasing most rapidly and 22.43% [95th percentile Bayesian credible interval (CrI): 5.24 to 41.42] of neonatal sepsis deaths are caused by meropenem-resistant K. pneumoniae. Globally, we estimate that maternal vaccination could avert 80,258 [CrI: 18,084 to 189,040] neonatal deaths and 399,015 [CrI: 334,523 to 485,442] neonatal sepsis cases yearly worldwide, accounting for more than 3.40% [CrI: 0.75 to 8.01] of all neonatal deaths. The largest relative benefits are in Africa (Sierra Leone, Mali, Niger) and South-East Asia (Bangladesh) where vaccination could avert over 6% of all neonatal deaths. Nevertheless, our modeling only considers country-level trends in K. pneumoniae neonatal sepsis deaths and is unable to consider within-country variability in bacterial prevalence that may impact the projected burden of sepsis. A K. pneumoniae maternal vaccine could have widespread, sustained global benefits as AMR in K. pneumoniae continues to increase.

Influence of adaptive signal control technology (ASCT) on severity of intersection-related crashes

Adaptive signal control technology (ASCT) is an advanced traffic control system that optimizes signal timing based on real-time traffic demand. ASCT can potentially improve the operation and safety of intersections by establishing dynamic coordination among signalized intersections in real-time. This study used a binary Bayesian logit model with random effects, which accounts for unobserved heterogeneity, to explore the impacts of ASCT on the severity of intersection-related crashes in Florida. Two distinct ASCT types (Type I and II) were analyzed to assess their impacts on crash severity. The analysis revealed that ASCT reduced the likelihood of a fatal plus injury (FI) crash by 14.6%. This reduction was significant at a 90% Bayesian credible interval (BCI). Also, each ASCT type (Type I and II) showed a potential reduction in the likelihood of a FI crash, although the decrease was not significant at a 90% BCI. Other factors such as driving under the influence, angle crashes, dark lighting conditions, posted speed limit, and median along a minor approach, were associated with a higher risk of a FI crash. Transportation agencies could use the study results to justify the deployment and expansion of ASCT at signalized intersections with a high frequency of severe crashes.

Low-intensity climbing with blood flow restriction over 5 weeks increases grip and elbow flexor endurance in advanced climbers: A randomized controlled trial

ABSTRACT Grip and elbow flexor strength and endurance are crucial performance surrogates in competitive climbing. Thus, we examined the effects of blood flow restricted (BFR) climbing on grip and elbow flexor performance. Fifteen trained climbers (8 females; 20.8 ± 7.0 yrs; 1.72 ± 0.08 m; 63.0 ± 9.7 kg; 21.7 ± 2.7 IRCRCA grade) were either assigned to the intervention (BFR) or control (noBFR) group, using the minimization method (Strata: age, height, body mass, gender, and IRCRA grade). While BFR was used during low-intensity climbing training (2-times 10 min/session; 3-times/week), noBFR followed identical training protocols without BFR over 5 weeks. BFR of the upper limb was applied via customized pneumatic cuffs (occlusion pressure: 120 ± 23 mmHg, 75%; occlusion pressure). Endurance and strength performances were assessed via one-handed rung pulling (GripSTRENGTH), one-handed bent arm lock off at 90° (ArmSTRENGTH), static-intermitted finger hang (GripENDURANCE), and bent arm hang (ArmENDURANCE). Bayesian credible intervals revealed increased GripENDURANCE (+21 s (95% credible interval: −2 to 43 s)) and ArmENDURANCE +11 s (−5 to 27 s); adaptations via BFR. In contrast, GripSTRENGTH +4 N (−40 to 48 N) and ArmSTRENGTH +4 N (−68 to 75 N) were not affected by the BFR intervention. Fifteen cumulative sessions of BFR application with a cumulative total BFR load of 5 h over a 5 weeks macrocycle remarkably increased grip and elbow flexor endurance. Thus, BFR might serve as a promising means to improve relevant performance surrogates in trained climbers.

Tailoring capture-recapture methods to estimate registry-based case counts based on error-prone diagnostic signals.

Surveillance research is of great importance for effective and efficient epidemiological monitoring of case counts and disease prevalence. Taking specific motivation from ongoing efforts to identify recurrent cases based on the Georgia Cancer Registry, we extend recently proposed "anchor stream" sampling design and estimation methodology. Our approach offers a more efficient and defensible alternative to traditional capture-recapture (CRC) methods by leveraging a relatively small random sample of participants whose recurrence status is obtained through a principled application of medical records abstraction. This sample is combined with one or more existing signaling data streams, which may yield data based on arbitrarily non-representative subsets of the full registry population. The key extension developed here accounts for the common problem of false positive or negative diagnostic signals from the existing data stream(s). In particular, we show that the design only requires documentation of positive signals in these non-anchor surveillance streams, and permits valid estimation of the true case count based on an estimable positive predictive value (PPV) parameter. We borrow ideas from the multiple imputation paradigm to provide accompanying standard errors, and develop an adapted Bayesian credible interval approach that yields favorable frequentist coverage properties. We demonstrate the benefits of the proposed methods through simulation studies, and provide a data example targeting estimation of the breast cancer recurrence case count among Metro Atlanta area patients from the Georgia Cancer Registry-based Cancer Recurrence Information and Surveillance Program (CRISP) database.

Prevalence of respiratory disease in Irish preweaned dairy calves using hierarchical Bayesian latent class analysis.

Bovine respiratory disease (BRD) has a significant impact on the health and welfare of dairy calves. It can result in increased antimicrobial usage, decreased growth rate and reduced future productivity. There is no gold standard antemortem diagnostic test for BRD in calves and no estimates of the prevalence of respiratory disease in seasonal calving dairy herds. To estimate BRD prevalence in seasonal calving dairy herds in Ireland, 40 dairy farms were recruited and each farm was visited once during one of two calving seasons (spring 2020 & spring 2021). At that visit the prevalence of BRD in 20 calves between 4 and 6 weeks of age was determined using thoracic ultrasound score (≥3) and the Wisconsin respiratory scoring system (≥5). Hierarchical Bayesian latent class analysis was used to estimate the calf-level true prevalence of BRD, and the within-herd prevalence distribution, accounting for the imperfect nature of both diagnostic tests. In total, 787 calves were examined, of which 58 (7.4%) had BRD as defined by a Wisconsin respiratory score ≥5 only, 37 (4.7%) had BRD as defined by a thoracic ultrasound score of ≥3 only and 14 (1.8%) calves had BRD based on both thoracic ultrasound and clinical scoring. The primary model assumed both tests were independent and used informed priors for test characteristics. Using this model the true prevalence of BRD was estimated as 4%, 95% Bayesian credible interval (BCI) (1%, 8%). This prevalence estimate is lower or similar to those found in other dairy production systems. Median within herd prevalence varied from 0 to 22%. The prevalence estimate was not sensitive to whether the model was constructed with the tests considered conditionally dependent or independent. When the case definition for thoracic ultrasound was changed to a score ≥2, the prevalence estimate increased to 15% (95% BCI: 6%, 27%). The prevalence of calf respiratory disease, however defined, was low, but highly variable, in these seasonal calving dairy herds.