An integrative latent class model of heterogeneous data modalities for diagnosing kidney obstruction.

Radionuclide imaging plays a critical role in the diagnosis and management of kidney obstruction. However, most practicing radiologists in US hospitals have insufficient time and resources to acquire training and experience needed to interpret radionuclide images, leading to increased diagnostic errors. To tackle this problem, Emory University embarked on a study that aims to develop a computer-assisted diagnostic (CAD) tool for kidney obstruction by mining and analyzing patient data comprised of renogram curves, ordinal expert ratings on the obstruction status, pharmacokinetic variables, and demographic information. The major challenges here are the heterogeneity in data modes and the lack of gold standard for determining kidney obstruction. In this article, we develop a statistically principled CAD tool based on an integrative latent class model that leverages heterogeneous data modalities available for each patient to provide accurate prediction of kidney obstruction. Our integrative model consists of three sub-models (multilevel functional latent factor regression model, probit scalar-on-function regression model, and Gaussian mixture model), each of which is tailored to the specific data mode and depends on the unknown obstruction status (latent class). An efficient MCMC algorithm is developed to train the model and predict kidney obstruction with associated uncertainty. Extensive simulations are conducted to evaluate the performance of the proposed method. An application to an Emory renal study demonstrates the usefulness of our model as a CAD tool for kidney obstruction.

A novel diagnostic model for tuberculous meningitis using Bayesian latent class analysis.

BACKGROUND: Diagnosis of tuberculous meningitis (TBM) is hampered by the lack of a gold standard. Current microbiological tests lack sensitivity and clinical diagnostic approaches are subjective. We therefore built a diagnostic model that can be used before microbiological test results are known. METHODS: We included 659 individuals aged [Formula: see text] years with suspected brain infections from a prospective observational study conducted in Vietnam. We fitted a logistic regression diagnostic model for TBM status, with unknown values estimated via a latent class model on three mycobacterial tests: Ziehl-Neelsen smear, Mycobacterial culture, and GeneXpert. We additionally re-evaluated mycobacterial test performance, estimated individual mycobacillary burden, and quantified the reduction in TBM risk after confirmatory tests were negative. We also fitted a simplified model and developed a scoring table for early screening. All models were compared and validated internally. RESULTS: Participants with HIV, miliary TB, long symptom duration, and high cerebrospinal fluid (CSF) lymphocyte count were more likely to have TBM. HIV and higher CSF protein were associated with higher mycobacillary burden. In the simplified model, HIV infection, clinical symptoms with long duration, and clinical or radiological evidence of extra-neural TB were associated with TBM At the cutpoints based on Youden's Index, the sensitivity and specificity in diagnosing TBM for our full and simplified models were 86.0% and 79.0%, and 88.0% and 75.0% respectively. CONCLUSION: Our diagnostic model shows reliable performance and can be developed as a decision assistant for clinicians to detect patients at high risk of TBM. Diagnosis of tuberculous meningitis is hampered by the lack of gold standard. We developed a diagnostic model using latent class analysis, combining confirmatory test results and risk factors. Models were accurate, well-calibrated, and can support both clinical practice and research.

Global patterns of cancer transitions: A modelling study.

Cancer is a major contributor to global disease burden. Many countries experienced or are experiencing the transition that non-infection-related cancers replace infection-related cancers. We aimed to characterise burden changes for major types of cancers and identify global transition patterns. We focused on 10 most common cancers worldwide and extracted age-standardised incidence and mortality in 204 countries and territories from 1990 to 2019 through the Global Burden of Disease Study. Two-stage modelling design was used. First, we applied growth mixture models (GMMs) to identify distinct trajectories for incidence and mortality of each cancer type. Next, we performed latent class analysis to detect cancer transition patterns based on the categorisation results from GMMs. Kruskal-Wallis H tests were conducted to evaluate associations between transition patterns and socioeconomic indicators. Three distinct patterns were identified as unfavourable, intermediate and favourable stages. Trajectories of lung and breast cancers had the strongest association with transition patterns among men and women. The unfavourable stage was characterised by rapid increases in lung, breast and colorectal cancers alongside stable or decreasing burden of gastric, cervical, oesophageal and liver cancers. In contrast, the favourable stage exhibited rapid declines in most cancers. The unfavourable stage was associated with lower sociodemographic index, health expenditure, gross domestic product per capita and higher maternal mortality ratio (P < .001 for all associations). Our findings suggest that unfavourable, intermediate and favourable transition patterns exist. Countries and territories in the unfavourable stage tend to be socioeconomically disadvantaged, and tailored intervention strategies are needed in these resource-limited settings.

Simplifying the estimation of diagnostic testing accuracy over time for high specificity tests in the absence of a gold standard.

Many different methods for evaluating diagnostic test results in the absence of a gold standard have been proposed. In this paper, we discuss how one common method, a maximum likelihood estimate for a latent class model found via the Expectation-Maximization (EM) algorithm can be applied to longitudinal data where test sensitivity changes over time. We also propose two simplified and nonparametric methods which use data-based indicator variables for disease status and compare their accuracy to the maximum likelihood estimation (MLE) results. We find that with high specificity tests, the performance of simpler approximations may be just as high as the MLE.

Joint clustering multiple longitudinal features: A comparison of methods and software packages with practical guidance.

Clustering longitudinal features is a common goal in medical studies to identify distinct disease developmental trajectories. Compared to clustering a single longitudinal feature, integrating multiple longitudinal features allows additional information to be incorporated into the clustering process, which may reveal co-existing longitudinal patterns and generate deeper biological insight. Despite its increasing importance and popularity, there is limited practical guidance for implementing cluster analysis approaches for multiple longitudinal features and evaluating their comparative performance in medical datasets. In this paper, we provide an overview of several commonly used approaches to clustering multiple longitudinal features, with an emphasis on application and implementation through R software. These methods can be broadly categorized into two categories, namely model-based (including frequentist and Bayesian) approaches and algorithm-based approaches. To evaluate their performance, we compare these approaches using real-life and simulated datasets. These results provide practical guidance to applied researchers who are interested in applying these approaches for clustering multiple longitudinal features. Recommendations for applied researchers and suggestions for future research in this area are also discussed.

A latent functional approach for modeling the effects of multidimensional exposures on disease risk.

Understanding the relationships between exposure and disease incidence is an important problem in environmental epidemiology. Typically, a large number of these exposures are measured, and it is found either that a few exposures transmit risk or that each exposure transmits a small amount of risk, but, taken together, these may pose a substantial disease risk. Further, these exposure effects can be nonlinear. We develop a latent functional approach, which assumes that the individual effect of each exposure can be characterized as one of a series of unobserved functions, where the number of latent functions is less than or equal to the number of exposures. We propose Bayesian methodology to fit models with a large number of exposures and show that existing Bayesian group LASSO approaches are a special case of the proposed model. An efficient Markov chain Monte Carlo sampling algorithm is developed for carrying out Bayesian inference. The deviance information criterion is used to choose an appropriate number of nonlinear latent functions. We demonstrate the good properties of the approach using simulation studies. Further, we show that complex exposure relationships can be represented with only a few latent functional curves. The proposed methodology is illustrated with an analysis of the effect of cumulative pesticide exposure on cancer risk in a large cohort of farmers.

Uncovering circadian rhythms in metabolic longitudinal data: A Bayesian latent class modeling approach.

Researchers in biology and medicine have increasingly focused on characterizing circadian rhythms and their potential impact on disease. Understanding circadian variation in metabolomics, the study of chemical processes involving metabolites may provide insight into important aspects of biological mechanism. Of scientific importance is developing a statistical rigorous approach for characterizing different types of 24-hour patterns among high dimensional longitudinal metabolites. We develop a latent class approach to incorporate variation in 24-hour patterns across metabolites where profiles are modeled with finite mixtures of distinct shape-invariant circadian curves that themselves incorporate variation in amplitude and phase across metabolites. An efficient Markov chain Monte Carlo sampling is used to carry out Bayesian posterior computation. When the model was fit separately by individual to the data from a small group of participants, two distinct 24-hour rhythms were identified, with one being sinusoidal and the other being more complex with multiple peaks. Interestingly, the latent pattern associated with circadian variation (simple sinusoidal curve) had a similar phase across the three participants, while the more complex latent pattern reflecting diurnal variation differed across individual. The results suggested that this modeling framework can be used to separate 24-hour rhythms into an endogenous circadian and one or more exogenous diurnal patterns in describing human metabolism.

Heterogeneous Preferences for Colorectal Cancer Screening in Germany: Results of a Discrete Choice Experiment.

OBJECTIVES: Colorectal cancer (CRC) screening tests differ in benefits, harms, and processes, making individual informed decisions preference based. The objective was to analyze the preferences of insurees in Germany for characteristics of CRC screening modalities. METHODS: A generic discrete choice experiment with 2-alternative choice sets and 6 attributes (CRC mortality, CRC incidence, complications, preparation, need for transportation, and follow-up; 3 levels each) depicting characteristics of fecal testing, sigmoidoscopy, and colonoscopy was generated. Participants completed 8 choice tasks. Internal validity was tested using a within-set dominated pair. Between June and October 2020, written questionnaires were sent to a stratified random sample (n = 5000) of 50-, 55-, and 60-year-old insurees of the AOK (Allgemeine Ortskrankenkasse) Lower Saxony, who had previously received an invitation to participate in the organized screening program including evidence-based information. Preferences were analyzed using conditional logit, mixed logit, and latent-class model. RESULTS: From 1282 questionnaires received (26% [1282 of 4945]), 1142 were included in the analysis. Approximately 42% of the respondents chose the dominated alternative in the internal validity test. Three heterogeneous preference classes were identified. Most important attributes were preparation (class 1; n = 505, 44%), CRC mortality (class 2; n = 347, 30%), and CRC incidence (class 3; n = 290, 25%). Contrary to a priori expectations, a higher effort was preferred for bowel cleansing (class 1) and accompaniment home (classes 1 and 2). CONCLUSION: Internal validity issues of choice data need further research and warrant attention in future discrete choice experiment surveys. The observed preference heterogeneity suggests different informational needs, although the underlying reasons remained unclear.

Estimating sensitivity and specificity of diagnostic tests using latent class models that account for conditional dependence between tests: a simulation study.

BACKGROUND: Latent class models are increasingly used to estimate the sensitivity and specificity of diagnostic tests in the absence of a gold standard, and are commonly fitted using Bayesian methods. These models allow us to account for 'conditional dependence' between two or more diagnostic tests, meaning that the results from tests are correlated even after conditioning on the person's true disease status. The challenge is that it is not always clear to researchers whether conditional dependence exists between tests and whether it exists in all or just some latent classes. Despite the increasingly widespread use of latent class models to estimate diagnostic test accuracy, the impact of the conditional dependence structure chosen on the estimates of sensitivity and specificity remains poorly investigated. METHODS: A simulation study and a reanalysis of a published case study are used to highlight the impact of the conditional dependence structure chosen on estimates of sensitivity and specificity. We describe and implement three latent class random-effect models with differing conditional dependence structures, as well as a conditional independence model and a model that assumes perfect test accuracy. We assess the bias and coverage of each model in estimating sensitivity and specificity across different data generating mechanisms. RESULTS: The findings highlight that assuming conditional independence between tests within a latent class, where conditional dependence exists, results in biased estimates of sensitivity and specificity and poor coverage. The simulations also reiterate the substantial bias in estimates of sensitivity and specificity when incorrectly assuming a reference test is perfect. The motivating example of tests for Melioidosis highlights these biases in practice with important differences found in estimated test accuracy under different model choices. CONCLUSIONS: We have illustrated that misspecification of the conditional dependence structure leads to biased estimates of sensitivity and specificity when there is a correlation between tests. Due to the minimal loss in precision seen by using a more general model, we recommend accounting for conditional dependence even if researchers are unsure of its presence or it is only expected at minimal levels.

Many phish in the C $\mathcal{C}$ : A coexisting-choice-criteria model of security behavior.

Normative decision theory proves inadequate for modeling human responses to the social-engineering campaigns of advanced persistent threat (APT) attacks. Behavioral decision theory fares better, but still falls short of capturing social-engineering attack vectors which operate through emotions and peripheral-route persuasion. We introduce a generalized decision theory, under which any decision will be made according to one of multiple coexisting choice criteria. We denote the set of possible choice criteria by C $\mathcal {C}$ . Thus, the proposed model reduces to conventional Expected Utility theory when | C EU | = 1 $|\mathcal {C}_{\text{EU}}|=1$ , while Dual-Process (thinking fast vs. thinking slow) decision making corresponds to a model with | C DP | = 2 $|\mathcal {C}_{\text{DP}}|=2$ . We consider a more general case with | C | ≥ 2 $|\mathcal {C}|\ge 2$ , which necessitates careful consideration of how, for a particular choice-task instance, one criterion comes to prevail over others. We operationalize this with a probability distribution that is conditional upon traits of the decisionmaker as well as upon the context and the framing of choice options. Whereas existing signal detection theory (SDT) models of phishing detection commingle the different peripheral-route persuasion pathways, in the present descriptive generalization the different pathways are explicitly identified and represented. A number of implications follow immediately from this formulation, ranging from the conditional nature of security-breach risk to delineation of the prerequisites for valid tests of security training. Moreover, the model explains the "stepping-stone" penetration pattern of APT attacks, which has confounded modeling approaches based on normative rationality.

Willingness to Pay to Reduce the Ecological and Health Risks from Water Pollution in Emerging Urban Regions: The Case of the Red River Delta in Vietnam.

The soaring economic development of export activities of handicrafts centralized in emerging urban regions in Vietnam has accelerated the increase in the occurrence of diseases and threats to ecosystems induced by water pollution. We design a discrete choice experiment to elicit the willingness-to-pay of handicraft enterprises to restore the environment and diminish health risks from polluted wastewater through water quality improvement under different scenarios. Estimates from five latent classes reveal that one-half of entrepreneurs strongly value the provision of wastewater treatment services, and their decisions are mostly driven by preferences to reduce the risk of sickness caused by water pollution. This finding lends support to the argument that self-interested preferences predominate pro-environmental behavior in the readiness to pay for water quality services. While entrepreneurs' preferences attributed to ecological remediation seem to vary according to their educational background, the status-quo group shows low environmental awareness. This divergent behavioral pattern suggests that the design of wastewater management policies requires a mixture of measures that aim at different groups of individuals pursuing economic incentives and the creation of awareness.

Performance characteristics of two real-time TaqMan polymerase chain reaction assays for the detection of WSSV in clinically diseased and apparently healthy prawns.

This study aimed to generate data on performance characteristics for 2 real-time TaqMan PCR assays (CSIRO and WOAH WSSV qPCRs) for the purposes of (1) detection of white spot syndrome virus (WSSV) in clinically diseased prawns and (2) detection of WSSV in apparently healthy prawns. Analytical sensitivity of both assays was 2 to 20 genome copies per reaction, and analytical specificity was 100% after testing nucleic acid from 9 heterologous prawn pathogens and 4 prawn species. Results obtained after testing more than 20 000 samples in up to 559 runs with the CSIRO WSSV qPCR and up to 293 runs with the WOAH WSSV qPCR demonstrated satisfactory repeatability for both assays. Both assays demonstrated median diagnostic sensitivity (DSe) 100% (95% CI: 94.9-100%) when testing clinically diseased prawns. When 1591 test results from apparently healthy prawns were analysed by Bayesian latent class analysis, median DSe and diagnostic specificity (DSp) were 82.9% (95% probability interval [PI]: 75.0-90.2%) and 99.7% (95% PI: 98.6-99.99%) for the CSIRO WSSV qPCR and 76.8% (95% PI: 68.9-84.9%) and 99.7% (95% PI: 98.7-99.99%) for the WOAH WSSV qPCR. When both assays were interpreted in parallel, median DSe increased to 98.3 (95% PI: 91.6-99.99%), and median DSp decreased slightly to 99.4% (95% PI: 97.9-99.99%). Routine testing of quantified positive controls by laboratories in the Australian laboratory network demonstrated satisfactory reproducibility of the CSIRO WSSV qPCR assay. Both assays demonstrated comparable performance characteristics, and the results contribute to the validation data required in the WOAH validation pathway for the purposes of detection of WSSV in clinically diseased and apparently healthy prawns.

Development and validation of a clinical respiratory disease scoring system for guiding treatment decisions in veal calves using a Bayesian framework.

Active infectious bovine respiratory disease (BRD) is an infection of the airways that needs to be diagnosed correctly so that appropriate treatment can be initiated. The simplest and most practical test to detect active BRD in dairy calves raised for veal is the detection and interpretation of clinical signs by producers or technicians. However, the clinical scoring system currently available for veal calves lacks sensitivity and specificity, contributing to economic losses and high use of antimicrobials. An accurate and reliable batch-level test to detect active BRD is essential to tailor antimicrobial use and reduce economic losses in veal calves. The objective of this study was therefore to develop and validate a new veal calf respiratory clinical scoring system (VcCRS), including reliable clinical signs (cough, ear droop or head tilt) and increased rectal temperature to detect active BRD in batches of veal calves housed individually, and to describe the accuracy of the scoring system for identifying batches of veal calves to treat. During 2017 to 2018, clinical examination, thoracic ultrasonography (TUS) and a haptoglobin concentration (Hap) were prospectively performed on 800 veal calves housed individually in Québec, Canada. Deep nasopharyngeal swabs were performed on 250 veal calves. A Bayesian latent class model accounting for imperfect accuracy of TUS and Hap was used to obtain weights for the clinical signs and develop the VcCRS. The VcCRS was then validated externally in 3 separate data sets. Finally, the applicability of the VcCRS at batch level was determined. We found that calves with 2 of the following findings-cough, unilateral or bilateral ear droop or head tilt, or increased rectal temperature ≥39.7°C-were considered positive and had a 31% chance of having active BRD. Without at least 2 of these 2 findings, a calf had a 100% chance of not having active BRD. At the batch level, we found that a batch with ≥3 positive calves among 10 calves sampled 2 wk after arrival at the fattening unit had a 94% chance of having an active BRD prevalence ≥10%. A batch with <3 positive calves had a 95% chance of not having an active BRD prevalence ≥10%. In this study, we developed a simple individual and batch-level score that is reliable across examiners and performs effectively in the detection of active BRD in veal calves. The implementation of this VcCRS in the veal calf industry would promote the elaboration of a protocol tailoring antimicrobial use.

The Data-Adaptive Fellegi-Sunter Model for Probabilistic Record Linkage: Algorithm Development and Validation for Incorporating Missing Data and Field Selection.

BACKGROUND: Quality patient care requires comprehensive health care data from a broad set of sources. However, missing data in medical records and matching field selection are 2 real-world challenges in patient-record linkage. OBJECTIVE: In this study, we aimed to evaluate the extent to which incorporating the missing at random (MAR)-assumption in the Fellegi-Sunter model and using data-driven selected fields improve patient-matching accuracy using real-world use cases. METHODS: We adapted the Fellegi-Sunter model to accommodate missing data using the MAR assumption and compared the adaptation to the common strategy of treating missing values as disagreement with matching fields specified by experts or selected by data-driven methods. We used 4 use cases, each containing a random sample of record pairs with match statuses ascertained by manual reviews. Use cases included health information exchange (HIE) record deduplication, linkage of public health registry records to HIE, linkage of Social Security Death Master File records to HIE, and deduplication of newborn screening records, which represent real-world clinical and public health scenarios. Matching performance was evaluated using the sensitivity, specificity, positive predictive value, negative predictive value, and F1-score. RESULTS: Incorporating the MAR assumption in the Fellegi-Sunter model maintained or improved F1-scores, regardless of whether matching fields were expert-specified or selected by data-driven methods. Combining the MAR assumption and data-driven fields optimized the F1-scores in the 4 use cases. CONCLUSIONS: MAR is a reasonable assumption in real-world record linkage applications: it maintains or improves F1-scores regardless of whether matching fields are expert-specified or data-driven. Data-driven selection of fields coupled with MAR achieves the best overall performance, which can be especially useful in privacy-preserving record linkage.

A Bayesian latent class model for predicting gestational age in health administrative data.

Health administrative data are oftentimes of limited use in epidemiological study on drug safety in pregnancy, due to lacking information on gestational age at birth (GAB). Although several studies have proposed algorithms to estimate GAB using claims database, failing to incorporate the unique distributional shape of GAB, can introduce bias in estimates and subsequent modeling. Hence, we develop a Bayesian latent class model to predict GAB. The model employs a mixture of Gaussian distributions with linear covariates within each class. This approach allows modeling heterogeneity in the population by identifying latent subgroups and estimating class-specific regression coefficients. We fit this model in a Bayesian framework conducting posterior computation with Markov Chain Monte Carlo methods. The method is illustrated with a dataset of 10,043 Rhode Island Medicaid mother-child pairs. We found that the three-class and six-class mixture specifications maximized prediction accuracy. Based on our results, Medicaid women were partitioned into three classes, featured by extreme preterm or preterm birth, preterm or" early" term birth, and" late" term birth. Obstetrical complications appeared to pose a significant influence on class-membership. Altogether, compared to traditional linear models our approach shows an advantage in predictive accuracy, because of superior flexibility in modeling a skewed response and population heterogeneity.

Environmental information disclosure and public choice decisions for urban river restoration: A comparative study between Brussels (Belgium) and Guangzhou (China).

Worldwide environmental information disclosure (EID) has been widely promoted as a policy approach to establish transparent governments, enhance public environmental awareness, and foster participatory environmental governance. While information disclosure and transparency are inherently incentivised within democratic regimes, how and through what pathways an increased flow of environmental information in the absence of democracy could lead to favourable public support for environmental/ecological projects remain under-investigated. Particularly, there exists very limited literature which compares how EID is associated with public environmental choices between different sociopolitical contexts. Taking Brussels (Belgium) and Guangzhou (China) as a comparative case, this study examines the association between citizens' perceived trustworthiness of various environmental information sources and their choice decisions regarding urban river restoration initiatives in contrasting socialpolitical contexts. Latent class modelling of two paralleled discrete choice experiments unveils a consistent classification of three distinctive groups for each city and also the combined sample, including Enthusiastic Supporters (Class 1, who are cost-insensitive and supportive of all proposed changes), Pragmatic Supporters (Class 2, who are cost-sensitive, prefer some changes they favour), and Non-Supporters (Class 3, who are unwilling to support the proposed initiatives). Incorporating respondents' trustworthiness in information sources as covariates in class membership likelihood function, respondents' membership is found to be associated solely with the most trusted information source, i.e., social contacts in Guangzhou, third parties in Brussels, and social contacts for the whole sample. Holding trust toward the most-trusted information source can increase the probability of being a member of Class 1, otherwise, more likely being a member of Class 3. Taken together with the insignificance of the variable denoting a respondent's city in explaining class membership, this study reveals that the variations in the EID levels (matured vs. emerging) and sociopolitical contexts (democratic vs. non-democratic) cannot significantly shape citizens' environmental decisions. Instead, it is respondents' perceived trustworthiness of information outlets that plays a positive role in their supportive decisions. These analytical results offer new insights about the role of EID in environmental governance and call for instilling institutional trust in China and relational trust in Belgium for facilitating effective communication and pro-environmental behaviours across the whole community.

Students' preferences for returning to colleges and universities during the COVID-19 pandemic: A discrete choice experiment.

Importance: When an emerging infectious disease outbreak occurs, such as COVID-19, institutions of higher education (IHEs) must weigh decisions about how to operate their campuses. These decisions entail whether campuses should remain open, how courses should be delivered (in-person, online, or a mixture of the two), and what safety plans should be enacted for those on campus. These issues have weighed heavily on campus administrators during the on-going COVID-19 pandemic. However, there is still limited knowledge about how such decisions affect students' enrollment decisions and campus safety in practice when considering compliance. Objectives: To assess 1) students' willingness to comply with health protocols and contrast their perception of their classmates' compliance, 2) whether students prefer in-person or online learning during a pandemic, and 3) the importance weights of different aspects of campus operations (i.e., modes of course delivery and safety plans) for students when they decide to enroll or defer. Design setting and participants: An internet-based survey of college students took place from June 25, 2020 to July 10, 2020. Participants included 398 industrial engineering students at the Georgia Institute of Technology, a medium-size public university in Atlanta, Georgia. The survey included a discrete choice experiment with questions that asked students to choose whether to enroll or defer when presented with hypothetical scenarios related to Fall 2020 modes of course delivery and aspects of campus safety. The survey also asked students about expected compliance with health protocols, whether they preferred in-person or online courses, and sociodemographic information. Main outcomes and measures: We examine students' willingness to comply with potential health protocols. We estimated logistic regression models to infer significant factors that lead to a student's choice between in-person and online learning. Additionally, we estimated discrete choice models to infer the importance of different modes of course delivery and safety measures to students when deciding to enroll or defer. Results: The survey response rate was 20.8%. A latent class model showed three classes of students: those who were "low-concern" (comprising a 29% expected share of the sample), those who were "moderate-concern" (54%) and those who were "high-concern" (17%). We found that scenarios that offered an on-campus experience with large classes delivered online and small classes delivered in-person, strict safety protocols in terms of mask-wearing, testing, and residence halls, and lenient safety protocols in terms of social gatherings were broadly the scenarios with the highest expected enrollment probabilities. The decision to enroll or defer for all students was largely determined by the mode of delivery for courses and the safety measures on campus around COVID-19 testing and mask-wearing. A logistic regression model showed that a higher perceived risk of infection of COVID-19, a more suitable home environment, being older, and being less risk-seeking were significant factors for a person to choose online learning. Students stated for themselves and their classmates that they would comply with some but not all health protocols against COVID-19, especially those limiting social gatherings. Conclusions and relevance: The majority of students indicated a preference to enroll during the COVID-19 pandemic so long as sufficient safety measures were put in place and all classes were not entirely in-person. As IHEs consider different options for campus operations during pandemics, they should consider the heterogeneous preferences among their students. Offering flexibility in course modes may be a way to appeal to many students who vary in terms of their concern about the pandemic. At the same time, since students overall preferred some safety measures placed around mask-wearing and COVID-19 testing on campus, IHEs may want to recommend or require wearing masks and doing some surveillance tests for all students, faculty, and staff. Students were expecting themselves and their fellow classmates to comply with some but not all health protocols, which may help IHEs identify protocols that need more education and awareness, like limits on social gatherings and the practice of social distancing at social gatherings.

Evaluation of Nanopore Sequencing as a Diagnostic Tool for the Rapid Identification of Mycoplasma bovis from Individual and Pooled Respiratory Tract Samples.

Rapid identification of Mycoplasma bovis infections in cattle is a key factor to guide antimicrobial therapy and biosecurity measures. Recently, Nanopore sequencing became an affordable diagnostic tool for both clinically relevant viruses and bacteria, but the diagnostic accuracy for M. bovis identification is undocumented. Therefore, in this study Nanopore sequencing was compared to rapid identification of M. bovis with matrix-assisted laser desorption ionization-time of flight mass spectrometry (RIMM) and a triplex real-time PCR assay in a Bayesian latent class model (BLCM) for M. bovis in bronchoalveolar lavage fluid (BALf) samples obtained from calves. In practice, pooling of samples is often used to save money, but the influence on diagnostic accuracy has not been described for M. bovis. Therefore, a convenience sample of 17 pooled samples containing 5 individual BALf samples per farm was analyzed as well. The results for the pooled samples were compared with those for the individual samples to determine sensitivity and specificity. The BLCM showed good sensitivity (77.3% [95% credible interval, 57.8 to 92.8%]) and high specificity (97.4% [91.5 to 99.7%]) for Nanopore sequencing, compared to RIMM (sensitivity, 93.0% [76.8 to 99.5%]; specificity, 91.3% [82.5 to 97.0%]) and real-time PCR (sensitivity, 94.6% [89.7 to 97.7%]; specificity, 86.0% [76.1 to 93.6%]). Sensitivity and specificity of pooled analysis for M. bovis were 85.7% (95% confidence interval, 59.8 to 111.6%) and 90.0% (71.4 to 108.6%%), respectively, for Nanopore sequencing and 100% (100% to 100%) and 88.9% (68.4 to 109.4%) for RIMM. In conclusion, Nanopore sequencing is a rapid, reliable tool for the identification of M. bovis. To reduce costs and increase the chance of M. bovis identification, pooling of 5 samples for Nanopore sequencing and RIMM is possible.

Estimation of diagnostic test accuracy: A "Rule of Three" for data with repeated observations but without a gold standard.

This article considers how to estimate the accuracy of a diagnostic test when there are repeated observations, but without the availability of a gold standard or reference test. We identify conditions under which the structure of the observed data is rich enough to provide sufficient degrees of freedom, such that a suitable latent class model can be fitted with identifiable accuracy parameters. We show that a Rule of Three applies, specifying that accuracy can be evaluated as long as there are at least three observations per individual with the given test. This rule also applies if the three observations arise from combinations of different test methods, or from a sequential design in which individuals are tested for a maximum number of times with the same test but stopping if a positive (or negative) result occurs. The rule pertains to tests having an arbitrary number of response categories. Accuracy is evaluated by parameters reflecting rates of misclassification among the response categories, and the model also provides estimates of the underlying distribution of the true disease state. These ideas are illustrated by data from two medical studies. Issues discussed include the advantages and disadvantages of analyzing the response variable as binary or multinomial, as well as the feasibility of testing goodness of fit when the model incorporates a large number of parameters. Comparisons are possible between models that do or do not assume equal accuracy rates for the observations, and between models where certain misclassification parameters are or are not assumed to be zero.

Evaluation using latent class models of the diagnostic performances of three ELISA tests commercialized for the serological diagnosis of Coxiella burnetii infection in domestic ruminants.

ELISA methods are the diagnostic tools recommended for the serological diagnosis of Coxiella burnetii infection in ruminants but their respective diagnostic performances are difficult to assess because of the absence of a gold standard. This study focused on three commercial ELISA tests with the following objectives (1) assess their sensitivity and specificity in sheep, goats and cattle, (2) assess the between- and within-herd seroprevalence distribution in these species, accounting for diagnostic errors, and (3) estimate optimal sample sizes considering sensitivity and specificity at herd level. We comparatively tested 1413 cattle, 1474 goat and 1432 sheep serum samples collected in France. We analyzed the cross-classified test results with a hierarchical zero-inflated beta-binomial latent class model considering each herd as a population and conditional dependence as a fixed effect. Potential biases and coverage probabilities of the model were assessed by simulation. Conditional dependence for truly seropositive animals was high in all species for two of the three ELISA methods. Specificity estimates were high, ranging from 94.8% [92.1; 97.8] to 99.2% [98.5; 99.7], whereas sensitivity estimates were generally low, ranging from 39.3 [30.7; 47.0] to 90.5% [83.3; 93.8]. Between- and within-herd seroprevalence estimates varied greatly among geographic areas and herds. Overall, goats showed higher within-herd seroprevalence levels than sheep and cattle. The optimal sample size maximizing both herd sensitivity and herd specificity varied from 3 to at least 20 animals depending on the test and ruminant species. This study provides better interpretation of three widely used commercial ELISA tests and will make it possible to optimize their implementation in future studies. The methodology developed may likewise be applied to other human or animal diseases.