Bayesian Latent Class Analysis Research Articles

Sexual behaviors and risk of extended-spectrum β-lactamase-producing Enterobacterales carriage: a cross-sectional analysis

Sexual transmission of extended-spectrum β-lactamase-producing Enterobacterales (ESBL-E) is suspected. We aimed to identify clusters of sexual behavior associated with ESBL-E carriage among individuals at risk of sexually transmitted infections (STI). In this cross-sectional study, patients attending an STI-screening center and human immunodeficiency virus outpatient clinic in Paris, France between 2018 and 2019 were asked questions on the following sexual activities in the last 6 months: receptive/insertive anal intercourse, passive/active rimming, receptive/insertive fellatio, receptive/insertive fisting, receptive/insertive fingering, active/passive cunnilingus. ESBL-E carriage was determined from rectal swabs. Bayesian latent class analysis was used to identify clusters of sexual activity, which were then associated with ESBL-E carriage using logistic regression. Among 1211 men who have sex with men (MSM), those belonging to two latent classes with higher prevalence of insertive fingering and active rimming (ESBL-E prevalence=15.3%, N = 124 and 16.0%, N = 100) and one class with higher proportions of all behaviors (24.3%, N = 70) had a higher risk of ESBL-E carriage compared to those in a class with few sexual behaviors (7.3%, N = 259) after adjustment. Among 439 other men and 479 women, no clear associations between sexual clusters and ESBL-E carriage were observed. Sexual behaviors are associated with varying degrees of ESBL-E carriage, particularly among MSM.

Bayesian accuracy estimates for diagnostic tests to detect tuberculosis in captive sun bears (Helarctos malayanus) and Asiatic black bears (Ursus thibetanus) in Cambodia and Vietnam.

Effective control of tuberculosis (TB) depends on early diagnosis of disease, yet available tests are unable to perfectly detect infected individuals. In novel hosts diagnostic testing methods for TB are extrapolated from other species, with unknown accuracy. The primary challenge to evaluating the accuracy of TB tests is the lack of a perfect reference test. Here we use a Bayesian latent class analysis approach to evaluate five tests available for ante-mortem detection of pulmonary TB in captive sun bears and Asiatic black bears in Southeast Asia. Using retrospective results from screening of 344 bears at three rescue centres, we estimate accuracy parameters for thoracic radiography, a serological assay (DPP VetTB), and three microbiological tests (microscopy, PCR (Xpert MTB/RIF, Xpert MTB/RIF Ultra), mycobacterial culture) performed on bronchoalveolar lavage samples. While confirming the high specificities (≥ 0.99) of the three microbiological tests, our model demonstrated their sub-optimal sensitivities (<0.7). Thoracic radiography was the only diagnostic method with sensitivity (0.95, 95% BCI: 0.76, 0.998) and specificity (0.95, 95% BCI: 0.91, 0.98) estimated above 0.9. We recommend caution when interpreting DPP VetTB results, with the increased sensitivity resulting from treatment of weakly visible reactions as positive accompanied by a drop in specificity, and we illustrate how the diagnostic value of weak DPP VetTB reactions is particularly reduced if disease prevalence and/or clinical suspicion is low. Conversely, the reduced utility of negative microbiological tests on bronchoalveolar lavage fluid samples when prevalence and/or clinical suspicion is high is demonstrated. Taken together our results suggest multiple tests should be applied and accompanied by consideration of the testing context, to minimise the consequences of misclassification of disease status of bears at risk of TB in sanctuary settings.

Accuracy of Xpert and alternative sampling methods to diagnose childhood pulmonary tuberculosis, a prospective cohort study

ObjectivesThe aim of the study was to estimate sensitivity and specificity of childhood pulmonary tuberculosis (CPTB) diagnostic tests, including Xpert and alternative samples such as nasopharyngeal aspirate and stool. We used latent class analysis to overcome the lack of gold standard. MethodsWe included 1165 children suspected of having CPTB in a prospective cohort study conducted in Ivory Coast, Madagascar, and Cameroon. We used Bayesian latent class analysis to evaluate the performance of multiple diagnostic tests for CPTB: smear microscopy, mycobacterial culture, and Xpert MTB/RIF carried out on different types of samples (sputum after 10 years of age, gastric aspirate before 10 of age, nasopharyngeal aspirate, stool), tuberculin skin test (TST), and chest X-ray. ResultsMedian age was 3.5 years (95% credible interval [1.3–8.2]). Smear microscopy was highly specific in all types of samples but lacked sensitivity (sputum 31% (95% credible interval [18–46]), gastric aspirate 36% [27–45], nasopharyngeal aspirate 24% [17–32], stool 24% [17–32]). Culture sensitivity was slightly higher in gastric (75% [65–83]) than nasopharyngeal aspirate (64% [54–72]). Xpert sensitivity was similar in gastric (69% [59–78]) and nasopharyngeal aspirate (66% [57–74]) but lower in sputum (58% [43–74]) and stools (53% [44–62]). Xpert was highly specific in all respiratory samples (sputum 96% [96–100], gastric aspirate 100% [99–100], nasopharyngeal aspirate 100% [99–100]). ConclusionsXpert performed on nasopharyngeal aspirate shows similar accuracy than culture. It was also similar to that in gastric aspirate. It highlights it’s an interesting CPTB diagnostic combination.

Evaluation of Molecular Assays for Diagnosis of Amoebic Liver Abscess in India with Bayesian Latent Class Analysis.

Amoebic liver abscess (ALA) is the most common extra-intestinal complication of Entamoeba histolytica, accounting for 50,000 deaths annually, and is endemic in South Asia. Diagnosis based on microscopic examination is insensitive, and serological assays are not discerning of current infections in endemic settings with high exposure. For a rapid and confirmatory laboratory diagnosis of ALA, the performance of a polymerase chain reaction (PCR), quantitative real time PCR (qPCR), digital droplet PCR (ddPCR), and a loop-mediated isothermal amplification (LAMP) assay that detects E. histolytica DNA in liver abscess pus, and a lectin antigen detection ELISA were evaluated against clinical diagnosis (based on predefined criteria) as the gold standard. Owing to the lack of a laboratory gold standard, a Bayesian latent class analysis approach was also used to determine sensitivity and specificity of these assays. In the latent class analysis, qPCR and ddPCR showed the highest sensitivity (98% and 98.1%) and specificity (both 96.6%), and although clinical diagnosis had a comparable sensitivity to qPCR and ddPCR (95.2%), poorer specificity (64.3%) was seen. Kappa agreement analysis showed that qPCR and ddPCR had a perfect agreement of 1 followed by an agreement of 0.76 (95% CI: 0.64-0.88) with PCR. Considering the performance characteristics and relative ease of setting up qPCR as well as the wide availability of qPCR equipment needed, this would be the most optimal assay for rapid, confirmatory, molecular diagnosis of ALA in the tertiary care laboratory setting in India, whereas further optimization of LAMP or antibody-based detection is required for use at smaller or secondary hospitals.

Evaluation of three serological tests for the diagnosis of Brucella suis in dogs using Bayesian latent class analysis

Brucella suis infection of dogs is an emerging issue worldwide requiring specific management to address zoonotic risks and animal welfare concerns. Diagnosis in dogs is routinely based on serological testing, but to date these tests have only been validated for use in production animal species and humans. This study aimed to assess the diagnostic performance of three commonly used serological tests in dogs. Canine sera (n = 989) were tested with the Rose Bengal rapid plate agglutination test (RBRPT), the complement fixation test (CFT) and a competitive ELISA (C-ELISA). Diagnostic test performance was evaluated using a three test, two population Bayesian latent class analysis accounting for conditional dependence between the three tests. Positive and negative predictive values (PPV, NPV) were calculated for a range of expected prevalence estimates for the individual tests and test combinations interpreted in series and parallel. The RBRPT showed the highest individual Se of 0.902 (95 % posterior credible interval [PCI] 0.759–0.978) and the CFT the highest individual diagnostic specificity (Sp) of 0.914 (95 % PCI 0.886–0.946). The C-ELISA had marginally the best overall diagnostic performance (Youden’s index = 0.807). The CFT and the C-ELISA interpreted in parallel returned the highest combined Se and Sp (0.988 and 0.885, respectively). All tests returned NPVs of > 0.982 in 2–8 % prevalence settings. Series interpretation of the three-test combination as well as the two-test combinations of the RBRPT and the C-ELISA and the CFT and the C-ELISA produced a PPV of 0.502 when the estimated prevalence was 8 %. While all tests are suitable for the detection of B. suis antibodies in dogs, they should not be interpreted in isolation as their diagnostic value is dependent on the pre-test probability of the disease. As such they are useful tools for the diagnosis of B. suis in dogs when exposure, history and clinical presentation indicate a risk of brucellosis.

Accuracy of CAD4TB (Computer-Aided Detection for Tuberculosis) on paediatric chest radiographs.

Computer-aided detection (CAD) systems hold promise for improving tuberculosis (TB) detection on digital chest radiographs. However, data on their performance in exclusively paediatric populations are scarce. We conducted a retrospective diagnostic accuracy study evaluating the performance of CAD4TBv7 (Computer-Aided Detection for Tuberculosis version 7) using digital chest radiographs from well-characterised cohorts of Gambian children aged <15 years with presumed pulmonary TB. The children were consecutively recruited between 2012 and 2022. We measured CAD4TBv7 performance against a microbiological reference standard (MRS) of confirmed TB, and also performed Bayesian latent class analysis (LCA) to address the inherent limitations of the MRS in children. Diagnostic performance was assessed using the area under the receiver operating characteristic curve (AUROC) and point estimates of sensitivity and specificity. A total of 724 children were included in the analysis, with confirmed TB in 58 (8%), unconfirmed TB in 145 (20%) and unlikely TB in 521 (72%). Using the MRS, CAD4TBv7 showed an AUROC of 0.70 (95% CI 0.60-0.79), and demonstrated sensitivity and specificity of 19.0% (95% CI 11-31%) and 99.0% (95% CI 98.0-100.0%), respectively. Applying Bayesian LCA with the assumption of conditional independence between tests, sensitivity and specificity estimates for CAD4TBv7 were 42.7% (95% CrI 29.2-57.5%) and 97.9% (95% CrI 96.6-98.8%), respectively. When allowing for conditional dependence between culture and Xpert assay, CAD4TBv7 demonstrated a sensitivity of 50.3% (95% CrI 32.9-70.0%) and specificity of 98.0% (95% CrI 96.7-98.9%). Although CAD4TBv7 demonstrated high specificity, its suboptimal sensitivity underscores the crucial need for optimisation of CAD4TBv7 for detecting TB in children.

Prevalence, patterns and predictors of paranormal beliefs in The Netherlands: a several-analysts approach

Paranormal beliefs encompass a wide variety of phenomena, including the existence of supernatural entities such as ghosts and witches, as well as extraordinary human abilities such as telepathy and clairvoyance. In the current study, we used a nationally representative sample ( N = 2534 ) to investigate the presence and correlates of paranormal beliefs among the secular Dutch population. The results indicated that most single paranormal phenomena (e.g. belief in clairvoyance) are endorsed by 10–20% of Dutch respondents; however, 55.6% of respondents qualify as paranormal believers based on the preregistered criterion that they believe in at least one phenomenon with considerable certainty. In addition, we invited four analysis teams with different methodological expertise to assess the structure of paranormal beliefs using traditional factor analysis, network analysis, Bayesian network analysis and latent class analysis (LCA). The teams’ analyses indicated adequate fit of a four-factor structure reported in a 1985 study, but also emphasized different conclusions across techniques; network analyses showed evidence against strong connectedness within most clusters, and suggested a five-cluster structure. The application of various analytic techniques painted a nuanced picture of paranormal beliefs and believers in The Netherlands and suggests that despite increased secularization, subgroups of the general population still believe in paranormal phenomena.

Ddtlcm: An R package for overcoming weak separation in Bayesian latent class analysis via tree-regularization

ddtlcm: An R package for overcoming weak separation in Bayesian latent class analysis via tree-regularization

Comparison of test performance of a conventional PCR and two field-friendly tests to detect Coxiella burnetii DNA in ticks using Bayesian latent class analysis.

Coxiella burnetii (C. burnetii)-infected livestock and wildlife have been epidemiologically linked to human Q fever outbreaks. Despite this growing zoonotic threat, knowledge of coxiellosis in wild animals remains limited, and studies to understand their epidemiologic role are needed. In C. burnetii-endemic areas, ticks have been reported to harbor and spread C. burnetii and may serve as indicators of risk of infection in wild animal habitats. Therefore, the aim of this study was to compare molecular techniques for detecting C. burnetii DNA in ticks. In total, 169 ticks from wild animals and cattle in wildlife conservancies in northern Kenya were screened for C. burnetii DNA using a conventional PCR (cPCR) and two field-friendly techniques: Biomeme's C. burnetii qPCR Go-strips (Biomeme) and a new C. burnetii PCR high-resolution melt (PCR-HRM) analysis assay. Results were evaluated, in the absence of a gold standard test, using Bayesian latent class analysis (BLCA) to characterize the proportion of C. burnetii positive ticks and estimate sensitivity (Se) and specificity (Sp) of the three tests. The final BLCA model included main effects and estimated that PCR-HRM had the highest Se (86%; 95% credible interval: 56-99%), followed by the Biomeme (Se = 57%; 95% credible interval: 34-90%), with the estimated Se of the cPCR being the lowest (24%, 95% credible interval: 10-47%). Specificity estimates for all three assays ranged from 94 to 98%. Based on the model, an estimated 16% of ticks had C. burnetii DNA present. These results reflect the endemicity of C. burnetii in northern Kenya and show the promise of the PCR-HRM assay for C. burnetii surveillance in ticks. Further studies using ticks and wild animal samples will enhance understanding of the epidemiological role of ticks in Q fever.

Simultaneous alleviation of verification and reference standard biases in a community-based tuberculosis screening study using Bayesian latent class analysis.

Estimation of prevalence and diagnostic test accuracy in tuberculosis (TB) prevalence surveys suffer from reference standard and verification biases. The former is attributed to the imperfect reference test used to bacteriologically confirm TB disease. The latter occurs when only the participants screening positive for any TB-compatible symptom or chest X-ray abnormality are selected for bacteriological testing (verification). Bayesian latent class analysis (LCA) alleviates the reference standard bias but suffers verification bias in TB prevalence surveys. This work aims to identify best-practice approaches to simultaneously alleviate the reference standard and verification biases in the estimates of pulmonary TB prevalence and diagnostic test performance in TB prevalence surveys. We performed a secondary analysis of 9869 participants aged ≥15 years from a community-based multimorbidity screening study in a rural district of KwaZulu-Natal, South Africa (Vukuzazi study). Participants were eligible for bacteriological testing using Xpert Ultra and culture if they reported any cardinal TB symptom or had an abnormal chest X-ray finding. We conducted Bayesian LCA in five ways to handle the unverified individuals: (i) complete-case analysis, (ii) analysis assuming the unverified individuals would be negative if bacteriologically tested, (iii) analysis of multiply-imputed datasets with imputation of the missing bacteriological test results for the unverified individuals using multivariate imputation via chained equations (MICE), and simultaneous imputation of the missing bacteriological test results in the analysis model assuming the missing bacteriological test results were (iv) missing at random (MAR), and (v) missing not at random (MNAR). We compared the results of (i)-(iii) to the analysis based on a composite reference standard (CRS) of Xpert Ultra and culture. Through simulation with an overall true prevalence of 2.0%, we evaluated the ability of the models to alleviate both biases simultaneously. Based on simulation, Bayesian LCA with simultaneous imputation of the missing bacteriological test results under the assumption that the missing data are MAR and MNAR alleviate the reference standard and verification biases. CRS-based analysis and Bayesian LCA assuming the unverified are negative for TB alleviate the biases only when the true overall prevalence is <3.0%. Complete-case analysis produced biased estimates. In the Vukuzazi study, Bayesian LCA with simultaneous imputation of the missing bacteriological test results under the MAR and MNAR assumptions produced overall PTB prevalence of 0.9% (95% Credible Interval (CrI): 0.6-1.9) and 0.7% (95% CrI: 0.5-1.1) respectively alongside realistic estimates of overall diagnostic test sensitivity and specificity with substantially overlapping 95% CrI. The CRS-based analysis and Bayesian LCA assuming the unverified were negative for TB produced 0.7% (95% CrI: 0.5-0.9) and 0.7% (95% CrI: 0.5-1.2) overall PTB prevalence respectively with realistic estimates of overall diagnostic test sensitivity and specificity. Unlike CRS-based analysis, Bayesian LCA of multiply-imputed data using MICE mitigates both biases. The findings demonstrate the efficacy of these advanced techniques in alleviating the reference standard and verification biases, enhancing the robustness of community-based screening programs. Imputing missing values as negative for bacteriological tests is plausible under realistic assumptions.

Estimation of sensitivity and specificity of bulk tank milk PCR and 2 antibody ELISA tests for herd-level diagnosis of Mycoplasma bovis infection using Bayesian latent class analysis

Estimation of sensitivity and specificity of bulk tank milk PCR and 2 antibody ELISA tests for herd-level diagnosis of Mycoplasma bovis infection using Bayesian latent class analysis

Validity of luminometry and bacteriological tests for diagnosing intramammary infection at dry-off in dairy cows

The objective of this cross-sectional study was to estimate the validity of laboratory culture, Petrifilm and Tri-Plate on-farm culture systems, and luminometry to correctly identify IMI at dry-off in dairy cows, considering all tests as imperfect. From September 2020 until December 2021, we collected composite milk samples from cows before dry-off and divided them into 4 aliquots for the luminometry test, the Petrifilm (aerobic count), the Tri-Plate, and the laboratory culture. While we assessed multiple thresholds of relative light units (RLU) for the luminometry, we used thresholds of ≥100 cfu/mL for the laboratory culture, ≥ 50 cfu/mL for the Petrifilm, and ≥1 cfu for the Tri-Plate. We fitted Bayesian latent class analysis (LCA) models to estimate the sensitivity (Se) and specificity (Sp) for each test to identify IMI, with 95% credibility interval (BCI). Using different prevalence measures (0.30, 0.50, and 0.70), we calculated the predictive values (PV) and misclassification cost terms (MCT) at different false-negative to false-positive ratios (FN:FP). A total of 333 cows were enrolled in the study from one commercial Holstein herd. The validity of the luminometry was poor for all thresholds, with Se of 0.51 (95% BCI = 0.43-0.59) and Sp of 0.38 (95% BCI = 0.26-0.50) when using a threshold of ≥150 RLU. The laboratory culture had Se of 0.93 (95% BCI = 0.85-0.98) and Sp of 0.69 (95% BCI = 0.49-0.89), the Petrifilm had Se of 0.91 (95% BCI = 0.80-0.98) and Sp of 0.71 (95% BCI = 0.51-0.90), and the Tri-Plate had Se of 0.65 (95% BCI = 0.53-0.82) and Sp of 0.85 (95% BCI = 0.66-0.97). Bacteriological tests had good PVs, with comparable positive PV for all 3 tests, but lower negative PV for the Tri-Plate compared with the laboratory culture and the Petrifilm. For a prevalence of IMI of 0.30, all 3 tests had similar MCT, but for prevalence of 0.50 and 0.70, the Tri-Plate had higher MCT in scenarios where leaving a cow with IMI untreated is considered to have greater detrimental impacts than treating a healthy cow (i.e., FN:FP of 3:1). Our results showed that the bacteriological tests have adequate validity to diagnose IMI at dry-off, but the luminometry does not. We concluded that, while luminometry is not useful to identify IMI at dry-off, the Petrifilm and Tri-Plate tests performed similarly to the laboratory culture, depending on the prevalence and the importance of the FP and FN results.

Performance of various interpretations of clinical scoring systems for diagnosis of respiratory disease in dairy calves in a temperate climate using Bayesian latent class analysis

Bovine respiratory disease (BRD) presents a challenge to farmers all over the globe, not only because it can have significant impacts on welfare and productivity, but also because diagnosis can prove challenging. Several clinical scoring systems have been developed to aid farmers in making consistent early diagnosis, 2 examples being the Wisconsin (WCS) and the California (CALIF) systems. Neither of these systems were developed in or for use in a temperate environment. As environment may lead to changes in BRD presentation, the weightings and cutoffs designed for one environmental presentation of BRD may not be appropriate when used in a temperate climate. Additionally, the interpretation of the scores recorded varies between studies; this may also influence conclusions. Hence, the objective of this work was to investigate the sensitivity (Se) and specificity (Sp) of these tests in a temperate climate and investigate the influence of varying the interpretation on the performance of the WCS. In this prospective study, 98 commercial spring-calving dairy farms were recruited (40 randomly, 58 targeted) and visited. Thoracic ultrasound and WCS were performed on 20 randomly sampled calves between 4 and 6 wk of age on each farm. On a subset of 32 farms, the CALIF score was also undertaken. The data were then used in a hierarchical Bayesian latent class model to estimate the Se and Sp of 5 different interpretations of the Wisconsin clinical score and 1 interpretation of the California clinical score. In total, 1,936 calves were examined. The Se of the Wisconsin score varied from 0.336 to 0.577 depending on the interpretation used, and the Sp varied from 0.943 to 0.977. The Se of the California score was 0.563 (95% Bayesian credible interval [BCI]: 0.452, 0.681) and the Sp was 0.919 (95% BCI: 0.899, 0.937). In conclusion, the performances of the clinical scores in a temperate environment were similar to previously published work from more extreme climates; however, the performance varied widely depending on the score interpretation. Authors should justify their use of a particular clinical score interpretation to improve clarity in publications.

Evaluation of sensitivity and specificity of Kato-Katz and circulating cathodic antigen in terms of Schistosoma japonicum using latent class analysis

Schistosoma japonicum is endemic in the Philippines. The Kato-Katz (KK) method was used to diagnose S. japonicum. This is impractical, particularly when the sample size is limited. Knowledge on point-of-care circulating cathodic antigen (CCA) test performance for S. japonicum is limited. Determining the sensitivity and specificity of new diagnostics is difficult when the gold standard test is less effective or absent. Latent class analysis (LCA) can address some limitations. A total of 484 children and 572 adults from the Philippines were screened for S. japonicum. We performed Bayesian LCA to estimate the infection prevalence, sensitivity and specificity of each test by stratifying them into two age groups. Observed prevalence assessed by KK was 50.2% and 31.8%, and by CCA was 89.9% and 66.8%, respectively. Using Bayesian LCA, among children, the sensitivity and specificity of CCA were 94.8% (88.7–99.4) and 21.5% (10.5–36.1) while those of KK were 66.0% (54.2–83.3) and 78.1% (61.1–91.3). Among adults, the sensitivity and specificity of CCA were 86.4% (76.6–96.9) and 62.8% (49.1–81.1) while those of KK were 43.6% (35.1–53.9) and 85.5% (75.8–94.6). Overall, CCA was more sensitive than KK, regardless of the age group at diagnosis, as KK was more specific. KK and CCA have different diagnostic performance, which should inform their use in the planning and implementation of S. japonicum control programs.

Diagnostic performance of Mycoplasmopsis bovis antibody ELISA tests on bulk tank milk from dairy herds

BackgroundTesting of bulk tank milk (BTM) for Mycoplasmopsis bovis (previously Mycoplasma bovis) antibodies is increasingly popular. However the performance of some commercially available tests is unknown, and cutoff values possibly need to be adjusted in light of the purpose. Therefore, the aim of this study was to compare the diagnostic performance of three commercially available M. bovis antibody ELISAs on BTM, and to explore optimal cutoff values for screening purposes. A prospective diagnostic test accuracy study was performed on 156 BTM samples from Belgian and Swiss dairy farms using Bayesian Latent Class Analysis. Samples were initially classified using manufacturer cutoff values, followed by generated values.ResultsFollowing the manufacturer’s guidelines, sensitivity of 91.4%, 25.6%, 69.2%, and specificity of 67.2%, 96.8%, 85.8% were observed for ID-screen, Bio K432, and Bio K302, respectively. Optimization of cutoffs resulted in a sensitivity of 89.0%, 82.0%, and 85.5%, and a specificity of 83.4%, 75.1%, 77.2%, respectively.ConclusionsThe ID-screen showed the highest diagnostic performance after optimization of cutoff values, and could be useful for screening. Both Bio-X tests may be of value for diagnostic or confirmation purposes due to their high specificity.

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

BackgroundDiagnosis 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.MethodsWe included 659 individuals aged ≥16\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ge 16$$\\end{document} 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.ResultsParticipants 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.ConclusionOur diagnostic model shows reliable performance and can be developed as a decision assistant for clinicians to detect patients at high risk of TBM.SummaryDiagnosis 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.

Comparison of the Automated OvaCyte Telenostic Faecal Analyser versus the McMaster and Mini-FLOTAC Techniques in the Estimation of Helminth Faecal Egg Counts in Equine.

Gastrointestinal helminth parasites continue to be a significant threat to the health of equine. OvaCyte Telenostic (OCT) (Telenostic Ltd., Kilkenny, Ireland) has developed an automated digital microscope utilising Artificial Intelligence to identify and count the clinically important helminth species in equine, bovine, and ovine host species. In this paper, the performance of the OCT analyser was evaluated for the detection and counting of equine helminth species parasites and its performance compared to the currently accepted benchmark methods of faecal egg counts being the McMaster and the Mini-FLOTAC techniques. A pairwise comparison of tests was assessed based on the correlation of egg counts and Cohen's kappa agreement statistics for dichotomized outcomes. Bayesian latent class analysis was used to estimate the sensitivity and specificity of all three techniques in the absence of a gold standard for four helminth parasites (strongyles, Anoplocephala spp., Parascaris spp. and Strongyloides westeri). Based on the analysis of 783 equine faecal samples, we found a high level (ρ ≥ 0.94) of correlation between each pairwise comparison of techniques for strongyle egg counts. Cohen's kappa agreement between techniques was high for strongyles and S. westeri, moderate for Parascaris spp., and low for Anoplocephala spp. All three techniques had a high sensitivity and specificity (>0.90) for strongyles. Across helminth parasites, the sensitivity of the OCT was the highest of the three techniques evaluated for strongyles (0.98 v 0.96 and 0.94), Anoplocephala spp. (0.86 v 0.44 and 0.46) and Parascaris spp. (0.96 v 0.83 and 0.96); but lowest for S. westeri (0.74 v 0.88 and 0.88), compared to McMaster and Mini-FLOTAC, respectively. In terms of specificity, OCT was the lowest in two species (Parascaris spp. 0.96, Anoplocephala spp. 0.95). In conclusion, OCT has a sensitivity and specificity statistically similar to both McMaster and Mini-FLOTAC, and had a higher correlation with Mini-FLOTAC. The OCT point of care faecal analyser offers improved workflow, test turn-around time and does not require trained laboratory personnel to operate or interpret the results.

Diagnostic accuracy of qPCR and microscopy for cutaneous leishmaniasis in rural Ecuador: A Bayesian latent class analysis.

Clinical and laboratory diagnosis of cutaneous leishmaniasis (CL) is hampered by under-ascertainment of direct microscopy. This study compared the diagnostic accuracy of qPCR on DNA extracted from filter paper to the accuracy of direct smear slide microscopy in participants presenting with a cutaneous lesion suspected of leishmaniasis to 16 rural healthcare centers in the Ecuadorian Amazon and Pacific regions, from January 2019 to June 2021. We used Bayesian latent class analysis to estimate test sensitivity, specificity, likelihood ratios (LR), and predictive values (PV) with their 95% credible intervals (95%CrI). The impact of sociodemographic and clinical characteristics on predictive values was assessed as a secondary objective. Of 320 initially included participants, paired valid test results were available and included in the diagnostic accuracy analysis for 129 from the Amazon and 185 from the Pacific region. We estimated sensitivity of 68% (95%CrI 49% to 82%) and 73% (95%CrI 73% to 83%) for qPCR, and 51% (95%CrI 36% to 66%) and 76% (95%CrI 65% to 86%) for microscopy in the Amazon and Pacific region, respectively. In the Amazon, with an estimated disease prevalence among participants of 73%, negative PV for qPCR was 54% (95%CrI 5% to 77%) and 44% (95%CrI 4% to 65%) for microscopy. In the Pacific, (prevalence 88%) the negative PV was 34% (95%CrI 3% to 58%) and 37% (95%CrI 3% to 63%). The addition of qPCR parallel to microscopy in the Amazon increases the observed prevalence from 38% to 64% (+26 (95%CrI 19 to 34) percentage points). The accuracy of either qPCR on DNA extracted from filter paper or microscopy for CL diagnosis as a stand-alone test seems to be unsatisfactory and region-dependent. We recommend further studies to confirm the clinically relevant increment found in the diagnostic yield due to the addition of qPCR.

Modelling variation in test sensitivity for monitoring leptospirosis in beef cattle

When Bayesian latent class analysis is used for diagnostic test data in the absence of a gold standard test, it is common to assume that any unknown test sensitivities and specificities are constant across different populations. Indeed this assumption is often necessary for model identifiability. However there are a number of practical situations, depending on the type of test and the nature of the disease, where this assumption may not be true. We present a case study of using a microscopic agglutination test to diagnose leptospiroris infection in beef cattle, which strongly suggests that sensitivity in particular varies among herds. We develop and fit an alternative model in which sensitivity is related to within-herd prevalence, and discuss the statistical and epidemiological implications.

Two-phase Bayesian latent class analysis to assess diagnostic test performance in the absence of a gold standard: COVID-19 serological assays as a proof of concept.

In this proof-of-concept study, which included blood donor samples, we aimed to demonstrate how Bayesian latent class models (BLCMs) could be used to estimate SARS-CoV-2 seroprevalence in the absence of a gold standard assay under a two-phase sampling design. To this end, 6810 plasma samples from blood donors who resided in Québec (Canada) were collected from May to July 2020 and tested for anti-SARS-CoV-2 antibodies using seven serological assays (five commercial and two non-commercial). SARS-CoV-2 seroprevalence was estimated at 0.71% (95% credible interval [CrI] = 0.53%-0.92%). The cPass assay had the lowest sensitivity estimate (88.7%; 95% CrI = 80.6%-94.7%), while the Héma-Québec assay had the highest (98.7%; 95% CrI = 97.0%-99.6%). The estimated low seroprevalence (which indicates a relatively limited spread of SARS-CoV-2 in Quebec) might change rapidly-and this tool, developed using blood donors, could enable a rapid update of the prevalence estimate in the absence of a gold standard. Further, the present analysis illustrates how a two-stage BLCM sampling design, along with blood donor samples, can be used to estimate the performance of new diagnostic tests and inform public health decisions regarding a new or emerging disease for which a perfect reference standard does not exist.