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Lot Quality Assurance Sampling (LQAS) plans are widely used for health monitoring purposes. We propose a systematic approach to design multiple-objective LQAS plans that meet user-specified type 1 and 2 error rates and targets for selected diagnostic accuracy metrics. These metrics may include sensitivity, specificity, positive predictive value, and negative predictive value in high or low anticipated prevalence rate populations. We use Mixed Integer Nonlinear Programming (MINLP) tools to implement our design methodology. Our approach is flexible in that it can directly generate classic LQAS plans that control error rates only and find optimal LQAS plans that meet multiple objectives in terms of diagnostic metrics. We give examples, compare results with the classic LQAS and provide an application using a malaria outcome indicator survey in Mozambique.

Objective: To translate and psychometrically validate the center for epidemiological studies for depression scale (CES-DC) for Pakistani children. Study Design: Cross-sectional study. Place and Duration of Study: Various schools in Rawalpindi, from Feb to Mar 2021. Methodology: Present study comprised of translation, cross-language validation and investigation of psychometric properties of the scale into Urdu Language. Urdu translated version and strength and difficulty questionnaire were administered on 154 primary school children from various private schools situated in Rawalpindi district. Cronbach’s alpha reliability, item-total correlation and Pearson’s correlation coefficient were used for analysis. Results: Results revealed that this scale had good internal consistency and reliability (Urdu-version α=0.73; English-version α = 0.65) along with correlation coefficients over a period of 15 days (r=0.96). Cronbach’s alpha value of scales was found to be fairly high (r=0.78 and r=0.81). Item-total correlation of translated scale was also satisfactory (ranged from 0.04-0.58) and Pearson’s correlation of translated version with strength and difficulty questionnaire indicated good convergent validity (r= 0.54; p<0.01). Conclusion: Results supported the feasibility, reliability and validity of the Urdu version of this scale to screen depression among Urdu speaking pediatric Pakistani population.

Background: The global obesity epidemic is a major public health concern, and accurate diagnosis is essential for identifying at-risk individuals. Three-dimensional (3D) body scanning technology offers several advantages over the standard practice of tape measurements for diagnosing obesity. This study was conducted to validate body scan data from a German population-based cohort and explore clinical implications of this technology in the context of metabolic syndrome. Methods: We performed a cross-sectional analysis of 354 participants from the Study of Health in Pomerania that completed a 3D body scanning examination. The agreement of anthropometric data obtained from 3D body scanning with manual tape measurements was analyzed using correlation analysis and Bland–Altman plots. Classification agreement regarding abdominal obesity based on IDF guidelines was assessed using Cohen’s kappa. The association of body scan measures with metabolic syndrome components was explored using correlation analysis. Results: Three-dimensional body scanning showed excellent validity with slightly larger values that presumably reflect the true circumferences more accurately. Metabolic syndrome was highly prevalent in the sample (31%) and showed strong associations with central obesity. Using body scan vs. tape measurements of waist circumference for classification resulted in a 16% relative increase in the prevalence of abdominal obesity (61.3% vs. 52.8%). Conclusions: These results suggest that the prevalence of obesity may be underestimated using the standard method of tape measurements, highlighting the need for more accurate approaches.

Background The awareness of autism spectrum condition (ASC) and the estimated prevalence rate are lower in the context of the Chinese mainland, compared to western countries. The Chinese adaptation of the Autism Diagnostic Observation Schedule – Generic (ADOS-G) has been widely used in the diagnostic process of ASC for many years despite its psychometric properties not having been established in a large clinical sample. Thus, the purpose of this study was to evaluate the metrics of the ADOS-G in a well characterized sample from a renowned child developmental-behavioural centre that provides clinical services to a nationwide reach of people in China. Methods The present study analysed a large retrospective record review of individuals who visited the centre’s outpatient clinic between January of 2018 and August of 2021. Participants in this study were divided into ASC ( n  = 2330) and non-ASC ( n  = 473) groups according to their clinical diagnosis. All participants completed the ADOS-G Chinese version, with 1333 administered Module 1, 867 Module 2 and 603 Module 3. Psychometric properties of the ADOS-G Chinese version were evaluated in terms of item characteristics, internal consistency, concurrent validity, and convergent validity. Results The ADOS-G Chinese version demonstrated different levels of internal consistency reliability for different domains, with low Cronbach alpha values for Communication domains (0.40-0.60), but higher for the Communication and Social Interaction combined domains (above 0.75). Evaluation of validity yielded good sensitivity (> 90%) and positive predictive values (> 0.85), but low specificity (0.15-0.60) and negative predictive values (around 0.60). Agreement between ADOS-G diagnosis and clinician’s diagnosis was fair (Cohen’s kappa ranging from 0.20 to 0.35 for different modules), but low between the ADOS-G and ADI-R (< 0.15). Almost all items of the ADOS-G differentiated between ASC and non-ASC. Limitations Sample sizes were unevenly distributed between ADOS-G Modules 1 to 3, with Module 1 having the largest sample and none for Module 4. Retest reliability could not be assessed with the retrospective chart data. Conclusions This study is the first to examine the ADOS-G Chinese adapted version in a large clinical sample in the Chinese mainland. The ADOS-G contributes valuable information to support the clinical diagnosis of ASC, though additional research into its psychometric properties and cultural adaptation in the Chinese mainland context may be necessary. This study highlights the importance of cultural validation and the considerations required when interpreting ADOS results in non-Western settings.

The Upper Vistula River basin was divided into pooling groups with similar dimensionless frequency distributions of annual maximum river discharge. The cluster analysis and the Hosking and Wallis (HW) L-moment-based method were used to divide the set of 52 mid-sized catchments into disjoint clusters with similar morphometric, land use, and rainfall variables, and to test the homogeneity within clusters. Finally, three and four pooling groups were obtained alternatively. Two methods for identification of the regional distribution function were used, the HW method and the method of Kjeldsen and Prosdocimi based on a bivariate extension of the HW measure. Subsequently, the flood quantile estimates were calculated using the index flood method. The ordinary least squares (OLS) and the generalised least squares (GLS) regression techniques were used to relate the index flood to catchment characteristics. Predictive performance of the regression scheme for the southern part of the Upper Vistula River basin was improved by using GLS instead of OLS. The results of the study can be recommended for the estimation of flood quantiles at ungauged sites, in flood risk mapping applications, and in engineering hydrology to help design flood protection structures.

Thin-Plate Spline Generalized Linear Models (TPS-GLMs) are an extension of Semiparametric Generalized Linear Models (SGLMs), because they allow a smoothing spline to be extended to two or more dimensions. This class of models allows modeling a set of data in which it is desired to incorporate the non-linear joint effects of some covariates to explain the variability of a certain variable of interest. In the spatial context, these models are quite useful, since they allow the effects of locations to be included, both in trend and dispersion, using a smooth surface. In this work, we extend the local influence technique for the TPS-GLM model in order to evaluate the sensitivity of the maximum penalized likelihood estimators against small perturbations in the model and data. We fit our model through a joint iterative process based on Fisher Scoring and weighted backfitting algorithms. In addition, we obtained the normal curvature for the case-weight perturbation and response variable additive perturbation schemes, in order to detect influential observations on the model fit. Finally, two data sets from different areas (agronomy and environment) were used to illustrate the methodology proposed here.

Emergency department triage systems can be considered prediction rules with an ordinal outcome, where different directions of misclassification have different clinical consequences. We evaluated strategies to compare the performance of triage systems and aimed to propose a set of performance measures that should be used in future studies. We identified performance measures based on literature review and expert knowledge. Their properties are illustrated in a case study evaluating two triage modifications in a cohort of 14,485 pediatric emergency department visits. Strengths and weaknesses of the performance measures were systematically appraised. Commonly reported performance measures are measures of statistical association (34/60 studies) and diagnostic accuracy (17/60 studies). The case study illustrates that none of the performance measures fulfills all criteria for triage evaluation. Decision curves are the performance measures with the most attractive features but require dichotomization. In addition, paired diagnostic accuracy measures can be recommended for dichotomized analysis, and the triage-weighted kappa and Nagelkerke's R2 for ordinal analyses. Other performance measures provide limited additional information. When comparing modifications of triage systems, decision curves and diagnostic accuracy measures should be used in a dichotomized analysis, and the triage-weighted kappa and Nagelkerke's R2 in an ordinal approach.

The development of automated driving will profit from an agreed-upon methodology to evaluate human–machine interfaces. The present study examines the role of feedback on interaction performance provided directly to participants when interacting with driving automation (i.e., perceived ease of use). In addition, the development of ratings itself over time and use case specificity were examined. In a driving simulator study, N = 55 participants completed several transitions between Society of Automotive Engineers (SAE) level 0, level 2, and level 3 automated driving. One half of the participants received feedback on their interaction performance immediately after each use case, while the other half did not. As expected, the results revealed that participants judged the interactions to become easier over time. However, a use case specificity was present, as transitions to L0 did not show effects over time. The role of feedback also depended on the respective use case. We observed more conservative evaluations when feedback was provided than when it was not. The present study supports the application of perceived ease of use as a diagnostic measure in interaction with automated driving. Evaluations of interfaces can benefit from supporting feedback to obtain more conservative results.

Background Measures of diagnostic test accuracy provide evidence of how well a test correctly identifies or rules-out disease. Commonly used diagnostic accuracy measures (DAMs) include sensitivity and specificity, predictive values, likelihood ratios, area under the receiver operator characteristic curve (AUROC), area under precision-recall curves (AUPRC), diagnostic effectiveness (accuracy), disease prevalence, and diagnostic odds ratio (DOR) etc. Most available analysis tools perform accuracy testing for a single diagnostic test using summarized data. We developed a SAS macro for evaluating multiple diagnostic tests using individual-level data that creates a 2 × 2 summary table, AUROC and AUPRC as part of output. Methods The SAS macro presented here is automated to reduce analysis time and transcription errors. It is simple to use as the user only needs to specify the input dataset, “standard” and “test” variables and threshold values. It creates a publication-quality output in Microsoft Word and Excel showing more than 15 different accuracy measures together with overlaid AUROC and AUPRC graphics to help the researcher in making decisions to adopt or reject diagnostic tests. Further, it provides for additional variance estimation methods other than the normal distribution approximation. Results We tested the macro for quality control purposes by reproducing results from published work on evaluation of multiple types of dried blood spots (DBS) as an alternative for human immunodeficiency virus (HIV) viral load (VL) monitoring in resource-limited settings compared to plasma, the gold-standard. Plasma viral load reagents are costly, and blood must be prepared in a reference laboratory setting by a qualified technician. On the other hand, DBS are easy to prepare without these restrictions. This study evaluated the suitability of DBS from venous, microcapillary and direct spotting DBS, hence multiple diagnostic tests which were compared to plasma specimen. We also used the macro to reproduce results of published work on evaluating performance of multiple classification machine learning algorithms for predicting coronary artery disease. Conclusion The SAS macro presented here is a powerful analytic tool for analyzing data from multiple diagnostic tests. The SAS programmer can modify the source code to include other diagnostic measures and variance estimation methods. By automating analysis, the macro adds value by saving analysis time, reducing transcription errors, and producing publication-quality outputs.