Missing patient registrations in the Dutch National Trauma Registry of Southwest Netherlands: Prevalence and epidemiology

INTRODUCTION

Health care patient records have been digitalised the past twenty years, and registries have been automated. Missing registrations are common, and can result in selection bias.

OBJECTIVE

To assess the prevalence and characteristics of missed registrations in a Dutch regional trauma registry.

METHODS

An automatically generated trauma registry export was done for ten out of eleven hospitals in trauma region Southwest Netherlands, between June 1 and August 31, 2020. Second, lists were checked for being falsely flagged as 'non-trauma'. Finally, a list was generated with trauma tick box flagged as 'trauma' but were not automatically in the export due to administrative errors. Automated and missed registration datasets were compared on patient characteristics and logistic regression models were run with random intercepts and missed registration as outcome variable on the complete dataset.

RESULTS

A total of 2,230 automated registrations and 175 (7.3 %) missed registrations were included for the Dutch National Trauma Registry, ranging from 1 to 14 % between participating hospitals. Patients of the missed registration dataset had characteristics of a higher level of care, compared with patients of automated registrations. Level of trauma care (level II OR 0.464 95 % CI 0.328-0.666, p < 0.001; level III OR 0.179 95 % CI 0.092-0.325, p < 0.001), major trauma (OR 2.928 95 % CI 1.792-4.65, p < 0.001), ICU admission (OR 2.337 95 % CI 1.792-4.650, p < 0.001), and surgery (OR 1.871 95 % CI 1.371-2.570, p < 0.001) were potential predictors for missed registrations in multivariate logistic regression analysis.

CONCLUSION

Missed registrations occur frequently and the rate of missed registrations differs greatly between hospitals. Automated and missed registration datasets display differences related to patients requiring more intensive care, which held for the major trauma subset. Checking for missed registrations is time consuming, automated registration lists need a human touch for validation and to be complete.

Creating a Bot-tleneck for malicious AI: Psychological methods for bot detection

The standard approach for detecting and preventing bots from doing harm online involves CAPTCHAs. However, recent AI research, including our own in this manuscript, suggests that bots can complete many common CAPTCHAs with ease. The most effective methodology for identifying potential bots involves completing image-processing, causal-reasoning based, free-response questions that are hand coded by human analysts. However, this approach is labor intensive, slow, and inefficient. Moreover, with the advent of Generative AI such as GPT and Bard, it may soon be obsolete. Here, we develop and test various automated, bot-screening questions, grounded in psychological research, to serve as a proactive screen against bots. Utilizing hand coded free-response questions in the naturalistic domain of MTurkers recruited for a Qualtrics survey, we identify 18.9% of our sample to be potential bots, whereas Google's reCAPTCHA V3 identified only 1.7% to be potential bots. We then look at the performance of these potential bots on our novel bot-screeners, each of which has different strengths and weaknesses but all of which outperform CAPTCHAs.

Design of 2D/2D β-Ni(OH)2/ZnO heterostructures via photocatalytic deposition of nickel for sonophotocatalytic degradation of tetracycline and modeling with three supervised machine learning algorithms

Due to the expansive use of tetracycline antibiotics (TCs) to treat various infectious diseases in humans and animals, their presence in the environment has created many challenges for human societies. Therefore, providing green and cost-effective solutions for their effective removal has become an urgent need. Here, we will introduce 2D/2D p-n heterostructures that exhibit excellent sonophotocatalytic/photocatalytic properties for water-soluble pollutant removal. In this contribution, for the first time, β- Ni(OH)2 nanosheets were synthesized through visible-light-induced photodeposition of different amounts of nickel on ZnO nanosheets (β-Ni(x)/ZNs) to fabricate 2D/2D p-n heterostructures. The PXRD patterns confirmed the formation of wurtzite phase for ZNs and the hexagonal crystal structure of β-Ni(OH)2. The FESEM and TEM micrographs showed that the β-Ni(OH)2 sheets were dispersed on the surface of ZNs and formed 2D/2D p-n heterojunction in β-Ni(x)/ZNs samples. With the photodeposition of β-Ni(OH)2 nanosheets on ZNs, the surface area, pore volume, and pore diameter of β-Ni(x)/ZNs heterostructures have increased compared to ZNs, which can have a positive effect on the sonophotocatalytic/photocatalytic performance of ZNs. The degradation experiments showed that β-Ni(0.1)/ZNs and β-Ni(0.4)/ZNs have the highest degradation percentage in photocatalytic (51 %) and sonophotocatalytic (71 %) degradation of TC, respectively. Finally, the sonophotocatalytic/photocatalytic degradation process of TC was systematically validated through modeling with three powerful and supervised machine learning algorithms, including Support Vector Regression (SVR), Artificial Neural Networks (ANNs), and Stochastic Gradient Boosting (SGB). Five statistical criteria including R2, SAE, MSE, SSE, and RMSE were calculated for model validation. It was observed that the developed SGB algorithm was the most reliable model for predicting the degradation percent of TC. The results revealed that using fabricated 2D/2D p-n heterojunctions (β-Ni(x)/ZNs) is more sustainable than the conventional ZnO photocatalytic systems in practical applications.

Validating pertussis data measures using electronic medical record data in Ontario, Canada 1986-2016

Background

Pertussis is a reportable disease in many countries, but ascertainment bias has limited data accuracy. This study aims to validate pertussis data measures using a reference standard that incorporates different suspected case severities, allowing for the impact of case severity on accuracy and detection to be explored.

Methods

We evaluated 25 pertussis detection algorithms in a primary care electronic medical record database between January 1, 1986 and December 30, 2016. We estimated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). We used sensitivity analyses to explore areas of uncertainty and evaluated reasons for lack of detection.

Results

The algorithm including all data measures achieved the highest sensitivity at 20.6%. Sensitivity increased to 100% after reclassifying symptom-only cases as non-cases, but the PPV remained low. Age at first episode was significantly associated with detection in half of the tested scenarios, and false negatives often had some history of immunization.

Conclusions

Sensitivity improved by reclassifying symptom-only cases but remained low unless multiple data sources were used. Results demonstrate a trade-off between PPV and sensitivity. EMRs can enhance detection through patient history and clinical note data. It is essential to improve case identification of older individuals with vaccination history to reduce ascertainment bias.

On thresholds for controlling negative particle (PM2.5) readings in air quality reporting

Ambient PM2.5 (particles less than 2.5 μm in diameter) is monitored in many countries including Australia. Occasionally PM2.5 instruments may report negative measurements, although in realty the ambient air can never contain negative amounts of particles. Some negative readings are caused by instrument faults or procedural errors, thus can be simply invalidated from air quality reporting. There are occasions, however, when negative readings occur due to other factors including technological or procedural limitations. Treatment of such negative data requires consideration of factors such as measurement uncertainty, instrument noise and risk for significant bias in air quality reporting. There is very limited documentation on handling negative PM2.5 data in the literature. This paper demonstrates how a threshold is determined for controlling negative hourly PM2.5 readings in the New South Wales (NSW) air quality data system. The investigation involved a review of thresholds used in different data systems and an assessment of instrument measurement uncertainties, zero air test data and impacts on key reporting statistics when applying different thresholds to historical datasets. The results show that a threshold of -10.0 μg/m3 appears optimal for controlling negative PM2.5 data in public reporting. This choice is consistent with the measurement uncertainty estimates and the zero air test data statistics calculated for the NSW Air Quality Monitoring Network, and is expected not to have significant impacts on key compliance reporting statistics such as data availability and annual average pollution levels. The analysis can be useful for air quality monitoring in other Australian jurisdictions or wider context.

Data quality validation of the Spanish Incisional Hernia Surgery Registry (EVEREG): pilot study

PURPOSE

The Spanish Incisional Hernia Surgery Registry (EVEREG) was promoted by the Abdominal Wall Section of the Spanish Association of Surgeons, starting data collection in July 2012 and currently has more than 14,000 cases. The objective of this study was to validate the data collected through a pilot audit process.

METHODS

A sample of hospitals participating in the EVEREG registry since the beginning was selected. Patients registered in these centers in the 2012-2020 period were included. A stratified random sampling was carried out, with the inclusion of 10% of registered cases per center with a minimum of 20 cases per center. At each participating center, two researchers not belonging to the center undergoing the audit checked (on site or telematically) the concordance between the data in the registry and the data contained in the case history of each patient.

RESULTS

330 patients have been analyzed, out of a total of 2673 registered, in 9 participating centers. The average accuracy has been 95.7%. Incorrect data 1.5% and missing data 2.3% CONCLUSION: The group of pilot hospitals from this EVERG incisional hernia surgery registry shows a very high precision of 95.7%. The confirmation of these findings in all the centers participating in the registry will make it possible to guarantee the quality of the studies made and their comparability with other similar national registries.

TRIAL REGISTRATION

nnTrial registration number: ClinicalTrials.gov ID:NCT03899012.

A multipurpose TNM stage ontology for cancer registries

Background

Population-based cancer registries are a critical reference source for the surveillance and control of cancer. Cancer registries work extensively with the internationally recognised TNM classification system used to stage solid tumours, but the system is complex and compounded by the different TNM editions in concurrent use. TNM ontologies exist but the design requirements are different for the needs of the clinical and cancer-registry domains. Two TNM ontologies developed specifically for cancer registries were designed for different purposes and have limitations for serving wider application. A unified ontology is proposed to serve the various cancer registry TNM-related tasks and reduce the multiplication effects of different ontologies serving specific tasks. The ontology is comprehensive of the rules for TNM edition 7 as required by cancer registries and designed on a modular basis to allow extension to other TNM editions.

Results

A unified ontology was developed building on the experience and design of the existing ontologies. It follows a modular approach allowing plug in of components dependent upon any particular TNM edition. A Java front-end was developed to interface with the ontology via the Web Ontology Language application programme interface and enables batch validation or classification of cancer registry records. The programme also allows the means of automated error correction in some instances. Initial tests verified the design concept by correctly inferring TNM stage and successfully handling the TNM-related validation checks on a number of cancer case records, with a performance similar to that of an existing ontology dedicated to the task.

Conclusions

The unified ontology provides a multi-purpose tool for TNM-related tasks in a cancer registry and is scalable for different editions of TNM. It offers a convenient way of quickly checking validity of cancer case stage information and for batch processing of multi-record data via a dedicated front-end programme. The ontology is adaptable to many uses, either as a standalone TNM module or as a component in applications of wider focus. It provides a first step towards a single, unified TNM ontology for cancer registries.

Data parsing in mass spectrometry imaging using R Studio and Cardinal: A tutorial

Mass spectrometry imaging (MSI) has emerged as a rapidly expanding field in the MS community. The analysis of large spectral data is further complicated by the added spatial dimension of MSI. A plethora of resources exist for expert users to begin parsing MSI data in R, but there is a critical lack of guidance for absolute beginners. This tutorial is designed to serve as a one-stop guide to start using R with MSI data and describe the possibilities that data science can bring to MSI analysis.

Experimental Design for Time-Series RNA-Seq Analysis of Gene Expression and Alternative Splicing

RNA-sequencing (RNA-seq) is currently the method of choice for analysis of differential gene expression. To fully exploit the wealth of data generated from genome-wide transcriptomic approaches, the initial design of the experiment is of paramount importance. Biological rhythms in nature are pervasive and are driven by endogenous gene networks collectively known as circadian clocks. Measuring circadian gene expression requires time-course experiments which take into account time-of-day factors influencing variability in expression levels. We describe here an approach for characterizing diurnal changes in expression and alternative splicing for plants undergoing cooling. The method uses inexpensive everyday laboratory equipment and utilizes an RNA-seq application (3D RNA-seq) that can handle complex experimental designs and requires little or no prior bioinformatics expertise.

Anti-clustering in the national SARS-CoV-2 daily infection counts

The noise in daily infection counts of an epidemic should be super-Poissonian due to intrinsic epidemiological and administrative clustering. Here, we use this clustering to classify the official national SARS-CoV-2 daily infection counts and check for infection counts that are unusually anti-clustered. We adopt a one-parameter model of ϕ i ' infections per cluster, dividing any daily count n i into n i / ϕ i ' 'clusters', for 'country' i. We assume thatn i / ϕ i ' on a given day j is drawn from a Poisson distribution whose mean is robustly estimated from the four neighbouring days, and calculate the inferred Poisson probability P i j ' of the observation. The P i j ' values should be uniformly distributed. We find the value ϕ i that minimises the Kolmogorov-Smirnov distance from a uniform distribution. We investigate the (ϕ i , N i ) distribution, for total infection count N i . We consider consecutive count sequences above a threshold of 50 daily infections. We find that most of the daily infection count sequences are inconsistent with a Poissonian model. Most are found to be consistent with the ϕ i model. The 28-, 14- and 7-day least noisy sequences for several countries are best modelled as sub-Poissonian, suggesting a distinct epidemiological family. The 28-day least noisy sequence of Algeria has a preferred model that is strongly sub-Poissonian, with ϕ i 28 < 0.1 . Tajikistan, Turkey, Russia, Belarus, Albania, United Arab Emirates and Nicaragua have preferred models that are also sub-Poissonian, with ϕ i 28 < 0.5 . A statistically significant (P τ < 0.05) correlation was found between the lack of media freedom in a country, as represented by a high Reporters sans frontieres Press Freedom Index (PFI2020), and the lack of statistical noise in the country's daily counts. The ϕ i model appears to be an effective detector of suspiciously low statistical noise in the national SARS-CoV-2 daily infection counts.

Implementing an automated monitoring process in a digital, longitudinal observational cohort study

Background

Clinical data collection requires correct and complete data sets in order to perform correct statistical analysis and draw valid conclusions. While in randomized clinical trials much effort concentrates on data monitoring, this is rarely the case in observational studies- due to high numbers of cases and often-restricted resources. We have developed a valid and cost-effective monitoring tool, which can substantially contribute to an increased data quality in observational research.

Methods

An automated digital monitoring system for cohort studies developed by the German Rheumatism Research Centre (DRFZ) was tested within the disease register RABBIT-SpA, a longitudinal observational study including patients with axial spondyloarthritis and psoriatic arthritis. Physicians and patients complete electronic case report forms (eCRF) twice a year for up to 10 years. Automatic plausibility checks were implemented to verify all data after entry into the eCRF. To identify conflicts that cannot be found by this approach, all possible conflicts were compiled into a catalog. This “conflict catalog” was used to create queries, which are displayed as part of the eCRF. The proportion of queried eCRFs and responses were analyzed by descriptive methods. For the analysis of responses, the type of conflict was assigned to either a single conflict only (affecting individual items) or a conflict that required the entire eCRF to be queried.

Results

Data from 1883 patients was analyzed. A total of n = 3145 eCRFs submitted between baseline (T0) and T3 (12 months) had conflicts (40–64%). Fifty-six to 100% of the queries regarding eCRFs that were completely missing were answered. A mean of 1.4 to 2.4 single conflicts occurred per eCRF, of which 59–69% were answered. The most common missing values were CRP, ESR, Schober’s test, data on systemic glucocorticoid therapy, and presence of enthesitis.

Conclusion

Providing high data quality in large observational cohort studies is a major challenge, which requires careful monitoring. An automated monitoring process was successfully implemented and well accepted by the study centers. Two thirds of the queries were answered with new data. While conventional manual monitoring is resource-intensive and may itself create new sources of errors, automated processes are a convenient way to augment data quality.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13075-021-02563-2.

Experimental data on water vapour adsorption on silica gel in fully packed and Z-annulus packed beds

Experimental data on water vapour adsorption on silica gel in four packed bed configurations namely fully packed bed (FPB), large annulus packed bed (LAPB), medium annulus packed bed (MAPB) and small annulus packed bed (SAPB) are presented. Raw temperature data from designated mass transfer zones (MTZ) in the packed beds and on their corresponding walls are presented along with data of the inlet and outlet moist air conditions. Pressure transducers installed at the inlet and outlet provided pressure data. The presented data also covers the material properties of the silica gel for adsorption obtained through material testing and analysis in the laboratory. With detailed experimental methodology and comprehensive material and water vapour adsorption data, this article can help other researchers to validate and verify the performance of their adsorption systems. The material property data presented can also help investigators to use appropriate experimentally determined property values of silica gel in their theoretical studies. Furthermore, this data can serve as a basis of comparison for heat and mass transfer in other experimental adsorption systems.

An ontology-based approach for developing a harmonised data-validation tool for European cancer registration

Background

Population-based cancer registries constitute an important information source in cancer epidemiology. Studies collating and comparing data across regional and national boundaries have proved important for deploying and evaluating effective cancer-control strategies. A critical aspect in correctly comparing cancer indicators across regional and national boundaries lies in ensuring a good and harmonised level of data quality, which is a primary motivator for a centralised collection of pseudonymised data. The recent introduction of the European Union’s general data-protection regulation (GDPR) imposes stricter conditions on the collection, processing, and sharing of personal data. It also considers pseudonymised data as personal data. The new regulation motivates the need to find solutions that allow a continuation of the smooth processes leading to harmonised European cancer-registry data. One element in this regard would be the availability of a data-validation software tool based on a formalised depiction of the harmonised data-validation rules, allowing an eventual devolution of the data-validation process to the local level.

Results

A semantic data model was derived from the data-validation rules for harmonising cancer-data variables at European level. The data model was encapsulated in an ontology developed using the Web-Ontology Language (OWL) with the data-model entities forming the main OWL classes. The data-validation rules were added as axioms in the ontology. The reasoning function of the resulting ontology demonstrated its ability to trap registry-coding errors and in some instances to be able to correct errors.

Conclusions

Describing the European cancer-registry core data set in terms of an OWL ontology affords a tool based on a formalised set of axioms for validating a cancer-registry’s data set according to harmonised, supra-national rules. The fact that the data checks are inherently linked to the data model would lead to less maintenance overheads and also allow automatic versioning synchronisation, important for distributed data-quality checking processes.

Comment on "A global-scale ecological niche model to predict SARS-CoV-2 coronavirus infection rate", author Coro

In this letter we present comments on the article “A global-scale ecological niche model to predict SARS-CoV-2 coronavirus” by Coro published in 2020.

Validating a transnational fracture treatment registry using a standardized method

AIM

Subsequent to a three-month pilot phase, recruiting patients for the newly established BFCC (Baltic Fracture Competence Centre) transnational fracture registry, a validation of the data quality needed to be carried out, applying a standardized method.

METHOD

During the literature research, the method of "adaptive monitoring" fulfilled the requirements of the registry and was applied. It consisted of a three-step audit process; firstly, scoring of the overall data quality, followed by source data verification of a sample size, relative to the scoring result, and finally, feedback to the registry on measures to improve data quality. Statistical methods for scoring of data quality and visualisation of discrepancies between registry data and source data were developed and applied.

RESULTS

Initially, the data quality of the registry scored as medium. During source data verification, missing items in the registry, causing medium data quality, turned out to be absent in the source as well. A subsequent adaptation of the score evaluated the registry's data quality as good. It was suggested to add variables to some items in order to improve the accuracy of the registry.

DISCUSSION

The application of the method of adaptive monitoring has only been published by Jacke et al., with a similar improvement of the scoring result following the audit process. Displaying data from the registry in graphs helped to find missing items and discover issues with data formats. Graphically comparing the degree of agreement between the registry and source data allowed to discover systematic faults.

CONCLUSIONS

The method of adaptive monitoring gives a substantiated guideline for systematically evaluating and monitoring a registry's data quality and is currently second to none. The resulting transparency of the registry's data quality could be helpful in annual reports, as published by most major registries. As the method has been rarely applied, further successive applications in established registries would be desirable.

Wrangling environmental exposure data: guidance for getting the best information from your laboratory measurements

BACKGROUND

Environmental health and exposure researchers can improve the quality and interpretation of their chemical measurement data, avoid spurious results, and improve analytical protocols for new chemicals by closely examining lab and field quality control (QC) data. Reporting QC data along with chemical measurements in biological and environmental samples allows readers to evaluate data quality and appropriate uses of the data (e.g., for comparison to other exposure studies, association with health outcomes, use in regulatory decision-making). However many studies do not adequately describe or interpret QC assessments in publications, leaving readers uncertain about the level of confidence in the reported data. One potential barrier to both QC implementation and reporting is that guidance on how to integrate and interpret QC assessments is often fragmented and difficult to find, with no centralized repository or summary. In addition, existing documents are typically written for regulatory scientists rather than environmental health researchers, who may have little or no experience in analytical chemistry.

OBJECTIVES

We discuss approaches for implementing quality assurance/quality control (QA/QC) in environmental exposure measurement projects and describe our process for interpreting QC results and drawing conclusions about data validity.

DISCUSSION

Our methods build upon existing guidance and years of practical experience collecting exposure data and analyzing it in collaboration with contract and university laboratories, as well as the Centers for Disease Control and Prevention. With real examples from our data, we demonstrate problems that would not have come to light had we not engaged with our QC data and incorporated field QC samples in our study design. Our approach focuses on descriptive analyses and data visualizations that have been compatible with diverse exposure studies with sample sizes ranging from tens to hundreds of samples. Future work could incorporate additional statistically grounded methods for larger datasets with more QC samples.

CONCLUSIONS

This guidance, along with example table shells, graphics, and some sample R code, provides a useful set of tools for getting the best information from valuable environmental exposure datasets and enabling valid comparison and synthesis of exposure data across studies.

Reproducibility and Crossplatform Validation of Reverse-Phase Protein Array Data

Reverse-phase protein array (RPPA) technology is a high-throughput antibody- and microarray-based approach for the rapid profiling of levels of proteins and protein posttranslational modifications in biological specimens. The technology consumes small amounts of samples, can sensitively detect low-abundance proteins and posttranslational modifications, enables measurements of multiple signaling pathways in parallel, has the capacity to analyze large sample numbers, and offers robust interexperimental reproducibility. These features of RPPA experiments have motivated and enabled the use of RPPA technology in various biomedical, translational, and clinical applications, including the delineation of molecular mechanisms of disease, profiling of druggable signaling pathway activation, and search for new prognostic markers. Owing to the complexity of many of these applications, such as developing multiplex protein assays for diagnostic laboratories or integrating posttranslational modification-level data using large-scale proteogenomic approaches, robust and well-validated data are essential. There are many distinct components of an RPPA workflow, and numerous possible technical setups and analysis parameter options exist. The differences between RPPA platform setups around the world offer opportunities to assess and minimize interplatform variation. Crossplatform validation may also aid in the evaluation of robust, platform-independent protein markers of disease and response to therapy.

Experimental design for evaluating WWTP data by linear mass balances

A stepwise experimental design procedure to obtain reliable data from wastewater treatment plants (WWTPs) was developed. The proposed procedure aims at determining sets of additional measurements (besides available ones) that guarantee the identifiability of key process variables, which means that their value can be calculated from other, measured variables, based on available constraints in the form of linear mass balances. Among all solutions, i.e. all possible sets of additional measurements allowing the identifiability of all key process variables, the optimal solutions were found taking into account two objectives, namely the accuracy of the identified key variables and the cost of additional measurements. The results of this multi-objective optimization problem were represented in a Pareto-optimal front. The presented procedure was applied to a full-scale WWTP. Detailed analysis of the relation between measurements allowed the determination of groups of overlapping mass balances. Adding measured variables could only serve in identifying key variables that appear in the same group of mass balances. Besides, the application of the experimental design procedure to these individual groups significantly reduced the computational effort in evaluating available measurements and planning additional monitoring campaigns. The proposed procedure is straightforward and can be applied to other WWTPs with or without prior data collection.

Aggregation, Validation, and Generalization of Qualitative Data - Methodological and Practical Research Strategies Illustrated by the Research Process of an empirically Based Typology

The article deals with the question of how aggregated data which allow for generalizable insights can be generated from single-case based qualitative investigations. Thereby, two central challenges of qualitative social research are outlined: First, researchers must ensure that the single-case data can be aggregated and condensed so that new collective structures can be detected. Second, they must apply methods and practices to allow for the generalization of the results beyond the specific study. In the following, we demonstrate how and under what conditions these challenges can be addressed in research practice. To this end, the research process of the construction of an empirically based typology is described. A qualitative study, conducted within the framework of the Luxembourg Youth Report, is used to illustrate this process. Specifically, strategies are presented which increase the likelihood of generalizability or transferability of the results, while also highlighting their limitations.

Enhanced data validation strategy of air quality monitoring network

Quick validation and detection of faults in measured air quality data is a crucial step towards achieving the objectives of air quality networks. Therefore, the objectives of this paper are threefold: (i) to develop a modeling technique that can be used to predict the normal behavior of air quality variables and help provide accurate reference for monitoring purposes; (ii) to develop fault detection method that can effectively and quickly detect any anomalies in measured air quality data. For this purpose, a new fault detection method that is based on the combination of generalized likelihood ratio test (GLRT) and exponentially weighted moving average (EWMA) will be developed. GLRT is a well-known statistical fault detection method that relies on maximizing the detection probability for a given false alarm rate. In this paper, we propose to develop GLRT-based EWMA fault detection method that will be able to detect the changes in the values of certain air quality variables; (iii) to develop fault isolation and identification method that allows defining the fault source(s) in order to properly apply appropriate corrective actions. In this paper, reconstruction approach that is based on Midpoint-Radii Principal Component Analysis (MRPCA) model will be developed to handle the types of data and models associated with air quality monitoring networks. All air quality modeling, fault detection, fault isolation and reconstruction methods developed in this paper will be validated using real air quality data (such as particulate matter, ozone, nitrogen and carbon oxides measurement).

Assessing the validity of commercial and municipal food environment data sets in Vancouver, Canada

OBJECTIVE

The present study assessed systematic bias and the effects of data set error on the validity of food environment measures in two municipal and two commercial secondary data sets.

DESIGN

Sensitivity, positive predictive value (PPV) and concordance were calculated by comparing two municipal and two commercial secondary data sets with ground-truthed data collected within 800 m buffers surrounding twenty-six schools. Logistic regression examined associations of sensitivity and PPV with commercial density and neighbourhood socio-economic deprivation. Kendall's τ estimated correlations between density and proximity of food outlets near schools constructed with secondary data sets v. ground-truthed data.

SETTING

Vancouver, Canada.

SUBJECTS

Food retailers located within 800 m of twenty-six schools RESULTS: All data sets scored relatively poorly across validity measures, although, overall, municipal data sets had higher levels of validity than did commercial data sets. Food outlets were more likely to be missing from municipal health inspections lists and commercial data sets in neighbourhoods with higher commercial density. Still, both proximity and density measures constructed from all secondary data sets were highly correlated (Kendall's τ>0·70) with measures constructed from ground-truthed data.

CONCLUSIONS

Despite relatively low levels of validity in all secondary data sets examined, food environment measures constructed from secondary data sets remained highly correlated with ground-truthed data. Findings suggest that secondary data sets can be used to measure the food environment, although estimates should be treated with caution in areas with high commercial density.

Validation of multisource electronic health record data: an application to blood transfusion data

Background

Although data from electronic health records (EHR) are often used for research purposes, systematic validation of these data prior to their use is not standard practice. Existing validation frameworks discuss validity concepts without translating these into practical implementation steps or addressing the potential influence of linking multiple sources. Therefore we developed a practical approach for validating routinely collected data from multiple sources and to apply it to a blood transfusion data warehouse to evaluate the usability in practice.

Methods

The approach consists of identifying existing validation frameworks for EHR data or linked data, selecting validity concepts from these frameworks and establishing quantifiable validity outcomes for each concept. The approach distinguishes external validation concepts (e.g. concordance with external reports, previous literature and expert feedback) and internal consistency concepts which use expected associations within the dataset itself (e.g. completeness, uniformity and plausibility). In an example case, the selected concepts were applied to a transfusion dataset and specified in more detail.

Results

Application of the approach to a transfusion dataset resulted in a structured overview of data validity aspects. This allowed improvement of these aspects through further processing of the data and in some cases adjustment of the data extraction. For example, the proportion of transfused products that could not be linked to the corresponding issued products initially was 2.2% but could be improved by adjusting data extraction criteria to 0.17%.

Conclusions

This stepwise approach for validating linked multisource data provides a basis for evaluating data quality and enhancing interpretation. When the process of data validation is adopted more broadly, this contributes to increased transparency and greater reliability of research based on routinely collected electronic health records.

Electronic supplementary material

The online version of this article (doi:10.1186/s12911-017-0504-7) contains supplementary material, which is available to authorized users.

Validating Safecast data by comparisons to a U. S. Department of Energy Fukushima Prefecture aerial survey

Safecast is a volunteered geographic information (VGI) project where the lay public uses hand-held sensors to collect radiation measurements that are then made freely available under the Creative Commons CC0 license. However, Safecast data fidelity is uncertain given the sensor kits are hand assembled with various levels of technical proficiency, and the sensors may not be properly deployed. Our objective was to validate Safecast data by comparing Safecast data with authoritative data collected by the U. S. Department of Energy (DOE) and the U. S. National Nuclear Security Administration (NNSA) gathered in the Fukushima Prefecture shortly after the Daiichi nuclear power plant catastrophe. We found that the two data sets were highly correlated, though the DOE/NNSA observations were generally higher than the Safecast measurements. We concluded that this high correlation alone makes Safecast a viable data source for detecting and monitoring radiation.

Mass Spectral Library Quality Assurance by Inter-Library Comparison

A method to discover and correct errors in mass spectral libraries is described. Comparing across a set of highly curated reference libraries compounds that have the same chemical structure quickly identifies entries that are outliers. In cases where three or more entries for the same compound are compared, the outlier as determined by visual inspection was almost always found to contain the error. These errors were either in the spectrum itself or in the chemical descriptors that accompanied it. The method is demonstrated on finding errors in compounds of forensic interest in the NIST/EPA/NIH Mass Spectral Library. The target list of compounds checked was the Scientific Working Group for the Analysis of Seized Drugs (SWGDRUG) mass spectral library. Some examples of errors found are described. A checklist of errors that curators should look for when performing inter-library comparisons is provided. Graphical Abstract ᅟ.

Keemei: cloud-based validation of tabular bioinformatics file formats in Google Sheets

BACKGROUND

Bioinformatics software often requires human-generated tabular text files as input and has specific requirements for how those data are formatted. Users frequently manage these data in spreadsheet programs, which is convenient for researchers who are compiling the requisite information because the spreadsheet programs can easily be used on different platforms including laptops and tablets, and because they provide a familiar interface. It is increasingly common for many different researchers to be involved in compiling these data, including study coordinators, clinicians, lab technicians and bioinformaticians. As a result, many research groups are shifting toward using cloud-based spreadsheet programs, such as Google Sheets, which support the concurrent editing of a single spreadsheet by different users working on different platforms. Most of the researchers who enter data are not familiar with the formatting requirements of the bioinformatics programs that will be used, so validating and correcting file formats is often a bottleneck prior to beginning bioinformatics analysis.

MAIN TEXT

We present Keemei, a Google Sheets Add-on, for validating tabular files used in bioinformatics analyses. Keemei is available free of charge from Google's Chrome Web Store. Keemei can be installed and run on any web browser supported by Google Sheets. Keemei currently supports the validation of two widely used tabular bioinformatics formats, the Quantitative Insights into Microbial Ecology (QIIME) sample metadata mapping file format and the Spatially Referenced Genetic Data (SRGD) format, but is designed to easily support the addition of others.

CONCLUSIONS

Keemei will save researchers time and frustration by providing a convenient interface for tabular bioinformatics file format validation. By allowing everyone involved with data entry for a project to easily validate their data, it will reduce the validation and formatting bottlenecks that are commonly encountered when human-generated data files are first used with a bioinformatics system. Simplifying the validation of essential tabular data files, such as sample metadata, will reduce common errors and thereby improve the quality and reliability of research outcomes.

Lessons learned from initial reporting of carbapenem-resistant Enterobacteriaceae in New York State hospitals, 2013-2014

BACKGROUND

Carbapenem-resistant Enterobacteriaceae (CRE) are an urgent concern in health care in the United States because of high attributable mortality and versatile resistance mechanisms. CRE reporting was mandated in New York State (NYS) hospitals in July 2013.

METHODS

Infection preventionists from the NYS Department of Health audited hospital-reported CRE data by comparing laboratory records with cases reported to the National Healthcare Safety Network (NHSN). Information regarding microbiology laboratory testing methodologies was obtained through a survey in October 2013.

RESULTS

There were 1,151 CRE laboratory reports audited, with 13.6% determined not to have been reported to the NHSN when they should have been and 4.6% determined to be reported in error. There were a variety of errors, including lapses in surveillance and misinterpretation of the surveillance definition.

CONCLUSION

Educational initiatives that include microbiology laboratory staff, improvements in the use of laboratory information systems to communicate with infection prevention, and updated NHSN definitions should improve the accuracy and consistency of CRE reporting in NYS.

Structural redundancy of data from wastewater treatment systems. Determination of individual balance equations

Although data reconciliation is intensely applied in process engineering, almost none of its powerful methods are employed for validation of operational data from wastewater treatment plants. This is partly due to some prerequisites that are difficult to meet including steady state, known variances of process variables and absence of gross errors. However, an algorithm can be derived from the classical approaches to data reconciliation that allows to find a comprehensive set of equations describing redundancy in the data when measured and unmeasured variables (flows and concentrations) are defined. This is a precondition for methods of data validation based on individual mass balances such as CUSUM charts. The procedure can also be applied to verify the necessity of existing or additional measurements with respect to the improvement of the data's redundancy. Results are given for a large wastewater treatment plant. The introduction aims at establishing a link between methods known from data reconciliation in process engineering and their application in wastewater treatment.

Validation of the surveillance and reporting of central line-associated bloodstream infection denominator data

BACKGROUND

While the main focus of validating central line-associated infections (CLABIs) has been applying strict definitions to identify cases, assessing the denominator counts has received less attention. This study evaluates the accuracy of the reporting of CLABSI denominator patient-day (PD) and central line-day (CLD) counts to the National Healthcare Safety Network (NHSN) system in one state.

METHODS

The Connecticut Department of Public Health (CT DPH) performed a blinded retrospective chart review on the collection of CLABSI PD and CLD on 9 selected days during the fourth quarter of 2009 from 23 acute care hospitals.

RESULTS

Overall, 1,988 intensive care unit patient charts were reviewed. Comparison of hospital and CT DPH counts identified over-reporting by 300 PD (17.2%) and 200 CLD (21.7%) with 17 hospitals (74%) collecting data manually. PD manual collection methods were more accurate than electronic methods (P < .01). For CLD, there was no significant difference in collection method (P > .05). Wednesday PD counts were more accurate than Monday (P < .05) or Saturday (P < .05). For CLD counts, there was no significant difference among the 3 days (P > .05).

CONCLUSION

Our results provide some evidence for the prerequisite internal validation of denominator data by hospitals before reporting to the national surveillance system.

Assessment of the quality of publicly reported central line-associated bloodstream infection data in Colorado, 2010

BACKGROUND

Validation of self-reported health care-associated infection data is essential to verify correct understanding of definition criteria, surveillance practices, and reporting integrity. Recent studies have found significant under-reporting of central line-associated bloodstream infections (CLABSI) leading Colorado Department of Public Health and Environment to examine the quality of Colorado's CLABSI data.

METHODS

Trained Colorado Department of Public Health and Environment staff members performed onsite validation visits that included interviews with infection preventionists to assess surveillance practices and retrospective chart reviews of patients with positive blood cultures in specific intensive care units (adult and neonatal) and long-term acute care hospitals during the first quarter of 2010.

RESULTS

Fifty-five CLABSIs from the original sample were identified; 33 (60%) in the adult intensive care unit, 7 (12.7%) in the neonatal intensive care unit, and 15 (27.3%) in the long-term acute care hospital. Of the 55 CLABSIs identified by reviewers, 18 (32.7%) were not reported by the hospitals, 37 CLABSIs (67.3%) were reported correctly into the National Healthcare Safety Network, and 1 CLABSI was over-reported.

CONCLUSIONS

There was wide variation noted in surveillance practices as well as in application of definition criteria. With 33% under-reported cases, it was concluded that ongoing validation of health care-associated infection data is necessary.