Performed Data Quality Research Articles

Speed and accuracy investigations of neural network algorithms for ionospheric modelling at an equatorial region

Neural networks are very efficient tools for modeling, including ionospheric modeling. The training algorithm is important for achieving the optimum performance of the trained network. This research is therefore meant to evaluate and compare the performances of ten neural network training algorithms based on their prediction accuracies, and the duration/times taken by each of the algorithms to establish the optimum result. The neural networks were trained using electron density measurements by Radio Occultation (RO) technique from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) satellites. Data for the period 2006 through 2021 was used. The networks were trained using about 2.9 million data points collected from the Kano region, Nigeria (5-degree rectangular region around geographic: 12.00° N, 8.59° E) after performing data quality control. The training algorithms considered in the work include: Bayesian Regularization (BR); Broyden-Fletcher-Goldfarb-Shanno Quasi-Newton (BFG); Conjugate Gradient with Powell/Beale (CGB); Fletcher-Reeves Conjugate Gradient (CGF); Gradient descent with momentum and adaptive learning rate (GDX); Levenberg Marquardt (LM); One Step Secant (OSS); Polak-Ribiére Conjugate Gradient (CGP); Resilient Backpropagation (RP); and Scaled Conjugate Gradient (SCG). The results showed that the BR and the LM algorithms gave the best performances in minimizing the errors of prediction (the mean RMSEs are respectively 112 and 114 ×103 electrons/cm3), but the RP algorithm, which came third in terms of accuracy, was significantly faster than both the LM and BR algorithms. The worst-performing algorithm in terms of accuracy was the GDX algorithm, although it was the fastest algorithm. The BFG algorithm was the worst-performing algorithm in terms of a combination of speed and accuracy. The developed neural network model was validated using ionosonde electron density measurements obtained from Ilorin, Nigeria (geographic: 8.5° N, 4.5° E; geomagnetic: 1.8° S). A comparison of the neural network, the NeQuick, and the IRI model predictions relative to the ionosonde measurements indicate that the neural network model was the best-performing model; the NN model predictions minimized the mean absolute errors (MAEs) in ∼44% of 399 ionosonde profiles investigated, the IRI model did so in ∼32%, and the NeQuick did so in ∼24%. The MAEs of the NeQuick however exhibited the best (least) variance. In overall, the NN model gave the least (best) mean of the MAEs (∼73 × 103 cm−3), compared to ∼82 × 103 cm−3 given by both the NeQuick and the IRI models, further supporting the idea that neural networks are excellent for present-day ionospheric modeling.

Interventional analytics in skilled nursing facilities associated with reduced readmissions.

To assess differences in longitudinal profiles for 30-day risk-adjusted readmission rates in skilled nursing facilities (SNFs) associated with Penn Medicine's Lancaster General Hospital (LGH) that implemented an interventional analytics (IA) platform vs other LGH facilities lacking IA vs other SNFs in Pennsylvania vs facilities in all other states. Retrospective longitudinal analysis of CMS readmissions data from 2017 through 2022, and cross-sectional analysis using CMS quality metrics data. CMS SNF quality performance data were aggregated and compared with risk-adjusted readmissions by facility and time period. Each SNF was assigned to a cohort based on location, referral relationship with LGH, and whether it had implemented IA. Multivariable mixed effects modeling was used to compare readmissions by cohort, whereas quality measures from the fourth quarter of 2022 were compared descriptively. LGH profiles differed significantly from both state and national profiles, with LGH facilities leveraging IA demonstrating an even greater divergence. In the most recent 12 months ending in the fourth quarter of 2022, LGH SNFs with IA had estimated readmission rates that were 15.24, 12.30, and 13.06 percentage points lower than the LGH SNFs without IA, Pennsylvania, and national cohorts, respectively (all pairwise P < .0001). SNFs with IA also demonstrated superior CMS claims-based quality metric outcomes for the 12 months ending in the fourth quarter of 2022. SNFs implementing the studied IA platform demonstrated statistically and clinically significant superior risk-adjusted readmission rate profiles compared with peers nationally, statewide, and within the same SNF referral network (P < .0001). A more detailed study on the use of IA in this setting is warranted.

A Comprehensive Study of Surface Finishes for High Frequency/High Speed Applications

The introduction of 5G/6G created growing demand for faster rates of data transfer and operation at higher frequencies, pushing signals to travel towards the outer edges of conductors. Therefore, the surface finish applied over the copper circuitry is now gaining more attention. It has been previously shown that the low conductivity and magnetic properties of the Electroless Nickel (EN) layer negatively affect electrical signals as they travel along the conductor outer surfaces, leading to insertion losses. Subsequent studies show that reducing the EN thickness can offset some of the insertion losses observed, leading to a reduction in insertion losses. In more recent times “Nickel Free’ finishes, such as EPIG (Electroless Palladium - Immersion Gold, no EN) have been promoted as solutions to improving signal losses at higher frequency. Exactly how much high frequency performance these new “nickel-free” finishes by the user has not yet been fully quantified, and what are the considerations or trade-off’s for solderjoint reliability with smaller diameter solder spheres when assembled using these newer surface finishes? Comprehensive studies of signal loss, solderjoint reliability testing (ball shear, drop shock, electromigration) and other critical to quality performance data were undertaken for standard ENEPIG, Thinner EN ENEPIG’s, EPIG (no EN) as well Immersion Silver, OSP and a new Ag/Au surface finish. All surface finishes were compared across the performance requirement matrix and a Quality Function Deployment was constructed to produce a data driven tool to align surface finish performance against design needs, allowing for optimal surface finish selection for each application.

Quality measures for the care of pediatric patients with obstructive sleep apnea: 2023 update after measure maintenance.

Lloyd RM, Crawford T, Donald R, etal. Quality measures for the care of pediatric patients with obstructive sleep apnea: 2023 update after measure maintenance. J Clin Sleep Med. 2024;20(1):127-134.

An Integrated Framework for Data Quality Fusion in Embedded Sensor Systems.

The advancement of embedded sensor systems allowed the monitoring of complex processes based on connected devices. As more and more data are produced by these sensor systems, and as the data are used in increasingly vital areas of applications, it is of growing importance to also track the data quality of these systems. We propose a framework to fuse sensor data streams and associated data quality attributes into a single meaningful and interpretable value that represents the current underlying data quality. Based on the definition of data quality attributes and metrics to determine real-valued figures representing the quality of the attributes, the fusion algorithms are engineered. Methods based on maximum likelihood estimation (MLE) and fuzzy logic are used to perform data quality fusion by utilizing domain knowledge and sensor measurements. Two data sets are used to verify the proposed fusion framework. First, the methods are applied to a proprietary data set targeting sample rate inaccuracies of a micro-electro-mechanical system (MEMS) accelerometer and second, to the publicly available Intel Lab Data set. The algorithms are verified against their expected behavior based on data exploration and correlation analysis. We prove that both fusion approaches are capable of detecting data quality issues and providing an interpretable data quality indicator.

Quality control procedures and metrics for resting-state functional MRI.

The monitoring and assessment of data quality is an essential step in the acquisition and analysis of functional MRI (fMRI) data. Ideally data quality monitoring is performed while the data are being acquired and the subject is still in the MRI scanner so that any errors can be caught early and addressed. It is also important to perform data quality assessments at multiple points in the processing pipeline. This is particularly true when analyzing datasets with large numbers of subjects, coming from multiple investigators and/or institutions. These quality control procedures should monitor not only the quality of the original and processed data, but also the accuracy and consistency of acquisition parameters. Between-site differences in acquisition parameters can guide the choice of certain processing steps (e.g., resampling from oblique orientations, spatial smoothing). Various quality control metrics can determine what subjects to exclude from the group analyses, and can also guide additional processing steps that may be necessary. This paper describes a combination of qualitative and quantitative assessments to determine the quality of fMRI data. Processing is performed using the AFNI data analysis package. Qualitative assessments include visual inspection of the structural T1-weighted and fMRI echo-planar images, functional connectivity maps, functional connectivity strength, and temporal signal-to-noise maps concatenated from all subjects into a movie format. Quantitative metrics include the acquisition parameters, statistics about the level of subject motion, temporal signal-to-noise ratio, smoothness of the data, and the average functional connectivity strength. These measures are evaluated at different steps in the processing pipeline to catch gross abnormalities in the data, and to determine deviations in acquisition parameters, the alignment to template space, the level of head motion, and other sources of noise. We also evaluate the effect of different quantitative QC cutoffs, specifically the motion censoring threshold, and the impact of bandpass filtering. These qualitative and quantitative metrics can then provide information about what subjects to exclude and what subjects to examine more closely in the analysis of large datasets.

Consistency as a Data Quality Measure for German Corona Consensus Items Mapped from National Pandemic Cohort Network Data Collections.

As a national effort to better understand the current pandemic, three cohorts collect sociodemographic and clinical data from coronavirus disease 2019 (COVID-19) patients from different target populations within the German National Pandemic Cohort Network (NAPKON). Furthermore, the German Corona Consensus Dataset (GECCO) was introduced as a harmonized basic information model for COVID-19 patients in clinical routine. To compare the cohort data with other GECCO-based studies, data items are mapped to GECCO. As mapping from one information model to another is complex, an additional consistency evaluation of the mapped items is recommended to detect possible mapping issues or source data inconsistencies. The goal of this work is to assure high consistency of research data mapped to the GECCO data model. In particular, it aims at identifying contradictions within interdependent GECCO data items of the German national COVID-19 cohorts to allow investigation of possible reasons for identified contradictions. We furthermore aim at enabling other researchers to easily perform data quality evaluation on GECCO-based datasets and adapt to similar data models. All suitable data items from each of the three NAPKON cohorts are mapped to the GECCO items. A consistency assessment tool (dqGecco) is implemented, following the design of an existing quality assessment framework, retaining their-defined consistency taxonomies, including logical and empirical contradictions. Results of the assessment are verified independently on the primary data source. Our consistency assessment tool helped in correcting the mapping procedure and reveals remaining contradictory value combinations within COVID-19 symptoms, vital signs, and COVID-19 severity. Consistency rates differ between the different indicators and cohorts ranging from 95.84% up to 100%. An efficient and portable tool capable of discovering inconsistencies in the COVID-19 domain has been developed and applied to three different cohorts. As the GECCO dataset is employed in different platforms and studies, the tool can be directly applied there or adapted to similar information models.

Peculiarities of regulatory and legal regulation of notarial activities of consular institutions

This article discloses the specifics of the regulatory and legal regulation of the notarial activity of consular institutions. It was noted that ensuring the proper level of training and qualification of consular notaries. For this purpose, it is possible to carry out regular retraining and professional development of specialists, organize seminars and trainings. Attention is focused on the need to establish clear rules and procedures for notarial acts. Consular offices can create an internal document that contains clear instructions on how to perform different types of notarial acts. Ensuring the accessibility and convenience of the procedure for performing notarial acts. For this purpose, it is possible to consider the possibility of expanding the work schedule of consular institutions, making it possible to make an appointment for a visit, and introducing other tools to facilitate the procedure for performing notarial acts. It is necessary to establish a quality control system for the performance of notarial acts. To do this, you can create a feedback system with customers of services, conduct regular monitoring of the quality of service performance and study statistical data on the performance of notarial acts. The following steps can be proposed to expand the system of quality control of the performance of notarial acts by consular institutions of Ukraine: 1) to introduce quality standards for the performance of notarial acts, which provide for the identification of quality criteria that take into account the best European practices and legislation and, accordingly, to determine the evaluation and assessment of the performance of notarial acts, which will include a rating of customer satisfaction, analysis of feedback on the performance of services, monitoring of compliance with established standards and other parameters; 2) to develop a system of monitoring and reporting on the quality of the performance of notarial actions, which will provide for the collection, analysis and reporting of the results of the assessment of the quality of the performance of services; 3) to develop and implement a satisfaction survey system for clients who have already used the services of the consular office and conduct an analysis to improve the quality of services; 4) to introduce training courses once every 2 years for consular notaries and other employees on the quality of performance of notarial actions and compliance with standards; 5) conduct an audit of consular notarial services once a year, in order to ensure compliance with standards and best practices; 6) to establish responsibility for violation of norms defining the rules and procedure for providing consular services by consular institutions.

Electronic health record data quality variability across a multistate clinical research network.

Electronic health record (EHR) data have many quality problems that may affect the outcome of research results and decision support systems. Many methods have been used to evaluate EHR data quality. However, there has yet to be a consensus on the best practice. We used a rule-based approach to assess the variability of EHR data quality across multiple healthcare systems. To quantify data quality concerns across healthcare systems in a PCORnet Clinical Research Network, we used a previously tested rule-based framework tailored to the PCORnet Common Data Model to perform data quality assessment at 13 clinical sites across eight states. Results were compared with the current PCORnet data curation process to explore the differences between both methods. Additional analyses of testosterone therapy prescribing were used to explore clinical care variability and quality. The framework detected discrepancies across sites, revealing evident data quality variability between sites. The detailed requirements encoded the rules captured additional data errors with a specificity that aids in remediation of technical errors compared to the current PCORnet data curation process. Other rules designed to detect logical and clinical inconsistencies may also support clinical care variability and quality programs. Rule-based EHR data quality methods quantify significant discrepancies across all sites. Medication and laboratory sources are causes of data errors.

A review of exploratory factor analysis in tourism and hospitality research: Identifying current practices and avenues for improvement

We perform a literature review of exploratory factor analysis (EFA) in six premier tourism and hospitality journals to (1) identify best practices, (2) ensure that researchers apply these best practices, and (3) enable future authors to be more precise in their reporting of EFA. Our results identify that researchers (1) frequently obtain large sample sizes and perform data quality checks, (2) overwhelmingly prefer the principal component approach rather than the common factor approach, (3) are reliant on varimax and other orthogonal rotations, (4) utilize the Kaiser criterion rather than more supported approaches, (5) produce large primary and secondary loadings, and (6) have never identified concerning common method variance via Harman’s one-factor test. We urge researchers to utilize oblique rotations and supported factor retention approaches, and we recommend that researchers should place more attention on their secondary loadings. We lastly call for a moratorium on Harman’s one-factor test.

Extracting historical flood locations from news media data by the named entity recognition (NER) model to assess urban flood susceptibility

Flood susceptibility assessment for identifying flood-prone areas plays a significant role in flood hazard mitigation. Machine learning is an optional assessment method because of its high objectivity and computational efficiency, but how to get enough and accurate information of historical flood locations to train the machine learning models has been a key problem. In recent years, news media data from both news websites and social media accounts has emerged as a promising source for natural science studies. However, the application of news media data in urban flood susceptibility assessment is still inadequate. This study proposed an approach to fill this gap. Firstly, flood locations were extracted from news media data based on a named entity recognition (NER) model. Then, a frequency or distance-based data quality control method was employed to improve the representativeness of the extracted flooded locations. Finally, flood conditioning factors with information of historical flood locations were input into a Support Vector Machine (SVM) model for flood susceptibility assessment. We took the central city of Dalian, China as a case study. The T-test results show that there was no significant difference between the distributions of most flood conditioning factors at the flood locations from the news media data and the official planning report. In the obtained flood susceptibility map, the high flood susceptibility areas got a recall of 90% compared with the high flood hazard areas in the planning report. Performing data quality control in the frequency-based method can improve the precision of the flood susceptibility map by up to 5%, while the distance-based method is ineffective. This study provides an example and offers the value of applying new data sources and modern deep learning techniques for urban flood management.

The IeDEA harmonist data toolkit: A data quality and data sharing solution for a global HIV research consortium

We describe the design, implementation, and impact of a data harmonization, data quality checking, and dynamic report generation application in an international observational HIV research network. The IeDEA Harmonist Data Toolkit is a web-based application written in the open source programming language R, employs the R/Shiny and RMarkdown packages, and leverages the REDCap data collection platform for data model definition and user authentication. The Toolkit performs data quality checks on uploaded datasets, checks for conformance with the network’s common data model, displays the results both interactively and in downloadable reports, and stores approved datasets in secure cloud storage for retrieval by the requesting investigator. Including stakeholders and users in the design process was key to the successful adoption of the application. A survey of regional data managers as well as initial usage metrics indicate that the Toolkit saves time and results in improved data quality, with a 61% mean reduction in the number of error records in a dataset. The generalized application design allows the Toolkit to be easily adapted to other research networks.

The data analysis and validation engine: an application of artificial intelligence in the improvement of COVID-19 data management

Background: The proper management of healthcare data is fundamental to the health system processes; artificial intelligence has proven its value in these processes. Artificial intelligence can simplify the management of information, improve data security, and automate data flow. It is also useful in the analysis and interpretation of big data. Hence, it has the possibility of screening and diagnosing diseases, categorizing disease severity, detecting therapeutic agents, and forecasting outbreak spots.Methods: A data analysis and validation engine was developed to perform data quality control checks, classify addresses, and generate epidemiology numbers using the index and parse command on the command-line interface of DAVE.Results: DAVE correctly formatted data and created a local copy of the datastore and the index. It also returned previous EPID numbers to each entry and assigned a new EPID number to missed entries. DAVE imported the entries into the data template of the existing data management tool and generated a sample manifest that is then sent to the Laboratory. The data flow from the point of collection to storage and reporting was assessed as 100% accurate without errors and in real-time; there was also the ability to roll back if any error occurred.Conclusions: DAVE is a semi-autonomous system that operates with minimal human intervention; it is automatically faster as it leverages computing power to parse, store, and retrieve data while practically eliminating the need for manual data quality assessment. The DAVE functionality can be extended to incorporate additional features like forecasting outbreaks of emerging/re-emerging diseases, categorizing the severity of diseases and analysis of data in our setting.

0020 Investigation of Seasonal Changes in Self-Reported Sleep Quality and Psychomotor Vigilance Task Outcomes: Results From the Ultra Long-Term Sleep (ULTS) Study

Abstract Introduction Although sleep is fundamental for human well-being, factors that contribute to an individual’s experience and report of sleep quality remain poorly understood. Utilizing that sleepiness is known to impact vigilance performance, this study sets out to explore how self-reported sleep quality changes with behavioral performance and how this variation is affected by seasonal changes. Methods This work is an interim analysis of self-reported sleep quality and behavioral performance data collected in the Ultra Long-term Sleep (ULTS) study (ClinicalTrials.gov Identifier: NCT04513743). In the study 20 healthy participants (average 33±13 years of age) were enrolled for 365 continuous days to observe the seasonal variation in sleep and cognitive performance. The outcome from the daily psychomotor vigilance task (PVT) and sleep questionnaire is analyzed and reported. The sleep questionnaire is a composite of questions from the Sleep Satisfaction Tool, the Karolinska Sleep Diary, questions regarding feelings of pain and outside disturbances, easiness of waking up, and the Karolinska Sleepiness Scale (KSS). The PVT was designed for self-administered high-frequency testing and has a short 3-minute test period. Monthly changes were examined from June to November. This period was chosen since all subjects were active. Results The first eight participants have now completed the study. Repeated measures correlations between KSS and mean PVT reaction time showed moderate but highly robust associations between the two measures over time (r=0.2; 95% CI 0.18-0.23). From the data collected thus far from the entire population, fastest PVT mean reaction times were found on Saturdays and slowest on Tuesdays. Similarly, KSS had best scores in weekends. There was an overall increase in mean PVT reaction time during the investigated period from June to November. This was also observed in KSS. Conclusion Our findings show a moderate correlation between the mean PVT reaction time and Karolinska Sleepiness Scale with up to 365 datapoints per subject with weekly and seasonal trends observed. Support (If Any) This research is supported by Innovation Fund Denmark.

EzQTL: A Web Platform for Interactive Visualization and Colocalization of QTLs and GWAS Loci

Genome-wide association studies (GWAS) have identified thousands of genomic loci associated with complex diseases and traits, including cancer. The vast majority of common trait-associated variants identified via GWAS fall in non-coding regions of the genome, posing a challenge in elucidating the causal variants, genes, and mechanisms involved. Expression quantitative trait locus (eQTL) and other molecular QTL studies have been valuable resources in identifying candidate causal genes from GWAS loci through statistical colocalization methods. While QTL colocalization is becoming a standard analysis in post-GWAS investigation, an easy web tool for users to perform formal colocalization analyses with either user-provided or public GWAS and eQTL datasets has been lacking. Here, we present ezQTL, a web-based bioinformatic application to interactively visualize and analyze genetic association data such as GWAS loci and molecular QTLs under different linkage disequilibrium (LD) patterns (1000 Genomes Project, UK Biobank, or user-provided data). This application allows users to perform data quality control for variants matched between different datasets, LD visualization, and two-trait colocalization analyses using two state-of-the-art methodologies (eCAVIAR and HyPrColoc), including batch processing. ezQTL is a free and publicly available cross-platform web tool, which can be accessed online at https://analysistools.cancer.gov/ezqtl.

A newly reconciled dataset for identifying sea level rise and variability in Dublin Bay

Abstract. We provide an updated sea level dataset for Dublin for the period 1938–2016 at yearly resolution. Using a newly collated sea level record for Dublin Port, as well as two nearby tide gauges at Arklow and Howth Harbour, we perform data quality checks and calibration of the Dublin Port record by adjusting the biased high water level measurements that affect the overall calculation of mean sea level (MSL). To correct these MSL values, we use a novel Bayesian linear regression that includes the mean low water values as a predictor in the model. We validate the re-created MSL dataset and show its consistency with other nearby tide gauge datasets. Using our new corrected dataset, we estimate a rate of sea level rise of 1.1 mm yr−1 during 1953–2016 (95 % credible interval from 0.6 to 1.6 mm yr−1), and a rate of 7 mm yr−1 during 1997–2016 (95 % credible interval from 5 to 8.8 mm yr−1). The overall sea level rise is in line with expected trends, but large multidecadal variability has led to higher rates of rise in recent years.

Physicians’ Choice of Board Certification Activity Is Unaffected by Baseline Quality of Care: The TRADEMaRQ Study

Physicians' use of self-assessment to guide quality improvement or board certification activities often does not correlate with more objective measures, and they may spend valuable time on activities that support their strengths instead of addressing gaps. Our objective was to study whether viewing quality measures, with peer comparisons, would affect the selection of certification activities. We conducted a cluster-randomized controlled trial-the Trial of Data Exchange for Maintenance of certification and Raising Quality (TRADEMaRQ)-with 4 partner organizations during 2015-2017. Physicians were presented their quality data within their online certification portfolios before (intervention) vs after (control) they chose board certification activities. The primary outcome was whether the selected activity addressed a quality gap (a quality area in which the physician scored below the mean for the study population). Of 2,570 invited physicians, 254 physicians completed the study: 130 in the intervention group and 124 in the control group. Nearly one-fifth of participating physicians did not complete any certification activities during the study. A sizable minority of those in the intervention group, 18.4%, never reviewed their quality dashboard. Overall, just 27.2% of completed certification activities addressed a quality gap, and there was no significant difference in this outcome in the intervention group vs the control group in either bivariate or adjusted analyses (odds ratio = 1.28; 95% CI, 0.90-1.82). Physicians did not use quality performance data in choosing certification activities. Certification boards are being pressed to make their programs relevant to practice, less burdensome, and supportive of quality improvement in alignment with value-based payment models. Using practice data to drive certification choices would meet these goals.

Interference Management in Small Cell System

The rapid increase in quality performance and high data rate make the small cell an attractive solution in the next generation of a cellular mobile network. The Femtocell is a small cell that can boost services to four or five users in low price, and it can guarantee high data rate and capacity within the indoor residential. The Macrocell is a base station with high power that can cover up to 200 miles of cell radius. The propagative of Femtocell over macrocell can cause interference between them as they use the same spectrum. In our paper, first we explain interference management algorithms; such as power control algorithm, Fractional Frequency Reuse (FFR) algorithm and resource scheduling algorithm. Also, we propose an algorithm for interference management that combines the fixed power control algorithm, Dynamic Fractional Frequency Reuse (DFFR) algorithm, and resource scheduling algorithm to mitigate the interference. Simulation results show that the proposed algorithm improves the signal to interference plus noise ratio (SINR), throughput and reduces the interference from neighbor cells.

Trans-eQTLs of the CYP3A4 and CYP3A5 associated with tacrolimus trough blood concentration in Chinese renal transplant patients.

This study aimed to systematically investigate trans-eQTLs of CYP3A4 and CYP3A5 affecting tacrolimus trough blood concentrations in Chinese renal transplant patients. We used Plink v1.90 to perform data quality control and linear regression analysis on GTEx v8 data. SNPs with p-value <0.05 were selected and the GTEx eQTL Calculator was used to further prioritize the eQTLs of CYP3A4 and CYP3A5 in the liver and small intestine. The eQTLs with a p-value <5×10-5 and MAF≥0.05 in the CHB population were selected as candidate eQTLs. The genotyping of candidate eQTLs was performed using high-resolution melting (HRM) assays and Sanger DNA sequencing. This study included 845 Chinese renal transplant patients who received tacrolimus as an immunosuppressive agent. Association between 103 candidate eQTLs and log-transformed tacrolimus concentration/dose ratio (log (C0/D)) in this cohort was conducted using the SNPassoc package of R software. In the end, a total of 75,632 liver eQTLs of CYP3A4, 69,558 liver eQTLs of CYP3A5, 48,596 small intestine eQTLs of CYP3A4 and 28,616 small intestine eQTLs of CYP3A5 were obtained using the GTEx v8 eQTL Calculator. Of the 103 candidate eQTLs, rs75727207, rs181294422 and rs28522676 were significantly associated with tacrolimus log(C0/D) in different genetic models. We discovered a substantial number of novel eQTLs of CYP3A4 and CYP3A5 in liver and small intestine, also found that rs75727207, rs181294422 and rs28522676 may affect tacrolimus trough blood concentrations in Chinese renal transplant patients.

Identifying Data Quality Dimensions for Person-Generated Wearable Device Data: Multi-Method Study.

BackgroundThere is a growing interest in using person-generated wearable device data for biomedical research, but there are also concerns regarding the quality of data such as missing or incorrect data. This emphasizes the importance of assessing data quality before conducting research. In order to perform data quality assessments, it is essential to define what data quality means for person-generated wearable device data by identifying the data quality dimensions.ObjectiveThis study aims to identify data quality dimensions for person-generated wearable device data for research purposes.MethodsThis study was conducted in 3 phases: literature review, survey, and focus group discussion. The literature review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guideline to identify factors affecting data quality and its associated data quality challenges. In addition, we conducted a survey to confirm and complement results from the literature review and to understand researchers’ perceptions on data quality dimensions that were previously identified as dimensions for the secondary use of electronic health record (EHR) data. We sent the survey to researchers with experience in analyzing wearable device data. Focus group discussion sessions were conducted with domain experts to derive data quality dimensions for person-generated wearable device data. On the basis of the results from the literature review and survey, a facilitator proposed potential data quality dimensions relevant to person-generated wearable device data, and the domain experts accepted or rejected the suggested dimensions.ResultsIn total, 19 studies were included in the literature review, and 3 major themes emerged: device- and technical-related, user-related, and data governance–related factors. The associated data quality problems were incomplete data, incorrect data, and heterogeneous data. A total of 20 respondents answered the survey. The major data quality challenges faced by researchers were completeness, accuracy, and plausibility. The importance ratings on data quality dimensions in an existing framework showed that the dimensions for secondary use of EHR data are applicable to person-generated wearable device data. There were 3 focus group sessions with domain experts in data quality and wearable device research. The experts concluded that intrinsic data quality features, such as conformance, completeness, and plausibility, and contextual and fitness-for-use data quality features, such as completeness (breadth and density) and temporal data granularity, are important data quality dimensions for assessing person-generated wearable device data for research purposes.ConclusionsIn this study, intrinsic and contextual and fitness-for-use data quality dimensions for person-generated wearable device data were identified. The dimensions were adapted from data quality terminologies and frameworks for the secondary use of EHR data with a few modifications. Further research on how data quality can be assessed with respect to each dimension is needed.