The accuracy of race & ethnicity data in US based healthcare databases: A systematic review.

BACKGROUND: The availability and accuracy of data on a patient's race/ethnicity varies across databases. Discrepancies in data quality can negatively impact attempts to study health disparities. METHODS: We conducted a systematic review to organize information on the accuracy of race/ethnicity data stratified by database type and by specific race/ethnicity categories. RESULTS: The review included 43 studies. Disease registries showed consistently high levels of data completeness and accuracy. EHRs frequently showed incomplete and/or inaccurate data on the race/ethnicity of patients. Databases had high levels of accurate data for White and Black patients but relatively high levels of misclassification and incomplete data for Hispanic/Latinx patients. Asians, Pacific Islanders, and AI/ANs are the most misclassified. Systems-based interventions to increase self-reported data showed improvement in data quality. CONCLUSION: Data on race/ethnicity that is collected with the purpose of research and quality improvement appears most reliable. Data accuracy can vary by race/ethnicity status and better collection standards are needed.

EUAdb: A resource for COVID-19 test development and comparison.

Due to the sheer number of COVID-19 (coronavirus disease 2019) cases there is a need for increased world-wide SARS-CoV-2 testing capability that is both efficient and effective. Having open and easy access to detailed information about these tests, their sensitivity, the types of samples they use, etc. would be highly useful to ensure their reproducibility, to help clients compare and decide which tests would be best suited for their applications, and to avoid costs of reinventing similar or identical tests. Additionally, this resource would provide a means of comparing the many innovative diagnostic tools that are currently being developed in order to provide a foundation of technologies and methods for the rapid development and deployment of tests for future emerging diseases. Such a resource might thus help to avert the delays in testing and screening that was observed in the early stages of the pandemic and plausibly led to more COVID-19-related deaths than necessary. We aim to address these needs via a relational database containing standardized ontology and curated data about COVID-19 diagnostic tests that have been granted Emergency Use Authorizations (EUAs) by the FDA (US Food and Drug Administration). Simple queries of this actively growing database demonstrate considerable variation among these tests with respect to sensitivity (limits of detection, LoD), controls and targets used, criteria used for calling results, sample types, reagents and instruments, and quality and amount of information provided.

The SARS-CoV-2 pandemic: the race to trace: contact tracing scale-up in San Francisco-early lessons learned.

In order to effectively control spread of coronavirus 2019 (COVID-19), it is essential that jurisdictions have the capacity to rapidly trace close contacts of each and every case. Best practice guidance on how to implement such programs is urgently needed. We describe the early experience in the City and County of San Francisco (CCSF), where the City's Department of Health expanded contact tracing capability in anticipation of changes in San Francisco's 'shelter in place' order between April and June 2020. Important prerequisites to successful scale-up included a rapid expansion of the COVID-19 response workforce, expansion of testing capability, and other containment resources. San Francisco's scale-up offers a model for how other jurisdictions can rapidly mobilize a workforce. We underscore the importance of an efficient digital case management system, effective training, and expansion of supportive service programs for those in quarantine or isolation, and metrics to ensure continuous performance improvement.

Automated production of research data marts from a canonical fast healthcare interoperability resource data repository: applications to COVID-19 research.

OBJECTIVE: The rapidly evolving COVID-19 pandemic has created a need for timely data from the healthcare systems for research. To meet this need, several large new data consortia have been developed that require frequent updating and sharing of electronic health record (EHR) data in different common data models (CDMs) to create multi-institutional databases for research. Traditionally, each CDM has had a custom pipeline for extract, transform, and load operations for production and incremental updates of data feeds to the networks from raw EHR data. However, the demands of COVID-19 research for timely data are far higher, and the requirements for updating faster than previous collaborative research using national data networks have increased. New approaches need to be developed to address these demands. METHODS: In this article, we describe the use of the Fast Healthcare Interoperability Resource (FHIR) data model as a canonical data model and the automated transformation of clinical data to the Patient-Centered Outcomes Research Network (PCORnet) and Observational Medical Outcomes Partnership (OMOP) CDMs for data sharing and research collaboration on COVID-19. RESULTS: FHIR data resources could be transformed to operational PCORnet and OMOP CDMs with minimal production delays through a combination of real-time and postprocessing steps, leveraging the FHIR data subscription feature. CONCLUSIONS: The approach leverages evolving standards for the availability of EHR data developed to facilitate data exchange under the 21st Century Cures Act and could greatly enhance the availability of standardized datasets for research.

A Systematic Approach of Data Collection and Analysis in Medical Imaging Research.

BACKGROUND: Obtaining the right image dataset for the medical image research systematically is a tedious task. Anatomy segmentation is the key step before extracting the radiomic features from these images. OBJECTIVE: The purpose of the study was to segment the 3D colon from CT images and to measure the smaller polyps using image processing techniques. This require huge number of samples for statistical analysis. Our objective was to systematically classify and arrange the dataset based on the parameters of interest so that the empirical testing becomes easier in medical image research. MATERIALS AND METHODS: This paper discusses a systematic approach of data collection and analysis before using it for empirical testing. In this research the image were considered from National Cancer Institute (NCI). TCIA from NCI has a vast collection of diagnostic quality images for the research community. These datasets were classified before empirical testing of the research objectives. The images in the TCIA collection were acquired as per the standard protocol defined by the American College of Radiology. Patients in the age group of 50-80 years were involved in various clinical trials (multicenter). The dataset collection has more than 10 billion of DICOM images of various anatomies. In this study, the number of samples considered for empirical testing was 300 (n) acquired from both supine and prone positions. The datasets were classified based on the parameters of interest. The classified dataset makes the dataset selection easier during empirical testing. The images were validated for the data completeness as per the DICOM standard of the 2020b version. A case study of CT Colonography dataset is discussed. CONCLUSION: With this systematic approach of data collection and classification, analysis will be become more easier during empirical testing.
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Reporting and design of randomized controlled trials for COVID-19: A systematic review.

BACKGROUND: The novel coronavirus 2019 (COVID-19) pandemic has mobilized global research at an unprecedented scale. While challenges associated with the COVID-19 trial landscape have been discussed previously, no comprehensive reviews have been conducted to assess the reporting, design, and data sharing practices of randomized controlled trials (RCTs). PURPOSE: The purpose of this review was to gain insight into the current landscape of reporting, methodological design, and data sharing practices for COVID-19 RCTs. DATA SOURCES: We conducted three searches to identify registered clinical trials, peer-reviewed publications, and pre-print publications. STUDY SELECTION: After screening eight major trial registries and 7844 records, we identified 178 registered trials and 38 publications describing 35 trials, including 25 peer-reviewed publications and 13 pre-prints. DATA EXTRACTION: Trial ID, registry, location, population, intervention, control, study design, recruitment target, actual recruitment, outcomes, data sharing statement, and time of data sharing were extracted. DATA SYNTHESIS: Of 178 registered trials, 112 (62.92%) were in hospital settings, median planned recruitment was 100 participants (IQR: 60, 168), and the majority (n = 166, 93.26%) did not report results in their respective registries. Of 35 published trials, 31 (88.57%) were in hospital settings, median actual recruitment was 86 participants (IQR: 55.5, 218), 10 (28.57%) did not reach recruitment targets, and 27 trials (77.14%) reported plans to share data. CONCLUSIONS: The findings of our study highlight limitations in the design and reporting practices of COVID-19 RCTs and provide guidance towards more efficient reporting of trial results, greater diversity in patient settings, and more robust data sharing.

Developing a Community of Practice: Building the Research Data Management Librarian Academy.

The Research Data Management Librarian Academy (RDMLA) is a free, online global professional development program designed by librarians for librarians working in research-intensive environments. Developed through a unique partnership that includes a Library and Information Sciences academic program, research and health sciences libraries, and industry, the RDMLA's inception, development, and launch provide helpful insights into the creation of online professional development courses. The RDMLA team's experience building the course's curriculum with an instructional designer (ID) and evaluating the operation and usefulness of the course's content through usability testing provides valuable lessons learned for librarians constructing an online continuing education (CE) course.

Timely access to trial data in the context of a pandemic: the time is now.

OBJECTIVE: Clinical trial data sharing has the potential to accelerate scientific progress, answer new lines of scientific inquiry, support reproducibility and prevent redundancy. Vivli, a non-profit organisation, operates a global platform for sharing of individual participant-level trial data and associated documents. Sharing of these data collected from each trial participant enables combining of these data to drive new scientific insights or assess reproducibility-not possible with the aggregate or summary data tables historically made available. We report on our initial experience including key metrics, lessons learned and how we see our role in the data sharing ecosystem. We also describe how Vivli is addressing the needs of the COVID-19 challenge through a new dedicated portal that provides a direct search function for COVID-19 studies, availability for fast-tracked request review and data sharing. DATA SUMMARY: The Vivli platform was established in 2018 and has partnered with 28 diverse members from industry, academic institutions, government platforms and non-profit foundations. Currently, 5400 trials representing 3.6 million participants are shared on the platform. From July 2018 to September 2020, Vivli received 201 requests. To date, 106 of 201 requests received approval, 5 have been declined, 27 withdrew and 27 are in the revision stage. CONCLUSIONS: The pandemic has only magnified the necessity for data sharing. If most data are shared and in a manner that allows interoperability, then we have hope of moving towards a cohesive scientific understanding more quickly not only for COVID-19 but also for all diseases. Conversely, if only isolated pockets of data are shared then society loses the opportunity to close vital gaps in our understanding of this rapidly evolving epidemic. This current challenge serves to highlight the value of data sharing platforms-critical enablers that help researchers build on prior knowledge.

Determinants of improved data consistency across routine immunization data tools for health facilities in Kano State, Nigeria.

INTRODUCTION: in this study, determinants of improved data consistency for routine immunization information at health facilities was measured to identify associated factors. METHODS: between June and August 2015, 1055 HFs were visited across 44 Local Government Areas in Kano state. We assessed data consistency, frequency of supportive supervision visits, availability of trained staff and attendance to monthly LGA RI review meetings. We compared RI monthly summary forms (MSF) versus national health management information system summary form (NHMIS) and vaccine management form 1a (VM1a) versus HF vaccine utilization summary monthly summary (HFVUM) for consistency. Data consistency at HF was determined at <+10% between number of children reportedly immunized, and doses of vaccine opened using 3 antigens (BCG, Penta and Measles). Levels of discrepancy <10% were considered as good data consistency. Bivariate and multivariate analysis used to determine association. RESULTS: data Consistency was observed in 195 (18.5%) HFs between (MSF vs NHMIS) and 90 (8.5%) HFs between (VM1a vs HFVUM). Consistency between MSF vs NHMIS was associated with receiving one or more SS visits in the previous month (p=0.001), data collection tools availability (p=0.001), recent attendance to monthly LGA RI review meeting and availability of trained staff. Data consistency between VM1a form and the HF VU summary was associated with a recent documented SS visit (p=0.05) and availability of trained staff (p=0.05). CONCLUSION: low level of data consistency was observed in Kano. Enhanced SS visits and availability of trained staff are associated with improved data quality.

Por que a desagregação de dados é fundamental durante uma pandemia?

Desagregação de dados é a separação de informações compiladas em unidades menores para elucidar tendências e padrões subjacentes. Dados desagregados de alta qualidade, acessíveis, confiáveis, oportunos, abertos e confiáveis ​​são críticos para gerar informações valiosas para a tomada de decisões em tempo real.

Why Data Disaggregation is key during a pandemic?

Data Disaggregation is the separation of compiled information into smaller units to elucidate underlying trends and patterns. High quality, accessible, trusted, timely, open, and reliable disaggregated data is critical to generating valuable information for decision-making in real time.

¿Por qué es importante el desglose de datos durante una pandemia?

El desglose de datos se refiere a la separación de la información recabada en unidades más pequeñas para dilucidar las tendencias y los patrones subyacentes. Los datos desglosados de alta calidad, accesibles, fiables, oportunos, abiertos y fidedignos son fundamentales para generar información valiosa para la toma de decisiones en tiempo real.

COVID-19: orientación y herramienta para hacer encuestas / COVID-19: guidance and tool for conducting surveys

Este documento ofrece orientación para los Estados Miembros de la Región de Europa de la OMS que deseen realizar estudios sobre las apreciaciones comportamentales relacionadas con la COVID-19. El brote pandémico de la COVID-19 está generando una carga abrumadora para los sistemas y las autoridades de salud, que deben responder con intervenciones, políticas y mensajes eficaces y apropiados.Una respuesta antipandémica o una fase de transición mal gestionadas e inoportunas pueden menoscabar los logros obtenidos de manera colectiva. La pandemia y sus restricciones pueden haber afectado el bienestar físico y mental, la cohesión social y la estabilidad económica, así como la resiliencia y la confianza de los individuos y las comunidades.

Practical issues encountered while determining Minimal Clinically Important Difference in Patient-Reported Outcomes.

BACKGROUND: Using a real dataset, we highlighted several major methodological issues raised by the estimation of the Minimal Clinically Important Difference (MCID) of a Patient-Reported Outcomes instrument. We especially considered the management of missing data and the use of more than two times of measurement. While inappropriate missing data management and inappropriate use of multiple time points can lead to loss of precision and/or bias in MCID estimation, these issues are almost never dealt with and require cautious considerations in the context of MCID estimation. METHODS: We used the LIGALONGO study (French Randomized Controlled Trial). We estimated MCID on the SF-36 General Health score by comparing many methods (distribution or anchor-based). Different techniques for imputation of missing data were performed (simple and multiple imputations). We also consider all measurement occasions by longitudinal modeling, and the dependence of the score difference on baseline. RESULTS: Three hundred ninety-three patients were studied. With distribution-based methods, a great variability in MCID was observed (from 3 to 26 points for improvement). Only 0.2 SD and 1/3 SD distribution methods gave MCID values consistent with anchor-based methods (from 4 to 7 points for improvement). The choice of missing data imputation technique clearly had an impact on MCID estimates. Simple imputation by mean score seemed to lead to out-of-range estimate, but as missing not at random mechanism can be hypothesized, even multiple imputations techniques can have led to an slight underestimation of MCID. Using 3 measurement occasions for improvement led to an increase in precision but lowered estimates. CONCLUSION: This practical example illustrates the substantial impact of some methodological issues that are usually never dealt with for MCID estimation. Simulation studies are needed to investigate those issues. TRIAL REGISTRATION: NCT01240772 (ClinicalTrials.gov) registered on November 15, 2010.

Security analysis and secure channel-free certificateless searchable public key authenticated encryption for a cloud-based Internet of things.

With the rapid development of informatization, an increasing number of industries and organizations outsource their data to cloud servers, to avoid the cost of local data management and to share data. For example, industrial Internet of things systems and mobile healthcare systems rely on cloud computing's powerful data storage and processing capabilities to address the storage, provision, and maintenance of massive amounts of industrial and medical data. One of the major challenges facing cloud-based storage environments is how to ensure the confidentiality and security of outsourced sensitive data. To mitigate these issues, He et al. and Ma et al. have recently independently proposed two certificateless public key searchable encryption schemes. In this paper, we analyze the security of these two schemes and show that the reduction proof of He et al.'s CLPAEKS scheme is incorrect, and that Ma et al.'s CLPEKS scheme is not secure against keyword guessing attacks. We then propose a channel-free certificateless searchable public key authenticated encryption (dCLPAEKS) scheme and prove that it is secure against inside keyword guessing attacks under the enhanced security model. Compared with other certificateless public key searchable encryption schemes, this scheme has higher security and comparable efficiency.

Connecting Data to Value: An Operating Model for Healthcare Advanced Analytics.

Artificial intelligence offers the promise to revolutionize the way healthcare is delivered in the future. To capitalize on the value of advanced analytics and artificial intelligence, organizations must focus on building organizational capabilities. This report shares an operating model for insight and change in healthcare comprising six key components: analytics technology and operations, data governance, change and automation, advanced analytics and insights, analytics literacy and strategy and relationship management. The adoption of the proposed model will build core capabilities that will enable organizations to connect data to decision making and realize value from its investment in advanced analytics.

Burning Issues in Health Technology Assessment and Policy Making: What's Keeping Senior Health Technology Assessment Users and Producers up at Night?

A key working session, held as part of the Health Technology Assessment international (HTAi) Global Policy Forum meeting asks members to share "What's Keeping Me Up At Night." Members-senior thought leaders from health technology assessment (HTA) agencies, payer organizations, industry, and the HTAi Board-share without fear or favor the thorny issues related to HTA that are challenging them now or likely to do so in the near future. This article contains a reflection on the discussions at this session over the last 2 years and focuses on the recurrent and repeated themes: internal and external stakeholder involvement in HTA processes; globalization of HTA and the future of HTA (namely innovative technologies, tide of data and the "war for talent"). While the aim of these informal sessions is not to produce solutions, it reinforces the importance of developing a truly multi-stakeholder HTA community with working relationships built on mutual trust and long-standing engagement.

Evaluación del impacto de la apnea obstructiva del sueño (OSA) Gestión de la calidad de vida (QoL) en pacientes pediátricos: un estudio transversal / Assessing the Impact of Obstructive Sleep Apnea (OSA) Management on Quality of Life (QoL) in Pediatric Patients: A Cross-Sectional Study

Objective: The study aims to assess the impact of obstructive sleep apnea on quality of life in pediatric patients along with their management approaches. Methodology: The study has applied a cross-sectional design to recruit children, visiting ENT clinics with the suggestion of obstructive sleep apnea. History and OSA assessment were collected for these children from the hospital. Besides this, OSA-18 questionnaire was used to collect the data, translated into Arabic language for respondents' feasibility. Results: A total of 24 patients (40%) were able to cure with medical treatment, whereas 36 patients (60%) were provided with surgical treatment. There were significant association between medical management and parental smoking (p-value=0.011), OSA score (p-value-0.003) and the size of adenoid (p-value=0.008). A significant association was also shown between size of adenoid and severity of OSA18 (p-value=0.031). No significant difference was shown between parental smoking, allergic rhinitis and severity of OSA. Conclusion: Although the surgical management is still the main modality in treating OSA, medical treatment plays an important role especially in cases with mild OSA, small adenoids and indoor smoking.