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1.
BMC Public Health ; 22(1): 716, 2022 Apr 11.
Article in English | MEDLINE | ID: covidwho-1785149

ABSTRACT

BACKGROUND: The COVID-19 epidemic has differentially impacted communities across England, with regional variation in rates of confirmed cases, hospitalisations and deaths. Measurement of this burden changed substantially over the first months, as surveillance was expanded to accommodate the escalating epidemic. Laboratory confirmation was initially restricted to clinical need ("pillar 1") before expanding to community-wide symptomatics ("pillar 2"). This study aimed to ascertain whether inconsistent measurement of case data resulting from varying testing coverage could be reconciled by drawing inference from COVID-19-related deaths. METHODS: We fit a Bayesian spatio-temporal model to weekly COVID-19-related deaths per local authority (LTLA) throughout the first wave (1 January 2020-30 June 2020), adjusting for the local epidemic timing and the age, deprivation and ethnic composition of its population. We combined predictions from this model with case data under community-wide, symptomatic testing and infection prevalence estimates from the ONS infection survey, to infer the likely trajectory of infections implied by the deaths in each LTLA. RESULTS: A model including temporally- and spatially-correlated random effects was found to best accommodate the observed variation in COVID-19-related deaths, after accounting for local population characteristics. Predicted case counts under community-wide symptomatic testing suggest a total of 275,000-420,000 cases over the first wave - a median of over 100,000 additional to the total confirmed in practice under varying testing coverage. This translates to a peak incidence of around 200,000 total infections per week across England. The extent to which estimated total infections are reflected in confirmed case counts was found to vary substantially across LTLAs, ranging from 7% in Leicester to 96% in Gloucester with a median of 23%. CONCLUSIONS: Limitations in testing capacity biased the observed trajectory of COVID-19 infections throughout the first wave. Basing inference on COVID-19-related mortality and higher-coverage testing later in the time period, we could explore the extent of this bias more explicitly. Evidence points towards substantial under-representation of initial growth and peak magnitude of infections nationally, to which different parts of the country contribute unequally.


Subject(s)
COVID-19 , Bayes Theorem , COVID-19/epidemiology , Cost of Illness , Humans , Information Storage and Retrieval , SARS-CoV-2
2.
Ciênc. Saúde Colet ; 25(supl.1): 2479-2486, Mar. 2020. graf
Article in Portuguese | WHO COVID, LILACS (Americas) | ID: covidwho-1725052

ABSTRACT

Resumo O presente ensaio busca discutir as implicações do isolamento social devido à pandemia do COVID-19 para o uso intensivo da internet entre crianças e adolescentes e suas possíveis consequências para a prática de violências autoinflingidas. Discutimos brevemente o potencial ansiogênico e a reprodução de um "medo global" que se consolidam com a exposição maciça e sem mediação dos conteúdos consumidos, que podem aumentar as vulnerabilidades para estresse e ideações suicidas. Centramos nosso debate sobre práticas "recreativas", denominadas de "desafios" com poder autolesivo, realizados por adolescentes no site Youtube. Essa prática revelou-se crescente a partir das medidas de isolamento social. Nossa reflexão sobre esses riscos é feita a partir da perspectiva teórica da sociabilidade digital, e suas implicações nas interações de adolescentes mediadas pela internet.


Abstract This essay aimed to discuss the implications of social isolation due to the COVID-19 pandemic for the intensive use of the internet among children and adolescents and its possible consequences for the practice of self-inflicted violence. We briefly discussed the anxiogenic potential and the reproduction of a "global fear" that are consolidated with the massive and unmediated exposure of the content consumed, which can increase the vulnerabilities to stress and suicidal ideas. We centered our debate on "recreational" practices, called "challenges" with self-harm power, carried out by teenagers on the YouTube website. This practice has been shown to increase with the social isolation measures. Our reflection on these risks builds on the theoretical perspective of digital sociability, and its implications for the internet-mediated interactions of adolescents.


Subject(s)
Humans , Child , Adolescent , Pneumonia, Viral/psychology , Pneumonia, Viral/epidemiology , Social Isolation/psychology , Self-Injurious Behavior/psychology , Internet/statistics & numerical data , Pandemics , Betacoronavirus , Anxiety/psychology , Self Concept , Stress, Psychological/etiology , Time Factors , Information Storage and Retrieval/statistics & numerical data , Behavior, Addictive , Coronavirus Infections , Coronavirus Infections/psychology , Coronavirus Infections/epidemiology , Fear , Social Media/statistics & numerical data
3.
BMJ Open ; 12(2): e055562, 2022 02 14.
Article in English | MEDLINE | ID: covidwho-1691306

ABSTRACT

OBJECTIVE: To investigate macro-scale estimators of the variations in COVID-19 cases and deaths among countries. DESIGN: Epidemiological study. SETTING: Country-based data from publicly available online databases of international organisations. PARTICIPANTS: The study involved 170 countries/territories, each of which had complete COVID-19 and tuberculosis data, as well as specific health-related estimators (obesity, hypertension, diabetes and hypercholesterolaemia). PRIMARY AND SECONDARY OUTCOME MEASURES: The worldwide heterogeneity of the total number of COVID-19 cases and deaths per million on 31 December 2020 was analysed by 17 macro-scale estimators around the health-related, socioeconomic, climatic and political factors. In 139 of 170 nations, the best subsets regression was used to investigate all potential models of COVID-19 variations among countries. A multiple linear regression analysis was conducted to explore the predictive capacity of these variables. The same analysis was applied to the number of deaths per hundred thousand due to tuberculosis, a quite different infectious disease, to validate and control the differences with the proposed models for COVID-19. RESULTS: In the model for the COVID-19 cases (R2=0.45), obesity (ß=0.460), hypertension (ß=0.214), sunshine (ß=-0.157) and transparency (ß=0.147); whereas in the model for COVID-19 deaths (R2=0.41), obesity (ß=0.279), hypertension (ß=0.285), alcohol consumption (ß=0.173) and urbanisation (ß=0.204) were significant factors (p<0.05). Unlike COVID-19, the tuberculosis model contained significant indicators like obesity, undernourishment, air pollution, age, schooling, democracy and Gini Inequality Index. CONCLUSIONS: This study recommends the new predictors explaining the global variability of COVID-19. Thus, it might assist policymakers in developing health policies and social strategies to deal with COVID-19. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov Registry (NCT04486508).


Subject(s)
COVID-19 , Health Policy , Humans , Information Storage and Retrieval , Regression Analysis , SARS-CoV-2
4.
BMJ Open ; 12(2): e054376, 2022 02 01.
Article in English | MEDLINE | ID: covidwho-1673438

ABSTRACT

OBJECTIVES: Develop a novel algorithm to categorise alcohol consumption using primary care electronic health records (EHRs) and asses its reliability by comparing this classification with self-reported alcohol consumption data obtained from the UK Biobank (UKB) cohort. DESIGN: Cross-sectional study. SETTING: The UKB, a population-based cohort with participants aged between 40 and 69 years recruited across the UK between 2006 and 2010. PARTICIPANTS: UKB participants from Scotland with linked primary care data. PRIMARY AND SECONDARY OUTCOME MEASURES: Create a rule-based multiclass algorithm to classify alcohol consumption reported by Scottish UKB participants and compare it with their classification using data present in primary care EHRs based on Read Codes. We evaluated agreement metrics (simple agreement and kappa statistic). RESULTS: Among the Scottish UKB participants, 18 838 (69%) had at least one Read Code related to alcohol consumption and were used in the classification. The agreement of alcohol consumption categories between UKB and primary care data, including assessments within 5 years was 59.6%, and kappa was 0.23 (95% CI 0.21 to 0.24). Differences in classification between the two sources were statistically significant (p<0.001); More individuals were classified as 'sensible drinkers' and in lower alcohol consumption levels in primary care records compared with the UKB. Agreement improved slightly when using only numerical values (k=0.29; 95% CI 0.27 to 0.31) and decreased when using qualitative descriptors only (k=0.18;95% CI 0.16 to 0.20). CONCLUSION: Our algorithm classifies alcohol consumption recorded in Primary Care EHRs into discrete meaningful categories. These results suggest that alcohol consumption may be underestimated in primary care EHRs. Using numerical values (alcohol units) may improve classification when compared with qualitative descriptors.


Subject(s)
Biological Specimen Banks , Electronic Health Records , Adult , Aged , Alcohol Drinking/epidemiology , Algorithms , Cross-Sectional Studies , Humans , Information Storage and Retrieval , Middle Aged , Primary Health Care , Reproducibility of Results , Scotland/epidemiology
5.
J Biomed Inform ; 127: 104005, 2022 03.
Article in English | MEDLINE | ID: covidwho-1670671

ABSTRACT

Consumers from non-medical backgrounds often look for information regarding a specific medical information need; however, they are limited by their lack of medical knowledge and may not be able to find reputable resources. As a case study, we investigate reducing this knowledge barrier to allow consumers to achieve search effectiveness comparable to that of an expert, or a medical professional, for COVID-19 related questions. We introduce and evaluate a hybrid index model that allows a consumer to formulate queries using consumer language to find relevant answers to COVID-19 questions. Our aim is to reduce performance degradation between medical professional queries and those of a consumer. We use a universal sentence embedding model to project consumer queries into the same semantic space as professional queries. We then incorporate sentence embeddings into a search framework alongside an inverted index. Documents from this index are retrieved using a novel scoring function that considers sentence embeddings and BM25 scoring. We find that our framework alleviates the expertise disparity, which we validate using an additional set of crowdsourced-consumer-queries even in an unsupervised setting. We also propose an extension of our method, where the sentence encoder is optimised in a supervised setup. Our framework allows for a consumer to search using consumer queries to match the search performance with that of a professional.


Subject(s)
COVID-19 , Information Storage and Retrieval , Humans , Natural Language Processing , SARS-CoV-2 , Unified Medical Language System
6.
Stud Health Technol Inform ; 289: 118-122, 2022 Jan 14.
Article in English | MEDLINE | ID: covidwho-1643435

ABSTRACT

In early 2021, the European Commission presented a proposal to introduce an EU Digital COVID Certificate, which should enable safe border crossings for citizens within the EU during the COVID-19 pandemic. Subsequently, all EU Member States successfully introduced the EU Digital COVID Certificate by 1 July 2021. This article focuses on a review of the technological and process aspects identified in the introduction of the EU Digital COVID Certificate in Slovenia. The research applies a case study framework, including focus group discussions, as the primary data collection method. The research findings expose the technological and process complexities related to the dispersed data sources and fairly intricate and copious business rules used for the creation of the EU Digital COVID Certificate. Moreover, the study implies that the ad hoc introduction of such demanding and sensitive digital solutions in the future will not be possible without the establishment of effective national health information infrastructures across the EU.


Subject(s)
COVID-19 , Pandemics , Humans , Information Storage and Retrieval , SARS-CoV-2 , Slovenia
7.
PLoS One ; 17(1): e0262609, 2022.
Article in English | MEDLINE | ID: covidwho-1643269

ABSTRACT

BACKGROUND: The use of linked healthcare data in research has the potential to make major contributions to knowledge generation and service improvement. However, using healthcare data for secondary purposes raises legal and ethical concerns relating to confidentiality, privacy and data protection rights. Using a linkage and anonymisation approach that processes data lawfully and in line with ethical best practice to create an anonymous (non-personal) dataset can address these concerns, yet there is no set approach for defining all of the steps involved in such data flow end-to-end. We aimed to define such an approach with clear steps for dataset creation, and to describe its utilisation in a case study linking healthcare data. METHODS: We developed a data flow protocol that generates pseudonymous datasets that can be reversibly linked, or irreversibly linked to form an anonymous research dataset. It was designed and implemented by the Comprehensive Patient Records (CPR) study in Leeds, UK. RESULTS: We defined a clear approach that received ethico-legal approval for use in creating an anonymous research dataset. Our approach used individual-level linkage through a mechanism that is not computer-intensive and was rendered irreversible to both data providers and processors. We successfully applied it in the CPR study to hospital and general practice and community electronic health record data from two providers, along with patient reported outcomes, for 365,193 patients. The resultant anonymous research dataset is available via DATA-CAN, the Health Data Research Hub for Cancer in the UK. CONCLUSIONS: Through ethical, legal and academic review, we believe that we contribute a defined approach that represents a framework that exceeds current minimum standards for effective pseudonymisation and anonymisation. This paper describes our methods and provides supporting information to facilitate the use of this approach in research.


Subject(s)
Biomedical Research/methods , Confidentiality , Data Anonymization , Biomedical Research/ethics , Datasets as Topic , Electronic Data Processing/ethics , Electronic Data Processing/methods , Electronic Health Records/organization & administration , Humans , Information Storage and Retrieval , United Kingdom
8.
Lancet ; 399(10323): 437-446, 2022 01 29.
Article in English | MEDLINE | ID: covidwho-1641746

ABSTRACT

BACKGROUND: The SARS-CoV-2 omicron variant of concern was identified in South Africa in November, 2021, and was associated with an increase in COVID-19 cases. We aimed to assess the clinical severity of infections with the omicron variant using S gene target failure (SGTF) on the Thermo Fisher Scientific TaqPath COVID-19 PCR test as a proxy. METHODS: We did data linkages for national, South African COVID-19 case data, SARS-CoV-2 laboratory test data, SARS-CoV-2 genome data, and COVID-19 hospital admissions data. For individuals diagnosed with COVID-19 via TaqPath PCR tests, infections were designated as either SGTF or non-SGTF. The delta variant was identified by genome sequencing. Using multivariable logistic regression models, we assessed disease severity and hospitalisations by comparing individuals with SGTF versus non-SGTF infections diagnosed between Oct 1 and Nov 30, 2021, and we further assessed disease severity by comparing SGTF-infected individuals diagnosed between Oct 1 and Nov 30, 2021, with delta variant-infected individuals diagnosed between April 1 and Nov 9, 2021. FINDINGS: From Oct 1 (week 39), 2021, to Dec 6 (week 49), 2021, 161 328 cases of COVID-19 were reported in South Africa. 38 282 people were diagnosed via TaqPath PCR tests and 29 721 SGTF infections and 1412 non-SGTF infections were identified. The proportion of SGTF infections increased from two (3·2%) of 63 in week 39 to 21 978 (97·9%) of 22 455 in week 48. After controlling for factors associated with hospitalisation, individuals with SGTF infections had significantly lower odds of admission than did those with non-SGTF infections (256 [2·4%] of 10 547 vs 121 [12·8%] of 948; adjusted odds ratio [aOR] 0·2, 95% CI 0·1-0·3). After controlling for factors associated with disease severity, the odds of severe disease were similar between hospitalised individuals with SGTF versus non-SGTF infections (42 [21%] of 204 vs 45 [40%] of 113; aOR 0·7, 95% CI 0·3-1·4). Compared with individuals with earlier delta variant infections, SGTF-infected individuals had a significantly lower odds of severe disease (496 [62·5%] of 793 vs 57 [23·4%] of 244; aOR 0·3, 95% CI 0·2-0·5), after controlling for factors associated with disease severity. INTERPRETATION: Our early analyses suggest a significantly reduced odds of hospitalisation among individuals with SGTF versus non-SGTF infections diagnosed during the same time period. SGTF-infected individuals had a significantly reduced odds of severe disease compared with individuals infected earlier with the delta variant. Some of this reduced severity is probably a result of previous immunity. FUNDING: The South African Medical Research Council, the South African National Department of Health, US Centers for Disease Control and Prevention, the African Society of Laboratory Medicine, Africa Centers for Disease Control and Prevention, the Bill & Melinda Gates Foundation, the Wellcome Trust, and the Fleming Fund.


Subject(s)
COVID-19/physiopathology , Hospitalization/statistics & numerical data , SARS-CoV-2/genetics , Severity of Illness Index , Adolescent , Adult , COVID-19/epidemiology , COVID-19/virology , COVID-19 Nucleic Acid Testing , Child , Child, Preschool , Female , Genome, Viral , Humans , Information Storage and Retrieval , Logistic Models , Male , Middle Aged , Multivariate Analysis , Odds Ratio , South Africa/epidemiology , Young Adult
9.
J Biomed Inform ; 127: 104002, 2022 03.
Article in English | MEDLINE | ID: covidwho-1639382

ABSTRACT

OBJECTIVE: The large-scale collection of observational data and digital technologies could help curb the COVID-19 pandemic. However, the coexistence of multiple Common Data Models (CDMs) and the lack of data extract, transform, and load (ETL) tool between different CDMs causes potential interoperability issue between different data systems. The objective of this study is to design, develop, and evaluate an ETL tool that transforms the PCORnet CDM format data into the OMOP CDM. METHODS: We developed an open-source ETL tool to facilitate the data conversion from the PCORnet CDM and the OMOP CDM. The ETL tool was evaluated using a dataset with 1000 patients randomly selected from the PCORnet CDM at Mayo Clinic. Information loss, data mapping accuracy, and gap analysis approaches were conducted to assess the performance of the ETL tool. We designed an experiment to conduct a real-world COVID-19 surveillance task to assess the feasibility of the ETL tool. We also assessed the capacity of the ETL tool for the COVID-19 data surveillance using data collection criteria of the MN EHR Consortium COVID-19 project. RESULTS: After the ETL process, all the records of 1000 patients from 18 PCORnet CDM tables were successfully transformed into 12 OMOP CDM tables. The information loss for all the concept mapping was less than 0.61%. The string mapping process for the unit concepts lost 2.84% records. Almost all the fields in the manual mapping process achieved 0% information loss, except the specialty concept mapping. Moreover, the mapping accuracy for all the fields were 100%. The COVID-19 surveillance task collected almost the same set of cases (99.3% overlaps) from the original PCORnet CDM and target OMOP CDM separately. Finally, all the data elements for MN EHR Consortium COVID-19 project could be captured from both the PCORnet CDM and the OMOP CDM. CONCLUSION: We demonstrated that our ETL tool could satisfy the data conversion requirements between the PCORnet CDM and the OMOP CDM. The outcome of the work would facilitate the data retrieval, communication, sharing, and analysis between different institutions for not only COVID-19 related project, but also other real-world evidence-based observational studies.


Subject(s)
COVID-19 , COVID-19/epidemiology , Databases, Factual , Electronic Health Records , Humans , Information Storage and Retrieval , Pandemics , SARS-CoV-2
10.
Nat Methods ; 18(12): 1496-1498, 2021 12.
Article in English | MEDLINE | ID: covidwho-1612200

ABSTRACT

The rapid pace of innovation in biological imaging and the diversity of its applications have prevented the establishment of a community-agreed standardized data format. We propose that complementing established open formats such as OME-TIFF and HDF5 with a next-generation file format such as Zarr will satisfy the majority of use cases in bioimaging. Critically, a common metadata format used in all these vessels can deliver truly findable, accessible, interoperable and reusable bioimaging data.


Subject(s)
Computational Biology/instrumentation , Computational Biology/standards , Metadata , Microscopy/instrumentation , Microscopy/standards , Software , Benchmarking , Computational Biology/methods , Data Compression , Databases, Factual , Information Storage and Retrieval , Internet , Microscopy/methods , Programming Languages , SARS-CoV-2
11.
Int J Environ Res Public Health ; 18(24)2021 12 20.
Article in English | MEDLINE | ID: covidwho-1580709

ABSTRACT

The use of WhatsApp in health care has increased, especially since the COVID-19 pandemic, but there is a need to safeguard electronic patient information when incorporating it into a medical record, be it electronic or paper based. The aim of this study was to review the literature on how clinicians who use WhatsApp in clinical practice keep medical records of the content of WhatsApp messages and how they store WhatsApp messages and/or attachments. A scoping review of nine databases sought evidence of record keeping or data storage related to use of WhatsApp in clinical practice up to 31 December 2020. Sixteen of 346 papers met study criteria. Most clinicians were aware that they must comply with statutory reporting requirements in keeping medical records of all electronic communications. However, this study showed a general lack of awareness or concern about flaunting existing privacy and security legislation. No clear mechanisms for record keeping or data storage of WhatsApp content were provided. In the absence of clear guidelines, problematic practices and workarounds have been created, increasing legal, regulatory and ethical concerns. There is a need to raise awareness of the problems clinicians face in meeting these obligations and to urgently provide viable guidance.


Subject(s)
COVID-19 , Pandemics , Humans , Information Storage and Retrieval , Privacy , SARS-CoV-2
12.
J Med Internet Res ; 23(2): e25682, 2021 02 24.
Article in English | MEDLINE | ID: covidwho-1574621

ABSTRACT

BACKGROUND: Since the outbreak of COVID-19, the development of dashboards as dynamic, visual tools for communicating COVID-19 data has surged worldwide. Dashboards can inform decision-making and support behavior change. To do so, they must be actionable. The features that constitute an actionable dashboard in the context of the COVID-19 pandemic have not been rigorously assessed. OBJECTIVE: The aim of this study is to explore the characteristics of public web-based COVID-19 dashboards by assessing their purpose and users ("why"), content and data ("what"), and analyses and displays ("how" they communicate COVID-19 data), and ultimately to appraise the common features of highly actionable dashboards. METHODS: We conducted a descriptive assessment and scoring using nominal group technique with an international panel of experts (n=17) on a global sample of COVID-19 dashboards in July 2020. The sequence of steps included multimethod sampling of dashboards; development and piloting of an assessment tool; data extraction and an initial round of actionability scoring; a workshop based on a preliminary analysis of the results; and reconsideration of actionability scores followed by joint determination of common features of highly actionable dashboards. We used descriptive statistics and thematic analysis to explore the findings by research question. RESULTS: A total of 158 dashboards from 53 countries were assessed. Dashboards were predominately developed by government authorities (100/158, 63.0%) and were national (93/158, 58.9%) in scope. We found that only 20 of the 158 dashboards (12.7%) stated both their primary purpose and intended audience. Nearly all dashboards reported epidemiological indicators (155/158, 98.1%), followed by health system management indicators (85/158, 53.8%), whereas indicators on social and economic impact and behavioral insights were the least reported (7/158, 4.4% and 2/158, 1.3%, respectively). Approximately a quarter of the dashboards (39/158, 24.7%) did not report their data sources. The dashboards predominately reported time trends and disaggregated data by two geographic levels and by age and sex. The dashboards used an average of 2.2 types of displays (SD 0.86); these were mostly graphs and maps, followed by tables. To support data interpretation, color-coding was common (93/158, 89.4%), although only one-fifth of the dashboards (31/158, 19.6%) included text explaining the quality and meaning of the data. In total, 20/158 dashboards (12.7%) were appraised as highly actionable, and seven common features were identified between them. Actionable COVID-19 dashboards (1) know their audience and information needs; (2) manage the type, volume, and flow of displayed information; (3) report data sources and methods clearly; (4) link time trends to policy decisions; (5) provide data that are "close to home"; (6) break down the population into relevant subgroups; and (7) use storytelling and visual cues. CONCLUSIONS: COVID-19 dashboards are diverse in the why, what, and how by which they communicate insights on the pandemic and support data-driven decision-making. To leverage their full potential, dashboard developers should consider adopting the seven actionability features identified.


Subject(s)
COVID-19 , Data Display , Information Dissemination , Internet , Adult , Computer Graphics , Disease Outbreaks , Female , Humans , Information Storage and Retrieval , Male , Pandemics , SARS-CoV-2 , Young Adult
14.
Epidemics ; 38: 100534, 2022 03.
Article in English | MEDLINE | ID: covidwho-1549782

ABSTRACT

For emerging epidemics such as the COVID-19 pandemic, quantifying travel is a key component of developing accurate predictive models of disease spread to inform public health planning. However, in many LMICs, traditional data sets on travel such as commuting surveys as well as non-traditional sources such as mobile phone data are lacking, or, where available, have only rarely been leveraged by the public health community. Evaluating the accuracy of available data to measure transmission-relevant travel may be further hampered by limited reporting of suspected and laboratory confirmed infections. Here, we leverage case data collected as part of a COVID-19 dashboard collated via daily reports from the Malagasy authorities on reported cases of SARS-CoV-2 across the 22 regions of Madagascar. We compare the order of the timing of when cases were reported with predictions from a SARS-CoV-2 metapopulation model of Madagascar informed using various measures of connectivity including a gravity model based on different measures of distance, Internal Migration Flow data, and mobile phone data. Overall, the models based on mobile phone connectivity and the gravity-based on Euclidean distance best predicted the observed spread. The ranks of the regions most remote from the capital were more difficult to predict but interestingly, regions where the mobile phone connectivity model was more accurate differed from those where the gravity model was most accurate. This suggests that there may be additional features of mobility or connectivity that were consistently underestimated using all approaches but are epidemiologically relevant. This work highlights the importance of data availability and strengthening collaboration among different institutions with access to critical data - models are only as good as the data that they use, so building towards effective data-sharing pipelines is essential.


Subject(s)
COVID-19 , SARS-CoV-2 , COVID-19/epidemiology , Humans , Information Storage and Retrieval , Madagascar/epidemiology , Pandemics , United States
15.
Nucleic Acids Res ; 50(D1): D11-D19, 2022 01 07.
Article in English | MEDLINE | ID: covidwho-1546006

ABSTRACT

The European Bioinformatics Institute (EMBL-EBI) maintains a comprehensive range of freely available and up-to-date molecular data resources, which includes over 40 resources covering every major data type in the life sciences. This year's service update for EMBL-EBI includes new resources, PGS Catalog and AlphaFold DB, and updates on existing resources, including the COVID-19 Data Platform, trRosetta and RoseTTAfold models introduced in Pfam and InterPro, and the launch of Genome Integrations with Function and Sequence by UniProt and Ensembl. Furthermore, we highlight projects through which EMBL-EBI has contributed to the development of community-driven data standards and guidelines, including the Recommended Metadata for Biological Images (REMBI), and the BioModels Reproducibility Scorecard. Training is one of EMBL-EBI's core missions and a key component of the provision of bioinformatics services to users: this year's update includes many of the improvements that have been developed to EMBL-EBI's online training offering.


Subject(s)
Computational Biology/education , Computational Biology/methods , Databases, Factual , Academies and Institutes , Artificial Intelligence , COVID-19 , Databases, Factual/economics , Databases, Factual/statistics & numerical data , Databases, Pharmaceutical , Databases, Protein , Europe , Genome, Human , Humans , Information Storage and Retrieval , RNA, Untranslated/genetics , SARS-CoV-2/genetics
16.
J Am Med Inform Assoc ; 29(4): 677-685, 2022 03 15.
Article in English | MEDLINE | ID: covidwho-1545999

ABSTRACT

OBJECTIVE: Obtaining electronic patient data, especially from electronic health record (EHR) systems, for clinical and translational research is difficult. Multiple research informatics systems exist but navigating the numerous applications can be challenging for scientists. This article describes Architecture for Research Computing in Health (ARCH), our institution's approach for matching investigators with tools and services for obtaining electronic patient data. MATERIALS AND METHODS: Supporting the spectrum of studies from populations to individuals, ARCH delivers a breadth of scientific functions-including but not limited to cohort discovery, electronic data capture, and multi-institutional data sharing-that manifest in specific systems-such as i2b2, REDCap, and PCORnet. Through a consultative process, ARCH staff align investigators with tools with respect to study design, data sources, and cost. Although most ARCH services are available free of charge, advanced engagements require fee for service. RESULTS: Since 2016 at Weill Cornell Medicine, ARCH has supported over 1200 unique investigators through more than 4177 consultations. Notably, ARCH infrastructure enabled critical coronavirus disease 2019 response activities for research and patient care. DISCUSSION: ARCH has provided a technical, regulatory, financial, and educational framework to support the biomedical research enterprise with electronic patient data. Collaboration among informaticians, biostatisticians, and clinicians has been critical to rapid generation and analysis of EHR data. CONCLUSION: A suite of tools and services, ARCH helps match investigators with informatics systems to reduce time to science. ARCH has facilitated research at Weill Cornell Medicine and may provide a model for informatics and research leaders to support scientists elsewhere.


Subject(s)
Biomedical Research , COVID-19 , Electronic Health Records , Electronics , Humans , Information Storage and Retrieval , Research Personnel
18.
Stud Health Technol Inform ; 286: 21-25, 2021 Nov 08.
Article in English | MEDLINE | ID: covidwho-1512001

ABSTRACT

Under pandemic conditions, it is important to communicate local infection risks to better enable the general population to adjust their behaviors accordingly. In Japan, our team operates a popular non-government and not-for-profit dashboard project - "Japan LIVE Dashboard" - which allows the public to easily grasp the evolution of the pandemic on the internet. We presented the Dashboard design concept with a generic framework integrating socio-technical theories, disease epidemiology and related contexts, and evidence-based approaches. Through synthesizing multiple types of reliable and real-time local data sources from all prefectures across the country, the Dashboard allows the public access to user-friendly and intuitive disease visualization in real time and has gained an extensive online followership. To date, it has attracted c.30 million visits (98% domestic access) testifying to the reputation it has acquired as a user-friendly portal for understanding the progression of the pandemic. Designed as an open-source solution, the Dashboard can also be adopted by other countries as well as made applicable for other emerging outbreaks in the future. Furthermore, the conceptual design framework may prove applicable into other ehealth scaled for global pandemics.


Subject(s)
COVID-19 , Humans , Information Storage and Retrieval , Japan/epidemiology , Pandemics , SARS-CoV-2
19.
Euro Surveill ; 26(38)2021 09.
Article in English | MEDLINE | ID: covidwho-1496926

ABSTRACT

Through deterministic data linkage of health registries, mRNA vaccine effectiveness (VE) against COVID-19-related hospitalisations and deaths was measured in 1,880,351 older adults. VE against hospitalisations was 94% (95% confidence interval (CI): 88-97) and 82% (95% CI: 72-89) for those 65-79 and ≥ 80 years old, with no evidence of waning 98 days after dose two. VE against mortality was 96% (95% CI: 92-98) and 81% (95% CI: 74-87) in these two age groups.


Subject(s)
COVID-19 , Vaccines , Aged , Aged, 80 and over , COVID-19 Vaccines , Cohort Studies , Hospitalization , Humans , Information Storage and Retrieval , Portugal/epidemiology , RNA, Messenger , Registries , SARS-CoV-2
20.
Int J Environ Res Public Health ; 18(21)2021 Oct 28.
Article in English | MEDLINE | ID: covidwho-1488569

ABSTRACT

Population-based data linkage has a long history in Australia from its beginnings in Western Australia in the 1970s to the coordinated national data linkage infrastructure that exists today. This article describes the journey from an idea to a national data linkage network which has impacts on the health and well-being of Australians from preventing developmental anomalies to responding to the COVID-19 pandemic. Many enthusiastic and dedicated people have contributed to Australia's data linkage capability over the last 50 years. They have managed to overcome a number of challenges including gaining stakeholder and community support; navigating complex legal and ethical environments; establishing cross-jurisdictional collaborations, and gaining ongoing financial support. The future is bright for linked data in Australia as the infrastructure built over the last 50 years provides a firm foundation for further expansion and development, ensuring that Australia's linked health and human services data continues to be available to address the evolving challenges of the next half century.


Subject(s)
COVID-19 , Pandemics , Australia/epidemiology , Humans , Information Storage and Retrieval , SARS-CoV-2
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