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1.
Artif Intell Med ; 114: 102053, 2021 04.
Artículo en Inglés | MEDLINE | ID: mdl-33875160

RESUMEN

MOTIVATION: In the age of big data, the amount of scientific information available online dwarfs the ability of current tools to support researchers in locating and securing access to the necessary materials. Well-structured open data and the smart systems that make the appropriate use of it are invaluable and can help health researchers and professionals to find the appropriate information by, e.g., configuring the monitoring of information or refining a specific query on a disease. METHODS: We present an automated text classifier approach based on the MEDLINE/MeSH thesaurus, trained on the manual annotation of more than 26 million expert-annotated scientific abstracts. The classifier was developed tailor-fit to the public health and health research domain experts, in the light of their specific challenges and needs. We have applied the proposed methodology on three specific health domains: the Coronavirus, Mental Health and Diabetes, considering the pertinence of the first, and the known relations with the other two health topics. RESULTS: A classifier is trained on the MEDLINE dataset that can automatically annotate text, such as scientific articles, news articles or medical reports with relevant concepts from the MeSH thesaurus. CONCLUSIONS: The proposed text classifier shows promising results in the evaluation of health-related news. The application of the developed classifier enables the exploration of news and extraction of health-related insights, based on the MeSH thesaurus, through a similar workflow as in the usage of PubMed, with which most health researchers are familiar.


Asunto(s)
Comunicación en Salud/normas , MEDLINE/organización & administración , Medical Subject Headings , Investigación/organización & administración , Macrodatos , Clasificación , Diabetes Mellitus/epidemiología , Humanos , MEDLINE/normas , Salud Mental/estadística & datos numéricos , Semántica
3.
Sensors (Basel) ; 21(7)2021 Mar 28.
Artículo en Inglés | MEDLINE | ID: mdl-33800574

RESUMEN

Obesity is a major public health problem worldwide, and the prevalence of childhood obesity is of particular concern. Effective interventions for preventing and treating childhood obesity aim to change behaviour and exposure at the individual, community, and societal levels. However, monitoring and evaluating such changes is very challenging. The EU Horizon 2020 project "Big Data against Childhood Obesity (BigO)" aims at gathering large-scale data from a large number of children using different sensor technologies to create comprehensive obesity prevalence models for data-driven predictions about specific policies on a community. It further provides real-time monitoring of the population responses, supported by meaningful real-time data analysis and visualisations. Since BigO involves monitoring and storing of personal data related to the behaviours of a potentially vulnerable population, the data representation, security, and access control are crucial. In this paper, we briefly present the BigO system architecture and focus on the necessary components of the system that deals with data access control, storage, anonymisation, and the corresponding interfaces with the rest of the system. We propose a three-layered data warehouse architecture: The back-end layer consists of a database management system for data collection, de-identification, and anonymisation of the original datasets. The role-based permissions and secured views are implemented in the access control layer. Lastly, the controller layer regulates the data access protocols for any data access and data analysis. We further present the data representation methods and the storage models considering the privacy and security mechanisms. The data privacy and security plans are devised based on the types of collected personal, the types of users, data storage, data transmission, and data analysis. We discuss in detail the challenges of privacy protection in this large distributed data-driven application and implement novel privacy-aware data analysis protocols to ensure that the proposed models guarantee the privacy and security of datasets. Finally, we present the BigO system architecture and its implementation that integrates privacy-aware protocols.


Asunto(s)
Macrodatos , Seguridad Computacional , Niño , Confidencialidad , Data Warehousing , Prestación de Atención de Salud , Humanos , Privacidad
4.
Proc Natl Acad Sci U S A ; 118(14)2021 04 06.
Artículo en Inglés | MEDLINE | ID: mdl-33790010

RESUMEN

Increasingly, human behavior can be monitored through the collection of data from digital devices revealing information on behaviors and locations. In the context of higher education, a growing number of schools and universities collect data on their students with the purpose of assessing or predicting behaviors and academic performance, and the COVID-19-induced move to online education dramatically increases what can be accumulated in this way, raising concerns about students' privacy. We focus on academic performance and ask whether predictive performance for a given dataset can be achieved with less privacy-invasive, but more task-specific, data. We draw on a unique dataset on a large student population containing both highly detailed measures of behavior and personality and high-quality third-party reported individual-level administrative data. We find that models estimated using the big behavioral data are indeed able to accurately predict academic performance out of sample. However, models using only low-dimensional and arguably less privacy-invasive administrative data perform considerably better and, importantly, do not improve when we add the high-resolution, privacy-invasive behavioral data. We argue that combining big behavioral data with "ground truth" administrative registry data can ideally allow the identification of privacy-preserving task-specific features that can be employed instead of current indiscriminate troves of behavioral data, with better privacy and better prediction resulting.


Asunto(s)
Macrodatos , Educación a Distancia , Estudiantes/estadística & datos numéricos , Humanos , Aprendizaje , Aprendizaje Automático
5.
OMICS ; 25(4): 249-254, 2021 04.
Artículo en Inglés | MEDLINE | ID: mdl-33794130

RESUMEN

Digital health is a rapidly emerging field that offers several promising potentials: health care delivery remotely, in urban and rural areas, in any time zone, and in times of pandemics and ecological crises. Digital health encompasses electronic health, computing science, big data, artificial intelligence, and the Internet of Things, to name but a few technical components. Digital health is part of a vision for systems medicine. The advances in digital health have been, however, uneven and highly variable across communities, countries, medical specialties, and societal contexts. This article critically examines the determinants of digital health (DDH). DDH describes and critically responds to inequities and differences in digital health theory and practice across people, places, spaces, and time. DDH is not limited to studying variability in design and access to digital technologies. DDH is situated within a larger context of the political determinants of health. Hence, this article presents an analysis of DDH, as seen through political science, and the feminist studies of technology and society. A feminist lens would strengthen systems-driven, historically and critically informed governance for DDH. This would be a timely antidote against unchecked destructive/extractive governance narratives (e.g., technocracy and patriarchy) that produce and reproduce the health inequities. Moreover, feminist framing of DDH can help cultivate epistemic competence to detect and reject false equivalences in how we understand the emerging digital world(s). False equivalence, very common in the current pandemic and post-truth era, is a type of flawed reasoning in decision-making where equal weight is given to arguments with concrete material evidence, and those that are conjecture, untrue, or unjust. A feminist conceptual lens on DDH would help remedy what I refer to in this article as "the normative deficits" in science and technology policy that became endemic with the rise of neoliberal governance since the 1980s in particular. In this context, it is helpful to recall the feminist writer Ursula K. Le Guin. Le Guin posed "what if?" questions, to break free from oppressive narratives such as patriarchy and re-imagine technology futures. It is time to envision an emancipated, equitable, and more democratic world by asking "what if we lived in a feminist world?" That would be truly awesome, for everyone, women and men, children, youth, and future generations, to steer digital technologies and the new field of DDH toward broadly relevant, ethical, experiential, democratic, and socially responsive health outcomes.


Asunto(s)
/epidemiología , Feminismo , Disparidades en Atención de Salud/ética , Pandemias/prevención & control , /patogenicidad , Inteligencia Artificial/tendencias , Macrodatos , Prestación de Atención de Salud/ética , Femenino , Humanos , Política , Salud Pública/tendencias
6.
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue ; 33(3): 338-343, 2021 Mar.
Artículo en Chino | MEDLINE | ID: mdl-33834977

RESUMEN

OBJECTIVE: To explore a medical big data algorithm to screen the core indicators in clinical database that can be used to evaluate the prognosis of elderly patients with pneumonia. METHODS: Based on the clinical database of a Beijing Chaoyang Hospital Consortium Chaoyang Emergency Ward in Beijing Chaoyang Hospital, Capital Medical University, patients with pulmonary infection were selected through the big data retrieval technology. According to the prognosis at the time of discharge, they were divided into death group and survival group. The general data of patients were collected, including gender, age, blood gas and laboratory indices. A computer language called Python was used to make batch calculations of key indicators that affect mortality in elderly patients with pneumonia. Logistic regression analysis was used to analyze the relationship between laboratory indicators and patients' prognosis. Receiver operating characteristic curve (ROC curve) was drawn to analyze the predictive value of screening method for patients' prognosis. RESULTS: A total of 265 patients were included in the study, 64 died and 201 survived. The data of the first detection indexes of each patient after admission were collected, and 23 key indicators with significant differences were selected from 472 indicators: blood routine indicators (n = 7), blood gas indicators (n = 3), tumor markers indicators (n = 3), coagulation related indicators (n = 4), and nutrition and organ function indicators (n = 6). (1) The key indicators of blood gas in patients died of pneumonia: Cl- was 97-111 mmol/L in 51.6% (33 cases) of patients, lactic acid (Lac) was 0.5-2.5 mmol/L in 81.2% (52 cases) of patients, and H+ was 0-46 mmol/L in 87.5% (56 cases) of patients. (2) The key indicators of blood routine of patients died of pneumonia: hemoglobin count (Hb) of 46.9% (30 cases) patients was 80-109 g/L, the eosinophils proportions (EOS%) in 67.2% (43 cases) patients was 0.000-0.009, the lymphocytes proportions (LYM%) in 51.6% (33 cases) patients was 0.00-0.09, the red blood cell count (RBC) in 50.0% (32 cases) patients was (3.0-3.9)×1012/L, the white blood cell count (WBC) in 54.7% (35 cases) patients was (0.0-9.9)×109/L, and the red blood cell volume distribution width coefficientof variability (RDW-CV) in 48.4% (31 cases) patients was 10.0%-14.9%, serum C-reactive protein (CRP) was 0.0-49.9 mg/L in 48.4% (31 cases) patients. (3) The key indicators of tumor markers in patients died of pneumonia: 76.6% (49 cases) of patients had negative free prostate specific antigen/total prostate specific antigen (FPSA/TPSA, the ratio was 0), 92.2% (59 cases) had cytokeratin 19 fragment (CYFRA21-1) between 0.0-11.0 µg/L, and 75.0% (48 cases) had carbohydrate antigen 125 (CA125) between 0-104 kU/L. (4) The key coagulation indexes of patients died of pneumonia: 68.8% (44 cases) of patients had activated partial thromboplastin time (APTT) of 57-96 s, 73.4% (47 cases) of patients had D-dimer of 0-6 mg/L, 93.8% (60 cases) of patients had thrombin time (TT) of 14-22 s, and 89.1% (57 cases) of patients had adenosine diphosphate (ADP) inhibition rate of 0%-53%. (5) Nutrition and organ function key indicatorsin patients died of pneumonia: 92.2% (59 cases) of brain natriuretic peptide (BNP) in patients with 0, 46.9% (30 cases) of patients had prealbumin (PA) of 71-140 mg/L, 90.6% (58 cases) of the patients with uric acid (UA) for 21-41 µmol/L, 75.0% (48 cases) of the patients with albumin (Alb) to 10-20 g/L, 93.5% (60 cases) of patients had albumin/globulin ratio (A/G ratio) of 0-0.9, 84.4% (54 cases) of the patients with lactate dehydrogenase (LDH) from 0-6.68 µmol/L×s-1×L-1. (6) Logistic regression analysis and ROC curve analysis: Logistic regression analysis showed that PA and Lac were the prognostic factors. PA could reduce the risk of death by 0.9%, Lac could increase the risk of death by 69.4%; the area under ROC curve (AUC) between laboratory indicators and the prediction effect of death prediction model for patients' prognosis was 0.80, which showed that the classification effect was better, and this study model could better predict the prognosis of elderly patients with pneumonia. CONCLUSIONS: By using big data technology, 23 core indicators for evaluating the prognosis of elderly patients with pneumonia can be screened from the clinical database of emergency ward, which provides a new perspective and method for clinical evaluation of the prognosis of elderly patients with pneumonia.


Asunto(s)
Macrodatos , Neumonía , Anciano , Antígenos de Neoplasias , Servicio de Urgencia en Hospital , Hospitales , Humanos , Queratina-19 , Masculino , Neumonía/diagnóstico , Pronóstico , Curva ROC , Estudios Retrospectivos
7.
Sensors (Basel) ; 21(7)2021 Mar 24.
Artículo en Inglés | MEDLINE | ID: mdl-33805218

RESUMEN

The COVID-19 epidemic has caused a large number of human losses and havoc in the economic, social, societal, and health systems around the world. Controlling such epidemic requires understanding its characteristics and behavior, which can be identified by collecting and analyzing the related big data. Big data analytics tools play a vital role in building knowledge required in making decisions and precautionary measures. However, due to the vast amount of data available on COVID-19 from various sources, there is a need to review the roles of big data analysis in controlling the spread of COVID-19, presenting the main challenges and directions of COVID-19 data analysis, as well as providing a framework on the related existing applications and studies to facilitate future research on COVID-19 analysis. Therefore, in this paper, we conduct a literature review to highlight the contributions of several studies in the domain of COVID-19-based big data analysis. The study presents as a taxonomy several applications used to manage and control the pandemic. Moreover, this study discusses several challenges encountered when analyzing COVID-19 data. The findings of this paper suggest valuable future directions to be considered for further research and applications.


Asunto(s)
Macrodatos , Ciencia de los Datos , Pandemias , /epidemiología , Humanos , Pandemias/prevención & control
8.
J Med Internet Res ; 23(4): e27275, 2021 04 13.
Artículo en Inglés | MEDLINE | ID: mdl-33847586

RESUMEN

BACKGROUND: Although the potential of big data analytics for health care is well recognized, evidence is lacking on its effects on public health. OBJECTIVE: The aim of this study was to assess the impact of the use of big data analytics on people's health based on the health indicators and core priorities in the World Health Organization (WHO) General Programme of Work 2019/2023 and the European Programme of Work (EPW), approved and adopted by its Member States, in addition to SARS-CoV-2-related studies. Furthermore, we sought to identify the most relevant challenges and opportunities of these tools with respect to people's health. METHODS: Six databases (MEDLINE, Embase, Cochrane Database of Systematic Reviews via Cochrane Library, Web of Science, Scopus, and Epistemonikos) were searched from the inception date to September 21, 2020. Systematic reviews assessing the effects of big data analytics on health indicators were included. Two authors independently performed screening, selection, data extraction, and quality assessment using the AMSTAR-2 (A Measurement Tool to Assess Systematic Reviews 2) checklist. RESULTS: The literature search initially yielded 185 records, 35 of which met the inclusion criteria, involving more than 5,000,000 patients. Most of the included studies used patient data collected from electronic health records, hospital information systems, private patient databases, and imaging datasets, and involved the use of big data analytics for noncommunicable diseases. "Probability of dying from any of cardiovascular, cancer, diabetes or chronic renal disease" and "suicide mortality rate" were the most commonly assessed health indicators and core priorities within the WHO General Programme of Work 2019/2023 and the EPW 2020/2025. Big data analytics have shown moderate to high accuracy for the diagnosis and prediction of complications of diabetes mellitus as well as for the diagnosis and classification of mental disorders; prediction of suicide attempts and behaviors; and the diagnosis, treatment, and prediction of important clinical outcomes of several chronic diseases. Confidence in the results was rated as "critically low" for 25 reviews, as "low" for 7 reviews, and as "moderate" for 3 reviews. The most frequently identified challenges were establishment of a well-designed and structured data source, and a secure, transparent, and standardized database for patient data. CONCLUSIONS: Although the overall quality of included studies was limited, big data analytics has shown moderate to high accuracy for the diagnosis of certain diseases, improvement in managing chronic diseases, and support for prompt and real-time analyses of large sets of varied input data to diagnose and predict disease outcomes. TRIAL REGISTRATION: International Prospective Register of Systematic Reviews (PROSPERO) CRD42020214048; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=214048.


Asunto(s)
Macrodatos , Enfermedades Cardiovasculares , Ciencia de los Datos , Prestación de Atención de Salud/estadística & datos numéricos , Diabetes Mellitus , Trastornos Mentales , Neoplasias , Adolescente , Adulto , Anciano , Enfermedades Cardiovasculares/diagnóstico , Enfermedades Cardiovasculares/terapia , Diabetes Mellitus/diagnóstico , Diabetes Mellitus/terapia , Femenino , Humanos , Masculino , Trastornos Mentales/diagnóstico , Trastornos Mentales/terapia , Persona de Mediana Edad , Neoplasias/diagnóstico , Neoplasias/terapia , Pronóstico , Revisiones Sistemáticas como Asunto , Adulto Joven
9.
Ann Fam Med ; 19(2): 135-140, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-33685875

RESUMEN

The use of big data containing millions of primary care medical records provides an opportunity for rapid research to help inform patient care and policy decisions during the first and subsequent waves of the coronavirus disease 2019 (COVID-19) pandemic. Routinely collected primary care data have previously been used for national pandemic surveillance, quantifying associations between exposures and outcomes, identifying high risk populations, and examining the effects of interventions at scale, but there is no consensus on how to effectively conduct or report these data for COVID-19 research. A COVID-19 primary care database consortium was established in April 2020 and its researchers have ongoing COVID-19 projects in overlapping data sets with over 40 million primary care records in the United Kingdom that are variously linked to public health, secondary care, and vital status records. This consensus agreement is aimed at facilitating transparency and rigor in methodological approaches, and consistency in defining and reporting cases, exposures, confounders, stratification variables, and outcomes in relation to the pharmacoepidemiology of COVID-19. This will facilitate comparison, validation, and meta-analyses of research during and after the pandemic.


Asunto(s)
/epidemiología , Consenso , Bases de Datos Factuales/normas , Sistemas de Registros Médicos Computarizados/normas , Atención Primaria de Salud/organización & administración , Vigilancia en Salud Pública , Macrodatos , Humanos , Farmacoepidemiología , Salud Pública , Reino Unido/epidemiología
10.
Undersea Hyperb Med ; 48(1): 57-58, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-33648034

RESUMEN

Decompression sickness (DCS) remains a major operational concern for diving operations, submarine escape and high-altitude jumps. Aside from DCS symptoms, venous gas emboli (VGE) detected with ultrasound post-dive are often used as a marker of decompression stress in humans, with a specificity of 100% even though the sensitivity is poor [1]. Being non-invasive, portable and non-ionizing, ultrasound is particularly suited to regular and repeated monitoring. It could help elucidate inter- and intra-subject variability in VGE and DCS susceptibility, but analyzing these recordings remains a cumbersome task [2].


Asunto(s)
Macrodatos/provisión & distribución , Enfermedad de Descompresión/diagnóstico por imagen , Buceo/estadística & datos numéricos , Embolia Aérea/diagnóstico por imagen , Sistema de Registros/normas , Ultrasonografía Doppler/estadística & datos numéricos , Humanos
12.
Curr Cardiol Rep ; 23(5): 46, 2021 03 15.
Artículo en Inglés | MEDLINE | ID: mdl-33721129

RESUMEN

PURPOSE OF REVIEW: Technical advances have facilitated high-throughput measurements of the genome in the context of cardiovascular biology. These techniques bring a deluge of gargantuan datasets, which in turn present two fundamentally new opportunities for innovation-data processing and knowledge integration-toward the goal of meaningful basic and translational discoveries. RECENT FINDINGS: Big data, integrative analyses, and machine learning have brought cardiac investigations to the cutting edge of chromatin biology, not only to reveal basic principles of gene regulation in the heart, but also to aid in the design of targeted epigenetic therapies. SUMMARY: Cardiac studies using big data are only beginning to integrate the millions of recorded data points and the tools of machine learning are aiding this process. Future experimental design should take into consideration insights from existing genomic datasets, thereby focusing on heretofore unexplored epigenomic contributions to disease pathology.


Asunto(s)
Ciencia de los Datos , Genómica , Macrodatos , Regulación de la Expresión Génica , Humanos , Aprendizaje Automático
13.
14.
BMC Med Inform Decis Mak ; 21(1): 87, 2021 03 06.
Artículo en Inglés | MEDLINE | ID: mdl-33676513

RESUMEN

Over the last decades, the face of health care has changed dramatically, with big improvements in what is technically feasible. However, there are indicators that the current approach to evaluating evidence in health care is not holistic and hence in the long run, health care will not be sustainable. New conceptual and normative frameworks for the evaluation of health care need to be developed and investigated. The current paper presents a novel framework of justifiable health care and explores how the use of artificial intelligence and big data can contribute to achieving the goals of this framework.


Asunto(s)
Inteligencia Artificial , Macrodatos , Prestación de Atención de Salud , Instituciones de Salud , Humanos
15.
BMC Bioinformatics ; 22(1): 144, 2021 Mar 22.
Artículo en Inglés | MEDLINE | ID: mdl-33752596

RESUMEN

BACKGROUND: Storage of genomic data is a major cost for the Life Sciences, effectively addressed via specialized data compression methods. For the same reasons of abundance in data production, the use of Big Data technologies is seen as the future for genomic data storage and processing, with MapReduce-Hadoop as leaders. Somewhat surprisingly, none of the specialized FASTA/Q compressors is available within Hadoop. Indeed, their deployment there is not exactly immediate. Such a State of the Art is problematic. RESULTS: We provide major advances in two different directions. Methodologically, we propose two general methods, with the corresponding software, that make very easy to deploy a specialized FASTA/Q compressor within MapReduce-Hadoop for processing files stored on the distributed Hadoop File System, with very little knowledge of Hadoop. Practically, we provide evidence that the deployment of those specialized compressors within Hadoop, not available so far, results in better space savings, and even in better execution times over compressed data, with respect to the use of generic compressors available in Hadoop, in particular for FASTQ files. Finally, we observe that these results hold also for the Apache Spark framework, when used to process FASTA/Q files stored on the Hadoop File System. CONCLUSIONS: Our Methods and the corresponding software substantially contribute to achieve space and time savings for the storage and processing of FASTA/Q files in Hadoop and Spark. Being our approach general, it is very likely that it can be applied also to FASTA/Q compression methods that will appear in the future. AVAILABILITY: The software and the datasets are available at https://github.com/fpalini/fastdoopc.


Asunto(s)
Compresión de Datos , Genómica , Programas Informáticos , Algoritmos , Macrodatos
16.
PLoS One ; 16(3): e0249145, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-33780496

RESUMEN

Taking the Guangdong-Hong Kong-Macao Greater Bay Area as the research area, this paper used OD cluster analysis based on Baidu migration data from January 11 to January 25 (before the sealing-off of Wuhan) and concluded that there is a significant correlation 1the migration level from Wuhan to the GBA and the epidemic severity index. This paper also analyzed the migration levels and diffusivity of the outer and inner cities of the GBA. Lastly, four evaluation indexes were selected to research the possibility of work resumption and the rating of epidemic prevention and control through kernel density estimation. According to the study, the amount of migration depends on the geographical proximity, relationship and economic development of the source region, and the severity of the epidemic depends mainly on the migration volume and the severity of the epidemic in the source region. The epidemic risk is related not only to the severity of the epidemic in the source region but also to the degree of urban traffic development and the degree of urban openness. After the resumption of work, the pressure of epidemic prevention and control has been concentrated mainly in Shenzhen and Canton; the further away a region is from the core cities, the lower the pressure in that region. The mass migration of the population makes it difficult to control the epidemic effectively. The study of the relationship between migration volume, epidemic severity and epidemic risk is helpful to further analyze transmission types and predict the trends of the epidemic.


Asunto(s)
/prevención & control , Análisis Espacio-Temporal , Macrodatos , /transmisión , China/epidemiología , Epidemias , Humanos , Modelos Teóricos , Población Urbana
17.
OMICS ; 25(3): 169-175, 2021 03.
Artículo en Inglés | MEDLINE | ID: mdl-33719569

RESUMEN

Big data in both the public domain and the health care industry are growing rapidly, for example, with broad availability of next-generation sequencing and large-scale phenomics datasets on patient-reported outcomes. In parallel, we are witnessing new research approaches that demand sharing of data for the benefit of planetary society. Health data cooperatives (HDCs) is one such approach, where health data are owned and governed collectively by citizens who take part in the HDCs. Data stored in HDCs should remain readily available for translation to public health practice but at the same time, governed in a critically informed manner to ensure data integrity, veracity, and privacy, to name a few pressing concerns. As a solution, we suggest that data generated from high-throughput omics research and phenomics can be stored in an open cloud platform so that researchers around the globe can share health data and work collaboratively. We describe here the Global Open Health Data Cooperatives Cloud (GOHDCC) as a proposed cloud platform-based model for the sharing of health data between different HDCCs around the globe. GOHDCC's main objective is to share health data on a global scale for robust and responsible global science, research, and development. GOHDCC is a citizen-oriented model cooperatively governed by citizens. The model essentially represents a global sharing platform that could benefit all stakeholders along the health care value chain.


Asunto(s)
Macrodatos , Nube Computacional , Salud Global , Difusión de la Información , Cooperación Internacional , /virología , Prestación de Atención de Salud , Secuenciación de Nucleótidos de Alto Rendimiento , Humanos , /genética
18.
Sci Rep ; 11(1): 5943, 2021 03 15.
Artículo en Inglés | MEDLINE | ID: mdl-33723282

RESUMEN

Mobile phones have been used to monitor mobility changes during the COVID-19 pandemic but surprisingly few studies addressed in detail the implementation of practical applications involving whole populations. We report a method of generating a "mobility-index" and a "stay-at-home/resting-index" based on aggregated anonymous Call Detail Records of almost all subscribers in Hungary, which tracks all phones, examining their strengths and weaknesses, comparing it with Community Mobility Reports from Google, limited to smartphone data. The impact of policy changes, such as school closures, could be identified with sufficient granularity to capture a rush to shops prior to imposition of restrictions. Anecdotal reports of large scale movement of Hungarians to holiday homes were confirmed. At the national level, our results correlated well with Google mobility data, but there were some differences at weekends and national holidays, which can be explained by methodological differences. Mobile phones offer a means to analyse population movement but there are several technical and privacy issues. Overcoming these, our method is a practical and inexpensive way forward, achieving high levels of accuracy and resolution, especially where uptake of smartphones is modest, although it is not an alternative to smartphone-based solutions used for contact tracing and quarantine monitoring.


Asunto(s)
Macrodatos , Computadoras de Mano , Movilidad Social/estadística & datos numéricos , /prevención & control , Trazado de Contacto , Geografía Médica , Humanos , Hungría/epidemiología , Vigilancia en Salud Pública
19.
BMC Infect Dis ; 21(1): 146, 2021 Feb 05.
Artículo en Inglés | MEDLINE | ID: mdl-33546618

RESUMEN

BACKGROUND: In December 2019, a pneumonia caused by SARS-CoV-2 emerged in Wuhan, China and has rapidly spread around the world since then. This study is to explore the patient characteristics and transmission chains of COVID-19 in the population of Gansu province, and support decision-making. METHODS: We collected data from Gansu Province National Health Information Platform. A cross-sectional study was conducted, including patients with COVID-19 confirmed between January 23 and February 6, 2020, and analyzed the gender and age of the patients. We also described the incubation period, consultation time and sources of infection in the cases, and calculated the secondary cases that occurred within Gansu for each imported case. RESULTS: We found thirty-six (53.7%) of the patients were women and thirty-one (46.3%) men, and the median ages were 40 (IQR 31-53) years. Twenty-eight (41.8%) of the 67 cases had a history of direct exposure in Wuhan. Twenty-five (52.2%) cases came from ten families, and we found no clear reports of modes of transmission other than family clusters. The largest number of secondary cases linked to a single source was nine. CONCLUSION: More women than men were diagnosed with COVID-19 in Gansu Province. Although the age range of confirmed cases of COVID-19 in Gansu Province covered almost all age groups, most patients with confirmed COVID-19 tend to be middle aged persons. The most common suspected mode of transmission was through family cluster. Gansu and other settings worldwide should continue to strengthen the utilization of big data in epidemic control.


Asunto(s)
Macrodatos , /epidemiología , Adolescente , Adulto , Anciano , Anciano de 80 o más Años , Niño , Preescolar , China/epidemiología , Estudios Transversales , Femenino , Humanos , Lactante , Recién Nacido , Masculino , Persona de Mediana Edad , Factores de Riesgo , Factores Sexuales , Adulto Joven
20.
Clin Implant Dent Relat Res ; 23(2): 228-235, 2021 Apr.
Artículo en Inglés | MEDLINE | ID: mdl-33554462

RESUMEN

BACKGROUND: Very little information is available on the general health of elderly who are provided with an implant-retained overdenture (IOD). PURPOSE: The general health status of three groups of elderly (≥75 years) were compared: those with a natural dentition (ND), those treated with an implant-retained overdenture (IOD), and those wearing a conventional denture (CD). MATERIALS AND METHODS: Data on healthcare costs were obtained from records of Dutch health insurers that are collected by Vektis. Data on general health (chronic diseases, medication use, and polypharmacy) were acquired for elderly patients with a ND, an IOD, and a CD in 2009 and 2017. Data on the general health of elderly who received an IOD were also acquired from 2010 through 2016. RESULTS: On average, the general health of elderly who received an IOD was comparable to general health of elderly with a ND and was better than the general health of elderly with a CD (lower prevalence of diabetes, cardiac disease, and hypertension). The general health profile of elderly receiving an IOD was consistent during all years. CONCLUSIONS: The general health of elderly with a ND or IODs is better than those with CDs.


Asunto(s)
Implantes Dentales , Prótesis de Recubrimiento , Anciano , Macrodatos , Estudios Transversales , Prótesis Dental de Soporte Implantado , Retención de Dentadura , Dentadura Completa Inferior , Estado de Salud , Humanos , Mandíbula , Satisfacción del Paciente
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