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1.
Acad Pediatr ; 22(3S): S119-S124, 2022 04.
Artigo em Inglês | MEDLINE | ID: mdl-35339238

RESUMO

BACKGROUND: Measuring quality at varying levels of the health care system requires attribution, a process of determining the patients and services for which each level is responsible. However, it is important to ensure that attribution approaches are equitable; otherwise, individuals may be assigned differentially based upon social determinants of health. METHODS: First, we used Medicaid claims (2010-2018) from Michigan to assess the proportion of children with sickle cell anemia who had less than 12 months enrollment within a single Medicaid health plan and could therefore not be attributed to a specific health plan. Second, we used the Medicaid Analytic eXtract data (2008-2009) from 26 states to simulate adapting the 30-Day Pediatric All-Condition Readmission measure to the Accountable Care Organization (ACO) level and examined the proportion of readmissions that could not be attributed. RESULTS: For the sickle cell measure, an average of 300 children with sickle cell anemia were enrolled in Michigan Medicaid each year. The proportion of children that could not be attributed to a Medicaid health plan ranged from 12.2% to 89.0% across years. For the readmissions measure, of the 1,051,365 index admissions, 22% were excluded in the ACO-level analysis because of being unable to attribute the patient to a health plan for the 30 days post discharge. CONCLUSIONS: When applying attribution models, it is essential to consider the potential to induce health disparities. Differential attribution may have unintentional consequences that deepen health disparities, particularly when considering incentive programs for health plans to improve the quality of care.


Assuntos
Organizações de Assistência Responsáveis , Anemia Falciforme , Assistência ao Convalescente , Anemia Falciforme/terapia , Criança , Agregação de Dados , Humanos , Medicaid , Alta do Paciente , Estados Unidos
2.
Sensors (Basel) ; 22(4)2022 Feb 14.
Artigo em Inglês | MEDLINE | ID: mdl-35214354

RESUMO

Abnormal electricity data, caused by electricity theft or meter failure, leads to the inaccuracy of aggregation results. These inaccurate results not only harm the interests of users but also affect the decision-making of the power system. However, the existing data aggregation schemes do not consider the impact of abnormal data. How to filter out abnormal data is a challenge. To solve this problem, in this study, we propose a lightweight and privacy-friendly data aggregation scheme against abnormal data, in which the valid data can correctly be aggregated but abnormal data will be filtered out during the aggregation process. This is more suitable for resource-limited smart meters, due to the adoption of lightweight matrix encryption. The automatic filtering of abnormal data without additional processes and the detection of abnormal data sources are where our protocol outperforms other schemes. Finally, a detailed security analysis shows that the proposed scheme can protect the privacy of users' data. In addition, the results of extensive simulations demonstrate that the additional computation cost to filter the abnormal data is within the acceptable range, which shows that our proposed scheme is still very effective.


Assuntos
Segurança Computacional , Privacidade , Algoritmos , Confidencialidade , Agregação de Dados
3.
Ann Epidemiol ; 65: 1-14, 2022 01.
Artigo em Inglês | MEDLINE | ID: mdl-34419601

RESUMO

Outbreaks of infectious diseases, such as influenza, are a major societal burden. Mitigation policies during an outbreak or pandemic are guided by the analysis of data of ongoing or preceding epidemics. The reproduction number, R0, defined as the expected number of secondary infections arising from a single individual in a population of susceptibles is critical to epidemiology. For typical compartmental models such as the Susceptible-Infected-Recovered (SIR) R0 represents the severity of an epidemic. It is an estimate of the early-stage growth rate of an epidemic and is an important threshold parameter used to gain insights into the spread or decay of an outbreak. Models typically use incidence counts as indicators of cases within a single large population; however, epidemic data are the result of a hierarchical aggregation, where incidence counts from spatially separated monitoring sites (or sub-regions) are pooled and used to infer R0. Is this aggregation approach valid when the epidemic has different dynamics across the regions monitored? We characterize bias in the estimation of R0 from a merged data set when the epidemics of the sub-regions, used in the merger, exhibit delays in onset. We propose a method to mitigate this bias, and study its efficacy on synthetic data as well as real-world influenza and COVID-19 data.


Assuntos
COVID-19 , Epidemias , Número Básico de Reprodução , Agregação de Dados , Surtos de Doenças , Humanos , Pandemias , SARS-CoV-2
4.
PLoS One ; 16(12): e0260634, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34914706

RESUMO

Compressive Sensing (CS) based data collection schemes are found to be effective in enhancing the data collection performance and lifetime of IoT based WSNs. However, they face major challenges related to key distribution and adversary attacks in hostile and complex network deployments. As a result, such schemes cannot effectively ensure the security of data. Towards the goal of providing high security and efficiency in data collection performance of IoT based WSNs, we propose a new security scheme that amalgamates the advantages of CS and Elliptic Curve Cryptography (ECC). We present an efficient algorithms to enhance the security and efficiency of CS based data collection in IoT-based WSNs. The proposed scheme operates in five main phases, namely Key Generation, CS-Key Exchange, Data Compression with CS Encryption, Data Aggregation and Encryption with ECC algorithm, and CS Key Re-generation. It considers the benefits of ECC as public key algorithm and CS as encryption and compression method to provide security as well as energy efficiency for cluster based WSNs. Also, it solves the CS- Encryption key distribution problem by introducing a new key sharing method that enables secure exchange of pseudo-random key between the BS and the nodes in a simple way. In addition, a new method is introduced to safeguard the CS scheme from potential security attacks. The efficiency of our proposed technique in terms of security, energy consumption and network lifetime is proved through simulation analysis.


Assuntos
Segurança Computacional , Internet das Coisas , Tecnologia sem Fio , Algoritmos , Confidencialidade , Agregação de Dados
5.
Math Biosci Eng ; 18(6): 7539-7560, 2021 09 02.
Artigo em Inglês | MEDLINE | ID: mdl-34814262

RESUMO

Mobile health networks (MHNWs) have facilitated instant medical health care and remote health monitoring for patients. Currently, a vast amount of health data needs to be quickly collected, processed and analyzed. The main barrier to doing so is the limited amount of the computational storage resources that are required for MHNWs. Therefore, health data must be outsourced to the cloud. Although the cloud has the benefits of powerful computation capabilities and intensive storage resources, security and privacy concerns exist. Therefore, our study examines how to collect and aggregate these health data securely and efficiently, with a focus on the theoretical importance and application potential of the aggregated data. In this work, we propose a novel design for a private and fault-tolerant cloud-based data aggregation scheme. Our design is based on a future ciphertext mechanism for improving the fault tolerance capabilities of MHNWs. Our scheme is privatized via differential privacy, which is achieved by encrypting noisy health data and enabling the cloud to obtain the results of only the noisy sum. Our scheme is efficient, reliable and secure and combines different approaches and algorithms to improve the security and efficiency of the system. Our proposed scheme is evaluated with an extensive simulation study, and the simulation results show that it is efficient and reliable. The computational cost of our scheme is significantly less than that of the related scheme. The aggregation error is minimized from ${\rm{O}}\left( {\sqrt {{\bf{w + 1}}} } \right)$ in the related scheme to O(1) in our scheme.


Assuntos
Segurança Computacional , Privacidade , Algoritmos , Computação em Nuvem , Confidencialidade , Agregação de Dados , Humanos
6.
Hepatol Commun ; 5(10): 1721-1736, 2021 10.
Artigo em Inglês | MEDLINE | ID: mdl-34558825

RESUMO

Farnesoid X receptor (FXR) is a nuclear receptor that controls gene regulation of different metabolic pathways and represents an upcoming drug target for various liver diseases. Several data sets on genome-wide FXR binding in different species and conditions exist. We have previously reported that these data sets are heterogeneous and do not cover the full spectrum of potential FXR binding sites. Here, we report the first meta-analysis of all publicly available FXR chromatin immunoprecipitation sequencing (ChIP-seq) data sets from mouse, rat, and human across different conditions using a newly generated analysis pipeline. All publicly available single data sets were biocurated in a standardized manner and compared on every relevant level from raw reads to affected functional pathways. Individual murine data sets were then virtually merged into a single unique "FXR binding atlas" spanning all potential binding sites across various conditions. Comparison of the single biocurated data sets showed that the overlap of FXR binding sites between different species is modest and ranges from 48% (mouse-human) to 55% (mouse-rat). Moreover, in vivo data among different species are more similar than human in vivo data compared to human in vitro data. The consolidated murine global FXR binding atlas virtually increases sequencing depth and allows recovering more and novel potential binding sites and signaling pathways that were missed in the individual data sets. The FXR binding atlas is publicly searchable (https://fxratlas.tugraz.at). Conclusion: Published single FXR ChIP-seq data sets and large-scale integrated omics data sets do not cover the full spectrum of FXR binding. Combining different individual data sets and creating an "FXR super-binding atlas" enhances understanding of FXR signaling capacities across different conditions. This is important when considering the potential wide spectrum for drugs targeting FXR in liver diseases.


Assuntos
Sequenciamento de Cromatina por Imunoprecipitação , Agregação de Dados , Bases de Dados Genéticas , Receptores Citoplasmáticos e Nucleares/genética , Animais , Sítios de Ligação/genética , Regulação da Expressão Gênica/genética , Humanos , Camundongos , Ligação Proteica/genética , Ratos , Transdução de Sinais/genética
7.
PLoS One ; 16(9): e0256919, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34473784

RESUMO

Structured protocols offer a transparent and systematic way to elicit and combine/aggregate, probabilistic predictions from multiple experts. These judgements can be aggregated behaviourally or mathematically to derive a final group prediction. Mathematical rules (e.g., weighted linear combinations of judgments) provide an objective approach to aggregation. The quality of this aggregation can be defined in terms of accuracy, calibration and informativeness. These measures can be used to compare different aggregation approaches and help decide on which aggregation produces the "best" final prediction. When experts' performance can be scored on similar questions ahead of time, these scores can be translated into performance-based weights, and a performance-based weighted aggregation can then be used. When this is not possible though, several other aggregation methods, informed by measurable proxies for good performance, can be formulated and compared. Here, we develop a suite of aggregation methods, informed by previous experience and the available literature. We differentially weight our experts' estimates by measures of reasoning, engagement, openness to changing their mind, informativeness, prior knowledge, and extremity, asymmetry or granularity of estimates. Next, we investigate the relative performance of these aggregation methods using three datasets. The main goal of this research is to explore how measures of knowledge and behaviour of individuals can be leveraged to produce a better performing combined group judgment. Although the accuracy, calibration, and informativeness of the majority of methods are very similar, a couple of the aggregation methods consistently distinguish themselves as among the best or worst. Moreover, the majority of methods outperform the usual benchmarks provided by the simple average or the median of estimates.


Assuntos
Agregação de Dados , Prova Pericial , Processos Grupais , Julgamento , Modelos Estatísticos , Conscientização , Teorema de Bayes , Previsões/métodos , Humanos , Psicologia/métodos , Opinião Pública , Pesquisadores/psicologia , Estudantes/psicologia
8.
Health Serv Res ; 56(6): 1262-1270, 2021 12.
Artigo em Inglês | MEDLINE | ID: mdl-34378181

RESUMO

OBJECTIVE: To examine how estimates of the association between nurse staffing and patient length of stay (LOS) change with data aggregation over varying time periods and settings, and statistical controls for unobserved heterogeneity. DATA SOURCES/STUDY SETTING: Longitudinal secondary data from October 2002 to September 2006 for 215 intensive care units and 438 general acute care units at 143 facilities in the Veterans Affairs (VA) health care system. RESEARCH DESIGN: This retrospective observational study used unit-level panel data to analyze the association between nurse staffing and LOS. This association was measured over both a month-long and a year-long period, with and without fixed effects. DATA COLLECTION: We used VA administrative data to obtain patient data on the severity of illness and LOS, as well as labor hours and wages for each unit by month. PRINCIPAL FINDINGS: Overall, shorter LOS was associated with higher nurse staffing hours and lower proportions of hours provided by licensed professional nurses (LPNs), unlicensed personnel, and contract staff. Estimates of the association between nurse staffing and LOS changed in magnitude when aggregating data over years instead of months, in different settings, and when controlling for unobserved heterogeneity. CONCLUSIONS: Estimating the association between nurse staffing and LOS is contingent on the time period of analysis and specific methodology. In future studies, researchers should be aware of these differences when exploring nurse staffing and patient outcomes.


Assuntos
Agregação de Dados , Tempo de Internação/estatística & dados numéricos , Recursos Humanos de Enfermagem no Hospital/estatística & dados numéricos , Admissão e Escalonamento de Pessoal/estatística & dados numéricos , Demandas Administrativas em Assistência à Saúde/estatística & dados numéricos , Idoso , Feminino , Humanos , Estudos Longitudinais , Masculino , Estudos Retrospectivos , Índice de Gravidade de Doença , Fatores de Tempo , Estados Unidos , United States Department of Veterans Affairs
9.
Accid Anal Prev ; 160: 106313, 2021 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-34365043

RESUMO

The American Association of State Highway and Transportation Officials' Highway Safety Manual (HSM) includes a collection of safety performance functions (SPFs) or statistical models to estimate the expected crash frequency of roadway segments, intersections, and interchanges. These models are applied in several steps of the safety management process, including to screen the road network for opportunities to improve safety and to evaluate the performance of safety countermeasure deployments. The SPFs in the HSM are generally estimated using negative binomial regression modeling. In some instances, they are estimated using annual crash frequency and site-specific (e.g., traffic volume) data, while in other instances they are estimated using aggregate crash frequency and site-specific data. This paper explores the differences that result from estimating SPFs using aggregate versus disaggregate data using the same methods as those used to estimate the SPFs in the HSM. A synthetic dataset was first used to conduct these comparisons - these data were generated in a manner that is consistent with the properties of the negative binomial distribution. Then, an observational dataset from Pennsylvania was used to compare the SPFs from both aggregate and disaggregate data. The results show that SPFs estimated using the panel (disaggregate) data and aggregated data provide similar model coefficients, although some differences may sometimes arise. However, the overdispersion parameter obtained using each dataset can differ significantly. These differences result in systematic biases in calculations of expected crash frequency when Empirical Bayes adjustments are applied, which - as the paper demonstrates - could lead to different outcomes in a network screening exercise. Overall, these results reveal that aggregating crash data might result in biased SPF outputs and lead to inconsistent Empirical Bayes adjustments.


Assuntos
Agregação de Dados , Planejamento Ambiental , Acidentes de Trânsito/prevenção & controle , Teorema de Bayes , Humanos , Modelos Estatísticos , Segurança , Gestão da Segurança
10.
Biosystems ; 207: 104451, 2021 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-34146627

RESUMO

The present study aims to propose a dynamic interactive self-organizing aggregation (DISA) method for swarm robots. The proposed method was determined by way of the movement of swarm robots, obstacle, and robot sensors. The controller used in the DISA method helps the state selector decide through the utilization of these sensors. Systematic simulations were conducted a different number of robots {10, 25, 50}, different detection radii {3, 4} and different arena sizes {40 × 40, 50 × 50, 60 × 60}. The performance of aggregation behavior was compared with other aggregation methods recommended in literature using Total Distance (TD) between robots, Cluster Metrics (CM), Expected Cluster Size (ECS) metric and aggregation completion time. Moreover, noise at different intensities was applied to sensor inputs of the robots. The robustness of the effect of increasing noise on aggregation behavior was examined comparatively. Consequently, the simulation results based on the other compared methods indicated that the utilization of the proposed DISA method led to a higher performance by 88% in the ECS and CM metric as well as in all TD metric measurements and aggregation completion time results.


Assuntos
Algoritmos , Simulação por Computador , Agregação de Dados , Robótica/métodos , Análise por Conglomerados , Robótica/instrumentação
11.
Am J Epidemiol ; 190(10): 1977-1992, 2021 10 01.
Artigo em Inglês | MEDLINE | ID: mdl-33861317

RESUMO

Genotype-phenotype association studies often combine phenotype data from multiple studies to increase statistical power. Harmonization of the data usually requires substantial effort due to heterogeneity in phenotype definitions, study design, data collection procedures, and data-set organization. Here we describe a centralized system for phenotype harmonization that includes input from phenotype domain and study experts, quality control, documentation, reproducible results, and data-sharing mechanisms. This system was developed for the National Heart, Lung, and Blood Institute's Trans-Omics for Precision Medicine (TOPMed) program, which is generating genomic and other -omics data for more than 80 studies with extensive phenotype data. To date, 63 phenotypes have been harmonized across thousands of participants (recruited in 1948-2012) from up to 17 studies per phenotype. Here we discuss challenges in this undertaking and how they were addressed. The harmonized phenotype data and associated documentation have been submitted to National Institutes of Health data repositories for controlled access by the scientific community. We also provide materials to facilitate future harmonization efforts by the community, which include 1) the software code used to generate the 63 harmonized phenotypes, enabling others to reproduce, modify, or extend these harmonizations to additional studies, and 2) the results of labeling thousands of phenotype variables with controlled vocabulary terms.


Assuntos
Estudos de Associação Genética/métodos , Fenômica/métodos , Medicina de Precisão/métodos , Agregação de Dados , Humanos , Disseminação de Informação , National Heart, Lung, and Blood Institute (U.S.) , Fenótipo , Avaliação de Programas e Projetos de Saúde , Estados Unidos
13.
Lancet Diabetes Endocrinol ; 9(4): 203-211, 2021 04.
Artigo em Inglês | MEDLINE | ID: mdl-33636102

RESUMO

BACKGROUND: Diabetes prevalence is increasing in most places in the world, but prevalence is affected by both risk of developing diabetes and survival of those with diabetes. Diabetes incidence is a better metric to understand the trends in population risk of diabetes. Using a multicountry analysis, we aimed to ascertain whether the incidence of clinically diagnosed diabetes has changed over time. METHODS: In this multicountry data analysis, we assembled aggregated data describing trends in diagnosed total or type 2 diabetes incidence from 24 population-based data sources in 21 countries or jurisdictions. Data were from administrative sources, health insurance records, registries, and a health survey. We modelled incidence rates with Poisson regression, using age and calendar time (1995-2018) as variables, describing the effects with restricted cubic splines with six knots for age and calendar time. FINDINGS: Our data included about 22 million diabetes diagnoses from 5 billion person-years of follow-up. Data were from 19 high-income and two middle-income countries or jurisdictions. 23 data sources had data from 2010 onwards, among which 19 had a downward or stable trend, with an annual estimated change in incidence ranging from -1·1% to -10·8%. Among the four data sources with an increasing trend from 2010 onwards, the annual estimated change ranged from 0·9% to 5·6%. The findings were robust to sensitivity analyses excluding data sources in which the data quality was lower and were consistent in analyses stratified by different diabetes definitions. INTERPRETATION: The incidence of diagnosed diabetes is stabilising or declining in many high-income countries. The reasons for the declines in the incidence of diagnosed diabetes warrant further investigation with appropriate data sources. FUNDING: US Centers for Disease Control and Prevention, Diabetes Australia Research Program, and Victoria State Government Operational Infrastructure Support Program.


Assuntos
Agregação de Dados , Diabetes Mellitus Tipo 2/diagnóstico , Diabetes Mellitus Tipo 2/economia , Saúde Global/tendências , Renda/tendências , Internacionalidade , Diabetes Mellitus Tipo 2/epidemiologia , Humanos , Incidência
15.
Clin Appl Thromb Hemost ; 26: 1076029620931200, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32936691

RESUMO

In the current era of patient empowerment and precision medicine, access to timely information is critical to decision-making. Unfortunately, we currently lack patient-specific, real-time data about clinical presentation, risk of thrombotic or hemorrhagic events, key risk factors, and adverse outcomes in patients with venous thromboembolism (VTE). Accordingly, the Registro Informatizado Enfermedad TromboEmbólica (RIETE) investigators developed a tool to provide an open-source, real-time graphic representation of VTE-related data derived from over 90 000 patients with confirmed VTE. This information is intended to facilitate discussion in the informed decision-making process. The current article describes the aims, rationale, methods, and ongoing and future efforts of the real-time VTE infographics developed by the RIETE registry collaborators.


Assuntos
Tromboembolia Venosa/epidemiologia , Agregação de Dados , Feminino , Humanos , Masculino , Estudos Prospectivos , Sistema de Registros , Medição de Risco , Resultado do Tratamento
16.
Artigo em Inglês | MEDLINE | ID: mdl-32823719

RESUMO

Time series analysis in epidemiological studies is typically conducted on aggregated counts, although data tend to be collected at finer temporal resolutions. The decision to aggregate data is rarely discussed in epidemiological literature although it has been shown to impact model results. We present a critical thinking process for making decisions about data aggregation in time series analysis of seasonal infections. We systematically build a harmonic regression model to characterize peak timing and amplitude of three respiratory and enteric infections that have different seasonal patterns and incidence. We show that irregularities introduced when aggregating data must be controlled during modeling to prevent erroneous results. Aggregation irregularities had a minimal impact on the estimates of trend, amplitude, and peak timing for daily and weekly data regardless of the disease. However, estimates of peak timing of the more common infections changed by as much as 2.5 months when controlling for monthly data irregularities. Building a systematic model that controls for data irregularities is essential to accurately characterize temporal patterns of infections. With the urgent need to characterize temporal patterns of novel infections, such as COVID-19, this tutorial is timely and highly valuable for experts in many disciplines.


Assuntos
Betacoronavirus/isolamento & purificação , Infecções por Coronavirus/epidemiologia , Agregação de Dados , Pneumonia Viral/epidemiologia , Estações do Ano , COVID-19 , Estudos de Coortes , Infecções por Coronavirus/virologia , Humanos , Incidência , Modelos Teóricos , Pandemias , Pneumonia Viral/virologia , SARS-CoV-2 , Estudos de Tempo e Movimento
17.
J Clin Epidemiol ; 127: 142-150, 2020 11.
Artigo em Inglês | MEDLINE | ID: mdl-32798713

RESUMO

BACKGROUND AND OBJECTIVES: The Cochrane Central Register of Controlled Trials (CENTRAL) is compiled from a number of sources, including PubMed and Embase. Since 2017, we have increased the number of sources feeding into CENTRAL and improved the efficiency of our processes through the use of application programming interfaces, machine learning, and crowdsourcing.Our objectives were twofold: (1) Assess the effectiveness of Cochrane's centralized search and screening processes to correctly identify references to published reports which are eligible for inclusion in Cochrane systematic reviews of randomized controlled trials (RCTs). (2) Identify opportunities to improve the performance of Cochrane's centralized search and screening processes to identify references to eligible trials. METHODS: We identified all references to RCTs (either published journal articles or trial registration records) with a publication or registration date between 1st January 2017 and 31st December 2018 that had been included in a Cochrane intervention review. We then viewed an audit trail for each included reference to determine if it had been identified by our centralized search process and subsequently added to CENTRAL. RESULTS: We identified 650 references to included studies with a publication year of 2017 or 2018. Of those, 634 (97.5%) had been captured by Cochrane's Centralised Search Service. Sixteen references had been missed by the Cochrane's Centralised Search Service: six had PubMed-not-MEDLINE status, four were missed by the centralized Embase search, three had been misclassified by Cochrane Crowd, one was from a journal not indexed in MEDLINE or Embase, one had only been added to Embase in 2019, and one reference had been rejected by the automated RCT machine learning classifier. Of the sixteen missed references, eight were the main or only publication to the trial in the review in which it had been included. CONCLUSION: This analysis has shown that Cochrane's centralized search and screening processes are highly sensitive. It has also helped us to understand better why some references to eligible RCTs have been missed. The CSS is playing a critical role in helping to populate CENTRAL and is moving us toward making CENTRAL a comprehensive repository of RCTs.


Assuntos
Bases de Dados Bibliográficas , Armazenamento e Recuperação da Informação/métodos , Ensaios Clínicos Controlados Aleatórios como Assunto , Sistema de Registros , Revisões Sistemáticas como Assunto , Crowdsourcing/estatística & dados numéricos , Agregação de Dados , Bases de Dados Bibliográficas/estatística & dados numéricos , Humanos , Armazenamento e Recuperação da Informação/estatística & dados numéricos , MEDLINE , Aprendizado de Máquina , PubMed , Sistema de Registros/estatística & dados numéricos , Estudos Retrospectivos , Sensibilidade e Especificidade
18.
Clin Chem Lab Med ; 59(1): 117-125, 2020 08 06.
Artigo em Inglês | MEDLINE | ID: mdl-32759402

RESUMO

Objectives: External quality assessment (EQA) with commutable samples is used for assessing agreement of results for patients' samples. We investigated the feasibility to aggregate results from four different EQA schemes to determine the bias between different measurement procedures and a reference target value. Methods: We aggregated EQA results for creatinine from programs that used commutable EQA material by calculating the relative difference between individual participant results and the reference target value for each sample. The means and standard errors of the means were calculated for the relative differences. Results were partitioned by methods, manufacturers and instrument platforms to evaluate the biases for the measurement procedures. Results: Data aggregated for enzymatic methods had biases that varied from -8.2 to 3.8% among seven instrument platforms for creatinine at normal concentrations (61-85 µmol/L). EQA schemes differed in the evidence provided about the commutability of their samples, and in the amount of detail collected from participants regarding the measurement procedures which limited the ability to sub-divide aggregated data by instrument platforms and models. Conclusions: EQA data could be aggregated from four different programs using different commutable samples to determine bias among different measurement procedures. Criteria for commutability for EQA samples as well as standardization of reporting the measurement methods, reagents, instrument platforms and models used by participants are needed to improve the ability to aggregate the results for optimal assessment of performance of measurement procedures. Aggregating data from a larger number of EQA schemes is feasible to assess trueness on a global scale.


Assuntos
Análise Química do Sangue/normas , Creatinina/sangue , Análise Química do Sangue/estatística & dados numéricos , Agregação de Dados , Estudos de Viabilidade , Humanos , Países Baixos , Noruega , Controle de Qualidade , Reino Unido , Estados Unidos
19.
J Med Internet Res ; 22(6): e19787, 2020 06 19.
Artigo em Inglês | MEDLINE | ID: mdl-32501803

RESUMO

BACKGROUND: In the context of home confinement during the coronavirus disease (COVID-19) pandemic, objective, real-time data are needed to assess populations' adherence to home confinement to adapt policies and control measures accordingly. OBJECTIVE: The aim of this study was to determine whether wearable activity trackers could provide information regarding users' adherence to home confinement policies because of their capacity for seamless and continuous monitoring of individuals' natural activity patterns regardless of their location. METHODS: We analyzed big data from individuals using activity trackers (Withings) that count the wearer's average daily number of steps in a number of representative nations that adopted different modalities of restriction of citizens' activities. RESULTS: Data on the number of steps per day from over 740,000 individuals around the world were analyzed. We demonstrate the physical activity patterns in several representative countries with total, partial, or no home confinement. The decrease in steps per day in regions with strict total home confinement ranged from 25% to 54%. Partial lockdown (characterized by social distancing measures such as school closures, bar and restaurant closures, and cancellation of public meetings but without strict home confinement) does not appear to have a significant impact on people's activity compared to the pre-pandemic period. The absolute level of physical activity under total home confinement in European countries is around twofold that in China. In some countries, such as France and Spain, physical activity started to gradually decrease even before official commitment to lockdown as a result of initial less stringent restriction orders or self-quarantine. However, physical activity began to increase again in the last 2 weeks, suggesting a decrease in compliance with confinement orders. CONCLUSIONS: Aggregate analysis of activity tracker data with the potential for daily updates can provide information regarding adherence to home confinement policies.


Assuntos
Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/prevenção & controle , Agregação de Dados , Análise de Dados , Monitores de Aptidão Física , Locomoção , Pandemias/prevenção & controle , Pneumonia Viral/epidemiologia , Pneumonia Viral/prevenção & controle , Isolamento Social , Adulto , Betacoronavirus , COVID-19 , Infecções por Coronavirus/transmissão , Europa (Continente) , Feminino , França , Humanos , Masculino , Pessoa de Meia-Idade , Pneumonia Viral/transmissão , SARS-CoV-2 , Espanha
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