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

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

Detection of overdose and underdose prescriptions-An unsupervised machine learning approach.

Overdose prescription errors sometimes cause serious life-threatening adverse drug events, while underdose errors lead to diminished therapeutic effects. Therefore, it is important to detect and prevent these errors. In the present study, we used the one-class support vector machine (OCSVM), one of the most common unsupervised machine learning algorithms for anomaly detection, to identify overdose and underdose prescriptions. We extracted prescription data from electronic health records in Kyushu University Hospital between January 1, 2014 and December 31, 2019. We constructed an OCSVM model for each of the 21 candidate drugs using three features: age, weight, and dose. Clinical overdose and underdose prescriptions, which were identified and rectified by pharmacists before administration, were collected. Synthetic overdose and underdose prescriptions were created using the maximum and minimum doses, defined by drug labels or the UpToDate database. We applied these prescription data to the OCSVM model and evaluated its detection performance. We also performed comparative analysis with other unsupervised outlier detection algorithms (local outlier factor, isolation forest, and robust covariance). Twenty-seven out of 31 clinical overdose and underdose prescriptions (87.1%) were detected as abnormal by the model. The constructed OCSVM models showed high performance for detecting synthetic overdose prescriptions (precision 0.986, recall 0.964, and F-measure 0.973) and synthetic underdose prescriptions (precision 0.980, recall 0.794, and F-measure 0.839). In comparative analysis, OCSVM showed the best performance. Our models detected the majority of clinical overdose and underdose prescriptions and demonstrated high performance in synthetic data analysis. OCSVM models, constructed using features such as age, weight, and dose, are useful for detecting overdose and underdose prescriptions.

Factors related to lower limb performance in children and adolescents aged 7 to 17 years: A systematic review with meta-analysis.

BACKGROUND: The literature identifies several factors that are associated with lower limb performance (LLP). However, there is little consensus on which factors have the major associations with LLP. OBJECTIVE: Examine, analyze and summarize the scientific evidence on the factors associated with the performance of LLP in children and adolescents of both sexes aged between 7 and 17 years. DESIGN: This systematic review was conducted following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement and was registered in PROSPERO. DATA SOURCES: A systematic literature search of five electronic databases (i.e., SPORTDiscus, PubMed, CINAHL, Google Scholar, and SCOPUS) with date restrictions was conducted (2010 to 2021). ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Eligibility criteria included (i) a study published between 2010 and 2021; (ii) a research study with observational design; (iii) a study analyzing LLP; and (iv) a sample composed of young people between 7 and 17 years old (regardless of sex). ANALYSES: Literature analysis was carried out in English and Portuguese between 2018 and 2021, "blindly" by two researchers. For data sorting, Rayyan® was used. Data extraction and evidence analysis were performed "blindly", using the Loney scale. The minimum items for observational studies were analyzed by the STROBE checklist. Meta-analyses were conducted based on age group (Childhood [7 to 11 Yrs] and Adolescence [12 to 17 Yrs]) and puberty stages (i.e., Prepupertal and Pubertal). The heterogeneity between the samples of the studies was assessed using the "Cochran's Q" and "I^2" statistics. Meta-regression analyses were performed to check the factors related to heterogeneity of the studies and to check the associations between chronological age and LLP. RESULTS: The literature search resulted in 1,109,650 observational studies of which 39 were included in this review. Through Meta-analysis and Meta-regressions, it was possible to indicate that advancing chronological age related to increased LLP (p<0.01), and that in relation to puberty stages pubertal subjects had higher LLP than their pre-pubertal peers (p<0.01). DISCUSSION: The main findings of the present systematic review suggest that as chronological age advances (childhood to adolescence), neuromuscular systems mature and this may be due to advancing puberty, which is also associated with an increase in LLP. CONCLUSION: The factors associated with lower limbs performance are still inconsistent in the literature. However, advancing chronological age and stage of puberty are both associated with increased lower limbs performance. TRIAL REGISTRATION: ID-PROSPERO-CRD42020137925.

Analysing the administration of an intermediate level of outpatient palliative care in Germany and developing recommendations for improvement (Polite): A study protocol for a mixed-methods study.

BACKGROUND: To date, there has been no systematic research on the intermediate level service (level 2) in outpatient palliative care that was introduced in Germany in 2017. Accordingly, the Polite research project aims at: (1) investigating the current state of level 2 palliative care and (2) developing recommendations for its optimisation. METHODS: The multi-perspective, observational study will follow a mixed-methods approach across two study phases. In phase 1a, quantitative routinely collected data from a statutory general local health insurance provider will be used to identify patients who received level 2 or other outpatient palliative care in the years 2017-2019. In phase 1b, a questionnaire will be sent to all registered general practitioners (GPs) in Lower Saxony to collect information on the number and characteristics of physicians offering level 2 palliative care. In phase 1c, a quantitative, standardised online questionnaire for teams providing specialised outpatient palliative care will be administered to assess the interfaces of level 2 palliative care. In phase 2a, the results from phases 1a-c will be discussed in an expert workshop with the aim of developing ideas to adapt and optimise level 2 outpatient palliative care. Finally, in phase 2b, the empirically derived recommendations from phases 1 and 2a will be agreed upon via a multi-round Delphi survey involving experts with sufficient influence to promote the project results and recommendations nationally. DISCUSSION: The results of the project will facilitate the optimisation of outpatient palliative care, as well as its administration, nationwide. TRIAL REGISTRATION: The study was registered in the German Clinical Trials Register (Deutsches Register Klinischer Studien) (Registration N° DRKS00024785); date of registration: 06th May 2021) and is searchable under the International Clinical Trials Registry Platform Search Portal of the World Health Organization, under the German Clinical Trials Register number.

Evidence on access to healthcare information by women of reproductive age in low- and middle-income countries: Scoping review.

BACKGROUND: A majority of women of reproductive age in low- and middle-income countries (LMICs) are not able to access healthcare information due to different factors. This scoping review aimed to map the literature on access to healthcare information by women of reproductive age in LMICs. METHODS: The literature search was conducted through the following databases: Google Scholar, Science Direct, PubMed, EBSCOhost (Academic search complete, CINAHL with full text, MEDLINE with full text, MEDLINE, and PsycINFO), Emerald, Embase, published and peer-reviewed journals, organizational projects, reference lists, and grey literature. RESULTS: A total of 377 457 articles were identified from all the databases searched. Of these, four articles met inclusion criteria after full article screening and were included for data extraction. The themes that emerged from our study are as follows: accessibility, financial accessibility/affordability, connectivity, and challenges. This study demonstrated that there are minimal interventions that enable women of reproductive age to access healthcare information in terms of accessibility, financial accessibility, and connectivity. CONCLUSION: The findings of the study revealed poor access and utilization of healthcare information by women of reproductive age. We, therefore, recommend primary studies in other LMICs to determine the accessibility, financial accessibility, connectivity, and challenges faced by women of reproductive age in LMICs.

More than half of systematic reviews have relevant core outcome sets.

OBJECTIVES: Using recent systematic reviews (SRs), our objectives were to: (1) develop a framework to assess whether a given COS is relevant to the scope of a SR; (2) examine the proportion of SRs for which relevant COS exist; and (3) for SRs for which COS exist, examine the extent to which outcomes in the COS and outcomes in the SR match. STUDY DESIGN AND SETTING: We included a sample of SRs published by the Agency for Healthcare Research and Quality Evidence-based Practice Center Program between January 1, 2018 and October 12, 2020. We searched for potentially relevant COS from the Core Outcome Measures for Effectiveness Trials (COMET) database. We assessed the matching between outcomes recommended by COS and those included in corresponding SRs. When outcomes were matched, we considered matches to be specific (i.e., exact) or general (i.e., non-specific). RESULTS: Sixty-seven SRs met criteria. We found relevant COS for 36 of 67 SRs (54%). Our framework for comparing the scope of a SR and a COS describes 16 scenarios arising when the breadth of the populations and the interventions are considered. The framework guides systematic reviewers to determine whether a COS is very likely to be relevant, may be relevant, or unlikely to be relevant. Sixty-two percent of outcomes in COS (interquartile range, 40% - 80%) were either specific or general matches to outcomes in SRs. CONCLUSION: We found a COS with relevant scope for more than half of the SRs in our sample, with almost two-thirds of the recommended core outcomes matched to outcomes chosen for the SRs. Consideration of COS appears relevant for SR planning and our framework for assessing relevance of a given COS may help with this process.

Probabilistic linkage without personal information successfully linked national clinical datasets.

BACKGROUND: Probabilistic linkage can link patients from different clinical databases without the need for personal information. If accurate linkage can be achieved, it would accelerate the use of linked datasets to address important clinical and public health questions. OBJECTIVE: We developed a step-by-step process for probabilistic linkage of national clinical and administrative datasets without personal information, and validated it against deterministic linkage using patient identifiers. STUDY DESIGN AND SETTING: We used electronic health records from the National Bowel Cancer Audit and Hospital Episode Statistics databases for 10,566 bowel cancer patients undergoing emergency surgery in the English National Health Service. RESULTS: Probabilistic linkage linked 81.4% of National Bowel Cancer Audit records to Hospital Episode Statistics, vs. 82.8% using deterministic linkage. No systematic differences were seen between patients that were and were not linked, and regression models for mortality and length of hospital stay according to patient and tumour characteristics were not sensitive to the linkage approach. CONCLUSION: Probabilistic linkage was successful in linking national clinical and administrative datasets for patients undergoing a major surgical procedure. It allows analysts outside highly secure data environments to undertake linkage while minimizing costs and delays, protecting data security, and maintaining linkage quality.

Suicide along the Australian coast: Exploring the epidemiology and risk factors.

Suicide is an increasing global concern with multiple risk factors, yet location-based understanding is limited. In Australia, surf lifesavers (SLS) and lifeguards patrol the coast, performing rescues and assisting injured people, including people who suicide. This study is a descriptive epidemiological analysis of Australian coastal suicide deaths. The results will be used to inform training and support surf lifesaving personnel and suicide prevention organisations. This is a population-based cross-sectional study of suicide deaths at Australian coastal locations (between 1 January 2005 and 31 December 2019). Data were sourced from the National Coronial Information System and SLS Australia's Incident Report Database. Analyses explored decedent, incident, and risk factors by sex and method. Across the study period, there were 666 coastal suicide deaths (71.0% male, 43.4% jumping from high places [X80]). Males were more likely to suicide by other means (hanging, self-poisoning, firearm discharge; n = 145, 83.8%), compared to females who were more likely to suicide by drowning ([X71]; n = 77, 37.7%). In one third (n = 225, 38.3%) toxicology was a contributing factor. The risk of coastal suicides was 10.3 times higher during the seven-days prior to their birthday (p<0.001). Evidence of mental ill health was reported in 61.4% (n = 409) of cases and evidence of suicidal behaviour was reported for 37.4% of decedents (n = 249), more prevalent in females. SLS responded in 10.7% (n = 71) of coastal suicides (most jumps from high places; n = 36, 50.7%). Coastal suicides differ to national trends suggesting that location-based differences should be considered during development of preventative and protective measures, especially at a community level. Accessibility, availability, perceived lethality and symbolic qualities are proposed to influence suicide location decisions. These results will guide support and education strategies for surf lifesaving personnel, contributes to established, ongoing suicide surveillance efforts (including hot-spot identification) and add to the limited literature exploring place-based suicide.

Identical summary statistics were uncommon in randomized trials and cohort studies.

OBJECTIVE: To examine the proposition that identical summary statistics (mean and/or SD) in different randomized controlled trials (RCT) or clinical cohorts can be explained by common or homogeneous source populations. STUDY DESIGN: We estimated the probability of identical summary data in studies with high proportions of identical summary statistics, in simulations, and in control datasets. RESULTS: The probability of both an identical mean and an identical SD for a variable in separate RCT is low (<~3%), unless the variable is rounded to 1 significant figure. In two RCT with identical summary statistics for 16 of 39 shared variables, simulations indicated the probability of the observed matches was <1 in 100,000. In 34 clinical cohorts with publication integrity concerns, the proportion of summary statistics from variables reported in ≥10 studies that were identical in ≥2 cohorts were high (42% for means, 52% for SD, and 29% for both), and improbable based on simulations and comparisons to control datasets. CONCLUSIONS: The likelihood of multiple identical summary statistics within an individual RCT or across a body of RCT or cohort studies by the same research group is low, especially when both the mean, and the SD are identical, unless the variables are rounded to 1 significant figure.

Integrating contextual sentiment analysis in collaborative recommender systems.

Recently. recommender systems have become a very crucial application in the online market and e-commerce as users are often astounded by choices and preferences and they need help finding what the best they are looking for. Recommender systems have proven to overcome information overload issues in the retrieval of information, but still suffer from persistent problems related to cold-start and data sparsity. On the flip side, sentiment analysis technique has been known in translating text and expressing user preferences. It is often used to help online businesses to observe customers' feedbacks on their products as well as try to understand customer needs and preferences. However, the current solution for embedding traditional sentiment analysis in recommender solutions seems to have limitations when involving multiple domains. Therefore, an issue called domain sensitivity should be addressed. In this paper, a sentiment-based model with contextual information for recommender system was proposed. A novel solution for domain sensitivity was proposed by applying a contextual information sentiment-based model for recommender systems. In evaluating the contributions of contextual information in sentiment-based recommendations, experiments were divided into standard rating model, standard sentiment model and contextual information model. Results showed that the proposed contextual information sentiment-based model illustrates better performance as compared to the traditional collaborative filtering approach.

Digitized smart surveillance and micromanagement using information technology for malaria elimination in Mangaluru, India: an analysis of five-year post-digitization data.

BACKGROUND: Malaria control system (MCS), an Information technology (IT)-driven surveillance and monitoring intervention is being adopted for elimination of malaria in Mangaluru city, Karnataka, India since October 2015. This has facilitated 'smart surveillance' followed by required field response within a timeline. The system facilitated data collection of individual case, data driven mapping and strategies for malaria elimination programme. This paper aims to present the analysis of post-digitization data of 5 years, discuss the current operational functionalities of MCS and its impact on the malaria incidence. METHODS: IT system developed for robust malaria surveillance and field response is being continued in the sixth year. Protocol for surveillance control was followed as per the national programme guidelines mentioned in an earlier publication. Secondary data from the malaria control system was collated and analysed. Incidence of malaria, active surveillance, malariogenic conditions and its management, malariometric indices, shrinking malaria maps were also analysed. RESULTS: Smart surveillance and subsequent response for control was sustained and performance improved in five years with participation of all stakeholders. Overall malaria incidence significantly reduced by 83% at the end of 5 years when compared with year of digitization (DY) (p < 0.001). Early reporting of new cases (within 48 h) was near total followed by complete treatment and vector control. Slide positivity rate (SPR) decreased from 10.36 (DY) to 6.5 (PDY 5). Annual parasite incidence (API) decreased from 16.17 (DY) to 2.64 (PDY 5). There was a negative correlation between contact smears and incidence of malaria. Five-year data analyses indicated declining trends in overall malaria incidence and correlation between closures by 14 days. The best impact on reduction in incidence of malaria was recorded in the pre-monsoon months (~ 85%) compared to lower impact in July-August months (~ 40%). CONCLUSION: MCS helped to micromanage control activities, such as robust reporting, incidence-centric active surveillance, early and complete treatment, documentation of full treatment of each malaria patient, targeted mosquito control measures in houses surrounding reported cases. The learnings and analytical output from the data helped to modify strategies for control of both disease and the vector, heralding the city into the elimination stage.

Clinicopathological characteristics and prognostic factors for intrahepatic cholangiocarcinoma: a population-based study.

We aimed to explore the clinicopathological features and survival-related factors for intrahepatic cholangiocarcinoma (ICC). Eligible data were extracted from the Surveillance, Epidemiology and End Results (SEER) database from 2004 to 2015. Totally, 4595 ICC patients were collected with a male to female ratio of nearly 1:1. The higher proportion of ICC patients was elderly, tumor size ≥ 5 cm and advanced AJCC stage. Most patients (79.2%) have no surgery, while low proportion of patients receiving radiotherapy (15.1%). The median survival was 7.0 months (range 0-153 months). The 5-year CSS and OS rates were 8.96% and 7.90%. Multivariate analysis found that elderly age (aged ≥ 65 years old), male, diagnosis at 2008-2011, higher grade, tumor size ≥ 5 cm, and advanced AJCC stage were independent factors for poorer prognosis; while API/AI (American Indian/AK Native, Asian/Pacific Islander) race, married, chemotherapy, surgery and radiotherapy were independent favorable factors in both CSS and OS. Furthermore, stratified analysis found that chemotherapy and radiotherapy improved CSS and OS in patients without surgery. Age, sex, race, years of diagnosis, married status, grade, tumor size, AJCC stage, surgery, chemotherapy and radiotherapy were significantly related to prognosis of ICC. Chemotherapy and radiotherapy could significantly improve survival in patients without surgery.

Decreasing trends in cholangiocarcinoma incidence and relative survival in Khon Kaen, Thailand: An updated, inclusive, population-based cancer registry analysis for 1989-2018.

BACKGROUND: Cholangiocarcinoma (CCA) is a leading cause of cancer death in northeastern Thailand. We reported on the incidence of CCA using only one method. In the current study, we used three different statistical methods to forecast future trends and estimate relative survival. METHODS: We reviewed the CCA cases diagnosed between 1989 and 2018 recorded in the population-based Khon Kaen Cancer Registry (KKCR). Annual percent change (APC) was calculated to quantify the incidence rate trends using Joinpoint regression. Age-period-cohort models (APC model) were used to examine the temporal trends of CCA by age, calendar year, and birth cohort. We projected the incidence of CCA up to 2028 using three independent approaches: the Joinpoint, Age-period-cohort, and Nordpred models. Survival assessments were based on relative survival (RS). RESULTS: The respective APC in males and females decreased significantly (-3.1%; 95%CI: -4.0 to -2.1 and -2.4%; 95%CI: -3.6 to -1.2). The APC model-AC-P for male CCA-decreased according to a birth-cohort. The CCA incidence for males born in 1998 was 0.09 times higher than for those born in 1966 (Incidence rate ratios, IRR = 0.09; 95%CI: 0.07 to 0.12). The relative incidence for female CCA similarly decreased according to a birth-cohort (IRR = 0.11; 95%CI: 0.07 to 0.17). The respective projection for the age-standardized rate for males and females for 2028 will be 7.6 per 100,000 (102 patients) and 3.6 per 100,000 (140 patients). The five-year RS for CCA was 10.9% (95%CI: 10.3 to 11.6). CONCLUSION: The incidence rate of CCA has decreased. The projection for 2028 is that the incidence will continue to decline. Nevertheless, the survival of patients with CCA remains poor.

A novel tool for patient data management in the ICU-Ensuring timely and accurate vital data exchange among ICU team members.

OBJECTIVE: The coronavirus pandemic has highlighted the need to simplify data collection for critically-ill patients, particularly for physicians relocated to the ICU setting. Herein we present a simple, reproducible, and highly-customizable manual-entry tool to track ICU patients using new HIPAA-compliant Google Big Query technology for parsing large datasets. This innovative flow chart is useful and could be modified to serve the particular needs of different sub-specialists, particularly those that either rely heavily on hand-written notes or experience poor electronic medical record (EMR) penetration. METHODS: The tool was developed using a combination of three Google Enterprise features: Google Forms for data input, Google Sheets for data output, and Google Big Query for data parsing. Code was written in SQL. Sheets functions were used to transpose and filter parsed data. White and black box tests were performed to examine functionality. RESULTS: Our tool was successfully able to collect and output fictional patient data across all 57 data points specified by the intensivists and surgeons of Cardiovascular Department of Mt. Sinai Morningside Hospital. CONCLUSION: The functional tests performed demonstrate use of the tool. Though originally conceived to simplify patient data collection for newly relocated physicians to the ICU, our tool also overcomes financial and technological barriers previously described in low-income countries that could dramatically improve patient care and provide data to power future studies in these regions. With the original code provided, implementers may adapt our tool to best meet the requirements of their clinical setting and protocols during this very challenging time.

Systematic scoping review identifies heterogeneity in outcomes measured in adolescent depression clinical trials.

OBJECTIVES: The objective of this review was to identify outcomes reported in adolescent major depressive disorder trials and quantify outcome heterogeneity. STUDY DESIGN AND SETTING: Three databases were searched to identify trials evaluating therapies for major depressive disorder in adolescents published from 2008 to 2017. Identified outcomes were thematically grouped and mapped into predefined outcome core areas (physiological/clinical, life impact, resource use, adverse events, and death). Outcome heterogeneity was quantified using descriptive analyses. RESULTS: Of 2,686 articles yielded from the search, 42 articles describing 32 trials were included. A total of 434 outcomes measured using 118 different outcome measurement instruments were grouped into 86 unique outcome terms. Most outcome terms mapped to the physiological/clinical core area (62%), followed by the life impact (27%). Nearly half (45%) were reported in only a single trial each. Of 18 primary outcomes reported, 13 (72%) were each only reported in a single trial. "Depressive symptom severity", reported in 30 trials (94%), was measured using 19 different outcome measurement instruments. CONCLUSION: Heterogeneity exists in the outcomes and outcome measurement instruments used in adolescent depression trials. To enable reproducibility, comparison, and synthesis of trial results, a standard set of agreed-on outcomes and methods of measurement is needed.

Impact of tumor heterogeneity and tissue sampling for genetic mutation testing: a systematic review and post hoc analysis.

OBJECTIVE: The objective of the study was to identify guidelines to assist systematic reviewers or clinical researchers in identifying sampling bias due to tumor heterogeneity (TH) in solid cancers assayed for somatic mutations. We also assessed current reporting standards to determine the impact of TH on sample bias. STUDY DESIGN AND SETTING: We conducted a systematic review searching 13 databases (to January 2019) to identify guidelines. A post hoc analysis was performed using 12 prostate tumor somatic mutation data sets from a previous systematic review to assess reporting on TH. RESULTS: Searches identified 2,085 records. No formal guidelines were identified. Forty publications contained incidental recommendations across five major themes: using multiple tumor samples (n = 29), sample purity thresholds (n = 14), using specific sequencing methods (n = 8), using liquid biopsies (n = 4), and microdissection (n = 4). In post hoc analyses, 50% (6 of 12) clearly reported pathology methods. Forty-two percent (5 of 12) did not report pathology results. Forty-two percent (5 of 12) confirmed the pathology of the sample by direct diagnosis rather than inference. Forty-two percent (5 of 12) used multiple samples per patient. Fifty-eight percent (7 of 12) reported on tumor purity (reported ranges 10% to 100%). CONCLUSIONS: As precision medicine progresses to the clinic, guidelines are required to help evidence-based decision makers understand how TH may impact sample bias. Authors need to clearly report pathology methods and results and tumor purity methods and results.

Por que a desagregação de dados é essencial durante pandemias / Por qué es importante la desagregación de datos durante una pandemia / Why Data Disaggregation is key during a pandemic

O que é desagregação de dados? O termo dados desagregados se refere à separação das informações coletadas em unidades menores para revelar tendências e padrões subjacentes. Os dados compilados podem vir de diversas fontes (setores público e privado e organizações nacionais e internacionais) e ter diversas variáveis, ou "dimensões". Para melhor entender uma situação, os dados são agrupados por dimensão, como idade, sexo, área geográfica, escolaridade, etnia ou outras variáveis socioeconômicas. Por que precisamos de dados desagregados durante uma pandemia? Quando ocorre uma pandemia, uma resposta adequada e eficiente requer a identificação e caracterização dos fatores que desaceleram ou aceleram a transmissão e das populações mais vulneráveis. Dados desagregados de alta qualidade, acessíveis, seguros, atuais, abertos e confiáveis são fundamentais a fim de gerar informações valiosas para a tomada de decisões em tempo real. Por exemplo, para determiner se uma intervenção (como a autotriagem em massa) é efetiva, precisamos saber a proporção da população que foi testada. Isso pode exigir análise por idade, área geográfica e/ou outras variáveis socioeconômicas...

¿Qué significa la desagregación de datos? La desagregación de datos se refiere a la separación de la información recabada en unidades más pequeñas para dilucidar las tendencias y los patrones subyacentes. Los datos recabados pueden provenir de múltiples fuentes (los sectores público y privado, y organizaciones nacionales e internacionales) y tener múltiples variables o "dimensiones". Para mejorar la comprensión de una situación, los datos se agrupan por dimensión, como edad, sexo, zona geográfica, educación, etnicidad u otras variables socioeconómicas. ¿Por qué necesitamos datos desagregados durante una pandemia? Cuando hay una pandemia, una respuesta apropiada y eficaz requiere que determinemos y caractericemos los factores que enlentecen o aceleran la transmisión y los grupos poblacionales que son más vulnerables. Los datos desagregados de alta calidad, accesibles, fiables, oportunos, abiertos y fidedignos son fundamentales para generar información valiosa para la toma de decisiones en tiempo real. Por ejemplo, a fin de determinar si una intervención (como el autotamizaje masivo) es eficaz, tenemos que saber qué proporción de la población ha sido objeto de la prueba. Esto puede requerir un análisis por edad, zona geográfica u otras variables socioeconómicas...

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

Trauma Data Quality Improvement: One Center's Experience With Telecommuting and Paperless Data Management.

The American College of Surgeons requires that trauma centers collect and enter data into the National Trauma Data Registry in compliance with the National Trauma Data Standard. ProMedica supports employment of 4 trauma data analysts who are responsible for entering information in a timely manner, validating the data, and analyzing data to evaluate established benchmarks and support the performance improvement and patient safety process. Historically, these analysts were located on-site at ProMedica Toledo Hospital. In 2017, a proposal was developed including modifications to data collection to streamline processes, move toward paperless documentation, and allow for the analysts to telecommute. To measure the effect of these changes, the timeliness of data entry, rate of data validation, productivity, and staff satisfaction were measured. After the transition to electronic data management and home-based workstations, registry data were being entered within 30 days and 100% of cases were being validated, without sacrificing effective and efficient communication between in-hospital and home-based staff. The institution also benefitted from reduced expense for physical space, employee turnover, and decreased employee absenteeism. The analysts appreciated benefits related to time, travel, environment, and job satisfaction.It is feasible to transition trauma data analysts to a work-from-home situation. An all-electronic system of data management and communication makes such an arrangement possible and sustainable. This quality improvement project solved a workspace issue and was beneficial to the trauma program overall, with the timeliness and validation of data entry vastly improved.

Bayesian analysis from phase III trials was underused and poorly reported: a systematic review.

OBJECTIVES: We assessed the reporting of replicability items with the BayesWatch guidelines and crucial items of Bayesian analyses with the Reporting Of Bayes Used in clinical STudies (ROBUST) guidelines in reports of phase III trials. STUDY DESIGN AND SETTING: A literature search of Cochrane CENTRAL, Ovid MEDLINE, and EMBASE database was conducted to identify phase III trials using Bayesian methods for their primary outcome. Two reviewers performed the study selection process independently. RESULTS: From 5,404 articles retrieved, 49 reports of trials were included. The median [interquartile range] number of correctly reported items was four of seven [3-5] for ROBUST guidelines and seven of 10 [6-8] for BayesWatch guidelines. The proportion of correct reporting widely varied among items. The most frequently reported items from BayesWatch were the least specific (objectives, designs, and reporting of results). Specific Bayesian items (analytic technique and prior specifications) were poorly reported. Priors were given for two-thirds of studies and were justified for 22%. Sensitivity analyses were reported for 57% of trials. CONCLUSION: Bayesian methods are underused in phase III trials. Reports frequently lack descriptions of essential methodological components of the Bayesian analysis, such as the description of priors, which remains a critical key to interpreting Bayesian analyses. PROSPERO: CRD42017054929.