Development of minimal basic data set to report COVID-19

Background: Effective surveillance of COVID-19 highlights the importance of rapid, valid, and standardized information to crisis monitoring and prompts clinical interventions. Minimal basic data set (MBDS) is a set of metrics to be collated in a standard approach to allow aggregated use of data for clinical purposes and research. Data standardization enables accurate comparability of collected data, and accordingly, enhanced generalization of findings. The aim of this study is to establish a core set of data to characterize COVID-19 to consolidate clinical practice. Methods: A 3-step sequential approach was used in this study: (1) an elementary list of data were collected from the existing information systems and data sets; (2) a systematic literature review was conducted to extract evidence supporting the development of MBDS; and (3) a 2-round Delphi survey was done for reaching consensus on data elements to include in COVID-19 MBDS and for its robust validation. Results: In total, 643 studies were identified, of which 38 met the inclusion criteria, where a total of 149 items were identified in the data sources. The data elements were classified by 3 experts and validated via a 2-round Delphi procedure. Finally, 125 data elements were confirmed as the MBDS. Conclusion: The development of COVID-19 MBDS could provide a basis for meaningful evaluations, reporting, and benchmarking COVID-19 disease across regions and countries. It could also provide scientific collaboration for care providers in the field, which may lead to improved quality of documentation, clinical care, and research outcomes

Reliability and Validity of the Pain Anxiety Symptom Scale in Persian Speaking Chronic Low Back Pain Patients

Study Design. Psychometric testing of the Persian version of Pain Anxiety Symptom Scale 20. Objective. The aim of this study was to assess the reliability and construct validity of the PASS-20 in nonspecific chronic low back pain (LBP) patients. Summary of Background Data. The PASS-20 is a self-report questionnaire that assesses pain-related anxiety. The Psychometric properties of this instrument have not been assessed in Persian-speaking chronic LBP patients. Methods. One hundred and sixty participants with chronic LBP completed the Persian version of PASS-20, Tampa Scale of Kinesiophobia (TSK), Fear-Avoidance Beliefs Questionnaire (FABQ), Pain Catastrophizing Scale (PCS), trait form of the State-Trait Anxiety (STAI-T), Oswestry Low Back Pain Disability Index (ODI), Beck Depression Inventory (BDI-II), and Visual Analogue Scale (VAS). To evaluate test-retest reliability, 60 patients filled out the PASS-20, 6 to 8 days after the first visit. Test-retest reliability (intraclass correlation coefficient [ICC], standard error of measurement [SEM], and minimal detectable change [MDC]), internal consistency, dimensionality, and construct validity were examined. Results. The ICCs of the PASS-20 subscales and total score ranged from 0.71 to 0.8. The SEMs for PASS-20 total score was 7.29 and for the subscales ranged from 2.43 to 2.98. The MDC for the total score was 20.14 and for the subscales ranged from 6.71 to 8.23. The Cronbach alpha values for the subscales and total score ranged from 0.70 to 0.91. Significant positive correlations were found between the PASS-20 total score and PCS, TSK, FABQ, ODI, BDI, STAI-T, and pain intensity. Conclusion. The Persian version of the PASS-20 showed acceptable psychometric properties for the assessment of pain-related anxiety in Persian-speaking patients with chronic LBP. © 2017 Wolters Kluwer Health, Inc. All rights reserved

Development a Minimum Data Set of the Health Information Exchange for Computerized HIV Reporting in Iran

Background: The number of people living with HIV has increased in Iran. Creating standard templates for reporting HIV in Iran that can fulfil the needs of all beneficiaries is a basic necessity for the foundation of integrates health information systems. Objective: The aim of this study was to determine the Minimum Data Set (MDS) that is needed in the HIV/AIDS information exchange. Materials and methods: This descriptive and cross-sectional study was performed in 2016. Data were collected from internet resources by using a checklist. The necessary data elements for designing HIV MDS were identified. In order to make a consensus about the data elements, the decision Delphi technique was applied using a questionnaire. The content validity and reliability of questionnaire were assessed by expert's opinions and test-retest method, respectively. Results: An MDS of HIV was developed. The proposed MDS was divided into three data categories includes nonclinical, clinical and supportive with 10, six and three Data classes and 73, 63 and 24 data elements respectively. Discussion: The primary challenge of HIV/AIDS care systems in Iran is insufficient attention to support and consulting programmes as well as the lack of adequate information for accurate and efficient policy and decision-making. Therefore, the existing MDS has been designed to meet the needs of all groups of care providers, Health politicians, healthcare managers, administrative staff, researchers, public health practitioners and support groups (rehabilitation). Finally, it is suggested to use messaging protocols for HIV/AIDS information exchange

Developing a Minimum Dataset for a Mobile-based Contact Tracing System for the COVID-19 Pandemic

Context: Contact tracing is a cornerstone community-based measure for augmenting public health response preparedness to epidemic diseases such as the current coronavirus disease 2019 (COVID-19). However, there is no an agreed data collection tool for the unified reporting of COVID-19 contact tracing efforts at the national level. Objectives: The purpose of this research was to determine the COVID-19 Contact Tracing Minimal Dataset (COV-CT-MDS) as a prerequisite to develop a mobile-based contact tracing system for the COVID-19 outbreak. Methods: This study was carried out in 2020 by a combination of literature review coupled with a two-round Delphi survey. First, the probable data elements were identified using an extensive literature review in scientific databases, including PubMed, Scopus, ProQuest, Science Direct, and Web of Science (WOS). Then, the core data elements were validated using a two-round Delphi survey. Results: Out of 388 articles, 24 were eligible to be included in the study. By the full-text study of the included articles and after the Delphi survey, the designed COV-CT-MDS was categorized into two clinical and administrative data sections, nine data classes, and 81 data fields. Conclusions: COV-CT-MDS is an efficient and valid tool that could provide a basis for collecting comprehensive and standardized data on COVID-19 contact tracing. It could also provide scientific teamwork for health care authorities, which may lead to the enhanced quality of documentation, research, and surveillance outcomes. © 2021, Author(s)

Large-scale image retrieval using local binary patterns and iterative quantization

Hashing algorithm is an efficient approximate searching algorithm for large-scale image retrieval. Learning binary code is a key step to improve its performance and it is still an ongoing challenge. The inputs of Hashing affects its performance. This paper proposes a method to improve the efficiency of learning binary code by improving the suitableness of the Hashing algorithms inputs by employing local binary patterns in extracting image features. This approach results in more compact code, less memory and computational requirement and higher performance. The reasons behind these achievements are the binary nature and high efficiency in feature generation of local binary pattern. The performance analysis consists of using CIFAR-10 and precision vs. recall rate as dataset and evaluation criteria respectively. The simulations compare the new algorithm with three state of the art and along the line algorithms from three points of view; the hashing code size, memory space and computational cost, and the results demonstrate the effectiveness of the new approach

Common data elements and features of brucellosis health information management system

Introduction: A key step in constructing any health information management system (HIMS) is to decide on a set of minimal yet comprehensive data items. The consensus dataset would be homogenous between healthcare settings and can pave the way for scientific collaborations. Iran is the fourth endemic country for brucellosis in the world. Despite its huge burden on society, the economy, and the environment, there is no agreed-upon minimum data set (MDS) for reporting this disease, and the data collected are rarely homogenous or directly comparable. Objective: To establish the brucellosis MDS that may enable homogeneity in data collection, data reporting, and data exchange among various HIMSs. Methods: A two-step process, including an extensive literature search and a two-round Delphi survey, was performed to foster consensus about the required data items. The collected data were analyzed using SPSS V22 (SPSS Inc., Chicago, IL). Results: The final MDS platform of our study contained 134 items divided into five main categories of administrative information, epidemiology, diagnosis investigation, complications, and signs and symptoms. Conclusion: This study provided a practical MDS for brucellosis that can help collect unified and comprehensive data for electronic health record systems (EHRs), disease surveillance, and registries, and easily integrate them with other HIMSs. The developed MDS can promote the collaboration of policy-makers, healthcare providers, and researchers to prevent, control, and manage brucellosis. © 202

Designing a communication protocol for acquired immunodeficiency syndrome information exchange

INTRODUCTION: Interoperability will provide similar understanding on the meaning of communicated messages to intelligent systems and their users. This feature is essential for controlling and managing contagious diseases which threaten public health, such as acquired immunodeficiency syndrome (AIDS). The aim of this study was also designing communication protocols for normalizing the content and structure of intelligent messages in order to optimize the interoperability. MATERIALS AND METHODS: This study used a checklist to extract information content compatible with minimum data set (MDS) of AIDS. After coding information content through selected classification and nomenclature systems, the reliability and validity of codes were evaluated by external agreement method. The MindMaple software was used for mapping the information content to Systematized Nomenclature of Medicine-Clinical Terminology (SNOMED-CT) integrated codes. Finally, the Clinical Document Architecture (CDA) format was used for standard structuring of information content. RESULTS: The information content standard format, compatible selected classification, or nomenclature system and their codes were determined for all information contents. Their corresponding codes in SNOMED-CT were structured in the form of CDA body and title. CONCLUSION: The complex and multidimensional nature of AIDS requires the participation of multidisciplinary teams from different organizations, complex analyzes, multidimensional and complex information modeling, and maximum interoperability. In this study, the use of CDA structure along with SNOMED-CT codes is completely compatible with optimal interoperability needs for AIDS control and management

Determination of the most important diagnostic criteria for COVID-19: A step forward to design an intelligent clinical decision support system

Background & Objective: Since the clinical and epidemiologic characteristics of coronavirus disease 2019 (COVID-19) is not well known yet, investigating its origin, etiology, diagnostic criteria, clinical manifestations, risk factors, treatments, and other related aspects is extremely important. In this situation, clinical experts face many uncertainties to make decision about COVID-19 prognosis based on their judgment. Accordingly, this study aimed to determine the diagnostic criteria for COVID-19 as a prerequisite to develop clinical diagnostic models. Materials & Methods: In this retrospective study, the Enter method of the binary logistic regression (BLR) and the Forward Wald method were used to measure the odds ratio (OR) and the strength of each criterion, respectively. P-value<0.05 was considered as statistically significant for bivariate correlation coefficient. Results: Phi and Cramer’s V correlation coefficient test showed that 12 diagnostic criteria were statistically important; measuring OR revealed that six criteria had the best diagnostic power. Finally, true classification rate and the area under receiver operative characteristics curve (AUC) were calculated as 90.25 and 0.835, respectively. Conclusion: Identification of diagnostic criteria has become the standard approach for disease modeling; it helps to design decision support tools. After analyzing and comparing six diagnostic performance measures, we observed that these variables have a high diagnostic power for COVID-19 detection. © 2021