Online journal of public health informatics最新文献

{"title":"A Data Driven Approach for Prioritizing COVID-19 Vaccinations in the Midwestern United States.","authors":"Greg Arling,&nbsp;Matthew Blaser,&nbsp;Michael D Cailas,&nbsp;John R Canar,&nbsp;Brian Cooper,&nbsp;Joel Flax-Hatch,&nbsp;Peter J Geraci,&nbsp;Kristin M Osiecki,&nbsp;Apostolis Sambanis","doi":"10.5210/ojphi.v13i1.11621","DOIUrl":"https://doi.org/10.5210/ojphi.v13i1.11621","url":null,"abstract":"<p><p>Considering the potential for widespread adoption of social vulnerability indices (SVI) to prioritize COVID-19 vaccinations, there is a need to carefully assess them, particularly for correspondence with outcomes (such as loss of life) in the context of the COVID-19 pandemic. The University of Illinois at Chicago School of Public Health Public Health GIS team developed a methodology for assessing and deriving vulnerability indices based on the premise that these indices are, in the final analysis, classifiers. Application of this methodology to several Midwestern states with a commonly used SVI indicates that by using only the SVI rankings there is a risk of assigning a high priority to locations with the lowest mortality rates and low priority to locations with the highest mortality rates. Based on the findings, we propose using a two-dimensional approach to rationalize the distribution of vaccinations. This approach has the potential to account for areas with high vulnerability characteristics as well as to incorporate the areas that were hard hit by the pandemic.</p>","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"13 1","pages":"e5"},"PeriodicalIF":0.0,"publicationDate":"2021-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075414/pdf/ojphi-13-1-e5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38860337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tracking COVID-19 burden in India: A review using SMAART RAPID tracker.","authors":"Ashish Joshi, Harpreet Kaur, L Nandini Krishna, Shruti Sharma, Gautam Sharda, Garima Lohra, Ashruti Bhatt, Ashoo Grover","doi":"10.5210/ojphi.v13i1.11456","DOIUrl":"10.5210/ojphi.v13i1.11456","url":null,"abstract":"<p><strong>Objective: </strong>India has seen a rapid rise in COVID-19 cases. Examine spatiotemporal variation of COVID-19 burden Tracker across Indian states and union territories using SMAART RAPID Tracker.</p><p><strong>Method: </strong>We used SMAART RAPID Tracker to visually display COVID-19 spread in space and time across various states and UTs of India. Data gathered from publicly available government information sources. Data analysis on COVID-19 conducted from March 1 2020 to October 1 2020. Variables recorded include COVID-19 cases and fatality, 7-day average change, recovery rate, labs and tests. Spatial and temporal trends of COVID-19 spread across Indian states and UTs is presented.</p><p><strong>Result: </strong>The total number of COVID-19 cases were 63, 12,584 and total fatality was 86,821 (October 1 2020). More than 85,000 new cases of COVID-19 were reported. There were 1,867 total COVID-19 labs throughout India. More than half of them were Government labs. The total number of COVID-19 tests was 76,717,728 and total recovered COVID-19 cases was 5,273,201. Results show an overall decline in the 7-day average change of new COVID-19 cases and new COVID-19 fatality. States such as Maharashtra, Chandigarh, Puducherry, Goa, Karnataka and Andhra Pradesh continue to have high COVID-19 infectivity rate.</p><p><strong>Discussion: </strong>Findings highlight need for both national guidelines combined with state specific recommendations to help manage the spread of COVD-19.</p><p><strong>Conclusion: </strong>The heterogeneity represented in India in terms of its geography and various population groups highlight the need of state specific approach to monitor and combat the ongoing pandemic. This would further facilitate the tailored approach for each state to mitigate and contain the spread of the disease.</p>","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"13 1","pages":"e4"},"PeriodicalIF":0.0,"publicationDate":"2021-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075416/pdf/ojphi-13-1-e4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38860335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A second wave of COVID-19 in Cook County: What lessons can be applied?","authors":"Gregory W Arling,&nbsp;Matthew Blaser,&nbsp;Michael D Cailas,&nbsp;John R Canar,&nbsp;Brian Cooper,&nbsp;Peter J Geraci,&nbsp;Kristin M Osiecki,&nbsp;Apostolis Sambanis","doi":"10.5210/ojphi.v12i2.11506","DOIUrl":"https://doi.org/10.5210/ojphi.v12i2.11506","url":null,"abstract":"<p><p>During the ongoing public health crisis, many agencies are reporting COVID-19 health outcome information based on the overall population. This practice can lead to misleading results and underestimation of high risk areas. To gain a better understanding of spatial and temporal distribution of COVID-19 deaths; the long term care facility (LTCF) and household population (HP) deaths must be used. This approach allows us to better discern high risk areas and provides policy makers with reliable information for community engagement and mitigation strategies. By focusing on high-risk LTCFs and residential areas, protective measures can be implemented to minimize COVID-19 spread and subsequent mortality.  These areas should be a high priority target when COVID-19 vaccines become available.</p>","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"12 2","pages":"e15"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7758067/pdf/ojphi-12-2-e15.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39114620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Mobile Phone Data Collection Tool, Surveda, for Noncommunicable Disease Surveillance in Five Low- and Middle-income Countries.","authors":"Yang Song,&nbsp;Rachael Phadnis,&nbsp;Jennifer Favaloro,&nbsp;Juliette Lee,&nbsp;Charles Q Lau,&nbsp;Manuel Moreira,&nbsp;Leenisha Marks,&nbsp;Matías García Isaía,&nbsp;Jason Kim,&nbsp;Veronica Lea","doi":"10.5210/ojphi.v12i2.10574","DOIUrl":"https://doi.org/10.5210/ojphi.v12i2.10574","url":null,"abstract":"<p><strong>Objectives: </strong>The Noncommunicable Disease (NCD) Mobile Phone Survey, a component of the Bloomberg Philanthropies Data for Health Initiative, determines the prevalence of NCDs and their associated risk factors and demonstrates the use of mobile phone administered surveys to supplement periodic national household surveys. The NCD Mobile Phone Survey uses Surveda to administer the survey; Surveda is an open-source, multi-modal software specifically developed for the project. The objective of the paper is to describe Surveda, review data collection methods used in participating countries and discuss how Surveda and similar approaches can improve public health surveillance.</p><p><strong>Methods: </strong>Surveda features full-service survey design and implementation through a web application and collects data via Short Messaging Service (SMS), Interactive Voice Response (IVR) and/or mobile web. Surveda's survey design process employs five steps: creating a project, creating questionnaires, designing and starting a survey, monitoring survey progress, and exporting survey results.</p><p><strong>Results: </strong>The NCD Mobile Phone Survey has been successfully conducted in five countries, Zambia (2017), Philippines (2018), Morocco (2019), Malawi (2019), and Sri Lanka (2019), with a total of 23,682 interviews completed.</p><p><strong>Discussion: </strong>This approach to data collection demonstrates that mobile phone surveys can supplement face-to-face data collection methods. Furthermore, Surveda offers major advantages including automated mode-switch, question randomization and comparison features.</p><p><strong>Conclusion: </strong>Accurate and timely survey data informs a country's abilities to make targeted policy decisions while prioritizing limited resources. The high acceptance of Surveda demonstrates that the use of mobile phones for surveillance can deliver accurate and timely data collection.</p>","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"12 2","pages":"e13"},"PeriodicalIF":0.0,"publicationDate":"2020-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7758069/pdf/ojphi-12-2-e13.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39114618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards Unified Data Exchange Formats for Reporting Molecular Drug Susceptibility Testing.","authors":"Wilfred Bonney,&nbsp;Sandy F Price,&nbsp;Swapna Abhyankar,&nbsp;Riki Merrick,&nbsp;Varsha Hampole,&nbsp;Tanya A Halse,&nbsp;Charles DiDonato,&nbsp;Tracy Dalton,&nbsp;Beverly Metchock,&nbsp;Angela M Starks,&nbsp;Roque Miramontes","doi":"10.5210/ojphi.v12i2.10644","DOIUrl":"https://doi.org/10.5210/ojphi.v12i2.10644","url":null,"abstract":"<p><strong>Background: </strong>With the rapid development of new advanced molecular detection methods, identification of new genetic mutations conferring pathogen resistance to an ever-growing variety of antimicrobial substances will generate massive genomic datasets for public health and clinical laboratories. Keeping up with specialized standard coding for these immense datasets will be extremely challenging. This challenge prompted our effort to create a common molecular resistance Logical Observation Identifiers Names and Codes (LOINC) panel that can be used to report any identified antimicrobial resistance pattern.</p><p><strong>Objective: </strong>To develop and utilize a common molecular resistance LOINC panel for molecular drug susceptibility testing (DST) data exchange in the U.S. National Tuberculosis Surveillance System using California Department of Public Health (CDPH) and New York State Department of Health as pilot sites.</p><p><strong>Methods: </strong>We developed an interface and mapped incoming molecular DST data to the common molecular resistance LOINC panel using Health Level Seven (HL7) v2.5.1 Electronic Laboratory Reporting (ELR) message specifications through the Orion Health™ Rhapsody Integration Engine v6.3.1.</p><p><strong>Results: </strong>Both pilot sites were able to process and upload/import the standardized HL7 v2.5.1 ELR messages into their respective systems; albeit CDPH identified areas for system improvements and has focused efforts to streamline the message importation process. Specifically, CDPH is enhancing their system to better capture parent-child elements and ensure that the data collected can be accessed seamlessly by the U.S. Centers for Disease Control and Prevention.</p><p><strong>Discussion: </strong>The common molecular resistance LOINC panel is designed to be generalizable across other resistance genes and ideally also applicable to other disease domains.</p><p><strong>Conclusion: </strong>The study demonstrates that it is possible to exchange molecular DST data across the continuum of disparate healthcare information systems in integrated public health environments using the common molecular resistance LOINC panel.</p>","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"12 2","pages":"e14"},"PeriodicalIF":0.0,"publicationDate":"2020-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7758061/pdf/ojphi-12-2-e14.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39114619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generation and Classification of Activity Sequences for Spatiotemporal Modeling of Human Populations.","authors":"Albert M Lund,&nbsp;Ramkiran Gouripeddi,&nbsp;Julio C Facelli","doi":"10.5210/ojphi.v12i1.10588","DOIUrl":"https://doi.org/10.5210/ojphi.v12i1.10588","url":null,"abstract":"<p><p>Human activity encompasses a series of complex spatiotemporal processes that are difficult to model but represent an essential component of human exposure assessment. A significant empirical data source, like the American Time Use Survey (ATUS), can be leveraged to model human activity. However, tractable models require a better stratification of activity data to inform about different, but classifiable groups of individuals, that exhibit similar activity sequences and mobility patterns. Using machine learning algorithms, we developed an unsupervised classification and sequence generation method that is capable of generating coherent and stochastic sequences of activity from the ATUS data. This classification, when combined with any spatiotemporal exposure profile, allows the development of stochastic models of exposure patterns and records for groups of individuals exhibiting similar activity behaviors.</p>","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"12 1","pages":"e9"},"PeriodicalIF":0.0,"publicationDate":"2020-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7462521/pdf/ojphi-12-1-e9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38460674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study of feasibility and effectiveness of ASHA-Soft (Online Payment and Performance Monitoring System) in Rajasthan.","authors":"Nitin K Joshi,&nbsp;Pankaj Bhardwaj,&nbsp;Praveen Suthar,&nbsp;Vibha Joshi","doi":"10.5210/ojphi.v12i1.10662","DOIUrl":"https://doi.org/10.5210/ojphi.v12i1.10662","url":null,"abstract":"<p><strong>Objective: </strong>ASHA-Soft is the pioneer e-Health program which was launched to manage online payment and for monitoring performance of ASHA workers in Rajasthan. There is a paucity of studies which documents the feasibility and effectiveness of this program with aim to assess the feasibility and effectiveness of ASHA-Soft program.</p><p><strong>Methods: </strong>Study was conducted in Jodhpur using quantitative and qualitative method. Primary and secondary data approach was used to assess feasibility and effectiveness of ASHA-Soft. Purposive sampling was done to recruit 150 ASHA workers having experience of more than 5 years to capture the perception before and after implementation of ASHA-Soft. Qualitative data was also obtained from ASHA workers and key stakeholders. To assess the effectiveness secondary data was obtained from various sources was analyzed.</p><p><strong>Results: </strong>Mean age of participants were 35.51<b>+</b> 6.7 years. Most of ASHAs agreed that ASHA-Soft mediated timely payment (68%) and payment according to their performance (81%). It also increased their motivational level (96%).There were no significant difference in different work experience of ASHAs and perception towards ASHA-Soft regarding timely payment (p=0.99), improving quality of life (p=0.66) and motivation level (p=0.40). This program has provided standard online procedure of online payment and monitoring for ASHAs. Incentives received by ASHAs increased to 77%, performance increased by 7% and 9% for maternal health and child health respectively within one year of its initial implementation.</p><p><strong>Conclusions: </strong>Study finding demonstrate that ASHA-Soft program is acceptable to the users and is effective in terms of meeting organizational requirement.</p>","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"12 1","pages":"e12"},"PeriodicalIF":0.0,"publicationDate":"2020-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7462604/pdf/ojphi-12-1-e12.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38364088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving Accuracy for Diabetes Mellitus Prediction by Using Deepnet.","authors":"Riyad Alshammari, Noorah Atiyah, Tahani Daghistani, Abdulwahhab Alshammari","doi":"10.5210/ojphi.v12i1.10611","DOIUrl":"10.5210/ojphi.v12i1.10611","url":null,"abstract":"<p><p>Diabetes is a salient issue and a significant health care concern for many nations. The forecast for the prevalence of diabetes is on the rise. Hence, building a prediction machine learning model to assist in the identification of diabetic patients is of great interest. This study aims to create a machine learning model that is capable of predicting diabetes with high performance. The following study used the BigML platform to train four machine learning algorithms, namely, Deepnet, Models (decision tree), Ensemble and Logistic Regression, on data sets collected from the Ministry of National Guard Hospital Affairs (MNGHA) in Saudi Arabia between the years of 2013 and 2015. The comparative evaluation criteria for the four algorithms examined included; Accuracy, Precision, Recall, F-measure and PhiCoefficient. Results show that the Deepnet algorithm achieved higher performance compared to other machine learning algorithms based on various evaluation matrices.</p>","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"12 1","pages":"e11"},"PeriodicalIF":0.0,"publicationDate":"2020-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7462602/pdf/ojphi-12-1-e11.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38364087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical Decision Support for Immunization Uptake and Use in Immunization Health Information Systems.","authors":"Lauren Shrader,&nbsp;Stuart Myerburg,&nbsp;Eric Larson","doi":"10.5210/ojphi.v12i1.10602","DOIUrl":"https://doi.org/10.5210/ojphi.v12i1.10602","url":null,"abstract":"<p><strong>Context: </strong>In the United States, immunization recommendations and their associated schedules are developed by the Advisory Committee on Immunization Practices (ACIP). To assist with the translation process and better harmonize the outcomes of existing clinical decision support tools, the Centers for Disease Control and Prevention (CDC) created clinical decision support for immunization (CDSi) resources for each set of ACIP recommendations. These resources are continually updated and refined as new vaccine recommendations and clarifications become available and will be available to health information systems for a coronavirus disease 2019 (COVID-19) vaccine when one becomes available for use in the United States Objectives: To assess awareness of CDSi resources, whether CDSi resources were being used by immunization-related health information systems, and perceived impact of CDSi resources on stakeholders' work Design: Online surveys conducted from 2015-2019 including qualitative and quantitative questions Participants: The main and technical contact from each of the 64 CDC-funded immunization information system (IIS) awardees, IIS vendors, and electronic health record vendors Results: Awareness of at least one resource increased from 75% of respondents in 2015 to 100% in 2019. Use of at least one CDSi resource also increased from 47% in 2015 to 78% in 2019. About 80% or more of users of CDSi are somewhat or very highly satisfied with the resources and report a somewhat or very positive impact from using them Conclusion: As awareness and use of CDSi resources increases, the likelihood that patients receive recommended immunizations at the right time will also increase. Rapid and precise integration of vaccine recommendations into health information systems will be particularly important when a COVID-19 vaccine becomes available to help facilitate vaccine implementation.</p>","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"12 1","pages":"e10"},"PeriodicalIF":0.0,"publicationDate":"2020-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7462520/pdf/ojphi-12-1-e10.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38364086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Current Approaches and Trends in Graduate Public Health Informatics Education in the United States: Four Case Studies from the Field.","authors":"Diane G Schwartz,&nbsp;Scott P McGrath,&nbsp;Karen A Monsen,&nbsp;Brian E Dixon","doi":"10.5210/ojphi.v12i1.10703","DOIUrl":"https://doi.org/10.5210/ojphi.v12i1.10703","url":null,"abstract":"<p><p><b>Background:</b>Public Health Informatics (PHI) has taken on new importance in recent years as health and well-being face a number of challenges, including environmental disasters, emerging infectious diseases, such as Zika, Ebola and SARS-CoV-2, the growing impact of the Influenza virus, the opioid epidemic, and social determinants of health. Understanding the relationship between climate change and the health of populations adds further complexity to global health issues. <b>Objectives:</b> To describe four examples of curricula that exist in U.S. based graduate-level public and population health informatics training programs. <b>Methods:</b> Biomedical informatics educators are challenged to provide learners with relevant, interesting, and meaningful educational experiences in working with and learning from the many data sources that comprise the domain of PHI. Programs at four institutions were reviewed to examine common teaching practices that stimulate learners to explore the field of public health informatics. <b>Results:</b> Four case studies represent a range of pedagogical approaches to meeting the requirements of three established accreditation/certification agencies relevant to PHI education. Despite their differences, each program achieved the established learning objectives along with a substantive record of student learning achievements. <b>Conclusion:</b> The overarching goal of empowering learners to serve an active and dynamic role in enhancing preventive measures, informing policy, improving personal health behaviors, and clarifying issues such as quality, cost of care, and the social determinants of health, are essential components of PHI education and training, and must receive additional consideration now and in the future by educators, policy makers, administrators, and government officials.</p>","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"12 1","pages":"e7"},"PeriodicalIF":0.0,"publicationDate":"2020-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7386057/pdf/ojphi-12-1-e7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38221008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}