scholarly journals Electronic Data Management in Public Health and Humanitarian Crises. Upgrades, Scalability and Impact of ODK

2020 ◽  
Author(s):  
Michael Marks ◽  
Sham Lal ◽  
Hannah Brindle ◽  
Pierre-Stéphane Gsell ◽  
Matthew MacGregor ◽  
...  
Keyword(s):  
Public Health ◽  
Data Collection ◽  
Clinical Research ◽  
Data Management ◽  
Open Source ◽  
Large Scale ◽  
World Health ◽  
Electronic Data ◽  
Diverse Range ◽  
Health And Social Care

Abstract BackgroundODK provides software and standards that are popular solutions for off-grid electronic data collection and are the basis of related products like CommCare, Enketo, Ona, SurveyCTO and KoBoToolbox. In combination with the use of statistical analysis software such as R, these tools provide fully open-source options for off-grid use in public health data collection, management, analysis and reporting.ResultsNew functions were developed to facilitate the use of ODK, Enketo and R in large scale data collection, aggregation, monitoring and near-real-time analysis during clinical research in health emergencies. We present open-source enhancements to ODK that include a built-in audit-trail, a framework and companion app for biometric registration of ISO/IEC 19794-2 fingerprint templates, enhanced performance features, better scalability for studies featuring millions of data form submissions, increased options for parallelisation of research projects, and pipelines for automated management and analysis of data. We also developed novel encryption protocols for enhanced web-form security in Enketo. During the 2018-2020 Ebola epidemic in the North Kivu & Ituri regions of Democratic Republic of Congo, ODK, Enketo and R were leveraged to support the DRC Ministère de la Santé RDC and World Health Organization in their efforts to administer an experimental vaccine (VSV-Zebov-GP) as part of their strategy to control the transmission of infection. Against the backdrop of a complex and challenging epidemic response, our enhanced platform of open tools was used to collect and manage data from more than 280,000 eligible study participants who received VSV under informed consent. These data were used to determine whether the VSV-Zebov-GP was safe and effective and to guide daily field operations.ConclusionsWe present open source developments that make electronic data management during clinical research and health emergencies more viable and robust. These developments will also enhance and expand the functionality of a diverse range of data collection platforms (Ona, KoBoToolbox etc.) that are based on the ODK software and standards.FundingThis research is funded by the Department of Health and Social Care using UK Aid funding and is managed by the NIHR (PR-OD-1017-20001).

scholarly journals Electronic Data Management for Vaccine Trials in Low Resource Settings: Upgrades, Scalability, and Impact of ODK

2021 ◽  
Vol 9 ◽  
Author(s):  
Michael Marks ◽  
Sham Lal ◽  
Hannah Brindle ◽  
Pierre-Stéphane Gsell ◽  
Matthew MacGregor ◽  
...  
Keyword(s):  
Data Collection ◽  
Clinical Research ◽  
Data Management ◽  
Open Source ◽  
Large Scale ◽  
Democratic Republic Of Congo ◽  
World Health ◽  
Electronic Data ◽  
Diverse Range ◽  
Electronic Data Collection

Background: ODK provides software and standards that are popular solutions for off-grid electronic data collection and has substantial code overlap and interoperability with a number of related software products including CommCare, Enketo, Ona, SurveyCTO, and KoBoToolbox. These tools provide open-source options for off-grid use in public health data collection, management, analysis, and reporting. During the 2018–2020 Ebola epidemic in the North Kivu and Ituri regions of Democratic Republic of Congo, we used these tools to support the DRC Ministère de la Santé RDC and World Health Organization in their efforts to administer an experimental vaccine (VSV-Zebov-GP) as part of their strategy to control the transmission of infection.Method: New functions were developed to facilitate the use of ODK, Enketo and R in large scale data collection, aggregation, monitoring, and near-real-time analysis during clinical research in health emergencies. We present enhancements to ODK that include a built-in audit-trail, a framework and companion app for biometric registration of ISO/IEC 19794-2 fingerprint templates, enhanced performance features, better scalability for studies featuring millions of data form submissions, increased options for parallelization of research projects, and pipelines for automated management and analysis of data. We also developed novel encryption protocols for enhanced web-form security in Enketo.Results: Against the backdrop of a complex and challenging epidemic response, our enhanced platform of open tools was used to collect and manage data from more than 280,000 eligible study participants who received VSV-Zebov-GP under informed consent. These data were used to determine whether the VSV-Zebov-GP was safe and effective and to guide daily field operations.Conclusions: We present open-source developments that make electronic data management during clinical research and health emergencies more viable and robust. These developments will also enhance and expand the functionality of a diverse range of data collection platforms that are based on the ODK software and standards.

scholarly journals Electronic data management for vaccine trials in low resource settings: Upgrades, scalability and impact of ODK

2021 ◽  
Author(s):  
Michael Marks ◽  
Sham Lal ◽  
Hannah Brindle ◽  
Pierre-Stéphane Gsell ◽  
Matthew MacGregor ◽  
...  
Keyword(s):  
Data Collection ◽  
Clinical Research ◽  
Data Management ◽  
Open Source ◽  
Social Care ◽  
World Health ◽  
Electronic Data ◽  
Diverse Range ◽  
Department Of Health ◽  
Health And Social Care

ABSTRACTBackgroundODK provides software and standards that are popular solutions for off-grid electronic data collection and has substantial code overlap and interoperability with a number of related software products including CommCare, Enketo, Ona, SurveyCTO and KoBoToolbox. In combination with the use of statistical analysis software such as R, these tools provide fully open-source options for off-grid use in public health data collection, management, analysis and reporting. During the 2018-2020 Ebola epidemic in the North Kivu & Ituri regions of Democratic Republic of Congo, we leveraged ODK and other tools to support the DRC Ministère de la Santé RDC and World Health Organization in their efforts to administer an experimental vaccine (VSV-Zebov-GP) as part of their strategy to control the transmission of infection.MethodNew functions were developed to facilitate the use of ODK, Enketo and R in large scale data collection, aggregation, monitoring and near-real-time analysis during clinical research in health emergencies. We present open-source enhancements to ODK that include a built-in audit-trail, a framework and companion app for biometric registration of ISO/IEC 19794-2 fingerprint templates, enhanced performance features, better scalability for studies featuring millions of data form submissions, increased options for parallelization of research projects, and pipelines for automated management and analysis of data. We also developed novel encryption protocols for enhanced web-form security in Enketo.ResultsAgainst the backdrop of a complex and challenging epidemic response, our enhanced platform of open tools was used to collect and manage data from more than 280,000 eligible study participants who received VSV-Zebov-GP under informed consent. These data were used to determine whether the VSV-Zebov-GP was safe and effective and to guide daily field operations.ConclusionsWe present open-source developments that make electronic data management during clinical research and health emergencies more viable and robust. These developments will also enhance and expand the functionality of a diverse range of data collection platforms (Ona, KoBoToolbox etc.) that are based on the ODK software and standards.FundingThis research is funded by the Department of Health and Social Care using UK Aid funding and is managed by the NIHR (PR-OD-1017-20001). The views expressed in this publication are those of the authors and not necessarily those of the Department of Health and Social Care.

Electronic Synoptic Operative Reporting for Thyroid Surgery using an Electronic Data Management System: Potential for Prospective Multicenter Data Collection

2010 ◽  
Vol 18 (3) ◽  
pp. 762-766 ◽  
Author(s):  
N. Gopalakrishna Iyer ◽  
Iain J. Nixon ◽  
Frank Palmer ◽  
Ian Ganly ◽  
Snehal G. Patel ◽  
...  
Keyword(s):  
Data Collection ◽  
Data Management ◽  
Thyroid Surgery ◽  
Management System ◽  
Data Management System ◽  
Electronic Data

scholarly journals Data quality and cost-effectiveness analyses of electronic and paper-based interviewer-administered public health surveys: a systematic review (Preprint)

2020 ◽  
Author(s):  
Atinkut Alamirrew Zeleke ◽  
Tolga Naziyok ◽  
Fleur Fritz ◽  
Lara Christianson ◽  
Rainer Röhrig
Keyword(s):  
Public Health ◽  
Cost Effectiveness ◽  
Data Collection ◽  
Data Quality ◽  
Cost Reduction ◽  
Health Surveys ◽  
Collection System ◽  
Electronic Data ◽  
Data Collection System ◽  
Electronic Data Collection

BACKGROUND Population-level survey (PLS) is an essential standard method used in public health research. It supports to quantify sociodemographic events and support public health policy development and intervention designs with evidence. During survey, data collection mechanisms seem the most determinant to avoid mistakes before they happen. The use of electronic devices such as smartphones and tablet computers improve the quality and cost-effectiveness of public health surveys. However, there is a lack of systematically analyzed evidence to show the potential impact of electronic-based data collection tools on data quality and cost reduction in interviewer-administered surveys compared to the standard paper-based data collection system OBJECTIVE This systematic review aims to evaluate the impact of interviewer-administered electronic device data collection methods concerning data quality and cost reduction in PLS compared to the traditional paper-based methods. METHODS A systematic search was conducted in MEDLINE, CINAHL, PsycINFO, the Web of Science, EconLit and Cochrane CENTRAL, and CDSR to identify relevant studies from 2008 to 2018. We included randomized and non-randomized studies that examine data quality and cost reduction outcomes. Moreover, usability, user experience, and usage parameters from the same studies were included. Two independent authors screened the title, abstract, and finally extracted data from the included papers. A third author mediated in case of disagreement. The review authors used EndNote for de-duplication and Rayyan for screening RESULTS The search strategy from the electronic databases found 3,817 articles. After de-duplication, 2,533 articles were screened, and 14 articles fulfilled the inclusion criteria. None of the studies was designed as a randomized control trial. Most of the studies have a quasi-experimental design, like comparative experimental evaluation studies nested on the other ongoing cross-sectional surveys. 4 comparative evaluations, 2 pre-post intervention comparative evaluation, 2 retrospectives comparative evaluation, and 4 one arm non-comparative studies were included in our review. Meta-analysis was not possible because of the heterogeneity in study design, the type, and level of outcome measurements and the study settings. Individual article synthesis showed that data from electronic data collection systems possessed good quality data and delivered faster when compared to the paper-based data collection system. Only two studies linked the cost and data quality outcomes to describe the cost-effectiveness of electronic-based data collection systems. Despite the poor economic evaluation qualities, most of the reported results were in favor of EDC for the large-scale surveys. The field data collectors reported that an electronic data collection system was a feasible, acceptable and preferable tool for their work. Onsite data error prevention, fast data submission, and easy to handle devices were the comparative advantages of electronic data collection systems. Technical difficulties, accidental data loss, device theft, security concerns, power surges, and internet connection problems were reported as challenges during the implementation. CONCLUSIONS Though positive evidence existed about the comparative advantage of electronic data capture over paper-based tools, the included studies were not methodologically rigorous enough to combine. We need more rigorous studies that demonstrate the comparative evidence of paper and electronic-based data collection systems in public health surveys on data quality, work efficiency, and cost reduction CLINICALTRIAL The review protocol is registered in the International Prospective Register for Systematic Reviews (PROSPERO) CRD42018092259. The protocol of this article was also pre-published (JMIR Res Protoc 2019;8(1): e10678 doi:10.2196/10678).

scholarly journals Large Scale Survey Research with Older Adults/Persons with Disabilities in a Public Health Crisis

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 1019-1019
Author(s):  
Sarah Dys ◽  
Hannah Huebner ◽  
Norma Carrillo-Van Tongeren ◽  
Courtney Sirk ◽  
Harold Urman ◽  
...  
Keyword(s):  
Public Health ◽  
Older Adults ◽  
Data Collection ◽  
Large Scale ◽  
Technical Assistance ◽  
People With Disabilities ◽  
Training Data ◽  
Field Staff ◽  
Persons With Disabilities ◽  
Health Crisis

Abstract Best practice for measuring quality improvement and consumer satisfaction of health and human services for older adults and people with disabilities relies on in-person survey administration. This poster highlights adaptation strategies undertaken across three large-scale evaluation studies of program/service delivery conducted during the COVID-19 pandemic, necessitating a departure from in-person techniques: 1) Integrated Satisfaction Measurement for the Program of All-Inclusive Care for the Elderly (I-SAT-PACE), 2) National Core Indicators- Aging and Disabilities/Intellectual and Developmental Disabilities (NCI-AD/IDD), and 3) Assisted Living Resident Quality of Life (AL-QOL). Data collection for these projects occurred from September 2020 to August 2021, providing an opportunity to showcase project adaptation over the course of the pandemic. Using project implementation examples across 15 states and approximately 10,100 participants, we discuss implications for successful survey coordination, interviewer training, data collection, and participant/stakeholder engagement during a public health emergency. Strategies included pivoting to phone, Zoom, and paper-based data collection and increasing technical assistance for field staff and participants. Project teams were able to increase access to participation by implementing multimodal survey delivery, mitigate coronavirus exposure, continue collecting older adults and people with disabilities’ experiences, and compare results based on method of delivery. Technology barriers, field staff dropout, need for larger sample sizes, and inclusion of participants with dementia, hearing, and speech impairments present important tradeoffs to consider. These examples indicate it is possible to administer hybrid data collection methods across populations with varying cognitive and physical abilities without compromising data quality.

scholarly journals Prevalence, Risk Factors, and Genetic Characterization of Extended-Spectrum Beta-Lactamase Escherichia coli Isolated From Healthy Pregnant Women in Madagascar

2021 ◽  
Vol 12 ◽  
Author(s):  
Milen Milenkov ◽  
Saida Rasoanandrasana ◽  
Lalaina Vonintsoa Rahajamanana ◽  
Rivo Solo Rakotomalala ◽  
Catherine Ainamalala Razafindrakoto ◽  
...  
Keyword(s):  
Public Health ◽  
Risk Factors ◽  
Pregnant Women ◽  
Large Scale ◽  
Genomic Diversity ◽  
World Health ◽  
Health Concern ◽  
Snp Analysis ◽  
Phylogenetic Groups ◽  
E Coli

Antimicrobial resistance is a major public health concern worldwide affecting humans, animals and the environment. However, data is lacking especially in developing countries. Thus, the World Health Organization developed a One-Health surveillance project called Tricycle focusing on the prevalence of ESBL-producing Escherichia coli in humans, animals, and the environment. Here we present the first results of the human community component of Tricycle in Madagascar. From July 2018 to April 2019, rectal swabs from 492 pregnant women from Antananarivo, Mahajanga, Ambatondrazaka, and Toamasina were tested for ESBL-E. coli carriage. Demographic, sociological and environmental risk factors were investigated, and E. coli isolates were characterized (antibiotic susceptibility, resistance and virulence genes, plasmids, and genomic diversity). ESBL-E. coli prevalence carriage in pregnant women was 34% varying from 12% (Toamasina) to 65% (Ambatondrazaka). The main risk factor associated with ESBL-E. coli carriage was the rainy season (OR = 2.9, 95% CI 1.3–5.6, p = 0.009). Whole genome sequencing was performed on 168 isolates from 144 participants. blaCTX–M–15 was the most frequent ESBL gene (86%). One isolate was resistant to carbapenems and carried the blaNDM–5 gene. Most isolates belonged to commensalism associated phylogenetic groups A, B1, and C (90%) and marginally to extra-intestinal virulence associated phylogenetic groups B2, D and F (10%). Multi locus sequence typing showed 67 different sequence types gathered in 17 clonal complexes (STc), the most frequent being STc10/phylogroup A (35%), followed distantly by the emerging STc155/phylogroup B1 (7%), STc38/phylogroup D (4%) and STc131/phylogroup B2 (3%). While a wide diversity of clones has been observed, SNP analysis revealed several genetically close isolates (n = 34/168) which suggests human-to-human transmissions. IncY plasmids were found with an unusual prevalence (23%), all carrying a blaCTX–M–15. Most of them (85%) showed substantial homology (≥85%) suggesting a dissemination of IncY ESBL plasmids in Madagascar. This large-scale study reveals a high prevalence of ESBL-E. coli among pregnant women in four cities in Madagascar associated with warmth and rainfall. It shows the great diversity of E. coli disseminating throughout the country but also transmission of specific clones and spread of plasmids. This highlights the urgent need of public-health interventions to control antibiotic resistance in the country.

scholarly journals RADAR-Base: Open Source Mobile Health Platform for Collecting, Monitoring, and Analyzing Data Using Sensors, Wearables, and Mobile Devices

10.2196/11734 ◽  
2019 ◽  
Vol 7 (8) ◽  
pp. e11734 ◽  
Author(s):  
Yatharth Ranjan ◽  
Zulqarnain Rashid ◽  
Callum Stewart ◽  
Pauline Conde ◽  
Mark Begale ◽  
...  
Keyword(s):  
High Resolution ◽  
Data Collection ◽  
Open Source ◽  
Mobile Health ◽  
Large Scale ◽  
Streaming Data ◽  
Security And Privacy ◽  
The European Union ◽  
Wide Range ◽  
Remote Assessment

Background With a wide range of use cases in both research and clinical domains, collecting continuous mobile health (mHealth) streaming data from multiple sources in a secure, highly scalable, and extensible platform is of high interest to the open source mHealth community. The European Union Innovative Medicines Initiative Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) program is an exemplary project with the requirements to support the collection of high-resolution data at scale; as such, the Remote Assessment of Disease and Relapse (RADAR)-base platform is designed to meet these needs and additionally facilitate a new generation of mHealth projects in this nascent field. Objective Wide-bandwidth networks, smartphone penetrance, and wearable sensors offer new possibilities for collecting near-real-time high-resolution datasets from large numbers of participants. The aim of this study was to build a platform that would cater for large-scale data collection for remote monitoring initiatives. Key criteria are around scalability, extensibility, security, and privacy. Methods RADAR-base is developed as a modular application; the backend is built on a backbone of the highly successful Confluent/Apache Kafka framework for streaming data. To facilitate scaling and ease of deployment, we use Docker containers to package the components of the platform. RADAR-base provides 2 main mobile apps for data collection, a Passive App and an Active App. Other third-Party Apps and sensors are easily integrated into the platform. Management user interfaces to support data collection and enrolment are also provided. Results General principles of the platform components and design of RADAR-base are presented here, with examples of the types of data currently being collected from devices used in RADAR-CNS projects: Multiple Sclerosis, Epilepsy, and Depression cohorts. Conclusions RADAR-base is a fully functional, remote data collection platform built around Confluent/Apache Kafka and provides off-the-shelf components for projects interested in collecting mHealth datasets at scale.

scholarly journals EXISTING STRATEGIES FOR ELECTRONIC DATA COLLECTION BY ELDER ABUSE MULTI-DISCIPLINARY TEAMS

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S423-S423
Author(s):  
Tony Rosen ◽  
David Burnes ◽  
Darin Kirchin ◽  
Alyssa Elman ◽  
Risa Breckman ◽  
...  
Keyword(s):  
Data Collection ◽  
Social Services ◽  
Large Scale ◽  
Elder Abuse ◽  
The United States ◽  
Third Party ◽  
Snowball Sampling ◽  
Electronic Data ◽  
Data Set ◽  
Electronic Data Collection

Abstract Elder abuse cases often require integrated responses from social services, medicine, civil legal, and criminal justice. Multi-disciplinary teams (MDTs), which meet periodically to discuss and coordinate interventions for complex cases, have developed in many communities. Little is known about how these MDTs collect case-level data. Our objective was to describe existing strategies of case-level electronic data collection conducted by MDTs across the United States as a preliminary step in developing a comprehensive database strategy. To identify MDTs currently collecting data electronically, we used a snowball sampling approach discussing with national leaders. We also sent an e-mail to the National Center for Elder Abuse listserv inviting participation. We identified and reviewed 11 databases from MDTs. Strategies for and comprehensiveness of data collection varied widely. Databases used ranged from a simple spreadsheet to a customized Microsoft Access database to large databases designed and managed by a third-party vendor. Total data fields collected ranged from 12-338. Types of data included intake/baseline case/client information, case tracking/follow-up, and case closure/outcomes. Information tracked by many MDTs, such as type of mistreatment, was not captured in a single standard fashion. Documentation about data entry processes varied from absent to detailed. We concluded that MDTs currently use widely varied strategies to track data electronically and are not capturing data in a standardized fashion. Many MDTs collect only minimal data. Based on this, we have developed recommendations for a minimum data set and optimal data structure. If widely adopted, this would potentially improve ability to conduct large-scale comparative research.

scholarly journals National surveillance using mobile systems for health monitoring: complexity, functionality and feasibility

2019 ◽  
Vol 19 (S1) ◽  
Author(s):  
Yages Singh ◽  
Debra Jackson ◽  
Sanjana Bhardwaj ◽  
Natasha Titus ◽  
Ameena Goga
Keyword(s):  
South Africa ◽  
Data Collection ◽  
Data Management ◽  
Mobile Phones ◽  
Large Scale ◽  
Turnaround Time ◽  
Mobile Systems ◽  
Data Errors ◽  
Nationally Representative ◽  
Reduced Data

Abstract Background Although the use of technology viz. mobile phones, personalised digital assistants, smartphones, notebook and tablets to monitor health and health care (mHealth) is mushrooming, only small, localised studies have described their use as a data collection tool. This paper describes the complexity, functionality and feasibility of mHealth for large scale surveillance at national and sub-national levels in South Africa, a high HIV-prevalence setting. Methods In 2010, 2011–12 and 2012–13 three nationally representative surveys were conducted amongst infants attending 580 facilities across all 51 districts, within all nine provinces of South Africa, to monitor the effectiveness of the programme to prevent mother-to-child transmission of HIV (PMTCT). In all three surveys a technical protocol and iterative system for mobile data collection was developed. In 2012–13 the system included automated folders to store information about upcoming interviews. Paper questionnaires were used as a back-up, in case of mHealth failure. These included written instructions per question on limits, skips and compulsory questions. Data collectors were trained on both systems. Results In the 2010, 2011–12 and 2012–2013 surveys respectively, data from 10,554, 10,071, and 10,536 interviews, and approximately 186 variables per survey were successfully uploaded to 151 mobile phones collecting data from 580 health facilities in 51 districts, across all nine provinces of South Africa. A technician, costing approximately U$D20 000 p.a. was appointed to support field-based staff. Two percent of data were gathered using paper- questionnaires. The time needed for mHealth interviews was approximately 1,5 times less than the time needed for paper questionnaires 30–45 min versus approximately 120 min (including 60–70 min for the interview with an additional 45 min for data capture). In 2012–13, 1172 data errors were identified via the web-based console. There was a four-week delay in resolving data errors from paper-based surveys compared with a 3-day turnaround time following direct capture on mobile phones. Conclusion Our experiences demonstrate the feasibility of using mHealth during large-scale national surveys, in the presence of a supportive data management team. mHealth systems reduced data collection time by almost 1.5 times, thus reduced data collector costs and time needed for data management.

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