scholarly journals Implementing effective community-based surveillance in research studies of maternal, newborn and infant outcomes in low resource settings

2022 ◽  
Vol 19 (1) ◽  
Author(s):  
Caitlin Shannon ◽  
Chris Hurt ◽  
Seyi Soremekun ◽  
Karen Edmond ◽  
Sam Newton ◽  
...  
Keyword(s):  
Data Collection ◽  
Health Outcomes ◽  
Real Time ◽  
Quality Data ◽  
Research Centre ◽  
London School ◽  
Infant Outcomes ◽  
Low Resource ◽  
Maternal And Infant Outcomes ◽  
Research Studies

Abstract Background Globally adopted health and development milestones have not only encouraged improvements in the health and wellbeing of women and infants worldwide, but also a better understanding of the epidemiology of key outcomes and the development of effective interventions in these vulnerable groups. Monitoring of maternal and child health outcomes for milestone tracking requires the collection of good quality data over the long term, which can be particularly challenging in poorly-resourced settings. Despite the wealth of general advice on conducting field trials, there is a lack of specific guidance on designing and implementing studies on mothers and infants. Additional considerations are required when establishing surveillance systems to capture real-time information at scale on pregnancies, pregnancy outcomes, and maternal and infant health outcomes. Main body Based on two decades of collaborative research experience between the Kintampo Health Research Centre in Ghana and the London School of Hygiene and Tropical Medicine, we propose a checklist of key items to consider when designing and implementing systems for pregnancy surveillance and the identification and classification of maternal and infant outcomes in research studies. These are summarised under four key headings: understanding your population; planning data collection cycles; enhancing routine surveillance with additional data collection methods; and designing data collection and management systems that are adaptable in real-time. Conclusion High-quality population-based research studies in low resource communities are essential to ensure continued improvement in health metrics and a reduction in inequalities in maternal and infant outcomes. We hope that the lessons learnt described in this paper will help researchers when planning and implementing their studies.

Leveraging Polio GIS platforms in the African Region for mitigating Covid-19 contact tracing challenges (Preprint)

2020 ◽  
Author(s):  
Godwin Ubong Akpan ◽  
Isah Mohammed Bello ◽  
Kebba Touray ◽  
Reuben Ngofa ◽  
Daniel Oyaole ◽  
...  
Keyword(s):  
Data Collection ◽  
Real Time ◽  
Critical Role ◽  
Open Data ◽  
Contact Tracing ◽  
Quality Data ◽  
Time Data ◽  
Outbreak Response ◽  
African Region ◽  
Health Crisis

BACKGROUND The growth of the novel coronavirus 2019 (COVID-19) pandemic in Africa is an urgent public health crisis. Estimated models project over 150,000 deaths and 4,600,000 hospitalizations in the first year of disease in the absence of adequate interventions. Electronic contact tracing, therefore, offers a critical role in decreasing COVID-19 transmission; yet if not conducted properly can rapidly become a bottleneck for synchronized data collection, case detection, and case management. While the continent is currently reporting relatively low COVID-19 cases, digitized contact tracing mechanisms are necessary for standardizing real-time reporting of new chains of infection to quickly reverse growing trends and halt the pandemic. OBJECTIVE The aim of this study is describing an effective contact tracing smartphone app developed with expertise and experience gained from the numerous digital apps that the Polio programme has used to successfully support disease surveillance and immunization assessment in the African Region. A secondary objective is to describe how we leveraged Polio GIS resources to enhance existing contact tracing solutions to be more efficient through the connection to real-time data visualization platforms. METHODS We propose the use of a hybrid Open Data Kit (ODK) electronic COVID-19 contact registra- tion form that automates contacts and follow-ups. A proof-of-concept form on ODK has been developed that integrates collected contact tracing information from multiple platforms to generate an interactive regional dashboard to monitor the COVID-19 response. Analytics outputs extrapolate key outbreak response indi- cators such as timeliness, completeness and outcomes of contact tracing including new positive cases. This system allows multiple outbreak outputs to be monitored including sources of new infection for immediate response with minimal disruption to existing contact tracing tools. RESULTS Standardized electronic registration of COVID-19 contacts and follow-up using ODK has en- hanced monitoring of contact tracing. Countries and communities have increased their capacity to track COVID-19 cases and contacts in the general population quickly based on the onset of signs or symptoms. Registered contacts for contact tracing are matched to their respective cases more efficiently and for con- tacts that can engage in self-reporting, the anonymity of self-reporting. The country-specific results suggest that higher adoption rates of the tools may result in better quality data on the pandemic and elicited better decisions for a response. CONCLUSIONS Our proposed contact tracing solution which uses ODK based tools on smartphones and visualization bridge systems presents a scalable and easy to implement solution, that collects and aggregates good quality contact data with geographic information that can help make spatial based decisions and preserves privacy while demonstrating the potential to help make better decisions in response to an epidemic or pandemic outbreak. This application has been applied to the current COVID-19 pandemic and can also be used for other epidemics or pandemics in the future, to achieve quality data collection for better decision making.

scholarly journals A Health Informatics Reporting System for Technology Illiterate Workforce Using Mobile Phone

2019 ◽  
Vol 10 (02) ◽  
pp. 348-357 ◽  
Author(s):  
Muhammad Imran Afzal Durrani ◽  
Noman Sohaib Qureshi ◽  
Nadeem Ahmad ◽  
Tabbasum Naz ◽  
Alessia Amelio
Keyword(s):  
Data Collection ◽  
Maternal Mortality ◽  
Real Time ◽  
Surveillance System ◽  
Reporting System ◽  
World Health ◽  
Quality Data ◽  
Mortality Data ◽  
User Centered Design ◽  
User Centered

Abstract Background The reduction and control over neonatal, infant, and maternal mortality is a collective mission of the World Health Organization under United Nations. Methods This article summarizes the automation of verbal autopsy reporting for neonatal, infant, and maternal mortality with primary focus on user-centered design for technologically illiterate workforce with minimum available resources. The diminution in neonatal, infant, and maternal deaths is not possible until grassroot level quality data are available for mortality. The estimated data are less effective for developing countries like Pakistan because it has heterogeneous demographic pockets with respect to mortality causes. The Neonatal, Infant, and Maternal Death E-surveillance System is a project in which a real-time reporting system is innovated that is useful in detecting the causes of mortality and effective in adopting appropriate countermeasure policies. In a pilot study, the system was implemented initially in nine districts of Punjab, Pakistan. The initial system was refined after getting detailed feedback from district management staff including Lady Health Workers and Lady Health Supervisors. The refined surveillance system was finally implemented in all 36 districts of Punjab, Pakistan. Results The results exhibited 31% improvement in infant data collection and 6% improvement in maternal data collection regarding mortality. Conclusion This research will be helpful in achieving the milestone of gathering real-time mortality data from grassroot level using user-centered design methodology.

Enhancing Clinical Supervision Using High-Definition Real-Time Digital Recording, Annotation, and Bluetooth Technology

2019 ◽  
Vol 4 (2) ◽  
pp. 356-362
Author(s):  
Jennifer W. Means ◽  
Casey McCaffrey
Keyword(s):  
Graduate Students ◽  
Data Collection ◽  
Real Time ◽  
Clinical Supervision ◽  
Video Recording ◽  
Advanced Technology ◽  
High Definition ◽  
Recording Technology ◽  
Self Confidence ◽  
Additional Data Collection

Purpose The use of real-time recording technology for clinical instruction allows student clinicians to more easily collect data, self-reflect, and move toward independence as supervisors continue to provide continuation of supportive methods. This article discusses how the use of high-definition real-time recording, Bluetooth technology, and embedded annotation may enhance the supervisory process. It also reports results of graduate students' perception of the benefits and satisfaction with the types of technology used. Method Survey data were collected from graduate students about their use and perceived benefits of advanced technology to support supervision during their 1st clinical experience. Results Survey results indicate that students found the use of their video recordings useful for self-evaluation, data collection, and therapy preparation. The students also perceived an increase in self-confidence through the use of the Bluetooth headsets as their supervisors could provide guidance and encouragement without interrupting the flow of their therapy sessions by entering the room to redirect them. Conclusions The use of video recording technology can provide opportunities for students to review: videos of prospective clients they will be treating, their treatment videos for self-assessment purposes, and for additional data collection. Bluetooth technology provides immediate communication between the clinical educator and the student. Students reported that the result of that communication can improve their self-confidence, perceived performance, and subsequent shift toward independence.

Collecting High-Quality Data

2018 ◽  
Author(s):  
Mary Kay Gugerty ◽  
Dean Karlan
Keyword(s):  
Data Collection ◽  
Social Desirability ◽  
Measurement Errors ◽  
Monitoring And Evaluation ◽  
Quality Data ◽  
Social Desirability Bias ◽  
High Quality ◽  
High Quality Data ◽  
Evaluation Systems ◽  
Demand Effect

Without high-quality data, even the best-designed monitoring and evaluation systems will collapse. Chapter 7 introduces some the basics of collecting high-quality data and discusses how to address challenges that frequently arise. High-quality data must be clearly defined and have an indicator that validly and reliably measures the intended concept. The chapter then explains how to avoid common biases and measurement errors like anchoring, social desirability bias, the experimenter demand effect, unclear wording, long recall periods, and translation context. It then guides organizations on how to find indicators, test data collection instruments, manage surveys, and train staff appropriately for data collection and entry.

933 Maternal and infant outcomes in those undergoing TOLAC by hospital volume

2021 ◽  
Vol 224 (2) ◽  
pp. S579
Author(s):  
Brooke F. Mischkot ◽  
Alyssa R. Hersh ◽  
Bharti Garg ◽  
Aaron B. Caughey
Keyword(s):  
Hospital Volume ◽  
Infant Outcomes ◽  
Maternal And Infant Outcomes

scholarly journals DAViS: a unified solution for data collection, analyzation, and visualization in real-time stock market prediction

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Suppawong Tuarob ◽  
Poom Wettayakorn ◽  
Ponpat Phetchai ◽  
Siripong Traivijitkhun ◽  
Sunghoon Lim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Data Collection ◽  
Stock Market ◽  
Real Time ◽  
Stock Prices ◽  
Contextual Information ◽  
Online News ◽  
Stock Prediction ◽  
Stock Market Prediction

AbstractThe explosion of online information with the recent advent of digital technology in information processing, information storing, information sharing, natural language processing, and text mining techniques has enabled stock investors to uncover market movement and volatility from heterogeneous content. For example, a typical stock market investor reads the news, explores market sentiment, and analyzes technical details in order to make a sound decision prior to purchasing or selling a particular company’s stock. However, capturing a dynamic stock market trend is challenging owing to high fluctuation and the non-stationary nature of the stock market. Although existing studies have attempted to enhance stock prediction, few have provided a complete decision-support system for investors to retrieve real-time data from multiple sources and extract insightful information for sound decision-making. To address the above challenge, we propose a unified solution for data collection, analysis, and visualization in real-time stock market prediction to retrieve and process relevant financial data from news articles, social media, and company technical information. We aim to provide not only useful information for stock investors but also meaningful visualization that enables investors to effectively interpret storyline events affecting stock prices. Specifically, we utilize an ensemble stacking of diversified machine-learning-based estimators and innovative contextual feature engineering to predict the next day’s stock prices. Experiment results show that our proposed stock forecasting method outperforms a traditional baseline with an average mean absolute percentage error of 0.93. Our findings confirm that leveraging an ensemble scheme of machine learning methods with contextual information improves stock prediction performance. Finally, our study could be further extended to a wide variety of innovative financial applications that seek to incorporate external insight from contextual information such as large-scale online news articles and social media data.

Sign in / Sign up

Export Citation Format

Share Document