Data Collection Capacity Research Articles

Play the Pain: A Digital Strategy for Play-Oriented Research and Action.

The value of understanding patients' illness experience and social contexts for advancing medicine and clinical care is widely acknowledged. However, methodologies for rigorous and inclusive data gathering and integrative analysis of biomedical, cultural, and social factors are limited. In this paper, we propose a digital strategy for large-scale qualitative health research, using play (as a state of being, a communication mode or context, and a set of imaginative, expressive, and game-like activities) as a research method for recursive learning and action planning. Our proposal builds on Gregory Bateson's cybernetic approach to knowledge production. Using chronic pain as an example, we show how pragmatic, structural and cultural constraints that define the relationship of patients to the healthcare system can give rise to conflicted messaging that impedes inclusive health research. We then review existing literature to illustrate how different types of play including games, chatbots, virtual worlds, and creative art making can contribute to research in chronic pain. Inspired by Frederick Steier's application of Bateson's theory to designing a science museum, we propose DiSPORA (Digital Strategy for Play-Oriented Research and Action), a virtual citizen science laboratory which provides a framework for delivering health information, tools for play-based experimentation, and data collection capacity, but is flexible in allowing participants to choose the mode and the extent of their interaction. Combined with other data management platforms used in epidemiological studies of neuropsychiatric illness, DiSPORA offers a tool for large-scale qualitative research, digital phenotyping, and advancing personalized medicine.

"Real-world" first-episode psychosis care in Massachusetts: Lessons learned from a pilot implementation of harmonized data collection.

Increasing evidence points to the value of coordinated specialty care (CSC) for early intervention in psychotic disorders. This report characterizes clinical and socio-demographic features of patients at CSC programs in Massachusetts (MA), assessed by a standardized battery incorporated into "real-world" clinical care. The MA psychosis network for early treatment developed a pilot battery to coordinate assessments across six CSC clinics. Programs reported baseline, 6-month, and 12-month data from a sample of 287 patients with intake dates ranging from April 2015 to December 2020. Patients showed improvements in functioning, emergency service use and several symptom domains at 6 and 12 months. Missing data proved to be a limitation. Patients improved on several meaningful domains within the first year of CSC treatment. Future implementation efforts in cross-program data collection should consider strategies to circumvent limitations related to heterogeneity between clinics, patient discharge and clinics' capacity for data collection.

Implementation research on measuring quality in primary care: balancing national needs with learning from the Eastern Mediterranean Region.

Universal Health Coverage (UHC) is a core element of ensuring healthy lives, marking the third Sustainable Development Goal. It requires providing quality primary health-care (PHC) services. Assessment of quality of care considering a wide variety of contexts is a challenge. This study lists practical indicators to enhance the quality of PHC. Demonstrating quality indicators for PHC that are feasible, comprehensive and adaptable to wide array of health systems and resource settings. We applied the research framework: Exploration, Preparation, Implementation and Sustainment. Exploration included a scoping review to identify quality indicators. Preparation included an eDelphi to refine the primary indicators' list. A panel of 27 experts reviewed the list that was later pilot tested in PHC facilities. The outcomes were presented to two further expert consultations, to refine indicators and plan for broader testing. Implementation included testing the indicators through a five-step process in 40 facilities. A regional consultation in May 2016 discussed the testing outcomes. Initial efforts identified 83 quality indicators at the PHC level that were then refined to a 34-indicator list covering the six domains of quality. A toolkit was also developed to test the feasibility of each indicator measurement, data availability, challenges and gaps. Pilot testing provided insight into modifying and adding some indicators. Wide variability was encountered within and in between facilities, and timely initiation of antenatal care, for example, ranged 31-90% in Oman and 11-98% in Tunisia. Indicators were highly feasible, and 29 out of 34 were measured in 75% of facilities or more. While challenges included gaps in capacity for data collection, the tool showed high adaptability to the local context and was adopted by countries in the Eastern Mediterranean Region (EMR) including Libya, Oman, Iran, Pakistan, Sudan and Palestine. Stakeholders agreed on the high relevance and applicability of the proposed indicators that have been used to inform improvement. A cross-regional set of 34 quality indicators of PHC in the EMR was developed and adopted by a diverse group of countries. The toolkit showed high feasibility in pilot testing reflecting the practicality needed to encourage local uptake and sustainability. The core quality indicators are highly adaptable to different local and regional contexts regardless of current PHC strength or available resources. Continuous evaluation and sharing lessons of implementation and use are needed to ascertain the indicators' effectiveness in driving improvements in PHC and to refine and strengthen the evidence supporting the set of indicators for wider adoption.

An Assessment of Occurrences of Thunderstorm as an Indicator of Climate Change : A Case Study of Potiskum and its Environs, Yobe State, Nigeria

This study examines the occurrences of thunderstorm and rainfall as indicator of climate change in Potiskum and its environs through exploring the decadal recorded data of rainfall and thunderstorm. The data obtained were used to analyze the changes and activities of thunderstorms with rainfall, and thunderstorms without rainfall for each month of the 30 years under study. A descriptive and Inferential statistics was employed to determine the frequency and trend of thunderstorm activities as well as the intensity of rainfall. The analysis indicated that there was decrease in thunderstorm occurrences from first decade (1987 - 1996), second (1997 - 2006) to third decade (2007 - 2016). The results also showed that there was an increase of rainfall intensity in the third decade and decrease in second decade. The results further indicated that there was a climatic change in thunderstorm and rainfall activities in Potiskum and its environs during the study period, and this serves as good indicator of climate change in the study area. Therefore, it is recommended that Nigeria should develop capacity for research and data collection, to monitor climate change impacts, formulate and implement policies to protect natural resources, including forests, and conserve energy based on clean and low carbon technologies.

Abstract 31: Impact of the COVID-19 Pandemic on Cancer Registry Operations in the Caribbean

Abstract Purpose: Cancer registration is an integral part of national disease surveillance, and actions must be taken to maintain operation of such essential systems, even during concurrent public health emergencies. This abstract summaries the impact of the COVID-19 pandemic on operations of population-based cancer registries (PBCRs) in the Caribbean. Methods: In May 2020, the International Agency for Research on Cancer (IARC) issued a worldwide survey to members of the International Association of Cancer Registries (IACR) to assess the impact of the COVID-19 pandemic on PBCRs. In November, the IARC Caribbean Hub renewed this call for responses from IACR members plus additional Caribbean PBCRs. The experience of ten of thirteen registries contacted is presented. Results: Survey responses were received from PBCRs in the Bahamas, Barbados, Bermuda, Cuba, Dominican Republic, Guyana, Jamaica, Martinique, Puerto Rico, and Trinidad and Tobago. Five (50%) registries reported that the COVID-19 pandemic negatively affected operations. These same registries reported a reduction in data collection capacity ranging from 28% to 100%. Reasons for data collection disruptions included an inability to fully access physical data sources and a reduced capacity or inability to receive cancer notifications and files. Where data collection was impacted, all registries expected missing cancer cases to be registered later in the next 12-15 months. Remote working was one strategy implemented by unaffected registries. One (10%) registry reported an impact on registry financing, and two (20.0%) reported that staffing was affected. Conclusions: Half of responding Caribbean registries reported having been negatively affected by the COVID-19 pandemic, particularly regarding data collection. Improved access to physical data sources for registration activities could mitigate continuing operational challenges in the current pandemic. Accelerating the development of electronic records and direct source abstraction via remote electronic access could support continuous operation of cancer registries amidst future emergencies or operational disruptions. Citation Format: Sarah Quesnel-Crooks, Rachel Hanisch, Isabelle Soerjomataram, Brenda Edwards, Arica White, Betsy Kohler, Ann Chao, Les Mery, Glennis Andall-Brereton. Impact of the COVID-19 Pandemic on Cancer Registry Operations in the Caribbean [abstract]. In: Proceedings of the 9th Annual Symposium on Global Cancer Research; Global Cancer Research and Control: Looking Back and Charting a Path Forward; 2021 Mar 10-11. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2021;30(7 Suppl):Abstract nr 31.

A Survey on Some Big Data Applications Tools and Technologies

Big Data is a broad area that deals with enormous chunks of data sets. It is a word for enormous data sets having huge volume, more diverse structures of data originating from diverse sources are growing rapidly. Many data being generated because of fast data transmission between devices concerning different sectors like healthcare, science, media, business, entertainment and engineering. Data collection capacity and its storage is big concern. Apache Hadoop software is a store of accessible source programs to store big data and perform analytics and various other operations related to big data. Many organizations base their decisions by extracting knowledge from huge and complex data, because of this prime cause of decision making, Big Data has to be accurately classified and analyzed. In order to overcome the complex challenges encountered by Big Data, various Big Data tools and technologies have developed. Big Data Applications, tools and technologies used to handle it are briefly discussed in this paper.

Will college promise programs improve or reduce equity? Understanding the contextual conditions that influence program implementation

Although “free tuition” programs are politically popular, some worry that these programs will exacerbate inequity. Scholars note that program outcomes depend on implementation, but few have probed the contextual conditions that contribute to differences in implementation or the consequences of implementation for equity. To address this knowledge gap, we draw on conceptual models of implementation fidelity and case studies of last-dollar, free tuition programs at four community colleges. The consequences of an implemented program for equity depend on program content and coverage and are moderated by programmatic and organizational conditions. For the studied programs, implemented content includes the financial award and non-financial academic supports. Coverage is determined by eligibility requirements. Program content and coverage are moderated by programmatic characteristics, including program goals and placement in the organizational structure, program staffing, and recruitment strategies. Organizational conditions, including sources and availability of funding, availability of synergistic programs, capacity for data collection and evaluation, and perceptions of the community college also moderate implementation. The results inform understanding of how to implement programs at community colleges that increase equity in particular contexts.

A hierarchical machine learning framework for the analysis of large scale animal movement data

BackgroundIn recent years the field of movement ecology has been revolutionized by our ability to collect high-accuracy, fine scale telemetry data from individual animals and groups. This growth in our data collection capacity has led to the development of statistical techniques that integrate telemetry data with random walk models to infer key parameters of the movement dynamics. While much progress has been made in the use of these models, several challenges remain. Notably robust and scalable methods are required for quantifying parameter uncertainty, coping with intermittent location fixes, and analysing the very large volumes of data being generated.MethodsIn this work we implement a novel approach to movement modelling through the use of multilevel Gaussian processes. The hierarchical structure of the method enables the inference of continuous latent behavioural states underlying movement processes. For efficient inference on large data sets, we approximate the full likelihood using trajectory segmentation and sample from posterior distributions using gradient-based Markov chain Monte Carlo methods.ResultsWhile formally equivalent to many continuous-time movement models, our Gaussian process approach provides flexible, powerful models that can detect multiscale patterns and trends in movement trajectory data. We illustrate a further advantage to our approach in that inference can be performed using highly efficient, GPU-accelerated machine learning libraries.ConclusionsMultilevel Gaussian process models offer efficient inference for large-volume movement data sets, along with the fitting of complex flexible models. Applications of this approach include inferring the mean location of a migration route and quantifying significant changes, detecting diurnal activity patterns, or identifying the onset of directed persistent movements.

Brain injuries: health care capacity and policy in Georgia.

:Background:Over 90% of morbidity and mortality associated with traumatic brain injury (TBI) occurs in low- and middle-income countries. Lack of reliable, high-quality data regarding TBI prevention and care hinders the ability to reduce TBI burden. We sought to identify current TBI data collection practices and capacity in Georgia, focusing on pre-hospital, hospital, and rehabilitation treatment.Methods:The eight level I and two level II Trauma Hospitals in Georgia with the highest number of TBI admissions in 2017 were selected for study. A semi-structured survey about various aspects of TBI care was designed and semi-structured interviews of healthcare providers treating TBI patients (e.g. neurologists, neurosurgeons) were conducted based on this survey. Results:Pre-hospital triage protocols were not routinely used to match patient treatment needs with hospital capacity. All hospitals provided specialist care for TBI 24 hours/day. MRI was available at only three (30%) centers, and in-hospital rehabilitation units were available in only one (10%). No center used a defined protocol for treating TBI patients and no national protocol exists.Conclusions:Even among the largest, most highly specialized hospitals in Georgia, TBI care varies in terms of diagnostic and treatment protocols. While TBI specialists are available, diagnostic equipment often is not. Gaps in pre-hospital coordination and access to rehabilitation services exist and provide areas of focus for future investment in reducing TBI burden.

Monitoring the status of selected health related sustainable development goals: methods and projections to 2030

ABSTRACT Background: Monitoring Sustainable Development Goal indicators (SDGs) and their targets plays an important role in understanding and advocating for improved health outcomes for all countries. We present the United Nations (UN) Inter-agency groups’ efforts to support countries to report on SDG health indicators, project progress towards 2030 targets and build country accountability for action. Objective: We highlight common principles and practices of each Inter-agency group and the progress made towards SDG 3 targets using seven health indicators as examples. The indicators used provide examples of best practice for modelling estimates and projections using standard methods, transparentdata collection and country consultations. Methods: Practices common to the UN agencies include multi-UN agency participation, expert groups to advise on estimation methods, transparent publication of methods and data inputs, use of UN-derived population estimates, country consultations, and a common reporting platform to present results. Our seven examples illustrate how estimates, using mostly Bayesian models, make use of country data to track progress towards SDG targets for 2030. Results: Progress has been made over the past decade. However, none of the seven indicators are on track to achieve their respective SDG targets by 2030. Accelerated efforts are needed, especially in low- and middle-income countries, to reduce the burden of maternal, child, communicable and noncommunicable disease mortality, and to provide access to modern methods of family planning to all women. Conclusion: Our analysis shows the benefit of UN interagency monitoring which prioritizes transparent country data sources, UN population estimates and life tables, and rigorous but replicable modelling methods. Countries are supported to build capacity for data collection, analysis and reporting. Through these monitoring efforts we support countries to tackle even the most intransient health issues, including the pandemic caused by SARS-CoV-2 that is reversing the hard-earned gains of all countries.

Crossing Silos and Networking to Advance Environmental Health Disaster Research

No change in the environment is as extreme, or often as sudden, as the change that comes after a disaster. The Disaster Research Response (DR2) Program, championed by the National Institutes of Health (NIH) is an important component in understanding environmental health threats and issues following a disaster. While an increasing number of environmental health researchers are taking steps to prepare for, and to conduct, research in disaster environments, they often work in disciplinary silos, whereas the impacts of disasters are often crosscutting, impacting divergent communities, scientific disciplines, and across jurisdictional borders. Previous responses to disasters like the Deepwater Horizon Oil Spill have inspired efforts to improve the nation’s capacity to perform timely interdisciplinary disaster research. Many new efforts are springing up to promote transformative collaborations in disaster research. This session will include a mix of discussions highlighting specific situations involving applied epidemiologic research or efforts to improve the process for such research to be performed. Speakers begin by explaining the importance of disaster research to environmental health. Presenters will then discuss the development and accomplishments of a new environmental health sciences disaster research community of practice and ongoing efforts to facilitate behavioral, geoscience, and environmental health disaster research. Efforts to develop international networks, as well as to foster new strategies for applied research for humanitarian crises will be presented. A discussion will include ongoing efforts to develop IRB pre-reviewed protocols, guidance, and tools to help support and expedite timely research. Additional discussions will include examples of rapid collaborations among academic institutions after a major hurricane and a recent disaster workshop that sought to reach outside environmental health to the clinical care setting to harmonize and build capacity for disaster data collection and sharing. Presenters will share experiences, case studies, and lessons learned as part of these evolving efforts. Please describe how the symposia relates to the meeting theme of Advancing Environmental Health in a Changing World. (100 words max): Disasters and accidents are an extreme example of changing environments in a changing world. In seemingly the blink of an eye, a community can be impacted by contamination, floodwaters, or wildfire smoke and suddenly be concerned about environmental exposures they never considered previously. The growing field of disaster research, championed by the NIH Disaster Research Response Program (DR2), the NSF Natural Hazards Center, Fogarty International Center, and others seek to advance transdisciplinary health research, and place environmental health into disaster response planning, for changing and challenging situations. Recent efforts, in the US and globally, to conduct disaster research and to bring together researchers have begun to advance this important field of research.

Estimating the Impact of Research Practitioner Partnerships on evidence-based Program Implementation*

Evidence-based program implementation in criminal justice and community settings is complex and rarely encounters an ideal set of conditions. Implementation failure may be more common than success, and the field has only started to seriously consider how to improve dissemination and implementation. Research-practitioner partnerships are a potential mechanism for achieving implementation success. Using an implementation science framework, we examine the impact of researcher involvement and activity on implementation outcomes in a sample of 49 community-based multi-agency initiatives to improve public safety. Our findings suggest that projects focused more heavily on data and analysis from the outset achieved better implementation outcomes across multiple domains, including fidelity, penetration and dosage, complexity, execution, and sustainability. Similar improvements in outcomes were not associated with more robust data collection, access, or analytic capacity.

Fire Risk Assessment in Dense Urban Areas Using Information Fusion Techniques

A comprehensive fire risk assessment is very important in dense urban areas as it provides an estimation of people at risk and property. Fire policy and mitigation strategies in developing countries are constrained by inadequate information, which is mainly due to a lack of capacity and resources for data collection, analysis, and modeling. In this research, we calculated the fire risk considering two aspects, urban infrastructure and the characteristics of a high-rise building for a dense urban area in Zanjan city. Since the resources for this purpose were rather limited, a variety of information was gathered and information fusion techniques were conducted by employing spatial analyses to produce fire risk maps. For this purpose, the spatial information produced using unmanned aerial vehicles (UAVs) and then attribute data (about 150 characteristics of each high-rise building) were gathered for each building. Finally, considering high-risk urban infrastructures, like the position of oil and gas pipes and electricity lines and the fire safety analysis of high-rise buildings, the vulnerability map for the area was prepared. The fire risk of each building was assessed and its risk level was identified. Results can help decision-makers, urban planners, emergency managers, and community organizations to plan for providing facilities and minimizing fire hazards and solve some related problems to reduce the fire risk. Moreover, the results of sensitivity analysis (SA) indicate that the social training factor is the most effective causative factor in the fire risk.

Hospital staff participation in a national hip fracture audit: facilitators and barriers

SummaryTo ensure meaningful results in a clinical audit, as many hospitals as possible should participate. To optimise participation, the data collection process should either be performed by additional staff or be automated. Active participation may be promoted by offering relevant external parties insight into the actual quality of care.PurposeThe aim of the study was to identify which facilitators and barriers experienced by hospital staff are associated with participation in the ongoing nationwide multidisciplinary Dutch Hip Fracture Audit (DHFA).MethodsA survey including questions about the respondents’ characteristics, hospital level of participation and factors of influence on DHFA participation was sent to hip fracture surgeons. The factors were based on results of semi-structured interviews held with hospital staff involved in hip fracture care. Univariable and multivariable logistic regression analyses were used to establish which respondent characteristics and factors were associated with participation and active participation (≥ 80% of patients registered) in the DHFA. Factors significantly increasing the (active) participation in the DHFA were classified as facilitators, and factors significantly decreasing the (active) participation in the DHFA as barriers.ResultsOne hundred nine surgeons filled out the questionnaire. The factors most agreed on were availability of staffing capacity for data collection and automated data import. A lower intention to participate was associated with being an academic surgeon (odds ratio, 0.15; 95% confidence interval, 0.04–0.52) and an orthopaedic surgeon (odds ratio, 0.30; 95% confidence interval, 0.10–0.90). Data sharing with relevant external parties was associated with active participation (odds ratio, 3.19; 95% confidence interval, 1.14–8.95).ConclusionsTo improve participation in a nationwide clinical audit, it seems that the data collection should either be performed by additional staff or be automated. Active participation is facilitated if audit data is made available to other parties, such as insurers, healthcare authorities or policymakers.

The Case for Studying Criminal Nonfatal Shootings: Evidence from Four Midwest Cities

Using law enforcement data from four Midwest communities, we document the similarities and differences between criminal nonfatal and fatal shooting incidents, including the spatial dimensions of the events. We present a definition for a nonfatal shooting incident that guides our victim and incident characteristic comparisons. Our work suggests that law enforcement agencies should build capacity for standardized data collection surrounding gun violence to include nonfatal shootings especially for use in evaluations of gun violence prevention strategies.

UAV wildlife radiotelemetry: System and methods of localization

Abstract The majority of bird and bat species are incapable of carrying tags that transmit their position to satellites. Given fundamental power requirements for such communication, burdened mass guidelines and battery technology, this constraint necessitates the continued use of very high frequency (VHF) radio beacons. As such, efforts should be made to mitigate their primary deficiencies: detection range, localization time and localization accuracy. The integration of a radiotelemetry system with an unmanned aerial vehicle (UAV) could significantly improve the capacity for data collection from VHF tags. We present a UAV‐integrated radiotelemetry system that relies on open source hardware and software. Localization methods, including signal processing, bearing estimation based on principal component analysis, localization techniques and test results, are discussed. Using a low‐power beacon applicable for bats and small birds, testing showed that the improved vantage of the UAV‐radiotelemetry system (UAV‐RT) provided significantly higher received signal power compared to the low‐level flights (maximum range beyond 1.4 km). Flight testing of localization methods showed median bearing errors between 2.3° and 6.8°, with localization errors of between 5% and 14% of the distance to the tag. In a direct comparison to an experienced radiotelemetry user, the UAV‐RT system provided bearing and localization estimates with 53% less error. This paper introduces the core functionality and use methods of the UAV‐RT system, while presenting baseline localization performance metrics. An associated website hosts plans for assembly and software installation. The methods of UAV‐RT use for tag detection will be further developed in future works. For both the detection and localization problems, the mobility of a flying asset drastically reduces tracker time requirements. A 7‐min flight would be sufficient to collect five equally spaced bearing estimates over a 1‐km transect. The use of a software‐defined radio on the UAV‐RT system will allow for the simultaneous detection and localization of multiple tags.

Building capacity for injury prevention: a process evaluation of a replication of the Cardiff Violence Prevention Programme in the Southeastern USA

ObjectivesViolence is a major public health problem in the USA. In 2016, more than 1.6 million assault-related injuries were treated in US emergency departments (EDs). Unfortunately, information about the magnitude...

Potential Role of Machine Learning in Oncology

Machine learning (ML) is the ability of computers to learn from data autonomously. It is a core branch of artificial intelligence (AI), which is defined as the ability of a machine to replicate the intellectual processes of humans independently.1,2 The evolution of the microprocessor for home computers resulted in increased computing speed, efficient data collection, storage, and retrieval capacity. Thus AI techniques have evolved, which led to the discovery of artificial neural networks which are computer modeling algorithms mimicking the human brain.3-6 AI has developed to an extent that it can exceed the human brain in board games such as chess, television games, image recognition, and spam e-mail filtering. In addition to engineering, arts, and finance, extensive progress has been made to integrate ML in the field of medicine, especially oncology.

Jullien’s 1817 Esquisse: Toward a “Science” of Comparative Higher Education?

This article links comparative and international higher education research to ideas put forward in the 1817 pamphlet by Marc-Antoine Jullien, Esquisse, that is widely recognized as a foundational work for the field of comparative education, including providing its name. The paper describes how Jullien’s ideas in Esquisse are reflected in the contemporary work of the International Bureau of Education (IBE) and the UNESCO Institute of Statistics (UIS), including examples of comparative higher education tables analogous to those first described in Esquisse. The positivist approach advocated by Jullien is linked to contemporary research such as international rankings of higher education institutions (league tables) and surveys of the professoriate. It concludes with implications for future directions of comparative education research that are more “scientific” because they embed the increasingly sophisticated capacity for measurement and data collection within systematic conceptual frameworks as well as ever more rigorous quantitative and qualitative methodological techniques.

Study on the quality evaluation method for undergraduate thesis based on discrete Hopfield neural network

In terms of the poor evaluation system of graduation theses, which is easy to be influenced by many human factors, we try to establish a more scientific and objective evaluation model and method of graduation thesis quality. Firstly, the index system of graduation thesis quality evaluation model was constructed, including topic selection, professional knowledge level, data collection capacity, ability to use knowledge, level of guidance teacher, and quality of the paper. Secondly, using expert scoring method, combining Matlab programming and applying AHP, the weight of the quality evaluation elements of graduation thesis was obtained, and the key factors affecting the quality level of graduation thesis were determined. Finally, a discrete Hopfield neural network model for the quality level of graduation thesis was established, and 120 graduation theses were selected for quality level simulation evaluation, and compared with the ideal evaluation results. The results showed that the discrete Hopfield neural network model is correct and it can effectively provide objective, scientific and normative evaluation for the quality of graduation theses. Key words: Undergraduate graduation thesis; AHP; Hopfield; Quality evaluation method