Innovations In Data Collection Research Articles

The Demographic Frame

The Demographic Frame is a comprehensive, secure, access-controlled database of person-level data consisting of demographic, social, and economic characteristics of individuals derived from census, survey, administrative, and third-party data sources. The Demographic Frame is facilitating innovations in data collection, processing, analyses, and products for census and survey programs.

Representation of regeneration dynamics in growth and yield models: a review

Models of forest regeneration dynamics have been less widely applied in forest management than those representing growth and mortality in later stages of stand development, in spite of the critical role of regeneration in maintaining forest ecosystems. This omission is demonstrated by a review of pertinent literature and examined in the context of reforestation in the province of Alberta. Regeneration assessments in Alberta are undertaken before the regeneration phase of stand development is complete. As a result, existing growth and yield models, used to predict whether regeneration performance will meet management objectives, do not adequately represent juvenile mortality, ingress of natural regeneration, stand density, and the responses of ingress and mortality to reforestation treatments. A long-term experiment monitoring regeneration of lodgepole pine stands following harvest has over the last 20 years attempted to address some of the resulting challenges. Opportunities and needs for regeneration modelling include extension to other boreal species and ecotypes, incorporation of climatic variables, and innovations in data collection and analytical techniques. Steps are recommended for expediting the required research. 

Enabling data sharing and utilization for African population health data using OHDSI tools with an OMOP-common data model.

The COVID-19 pandemic has spurred the use of AI and DS innovations in data collection and aggregation. Extensive data on many aspects of the COVID-19 has been collected and used to optimize public health response to the pandemic and to manage the recovery of patients in Sub-Saharan Africa. However, there is no standard mechanism for collecting, documenting and disseminating COVID-19 related data or metadata, which makes the use and reuse a challenge. INSPIRE utilizes the Observational Medical Outcomes Partnership (OMOP) as the Common Data Model (CDM) implemented in the cloud as a Platform as a Service (PaaS) for COVID-19 data. The INSPIRE PaaS for COVID-19 data leverages the cloud gateway for both individual research organizations and for data networks. Individual research institutions may choose to use the PaaS to access the FAIR data management, data analysis and data sharing capabilities which come with the OMOP CDM. Network data hubs may be interested in harmonizing data across localities using the CDM conditioned by the data ownership and data sharing agreements available under OMOP's federated model. The INSPIRE platform for evaluation of COVID-19 Harmonized data (PEACH) harmonizes data from Kenya and Malawi. Data sharing platforms must remain trusted digital spaces that protect human rights and foster citizens' participation is vital in an era where information overload from the internet exists. The channel for sharing data between localities is included in the PaaS and is based on data sharing agreements provided by the data producer. This allows the data producers to retain control over how their data are used, which can be further protected through the use of the federated CDM. Federated regional OMOP-CDM are based on the PaaS instances and analysis workbenches in INSPIRE-PEACH with harmonized analysis powered by the AI technologies in OMOP. These AI technologies can be used to discover and evaluate pathways that COVID-19 cohorts take through public health interventions and treatments. By using both the data mapping and terminology mapping, we construct ETLs that populate the data and/or metadata elements of the CDM, making the hub both a central model and a distributed model.

Adapting the 'First 2000 Days maternal and child healthcare framework' in the aftermath of the COVID-19 pandemic: ensuring equity in the new world.

The purpose of this perspective article is to emphasise the importance of the 'First 2000 Days' policy of life from conception to age five, and to propose new directions in which the policy's implementation could be extended for the benefit of children and families. The proposed approach highlights principles of responsiveness, integration, sustainability and equity, specifying initiatives that embody the kind of innovation each principle aspires to. The article also proposes innovations in data collection and linkages that would strengthen the implementation of first 2000 days policies and frameworks. This perspective proposes a framework that could improve health systems implementation of services in the first 5 years of life, by proposing a well-coordinated continuum of services with integrated physical and digital solutions. This has the potential to transform how the health system monitors and responds to children and families' needs in the critical early years of life during and beyond the current pandemic.

Urban seas as hotspots of stress in the Anthropocene ocean: The Salish Sea example

Coastal seas and estuaries are among the most productive ecosystems on Earth and have long attracted human activity. Yet, urbanization pressures are intense and are compounded by accelerating climate stresses. Urban seas are now hotspots of stress in the Anthropocene ocean. The Salish Sea stands out as one of a few highly functioning urban seas in the world, boasting ecological riches and thriving coastal communities and industries, including tourism. For over 10,000 years the region has supported Indigenous peoples; now it is home to a growing population of almost nine million people, concentrated in and near the major cities of Seattle, Washington, and Vancouver, British Columbia. Increasing urbanization combined with intensifying climate stress is degrading the Salish Sea and acutely affecting communities already experiencing marginalization. Current environmental impacts include acidifying waters, hypoxia, and intense heat waves, all of which have had measurable impacts within the ecosystem. A recent synthesis of this system identified key domains for solutions, which we generalize here for invoking positive change in global urban seas: 1) innovation in data collection, curation, and integration using a systems approach in science and management; 2) sharing place-based knowledge to sustain community-based action; and 3) aligning science and policy with ecosystem boundaries. The differing governance and socio-political settings across two countries and numerous Indigenous nations creates a complex challenge in ecosystem management. Developing actionable solutions for people and the biota of the Salish Sea can create a global example of a sustainably managed urban sea with transferable insights to other urban seas in need of revitalization around the world.

Advances and innovations in methods for collecting egocentric network data

This article reviews recent methodological research that bears on the collection of egocentric network data. It begins with background on setting egocentric network boundaries and principal types of instruments that obtain information about such networks. It then discusses innovations in data collection and studies of data quality. The bulk of these address questions about “name generator” instruments that obtain information about the alters and relationships in a subject's network. Among topics receiving substantial attention in recent research are mitigation of respondent burden, interviewer effects, survey mode, and the performance of name generators in longitudinal studies. Potentially fruitful innovations supplement conventional question-and-answer surveys with visual elements that promise to better engage respondents and reduce the demands that name generator-based data collection poses. We close by highlighting both accomplishments of this body of research and some open issues.

Brazilian National Household Health Survey and PCAT: the case of Mato Grosso do Sul, Brazil

Abstract Brazil is divided into five administrative regions, 27 federation units and 5,570 municipalities. Mato Grosso do Sul is one of the states located in the Midwest region and has 1.6 million km2 and a resident population of 2.8 million inhabitants, that is, it has an even lower demographic density than its region - only 7.8 inhabitants/km2. Mato Grosso do Sul has part of the Pantanal, a biome considered the largest continuous floodplain in the world, rich in biodiversity. For this reason, displacements for data collection in household surveys combine roads and rivers. In 2019, the Brazilian National Institute of Geography and Statistics (Istituto Nazionale di Statistica del Brasile) in partnership with the Ministry of Health launched the world's largest household sample survey, the National Health Survey (PNS-2019), in which part of its questions included the use of Primary Care Assessment Tool (PCAT, adult version), created by professors Barbara Starfield and Leiyu Shi in the 2000s. IBGE interviewers visited more than 100,000 households across the country. In Mato Grosso do Sul, more than 3,000 households were surveyed. In this work, we present the data collection instrument used by IBGE and its multiple analysis possibilities in the scope of primary health care, crossing the variables from other questionnaire modules in order to compare the results from Brazil with the state of Mato Grosso do Sul and its capital, Campo Grande. Developing a baseline and measuring the attributes of primary health care in each of the Brazilian states is another step towards giving health policy accountability, towards strong primary care. IBGE's experience in household surveys and innovation in data collection in primary care is an example for the world that yes, it is possible to develop statistically representative national sample surveys and make them perennial in their regular household surveys, by the time World Health Organization (WHO) discusses universal health coverage. Key messages Evaluation of primary care using an internationally validated instrument is possible on national bases with random household sample surveys. A questionnaire elaborated academically can be used as an instrument of public policy to evaluate nationwide health services.

Digital Platforms as a Method of Invention for Infection Surveillance.

Background: The history of large-scale technological advances, such as the digital revolution in our era, suggests that core technologies yield wide benefits by serving as a method of invention, spawning new tools and techniques that surpass the performance of their predecessors. Methods: Digital platforms provide a method of invention in the health sector by enabling innovations in data collection, use, and sharing. Although wide adoption of computerized information technology in healthcare has produced mixed results, the advent of mobile health (mHealth) creates new opportunities for device-mediated advances in surgical and public health practice. Conclusion: Mobile solutions for collecting, using, and sharing patient-generated health data after surgery can yield important benefits for post-operative monitoring, whether the data are used to evaluate and manage individual patients or track infections and other outcomes in patient populations.

In situ studies: electrochemistry and scattering

Abstract Given the global relevance of sustainable energy and energy security, the study of electrochemistry and the development of energy storage devices is a very active area of research. As researchers attempt to overcome issues with current energy storage devices, innovative methods are required to probe deeper into the working mechanisms of devices. The ability to study electrochemistry as it takes place in electrochemical energy storage devices has been enabled by continuous developments in instrumentation, improved accessibility for researchers and also innovations in data collection and cell design. In this short review, we highlight several studies that make use of innovations in data collection, data interpretation, or device design, to study electrochemical energy storage devices using scattering methods.

Evidence-Based Monitoring of International Migration Flows in Europe

Abstract In Europe, the monitoring and management of migration flows are high on the political agenda. Evidence-based monitoring calls for adequate data, which do not exist. The sources of data on international migration differ significantly between countries in Europe and the initiatives to improve data collection and produce comparable data, including new legislation, did not yield the expected outcome. Scientists have developed statistical models that combine quantitative and qualitative data from different sources to derive at estimates of migration flows that account for differences in definition, undercoverage, undercount and other measurement problems. Official statisticians are reluctant to substitute estimates for measurements. This article reviews the progress made over the last decades and the challenges that remain. It concludes with several recommendations for better international migration data/estimates. They range from improved cooperation between actors to innovation in data collection and modelling.

People-Centered Innovations in Data Collection: The Oregon Experience.

People-Centered Innovations in Data Collection: The Oregon Experience.

Mobile Phone-Based Ecological Momentary Intervention to Reduce Young Adults' Alcohol Use in the Event: A Three-Armed Randomized Controlled Trial.

BackgroundReal-time ecological momentary interventions have shown promising effects in domains other than alcohol use; however, only few studies regarding ecological momentary interventions for alcohol use have been conducted thus far. The increasing popularity of smartphones offers new avenues for intervention and innovation in data collection.ObjectiveWe aimed to test the efficacy of an ecological momentary intervention, comprising mobile Web-based ecological momentary assessments (EMAs) and text messaging (short message service, SMS) brief interventions, delivered during drinking events using participants’ mobile phones.MethodsWe conducted a three-armed randomized controlled trial to assess the effect of a mobile Web-based ecological momentary assessment with texting feedback on self-reported alcohol consumption and alcohol-related harms in young adults. Participants were enrolled from an existing observational cohort study of young adults screened for risky drinking behavior. The intervention group (ecological momentary intervention group) completed repeated ecological momentary assessments during 6 drinking events and received immediate texting-based feedback in response to each ecological momentary assessment. The second group (ecological momentary assessment group) completed ecological momentary assessments without the brief intervention, and the third did not receive any contact during the trial period. Recent peak risky single-occasion drinking was assessed at the baseline and follow-up using telephone interviews. We used a random effects mixed modeling approach using maximum likelihood estimation to provide estimates of differences in mean drinking levels between groups between baseline and 12-week follow-up.ResultsA total of 269 participants were randomized into the 3 groups. The ecological momentary intervention group exhibited a small and nonsignificant increase between baseline and follow-up in (geometric) the mean number of standard drinks consumed at the most recent heavy drinking occasion (mean 12.5 vs 12.7). Both ecological momentary assessment and control groups exhibited a nonsignificant decrease (ecological momentary assessment: mean 13.8 vs 11.8; control: mean 12.3 vs 11.6); these changes did not differ significantly between groups (Wald χ22 1.6; P=.437) and the magnitude of the effects of the intervention were markedly small. No other significant differences between groups on measures of alcohol consumption or related harms were observed. The intervention acceptability was high despite the technical problems in delivery.ConclusionsWith a small number of participants, this study showed few effects of an SMS-based brief intervention on peak risky single-occasion drinking. Nevertheless, the study highlights areas for further investigation into the effects of EMI on young adults with heavy alcohol consumption.Trial RegistrationAustralian New Zealand Clinical Trials Registry ACTRN12616001323415; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=369534 (Archived by WebCite at http://www.webcitation.org/7074mqwcs)

Making the case for simulation: Unlocking carbon reduction through simulation of individual ‘middle actor’ behaviour

This paper makes the case for agent-based modelling as a route to unlocking the potential of existing buildings to reduce energy demand and contribute to achieving carbon reduction targets. The construction of a model to simulate this system requires significant innovation in data collection and handling. The need to focus on ‘middle actors’ in construction – specifically the tradesmen who carry out repair, maintenance and renovation – in order to reduce energy demand in existing buildings is described. This is the first work that proposes to simulate these actors. After identifying this opportunity, the paper considers what modelling techniques are required to describe the possible effects of changes to middle actor behaviour across the construction industry. Having discussed the different types of data needed, the paper uses the ‘overview, design, detail’ approach to describe how an agent-based model might be developed, using rule sets derived from middle actor data. Finally, the types of interventions that might be tested are outlined, indicating how policy and practice could be informed by the proposed modelling approach.

Integration of sex/gender into environmental health research. Results of the interdisciplinary research network Sex/Gender-Environment-Health (GeUmGe-NET)

The comprehensive consideration of sex/gender in health research is essential to increase relevance and validity of research results. Contrary to other areas of health research, there is no systematic summary of the current state of research on the significance of sex/gender in environmental health. Within the interdisciplinary research network Sex/Gender-Environment-Health (GeUmGe-NET) the current state of integration of sex/gender aspects or, respectively, gender theoretical concepts into research was systematically assessed within selected topics of the research areas environmental toxicology, environmental medicine, environmental epidemiology and public health research on environment and health. Knowledge gaps and research needs were identified in all research areas. Furthermore, the potential for methodological advancements by using gender theoretical concepts was depicted. Adialogue between biomedical research, public health research, and gender studies was started with the research network GeUmGe-NET. This dialogue has to be continued particularly regarding acommon testing of methodological innovations in data collection and data analysis. Insights of this interdisciplinary research are relevant for practice areas such as environmental health protection, health promotion, environmental justice, and environmental health monitoring.

Mathematical models of infection transmission in healthcare settings: recent advances from the use of network structured data.

Mathematical modeling approaches have brought important contributions to the study of pathogen spread in healthcare settings over the last 20 years. Here, we conduct a comprehensive systematic review of mathematical models of disease transmission in healthcare settings and assess the application of contact and patient transfer network data over time and their impact on our understanding of transmission dynamics of infections. Recently, with the increasing availability of data on the structure of interindividual and interinstitution networks, models incorporating this type of information have been proposed, with the aim of providing more realistic predictions of disease transmission in healthcare settings. Models incorporating realistic data on individual or facility networks often remain limited to a few settings and a few pathogens (mostly methicillin-resistant Staphylococcus aureus). To respond to the objectives of creating improved infection prevention and control measures and better understanding of healthcare-associated infections transmission dynamics, further innovations in data collection and parameter estimation in modeling is required.

Moving beyond the household: Innovations in data collection on kinship

Across settings, it has been shown that the co-residential household is an insufficient measure of family structure and support. However, it continues to be the primary means of population data collection. To address this problem, we developed a new instrument, the Kinship Support Tree (KST), to collect kinship structure and support data on co-residential and non-residential kin and tested it on a sample of 462 single mothers and their children in a slum community in Nairobi, Kenya. This instrument is unique in four important ways: (1) it is not limited to the co-residential household; (2) it distinguishes potential from functional kin; (3) it incorporates multiple geospatial measures; and (4) it collects data on kin relationships specifically for children. In this paper, we describe the KST instrument, assess the data collected in comparison to data from household rosters, and consider the challenges and feasibility of administration of the KST.

Transforming Clinical Data into Actionable Prognosis Models: Machine-Learning Framework and Field-Deployable App to Predict Outcome of Ebola Patients.

BackgroundAssessment of the response to the 2014–15 Ebola outbreak indicates the need for innovations in data collection, sharing, and use to improve case detection and treatment. Here we introduce a Machine Learning pipeline for Ebola Virus Disease (EVD) prognosis prediction, which packages the best models into a mobile app to be available in clinical care settings. The pipeline was trained on a public EVD clinical dataset, from 106 patients in Sierra Leone.Methods/Principal FindingsWe used a new tool for exploratory analysis, Mirador, to identify the most informative clinical factors that correlate with EVD outcome. The small sample size and high prevalence of missing records were significant challenges. We applied multiple imputation and bootstrap sampling to address missing data and quantify overfitting. We trained several predictors over all combinations of covariates, which resulted in an ensemble of predictors, with and without viral load information, with an area under the receiver operator characteristic curve of 0.8 or more, after correcting for optimistic bias. We ranked the predictors by their F1-score, and those above a set threshold were compiled into a mobile app, Ebola CARE (Computational Assignment of Risk Estimates).Conclusions/SignificanceThis method demonstrates how to address small sample sizes and missing data, while creating predictive models that can be readily deployed to assist treatment in future outbreaks of EVD and other infectious diseases. By generating an ensemble of predictors instead of relying on a single model, we are able to handle situations where patient data is partially available. The prognosis app can be updated as new data become available, and we made all the computational protocols fully documented and open-sourced to encourage timely data sharing, independent validation, and development of better prediction models in outbreak response.

Innovations in health and demographic surveillance systems to establish the causal impacts of HIV policies.

Health and demographic surveillance systems (HDSS), in conjunction with HIV treatment cohorts, have made important contributions to our understanding of the impact of HIV treatment and treatment-related interventions in sub-Saharan Africa. The purpose of this review is to describe and discuss innovations in data collection and data linkage that will create new opportunities to establish the impacts of HIV treatment, as well as policies affecting the treatment cascade, on population health and economic and social outcomes. Novel approaches to routine collection of biomarkers, behavioural data, spatial data, social network information, migration events and mobile phone records can significantly strengthen the potential of HDSS to generate exposure and outcome data for causal analysis of HIV treatment impact and policies affecting the HIV treatment cascade. Additionally, by linking HDSS data to health service administration, education and welfare service records, researchers can substantially broaden opportunities to establish how HIV treatment affects health and economic outcomes when delivered through public sector health systems and at scale. As the HIV treatment scaleup in sub-Saharan Africa enters its second decade, it is becoming increasingly important to understand the long-term causal impacts of large-scale HIV treatment and related policies on broader population health outcomes, such as noncommunicable diseases, as well as on economic and social outcomes, such as family welfare and children's educational attainment. By collecting novel data and linking existing data to public sector records, HDSS can create near-unique opportunities to contribute to this research agenda.

Implementation of a Care Pathway for Primary Palliative Care in 5 research clusters in Belgium: quasi-experimental study protocol and innovations in data collection (pro-SPINOZA)

BackgroundStarting with early identification of palliative care patients by general practitioners (GPs), the Care Pathway for Primary Palliative Care (CPPPC) is believed to help primary health care workers to deliver patient- and family-centered care in the last year of life. The care pathway has been pilot-tested, and will now be implemented in 5 Belgian regions: 2 Dutch-speaking regions, 2 French-speaking regions and the bilingual capital region of Brussels. The overall aim of the CPPPC is to provide better quality of primary palliative care, and in the end to reduce the hospital death rate.The aim of this article is to describe the quantitative design and innovative data collection strategy used in the evaluation of this complex intervention.Methods/DesignA quasi-experimental stepped wedge cluster design is set up with the 5 regions being 5 non-randomized clusters. The primary outcome is reduced hospital death rate per GPs’ patient population. Secondary outcomes are increased death at home and health care consumption patterns suggesting high quality palliative care.Per research cluster, GPs will be recruited via convenience sampling. These GPs -volunteering to be involved will recruit people with reduced life expectancy and their informal care givers. Health care consumption data in the last year of life, available for all deceased people having lived in the research clusters in the study period, will be used for comparison between patient populations of participating GPs and patient populations of non-participating GPs. Description of baseline characteristics of participating GPs and patients and monitoring of the level of involvement by GPs, patients and informal care givers will happen through regular, privacy-secured web-surveys.Web-survey data and health consumption data are linked in a secure way, respecting Belgian privacy laws.DiscussionTo evaluate this complex intervention, a quasi-experimental stepped wedge cluster design has been set up. Context characteristics and involvement level of participants are important parameters in evaluating complex interventions. It is possible to securely link survey data with health consumption data. By appealing to IT solutions we hope to be able to partly reduce respondent burden, a known problem in palliative care research.Trial registrationClinicalTrials.gov Identifier: NCT02266069.

Innovations in data collection, management, and archiving for systematic reviews.

Data abstraction is a key step in conducting systematic reviews because data collected from study reports form the basis of appropriate conclusions. Recent methodological standards and expectations highlight several principles for data collection. To support implementation of these standards, this article provides a step-by-step tutorial for selecting data collection tools; constructing data collection forms; and abstracting, managing, and archiving data for systematic reviews. Examples are drawn from recent experience using the Systematic Review Data Repository for data collection and management. If it is done well, data collection for systematic reviews only needs to be done by 1 team and placed into a publicly accessible database for future use. Technological innovations, such as the Systematic Review Data Repository, will contribute to finding trustworthy answers for many health and health care questions.