Improving Data Infrastructure for Person-Centered Outcomes Research on Intellectual and Developmental Disabilities

Individuals with intellectual and developmental disabilities (IDD) continue to experience disparities in health and well-being despite improved provisions of person-centered care. Patient-centered outcomes research (PCOR) translates evidence into practice for meaningful outcomes. This piece describes findings from an environmental scan and stakeholder outreach to identify and prioritize opportunities to enhance IDD PCOR data infrastructure. These opportunities include developing a standardized research definition; advancing data standards for service systems; improving capture of IDD at point of care; developing standardized outcome measures; and encouraging Medicaid data use for IDD research. Within this piece, we discuss the implications of addressing data gaps for enhanced research. While the identified activities provide a path towards advancing IDD PCOR data infrastructure, collaborative efforts between government, researchers, and others are paramount.

Designing Electronic Data Capture Systems for Sustainability in Low-Resource Settings: Viewpoint With Lessons Learned From Ethiopia and Myanmar

Electronic data capture (EDC) is a crucial component in the design, evaluation, and sustainment of population health interventions. Low-resource settings, however, present unique challenges for developing a robust EDC system due to limited financial capital, differences in technological infrastructure, and insufficient involvement of those who understand the local context. Current literature focuses on the evaluation of health interventions using EDC but does not provide an in-depth description of the systems used or how they are developed. In this viewpoint, we present case descriptions from 2 low- and middle-income countries: Ethiopia and Myanmar. We address a gap in evidence by describing each EDC system in detail and discussing the pros and cons of different approaches. We then present common lessons learned from the 2 case descriptions as recommendations for considerations in developing and implementing EDC in low-resource settings, using a sociotechnical framework for studying health information technology in complex adaptive health care systems. Our recommendations highlight the importance of selecting hardware compatible with local infrastructure, using flexible software systems that facilitate communication across different languages and levels of literacy, and conducting iterative, participatory design with individuals with deep knowledge of local clinical and cultural norms.

Data as symptom: Doctors' responses to patient-provided data in general practice

People are increasingly able to generate their own health data through new technologies such as wearables and online symptom checkers. However, generating data is one thing, interpreting them another. General practitioners (GPs) are likely to be the first to help with interpretations. Policymakers in the European Union are investing heavily in infrastructures to provide GPs access to patient measurements. But there may be a disconnect between policy ambitions and the everyday practices of GPs. To investigate this, we conducted semi-structured interviews with 23 Danish GPs. According to the GPs, patients relatively rarely bring data to them. GPs mostly remember three types of patient-generated data that patients bring to them for interpretation: heart and sleep measurements from wearables and results from online symptom checkers. However, they also spoke extensively about data work with patient queries concerning measurements from the GPs' own online Patient Reported Outcome system and online access to laboratory results. We juxtapose GP reflections on these five data types and between policy ambitions and everyday practices. These data require substantial recontextualization work before the GPs ascribe them evidential value and act on them. Even when they perceived as actionable, patient-provided data are not approached as measurements, as suggested by policy frameworks. Rather, GPs treat them as analogous to symptoms-that is to say, GPs treat patient-provided data as subjective evidence rather than authoritative measures. Drawing on Science and Technology Studies (STS) literature,we suggest that GPs must be part of the conversation with policy makers and digital entrepreneurs around when and how to integrate patient-generated data into healthcare infrastructures.

State Experiences Linking Medicaid Data With Birth Certificates and Other Data Sources

BACKGROUND

Many states link Medicaid claims with birth certificates or other data, often to inform programs and policies aimed at improving maternal and child health (MCH).

OBJECTIVES

To develop an up-to-date understanding of the extent of the use of linked Medicaid claims for MCH research by state.

RESEARCH DESIGN

We completed a structured literature review, developed an inventory of linkage efforts, and facilitated semistructured discussions with representatives from 9 states with established Medicaid claims data linkages to understand the technical details of linkages, experiences creating and maintaining linkages, and barriers or facilitators to establishing linkages.

RESULTS

We identified 45 peer-reviewed journal articles representing 22 states that used linked Medicaid data to study MCH and 33 states and territories that publicly report on Medicaid data linkages for a total of 39 states with any in-scope linkage. Discussions revealed that linkages often arose from the desire to answer a specific question or evaluate a program but then expanded to other use cases and that most states enable external researchers to access data for analysis. Respondents provided a few examples of where linked birth certificate data were used for health outcomes research.

CONCLUSION

Additional resources including technical assistance for identifying best practices along with interagency collaboration could overcome barriers and facilitate a coordinated and consolidated approach across states.

Highlighting the value of Alzheimer's disease-focused registries: lessons learned from cancer surveillance

Like cancer, Alzheimer's disease and related dementias (ADRD) comprise a global health burden that can benefit tremendously from the power of disease registry data. With an aging population, the incidence, treatment, and mortality from ADRD is increasing and changing rapidly. In the same way that current cancer registries work toward prevention and control, so do ADRD registries. ADRD registries maintain a comprehensive and accurate registry of ADRD within their state, provide disease prevalence estimates to enable better planning for social and medical services, identify differences in disease prevalence among demographic groups, help those who care for individuals with ADRD, and foster research into risk factors for ADRD. ADRD registries offer a unique opportunity to conduct high-impact, scientifically rigorous research efficiently. As research on and development of ADRD treatments continue to be a priority, such registries can be powerful tools for conducting observational studies of the disease. This perspectives piece examines how established cancer registries can inform ADRD registries' impact on public health surveillance, research, and intervention, and inform and engage policymakers.

Availability of open data for spatial public health research

Background: Preventive and health-promoting policies can guide (place- and space-specific) factors influencing human health, such as the physical and social environment. Required is data that can lead to a more nuanced decision-making process and identify both existing and future challenges. Along with the rise of new technologies, and thus the multiple opportunities to use and process data, new options have emerged to measure and monitor factors that affect health. Thus, in recent years, several gateways for open data (including governmental and geospatial data) have become available. At present, an increasing number of research institutions as well as (state and private) companies and citizens' initiatives are providing data. However, there is a lack of overviews covering the range of such offerings regarding health. In particular, for geographically differentiated analyses, there are challenges related to data availability at different spatial levels and the growing number of data providers. Objectives: This paper aims to provide an overview of open data resources available in the context of space and health to date. It also describes the technical and legal conditions for using open data. Results: An up-to-date summary of results including information on relevant data access and terms of use is provided along with a web visualization. All data is available for further use under an open license.

A global agenda for advancing freshwater biodiversity research

Global freshwater biodiversity is declining dramatically, and meeting the challenges of this crisis requires bold goals and the mobilisation of substantial resources. While the reasons are varied, investments in both research and conservation of freshwater biodiversity lag far behind those in the terrestrial and marine realms. Inspired by a global consultation, we identify 15 pressing priority needs, grouped into five research areas, in an effort to support informed stewardship of freshwater biodiversity. The proposed agenda aims to advance freshwater biodiversity research globally as a critical step in improving coordinated actions towards its sustainable management and conservation.

An agenda-setting paper on data sharing platforms: euCanSHare workshop

Various data sharing platforms are being developed to enhance the sharing of cohort data by addressing the fragmented state of data storage and access systems. However, policy challenges in several domains remain unresolved. The euCanSHare workshop was organized to identify and discuss these challenges and to set the future research agenda. Concerns over the multiplicity and long-term sustainability of platforms, lack of resources, access of commercial parties to medical data, credit and recognition mechanisms in academia and the organization of data access committees are outlined. Within these areas, solutions need to be devised to ensure an optimal functioning of platforms.

The Role of Large-Scale Data Infrastructure in Developing Next-Generation Deep Brain Stimulation Therapies

Advances in neuromodulation technologies hold the promise of treating a patient's unique brain network pathology using personalized stimulation patterns. In service of these goals, neuromodulation clinical trials using sensing-enabled devices are routinely generating large multi-modal datasets. However, with the expansion of data acquisition also comes an increasing difficulty to store, manage, and analyze the associated datasets, which integrate complex neural and wearable time-series data with dynamic assessments of patients' symptomatic state. Here, we discuss a scalable cloud-based data platform that enables ingestion, aggregation, storage, query, and analysis of multi-modal neurotechnology datasets. This large-scale data infrastructure will accelerate translational neuromodulation research and enable the development and delivery of next-generation deep brain stimulation therapies.

Essential requirements for establishing and operating data trusts: practical guidance co-developed by representatives from fifteen canadian organizations and initiatives

INTRODUCTION

Increasingly, the label "data trust" is being applied to repeatable mechanisms or approaches to sharing data in a timely, fair, safe, and equitable way. However, there is an absence of practical guidance regarding how to establish and operate a data trust.

AIM AND APPROACH

In December 2019, the Canadian Institute for Health Information and the Vector Institute for Artificial Intelligence convened a working meeting of 19 people representing 15 Canadian organizations/initiatives involved in data sharing, most of which focus on public sector health data. The objective was to identify essential requirements for the establishment and operation of data trusts in the Canadian context. Preliminary requirements were discussed during the meeting and then refined as authors contributed to this manuscript.

RESULTS

Twelve minimum specification requirements ("min specs") for data trusts were identified. The foundational min spec is that data trusts must meet all legal requirements, including legal authority to collect, hold or share data. In addition, there was agreement that data trusts must have (i) an accountable governing body to ensure that the data trust achieves its stated purpose and is transparent, (ii) comprehensive data management including clear processes and qualified individuals responsible for the collection, storage, access, disclosure and use of data, (iii) training and accountability requirements for all data users and (iv) ongoing public and stakeholder engagement.

CONCLUSIONS

Practical guidance for the establishment and operation of data trusts was articulated in the form of 12 min specs requirements. The 12 min specs are a starting point. Future work to refine and strengthen them with members of the public, companies, and additional research data stakeholders from within and outside of Canada, is recommended.

Sharing health research data - the role of funders in improving the impact

Recent public health emergencies with outbreaks of influenza, Ebola and Zika revealed that the mechanisms for sharing research data are neither being used, or adequate for the purpose, particularly where data needs to be shared rapidly. A review of research papers, including completed clinical trials related to priority pathogens, found only 31% (98 out of 319 published papers, excluding case studies) provided access to all the data underlying the paper - 65% of these papers give no information on how to find or access the data. Only two clinical trials out of 58 on interventions for WHO priority pathogens provided any link in their registry entry to the background data. Interviews with researchers revealed a reluctance to share data included a lack of confidence in the utility of the data; an absence of academic-incentives for rapid dissemination that prevents subsequent publication and a disconnect between those who are collecting the data and those who wish to use it quickly.  The role of the funders of research needs to change to address this. Funders need to engage early with the researchers and related stakeholders to understand their concerns and work harder to define the more explicitly the benefits to all stakeholders.  Secondly, there needs to be a direct benefit to sharing data that is directly relevant to those people that collect and curate the data. Thirdly more work needs to be done to realise the intent of making data sharing resources more equitable, ethical and efficient.  Finally, a checklist of the issues that need to be addressed when designing new or revising existing data sharing resources should be created. This checklist would highlight the technical, cultural and ethical issues that need to be considered and point to examples of emerging good practice that can be used to address them.

Using the Annual Social and Economic Supplement as Part of a Current Population Survey Panel

The Annual Social and Economic Supplement (ASEC) is the most widely used type of Current Population Survey (CPS) data, but it is cumbersome to use the ASEC as part of a longitudinal CPS panel, especially linking to non-March months. In this paper, we detail the challenges associated with linking the ASEC to monthly CPS data, outline the creation of an identifier that links the ASEC and the March Basic Monthly data from 1989 through 2017, and provide substantive examples that illustrate the value of combining the ASEC with monthly data. The variable, MARBASECID, which we created to link ASEC and March monthly CPS data, represents a significant contribution to social and economic data infrastructure, saving individual researchers from having to duplicate the effort required to create linkages between ASEC and monthly CPS data.

Infrastructure to support learning health systems: are we there yet? Innovative solutions and lessons learned from American Recovery and Reinvestment Act CER investments

The 11 big health data networks participating in the AcademyHealth Electronic Data Methods Forum represent cutting-edge efforts to harness the power of big health data for research and quality improvement. This paper is a comparative case study based on site visits conducted with a subset of these large infrastructure grants funded through the Recovery Act, in which four key issues emerge that can inform the evolution of learning health systems, including the importance of acknowledging the challenges of scaling specialized expertise needed to manage and run CER networks; the delicate balance between privacy protections and the utility of distributed networks; emerging community engagement strategies; and the complexities of developing a robust business model for multi-use networks.

Building electronic data infrastructure for comparative effectiveness research: accomplishments, lessons learned and future steps

There are large gaps in our knowledge on the potential impact of diagnostics and therapeutics on outcomes of patients treated in the real world. Comparative effectiveness research aims to fill these gaps to maximize effectiveness of these interventions. Health information technology has the potential to dramatically improve the practice of medicine and of research. This is an overview of about US$100 million of American Recovery and Reinvestment Act investment in 12 projects managed by the Agency for Healthcare Research and Quality to build an electronic clinical data infrastructure that connects research with healthcare delivery. The achievements and lessons learned from these projects provided a foundation for the National Patient-Centered Clinical Research Network (PCORnet)and will help to guide future infrastructure development needed to build an efficient, scalable and sustainable learning health system.

American Recovery and Reinvestment Act investments in data infrastructure

AIM

This article describes American Reinvestment and Recovery Act comparative effectiveness research data infrastructure (DI) investments and identifies facilitators and barriers to implementation.

MATERIALS & METHODS

We reviewed original project proposals, conducted an investigator survey and interviewed project officers and principal investigators.

RESULTS

DI projects assembled or enhanced existing clinical datasets, established linkages between public and private data sources and built infrastructure. Facilitators included building on existing relationships across organizations and making collection as seamless as possible for clinicians.

CONCLUSION

To sustain DI, investigators should reduce the burden of comparative effectiveness research data collection on practices, adequately address data privacy and security issues, resolve or lessen the impact of data-linking issues and build research capacity for other investigators and clinicians.

The Future of NCES's Longitudinal Student Surveys: Balancing Bold Vision and Realism

The National Center for Education Statistics' (NCES) longitudinal student surveys have long been exceptionally useful for many purpose. Despite their many virtues, however, these surveys cannot be used to monitor trends at short time intervals, they do not allow for flexible changes to survey content, they cannot generally be used to infer policy effects, they are not useful for international comparisons, and they are of limited value to local stakeholders. NCES should consider doing to its longitudinal students surveys what the Census Bureau did to the decennial census long form and what NORC has long done for the General Social Survey: Move to annual rotating panels and allow outside investigators to field (and fund) supplemental topical modules. NCES should also continue to work with the research community to explore new survey content areas and modes of observation, improve the quality of spatial measures, and pursue record linkage to administrative data.

A national data infrastructure for patient-centered outcomes research

Concerted efforts are underway to improve healthcare decision-making through patient-centered outcomes research. These efforts are supported by the Patient-Centered Outcomes Research Trust Fund, which was established within the Patient Protection and Affordable Care Act. This article focuses on describing national data infrastructure efforts that support patient-centered outcomes research. A national data infrastructure has the potential to decrease research costs and improve research throughput. We describe early and current efforts that demonstrated this potential, how the national effort is utilizing the lessons learned from these predecessor efforts and remaining challenges.