Data

This post is the final entry in our series about the use of race and ethnicity in population health research. Our previous posts have detailed information around defining, measuring, coding, and analyzing data on race and ethnicity.

This work seeks to shift awareness and practice by centering racial equity and community voice within the context of data integration and use that supports power sharing and building across agencies and community members.

This report explains the Executive Order on Advancing Racial Equity and Support for Underserved Communities Through the Federal Government (“Equity EO”) launched a whole-of-government effort to incorporate the principle of equity throughout the federal government. Recognizing that the ability to conduct equity assessments and identify and remove barriers to equitable access to government programs is contingent on gathering the necessary data, President Biden ordered the formation of the Equitable Data Working Group. The President directed the Working Group to study existing federal data collection policies, programs, and infrastructure to identify inadequacies and provide recommendations that lay out a strategy for increasing data available for measuring equity and representing the diversity of the American people. The following recommendations are then discussed: make disaggregated data the norm while protecting privacy; catalyze existing federal infrastructure to leverage underused data; build capacity for robust equity assessment for policymaking and program implementation; galvanize diverse partnerships across levels of government and the research community; and be accountable to the American public. Key administration actions that address each of the recommendations are described.

The chief research officer of the Seattle Indian Health Board is creating programs and databases that are not based on Western concepts to better serve indigenous communities.

Yeshimabeit Milner, founder and executive director of Data for Black Lives, calls for rejecting the concentration of Big Data in the hands of a few to challenge the structures that allow data to be wielded as a weapon of immense political influence.

This article highlights synergies between antiracist principles and community-based participatory research (CBPR), examines the potential for CBPR to promote antiracist research and praxis, illustrates structural barriers to antiracist CBPR praxis, and offers examples of CBPR actions taken to disrupt structural racism. It also provides recommendations for the next generation of antiracist CBPR.

To unlock the full potential of data, researchers and analysts must consider and apply equity at every step of the research process. Ensuring responsible data collection, representing the communities surveyed accurately, and incorporating community input whenever possible will lead to more equitable data analyses and visualizations. Although there is no one-size-fits-all approach to working with data, for researchers to truly do no harm, they must build their work on a foundation of empathy. This article details three ways you can make your data analysis and communication more equitable and inclusive.

A recent literature review revealed most studies of social need interventions were poorly designed, inadequately documented, and inconsistently presented. In this post, Robert Dubois of the National Pharmaceutical Council, an AcademyHealth Organizational Member, outlines the state of the research and provides recommendations to improve study design quality.

This expert roundtable focuses on how the disaggregation of data, especially race and ethnicity data, can improve equity.

On this week?s episode of The Dose, host Joel Bervell speaks with Dr. Ziad Obermeyer, from the UC Berkeley School of Public Health, about the potential of AI to inform health outcomes ? for better and for worse.

The Commission’s report and recommendations make it clear that in our current system, data on health inequities are divorced from the history and community conditions that shape poor health outcomes.

This paper provides recommendations on how to appropriately engage in scientific inquiry aimed at understanding racial health inequities. Race should not be used as a measure of biologic difference, but as a proxy for exposure to systemic racism.

This guide was developed in response to librarians fielding multiple requests from UMN researchers looking to incorporate anti-racism into their research practices. This guide shares racist research systems and practices, followed by resources for mitigating those problematic systems and practices, but we wholeheartedly acknowledge that this guide is not a “solution” to the issues of racism embedded in research.

Drawing on field experience and scientific literature, we explore the connection between citizen science and Indigenous local knowledge and demonstrate approaches for how citizen science can generate useful knowledge while at the same time strengthening Indigenous local knowledge systems.

Given the significant nature of the Big Data (BD) and Big Data Analytics (BDA), this paper presents a state-of-the-art review that presents a holistic view of the BD challenges and BDA methods theorized/proposed/employed by organizations to help others understand this landscape with the objective of making robust investment decisions.

Background: Multiple factors, including experiences with unethical research practices, have made some Indigenous groups in the United States and Canada reticent to participate in potentially beneficial health-related research. Yet, Indigenous peoples have also expressed a willingness to participate in research when certain conditions related to the components of data management?including data collection, analysis, security and storage, sharing, dissemination, and withdrawal?are met. A scoping review was conducted to better understand the terms of data management employed in healthrelated research involving Indigenous communities in the United States and Canada. Methods: PubMed, Embase, PsychINFO, and Web of Science were searched using terms related to the populations and topics of interest. Results were screened and articles deemed eligible for inclusion were extracted for content on data management, community engagement, and community-level research governance. Results: The search strategy returned 734 articles. 31 total articles were extracted,
of which nine contained in-depth information on data management and underwent detailed extraction. All nine articles reported the development and implementation of data management tools, including research ethics codes, data-sharing agreements, and biobank access policies. These articles reported that communities were involved in activities and decisions related to data collection (n=7), data analysis (n=5), data-sharing (n=9), dissemination (n=7), withdrawal (n=4), and development of data management tools (n=9). The articles also reported that communities had full or shared ownership of (n=5), control over (n=9), access to (n=1), and possession of data (n=5). All nine articles discussed the role of community engagement in research and communitylevel research governance as means for aligning the terms of data management with the values, needs, and interests of communities. Conclusions: There is need for more research and improved reporting on data management in health-related research involving Indigenous peoples in the United States and Canada. Findings from this review can provide guidance for the identification of data management terms and practices that may be acceptable to Indigenous communities considering participation in health-related research.

This toolkit is designed to highlight the opportunities and outline the challenges specific to greater use of technology in the service of diversity, equity and inclusion efforts. It is designed for organizational leaders, Chief Diversity and Inclusion Officers (CDIOs), and others actively working to promote diverse, equitable and inclusive workplaces globally. It is intended to complement a range of related publications produced by the World Economic Forum?s Platform for Shaping the Future of the New Economy and Society: ?HR4.0: Shaping People Strategies in the Fourth Industrial Revolution?, developed in collaboration with the Forum?s community of Chief Human Resources Officers (CHROs) and a guide to sound decision-making in the context of the coronavirus pandemic, ?Workforce Principles for the COVID-19 Pandemic: Stakeholder Capitalism in a Time of Crisis?.

This publication presents the final recommendations of an external advisory group AcademyHealth convened to help shape a sustainable, action-oriented strategy to address diversity, equity, & inclusion in the field

For this follow-up volume, Do No Harm Guide: Additional Perspectives on Data Equity, we handed the pen to data experts and practitioners whose voices have been traditionally underrepresented.

In this guide and its associated toolkits, we focus on how data practitioners can approach their work through a lens of diversity, equity, and inclusion to encourage thoughtfulness in how analysts work with and present their data.

The COVID-19 pandemic has inequitably impacted Indigenous communities in the United States. In this emergency state that highlighted existing inadequacies in US government and tribal public health infrastructures, many tribal nations contracted with commercial entities and other organization types to conduct rapid diagnostic and antibody testing, often based on proprietary technologies specific to the novel pathogen. They also partnered with public-private enterprises on clinical trials to further the development of vaccines. Indigenous people contributed biological samples for assessment and, in many cases, broadly consented for indefinite use for future genomics research. A concern is that the need for crisis aid may have placed Indigenous communities in a position to forego critical review of data use agreements by tribal research governances. In effect, tribal nations were placed in the unenviable position of trading short-term public health assistance for long-term, unrestricted access to Indigenous genomes that may disempower future tribal sovereignties over community members’ data. Diagnostic testing, specimen collection, and vaccine research is ongoing; thus, our aim is to outline pathways to trust that center current and future equitable relationship-building between tribal entities and public-private interests. These pathways can be utilized to increase Indigenous communities’ trust of external partners and share understanding of expectations for and execution of data protections. We discuss how to navigate genomic-based data use agreements in the context of pathogen genomics. While we focus on US tribal nations, Indigenous genomic data sovereignties relate to global Indigenous nations regardless of colonial government recognition.

This article examines what it means to use equity as a guiding principle throughout the components and functions of a modern public health data system.

This paper explores the rise & consequences of ?health equity tourism?, a phenomenon where previously unengaged investigators pivot into health equity research without developing the necessary scientific expertise for high-quality work.

This resource is intended to provide state policymakers with a suite of state approaches to address health disparities ? from targeted to cross-agency comprehensive strategies. Each section below comprises best practices and state
examples from a geographically diverse set of states to guide state policymakers to the opportunity that best suits their states? needs and capacity. Also included is a lexicon, recognizing the importance of common definitions in this domain, along with links to key tools and resources.

This expert roundtable focuses on how the COVID-19 pandemic brought to light the serious and pervasive data gaps facing marginalized groups and what cross-cutting themes the panels found in their work.

In this issue we have some articles that instead attempt to tackle some of the fundamental philosophical challenges of data science

Public research funding agencies and research organisations are increasingly accountable for the wider impacts of the research they support. While research impact assessment (RIA) frameworks and tools exist, little is known and shared of how these organisations implement RIA activities in practice.

Racial and ethnic health disparities have been documented in the United States for over a century. In 1985 the Heckler Report provided the first national summary of these disparities, leading to the creation of the Department of Health and Human Services Office of Minority Health. In the 1990s and 2000s the study of gaps in health care access, utilization, and outcomes by race and ethnicity grew rapidly, but confronted a critical limitation of the available data: the lack of standardized, self-identified race and ethnicity. The COVID-19 pandemic provided stark proof that data limitations are far from being addressed, which has real consequences for the study and practice of public health. Though data have improved since the beginning of the pandemic, the Centers for Disease Control and Prevention can only identify the race and ethnicity of less than 60% of individuals testing positive for COVID-19 or receiving vaccines, significantly limiting the ability of policymakers and health care stakeholders to measure and improve equity in the pandemic?s effects and mitigation. This Roadmap builds on an earlier report, Federal Action Is Needed to Improve Race and Ethnicity Data in Health Programs,1 in three critical respects. First, it expands on that report?s summary of the current state of race and ethnicity data in health care programs, offering more detail about whether and how race and ethnicity data are collected across a range of insurance programs, federally administered health systems and public health databases. Second, it summarizes a range of barriers to improving collection and use of race and ethnicity data, and identifies general principles for improving the data. Finally, it expands the range of recommendations for improving the data, considering not only actions the federal government could take, but also identifying actions for states and the private sector. To advance these goals, the project team carried out a targeted search for information on the completeness and quality of race and ethnicity data; an environmental scan to identify previous reports summarizing challenges to collection and use of race and ethnicity data; and key informant interviews to better define and understand barriers and opportunities. The environmental scan and informant interviews pointed to a consistent set of barriers faced by health care organizations, including: (1) Legal and privacy concerns around collection and use of race and ethnicity data. (2) Lack of standardized collection procedures and category definitions. (3) Technical barriers to collection and storage of data. (4) Cost of collection and lack of financial incentives or program requirements to collect race and ethnicity data. (5) Lack of staff and resources in health care organizations to analyze and use data once collected. (6) Resistance from patients and clinical providers to collection and use of race and ethnicity data. Despite these challenges, prior reports and data from the key informant interviews pointed to several opportunities to improve collection and use of race and ethnicity data: (1) Highlight early, successful adopters of expanded race and ethnicity data collection. (2) Disseminate existing technical support resources and data standards. (3) Educate patients and providers about the potential of improved race and ethnicity data to improve outcomes and equity. (4) Provide incentives to encourage data collection and finance necessary technology investments and staffing. (5) Where incentives fail to produce action, consider mandates (e.g., require collection to meet certain standards as a condition of participating in federal programs or demonstration projects). (6) Identify existing resources that could be leveraged to improve analysis of health equity until consistent, complete, self-reported race and ethnicity data are available. The report concludes with a series of recommendations for federal and state regulators and legislators, health systems and health insurance companies, and a range of other health sector stakeholders. Recommendations are grouped under the following themes: (1) Improve data collection, storage and transfer systems. (2) Evaluate and expand incentives and requirements to collect. (3) Provide updated technical assistance to stakeholders. (4) Review, clarify and, if necessary, amend regulations.

Our latest episode highlights how the current equity efforts can integrate with established performance management practices. It also discusses how health agencies can use planning documents to integrate equity in health assessments, health improvement plans, strategic plans, and performance management systems.

This tool describes how to conduct intensive equity assessments of existing programs, policies, and processes. The tool first describes the scoping work that needs to be done before conducting an equity assessment and then describes and provides guiding questions for each of the six steps in the in-depth equity assessment process.

The Lancet REWARD (REduce research Waste And Reward Diligence) Campaign invites everyone involved in biomedical research to critically examine the way they work to reduce waste and maximize efficiency.

Despite decades of research exposing health disparities between populations and communities in the US, health equity goals remain largely unfulfilled. We argue these failures call for applying an equity lens in the way we approach data systems, from collection and analysis to interpretation and distribution. Hence, health equity requires data equity. There is notable federal interest in policy changes and federal investments to improve health equity. With this, we outline the opportunities to align these health equity goals with data equity by improving the way communities are engaged and how population data are collected, analyzed, interpreted, made accessible, and distributed. Policy priority areas for data equity include increasing the use of disaggregated data, increasing the use of currently underused federal data, building capacity for equity assessments, developing partnerships between government and community, and increasing data
accountability to the public.

Examines the measurement of sex, gender identity, & sexual orientation to produce recommendations for specific measures that can be used in surveys & research, administrative, clinical, & other health settings.

The goal of this commentary is to inspire the use of up-to-date and theoretically driven approaches to increase discourse among public health researchers on capturing racism as well as to improve evidence of its role as the fundamental cause of racial health inequities.

We present the steps taken with a forward-looking perspective on the challenges laying ahead, in particular the necessary change of the rewards and incentives system for researchers (for which various actors are co-responsible and which goes beyond the mandate of the European Commission). Finally, we discuss the role of artificial intelligence (AI) within an open science perspective.

This brief introduces the Belmont Report?s principles and provides selected principle-aligned practices and resources to help data experts at all levels integrate the principles into their work and move toward more equitable data practice.

The Commission’s report and recommendations make it clear that in our current system, data on health inequities are divorced from the history and community conditions that shape poor health outcomes.

This article identifies where the technology and data sectors can contribute skills, expertise, and assets in support of innovative workforce models and augment the development of public health workforce competencies.

Use the Data Equity Framework to break up your data work into manageable parts and go through an intentional, equity-oriented process to make the key decisions along the way.

Our aim is to lay the groundwork for an ethically responsible development of AI in the domains of health research, clinical practice and public health.

This article summarizes some of the challenges around data sharing and reuse and identifies where the technology and data sectors can contribute to fill current gaps to promote interoperability and data stewardship.

We provide recommendations on how to implement systems-level change and educational reform to infuse racial equity in future policy and practice for Asian American communities.

This content was initially created to inform federal staff at the U.S. Department of Health and Human Services. In an effort to increase collaboration and share promising practices, the Office of the Assistant Secretary for Planning and Evaluation has made this tool available for both public and private partners.

Abdul Shaikh facilitates a discussion on emerging methods to advance health equity. Lisa Goldman Rosas presents on community-based participatory research, and Suzanne Tamang discusses how using big data can improve outcomes.

In this session, presenters Ninez Ponce and Juanita Chinn describe approaches to improving data collection and analysis with Asian/Pacific Islander, Native American people, and other under-represented groups.

Rashida Dorsey leads a conversation with J?Mag Karbeah, Reginald Tucker-Seeley, and Jameta Barlow on topic areas including structural racism, measuring and reporting health disparities, and understanding intersectionality.

The We All Count training program is made up of three levels, taking you from wanting to improve the equity in your data work to a high level of practical competency at actually doing it.

Our courses are grounded in the revolutionary Data Equity Framework: a set of real-world tools and systems that identify key equity choice points in your work and teach you how to make those choices in a way that makes your projects more fair, more rigorous, more accurate, and more successful.

This article summarizes what data should be included and identifies where the technology and data sectors can contribute to fill current gaps to measure equity, positive health, and well-being.

What we?re realizing as a community, is that we?re leaving an enormous amount of value on the table, and that if we can do a better job of capturing, preserving, and making available more of the research workflow, we?ll drive better transparency and reliability of the research conclusions, and improve efficiency and the return on the investment we make in research funding.

If we seek to improve community and population health through effective interventions targeting social and environmental contexts, who will drive the change? On the basis of our experience in Durham County, North Carolina, we have learned that successful interventions must rely, at least in part, on stakeholders outside the public health and health care sectors, including those in business, education, philanthropy, nonprofit organizations, community development, and government.