To Address Women's Health Inequity,It Must First Be Measured

Abstract

Research and data collection related to what is historically known as “women's health” is consistently underfunded and marginalizes the health risks and experiences of women of color and transgender people. In the wake of the pandemic, the United States has an opportunity to redesign and reimagine a modern public health data infrastructure that centers equity and elevates the health and well-being of under-represented communities, including the full spectrum of gender identities. This piece offers a blueprint for transformational change in how the United States collects, interprets, and shares critical data to deliver greater health justice for all.

Introduction

It is time to acknowledge that the United States needs a dramatic reset when it comes to “women's health” and how we measure it. For decades, our public health system has not only marginalized women's health but also viewed it through a flawed monolithic exclusionary lens. Research and data collection related to women's health have been consistently underfunded and excluded the health risks and lived experiences of important populations,¹ such as women of color and transgender people. In this piece, we intend to instigate a conversation on the urgent need for transformational reform in the United States public health data infrastructure that centers equity by including sex assigned at birth, gender identity, race, ethnicity, and other statuses, to achieve more inclusive health policies.

The scarcity of real-time disaggregated data—across variables such as sex and gender identity, race, ethnicity, geographic location, age, and disability—contributed² to the devastating toll of COVID-19 and systemic disparities in health outcomes³ among minority and marginalized populations. The lesson is clear: we cannot fix what we do not accurately measure. This country's data infrastructure must be fundamentally redesigned to center equity and elevate the health and well-being of marginalized populations.

To address our nation's enduring and chronic data failures, the Robert Wood Johnson Foundation⁴ created a National Commission to Transform Public Health Data Systems,⁵ with the goal of creating recommendations⁶ on how to build a modern equity-centered public health data infrastructure that helps policymakers identify problems, target interventions, and allocate resources to those most in need. Informing the Commission's deliberations were five expert panels, which examined population-specific data gaps that contribute to health inequities among American Indians/Alaska Natives, Black people/African Americans, lesbian, gay, bisexual, transgender, queer or questioning, intersex, asexual, and more (LGBTQ+) communities, people living with disabilities, and women.

The women expert panel⁷ was tasked with identifying the change needed in data systems to achieve greater sex and gender equity in health outcomes among women. Recognizing the intersectionality among population-specific groups, panelists were selected across race, ethnicity, sex, gender identity, lived experiences, geographic location and backgrounds in women's health research, policy, maternal, and public health. Collectively, participants represented perspectives from government and nongovernment sectors, and public and nonpublic health systems. Central to this expert panel's deliberations were in-depth discussions and analysis of the glaring gaps in women's health equity, across a span of intersecting identities, stemming from the historic lack of inclusion of women in health research and the dearth of accurate, disaggregated sex, and gender data.

We reference studies examining differences between women and men. Most include only cisgender people and/or were not clear as to whether transgender people were included. When we refer to “transgender people,” we mean transgender women and men. Furthermore, we acknowledge the consequences gender identity goes beyond how a person identifies, and includes multiple factors such as access to resources, roles and practices, norms, beliefs, decision-making power and autonomy, laws, policy, and institutions.⁸

The Impact of Data Failures on Women's Health Equity

The pandemic provided a stark reminder of how the intersectionality of race, gender identity, and social determinants of health factor into a person's risk for poor health and disease-related outcomes. Although data found that more men than women⁹ in the United States died from the COVID-19 virus, critical variables such as race and line of work obscure the risk of death for women from certain communities.* Studies in Georgia and Michigan showed the COVID death rate for Black women three times as high¹⁰ as that for White and Asian men, with disparities further exacerbated by the lack of uniform reporting of race/ethnicity in COVID-19 studies.¹¹ Without examining the intersections of race and gender, researchers stated, “the blanket claim that women with COVID-19 fare better than men, makes invisible the high death rate among Black women.”*

Women hold 76%¹² of the nation's health care jobs and account for the vast majority of frontline service workers,¹³ making them especially vulnerable to exposure. In addition, they are four times more likely¹⁴ to suffer from long COVID.* Alongside longer lifespans¹⁵ and different comorbidities, the ramifications from COVID infections could potentially be more severe and costly for women over time. Without tracking and measuring these data points, we cannot fully understand who is at the greatest risk and the potential lifelong debilitating impacts.

Cisgender women—especially women of color—have been historically and contemporarily to be excluded from biomedical research. Only as recently as 2016 did the National Institutes of Health (NIH) institute a policy that sex as a biological variable be factored into its funded research. This policy aimed to ensure that differences according to sex assigned at birth are considered, but one unintended effect is erasure of transgender people in research. Research institutes and organizations continue to consistently underfund research¹⁶ on women, even when it comes to diseases that have the greatest impact on women's health. A recent analysis of NIH research funding patterns reveals there are roughly three times as many diseases¹⁷ as female-favored diseased in the NIH portfolio.

Furthermore, male-favored diseases are significantly more likely to be overfunded and female-favored diseases more likely to be underfunded.* Nationwide, only one-third¹⁸ of cardiovascular disease clinical trial subjects are assigned female at birth, despite the fact that it is the number one killer of women in the United States.* These glaring inequities are all the more irrational considering the immense societal return on investment that could be achieved through greater women's health research. Another study¹⁹ examining NIH's 2019 funding decisions revealed that a mere 4.5% of funding on coronary artery disease (CAD) addressed questions related to how the disease impacts women, but that doubling NIH funding for research on CAD in women would produce a return on investment of 9500% over 30 years.*

Gender, race, and age biases persist in clinical trials for life-saving vaccines and drugs. Although the FDA's 2020 Drug Trials Snapshots²⁰ report shows that women made up >50% of drug trial participants, overall—among both women and men—only 8% of all trial participants were Black or African American, 11% were Hispanic, and 30% were over the age of 65. Pregnant people were excluded from COVID vaccine trials, leading to vaccine hesitancy²¹ among this population—even though global studies²² showed that they face significantly higher mortality rates from COVID-19. Unfortunately, it is difficult to determine the true extent of health inequities and impact of the pandemic due to the lack of data on minorities and marginalized populations, including cisgender women of color and transgender people.

Standardizing Data Collection Around Sex and Gender Identity

Developing intentional consistent definitions of sex and gender data and establishing routine data collection are essential to identifying health disparities and achieving health equity. Data collection efforts must recognize evolving terminology related to the spectrum of gender identity, and the range of ways in which people—both cisgender and trans—experience health and the health care system, to capture a holistic understanding of health and its impact over time.

A 2022 report²³ from the National Academies of Sciences, Engineering, and Medicine calls on the NIH to standardize language and practices used for data collection on sex and gender, “including collecting gender data by default, and not conflating gender with sex as a biological variable.” The report argues that, in most contexts, collecting data on gender is more relevant than collecting data on sex as a biological variable, and offers the following five guiding principles²⁴ for data collection (Table 1).

Table 1.

National Academies of Science, Engineering, and Medicine's Guiding Principles for data collection on sex, gender, and sexual orientation

Guiding principles for data collection on sex, gender, and sexual orientation
	Source: National Academies of Sciences, Engineering, and Medicine
Inclusiveness	People deserve to be counted. Everyone should be able to see themselves and their identities represented in surveys and other data collection instruments.
Precision	Use precise terminology that reflects the complex and multidimensional nature of sex, gender, and sexual orientation.
Autonomy	Respect identity and autonomy. Data collection must allow respondents to self-identify whenever possible.
Parsimony	Collect only necessary data, gathered in pursuit of a specific and well-defined goal.
Privacy	Use data in a manner that benefits respondents and respects their privacy and confidentiality. Research findings should be shared with respondents to ensure the benefit, and data should be shared only under rigorous privacy and confidentiality standards.

As inclusive language practices evolve around sex and gender identity in the scientific and medical communities, we must proceed with intention on the use of language and definitions, to center equity in the approach to data collection, and to continually reassess definitions to ensure findings are correctly interpreted, shared, and compared across community-based and national data systems.

Building Women's Health Equity Through Data Systems Transformation

Research and data on women's health are inextricably linked to many facets of identity and other social determinants, impacting health outcomes. Yet our outdated data systems are ill-equipped to capture this vital information. Health care systems that are designed to routinely collect and report a standardized and comprehensive set of essential data points must be structured to allow for intersectional analysis between variables and remain nimble as data are collected over time. The ability to pull data by sex or gender identity, as well as by race, ethnicity, disability, employment, and socioeconomic status, and other variables, will help identify the biggest health gaps and prioritize areas of focus.

After thoughtful deliberations, the women expert panel reached unanimous agreement on four overarching recommendations and calls-to-action to reinvent America's public health data systems, with an eye toward women's health equity (Table 2).

Table 2.

Recommendations and Calls-to-Action

Recommendation 1: Standardization of essential data points
It is imperative that we build a new public health data infrastructure cemented in a foundation of standardized, comprehensive essential data points to identify women's health inequities across one's “life course” and help shape policies to address disparities. The standardization of data collection best practices will facilitate intersectional analysis between variables, such as sex and gender identity, and other social determinants impacting health outcomes. Also critical is the standardization of definitions being used in the collection of sex and gender identity data to ensure a complete and accurate understanding of health inequities across the spectrum of sex and gender.	Agree on a set of guidelines, goals, objectives, measurable outcomes, and benchmarks for success.Create and adopt universal data elements and standards—including consistent standardized definitions of sex and gender identity—within an overarching data governance structure applied across data systems.Ensure adequate sustainable funding for standardized data collection, management, integration, collaboration, reporting, and evaluation.

Recommendation 1: Standardization of essential data points

Rationale

Calls-to-action

It is imperative that we build a new public health data infrastructure cemented in a foundation of standardized, comprehensive essential data points to identify women's health inequities across one's “life course” and help shape policies to address disparities. The standardization of data collection best practices will facilitate intersectional analysis between variables, such as sex and gender identity, and other social determinants impacting health outcomes. Also critical is the standardization of definitions being used in the collection of sex and gender identity data to ensure a complete and accurate understanding of health inequities across the spectrum of sex and gender.

Agree on a set of guidelines, goals, objectives, measurable outcomes, and benchmarks for success.Create and adopt universal data elements and standards—including consistent standardized definitions of sex and gender identity—within an overarching data governance structure applied across data systems.Ensure adequate sustainable funding for standardized data collection, management, integration, collaboration, reporting, and evaluation.

Recommendation 2: interoperability and interconnectivity of data and systems
America's siloed disconnected health care data systems have made it extremely difficult to measure women's health data in a holistic impactful way. Currently, the lack of interoperability among systems makes it largely impossible to integrate social determinants of health into data gathering processes. Interoperability between data systems stymies the critical sharing and analysis of disaggregated health data across platforms and communities. This, in turn, prevents women from receiving preventive quality care and treatment, especially among those most at risk for developing serious illness and disease.	Establish interoperability and ability to overlay or connect data sets to support longitudinal overtime analysis.Prioritize high-value data sets and systems for conformity and interoperability.Identify specific gender-based disparities and how gender discrimination interacts with other oppressions, and targeted data connectivity initiatives to address them.Advocate for policy change to address barriers to data sharing and interoperability.

Recommendation 2: interoperability and interconnectivity of data and systems

Rationale

Calls-to-action

America's siloed disconnected health care data systems have made it extremely difficult to measure women's health data in a holistic impactful way. Currently, the lack of interoperability among systems makes it largely impossible to integrate social determinants of health into data gathering processes. Interoperability between data systems stymies the critical sharing and analysis of disaggregated health data across platforms and communities. This, in turn, prevents women from receiving preventive quality care and treatment, especially among those most at risk for developing serious illness and disease.

Establish interoperability and ability to overlay or connect data sets to support longitudinal overtime analysis.Prioritize high-value data sets and systems for conformity and interoperability.Identify specific gender-based disparities and how gender discrimination interacts with other oppressions, and targeted data connectivity initiatives to address them.Advocate for policy change to address barriers to data sharing and interoperability.

Recommendation 3: longitudinal tracking for lifespan measurement
Health data captured and linked over time and across an individual's lifespan will improve long-term health outcomes for women. In rebuilding America's data infrastructure, it is critical that our data systems have the capability of interacting throughout one's health journey. The health transitions traditionally experienced by cisgender women are also important for transgender people across their lives—from pediatric to adult care, preconception into pregnancy and postpartum for those of childbearing capabilities, and finally, life stage transitions such as menopause through to Medicare—allow us to identify data linkage failures, the lack of collection of multiple data points, and other gaps and areas of vital need in women's health.	Develop a long-term roadmap and timelines for milestones for implementation of health data transformation with an eye toward lifespan measurement and strengthening women's health equity.Implement a strategy for tracking and addressing data gaps that affect women's health equity over time.Make integrated “lifespan” data accessible for a wide range of stakeholders to examine and assess progress in women's health care and equity over time.

Recommendation 3: longitudinal tracking for lifespan measurement

Rationale

Calls-to-action

Health data captured and linked over time and across an individual's lifespan will improve long-term health outcomes for women. In rebuilding America's data infrastructure, it is critical that our data systems have the capability of interacting throughout one's health journey. The health transitions traditionally experienced by cisgender women are also important for transgender people across their lives—from pediatric to adult care, preconception into pregnancy and postpartum for those of childbearing capabilities, and finally, life stage transitions such as menopause through to Medicare—allow us to identify data linkage failures, the lack of collection of multiple data points, and other gaps and areas of vital need in women's health.

Develop a long-term roadmap and timelines for milestones for implementation of health data transformation with an eye toward lifespan measurement and strengthening women's health equity.Implement a strategy for tracking and addressing data gaps that affect women's health equity over time.Make integrated “lifespan” data accessible for a wide range of stakeholders to examine and assess progress in women's health care and equity over time.

Recommendation 4: multisector buy-in for data systems transformation
It is essential that we build the diverse multisector stakeholder buy-in required to champion the transformation of America's public health data systems, with a focus on women's health equity. Individuals and organizations from health care delivery, financing, patient and consumer advocacy, research, and regulatory sectors should be involved in developing and implementing a comprehensive approach to improving data integrity and interconnectedness across the health care landscape. Multisector collaboration will ensure public and private data collection systems adhere to standardized best practices to collect critical health variables that include both biological sex and gender identity, and other social determinants such as race/ethnicity, to ensure intersectional impacts can be accurately identified and addressed.	Assess funding needs and opportunities and develop a cost-benefit analysis to support funding requests across sectors.Integrate a wide-ranging set of stakeholders, including advocates for high-risk populations impacted by systemic health inequities.Raise awareness across sectors on health disparities experienced by women and the important role of data in correcting these inequities.

Recommendation 4: multisector buy-in for data systems transformation

Rationale

Calls-to-action

It is essential that we build the diverse multisector stakeholder buy-in required to champion the transformation of America's public health data systems, with a focus on women's health equity. Individuals and organizations from health care delivery, financing, patient and consumer advocacy, research, and regulatory sectors should be involved in developing and implementing a comprehensive approach to improving data integrity and interconnectedness across the health care landscape. Multisector collaboration will ensure public and private data collection systems adhere to standardized best practices to collect critical health variables that include both biological sex and gender identity, and other social determinants such as race/ethnicity, to ensure intersectional impacts can be accurately identified and addressed.

Assess funding needs and opportunities and develop a cost-benefit analysis to support funding requests across sectors.Integrate a wide-ranging set of stakeholders, including advocates for high-risk populations impacted by systemic health inequities.Raise awareness across sectors on health disparities experienced by women and the important role of data in correcting these inequities.

Conclusion

Now is the time to widen our lens on “women's health” to include research and data across the full spectrum of gender identities, sex, race, ethnicity, and other variables to address systemic health disparities. The United States has an unprecedented opportunity—and obligation—to rebuild a modern public health data infrastructure, cemented in a foundation of equity and inclusion. The recommendations outlined earlier by the women expert panel on population-specific data gaps, convened by the Robert Wood Johnson Foundation, offer a blueprint for transformational change in how the United States collects, interprets, and shares critical data on sex and gender that is inclusive of all numerical gender minorities. This disruptive model for data systems reform has enormous potential to transform care, enhance equity, and create empowerment through better health outcomes for generations to come.

Footnotes

Acknowledgments

All persons who have made substantial contributions to the study reported in the article, but who do not meet the criteria for authorship, have given us their written permission to be mentioned here. The authors thank the following individuals: additional members of the women expert panel Wanda D. Barfield, MD, MPH; Juanita Chinn, PhD; Andria Cornell, MPH; Joia Crear-Perry, MD; Georgian Dukes, MHA; Janet Hamilton, MPH, Mara Kiesling; Giannina Ong, MA; and Karen Ellis, MMS Education, expert panel coordinator.

Authors' Contributions

Conceptualization, project administration, writing—original draft, and writing—review and editing by K.G.S. Conceptualization and writing—review and editing by C.E.B., K.K., and S.F.W.

Authorship Statement

All persons who meet authorship criteria are listed as authors, and all authors certify that they have participated sufficiently in the study to take a public responsibility for the content, including participation in the concept, design, analysis, writing, or revision of the article. Furthermore, each author certifies that this material or similar materials has not been and will not be submitted to or published in any other publication before its appearance in Health Equity.

Author Disclosure Statement

No competing financial interests exist.

Funding Information

Funding for the women's panel convening and findings provided by the Robert Wood Johnson Foundation.

Abbreviations Used

References

Bird

CE.

RAND Corporation. Underfunding of Research in Women's Health Issues is the Biggest Missed Opportunity in Health Care. Santa Monica, CA; 2022. Available from: https://www.rand.org/blog/2022/02/underfunding-of-research-in-womens-health-issues-is.html [Last accessed: June 23, 2022].

Knowledge at Wharton. Why the U.S. Needs Better COVID Data Collection. Philadelphia, PA; 2022. Available from: https://knowledge.wharton.upenn.edu/article/why-the-u-s-needs-better-covid-data-collection [Last accessed: June 23, 2022].

Centers for Disease Control and Prevention. Hospitalization and Death by Race/Ethnicity. Atlanta, GA; 2022. Available from: https://www.cdc.gov/coronavirus/2019-ncov/covid-data/investigations-discovery/hospitalization-death-by-race-ethnicity.html [Last accessed: June 23, 2022].

Robert Wood Johnson Foundation. Princeton, New Jersey; n.d. Available from: https://www.rwjf.org [Last accessed: June 23, 2022].

Robert Wood Johnson Foundation. Transforming Public Health Data Systems. Princeton, New Jersey; 2022. Available from: https://www.rwjf.org/en/library/research/2021/09/transforming-public-health-data-systems.html [Last accessed: June 23, 2022].

Robert Wood Johnson Foundation. Charting a Course for an Equity-Centered Data System: Recommendations from the National Commission to Transform Public Health Data Systems. Princeton, NJ; 2021. Available from: https://www.nationalcollaborative.org/wp-content/uploads/2021/10/RWJF-Transforming-Public-Health-Data-Systems.pdf [Last accessed: June 23, 2022].

Robert Wood Johnson Foundation. Transforming Public Health Data Systems. Princeton, NJ; 2021; pp. 35–42. Available from: https://www.rwjf.org/content/dam/farm/reports/reports/2021/rwjf467001 [Last accessed: June 23, 2022].

Morgan

Rosemary

, Garrison-Desany

, Hobbs

, et al. Strengthening effectiveness evaluations through gender integration to improve programs for women, newborn, child, and adolescent health. Available from: https://pubmed.ncbi.nlm.nih.gov/36098954 [Last accessed October 27, 2022].

Reeves

, Deng

The Brookings Institution. At least 65,000 more men than women have died from COVID-19 in the US. Washington, DC; 2021. Available from: https://www.brookings.edu/blog/up-front/2021/10/19/at-least-65000-more-men-than-women-have-died-from-covid-19-in-the-us/#:~:text=The%20gender%20gap%20in%20death,deaths%20and%20296%2C567%20female%20deaths [Last accessed: June 23, 2022].

10.

Harvard

TH.

Chan School of Public Health. Boston, MA; 2021. Available from: https://www.hsph.harvard.edu/news/hsph-in-the-news/sex-disparities-covid-black-women [Last accessed: June 23, 2022].

11.

Borno Hala

, Zhang

, Gomez

COVID-19 disparities: An urgent call for race reporting and representation in research. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7391979 [Last accessed: September 15, 2022].

12.

Day

, Christnacht

United States Census Bureau. Women Hold 76% of All Health Care Jobs, Gaining in Higher-Paying Occupations. Suitland, MD; 2019. Available from: https://www.census.gov/library/stories/2019/08/your-health-care-in-womens-hands.html [Last accessed: June 23, 2022].

13.

TIME'S UP Foundation. From health care to home care, restaurants to grocery stores, women workers, low-paid workers, and women of color are on the front lines of the COVID-19 pandemic. Washington, DC; n.d. Available from: https://timesupfoundation.org/work/women-on-the-front-lines [Last accessed: June 23, 2022].

14.

Cox

The Guardian. Why are women more prone to long Covid? London, UK; 2021. Available from: https://www.theguardian.com/society/2021/jun/13/why-are-women-more-prone-to-long-covid [Last accessed: June 23, 2022].

15.

Baum

, Musolino

, Gesesew

, et al. New perspective on why women live longer than men: An exploration of power, gender, social determinants, and capitals. Int J Environ Res Public Health, 2021; 18(2):661; doi: 10.3390/ijerph18020661

16.

Baird

, Zaber

, Chen

, et al. Women's Health Access Matters. The WHAM Report: The Case To Fund Women's Health Research. Greenwich, CT; 2022. Available from: https://thewhamreport.org/wp-content/uploads/2022/02/TheWHAMReport_crosscutting-compressed.pdf [Last accessed: June 23, 2022].

17.

Mirin

AA.

Gender disparity in the funding of diseases by the U.S. National Institutes of Health. J Womens Health, 2021; 30(7): 956–953; doi: 10.1089/jwh.2020.8682

18.

Westervelt

The Guardian. The medical research gender gap: How excluding women from clinical trials is hurting our health. London, UK; 2015. Available from: https://www.theguardian.com/lifeandstyle/2015/apr/30/fda-clinical-trials-gender-gap-epa-nih-institute-of-medicine-cardiovascular-disease [Last accessed: June 23, 2022].

19.

Baird

, Zaber

, Dick

, et al. RAND Corporation. Societal Impact of Research Funding for Women's Health in Coronary Artery Disease. Santa Monica, CA; 2021. Available from: https://www.rand.org/pubs/research_reports/RRA708-2.html [Last accessed: June 23, 2022].

20.

U.S. Food and Drug Administration. Drug Trials Snapshots Summary Report. Silver Spring, MD; 2020. Available from: https://www.fda.gov/media/145718/download [Last accessed: June 23, 2022].

21.

Parkins

Clinical Trials Arena. Lack of trials in pregnant people driving Covid-19 hesitancy. London, UK; 2021. Available from: https://www.clinicaltrialsarena.com/analysis/lack-of-trials-in-pregnant-people-driving-covid-19-vaccine-hesitancy [Last accessed: June 23, 2022].

22.

Science Daily. Pregnant women with COVID-19 face high mortality rate. Rockville, MD; 2021. Available from: https://www.sciencedaily.com/releases/2021/04/210422181856.htm [Last accessed: June 23, 2022].

23.

Bates

, Chin

, Becker

(eds). Measuring Sex, Gender Identity, and Sexual Orientation. The National Academies Press: Washington, DC; 2022.

24.

The National Academies of Sciences, Engineering, and Medicine. NIH Should Standardize Questions Used to Collect Sex, Gender, and Sexual Orientation Data in Studies and Surveys, Says New Report. Washington, DC; 2022. Available from: https://www.nationalacademies.org/news/2022/03/nih-should-standardize-questions-used-to-collect-sex-gender-and-sexual-orientation-data-in-studies-and-surveys-says-new-report [Last accessed: June 23, 2022].