Connecting state and local government leaders
COMMENTARY | While data is a critical tool in the fight against the Covid-19 pandemic, glaring reporting disparities around race and socioeconomic status remain. Policymakers need to ensure they are using an equitable measurement approach to ensure they are serving the needs of all communities.
Data has been a vital tool for state and local governments to assess current needs, allocate resources and evaluate progress in responding to the Covid-19 pandemic. Yet, depending on how metrics are reported, data can either highlight or obscure huge disparities in rates of infection, testing, death and economic losses. This is particularly true for communities of color that have been hit hardest by the virus due to long-term health and economic inequities.
As policymakers try to balance public health, economic concerns and community wellbeing, they need guidance on how to use measurement tools equitably. Last year, our team began examining how measurement—the process of making sense of data through metrics—supports efforts to align health care, public health and human services towards more equitable outcomes. We reviewed more than 40 such efforts and talked with stakeholders about what works and what doesn’t.
What we’ve learned is policymakers and leaders can use measurement to help systems work towards more equitable health outcomes by disaggregating data to identify inequities; partnering with communities to identify actionable metrics; and balancing decision-making among stakeholders.
How to Use Measurement Equitably
Disaggregate data to identify inequities. Communities of color have faced long-standing structural inequities that limited their access to affordable healthcare, safe environments, economic opportunity and nutritious food. Covid-19 has exacerbated these inequities, yet aggregate infection and death rates have obscured these disparities and the scope of the problem. Therefore, it’s critical that data is reported by race and ethnicity so that state and local leaders can adequately and appropriately respond to meet these communities’ specific needs.
For example, after the Chicago Tribune reported that black Chicagoans were dying of Covid-19 at nearly six times the rate of white residents, Illinois Gov. J.B. Pritzker expanded testing in communities with predominantly black residents. Meanwhile, Chicago Mayor Lori Lightfoot created a racial equity rapid response team to address the disproportionate impact of the disease in black communities. While many states and some local jurisdictions are now reporting at least some Covid-19 data by race and ethnicity, gaps remain. A recent policy recommendation in Connecticut, for example, suggests strengthening efforts to disaggregate data by standardizing and expanding what data is collected about race, ethnicity and language and publicly reporting it. The State Health Access Data Assistance Center offers best practices for creating Covid-19 data dashboards that disaggregate by race, ethnicity, geographic areas, language, socioeconomic and disability status.
Create measurement strategies in partnership with the community. Engaging community members early and often as partners in decision-making will ensure that measurement efforts focus on what matters most to communities and becomes a tool for building trust. This is especially important for historically marginalized groups like communities of color, immigrants, LGBTQ+ individuals, people from low income households and people experiencing homelessness. Community-centered measurement relies on equal partnership between community members and other stakeholders at all stages including decisions about what to measure and why. For example, the San Antonio 2020 visioning initiative selected measures in partnership with a Steering Committee of community members and gathered input via public meetings, surveys and focus groups from all communities. By doing so, they were able to create a shared vision and prioritize efforts based on community-identified needs. Lessons learned from engaging patients and caregivers in healthcare measurement efforts also offers additional examples for how to engage community as partners in measurement, and the benefits of doing so.
Balance decision-making among stakeholders enables collaboration. Too often, data exists in silos, leading decision makers to work with an incomplete understanding of the intersectionality of issues and lessening the effectiveness of government services. Pre-pandemic, we found examples of alignment efforts across systems such as healthcare and housing to collaborate on data collection. For example, the medical, public health and housing sectors in Los Angeles collaborated to combine data on Covid-19 infections, medical assessments and data on transient housing availability to implement a program aimed at reducing the spread of Covid-19 among people lacking stable housing. This alignment effort provided a complete picture and was balanced so that no one system, agency or perspective dominated the data sources used or metrics calculated. While there is no one or right way to balance decisions about what to measure or how to use metrics, it is immensely important that all stakeholders involved feel empowered to foster true partnership and align goals. Balancing decision-making ensures everyone has a part in measurement that they can recognize, identify with and act on.
Key Questions for Policymakers
In moving forward, policymakers can begin shaping an equitable measurement strategy by asking:
- What disparities might be hiding in the existing data? How do metrics vary by race, ethnicity, age, gender identity, income, education or immigration status?
- Who defined the metrics we use? What was the role of community members in selecting metrics, collecting data and understanding the data?
- Who holds decision-making power in what data to use and how to act on it? How can I balance decision-making among the many stakeholders who will be impacted by decisions based on this data, including community members?
Many resources are available to help policymakers begin answering these questions, including Covid-19 data by race and ethnicity, guidance to states on how to collect and disaggregate Covid-19 data and a guidebook for building evidence in partnership with communities.
Charting a new path forward
Looking ahead, the long process of recovery from the pandemic will require collective efforts across systems of medical care, public health, housing, education, transportation, justice and human services to address the grave disparities in health and economic wellbeing highlighted by the Covid-19 pandemic. With that in mind, it’s imperative that state and local officials incorporate equity at the outset of data collection and measurement efforts to ensure they’re taking actions that will ultimately improve the health and wellbeing of everyone in their communities.
Tamika Cowans is a qualitative Researcher at the American Institutes for Research in the Health Services and Systems practice area. Ellen Schultz is a Senior Researcher at the American Institutes for Research with nationally recognized expertise in quality measurement. Trenita Childers is a qualitative and mixed methods Researcher at the American Institutes for Research with expertise in social determinants of health and eliminating health disparities. Tandrea Hilliard is a Senior Researcher at the American Institutes for Research with expertise in health disparities, chronic disease prevention and management, and patient-centered care. Maliha Ali is a Researcher at the American Institutes for Research with research experience across the spectrum of public health and health care services research.