From the Chief Medical Officer: What Needs to Change to Achieve Better Health Equity Metrics

June 26, 2023 | Marcus Plescia

Last month, I had the pleasure of attending and supporting the ASTHO Public Health TechXpo and Futures Forum in Chicago, an annual convening of public health leaders and experts across the technology and public health sectors. Positioned at a critical juncture in the public health story, the convening served as a vantage point to better understand the unique challenges that the public health field can expect to face in the coming years.

Unsurprisingly, none of the week’s conversations or sessions went long before turning to the need to address health equity. For too many years, advancements in public health technology and data modernization have left behind some of our most underrepresented and disenfranchised populations. The week was a reminder that true growth means no one is left behind.

These conversations felt especially resonant in light of the glaring data gaps that emerged throughout the COVID-19 response. According to one CDC report, in September 2020, demographic information on race was missing from as much as 35.8% of COVID-19 cases, while ethnicity data was missing from 47.2% of cases. Despite major concerns and planning for an equitable roll-out of the COVID-19 vaccine, in May 2021, race or ethnicity information was missing for nearly 69 million vaccinated people—or 44%—in CDC data.

Meanwhile, the Council of State and Territorial Epidemiologists has reported that race and ethnicity data is not complete for any of the four major public health surveillance systems, with electronic lab reporting lowest at only 29%. Data is the best tool we have to identify opportunities for change. As such, these gaps were not only worrisome; they were harmful, directly resulting in the misallocation of attention and resources throughout the pandemic.

In one of the TechXpo’s flagship presentations, I sat down with healthcare, technology, and policy experts from across the field to discuss the future of measuring health equity in a world of rapidly evolving data. This group included Garfield Clunie, president of the National Medical Association; Brooke Cunningham (SHO-MN); Gabriel Seidman, director of policy at the Ellison Institute for Transformative Medicine; and Rhonda Randall, board member of the United Health Foundation.

What Must Change

One of the biggest culprits for widening data disparities between races and ethnicities is not whether data is collected, but how. For our experts, this means asking the right types of questions to foster the right types of data. “’Multi-Racial’ is a terrible category,” said Cunningham. “I would encourage us to disaggregate race—and to be specific about it.” In other words, our data collection methods must reflect the diversity of the lived conditions and identities of the populations who we are tasked with serving. The finer and more specific our data points, the more targeted and efficient the public health response promises to be.

“What is the point of collecting data if you’re not going to use it? If you’re not going to demonstrate its value to the people you’re collecting data from?” These are the questions that Randall posed to the panel, and which set the tone of the conversation. Our speakers shared stories of mounting skepticism around data collection in communities where public health trust has been degraded. To repair this trust and form a more complete statistical picture, our experts emphasized the need for the data collection process to begin inside communities, by partnering with community liaisons and leaders.

Our panel also put special emphasis on the need for strong and more effective data modelling. It is almost a cliché in public health that data drives action. However, it is then fair to ask: What drives the collection of data? The answer is probably multiple. However, one driving answer might be the accumulation of models that help us to turn jumbles of data into a coherent story. “We need models that have methods for accounting for social risk,” said Cunningham. “And then we need to do a better job at making our analyses accessible to the communities who we’re modelling.”

Making Data Come to Life

Throughout the TechXpo, there was ample conversation around strategies for communicating data in ways that are accessible, appropriate, and relevant. We need novel data dashboards, and better cross-integration of these dashboards across and between jurisdictions. We considered new ways to model data, such that our analyses tell a more inclusive story and do a better job of reflecting the lived reality of a community.

However, maybe the most effective way of backing up our data is by doing what people have always done: Showing up and telling a story. “When talking to [community decision-makers and legislators], how many times did I hear the phrase, “The advocates want, the advocates want, the advocates want…?’” said Cunningham “Well, what does the community want? It is important for the community members to show up themselves, to demonstrate and prove a need.”

Over the course of the TechXpo, an almost ironic theme continued to emerge: We do not just need modernization. In many ways, public health’s technological ceiling is lightyears ahead of where it was even a few years ago. However, in many places—and particularly in underrepresented populations—the technological floor is still largely where it was decades ago. We need to demonstrate a need to modernize our data systems across the board, and to think about how we use this data when we think about what data we collect.

When Will We Turn the Corner?

Some of these proposed solutions (e.g., building networks of community trust) are missions for the long-term. However, head agencies can start collecting better data as soon as today. Health plans and insurers can set internal standards to collect better data on their enrollees. CMS can leverage better data collection in clinical practice and ONC can assure these fields are built into EHR systems.

While states often have less regulatory leverage, then can change any existing laws barring the collection or federal reporting of race and ethnicity data, expand the development and use of Health Information Exchanges, and use these exchanges or other state datasets to cross match data sources that are incomplete.

In the coming years, there must be certain standards that we hold ourselves to, and certain benchmarks we must meet. However, we also cannot afford to mistake this progression for absolute success. “We’re not done just because we did this in the last two years,” said Cunningham. “We need to keep doing this work. The way we view race and gender will continue to change.”

It is critical that when we look ahead to the future of health equity and data that we remain fluid to needs of the moment. For now, we can prepare for this future by building data systems that are flexible enough to account for such a future.