The pandemic has brought to light that data collection on race/ethnicity, language, sexual orientation, and gender identity is often limited in federal and state health programs, as well as commercial insurance. . A panel organized by the Alliance for Health Policy recently discussed barriers that limit the collection and analysis of health data to inform policymaking.
Collecting this demographic data is essential to get a complete picture of the state of disparities, to understand how different factors drive disparities, to direct resources and efforts to advance equity, and to measure progress and establish accountability for achieving equity, as well as identifying best practices or strategies to advance equity, said Samantha Artiga, vice president and program director for racial equity and health policy at the Kaiser Family Foundation. “When we’re running out of data or when the data has quality issues, it’s much more difficult to achieve all of these goals,” she said.
Despite the existence of some federal standards for collecting this type of data, there are gaps and limitations in the racial and ethnic health data available today, Artiga said. “These gaps and limitations have been around for a long time, but I think they’ve really been amplified and brought increased attention throughout the COVID-19 pandemic, when the lack of comprehensive race and ethnicity data has been recognized as a barrier to both understanding disparities and addressing them.”
Artiga noted that some datasets simply don’t have race/ethnicity data. “Where race/ethnicity data is available, data is often insufficient for small population groups, particularly Native Americans and Alaska Natives, as well as Hawaiians and other Pacific Islanders. “, she said. “We also often lack data for subgroups within these broad racial and ethnic categories.”
The five minimum racial/ethnic categories required by federal programs such as Medicaid are very broad and within them there is a wide diversity of populations who may have very different health and health care experiences. “We also found inconsistencies in racial and ethnic classifications, so these federal minimum standards apply to federal datasets, but not to other datasets,” she said.
At the state level, she explained, some states have not reported vaccination by race/ethnicity during the pandemic. “We saw a lot of variation between states in their different categorizations and reporting of racial and ethnic data. Many states did not report data for smaller population groups, including American Indians, Alaska Natives, and Native Hawaiians and other Pacific Islanders. And very few states reported data in a way that allowed for intersectional analysis.
While the focus on health equity is crucial, making these changes in data collection can be quite challenging, said Niall Brennan, chief analytics and privacy officer at Clarify Health. . “Aspirations of where we want to go come up against a brick wall of reality and as a person and a data enthusiast, it is both personally and professionally very frustrating not to have this information,” said he declared. “Our data collection mechanisms are not really optimized for this type of data. We have a universal claims processing and administrative data infrastructure. Many of them were designed 20, 30, 40, 50 years ago when collecting this information was not considered important or was not a priority. And then we have this massive, rapidly evolving and emerging set of more modern data systems – all of our investments in clinical health records. There’s been incredible progress, but a huge throughput limiting factor is both the inability of data to be truly interoperable and flow, and also, to some extent — almost too much data in electronic health records and a sort of separation of wheat from joking and figuring out what’s important.
Prior to Clarify, Brennan was President and CEO of the Health Care Cost Institute, where he oversaw HCCI’s global research program, highlighting trends in US healthcare spending and the factors driving those trends. Prior to that, he served as Chief Data Officer at the Centers for Medicare & Medicaid Services.
Another reason this type of data collection is so difficult involves trust, he said. Many people are unaware or afraid of how this information might be used, even though people’s intentions are truly pure. “You can’t change what you can’t measure, but a lot of the people we’re trying to capture this more granular information about are people who have been repeatedly let down over many generations by the healthcare system and by society. society in general,” he said. “When you start making these well-intentioned efforts to try to harvest the information, you may encounter some resistance because people don’t know how it’s going to be used.
Elizabeth Lukanen, MPH, deputy director of the State Health Access Data Assistance Center (SHADAC), a health policy research program at the University of Minnesota School of Public Health, said states recognize they have problems of quality with their data. There are currently four states that have unusable data, according to CMS’s assessment, and a large handful where it is truly unusable, she said. “What many states are doing is really committing to improving their race/ethnicity data in Medicaid and we’re working with them to try to improve that data.”
“As we have worked with states to try to think about how to improve this data, we have come up with two streams of activities. One is to improve the existing data that they have,” Lukanen said. “We We’ve worked with states to leverage other data they may have to triangulate data within the Medicaid program, which often means identifying individuals in other data systems they have access to.
The second approach is more of a statistical methodology where you fill in the gaps in your data using other information about the individual. “If my data was missing, it would look at my last name and maybe where I live and try to give an indication of what my race might be,” she explained. “You can imagine there are problems with this methodology. but it is a means that States can use to fill in some of the gaps. Finally, we always recommend that states validate their data against other sources of race/ethnicity data, and probably the best source is census data.
Irene Dankwa-Mullan, MD, MPH, is director of health equity and associate director of health at Merative (formerly IBM Watson Health). She noted that sometimes our current practices reinforce norms of homogeneity within Black, Hispanic and Indigenous communities, “but we know there are differences within groups, or differences between population groups, or different risk characteristics or attributes that may contribute to their results or their risk or resilience.
Dankwa-Mullan said we might also miss the influence of environmental, occupational, occupational, location, and lifecycle exposure on health outcomes. She also used the term “data empathy” to describe the knowledge or experience about the people or places or factors that actually constitute the data. A lack of awareness of these data sources or an inability to recognize them “results in our inability to optimize the algorithms or models that go into decision-making processes.”
