From the prevalence of primary care providers to the food available, where we live influences our health. Numerous researchers have examined persistent health inequities present within a state, county, or ZIP code. But pinpointing differences on an extremely local level can offer even more insights leading to tailored approaches to address health equity disparities.
Census tract data can reveal hidden differences
Collecting and analyzing local-level data can reveal striking differences that might have been obscured on a larger scale.
Take the example of Allegheny County, Pennsylvania, which has a life expectancy of 77.4 years according to a 2020 study published in Proceedings of the National Academy of Sciences. What happens to this figure, though, if the county is divided into Census tracts? Within that same county, researchers found a tract with a life expectancy of 62 years and another with 86 years, representing a 24-year difference.
Similar disparities in life expectancy were found in other counties throughout the country. Even more, variables such as race were “strongly associated with life expectancy" when looking at this local level, the study found.
Life expectancy is “a pretty local phenomenon"
“Our study shows that as far as geographic variation in life expectancy is concerned, it's a pretty local phenomenon," noted S (Subu) V Subramanian, professor of population health and geography at Harvard University and co-author of the study, in a Harvard press release.
These same findings play out in numerous communities across the United States, underscoring the importance of examining health data at a local level.
Local-level data analysis prompts surprising COVID-19 findings
Another interesting example of using local-level data to identify health equity gaps comes from researchers at the University of Minnesota who examined COVID-19 mortality during the pandemic at an extremely local level, findings published in a 2021 Health Affairs paper. While it had been well established that historically disadvantaged populations experienced higher COVID-19 mortality rates, the role of socioeconomic determinants was unclear, other researchers had noted.
This local analysis, which also used Census tract data, found that Black, Indigenous, and other people of color had much higher mortality rates compared to White people in areas that had the same economic circumstances. In other words, the differences couldn't be attributed to the fact “that White people live in more advantaged areas," the authors wrote.
Super local data provided other health equity insights, too, when comparing mortality rates among Black, Indigenous, and other people of color from one neighborhood to another. In the most disadvantaged neighborhoods, the adjusted mortality rate was 639 deaths per 100,000 population. The most advantaged neighborhoods had a 229 per 100,000 adjusted mortality rate. Living in the poorest neighborhoods had an outsized impact on mortality.
Heat mapping visually highlights inequities
It can be helpful to see these local level geographical differences visually. Creating heat maps of the geographical findings can help display these inequities in a user-friendly way. Take the example of researchers who calculated how much lead was present in the soil of Santa Ana, California, findings published in Science of the Total Environment.
Higher lead concentrations can lead to poorer health outcomes for children, a problem that disproportionately affects low income and historically marginalized communities. After testing soil lead concentrations by census tract throughout the county, researchers found that lower income residents had five times higher concentrations of lead in their soil than people in high-income Census tracts.
This finding becomes even more striking when overlaid on a map where a clear cluster emerges in the center of the region, while the surrounding outer areas have much less lead present. Interestingly, recent work has explored the association between these high soil-lead levels and their proximity to the nearest historic roadway, according to findings published in Environmental Research.
AI tool displays Census tract data to help improve health equity outcomes
Our organization has developed an artificial intelligence (AI) tool that allows us to analyze data at the Census tract level. The tool, RTI Rarity™, draws on more than 150 social determinants of health to create Local Social Inequity (LSI) scores at the Census tract level.
This extremely local health data is useful in creating interactive state maps that clearly visualize these differences. Individual Census tracts can be viewed along with key data on racial and ethnic compositions, health measures such as life expectancy, binge drinking, and smoking prevalence, and links to local resources to address health-related social needs.
The tool demonstrates how high levels of variation can exist even within a small geographic region. RTI International's Dr. Stephanie Hawkins described using the tool to help understand the implications of her own experience growing up in New York in a recent goRed webinar held by the American Heart Association. The webinar centered around health equity: “The most fascinating thing, though, that blew my mind about our RTI Rarity tool is: By moving 3.5 miles, I gained four years to my life expectancy."
Local data matters for health systems, too
These scores can be useful on a health system level too, helping organizations account for social risk in value-based payments, as Dr. Lisa M. Lines, RTI International Senior Health Services Researcher, describes in this video.
The scores can be easily incorporated into existing risk adjusted payment models that predict health care expenditures. That way, a patient's risk factors are more accurately captured. This ensures that providers aren't punished for factors outside their control and won't have an incentive to selectively choose patients.
Dr. Lines, in her role as director of the RTI Rarity initiative, will join Dr. Amy Helwig, RTI Health Advance executive vice president, and Dr. Claudia Uribe, head of evidence generation, to co-present the session, "Using Next-Gen Analytics to Create a Local Social Inequity Score and Applications for HEA Plus" at the NCQA Innovation Summit. Their talk will discuss how RTI Rarity can be used for conducting a health equity gap analysis.
Using granular information to tailor interventions
Without this kind of granular data, these local-level differences may persist largely unseen. But identifying the differences also allows us to pinpoint where we need to spend more time and resources. It can also help us tailor outreach and other interventions to address these inequities.
In the lead soil example, the findings could lead to mitigation efforts in areas where children commonly play. It could also lead to broader environmental justice efforts and soil remediation. Similarly, looking at vaccination rates by Census tract could lead to door-to-door public health outreach, or perhaps adding vaccination clinics in convenient community centers.
For health measures, zooming into a particular neighborhood might help identify factors that are leading to poorer outcomes, such as unmet transportation needs or food insecurity. Similarly, identifying the highest smoking rates and binge drinking rates might lead to special programs and services to address the community's needs.
Cultural humility should guide effective interventions
When considering community outreach to address inequities, it's important to use a culturally humble approach to address inequities. Interventions should consider factors such as people's preferred language, history, and cultural traditions.
Involving the affected communities in the development of these strategies is key. This means interacting and inquiring from a place of humility, and not assuming we know the best approach. Meeting with key stakeholders in a community can help us best understand the factors contributing to these health measures and the most meaningful steps to address the equity gaps.
Close view can alter outcomes
While health equity data on all scales is important, the most local level can offer important details that may otherwise be buried within the larger picture. By pulling out and identifying these granular findings, we can tailor interventions in culturally humble ways to address health inequities.
Harnessing the potential of local data
RTI Health Advance's diverse data sets incorporate social determinants of health and other highly local level information. Our experts can help you use these tools to understand and address the root causes of health inequities on all scales.