The current covid-19 pandemic has shined the spotlight on longstanding health inequities for people of color. According to the Centers for Disease Control and Prevention, compared to the general United States population, African Americans are 1.4 times more likely to contract the coronavirus, and 2.8 times more likely to die from covid-19. Similarly, Native Americans and Hispanics/Latinos are nearly twice as likely to be infected by coronavirus, and 2.5 to 2.8 times more likely to die from it.
Underlying these statistics are significant structural, social, and spatial issues. But why is this? And how do we begin to quantify and address the nested problems of public health inequality?
Understanding the geography of health inequity
One tool that can help us understand the higher coronavirus infection and death rate among people of color is mapping produced by a geographic information system (GIS). GIS correlates geography to key issues by layering relevant, sometimes seemingly disparate data to achieve clarity on complex situations.
For instance, one of the first things GIS users and epidemiologists mapped in the pandemic was the locations of vulnerable populations. Each layer of data took into account various contributing factors to such vulnerability. These include potential exposure through essential jobs; disease susceptibility for seniors and people with certain health conditions; the risk of transmission for public transit commuters and those in group living situations; and socioeconomic disadvantages through poverty, inadequate education, and lack of health insurance. The dynamic analyses that GIS enabled immediately guided actions by first responders and gave epidemiologists an evidenced-based way to assess vulnerability against hospital accessibility and capacity.
As awareness of the disproportionate number of deaths in communities of color grew, the same tool was applied to understand the causes behind this inequity, which, in turn, can aid in defining and developing potential solutions.
It’s been long understood that people living in inner cities face conditions that have clear correlations to overall health. These include income and education disparity, a low percentage of home ownership, increased exposure to neighborhood pollution, and reduced access to wellness care and reasonably priced fresh food. Another important dataset relevant to the covid crisis is the disproportionate percentage of people of color in service jobs that put them into daily close contact with the virus.
“GIS can help identify where outcome disparities exist, perform analysis to understand root causes, and focus mitigation efforts on places where systemic racism concentrates causal factors,” says Este Geraghty, chief medical officer and health solutions director at GIS vendor Esri. By analyzing all relevant data on a GIS-based smart map, Geraghty says leaders are poised to uncover localized insights that drive potential solutions. This means, “we can provide stopgaps until we have fully equitable systems, ensuring that one day everyone will have the same opportunity to reach their full health potential.”
Geraghty adds, “If you can’t understand all of the contributing factors in context, you might not anticipate potential problems or solutions.”
GIS for effective covid-19 vaccine distribution
Another pandemic-related problem tied closely to geography is how to get covid vaccines to the public in an equitable, safe, and effective manner. GIS provides the tools to analyze prioritized needs, plan distribution networks, guide deliveries, see the real-time status of inoculation missions, and monitor overall progress.
Geraghty developed a covid vaccine distribution approach using GIS. She explains that the first step is to map those facilities currently suitable for distributing the vaccine to the public. Since some vaccines need ultra-cold storage, facilities will have to be differentiated according to that and other storage capabilities. As part of the facility dataset, Geraghty says, GIS can also be used to calculate how many vaccines each facility’s staff can potentially administer in a day. In addition to hospitals, other facility types will need to be considered based on their ability to deliver the vaccine to underserved and remote populations. Facilities might include university health clinics, independent and retail pharmacies, and potentially even work sites willing and able to inoculate employees, among others.
The next step involves mapping the population—not only their locations and numbers, but also according to the categories recommended by the CDC guidance and state-based plans for the phased rollout of the vaccine.
By correlating these two layers of data on the map (facilities and population), it becomes clear which communities aren’t within a reasonable travel time to a vaccination location, based on multiple modes of travel (for example, driving, walking, public transit).
Geraghty explains, “That geographic perspective will help find any gaps. Who is left out? Where are the populations that aren’t within the range of identified facilities?” This is where GIS can improve decision-making by finding options to fill gaps and make sure that everyone has access to the vaccine.
In areas where GIS analysis identifies “gaps” on the map, such as communities or rural areas that aren’t being reached, Geraghty envisions pop-up clinics in places like school gyms, or drive-throughs in large parking lots, or, in some circumstances, personal outreach. For example, Geraghty explains, “People experiencing homelessness may be less likely to show up at a clinic to get a vaccine, so you may have to reach out to them.”
Public communication about vaccination progress offers another opportunity for mapping and spatial thinking. For example, an updated map could give a clear picture of how many people have been vaccinated in different parts of a state or county. The same map could help people figure out when it’s their turn to be vaccinated and where they can go to receive their vaccine. Maps could even help community residents compare wait times among different facilities to guide their choices and offer the best possible experiences.
Geraghty says that organizing covid vaccine distribution in this way can represent hope for people. “If we take this logical and strategic perspective, we can be more efficient in vaccine delivery and enjoy our normal activities much sooner.”
Vulnerable populations, geographic insights
Long before the world was forced to struggle with covid, the connection between geography and solving public health and social issues was very clear. Using GIS to address homelessness is one example.
In Los Angeles County, GIS has been used to map the homeless population by location, and also document and analyze the risk factors that create homelessness in each community. GIS analysis revealed that a predominant risk factor for homelessness in the northern, and especially northwestern part of the county, was veterans with post-traumatic stress disorder (PTSD). Conversely, in the northeast area, the predominant risk factor creating new homelessness was women and children escaping domestic violence.
In Snohomish County, Washington, health-care workers hit the streets to gather the data needed to facilitate such risk-factor mapping. They used GIS to perform the biannual survey and census of homeless people, gathering details on the conditions and needs of 400 people in short order. They collected standard information like the age of people in camps and whether any were veterans and reported whether they saw needles used for drugs.
Once location-specific differences like these are identified, appropriate resources can be deployed on a community-by-community basis, such as targeted social and health services to help specifically with domestic violence, PTSD, addiction, joblessness, or other identified root causes. “Using a geographic perspective, you can allocate resources, which are always limited, in ways that do the most good,” Geraghty says.
Lessons from the pandemic
Addressing disparities related to living conditions, locations, and genetics has always been a factor of disease spread and mortality, but it has never been tracked, measured, and analyzed on such a scale. However, confronting the covid crisis has been an ongoing case of catch-up, trying to find and correlate critical data to save lives, and Geraghty doesn’t want to see that level of frenetic activity repeated.
“Building strong public health preparedness systems means having foundational data ready,” she explains. “For instance, where, relative to the population, are the hospitals, the shelters, blood banks, and key infrastructure? Who are the community players and partners, and what services can they provide, and where?” In March, at the start of the pandemic, there was no comprehensive map of how many beds each hospital had, what percentage were intensive care beds, the number of ventilators available, and how much personal protection equipment was easily obtainable, and from where. “For anything that is health-related infrastructure,” explains Geraghty, “you should have a baseline map and data that you keep updated, as well as population demographic data.”
The crisis has also brought to light other issues; for example, better and more data sharing is needed, as well as clearer governance for which data are acceptable to share, so nothing will delay essential communications among institutions in the next crisis. And improved system interoperability ensuring key systems can work together to keep data fresh and reaction times quick should be a priority. The covid-19 pandemic has been a tragedy in terms of the human toll. But if we can learn from it, perhaps we can make corrections so that all communities and future generations can look forward to better, longer, and healthier lives.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.
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