The current covid-19 pandemic has shone the spotlight on long-standing health inequalities for people of color. According to the Centers for Disease Control and Prevention, compared to the general population of the United States, African Americans are 1.4 times more likely to contract the coronavirusand 2.8 times more likely to die from covid-19. Likewise, Native Americans and Hispanics / Latinos are almost twice as likely to be infected with the coronavirus and 2.5 to 2.8 times more likely to die from it.
Underlying these statistics are important structural, social and spatial issues. But why is this? And how do you start to quantify and solve the intricate problems of public health inequalities?
Understanding the geography of health inequalities
One tool that can help us understand the higher rate of coronavirus infection and death in people of color is mapping produced by a geographic information system (GIS). GIS correlates geography with key issues by overlaying relevant, sometimes seemingly disparate, data to clarify complex situations.
For example, one of the first things GIS users and epidemiologists mapped in the pandemic was the location of vulnerable populations. Each data layer took into account various factors contributing to this vulnerability. These include potential exposure to essential jobs; the vulnerability to diseases of the elderly and of people with certain health problems; the risk of transmission for transit commuters and those living in groups; and socio-economic disadvantages due to poverty, inadequate education and lack of health insurance. The dynamic analyzes that GIS enabled immediately guided the actions of first responders and gave epidemiologists an evidence-based means of assessing vulnerability against accessibility and hospital capacity.
As awareness of the disproportionate number of deaths in communities of color increased, the same tool has been applied to understand the causes of this inequality, which, in turn, can help define and develop potential solutions.
It has long been known that people living in city centers face conditions that have clear correlations with overall health. These include income and education disparity, a low percentage of home ownership, increased exposure to neighborhood pollution, and reduced access to welfare care. and fresh food at reasonable prices. Another important data set regarding the covid crisis is the disproportionate percentage of people of color in service jobs that puts them in close contact with the virus on a daily basis.
“GIS can help identify disparities in outcomes, perform analysis to understand root causes, and focus mitigation efforts on where systemic racism concentrates causative factors,” says Este Geraghty, Chief Medical Officer and Director of health solutions from GIS provider Esri. By analyzing all the relevant data on a GIS-based smart map, Geraghty says leaders are ready to uncover localized information that leads to potential solutions. This means that “we can provide interim solutions until we have fully equitable systems, ensuring that one day everyone has the same chance to reach their full health potential.”
Geraghty adds, “If you can’t understand all of the contributing factors in context, you may not anticipate potential problems or solutions.”
GIS for efficient distribution of the Covid-19 vaccine
Another closely related geography-related issue with the pandemic is how to deliver covid vaccines to the public in a fair, safe, and effective manner. The GIS provides the tools to analyze priority needs, plan distribution networks, guide deliveries, see the real-time status of inoculation missions and monitor overall progress.
Geraghty has developed an approach for delivering covid vaccines using GIS. She explains that the first step is to map the facilities currently able to distribute the vaccine to the public. Since some vaccines require ultra-cold storage, facilities will need to be differentiated based on this and other storage capacities. As part of the facility’s dataset, says Geraghty, the GIS can also be used to calculate the number of vaccines staff at each facility can potentially administer in a day. In addition to hospitals, other types of facilities should be considered based on their capacity to deliver the vaccine to underserved and remote populations. Facilities can include university health clinics, independent and retail pharmacies, and potentially even work sites willing and able to vaccinate employees, among others.
The next step is to map the population – not only their locations and numbers, but also according to categories recommended by CDC guidelines and state-based plans for phased vaccine deployment.
By correlating these two layers of data on the map (facilities and population), it becomes clear which communities are not within a reasonable travel time to an immunization location, based on multiple modes of travel (e.g., driving, walking, public transport.).
Geraghty explains, “This geographic perspective will help find the gaps. Who is left out? Where are the populations located who are not within the perimeter of the identified facilities? This is where MIS can improve decision making by finding options to fill in the gaps and make sure everyone has access to the vaccine.
In areas where GIS analysis identifies ‘gaps’ on the map, such as communities or rural areas that are not reached, Geraghty is considering pop-up clinics in places like school gyms, or drive-thru in large car parks, or, in certain circumstances, personal awareness. For example, Geraghty explains, “Homeless people may be less likely to come to a clinic for a shot, so you may need to contact them.”
Public communication on the progress of immunization offers another opportunity for mapping and spatial reflection. For example, an updated map could give a clear picture of the number of people vaccinated in different parts of a state or county. The same card could help people know when it is their turn to get the vaccine and where they can go to get their vaccine. The maps could even help community residents compare wait times between different facilities to guide their choices and deliver the best possible experiences.
Geraghty says that organizing the distribution of covid vaccines in this way can be a hope for people. “If we take this logical and strategic perspective, we can be more efficient in administering vaccines and enjoying our normal activities much sooner.”
Vulnerable populations, geographic perspectives
Long before the world was forced to fight covid, the link between geography and solving public health and social issues was very clear. The use of GIS to tackle homelessness is one example.
In Los Angeles County, GIS has been used to map the homeless population by location, as well as to document and analyze the risk factors that create homelessness in each community. The GIS analysis found that a predominant risk factor for homelessness in the north, and in particular the northwestern county, was veterans suffering from post-traumatic stress disorder (PTSD). Conversely, in the northeast region, the main risk factor for the creation of new homelessness was women and children fleeing domestic violence.
In Snohomish County, Wash., Health workers have taken to the streets to collect the data needed to aid in this risk factor mapping. They used the GIS to conduct the semi-annual homelessness survey and census, gathering details of the conditions and needs of 400 people in a short period of time. They collected standard information such as the ages of people in the camps and whether any were veterans and indicated whether they had seen needles used for drugs.
Once place specific differences like these are identified, appropriate resources can be deployed community by community, such as targeted health and social services to specifically help address domestic violence, PTSD, substance abuse , unemployment or other identified root causes. “By using a geographic perspective, you can allocate resources, which are always limited, in a way that does the most good,” says Geraghty.
Lessons from pandemic
The fight against disparities linked to living conditions, places and genetics has always been a factor in the spread of disease and mortality, but it has never been followed, measured and analyzed on such a scale. However, dealing with the covid crisis has been an ongoing case of catching up, trying to find and correlate critical data to save lives, and Geraghty doesn’t want to see this level of frantic activity repeat itself.
“Building strong public health preparedness systems means having baseline data ready,” she explains. “For example, where in relation to the population are hospitals, shelters, blood banks and key infrastructure located? Who are the community actors and partners, what services can they provide and where? In March, at the start of the pandemic, there was no complete map of the number of beds in each hospital, the percentage of intensive care beds, the number of ventilators available, the amount of personal protective equipment easily. accessible and from where. “For anything health related infrastructure,” says Geraghty, “you should have a base map and data that you keep up to date, as well as demographic data on the population.”
The crisis has also brought to light other problems; for example, better and more data sharing is needed, as well as clearer governance where data is acceptable to share, so that nothing will delay essential communications between institutions in the next crisis. And improving system interoperability ensuring that key systems can work together to keep data up-to-date and rapid reaction times should be a priority. The covid-19 pandemic has been a tragedy in terms of the human toll. But if we can learn from this, perhaps we can make corrections so that all communities and future generations can hope for better, longer and healthier lives.
This content was produced by Insights, the personalized content arm of MIT Technology Review. It was not written by the editorial staff of MIT Technology Review.