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.
The hunter-gatherer groups at the heart of a microbiome gold rush
The first step to finding out is to catalogue what microbes we might have lost. To get as close to ancient microbiomes as possible, microbiologists have begun studying multiple Indigenous groups. Two have received the most attention: the Yanomami of the Amazon rainforest and the Hadza, in northern Tanzania.
Researchers have made some startling discoveries already. A study by Sonnenburg and his colleagues, published in July, found that the gut microbiomes of the Hadza appear to include bugs that aren’t seen elsewhere—around 20% of the microbe genomes identified had not been recorded in a global catalogue of over 200,000 such genomes. The researchers found 8.4 million protein families in the guts of the 167 Hadza people they studied. Over half of them had not previously been identified in the human gut.
Plenty of other studies published in the last decade or so have helped build a picture of how the diets and lifestyles of hunter-gatherer societies influence the microbiome, and scientists have speculated on what this means for those living in more industrialized societies. But these revelations have come at a price.
A changing way of life
The Hadza people hunt wild animals and forage for fruit and honey. “We still live the ancient way of life, with arrows and old knives,” says Mangola, who works with the Olanakwe Community Fund to support education and economic projects for the Hadza. Hunters seek out food in the bush, which might include baboons, vervet monkeys, guinea fowl, kudu, porcupines, or dik-dik. Gatherers collect fruits, vegetables, and honey.
Mangola, who has met with multiple scientists over the years and participated in many research projects, has witnessed firsthand the impact of such research on his community. Much of it has been positive. But not all researchers act thoughtfully and ethically, he says, and some have exploited or harmed the community.
One enduring problem, says Mangola, is that scientists have tended to come and study the Hadza without properly explaining their research or their results. They arrive from Europe or the US, accompanied by guides, and collect feces, blood, hair, and other biological samples. Often, the people giving up these samples don’t know what they will be used for, says Mangola. Scientists get their results and publish them without returning to share them. “You tell the world [what you’ve discovered]—why can’t you come back to Tanzania to tell the Hadza?” asks Mangola. “It would bring meaning and excitement to the community,” he says.
Some scientists have talked about the Hadza as if they were living fossils, says Alyssa Crittenden, a nutritional anthropologist and biologist at the University of Nevada in Las Vegas, who has been studying and working with the Hadza for the last two decades.
The Hadza have been described as being “locked in time,” she adds, but characterizations like that don’t reflect reality. She has made many trips to Tanzania and seen for herself how life has changed. Tourists flock to the region. Roads have been built. Charities have helped the Hadza secure land rights. Mangola went abroad for his education: he has a law degree and a master’s from the Indigenous Peoples Law and Policy program at the University of Arizona.
The Download: a microbiome gold rush, and Eric Schmidt’s election misinformation plan
Over the last couple of decades, scientists have come to realize just how important the microbes that crawl all over us are to our health. But some believe our microbiomes are in crisis—casualties of an increasingly sanitized way of life. Disturbances in the collections of microbes we host have been associated with a whole host of diseases, ranging from arthritis to Alzheimer’s.
Some might not be completely gone, though. Scientists believe many might still be hiding inside the intestines of people who don’t live in the polluted, processed environment that most of the rest of us share. They’ve been studying the feces of people like the Yanomami, an Indigenous group in the Amazon, who appear to still have some of the microbes that other people have lost.
But there is a major catch: we don’t know whether those in hunter-gatherer societies really do have “healthier” microbiomes—and if they do, whether the benefits could be shared with others. At the same time, members of the communities being studied are concerned about the risk of what’s called biopiracy—taking natural resources from poorer countries for the benefit of wealthier ones. Read the full story.
Eric Schmidt has a 6-point plan for fighting election misinformation
—by Eric Schmidt, formerly the CEO of Google, and current cofounder of philanthropic initiative Schmidt Futures
The coming year will be one of seismic political shifts. Over 4 billion people will head to the polls in countries including the United States, Taiwan, India, and Indonesia, making 2024 the biggest election year in history.
Navigating a shifting customer-engagement landscape with generative AI
A strategic imperative
Generative AI’s ability to harness customer data in a highly sophisticated manner means enterprises are accelerating plans to invest in and leverage the technology’s capabilities. In a study titled “The Future of Enterprise Data & AI,” Corinium Intelligence and WNS Triange surveyed 100 global C-suite leaders and decision-makers specializing in AI, analytics, and data. Seventy-six percent of the respondents said that their organizations are already using or planning to use generative AI.
According to McKinsey, while generative AI will affect most business functions, “four of them will likely account for 75% of the total annual value it can deliver.” Among these are marketing and sales and customer operations. Yet, despite the technology’s benefits, many leaders are unsure about the right approach to take and mindful of the risks associated with large investments.
Mapping out a generative AI pathway
One of the first challenges organizations need to overcome is senior leadership alignment. “You need the necessary strategy; you need the ability to have the necessary buy-in of people,” says Ayer. “You need to make sure that you’ve got the right use case and business case for each one of them.” In other words, a clearly defined roadmap and precise business objectives are as crucial as understanding whether a process is amenable to the use of generative AI.
The implementation of a generative AI strategy can take time. According to Ayer, business leaders should maintain a realistic perspective on the duration required for formulating a strategy, conduct necessary training across various teams and functions, and identify the areas of value addition. And for any generative AI deployment to work seamlessly, the right data ecosystems must be in place.
Ayer cites WNS Triange’s collaboration with an insurer to create a claims process by leveraging generative AI. Thanks to the new technology, the insurer can immediately assess the severity of a vehicle’s damage from an accident and make a claims recommendation based on the unstructured data provided by the client. “Because this can be immediately assessed by a surveyor and they can reach a recommendation quickly, this instantly improves the insurer’s ability to satisfy their policyholders and reduce the claims processing time,” Ayer explains.
All that, however, would not be possible without data on past claims history, repair costs, transaction data, and other necessary data sets to extract clear value from generative AI analysis. “Be very clear about data sufficiency. Don’t jump into a program where eventually you realize you don’t have the necessary data,” Ayer says.
The benefits of third-party experience
Enterprises are increasingly aware that they must embrace generative AI, but knowing where to begin is another thing. “You start off wanting to make sure you don’t repeat mistakes other people have made,” says Ayer. An external provider can help organizations avoid those mistakes and leverage best practices and frameworks for testing and defining explainability and benchmarks for return on investment (ROI).
Using pre-built solutions by external partners can expedite time to market and increase a generative AI program’s value. These solutions can harness pre-built industry-specific generative AI platforms to accelerate deployment. “Generative AI programs can be extremely complicated,” Ayer points out. “There are a lot of infrastructure requirements, touch points with customers, and internal regulations. Organizations will also have to consider using pre-built solutions to accelerate speed to value. Third-party service providers bring the expertise of having an integrated approach to all these elements.”