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Your Zip Code Shouldn't Be a Death Sentence



The COVID-19 pandemic has put a spotlight on racial and socioeconomic inequities in healthcare, but it remains to be seen whether the industry will adapt based on lessons from the experience. 

According to CDC data, Black people are 2.9 times more likely to be hospitalized and 1.9 times more likely to die from COVID-19 than white people, and Hispanic people are 2.8 times more likely to be hospitalized and 2.3 times more likely to die from COVID-19. Despite these higher rates of infection and hospitalization, Black and Hispanic people have received smaller shares of vaccinations compared to the rest of the population.

While this data is upsetting, it is not a surprise. In fact, as Komodo Heath CEO Arif Nathoo explained in a recent STAT Health Tech Summit session with executive editor Rick Berke, these results could have been predicted based on zip code. Sharing research conducted using our Healthcare Map™, Arif described how we found strong links between the racial composition of a neighborhood and the likelihood of getting COVID-19 or being vaccinated. Specifically, we found that people living in a zip code that is predominantly Black or Hispanic are 50% more likely to be diagnosed with COVID-19, yet they are 75% less likely to be vaccinated. 

Dr. Nathoo discussed how information like this needs to be a part of the serious self-examination of healthcare in the aftermath of the pandemic to better understand where utilization is happening and where it is not – and to rework the system to meet the needs of those underserved populations.

The full conversation is transcribed below.

Rick Berke: Arif, great to see you. It seems like a totally perfect segue to go from City block to talk about Komodo Health, given what you've done with identifying inequities and using data to do that. Could you talk a little about Komodo and give a little background about the company and why you left McKinsey to start this? 

Arif Nathoo: Thanks, Rick. I really appreciate being here to share some thoughts on this. One of the things that we talk about with inequity in the system is that, these [disparities] are known things about our system. We have the ability to observe it; we have the ability to see inequity. And one of the things that I realized years ago is not only am I passionate about taking care of populations, but using data to do that. We were at a beautiful place about 10 years ago, where we were starting to see an incredible amount of patient-level data being generated to do this; and for me, being able to tie what we saw on the ground with what we saw in the data had enormous amounts of opportunity; and so we started Komodo with this thesis that if we could tap into the way that patient-level data was being generated, we could use that to understand populations at scale, we could find inequities, we could find opportunities to improve care, and we could address them aggressively.

Arif: The market that we started with was Life Sciences. Now, these big pharmaceutical companies, they basically decide who to go talk to based on the prescription patterns of doctors; they say, "Okay, who's the high prescriber in this area? I'm going to send you a bunch of reps to go figure out how you should be using my new product." And, unfortunately, that misses so many parts of the population where main care isn't at standard of care, where drugs aren't being used in the right way. So we said, "Well, if we could start studying the patient and really understanding the needs of the patient, we could reinvent a better system that's based off of data and then companies can take advantage of using that to make better decisions on how they address care." So it's really the personal passion that I have around care as well as data that led to the creation of Komodo, and it's been an amazing journey since then.

Rick: And looking at ways of categorizing, you've made the point, we're calling this session, Zip Code is Not a Death Sentence. What do you mean by that? 

Arif: Yeah, it's a great question. The reality is that when you look at our system, you see multiple different systems out there, you see certain geographies where you get an incredible standard of care, you've got physicians who are really well-trained; and the amount of money that gets poured into that environment by pharmaceutical companies for clinical trials or even for promotion, all the way to the way that providers engage that population, are completely different. We've studied this across the board in so many different ways. So if you just take vaccine equity, which is what we're talking about these days, especially with the COVID vaccination patterns, and you look in zip codes that tend to be whiter on average, you'll see a much higher rate of vaccination and a much lower rate of COVID. And so, conversely, if you look at zip codes where there's a higher Black population, you see a 75% lower likelihood that you'll be vaccinated and 50% higher likelihood that you'll get COVID. And to us, these inequities exist all across the board, and you can look at that from vaccines to cancer care, and you'll see that. And so one of the theses that we've been following – really for the last five or six years – is how we can use data to better predict where inequity exists, and then how we can use data to better address that inequity.

Rick: It seems like with COVID, this has been a moment for people to really focus on inequities in a way they never did before. How has that affected you and your company in what you've been doing in the last year? 

Arif: It's created many moments from when we saw the pandemic really starting to rage, we were able to observe what parts of the healthcare system were shutting down and at what speed. When people stopped seeing their doctors, what kind of folks were not seeing their doctors anymore? What kind of screening wasn't being done? What kind of testing wasn't being done? What kind of therapies weren't being delivered? And then over the course of the pandemic, you have this sort of start-and-stop motion, and you can really trace that over the past year. And for us, what's been really interesting for our business is that so many of the questions that payers, or providers, or Life Science companies are asking have to do with how the system is actually reforming in the wake of the pandemic; and that's an amazing area where data is this incredible equalizer. We can use data to understand these patterns, we can see those patterns and we can inform better policy as a result of it.

Rick: Are you personally more a data guy or more a clinician guy? Your passion really seems to be data.

Arif: I love the notion that data consciousness and that the use of data can actually change the way that we think about care provision. I see so many wonderful experiments that are done at different institutions or with different populations, but the power is actually to bring that all up at the national level to look at these patterns across geographies, to look at where experiments are working, to figure out ways to scale them up; and so what I'm passionate about is basically using data to achieve better outcomes. If you look at the mission of Komodo Health, it's to reduce the burden of disease, so we hold ourselves to the standard of not just saying, "Hey, here's a bunch of data, go figure it out;" it's us saying, "How do we use this to actually reduce disease burden? Where do we see meaningful opportunities to accomplish that?"

Rick: I'm intrigued by your Healthcare Map™, can you explain what that is? And it's not quite a map in the traditional form, right? 

Arif: We think about maps as a way to chart a course from A to B – and the way that we've assembled the Healthcare Map is to think about this entire ecosystem of different providers, different insurers, and different ways that individuals seek care. So if you actually trace the journey of a patient, and you trace thousands and tens of thousands, then millions, and then hundreds of millions of patients, you start to see these patterns and they form this incredible Map. We see how patients practically enter the system, we see which ones receive standards of care, which ones don't, and all of that informs so much about them from a policy point of view, as well as from a commercial point of view. To us, the Map is really this incredible foundation on which we can study the healthcare system result by looking at patients, or payers, or providers to better understand the needs of the system.

Rick: You had told me that there's been stunning... You used the word stunning, for some of the data that you've uncovered in your work. Can you give some examples of stunning findings? 

Arif: So one of the most amazing opportunities we get to do when we work with data is to take questions that everyone's asking and look at them and tell us what the data says about those opportunities. So when we did the vaccine equity research and we started to look at zip codes that were really predominantly Black populations or Latinx populations, we're seeing a massive correlation with lower vaccination rates; that's not true of Native American populations and not true of white populations. And we talk a lot about the Native American communities conducting very organized vaccination efforts, and you can see that difference play out in the data. And so you may say, "Well, that's not stunning, we know these things." So what's stunning to me is that, well, we could have predicted this was going to happen. We saw the data across the pandemic around where COVID was happening.

Arif: And if you wanna talk about creating better equity, you have to be able to use that to make better decisions, and so what I find stunning is that we almost didn't learn anything from the past year. We basically set up a system that we looked at, we set up vaccine sites and we created a really cumbersome process on your iPhone that requires all this information, and we basically favored the technology set, and that means that populations that really needed the vaccine the most, or really have the highest chances of comorbidities if they were to get COVID, were being addressed; and I think that's the stunning news for us.

Rick: And in our final 30 seconds, if we can use data, as you're saying, to predict what's ahead – what's ahead for us this year that people may not be thinking about? 

Arif: Well, I think the re-entry in the system is going to be completely fascinating. So we've seen telemedicine adoption in certain areas – really in the wealthier zip codes that have a much higher rate compared to the poorer zip codes – by a factor of two to three. So what I think is going to be fascinating is, as healthcare is being remade with certain services moving into virtualized environments, it's going to create inequity in other places, and I think that's really the opportunity for us to study where that utilization is happening, where it's not, and then shape a system that's designed to meet the needs of the population that are not being addressed. And so I think the next year is going to be all about examining, then reopening, as things move back and forth and we really find those areas where high disease burden exist, where access to care is really low, and use that to form better policy. It's going to be a very interesting set of observations in time as we go into the coming year.

Rick: Put your seatbelt on for what's ahead. Arif, thank you. It was a pleasure talking to you. I really appreciate your being here. And let me now turn it back to our emcee.

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