The vaccine rollout is following patterns that are all too familiar — but we can change that
Newscasters sounded surprised when they announced for the first time that COVID-19 was disproportionately affecting Black, Indigenous, and people of color. As they presented graphs that showed higher rates of infection, hospitalization, and death, their tone was one of mild shock.
Similar surprise– and similar graphs– returned when they reported on the slowed, uneven vaccine rollout in poorer communities.
But this news did not surprise me at all, because I’ve spent my career working with patient-level health data. The steep upward curve on graphs that show worse health outcomes for BIPOC patients is a curve I know only too well stemming decades ago when I was in medical school, and more times than I can count since then.
BIPOC in America are getting substandard medical care and suffering higher rates of acute and chronic illness, and healthcare data are one of the most powerful tools we have if we intend to turn this alarming reality around. Creating a better system depends on a commitment to helping solve healthcare inequities by the entities who are holding this data. After all, data has been used to target populations with unhealthy products from cigarettes to fast food and contributed to the health disparities we see today.
The troves of data held by insurance companies and healthcare providers have the potential to help us break from the healthcare disparities of the past, seize the moment to ensure an effective, equitable rollout of COVID vaccines, and finally address disparity in care well after this current crisis is behind us.
We all have a role to play, a moral imperative to expose—and where possible, help remedy—gaping inequities in our system. Providers and payers are in a position today to do just that.
Memorable Med School Lecture
The first time I saw a graph like the one about COVID-19 impacting BIPOC was in a medical school lecture that included a section on the health effects of smoking. The data presented by the professor showed that BIPOC took up smoking in greater numbers and had far higher rates of smoking-related illnesses.
My thought at the time was that the people represented in the data were simply making poor lifestyle choices. I had no reason to think otherwise because there was no other data to inform my viewpoint.
But my opinion on this topic changed dramatically when more data was introduced: Our professor pointed out that cigarette companies, in Boston at least, were advertising far more aggressively in neighborhoods with higher populations of Black and Hispanic residents. This targeting was systematic and lasted over years.
More complete data made all the difference.
Since healthcare policy—which can widen or close disparities in healthcare—is always informed by data, the more data we can introduce, the more informed our country’s decision-makers will be. This is true in the rollout of vaccines – as it is in every other initiative in healthcare.
A More Complete Picture
More data means deeper understanding, and we need to understand far more than just how many doses of vaccine have been administered in each neighborhood, city, and state in the country. We need to know what’s working and what isn’t, as well as who is best able to answer that question.
Today, for example, we can see that 10% of the residents in one county have gotten the vaccine, compared to 20% in another county. But who are these residents, and where are they most comfortable getting vaccinated? Will it be more effective to send the next shipment of doses to local health clinics, or to churches and food banks?
Early on in the vaccine rollout there appeared to be very little socio-economic context, which means there was a limited insight into whether or not we could be doing a better job of getting the most doses to the most people in the shortest time. And we need to know. Not only for vaccine distribution but to answer all of the other healthcare questions that will exist long past the current crisis, such as what are the most effective treatments for chronic conditions in vulnerable populations.
The answer lies in combining clinical data in electronic health records, claims data in payers’ systems, and data from public health sources with county-level data on poverty, food availability, and other so-called determinants of health. Layering in other types of data such as the weather can provide an even deeper analysis.
There is a unique opportunity to make this happen today, with a government deadline on data interoperability right around the corner. Furthermore, initiatives like the Gravity Project are building standards-based codes that will enable providers to uniformly identify someone who may lack food directly in their electronic medical record. While some have argued that this could infringe upon privacy, I believe we have a moral responsibility to have this data in a structured format so that we have a more complete picture of an individual.
The next question is who is best able to generate insights from data that can solve inequities in healthcare. Here I believe that all healthcare is local. Analytics is best done by the people who understand the problems in a local market and know the community, doctors, and resources, rather than a research center in some obscure location that applies an algorithm to a local market.
In a prior life, we conducted analyses that showed a certain county’s HbA1C levels were much higher than neighboring counties adjusting for standard variables such as age, gender, and other comorbidities. Once adjusted for country of origin, the disparities across counties vanished showcasing the root cause of the issue as the dietary habits of subsets of the population based on their ethnic background. With this understanding, we were able to position culturally sensitive interventions that began to narrow the divide. Our understanding of the local community allowed us to more accurately understand the root cause of the issue and effectively address them.
The racial and economic disparities in healthcare delivery are nothing new. As a doctor, I’ve seen the same kinds of disheartening graphs and charts all the way back to my med school days. What is new is our opportunity to change this, not just for vaccines in a country desperate to get back to normal life — but to right all the historical inequities such as chronic disease treatment that will have an even bigger impact moving forward.
Data is the key to avoiding the patterns that have been all too common for far too many years. It’s up to healthcare’s major stakeholders to seize this moment to make it happen.
About Minal Patel, CEO, Abacus Insights
Minal is a serial physician entrepreneur who founded Abacus Insights to serve payer needs around data integration and insight generation. Prior to this he has held senior roles at payer organizations most recently serving as SVP and Chief Strategy Officer of Horizon BCBSNJ.