Using data analytics to prioritize COVID-19 vaccine distribution

In late August, the federal government predicted a limited amount of COVID-19 vaccines would be available at the beginning of November, and that healthcare systems in all 50 states should be ready to implement an inoculation plan by this date. However, only targeted populations will be eligible for receipt – begging the questions: who should be immunized first, and how should existing epidemiological data be used to help guide the decision-making process?
Medical Value Sets - COVID-19

In August, the Centers for Disease Control (CDC) sent state and local public health officials three letters detailing operational plans for distributing two unidentified candidates for a vaccine against COVID-19. The strategies outlined in these letters assume that initial doses of vaccine will be available for targeted populations only, raising ethical questions around how to appropriately allocate this resource.

Getting priorities straight

In response to the 2009 H1N1 epidemic, the CDC’s Advisory Committee on Immunization Practices (ACIP) created a prioritization list with five tiers, which was intended to help healthcare professionals appropriately distribute a scarce vaccine. Healthcare personnel make up the first tier, followed by essential military support, utilities workers, high-risk adults, and healthy adults in tiers two through five, respectively. Federal and state public health departments are considering using a similar approach for COVID-19.

What will such a list look like this time around? In response to a joint request from the National Institutes of Health and the CDC, the National Academies of Sciences, Engineering, and Medicine formed an ad-hoc committee to design a framework for the equitable allocation of the COVID-19 vaccine, adding persons with underlying conditions in the highest priority tier. The ACIP will likely soon vote on whether or not to accept this new change.

How can HIT help?

The data needed to formulate a prioritization list could come from several sources. Patient-level data can identify groups that are more likely to contract severe COVID-19. Regional testing data can identify hotspots, which could shape prioritization based on location. And, demographic and epidemiological data can help identify groups disproportionately impacted by the disease helping to show where the vaccine will have the greatest impact.

Data from these disparate sources will need to be standardized and integrated into a single system. To do so quickly and accurately will require automated tools to normalize the data and identify specific cohorts. Well-defined medical value sets can be designed to stratify the population based on risk, likelihood of benefit, and utility. These cohort definitions could then be used by decision support algorithms to help in the implementation of best practice guidelines for vaccination.

However, the likelihood that this can be accomplished without a significant investment in data systems is low. The confusion about how the healthcare system can be prepped to rapidly administer 330 million doses of different COVID vaccines has raised serious concerns. Furthermore, the government has a contract with Deloitte and Salesforce to design and implement a new Vaccine Administration Management System (VAMS), but this system has not yet been released.

With a COVID-19 vaccine on the horizon, public health departments will have to come up with a distribution plan, and soon. With the right data, policy makers can make informed decisions when designing a fair and equitable distribution strategy.

For more information on the ongoing development, testing, and roll-out of COVID-19 vaccines, download IMO’s new white paper, Traveling at the speed of light: Health IT’s role in administering the COVID-19 vaccine.

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