Data Bridges the Knowledge Gaps in the Fight Against COVID-19

If governments and healthcare providers thought it was difficult to plan for COVID-19 testing and contact tracing, the public vaccination process will be another monumental challenge.

The Advisory Committee on Immunization Practices, an independent panel that reports to the Centers for Disease Control and Prevention (CDC), recommended that frontline healthcare workers should be first in line to receive the vaccine, followed by employees and residents of long-term care facilities, the elderly, and those with underlying health conditions. But then who gets the vaccine next? And what is the best way to prioritize populations?

Another hurdle in the public vaccination process is that there is not near enough doses to vaccinate the U.S. population, especially when you consider that each person will need two doses of both the Pfizer and Moderna vaccines.

Many questions are left unanswered, but with every unprecedented challenge over the past nine months, comes an opportunity for governments and healthcare providers to collaborate and consider novel solutions to answers these questions and address this global health issue. Coordinated large-scale COVID-19 data registries representing diverse patient populations across various age groups, races, ethnicity, and regions are the commonsense solution we need now. Data registries can rapidly identify high-risk patients and prioritize immunization efforts, help ease public health concerns around vaccinations, and support the long-term surveillance and management of COVID-19.

Large-Scale Data Registries Can Rapidly Identify High-Risk Patients and Prioritize Immunization Efforts

Take Singapore for example; the country’s small population size and effective control measures limited the number of COVID-19 cases and mortality rate, but this also means there is a lack of data to power predictive tools for things like mortality. If Singapore healthcare providers were to rely on Singapore-only data, their findings might be limited, and they will not be able to make data-driven decisions. However, the Ministry of Health Office of Healthcare Transformation leveraged a U.S. based COVID-19 registry, which is a much broader data set with much more mortality data.

The U.S. registry had over 134,000 confirmed and 1,109,000 suspected COVID-19 cases as of August 2020, allowing data analysts to leverage this large-scale data to drive population-level insights about surveillance, testing, capacity planning, and treatment response. The registry included a representative population of various ages, races, and ethnicity. Together we were able to build a predictive mortality tool to determine who was more likely to die from COVID-19 based on age, gender, and other factors.

From this data, the Ministry was able to observe patterns, such as older adults have a higher risk of COVID-19 mortality; males are more at risk for having the worst COVID-19 outcomes and death; and patients with COVID-19 and cancer have a higher risk of death.

These data-powered models can support the early allocation of vaccines to ensure that those with the highest risks are being prioritized. Further, when we link vaccine data with patient’s medical history, we can start to understand which vaccines are most effective for which type of populations and are the safest to use – i.e., for pregnant women and children, this vaccine works best with minimal side effects.

Data Registries Can Help Ease Public Health Concerns Around Vaccinations

Public health concerns are one of the greatest challenges right now – and one of the most important areas for governments and healthcare providers to manage. The World Economic Forum suggests that the standard vaccination development process is typically more than 10 years. With the COVID-19 vaccine, there was a huge urgency to accelerate the process and release the vaccine within a year. Now, governments and healthcare organizations must overcome the burden of insufficient clinical trial data to understand the effects of the vaccine on the wider population or on specific demographics, such as children, pregnant women, the elderly, or those with pre-existing conditions.

Further complicating the issue is that there is not enough data to tell the preventative effect of the vaccine to determine whether a double dose of vaccination is sufficient after 1 year or if repeated vaccinations are needed at regular intervals, or if those who have had COVID-19 already and have antibodies need the vaccine right away. Answering these questions around vaccine safety and efficacy with long-term surveillance data to inform studies will be critical in assuring the public that the vaccine is safe for them.

Data Registries Can Support the Long-Term Management of COVID-19

Globally, there are certain countries that are more affected by COVID-19 than others, but this doesn’t mean that those who are not affected now will not be affected later. When that happens, these countries will want as much data as possible to understand the best way to deal with the virus. They can learn from the countries that have been in their situation before and make data-driven decisions like – prioritization of COVID-19 testing, prioritization of hospital beds, optimizing treatment, and more.

In Singapore, we are working with one of the public health clusters to compare the treatment of COVID-19 between the US and Singapore – hopefully, we can learn from one another. Ultimately, we can learn best practices globally for managing the pandemic if we can share data and knowledge to improve the health outcomes of people around the world. A good data and analytics strategy and infrastructure, such as a data registry, helps drive better data-driven conversations around decision making around vaccine distribution and vaccine management globally.