Health Policy researcher uses modelling to assess disparities in COVID-19 vaccine uptake and promote equity at the state level
May 19, 2021. Modelling highlights disparities and charts paths to unlocking more equitable COVID-19 vaccination for disadvantaged populations within states and across the US.
That was the key takeaway from an April 28th New Frontiers in Health Equity & Precision Population Health Seminar hosted by the Center for Population Health Sciences (PHS) and featuring Marissa Reitsma, a PhD Student in Health Policy and a Knight-Hennessy Scholar.
Reitsma and colleagues working in the SC-COSMO Modeling Consortium (including PhD graduate in Management Science and Engineering Anneke Claypool, and faculty project leads Jeremy Goldhaber-Fiebert and Joshua Salomon) and the Stanford Prevention Policy Modeling Lab developed a model for assessing the effectiveness of different COVID-19 vaccination strategies on vaccine coverage and uptake for disadvantaged populations.
Reitsma emphasized that (as of late April 2021) there are still people who want the vaccine who do not currently have access [see Figure 1], stating, “What we find is that, across the country, Black and Hispanic populations, as well as Asian populations in some regions, have faced access barriers that have resulted in lower coverage than their acceptance levels would suggest that they have.” She explained that there are a number of barriers that could reduce access to COVID-19 vaccines, including, but not necessarily limited to, not having the technology or time required to make a vaccination appointment, language barriers, lack of transportation to the vaccination site, or even being unable to take the required time off of work to get the vaccine.
Simulation modelling enabled Reitsma and her team to delve deep into the data surrounding the impact of various aspects of vaccination campaigns--including eligibility criteria, supply allocation rules, accessibility, and vaccine confidence--on coverage and uptake levels by race/ethnicity, and to conclude that some strategies are more equitable than others. For example, they estimated that age-based eligibility criteria, which were broadly utilized from January through April, resulted in a smaller proportion of deaths averted among Black and Hispanic populations, compared to White populations. Unequal vaccination rates due to access barriers faced by Black and Hispanic adults resulted in even greater disparities in outcomes.
Reitsma and the team modeled intervention scenarios to explore how these gaps in outcomes could feasibly be closed. They found that, during the past several months when demand for the vaccine exceeded available supply, intervention schemes geared towards allocating a greater proportion of vaccines to disadvantaged areas (based on census tracts or zip codes) could speed uptake and "roughly offset the impact of access barriers to result in equalization of coverage." Combining increased allocation of doses to disadvantaged areas with strategies for reducing access barriers further increases uptake rates and provides an "equity boost that's needed to mitigate the anti-equity effects of age-based eligibility criteria." She warns that, even in the best case scenario, equity gaps are difficult to close under strictly age-based eligibility schemes.
In February 2021, the team collaborated with the California Department of Public Health (CDPH) to use Reitsma’s model to evaluate the impact of policies surrounding the state’s distribution of COVID-19 vaccines. The model results highlighted the risks of growing disparities in COVID-19 outcomes under age-based eligibility criteria, and the opportunity to close these gaps through supply allocation. On March 3rd, the state announced their plan to allocate an additional 20% of vaccines to disadvantaged areas, identified by zip code. Though she reflected “Zip codes are a somewhat blunt instrument that can’t perfectly capture or prioritize marginalized populations,” Reitsma commended California state for their strategic vaccine equity plan and expressed pride in the real-world impacts her team’s models have already had.
Assessing vaccine rollout and equity within states
With the recent national shift to universal adult vaccine eligibility (effective as of April 19th), Reitsma and team quickly pivoted to adapt their modeling framework. They hypothesized that universal eligibility could threaten to increase disparities if demand for vaccination far outstripped supply. Applying their model to data gathered from each US state, Reitsma and her team noted that disparities in uptake ranged significantly from state to state, and vaccine acceptance levels also varied greatly within states and across various geographic areas and counties.
Models based on vaccine uptake rates through the end of March projected that in all states but three, people of color were on pace to reach the threshold of 75% vaccine coverage among adults slower than White people. In 16 states, people of color were projected to reach this coverage threshold an entire month after White people, assuming observed disparities in uptake continued. Simulating various vaccine rollout scenarios under universal adult eligibility, they concluded that interventions to eliminate access barriers and increase vaccine confidence among marginalized populations could narrow, but not completely eliminate, gaps in coverage. They found that place-based allocation strategies could help to further accelerate vaccination in disadvantaged communities.
“Measuring the effectiveness of interventions is critical,” Reitsma asserted, “and we are just getting to the point now where we have accumulated enough evidence to run those econometric analyses and hopefully disentangle some idea of what is an effective intervention, and then target those interventions to either very small geographic areas or specific socio demographic groups...” Modeling efforts are absolutely required to fill in data gaps on who, and where, those populations are.
Next steps towards more equitable COVID-19 vaccination
Reitsma closed the question and answer portion of the event by considering future steps to eliminating disparities in COVID-19 vaccination coverage. She suggested that community-based outreach strategies may be needed to reach the “hard-to-reach or hard-to-convince” populations, now that vaccination rates are slowing and states are increasingly finding that supply is no longer the limiting factor. Modeling can help states to identify where to direct their efforts to increase uptake, and can also help to hold states accountable to ensuring that marginalized populations are not left behind.
Reitsma’s presentation and the modelling work she and her team have undertaken serve as an important reminder that COVID-19 vaccination and general health efforts geared solely towards equalization or equality (in which we assume everyone is starting from the same place and needs the same resources) are not enough: we have to push towards equity, towards understanding and meeting the diverse needs of people so we can all lead happy and healthy lives.
This research was supported by a Stanford Population Health Sciences Pilot Grant through the Stanford Clinical and Translational Science Award to Spectrum, the State of California, Stanford’s Knight-Hennessy Scholars program, the Centers for Disease Control and Prevention though the Council of State and Territorial Epidemiologists, and the National Institute on Drug Abuse.
By Katie M. Kanagawa, Ph.D., M.A., Communication & Public Relations Manager for the Center for Population Health Sciences, the Department of Epidemiology & Population Health, and the Department of Biomedical Data Science
More research by Marissa Reitsma & colleagues
- Addressing racial/ethnic disparities in the COVID-19 vaccination campaign (Preprint)
- Racial/Ethnic Disparities In COVID-19 Exposure Risk, Testing, And Cases At The Subcounty Level In California (published in Health Affairs)
- In California, the pandemic hits Latinos hard (Stanford Medicine News release)