Geographically-targeted COVID-19 vaccination is more equitable than age-based thresholds alone

Why we should not 'just use age' for COVID-19 vaccine prioritisation

Older age is one of the greatest risk factors for severe outcomes from COVID-19. If we believe it is important to use limited supplies of COVID-19 vaccines to protect the most vulnerable and prevent deaths, then available doses should be allocated with significant priority to older adults. Yet, we should resist the conclusion that age should be the sole criterion for COVID-19 vaccine prioritisation or that no younger populations (eg, those under the age of 60) should be prioritised until all older adults have been vaccinated. This article examines arguments that are commonly presented to abandon 'complex' vaccine prioritisation schemes in favour of 'just using age' (eg, prioritising those 80 years of age and older and then decreasing in a 5-year age bands until the entire population has had the opportunity to be vaccinated), and articulates the ethical reasons why these arguments are not persuasive.

Prioritizing COVID-19 vaccination efforts and dose allocation within Madagascar

Background

While mass COVID-19 vaccination programs are underway in high-income countries, limited availability of doses has resulted in few vaccines administered in low and middle income countries (LMICs). The COVID-19 Vaccines Global Access (COVAX) is a WHO-led initiative to promote vaccine access equity to LMICs and is providing many of the doses available in these settings. However, initial doses are limited and countries, such as Madagascar, need to develop prioritization schemes to maximize the benefits of vaccination with very limited supplies. There is some consensus that dose deployment should initially target health care workers, and those who are more vulnerable including older individuals. However, questions of geographic deployment remain, in particular associated with limits around vaccine access and delivery capacity in underserved communities, for example in rural areas that may also include substantial proportions of the population.

Methods

To address these questions, we developed a mathematical model of SARS-CoV-2 transmission dynamics and simulated various vaccination allocation strategies for Madagascar. Simulated strategies were based on a number of possible geographical prioritization schemes, testing sensitivity to initial susceptibility in the population, and evaluating the potential of tests for previous infection.

Results

Using cumulative deaths due to COVID-19 as the main outcome of interest, our results indicate that distributing the number of vaccine doses according to the number of elderly living in the region or according to the population size results in a greater reduction of mortality compared to distributing doses based on the reported number of cases and deaths. The benefits of vaccination strategies are diminished if the burden (and thus accumulated immunity) has been greatest in the most populous regions, but the overall strategy ranking remains comparable. If rapid tests for prior immunity may be swiftly and effectively delivered, there is potential for considerable gain in mortality averted, but considering delivery limitations modulates this.

Conclusion

At a subnational scale, our results support the strategy adopted by the COVAX initiative at a global scale.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-13150-8.

Increasing concentration of COVID-19 by socioeconomic determinants and geography in Toronto, Canada: an observational study

BACKGROUND

Inequities in the burden of COVID-19 were observed early in Canada and around the world, suggesting economically marginalized communities faced disproportionate risks. However, there has been limited systematic assessment of how heterogeneity in risks has evolved in large urban centers over time.

PURPOSE

To address this gap, we quantified the magnitude of risk heterogeneity in Toronto, Ontario from January to November 2020 using a retrospective, population-based observational study using surveillance data.

METHODS

We generated epidemic curves by social determinants of health (SDOH) and crude Lorenz curves by neighbourhoods to visualize inequities in the distribution of COVID-19 and estimated Gini coefficients. We examined the correlation between SDOH using Pearson-correlation coefficients.

RESULTS

Gini coefficient of cumulative cases by population size was 0.41 (95% confidence interval [CI]:0.36-0.47) and estimated for: household income (0.20, 95%CI: 0.14-0.28); visible minority (0.21, 95%CI:0.16-0.28); recent immigration (0.12, 95%CI:0.09-0.16); suitable housing (0.21, 95%CI:0.14-0.30); multigenerational households (0.19, 95%CI:0.15-0.23); and essential workers (0.28, 95%CI:0.23-0.34).

CONCLUSIONS

There was rapid epidemiologic transition from higher- to lower-income neighborhoods with Lorenz curve transitioning from below to above the line of equality across SDOH. Moving forward necessitates integrating programs and policies addressing socioeconomic inequities and structural racism into COVID-19 prevention and vaccination programs.

Disparities in SARS-CoV-2 Vaccination-to-Infection Risk During the COVID-19 Pandemic in Massachusetts

This cohort study examines the alignment of vaccination and SARS-CoV-2 risk in Massachusetts by creating and applying a vaccination-to-infection risk ratio.

Increasing concentration of COVID-19 by socioeconomic determinants and geography in Toronto, Canada: an observational study

BACKGROUND

Inequities in the burden of COVID-19 were observed early in Canada and around the world suggesting economically marginalized communities faced disproportionate risks. However, there has been limited systematic assessment of how heterogeneity in risks has evolved in large urban centers over time.

PURPOSE

To address this gap, we quantified the magnitude of risk heterogeneity in Toronto, Ontario from January-November, 2020 using a retrospective, population-based observational study using surveillance data.

METHODS

We generated epidemic curves by social determinants of health (SDOH) and crude Lorenz curves by neighbourhoods to visualize inequities in the distribution of COVID-19 and estimated Gini coefficients. We examined the correlation between SDOH using Pearson-correlation coefficients.

RESULTS

Gini coefficient of cumulative cases by population size was 0.41 (95% confidence interval [CI]:0.36-0.47) and estimated for: household income (0.20, 95%CI: 0.14-0.28); visible minority (0.21, 95%CI:0.16-0.28); recent immigration (0.12, 95%CI:0.09-0.16); suitable housing (0.21, 95%CI:0.14-0.30); multi-generational households (0.19, 95%CI:0.15-0.23); and essential workers (0.28, 95%CI:0.23-0.34).

CONCLUSIONS

There was rapid epidemiologic transition from higher to lower income neighbourhoods with Lorenz curve transitioning from below to above the line of equality across SDOH. Moving forward necessitates integrating programs and policies addressing socioeconomic inequities and structural racism into COVID-19 prevention and vaccination programs.

Modeling the impact of racial and ethnic disparities on COVID-19 epidemic dynamics

Background

The impact of variable infection risk by race and ethnicity on the dynamics of SARS-CoV-2 spread is largely unknown.

Methods

Here, we fit structured compartmental models to seroprevalence data from New York State and analyze how herd immunity thresholds (HITs), final sizes, and epidemic risk change across groups.

Results

A simple model where interactions occur proportionally to contact rates reduced the HIT, but more realistic models of preferential mixing within groups increased the threshold toward the value observed in homogeneous populations. Across all models, the burden of infection fell disproportionately on minority populations: in a model fit to Long Island serosurvey and census data, 81% of Hispanics or Latinos were infected when the HIT was reached compared to 34% of non-Hispanic whites.

Conclusions

Our findings, which are meant to be illustrative and not best estimates, demonstrate how racial and ethnic disparities can impact epidemic trajectories and result in unequal distributions of SARS-CoV-2 infection.

Funding

K.C.M. was supported by National Science Foundation GRFP grant DGE1745303. Y.H.G. and M.L. were funded by the Morris-Singer Foundation. M.L. was supported by SeroNet cooperative agreement U01 CA261277.