Table 1 shows the variation across NYC MODZCTAs of the total population, proportion 65 + , median household income, mortality rate, and vaccination coverage by wealth quintile on March 27, 2021, and March 19, 2022.

The difference in the total population between the smallest and largest MODZCTAs ranged from 3260 to over 108,000 people. There was a 30-fold range in proportion of 65 + (less than 1 to 31%) and a tenfold range in median household income ($23,337 to $250,001). COVID-19 mortality in 2021 was four times higher in the highest versus lowest mortality area (397/100,000 vs 94/100,000).

We explored the relationship between age-wealth, age-vaccination coverage, and wealth-vaccination coverage across MODZCTAs, focusing on vaccination coverage by the last week of March, just before everyone aged 30 + became eligible for vaccination.

The mean vaccination rate for 65 + ranged from 52.8% in the poorest quintile to 74.6% in the wealthiest. The maximum coverage was 99.0% among those 65 + in the wealthiest quintile versus 67.9% in the poorest. For those 45–64 years, the difference in mean coverage between the wealthiest and poorest quintiles was 25% (60% versus 34.6%). A year later, when vaccines were widely available, 65 + residents had median vaccination coverage exceeding 87%, including in the lowest wealth quintile (Table 1).

The proportion of 65 + vaccinated three months following vaccine introduction and the overall death rates by geographical area for 2021 are almost mirror images with opposite polarity. Areas with a higher proportion of 65 + vaccinated tended to have lower death rates, suggesting an inverse spatial relationship between the proportion vaccinated and the death rate (Fig. 1).

Proportion vaccinated with at least one dose by MODZCTA  65+ by the week of March 27, 2021 (left) and overall death rates by MODZCTA 2021 (right) 

We also observed a positive correlation between weighted median income (wealth) and vaccination coverage for 65 + and an inverse correlation between wealth and mortality (Fig. 2).

Wealth and  65+ vaccination rate (left) and wealth and mortality rate (right)

Figure 3 shows a boxplot of vaccination coverage, by age group, for each income quintile by March 27, 2021. The plot shows a consistent pattern whereby, for any age group, there is a monotonic and increasing relationship between wealth and vaccination coverage. However, as up to March 27, 2021, the vaccine was not generally available to people under 50; it is striking that (i) so many younger people were being vaccinated and (ii) wealthier people had higher coverage across all age groups.

Percentage vaccinated by wealth quintile and age group

We reanalyzed vaccination coverage using absolute numbers of doses to assess the potential “misallocation” of vaccines from older to younger age groups. We used coverage rates to derive doses delivered by age group by multiplying coverage rates by the population, per MODZCTA. This provided the minimum number of doses available during this period. From this, we identified that by March 27, 2021, MODZCTAs in the wealthiest quintiles had received enough vaccines to cover 41.6% of their total population. Vaccine availability dropped monotonically by wealth quintile: 31.6% coverage for the second wealthiest, 27.3% coverage for the middle, 24.9% for the second poorest, and 20.7% coverage for the poorest quintile.

We estimated the “misallocated doses” as any doses received by a person under 45 years before March 27, 2021, as by this date, only people older than 50 years were eligible for vaccination unless they were in high-risk health or employment categories. As described in the discussion section, we would expect to observe a larger proportion of these individuals living in the poorest quintiles and commensurate “overallocation.”

The greatest estimated overallocation of doses, however, occurred in the wealthiest quintiles.

Among them, 38.6% of doses were “misallocated” from older (65 +) to younger (0–17, 18–24, 25–44) age groups. The “misallocation rate” dropped monotonically by wealth quintile.

Moreover, if vaccination had been restricted to 65 + , there would have been enough vaccines for this age group in NYC 1.19 times over. By quintile, if in the poorest all doses delivered had only been administered to older New Yorkers, there would only have been doses to vaccinate 96.4% of the 65 + age group. In contrast, in the wealthiest, if vaccinating only older adults, there would have been 69% excess doses.

To explore the statistical relationship between mortality rates and the centered values for the percentage of the population 65 + in each MODZCTA, the vaccination coverage in the 65 + , and the median household income, we fit a “full factorial” OLS regression model. This model enables the study of the effects of multiple factors simultaneously on a response variable.

The Moran I test of the residuals, based on spatial autocorrelation, revealed strong evidence of positive spatial autocorrelation (I = 0.299, p < 0.0001), implying that locations close together exhibited values more similar than expected by chance alone.

To overcome the spatial correlation in the error observed in the initial model, we used a spatial error model. We tested the residuals using a Monte Carlo version of the Moran I test, which detected no significant spatial autocorrelation (I =  − 0.053, p = 0.799). The absence of significant spatial autocorrelation in the residuals suggests that the spatial error model successfully accounts for the spatial dependence present in the data. The model results, including coefficient estimates and statistical significance, are shown in Table 2.

Except for one MODZCTA (with an exceptionally higher-than-average proportion of older adults) [24] mortality was higher in lower-income areas and areas with a greater proportion aged 65 + . Those areas were also less vaccinated.

The fitted spatial error model provides insights into the factors affecting the COVID-related mortality rate in MODZCTAs. First, assuming a MODZCTA with centered values for income ($74 K), percentage 65 + (14.5%), and vaccination rate in 65 + (14.5%), the expected mortality rate for the period 2021 is 125.2 per 100,000 people.

The coefficient for the percentage of the population 65 + was positive and statistically significant (b = 5.82, p < 0.001), indicating that, with centering, a 1% increase in the percentage of 65 + is associated with a 5.8% increase in the mortality rate (p < 0.0001), holding all other variables constant. Median household income had a negative and significant effect (b =  − 1.09, p < 0.001); a $1000 increase in median income is associated with a 1.1% decrease in the mortality rate (p < 0.0001), holding all other variables constant. A 1% increase in vaccination coverage in the 65 + age group is associated with a small (− 0.3056; p = 0.3458) decrease in the mortality rate. While not statistically significant, it is in the expected direction.

The vaccination coverage among 65 + had a negative but non-significant coefficient (b =  − 0.31, p = 0.35), aligned with the protective effect of vaccination against mortality risk.

There were significant interactions between the percentage of 65 + , median income, and vaccination coverage. The negative interaction between the percentage of 65 + and income (b =  − 0.10, p < 0.001) indicated that the mortality effect of a larger elderly population diminished in higher-income areas. The positive income by vaccination coverage interaction (b = 0.02, p = 0.001) suggested that vaccination played a greater role in lowering mortality in higher-income regions. Finally, the three-way interaction between the percentage of 65 + , income, and vaccination coverage was positive and significant (b = 0.003, p = 0.002), implying a complex relationship between age, income, vaccination, and mortality.

While coefficient estimates were robust, accounting for spatial autocorrelation in the error term improved model fit and controlled for potential bias. Formal interpretation of the spatial structure is necessarily limited.