COVID-19 Incidence and Hospitalization Rates are Inversely Related to Vaccination Coverage Among the 112 Most Populous Counties in the United States

New research supports the conclusion that vaccination substantially reduces the risk of severe infection.


We tested whether COVID-19 incidence and hospitalization rates were inversely related to vaccination coverage among the 112 most populous counties in the United States, each with a population exceeding 600,000, and together with a combined total population of 147 million persons.

We measured vaccination coverage as the percent of the total population fully vaccinated as of July 15, 2021, with the exception of 11 Texas counties, where the cutoff date was July 14, 2021. We measured COVID-19 incidence as the number of confirmed cases per 100,000 population during the 14-day period ending August 12, 2021. We measured hospitalization rates as the number of confirmed COVID-19 admissions per 100,000 population during the same 14-day period.

COVID-19 incidence was significantly higher among counties in the lower half of the distribution of vaccination coverage (incidence 543.8 per 100,000 among 56 counties with mean coverage 42.61%) than among counties in the upper half of the distribution of coverage (incidence 280.7 per 100,000 among 56 counties with mean coverage 57.37%, p < 0.0001). Hospital admissions were also significantly higher among counties in the lower half of the distribution (55.37 per 100,000) than in the upper half of the distribution (20.48 per 100,000, p < 0.0001).

In log-linear regression models, a 10-percentage-point increase in vaccination coverage was associated with a 28.3% decrease in COVID-19 incidence (95% confidence interval, 16.8 – 39.7%), a 44.9 percent decrease in the rate of COVID-19 hospitalization (95% CI, 28.8 – 61.0%), and a 16.6% decrease in COVID-19 hospitalizations per 100 cases (95% CI, 8.4 – 24.8%).

Higher vaccination coverage is associated not only with significantly lower COVID-19 incidence, but also significantly less severe cases of the disease.

COVID-19 Vaccination Mandates for School and Work Are Sound Public Policy

by Karen Mulligan and Jeffrey E. Harris, University of Southern California, Schaeffer Center for Health Economics & Policy, White Paper Released July 7, 2021

Key Takeaways

  • Vaccine mandates for employees are emerging as the most feasible and effective policy to reduce the risk of disastrous COVID-19 outbreaks in the workplace.
  • Higher education and healthcare are leading the way in vaccine mandates, while other sectors are beginning to follow their lead.
  • State laws barring vaccine mandates will become increasingly unenforceable as they come into conflict with private employer initiatives to protect workers and consumers.
  • Concerns that verification of vaccination status is unworkable or that the Food and Drug Administration has approved COVID-19 vaccines only on an emergency basis will prove to be overblown.
  • While a clearly articulated, unified federal policy on vaccine mandates remains lacking, independent agencies such as the Equal Employment Opportunity Commission, the Occupational Safety and Health Administration, and the Centers for Disease Control have already begun to step into the void.


In the absence of clearly articulated federal and state policies, private employers have had to adopt their own vaccination policies to reduce the risk of a disastrous COVID-19 outbreak among returning workers. Higher education and healthcare have led the way in establishing vaccination mandates for students and employees, and there are strong indications that other sectors will follow suit. This trend is reinforced by the emergence of markets for vaccination-only services, as well as market-based approaches to vaccination verification. While workplace vaccine mandates have already proved feasible and effective, the alternative of mixing unmasked vaccinated with masked unvaccinated workers remains untested. State laws that prohibit vaccination mandates will prove unenforceable as they increasingly conflict with the policies of large private employers. Vaccine mandates are not only inevitable, but they are sound public policy. The federal government can play an important role in endorsing workplace mandates while protecting the most vulnerable and reducing disparities in disease burden.

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3 Million Doctors and Nurses Have Had the COVID-19 Vaccine. You Can, Too.

We have (nearly) forgotten what we learned from the anti-smoking campaigns of the sixties and seventies.

On December 1, 1966, a lawyer in his 20s named John Banzhaf – later to become a professor at George Washington University Law School – petitioned WCBS-TV in New York City to provide free air time to respond to the pro-smoking views embedded in the cigarette commercials that the television station had been broadcasting. After two years of litigation, the United States Court of Appeals for the District of Columbia Circuit, in an opinion written by Chief Judge David L. Bazelon, found in Banzhaf’s favor. The Court upheld a ruling by the Federal Communications Commission requiring every TV station to broadcast one anti-smoking public service announcement during prime time for every three cigarette commercials it aired.

There followed a sustained period of prime-time anti-smoking ads, running night after night until the U.S. Congress finally banned all TV and radio advertising of cigarettes effective January 2, 1971. From 1968–1969, per capita cigarette consumption in the U.S. fell by 4.6 percent — a drop in smoking rates not seen since the lung cancer scare of December 1953, and not seen again until a major, nationwide increase in the federal cigarette excise tax in 1983.

Here, we reproduce what was arguably the most influential public service advertisement of the period, sponsored by the American Cancer Society, lasting all of eight seconds.

Combining Trust in Healthcare Providers with Peer Influence

The American Cancer Society public service ad worked because it combined two critical themes. First, the television-viewing public trusted healthcare providers – physicians, in particular – to offer reliable advice. Second, the salient reference to a large round number with lots of trailing zeros added the element of peer influence. The fusion of these two themes sent a clear message to many who doubted a connection between smoking and disease, or who felt they didn’t have the willpower to quit: All those doctors must know something.

When it comes to COVID-19 vaccine hesitancy, there is by now no shortage of commentators rightly observing that physicians and nurses can be vital vaccine messengers, and we, too, have joined in the chorus. Nor is there a paucity of position papers propounding the power of peer influence, and we’ve again joined the crowd. The new We Can Do This campaign is based, in great part, on the idea that everybody’s doing it.

So let’s bring back the nearly forgotten motif from a half-century ago. To that end, we first need to do some arithmetic. An estimated 84.5% of the nation’s 3.8 million registered nurses work in healthcare. There are 985,000 actively practicing physicians in the United States. A recent Kaiser Family Foundation survey, in the field during February 11 – March 7, 2021, reported that 68 percent of doctors and nurses in the United States had received at least one vaccine dose. (We do not include those expressing an intention to be vaccinated.) Putting all the numbers together, and recognizing that the survey data are already two months old, we end up with a conservative tally of at least 3 million vaccinated doctors and nurses.

So, here is our reincarnation of the American Cancer Society public service ad of 1969.

Social Desirability Bias: The Achilles Heel of COVID-19 Vaccine Surveys

In 1978, a decade after the prime-time, anti-smoking TV ads had begun to put the brakes on the 20th century tobacco juggernaut, Ken Warner, a young economist on the faculty of the University of Michigan School of Public Health in Ann Arbor, pointed out how survey-based estimates of cigarette smoking rates fell increasingly short of actual consumption based on tax receipts. With smoking rates then on the decline, Prof. Warner worried that survey respondents were ever more reluctant to tell the interviewer that they were engaging in a “socially undesirable” activity.

Even at the time, social desirability bias (SDB) had been a concern of survey experts. Rather than reveal their true intentions or beliefs, the problem was that people would tell the interviewer what they thought the interviewer wanted to hear. During the intervening 43 years, there has been an abundance of studies of the influence of SDB in surveys of such personal health-related behaviors as substance abuse and physical activity. Extensive reviews have been published. The problem has persisted even in apparently anonymous online surveys. In the modern version of SDB, survey researchers worry that respondents, ever wary of empty assurances of anonymity, will click on the button they want the online survey software to record.

When it comes to surveys of intentions to get vaccinated against COVID-19, social desirability bias has once again reared its ugly head. A handful of academic studies have at least acknowledged the problem, but only in passing. A worldwide review of vaccine hesitancy does not mention this problem at all. Nor does a recent interpretive synthesis in a major medical journal.

The Kaiser Family Foundation vaccine monitor recently reported that the proportion of U.S. adults who said they’d already received one dose of the vaccine or intended to do so as soon as possible increased from 61 percent in March to 64 percent in April. Polling data from the Pew Research Center, in the field on February 21, indicated that 69 percent of U.S. adults either had already received one dose of the vaccine or were probably or definitely planning to get vaccinated. Yet the Center Center for Disease Control’s Vaccine Tracker reports that 58 percent of the U.S. adult population has actually had at least one vaccine dose and only 44 percent had been fully vaccinated. These data would suggest that there remains considerable excess demand for the COVID-19 vaccine. Yet there are signs that demand has slowed, and some states are returning allotted vaccine doses because of excess supply.

The concern here is not just that a particular survey at a particular point in time may have overstated vaccination intentions. The more knotty problem is that the increasing promotion of vaccination itself enhances the magnitude of the social desirability bias and thus exaggerates the apparent rise in vaccine acceptance. That’s exactly what Prof. Warner worried about in an entirely different context more than four decades ago. What made smoking increasingly undesirable way back then was the growing movement of the 1970s to restrict smoking in public places.

Continuum of Resistance to Behavior Change

As the daily pace of vaccination has decelerated in the United States, a new wave of commentary has become fashionable. The basic theme is that vaccine hesitancy is deeply ingrained in some people because of their distrust of the medical establishment or authority in general, or their core beliefs about personal freedom or the inviolability of the body, or their hardened sense of fatalism.

What we learned from the anti-smoking campaigns is that resistance to behavioral change lies along a continuum. At one end of the spectrum, tens of millions of smokers quit when they got the facts about tobacco and health, and tens of millions of people lined up to get their shot when they got the facts about the efficacy of the COVID-19 vaccines. At the other end, many smokers remained addicted and in denial, but came around when they could no longer bear the ostracism of having to smoke outside the office in the cold, or when they had to pay more for life insurance, or when they suddenly found themselves tethered to a heart monitor in the intensive care unit. Others who remain hesitant to get vaccinated will similarly come around when they find out – as inevitably will be the case – that proof of vaccination has become the access key to open the portal to life as we once knew it.

An Even Deeper Look at Vaccination Rates in New York City

Vaccination and seropositivity. The hyper-vaccinated Upper East and Upper West Sides. African American vaccination rates lag those of Spanish speakers.

In A Deeper Look at Vaccination Inequity in Los Angeles County, we delved into the inequities of the COVID-19 vaccine rollout in the largest county in the United States. Here, we head eastward to the Big Apple. This time around, we go beyond concerns of fairness and begin to worry about vaccine hesitancy.

Vaccination and Seropositivity

The map on the left-hand side of Figure 1 shows the percentage of the adult population that has so far received at least one dose of a COVID-19 vaccine. The map on the right shows the corresponding percentage of adults who have so far had a positive antibody test for SARS-CoV-2. The two maps look like negatives of each other.

Figure 1. First-Dose COVID-19 Vaccination Rates (Left) and Cumulative Seropositivity Rates (Right) among Adults in New York City by Zip Code Tabulation Area (ZCTA) as of March 27,2021. Sources: New York City Health Department Vaccination and Antibody Testing Date

As we noted in A Deeper Look, many people who’ve already come down with COVID-19 don’t think they need to be vaccinated. (Consider Sen. Rand Paul’s provocative tweet of November 17. ) While there is now a strong medical case that those already infected should get vaccinated as well – In fact, we’re doing just that at our own community health center. – the perception that one is naturally protected is nonetheless a critical determinant of vaccine demand.

Prior studies of equity in vaccination rollout – and, in particular, studies of disparities in racial and ethnic vaccination rates – have ignored this important negative relationship between seropositivity and vaccination rates.

Two-Way Plot

Map comparisons may be illuminating, but they’re hard to work with. So let’s convert the negative relationship for New York City into a two-way plot.

Figure 2 plots the percentage of adults who have had at least one COVID-19 vaccination against the percentage of adults who have had a positive antibody test. As in A Deeper Look, this two-way plot allows us to quantitatively test the relationship between past COVID-19 infection and the demand for vaccination.

Figure 2. Percent of Adults with at Least One COVID-19 Vaccination Versus Percent of Adults Antibody Positive for SARS-CoV-2 Among 167 Zip Code Tabulation Areas (ZCTAs) in New York City as of March 27, 2021. The size of each point corresponds to the number of persons tested in each ZCTA. The overlaid line is the weighted least squares fit, where the weights are numbers of persons tested in each ZCTA. The slope of the fitted line is –0.718, with 95% confidence interval –0.819 to –0.616. Some ZCTAs have been specifically identified.

In contrast to our two-way plot for Los Angeles County, here we rely on New York City’s database of antibody tests for prior COVID-19 infection. The estimated slope of the fitted line is –0.71, which means that, in the absence of any other confounding factors, 71 out of 100 previously infected individuals have so far chosen to deter vaccination.

Vaccination itself may eventually cause some currently marketed antibody tests to turn positive. If so, the trend seen in Figure 2 would understate the actual deterrent effect of prior infection on vaccine demand, so that the real number would be greater than 71 out of 100. Still, this bias is likely to be small, as the vast majority of antibody tests were done before the U.S. vaccination rollout.

In Figure 2, we’ve drawn attention to a few specific Zip Code Tabulation Areas (ZCTAs). At the upper left, there is a cluster of nine ZCTAs located in the Upper East Side and Upper West Side, affluent areas bordering Central Park. At the extreme right, we see Corona in Queens (11368), where 48 percent of adults so far tested have had detectable antibodies against coronavirus, followed by Borough Park in Brooklyn (11219), where the seropositivity rate has reached 46 percent. Tracking the outliers from right to left, we see East Elmhurst (11369), Jackson Heights (11372) and Elmurst (11373) in Queens. Along with Corona, these contiguous ZCTAs formed the Queens-Elmhurst hotspot that emerged in late March 2020 in the early days of New York City’s epidemic.

Upper East Side – Upper West Side

Figure 2 should not be interpreted to mean that prior SARS-CoV-2 infection is the only determinant of the likelihood of vaccination. It’s just that we need to take prior infection into account when we analyze other potential determinants.

To that end, Figure 3 shows the influence of household income. This new figure is the same as Figure 2 except for the color coding. The darker-colored points represent ZCTAs with a median household income of at least $67,800 (the median of the medians), while the lighter-color points show the ZCTAs with a median household income below that threshold. The data on income and other demographics come from the Census Bureau’s American Community Survey.

Figure 3. The Two-Way Plot of Figure 2 Broken Down by the Median Household Income of Each ZCTA. Source for household income data: American Community Survey 2015–2019.

If higher household income in fact enhances vaccination rates – even after accounting for prior infection – then we would expect to see the darker-colored points situated above the line and lighter-colored points situated below the line.

Figure 3 shows us that income indeed matters . Its influence, however, is concentrated in the cluster of outlier points in the upper left, all of which correspond to ZCTAs on the affluent Upper East Side and Upper West Side, where median family incomes exceed $120,000 annually. To be sure,there is an obvious relationship between income and the likelihood of having ever been infected. However, in contrast to our findings for Los Angeles County in A Deeper Look, there is no income gradient in vaccination rates at any given level antibody-test positivity.

Figure 4 shows the corresponding plot to gauge the influence of age. Again, we’ve copied over Figure 2, but this time the darker color-code points correspond to those ZCTAs with a higher proportion of senior citizens. The figure confirms that age matters, even when we take prior infection into account. Like Figure 3, the influence of age is concentrated in those ZCTAs with the lowest prior infection rates, especially the cluster located on the Upper East Side and Upper West Side.

Figure 4. The Two-Way Plot of Figure 2 Broken Down by the Median Household Income of Each ZCTA. Source for age data: American Community Survey 2015–2019.

Black and African Americans

Figure 5 once again reproduces our basic Figure 2, but this time we’ve broken down the data points by the proportion of individuals in each ZCTA who describe themselves as Black or African American. In contrast to Figures 3 and 4, we’ve shaded the data points in three colors. The lightest shade represents 84 ZCTAs with less than 10.3 percent (the median across all ZCTAs), while the two darker shades identify the 83 ZCTAs with a proportion of Black or African Americans above that cutoff. The very darkest shade represents 47 ZCTAs that are further distinguished as being significantly below the fitted line.

Figure 5. The Two-Way Plot of Figure 2 Broken Down by the Proportion of Individuals Self-Declaring as Black or African American. The darkest data points represent ZCTAs with a high proportion of Black or African Americans that fall more than 2 standard errors of prediction below the fitted line. Source for data on proportion of Black or African Americans: American Community Survey 2015–2019.

Figure 5 shows a pattern quite different from those in Figures 3 and 4. While ZCTAs with a higher household income or a higher proportion of elderly residents have higher vaccination rates, the opposite trend is observed for the proportion of Black and African Americans. Moreover, the lower vaccination rates among ZCTAs with more Black and African Americans is seen for a wide range of antibody-test positivity.

Figure 6 uses this color scheme to identify the corresponding ZCTAs in our map of New York City. As above, the two darker shades identify those neighborhoods with a higher proportion of Black or African American residents. What is most striking is that the very darkest shaded ZCTAs tend to be grouped into clusters of spatially contiguous zones.

Figure 6. Data Points of Figure 4 Plotted on a Map of New York City ZCTAs.

Spanish Speakers

Figure 7 repeats the same exercise for the proportion of persons in each ZCTA who describe themselves as Spanish speakers. In contrast to the case of Black and African Americans in Figure 6, a higher proportion of Spanish speakers is associated with a higher vaccination rate. This is particularly the case for the ZCTAs that made up the Queens-Elmhurst hotspot described in Figure 2.

Figure 7. The Two-Way Plot of Figure 2 Broken Down by the Proportion of Individuals Self-Declaring as Spanish Speakers. Source for data on proportion of Spanish speakers: American Community Survey 2015–2019..

To construct Figure 7, we measured the proportion of individuals in each community describing themselves as Spanish speakers. We saw virtually the same pattern when we instead measured the proportion of individuals in each ZCTA who described themselves as Latino or Hispanic. The only difference was that the median proportion of self-described Latino/Hispanic persons was 19.5 percent.

Statistical Breakdown

Figure 8 shows the results of our statistical breakdown of the influence of all five factors on the likelihood of vaccination to date in New York City. The basic idea is to estimate the effect of each individual factor while holding the remaining four factors constant. The details, which mirror the approach we took for Los Angeles County in A Deeper Look, have been relegated to the Technical Notes below.

Figure 8. Effect of Each Binary Variable on the Percent of Adults Vaccinated in a ZCTA. The estimated effects are taken from Table 1 in the Technical Notes.

A low seropositivity rate (that is, fewer than 26.4 of adults having tested antibody-positive for coronavirus) was the most important factor. The proportion of adults vaccinated was 6.4 percentage points higher in those ZCTAs with a low seropositivity rate. The next two most important factors were a high median household income (4.6 percentage points) and a high proportion of seniors (4.3 percentage points). A high proportion of Spanish speakers raised the likelihood of vaccination by 2.9 percentage points, while a high proportion of Black/African Americans lowered the likelihood of vaccination by 2.2 percentage points.

A Cautionary Note on Vaccine Hesitancy

It comes as no surprise that the rollout of COVID-19 vaccinations in New York City exhibits significant evidence of economic inequality. What is more unexpected is that the inequality is so highly concentrated in a small area bounding both sides of Central Park in Upper Manhattan.

But that is not the real take-home lesson of this deeper look at vaccination rates in New York City. Once we account of other factors influencing demand or access – prior rates of COVID-19, median household income and proportion of senior citizens– we find that Spanish-speaking individuals have a higher than expected vaccination rate, while Black and African American individuals have a lower than expected rate.

One partial explanation is that the city government’s prioritization of vaccine distribution, in combination with private efforts, is already having an impact – especially in those Latino communities in Queens that were hardest hit by COVID-19 last spring. That may explain why the data points for East Elmhurst (11369), Jackson Heights (11372) and Elmurst (11373) in Queens are so far above the line in Figure 7.

But what about the finding that the data points for predominantly Black and African American communities remain below the line, as we saw in Figure 5? And why do the communities farthest below the line seem to be clustered, as we saw in the map of Figure 6?

Perhaps we’re simply observing a temporary state, a condition of disequilibrium. As vaccines become more plentiful, the divergence between the two groups may disappear. The deeper concern, however, is that we may be confronting a serious problem of persistent vaccine hesitancy.

Spatial Clustering and Peer Effects

As economists well know, the spatial clustering of ZCTAs in the map of Figure 6 has two competing interpretations. On the one hand, the individuals in these clusters may simply share things in common that have nothing to do with the demand for or access to COVID-19 vaccines. On the other hand, there is the distinct possibility of peer effects. That is, individuals in close social proximity learn from each other.

Much has already been written about the drivers of vaccine hesitancy among Black and African Americans. Our findings here suggest that the underlying beliefs driving vaccine hesitancy are learned, shared, and reinforced through highly local, personal interactions.

When a patient tells us, “I just don’t trust those vaccines,” he’s really saying, “We don’t trust those vaccines.” When another patient tells us, “I heard that people have died from the second dose,” she’s really saying, “My core influential group says that people have died from the second shot.”

If so, then our task as physicians and nurses is not simply to factually and dispassionately allay our individual patients’ concerns about vaccine safety and effectiveness.

We need to leave the comfort of our examining rooms and step out into the community.

Technical Details

As in A Deeper Look, we specified a multivariate statistical model of vaccination rates across 167 Zip Code Tabulation Areas (ZCTA), where each of the five explanatory variables was binary, that is, either low or high. For each explanatory variable, the cutoff between low and high was the median value for the sample. Table 1 below shows our detailed results.

Binary Explanatory VariableCoefficientLower RangeUpper Range
Low Confirmed Seropositivity6.434.438.42
High Median Household Income4,552.646.46
High Proportion Aged 65+4.262.386.14
High Proportion Spanish Speakers2.920.994.85
High Prop. Black/African American–2.16–4.01–0.32
Table 1. Linear regression of Percent Vaccinated against five binary variables: Low Seropositivity (up to 26.4%); High Median Household Income (greater than $67,800); High Proportion Aged 65+ (greater than 26.2%); High Proportion Spanish Speakers (greater than 12.4%); and High Proportion of Black/African Americans (greater than 10.3%). The data set included 167 ZCTAs with adult population greater than 9,000. The regression was weighted by the number of adults tested for SARS-CoV-2 antibodies in each ZCTA. The two right-most columns show the bounds of the estimated 95% confidence intervals. The adjusted R-squared statistic was 0.53. The estimated difference in coefficients between the High Proportion of Spanish Speakers and the High Proportion of Black/African Americans, equal to 5.08 percent, was statistically significant at the level p = 0.0005.

The use of binary explanatory variables allowed us to compare the magnitudes of each of the variables’ estimated effects. A high proportion of Spanish speakers in a ZCTA was associated with a 2.92 percentage point increase in the likelihood of vaccination, while a high proportion of Black or African Americans was associated with a 2.16 percentage point decrease. The spread between these coefficients, equal to 5.08 percentage points, was statistically significant.

Table 2 shows the results of an alternative linear multivariate model where each of the explanatory variables is continuous. The estimated coefficient of Percent Seropositivity was –0.38 (95% CI –0.49 to –0.27). That is, once we took all our explanatory factors in account, the impact of prior SARS-CoV-2 infection on vaccination rates was reduced by about half.

Continuous Explanatory VariableCoefficientLower RangeUpper Range
Percent Seropositivity–0.38–0.49–0.27
Median Household Income ($000)
Percent Aged 65+0.620.470.77
Percent Spanish Speakers0.130.090.17
Percent Black or African American–0.06–0.09–004
Table 2. Linear regression of Percent Vaccinated against five continuous variables. The adjusted R-squared statistic was 0.53.

The Repeated Setbacks of HIV Vaccine Development Laid the Groundwork for SARS-CoV-2 Vaccines

National Bureau of Economic Research Working Paper 28587

The decades-long effort to produce a workable HIV vaccine has hardly been a waste of public and private resources. To the contrary, the scientific know-how acquired along the way has served as the critical foundation for the development of vaccines against the novel, pandemic SARS-CoV-2 virus. We retell the real-world story of HIV vaccine research – with all its false leads and missteps – in a way that sheds light on the current state of the art of antiviral vaccines. We find that HIV-related R&D had more than a general spillover effect. In fact, the repeated failures of HIV vaccine trials have served as a critical stimulus to the development of successful vaccine technologies today. We rebut the counterargument that HIV vaccine development has been no more than a blind alley, and that recently developed vaccines against COVID-19 are really descendants of successful vaccines against Ebola, MERS, SARS-CoV-1 and human papillomavirus. These successful vaccines likewise owe much to the vicissitudes of HIV vaccine development.

The SARS-CoV-1 outbreak in 2002-2003 and the continuing reintroduction of MERS-CoV on the Arabian Peninsula a decade later sent a clear message to the public health and scientific communities well before the December 2019 outbreak of SARS-CoV-2 in Wuhan, China. At any minute, a novel, lethal respiratory coronavirus could emerge with the potential for pandemic human-to-human spread. Piggybacking on the major advances in virology, immunology and molecular biology achieved during the scientific confrontation with HIV, researchers already knew that human coronaviruses were positive-sense RNA viruses with a key spike glycoprotein that protruded from viral envelope. They already knew that the spike glycoprotein could bind to viral receptors on host target cells. The spike glycoprotein of SARS-Cov-1, they knew as well, could bind a receptor called ACE2 in human lungs. They knew it was coming and, at least from the scientific point of view of vaccine development, they were ready.

A Deeper Look at Vaccination Inequity in Los Angeles County

There’s more to the story than a simple comparison of two maps. The sources of inequity are systemic.

Figure 1.On the left: Map of the percentage of the population receiving at least one COVID-19 vaccine as of 2/24/2021. On the right: Map of the incidence of confirmed COVID-19 cases during the prior two weeks. Both maps cover 346 countywide statistical areas (CSAs) in Los Angeles County. Two islands not shown. Sources: Los Angeles County Department of Public Health, Long Beach Department of Health and Human Services, Pasadena Health Department.

The Los Angeles Times recently posted two maps in an attempt to demonstrate the striking inequities in the COVID-19 vaccine rollout in Los Angeles County, far and away the largest county by population in the United States. One map showed the geographic distribution of vaccines to date, while the other showed the corresponding distribution of recent COVID-19 cases throughout the county.

In Figure 1 above, we display our own rendering of these two maps. The distribution of vaccinations on the left certainly looks different from the distribution of recent cases on the right. In fact, except for the broad swath of the San Gabriel Mountains and Los Angeles National Forest that cut across the county, the two maps look like negatives of each other.

But that doesn’t tell the whole story.

People who’ve already had COVID-19 don’t think they need to be vaccinated.

There is, in fact, an entirely plausible, alternative explanation for the apparent negative relation between the rate of vaccination and the incidence of COVID-19 in a community. In particular, people who have already come down with COVID-19 do not perceive the need to be vaccinated.

We could debate the science underlying the need for past sufferers of COVID-19 to get vaccinated. On the one hand, recent evidence suggests that the natural immunity acquired from a SARS-CoV-2 infection is indeed lasting, at least for 6-8 months. On the other hand, a single dose of an mRNA vaccine appears to confer significantly more immune protection in a previously infected individual than in an infection-naive person.

But this isn’t about scientific need for a vaccination. It’s about the perceived need for a vaccination. As economists would put it, prior infection reduces the demand for vaccination.

A Two-Way Plot

Figure 2. Percentage of the population receiving at least one COVID-19 vaccine as of 2/24/2021 (vertical axis) versus percentage of the population with a confirmed case of COVID-19 as of 1/10/2021 (horizontal axis). The data plot is restricted to 204 CSAs with at least 10,000 population. The size of each point indicates the CSA population. The line was fit to the data by population-weighted regression. Source: Los Angeles County Department of Public Health.

In Figure 2 above, we’ve plotted the percentage vaccinated in each community against the cumulative number of confirmed COVID-19 cases in that community. We’ve restricted our plot to the 204 communities within Los Angeles County that have a population of 10,000 or more. (Technically, these communities are called countywide statistical areas, or CSAs.) The size of each data point corresponds to community’s population. Strictly speaking, the horizontal axis measures the cumulative proportion of individuals who ever had a confirmed case of COVID-19, while the map at the right in Figure 1 shows the geographic distribution of recently confirmed cases. Still, the two variables are highly correlated.

The regression line fitting the data points in Figure 2 has a slope of –0.85. This would mean that for every individual who has had a confirmed case of COVID-19 since the epidemic began, 0.85 individuals deferred vaccination.

There is plenty of evidence that the number of confirmed cases substantially understates the actual number of cases of COVID-19. However, a lot of those unreported cases have been asymptomatic people who still don’t know they were infected. When it comes to our analysis of the effect of past infection on the perceived need for immunization, those people don’t count.

To be conservative, let’s assume that the true number of symptomatic infections is 25% greater than the confirmed number. Then our slope would drop to –0.85 / 1.25 = –0.68. That would still imply that most of the negative correlation seen in the two-map comparison can simply be accounted for by deferrals among those who already got sick.

The outliers offer a clue.

Figure 2 has a number of notable outlier points. We’ve annotated the five most obvious ones. With the possible exception of the Century City neighborhood of Los Angeles, where you can still buy a condo for under $1 million, housing prices in the neighborhoods of Beverly Crest, Encino, and Tarzana, as well as the City of Beverly Hills are way, way through the roof. It would be a delicate understatement to say that these outliers are affluent communities.

That gives us a clue that, quite apart from the influence of prior SARS-CoV-2 infection, income has played a role in the distribution of vaccinations to date.

Taking Income into Account

In Figure 3 below, we’re redrawn our two-way plot, coloring the data points according to the median household income of each community. (We set the cutoff at $65,200, the median of the medians.) The higher income communities tend to be clustered at the left, where the cumulative incidence of COVID-19 infection is relatively low. But we can also see a vertical gradient, with lower income communities situated below higher income communities. This gradient implies that, for any level of cumulative COVID-19 infection, higher income still yields a higher vaccination rate.

Figure 3. The data points of Figure 2 have been redrawn with a two-color scheme according to the median income of each community. Additional source: American Community Survey 5-Year (2015–2019) Public Use Microdata Sample. Details of the crosswalk from Public Use Microdata Areas (PUMAs) to CSAs are given in a recent research paper, to appear in the Journal of Bioeconomics.

What about the elderly?

During most of the rollout to date, persons aged 65 years or more were classified as high priority for vaccination. So, in Figure 4 below,we carried out the same exercise as in Figure 3, recoloring our basic two-way plot to distinguish communities on the basis of the percentage of persons aged 65 or more.

Figure 4. The data points of Figure 2 have been redrawn with a two-color scheme according to the proportion of persons aged 65 years or more each community. Additional source: American Community Survey 5-Year (2015–2019) Public Use Microdata Sample. Details of the crosswalk from Public Use Microdata Areas (PUMAs) to CSAs are given in a recent research paper, to appear in the Journal of Bioeconomics.

Comparing Figures 3 and 4, we learn that the higher-income communities in Los Angeles County also tend to have a higher proportion of elderly persons. (The correlation coefficient was 0.78.) This strong correlation creates a nontrivial problem in the interpretation of our cross-community data. We had the impression from Figure 3 that higher income communities have fared better. But that may merely be a consequence of their higher proportion of persons 65+

An Attempted Statistical Solution

It is arguable that, at this juncture, we’ve reached a logical impasse. We might be able to pick out two communities which differ only by income and not by the proportion of elderly persons or the percent infected. But that sort of comparison would not necessarily tell us much about the equity of the vaccine rollout in the county as a whole.

One way out is to formulate a statistical model of each community’s vaccination rate in relation to our three factors: the cumulative proportion of confirmed COVID-19 cases; the median household income; and the percentage of persons aged 65 or more. We’ve done this in Table 1 below.

Binary Explanatory VariableCoefficientLower RangeUpper Range
Low Confirmed COVID-193.642.584.69
High Median Household Income 3.482.394.57
High Proportion Aged 65+1.900.811.95
Table 1. Linear regression of Percent Vaccinated against binary three variables: Low Confirmed COVID-19 (up to 8.9%); High Median Household Income (greater than $65,200); and High Proportion Aged 65+ (greater than 13.1%). The data set included the 204 CSAs in Figures 2–4. The regression was weighted by each community’s population. The two right-most columns show the bounds of the estimated 95% confidence intervals. The adjusted R-squared statistic was 0.60.

In Table 1, we treated each of the three explanatory variables as binary, that is, as either high or low. That allowed us to compare the magnitudes of each of the variables’ estimated effects. The table tells us that a low proportion of confirmed COVID-19 cases is associated with a 3.64 percentage point increase in a community’s vaccination rate. That’s very close to the estimated impact of a high median income. A high proportion of elderly persons, however, had an impact that was significantly lower.

For more on our statistical analysis, see the Technical Notes below.

A Deeper Look

Our results in Table 1 give us a reasonable idea of the magnitudes of the effects involved. A community with a low percentage of confirmed COVID-19 cases, with a high median income and a high proportion of elderly persons would have a vaccination rate 3.64 + 3.48 + 1.90 = 9.02 percent greater than a community at the other extreme. That’s the height of the bar in Figure 5 below, which shows each of our three estimated effects stacked atop one another. With a median vaccination rate of 13.1% to date, a combined increase of 9.02% is no small effect.

Figure 5. Effect of each binary explanatory variable on the percent vaccinated in a community. The estimated effects are taken from the coefficients in Table 1.

Our results confirm that the simple comparison of two maps in Figure 1 gives a distorted picture of the extent of inequality in the vaccine rollout to date in Los Angeles County. Still, when we took a deeper look at the data, we confirmed that a community’s median household income indeed had a significant positive impact on its vaccination rate.

How Inequality Works

Not a workday goes by without our witnessing first-hand the enormous inequities in access and outcomes that have been baked, seared, engraved and branded into our so-called system of healthcare. That our system of allocating lifesaving vaccinations is likewise unfair comes as no surprise.

But what is it about our system that makes the distribution of COVID-19 vaccines unfair?

Let’s put aside anecdotal evidence that persons with privilege have been able to jump the queue, and talk instead about systemic factors.

First, there is the hidden administration charge. When a patient receives a COVID-19 vaccine, he or she incurs no out-of-pocket expense. But the healthcare provider who administers the vaccine can charge the patient’s insurer for the service. Medicare, in particular, pays $28.39 for the administration of a single-dose vaccine and $16.94 for the first dose and $28.39 for the second dose of a two-dose vaccine. California Medicaid appears to be paying the same rate. This hidden administration charge creates a strong incentive in favor of vaccinating insured individuals. And to the extent that private insurers pay at higher rates, it creates an even stronger incentive to vaccinate privately insured individuals.

Second, there is the web-based sign-up. It obviously makes sense to set up an online appointment system. But the initial, crash-prone website was overloaded with page after page of required information to be filled out. In fact, one of the hidden functions of the website was to require registrants to upload images of the front and back of their insurance cards to make sure that providers could bill for the administration fee. This sign-up system heavily favored those with Internet access, especially those with laptops who could simultaneously open up ten browser windows – each with the required information automatically inserted by auto-fill – until they finally got in.

Third, there are the automobile-based vaccination sites. At the risk of overstating the obvious, lower-income people who rely on public transportation simply can’t wait in line for hours in their cars until it’s their turn.

Finally, there is the inexplicable but clear bias against community health centers. The current federal administration is launching an initiative to distribute vaccines directly to these community health centers. But for as yet unclear reasons, state- and county-level authorities don’t seem to have displayed the same enthusiasm. Yet community health centers are already strategically located to help the poorest people who would otherwise face serious barriers to access.

The Trusted Advisor Model

What’s more, community health centers, with their physicians, nurse practitioners and other providers standing ready to give personalized counseling, take full advantage of the trusted advisor model, which has been receiving increasing empirical support.

What if you heard that you should not get a mammogram right after your COVID-19 vaccine shot? Or what if you’re wondering whether the skin test for tuberculosis you had last week might interfere with your upcoming COVID-19 vaccine? Or what if you’re allergic to dust mites? Or what if you’re just plain suspicious of authoritative statements by experts who, despite appearing to speak English, don’t seem to be understandable?

Do you think a busy vaccinator at a mega-pod site will answer your questions? Or a busy pharmacist at one of the new sites to be rolled out in March 2021? With the best of intentions, they’ll suggest that you call your doctor for advice.

What doctor?

Technical Notes

Statistical models may help summarize complicated relationships between multiple variables, but they may also entail hidden, unverifiable assumptions. One such assumption here is that the contributions of our three factors are independent of each other. Still, alternative statistical models that we tested did not show any significant interactions between our explanatory factors. Another is that our binary distinction between high and low levels of each variable obscures more complicated nonlinear relationships. Our tests of models with continuous explanatory variables did not appear to support this possibility.

One such alternative test was a linear model with continuous explanatory variables, rather than the binary explanatory variables shown in Table 1. As shown in Table 2 below, the estimated slope coefficient for the cumulative percent of confirmed COVID-19 cases was -0.37 (95% CI, –0.51 to –0.23), which was less than half of the slope in the bivariate plot of Figure 2. That is, once we took all our explanatory factors in account, the impact of prior SARS-CoV-2 infection on vaccination rates was attenuated considerably. The estimated coefficient of a community’s median household income was 0.077 (95% CI, 0.046 to 0.108). That is, a $10,000 increase in a community’s median household income was associated with an increase of 0.77 percentage points in the vaccination rate.

Continuous Explanatory VariableCoefficientLower RangeUpper Range
Percent Confirmed COVID-19-0.37-0.51-0.23
Median Household Income ($000)+0.077+0.046+0.108
Percent Aged 65++0.45+0.26+0.64
Table 2. Linear regression of Percent Vaccinated against three continuous variables: Cumulative Percent Confirmed COVID-19 ; Median Household Income (in $thousands); and Percent of the Population Aged 65+. The data set included the 204 CSAs in Figures 2–4. The regression was weighted by each community’s population. The two right-most columns show the bounds of the estimated 95% confidence intervals. The adjusted R-squared statistic was 0.68.

Little Measurable Effect of Stay-at-Home Orders on Social Mobility: Los Angeles and Orange Counties

No significant long-term change in smartphone-tracking indicators can be detected since the Los Angeles Mayor’s order of December 2 or the California public health officer’s regional orders of December 3 and 6.

Figure 1 below shows the average time spent at home by smartphones located within the City of Los Angeles, within the remainder of Los Angeles County, and within Orange County before and after the issuance of city- and statewide stay-at-home orders at the beginning of December.

Figure 1. Mean Home Dwell Time Among Devices in the City of Los Angeles (Blue), the Remainder of Los Angeles County (Red), and Orange County (Gray), November 1 – December 22, 2020. The arrows show the December 2 data of Los Angeles Mayor Garcetti’s Stay-at-Home Order, as well as the December 3 and 6 dates of the state public health officer’s regional order and supplemental order.

Mayor Eric Garcetti’s December 2 order applied only to the City of Los Angeles, which is contained within Los Angeles County. The state public health officer’s order of December 3, followed by a supplemental order on December 6, applied to the entire Southern California region, including all of Orange and Los Angeles counties. (Municipalities within Los Angeles County but outside the City of Los Angeles include the cities of Beverly Hills, West Hollywood, Pasadena, Long Beach and Santa Monica, to name a few.)

Figure 2 below shows the corresponding trends in the percentage of devices staying completely at home.

Figure 2. Percent of Devices Staying Completely at Home Among All Candidate Devices, Including Those with No Activity., November 1 – December 22, 2020. As in Figure 1, the devices are classified according to their home location: the City of Los Angeles (blue), the remainder of Los Angeles County (red), and Orange County (gray). Also shown are the dates of recent city- and statewide stay-at-home orders.

Let’s look first at the trends outside the City of Los Angeles. Comparing Sunday November 29 with the following Sunday December 6 in Figure 1, we see that the December 2 and 3 orders may have been associated with about a half-hour increase in average stay-at-home time. But this short-term effect appears to have been dissipated during the subsequent two weeks. What’s more, Figure 2 suggests that if there was indeed a decrease in social mobility in Orange County or the rest of Los Angeles County, it started before the orders were issued.

Examining the blue trend lines for the City of Los Angeles in both Figures 1 and 2, we find it even harder to discern an impact. Again, if there was indeed a small short-term effect after the orders were issued, it appears to be gone two weeks later.

These two graphs do not show impressive drops in social mobility after the city- and statewide orders. There may have been some minor short-term reductions in out-of-home movement, but they have not been persistent.

Why So Little Impact?

The December 2-6 orders did more than require people to stay at home. Mayor Garcetti’s order, for example, prohibited “all public and private gatherings of any number of people from more than one household,” except for outdoor faith-based services and outdoor protests while wearing a mask. Acting State Public Health Officer Eric Pan’s order, for example, provided that “all retailers may operate indoors at no more than 20% capacity.” Perhaps these accompanying provisions have had a favorable effect in reducing coronavirus transmission.

Still, Garcetti’s order was entitled Targeted Safer at Home Order. Right off the bat, the order reads, “Subject only to the exceptions outlined in this Order, all persons living within the City of Los Angeles are hereby ordered to remain in their homes.” Pan’s order was entitled Regional Stay At Home Order. Paragraph #2 reads, “All individuals living in the Region shall stay home or at their place of residence except as necessary to conduct activities associated with the operation, maintenance, or usage of critical infrastructure.” Yet the evidence is that residents of Los Angeles and Orange counties did not stay at home any more or less than did before these stay-at-home orders.

The reflex interpretation of these findings is that they are simply one more manifestation of COVID-19 burnout and pandemic fatigue. Our own view is that there is now a serious problem of signal versus noise.

There have been so many orders and revised orders and supplemental orders that it has become nearly impossible to ascertain what restrictions on mobility are actually in effect. If we could have performed a focused survey of public awareness or an analysis of social media content, we’d want to know how many Southern Californians were even aware that the new stay-at-home orders were in effect.

Whatever the interpretation, these findings reinforce a critical conclusion.

Los Angeles is rapidly becoming the epicenter of the United States COVID-19 pandemic. Los Angeles County authorities need to start thinking beyond their standard public health toolbox.

What used to be right is wrong.

Technical Details

Our calculations are derived from the SafeGraph Social Distancing database, which follows the GPS pings of an anonymous panel of smartphones equipped with location-tracking software. Each mobile device is assigned a home or origin based on the census block group (CBG) where it commonly spends the night. All CBG codes in Orange County begin with the 6-character string 06059, while all CBG codes in Los Angeles County begin with the 6-character code 06037. For example, the CBG codes for the three census block groups within census tract 1011.10 in the City of Los Angeles would be: 060371011101, 060371011102, and 060371011103. We can thus use census tracts to distinguish CBGs belonging to the City of Los Angeles from other municipalities within Los Angeles County.

For each calendar day from November 1 through December 22 and each CBG in Los Angeles and Orange counties, we extracted a record showing the total number of devices (device_count), the number of devices staying completely at home (completely_home_device_count), and the mean in-home dwell time (mean_home_dwell_time). For each calendar date and each of the three major geographic subdivisions (City of Los Angeles, rest of Los Angeles County, Orange County), we computed the overall mean time staying at home (Figure 1) and the overall percentage of devices staying completely at home (Figure 2).