CHAPTER 11

COVID-19 Vaccine Politics and Policy in the United States: Implications for Democracy

Tanya Buhler Corbin and Courtney Page-Tan

At the onset of the COVID-19 pandemic, the US government, which should have been expected to take the lead in managing a national and international event under the guiding principles of federalism, instead delegated the primary responsibility for pandemic management to the states.1 As a result, state and local variations in COVID-19 policies were widespread,2 with partisanship politics playing a prominent role in pandemic management and policies.3

Uneven COVID-19 policies instituted across the nation resulted in sizable variations in health outcomes and death rates, with lower-income and minority communities suffering disproportionate numbers of cases and deaths early in the pandemic,4 despite early guidance from the Centers for Disease Control and Prevention (CDC) on reaching at-risk communities.5 In earlier work, we investigated the relationship between state and local policies and COVID-19 deaths and found that protective policies at the county level reduced deaths in communities ranking higher in social vulnerability.6 Although these protective policies (e.g., physical distancing and masking) were effective in reducing cases and deaths, the most effective prevention measure is vaccination.7

The efficacy and safety information about COVID-19 vaccines during the pandemic has been characterized by tumultuous politics, with partisanship driving policymaking and attitudes about the pandemic. Elected officials’ messaging to the public about vaccines differed between parties. Democrats more closely aligned their public messaging with CDC public health guidance than did Republicans.8 Republican elected officials who publicly supported vaccination increased their constituents’ likelihood of vaccination.9 Likewise, vaccine hesitancy was higher among people who relied on conservative news sources (e.g., Fox News) for information, which also correlated with beliefs in conspiracy theories.10

Vaccination rates were lower in Republican counties and correlated with support for President Donald Trump.11 While it is well established that messaging and partisanship affect the likelihood of getting COVID-19 vaccinations, it is unclear what role constituent preferences played in affecting the policies related to COVID-19 vaccinations. The political science axiom that elected officials prioritize reelection above all other considerations suggests that vaccine policies at the local level will align with constituent partisanship.12 Voters assign value to strong partisan ties, yielding distinct policies that reflect these partisan beliefs.13 Therefore, an investigation of local-level vaccination policies is important for understanding public health and pandemic management in a large, diverse democratic society. Thus, we hypothesize (1) that partisanship among constituents will align with policies when vaccine policymaking resides at the local level and (2) that vaccine policymaking is likely to reside with bureaucratic agencies composed of experts.

Vaccination Guidance: CDC, State, and Local Prioritization

The Food and Drug Administration approved the first US COVID-19 vaccine on December 11, 2020, for emergency use authorization, and the first vaccines were delivered on December 14, 2020.14 With initial demand exceeding supply, the CDC Advisory Committee on Immunization Practices developed guidance vaccination prioritization, recommending offering the vaccine to health care workers and residents in long-term facilities first (Phase 1a, issued December 1, 2020). Soon after, the advisory committee updated vaccine priorities: Vaccines should be offered to people ages seventy-five and older, and non–health care frontline essential workers (Phase 1b), including first responders, educators, workers in daycare, food and agriculture laborers, manufacturing workers, those in corrections, US Postal Service personnel, public transit workers, and grocery store clerks. During Phase 1c, eligibility should include people between sixty-five and seventy-four years, between sixteen and sixty-four years old with high-risk medical conditions, and other essential workers,15 including personnel in food service, transportation and logistics, construction, finance, information technology and communications, energy, media, the legal profession, public safety engineers, and wastewater.16 Phase 2 began when there were more vaccines available. In Phase 3, there were sufficient supplies for the entire population and thus a shift to unrestricted access.17

There were significant variations across state and local governments throughout the vaccination phases. Forty-five states followed the Phase 1a recommendations to prioritize health care workers and residents in long-term care, with some state deviations (e.g., Massachusetts vaccinated residents in temporary shelters and the incarcerated during Phase 1a), including more than twenty states providing further prioritization subcategories.18 About half of the states incorporated minorities or health equity considerations in priority populations. Some states made minorities a priority population group, while others used methods such as social vulnerability indices and health equity frameworks (e.g., Arizona, California, Georgia, Louisiana, New Jersey, Ohio, and Vermont).19 Regardless of prioritization protocols, state and local governments were operating on different timelines due to vaccine availability differences.

Data

Using collected data on COVID-19–related vaccine policies from the COVID Analysis and Mapping of Policies database,20 we analyzed local vaccine policies in thirty-one counties from California, Florida, Idaho, Kansas, Maryland, Nevada, Texas, and Virginia. Distribution of the vaccine policies by county are listed in Table 11.1. The majority of the vaccine policies in the database are from counties in California, Idaho, and Virginia, a limitation to our study because several local-level policies, albeit controversial at the state level in some states such as Florida and Texas, are absent. Authorizing authorities of COVID-19 vaccine policies by policymakers are listed in Table 11.2.

The database includes the following classifications for vaccine-related policies: (1) vaccine administration, distribution, and logistics; (2) vaccine cost, financing, and insurance; (3) vaccine exemption or alternative; (4) vaccine mandate; (5) vaccine prioritization; and (6) vaccine-related plan. Distribution of vaccine policies from the COVID AMP database are presented in Table 11.3.

Methods

Throughout the pandemic, vaccine policymaking became increasingly partisan. In 2021 while many Republicans supported the vaccine, the party also emphasized the right to choose, whereas Democrats moved to institute protective policies, including requiring vaccines for certain individuals, such as federal workers.21 We hypothesize that partisanship among constituents was an important predictor of vaccine policies designed to promote vaccine administration, prioritization, and uptake. To test for this, we use the National Neighborhood Data Archive Democratic partisanship index (DPI) (percent of votes cast in the past six years).22 We control for voter turnout (percent of eligible voters casting ballots), as reported by the National Neighborhood Data Archive and the US Census Bureau urban-rural classification,23 to control for this predictive factor. Previous work has shown lower rates of vaccine uptake in rural communities compared to urban communities,24 making this a potential confounder of our results. Finally, using data from the Community Resilience Indicators from the US Census Bureau, we control for the highest level of risk at the county level and the percentage of households with three or more risk factors. In the early stages of vaccine distribution, prioritization occurred in some historically marginalized communities,25 which could account for the adoption of some vaccine-related policies at the local level. Table 11.4 displays descriptive statistics for partisanship, voter turnout, percent urban, and percentage of households at high risk.

Hypothesis No. 1: When Vaccine Policymaking Resides at the Local Level, Partisanship Among Constituents Will Align with Policies

Results from the negative binomial regressions appear in Table 11.5. We found statistically significant and positive associations between partisanship and the total number of vaccine policies, administration policies, mandates, prioritization policies, and planning policies for COVID-19 vaccines. More specifically, for every unit increase of the DPI, holding all other variables constant, the difference in the logs of expected counts would be projected to increase by 10.05 (p < 0.01) for the total count of policies, an increase of 8.18 (p < 0.01) for the count of vaccine administration policies, an increase of 9.23 (p < 0.01) for the count of vaccine mandates, and an increase of 18.73 (p < 0.01) for vaccine prioritization policies, and an increase of 7.28 (p < 0.1) for vaccine planning policies. We excluded vaccine financing policies, as the models could not converge around the available data.

We also found statistically significant and positive associations between higher rates of voter turnout and vaccine prioritization (5.79, p < 0.05), statistically significant and negative associations between the percent urban and vaccine exemptions (−0.074, p < 0.01) and vaccine planning (−0.0497, p < 0.01), and statistically significant and negative associations between the percentage of households at the highest risk and total count of policies (−0.154, p < 0.05), count of vaccine administration policies (−0.253, p < 0.01), and count of vaccine prioritization policies (−0.218, p < 0.01). All models were statistically significant at the p < 0.001 level. The mean variance inflation factor for all models was 1.28, well below accepted thresholds.26

Findings from models 1–6 confirmed our first hypothesis. In jurisdictions with high DPI measures, such as Alexandria City, Virginia (75%), Arlington County, Virginia (75%), Contra Costa County, California (71%), Fairfax County, Virginia (63%), and Orange County, California (62%), policymakers implemented ten or more vaccine policies in 2020 and 2021. Similarly, we found evidence of counties with low DPI measures and high Republican partisanship index measures, implementing policies that reflect the highly politicized policymaking environment during the pandemic. For example, elected officials in Lander County, Nevada (78%), voted to ban vaccine passports proposed by county agencies and prohibited businesses from requiring proof of vaccination from their patrons or employees. This is similar to Florida Senate Bill 2006 aimed at “stemming the tide of local and state government overreach” by banning vaccine passports, other mandates, and business restrictions implemented at the local level to curb the spread of the virus.27

Hypothesis No. 2: Vaccine Policymaking Is Likely to Reside with Bureaucratic Agencies Composed of Experts

Results from our logistic regressions appear in Table 11.6. Our models indicate (Figure 11.1) that bureaucratic agencies were more likely to make policies on administration and prioritization compared to elected officials. Vaccine administration, distribution, and logistics include policies to coordinate the administration of the vaccine. For example, the district director of the Central District Health in Boise County, Idaho, issued a declaration that “Central District Health strongly encourages those in Group 1 and Subgroup 2.1 to seek a vaccine appointment including healthcare, school and childcare workers.”28 Vaccine prioritization includes policies to prioritize vaccine access among individuals and communities. These policies often granted early access to those with underlying conditions, age criterion, and occupational exposure risk. Some counties prioritized access by county ordinance to administer vaccines in underserved communities with referrals from Neighborhood Health or approved nonprofits.29

Holding all other variables at their mean, postestimation predictive margins reveal that the probability of making policies on vaccine administration, distribution, and logistics policies goes from 29 percent to 62 percent from elected officials to bureaucratic agencies (p < .01), and from 5 percent to 27 percent from elected officials to bureaucratic agencies for prioritization policymaking (p < .01).

However, on the contrary, our models also reveal that elected officials were more likely to make vaccine planning and vaccine mandate policies compared to bureaucratic agencies (see Figure 11.2). Vaccine-related plans include policies that establish testing procedures for providers and plans for coordinated information campaigns on vaccine administration. For example, the Board of Commissioners in Lander County, Nevada, required that all individuals receiving a vaccine must be provided information from the Vaccine Adverse Event Reporting System on any adverse side effects from receiving the vaccine.30 Vaccine mandates include policies that require individuals to be fully or partially vaccinated, including individuals such as government employees, patrons, and volunteers. For example, Boise, Idaho, mayor Lauren McLean issued an order requiring all newly hired city employees to be fully vaccinated two weeks before their start date to ensure “uninterrupted city services and programming, and to protect the health and safety of our community.”31

Again, holding all other variables at the mean, postestimation predictive margins reveal that the probability of implementing a vaccine mandate goes from 2 percent to 19 percent from bureaucratic agencies to elected officials (p < .01) and from 3 percent to 27 percent from bureaucratic agencies to elected officials for vaccine planning policies (p < .01). The variance inflation factor for the models was less than 1.62, which is well below the accepted threshold.32

Findings from models 7–11 provided mixed results for our second hypothesis. Our models revealed that bureaucratic agencies were more likely to implement vaccine administration, distribution, and logistics and prioritization, policies that were in tandem with the CDC’s manual on operational guidance.33 We found evidence of bureaucratic agencies implementing policies to comply with CDC guidance, particularly in Phase 1a. Examples include declarations made by the director of Central District Health in Boise, Idaho, authorizing prioritization of health care workers and seniors; vaccine events at public schools in Alexandria City, Virginia, ordered by the acting health director of the Alexandria Health Department; and Alpine County’s vaccine prioritization of individuals with chronic medical conditions such as cancer and chronic kidney disease.

Further, we found that policymaking by bureaucratic agencies potentially insulated politicized vaccine policies. While there is evidence of elected officials making policy counter to CDC guidance in counties with low DPI measures, we also found instances of policymaking by bureaucratic agencies in some counties with low DPI measures that were in line with federal guidelines. For example, the director of the Central District Health in the Idaho counties of Ada (44%), Boise (28%), Elmore (28%), and Valley (41%) issued a series of declarations prioritizing vaccines for health care, school, and childcare workers in January 2021.

Models 7–11 also revealed that elected officials were more likely than agency bureaucrats to enact policies ordering vaccine mandates and planning. Lauren McLean, the mayor of Boise City, Idaho, ordered that all new city employees be fully vaccinated. Similarly, Arlington County Public Schools and the county government mandated that all county employees be fully vaccinated. In April 2021, officials elected to the Board of Commissioners in Lander County, Nevada, implemented vaccine-related planning to ensure that all residents were informed of the risks and adverse events associated with receiving a COVID-19 vaccination. These two vaccine policy domains, along with vaccine exemptions and alternatives, were policymaking spaces subject to much politicization,34 as local-level officials and bureaucratic agencies were often at odds with state governments or electoral constituencies that opposed strict measures, leading to intense and ongoing legal battles. For example, in Houston, Texas, employees of the Houston Methodist Hospital unsuccessfully sued the hospital for an employee-wide vaccine mandate that was later dismissed by a federal judge in Texas.35 Similarly, in Florida, forty-three employees of Orange County Fire Rescue sued Orange County for its vaccine mandate requiring that city employees have at least one dose of the COVID-19 vaccine.36

Concluding Thoughts: Implications for Democracy

We concur with the policy literature that has demonstrated the importance of expertise in science policymaking. Likewise, we conclude that the responsibility for public health policies should rest with policy experts, with efforts made to depoliticize the policymaking process in public health decisions to the extent possible in a health crisis. Potential options include vesting the decision-making authority with public health experts rather than with elected or appointed officials such as committees and commissions. However, this raises the normative question of whether unelected bureaucrats are the preferred policymakers in a democracy. How do we reconcile democratic principles of popular sovereignty, which produced suboptimal outcomes for society at large, with the need to protect public health and safety? For public health experts to make policy decisions, they must have the trust of the public for effective implementation.

Additionally, this study reinforces the need for crises and disasters such as pandemics that transcend local and national boundaries to be managed at the national level. The Trump administration’s early abdication of federal responsibilities to underresourced state and local governments created the opportunity for hyperpartisanship at the local and state levels in the absence of federal policies. Vaccine hesitancy and resistance was exacerbated by conspiracy theories, social media misinformation, partisanship, and mistrust in government, and these attitudes affected the policies and the public health outcomes from COVID-19.

In the United States, where we traditionally would have expected to see a nationally coordinated and internationally cooperative vaccine policy plan for a global and nationwide crisis, instead the vaccine policies were a patchwork based on partisanship. While some variation is expected in a system of federalism, without a national-level policy foundation, state- and local-level policies were not always founded on the most current science. Proponents of federalism note that federalism can be a bastion for democracy, where local-level policymaking is more inclusive and understanding of local needs. However, more democracy doesn’t always lead to the best policy outcomes, nor does it ensure democratic outcomes even if the process is democratic. In this case, democracy contributed to uneven and unequal access to vaccines. In many states and localities, policies were based on partisanship over science. This dark side of federalism could have been mitigated by a strong national response and vaccine management. The Trump administration had no vaccine distribution plan; the Biden administration developed a plan in January 2021.

These results have implications for policymaking in democracies, where public health and the common good need to be balanced with democratic values, such as representation and freedom. Democracy can produce suboptimal public health policies and majority tyranny can reign, whereby policies are enacted that serve the majority and the powerful rather than the most vulnerable. In the case of COVID-19 the stakes were high, including uneven suffering, illness, and death. This study suggests that in future national and international transboundary crises and disasters, expertise must be bolstered and supported by elected officials to be effective.

Notes

1. 1.  Nolan McCaskill and Alice Miranda Ollstein, “Trump Administration Tells States to Step Up as Governors Plead for Aid,” Politico, April 5, 2020, https://www.politico.com/news/2020/04/05/white-house-trump-funding-states-coronavirus-165783; and Cynthia J. Bowling, Jonathan M. Fisk, and John C. Morris, “Seeking Patterns in Chaos: Transactional Federalism in the Trump Administration’s Response to the COVID-19 Pandemic,” American Review of Public Administration 50, no. 6–7 (2020): 512–18, https://doi.org/10.1177/0275074020941686.
2. 2.  Komla D. Dzigbede, Sarah Beth Gehl, and Katherine Willoughby, “Disaster Resiliency of US Local Governments: Insights to Strengthen Local Response and Recovery from the COVID-19 Pandemic,” Public Administration Review 80, no. 4 (2020): 634–43, https://doi.org/10.1111/puar.13249.
3. 3.  Donald F. Kettl, “States Divided: The Implications of American Federalism for COVID-19,” Public Administration Review 80, no. 4 (2020): 595–602, https://doi.org/10.1111/puar.13243; and Philip Rocco, Daniel Béland, and Alex Waddan, “Stuck in Neutral? Federalism, Policy Instruments, and Counter-Cyclical Responses to COVID-19 in the United States,” Policy and Society 39, no. 3 (2020): 458–77, https://doi.org/10.1080/14494035.2020.1783793.
4. 4.  Wyatt Koma et al., “Low-Income and Communities of Color at Higher Risk of Serious Illness if Infected with Coronavirus,” Kaiser Family Foundation, May 7, 2020, https://www.kff.org/coronavirus-covid-19/issue-brief/low-income-and-communities-of-color-at-higher-risk-of-serious-illness-if-infected-with-coronavirus.
5. 5.  Centers for Disease Control and Prevention, COVID-19 Vaccination Program Interim Operational Guidance Jurisdiction Operations, https://www.cdc.gov/vaccines/imz-managers/downloads/Covid-19-Vaccination-Program-Interim_Playbook.pdf.
6. 6.  Courtney Page-Tan and Tanya Buhler Corbin, “Protective Policies for All? An Analysis of COVID-19 Deaths and Protective Policies Among Low-, Medium-, and High-Vulnerability Groups,” Disasters 45, no. S1 (2021): S119–S145, https://doi.org/https://doi.org/10.1111/disa.12525.
7. 7.  “Prevention of SARS-CoV-2 Infection,” National Institutes of Health, last updated December 20, 2023, https://www.covid19treatmentguidelines.nih.gov/overview/prevention-of-sars-cov-2/.
8. 8.  Eden Engel-Rebitzer et al., “Partisan Differences in Legislators’ Discussion of Vaccination on Twitter During the COVID-19 Era: Natural Language Processing Analysis,” JMIR Infodemiology 2, no. 1 (2022), https://doi.org/10.2196/32372.
9. 9.  Sophia L. Pink et al., “Elite Party Cues Increase Vaccination Intentions Among Republicans,” Proceedings of the National Academy of Sciences 118, no. 32 (2021), https://doi.org/10.1073/pnas.2106559118.
10. 10.  Jeanette B. Ruiz and Robert A. Bell, “Predictors of Intention to Vaccinate Against COVID-19: Results of a Nationwide Survey,” Vaccine 39, no. 7 (2021): 1080–86, https://doi.org/10.1016/j.vaccine.2021.01.010; Daniel Romer and Kathleen Hall Jamieson, “Conspiracy Theories as Barriers to Controlling the Spread of COVID-19 in the US,” Social Science & Medicine 263 (October 2020), https://doi.org/10.1016/j.socscimed.2020.113356; Matthew Motta and Dominik Stecula, “The Influence of Partisan Media in the Face of Global Pandemic: How News Media Influenced COVID-19 Vaccine Hesitancy,” preprint, SocArXiv, September 16, 2021, https://doi.org/10.31235/osf.io/xj4nq; and Matteo Pinna, Léo Picard, and Christoph Goessmann, “Cable News and COVID-19 Vaccine Uptake,” Scientific Reports 12, no. 1 (2022), https://doi.org/10.1038/s41598-022-20350-0.
11. 11.  Don Albrecht, “Vaccination, Politics and COVID-19 Impacts,” BMC Public Health 22 (2022), https://doi.org/10.1186/s12889-021-12432-x; and Yongjin Choi and Ashley M. Fox, “Mistrust in Public Health Institutions Is a Stronger Predictor of Vaccine Hesitancy and Uptake Than Trust in Trump,” Social Science & Medicine 314 (December 2022), https://doi.org/10.1016/j.socscimed.2022.115440.
12. 12.  David R. Mayhew, “Congressional Elections: The Case of the Vanishing Marginals,” Polity 6, no. 3 (1974), https://www.journals.uchicago.edu/doi/10.2307/3233931.
13. 13.  Robert J. Franzese Jr., “Electoral and Partisan Cycles in Economic Policies and Outcomes,” Annual Review of Political Science 5 (2002): 369–421, https://doi.org/10.1146/annurev.polisci.5.112801.080924.
14. 14.  Centers for Disease Control and Prevention, “COVID-19 Vaccination Program Interim Operational Guidance Jurisdiction Operations,” October 29, 2020, https://www.cdc.gov/vaccines/imz-managers/downloads/COVID-19-Vaccination-Program-Interim_Playbook.pdf.
15. 15.  Kathleen Dooling et al., “The Advisory Committee on Immunization Practices’ Interim Recommendation for Allocating Initial Supplies of COVID-19 Vaccine—United States, 2020,” Morbidity and Mortality Weekly Report 69, no. 49 (2020): 1857–59, http://dx.doi.org/10.15585/mmwr.mm6949e1.
16. 16.  Dena Bunis, “Phase 1a, 1b or 1c? Understanding Coronavirus Vaccine Priority Groups,” AARP, December 21, 2020, https://www.aarp.org/health/conditions-treatments/info-2020/covid-vaccine-priority-groups.html.
17. 17.  Centers for Disease Control and Prevention, COVID-19 Vaccination Program Interim Operational Guidance Jurisdiction Operations.
18. 18.  Jennifer Kates, Josh Michaud, and Jennifer Tolbert, “How Are States Prioritizing Who Will Get the COVID-19 Vaccine First,” Kaiser Family Foundation, December 14, 2020, https://www.kff.org/policy-watch/how-are-states-prioritizing-who-will-get-the-Covid-19-vaccine-first/.
19. 19.  Josh Michaud et al., “States Are Getting Ready to Distribute COVID-19 Vaccines. What Do Their Plans Tell Us So Far,” Kaiser Family Foundation, November 18, 2020, https://www.kff.org/coronavirus-Covid-19/issue-brief/states-are-getting-ready-to-distribute-Covid-19-vaccines-what-do-their-plans-tell-us-so-far/.
20. 20.  Contributors to the database include the Georgetown University Center for Global Health Science and Security, Talus Analytics, the Nuclear Threat Initiative, and COVID Act Now.
21. 21.  Ronald Brownstein, “‘Everybody I Know Is Pissed Off’: New Polling Data Paint a More Complicated Picture About the Next Phase of the Pandemic,” The Atlantic, August 12, 2021, https://www.theatlantic.com/politics/archive/2021/08/vaccine-mandates-republicans-democrats/619735/.
22. 22.  Megan Chenoweth et al., “National Neighborhood Data Archive (NaNDA): Voter Registration, Turnout, and Partisanship by County, United States, 2004–2018,” University of Wisconsin–Madison, https://search.library.wisc.edu/catalog/9913613633902121.
23. 23.  “Urban and Rural,” US Census Bureau, last updated December 16, 2024, https://www.census.gov/programs-surveys/geography/guidance/geo-areas/urban-rural.html.
24. 24.  Bhavini Patel Murthy et al., “Disparities in COVID-19 Vaccination Coverage Between Urban and Rural Counties—United States, December 14, 2020–April 10, 2021,” Morbidity and Mortality Weekly Report 70, no. 20 (2021): 335–40, http://dx.doi.org/10.15585/mmwr.mm7109a2.
25. 25.  Erik T. Rosenstrom et al., “Can Vaccine Prioritization Reduce Disparities in COVID-19 Burden for Historically Marginalized Populations?,” PNAS Nexus 1, no. 1 (2022), https://doi.org/10.1093/pnasnexus/pgab004.
26. 26.  Roman Salmerón Gómez et al., “Collinearity Diagnostic Applied in Ridge Estimation Through the Variance Inflation Factor,” Journal of Applied Statistics 43, no. 10 (2016): 1831–49, https://doi.org/10.1080/02664763.2015.1120712.
27. 27.  Governor of Florida, “Governor Ron DeSantis Signs Landmark Legislation to Ban Vaccine Passports and Stem Government Overreach,” news release, May 3, 2021, https://www.flgov.com/2021/05/03/governor-ron-desantis-signs-landmark-legislation-to-ban-vaccine-passports-and-stem-government-overreach/.
28. 28.  Central District Health, “Those Qualified to Receive COVID-19 Vaccine in Group 1 and Subgroup 2.1 Encouraged to Seek Vaccine as Soon as Possible; Avoid Double Booking,” news release, January 26, 2021, https://cdh.idaho.gov/those-qualified-to-receive-covid-19-vaccine-in-group-1-and-subgroup-2-1-encouraged-to-seek-vaccine-as-soon-as-possible-avoid-double-booking/.
29. 29.  County of Arlington, Virginia, “Vaccine Equity Partnership Focuses on Underserved Communities,” news release, March 25, 2021, https://www.arlingtonva.us/About-Arlington/Newsroom/Articles/2021/Vaccine-Equity-Partnership-Focuses-on-Underserved-Communities.
30. 30.  “Re: Declaration of Emergency Directive 041,” Lander County, Nevada, Board of Lander County Commissioners, April 8, 2021.
31. 31.  City of Boise, “City of Boise to Ensure Services and Protect Community with Vaccine Requirement for New Hires. Mayor’s Office Media Relations,” news release, December 16, 2021, https://www.cityofboise.org/news/mayor/2021/december/city-of-boise-to-ensure-services-and-protect-community-with-vaccine-requirement-for-new-hires/.
32. 32.  Roman Salmerón Gómez et al., “Collinearity Diagnostic Applied in Ridge Estimation Through the Variance Inflation Factor,” Journal of Applied Statistics 43, no. 10 (2016): 1831–49, https://doi.org/10.1080/02664763.2015.1120712.
33. 33.  Centers for Disease Control and Prevention, COVID-19 Vaccination Program Interim Operational Guidance Jurisdiction Operations.
34. 34.  Toby Bolsen and Risa Palm, “Politicization and COVID-19 Vaccine Resistance in the US,” Progress in Molecular Biology and Translational Science 188, no. 1 (2022): 81–100, https://doi.org/10.1016/bs.pmbts.2021.10.002; and Alana Wise, “The Political Fight over Vaccine Mandates Deepens Despite Their Effectiveness,” NPR, October 17, 2021, https://www.npr.org/2021/10/17/1046598351/the-political-fight-over-vaccine-mandates-deepens-despite-their-effectiveness.
35. 35.  Sheila Kaplan, “A Judge Dismisses Houston Hospital Workers’ Lawsuit About Vaccine Mandates,” New York Times, June 13, 2021, https://www.nytimes.com/2021/06/13/health/houston-hospital-vaccine-mandate-lawsuit.html.
36. 36.  Shannon Butler, “Dozens of Orange County Firefighters File Lawsuit over Vaccine Mandate,” WFTV9, October 1, 2021, https://www.wftv.com/news/local/dozens-orange-county-firefighters-file-lawsuit-over-vaccine-mandate/OEOUDJLQFNAFPFES7FXEX4E5O4/.