Rurality Versus Readiness: The Relationship Between State-Level Connection and Capacity Variables and the Management of Medical Stockpiles for a Public Health Emergency

Abstract

This study investigates what factors contributed to the score a state received for managing its medical countermeasures stockpile pre-COVID-19. It is particularly interested in the relationship between a state’s level of rural population and its countermeasure management capacity. A fixed-effects regression analysis was run using data from 2016 to 2019 to test for a relationship between the percentage of rural population in a state and the states’ countermeasures management score, while controlling for other relevant social, economic, and political variables such as level of social associations, the segregation index, and the level of income inequality. Rurality and physicians per capita proved to be significant and negative. A subsequent analysis found that states with higher levels of rural populations have lower levels of COVID-19 vaccinations, even accounting for effective countermeasure management. This points to rural states having challenges in regard to medical countermeasures that cannot be completely solved with technocratic solutions.

Keywords

social capital rural states housing segregation income inequality medical countermeasures public health emergency preparedness pandemic response

Introduction

The emergence of a novel coronavirus at the dawning of a new decade indicates that infectious diseases will continue to be an important agenda item in regard to both national security and public health. Although these policy areas are certainly interconnected, they can sometimes be treated as separate issues. The Strategic National Stockpile, a federally funded reserve of medical resources, including vaccines and countermeasures, was established in the 1990s in anticipation of potential bioterrorism events (Nicholson et al. 2016). States and local governments are assessed by the federal government in regard to their ability to effectively manage these resources (CDC 2021). At the same time, the ability to immunize as much of the population as possible against influenza and other vaccine-preventable illnesses is a metric by which the public health establishment is evaluated. The media has documented the manner in which vaccination rates have fallen in some parts of the United States, due to mobilization of anti-vaccination advocates (Brumfiel 2021) coupled with distrust of government (Enten 2021, July 10) and distrust of the pharmaceutical industry (Grimes 2021).

Medical countermeasures are defined by the Food and Drug Administration as “FDA-regulated products (biologics, drugs, devices) that may be used in the event of a potential public health emergency” (FDA 2021). Countermeasures can be used to “diagnose, prevent, protect from, or treat conditions” associated with a range of threats, including emerging infectious diseases (FDA 2021). So at a time when the federal government, as well as state and local partners, are making strides in regard to the professionalization of countermeasures management prior to and during an emergency, a growing percentage of the public may be disinclined to accept such resources from the government. Both of these facets of government performance in regard to medical countermeasures are captured by National Health Security Preparedness Index. The year 2020 brought not only the emergence of COVID-19 but also the beginning of the largest vaccination effort in the history of the U.S. When considering the success of this effort and the potential for improvement in the future, it is useful to look at what factors have affected state performance in regard to the management of medical countermeasures in the past.

This study will focus on the NHSPI data regarding the management of medical countermeasures as the dependent variable, with most of the independent variables derived from the County Health Rankings data set. Of particular concern for this paper are variables measuring the percentage of rural area in the state, as well as the level of social associations, residential segregation, and income inequality in the state. The model also includes variables with which to measure health system capacity, education level, quality of health outcomes, overall economic strength, and overall state administrative capacity.

Rural Areas and Public Health Challenges

Rural areas confront technical, financial, and social-behavioral barriers to policy change, as Bagchi (2020) recently outlined in the context of expanding telehealth services. Leider et al. (2020) noted the disparities in regard to premature mortality between urban and rural areas. Mortalities decreased less in low-income rural areas than in comparable urban areas since 2005. Higher income urban areas have seen a decline in premature mortality since 2005, while higher income rural areas have seen increases in premature mortality. Melvin et al. (2020) note that states with a higher percentage of rural areas also tend to be less healthy. As noted by Melvin et al. rural areas have a disproportionate level of “older, poor, and underinsured residents” as well as “high rates of chronic illness” (p. 1). Cosby et al. (2019) describe a “rural mortality penalty” which has increased over the last three decades. The authors found the urban–rural disparity to be “persistent, growing, and large” from the 1970s to 2016 (p. 155). Santibañez et al. (2019) note that between 2004 and 2016 infectious disease threats that are a particular threat to rural areas, including those borne by mosquitoes, fleas, and ticks, tripled in the United States. In addition, rural areas must confront public health challenges from agriculture and livestock, forests, and outdoor recreation. The fact that rural populations are spread over such large areas also creates challenges for carrying out public health activities.

This is attributed in part to slower economic recovery from the Great Recession in rural areas compared to urban areas. Rural areas have significantly higher numbers of premature death due to the national five leading causes, including cancer and heart disease (Leider et al. 2020). White, middle class, and less educated rural residents have also been heavily impacted by the opioid crisis. Rural residents are also at higher risk of death from obesity and smoking (Cosby et al. 2019). Rural areas additionally seem to be at a disadvantage in regard to investments related to the social determinants of health, including housing, education, and access to healthy foods. Cosby et al. (2019) found education, race, and rurality to be strong predictors of mortality, with mortality being a particular concern in high poverty and largely rural communities. Melvin et al. (2020) noted that rural states like Mississippi have higher rates of poverty. Leider et al. also found disparities in regard to intergovernmental transfers (such as housing grants from the federal government), with rural areas again at a disadvantage despite the fact that intergovernmental transfers may make up a large proportion of rural government budgets. This disparity in particular has significant negative implications for public health departments and public health more generally in rural areas.

Historically, public health departments in rural areas have played a vital role in maintaining sustainable clinical care. Since the 1970s the federal government has called for public health departments to put less emphasis on clinical care and focus on matters of population-based health. May local public health departments have transitioned to a population health focus, although still providing some health services. Rural health departments tend to have smaller staffs while covering broader geographic areas, which puts them at a disadvantage in regard to providing services like population-based surveillance and prevention (Leider et al. 2020).

Some rural areas are not covered by a local health department, but rather by regional or state health departments. Smaller local health departments can have more challenges in regard to recruiting for positions like nurses and executives. An important aspect of this is pay, as nurses working for smaller health departments in rural areas are at a significant disadvantage compared to those working for private sector providers (Leider et al. 2020). As noted by Melvin et al. (2020), only 9 percent of physicians and 16 percent of registered nurses practice in rural areas. Melvin et al. (2020) do note that it is important to remember that hospitals are often financially disadvantaged and rely on high-profit elective services which have been curtailed during the COVID-19 pandemic. Rural hospitals tend to be smaller, have fewer intensive care beds, and fewer board-certified physicians. Rural residents often have to travel farther to receive care due in part to hospital closures and are further disadvantaged by the fact that 59 percent of uninsured rural residents live in states that opted not to expand Medicaid as part of the Affordable Care Act. Community health centers have long been a vital resource for rural areas, but since the start of the COVID-19 pandemic, there has been a drop in patient visits and many cases of staff being unable to work (Melvin et al. 2020).

While residents of rural areas tend to have lower quality of health and economic resources, rural areas do have some advantages to leverage. Among these are “social connectedness” and “self-reliance” (Leider et al. 2020, 1287). Stronger rural health departments can help communities take fuller advantage of these resources. Leider et al. (2020) provide five recommendations for improving rural public health, including the standardization of what it means to be “rural” in order to facilitate better eligibility for federal assistance. They also call for improving advocacy for rural public health by better identifying and defining a rural public health constituency and creating a preventative public health strategy for rural areas. Increasing investment in rural public health, reducing the stigma attached to receiving clinical care from local health departments in rural areas, and recognizing the individual needs of communities are also recommended.

Acceptance of public health policies varies significantly between urban and rural areas. For instance, rural areas are less likely to participate in tobacco control efforts, sex education, and HPV vaccination and to adopt Medicaid expansion as part of the Affordable Care Act. Research has also noted that rural public health departments have also failed to acknowledge the effects of racism on health inequities. Current policies to address rural health challenges are not dissimilar from approaches advocated in the early twentieth century (Santibañez et al. 2019).

Galarce, Minsky, and Viswanath (2011) found that urban residents were more likely to consider H1N1 influenza vaccine unsafe than those living in rural areas. In a brief for the National Rural Health Association, Gale et al. (2020, 3) noted that normal childhood immunization and influenza vaccine uptake tend to be lower in rural areas. Along with less access to healthcare services, the authors attribute to this “cultural skepticism,” “lower health literacy rates,” “lower perceived risk,” “travel barriers,” and mistrust of government among African American and American Indiana populations. Kirzinger, Munana, and Brodie (2021) noted that party affiliation is strongly associated with a person’s intention to take the COVID-19 vaccine, with Republicans much less likely than independents or Democrats to indicate a willingness to do so. In regard to vaccine uptake in rural area, studies have found a number of factors associated with getting children vaccinated for the human papillomavirus (HPV). Lai et al. (2016) found that younger parents, older teenagers, and previous willingness to vaccinate were associated with uptake. Fazekas, Brewer, and Smith (2008) found that most respondents to a survey were willing to vaccinate an adolescent daughter, with greater intention to vaccinate associated with knowing more about HPV, believing that there is a higher likelihood of getting HPV and suffering ill effects from cancer, and trusting in the effectiveness of the vaccine. Women surveyed indicated being less likely to vaccinate themselves than their children. Those women who indicated they were unlikely to vaccinate tend to be older and African American.

Kepka, Ulrich, and Coronado (2012) concluded that in order to get adolescent Hispanic women in rural areas fully vaccinated, vaccine education programs need to target their messaging toward both parents. Communication strategy also factored heavily into a study by Cates et al. (2009), which concluded that to improve uptake of HPV vaccine in Southern, rural states messaging needs to be specially tailored for the black parents of adolescent women. This was due to black respondents having less overall awareness of HPV, being less likely to view it as a serious threat to their child’s health, and believing that their daughter should be 17 years old to get the vaccine. There was also disparity between black and white respondents in regard to intention to vaccinate, although there were mixed results in regard to whether it was statistically significant.

More currently, with the COVID-19 vaccines, Cirruzzo (2020) notes evidence that skepticism remains high for that countermeasure among the black and Hispanic populations. Vaccine skepticism in rural areas continues to be an issue with COVID-19, with 35 percent of rural residents indicating that they probably or definitely would not get the vaccine, compared to a 27 percent average nationwide (Cirruzzo 2020). Kirzinger, Munana, and Brodie (2021) reported that 31 percent of rural residents indicated that they would definitely get the COVID-19 vaccine, lower than in urban and suburban areas (42 percent and 43 percent, respectively). This appears to be tied to rural residents having less concern about themselves or someone they know getting the virus and a belief that the extent of the threat posed by the virus has been exaggerated. Rural residents are also more likely to regard taking a vaccine as a personal decision as opposed to a societal obligation.

Rural Areas, Administrative Capacity, and Countermeasure Management

In addition to challenges facing rural populations more generally, research has also documented the particular challenges of rural public health departments as well. Eighty percent of local public health departments employ fewer than 50 people, creating challenges to responding effectively to emerging infectious diseases. In the study by Santibañez et al. (2019), all 14 rural districts studied reported staffing as a performance challenge. Beatty et al. (2018) noted that inadequate staffing presented a barrier to rural health departments receiving accreditation. Accreditation has been found to be an effective tool for generating improvement and change in public health departments, which has particular importance for rural areas (Beatty et al. 2016 as cited in Beatty et al. 2018). Lack of public health accreditation in rural departments also speaks to the larger problem of evidence-based practices being seen as too time-consuming and resource-intensive. Helpap (2019) investigated the willingness of rural areas to build up their “management capacity by hiring a professional administrator” (14). Helpap found that the choice to do so was associated with communities being larger, more Democratic, educated, and having greater wealth, characteristics that are lacking in rural communities most at risk from public health emergencies.

Lack of funding for public health at the local level, as well as decreasing funding from state and federal authorities, also complicate rural public health response. Local public health departments cooperating and sharing resources may be an effective strategy for addressing resource and capacity challenges. Historically and currently, local public health departments have had productive collaborations with churches, food pantries, and libraries. One area in which rural health departments have invested funding is training local public health nurses, who are viewed as not only improving health outcomes but also building social and cultural capital as well as mitigating racial and ethnic disparities (Ziller and Milkowski 2020).

Bekemeier et al. (2019) note the difficulties that public health officials in rural areas can have accessing and using data to improve decision-making regarding the health status of people in their areas. Such difficulties were exacerbated by inconsistent data quality, limited staff with expertise in data analysis, and diversity among rural areas. Due to privacy concerns, most publicly available data sets do not include a variable differentiating between rural and urban areas which makes it harder to distinguish the particular concerns of rural areas. Rural data sets may also be too small to yield statistically reliable results. The definition of rural also can vary significantly between federal data sets (Ziller and Milkowski 2020).

Other Variables Related to Social Connectedness

Putnam (1993) argued that higher levels of social capital and generalized trust encourage voluntary cooperation to resolve collective action problems (as cited in Rönnerstrand 2016). Research by Chuang et al. (2015) found evidence of associations between different types of social capital and willingness to vaccinate, with factors like government credibility and interpersonal networks showing evidence of influence as well. Lower levels of vacant housing, considered evidence of more social capital, have been found to be related to reduced infant mortality, while higher rates of homicide (considered evidence of lower levels of social capital) were related to higher infant mortality (White, Horton, and Simpson 2017).

Income inequality can lower the level of social capital in a state, which can have negative public health consequences. However, social capital can also mitigate some effects of income inequality. SNAP participants with medium or high levels of community social capital have been found to be less likely to be food insecure (Dean et al. 2014), while Recker and Moore (2016) found that states with higher levels of social capital experienced lower suicide rates.

Nagaoka, Fujiwara, and Ito (2012) found that higher levels of income inequality were associated with lower rates of measles vaccine coverage. Weaver and Rivello (2007) did not find an association between mortality and three different measures of income inequality; however, they do not rule out the possibility that other measures of income inequality could show significance. Hutchins et al. (2009) found that pandemic interventions needed to exhibit cultural competency in regard to minority populations, strengthening social assistance programs and minimizing the economic harm from interventions from social distancing and quarantines, and a communication plan that takes into account various languages and cultures.

It should be noted that all academic literature speaks to social capital being a universal good. Hawes (2017) found that social capital contributes to social empathy in homogeneous contexts, but in more diverse populations contributed to stricter social controls as evidenced by increased incarceration levels for African Americans. Hawes and McCrea (2018) continued the investigation of this link between the effects of social capital and population homogeneity, this time by focusing on immigration levels. They did by comparing state cash benefit levels after enactment of welfare reform in 1996 between low immigration and high immigration states. They found that cash benefit levels were higher in low immigration states, while those with higher levels of immigration had less generous cash benefits. Along a similar line, Zhu (2017) found that higher levels of social capital at the state level helped to reduce inequities in access to health care, but those effects were diminished or erased if states had a high degree of racial diversity.

Racial disparities have also been found to be associated with limited emergency preparedness. Bethel, Burke, and Britt (2013) researched the level of emergency preparedness for minority groups and found that while non-whites were less likely to have a month’s supply of prescription medicine on hand, black and English-speaking Hispanic families were more likely to have evacuation plans. The increased preparedness among these groups was attributed to racial inequities illuminated by Hurricane Katrina. Although distrust of government created by such examples encouraged higher levels of preparedness in some areas, Hutchins et al. (2009) and Bleser, Miranda, and Jean-Jacques (2016) found that distrust contributed to lower rates among non-Hispanic Blacks. In addition to distrust, Hutchins et al. (2009) notes that minority groups experience barriers to vaccination such as lower income and difficulty procuring personal identification. In a study examining racial disparities in hospitalizations during the H1N1 pandemic, DeBruin, Liaschenko, and Marshall (2012) noted that structural inequalities like access to care, underlying health conditions and care-seeking or self-care behaviors were blamed by the Centers for Disease Control and Prevention.

Segmentation of housing markets, among other factors, can contribute to unequal access to economic opportunities in ways that affect community environmental health (Alshutler et al. 1999; Conley 1999; Kain 1992; Keister 2000; Oliver and Shapiro 1995; Preston and McLafferty 1999, as cited in Morello-Frosch and Lopez, 2006). Aspects of social inequality, such as residential segregation, can limit options for addressing environmental and health problems by limiting access to health insurance and hindering engagement with elected officials (Morello-Frosch and Lopez, 2006). Uslaner (2009) argued that residential segregation leads to lower levels of trust, while integrated and diverse neighborhoods can lead to higher levels of trust if diverse networks are present.

Relevant Background on the Strategic National Stockpile

The first enabling statute for the Strategic National Stockpile, a system of government repositories of medical countermeasures, was the Public Health Security and Bioterrorism Preparedness Act of 2002. (Nicholson et al. 2016). The storage of SNS resources is governed by local, state, and federal policy (Nicholson et al. 2016). Working with SNS resources involves logistical burdens, including time, personnel, security, and personnel accreditation. There is a high rate of turnover among SNS program staff (Nicholson et al. 2016). During responses, federal authorities deliver material from their inventory to the states, who then disseminate it to local communities who must establish points of distribution. State systems vary significantly. The differences in distribution systems between U.S. states can be seen in a comparison of Texas and Michigan. In Texas, there are 18 warehouses for the storage of stockpile resources (run in partnership with private sector entities) throughout the state and the state is responsible for shipping the resources to open/closed PODs and local jurisdictions throughout the state. In Michigan, on the other hand, one warehouse handles receipt, storage and staging for the state, and when resources come into that warehouse they are distributed to 45 distribution nodes and hospitals, with 225 to 245 different shipment locations across the state. (Nicholson et al. 2016, 13). Sometimes aspects of state systems are emulated by other states. The need for improved information systems spurred states to emulate the Communicable Disease Reporting System developed in New Jersey to make it available to local health departments and laboratories (Ziskin and Harris 2007).

Federal guidelines are provided, but gaps must be filled in to address local issues (Whitworth 2006). Local governments are responsible for breaking down larger shipments sent by the federal government to the states (Havlak, Gorman, and Adams 2002). Local governments are expected to start dispensing materials from the stockpile 48 h after the governor’s request. It is recommended that state and local planners focus on preparing to dispense in the community with the largest population center and establish primary and secondary POD’s (Whitworth 2006). Research has indicated that local providers should be prepared to meet the pharmaceutical needs of 100 patients for three days due to the possibility of resupply delays from the SNS (Hanfling 2006).

Theoretical Implications for Public Health Emergency Response

While there has been considerable focus in the academic literature regarding what variables contribute to vaccine uptake, less attention has been paid to investigating variables influencing the quality of management when it comes to managing medical countermeasures. This research seeks to address that gap in the literature by investigating what relationship, if any, exists, between a state’s rurality and associated measures discussed above and its management of medical countermeasures. The theory at issue is that states with higher levels of rurality will be less effective in regard to the management of medical countermeasures due to weaknesses in an administrative capacity, but this can be mitigated by other state-level variables.

Statement of Hypotheses

Based on the relationships between these variables discussed in the literature, this study hypothesizes Greater socioeconomic inequality, segregation, and percentage of rural population will be associated with a wider gap between these variables. A higher level of social associations in a state will be positively associated with the management of countermeasures. Although there is evidence in the literature that a higher percentage of rural population could be positively associated with countermeasure management, this study hypothesizes that the relationship is negative because public health departments in more urban areas have a higher probability of accreditation and thus have better management capacity:

H1: A higher level of rural population in a state will be negatively related to the state’s countermeasure score.

H0: There is no relationship between rural population and the state’s countermeasure score.

H2: A higher level of social associations in a state will be positively related to the state’s countermeasure score.

H0: There is no relationship between social associations and a state’s countermeasure score.

H3: A higher level of residential segregation in a state will be negatively related to the state’s countermeasure score.

H0: There is no relationship between residential segregation and a state’s countermeasure score.

H4: A higher level of income inequality in a state will be negatively related to the state’s countermeasure score.

H0: There is no relationship between income inequality and a state’s countermeasure score.

Data and Methodology

The dependent variable for this study is the Medical Material Management Distribution Dispensing score from the National Health Security Preparedness Index. The NHSPI defines this subdomain as: “The ability to acquire, maintain (e.g., cold chain storage or other storage protocol), transport, distribute, and track medical materiel (e.g., pharmaceuticals, gloves, masks, and ventilators) before and during an incident and recover and account for unused medical materiel after an incident. This capability includes managing the research, development, and procurement of medical countermeasures in addition to the management and distribution of medical countermeasures. This is an index measure of three data points: the state has a written countermeasure management plan that includes elements from the Strategic National Stockpile, the number of pharmacists per 100,000 population in the state, and the percentage of hospitals in the state participating in a group purchasing arrangement” (National Health Security Preparedness Index 2020).¹ It should be noted that the primary reason for examining this issue of countermeasure management at the state level is because reliable data is much more readily available at that level of government than the county or city levels. This particular measure is useful as the most reliable measure available with which to compare countermeasure management across states and across years.

The primary independent variable is the percentage of a state’s population that lives in an area of the state designated as rural, derived from the County Health Rankings database. Another independent variable is the number of social associations in a state from the County Health Rankings database, which is a proxy measure of social capital. This is a measure of the number of active religious, civic, and social organizations active within a state per 10,000 population. Although using different data and measures for social associations, Kwak, Shah, and Lance Holbert (2004) found that participation in social associations including informal socializing, attending public events, and attending religious services were related to participation in community activities, thus generating social capital. It is the connection of this study that the County Health Rankings measure for social associations can similarly be used to measure social connectedness and therefore community social capital.

To control for other variables that could affect the relationship between social associations and countermeasure management at the state level, we include an index measure of residential segregation of black and white residents and the GINI index score as a measure of income inequality. (County Health Rankings 2020). Both the NHSPI data and the County Health Rankings data are projects of the Robert Wood Johnson Foundation. Panel data for the years 2016–2019 was collected for all 50 states. Washington, D.C. was excluded from the analysis as an outlier in regard to the percentage of rural populations and social associations. Descriptive statistics for the variables are provided in Table 1.

Table 1.

Descriptive Statistics of Independent/Dependent Variables.

Variable	Obs	Mean	S.D.	Min.	Max.
Rural%	200	26.521	14.511	5	61.3
Social associations	200	10.533	2.822	3.4	17
Income ratio	200	4.695	0.418	3.8	5.7
Segregation Index	200	61.135	8.671	41	78
Physicians per capita	200	76.915	12.774	53	113
Some college	200	64.998	4.748	53.3	75
Infant mortality	200	6.295	1.235	4	10
Household income	200	56,979.11	9,586.31	39,738	80,711
State employment	200	536.338	81.174	383	882.8
Management	200	5.0315	1.254	1	8

Table 2.

States with Percentage of Rural Population Below/Above Average.

Low Rural Pop. (%)	High Rural Pop. (%)
California	Minnesota
New Jersey	Louisiana
Nevada	Indiana
Massachusetts	Idaho
Hawaii	Missouri
Florida	Nebraska
Rhode Island	Wisconsin
Utah	Tennessee
Arizona	South Carolina
Illinois	Oklahoma
Connecticut	North Carolina
New York	Wyoming
Maryland	Iowa
Colorado	New Hampshire
Texas	North Dakota
Washington	Alabama
Delaware	Kentucky
Oregon	Arkansas
Pennsylvania	South Dakota
Ohio	Alaska
New Mexico	Montana
Virginia	Mississippi
Georgia	West Virginia
Michigan	Vermont
Kansas	Maine

In addition to these primary variables of interest, other variables have been included to account for other issues that could affect the level of countermeasure management in a state. The strength of a state’s health care infrastructure is measured with the number of primary care physicians per capita. The level of education in a state is measured with the percentage of a state’s population that has some college education. The general quality of health in a state is measured with the infant mortality rate in the state. The economic strength of a state is measured by the median household income. All of these variables are derived from the County Health Rankings database (County Health Rankings 2020). State government administrative capacity is accounted for by a measure of state employees per capita, which was taken from the “Rich State, Poor State” website, a project of the American Legislative Exchange Council (Rich States, Poor States 2021).

Fixed effects regression analysis with robust standard errors to control for effects associated with both years and states was used to test the relationship between the independent and control variables and the countermeasures score while controlling for other state-level measures. This methodological approach has been used in a number of recent, related studies, looking at issues such as the effect of partisanship on people’s willingness to limit social interactions in the interest of public health (Clinton et al. 2021) and behaviors to protect individual public health, policy preferences, and concerns about COVID-19 (Gadarian, Goodman, and Pepinsky 2021). This analysis as well as the descriptive statistics were conducted in Stata version 11. The results of the fixed-effects analysis are provided in Table 5.

Table 3.

States with Medical Countermeasure Management Scores Above/Below the Average.

Low CM Management	High CM Management
Alaska	Illinois
Nevada	Michigan
New Mexico	Kentucky
Louisiana	Ohio
California	Delaware
Florida	Connecticut
Idaho	Maine
Indiana	New Jersey
Utah	Maryland
Wyoming	New Hampshire
Georgia	North Carolina
Alabama	South Carolina
Arizona	Minnesota
Mississippi	Arkansas
Washington	Kansas
Hawaii	Missouri
Oklahoma	North Dakota
Tennessee	Pennsylvania
New York	West Virginia
Texas	Wisconsin
Vermont	Massachusetts
Virginia	Montana
Colorado	Oregon
	South Dakota
	Nebraska
	Iowa
	Rhode Island

Table 4.

States Categorized Based on their Percentage of Rural Population and Medical Countermeasure Score.

Low Rural/Management	Low Rural/High Management	High Rural/Low Management	High Rural/Management
Arizona	Connecticut	Alabama	Arkansas
California	Delaware	Alaska	Iowa
Colorado	Illinois	Idaho	Kentucky
Florida	Kansas	Indiana	Maine
Georgia	Maryland	Louisiana	Minnesota
Hawaii	Massachusetts	Mississippi	Missouri
Nevada	Michigan	Oklahoma	Montana
New Mexico	New Jersey	Tennessee	Nebraska
New York	Ohio	Vermont	New Hampshire
Texas	Oregon	Wyoming	North Carolina
Utah	Pennsylvania	North Dakota
Virginia	Rhode Island	South Carolina
Washington	South Dakota
	West Virginia
	Wisconsin

Table 5.

Fixed-Effects Regression Analysis Testing for Relationship Between State-Level Variables and Countermeasure Management Scores.

Variable	Coeff.
Rural%	−0.072 (0.028)*
Social associations	−0.046 (0.197)
Income ratio	1.139 (0.765)
Segregation index	−0.007 (0.040)
Physicians per capita	−0.097 (0.045)*
Some college	−0.105 (0.123)
Infant mortality	0.112 (0.086)
Household income	0.000 (0.000)
State employment	0.005 (0.005)

N = 200.

F = 32.34***.

Rho = 0.982.

Given the widely reported challenges that rural areas have had increasing the size of their vaccinated population, some additional analysis was conducted examining the association between the state data for the percentage of rural population and countermeasures management and the current levels of COVID-19 vaccines administered at the state level. To measure this, the data for the percentage of the fully vaccinated population in the state over the age of 18 from the CDC COVID Data Tracker was used (COVID Data Tracker 2021). The median for the percentage of rural population and countermeasure management were calculated for the states. The medians were used to divide the states into two groups for each category: high/low rural population states and states with high/low scores for countermeasure management. An independent samples t-test was conducted using Microsoft Excel to compare the current COVID-19 vaccination rates for each pair of states (see Tables 6 and 7).

Table 6.

Two-Sample t-test Assuming Unequal Variances Between Low/High Rural States on Vaccination Rates (Two-Tailed Test).

	Low Rural	High Rural
Mean	61.02	53.57
Variance	51.83	91.61
Observations	25	25
df	45
t Stat	3.11**

Table 7.

Two-Sample t-test Assuming Unequal Variances Between Low/High Countermeasure Management States on Vaccination Rates (Two-Tailed Test).

	Low Management	High Management
Mean	54.92	59.32
Variance	92.57	71.52
Observations	23	27
df	44
t Stat	−1.70*

Table 8.

Analysis of Variance Testing for the Differences in Vaccination Levels in States Based on Percentage of Rural Population and Countermeasure Management Scores.

SUMMARY
Groups	Count	Sum	Average	Variance
LRLM	13	763.6	58.739	49.854
LRHM	12	761.9	63.492	45.874
HRLM	10	499.5	49.95	111.309
HRHM	15	839.7	55.98	69.903
ANOVA
Source of variation	SS	df	MS	F	p-value
Between groups	1053.30	3	351.10	5.238	0.003
Within groups	3083.29	46	67.03
Total	4136.59	49

To investigate whether higher levels of countermeasure management capacity mitigate issues rural states had with increasing vaccination rates, the states were divided into four groups (Low Rural and Low Management, Low Rural and High Management, High Rural and Low Management, and High Rural and High Management.

Results

Although the F statistic indicates that the overall model is statistically significant, two variables in the fixed effects model achieved significance, the percentage of rural population in the state and physicians per capita. The rural percentage variable was significant and negative in the analysis, indicating that a higher percentage of rural population is associated with a lower countermeasures management score. This finding supports the literature indicating the significant public health challenges rural areas have historically faced, including funding, personnel, and other types of capacity challenges that hinder rural health departments in efforts like receiving accreditation (Beatty et al. 2018). At the beginning of the pandemic, there was evidence that certain rural states were proving effective in distributing and dispensing the COVID-19 vaccines compared to their more urban counterparts (Siegler 2021). However, this advantage dissipated considerably over time, leaving a number of rural states at the bottom of the list in regard to rates of vaccination.

The t-test based on the percentage of rural groups was found to be statistically significant at the 0.01 level using a one-tailed test (see Table 3), whereas the t-test based on the countermeasure management scores was found to be significant at the 0.05 level (see Table 4).

A one-way Analysis of Variance was conducted to test for a statistically significant difference in the mean COVID-19 vaccination rates among these groups. The results were statistically significant at the 0.01 level, and the means indicate that the states with lower levels of rural population and higher levels of countermeasure management capability had higher average vaccination rates, while states with higher percentages of rural populations and lower countermeasure scores did the worst in terms of COVID-19 vaccinations on average.

The findings from the fixed effects analysis and the follow-up t-test and ANOVA analyses support the idea that rural areas confront obstacles to vaccine uptake that are social and cultural in nature and cannot be fully addressed by competence and capacity in the public health realm.

The significant and negative finding for physicians per capita in the state was less intuitive. This variable was included in the model to account for the strength of the health care infrastructure in the state. One would anticipate that states with a stronger health infrastructure would perform better in regard to countermeasure management. However, this may provide evidence that leaders in states with stronger health care systems believe that these systems provide adequate protection and obviate the need for additional investments in public health functions. States with stronger health care systems may see lower overall disease which may create the sense that a state’s population is less likely to be heavily affected by a new pathogen. It is also important to note that health care is primarily a for-profit service in most states, and both medical institutions and state governments see economic value as a well social value in strengthening the health care system. Public health systems, on the other hand, are still regarded by some as “health care for the poor” (Garrett 2000). Stronger health care systems are likely to be useful when it comes to distributing and dispensing countermeasures, but the cost of regular management falls on underfunded public health systems.

Discussion

At present, the primary policy focus is on how to raise vaccination rates in rural areas to increase immunity levels against COVID-19 and its variants. The issues surrounding vaccine uptake in rural communities are varied and have been well-documented (Cates et al. 2009; Fazekas, Brewer, and Smith 2008; Kirzinger, Munana, and Brodie 2021, January 7; Lai et al. 2016). These issues are related to the problem of countermeasure management in that the public’s resistance to taking certain measures in response to a public health emergency, including vaccination, social distancing, and masking, makes it all the more important that public health authorities have the resources and capacity to provide resources as quickly as possible to those who are seeking and willing to accept aid. The appearance of incompetence or unprofessionalism in public health agencies regarding issues like countermeasure management can in fact feed into narratives that vaccines should not be trusted or public health authorities should not be listened to. Public health agencies need to be innovative, highly organized, and adaptable. This is particularly important when one considers that rural areas may be at a disadvantage when it comes to procuring new supplies in an emergency. Rural health agencies must make sure that the material in their stockpiles remains up-to-date and usable.

In regard to the rate of physicians per capita and the strength of the health care infrastructure, COVID-19 has demonstrated that during a pandemic health care infrastructure will not be sufficient to keep people safe or treat those that become ill. Health care, which largely focuses on individual health, is not a substitute for public health and its focus on the larger population. Rather than focusing on increasing the largely private health care capacity in their state, leaders need to be convinced of the social and economic benefit of investing in population health.

As of this writing, rural areas and their willingness to properly utilize the vaccines will likely be determinative as to how COVID-19 continues to affect the United States as a whole. As the U.S. seeks to learn from this experience, policymakers must remember how the pandemic has played out in rural areas and consider how future emergencies may play out as well. Future planning must focus on providing rural public health agencies with what they need to manage their challenges effectively, whether on a day-to-day basis or during an emergency. This must include not only properly resourcing public health but strategically examining rural health care systems as well.

Conclusion

Although this study indicates that before COVID-19 rural areas were not as effective in managing and dispensing medical countermeasures, the current vaccination effort should produce a wealth of new data about how rural communities perform in an urgent pandemic response. Careful attention must be given to the degree to which traditionally recognized barriers (access to care, inadequate staffing, geographic distance) and strengths (more social connectedness, strong social institutions) played important roles in the success of the vaccination effort. The success of messaging efforts targeted at rural residents will be important to study as well. Some rural states that have traditionally performed well in countermeasures management (West Virginia, North Dakota) have underperformed in regard to vaccinating those 18 years of age or older for COVID-19 (45.2 percent and 49.7 percent, respectively). Therefore, it will be valuable to compare the degree to which the National Health Security Preparedness Index scores were predictive of vaccination success. After all, the U.S. was rated as the most prepared country in terms of a pandemic before COVID-19, but the country’s performance has not demonstrated the correctness of that rating. Moving forward it will be important to reevaluate how we measure public health emergency preparedness for particular populations, such as rural areas. It will also be important to separate facts from truisms in regard to how we expect rural populations to behave in an emergency. One such truism is the presence of strong social capital in rural communities. Further research will either bear this out or help to recalibrate our perception of this important population.

There is certainly an argument to be made that it is important for rural areas to have the strongest public health and medical infrastructure possible given rural populations’ resistance to preventative actions. It could further be argued that the more that public health agencies and medical institutions are regarded as competent, professional, and well-resourced the more likely the public is to heed their advice. Furthering both of these aims will benefit from stronger social bonds between health agencies, medical providers, and the public at large. Although social capital as measured by social associations was not statistically significant in the preceding analysis, there is ample anecdotal and quantitative evidence that part of the reason for rural resistance to the COVID-19 vaccine is distrust of the public health establishment (Gale et al. 2020; Kirzinger, Munana, and Brodie 2020). This means that public health leaders in rural areas must not only focus on both technocratic proficiencies in being able to get vaccines distributed and disseminated but also communicate about vaccines in a way that will promote broader acceptance.

Footnotes

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author received no financial support for the research, authorship and/or publication of this article.

ORCID iD

Nathan Myers

Notes

Author Biography

Nathan Myers, PhD is a Professor in the Department of Political Science and Director of the Master of Public Administration at Indiana State University. His areas of research include public policy, public health emergency preparedness, and the governance of biotechnology. He is the author of “Pandemics and Polarization: Implications of Partisan Budgeting for Responding to Public Health Emergencies.” His work has been published in journals including Administration and Society, Public Integrity, Politics and Policy, Health Security, Review of Policy Research, Disaster Medicine and Public Health Emergency Preparedness, Journal of Emergency Management, and the Journal of Health and Human Services Administration, Myers serves as an academic advisor to the Continuity of Supply Initiative and assistant director of Indiana State’s Center for Genomic Advocacy.

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