Spatial Analysis of Vulnerability and Social Capital in Relation to COVID-19 Mortality in the 50 States of the U.S. in the First Year of the Pandemic

Abstract

The lack of social determinants of health has significantly influenced COVID-19 mortality; however, the few studies that have investigated the relationship among social capital, vulnerability, and COVID-19 mortality have either shown mixed directions of association or were not conducted at a larger scale on the state level. Our study aimed to fill this research gap. We aimed to test the association of comprehensive vulnerability and social capital measures and COVID-19 mortality in 50 states in the U.S. during the first year of the pandemic. We hypothesized that states with low social capital would register the highest mortality rates and high vulnerability would be proportional to a high number of deaths due to COVID-19 in the U.S. Methods: Our ecological analytic study used aggregate secondary data from nine open access databases. The outcome was COVID-19 mortality (January 2020 to July 2021). The main exposures were social capital and social vulnerability. We also tested 28 covariates and selected socioeconomic variables at the state level. Statistical analysis consisted of a multivariate technique factor analysis and a bivariate Local Indicators of Spatial Association (LISA) analysis. Findings: Social capital (SCI) and social vulnerability (SVI) explained 57% of the COVID-19 mortality rate variation (R² adjusted = 0.57). This analysis resulted in a statistically significant model (p ≤ 0.001), where SCI (β = 23.256, t = 2.649, p = 0.011) and SVI (β = 150.316, t = 4.235, p = 0.001) were predictors for COVID-19 mortality in the investigated period.

1. Introduction

Coronavirus disease (COVID-19) is caused by a positive-strand RNA enveloped virus, SARS-CoV-2, which infects many cell types of the human pulmonary system [1]. This highly transmissible virus was first identified in December 2019 in Wuhan, Hubei Province, China [2]. Shortly thereafter, the virus rapidly spread across the globe, reaching the United States (U.S.) on 21 January 2020 [3]. As of mid-November 2021, COVID-19 had claimed over 760,000 lives in the U.S. alone [4]. Medico-legal disputes over healthcare-related COVID-19 infections arose during the pandemic and have continued to seek settlement for hospital responsibility for preventable deaths that contributed to the total COVID-19 mortality [5,6]. The devastating impact of COVID-19 has catalyzed efforts to investigate the factors associated with COVID-19 mortality.

COVID-19 mortality research has focused on variables associated with pre-pandemic mortality, such as lifestyle diseases [7]. Hypertension, hyperlipidemia, diabetes mellitus, and obesity are risk factors for cardiovascular disease, the leading cause of death in the U.S., and each of these risk factors has been independently associated with a heightened risk for COVID-19 mortality [8,9,10,11,12,13,14]. Additionally, a medical history of stroke, kidney disease, respiratory diseases (excluding mild–moderate asthma), cancer, and dementia all elevate the risk for death due to COVID-19 [15].

Agedness is another factor that has been shown to increase COVID-19 disease severity and mortality [16,17,18]. While persons 65 years of age or older made up 17% of the U.S. population in 2020, they comprised more than 80% of the total COVID-19 deaths as of late March 2021 [16,18]. This disproportionate risk intensifies with older age; individuals 85 years of age and older have a 7900× greater chance of dying from COVID-19 than a 5–17-year-old reference group [19].

Social factors such as educational attainment, race, and household income have been shown to exert a strong influence on mortality rates pre-pandemic [20,21]. For example, a major risk factor for cardiovascular disease is being socioeconomically disadvantaged [11]. It even appears that low socioeconomic status may sometimes be at the root of cardiovascular disease; higher stress, lack of access to healthcare, and poor nutrition often accompany socioeconomic disadvantage and each increase the risk of developing coronary artery disease [22,23].

The existing research that has examined the social determinants of health and their association with COVID-19 mortality reaffirms the critical role of social factors in health outcomes. Poverty and minority race are significantly associated with elevated COVID-19 mortality rates [24,25,26,27,28]. For instance, a higher density of households at or below the federal poverty line, defined as USD 21,720 for a three-person household in 2020, within a county is significantly associated with higher COVID-19 mortality [24,25]. People of minority race have been disproportionately burdened by COVID-19; for example, non-Hispanic white persons make up 76.3% of the U.S. population in 2019 but only 45.1% of COVID-19 deaths as of late March 2021 [26,27]. Of the racial minorities in America, Hispanic individuals are notably at a higher risk, with a 2.3x greater chance of dying from COVID-19 than a non-Hispanic white person, according to CDC data [28]. In alignment with this information, Andrafsay and Goldman observed a life expectancy reduction for the Latino population by 3.03 years due to COVID-19, compared to a 0.94-year reduction amongst white populations in the U.S. [29]. Furthermore, the average Hispanic individual who dies from COVID-19 is significantly younger than the average non-Hispanic white person who dies from COVID-19 [30,31].

Among the numerous aforementioned social determinants of health, social capital has emerged as a prominent resource for improving quality and longevity of life [32,33,34]. The definition of social capital has seen many iterations across authors and disciplines, and there is no consensus among researchers on which definition is the best [35,36,37]. Social capital can be measured through different instruments, such as a single question or questionnaire [38,39], indices [40], and scales [41,42]. For the purpose of this study, as derived from Rostila [43], social capital is defined as “social resources that evolve in accessible social networks and social structures characterized by mutual trust… [that] in turn, facilitate access to various instrumental and expressive returns, which might benefit both the individual and the collective.” This definition allows us to investigate social capital through macrolevel indices while, according to Rostila, also reducing the confusion between the preconditions of the social capital and its core meaning [43].

The first attempt to describe the effects of social capital on overall mortality in the U.S. was conducted in 1997. This landmark study showed that states with low group membership, high perceived lack of fairness, high social mistrust, and high perceived lack of helpfulness were all significantly associated with higher all-cause mortality rates [20]. These results emphasized that the quality of an individual’s associational life influences their health and has encouraged further research into social capital as a determinant of health.

The association between social capital and COVID-19 mortality has been investigated to some extent on the international level. Evidence has shown mixed associations between individual aspects of social capital and COVID-19 mortality. Specifically, greater civic engagement, greater trust in state institutions, greater family bond/security, greater social and emotional support, and the norm of reciprocity amongst neighbors are all associated with lower COVID-19 mortality [44,45,46,47,48,49,50]. The negative association between these dimensions of social capital and COVID-19 mortality makes intuitive sense. A group which is more invested in improving the conditions of their community and demonstrates greater civic engagement would likely be more concerned about minimizing the spread of COVID-19 and have lower COVID-19 mortality rates. Similarly, a group with higher trust in state institutions is more likely to abide by state recommendations for social distancing, mask-wearing, and vaccination, which have each been shown to decrease COVID-19 infection and mortality [51]. As for a community with stronger family bond/security, this may contribute to lower COVID-19 mortality by individuals with stronger familial relationships fulfilling their social needs through activities that pose a lower risk for infection than going out in public to socialize. On the other hand, greater community trust and greater community attachment/participation are associated with higher COVID-19 mortality [44,45,50]. This positive association between community trust/attachment and COVID-19 mortality could be due to a community with stronger social ties adhering less to social distancing and consequently decreasing the virus doubling time [52].

The vast majority of previous research on social capital and COVID-19 mortality has been investigated worldwide. There is a research gap in examining the relationship among comprehensive indices of social capital, social vulnerability, and COVID-19 mortality in the U.S. In this study, we hypothesize that social capital has a negative association and social vulnerability has a positive association with COVID-19 mortality rates in the U.S. at the state level. Therefore, the purpose of this study was to test the relationship between COVID-19 mortality (from January 2020 to July 2021), social capital, social vulnerability, and selected comorbidities in the U.S. at the state level.

2. Materials and Methods

2.1. Study Design and Variables Investigated

This ecological analytic study used aggregate secondary data from the state level to investigate the relationship between social capital, social vulnerability, and COVID-19 mortality in the U.S. from January 2020 to July 2021. We utilized nine open access databases: Centers for Disease Control and Prevention (CDC) [53], United States Congress Joint Economic Committee Social Capital Project [40], United States Department of Agriculture Economic Research Service (U.S.DA ERS) [54], Kaiser Family Foundation [55], Statista [16], U.S.AFacts [27], Financial Reserve Economic Data (FRED) [56], Migration Policy Institute [57], and the United States Census Bureau [58]. We sought out data from each of these nine sources based on our review of the literature describing the associated factors with COVID-19 mortality. These evidence-based factors are summarized in our theoretical model (Figure 1).

The Centers for Disease Control and Prevention (CDC) protects the U.S. from health threats by providing health information in response to these threats [53]. The Social Capital Project is a multi-year research effort initiated by the U.S. Congress Joint Economic Committee in 2017 to investigate associational life in the U.S. [40]. The U.S.DA ERS sets out to “anticipate trends and emerging issues in agriculture, food, the environment, and rural America and to conduct high-quality, objective economic research to inform and enhance public and private decision making [54]”. The Kaiser Family Foundation is a non-profit organization which aims to provide trusted information on national health issues [55]. Statista is a German data portal that integrates thousands of diverse topics, including demographic measurements [16]. USAFacts is a not for profit, non-partisan civic initiative with the aim of making U.S. government data accessible to all Americans [27]. FRED is an online database created and maintained by the Federal Reserve Bank of St. Louis with the goal of making national, international, public, and private economic data more user-friendly [56]. The Migration Policy Institute is a nonpartisan organization that seeks to improve immigration and integration policies in the U.S. [57]. Lastly, the United States Census Bureau serves as the national leader in providing data on the economy and the people of the U.S. The data obtained from the Census were in the shapefile format, in geographic coordinates, enabling them to be used without the need for any treatment [58].

2.2. Outcome Measure

Our outcome was mortality due to COVID-19, calculated per 100,000 state residents, retrieved from the Centers for Disease Control and Prevention for all 50 states and Washington DC [4]. The numerator was the number of total deaths due to COVID-19 and the denominator was the population size in the same period of observation per 100,000 state residents registered since 21 January 2020 as of 5 July 2021.

2.3. Main Exposures

The main exposures of this study were as follows: 1. Social capital, measured by the social capital index (SCI) created by the Joint Economic Committee of the U.S. Congress [40]. This index consisted of the subindices family unity, family interaction, social support, community health, institutional health, collective efficacy, and philanthropic health (

Table 1. State-level social capital index according to seven dimensions. U.S Congress Joint Economic Committee, Social Capital Project.

Dimension Name	Dimension Description
Family Unity	Family Unity was measured by the following statistics: Share of births in past year to women who were unmarried; Share of women ages 35–44 who are currently married (and not separated); Share of own children living in a single-parent family.
Family Interaction	Family Interaction was measured by the following statistics: Share who report child spends at least 4 h per weekday in front of a TV; Share who report child spends at least 4 h per weekday on electronic device, excluding homework; Share who report someone in the family read to child every day in past weeks.
Social Support	Social Support was measured by the following statistics: Share saying they receive the emotional support they need only sometimes, rarely, or never; Average number of close friends reported by adults; Share of adults reporting they and their neighbors do favors for each other at least 1x/month; Share of adults reporting they can trust all or most of their neighbors.
Community Health	Community Health was measured by the following statistics: Share of adults who report having volunteered for a group in 2018; Share who report having attended a public meeting re. community affairs in past year; Share who report having worked with neighbors to fix/improve something in past year; Share of adults who served on a committee or as an officer of a group; Share who attended a meeting where political issues were discussed in past year; Share who took part in march/rally/protest/demonstration in past year; Membership organizations per 1000 people; Registered non-religious non-profits plus religious congregations per 1000 people.
Institutional Health	Institutional health was measured by the following statistics: Average (over 2012 and 2016) of votes in the presidential election per citizen age 18+; Mail-back response rates for 2010 census; Share of adults reporting some or great confidence in corporations to do what is right; Share of adults reporting some or great confidence in the media to do what is right; Share of adults reporting some or great confidence in public schools to do what is right.
Collective Efficacy	Collective efficacy was measured by violent crimes per 100,000 inhabitants.
Philanthropic Health	Philanthropic Health was measured by those who report having made a donation of >USD 25 to a charitable group in the past year.

). 2. Social vulnerability, measured by the Social Vulnerability Index (SVI) constructed by our team for the purpose of this study. This index is composed of the subindices Socioeconomic (Block 1: Demographics + Block 2: Socioeconomic + Block 3: Education) and Health Outcomes and Health Behaviors (Block 4: Health and Behavior) (

Table 2. Complete list of all 28 investigated covariates and corresponding descriptions.

Variables	Description	Year
% female	Percentage of the state population that is female.	2019
Population per square mile	Number of people per square mile.	2010
% of population born in a foreign country	Numerator: number of foreign born persons. Denominator: total population. Multiplier: 100.	2015–2019
Unemployment rate	Numerator: number of unemployed people. Denominator: total population. Multiplier: 100.	2019
% of children in families that receive public assistance	Numerator: number of children that receive public assistance. Denominator: total number of children in that same state. Multiplier: 100.	2019
% of persons aged 25+ years that are high school graduates or higher	Numerator: number of people aged 25+ that graduated from high school. Denominator: total number of adults aged 25+ in the state. Multiplier: 100.	2015–2019
% of persons aged 25 years+ with a bachelor’s degree or higher	Numerator: number of people aged 25+ that graduated from a 4-year higher education program. Denominator: total number of adults aged 25+ in the state. Multiplier: 100.	2015–2020
% of residents reporting fair or poor health	Numerator: number of Behavioral Risk Factor Surveillance System (BRFSS) survey respondents in the state reporting fair or poor health. Denominator: total number of BRFSS respondents in the state. Multiplier: 100.	2019
% of population with a disability under age 65 years	Numerator: number of people under the age of 65 that have a disability. Denominator: total population under the age of 65. Multiplier: 100.	2015–2019
Prevalence of diabetes among U.S. adults ≥ 18 years	Numerator: number of cases of diabetes (type 1 or 2) among people aged ≥ 18 years. Denominator: total population ≥ 18 years. Multiplier: 100,000	2017
Current smoking among adults aged ≥ 18 years	Numerator: number of current smokers aged ≥ 18. Denominator: total population aged ≥ 18 years. Multiplier: 100,000	2018
Deaths per 100,000 due to influenza/pneumonia	Numerator: number of deaths due to influenza. Denominator: total population. Multiplier: 100,000	2019
Binge drinking among adults ≥ 18 years	Numerator: number of men ≥18 who consume 5 or more drinks in about 2 h or women ≥18 who consume 4 or more drinks in about 2 h. Denominator: Total population aged ≥18.	2018
Coronary heart disease deaths per 100,000 U.S. adults ≥ 18 years	Numerator: number of people diagnosed with CHD aged ≥ 18. Denominator: total population aged ≥ 18 years. Multiplier: 100,000.	2017
CVA deaths per 100,000 U.S. adults (18+)	Numerator: number of people who died of CVA aged ≥18. Denominator: total population aged ≥ 18 years. Multiplier: 100,000.	2017
Prevalence of two or more chronic conditions among Medicare-enrolled persons aged ≥ 65	Numerator: number of people diagnosed with ≥ 2 chronic conditions among Medicare-enrolled persons aged ≥ 65. Denominator: total Medicare-enrolled persons aged ≥ 65.	2015
Median household income	Defined as the median income of a householder plus the income of all individuals 15 years or older.	2019
% adult obesity	Numerator: number of adults with BMI ≥ 30. Denominator: total adult population. Multiplier: 100.	2019
% obesity Asian alone	Numerator: number of Asian adults with BMI ≥ 30. Denominator: total adult population. Multiplier: 100.	2019
% obesity Black/African American alone	Numerator: number of Black/African American adults with BMI ≥ 30. Denominator: total adult population. Multiplier: 100.	2019
% obesity non-Hispanic white alone	Numerator: number of non-Hispanic white American adults with BMI ≥ 30. Denominator: total adult population. Multiplier: 100.	2019
% obesity Hispanic or Latino	Numerator: number of Hispanic or Latino adults with BMI ≥ 30. Denominator: total adult population. Multiplier: 100.	2019
% population in poverty	The Census Bureau uses a set of income thresholds that vary by family size and composition to determine who is in poverty. If a family’s total income is less than the threshold, then that family and every individual in it is considered in poverty.	2019
% persons without health insurance under 65 years	Numerator: number of persons without health insurance under 65 years. Denominator: total population under 65 years. Multiplier: 100.	2018
% rural inhabitants	Numerator: number of people living in rural areas in the state. Denominator: total population in the state. Multiplier: 100.	2010
% internet subscription	Numerator: number of households with broadband internet subscription. Denominator: total number of households. Multiplier: 100.	2015–2019
% birth to unmarried women	Numerator: number of births to unmarried women. Denominator: number of total births. Multiplier: 100.	2019
Average county % of population with ≥3 risk factors for COVID-19 mortality	The Community Resilience Estimates (CRE) groups the population estimates into 3 categories: 0 risk factors, 1–2 risk factors, and 3 plus risk factors. The data file includes the population estimate, estimate margin of error, rate, and rate margin of error for each of the 3 categories.	2014

) and was developed specifically for this present study. The models’ adequacy was statistically tested and confirmed (factor analysis, Kaiser–Meyer–Olkin test, Bartlett sphericity test, and communalities were satisfactorily tested), and a final score of low, medium, and high was generated for the measurement of social vulnerability.

2.4. Covariates

A total of 28 covariates were collected and initially grouped into four BLOCKS:

B1: Demographics (% female, population per square mile, % population foreign-born, % rural inhabitants, % births to unmarried women).

B2: Socioeconomic (unemployment rate, median household income, % population in poverty, % of persons without health insurance under the age of 65, % of population with internet subscription, % of children in families that receive public assistance).

B3: Education (% of persons aged 25 years+ that are high school graduates or higher, % of persons age 25 years+ with a bachelor’s degree or higher).

B4: Health and Behavior (% of residents reporting fair or poor health, % population with a disability under age 65 years, prevalence of diabetes among adults aged ≥ 18 years, current smoking among adults aged ≥ 18 years, deaths per 100,000 due to influenza/ pneumonia, binge drinking among adults ≥ 18 years, coronary heart disease deaths per 100,000 U.S. adults aged ≥ 18 years, Cerebrovascular Accident (CVA) deaths per 100,000 U.S. adults aged ≥ 18 years, prevalence of two or more chronic conditions among Medicare-enrolled persons aged ≥ 65, % adult obesity, % obesity Asian alone, % obesity Black/African American alone, % obesity non-Hispanic white alone, % obesity Hispanic or Latino, average county % of population with ≥ 3 risk factors for COVID-19).

A complete list of all investigated variables and their corresponding definitions is available in Table 2. The states used to define the macroregions included in this study had complete data on the investigated variables. All states met the inclusion criteria.

2.5. Statistical Analysis

We used multivariate factor analysis (FA) through R Software, version 1.0. The goal of this technique was to create two subindices, one reflecting the socioeconomic variables and the other representing health outcomes and health behaviors in each state in the U.S. Geographic Information System (GIS) TerraView 5.6.1 was used to create themed maps, the dataset, and spatial analysis. TerraView is an open-source software that allows the visualization, revision and manipulation of geographic data.

We conducted a bivariate Local Indicators of Spatial Association (LISA) analysis, an index to measure the association between the different locations of a spatially distributed variable, for the validation of the spatial correlation between the outcome variable, COVID-19 mortality, and the independent variables using the GeoDa 1.6.61 Software (Spatial Analysis Laboratory, University of Illinois, Urbana Champaign, United States). Themed maps were created with each variable pair, and later their statistical significance were verified. To evaluate the association between COVID-19 mortality rates in the U.S. and independent variables, Pearson correlation tests, and linear regressions were used. Lastly, we used the Moran Index to test the geographic correlation of each main exposure separately (social capital and vulnerability) and COVID-19 mortality at the state level. The Moran Index measures the spatial correlation using an auto covariant measure in the form of a crossed product [59].

3. Results

3.1. Spatial Analysis

We stratified the U.S. by COVID-19 mortality for the observed period (from January 2020 to July 2021). We observed that 23 (45.10%) of the 50 states and DC had high coronavirus mortality rates. The Southern region of the U.S. suffered from notably high mortality rates, with 47.83% of Southern states being classified as high COVID-19 mortality. In the descriptive analysis of the variables for the 50 states and DC, we found that the standardized mortality rate varied between 37 and 299 deaths per 100,000 residents, with the average and standard deviation of 169 ± 60.67 deaths per 100,000 residents. The distribution of COVID-19 mortality in U.S. macroregions can be seen in

Table 3. Distribution of COVID-19 mortality rates according to macroregions. U.S., 2020–2021.

Region	Population 1	Mortality Rate COVID-19 $(\frac{\# \mathit{o}\mathit{f} \mathit{t}\mathit{o}\mathit{t}\mathit{a}\mathit{l} \mathit{C}\mathit{O}\mathit{V}\mathit{I}\mathit{D}-19 \mathit{d}\mathit{e}\mathit{a}\mathit{t}\mathit{h}\mathit{s}}{\mathit{m}\mathit{a}\mathit{c}\mathit{r}\mathit{o}\mathit{r}\mathit{e}\mathit{g}\mathit{i}\mathit{o}\mathit{n} \mathit{p}\mathit{o}\mathit{p}\mathit{u}\mathit{l}\mathit{a}\mathit{t}\mathit{i}\mathit{o}\mathit{n}}\times \mathrm{100,000})$			Number of States 1
		Low (%)	Medium (%)	High (%)
Midwest	68,329,004	3 (23.08)	2 (13.33)	7 (30.43)	12
Northeast	56,688,552	5 (38.46)	3 (20.00)	2 (8.70)	10
South	124,874,699	3 (23.08)	2 (13.33)	11 (47.83)	16
West	78,347,268	2 (15.38)	8 (53.33)	3 (13.04)	13
Total	328,239,523	13 (25.49)	15(29.41)	23(45.10)	51
Source: DCEU/CDC.

¹ population of 2019, Census.gov. Note: The scores (low/medium/high) were calculated as follows: <30 percentile (low), 30–70 percentile range (medium), and values > 70 percentile (high). The data were collected for each state level separately (${\displaystyle \frac{\# of total COVID-19 deaths in a given state}{population of the same state}}\times \mathrm{100,000})$ and then calculated per microregion.

.

We found higher COVID-19 mortality rates localized to Arizona, Minnesota, Alabama, Pennsylvania, Connecticut, and South Carolina in the investigated period. We observed that the states with the lowest stocks of social capital included Nevada, Arizona, New México, Arkansas, Mississippi, Louisiana, and Florida. The states of Oregon, Idaho, Arizona, Minnesota, Mississippi, Alabama, Pennsylvania, South Carolina, and Florida showed the most social vulnerability (Figure 2).

To evaluate whether the distribution of social capital and social vulnerability in the U.S. was not random, we employed an auto spatial correlation test. We found an auto spatial correlation of the U.S. at the state level of the COVID-19 mortality distribution, the SCI (social capital index), and the SVI (Social Vulnerability Index).

The Moran Index results were 0.0946 and 0.1500, which is higher than the expected values of −0.0070 and 0.0020. This means that the states with high or low frequency of the SCI, states with high or low frequency of the SVI and states with high or low frequency of COVID-19 mortality rates are especially related to other states with the same investigated characteristics. These results were statistically significant (Figure 3).

3.2. Spatial Regression Analysis

It was observed that the SCI and the SVI are capable of explaining 57% of the COVID-19 mortality rate variation (R² adjusted = 0.57). This analysis resulted in a statistically significant model (p ≤ 0.001), where the SCI (β = 23.256, t = 2.649, p = 0.011) and the SVI (β = 150.316, t = 4.235, p = 0.001) are predictors for COVID-19 mortality in the investigated period (

Table 4. Spatial linear regression model with predictors of the dependent variable COVID-19 mortality rate in the U.S.

	Coefficients	Standard Deviation	t	p-Value
(Constant)	77.037	23.036	3.344	0.002
Social Capital Index	23.256	8.780	2.649	0.011
Social Vulnerability Index	150.316	35.492	4.235	0.001

).

4. Discussion

We found a moderate positive association between social vulnerability and mortality rates due to COVID-19 (r = 0.5158). We confirmed the hypothesis that states with a higher degree of social vulnerability have higher COVID-19 mortality rates but rejected the hypothesis that low social capital was associated with higher COVID-19 mortality rates in the United States in the first year of the pandemic.

The literature that concomitantly addresses exposures to social vulnerability and social capital over mortality due to COVID-19 is relatively sparse.

The current body of research considering the relationship between social vulnerability and COVID-19 mortality aligns with our finding that greater social vulnerability is associated with higher rates of COVID-19 mortality. Measures of vulnerability have been found to be strongly associated with select comorbidities [60]. A prominent component of social vulnerability is socioeconomic disadvantage, and studies have observed that lower high school graduation rates and lower household income are both strongly associated with greater COVID-19 mortality rates [26,61,62,63,64]. These findings have been consistent across many geographical locations, such as in the U.S. at both the state and county levels, as well as Chile, Brazil, and Peru [26,61,62,63,64]. There are many mechanisms that contribute to the disparity in COVID-19 mortality rates according to socioeconomic disadvantages. One likely contributor is that lower socioeconomic status is associated with more crowded living spaces, a greater reliance on public transportation, and higher likelihood of being an essential worker, all factors that increase risk of exposure to COVID-19, adding to the disparate mortality rates [65].

Furthermore, a socioeconomically disadvantaged community tends to suffer from a higher chronic disease burden which increases the chances of a severe and potentially fatal COVID-19 infection. A study by Islam et al. examined the relationship between U.S. county social vulnerability and COVID-19 mortality and infections. Social vulnerability was measured using the CDC Social Vulnerability Index, which consists of various socioeconomic and demographic factors such as the level of poverty, level of crowded housing, and chronic disease prevalence in each county [66]. A major finding of this study was the dose–response relationship between social vulnerability and COVID-19 mortality. For example, the first quintile for social vulnerability (least vulnerable quintile) had only 4 coronavirus deaths per 100,000 residents compared to the fifth quintile (most vulnerable) recording 31 deaths per 100,000 residents. A likely prominent contributor to these disparate mortality rates is a significantly higher prevalence of obesity, diabetes mellitus, chronic obstructive pulmonary disease, heart disease, and chronic kidney disease in higher quintiles [66]. Our results sustain the importance of meaningful analysis of the impact of the social determinants over the health of populations.

Although this study did not find a significant consistent correlation between social capital and COVID-19 mortality in the U.S. at the state level (we found a weak correlation at the national level), studies have shown that social capital is negatively associated with COVID-19 cases and COVID-19 mortality in U.S. counties [38,67,68]. For example, social capital was measured using the U.S. Congress county-level social capital index, consisting of measures such as family unity/interaction, trust/confidence in institutions, and community cohesion; it was observed that moving a county from the 25th percentile to the 75th percentile, of the social capital distribution among 2700 U.S. counties, led to a 18% and 5.7% decline in COVID-19 infections and COVID-19 deaths, respectively [38]. Similarly, social capital has been observed to be negatively associated with new COVID-19 cases in seven European countries and forty-seven Japanese prefectures [69,70].

The inverse relationship between social capital and COVID-19 mortality could be partially due to social capital fostering greater physical health in communities prior to the pandemic and therefore decreasing the severity of illness and deaths due to COVID-19 [71]. Our results have implications for health and public health practice at different levels. At the clinical level, it is important for clinicians and health personnel to bring elements of the social determinants of health such as social vulnerability and social capital into their practices to better address the health needs of populations taking into account the burden of social disadvantages [72,73,74,75]. The shift in clinical practice requires conscious investment, from health administrators, in providing continuing education to their health personnel [76]. However, it is important to note such issues are also beyond individual capacity due to the macrosocial nature of social capital and social vulnerability that could be addressed through specific public policies. Furthermore, multiple studies suggest that social capital may exert its protective effect through promoting behaviors that minimize viral spread. Most notably, a higher degree of institutional trust is associated with social distancing, mask wearing, decreased mobility for nonessential tasks such as retail and recreational activities, and a higher intent to become vaccinated [68,69,77,78,79,80]. In addition to institutional trust, relational trust drives viral-curbing behaviors; for example, citizens’ confidence that family members would help the individual if they were to fall ill had a strong positive association with compliance with pandemic-related mobility restrictions [81].

Limitations and Strengths

As a cross-sectional ecological analytic study, this study has important limitations to be discussed. Although we utilized secondary data from reliable sources, we cannot assure quality control of the investigated variables. Due to the cross-sectional design, any causal conclusion cannot be inferred from our results. Additionally, we cannot assume that the associations observed at the state level between social vulnerability and COVID-19 mortality exist at lower levels (counties, municipalities, and on the individual level), due to ecological fallacy. Another limitation refers to the measurement of our two main exposures. Both social capital and social vulnerability were measured by indices that might not be sensible enough to measure the complexity of these characteristics due to their finite number of factors tested by each index. Moreover, social capital and social vulnerability measures are not consensual among scholars; measures vary from a single question to structured questionnaires. In our study, we used a multidimensional social capital index from the U.S. Congress that we have used in one of our previous studies due to its comprehensiveness (five subdomains that contain factors supported by current scientific literature) [60]. We also understand the limitation of our outcome variable, COVID-19 mortality, since mortality is influenced by countless factors, and it is impossible to cover all factors in a single study. Nevertheless, we leveraged a careful approach in selecting 11 health outcomes and health behavioral related factorial loads as well as eight socioeconomic factorial loads. A key strength of our study is that we created a robust theoretical model based on the literature that has examined some of the main factors that influence COVID-19 mortality rates in the first year of the pandemic. Additionally, our research has reinforced the crucial importance of adding social determinants of health in the analysis of complex health-related outcomes such as mortality. A search on PubMed conducted by the authors of this present study on 20 November 2024 using the keywords “COVID-19 mortality” retrieved over 49,000 articles. When the search was narrowed to “social capital” AND “social vulnerability” AND “COVID-19” AND “mortality”, only one article was found [81]. This supports the unique approach of our study and its subsequent findings, which have provided a comprehensive investigation at state-level associations between social capital, vulnerability, and COVID-19 mortality not previously present in the field of social epidemiology in the United States.

5. Conclusions

In this study, we have identified that 57% of the variance in the COVID-19 mortality rate distribution was explained by social capital and social vulnerability. However, due to the complex cross-section between social capital and social vulnerability, a holistic understanding of their relationships to COVID-19 mortality outside of macrolevel analysis is difficult. States with lower levels of social vulnerability exhibited lower COVID-19 mortality. Therefore, it is merited that future studies investigate interventions to improve disparate social vulnerabilities at the community level. Specifically, interventions to improve trust, cohesion, and collective action could enhance the resilience of our communities when the next pandemic emerges through bolstered social capital. Furthermore, interventions to improve the affordability of healthy housing environments, access to quality education and health care, as well as an emphasis on availability and knowledge of how to consume fresh, nutrient-dense foods, will decrease social vulnerability and therefore decrease the risk for mortality from a communicable disease like COVID-19. Ultimately, our findings reinforce the urgent need of bringing the social determinants of health to public health research, practice, and action.