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Over the past thirty years, disaster scholars have highlighted that communities with stronger social infrastructure—including social ties that enable trust, mutual aid, and collective action—tend to respond to and recover better from crises. However, comprehensive measurements of social capital across communities have been rare. This study adapts Kyne and Aldrich’s (Risk Hazards Crisis Public Policy11, 61–86, 2020) county-level social capital index to the census-tract level, generating social capital indices from 2011 to 2018 at the census-tract, zipcode, and county subdivision levels. To demonstrate their usefulness to disaster planners, public health experts, and local officials, we paired these with the CDC’s Social Vulnerability Index to predict the incidence of COVID-19 in case studies in Massachusetts, Wisconsin, Illinois, and New York City. We found that social capital predicted 41–49% of the variation in COVID-19 outbreaks, and up to 90% with controls in specific cases, highlighting its power as diagnostic and predictive tools for combating the spread of COVID.
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Social capital’s impact on COVID‑19
outcomes at local levels
Timothy Fraser 1*, Courtney Page‑Tan 2 & Daniel P. Aldrich 3
Over the past thirty years, disaster scholars have highlighted that communities with stronger social
infrastructure—including social ties that enable trust, mutual aid, and collective action—tend to
respond to and recover better from crises. However, comprehensive measurements of social capital
across communities have been rare. This study adapts Kyne and Aldrich’s (Risk Hazards Crisis Public
Policy 11, 61–86, 2020) county‑level social capital index to the census‑tract level, generating social
capital indices from 2011 to 2018 at the census‑tract, zipcode, and county subdivision levels. To
demonstrate their usefulness to disaster planners, public health experts, and local ocials, we paired
these with the CDC’s Social Vulnerability Index to predict the incidence of COVID‑19 in case studies in
Massachusetts, Wisconsin, Illinois, and New York City. We found that social capital predicted 41–49%
of the variation in COVID‑19 outbreaks, and up to 90% with controls in specic cases, highlighting its
power as diagnostic and predictive tools for combating the spread of COVID.
Why do some communities experience greater outbreaks of COVID-19? As of the time of writing on February
1, 2022, COVID-19 has led to at least 74.3 million cases and 884,300 known deaths in the US, and over 5.67
million deaths worldwide1,2. ese outcomes represent major challenges to communities’ resilience, referring
to residents’ “capacity to adapt existing resources and skills to new situations and operating conditions”3. Past
studies highlighted that pandemic outcomes, like many disaster resilience outcomes, depend on more than the
capacity of governments and health care workers to respond4,5 and the social vulnerability of residents by race,
class, age, and gender69. However, crisis outcomes also correlate with the strength of community resources such
as social infrastructure accessible to members1013. Social capital—the social ties that enable trust, reciprocity, and
collective action14—serves as a key resource residents can draw upon before, during, and aer crisis to ensure
that they obtain mutual aid from family, friends, and neighbors and also gain access to key public or semi-public
goods from lawmakers15,16. Past scholars measured social capital using regional level responses from the Global
Social Survey17, custom neighborhood surveys in disaster zones18, and aggregate measures at the county level19.
However, the impact of COVID-19 varies widely by neighborhood. Scholars and local ocials need a clearer
measure of social ties at increasingly hyperlocal levels.
is study introduces new measures for social capital for every census tract, zipcode, and county subdivision
in the United States from 2010 to 2018. ese indices characterize the strength of bonding, bridging, and linking
social capital, as well as overall levels of social capital. Rather than reinventing the wheel, these measures draw
directly from the methodology of Kyne and Aldrich’s19 validated county level Social Capital Indices, extending it
to the census tract level. en, we average these census tract measures up to the zipcode and county subdivision
level to create broad coverage for measuring social capital. Finally, we apply these indices to predict COVID-19
outcomes in dierent regions of the United States, showing frequent associations with COVID-19 test positivity
is study makes three main contributions to the literature. First, we extend Kyne and Aldrich’s19 county level
measures of social capital to the census tract, zipcode, and county subdivision, enabling comparison of tens of
thousands of communities in terms of bonding, bridging, and linking social ties. is adds an important resource
to scholars and policymakers involved in disaster and pandemic response eorts, easily paired with the CDC’s
Social Vulnerability Index20 and the Baseline Resilience Indicators (BRIC)21.
Second, we nd that these local level measures of social capital are closely correlated with a key diagnostic
measure of COVID-19 spread, the percentage of COVID-19 tests returned positive. Our models accounted for up
to 90% of the variation in COVID-19 spread. is builds on recent research that suggests that social capital helps
residents adopt new behavioral norms like social distancing and masking and reduce COVID-19 spread1013,
while verifying it with a measure that adjusts for testing capacity.
1Political Science Department, Northeastern University, Boston, MA 02115, USA. 2Security and Emergency Services
Department, Embry-Riddle Aeronautical University, Daytona Beach, FL 32114, USA. 3Security and Resilience
Program, Department of Political Science, School of Public Policy & Urban Aairs, Northeastern University, Boston,
MA 02115, USA. *email:
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ird, we nd that social capital has divergent eects depending on the type of social capital present in a
community and the local context of each city or region. First, each kind of social capital index produces strong,
signicant associations with test positivity rates in multiple cases, and bonding social capital is linked to reduced
COVID-19 spread in regions at large, matching past studies10,12,22. However, in specic cities the eect of bonding
social capital frequently changes: communities with strong bonding ties promote COVID-19 spread through
insular social networks, while strong bridging ties help reduce that spread in urban environments. is matches
results from disaster studies, which highlight that while bonding social capital can help some communities, bridg-
ing ties aid most communities by fostering trust, reciprocity, and pro-social behavioral changes across dierent
racial, ethnic, religious, and political lines2325.
Literature review
Since the COVID-19 pandemic reached the US in early 2020 (or arguably earlier26), local, state, and federal
government agencies have made tracking key COVID-19 outcomes a top priority, including case rates, death
rates, test positivity rates, excess deaths rates, and vaccination rates. States governments across the country have
digitized measurements of these new pandemic resilience outcomes, creating online dashboards for health
ocials to more easily communicate with policymakers, the press, and the public, and to engender greater
trust and transparency in crisis information ow. While these dashboards provoked early pushback from state
government ocials in denial about the scope of the crisis, they have become powerful tools for highlighting
hotspots of vulnerability, as it became clear quickly that communities of color, working class neighborhoods,
front line workers in crowded areas, the aged, and residents with pre-existing conditions faced high health or
nancial risks from COVID. While some dashboards (see for example this Alabama dashboard27), embraced the
CDC’s Social Vulnerability Index, other key indicators of disaster resilience have been conspicuously absent in
pandemic planning dashboards and data monitoring. Below, we review past explanations from disaster scholar-
ship for pandemic resilience, including mobility, health care capacity, quality of health, governance capacity,
political partisanship, and social capital.
First, some communities might see higher rates of COVID-19 spread due to greater population mobility.
While past crises, like SARS and the Avian u28,29, highlighted that travel can spread viruses across borders,
this was complicated in the ongoing pandemic by the fact that many persons infected with COVID-19 remain
asymptomatic30. Among symptomatic patients, 95% of individuals manifest symptoms between 2 and 11days
aer contact with COVID, with a median of 5 days3133. Recent studies of COVID-19 spread have shown that
lockdowns, compulsory34 or not35, greatly reduced mobility and likely altered the course of the epidemic.
Second, communities with better staed and funded health care systems might respond more adroitly to
the pandemic. e capacity of health care systems, measured by better hospital quality and larger physician
workforces36,37, has been linked to better response quality both before4 and during COVID-1938. Similarly, more
capable governments which can eectively allocate resources, purchase appropriate protective gear, manage
information ow, enforce lockdowns, and ensure high shares of residents with health insurance may see better
health and pandemic outcomes34,39,40. Meta-analyses over the last 2years have highlighted common comorbidities
of COVID-194143. For example, Shakaib and colleagues’ meta analysis43 ranked comorbidities by prevalence as
(1) hypertension (~ 28.6% of patient deaths), (2) heart conditions and stroke (14.6% and 8.3%); diabetes (13.2%),
smoking and chronic lung disease (8.1% and 3.2%), chronic kidney and liver diseases (7.2% and 2.7%), and being
immunocompromised (4.8%). Obesity also co-occurs frequently with severe cases; in studies of New York City
hospital patients with severe cases of COVID-19, 35.8% to 60.6% reported obesity44,45; obese COVID-19 patients
tend to see 37% higher rates of death during hospitalization and risk of contracting pneumonia46.
ird, communities may see dierent mobility patterns depending on their levels of political partisanship.
Several notable Republican national and state election ocials consistently downplayed the COVID-19 pan-
demic, despite multiple superspreader events at the White House in 202047. ese sentiments played out among
the general public: By mid-July 2020, just 45% of Republican voters ranked COVID-19 as a major threat to the
country compared to 85% of Democrats48. Multiple studies have highlighted that Republican elected ocials
and voters have been less likely to adopt social distancing and other COVID-19 prevention measures than their
Democratic peers4951.
Fourth, some communities see worse resilience to crisis due to their levels of social vulnerability6. In both
pandemic and disaster studies, socially vulnerable residents fare worse in terms of resilience to crisis due to
employment challenges, limited mobility, and the fear of discrimination or retribution from neighbors or local
authorities. Groups more socially vulnerable to disaster including women and single parents47, families facing
unemployment or poverty52, the elderly53, the LGBTQ+ community54, the disabled55, and racial, religious, or
ethnic minorities56. During COVID, Black Americans have overwhelmingly felt a higher burden79,57, with 1 in
1000 African Americans dead from COVID-1958 at the same time as crushing nancial loss, with over 40% of
Black-owned businesses in the US closed permanently this past year59. However, previous research60 has found
that state and local governance has made a dierence in the pandemic for socially vulnerable communities.
Counties considered highly vulnerable based on the CDC’s Social Vulnerability Index that enacted face mask
requirements, gathering restrictions, and stay-at-home orders experienced reductions in the average number of
COVID-19 deaths, compared to similarly high vulnerability counties that did not enact these non-pharmaceutical
Finally, some communities, even facing high social vulnerability or weak health care response, have managed
better pandemic outcomes than others due to their social capital. Social capital—the social ties that bind together
a community and enable trust, reciprocity, and collective action14—serves as a key resource for residents respond-
ing to disaster24, closely correlated with health outcomes16 and recovery outcomes aer crisis18,61. Recent survey
and aggregator level research has demonstrated that residents and communities with stronger social capital were
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more likely to socially distance62, see fewer cases and fatalities10,11, and slower spread of COVID-1912. However,
disaster studies acknowledge the Janus-faced nature of social capital, in which social capital can generate both
positive and negative outcomes depending on the type of social ties fosters63. Indeed, recent studies of social
capital highlight that vertical social ties to government ocials are especially key, relative to others22,64.
Social capital comes in three forms: bonding, bridging, and linking social capital65. Bonding social capital
refers to close ties between members of the same social circles, facilitating trust and mutual aid among friends
and family members23,66. Bridging social capital describes association ties between members of dierent social
groups, built through workplaces67, unions68, volunteering17, sports clubs14, and local associations69. Bridging
ties are the lifeblood of democracy, helping residents build shared stake in their community and enabling close
cooperation during and aer crisis24,69. Finally, linking social capital refers to vertical ties connecting residents
to local, state, and national authorities15. ese linking ties help residents access key public goods from elected
ocials and instill trust in government25,70, which has been linked to better public compliance with public health
protocols during outbreaks71, including SARS72, Ebola73, and COVID-19 crises64. We might expect that linking
and bridging ties aid COVID-19 response, while bonding, insular ties might limit the spread of information and
lead to less resilient response under certain conditions63.
is study examines what kinds of communities experience greater outbreaks of COVID-19, and to what degree
local level measures of social capital and vulnerability can predict those outbreaks. As our main outcome of
interest, this study investigates COVID-19 test positivity rates (the percentage of tests that come back positive)74,
approximating the spread of COVID-19, widely adopted in past studies of community spread7579. While case
incidence rates are biased because some states test more than others, test positivity rates adjust for the number of
total tests performed in an area. ey are also now widely available in some cities and regions at extremely local
geographies—the census tract, zipcode, and county subdivision levels being the focus of this paper.
is study adapts existing, validated methodologies for measuring social capital at the county level19 to new
measures at the census tract, zipcode, and county subdivision levels. is study generated social capital indices
at the census tract, zipcode, and county subdivision levels, and applied them to case studies within and across
counties in Wisconsin, New York, Illinois, and Massachusetts. Each index was measured at the census tract
Figure1. Geographic distribution of social capital indices at the census-tract level. Violins depict distributions
of social capital indices for each of the 9 US Census-bureau designated geographic divisions in the US.
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level, and their distributions are visualized across US Census Divisions in Fig.1 to demonstrate their degree of
geographic variation.
As shown in Fig.2, these case studies demonstrated considerable variation in overall social capital com-
pared to the national median, with urban areas showing especially troublingly low levels of social capital. Below
we explore how this variation might shape resilience to COVID-19, summarizing ndings from these models
(TablesB1–B3). Model tables in the Supplementary Information appendix report the projected increase in log-
test positivity rates given an increase of one standard deviation in the predictor; all eect sizes are comparable
among predictors.
Variation in COVID‑19 spread explained. ese models explained considerable amounts of variation
in the outcome, as shown by the R2 statistics in TablesB1–B3. For models across counties in TableB1, our fully
specied models explained 47–55% of variation in test positivity rates. However, even simpler versions explained
high levels of variation too: Our models in TableB2 with just social capital measure predicted 41–49% (Models
1A, 2A, and 3A), increasing to 43–54% when paired with social vulnerability indicators (Models 1B, 2B, and
3B) (see “Methods”). is is considerable, especially considering the great dierences between dierent kinds
of counites involved (eg. all of Wisconsin). For models within counties, this includes anywhere from 8 to 89% of
variation in the outcome, and 30–90% including xed or random eects. e best explained cases were Brooklyn
(90%), Queens (81%) and Manhattan (87%)..
Figure2. Variation in overall social capital among case studies. is ugure depicts communities as zipcodes,
census tracts, or county subdivisions. Shading represents social capital, measured as a modied Z-score,
showing distance from the national median standardized by the median absolute dierence (MAD). White
indicates national median level of social capital. Blue shows MAD-calculated standard deviations above median,
while red depicts below median. Maps made in R (version 4.0.3) using the sf package (version 1.0-6)81.
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Figure3. Variation explained by model covariates over time. Points depict variation explained (R2 statistic)
from dozens of fully specied OLS models for each time-step. Indicates substantial variation explained by model
covariates, separate from time.
Figure4. Marginal eects of social capital on COVID-test positivity rates. Bands depict marginal eect on
test positivity rates as specied type of social capital varies by 2 standard deviations around the mean, holding
all other variables at means and modes. Bands reveal varied eect of social capital subtypes depending on
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Figure3 contextualizes this by displaying daily versions of these models among each case study. Our New
York City models explain the model variation (60–80% in most zipcodes), all other models explained anywhere
between 5 and 55% of variation, each crossing at least 40% at some point in the study period. Even when regressed
as dozens of individual models, in Fig.3, we see that model covariates can explain quite high levels of variation
over time.
Eects of social capital on COVID‑19 spread. is analysis produced three general trends, highlighted
in Fig.4 using marginal eects, calculated holding all covariates at their means while varying each specic type
of social capital. To ensure comparable eect sizes, all predictors in all models were rescaled as Z-scores before
modeling, so that beta coecients show the projected increase in the logged outcome as a predictor increases by
1 standard deviation from the mean (frequently called ‘standardized beta coecients’). Similarly, Fig.4 projects
the test-positivity rate with 95% condence intervals for an otherwise average community as each type of social
capital independently increases from −2 to 0 to 2 standard deviations from the mean level in each sample.
First, aer adjusting for social vulnerability, health care capacity and conditions, governance capacity, and par-
tisanship, we nd that linking social capital, a close proxy for trust in government, is oen negatively associated
with test positivity rates. is occurs frequently across all within county models, and is shown by the consistently
downward trending yellow bands in Fig.4. Signicant negative associations were found in TableB3 for Madison
(beta = −0.13, p < 0.001), Brooklyn (beta = −0.04, p < 0.001), Queens (b eta = −0.28, p < 0.003), and Manhattan
(beta = −0.04, p < 0.001). Similar negative trends were found at large in TableB1 for Wisconsin (beta = −0.13,
p < 0.001) and New York City (beta = −0.006, albeit with low signicance at p = 0.823); the main exception was
Massachusetts, which saw higher test-positivity rates (beta = 0.06, p = 0.058).
Second, we see mixed track records for bonding and bridging social capital. At large, bonding and bridging
ties were linked to lower test-positivity rates: Our across-county models in TableB1 show that communities
with strong bonding social ties saw lower test positivity rates, with signicant associations for Massachusetts
(beta = −0.14, p < 0.007) and Wisconsin (beta = −0.04, p < 0.001). Even moreso, communities with strong bridging
ties saw signicant negative associations for Massachusetts (beta = −0.85, p < 0.001), New York City (beta = −0.11,
p < 0.003), and Wisconsin (beta = −0.32, p < 0.001).
However, for within-county models in TableB3, the evidence for bonding and bridging ties diverges.
Bridging ties were linked to lower COVID-19 spread in Madison (beta = −0.05, p = 0.100) and Manhattan
(beta = −0.08, p < 0.001), with negative trends in Chicago with limited signicance (beta = −0.13, p = 0.696) and
Queens (beta = 0.03, p = 0.756). Similarly, bonding ties were linked to lower COVID-19 spread in Manhattan
(beta = −0.18, p < 0.001), with less signicant negative trends in Chicago (beta = −0.103, p = 0.684) and Madison
(beta = −0.06, p = 0.153).
However, several cases diverged in TableB3. Greater bonding ties were linked to greater disease spread in
Brooklyn (beta = 0.04, p < 0.001), the Bronx (beta = 0.08, p = 0.012), and Queens (beta = 0.66, p < 0.001). And
greater bridging ties were linked to greater spread in Brooklyn (beta = 0.03, p < 0.001). Indeed, these communities
were hard hit by COVID-19 early; Brooklyn is home to Spring Creek Towers, the subsidized housing develop-
ment which suered an outbreak leading their zipcode to face the highest death rate from COVID-19 in New
York in the rst 3 months of the outbreak, while the Bronx overall faced the highest rates for COVID-19 cases,
hospitalizations, and deaths in the rst 3 months80.
Figure5. COVID-19 test positivity rates in New York City. Zipcodes of 5 boroughs based on whether above or
below the median level of social capital in the NYC area. Blue lines depicting counties, a color scale depicting
distance from the median COVID-19 test positivity rate (shaded white), averaged over time. Maps made in R
(version 4.0.3) using the sf package (version 1.0-6)81.
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Yet this is not necessarily a contradictory nding. Past studies of disaster indicate that bonding social capital
is ckle24; it can help family, friends, and members of the same social circle team up and share resources, but it
can also promote insular social circles and inhibit the spread of quality information. In other words, we might
expect that in historically marginalized communities where trust in local and national authorities is limited, that
bonding social capital might not help these communities as much as bridging or linking social capital.
Spatial dierences in eects of social capital on covid‑19 spread. Figure5 highlights these trends
in three panels, which highlight using black borders the zipcodes with the highest bonding (le panel), bridging
(center panel), and linking social capital (right panel), compared to the COVID-19 test positivity rate, colored
from blue (far below median) to white (median) to red (far above median). We see that bridging ties aided
some parts of the city: many Bronx and Brooklyn zipcodes, with summarily low bridging social ties, saw high
COVID-19 outbreak levels, while Manhattan zipcodes with strong bridging ties saw low COVID-19 outbreaks.
In contrast, Staten Island experienced high test positivity rates on average, despite having strong bonding, bridg-
ing, and linking social capital, possibly leading to New York City’s overall skewed, positive eects of bridging and
linking social capital on COVID-19 spread.
ese ndings about bonding and bridging social capital at rst glance defy past ndings in disaster studies,
where aer adjusting for bonding, bridging, and linking social capital, bridging ties, fostered through associa-
tions, typically encourage greater resilience to crisis than bonding, in-group ties. However, the nature of COVID-
19 is especially challenging to bridging social capital. While linking social ties, as usual, help promote trust in
public health and disaster response, bridging ties are usually indicated by connectedness to multiple dierent
parts of society. In this case, that makes bridging residents in a community both capable of great good, reaching
out to neighbors and supporting each other, but also capable of spreading the virus to new groups.
However, as highlighted by bridging social capitals negative eects on COVID-19 spread within the Manhat-
tan, Madison, New York City, and Wisconsin at large, many communities with strong bridging social ties are
developing important innovations to respond to COVID, ranging from virtual religious services and clubs to
masked volunteer outreach and volunteering by teens out of school. For example, the Jewish Center located in
Manhattan’s Upper West Side, which typically can accommodate 500 of its faithful, now can only accommodate
60 members who must pre-register and have their temperature taken on site before entering. Similarly, the Dar
Al-Dawah mosque in Astoria, Queens now limits their in-person attendance to 64 people, requiring temperature
checks and hand sanitization at the door for entry. ose who worship in-person are required to wear a mask
and bring their own prayer rugs and place them in designated spots that allow for six feet of separation. Dar
Al-Dawah has also oered additional services during religious holidays to accommodate higher demand for
in-person, community worship82.
In other words, while strong bridging ties may not mean an automatic transition to resilience to the pandemic,
they represent an important reservoir that city and health care ocials can draw on to mobilize communities,
provide aid, and address the "twin pandemics" of COVID’s outright eects and its disproportionate eects on
Black Americans.
is study measured bonding, bridging, and linking social capital for each census tract, zipcode, and county
subdivision between 2010 and 2018 in the United States. en, we applied these indices to predicting COVID-
19 test positivity rates in Massachusetts, New York, Illinois, and Wisconsin, zooming in further to several urban
areas, including Chicago, IL, Madison, WI, and each of the ve boroughs of New York City. Broadly, we found
that linking social capital is frequently tied to less COVID-19 spread (with Massachusetts as a contrarian case),
while bonding and bridging social demonstrate mixed eects depending on the underlying social and racial
histories of these communities.
In particular, we nd that the eects of social capital are not uniform but divergent depending on whether
those social ties link in-groups (bonding), bridge dierent groups (bridging), or engender trust in ocials (link-
ing), and the local context in which they are built.
Contributions to the literature. Strong evidence was found supporting our hypothesis that social capital
indices are strong, signicant predictors of COVID-19 spread rates. Across counties, communities with stronger
bonding social capital see lower rates of COVID-19 spread. Recent research on matched samples of US coun-
ties showed similar results, where bonding social capital seemed to help families and friends shelter and rein-
forced norms to wear masks and physical distance22. is largely conrms the trends found in past studies10,12,
including those which use robust metrics like excess death rates22. It is worth noting that overall, when analyzed
descriptively in Fig.6s scatterplots, each type of social capital was negatively correlated with COVID-19 spread.
e main exception in Fig.6 is New York City, where linking social capital was related to worse spread. ere
are several reasons why this might be the case. First, Staten Island is home to 5% of New York City’s population,
but residents accounted for a quarter of COVID-19 fatalities in late 2020. e politically conservative borough
has struggled with low resident participation in public health measures, including diculty enforcing restric-
tions on indoor dining, residents refusing to wear masks83. And in Brooklyn, public health messaging has had
diculty penetrating some communities despite strong social ties. For example, tensions between Mayor Bill
DeBlasio and the Orthodox Jewish community over COVID-19 have ared since March 2020, when the mayor
sent police to break up a local rabbi’s funeral and disperse crowds to prevent COVID-19 transmission. Such
confrontations have been attributed to a decline in trust in public health measures and government response,
and helped circulate rumors discouraging COVID-19 testing84.
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However, aer applying statistical controls, we found that each type of social capital produced varying eects
depending on geography. When we zoomed into urban settings, bonding social ties took on mixed eects. In
three out of six urban areas studied, communities with stronger bonding ties on average saw higher rates of
COVID-19 spread, matching past ndings in disaster studies, where bonding social ties can promote insular
Further, bridging social capital showed varying eects, leading to lower spread in Massachusetts, Chicago,
Brooklyn, the Bronx, and Wisconsin (although these last two eects had lower statistical signicance, where
p > 0.10). We theorize that bridging ties help ameliorate the deleterious eects of bonding social capital when
present, but that since many bridging ties are formed in civil society groups and associations, they were occasion-
ally the cause of early superspreader events. is study suggests that local planners should carefully inventory
their community resources when responding to pandemics, in order to proactively channel bridging and linking
social capital early into better public response.
Limitations. Finally, this study came with several limitations. First, we relied on aggregate indicators
sourced annually from the American Community Survey to make inferences about the state of social capital in
communities. Future studies should ground-truth these measures by comparing them with survey responses in
local communities. Second, this study also relied on a handful of indicators at the zipcode or county level, which
were then averaged down onto census tracts, due to lack of availability. is primarily aected the bridging and
linking indices, which fortunately still retain much variability within counties.
Figure6. Social capital (mostly) reduces COVID-19 spread. Bivariate Scatterplots of social capital index scores
compared to test positivity rates over, with lines of best t depicting weak-to-strong negative associations for all
except linking social capital in New York City.
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ird, as discussed in the “Methods” section, we relied on county averages to ll in some indicators of
bridging social capital at the census tract level; future studies should identify comparable correlates or principal
components to approximate such measures. Fourth, this study focused on data from 2010 to 2018, due to the
limited availability of bridging and linking indicators, but future studies should extend this backwards to 2000.
Finally, this observational study showed associations between social capital and COVID-19 spread, but fur-
ther studies are needed to verify whether these associations could be causal. Other factors may also inuence
COVID-19 spread depending on geographic context, including pollution85,86 and disaster damage87; natural
and quasi-experiments may be helpful tools for further disentangling social capital’s eects from confounders22.
Despite these limitations, these local level indices produce a considerably granular picture of the state of social
capital in the US, far more so than previously available, and are a powerful tool for predicting the spread and
abatement of COVID-19.
Policy implications. In summary, this study analyzed the state of social capital in the United States between
2010 and 2018, generating estimates for bonding, bridging, and linking social capital in 72,877 census tracts over
9years. rough geographic aggregation, we have produced estimates at the census tract, zipcode, and county
subdivision level, which can serve as a resource for scholars and policymakers involved in disaster and pandemic
response and recovery eorts. Further, this study identied through 3 regional studies and 6 urban case studies
the considerable statistical power of these indices in predicting the spread of COVID-19.
We found considerable evidence that bonding social capital reduces COVID-19 spread at large, but especially
in our urban case studies, this trend reversed. Instead, several cities displayed divergent trends, known as the
Janus-faced nature of social capital63, where the insular, homophilous social networks fostered by bonding social
capital were associated with greater COVID-19 spread (eg. Brooklyn, the Bronx, & Queens), while the bridging
networks, trust, and reciprocity fostered by bridging social capital were associated with reduced COVID-19
spread in others cities (Madison, Manhattan, New York City at large, etc.),. Finally, this study found continued
evidence that linking social capital is oen negatively associated with COVID-19, matching past ndings that
trust in government and public health ocials is key to pandemic prevention and response22,64,7173. However,
the positive association between linking ties and COVID-19 spread in Massachusetts hint that these trends are
not set in stone and may uctuate depending on local crisis conditions.
ese ndings highlight that while horizontal social ties usually aid in community resilience, local policy-
makers should pay special attention to encouraging trust and reciprocity among residents. is is especially vital
considering that rising political polarization has been accompanied by pandemic denial from some state ocials
and rejection of social distancing and mask mandates among residents4951.
Future studies should apply these indices to examine how social ties aected the movements and patterns
of residents, economic activity, and physical and mental health during the pandemic—all important adjacent
indicators of risk and resilience during the COVID-19 crisis. Further, scholars and policymakers should use these
indices to identify communities as similar as possible to their own in terms of social capital in order to make
projections about their communities’ recovery trajectories. Finally, scholars should apply these indices to diag-
nosing and explaining a wide variety of other community resilience, highlighting the close relationships between
social capital and policy-relevant social outcomes, including health16, political polarization4951,88,89, adaptation
to climate change90, and resilience to future disasters25,55,91. We hope that these indices galvanize social capital
scholars to examine the role of social capital in their communities’ recovery and to encourage greater attention
to the role of residents and community planning in recovery and response to crisis.
is study examines what kinds of communities experience greater outbreaks of COVID-19, measuring to what
degree local-level measures of social capital and vulnerability predict those outbreaks. Below, we (1) outline our
methodology for adapting these measures to the local level, and (2) describe our modeling procedures for case
studies demonstrating the uses of these indices in predicting COVID-19 spread.
Measuring social capital at the local level. To measure social capital, we took the average of three
subindices (bonding, bridging, and linking) in each census tract, built out of 20 indicators total, listed in Table1.
ese indicators were outlined and validated for the original county level social capital index19, but we describe
them below as well.
First, to represent bonding social capital, we use 9 indicators describing how similar residents in a community
are in terms of race, age, class, gender, language, and communication capacity, because homophilous communi-
ties tend to have strong bonding social ties98,99. To represent race similarity, we used a fractionalization approach
to measure how fractionalized a community is into dierent racial categories, where 0 represents homogeneity
and 1 represents heterogeneity92. We repeated this approach for ethnicity similarity, using the share of residents
which identied as Latino or Hispanic, or not92, as well as similarity between genders by income95. To represent
educational equality, we calculated the negative absolute dierence between the share of residents with a college
education compared to those which did not graduate high school93. Each of the aforementioned negative meas-
ures were then reverse-coded, so that a low value denotes low heterogeneity while high values denote homoge-
neity. Finally, we measured four more positive measures of homogeneity. To represent employment equality, we
calculated the absolute dierence between the share of the employed and unemployed labor force, because this
indicates that most of the labor force is similarly employed96. To represent language competency, we used the
percentage of residents who speak English prociently; to represent communication capacity, we used the share
of households with a telephone; while to represent age, we used that share of residents below 65years of age21,94.
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Second, to represent bridging social capital, we use 6 indicators of membership in associations that can
bridge dierent parts of society. We measured the number of religious organizations93, civic organizations, social
advocacy organizations96, charitable organizations, and unions68, normalized per 10,000 residents. Each of these
was obtained from the Zipcode Business Patterns census, and then averaged to the census tract level. Finally, we
supplement this with one county tally not available at the zipcode level, the percentage of residents participating
in a fraternal order.
ird, to represent linking social capital, we use 5 indicators of connection and representation. To represent
political linkages, we gathered the share of voting age eligible citizens94, while to represent government linkages,
we measured the share of local, state, and federal government employees per capita97. We supplemented these
census tract level measures with one county level measure of political activities, namely the share of residents
who attended a political rally, speech, or organized protest that year95.
is culminated in four indices, including bonding, bridging, and linking social capital, as well as overall
social capital, which represents the average of its three subindices. To combine indicators, we use the mean,
because this best approximates the midpoint of a small number of indicators for a given census tract. is is
better compared to the median, which could potentially completely ignore the inuence of the lowest indicator
in favor of more commonly occurring values (for an example, see Fig.A1). Each index was measured at the
census tract level, and their distributions are visualized across US Census Divisions in Fig.1 to demonstrate
their degree of geographic variation.
is data comes with several caveats. e indicators for these indices, presented in Table1, are based on
various tallies pre-aggregated by the census to the lowest level at which they would provide these data (eg. tract,
zipcode, or county subdivision). It was not possible to test the geographic distributions of these tallied units (eg.
people or organizations) within a census tract, zipcode, or subdivision; that would require individual records
for each organization, but the census does not provide these, so as to preserve the privacy of respondents. ese
indices cannot explore spatial variation within a community; as such data becomes available, we encourage future
studies to test such questions. Fortunately, they are well suited to expose spatial variation in social capital among
communities, as shown below in Fig.1, and later, in Fig.2.
We present the distributions of our indicators using density plots in Appendix Fig.A2; indicators largely
retained a bell curve shape, except for a handful, such as language competency and communication capacity.
However, we view this as important evidence of variation in aspects of bonding social capital; it is important
to up- or downweight- communities’ bonding indices based on these observed trends, since such factors are
Table 1. Indicators for census-tract level social capital indices. a Filled in missing census tract values with
average value from census tracts in that county. Missing data tally reects aer imputing county median for
specially marked zipcode bridging social capital measures. b Used county level measure, because comparable
measures were unavailable at local levels.
Index Concept Indicator Eect on index Level Years Missing data (%)aLiterature
Race similarity Race fractionalization (0 = homogeneity, 1 = het-
erogeneity) Tract 2010–18 0.9% 92
Ethnicity similarity Ethnicity fractionalization (0 = homogeneity,
1 = heterogeneity) Tract 2010–18 0.9% 92
Education equality Negative absolute dierence between % of resi-
dents with college education vs. did not graduate
high school Tract 2010–18 1.4% 93,94
Race/income inequality Gini coecient (0 = equality, 1 = inequality) Tract 2010–18 1.2% 21
Employment equality Absolute dierence between % employed and
unemployed labor force + Tract 2011–18 1.0% 95
Gender income similarityaGender income fractionalization (0 = homogene-
ity, 1 = heterogeneity) Tract 2010–18 3.4% 68
Language competency % Procient English Speakers + Tract 2010–18 1% 94
Communication capacity % Households with telephone + Tract 2010–18 1.5% 21
Non-elder population % Below 65years of age + Tract 2010–18 1% 94
Religious Organizations Religious organizations per 10,000 persons + Zipcode 2012–16 0.4% 93
Civic OrganizationsaCivic organizations per 10,000 persons
Social Advocacy organizations per 10,000
persons + Zipcode 2012–16 0.7% (9.3%)
2.5% (22.0%) 96
Social embeddedness—charitable tiesaCharitable organizations per 10,000 persons + Zipcode 2012–16 2.3% (20.7%) 68
Social embeddedness—Fraternal tiesbMember of fraternal order (% of total) + County 2010 0% 68
Social embeddedness—Union ties Unions per 10,000 persons + Zipcode 2012–16 3% 68
Political Linkage % of voting age citizens eligible for voting + Tract 2010–18 1% 94
Local government linkage % local government employees (per capita) + Tract 2010–18 1% 97
State government linkage % state government employees (per capita) + Tract 2010–18 1% 97
Federal government linkage % federal government employees (per capita) + Tract 2010–18 1% 97
Political linkage-political activitiesb% Attended political rally, speech, or organized
protest + County 2010 0% 95
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Scientic Reports | (2022) 12:6566 |
closely involved in building in-group ties. e exception was for rates, including all bridging social capital indica-
tors (organizations normalized by population) and 3 linking indicators (government employees normalized by
population). Because rates are always right-skewed, we log-transformed these (before rescaling from 0 to 1) to
be more comparable to other variables. Finally, to deal with outliers, while retaining the general shape of these
distributions, we capped all indicators at their 2.5th and 97.5th percentiles.
Missing data. While 15 out of 20 indicators were missing 1% of data points or less, 3 indicators were missing
considerable amounts of data. ese variables included social advocacy groups (missing 22%), charitable organi-
zations (20.7%), and civic organizations (9.3%). While complete data is obviously preferable, these associational
measures are vital to the measurement of bridging social capital. Despite these missing data points, most of these
census tracts were surrounded by other census tracts where this data was widely available. As a result, we use a
two-stage imputation process, rst imputing missing data for these ve variables alone with each census tracts
county median value. is reduces missing data considerably, and ensures that our indices are geographically
consistent. Aer county median imputation, social advocacy groups lack only 2.5% of data points, charitable
organizations lack just 2.3%, and civic organizations lack just 0.7%. ese are much better levels of missing data.
To ll in remaining data points, we leverage the statistical power of our dataset of 72,877 census tract obser-
vations from 2010 to 2018, using multiple imputation using the Amelia II soware (version 1.76) in R (version
4.0.3)100. Using time-series imputation and melding results across 5 imputed datasets improves imputation for
missing data over time, because it lls in data points from, for example, 2013, by taking into consideration sen-
sible values given that census tract’s values in 2012 and 2014. While complete data is preferable, this two-handed
strategy helps keep missing data points geographically similar to those in their county and temporally similar
to observations of the same census tract over time. Our indices reect local level variation considerably better
than would a local level index that excludes these indicators.
Aggregation. Finally, we aggregated these census tract measures to the zipcode and county subdivision
levels as well. Since these geographies do not overlap perfectly, for each zipcode or county subdivision, we identi-
ed all census tracts that overlap with these jurisdictions and took the median index score for each index, which
best captures the most likely index score for that tract given its surroundings. We repeated this process with the
CDC’s 2018 Social Vulnerability Index20,101 and its four subcomponents, to enable comparisons between social
capital and social vulnerability indices at the census tract, zipcode, and county subdivision levels.
Variables. Finally, to demonstrate the value of these granular social capital indices, we modeled the eects
of social capital on COVID-19 spread in several case studies, to be discussed further below. is study uses the
following variables to represent key factors in COVID-19 spread.
As our outcome variable, we used the test positivity rate—the percentage of tests that return positive. is is a
useful outcome to measure, because it highlights how widespread infection is in a community, while controlling
for the amount of testing in a locale74. In contrast, straight case rates and death rates may miss unidentied cases.
(Other ideal measures, such as excess deaths rates, are not currently available below the county level). Test posi-
tivity rates are a useful proxy for the spread of COVID-19, because these rates closely correlate with COVID-19
case rates, rates of residents with COVID-19 antibodies77, hospital admissions rates75, and death rates76. Rates
of positive tests have been used to measure spread in studies in US counties102, Louisiana county subdivisions87,
and New York City zipcodes78,79, among others.
Since this outcome is right skewed, we log transformed it and applied linear models, as discussed below.
Because our outcome is a percentage, these models already adjust for the size of the population.
For each case study, we tested the eects of bonding, bridging, and linking social capital on test positivity
rates, controlling for the CDC’s for 4 social vulnerability sub-indices. ese include (1) socioeconomic status, (2)
minority status and language, (3) household composition and disability, and (4) housing type and transport20,94.
Additionally, we controlled for population mobility, measured over time at the county level, using the aver-
age daily change in workplace mobility, estimated with Google android user movement103. ese measures have
been frequently used to study COVID-19 and mobility (eg.11,51,62). We averaged movement between two and
eleven days prior to each observation, since 95% of individuals manifest symptoms between 2 and 11days aer
contact with COVID-193133.
Finally, we also controlled for several other constant county level traits. ese include health care capacity,
which we measured by averaging two rescaled indicators, including the number of primary care physicians per
100,000 residents in 201736 and the number of preventable hospital stays per 100,000 residents in 2017, which
we reverse scaled104. We also measured overall quality of health by averaging seven indicators. ese include the
precentage of residents identied as current smokers in 2017, drinking excessively in 2017, who reported being
physically inactive in 2016, who had diabetes in 2016, who were obese in 2016, who reported experiencing poor
physical health over 14days in a month in 2017, and the age-adjusted premature mortality rate (deaths under age
75) between 2016 and 2018. ese measures were gathered from the County Health Rankings105. We also control
for governance capacity, measured by the number of municipal employees per capita from the American Com-
munity Survey in 2018, and for partisanship, measured by the share of residents who voted for the Democratic
presidential candidate in 2016 using data from the MIT Elections Lab106.
Case studies. We applied this array of variables to two types of case studies, including (1) cases across coun-
ties and (2) cases within counties. ese cases were located in the Northeast, Mid-Atlantic, and Midwest, to
highlight indices in dierent regions and geographic levels, where COVID-19 outcome data had been reported
at these granular levels. (While it is rare for entire states to report these outcomes, many cities do).
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First, we examine variation across counties at our three levels of interest, drawing from (1) weekly test positiv-
ity rates over a 7-day period in 1393 Wisconsin census tracts from April 6, 2020 to November 16, 2020, (2) daily
test positivity rates over a 7day period in 178 New York City zipcode tabulation areas within the ve boroughs
from May 18 to October 28, 2020, and (3) weekly test positive rates over a 14day period in 350 Massachusetts
county subdivisions from September 30 to November 19, 2020.
Massachusetts, Wisconsin, and New York City used the rate of molecular tests (PRC tests)107109, while Chi-
cago’s test positivity rates combine both molecular and antigen test results110. Despite including antigen testing,
Chicago retained the same negative associations for bridging and linking social capital found in Wisconsin,
Madison, Manhattan, Queens, and New York City, although Chicago’s trends demonstrated lower statistical
signicance (p-values = 0.30–0.70) (see “Results”). Further, it is worth noting that the study period (2020) largely
predates mass-availability of antigen tests in local convenience stores, so during this period, we expect that
molecular tests reect well the state of testing across dierent cities.
For these cases, we model the eect of social capital indices, controlling for social vulnerability indices, at
the census tract, zipcode, or county subdivision, and we then control for county-level traits including mobility,
health care capacity, overall health quality, governance capacity, and partisanship.
Our primary goal in these models is to evaluate and compare the eects of bonding, bridging, and linking
social capital indices across dierent geographic contexts. In contrast, some dierences between control variables’
eects, eg. mobility or governance capacity, are to be expected, due to dierent levels of governance capacity
and COVID precautions (although shared eects among control variables are always a good sign). To test the
consistency of social capital eects, in TableB2, we break down our original TableB1 models across counties,
showing to what degree the eects of social capital indices remain consistent when using just our independent
variables with no controls (Models 1A, 2A and 3A), basic controls (Models 1B, 2B, and 3B), and full controls
(Models 1C, 2C, and 3C, the same as Models 1–3 in TableB1). In the results, we report associations which persist
across each level of controls, as a validity check to ensure our results are not simply due to model specication.
en, for each model, we compared xed eects by date, random eects by jurisdiction, and random eects
by jurisdiction nested within counties. e advantage here is that by nesting within multiple counties, we can
control for county wide traits. We used Hausman tests to choose between xed, random, and nested random
eects. Given a statistically signicant Hausman test (in this case, p < 0.001), we reported xed eects, and oth-
erwise reported nested random eects, which t better than random eects alone. Models tables are presented
in the Supplementary Information.
Second, we examine variation within counties at the zipcode level and census tract level (there are usually
too few county subdivisions to do this otherwise). We draw from 77 census tracts in Madison, WI from April
6, 2020 to November 16, 2020, from 60 zipcodes in Chicago, IL from March 7 to November 14, 2020, and from
44 zipcodes in Manhattan, 25 in the Bronx, 37 in Brooklyn, and 60 in Queens between May 18 and October 28,
2020. (Staten Island could not be modeled individually due to considerable collinearity between multiple social
capital and vulnerability indicators). Each model includes social capital and social vulnerability indicators, with
xed eects by date or random eects by jurisdiction. Since county variables are time-invariant or correlate
with temporal xed eects, they cannot be included in these models, which are intended to use as few variables
as possible, to demonstrate the considerable predictive power of these main indices together. Signicant Haus-
man tests (in this case, p < 0.001) led us to use xed eects for models of Madison, Brooklyn, and Manhattan,
while results of limited statistical signicance (p ~ 0.90) led us to use random eects for Chicago, the Bronx, and
Queens. ese are visualized in Fig.4, using marginal eects111 calculated in the ggeects package (version 1.1.1)
in R (version 4.0.3)114, as described in the “Results”.
Finally, we repeated all nine models (within counties and across counties) as simple OLS models for each
timestep to demonstrate the high predictive power of social capital and controls in each timestep. We use the
R2 statistic, the percentage of variance explained in the outcome, to represent this, and plot this over time for
each sample in Fig.3.
Goodness of t and validity. Figure3 required generating hundreds of models in a loop, but each of these
small OLS regression models fullled necessary assumptions112: First, no model demonstrated problematic col-
linearity in these models; all variance ination factor scores were below 10, the threshold for problematic colline-
arity. Second, there was no problematic heteroskedasticity; by modeling the data separately for each time period,
we removed any temporal relationships; further, heteroskedasticity aects standard errors, but not model-level
statistics like the R2 statistic, remedying any such concerns.
Further, in our reported models in TablesB1–B3, several models had highly collinear variables, usually
between social capital and social vulnerability variables. We performed a series of transformations on these
variables, which reduced collinearity for each variable, measured by the variance ination factor, to below 10,
the indicator for problematic levels, and near 2.5, the gold standard. In a handful of cases, problematic covari-
ates like governance capacity were dropped when transformations would not suce. ese steps are described
beneath each Supplementary Information table. Results of these models are presented above in the main text,
while model tables are listed in TablesB1–B3.
Ethics declarations. is study involved no human subjects and relied on only aggregate, publicly available
data and therefore did not require ethics review.
Data availability
All code necessary for replicating this study will be made available for replication on the Harvard Dataverse
(https:// doi. org/ 10. 7910/ DVN/ OSVCRC).
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Scientic Reports | (2022) 12:6566 |
Received: 4 September 2021; Accepted: 29 March 2022
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Author contributions
T.F., C.P., and D.A. wrote and edited the main manuscript text; T.F. designed methods and analysis; T.F. and C.P.
designed code and collected data; D.A. conceptualized framework and advised research. All authors reviewed
the manuscript.
Competing interests
e authors declare no competing interests.
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... When crisis strikes, communities with stronger social capital tend to evacuate (Fraser et al., 2022a), shelter (Fraser et al., 2021d), recover (Aldrich, 2012(Aldrich, , 2019, and rebuild (Page-Tan, 2021aFraser et al., 2021c) better than others, after floods, hurricanes, and fires (Fraser, 2022) and even during pandemics (Fraser et al., 2021c). Sites like libraries (Klinenberg, 2018), community centers (Aldrich and Kiyota, 2017), and parks (Takano et al., 2002) have been shown to make meaningful differences in neighborhood social capital (Small, 2009), health (Davern et al., 2017, quality of life (Montgomery, 2013), and resilience to crisis (Fraser, 2021a), including during COVID-19 (Aldrich, 2021;Fraser, 2021b;Fraser et al., 2022b). This is because they provide safer spaces for residents to interact in a masked, socially distanced capacity, and share important information with residents from all income and educational backgrounds. ...
... First, we compared correlations with indices of overall social capital, bonding, bridging, and linking social capital, which are census-tract level extensions (Fraser et al., 2022b) of a validated method for measuring social capital at the county-level (Kyne and Aldrich, 2020). These are the only available indices of social capital at such a granular level, while most social capital indices have been developed for the county level. ...
... These are the only available indices of social capital at such a granular level, while most social capital indices have been developed for the county level. These indices have been validated several times in other studies, showing strong internal and conceptual validity (See Kyne and Aldrich (2020); Fraser et al. (2021aFraser et al. ( , 2022b); this measurement approach has demonstrated expected associations in 10 more studies on community resilience, health, and environmental mobilization (See Appendix C for the full list of studies). ...
Scholars and policymakers increasingly recognize the value of social capital - the connections that generate and enable trust among people - in responding to and recovering from shocks and disasters. However, some communities have more social infrastructure, that is, sites that produce and maintain social capital, than others. Community centers, libraries, public pools, and parks serve as locations where people can gather, interact, and build social ties. Much research on urban spaces relies on Google maps because of its ubiquity and this article tests the degree to which it can accurately, reliably, and effectively capture social infrastructure. In this study, we map the social infrastructure of Boston using Google Maps Places API and then ground truth our measures, mapping social infrastructure on street corners with in-person site observations to evaluate the accuracy of available data. We find that though we may need to use multi-vectored measurement when trying to capture social infrastructure, Google maps serve as reliable measurements with a predictable, acceptable margin of error.
... At the economic level, people of low socioeconomic status suffer the social consequences of the virus in an amplified way because it is at high risk of impoverishment. Second, people with low social capital (Fraser et al., 2022) are more exposed to the virus: they have to accept risky jobs that they can provide for numerous social contacts; they often live in overcrowded houses; are forced to move repeatedly by public transport (Favretto et al., 2021). Third, of more clinical concern is the negative effect of the virus on the bodies of people with low social capital (Costa, 2022, Fraser et al., 2022, which live in disadvantaged conditions, as a result of the effect of the determinants of health on their state of wellness (Favretto et al., 2021). ...
... Second, people with low social capital (Fraser et al., 2022) are more exposed to the virus: they have to accept risky jobs that they can provide for numerous social contacts; they often live in overcrowded houses; are forced to move repeatedly by public transport (Favretto et al., 2021). Third, of more clinical concern is the negative effect of the virus on the bodies of people with low social capital (Costa, 2022, Fraser et al., 2022, which live in disadvantaged conditions, as a result of the effect of the determinants of health on their state of wellness (Favretto et al., 2021). ...
Full-text available
The globalization of contemporary society forces institutions to interact, in an ever-increasing way, with the concept of diversity. To date there is not a single definition, but the common element of the various notions concerns the particularity of the human in all its facets. The purpose of this paper is to analyze the relation between the concept of diversity and its relation to health. Diversity, in conclusion, becomes a challenge for any form of activity, both with research or with health implications. Above all, diversity presents itself as a challenge for the concrete and practical application of the paradigm of complexity.
... Usefully it centres social ties that enable trust, reciprocity and collective action and highlights the varying social and economic resources (such as friends, families and finances) particular groups have to respond to health crises such as epidemics and pandemics Fraser et al., 2022). Social capital theory provides a strengths-based approach to understanding marginalised groups responses to the pandemic that have been largely overlooked when the attention centres on homogenous national responses that overlook the dynamics of inter-ethnic social ties (Wong and Kohler, 2020). ...
... 15). Interestingly, another U.S. epidemiological study found that communities with strong bonding ties promote COVID-19 spread through insular social networks, while strong bridging ties help reduce that spread throughout the wider community (Fraser et al., 2022). This study highlights the importance of nuanced interrogation of the different levels of bridging, bonding and linking capital. ...
Full-text available
Later‐life migrants, as older people living away from their home nations, occupy multiply‐precarious positions in relation to national COVID‐19 pandemic responses. Concern has particularly centred on this group's increased risk of social and linguistic exclusion. We explore the perspectives of later‐life older Chinese and Koreans living in New Zealand during the nation's COVID‐19 lockdown of 2020. This paper presents a sub‐analysis of culturally‐matched interviews conducted with 3 Korean and 5 Chinese later‐life migrants. These participants are a sub‐sample of a larger qualitative interview study comprising 44 interviews. A social capital approach has been used to aid conceptualisation of participants' experiences and a reflexive thematic approach guided analysis. Despite their underrepresentation in national response efforts, Chinese and Korean later‐life migrants resourcefully participated in ethnically‐specific pandemic initiatives. Three themes identified were: (1) taking it seriously (2) already digitally literate (3) challenges and difficulties. Older Asian migrants engaged in a range of creative strategies to stay connected during COVID‐19 lockdowns which drew heavily on pre‐existing social capital. Future pandemic responses should seek to improve connectedness between the national government COVID‐19 response and older Korean and Chinese later‐life migrants.
... This is consistent with fundamental cause theory, which asserts that socioeconomic resources (e.g., education) are important for health outcomes, including risk of death from new diseases such as COVID-19 (Link and Phelan 1995;Phelan, Link, and Tehranifar 2010). In addition, social capital is associated with fewer COVID-19 cases and deaths (Borgonovi et al. 2021;Fraser, Aldrich, and Page-Tan 2021;Fraser, Page-Tan, and Aldrich 2022). Social capital reflects the resources and connectivity of communities, which is achieved through organizations and civic participation that share norms and values among members (Kawachi, Subramanian, and Kim 2008;Putnam 2000). ...
The authors examine how two state-level coronavirus disease 2019 (COVID-19) policy indices (one capturing economic support and one capturing stringency measures such as stay-at-home orders) were associated with county-level COVID-19 mortality from April through December 2020 and whether the policies were more beneficial for certain counties. Using multilevel negative binominal regression models, the authors found that high scores on both policy indices were associated with lower county-level COVID-19 mortality. However, the policies appeared to be most beneficial for counties with fewer physicians and larger shares of older adults, low-educated residents, and Trump voters. They appeared to be less effective in counties with larger shares of non-Hispanic Black and Hispanic residents. These findings underscore the importance of examining how state and local factors jointly shape COVID-19 mortality and indicate that the unequal benefits of pandemic policies may have contributed to county-level disparities in COVID-19 mortality.
... Resilient communities have much lower case-fatality rates of COVID-19, and the most-and least-resilient groups in the community are prone to interact with communities similar to theirs, with increased mortality once the disease invades [105,106] Social Capital (3%) Social capital helps residents adopt new behavioral norms [107] Social Assistance (4%) Patients with poor social resilience were sicker, but with no difference in mortality or discharge disposition after hospital admission [108] Vulnerable populations are one of the most critical elements of social resilience to COVID-19. Vulnerable populations in the community are important contributors to increased COVID-19 infection rates, including the elderly, adolescents, disability groups, and refugees (or international migrants); improving social resilience to COVID-19 requires going back to the root causes to help vulnerable populations address their problems, including psychological and physical issues [79][80][81][82][83][84][85][86]88]. ...
Full-text available
It has been more than two years since the outbreak of the COVID-19 epidemic at the end of 2019. Many scholars have introduced the “resilience” concept into COVID-19 prevention and control to make up for the deficiencies in traditional community governance. This study analyzed the progress in research on social resilience, which is an important component of community resilience, focusing on the current literature on the impact of social resilience on COVID-19, and proposed a generalized dimension to integrated previous relevant literature. Then, VOSviewer was used to visualize and analyze the current progress of research on social resilience. The PRISMA method was used to collate studies on social resilience to the pandemic. The result showed that many current policies are effective in controlling COVID-19, but some key factors, such as vulnerable groups, social assistance, and socioeconomics, affect proper social functioning. Some scholars have proposed effective solutions to improve social resilience, such as establishing an assessment framework, identifying priority inoculation groups, and improving access to technology and cultural communication. Social resilience to COVID-19 can be enhanced by both external interventions and internal regulation. Social resilience requires these two aspects to be coordinated to strengthen community and urban pandemic resilience.
... Economically, as McKibbin and Fernando (2020) highlighted, the short-and long-term fiscal and budgetary effects associated with COVID-19 point to the most significant recession in contemporary history. In addition, socially, the COVID-19 pandemic has influenced the daily lives of millions of people, from the obligation to follow social isolation rules to the planning and adoption of health measures (Saladino et al., 2020;Fraser et al., 2022). However, recent studies have shown that the impacts of the COVID-19 pandemic can be even broader, especially considering the potential effect of SARS-CoV-2 on non-target organisms (Charlie-Silva and . ...
The input of SARS-CoV-2 or its fragments into freshwater ecosystems (via domestic or hospital sewage) has raised concerns about its possible impacts on aquatic organisms. Thus, using mayfly larvae [Cloeon dipterum (L.), Ephemeroptera: Baetidae] as a model system, we aimed to evaluate the possible effects of the combined short exposure of SARS-CoV-2-derived peptides (named PSPD-2001, PSPD-2002, and PSPD-2003 – at 266.2 ng/L) with multiple emerging pollutants at ambient concentrations. After six days of exposure, we observed higher mortality of larvae exposed to SARS-CoV-2-derived peptides (alone or in combination with the pollutant mix) and a lower-body condition index than those unexposed larvae. In the “PSPD” and “Mix+PSPD” groups, the activity of superoxide dismutase, catalase, DPPH radical scavenging activity, and the total thiol levels were also lower than in the “control” group. In addition, we evidenced the induction of nitrosative stress (inferred by increased nitrite production) and reduced acetylcholinesterase activity by SARS-CoV-2-derived peptides. On the other hand, malondialdehyde levels in larvae exposed to treatments were significantly lower than in unexposed larvae. The values of the integrated biomarker response index and the principal component analysis (PCA) results confirmed the similarity between the responses of animals exposed to SARS-CoV-2-derived peptides (alone and in combination with the pollutant mix). Although viral peptides did not intensify the effects of the pollutant mix, our study sheds light on the potential ecotoxicological risk associated with the spread of the new coronavirus in aquatic environments. Therefore, we recommend exploring this topic in other organisms and experimental contexts.
Background: The USA struggled in responding to the COVID-19 pandemic, but not all states struggled equally. Identifying the factors associated with cross-state variation in infection and mortality rates could help to improve responses to this and future pandemics. We sought to answer five key policy-relevant questions regarding the following: 1) what roles social, economic, and racial inequities had in interstate variation in COVID-19 outcomes; 2) whether states with greater health-care and public health capacity had better outcomes; 3) how politics influenced the results; 4) whether states that imposed more policy mandates and sustained them longer had better outcomes; and 5) whether there were trade-offs between a state having fewer cumulative SARS-CoV-2 infections and total COVID-19 deaths and its economic and educational outcomes. Methods: Data disaggregated by US state were extracted from public databases, including COVID-19 infection and mortality estimates from the Institute for Health Metrics and Evaluation's (IHME) COVID-19 database; Bureau of Economic Analysis data on state gross domestic product (GDP); Federal Reserve economic data on employment rates; National Center for Education Statistics data on student standardised test scores; and US Census Bureau data on race and ethnicity by state. We standardised infection rates for population density and death rates for age and the prevalence of major comorbidities to facilitate comparison of states' successes in mitigating the effects of COVID-19. We regressed these health outcomes on prepandemic state characteristics (such as educational attainment and health spending per capita), policies adopted by states during the pandemic (such as mask mandates and business closures), and population-level behavioural responses (such as vaccine coverage and mobility). We explored potential mechanisms connecting state-level factors to individual-level behaviours using linear regression. We quantified reductions in state GDP, employment, and student test scores during the pandemic to identify policy and behavioural responses associated with these outcomes and to assess trade-offs between these outcomes and COVID-19 outcomes. Significance was defined as p<0·05. Findings: Standardised cumulative COVID-19 death rates for the period from Jan 1, 2020, to July 31, 2022 varied across the USA (national rate 372 deaths per 100 000 population [95% uncertainty interval [UI] 364-379]), with the lowest standardised rates in Hawaii (147 deaths per 100 000 [127-196]) and New Hampshire (215 per 100 000 [183-271]) and the highest in Arizona (581 per 100 000 [509-672]) and Washington, DC (526 per 100 000 [425-631]). A lower poverty rate, higher mean number of years of education, and a greater proportion of people expressing interpersonal trust were statistically associated with lower infection and death rates, and states where larger percentages of the population identify as Black (non-Hispanic) or Hispanic were associated with higher cumulative death rates. Access to quality health care (measured by the IHME's Healthcare Access and Quality Index) was associated with fewer total COVID-19 deaths and SARS-CoV-2 infections, but higher public health spending and more public health personnel per capita were not, at the state level. The political affiliation of the state governor was not associated with lower SARS-CoV-2 infection or COVID-19 death rates, but worse COVID-19 outcomes were associated with the proportion of a state's voters who voted for the 2020 Republican presidential candidate. State governments' uses of protective mandates were associated with lower infection rates, as were mask use, lower mobility, and higher vaccination rate, while vaccination rates were associated with lower death rates. State GDP and student reading test scores were not associated with state COVD-19 policy responses, infection rates, or death rates. Employment, however, had a statistically significant relationship with restaurant closures and greater infections and deaths: on average, 1574 (95% UI 884-7107) additional infections per 10 000 population were associated in states with a one percentage point increase in employment rate. Several policy mandates and protective behaviours were associated with lower fourth-grade mathematics test scores, but our study results did not find a link to state-level estimates of school closures. Interpretation: COVID-19 magnified the polarisation and persistent social, economic, and racial inequities that already existed across US society, but the next pandemic threat need not do the same. US states that mitigated those structural inequalities, deployed science-based interventions such as vaccination and targeted vaccine mandates, and promoted their adoption across society were able to match the best-performing nations in minimising COVID-19 death rates. These findings could contribute to the design and targeting of clinical and policy interventions to facilitate better health outcomes in future crises. Funding: Bill & Melinda Gates Foundation, J Stanton, T Gillespie, J and E Nordstrom, and Bloomberg Philanthropies.
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The purpose of this study is to examine how the COVID-19 epidemic has affected the working capital management practices of Amman Stock Exchange (ASE) companies. From 2012 to 2021, 101 firms were studied in the financial sector. The data was also examined using a Multiple Regression Model in the study. The results revealed that Covid-19 Pandemic has significant and negative effect on working capital management. According to the results, companies tended to take a relatively conservative approach to managing their working capital. More importantly, the data demonstrated that the COVID-19 pandemic crisis drove changes in working capital management practices. Companies with a high FL, QR, and CCC have attempted to increase their client base by prolonging the average age of their accounts receivable and decreasing the turnover rate of their liabilities, respectively. Companies with a greater CCC, as well as those whose principal current assets are accounts receivable, outperformed the other working capital management strategies.
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The current COVID-19 pandemic has affected societies across the world while its economic impact has cut deeper than any recession since the Second World War. Climate change is potentially an even more disruptive and complex global challenge. Climate change could cause social and economic damage far larger than that caused by COVID-19. The current pandemic has highlighted the extent to which societies need to prepare for disruptive global environmental crises. Although the dynamics of combating COVID-19 and climate change are different, the priorities for action are the same: behavioral change, international cooperation to manage shared challenges, and technology's role in advancing solutions. For a sustainable recovery from the COVID-19 crisis to be durable and resilient, a return to ‘business as usual’ and the subsequent often environmentally destructive economic activities must be avoided as they have significantly contributed to climate change. To avoid this, we draw lessons from the experiences of the waves of the COVID-19 pandemic and beyond to advance sustainable development.
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Partisan polarization significantly drives stress and anxiety among Americans, and recent aggregate-level studies suggest polarization may be shaping their health. This individual-level study uses a new representative dataset of 2752 US residents surveyed between December 2019 and January 2020, some U.S. residents report more days of poor physical and mental health per month than others. Using negative binomial models, zero inflated models, and visualizations, we find evidence that polarization is linked to declines in physical health: the more distant an individual feels politically from the average voter in their state, the worse health outcomes he or she reports. By uncovering the individual-level political correlates of health, this study aims to encourage further study and attention to the broader consequences of political polarization on American communities.
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This study investigates empirically how air pollution in earlier periods as measured by three air pollutants, namely N O 2, P M 10, and P M 2.5 may have affected the spread and fatality of COVID-19 in 31 European countries. Using panel data with fixed effects to examine the relationship between previous exposure to air pollution and COVID-19 new cases and COVID-19 deaths, we find that previous air pollution levels have both acted as an important factor in explaining the COVID-19 spread and its high fatality rate. This result may explain the negative impact that these pollutants may have on health and in particular on the respiratory functions that are mainly attacked by the virus. Supplementary information: The online version contains supplementary material available at doi:10.1007/s41885-021-00099-y.
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Objective This paper investigates the relationship between the positivity rate and numbers of deaths and intensive care unit (ICU) patients. In addition, it explores the use of the positivity rate as an indicator for the spread of COVID-19. Methods We used COVID-19 datasets for eight countries, including Canada, USA, UK, Italy, Belgium, Ireland, Colombia, and South Africa, and considered two correlation cases. The first case considers the correlation of the number of confirmed cases with each of the deaths and ICU patients. The second case considers the correlation of the positivity rate with each of the deaths and ICU patients. When obtaining the correlation, we considered different lagging periods between the date of confirming a case and the date of its ICU admittance or death. We compared the obtained correlation coefficient values for each of the two considered cases to explore whether the positivity rate is a better indicator for the spread of the disease than the confirmed cases. For each of the eight considered countries, we obtained the daily reproduction number using each of the confirmed cases and the positivity rate. The two obtained sets of reproduction number values for each country were statistically compared to investigate whether they are significantly different. Results When considering the daily positivity rate instead of the daily number of confirmed cases, the maximum correlation with the deaths is increased by 349.9% for the USA (the country with the highest increase) and 4.5% for the UK (the country with the lowest increase), with an average increase of 60.8% considering the eight countries. Considering the daily positivity rate instead of the daily number of confirmed cases caused the maximum correlation with the number of ICU patients to be increased by 74.7% for the USA (the country with the highest increase) and 2.2% for the UK (the country with the lowest increase), with an average increase of 25% over the considered countries. The results for the daily reproduction number obtained using the positivity rate are statistically different from those obtained using daily confirmed cases. Conclusion The results indicate that the positivity rate is a better indicator for the spread of the disease than the number of confirmed cases. Therefore, it is highly advised to use measures based on the positivity rate when indicating the spread of the disease and considering responses accordingly because these measures consider the daily number of tests and the confirmed cases.
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Accurate estimates of infection prevalence and seroprevalence are essential for evaluating and informing public health responses and vaccination coverage needed to address the ongoing spread of COVID-19 in each United States (U.S.) state. However, reliable, timely data based on representative population sampling are unavailable, and reported case and test positivity rates are highly biased. A simple data-driven Bayesian semi-empirical modeling framework was developed and used to evaluate state-level prevalence and seroprevalence of COVID-19 using daily reported cases and test positivity ratios. The model was calibrated to and validated using published state-wide seroprevalence data, and further compared against two independent data-driven mathematical models. The prevalence of undiagnosed COVID-19 infections is found to be well-approximated by a geometrically weighted average of the positivity rate and the reported case rate. Our model accurately fits state-level seroprevalence data from across the U.S. Prevalence estimates of our semi-empirical model compare favorably to those from two data-driven epidemiological models. As of December 31, 2020, we estimate nation-wide a prevalence of 1.4% [Credible Interval (CrI): 1.0%-1.9%] and a seroprevalence of 13.2% [CrI: 12.3%-14.2%], with state-level prevalence ranging from 0.2% [CrI: 0.1%-0.3%] in Hawaii to 2.8% [CrI: 1.8%-4.1%] in Tennessee, and seroprevalence from 1.5% [CrI: 1.2%-2.0%] in Vermont to 23% [CrI: 20%-28%] in New York. Cumulatively, reported cases correspond to only one third of actual infections. The use of this simple and easy-to-communicate approach to estimating COVID-19 prevalence and seroprevalence will improve the ability to make public health decisions that effectively respond to the ongoing COVID-19 pandemic.
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Rising partisan polarization in the American public over the last decade has been linked to stress and anxiety, raising questions about how communities and public health experts should respond. As the strength of an individual’s social network correlates with better health outcomes, could building a diverse set of connections moderate the effect of political polarization on an individual’s health? This study examines the role of social capital as a key intervening variable in the relationship between polarization and health. Drawing on a nationally representative survey of 2,752 U.S. residents conducted in December 2019 compared with county-level data, we use negative binomial, logit, and gamma models to examine the interaction between indicators of political polarization and bonding, bridging, and linking social capital on physical and mental health outcomes. We find consistent evidence that bonding social ties intervene to improve the physical and mental health of individuals in polarized communities, while bridging ties are related to worse health for politically isolated residents. By highlighting the relationship between polarization, social networks, and health, our findings shed light on how public health experts, and policymakers can improve health outcomes in polarized communities.
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Major disasters such as extreme weather events can magnify and exacerbate pre-existing social disparities, with disadvantaged populations bearing disproportionate costs. Despite the implications for equity and emergency planning, we lack a quantitative understanding of how these social fault lines translate to different behaviours in large-scale emergency contexts. Here we investigate this problem in the context of Hurricane Harvey, using over 30 million anonymized GPS records from over 150,000 opted-in users in the Greater Houston Area to quantify patterns of disaster-inflicted relocation activities before, during, and after the shock. We show that evacuation distance is highly homogenous across individuals from different types of neighbourhoods classified by race and wealth, obeying a truncated power-law distribution. Yet here the similarities end: we find that both race and wealth strongly impact evacuation patterns, with disadvantaged minority populations less likely to evacuate than wealthier white residents. Finally, there are considerable discrepancies in terms of departure and return times by race and wealth, with strong social cohesion among evacuees from advantaged neighbourhoods in their destination choices. These empirical findings bring new insights into mobility and evacuations, providing policy recommendations for residents, decision-makers, and disaster managers alike.
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Since its outbreak in 2019, the coronavirus disease (COVID-19) has become a pandemic, affecting more than 52 million people and causing more than 1 million mortalities globally till date. Current research reveals a wide array of disease manifestations and behaviors encompassing multiple organ systems in body and immense systemic inflammation, which have been summarized in this review. Data from a number of scientific reviews, research articles, case series, observational studies, and case reports were retrieved by utilizing online search engines such as Cochrane, PubMed, and Scopus from December 2019 to November 2020. The data for prevalence of signs and symptoms, underlying disease mechanisms and comorbidities were analyzed using SPSS version 25. This review will discuss a wide range of COVID-19 clinical presentations recorded till date, and the current understanding of both the underlying general as well as system specific pathophysiologic, and pathogenetic pathways. These include direct viral penetration into host cells through ACE2 receptors, induction of inflammosomes and immune response through viral proteins, and the initiation of system-wide inflammation and cytokine production. Moreover, peripheral organ damage and underlying comorbid diseases which can lead to short term and long term, reversible and irreversible damage to the body have also been studied. We concluded that underlying comorbidities and their pathological effects on the body contributed immensely and determine the resultant disease severity and mortality of the patients. Presently there is no drug approved for treatment of COVID-19, however multiple vaccines are now in use and research for more is underway.
Since the start of the pandemic, some U.S. communities have faced record storms, fires, and floods. Communities have confronted the increased challenge of curbing the spread of COVID-19 amid evacuation orders and short-term displacement that result from hazards. This raises the question of whether disasters, evacuations, and displacements have resulted in above-average infection rates during the COVID-19 pandemic. This study investigates the relationship between disaster intensity, sheltering-in-place, evacuation-related mobility, and contagion following Hurricane Zeta in Southeastern Louisiana and The Wildfires in Napa and Sonoma Counties, California, known as the Glass Fire. We draw on data from the county subdivision level and mapped and aggregated tallies of Facebook user movement from the Facebook Data for Good program’s GeoInsights Portal. We test the effects of disasters, evacuation, and shelter-in-place behaviors on COVID-19 spread using panel data models, matched panel models, and synthetic control experiments. Our findings suggest associations between disaster intensity and higher rates of COVID-19 cases. We also find that while sheltering-in-place led to decreases in the spread of COVID-19, evacuation-related mobility did not result in our hypothesized surge of cases immediately after the disasters. The findings from this study aim to inform policymakers and scholars about how to better respond to disasters during multi-crisis events, such as offering hotel accommodations to evacuees instead of mass shelters and updating intake and accommodation procedures at shelters, such as administration temperature screenings, offering hand sanitizing stations, and providing isolated areas for ill evacuees.
Protective policies have been unequally and inconsistently applied in the United States throughout the Covid-19 pandemic. This study investigates the relationship between state and local policies and Covid-19 deaths, combining three datasets: the Centers for Disease Control and Prevention's Social Vulnerability Index; local laws and regulations from the COVID Analysis and Mapping of Policies (AMP) database; and Covid-19 deaths by county reported by The New York Times. It examines, using propensity score matching, local policies and regulations as treatments during the crisis, and assesses how, inter alia, face mask requirements, gathering restrictions, stay-at-home orders, and social distancing mandates enacted at the county level altered Covid-19 deaths. The results indicate that the first three variables reduced average Covid-19 deaths in high-vulnerability communities. Despite clear gaps in federal policy guidance and coordinated policies, some efforts led by local and state governments promoted safer behaviour and lessened the impact of Covid-19 in communities, especially those with higher social vulnerability rates.
Much attention on the spread and impact of the ongoing pandemic has focused on institutional factors such as government capacity along with population-level characteristics such as race, income, and age. This paper draws on a growing body of evidence that bonding, bridging, and linking social capital - the horizontal and vertical ties that bind societies together - impact public health to explain why some U.S. counties have seen higher (or lower) excess deaths during the COVID19 pandemic than others. Drawing on county-level reports from the Centers for Disease Control and Prevention (CDC) since February 2020, we calculated the number of excess deaths per county compared to 2018. Starting with a panel dataset of county observations over time, we used coarsened exact matching to create smaller but more similar sets of communities that differ primarily in social capital. Controlling for several factors, including politics and governance, health care quality, and demographic characteristics, we find that bonding and linking social capital reduce the toll of COVID-19 on communities. Public health officials and community organizations should prioritize building and maintaining strong social ties and trust in government to help combat the pandemic.