Temporal trends in the association of social vulnerability and race/ethnicity with county-level COVID-19 incidence and outcomes in the USA: an ecological analysis

Social Vulnerability Index and All-Cause Mortality After Acute Ischemic Stroke, Medicare Cohort 2020-2023

Background

Inequities in stroke outcomes have existed for decades, and the COVID-19 pandemic amplified these inequities.

Objectives

This study examined the association between social vulnerability and all-cause mortality among Medicare beneficiaries hospitalized with acute ischemic stroke (AIS) during COVID-19 pandemic periods.

Methods

We analyzed data on Medicare fee-for-service beneficiaries aged ≥65 years hospitalized with AIS between April 1, 2020, and December 31, 2021 (followed until December 31, 2023) merged with county-level data from the 2020 Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry's Social Vulnerability Index (SVI). We used a Cox proportional hazard model to examine the association between SVI quartile and all-cause mortality.

Results

Among 176,123 Medicare fee-for-service beneficiaries with AIS, 29.9% resided in the most vulnerable counties (SVI quartile 4), while 14.9% resided in counties with least social vulnerability (SVI quartile 1). AIS Medicare beneficiaries living in the most vulnerable counties had the highest proportions of adults aged 65 to 74 years, non-Hispanic Black or Hispanic, severe stroke at admission, a history of COVID-19, and more prevalent comorbidities. Compared to those living in least vulnerable counties, AIS Medicare beneficiaries living in most vulnerable counties had significantly higher all-cause mortality (adjusted HR: 1.11, 95% CI: 1.08-1.14). The pattern of association was largely consistent in subgroup analyses by age group, sex, and race and ethnicity.

Conclusions

Higher social vulnerability levels were associated with increased all-cause mortality among AIS Medicare beneficiaries. To improve outcomes and address disparities, it may be important to focus efforts toward addressing social vulnerability.

Disparities in the real-world prescription pattern of oral anticoagulants for US Medicare beneficiaries: a geospatial analysis

OBJECTIVES

This study was conducted to examine urban-rural differences in the real-world prescribing pattern of oral anticoagulants and geographic variations in the prescribing pattern among clinicians serving Medicare beneficiaries in the USA.

DESIGN

A cross-sectional study.

SETTING

A real-world setting.

PARTICIPANTS

232 665 clinicians who prescribed oral anticoagulants for Medicare beneficiaries from the 2020 Medicare Provider Utilisation and Payment Data were classified as warfarin only, direct oral anticoagulants (DOACs) only or warfarin+DOAC prescribers.

MAIN OUTCOME MEASURES

Urban-rural differences in the prescribing patterns were examined using multivariate multinominal logistic regression analysis. A geospatial analysis was conducted to estimate standardised prescriber ratios (SPR) for DOAC only or warfarin only prescribers versus warfarin+DOAC prescribers to illustrate hot and cold spots for DOAC adoption in the USA.

RESULTS

92% of clinicians who prescribed oral anticoagulants prescribed DOAC in 2020. Clinicians from rural regions were more likely to prescribe warfarin only (adjusted OR=1.335, 95% CI=(1.281 to 1.391)) and DOAC only (adjusted OR=2.052, 95% CI=(1.999 to 2.106)), compared with clinicians from urban regions. Hot spots for SPR of DOAC only versus warfarin+DOAC prescribers were mostly found in California, southern and southeastern states; cold spots were mostly found in Minnesota and Iowa. Hot spots for SPR of warfarin only versus warfarin+DOAC prescribers were mostly found in several metropolitan areas on the west coast and in Midwest; cold spots were mostly found on the east coast.

CONCLUSIONS

Urban-rural status of clinicians was associated with their prescribing patterns of oral anticoagulants. The study identifies geographical heterogeneity in DOAC adoption and highlights gaps that may need to be addressed for increased accessibility of DOAC for patients in need.

Associations of County-Level Social Determinants of Health with COVID-19 Related Hospitalization Among People with HIV: A Retrospective Analysis of the U.S. National COVID Cohort Collaborative (N3C)

Individually, the COVID-19 and HIV pandemics have differentially impacted minoritized groups due to the role of social determinants of health (SDoH) in the U.S. Little is known how the collision of these two pandemics may have exacerbated adverse health outcomes. We evaluated county-level SDoH and associations with hospitalization after a COVID-19 diagnosis among people with (PWH) and without HIV (PWOH) by racial/ethnic groups. We used the U.S. National COVID Cohort Collaborative (January 2020-November 2023), a nationally-sampled electronic health record repository, to identify adults who were diagnosed with COVID-19 with HIV (n = 22,491) and without HIV (n = 2,220,660). We aggregated SDoH measures at the county-level and categorized racial/ethnic groups as Non-Hispanic (NH) White, NH-Black, Hispanic/Latinx, NH-Asian and Pacific Islander (AAPI), and NH-American Indian or Alaskan Native (AIAN). To estimate associations of county-level SDoH with hospitalization after a COVID-19 diagnosis, we used multilevel, multivariable logistic regressions, calculating adjusted relative risks (aRR) with 95% confidence intervals (95% CI). COVID-19 related hospitalization occurred among 11% of PWH and 7% of PWOH, with the highest proportion among NH-Black PWH (15%). In evaluating county-level SDoH among PWH, we found higher average household size was associated with lower risk of COVID-19 related hospitalization across racial/ethnic groups. Higher mean commute time (aRR: 1.76; 95% CI 1.10-2.62) and higher proportion of adults without health insurance (aRR: 1.40; 95% CI 1.04-1.84) was associated with a higher risk of COVID-19 hospitalization among NH-Black PWH, however, NH-Black PWOH did not demonstrate these associations. Differences by race and ethnicity exist in associations of adverse county-level SDoH with COVID-19 outcomes among people with and without HIV in the U.S.

Evolving patterns of COVID-19 mortality in US counties: A longitudinal study of healthcare, socioeconomic, and vaccination associations

The COVID-19 pandemic emphasized the need for pandemic preparedness strategies to mitigate its impacts, particularly in the United States, which experienced multiple waves with varying policies, population response, and vaccination effects. This study explores the relationships between county-level factors and COVID-19 mortality outcomes in the U.S. from 2020 to 2023, focusing on disparities in healthcare access, vaccination coverage, and socioeconomic characteristics. We conduct multi-variable rolling regression analyses to reveal associations between various factors and COVID-19 mortality outcomes, defined as Case Fatality Rate (CFR) and Overall Mortality to Hospitalization Rate (OMHR), at the U.S. county level. Each analysis examines the association between mortality outcomes and one of the three hierarchical levels of the Social Vulnerability Index (SVI), along with other factors such as access to hospital beds, vaccination coverage, and demographic characteristics. Our results reveal persistent and dynamic correlations between various factors and COVID-19 mortality measures. Access to hospital beds and higher vaccination coverage showed persistent protective effects, while higher Social Vulnerability Index was associated with worse outcomes persistently. Socioeconomic status and vulnerable household characteristics within the SVI consistently associated with elevated mortality. Poverty, lower education, unemployment, housing cost burden, single-parent households, and disability population showed significant associations with Case Fatality Rates during different stages of the pandemic. Vulnerable age groups demonstrated varying associations with mortality measures, with worse outcomes predominantly during the Original strain. Rural-Urban Continuum Code exhibited predominantly positive associations with CFR and OMHR, while it starts with a positive OMHR association during the Original strain. This study reveals longitudinal persistent and dynamic factors associated with two mortality rate measures throughout the pandemic, disproportionately affecting marginalized communities. The findings emphasize the urgency of implementing targeted policies and interventions to address disparities in the fight against future pandemics and the pursuit of improved public health outcomes.

Social-Economic Backgrounds to US County-Based COVID-19 Deaths: PLS-SEM Analysis

A complex interplay of social, economic, and environmental factors drove the COVID-19 epidemic. Understanding these factors is crucial in explaining the racial disparities observed in COVID-19 deaths. This research investigated various hypotheses, including ecological, racial, demographic, economic, and political party factors, to determine their impact on COVID-19 deaths. The study utilized data from the National Center for Health Statistics (NCHS), specifically focusing on COVID-19 deaths categorized by race and Hispanic origin in US counties, with over 100 recorded deaths as of July 11, 2022.

METHOD

To analyze the data, the study employed partial least squares (PLS) as the statistical approach, considering the presence of multicollinearity in the county-level socioeconomic data. SmartPLS4 software was utilized to illustrate paths depicting variance and covariance and to conduct significance tests. The analysis encompassed overall COVID-19 deaths and deaths among White, Black, and Hispanic Americans, utilizing the same latent variables and paths.

RESULTS

The results revealed that the number of residents aged 65 years or older in a county was the most influential predictor of COVID-19 deaths, irrespective of race. Economic factors emerged as the second strongest predictors. However, when considering each racial group separately, distinct factors aligned with the five hypotheses emerged as significant contributors to COVID-19 deaths. Furthermore, the diagrams illustrating the relationships between these factors (covariates) varied among racial groups, indicating that the underlying social influences differed across races.

DISCUSSION

In light of these findings, it becomes evident that a "one-size-fits-all" approach to prevention strategies is suboptimal. Instead, targeted prevention efforts tailored to specific racial and social classes at high risk of COVID-19 death could have provided more precise messaging and necessitate direct engagement.

Social Vulnerability May Underlie Racial Disparities in Peripartum Cardiomyopathy Outcomes

Objective

Black patients have disproportionately more cases of peripartum cardiomyopathy (PPCM) and more severe disease. To better understand these disparities, we examined the geographic distribution of patients with PPCM by race and evaluated associations between race and social vulnerability. We hypothesized that Black patients with PPCM are more likely than White patients to live in socially vulnerable communities.

Study Design

A retrospective cohort study of patients with PPCM defined by the National Institutes of Health, National Heart, Lung, and Blood Institute was conducted at a single center from January 2000 to November 2017. The US census tract for each patient was identified, and social vulnerability was assessed using the Centers for Disease Control and Prevention Social Vulnerability Index (SVI). Higher SVI values represent a more vulnerable community. SVI and select subcomponents were compared by self-reported race.

Results

Among 90 patients with PPCM (47 White, 43 Black), the ejection fraction at diagnosis was similar between groups, although Black patients were more likely to have an ejection fraction of ≤40% at 6 to 12 months postpartum. Black race was associated with living in areas of greater social vulnerability; mean SVI was significantly higher among Black individuals than among White individuals (.56 versus .33, P=.0003). Black patients lived in areas with more people living in poverty, higher unemployment, and more single-parent households.

Conclusion

Black patients with PPCM were more likely to have persistent left ventricular dysfunction and live in areas of greater social vulnerability. Strategies to achieve equitable social determinants of health are needed to improve health outcomes in Black patients with PPCM.

Association Between Social Vulnerability and SARS-CoV-2 Seroprevalence in Specimens Collected From Commercial Laboratories, United States, September 2021-February 2022

OBJECTIVE

We conducted a national US study of SARS-CoV-2 seroprevalence by Social Vulnerability Index (SVI) that included pediatric data and compared the Delta and Omicron periods during the COVID-19 pandemic. The objective of the current study was to assess the association between SVI and seroprevalence of infection-induced SARS-CoV-2 antibodies by period (Delta vs Omicron) and age group.

METHODS

We used results of infection-induced SARS-CoV-2 antibody assays of clinical sera specimens (N = 406 469) from 50 US states from September 2021 through February 2022 to estimate seroprevalence overall and by county SVI tercile. Bivariate analyses and multilevel logistic regression models assessed the association of seropositivity with SVI and its themes by age group (0-17, ≥18 y) and period (Delta: September-November 2021; Omicron: December 2021-February 2022).

RESULTS

Aggregate infection-induced SARS-CoV-2 antibody seroprevalence increased at all 3 SVI levels; it ranged from 25.8% to 33.5% in September 2021 and from 53.1% to 63.5% in February 2022. Of the 4 SVI themes, socioeconomic status had the strongest association with seroprevalence. During the Delta period, we found significantly more infections per reported case among people living in a county with high SVI (odds ratio [OR] = 2.76; 95% CI, 2.31-3.21) than in a county with low SVI (OR = 1.65; 95% CI, 1.33-1.97); we found no significant difference during the Omicron period. Otherwise, findings were consistent across subanalyses by age group and period.

CONCLUSIONS

Among both children and adults, and during both the Delta and Omicron periods, counties with high SVI had significantly higher SARS-CoV-2 antibody seroprevalence than counties with low SVI did. These disparities reinforce SVI's value in identifying communities that need tailored prevention efforts during public health emergencies and resources to recover from their effects.

An Application of Social Vulnerability Index to Infant Mortality Rates in Ohio Using Geospatial Analysis- A Cross-Sectional Study

BACKGROUND

Ohio ranks 43rd in the nation in infant mortality rates (IMR); with IMR among non-Hispanic black infants is three times higher than white infants.

OBJECTIVE

To identify the social factors determining the vulnerability of Ohio counties to IMR and visualize the spatial association between relative social vulnerability and IMR at county and census tract levels.

METHODS

The social vulnerability index (SVICDC) is a measure of the relative social vulnerability of a geographic unit. Five out of 15 social variables in the SVICDC were utilized to create a customized index for IMR (SVIIMR) in Ohio. The bivariate descriptive maps and spatial lag model were applied to visualize the quantitative relationship between SVIIMR and IMR, accounting for the spatial autocorrelation in the data.

RESULTS

Southeastern counties in Ohio displayed highest IMRs and highest overall SVIIMR; specifically, highest vulnerability to poverty, no high school diploma, and mobile housing. In contrast, extreme northwestern counties exhibited high IMRs but lower overall SVIIMR. Spatial regression showed five clusters where vulnerability to low per capita income in one county significantly impacted IMR (p = 0.001) in the neighboring counties within each cluster. At the census tract-level within Lucas county, the Toledo city area (compared to the remaining county) had higher overlap between high IMR and SVIIMR.

CONCLUSION

The application of SVI using geospatial techniques could identify priority areas, where social factors are increasing the vulnerability to infant mortality rates, for potential interventions that could reduce disparities through strategic and equitable policies.

Association of social vulnerability index and masking adherence among children enrolled in COVID-19 community research partnership study

BACKGROUND

Individuals with high social vulnerability index (SVI) have poorer outcomes with COVID-19. Masking reduces transmission of COVID-19 among children, but how SVI plays a role in masking behavior is unknown. We aimed to measure the association of SVI with masking adherence among children during the COVID-19 pandemic.

METHODS

We conducted a multi-site, prospective syndromic surveillance study among children aged 2 - 17 years in the Southeastern United States by daily electronic surveys which solicited symptoms of COVID-19-like illness, infection with or exposure to SARS-CoV-2, masking habits, and any receipt of COVID-19 vaccines. Parents/guardians submitted surveys for their children; adolescents 13 years and older could opt to submit their own surveys. Multivariable and univariate linear models were used to measure the associations of different predictors such as SVI with masking adherence.

RESULTS

One thousand four hundred sixty-one children from 6 states and 55 counties predominately from North and South Carolina were included in the analysis. Most children in the cohort were 5 - 11 years old, non-Hispanic White, from urban counties, and with low-moderate SVI. Overall masking adherence decreased over time, and older children had higher masking adherence throughout the study period compared with younger children. Children who resided in urban counties had greater masking adherence throughout the study period than those who resided in suburban or rural counties. Masking adherence was higher among children with both low and medium SVI than those with high SVI.

CONCLUSIONS

Despite being at risk for more severe outcomes with COVID-19, children with high SVI had lower levels of masking adherence compared to those with low SVI. Our findings highlight opportunities for improved and targeted messaging in these vulnerable communities.

Inequality in COVID-19 mortality in Quebec associated with neighbourhood-level vulnerability domains

OBJECTIVES

We measured disparities in COVID-19 mortality associated with increasing vulnerability to severe outcomes of infectious disease at the neighbourhood level to identify domains for prioritization of public interventions.

METHODS

In this retrospective ecological study, we calculated COVID-19 mortality rate ratios (RR) comparing neighbourhoods with the greatest vulnerability relative to lowest vulnerability using the five domains from the COVID-19 vulnerability index for Quebec using hospital data from the first year of the pandemic and vulnerability levels from 13,182 neighbourhoods. We estimated the attributable fraction to assess disparities in COVID-19 mortality associated with vulnerability. Domains covered biological susceptibility, sociocultural characteristics, socioeconomic characteristics, and indoor and outdoor risk factors for exposure to SARS-CoV-2.

RESULTS

Vulnerable neighbourhoods accounted for 60.7% of COVID-19 deaths between March 2020 and February 2021. Neighbourhoods with biological susceptibility accounted for 46.1% and indoor exposure for 44.6% of deaths. Neighbourhoods with socioeconomic vulnerability experienced 23.5%, outdoor exposure 14.6%, and sociocultural vulnerability 9.0% of deaths. Neighbourhoods with high relative vulnerability had 4.66 times greater risk of COVID-19 mortality compared with those with low vulnerability (95%CI 3.82-5.67). High vulnerability in the biological (RR 3.33; 95%CI 2.71-4.09), sociocultural (RR 1.50; 95%CI 1.27-1.77), socioeconomic (RR 2.08; 95%CI 1.75-2.48), and indoor (RR 3.21; 95%CI 2.74-3.76) exposure domains were associated with elevated risks of mortality compared with the least vulnerable neighbourhoods. Outdoor exposure was unassociated with mortality (RR 1.17; 95%CI 0.96-2.43).

CONCLUSION

Public intervention to protect vulnerable populations should be adapted to focus on domains most associated with COVID-19 mortality to ensure addressing local needs.

Differential associations of mask mandates on COVID-19 infection and mortality by community social vulnerability

BACKGROUND

The COVID-19 pandemic in the United States has disproportionately impacted communities deemed vulnerable to disease outbreaks. Our objectives were to test (1) whether infection and mortality decreased in counties in the most vulnerable (highest) tercile of the Social Vulnerability Index (SVI), and (2) whether disparities between terciles of SVI were reduced, as the length of mask mandates increased.

METHODS

Using the New York Times COVID-19 and the Centers for Disease Control and Prevention SVI and mask mandate datasets, we conducted negative binomial regression analyses of county-level COVID-19 cases and deaths from 1/2020-11/2021 on interactions of SVI and mask mandate durations.

RESULTS

Mask mandates were associated with decreases in mid-SVI cases (IRR: 0.79) and deaths (IRR: 0.90) and high-SVI cases (IRR: 0.89) and deaths (IRR: 0.88). Mandates were associated with the mitigation of infection disparities (Change in IRR: 0.92) and mortality disparities (Change in IRR: 0.85) between low and mid-SVI counties and mortality disparities between low and high-SVI counties (Change in IRR: 0.84).

DISCUSSION

Mask mandates were associated with reductions in COVID-19 infection and mortality and mitigation of disparities for mid and high-vulnerability communities.

CONCLUSIONS

Ongoing COVID-19 response efforts may benefit from longer-standing infection control policies, particularly in the most vulnerable communities.

Incidence of Stomach, Liver, and Colorectal Cancers by Geography and Social Vulnerability Among American Indian and Alaska Native Populations, 2010-2019

Social determinants of health and associated systems, policies, and practices are important drivers of health disparities. American Indian and Alaska Native (AI/AN) populations in the United States have elevated incidence rates of stomach, liver, and colorectal cancers compared with other racial/ethnic groups. In this study, we examined incidence rates of 3 types of gastrointestinal cancer among non-Hispanic AI/AN (NH-AI/AN) and non-Hispanic White (NHW) populations by geographic region and Social Vulnerability Index (SVI) score. Incident cases diagnosed during 2010-2019 were identified from population-based cancer registries linked with the Indian Health Service patient registration databases. Age-adjusted incidence rates (per 100,000 population) for stomach, liver, and colorectal cancers were compared within NH-AI/AN populations and between the NH-AI/AN and NHW populations by SVI score. Rates were higher among NH-AI/AN populations in moderate- and high-SVI-score counties in Alaska, the Southern Plains, and the East than in low-SVI counties. Incidence rates among NH-AI/AN populations were elevated when compared with NHW populations by SVI category. Results indicated that higher social vulnerability may drive elevated cancer incidence among NH-AI/AN populations. Additionally, disparities between NH-AI/AN and NHW populations persist even when accounting for SVI. Exploring social vulnerability can aid in designing more effective interventions to address root causes of cancer disparities among AI/AN populations.

Spatial patterns of COVID-19 and non-COVID-19 mortality across waves of infection in England, Wales, and Scotland

Recent studies have established the key individual-level risk factors of COVID-19 mortality such as age, gender, ethnicity, and socio-economic status. However, the spread of infectious diseases is a spatial and temporal process implying that COVID-19 mortality and its determinants may vary sub-nationally and over time. We investigate the spatial patterns of age-standardised death rates due to COVID-19 and their correlates across local authority districts in England, Wales, and Scotland across three waves of infection. Using a Spatial Durbin model, we explore within- and between-country variation and account for spatial dependency. Areas with a higher share of ethnic minorities and higher levels of deprivation had higher rates of COVID-19 mortality. However, the share of ethnic minorities and population density in an area were more important predictors of COVID-19 mortality in earlier waves of the pandemic than in later waves, whereas area-level deprivation has become a more important predictor over time. Second, during the first wave of the pandemic, population density had a significant spillover effect on COVID-19 mortality, indicating that the pandemic spread from big cities to neighbouring areas. Third, after accounting for differences in ethnic composition, deprivation, and population density, initial cross-country differences in COVID-19 mortality almost disappeared. COVID-19 mortality remained higher in Scotland than in England and Wales in the third wave when COVID-19 mortality was relatively low in all three countries. Interpreting these results in the context of higher overall (long-term) non-COVID-19 mortality in Scotland suggests that Scotland may have performed better than expected during the first two waves. Our study highlights that accounting for both spatial and temporal factors is essential for understanding social and demographic risk factors of mortality during pandemics.

US County-Level Variation in Preterm Birth Rates, 2007-2019

Importance

Preterm birth is a leading cause of preventable neonatal morbidity and mortality. Preterm birth rates at the national level may mask important geographic variation in rates and trends at the county level.

Objective

To estimate age-standardized preterm birth rates by US county from 2007 to 2019.

Design, Setting, and Participants

This serial cross-sectional study used data from the National Center for Health Statistics composed of all live births in the US between 2007 and 2019. Data analyses were performed between March 22, 2022, and September 29, 2022.

Main Outcomes and Measures

Age-standardized preterm birth (<37 weeks' gestation) and secondarily early preterm birth (<34 weeks' gestation) rates by county and year calculated with a validated small area estimation model (hierarchical bayesian spatiotemporal model) and percent change in preterm birth rates using log-linear regression models.

Results

Between 2007 and 2019, there were 51 044 482 live births in 2383 counties. In 2007, the national age-standardized preterm birth rate was 12.6 (95% CI, 12.6-12.7) per 100 live births. Preterm birth rates varied significantly among counties, with an absolute difference between the 90th and 10th percentile counties of 6.4 (95% CI, 6.2-6.7). The gap between the highest and lowest counties for preterm births was 20.7 per 100 live births in 2007. Several counties in the Southeast consistently had the highest preterm birth rates compared with counties in California and New England, which had the lowest preterm birth rates. Although there was no statistically significant change in preterm birth rates between 2007 and 2019 at the national level (percent change, -5.0%; 95% CI, -10.7% to 0.9%), increases occurred in 15.4% (95% CI, 14.1%-16.9%) of counties. The absolute and relative geographic inequalities were similar across all maternal age groups. Higher quartile of the Social Vulnerability Index was associated with higher preterm birth rates (quartile 4 vs quartile 1 risk ratio, 1.34; 95% CI, 1.31-1.36), which persisted across the study period. Similar patterns were observed for early preterm birth rates.

Conclusions and Relevance

In this serial cross-sectional study of county-level preterm and early preterm birth rates, substantial geographic disparities were observed, which were associated with place-based social disadvantage. Stability in aggregated rates of preterm birth at the national level masked increases in nearly 1 in 6 counties between 2007 and 2019.

Vaccine Mandates and Influenza Vaccination During the Pandemic

OBJECTIVES

To determine whether a state influenza vaccine mandate and elevated community coronavirus disease 2019 (COVID-19) severity affected a child's probability of receiving an influenza vaccine during the 2020-2021 influenza season, given the child's previous vaccination history.

METHODS

Longitudinal cohort study using enrollment and claims data of 71 333 children aged 6 months to 18 years living in Massachusetts, New Hampshire, and Maine, from a regional insurer. Schoolchildren in Massachusetts were exposed to a new influenza vaccine mandate in the 2020-2021 season. Community COVID-19 severity was measured using county-level total cumulative confirmed case counts between March 2020 and August 2020 and linked by zip codes. The primary outcome of interest was a claim for any influenza vaccine in the 2020-2021 season.

RESULTS

Children living in a state with a vaccine mandate during the 2020-2021 influenza season had a higher predicted probability of receiving an influenza vaccine than those living in states without a mandate (47.7%, confidence interval 46.4%-49.0%, vs 21.2%, confidence interval 18.8%-23.6%, respectively, for previous nonvaccinators, and 78.2%, confidence interval 77.4%-79.0%, vs 58.2%, confidence interval 54.7%-61.7%, for previous vaccinators); the difference was 6.5 percentage points greater among previous nonvaccinators (confidence interval 1.3%-11.7%). Previously vaccinated children had a lower predicted probability of receiving an influenza vaccine if they lived in a county with the highest COVID-19 severity compared with a county with low COVID-19 severity (72.1%, confidence interval 70.5%-73.7%, vs 77.3%, confidence interval 74.7%-79.9%).

CONCLUSIONS

Strategies to improve uptake of influenza vaccination may have differential impact based on previous vaccination status and should account for community factors.

Association of Social Vulnerability and COVID-19 Mortality Rates in Texas between 15 March 2020, and 21 July 2022: An Ecological Analysis

BACKGROUND

Despite the key role of social vulnerability such as economic disadvantage in health outcomes, research is limited on the impact of social vulnerabilities on COVID-19-related deaths, especially at the state and county level in the USA.

METHODS

We conducted a cross-sectional ecologic analysis of COVID-19 mortality by the county-level Minority Health Social Vulnerability Index (MH SVI) and each of its components in Texas. Negative binomial regression (NBR) analyses were used to estimate the association between the composite MH SVI (and its components) and COVID-19 mortality.

RESULTS

A 0.1-unit increase in the overall MH SVI (IRR, 1.27; 95% CI, 1.04-1.55; p = 0.017) was associated with a 27% increase in the COVID-19 mortality rate. Among the MH SVI component measures, only low socioeconomic status (IRR, 1.55; 95% CI, 1.28-1.89; p = 0.001) and higher household composition (e.g., proportion of older population per county) and disability scores (IRR, 1.47; 95% CI, 1.29-1.68; p < 0.001) were positively associated with COVID-19 mortality rates.

CONCLUSIONS

This study provides further evidence of disparities in COVID-19 mortality by social vulnerability and can inform decisions on the allocation of social resources and services as a strategy for reducing COVID-19 mortality rates and similar pandemics in the future.

Structural racism through Sundown towns and its relationship to COVID-19 local risk and racial and ethnic diversity

"Sundown towns" across the US prevented racial and ethnic minorities from living and working within their borders as they forced minorities to leave these towns after sunset. The objective of this study was to explore the relationship between sundown town status, COVID-19 local risk index and racial and ethnic diversity. A multi-level hierarchical model was used to examine the effect of historical segregation through sundown towns status on present day COVID-19 local risk index and city-level diversity. Over 2,400 Sundown towns were cataloged across the United States, with the greatest density in the Midwest. Sundown towns, which historically excluded racial and ethnic minorities, had significantly less city-level diversity and lower COVID-19 local risk index compared to non-Sundown towns. Findings show that Sundown towns perpetuate residual segregation which continues to impact current inequities in COVID-19 risk among racial and ethnic minorities at the neighborhood level. We recommend that public health officials for pandemic preparedness should devote greater resources to these historically segregated racial and ethnic minority areas because of the historic structural racism that has placed these places at higher risk.

Disparities in COVID-19 related outcomes in the United States by race and ethnicity pre-vaccination era: an umbrella review of meta-analyses

Background

Meta-analyses have investigated associations between race and ethnicity and COVID-19 outcomes. However, there is uncertainty about these associations' existence, magnitude, and level of evidence. We, therefore, aimed to synthesize, quantify, and grade the strength of evidence of race and ethnicity and COVID-19 outcomes in the US.

Methods

In this umbrella review, we searched four databases (Pubmed, Embase, the Cochrane Database of Systematic Reviews, and Epistemonikos) from database inception to April 2022. The methodological quality of each meta-analysis was assessed using the Assessment of Multiple Systematic Reviews, version 2 (AMSTAR-2). The strength of evidence of the associations between race and ethnicity with outcomes was ranked according to established criteria as convincing, highly suggestive, suggestive, weak, or non-significant. The study protocol was registered with PROSPERO, CRD42022336805.

Results

Of 880 records screened, we selected seven meta-analyses for evidence synthesis, with 42 associations examined. Overall, 10 of 42 associations were statistically significant (p ≤ 0.05). Two associations were highly suggestive, two were suggestive, and two were weak, whereas the remaining 32 associations were non-significant. The risk of COVID-19 infection was higher in Black individuals compared to White individuals (risk ratio, 2.08, 95% Confidence Interval (CI), 1.60-2.71), which was supported by highly suggestive evidence; with the conservative estimates from the sensitivity analyses, this association remained suggestive. Among those infected with COVID-19, Hispanic individuals had a higher risk of COVID-19 hospitalization than non-Hispanic White individuals (odds ratio, 2.08, 95% CI, 1.60-2.70) with highly suggestive evidence which remained after sensitivity analyses.

Conclusion

Individuals of Black and Hispanic groups had a higher risk of COVID-19 infection and hospitalization compared to their White counterparts. These associations of race and ethnicity and COVID-19 outcomes existed more obviously in the pre-hospitalization stage. More consideration should be given in this stage for addressing health inequity.

Longitudinal disparities in social determinants of health and COVID-19 incidence and mortality in the United States from the three largest waves of the pandemic

The United States experienced at least five COVID-19 waves linked with different mutated SARS-CoV-2 variants including Alpha, Delta and Omicron. In addition to the variants, the intensity, geographical distribution, and risk factors related to those waves also vary within socio-demographic characteristics and timeframes. In this project, we have examined the spatial and temporal pattern of COVID-19 in the USA and its associations with Social Determinants of Health (SDoH) by utilizing the County Health Rankings & Roadmaps (CHRR) dataset. Our epidemiologic investigation at the county level showed that the burden of COVID-19 cases and deaths is higher in counties with high percentages of smoking, number of preventable hospital stays, primary care physician rate, the average daily density of PM_2.5 and percentages of high proportions of Hispanic residents. In addition, the analysis also demonstrated that COVID-19 incidence and mortality had distinct characteristics in their association with SDoH variables. For example, the percentages of the population 65 and older had negative associations with incidence while a significant positive association with mortality. In addition to the elderly population, median household income, unemployment, and number of drug overdose deaths showed a mixed association with COVID-19 incidence and mortality. Our findings validate several influential factors found in the existing social epidemiology literature and highlight temporal associations between SDoH variables and COVID-19 incidence and mortality not yet frequently studied.

Socioeconomic disparities and concentration of the spread of the COVID-19 pandemic in the province of Quebec, Canada

BACKGROUND

Recent studies suggest that the risk of SARS-CoV-2 infection may be greater in more densely populated areas and in cities with a higher proportion of persons who are poor, immigrant, or essential workers. This study examines spatial inequalities in SARS-CoV-2 exposure in a health region of the province of Quebec in Canada.

METHODS

The study was conducted on the 1206 Canadian census dissemination areas in the Capitale-Nationale region of the province of Quebec. The observation period was 21 months (March 2020 to November 2021). The number of cases reported daily in each dissemination area was identified from available administrative databases. The magnitude of inequalities was estimated using Gini and Foster-Greer-Thorbecke (FGT) indices. The association between transmission and socioeconomic deprivation was identified based on the concentration of transmission in socially disadvantaged areas and on nonparametric regressions relating the cumulative incidence rate by area to ecological indicators of spatial disadvantage. Quantification of the association between median family income and degree of exposure of dissemination areas was supplemented by an ordered probit multiple regression model.

RESULTS

Spatial disparities were elevated (Gini = 0.265; 95% CI [0.251, 0.279]). The spread was more limited in the less densely populated areas of the Quebec City agglomeration and outlying municipalities. The mean cumulative incidence in the subsample made up of the areas most exposed to the pandemic was 0.093. The spread of the epidemic was concentrated in the most disadvantaged areas, especially in the densely populated areas. Socioeconomic inequality appeared early and increased with each successive pandemic wave. The models showed that areas with economically disadvantaged populations were three times more likely to be among the areas at highest risk for COVID-19 (RR = 3.55; 95% CI [2.02, 5.08]). In contrast, areas with a higher income population (fifth quintile) were two times less likely to be among the most exposed areas (RR = 0.52; 95% CI [0.32, 0.72]).

CONCLUSION

As with the H1N1 pandemics of 1918 and 2009, the SARS-CoV-2 pandemic revealed social vulnerabilities. Further research is needed to explore the various manifestations of social inequality in relation to the pandemic.

Epidemiological intelligence community network intervention: a community response for COVID-19 community transmission

BACKGROUND

Expanding and providing access to early detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) through testing community-based strategies among socially vulnerable communities (SVC) are critical to reducing health disparities. The Epidemiological Intelligence Community Network (EpI-Net) community-based intervention sought to increase coronavirus 2019 (COVID-19) testing uptake and prevention practices among SVC in Puerto Rico (PR). We evaluated EpI-Net's community leaders' capacity-building component by assessing pre-post COVID-19 public health workshops' tests' score changes and satisfaction among trained community leaders.

METHODS

A total of 24 community leaders from SVC in PR have completed four community workshops. Pre- and post-assessments were completed as part of the health promotors training program to evaluate participants' tests score changes and satisfaction outcomes.

RESULTS

Preliminary results showed: (1) high intervention retention levels of community leaders (85.7% acceptance rate); (2) change in post-test scores for community engagement strategies (p = 0.012); (3) change in post-test educational scores in COVID-19 prevention practices (p = 0.014); and (4) a change in scores in public health emergency management strategies (p < 0.001).

CONCLUSIONS

The overall workshop satisfaction was 99.6%. Community leaders have shown the importance of community capacity building as a key component for intervention feasibility and impact.

TRIAL REGISTRATION

Our study was retrospectively registered under the ClinicalTrial.gov ID NCT04910542.

Relationships Between Social Vulnerability and Coronavirus Disease 2019 Vaccination Coverage and Vaccine Effectiveness

BACKGROUND

Coronavirus disease 2019 (COVID-19) vaccination coverage remains lower in communities with higher social vulnerability. Factors such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exposure risk and access to healthcare are often correlated with social vulnerability and may therefore contribute to a relationship between vulnerability and observed vaccine effectiveness (VE). Understanding whether these factors impact VE could contribute to our understanding of real-world VE.

METHODS

We used electronic health record data from 7 health systems to assess vaccination coverage among patients with medically attended COVID-19-like illness. We then used a test-negative design to assess VE for 2- and 3-dose messenger RNA (mRNA) adult (≥18 years) vaccine recipients across Social Vulnerability Index (SVI) quartiles. SVI rankings were determined by geocoding patient addresses to census tracts; rankings were grouped into quartiles for analysis.

RESULTS

In July 2021, primary series vaccination coverage was higher in the least vulnerable quartile than in the most vulnerable quartile (56% vs 36%, respectively). In February 2022, booster dose coverage among persons who had completed a primary series was higher in the least vulnerable quartile than in the most vulnerable quartile (43% vs 30%). VE among 2-dose and 3-dose recipients during the Delta and Omicron BA.1 periods of predominance was similar across SVI quartiles.

CONCLUSIONS

COVID-19 vaccination coverage varied substantially by SVI. Differences in VE estimates by SVI were minimal across groups after adjusting for baseline patient factors. However, lower vaccination coverage among more socially vulnerable groups means that the burden of illness is still disproportionately borne by the most socially vulnerable populations.

Place, Race, and Case: Examining Racialized Economic Segregation and COVID-19 in Louisiana

Early COVID-19 pandemic data suggested racial/ethnic minority and low-income earning people bore the greatest burden of infection. Structural racism, the reinforcement of racial and ethnic discrimination via policy, provides a framework for understanding disparities in health outcomes like COVID-19 infection. Residential racial and economic segregation is one indicator of structural racism. Little attention has been paid to the relationship of infection to relative overall concentrations of risk (i.e., segregation of the most privileged from the most disadvantaged). We used ordinary least squares and geographically weighted regression models to evaluate the relationship between racial and economic segregation, measured by the Index of Concentration at the Extremes, and COVID-19 cases in Louisiana. We found a significant global association between racial segregation and cumulative COVID-19 case rate in Louisiana and variation across the state during the study period. The northwest and central regions exhibited a strong negative relationship indicating greater risk in areas with high concentrations of Black residents. On the other hand, the southeastern part of the state exhibited more neutral or positive relationships indicating greater risk in areas with high concentrations of White residents. Our findings that the relationship between racial segregation and COVID-19 cases varied within a state further support evidence that social and political determinants, not biological, drive racial disparities. Small area measures and measures of polarization provide localized information better suited to tailoring public health policy according to the dynamics of communities at the census tract level, which may lead to better health outcomes.

Interactive impacts of walkability, social vulnerability, & travel behavior on COVID-19 mortality: A hierarchical Bayesian spatial random parameter approach

While existing research highlights the built and social environment impacts on COVID-19 mortality, no empirical evidence exists on how the built and social environments may interact to influence COVID-19 mortality. This study presents a rigorous empirical assessment of the interactive impacts of social vulnerability and walkability on neighborhood-level COVID-19 mortality rates. Based in King County, WA, a unique data infrastructure is created by spatially integrating diverse census tract-level data on COVID-19 mortalities, walkability characteristics, social vulnerability, and travel behavior measures. Advanced Markov Chain Monte Carlo (MCMC) based Full Bayes hierarchical spatial random parameter models are developed to simultaneously capture spatial and unobserved random heterogeneity. Around 46% of the neighborhoods had opposite levels of walkability and social vulnerability. Compared to low walkability and high social vulnerability, neighborhoods with high walkability and low social vulnerability (i.e., best case scenario) had on average 20.2% (95% Bayesian CI: -37.2% to -3.3%) lower COVID-19 mortality rates. Analysis of the interactive impacts when only one of the social and built environment metrics was in a healthful direction revealed significant offsetting effects - suggesting that the underlying structural social vulnerability issues faced by our communities should be addressed first for the infectious disease-related health impacts of walkable urban design to be observed. Concerning travel behavior, the findings indicate that COVID-19 mortality rates may be reduced by discouraging auto use and encouraging active transportation. The study methodologically contributes by simultaneously capturing spatial and unobserved heterogeneity in a holistic Full Bayesian framework.

Minority Health Social Vulnerability Index and COVID-19 vaccination coverage - The United States, December 14, 2020-January 31, 2022

INTRODUCTION

In 2021, HHS Office of Minority Health and CDC developed a composite measure of social vulnerability called the Minority Health Social Vulnerability Index (MHSVI) to assess the needs of communities most vulnerable to COVID-19. The MHSVI extends the CDC Social Vulnerability Index with two new themes on healthcare access and medical vulnerability. This analysis examines COVID-19 vaccination coverage by social vulnerability using the MHSVI.

METHODS

County-level COVID-19 vaccine administration data among persons aged ≥18 years reported to CDC from 12/14/20 to 01/31/22 were analyzed. U.S. counties from 50 states and DC were categorized into tertiles of vulnerability (low, moderate, and high) for the composite MHSVI measure and each of the 34 indicators. Vaccination coverage (≥1 dose, primary series completion, and receipt of a booster dose) was calculated by tertiles for the composite MHSVI measure and each indicator.

RESULTS

Counties with lower per capita income, higher proportion of individuals with no high school diploma, living below poverty, ≥65 years of age, with a disability, and in mobile homes had lower vaccination uptake. However, counties with larger proportions of racial/ethnic minorities and individuals speaking English less than "very well" had higher coverage. Counties with fewer primary care physicians and greater medical vulnerabilities had lower ≥ 1 dose vaccination coverage. Furthermore, counties of high vulnerability had lower primary series completion and receipt of a booster dose. There were no clear patterns in COVID-19 vaccination coverage by tertiles for the composite measure.

CONCLUSION

Results from the new components in the MHSVI identify needs to prioritize persons in counties with greater medical vulnerabilities and limited access to health care, who are at greater risk for adverse COVID-19 outcomes. Findings suggest that using a composite measure to characterize social vulnerability might mask disparities in COVID-19 vaccination uptake that would have otherwise been observed using specific indicators.

A neighborhood-level analysis of association between social vulnerability and COVID-19 in ahvaz, Iran

Social vulnerability and society's resilience are two concepts frequently used to examine the capacity of social systems to prepare, absorb, and adapt to environmental hazards and shocks. With the emergence of the COVID-19 pandemic, the role of social vulnerability in dealing with risks has gained renewed attention. Assessing social vulnerability can help managers and planners prioritize budgets, develop prevention programs, and enhance risk preparedness. This study aimed to determine the association between social vulnerability and COVID-19 in the neighborhoods of Ahvaz, Iran. To assess the social vulnerability of Ahvaz neighborhoods, decision-making techniques (best-worst method and weighted aggregated sum product assessment method) and geographic information systems were applied. Moreover, to investigate the relationship between social vulnerability and COVID-19 cases, the Pearson correlation test was used. The results showed that the '20-meteri shahrdari' neighborhood has the highest level of social vulnerability, and the lowest level of social vulnerability among the neighborhoods of Ahvaz belongs to the neighborhood of 'Shahrak Naft'. There is a low inverse association between the integrated index of social vulnerability and the incidence of COVID-19 per 1000 people in Ahvaz. By revealing the most important details at the neighborhood level and levels of vulnerability, the results can inform effective planning actions at the neighborhood level.

Geographic heterogeneity of the epidemiological impact of the COVID-19 pandemic in Italy using a socioeconomic proxy-based classification of the national territory

Objectives

This study aimed to evaluate the differences in incidence, non-intensive care unit (non-ICU) and intensive care unit (ICU) hospital admissions, and COVID-19-related mortality between the "inner areas" of Italy and its metropolitan areas.

Study design

Retrospective population-based study conducted from the beginning of the pandemic in Italy (20 February 2020) to 31 March 2022.

Methods

The municipalities of Italy were classified into metropolitan areas, peri-urban/intermediate areas and "inner areas" (peripheral/ultra-peripheral). The exposure variable was residence in an "inner area" of Italy. Incidence of diagnosis of SARS-CoV-2 infection, non-ICU and ICU hospital admissions and death within 30 days from diagnosis were the outcomes of the study. COVID-19 vaccination access was also evaluated. Crude and age-standardized rates were calculated for all the study outcomes. The association between the type of area of residence and each outcome under study was evaluated by calculating the ratios between the standardized rates. All the analyses were stratified by period of observation (original Wuhan strain, Alpha variant, Delta variant, Omicron variant).

Results

Incidence and non-ICUs admissions rates were lower in "inner areas." ICU admission and mortality rates were much lower in "inner areas" in the early phases of the pandemic, but this protection progressively diminished, with a slight excess risk observed in the "inner areas" during the Omicron period. The greater vaccination coverage in metropolitan areas may explain this trend.

Conclusion

Prioritizing healthcare planning through the strengthening of the primary prevention policies in the peripheral areas of Italy is fundamental to guarantee health equity policies.

Identifying Optimal Locations for Potential Temporary Community Clinics During Public Health Emergencies

OBJECTIVES

This article argues that community healthcare clinics managed by dedicated medical professionals who are familiar with the special needs of the local community may serve as effective alternatives to centralized hospitals and medical facilities, which may be disconnected from these local communities.

BACKGROUND

The literature indicates that socioeconomic factors that affect an individual's ability to seek medical help when needed can cause vulnerability to public health emergencies. These factors include belonging to lower income populations, being African American, being dependent due to age (below 18 or above 65) or disability, being an immigrant, English-language ability, access to transportation means, and the strength of an individual's social network.

METHOD

This study aims at developing a multifaceted methodology to identify optimal locations for deployment of temporary healthcare clinics to address health disparity issues among socially vulnerable populations, especially during pandemics and public health crises. This case study looks at the Health Enterprise Zone (HEZ) in Baltimore and ranks Census tracts based on their vulnerability, using two novel health vulnerability indices and considering their locations.

RESULTS

Using the proposed methodology, the optimal tracts within the HEZ are identified as potential locations for deploying temporary healthcare clinics.

CONCLUSION

The analysis of vulnerabilities to public health emergencies based on socioeconomic factors can assist in identifying potential locations for setting up temporary healthcare clinics with the goal of assisting socially vulnerable populations during outbreaks and pandemics.

Community vulnerability to the COVID-19 pandemic: A narrative synthesis from an ecological perspective

Background

We aimed to conduct a narrative synthesis of components and indicators of community vulnerability to a pandemic and discuss their interrelationships from an ecological perspective.

Methods

We searched from PubMed, Embase, Web of Science, PsycINFO, and Scopus (updated to November 2021) for studies focusing on community vulnerability to a pandemic caused by novel respiratory viruses on a geographic unit basis . Studies that reported the associations of community vulnerability levels with at least one disease morbidity or mortality outcome were included.

Results

Forty-one studies were included. All were about the COVID-19 pandemic. Suitable temperature and humidity environments, advanced social and human development (including high population density and human mobility, connectivity, and occupations), and settings that intensified physical interactions are important indicators of vulnerability to viral exposure. However, the eventual pandemic health impacts are predominant in communities that faced environmental pollution, higher proportions of socioeconomically deprived people, health deprivation, higher proportions of poor-condition households, limited access to preventive health care and urban infrastructure, uneven social and human development, and racism. More stringent social distancing policies were associated with lower COVID-19 morbidity and mortality only in the early pandemic phases. Prolonged social distancing policies can disproportionately burden the socially disadvantaged and racially/ethnically marginalized groups.

Conclusions

Community vulnerability to a pandemic is foremost the vulnerability of the ecological systems shaped by complex interactions between the human and environmental systems.

Registration

PROSPERO (CRD42021266186).

Amplified effect of social vulnerability on health inequality regarding COVID-19 mortality in the USA: the mediating role of vaccination allocation

Background

Vaccination reduces the overall burden of COVID-19, while its allocation procedure may introduce additional health inequality, since populations characterized with certain social vulnerabilities have received less vaccination and been affected more by COVID-19. We used structural equation modeling to quantitatively evaluate the extent to which vaccination disparity would amplify health inequality, where it functioned as a mediator in the effect pathways from social vulnerabilities to COVID-19 mortality.

Methods

We used USA nationwide county (n = 3112, 99% of the total) level data during 2021 in an ecological study design. Theme-specific rankings of social vulnerability index published by CDC (latest data of 2018, including socioeconomic status, household composition & disability, minority status & language, and housing type & transportation) were the exposure variables. Vaccination coverage rate (VCR) during 2021 published by CDC was the mediator variable, while COVID-19 case fatality rate (CFR) during 2021 published by John Hopkinson University, the outcome variable.

Results

Greater vulnerabilities in socioeconomic status, household composition & disability, and minority status & language were inversely associated with VCR, together explaining 11.3% of the variance of VCR. Greater vulnerabilities in socioeconomic status and household composition & disability were positively associated with CFR, while VCR was inversely associated with CFR, together explaining 10.4% of the variance of CFR. Our mediation analysis, based on the mid-year data (30^th June 2021), found that 37.6% (mediation/total effect, 0.0014/0.0037), 10% (0.0003/0.0030) and 100% (0.0005/0.0005) of the effects in the pathways involving socioeconomic status, household composition & disability and minority status & language, respectively, were mediated by VCR. As a whole, the mediation effect significantly counted for 30.6% of COVID-19 CFR disparity. Such a mediation effect was seen throughout 2021, with proportions ranging from 12 to 32%.

Conclusions

Allocation of COVID-19 vaccination in the USA during 2021 led to additional inequality with respect to COVID-19 mortality. Viable public health interventions should be taken to guarantee an equitable deployment of healthcare recourses across different population groups.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-14592-w.

Exploring spatial distribution of social vulnerability and its relationship with the Coronavirus disease 2019: the Capital region of South Korea

Background & objective

The ongoing coronavirus disease 2019 (COVID-19) pandemic continues to cause death and socioeconomic problems worldwide. This study examined the spatial distribution of social vulnerability to COVID-19 and its relationship with the number of confirmed COVID-19 cases in 2020, focusing on the Capital region of South Korea.

Methods

A traditional social vulnerability index (SVI), healthy SVI, and the difference of each SVI were constructed in 2015 and 2019. The traditional SVI was constructed across five domains: age, socioeconomic disadvantage, housing, income, and environment. The healthy SVI domains were: prevention, health-related habits, chronic disease, healthcare infrastructure, and mortality. The spatial distribution of the traditional SVI, healthy SVI, and confirmed cases of COVID-19 was explored using ArcGIS 10.5. Pearson correlation was used to identify the relationship between confirmed COVID-19 cases and the two SVIs and their changes between 2015 and 2019. Four multiple linear regression models were used to identify the impact of the changes of the two SVIs on the confirmed COVID-19 cases for the three episodes and total period with control of population using STATA/MP 16.1.

Results

Confirmed COVID-19 cases were concentrated in a specific area of the Capital region. The traditional SVI was more vulnerable in the outer regions of the Capital region, and some central, western, and eastern areas reflected an increase in vulnerability. Healthy SVI was more vulnerable in the northern part of the Capital region, and increase in vulnerability showed in some central areas above Seoul. By multiple regression with the population controlled, the difference of the traditional SVI between 2015 and 2019 showed a positive relationship with the confirmed COVID-19 cases in all models at a significance level of 0.05, and the 2019 integrated SVI showed a negative relationship with confirmed COVID-19 cases in all models.

Conclusions

The results of this study showed that the confirmed COVID-19 cases are associated with increased traditional SVI vulnerability between 2015 and 2019 and have a high positive relationship with the spread of COVID-19. Policy efforts are needed to reduce confirmed COVID-19 cases among the vulnerable in regions with relatively increased traditional SVI.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-14212-7.

Race, Ethnicity, and Health Disparities in US Children With COVID-19: A Review of the Evidence and Recommendations for the Future

Coronavirus disease 2019 (COVID-19) is an important cause of morbidity in children in the United States (U.S.). Moreover, the U.S. has witnessed significant disparities affecting American Indian/Alaska Native, Black, and Hispanic/Latino children, stemming from systemic racism and social-structural inequalities and not differences in innate biological susceptibility. We review what is known on COVID-19 and health disparities in disease burden, access to care, pharmaceutical interventions, and clinical research in children, with a focus on the U.S. context. In addition, we propose strategies to communicate scientific data in ways that do not promote racism and biological susceptibility themes, and to address pediatric disparities in clinical infectious diseases research.

COVID-19 Outcomes Among the Hispanic Population of 27 Large US Cities, 2020-2021

Objectives. To examine racial/ethnic disparities in COVID-19 outcomes between Hispanics and Whites across 27 US jurisdictions whose health departments are members of the Big Cities Health Coalition (BCHC). Methods. Using surveillance data from the BCHC COVID-19 dashboard as of mid-June 2021, we computed crude incidence, age-adjusted hospitalization and mortality, and full vaccination coverage rates for Hispanics and Whites by city. We estimated relative and absolute disparities cumulatively and for 2020 and 2021 and explored associations between city-level social vulnerability and the magnitude of disparities. Results. In most of the cities with available COVID-19 incidence data, rates among Hispanics were 2.2 to 6.7 times higher than those among Whites. In all cities, Hispanics had higher age-adjusted hospitalization (1.5-8.6 times as high) and mortality (1.4-6.2 times as high) rates. Hispanics had lower vaccination coverage in all but 1 city. Disparities in incidence and hospitalizations narrowed in 2021, whereas disparities in mortality remained similar. Disparities in incidence, hospitalization, mortality, and vaccination rates were wider in cities with lower social vulnerability. Conclusions. A deeper exploration of racial/ethnic disparities in COVID-19 outcomes is essential to understand and prevent disparities among marginalized communities. (Am J Public Health. 2022;112(7): 1034-1044. https://doi.org/10.2105/AJPH.2022.306809).

Perspectives of Latinx Individuals Who Were Unvaccinated And Hospitalized for COVID-19: A Qualitative Study

Importance

Latinx individuals in the United States have lower COVID-19 vaccination rates and higher rates of COVID-19 infections, hospitalizations, and deaths than non-Latinx White individuals. Little is known about the perspectives of Latinx adults who had not received the COVID-19 vaccination and were hospitalized for COVID-19.

Objective

To describe the perspectives of Latinx individuals who were unvaccinated and subsequently hospitalized for COVID-19.

Design, Setting, and Participants

This qualitative study was conducted using semistructured phone interviews with 25 Latinx adults who were unvaccinated and survived a COVID-19 hospitalization in a public safety net hospital in Colorado from February to November 2021. Interviews were audio recorded, and transcripts were analyzed using thematic analysis.

Main Outcomes and Measures

Themes and subthemes of perspectives on vaccination.

Results

Among 25 adults (14 [56.0%] women, 11 [44.0%] men; mean [SD] age, 51 [15] years) who participated, all participants self-identified as Latino, Latina, or Latinx or Hispanic. There were 11 individuals who relied on emergency Medicaid (hospital coverage for Denver residents who are undocumented), while 10 individuals (40.0%) were essential workers and 13 individuals (52.0%) were unemployed. In interviews, 3 themes (with subthemes) were identified: factors associated with vaccination after hospitalization (subthemes: fear of death, avoiding hospitalization and reinfection, convinced COVID-19 is real, and responded to pressure from others), concerns about the COVID-19 vaccine (subthemes: experimental status and short timeline for production, contents of vaccine unknown or concerning, vaccine considered ineffective, worrisome immediate and long-term adverse effects, mixed and conflicting information, and government aimed to control or mark population through vaccination), and opportunities to improve vaccine uptake (subthemes; sharing personal experiences through social media, testimonials about minimal vaccine adverse effects, connecting with friends and family about the hospitalization experience, making the vaccine more accessible, and connecting with trusted sources of information).

Conclusions and Relevance

This study found that Latinx individuals who were unvaccinated and subsequently hospitalized for COVID-19 were motivated to engage in advocacy to encourage vaccination in their communities. These findings suggest that supporting patient advocacy after hospital discharge and continued efforts to create low-barrier, patient-informed public health strategies may be associated with increased vaccine uptake in Latinx communities.

Social Determinants and Indicators of COVID-19 Among Marginalized Communities: A Scientific Review and Call to Action for Pandemic Response and Recovery

Coronavirus disease 2019 (COVID-19) has placed massive socio-psychological, health, and economic burdens including deaths on countless lives; however, it has disproportionally impacted certain populations. Co-occurring Social Determinants of Health (SDoH) disparities and other underlying determinants have exacerbated the COVID-19 pandemic. This literature review sought to (1) examine literature focused on SDoH and COVID-19 outcomes ie, infectivity, hospitalization, and death rates among marginalized communities; and (2) identify SDoH disparities associated with COVID-19 outcomes. We searched electronic databases for studies published from October 2019 to October 2021. Studies that were selected were those intersecting SDoH indicators and COVID-19 outcomes and were conducted in the United States. Our review underscored the disproportionate vulnerabilities and adverse outcomes from COVID-19 that have impacted racial/ethnic minority communities and other disadvantaged groups (ie, senior citizens, and displaced/homeless individuals). COVID-19 outcomes were associated with SDoH indicators, ie, race/ethnicity, poverty, median income level, housing density, housing insecurity, health-care access, occupation, transportation/commuting patterns, education, air quality, food insecurity, old age, etc. Our review concluded with recommendations and a call to action to integrate SDoH indicators along with relevant health data when implementing intelligent solutions and intervention strategies to pandemic response/recovery among vulnerable populations.

Public health impact of covid-19 vaccines in the US: observational study

OBJECTIVE

To evaluate the impact of vaccine scale-up on population level covid-19 mortality and incidence in the United States.

DESIGN

Observational study.

SETTING

US county level case surveillance and vaccine administration data reported from 14 December 2020 to 18 December 2021.

PARTICIPANTS

Residents of 2558 counties from 48 US states.

MAIN OUTCOME MEASURES

The primary outcome was county covid-19 mortality rates (deaths/100 000 population/county week). The secondary outcome was incidence of covid-19 (cases/100 000 population/county week). Incidence rate ratios were used to compare rates across vaccination coverage levels. The impact of a 10% improvement in county vaccination coverage (defined as at least one dose of a covid-19 vaccine among adults ≥18 years of age) was estimated During the eras of alpha and delta variant predominance, the impact of very low (0-9%), low (10-39%), medium (40-69%), and high (≥70%) vaccination coverage levels was compared.

RESULTS

In total, 30 643 878 cases of covid-19 and 439 682 deaths associated with covid-19 occurred over 132 791 county weeks. A 10% improvement in vaccination coverage was associated with an 8% (95% confidence interval 8% to 9%) reduction in mortality rates and a 7% (6% to 8%) reduction in incidence. Higher vaccination coverage levels were associated with reduced mortality and incidence rates during the eras of alpha and delta variant predominance.

CONCLUSIONS

Higher vaccination coverage was associated with lower rates of population level covid-19 mortality and incidence in the US.

Geography of Disparity: Connecting COVID-19 Vulnerability and Social Determinants of Health in Colorado

The COVID-19 pandemic has drawn greater attention to social determinants of health and associated health inequities, which disproportionately affect vulnerable populations and places in the U.S. In this study, we explored geographic patterns of local-level COVID-19 vulnerability and associations with social and health determinants across Colorado. To conceptualize social and health determinants and how together they generate risk and exposure, we integrated the concepts of social vulnerability and syndemic to situate COVID-19 vulnerability within a broader hazards of place framework. Using geospatial statistics and GIS, we estimated census tract-level rates of COVID-19, which are not yet available in Colorado, and mapped areas of high and low incidence risk. We also developed composite indices that characterized social and health vulnerabilities to measure multivariate associations with COVID-19 rates. The findings revealed hotspots of persistent risk in mountain communities since the pandemic emerged in Colorado, as well as clusters of risk in the Urban Front Range's central and southern counties, and across many parts of eastern Colorado. Vulnerability analyses indicate that COVID-19 rates were associated with mental health and chronic conditions along with social determinants that represent inequities in education, income, healthcare access, and race/ethnicity (minority percent of population), which may have disproportionately exposed some communities more than others to infection and severe health outcomes. Overall, the findings provide geographic health information about COVID-19 and vulnerability context, which may better inform local decision-making for interventions and policies that support equity of social determinants of health.Supplemental data for this article is available online at https://doi.org/10.1080/08964289.2021.2021382 .

Contribution of Individual- and Neighborhood-Level Social, Demographic, and Health Factors to COVID-19 Hospitalization Outcomes

BACKGROUND

Although disparities in COVID-19 outcomes have been observed, factors contributing to these differences are not well understood.

OBJECTIVE

To determine whether COVID-19 hospitalization outcomes are related to neighborhood-level social vulnerability, independent of patient-level clinical factors.

DESIGN

Pooled cross-sectional study of prospectively collected data.

SETTING

38 Michigan hospitals.

PATIENTS

Adults older than 18 years hospitalized for COVID-19 in a participating site between March and December 2020.

MEASUREMENTS

COVID-19 outcomes included acute organ dysfunction, organ failure, invasive mechanical ventilation, intensive care unit stay, death, and discharge disposition. Social vulnerability was measured by the social vulnerability index (SVI), a composite measure of social disadvantage.

RESULTS

Compared with patients in low-vulnerability ZIP codes, those living in high-vulnerability ZIP codes were more frequently treated in the intensive care unit (29.0% vs. 24.5%); more frequently received mechanical ventilation (19.3% vs. 14.2%); and experienced higher rates of organ dysfunction (51.9% vs. 48.6%), organ failure (54.7% vs. 51.6%), and in-hospital death (19.4% vs. 16.7%). In mixed-effects regression analyses accounting for age, sex, and comorbid conditions, an increase in a patient's neighborhood SVI by 0.25 (1 quartile) was associated with greater likelihood of mechanical ventilation (increase of 2.1 percentage points), acute organ dysfunction (increase of 2.8 percentage points), and acute organ failure (increase of 2.8 percentage points) but was not associated with intensive care unit stay, mortality, or discharge disposition.

LIMITATION

Observational data focused on hospitalizations in a single state.

CONCLUSION

Hospitalized patients with COVID-19 from socially vulnerable neighborhoods presented with greater illness severity and required more intensive treatment, but once hospitalized they did not experience differences in hospital mortality or discharge disposition. Policies that target socially vulnerable neighborhoods and access to COVID-19 care may help ameliorate health disparities.

PRIMARY FUNDING SOURCE

Blue Cross Blue Shield of Michigan (BCBSM) and Blue Care Network as part of the BCBSM Value Partnerships Program, the Michigan Public Health Institute, and the Michigan Department of Health & Human Services.

Ethnicity and Clinical Outcomes in Patients Hospitalized for COVID-19 in Spain: Results from the Multicenter SEMI-COVID-19 Registry

(1) Background: This work aims to analyze clinical outcomes according to ethnic groups in patients hospitalized for COVID-19 in Spain. (2) Methods: This nationwide, retrospective, multicenter, observational study analyzed hospitalized patients with confirmed COVID-19 in 150 Spanish hospitals (SEMI-COVID-19 Registry) from 1 March 2020 to 31 December 2021. Clinical outcomes were assessed according to ethnicity (Latin Americans, Sub-Saharan Africans, Asians, North Africans, Europeans). The outcomes were in-hospital mortality (IHM), intensive care unit (ICU) admission, and the use of invasive mechanical ventilation (IMV). Associations between ethnic groups and clinical outcomes adjusted for patient characteristics and baseline Charlson Comorbidity Index values and wave were evaluated using logistic regression. (3) Results: Of 23,953 patients (median age 69.5 years, 42.9% women), 7.0% were Latin American, 1.2% were North African, 0.5% were Asian, 0.5% were Sub-Saharan African, and 89.7% were European. Ethnic minority patients were significantly younger than European patients (median (IQR) age 49.1 (40.5−58.9) to 57.1 (44.1−67.1) vs. 71.5 (59.5−81.4) years, p < 0.001). The unadjusted IHM was higher in European (21.6%) versus North African (11.4%), Asian (10.9%), Latin American (7.1%), and Sub-Saharan African (3.2%) patients. After further adjustment, the IHM was lower in Sub-Saharan African (OR 0.28 (0.10−0.79), p = 0.017) versus European patients, while ICU admission rates were higher in Latin American and North African versus European patients (OR (95%CI) 1.37 (1.17−1.60), p < 0.001) and (OR (95%CI) 1.74 (1.26−2.41), p < 0.001). Moreover, Latin American patients were 39% more likely than European patients to use IMV (OR (95%CI) 1.43 (1.21−1.71), p < 0.001). (4) Conclusion: The adjusted IHM was similar in all groups except for Sub-Saharan Africans, who had lower IHM. Latin American patients were admitted to the ICU and required IMV more often.

County-Level Social Vulnerability Is Positively Associated with Cardiometabolic Disease in Colorado

Cardiometabolic diseases are a group of interrelated diseases that pose greater burden among socially vulnerable communities. The social vulnerability index (SVI) identifies communities vulnerable to emergencies and may also help determine communities at risk of adverse chronic health outcomes. However, no studies have examined the relationship between the SVI and cardiometabolic health outcomes in Colorado or focused on rural settings. The aim of this ecological study was to determine whether the county-level SVI is associated with county-level cardiometabolic health indicators with a particular focus on rurality and racial/ethnic diversity. We obtained 2014 SVI scores from the Centers for Disease Control and Prevention (scored 0-1; higher = more vulnerable) and 2013-2015 cardiometabolic health estimates from the Colorado Department of Public Health and Environment. The distribution of social determinants of health was spatially evaluated. Bivariate relationships between the SVI and cardiometabolic indicators were estimated using simple linear regression models. The highest SVI scores were observed in rural areas, including the San Luis Valley (mean: 0.78, median: 0.91), Southeast (mean: 0.72, median: 0.73), and Northeast (mean: 0.66, median: 0.76) regions. Across Colorado, the SVI accounted for 41% of the variability in overweight and obesity prevalence (p < 0.001), 17% of the variability in diabetes prevalence (p = 0.001), and 58% of the age-adjusted myocardial infarction hospitalization rate (p < 0.001). SVI values may be useful in determining a community's burden of cardiometabolic diseases.

The effects of air pollution, meteorological parameters, and climate change on COVID-19 comorbidity and health disparities: A systematic review

Air pollutants, especially particulate matter, and other meteorological factors serve as important carriers of infectious microbes and play a critical role in the spread of disease. However, there remains uncertainty about the relationship among particulate matter, other air pollutants, meteorological conditions and climate change and the spread of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), hereafter referred to as COVID-19. A systematic review was conducted using PRISMA guidelines to identify the relationship between air quality, meteorological conditions and climate change, and COVID-19 risk and outcomes, host related factors, co-morbidities and disparities. Out of a total of 170,296 scientific publications screened, 63 studies were identified that focused on the relationship between air pollutants and COVID-19. Additionally, the contribution of host related-factors, co-morbidities, and health disparities was discussed. This review found a preponderance of evidence of a positive relationship between PM_2.5, other air pollutants, and meteorological conditions and climate change on COVID-19 risk and outcomes. The effects of PM_2.5, air pollutants, and meteorological conditions on COVID-19 mortalities were most commonly experienced by socially disadvantaged and vulnerable populations. Results however, were not entirely consistent, and varied by geographic region and study. Opportunities for using data to guide local response to COVID-19 are identified.

Spread of COVID-19 Vaccine Misinformation in the Ninth Inning: Retrospective Observational Infodemic Study

Background

Shortly after Pfizer and Moderna received emergency use authorizations from the Food and Drug Administration, there were increased reports of COVID-19 vaccine-related deaths in the Vaccine Adverse Event Reporting System (VAERS). In January 2021, Major League Baseball legend and Hall of Famer, Hank Aaron, passed away at the age of 86 years from natural causes, just 2 weeks after he received the COVID-19 vaccine. Antivaccination groups attempted to link his death to the Moderna vaccine, similar to other attempts misrepresenting data from the VAERS to spread COVID-19 misinformation.

Objective

This study assessed the spread of misinformation linked to erroneous claims about Hank Aaron’s death on Twitter and then characterized different vaccine misinformation and hesitancy themes generated from users who interacted with this misinformation discourse.

Methods

An initial sample of tweets from January 31, 2021, to February 6, 2021, was queried from the Twitter Search Application Programming Interface using the keywords “Hank Aaron” and “vaccine.” The sample was manually annotated for misinformation, reporting or news media, and public reaction. Nonmedia user accounts were also classified if they were verified by Twitter. A second sample of tweets, representing direct comments or retweets to misinformation-labeled content, was also collected. User sentiment toward misinformation, positive (agree) or negative (disagree), was recorded. The Strategic Advisory Group of Experts Vaccine Hesitancy Matrix from the World Health Organization was used to code the second sample of tweets for factors influencing vaccine confidence.

Results

A total of 436 tweets were initially sampled from the Twitter Search Application Programming Interface. Misinformation was the most prominent content type (n=244, 56%) detected, followed by public reaction (n=122, 28%) and media reporting (n=69, 16%). No misinformation-related content reviewed was labeled as misleading by Twitter at the time of the study. An additional 1243 comments on misinformation-labeled tweets from 973 unique users were also collected, with 779 comments deemed relevant to study aims. Most of these comments expressed positive sentiment (n=612, 78.6%) to misinformation and did not refute it. Based on the World Health Organization Strategic Advisory Group of Experts framework, the most common vaccine hesitancy theme was individual or group influences (n=508, 65%), followed by vaccine or vaccination-specific influences (n=110, 14%) and contextual influences (n=93, 12%). Common misinformation themes observed included linking the death of Hank Aaron to “suspicious” elderly deaths following vaccination, claims about vaccines being used for depopulation, death panels, federal officials targeting Black Americans, and misinterpretation of VAERS reports. Four users engaging with or posting misinformation were verified on Twitter at the time of data collection.

Conclusions

Our study found that the death of a high-profile ethnic minority celebrity led to the spread of misinformation on Twitter. This misinformation directly challenged the safety and effectiveness of COVID-19 vaccines at a time when ensuring vaccine coverage among minority populations was paramount. Misinformation targeted at minority groups and echoed by other verified Twitter users has the potential to generate unwarranted vaccine hesitancy at the expense of people such as Hank Aaron who sought to promote public health and community immunity.

Community predictors of COVID-19 cases and deaths in Massachusetts: Evaluating changes over time using geospatially refined data

BACKGROUND

The COVID-19 pandemic has highlighted the need for targeted local interventions given substantial heterogeneity within cities and counties. Publicly available case data are typically aggregated to the city or county level to protect patient privacy, but more granular data are necessary to identify and act upon community-level risk factors that can change over time.

METHODS

Individual COVID-19 case and mortality data from Massachusetts were geocoded to residential addresses and aggregated into two time periods: "Phase 1" (March-June 2020) and "Phase 2" (September 2020 to February 2021). Institutional cases associated with long-term care facilities, prisons, or homeless shelters were identified using address data and modeled separately. Census tract sociodemographic and occupational predictors were drawn from the 2015-2019 American Community Survey. We used mixed-effects negative binomial regression to estimate incidence rate ratios (IRRs), accounting for town-level spatial autocorrelation.

RESULTS

Case incidence was elevated in census tracts with higher proportions of Black and Latinx residents, with larger associations in Phase 1 than Phase 2. Case incidence associated with proportion of essential workers was similarly elevated in both Phases. Mortality IRRs had differing patterns from case IRRs, decreasing less substantially between Phases for Black and Latinx populations and increasing between Phases for proportion of essential workers. Mortality models excluding institutional cases yielded stronger associations for age, race/ethnicity, and essential worker status.

CONCLUSIONS

Geocoded home address data can allow for nuanced analyses of community disease patterns, identification of high-risk subgroups, and exclusion of institutional cases to comprehensively reflect community risk.

Differential Impact of Social Distancing on COVID-19 Spread in the U.S.: By Rurality and Social Vulnerability

Background

To quantify lessons learned to better prepare for similar pandemic crisis in the future, we assess the overall impact of social distancing on the daily growth rate of COVID-19 infections in the U.S. during the initial phase of the pandemic and the impacts' heterogeneity by urbanity and social vulnerability of the counties. The initial phase is chosen to purposely identify the essential and largest impact of the first-line of defense measure for similar pandemic: social distancing.

Methods

Spatial Durbin models with county fixed effects were used to account for spatial dependencies and identify spatial spillover effects and spatial heterogeneity.

Results

Besides the substantial curve flattening effects of social distancing, our results show significant spillover effects induced by neighboring counties' social distancing levels even in the absence of significant within-county effects. Urban and areas with high social vulnerability are the ones benefit the most from social distancing and high level of compliance is needed. Moderate level is enough in reaching the peak marginal impact in rural and areas with low social vulnerability.