Assessing capacity to social distance and neighborhood-level health disparities during the COVID-19 pandemic.
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ABSTRACT:
SUBMITTER: Carrion D
PROVIDER: S-EPMC7302284 | biostudies-literature | 2020 Jun
REPOSITORIES: biostudies-literature
Publications
Assessing capacity to social distance and neighborhood-level health disparities during the COVID-19 pandemic.
Carrión Daniel D Colicino Elena E Pedretti Nicolo Foppa NF Arfer Kodi B KB Rush Johnathan J DeFelice Nicholas N Just Allan C AC
medRxiv : the preprint server for health sciences 20200613
The COVID-19 pandemic has yielded disproportionate impacts on communities of color in New York City (NYC). Researchers have noted that social disadvantage may result in limited capacity to socially distance, and consequent disparities. Here, we investigate the role of neighborhood social disadvantage on the ability to socially distance, infections, and mortality. We combine Census Bureau and NYC open data with SARS-CoV-2 testing data using supervised dimensionality-reduction with Bayesian Weight ...[more]
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Project description:The COVID-19 pandemic has yielded disproportionate impacts on communities of color in New York City (NYC). Researchers have noted that social disadvantage may result in limited capacity to socially distance, and consequent disparities. We investigate the association between neighborhood social disadvantage and the ability to socially distance, infections, and mortality in Spring 2020. We combine Census Bureau and NYC open data with SARS-CoV-2 testing data using supervised dimensionality-reduction with Bayesian Weighted Quantile Sums regression. The result is a ZIP code-level index with weighted social factors associated with infection risk. We find a positive association between neighborhood social disadvantage and infections, adjusting for the number of tests administered. Neighborhood disadvantage is also associated with a proxy of the capacity to socially isolate, NYC subway usage data. Finally, our index is associated with COVID-19-related mortality.
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Project description:ObjectivesTo describe vaccine and booster uptake by neighborhood-level factors in California.MethodsWe examined trends in COVID-19 vaccination up to September 21, 2021, and boosters up to March 29, 2022 using data from the California Department of Public Health. Quasi-Poisson regression was used to model the association between neighborhood-level factors and fully vaccinated and boosted among ZIP codes. Sub-analyses on booster rates were compared among the 10 census regions.ResultsIn a minimally adjusted model, a higher proportion of Black residents was associated with lower vaccination (HR = 0.97; 95%CI: 0.96-0.98). However, in a fully adjusted model, proportion of Black, Hispanic/Latinx, and Asian residents were associated with higher vaccination rates (HR = 1.02; 95%CI: 1.01-1.03 for all). The strongest predictor of low vaccine coverage was disability (HR = 0.89; 95%CI: 0.86-0.91). Similar trends persisted for booster doses. Factors associated with booster coverage varied by region.ConclusionsExamining neighborhood-level factors associated with COVID-19 vaccination and booster rates uncovered significant variation within the large and geographically and demographically diverse state of California. Equity-based approaches to vaccination must ensure a robust consideration of multiple social determinants of health.
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Project description:Structural racism in police contact is an important driver of health inequities among the U.S. urban population. Hyper-policing and police violence in marginalized communities have risen to the top of the national policy agenda, particularly since protests in 2020. How did pandemic conditions impact policing? We assess neighborhood racial disparities in arrests after COVID-19 stay-at-home orders in Boston, Charleston, Pittsburgh, and San Francisco census tracts (January 2019-August 2020). Using interrupted time series models with census tract fixed effects, we report arrest rates across tract racial and ethnic compositions. In the weeks following stay-at-home orders, overall arrest rates were 39% lower (95% CI: 37-41%) on average compared to rates the year prior. Although arrest rates steadily increased thereafter, most tracts did not reach pre-pandemic arrest levels. However, despite declines in nearly all census tracts, the magnitude of racial inequities in arrests remained unchanged. During the initial weeks of the pandemic, arrest rates declined significantly in areas with higher Black populations, but average rates in Black neighborhoods remained higher than pre-pandemic arrest rates in White neighborhoods. These findings support urban policy reforms that reconsider police capacity and presence, particularly as a mechanism for enforcing public health ordinances and reducing racial disparities.
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Project description:Social distancing during a pandemic might be influenced by different attitudes: people may decide to reduce the risk and protect themselves from viral contagion, or they can opt to maintain their habits and be more exposed to the infection. To better understand the underlying motivating attitudes, we asked participants to indicate in an online platform the interpersonal distance from different social targets with professional/social behaviors considered more or less exposed to the virus. We selected five different social targets: a cohabitant, a friend working in a hospital, a friend landed from an international flight, a friend who is back from a cycling ride, or a stranger. In order to measure the realistic and the symbolic perceived threat, we administered the Brief 10-item COVID-19 threat scale. Moreover, in order to measure the risk attitude in different domains, the participants were also asked to fill in the Domain-Specific Risk-Taking DOSPERT scale. Results reveal a general preference for an increased distance from a stranger and the friends who are considered to be more exposed to the virus: the friend working in a hospital or landed from an international flight. Moreover, the interpersonal distance from friends is influenced by the perception of Realistic Threat measured through the Integrated Covid Threat Scale and the Health/Safety Risk Perception/Assumption as measured by the DOSPERT scale. Our results show the flexible and context-dependent nature of our representation of other people: as the social categories are not unchangeable fixed entities, the bodily (e.g., spatial) attitudes towards them are an object of continuous attunement.
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Project description:Importance:An association between social and neighborhood characteristics and health outcomes has been reported but remains poorly understood owing to complex multidimensional factors that vary across geographic space. Objectives:To quantify social determinants of health (SDOH) as multiple dimensions across the continental United States (the 48 contiguous states and the District of Columbia) at a small-area resolution and to examine the association of SDOH with premature mortality within Chicago, Illinois. Design, Setting, and Participants:In this cross-sectional study, census tracts from the US Census Bureau from 2014 were used to develop multidimensional SDOH indices and a regional typology of the continental United States at a small-area level (n?=?71?901 census tracts with approximately 312 million persons) using dimension reduction and clustering machine learning techniques (unsupervised algorithms used to reduce dimensions of multivariate data). The SDOH indices were used to estimate age-adjusted mortality rates in Chicago (n?=?789 census tracts with approximately 7.5 million persons) with a spatial regression for the same period, while controlling for violent crime. Main Outcomes and Measures:Fifteen variables, measured as a 5-year mean, were selected to characterize SDOH as small-area variations for demographic characteristics of vulnerable groups, economic status, social and neighborhood characteristics, and housing and transportation availability at the census-tract level. This SDOH data matrix was reduced to 4 indices reflecting advantage, isolation, opportunity, and mixed immigrant cohesion and accessibility, which were then clustered into 7 distinct multidimensional neighborhood typologies. The association between SDOH indices and premature mortality (defined as death before age 75 years) in Chicago was measured by years of potential life lost and aggregated to a 5-year mean. Data analyses were conducted between July 1, 2018, and August 30, 2019. Results:Among the 71?901 census tracts examined across the continental United States, a median (interquartile range) of 27.2% (47.1%) of residents had minority status, 12.1% (7.5%) had disabilities, 22.9% (7.6%) were 18 years and younger, and 13.6% (8.1%) were 65 years and older. Among the 789 census tracts examined in Chicago, a median (interquartile range) of 80.4% (56.3%) of residents had minority status, 10.2% (8.2%) had disabilities, 23.2% (10.9%) were 18 years and younger, and 9.5% (7.1%) were 65 years and older. Four SDOH indices accounted for 71% of the variance across all census tracts in the continental United States in 2014. The SDOH neighborhood typology of extreme poverty, which is of greatest concern to health care practitioners and policy advocates, comprised only 9.6% of all census tracts across the continental United States but characterized small areas of known public health crises. An association was observed between all SDOH indices and age-adjusted premature mortality rates in Chicago (R2?=?0.63; P?<?.001), even after accounting for violent crime and spatial structures. Conclusions and Relevance:The modeling of SDOH as multivariate indices rather than as a singular deprivation index may better capture the complexity and spatial heterogeneity underlying SDOH. During a time of increased attention to SDOH, this analysis may provide actionable information for key stakeholders with respect to the focus of interventions.
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Project description:INTRODUCTION:The COVID-19 pandemic will test the capacity of health systems worldwide and especially so in low- and middle-income countries. The objective of this study was to assess the surge capacity of the Kenyan of the Kenyan health system in terms of general hospital and ICU beds in the face of the COVID-19 pandemic. METHODS:We assumed that 2% of the Kenyan population get symptomatic infection by SARS-Cov-2 based on modelled estimates for Kenya and determined the health system surge capacity for COVID-19 under three transmission curve scenarios, 6, 12, and 18 months. We estimated four measures of hospital surge capacity namely: 1) hospital bed surge capacity 2) ICU bed surge capacity 3) Hospital bed tipping point, and 5) ICU bed tipping point. We computed this nationally and for all the 47 county governments. RESULTS:The capacity of Kenyan hospitals to absorb increases in caseload due to COVID-19 is constrained by the availability of oxygen, with only 58% of hospital beds in hospitals with oxygen supply. There is substantial variation in hospital bed surge capacity across counties. For example, under the 6 months transmission scenario, the percentage of available general hospital beds that would be taken up by COVID-19 cases varied from 12% Tharaka Nithi county, to 145% in Trans Nzoia county. Kenya faces substantial gaps in ICU beds and ventilator capacity. Only 22 out of the 47 counties have at least 1 ICU unit. Kenya will need an additional 1,511 ICU beds and 1,609 ventilators (6 months transmission curve) to 374 ICU beds and 472 ventilators (18 months transmission curve) to absorb caseloads due to COVID-19. CONCLUSION:Significant gaps exist in Kenya's capacity for hospitals to accommodate a potential surge in caseload due to COVID-19. Alongside efforts to slow and supress the transmission of the infection, the Kenyan government will need to implement adaptive measures and additional investments to expand the hospital surge capacity for COVID-19. Additional investments will however need to be strategically prioritized to focus on strengthening essential services first, such as oxygen availability before higher cost investments such as ICU beds and ventilators.
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Project description:Previous research finds that marriage is a privileged family form with health benefits. These health advantages may have shifted during the pandemic, as more time was spent at home and resources strained. This study compares differences in three health outcomes across relationship statuses between April and December 2020 using a nationally-representative US survey, the Household Pulse Survey (N = 1,422,733). As the pandemic progressed, larger differences emerged when comparing married and never married respondents’ probabilities of fair or poor health, depression, and anxiety as never married people had the steepest decline in health, even adjusting for pandemic-related stressors (e.g., food insufficiency). Yet, widowed and divorced/separated respondents’ greater probabilities of these three health outcomes compared to married respondents’ narrowed over this same period. During the pandemic, relationship status and self-rated health patterns were similar for men and women, but for mental health there was evidence that the growing advantage of marriage relative to never being married was more pronounced for men, whereas the shrinking advantage of marriage relative to being previously married was more pronounced for women. This study identifies the unique health needs for never married adults during the pandemic, demonstrating that social conditions around the pandemic likely exacerbated health disparities by relationship status.
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| S-EPMC10790234 | biostudies-literature
Project description:BackgroundThe coronavirus disease 2019 (COVID-19) pandemic has disproportionately affected socially disadvantaged populations. Whether disparities in COVID-19 incidence related to race/ethnicity and socioeconomic factors exist in the hemodialysis population is unknown.MethodsOur study involved patients receiving in-center hemodialysis in New York City. We used a validated index of neighborhood social vulnerability, the Social Vulnerability Index (SVI), which comprises 15 census tract-level indicators organized into four themes: socioeconomic status, household composition and disability, minority status and language, and housing type and transportation. We examined the association of race/ethnicity and the SVI with symptomatic COVID-19 between March 1, 2020 and August 3, 2020. COVID-19 cases were ascertained using PCR testing. We performed multivariable logistic regression to adjust for demographics, individual-level social factors, dialysis-related medical history, and dialysis facility factors.ResultsOf the 1378 patients on hemodialysis in the study, 247 (17.9%) developed symptomatic COVID-19. In adjusted analyses, non-Hispanic Black and Hispanic patients had significantly increased odds of COVID-19 compared with non-Hispanic White patients. Census tract-level overall SVI, modeled continuously or in quintiles, was not associated with COVID-19 in unadjusted or adjusted analyses. Among non-Hispanic White patients, the socioeconomic status SVI theme, the minority status and language SVI theme, and housing crowding were significantly associated with COVID-19 in unadjusted analyses.ConclusionsAmong patients on hemodialysis in New York City, there were substantial racial/ethnic disparities in COVID-19 incidence not explained by neighborhood-level social vulnerability. Neighborhood-level socioeconomic status, minority status and language, and housing crowding were positively associated with acquiring COVID-19 among non-Hispanic Whites. Our findings suggest that socially vulnerable patients on dialysis face disparate COVID-19-related exposures, requiring targeted risk-mitigation strategies.
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Project description:Background: This study uses a novel geographic approach to summarize the distribution of breast cancer in San Francisco and aims to identify the neighborhoods and racial/ethnic groups that are disproportionately affected by this disease.Methods: Nine geographic groupings were newly defined on the basis of racial/ethnic composition and neighborhood socioeconomic status. Distribution of breast cancer cases from the Greater Bay Area Cancer Registry in these zones were examined. Multivariable logistic regression models were used to determine neighborhood associations with stage IIB+ breast cancer at diagnosis. Cox proportional hazards regression was used to estimate the hazard ratios for all-cause and breast cancer-specific mortality.Results: A total of 5,595 invasive primary breast cancers were diagnosed between January 1, 2006 and December 31, 2015. We found neighborhood and racial/ethnic differences in stage of diagnosis, molecular subtype, survival, and mortality. Patients in the Southeast (Bayview/Hunter's Point) and Northeast (Downtown, Civic Center, Chinatown, Nob Hill, Western Addition) areas were more likely to have stage IIB+ breast cancer at diagnosis, and those in the East (North Beach, Financial District, South of Market, Mission Bay, Potrero Hill) and Southeast were more likely to be diagnosed with triple-negative breast cancers (TNBC). Compared with other racial/ethnic groups, Blacks/African Americans (B/AA) experienced the greatest disparities in breast cancer-related outcomes across geographic areas.Conclusions: San Francisco neighborhoods with lower socioeconomic status and larger minority populations experience worse breast cancer outcomes.Impact: Our findings, which reveal breast cancer disparities at sub-county geographic levels, have implications for population-level health interventions.
| S-EPMC6891202 | biostudies-literature