Inequities in the distribution of COVID-19: an adaptation of WHO's conceptual framework.
Ontology highlight
ABSTRACT:
SUBMITTER: Vasquez-Vera H
PROVIDER: S-EPMC8526437 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:We overcome a lack of frontline worker status information in most COVID-19 data repositories to document the extent to which occupation has contributed to COVID-19 disparities in the United States. Using national data from over a million U.S. respondents to a Facebook-Carnegie Mellon University survey administered from September 2020 to March 2021, we estimated the likelihoods of frontline workers, compared to non-frontline workers, 1) to ever test positive for SARs-Cov-2 and 2) to test positive for SARs-Cov-2 within the past two weeks. Net of other covariates including education level, county-level political environment, and rural residence, both healthcare and non-healthcare frontline workers had higher odds of having ever tested positive for SARs-Cov-2 across the study time period. Similarly, non-healthcare frontline workers were more likely to test positive in the previous 14 days. Conversely, healthcare frontline workers were less likely to have recently tested positive. Our findings suggest that occupational exposure has played an independent role in the uneven spread of the virus. In particular, non-healthcare frontline workers have experienced sustained higher risk of testing positive for SARs-Cov-2 compared to non-frontline workers. Alongside more worker protections, future COVID-19 and other highly infectious disease response strategies must be augmented by a more robust recognition of the role that structural factors, such as the highly stratified U.S. occupational landscape, have played in the uneven toll of the COVID-19 pandemic.
| S-EPMC8492358 | biostudies-literature
Project description:BackgroundIn 2020, early U.S. COVID-19 testing sites offered diagnostic capacity to patients and were important sources of epidemiological data about the spread of the novel pandemic disease. However, little research has comprehensively described American testing sites' distribution by race/ethnicity and sought to identify any relation to known disparities in COVID-19 outcomes.MethodsLocations of U.S. COVID-19 testing sites were gathered from 16 April to 28 May 2020. Geographic testing disparities were evaluated with comparisons of the demographic makeup of zip codes around each testing site versus Monte Carlo simulations, aggregated to statewide and nationwide levels. State testing disparities were compared with statewide disparities in mortality observed one to 3 weeks later using multivariable regression, controlling for confounding disparities and characteristics.ResultsNationwide, COVID-19 testing sites geographically overrepresented White residents on 7 May, underrepresented Hispanic residents on 16 April, 7 May and 28 May and overrepresented Black residents on 28 May compared with random distribution within counties, with new sites added over time exhibiting inconsistent disparities for Black and Hispanic populations. For every 1 percentage point increase in underrepresentation of Hispanic populations in zip codes with testing, mortality among the state's Hispanic population was 1.04 percentage points more over-representative (SE = 0.415, p = .01).ConclusionsAmerican testing sites were not distributed equitably by race during this analysis, often underrepresenting minority populations who bear a disproportionate burden of COVID-19 cases and deaths. With an easy-to-implement measure of geographic disparity, these results provide empirical support for the consideration of access when distributing preventive resources.
| S-EPMC8420583 | biostudies-literature
Project description:Major public health incidents such as COVID-19 typically have characteristics of being sudden, uncertain, and hazardous. If a government can effectively accumulate big data from various sources and use appropriate analytical methods, it may quickly respond to achieve optimal public health decisions, thereby ameliorating negative impacts from a public health incident and more quickly restoring normality. Although there are many reports and studies examining how to use big data for epidemic prevention, there is still a lack of an effective review and framework of the application of big data in the fight against major public health incidents such as COVID-19, which would be a helpful reference for governments. This paper provides clear information on the characteristics of COVID-19, as well as key big data resources, big data for the visualization of pandemic prevention and control, close contact screening, online public opinion monitoring, virus host analysis, and pandemic forecast evaluation. A framework is provided as a multidimensional reference for the effective use of big data analytics technology to prevent and control epidemics (or pandemics). The challenges and suggestions with respect to applying big data for fighting COVID-19 are also discussed.
| S-EPMC7503476 | biostudies-literature
Project description:BackgroundEmerging evidence indicates that illicit drug overdoses are increasing throughout the COVID-19 pandemic. There is a paucity of evidence on the causative pathways for this trend, but expert opinions, commentaries, and some reviews offer theoretical underpinnings.MethodsIn this rapid review, we collate the available published evidence, expert opinions, commentaries, and reviews on the unintended pathways between COVID-19 public health responses and increasing illicit drug overdoses. Using tenets of thematic analyses and grounded theory, we also offer a visual conceptual framework for these unintended pathways.ResultsOur framework focuses on five particular public health responses, namely social isolation/physical distancing/quarantine; staff/resource reallocations and reductions; closures of businesses and other places of employment; border closures and transportation restrictions; and the early release of people from prisons. As argued in the literature reviewed here, these public health responses have unintentionally created increased overdose risks by producing high risk use scenarios; increased risks of relapsing; disrupted addictions services and treatment; an increasingly toxic supply of drugs; and the risk of using with lowered tolerance.ConclusionsHealth care systems should respond to these pathways to mitigate the unintended consequences. Furthermore, the COVID-19 pandemic may represent an opportunity to enact proactive, progressive, and innovative solutions to an overdose crisis that will surely outlast the current pandemic.
| S-EPMC8545050 | biostudies-literature
Project description:Species invasions are a major driver of ecological change, are very difficult to control or reverse, and will increase with climate change and global trade. Invasion sciences consider how species in invaded environments adapt, but neither scientists nor policy makers consider human adaptation to invasive species and how this affects ecosystems and well-being. To address this, a framework conceptualising autochthonous human adaptation to invasions was developed based on the Human Adaptation to Biodiversity Change framework and a case study metasynthesis. Results show that adaptation occurs within different spheres of human activity and organisation at different social-ecological scales; responses have feedbacks within and across these spheres. Adaptation to invasives and other drivers is a set of highly contextual, complex, non-linear responses that make up pathways pursued over time. Most invasive species management and adaptation occurs 'from below,' and policies and planned control efforts should support autochthonous adaptation, rather than undermining it.
| S-EPMC6883017 | biostudies-literature
Project description: Not available
| S-BSST563 | biostudies-other
Project description:High COVID-19 mortality among Black communities heightened the pandemic's devastation. In the state of Louisiana, the racial disparity associated with COVID-19 mortality was significant; Black Americans accounted for 50% of known COVID-19-related deaths while representing only 32% of the state's population. In this paper, we argue that structural racism resulted in a synergistic framework of cumulatively negative determinants of health that ultimately affected COVID-19 deaths in Louisiana Black communities. We identify the spatial distribution of social, environmental, and economic stressors across Louisiana parishes using hot spot analysis to develop aggregate stressors. Further, we examine the correlation between stressors, cumulative health risks, COVID-19 mortality, and the size of Black populations throughout Louisiana. We hypothesized that parishes with larger Black populations (percentages) would have larger stressor values and higher cumulative health risks as well as increased COVID-19 mortality rates. Our results suggest two categories of parishes. The first group has moderate levels of aggregate stress, high population densities, predominately Black populations, and high COVID-19 mortality. The second group of parishes has high aggregate stress, lower population densities, predominantly Black populations, and initially low COVID-19 mortality that increased over time. Our results suggest that structural racism and inequities led to severe disparities in initial COVID-19 effects among highly populated Black Louisiana communities and that as the virus moved into less densely populated Black communities, similar trends emerged.
| S-EPMC9271191 | biostudies-literature
Project description:BackgroundThe COVID-19 pandemic led to mental health fallout in the US; yet research about mental health and COVID-19 primarily rely on samples that may overlook variance in regional mental health. Indeed, between-city comparisons of mental health decline in the US may provide further insight into how the pandemic is disproportionately affecting at-risk groups.PurposeThis study leverages social media and COVID-19-city infection data to measure the longitudinal (January 22- July 31, 2020) mental health effects of the COVID-19 pandemic in 20 metropolitan areas.MethodsWe used longitudinal VADER sentiment analysis of Twitter timelines (January-July 2020) for cohorts in 20 metropolitan areas to examine mood changes over time. We then conducted simple and multivariate Ordinary Least Squares (OLS) regressions to examine the relationship between COVID-19 infection city data, population, population density, and city demographics on sentiment across those 20 cities.ResultsLongitudinal sentiment tracking showed mood declines over time. The univariate OLS regression highlighted a negative linear relationship between COVID-19 city data and online sentiment (β = -.017). Residing in predominantly white cities had a protective effect against COVID-19 driven negative mood (β = .0629, p < .001).DiscussionOur results reveal that metropolitan areas with larger communities of color experienced a greater subjective well-being decline than predominantly white cities, which we attribute to clinical and socioeconomic correlates that place communities of color at greater risk of COVID-19.ConclusionThe COVID-19 pandemic is a driver of declining US mood in 20 metropolitan cities. Other factors, including social unrest and local demographics, may compound and exacerbate mental health outlook in racially diverse cities.
| S-EPMC8266050 | biostudies-literature
Project description:BackgroundIn the United States, distribution plans for the COVID-19 vaccination were established at the state level. However, some states, such as Connecticut, followed an age-based strategy without considering occupations or co-morbid conditions due to its simplicity in implementation. This strategy raised concerns about exacerbating health inequities because it did not prioritize vulnerable communities, specifically, minorities and low-income groups. The study aims to examine the vaccination inequities among different population groups for people aged 65+.MethodsA cross-sectional analysis of quantile-based independent sample t-test was employed to examine the relationship between eight social vulnerability indices (SVIs, i.e., below poverty, unemployed, without high school diploma, disability, minority, speaks English less than well, no vehicle, and mobile homes) and vaccination rates at the town level in Connecticut during the second phase of the vaccine distribution plan when individuals aged 65 and over were eligible. Negative binomial regressions were employed to further justify the relationships between SVIs and vaccination rates.ResultsThe report shows that the differences in vaccination rates were statistically significant between the most vulnerable and the least vulnerable towns with respect to six SVIs (i.e., below poverty, without high school diploma, disability, minority, speaks English less than well, and no vehicle). The vaccination gap was greater for people aged 75+ than people aged 65-74. Among the selected SVIs, below poverty was negatively correlated with the vaccination rate for 75+, and without high school diploma was negatively correlated with both rates.ConclusionsThis report reveals the significant health inequities in COVID-19 vaccination among the elderly population at the early vaccination phase. It can shed insights into health policy initiatives to improve vaccination coverage in the elderly communities, such as promoting onsite scheduling and increasing at-home vaccination services.
| S-EPMC8491089 | biostudies-literature
