Inequities in the distribution of COVID-19: an adaptation of WHO's conceptual framework.
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ABSTRACT:
SUBMITTER: Vasquez-Vera H
PROVIDER: S-EPMC8526437 | biostudies-literature |
REPOSITORIES: biostudies-literature
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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: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:Background Homeless people have a higher risk of COVID-19 infection, linked to several social, economic and environmental determinants, frequent comorbidities, obstacles to exercising their constitutional social and health rights, poor medical cover, and insufficient use of the healthcare system. Data on COVID-19 vaccine uptake and its main determinants are lacking for this underserved population.Objectives To construct and test a conceptual framework to model structural social determinants of COVID-19 vaccine uptake among underserved homeless populations, and to test this model to identify the determinants of COVID-19 vaccine uptake on the homeless population living in two metropolitan areas in France.Methods We implemented a multicenter cross-sectional survey from 15/11/2021 to 22/12/2021 in homeless adults in the city of Marseille and in the greater Paris area. Persons sheltered in migrant worker hostels or in emergency social shelters, members of the COVID HOMELESS cohort study in Marseille, and Travelers living in traditional housing were all eligible. A standardized face-to-face questionnaire was administered to the participants where they lived in various languages by trained interviewers. We used structural equation modeling to analyze the structural social determinants of COVID-19 vaccine uptake, the latter defined as receiving at least one dose.Results The participation rate was 64 %, accounting for 3811 participants. There were three main factors associated with greater vaccine uptake: i) opportunity, which included having a personal general practitioner (β = 0.05, p < 0.05), healthcare cover (β = 0.05, p < 0.05), and somebody to accompany the participant for medical appointments (β = -0.04, p < 0.05); ii) motivation, which included attitudes towards vaccination (β = 0.55, p < 0.05), press- and poster-based information (β = 0.03, p < 0.05), and vaccination history (β = 0.03, p < 0.05); iii) type of housing (β = 0.13, p < 0.05) and housing stability (β = 0.04, p < 0.05).Conclusion Our results highlight that housing exclusion is a structural social determinant of COVID-19 vaccine uptake in homeless people in France. They also underline the role which opportunity and motivation play in improving uptake in this underserved homeless population.
| S-EPMC10958472 | 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:Health technology assessments (HTAs) of vaccines typically focus on the direct health benefits to individuals and healthcare systems. COVID-19 highlighted the widespread societal impact of infectious diseases and the value of vaccines in averting adverse clinical consequences and in maintaining or resuming social and economic activities. Using COVID-19 as a case study, this research work aimed to set forth a conceptual framework capturing the broader value elements of vaccines and to identify appropriate methods to quantify value elements not routinely considered in HTAs. A two-step approach was adopted, combining a targeted literature review and three rounds of expert elicitation based on a modified Delphi method, leading to a conceptual framework of 30 value elements related to broader health effects, societal and economic impact, public finances, and uncertainty value. When applying the framework to COVID-19 vaccines in post-pandemic settings, 13 value elements were consensually rated highly important by the experts for consideration in HTAs. The experts reviewed over 10 methods that could be leveraged to quantify broader value elements and provided technical forward-looking recommendations. Limitations of the framework and the identified methods were discussed. This study supplements ongoing efforts aimed towards a broader recognition of the full societal value of vaccines.
| S-EPMC9961127 | biostudies-literature
Project description: Not available
| S-BSST563 | biostudies-other
Project description:Access to COVID-19 vaccines on the global scale has been drastically hindered by structural socio-economic disparities. Here, we develop a data-driven, age-stratified epidemic model to evaluate the effects of COVID-19 vaccine inequities in twenty lower middle and low income countries (LMIC) selected from all WHO regions. We investigate and quantify the potential effects of higher or earlier doses availability. In doing so, we focus on the crucial initial months of vaccine distribution and administration, exploring counterfactual scenarios where we assume the same per capita daily vaccination rate reported in selected high income countries. We estimate that more than 50% of deaths (min-max range: [54-94%]) that occurred in the analyzed countries could have been averted. We further consider scenarios where LMIC had similarly early access to vaccine doses as high income countries. Even without increasing the number of doses, we estimate an important fraction of deaths (min-max range: [6-50%]) could have been averted. In the absence of the availability of high-income countries, the model suggests that additional non-pharmaceutical interventions inducing a considerable relative decrease of transmissibility (min-max range: [15-70%]) would have been required to offset the lack of vaccines. Overall, our results quantify the negative impacts of vaccine inequities and underscore the need for intensified global efforts devoted to provide faster access to vaccine programs in low and lower-middle-income countries.
| S-EPMC10241610 | biostudies-literature
Project description:BACKGROUND:Prognostic research has many important purposes, including (i) describing the natural history and clinical course of health conditions, (ii) investigating variables associated with health outcomes of interest, (iii) estimating an individual's probability of developing different outcomes, (iv) investigating the clinical application of prediction models, and (v) investigating determinants of recovery that can inform the development of interventions to improve patient outcomes. But much prognostic research has been poorly conducted and interpreted, indicating that a number of conceptual areas are often misunderstood. Recent initiatives to improve this include the Prognosis Research Strategy (PROGRESS) and the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) Statement. In this paper, we aim to show how different categories of prognostic research relate to each other, to differentiate exploratory and confirmatory studies, discuss moderators and mediators, and to show how important it is to understand study designs and the differences between prediction and causation. MAIN TEXT:We propose that there are four main objectives of prognostic studies - description, association, prediction and causation. By causation, we mean the effect of prediction and decision rules on outcomes as determined by intervention studies and the investigation of whether a prognostic factor is a determinant of outcome (on the causal pathway). These either fall under the umbrella of exploratory (description, association, and prediction model development) or confirmatory (prediction model external validation and investigation of causation). Including considerations of causation within a prognostic framework provides a more comprehensive roadmap of how different types of studies conceptually relate to each other, and better clarity about appropriate model performance measures and the inferences that can be drawn from different types of prognostic studies. We also propose definitions of 'candidate prognostic factors', 'prognostic factors', 'prognostic determinants (causal)' and 'prognostic markers (non-causal)'. Furthermore, we address common conceptual misunderstandings related to study design, analysis, and interpretation of multivariable models from the perspectives of association, prediction and causation. CONCLUSION:This paper uses a framework to clarify some concepts in prognostic research that remain poorly understood and implemented, to stimulate discussion about how prognostic studies can be strengthened and appropriately interpreted.
| S-EPMC7325141 | biostudies-literature