scholarly journals A cluster analysis of the social and environmental health impacts of the COVID-19 pandemic among low-income youth

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Nadav Lev Sprague ◽  
Andrew G. Rundle ◽  
Christine C. Ekenga
Keyword(s):  
Cluster Analysis ◽  
Environmental Health ◽  
Low Income ◽  
Health Impacts ◽  
The Social

Environmental Risk Communication for the Clinician

PEDIATRICS ◽  
2003 ◽  
Vol 112 (Supplement_1) ◽  
pp. 211-217
Author(s):  
Mark Miller ◽  
Gina Solomon
Keyword(s):  
Social Context ◽  
Risk Communication ◽  
Environmental Health ◽  
Health Risks ◽  
Environmental Risk ◽  
Low Income ◽  
Team Approach ◽  
Environmental Health Risks ◽  
Qualitative Risk Assessment ◽  
The Social

Although they are accustomed to discussing risks in the medical arena through the process of informed consent, primary care clinicians may have difficulty communicating with their patients and communities about environmental health risks. Clinicians are generally trusted and can play important roles as educators, alert practitioners, or even advocates talking about environmental health risks with individuals and groups. Communication of risk requires an understanding of how scientists and clinicians assess risk—the process of quantitative or qualitative risk assessment. Risk is never a purely scientific issue; risk is perceived differently depending on some well-understood characteristics of the hazard, the individual perceiving the risk, and the social context. Many low-income communities of color have faced and continue to face disproportionate environmental exposures and disease burdens. The issue of environmental justice can significantly affect the context of a discussion about a specific environmental risk. The essence of risk communication has been well described and requires careful evaluation of the science and the social context, honesty, listening to and partnering with the community, and a clear, compassionate team approach.

scholarly journals Framing Environmental Health Decision-Making: The Struggle over Cumulative Impacts Policy

2021 ◽  
Vol 18 (8) ◽  
pp. 3947
Author(s):  
Devon C. Payne-Sturges ◽  
Thurka Sangaramoorthy ◽  
Helen Mittmann
Keyword(s):  
Environmental Health ◽  
Low Income ◽  
Social Forces ◽  
Cumulative Impacts ◽  
Policy Reforms ◽  
Policy Makers ◽  
The Social ◽  
Health Decision ◽  
Cumulative Risks ◽  
Analytical Tools

Little progress has been made to advance U.S. federal policy responses to growing scientific findings about cumulative environmental health impacts and risks, which also show that many low income and racial and ethnic minority populations bear a disproportionate share of multiple environmental burdens. Recent scholarship points to a “standard narrative” by which policy makers rationalize their slow efforts on environmental justice because of perceived lack of data and analytical tools. Using a social constructivist approach, ethnographic research methods, and content analysis, we examined the social context of policy challenges related to cumulative risks and impacts in the state of Maryland between 2014 and 2016. We identified three frames about cumulative impacts as a health issue through which conflicts over such policy reforms materialize and are sustained: (a) perceptions of evidence, (b) interpretations of social justice, and (c) expectations of authoritative bodies. Our findings illustrate that policy impasse over cumulative impacts is highly dependent on how policy-relevant actors come to frame issues around legislating cumulative impacts, rather than the “standard narrative” of external constraints. Frame analysis may provide us with more robust understandings of policy processes to address cumulative risks and impacts and the social forces that create health policy change.

scholarly journals Cumulative Impacts and COVID-19: Implications for Low-Income, Minoritized, and Health-Compromised Communities in King County, WA

2021 ◽  
Author(s):  
Carolyn Ingram ◽  
Esther Min ◽  
Edmund Seto ◽  
BJ Cummings ◽  
Stephanie Farquhar
Keyword(s):  
Environmental Health ◽  
Low Socioeconomic Status ◽  
Low Income ◽  
Census Tract ◽  
Determinants Of Health ◽  
Health Impacts ◽  
Outcome Variable ◽  
King County ◽  
Cumulative Impact ◽  
Census Tracts

AbstractFew studies have assessed how the intersection of social determinants of health and environmental hazards contributes to racial disparities in COVID-19. The aim of our study was to compare COVID-19 disparities in testing and positivity to cumulative environmental health impacts, and to assess how unique social and environmental determinants of health relate to COVID-19 positivity in Seattle, King County, WA, at the census tract level. Publicly available data (n = 397 census tracts) were obtained from Public Health–Seattle & King County, 2018 ACS 5-year estimates, and the Washington Tracking Network. COVID-19 testing and positive case rates as of July 12, 2020, were mapped and compared to Washington State Environmental Health Disparities (EHD) Map cumulative impact rankings. We calculated odds ratios from a series of univariable and multivariable logistic regression analyses using cumulative impact rankings, and community-level socioeconomic, health, and environmental factors as predictors and having ≥ 10% or < 10% census tract positivity as the binary outcome variable. We found a remarkable overlap between Washington EHD cumulative impact rankings and COVID-19 positivity in King County. Census tracts with ≥ 10 % COVID-19 positivity had significantly lower COVID-19 testing rates and higher proportions of people of color and faced a combination of low socioeconomic status–related outcomes, poor community health outcomes, and significantly higher concentrations of fine particulate matter (PM2.5). King County communities experiencing high rates of COVID-19 face a disproportionate cumulative burden of environmental and social inequities. Cumulative environmental health impacts should therefore systematically be considered when assessing for risk of exposure to and health complications resulting from COVID-19.

PAR Method

2010 ◽  
Vol 3 (3) ◽  
pp. 355-375 ◽  
Author(s):  
Michelle Billies
Keyword(s):  
New York ◽  
Low Income ◽  
Critical Consciousness ◽  
Design Method ◽  
Participatory Action ◽  
Community Members ◽  
Context Analysis ◽  
Gender Nonconforming ◽  
The Social ◽  
And Gender

The work of the Welfare Warriors Research Collaborative (WWRC), a participatory action research (PAR) project that looks at how low income lesbian, gay, bisexual, transgender, and gender nonconforming (LG-BTGNC) people survive and resist violence and discrimination in New York City, raises the question of what it means to make conscientization, or critical consciousness, a core feature of PAR. Guishard's (2009) reconceptualization of conscientization as “moments of consciousness” provides a new way of looking at what seemed to be missing from WWRC's process and analysis. According to Guishard, rather than a singular awakening, critical consciousness emerges continually through interactions with others and the social context. Analysis of the WWRC's process demonstrates that PAR researchers doing “PAR deep” (Fine, 2008)—research in which community members share in all aspects of design, method, analysis and product development—should have an agenda for developing critical consciousness, just as they would have agendas for participation, for action, and for research.

Empathy not quarantined: Association between people’s empathy to others and the COVID-19 pandemic situations based on a national survey study (Preprint)

2020 ◽  
Author(s):  
Zhibin Jiang ◽  
Fan Yang ◽  
Bu Zhong ◽  
Xuebing Qin
Keyword(s):  
Social Support ◽  
Social Media ◽  
National Survey ◽  
Low Income ◽  
Rural Areas ◽  
Critical Role ◽  
Affective Response ◽  
Survey Study ◽  
Income Levels ◽  
The Social

BACKGROUND The Covid-19 pandemic had turned the world upside down, but not much is known about how people’s empathy might be affected by the pandemic. OBJECTIVE This study examined 1) how empathy towards others might be influenced by the social support people obtained by using social media; and 2) how the individual demographics (e.g., age, income) may affect empathy. METHODS A national survey (N = 943) was conducted in China in February 2020, in which the participants read three real scenarios about low-income urban workers (Scenario I), small business owners in cities (Scenario II), and farmers in rural areas (Scenario III) who underwent hardship due to COVID-19. After exposure to others’ difficulties in the scenarios, the participants’ empathy and anxiety levels were measured. We also measured the social support they had by using social media. RESULTS Results show that social support not only positively impacted empathy, β = .30, P < .001 for Scenario I, β = .30, P < .001 for Scenario II, and β = .29, P < .001 for Scenario III, but also interacted with anxiety in influencing the degree to which participants could maintain empathy towards others, β = .08, P = .010 for Scenario I, and β = .07, P = .033 for scenario II. Age negatively predicted empathy for Scenario I, β = -.08, P = .018 and Scenario III, β = -.08, P = .009, but not for Scenario II, β = -.03, P = .40. Income levels – low, medium, high – positively predicted empathy for Scenario III, F (2, 940) = 8.10, P < .001, but not for Scenario I, F (2, 940) = 2.14, P = .12, or Scenario II, F (2, 940) = 2.93, P = .06. Participants living in big cities expressed greater empathy towards others for Scenario III, F (2, 940) = 4.03, P =.018, but not for Scenario I, F (2, 940) = .81, P = .45, or Scenario II, F (2, 940) = 1.46, P =.23. CONCLUSIONS This study contributes to the literature by discovering the critical role empathy plays in people’s affective response to others during the pandemic. Anxiety did not decrease empathy. However, those gaining more social support on social media showed more empathy for others. Those who resided in cities with higher income levels were more empathetic during the COVID-19 outbreak. This study reveals that the social support people obtained helped maintain empathy to others, making them resilient in challenging times.

scholarly journals Vertical position can affect categorization of the rich and the poor in Chinese culture

2021 ◽  
Vol 15 ◽  
pp. 183449092110257
Author(s):  
Qiong Li ◽  
Chen Deng ◽  
Bin Zuo ◽  
Xiaobin Zhang
Keyword(s):  
Low Income ◽  
Chinese Culture ◽  
Social Categorization ◽  
High Income ◽  
Vertical Position ◽  
The Poor ◽  
The Social ◽  
The Rich

This study explored whether vertical position affects social categorization of the rich and the poor. Experiment 1 used high- and low-income occupations as stimuli, and found participants categorized high-income occupations faster when they were presented in the top vertical position compared to the bottom vertical position. In Experiment 2, participants responded using either the “up” or “down” key to categorize high- and low-income occupations, and responded faster to high-income occupations with the “up” key and low-income occupations with the “down” key. In Experiment 3, names identified as belonging to either rich or poor individuals were presented at the top or bottom of a screen, and the results were the same as in Experiments 1 and 2. These findings suggest that social categorization based on wealth involved perceptual simulations of vertical position, and that vertical position affects the social categorization of the rich and the poor.

scholarly journals Heterogeneous Scarring Effects of Full-Year Nonemployment

2017 ◽  
Vol 107 (5) ◽  
pp. 369-373 ◽  
Author(s):  
Fatih Guvenen ◽  
Fatih Karahan ◽  
Serdar Ozkan ◽  
Jae Song
Keyword(s):  
Social Security ◽  
Low Income ◽  
Social Security Administration ◽  
Earnings Distribution ◽  
Employment Effects ◽  
Long Period ◽  
Earnings Losses ◽  
The Social ◽  
Scarring Effects

Drawing on administrative data from the Social Security Administration, we find that individuals that go through a long period of non-employment suffer large and long-term earnings losses (around 35-40 percent) compared to individuals with similar age and previous earnings histories. Importantly, these differences depend on past earnings, and are largest at the bottom and top of the earnings distribution. Focusing on workers that are employed 10 years after a period of long-term non-employment, we find much smaller earnings losses (8-10 percent). Furthermore, the large earnings losses of low-income individuals are almost entirely due to employment effects.

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