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Community health workers (CHWs) are uniquely positioned to improve health outcomes in immigrant communities; however, research on appropriate metrics for evaluating CHW attributes and mechanisms of effectiveness are limited. The objective of this paper is to characterize CHW attributes and pathways of action using adapted measures, develop a scale using these measures, and explore how findings can inform future CHW research and practice. The study analyzed pre- and post-intervention group data from one quasi-experimental and three randomized controlled-design parent trials assessing the impact of CHW-led group and individual health coaching on various health outcomes in four New York City immigrant communities. We conducted descriptive, bi-variate and principal components analysis to develop a 13-item scale assessing CHW attributes, roles, and pathways of action. The sample included 437 individuals completing the intervention arm of a CHW study. We found CHWs were reported to affect change through a number of mechanisms and participants expressed substantial communal concordance with the CHWs in terms of country of birth, language, and culture. Principal components analysis with promax rotation identified 13 items with three factors and high Cronbach's alphas: 1) valued interpersonal attributes of the CHW (alpha=0.784); 2) CHW as a bridge to health and non-health resources (alpha=0.857); and 3) providing accessibility beyond health providers (alpha=0.904). Socio-demographic characteristics and differences in CHW pathways of action were identified by community. Study findings can guide improved selection and training of CHWs. Further, measures identified in the principal components analysis can be used to guide future CHW evaluation efforts. [ABSTRACT FROM AUTHOR]

• Examine the concentration in gun violence shootings before and during the pandemic in three cities. • Examine how the increase in shooting hot spots impacted racial disparities in victimization rates. • Examine how the surge in gun violence during the pandemic increased disparities by place and race. • Between 36% and 55% of the increase in shootings occurred in the top decile of census block groups. • The spatial concentration of gun violence victimization by race-ethnicity was compounded during the pandemic. Gun violence rates increased in U.S. cities in 2020 and into 2021. Gun violence rates in U.S. cities is typically concentrated in racially segregated neighborhoods with higher poverty levels. However, poverty levels and demographics alone do not explain the high concentration of violence or its relative change over time. In this paper, we examine the extent to which the increase in shooting victimization in Philadelphia, New York, and Los Angeles during the 2020–2021 pandemic was concentrated in gun violence hot spots, and how the increase impacted race and ethnic disparities in shooting victimization rates. We find that 36% (Philadelphia), 47% (New York), and 55% (Los Angeles) of the increase in shootings observed during the period 2020–2021 occurred in the top decile of census block groups, by aggregate number of shootings, and that the race/ethnicity of victims in these gun violence hot spots were disproportionately Black and Hispanic. We discuss the implications of these findings as they relate to racial disparities in victimization and place-based efforts to reduce gun violence. [ABSTRACT FROM AUTHOR]

Based on a focus group study conducted in New York City (NYC), this paper examines the traditional staples (i.e., nostalgic foods) that Latinas regularly consume in the U.S., along with their beliefs regarding the impact of such foods on weight gain and related body image. Our research findings highlight the "double-bind" of nostalgic foods, defined by Latinas' retention of highly caloric familiar items along with their progressive abandonment of fresh produce and fruits. Despite participants' efforts to eat healthy staples from their homelands, they mostly kept foods perceived as unhealthy (e.g., fatty meats, fried foods). This phenomenon was informed by the "same-food paradox," represented by Latinas' beliefs that the same traditional foods that would make them lose weight in their native countries would lead them to gain weight in the U.S. Our qualitative data show that participants' concerns about their weight gain in the U.S. is in tune with their general body dissatisfaction, as indicated by our quantitative results. Finally, our findings reveal the role of stress in promoting Latinas' deleterious daily habits, including their consumption of fat-saturated snacks. Overall, these results speak to the cultural and structural barriers to healthy eating that financially strapped study participants experienced in NYC. In order to design successful public health interventions targeting Latinas, the nostalgic aspects of food preferences should be considered in conjunction with the barriers that keep them from engaging with healthier lifestyles in the U.S. [ABSTRACT FROM AUTHOR]

The aim of this paper is to assess the relationship between COVID-19-related deaths, economic growth, PM 10 , PM 2.5, and NO 2 concentrations in New York state using city-level daily data through two Machine Learning experiments. PM 2.5 and NO 2 are the most significant pollutant agents responsible for facilitating COVID-19 attributed death rates. Besides, we found only six out of many tested causal inferences to be significant and true within the AUPRC analysis. In line with the causal findings, a unidirectional causal effect is found from PM 2.5 to Deaths, NO 2 to Deaths, and economic growth to both PM 2.5 and NO 2. Corroborating the first experiment, the causal results confirmed the capability of polluting variables (PM 2.5 to Deaths, NO 2 to Deaths) to accelerate COVID-19 deaths. In contrast, we found evidence that unsustainable economic growth predicts the dynamics of air pollutants. This shows how unsustainable economic growth could increase environmental pollution by escalating emissions of pollutant agents (PM 2.5 and NO 2) in New York state. [Display omitted] • We assess the effect of economic growth, PM 2.5 and NO 2 variations on COVID-19 deaths. • We employ two experimental approaches based on Deep Machine Learning. • PM 2.5 and NO 2 are the most significant air pollutant responsible for COVID-19 deaths. • Increasing levels of economic growth spur air pollution in New York state. [ABSTRACT FROM AUTHOR]