Journal of Urban Health-Bulletin of the New York Academy of Medicine最新文献

Direct and indirect gun violence exposure (GVE) is associated with a broad range of detrimental health effects. However, much of this research has examined the effects of a single type of GVE (e.g., being shot) on discrete outcomes (e.g., daily pain, PTSD). Since people may experience numerous types of GVE (e.g., being threatened with a gun and hearing gunshots in their neighborhood) with broad effects on their well-being, we study the association between four types of direct and indirect GVE and five aspects of quality of life (overall, physical, psychological, social, and environmental). Using a representative sample of adults from nine states (N = 7455), we find that witnessing/hearing about a shooting in one's neighborhood was the most commonly experienced GVE associated with significant decreases in all five types of quality of life. Cumulative GVE was also associated with significant decreases in overall physical, psychological, social, and environmental quality of life. For example, individuals with four GVEs had an adjusted average physical quality of life that was 11.14 points lower and environmental quality of life that was 7.18 points lower than individuals with no GVE. Decreasing gun violence is a critical component of improving community health and well-being.

As part of a program evaluation of the New York City Test & Trace program (T2)-one of the largest such programs in the USA-we conducted a study to assess how implementing organizations (NYC Health + Hospitals, government agencies, CBOs) communicated information about the T2 program on Twitter. Study aims were as follows: (1) quantify user engagement of posts ("tweets") about T2 by NYC organizations on Twitter and (2) examine the emotional tone of social media users' T2-related tweets in our sample of 1987 T2-related tweets. Celebrities and CBOs generated more user engagement (0.26% and 0.07%, respectively) compared to government agencies (e.g., Mayor's Office, 0.0019%), reinforcing the value of collaborating with celebrities and CBOs in social media public health campaigns. Sentiment analysis revealed that positive tweets (46.5%) had higher user engagement than negative tweets (number of likes: R² = .095, p < .01), underscoring the importance of positively framing messages for effective public health campaigns.

On June 1, 2020, NYC Health + Hospitals, in partnership with the NYC Department of Health and Mental Hygiene, other city agencies, and a large network of community partners, launched the New York City Test & Trace (T2) COVID-19 response program to identify and isolate cases, reduce transmission through contact tracing, and provide support to residents during isolation or quarantine periods. In this paper, we describe lessons learned with respect to planning and implementation of case notification and contact tracing. Our findings are based on extensive document review and analysis of 74 key informant interviews with T2 leadership and frontline staff, cases, and contacts conducted between January and September 2022. Interviews elicited respondent background, history of program development, program leadership and structure, goals of the program, program evolution, staffing, data systems, elements of community engagement, trust with community, program reach, timeliness, equity, general barriers and challenges, general facilitators and best practices, and recommendations/improvement for the program. Facilitators and barriers revealed in the interviews primarily revolved around hiring and managing staff, data and technology, and quality of interactions with the public. Based on these facilitators and barriers, we identify suggestions to support effective planning and response for future case notification and contact tracing programs, including recommendations for planning during latent periods, case management and data systems, and processes for outreach to cases and contacts.

Gun-related crime continues to be an urgent public health and safety problem in cities across the US. A key question is: how are firearms diverted from the legal retail market into the hands of gun offenders? With close to 8 million legal firearm transaction records in California (2010-2020) linked to over 380,000 records of recovered crime guns (2010-2021), we employ supervised machine learning to predict which firearms are used in crimes shortly after purchase. Specifically, using random forest (RF) with stratified under-sampling, we predict any crime gun recovery within a year (0.2% of transactions) and violent crime gun recovery within a year (0.03% of transactions). We also identify the purchaser, firearm, and dealer characteristics most predictive of this short time-to-crime gun recovery using SHapley Additive exPlanations and mean decrease in accuracy variable importance measures. Overall, our models show good discrimination, and we are able to identify firearms at extreme risk for diversion into criminal hands. The test set AUC is 0.85 for both models. For the model predicting any recovery, a default threshold of 0.50 results in a sensitivity of 0.63 and a specificity of 0.88. Among transactions identified as extremely risky, e.g., transactions with a score of 0.98 and above, 74% (35/47 in the test data) are recovered within a year. The most important predictive features include purchaser age and caliber size. This study suggests the potential utility of transaction records combined with machine learning to identify firearms at the highest risk for diversion and criminal use soon after purchase.

Among sexual minority men (SMM), HIV and use of stimulants such as methamphetamine are linked with immune activation and systemic inflammation. Throughout the COVID-19 pandemic, SMM encountered financial challenges and structural obstacles that might have uniquely contributed to immune dysregulation and systemic inflammation, beyond the impacts of HIV and stimulant use. Between August 2020 and February 2022, 72 SMM with and without HIV residing in South Florida enrolled in a COVID-19 prospective cohort study. Multiple linear regression analyses examined unemployment, homelessness, and history of arrest as structural correlates of soluble markers of immune activation (i.e., sCD14 and sCD163) and inflammation (i.e., sTNF-α receptors I and II) at baseline after adjusting for HIV status, stimulant use, and recent SARS-CoV-2 infection. Enrolled participants were predominantly Latino (59%), gay-identified (85%), and with a mean age of 38 (SD, 12) years with approximately one-third (38%) of participants living with HIV. After adjusting for HIV status, SARS-CoV-2 infection, and recent stimulant use, unemployment independently predicted higher levels of sCD163 (β = 0.24, p = 0.04) and sTNF-α receptor I (β = 0.26, p = 0.02). Homelessness (β = 0.25, p = 0.02) and history of arrest (β = 0.24, p = 0.04) independently predicted higher levels of sCD14 after adjusting for HIV status, SARS-CoV-2 infection, and recent stimulant use. Independent associations exist between structural barriers and immune activation and systemic inflammation in SMM with and without HIV. Future longitudinal research should further elucidate complex bio-behavioral mechanisms linking structural factors with immune activation and inflammation.

Urban scaling is widely used to characterize the population dependence of city indicators including greenhouse gas emission. Here we analyze the population dependence of CO ${}_2$ and PM2.5 emissions and concentrations across all European cities. Our analysis reveals considerable variations in emissions among cities of comparable population size which are not captured by conventional urban scaling. We thus characterize these fluctuations by multi-parameter scaling functions and multifractal spectral analysis. We find that the distribution of emissions and population is multifractal while that of air pollution is not, leading to non-trivial relations between emission and pollution in some large cities. We also analyze the impact of forests in curbing emission and the impact of air pollution on health. Our work provides a detailed picture of the fluctuations in the scaling of urban metabolism in Europe and suggests a general strategy that goes beyond conventional urban scaling laws.

By providing spaces for recreation, physical activity, social gatherings, and time in nature, urban parks offer physical, mental, and social benefits to users. However, many urban residents face barriers to park use. The COVID-19 pandemic introduced new potential barriers to urban park access and use, including changes to daily life and employment, closure of park amenities and restrictions to public movement, and risk from the coronavirus itself. The mixed-methods PARCS study measured use and perceptions of a large urban park in St. Louis, Missouri before, during, and after local COVID-19 contingency measures and restrictions. We examine data from 1,157 direct observation assessments of park usership, an online survey of park users (n=561), interviews with key stakeholders (n=27), four focus groups (n=30), and a community-based participatory research sub-study (n=66) to comprehensively characterize the effects of the COVID-19 pandemic on park use. Park users who felt unsafe from the coronavirus experienced 2.65 higher odds of reducing park use. However, estimated park visits during COVID-19 contingency measures (n=5,023,759) were twice as high as post-contingency (n=2,277,496). Participants reported using the park for physical activity, recreation, time in nature, and socializing during the contingency period. Black, Hispanic/Latino, and young people were less likely to visit the park than others, suggesting an additional, disproportionate impact of the pandemic on minoritized and socioeconomically disadvantaged communities. This study highlights the role of public spaces like parks as resources for health and sites where urban health inequities can be alleviated in times of public crisis.

Depression is a relevant mental illness affecting hundreds of millions of people worldwide. As urbanization accelerates, agglomeration of populations has altered individual social network distances and life crowding, which in turn affects depressive prevalence. However, the association between depression and population agglomeration (PA) remains controversial. This study aims to explore whether and how PA could influence individual depression. Based on the China Health and Retirement Longitudinal Study (CHARLS) 2018, the empirical results showed that there was a U-shaped association between PA and individual CES-D scores. As PA increases, the risk of depression first decreases and then increases. CES-D was lowest at moderate aggregation. Dialect diversity (DD) was positively related to the incidence of individual depression. The higher the DD, the higher the risk of depression. Meanwhile, DD also played a moderating role in the association between PA and individual depression. Our observations suggest that the optimistic level of agglomeration for individual mental health is within 1500 to 2000 persons per square kilometer.

Racial residential segregation has been deemed a fundamental cause of health inequities. It is a result of historical and contemporary policies such as redlining that have created a geographic separation of races and corresponds with an inequitable distribution of health-promoting resources. Redlining and racial residential segregation may have contributed to racial inequities in COVID-19 vaccine administration in the early stages of public accessibility. We use data from the National Archives (historical redlining), Home Mortgage Disclosure Act (contemporary redlining), American Community Survey from 1940 (historical racial residential segregation) and 2015-2019 (contemporary racial residential segregation), and Washington D.C. government (COVID-19 vaccination administration) to assess the relationships between redlining, racial residential segregation, and COVID-19 vaccine administration during the early stages of vaccine distribution when a tiered system was in place due to limited supply. Pearson correlation was used to assess whether redlining and racial segregation, measured both historically and contemporarily, were correlated with each other in Washington D.C. Subsequently, linear regression was used to assess whether each of these measures associate with COVID-19 vaccine administration. In both historical and contemporary analyses, there was a positive correlation between redlining and racial residential segregation. Further, redlining and racial residential segregation were each positively associated with administration of the novel COVID-19 vaccine. This study highlights the ongoing ways in which redlining and segregation contribute to racial health inequities. Eliminating racial health inequities in American society requires addressing the root causes that affect access to health-promoting resources.

Existing literature has widely explored the individual roles of housing and neighborhood quality, and there is limited research examining their interactive effects on mental health. This 3-year cohort study utilized a longitudinal design to investigate the individual and interactive effects of housing and neighborhood quality on mental health among 962 community-dwelling adults in Hong Kong. Participants were asked to rate their residential qualities over the 3-year period. Mental health outcomes, including levels of psychological distress and common mental disorders (CMD), were assessed using the Revised Clinical Interview Schedule (CIS-R). Logistic regression and generalized linear models were used to examine the association between housing and neighborhood quality and CMD/psychological distress, adjusting for sociodemographic and residential characteristics and baseline mental disorders. Housing quality was associated with the 3-year CMD (adjusted OR 0.95; 95% CI 0.91 to 0.98). Likewise, neighborhood quality was associated with CMD over 3 years (adjusted OR 0.92; 95% CI 0.87 to 0.96). In a separate model including both quality measures, the effect of housing quality on CMD was attenuated, whereas the neighborhood impact remained significant (adjusted OR 0.92; 95% CI 0.87 to 0.98). Generalized linear models indicated that for participants residing in substandard housing, those with high neighborhood quality had lower CIS-R scores at follow-up compared to those with low neighborhood quality (p = 0.041). Better neighborhood quality alleviated the detrimental effects of poor housing quality on mental health. Planning for an enhanced neighborhood would improve population mental health in an urban environment.