Neighborhood Racial and Economic Segregation and Disparities in Violence During the COVID-19 Pandemic.

Objectives. To describe associations between neighborhood racial and economic segregation and violence during the COVID-19 pandemic. Methods. For 13 US cities, we obtained zip code-level data on 5 violence outcomes from March through July 2018 through 2020. Using negative binomial regressions and marginal contrasts, we estimated differences between quintiles of racial, economic, and racialized economic segregation using the Index of Concentration at the Extremes as a measure of neighborhood privilege (1) in 2020 and (2) relative to 2018 through 2019 (difference-in-differences). Results. In 2020, violence was higher in less-privileged neighborhoods than in the most privileged. For example, if all zip codes were in the least privileged versus most privileged quintile of racialized economic segregation, we estimated 146.2 additional aggravated assaults (95% confidence interval = 112.4, 205.8) per zip code on average across cities. Differences over time in less-privileged zip codes were greater than differences over time in the most privileged for firearm violence, aggravated assault, and homicide. Conclusions. Marginalized communities endure endemically high levels of violence. The events of 2020 exacerbated disparities in several forms of violence. Public Health Implications. To reduce violence and related disparities, immediate and long-term investments in low-income neighborhoods of color are warranted. (Am J Public Health. 2022;112(1):144-153. https://doi.org/10.2105/AJPH.2021.306540).

Home and Workplace Neighborhood Socioeconomic Status and Behavior-related Health: A Within-individual Analysis.

BACKGROUND: The influence of individual and home neighborhood socioeconomic status (SES) on health-related behaviors have been widely studied, but the majority of these studies have neglected the possible impact of the workplace neighborhood SES. OBJECTIVE: To examine within-individual associations between home and work place neighborhood SES and health-related behaviors in employed individuals. METHODS: We used participants from the Swedish Longitudinal Occupational Survey of Health who responded to a minimum of two surveys between 2012 and 2018. Data included 12,932 individuals with a total of 35,332 observations. We used fixed-effects analysis with conditional logistic regression to examine within-individual associations of home, workplace, as well as time-weighted home and workplace neighborhood SES index, with self-reported obesity, physical activity, smoking, excessive alcohol consumption, sedentary lifestyle, and disturbed sleep. RESULTS: After adjustment for covariates, participants were more likely to engage in risky alcohol consumption when they worked in a workplace that was located in the highest SES area compared to time when they worked in a workplace that was located in the lowest SES area (adjusted odds ratios 1.98; 95% confidence interval: 1.12 to 3.49). There was an indication of an increased risk of obesity when individuals worked in the highest compared to the time when they worked in the lowest neighborhood SES area (1.71; 1.02-2.87). No associations were observed for the other outcomes. CONCLUSION: These within-individual comparisons suggest that workplace neighborhood SES might have a role in health-related behaviors, particularly alcohol consumption.

County-level Social Vulnerability is Associated With Worse Surgical Outcomes Especially Among Minority Patients.

OBJECTIVE: We sought to characterize the association between patient county-level vulnerability with postoperative outcomes. SUMMARY BACKGROUND DATA: Although the impact of demographic-, clinical- and hospital-level factors on outcomes following surgery have been examined, little is known about the effect of a patient's community of residence on surgical outcomes. METHODS: Individuals who underwent colon resection, coronary artery bypass graft (CABG), lung resection, or lower extremity joint replacement (LEJR) were identified in the 2016 to 2017 Medicare database, which was merged with Center for Disease Control social vulnerability index (SVI) dataset at the beneficiary level of residence. Logistic regression models were utilized to estimate the probability of postoperative complications, mortality, readmission, and expenditures. RESULTS: Among 299,583 Medicare beneficiary beneficiaries who underwent a colectomy (n = 88,778, 29.6%), CABG (n = 109,564, 36.6%), lung resection (n = 30,401, 10.1%), or LEJR (n = 70,840, 23.6%).Mean SVI score was 50.2 (standard deviation: (25.2); minority patients were more likely to reside in highly vulnerable communities (low SVI: n = 3531, 5.8% vs high SVI: n = 7895, 13.3%; P < 0.001). After controlling for competing risk factors, the risk-adjusted probability of a serious complication among patients from a high versus low SVI county was 10% to 20% higher following colectomy [odds ratio (OR) 1.1 95% confidence intervals (CI) 1.1-1.2] or CABG (OR 1.2 95%CI 1.1-1.3), yet there no association of SVI with risk of serious complications following lung resection (OR 1.2 95%CI 1.0-1.3) or LEJR (OR 1.0 95%CI 0.93-1.2). The risk-adjusted probability of 30-day mortality was incrementally higher among patients from high SVI counties following colectomy (OR 1.1 95%CI 1.1-1.3), CABG (OR 1.4, 95%CI 1.2-1.5), and lung resection (OR 1.4 (95%CI 1.1-1.8), yet not LEJR (OR 0.95 95%CI 0.72-1.2). Black/minority patients undergoing a colectomy, CABG, or lung resection who lived in highly socially vulnerable counties had an estimate 28% to 68% increased odds of a serious complication and a 58% to 60% increased odds of 30-day mortality compared with a Black/minority patient from a low socially vulnerable county, as well as a markedly higher risk than White patients (all P > 0.05). CONCLUSIONS: Patients residing in vulnerable communities characterized by a high SVI generally had worse postoperative outcomes. The impact of social vulnerability was most pronounced among Black/minority patients, rather than White individuals. Efforts to ensure equitable surgical outcomes need to focus on both patient-level, as well as community-specific factors.

Evaluation of Neighborhood Socioeconomic Characteristics and Advance Care Planning Among Older Adults.

Importance: Advance care planning (ACP) is low among older adults with socioeconomic disadvantage. There is a need for tailored community-based approaches to increase ACP, but community patterns of ACP are poorly understood. Objective: To examine the association between neighborhood socioeconomic status (nSES) and ACP and to identify communities with both low nSES and low rates of ACP. Design, Setting, and Participants: This cross-sectional study examined University of California San Francisco electronic health record (EHR) data and place-based data from 9 San Francisco Bay Area counties. Participants were primary care patients aged 65 years or older and living in the San Francisco Bay Area in July 2017. Statistical analysis was performed from May to June 2020. Exposures: Patients' home addresses were geocoded and assigned to US Census tracts. The primary factor, nSES, an index combining area-level measures of income, education, poverty, employment, occupation, and housing or rent values, was divided into quintiles scaled to the distribution of all US Census tracts in the Bay Area (Q1 = lowest nSES). Covariates were from the EHR and included health care use (primary care, outpatient specialty, emergency department, and inpatient encounters in the prior year). Main Outcomes and Measures: ACP was defined as a scanned document (eg, advance directive), ACP Current Procedural Terminology code, or ACP note type in the EHR. Results: There were 13 104 patients included in the cohort-mean (SD) age was 75 (8) years, with 7622 female patients (58.2%), 897 patients (6.8%) identified as Black, 913 (7.0%) as Latinx, 3788 (28.9%) as Asian/Pacific Islander, and 748 (5.7%) as other minority race/ethnicity, and 2393 (18.3%) self-reported that they preferred to speak a non-English language. Of these, 3827 patients (29.2%) had documented ACP. The cohort was distributed across all 5 quintiles of nSES (Q1: 1426 patients [10.9%]; Q2: 1792 patients [13.7%]; Q3: 2408 patients [18.4%]; Q4: 3330 patients [25.4%]; Q5: 4148 patients [31.7%]). Compared with Q5 and after adjusting for health care use, all lower nSES quintiles showed a lower odds of ACP in a graded fashion (Q1: adjusted odds ratio [aOR] = 0.71 [95% CI, 0.61-0.84], Q2: aOR = 0.74 [95% CI, 0.64-0.86], Q3: aOR = 0.81 [95% CI, 0.71-0.93], Q4: aOR = 0.82 [95% CI, 0.72-0.93]. A bivariable map of ACP by nSES allowed identification of 5 neighborhoods with both low nSES and ACP. Conclusions and Relevance: In this study, lower nSES was associated with lower ACP documentation after adjusting for health care use. Using EHR and place-based data, communities of older adults with both low nSES and low ACP were identified. This is a first step in partnering with communities to develop targeted, community-based interventions to meaningfully increase ACP.

Home Language and Literacy Environments and Early Literacy Trajectories of Low-Socioeconomic Status Chilean Children.

This study used Latent Class Analysis to identify groups of children exposed to similar Home Language and Literacy Environments (HLLE) and explored whether belonging to a given HLLE group was related to children's language and early literacy growth from prekindergarten to kindergarten. Participants were 1,425 Chilean mothers and their children (Mage  = 52.52 months at baseline) from low-socioeconomic status households. Four HLLE groups were identified, which were associated with different trajectories of language and early literacy development. Children from groups whose mothers either read and talk about past events with them or teach them letters in addition to reading and talking about past events, showed higher relative vocabulary and letter knowledge. Implications for research and interventions are discussed.

Structural Racism, Historical Redlining, and Risk of Preterm Birth in New York City, 2013-2017.

Objectives. To assess if historical redlining, the US government's 1930s racially discriminatory grading of neighborhoods' mortgage credit-worthiness, implemented via the federally sponsored Home Owners' Loan Corporation (HOLC) color-coded maps, is associated with contemporary risk of preterm birth (< 37 weeks gestation).Methods. We analyzed 2013-2017 birth certificate data for all singleton births in New York City (n = 528 096) linked by maternal residence at time of birth to (1) HOLC grade and (2) current census tract social characteristics.Results. The proportion of preterm births ranged from 5.0% in grade A ("best"-green) to 7.3% in grade D ("hazardous"-red). The odds ratio for HOLC grade D versus A equaled 1.6 and remained significant (1.2; P < .05) in multilevel models adjusted for maternal sociodemographic characteristics and current census tract poverty, but was 1.07 (95% confidence interval = 0.92, 1.20) after adjustment for current census tract racialized economic segregation.Conclusions. Historical redlining may be a structural determinant of present-day risk of preterm birth.Public Health Implications. Policies for fair housing, economic development, and health equity should consider historical redlining's impacts on present-day residential segregation and health outcomes.

The Proximity of Spatial Clusters of Low Birth Weight and Risk Factors: Defining a Neighborhood for Focused Interventions.

BACKGROUND: Low birth weight (LBW) is associated with significant mortality and morbidity and remains a significant preventable problem. Risk factors include socioeconomic, demographics, and characteristics of the environment. Spatial analysis can uncover unusual frequencies of health problems in neighborhoods, eventually leading to insights for targeted interventions. OBJECTIVES: This study's goals were to 1. Evaluate the geographic distribution of spatial clusters of LBW births and maternal risk factors. 2. Determine the spatial relationship between risk factors and LBW. METHODS: This study obtained data on LBW newborns and risk factors from 19,013 births over 5 years (2012-2016) for Escambia County Census Tracts, extracted from FloridaCharts.com. Software was used to detect significant spatial clusters; these clusters were then plotted on a map. Poisson regression determined the statistical relationship between Census Tract risk factors and LBW. A separate analysis of the LBW cluster controlling for risk factors was also performed. RESULTS: All risk factor clusters resided in similar locations as the LBW cluster. The multiple Poisson regression model containing all risk factors fully explained the LBW cluster. On bivariate Poisson regression all risk factors in the Census Tract were significantly related to LBW whereas in multivariable Poisson regression, the proportion of births to African American women in the Census Tract remained significant after adjusting for other risk factors (p < 0.001). CONCLUSIONS FOR PRACTICE: Clusters of LBW and risk factors were located in the same region of the county, with the proportion of births to African American women in the Census Tract remaining significant on multiple Poisson Regression. Targeted interventions should be directed at the geographic level.

Metal air pollution partnership solutions: building an academic-government-community-industry collaboration to improve air quality and health in environmental justice communities in Houston.

BACKGROUND: From 2006 to 2011, the City of Houston received nearly 200 community complaints about air pollution coming from some metal recycling facilities. The investigation by the Houston Health Department (HHD) found that while operating within legal limits, emissions from facilities that use torch cutting, a technique generating metal aerosols, may increase health risks for neighboring residents. Choosing to use collaborative problem solving over legislative rulemaking, HHD reached out to The University of Texas Health Science Center at Houston (UTHealth) to further evaluate and develop plans to mitigate, if necessary, health risks associated with metal emissions from these facilities. METHODS: Utilizing a community-based participatory research approach, we constituted a research team from academia, HHD and an air quality advocacy group and a Community Advisory Board (CAB) to draw diverse stakeholders (i.e., frustrated and concerned residents and wary facility managers acting within their legal rights) into an equitable, trusting and respectful space to work together. Next, we investigated metal air pollution and inhalation health risks of adults living near metal recyclers and ascertained community views about environmental health using key informant interviews, focus groups and surveys. Finally, working collaboratively with the CAB, we developed neighborhood-specific public health action plans to address research findings. RESULTS: After overcoming challenges, the CAB evolved into an effective partnership with greater trust, goodwill, representation and power among members. Working together to translate and share health risk assessment results increased accessibility of the information. These results, coupled to community survey findings, set the groundwork for developing and implementing a stakeholder-informed action plan, which included a voluntary framework to reduce metal emissions in the scrap yard, improved lines of communication and environmental health leadership training. Tangible outcomes of enhanced capacity of our community and governmental partners included trained residents to conduct door-to-door surveys, adaptation of our field training protocol and survey by our community partner and development of a successful HHD program to engage residents to improve environmental health in their neighborhood. CONCLUSIONS: Academic-government-community-industry partnerships can reduce environmental health disparities in underserved neighborhoods near industrial facilities.

Longitudinal measures of neighborhood poverty and income inequality are associated with adverse birth outcomes in Texas.

BACKGROUND: Inequity in adverse birth outcomes between black and white women in the U.S. is persistent, despite decades of research and prevention efforts. Neighborhood environments are plausibly related to pre-pregnancy health and other risk factors for adverse birth outcomes and may help explain black/white inequities. Despite the fact that neighborhoods change over time, most prior work has relied upon cross-sectional measures of neighborhood economic contexts. METHODS: We used birth certificates for non-Hispanic black and white women in Texas (2009-2011, N = 470,896) to examine whether longitudinal measures of neighborhood economic context (poverty and income inequality, based on census tract data from 1990 to 2010) were associated with preterm birth, low birthweight and small-for-gestational-age (SGA) with hierarchical generalized linear models. We also tested whether (1) the longitudinal measures explained black/white inequities or (2) moderated the effect of race on the birth outcomes. Finally, we compared the models with longitudinal measures to models with cross-sectional measures of neighborhood economic context. RESULTS: Longitudinal measures of neighborhood economic context were associated with all three birth outcomes, but did not explain racial inequities. Except for income inequality and SGA, there was no evidence of moderation by race. Substituting cross-sectional measures of economic context for longitudinal ones resulted in similar findings. CONCLUSION: Policies that either address structural neighborhood-level economic disadvantage or mitigate the effects of such disadvantage are warranted to improve the health of mothers and prevent adverse birth outcomes.

Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone.

BACKGROUND: Although geriatric depression is prevalent, diagnosis using self-reporting instruments has limitations when measuring the depressed mood of older adults in a community setting. Ecological momentary assessment (EMA) by using wearable devices could be used to collect data to classify older adults into depression groups. OBJECTIVE: The objective of this study was to develop a machine learning algorithm to predict the classification of depression groups among older adults living alone. We focused on utilizing diverse data collected through a survey, an Actiwatch, and an EMA report related to depression. METHODS: The prediction model using machine learning was developed in 4 steps: (1) data collection, (2) data processing and representation, (3) data modeling (feature engineering and selection), and (4) training and validation to test the prediction model. Older adults (N=47), living alone in community settings, completed an EMA to report depressed moods 4 times a day for 2 weeks between May 2017 and January 2018. Participants wore an Actiwatch that measured their activity and ambient light exposure every 30 seconds for 2 weeks. At baseline and the end of the 2-week observation, depressive symptoms were assessed using the Korean versions of the Short Geriatric Depression Scale (SGDS-K) and the Hamilton Depression Rating Scale (K-HDRS). Conventional classification based on binary logistic regression was built and compared with 4 machine learning models (the logit, decision tree, boosted trees, and random forest models). RESULTS: On the basis of the SGDS-K and K-HDRS, 38% (18/47) of the participants were classified into the probable depression group. They reported significantly lower scores of normal mood and physical activity and higher levels of white and red, green, and blue (RGB) light exposures at different degrees of various 4-hour time frames (all P<.05). Sleep efficiency was chosen for modeling through feature selection. Comparing diverse combinations of the selected variables, daily mean EMA score, daily mean activity level, white and RGB light at 4:00 pm to 8:00 pm exposure, and daily sleep efficiency were selected for modeling. Conventional classification based on binary logistic regression had a good model fit (accuracy: 0.705; precision: 0.770; specificity: 0.859; and area under receiver operating characteristic curve or AUC: 0.754). Among the 4 machine learning models, the logit model had the best fit compared with the others (accuracy: 0.910; precision: 0.929; specificity: 0.940; and AUC: 0.960). CONCLUSIONS: This study provides preliminary evidence for developing a machine learning program to predict the classification of depression groups in older adults living alone. Clinicians should consider using this method to identify underdiagnosed subgroups and monitor daily progression regarding treatment or therapeutic intervention in the community setting. Furthermore, more efforts are needed for researchers and clinicians to diversify data collection methods by using a survey, EMA, and a sensor.

Socio-economic patterning of expenditures on 'out-of-home' food and non-alcoholic beverages by product and place of purchase in Britain.

While most food is consumed at home, food eaten out-of-home plays an important role in diets and it has been associated with higher energy intake and higher body weight. Beyond prepared meals, there is limited understanding of what foods people buy out-of-home and where they buy them from. This study analyses out-of-home food purchases by food groups and food outlet types, and estimates socio-economic differences in these expenditure patterns. We used a nationally representative product-level dataset of expenditures (n=2,734,987) on foods and non-alcoholic beverages for out-of-home consumption for 9,704 respondents in Great Britain (June 2015-December 2017). Population weighted estimates of per capita weekly expenditures and shares of expenditure were derived for four outlet types and eight food groups. We used linear multi-level modelling to determine differences in expenditure patterns by socio-economic status (SES) characterised via occupational social grade. Out-of-home purchases make up 25-39% of total food and beverage expenditures. Mid- and high-SES respondents spent nearly twice as much (£17.76 and £15.11 weekly), compared to low-SES respondents (£9.69) for out-ofhome food consumption. A third of expenditures across SES (36-37%) were spent in venues other than restaurants or fast-food and takeaway outlets. Meals accounted for 60% of expenditures, but a third was spent on beverages (10-12% non-alcoholic cold beverages, 17-18% hot beverages) and 9-10% on snacks. Mid- and low-SES respondents had a greater share of expenditure in takeaway and fast-food outlets, supermarkets and convenience stores, and on cold non-alcoholic beverages. Overall, low-SES respondents spent less on out-of-home foods but the share of this expenditure across different foods or outlets varied less. While restaurants, fast-food and takeaway outlets were a major source of out-of-home purchases, a significant proportion was spent in other outlets. Policies targeting out-of-home consumption should therefore consider the full range of foods as well as the diversity of places where they are sold.

Understanding Urban Health Inequalities: Methods and Design of the Heart Health Hoods Qualitative Project.

OBJECTIVE: Qualitative methods may help to understand features related to health urban inequalities as a way to include citizens' perceptions of their neighbourhoods in relation to their health-related behaviours. The aim of this article is to describe the methods and design of a qualitative urban health study. METHODS: The Heart Healthy Hoods (HHH) analyses cardiovascular health in an urban environment using mixed methods: electronic health records, quantitative individual questionnaires, physical examination, semi-structured Interviews (SSIs), focus groups (FGs) and participatory technics such as photovoice. This article focuses on the HHH qualitative methods and design. A case study was used to select three neighbourhoods in Madrid with different socioeconomic levels: low, medium, and high. The selection process for these three neighbourhoods was as follows: classification of all Madrid's neighbourhoods (128) according to their socioeconomic level; after ranking this classification, nine neighbourhoods, three by socioeconomic level, were short-listed; different urban sociology criteria and non-participant observation were used for the final selection of three neighbourhoods. After selecting the three neighbourhoods, thirty SSIs were held with residents and six SSIs were held with key informants. Finally, twenty-nine FGs will be conducted over the course of 8 months, between May and December of 2018. CONCLUSIONS: Systematization in the selection of neighbourhoods and the use of adequate techniques are essential for the qualitative study of urban health inequalities.

Broken (windows) theory: A meta-analysis of the evidence for the pathways from neighborhood disorder to resident health outcomes and behaviors.

The criminological "broken windows" theory (BWT) has inspired public health researchers to test the impact of neighborhood disorder on an array of resident health behaviors and outcomes. This paper identifies and meta-analyzes the evidence for three mechanisms (pathways) by which neighborhood disorder is argued to impact health, accounting for methodological inconsistencies across studies. A search identified 198 studies (152 with sufficient data for meta-analysis) testing any of the three pathways or downstream, general health outcomes. The meta-analysis found that perceived disorder was consistently associated with mental health outcomes, as well as substance abuse, and measures of overall health. This supported the psychosocial model of disadvantage, in which stressful contexts impact mental health and related sequelae. There was no consistent evidence for disorder's impact on physical health or risky behavior. Further examination revealed that support for BWT-related hypotheses has been overstated owing to data censoring and the failure to consistently include critical covariates, like socioeconomic status and collective efficacy. Even where there is evidence that BWT impacts outcomes, it is driven by studies that measured disorder as the perceptions of the focal individual, potentially conflating pessimism about the neighborhood with mental health.

Individual and neighborhood socioeconomic status in the prediction of liver transplantation among patients with liver disease: A population-based cohort study in Taiwan.

Given the fact that >80% of liver transplantations (LTs) were living donor liver transplantation (LDLT) in Taiwan, we conducted this study to assess whether patients with lower socioeconomic status are subject to a lower chance of receiving hepatic transplantation.This was a cohort study including 197,082 liver disease patients admitted in 1997 to 2013, who were at higher risk of LT. Personal monthly income and median family income of living areas were used to indicate individual and neighborhood socioeconomic status, respectively. Cox proportional hazard model that considered death as a competing risk event was used to estimate subdistribution hazard ratio (sHR) of LT in association with socioeconomic status.Totally 2204 patients received LT during follow-up, representing a cumulative incidence of 1.12% and an incidence rate of 20.54 per 10 person-years. After adjusting for potential confounders, including age, sex, co-morbidity, location/urbanization level of residential areas, we found that patients with < median monthly income experienced significantly lower incidence of LT (aHR = 0.802, 95% confidence interval (CI) = 0.717-0.898), but those with >- median monthly income had significantly elevated incidence of LT (aHR = 1.679, 95% CI = 1.482-1.903), as compared to those who were not actively employed. Additionally, compared to areas with the lowest quartile of median family income, the highest quartile of median family income was also associated with significantly higher incidence rate of LT (aHR = 1.248, 95% CI = 1.055-1.478).Higher individual and neighborhood socioeconomic status were significantly associated with higher incidence of LT among patients with higher risk of LT.