Interpreting a standardized and normalized measure of neighborhood socioeconomic status for a better understanding of health differences

Background Standardization and normalization of continuous covariates are used to ease the interpretation of regression coefficients. Although these scaling techniques serve different purposes, they are sometimes used interchangeably or confused for one another. Therefore, the objective of this study is to demonstrate how these scaling techniques lead to different interpretations of the regression coefficient in multilevel logistic regression analyses. Methods Area-based socioeconomic data at the census tract level were obtained from the 2015–2019 American Community Survey for creating two measures of neighborhood socioeconomic status (SES), and a hypothetical data on health condition (favorable versus unfavorable) was constructed to represent 3000 individuals living across 300 census tracts (i.e., neighborhoods). Two measures of neighborhood SES were standardized by subtracting its mean and dividing by its standard deviation (SD) or by dividing by its interquartile range (IQR), and were normalized into a range between 0 and 1. Then, four separate multilevel logistic regression analyses were conducted to assess the association between neighborhood SES and health condition. Results Based on standardized measures, the odds of having unfavorable health condition was roughly 1.34 times higher for a one-SD change or a one-IQR change in neighborhood SES; these reflect a health difference of individuals living in relatively high SES (relatively affluent) neighborhoods and those living in relatively low SES (relatively deprived) neighborhoods. On the other hand, when these standardized measures were replaced by its respective normalized measures, the odds of having unfavorable health condition was roughly 3.48 times higher for a full unit change in neighborhood SES; these reflect a health difference of individuals living in highest SES (most affluent) neighborhoods and those living in lowest SES (most deprived) neighborhoods. Conclusion Multilevel logistic regression analyses using standardized and normalized measures of neighborhood SES lead to different interpretations of the effect of neighborhood SES on health. Since both measures are valuable in their own right, interpreting a standardized and normalized measure of neighborhood SES will allow us to gain a more rounded view of the health differences of individuals along the gradient of neighborhood SES in a certain geographic location as well as across different geographic locations.

Associations Between Neighborhood SES Disadvantage and Feelings of Depression and Loneliness in Older Adults

Previous research suggests depressive symptoms and loneliness are increasingly prevalent among older adults living in lower-income neighborhoods. The purpose of this study was to examine the extent to which neighborhood socioeconomic status (SES) was associated with depressive symptoms and loneliness among a sample of older adults from the Healthy Heart and Mind Study (N = 165; mean age = 68.48 (SD = 6.26); 66.7% women; 40.6% African American). It was hypothesized that older adults living in neighborhoods with greater socioeconomic disadvantage would report more depressive symptoms and loneliness than those residing in neighborhoods with less socioeconomic disadvantage. Depression was assessed with the Beck Depression Inventory-II (BDI-II), and loneliness was assessed using the Revised University of California, Los Angeles (UCLA) Loneliness scale. Neighborhood SES was measured with the Area Deprivation Index (ADI), which allows rankings of neighborhoods by SES disadvantage both statewide and nationally. After controlling for demographic variables (age, sex, and race), linear regression analyses showed that greater neighborhood SES disadvantage was associated with higher depression scores (β = -.094; p = .041) and higher loneliness scores (β = -.258; p = .003). These findings highlight the importance of neighborhood context on mental health in older adults, as underserved populations are more likely to experience declines in mental health under strenuous circumstances. Future research should investigate the impact of neighborhood SES on mental health in aging adults.

Socioeconomic gradient in COVID-19 vaccination: evidence from Israel

Background Low socioeconomic status (SES) groups have been disproportionately affected by the COVID-19 pandemic. We aimed to examine COVID-19 vaccination rate by neighborhood SES and ethnicity in Israel, a country which has achieved high vaccination rates. Methods Data on vaccinations were obtained from the Israeli Ministry of Health’s open COVID-19 database, for December 20, 2020 to August 31, 2021. Correlation between vaccination rate and neighborhood SES was analyzed. Difference in vaccination rate between the first and second vaccine dose was analyzed by neighborhood SES and ethnicity. Findings A clear socioeconomic gradient was demonstrated, with higher vaccination rates in the higher SES categories (first dose: r = 0.66; second dose: r = 0.74; third dose: r = 0.92). Vaccination uptake was lower in the lower SES groups and in the Arab population, with the largest difference in uptake between Jewish and Arab localities for people younger than 60, and with the gap widening between first and third doses. Conclusions Low SES groups and the Arab ethnic minority demonstrated disparities in vaccine uptake, which were greater for the second and third, compared with the first vaccine dose. Strategies to address vaccination inequity will need to identify barriers, provide targeted information, and include trust-building in disadvantaged communities.

A Social Gradient of Cortical Thickness Relative to Age in Adolescence: Relations Among Neighborhood Socioeconomic Disadvantage, Advanced Cortical Thinning, and Depressive Symptoms

Background: Mental and physical health are affected by family and neighborhood socioeconomic status (SES). Accelerated biological aging in the context of lower SES is one mechanism that might contribute to underlying health disparities; few studies, however, have considered neighborhood SES in relation to neural markers of biological aging in adolescents. Methods: In 120 adolescents 13-18 years of age, we examined family and neighborhood SES in relation to biological aging in the brain, indexed by cortical thickness relative to chronological age. We also examined whether advanced cortical thinning relative to age was related to depressive symptoms and explored regions of interest.Results: Neighborhood socioeconomic disadvantage was uniquely associated with advanced cortical thinning in the left hemisphere (=-.20), which was related to more severe depressive symptoms (=-.33). In contrast, family income-to-needs was not significantly associated with cortical thickness age after controlling for relevant covariates. In exploratory, covariate-adjusted analyses of cortical thickness relative to age at the regional level, neighborhood socioeconomic disadvantage was associated with advanced cortical thinning in the left superior frontal gyrus (=-.27), fusiform gyrus (=-.20), and insula (=-.21). Of these regions, only advanced cortical thinning in the left superior frontal gyrus was associated with more severe depressive symptoms (=-.18). Conclusion: Our findings provide evidence for a social gradient of accelerated biological aging at the neural level during adolescence. Adolescents living in less advantaged communities have a thinner left hemisphere cortex than expected given their chronological age. Advanced cortical thinning may increase risk for depression in adolescence.

Association between Neighborhood Socioeconomic Status and Executive System Activation in Youth

Low socioeconomic status has been shown to have detrimental effects on cognitive performance, including working memory (WM). As executive systems that support WM undergo functional neurodevelopment during adolescence, environmental stressors at both the individual and community levels may have a particularly strong impact on cognitive outcomes. Here, we sought to examine how neighborhood socioeconomic status (SES) impacts task-related activation of the executive system during adolescence and to determine whether this effect mediates the relationship between neighborhood SES and WM performance. To address these questions, we studied 1,158 youths (age 8-22) that completed a fractal n-back WM task during fMRI at 3T as part of the Philadelphia Neurodevelopmental Cohort. We found that higher neighborhood SES was associated with greater activation of the executive system to WM load, including the bilateral dorsolateral prefrontal cortex, posterior parietal cortex, and precuneus. These associations remained significant when controlling for related factors like parental education and exposure to traumatic events. Furthermore, high dimensional multivariate mediation analysis identified two distinct patterns of brain activity within the executive system that significantly mediated the relationship between neighborhood SES and task performance. Together, these findings underscore the importance of neighborhood environment in shaping executive system function and WM in youth.

Associations Between Patient Neighborhood Characteristics and Inappropriate Antimicrobial Use

Background: Antimicrobials are among the most commonly prescribed medications in US hospitals; an estimated 50% of hospitalized patients receive an antimicrobial. Research has shown that antimicrobial prescriptions to vary by patient- and hospital-level factors; however, disparities by patient neighborhood characteristics have not been examined. We evaluated associations between hospital and neighborhood indicators of socioeconomic status (SES) and antimicrobial use (AU) for gram-positive bacterial infections (GPBs), and broad-spectrum use for community-acquired infections (BSCAs) and hospital-onset infections (BSHOs). Methods: This analysis was conducted among 86 acute-care hospitals in California that submitted AU data via the NHSN in 2019. Hospital-level AU was measured as standardized antimicrobial administration ratios (SAARs) calculated by dividing observed antimicrobial use by risk-adjusted predicted antimicrobial use for GPB, BSCA, and BSHO antimicrobial groupings and categorized as binary (>1 or <1); SAARs >1 indicate potential inappropriate prescribing. California Office of Statewide Health Planning and Development 2018 data were used to obtain hospital characteristics and patient age, race or ethnicity, insurance, and comorbidities (defined by Charlson comorbidity index) for hospitalizations where AU may have been indicated, based on International Classification of Diseases Tenth Revision (ICD-10) diagnosis codes. The California Healthy Places Index (HPI) was used to obtain composite neighborhood SES indicators for each patient at the ZIP code level, measured as tertiles. Covariates were aggregated to the hospital level. Poisson regressions were used to evaluate the association between hospital and neighborhood SES indicators and SAAR scores, controlling for potential hospital-level confounders. Results: Among 86 hospitals included in the analysis, the mean patient age for hospitalizations where AU may have been indicated was 66 years, the proportion of white patients was 55%, and the mean proportion of Medi-Cal users was 19%. After adjusting for confounders including age, race or ethnicity, insurance status, comorbidities, and number of hospital beds; higher median values of patient SES had a protective effect against hospitals having GP SAAR scores > 1 (relative risk [RR], 0.68; 95% CI, 0.50–0.93) but was not significantly associated with hospitals having BSCA SAAR scores >1 (RR, 0.79; 95% CI, 0.62–1.02) or BSHO SAAR scores >1 (RR, 0.80; 95% CI, 0.61–1.04). Conclusions: Considering SES in addition to summary antimicrobial use scores such as SAARs may help identify populations potentially at risk for inappropriate AU; however, patient-level information is still necessary to evaluate appropriateness of antimicrobial prescribing.Funding: NoDisclosures: None

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.

Neighborhood Poverty and Amygdala Response to Negative Face

Introduction: Considerable research has established a link between socioeconomic status (SES) and brain function. While studies have shown a link between poverty status and amygdala response to negative stimuli, a paucity of knowledge exists on whether neighborhood poverty is also independently associated with amygdala hyperactive response to negative stimuli. Purpose: Using functional brain imaging data, this study tested the association between neighborhood SES and the amygdala’s response to negative stimuli. Considering race as a sociological rather than a biological construct, we also explored racial heterogeneity in this association between non-Hispanic Black and non-Hispanic White youth. Methods: We borrowed the functional Magnetic Resonance Imaging (fMRI) data of the Adolescent Brain Cognitive Development (ABCD) study. The sample was 2,490 nine to ten years old non-Hispanic Black and non-Hispanic White adolescents. The independent variable was neighborhood income which was treated as a continuous measure. The primary outcomes were the right and left amygdala response to negative face during an N-Back task. Age, sex, race, marital status, and family SES were the covariates. To analyze the data, we used linear regression models. Results: Low neighborhood income was independently associated with a higher level of amygdala response to negative face. Similar results were seen for the right and left amygdala. These effects were significant net of race, age, sex, marital status, and family SES. An association between low neighborhood SES and higher left but not right amygdala response to negative face could be observed for non-Hispanic Black youth. No association between neighborhood SES and left or right amygdala response to negative face could be observed for non-Hispanic White youth. Conclusions: For American youth, particularly non-Hispanic Black youth, living in a poor neighborhood predicts the left amygdala reaction to negative face. This result suggested that Black youth who live in poor neighborhoods are at a high risk of poor emotion regulation. This finding has implications for policy making to reduce inequalities in undesired behavioral and emotional outcomes. Policy solutions to health inequalities should address inequalities in neighborhood SES.