Neighborhood-level Residential Isolation and Neighborhood Composition: Similar but Different.

Residential segregation has been considered as a potential cause of racial and/or ethnic disparities in health. Among the five dimensions of residential segregation, the isolation dimension has been conceived to play an essential role in disproportionately shaping the health of racial and ethnic minorities, particularly in urban or metropolitan areas. However, a noticeable amount of research studies has been using informal measures of neighborhood composition (i.e., proportions or percentages), which do not capture any of the five dimensions of residential segregation. Since the inappropriateness of measurement may obstruct a meaningful interpretation and an effective dissemination of research findings, a combination of graphical and non-graphical techniques was used to demonstrate the similarities and differences between formal measures of neighborhood-level residential isolation and informal measures of neighborhood composition. These were intended to provide intuitive and mutual understandings across academic disciples (e.g., city or urban planning, geography, public health, and sociology) and practitioners or professionals in multiple fields (e.g., community development workers, health service providers, policymakers, and social workers). Conceptual and methodological explanations with analytical discussions are also provided to differentiate and/or distinguish the two types of measures. While the concepts, methodologies, and research implications discussed herein are most relevant for research studies in urban or metropolitan areas of the United States, the general framework is also applicable to those of other industrialized counties.

Cancer risk from air toxics in relation to neighborhood isolation and sociodemographic characteristics: A spatial analysis of the St. Louis metropolitan area, USA.

BACKGROUND: A growing body of research has examined relationships between neighborhood characteristics and exposure to air toxics in the United States. However, a limited number of studies have addressed neighborhood isolation, a measure of spatial segregation. We investigated the spatial distribution of carcinogenic air toxics in the St. Louis metropolitan area and tested the hypothesis that neighborhood isolation and sociodemographic characteristics are associated with exposure to carcinogenic air toxics. METHODS: We obtained lifetime air toxics cancer risk data from the United States Environmental Protection Agency's National Air Toxic Assessment and sociodemographic data from the American Community Survey. We used geographic information systems to identify statistically significant clusters of census tracts with elevated all-site cancer risk due to air toxics in the St. Louis metropolitan area. Relative Risks (RR) were estimated for the association between neighborhood characteristics and air toxic hot spots. Using a local spatial isolation index to evaluate residential segregation, we also evaluated the association between neighborhood racial and economic isolation and air toxic hot spots. RESULTS: Approximately 14% (85 of the 615) of census tracts had elevated cancer risk due to air toxics (p < 0.01). These air toxic hot spots were independently associated with neighborhoods with high levels of poverty and unemployment and low levels of education. Census tracts with the highest levels of both racial isolation of Blacks and economic isolation of poverty were more likely to be located in air toxic hotspots than those with low combined racial and economic isolation (RR = 5.34; 95% CI = 3.10-9.22). CONCLUSIONS: These findings provide strong evidence of unequal distribution of carcinogenic air toxics in the St. Louis metropolitan area. Study results may be used to inform public health efforts to eliminate sociodemographic inequalities in exposure to air pollutants.

Spatializing Area-Based Measures of Neighborhood Characteristics for Multilevel Regression Analyses: An Areal Median Filtering Approach.

Area-based measures of neighborhood characteristics simply derived from enumeration units (e.g., census tracts or block groups) ignore the potential of spatial spillover effects, and thus incorporating such measures into multilevel regression models may underestimate the neighborhood effects on health. To overcome this limitation, we describe the concept and method of areal median filtering to spatialize area-based measures of neighborhood characteristics for multilevel regression analyses. The areal median filtering approach provides a means to specify or formulate "neighborhoods" as meaningful geographic entities by removing enumeration unit boundaries as the absolute barriers and by pooling information from the neighboring enumeration units. This spatializing process takes into account for the potential of spatial spillover effects and also converts aspatial measures of neighborhood characteristics into spatial measures. From a conceptual and methodological standpoint, incorporating the derived spatial measures into multilevel regression analyses allows us to more accurately examine the relationships between neighborhood characteristics and health. To promote and set the stage for informative research in the future, we provide a few important conceptual and methodological remarks, and discuss possible applications, inherent limitations, and practical solutions for using the areal median filtering approach in the study of neighborhood effects on health.

Area-based variations in obesity are more than a function of the food and physical activity environment : area-based variations in obesity.

This study examines the area-based variations in obesity from a community-based epidemiologic survey of Boston, MA, USA, using a geographic information system and multilevel modeling techniques. A combination of quantitative and qualitative methods was used to assess whether a function of the food and the physical activity (PA) environment can explain the body weight of residents. First, a series of multilevel analyses was conducted after accounting for the well-established individual determinants and capturing a wide range of environmental attributes to represent a more realistic portrayal of urban typology. Second, the results of multilevel analysis were framed into the theoretical model of area-based variations in obesity to qualitatively summarize the association of contextual factors with the body weight of residents. Based on the overall correlation, the area-based variations defined by a function of the food and PA environment seem to be insufficient in explaining the body weight of residents. By testing the cross-level interactions of gender and race/ethnicity with contextual factors, the results suggest that the concept of area-based variations in obesity will have to consider how residents behave differently within a given environment. More research is needed to better understand the contextual determinants of obesity so as to put forth population-wide interventions.

Census-Tract-Level Median Household Income and Median Family Income Estimates: A Unidimensional Measure of Neighborhood Socioeconomic Status?

Previous studies suggested either census-tract-level median household income (MHI) or median family income (MFI) estimates may be used as a unidimensional measure of neighborhood socioeconomic status (SES) in the United States (US). To better understand its general use, the purpose of this study was to assess the usefulness of MHI and MFI in a wide range of geographic areas. Area-based socioeconomic data at the census tract level were obtained from the 2000 Census as well as the 2005-2009, 2010-2014, and 2015-2019 American Community Survey. MHI and MFI were used as two simple measures of neighborhood SES. Based on the five area-based indexes developed in the US, several census-tract-level socioeconomic indicators were used to derive five composite measures of neighborhood SES. Then, a series of correlation analyses was conducted to assess the relationships between these seven measures in the State of California and its seven Metropolitan Statistical Areas. Two simple measures were very strongly and positively correlated with one another, and were also strongly or very strongly correlated, either positively or negatively, with five composite measures. Hence, the results of this study support an analytical thinking that simple measures and composite measures may capture the same dimension of neighborhood SES in different geographic areas.

Comparing proxy and formal measures of county-level racial isolation in race-stratified models: A case study in Tennessee, 2005-2014.

The purpose of this study was to understand whether proxy measures of county-level racial isolation (based on racial compositions) would yield similar results as the formal measures of county-level racial isolation (derived from the isolation index of P*). White (non-Hispanic White) and Black (non-Hispanic Black or African American) women residing in the State of Tennessee, USA, and diagnosed with a non-invasive or invasive breast cancer were considered as the study population. Individual-level variables were obtained from the Tennessee Cancer Registry data for the period between 2005 and 2014 (46,983 White women and 7,967 Black women), and county-level variables were obtained from the American Community Survey data for the periods of 2005-2009 and 2010-2014 (95 counties). Using breast cancer condition (non-invasive versus invasive) as the binary outcome of interest, a series of multilevel logistic regression analyses was conducted separately by race. After controlling for individual-level socio-demographic characteristics, proxy measure of county-level White isolation and county-level median household income were not associated with breast cancer condition, but formal measure of county-level White isolation was associated with lower odds of having an invasive breast cancer among White women. On the other hand, neither proxy nor formal measure of county-level Black isolation was associated with breast cancer condition, but county-level median household income was associated with lower odds of having an invasive breast cancer among Black women. These results suggest that using a proxy and formal measure of racial isolation may yield different results, and race-stratified analyses would be helpful for understanding a differential effect of racial isolation on Whites and Blacks. While more detailed examinations are needed in future studies, possible explanations on and reasons behind these findings are discussed.

Disparities in the prevalence of obesity in Boston: results from the Boston Area Community Health (BACH) survey.

OBJECTIVE: This study examined disparities in the prevalence of obesity to determine how future prevention and/or intervention efforts should be developed to remedy obesity. METHODS: We obtained individual information of sociodemographic characteristics, comorbid conditions, and lifestyle behaviors from the Boston Area Community Health (BACH) survey for 2002-2005. To account for the complex BACH sampling design, observations were weighted inversely to their probability of selection, and sampling weights were poststratified to the Boston population from the U.S. Census 2000. We tested all possible two- and three-way interaction terms from a multivariate logistic regression model. RESULTS: After controlling for individual determinants in detail and focusing on the population within a single city, the associations of sociodemographic characteristics, comorbid conditions, and lifestyle behaviors with obesity were consistent with previous findings. Notably, three two-way interaction terms were significantly associated with obesity: (1) race/ethnicity and gender, (2) gender and other people in the household, and (3) race/ethnicity and alcohol consumption. CONCLUSIONS: Future obesity prevention and/or intervention programs in Boston need to be primarily gender- and racially/ethnically specific to minimize cost and maximize results. Additional considerations are needed to take into account the differences in age, the presence of other people in the household, and education level.

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.

Capturing the two dimensions of residential segregation at the neighborhood level for health research.

Two conceptual and methodological foundations of segregation studies are that (i) segregation involves more than one group, and (ii) segregation measures need to quantify how different population groups are distributed across space. Therefore, percentage of population belonging to a group is not an appropriate measure of segregation because it does not describe how populations are spread across different areal units or neighborhoods. In principle, evenness and isolation are the two distinct dimensions of segregation that capture the spatial patterns of population groups. To portray people's daily environment more accurately, segregation measures need to account for the spatial relationships between areal units and to reflect the situations at the neighborhood scale. For these reasons, the use of local spatial entropy-based diversity index (SHi ) and local spatial isolation index (Si ) to capture the evenness and isolation dimensions of segregation, respectively, are preferable. However, these two local spatial segregation indexes have rarely been incorporated into health research. Rather ineffective and insufficient segregation measures have been used in previous studies. Hence, this paper empirically demonstrates how the two measures can reflect the two distinct dimensions of segregation at the neighborhood level, and argues conceptually and set the stage for their future use to effectively and meaningfully examine the relationships between residential segregation and health.

Perceived barriers to mammography among underserved women in a Breast Health Center Outreach Program.

BACKGROUND: To investigate perceived barriers to mammography among underserved women, we asked participants in the Siteman Cancer Center Mammography Outreach Registry-developed in 2006 to evaluate mobile mammography's effectiveness among the underserved-why they believed women did not get mammograms. METHODS: The responses of approximately 9,000 registrants were analyzed using multivariable logistic regression. We report adjusted odds ratios (OR) and 95% confidence intervals (CI) significant at 2-tailed P values less than .05. RESULTS: Fears of cost (40%), mammogram-related pain (13%), and bad news (13%) were the most commonly reported barriers. Having insurance was associated with not perceiving cost as a barrier (OR .44, 95% CI .40 to .49), but with perceiving fear of both mammogram-related pain (OR 1.39, 95% CI 1.21 to 1.60) and receiving bad news (OR 1.38, 95% CI 1.19 to 1.60) as barriers. CONCLUSION: Despite free services, underserved women continue to report experiential and psychological obstacles to mammography, suggesting the need for more targeted education and outreach in this population.

Effects of buffer size and shape on associations between the built environment and energy balance.

Uncertainty in the relevant spatial context may drive heterogeneity in findings on the built environment and energy balance. To estimate the effect of this uncertainty, we conducted a sensitivity analysis defining intersection and business densities and counts within different buffer sizes and shapes on associations with self-reported walking and body mass index. Linear regression results indicated that the scale and shape of buffers influenced study results and may partly explain the inconsistent findings in the built environment and energy balance literature.

Inhibition of proteasome by MG-132 treatment causes extra phragmoplast formation and cortical microtubule disorganization during M/G1 transition in synchronized tobacco cells.

The 26S proteasome plays essential roles in cell cycle progression in various types of cell. We previously reported that the inhibition of 26S proteasome activities by a proteasome inhibitor, MG-132, exclusively caused cell cycle arrest in synchronized tobacco BY-2 cells. Here we report a further observation of 26S proteasome involvement during M/G1 transition utilizing a transgenetic BY-2 cell line that stably expresses a GFP-alpha-tubulin fusion protein (BY-GT16). Interestingly, MG-132 treatment caused the arrest of cell cycle progression prior to entering the G1 phase. Indeed, phragmoplast-like structures were formed and cortical microtubules were not organized after the collapse of the original phragmoplasts. Additionally, actin microfilaments showed irregular rearrangements when further incubated with MG-132 and as the phragmoplast-like structures developed. Since these phragmoplast-like structures had a similar configuration and ability to form cell plates to that of the original phragmoplasts, we designated these phragmoplast-like structures as extra phragmoplasts. Furthermore, we showed that a tobacco kinesin-related polypeptide of 125 kDa (TKRP125) localized in the extra phragmoplasts and that its protein level remained unchanged during MG-132 treatment. We propose that TKRP125 might be one of the possible targets of the ubiquitin-proteasome degradation pathway during M/G1 transition.

Degradation of proliferating cell nuclear antigen by 26S proteasome in rice (Oryza sativa L.).

To study whether metabolic control of proliferating cell nuclear antigen (PCNA) during the cell cycle is similar to that of associated protein factors, two-hybrid analysis with PCNA from rice (Oryza sativa L. cv. Nipponbare) was performed. PCNA interacted with rice Rpt6, which is the ATPase subunit of 26S proteasome, both in vitro and in vivo, and the degradation of PCNA was disrupted by the proteasome in vivo. The tissue-specific expression pattern of the transcripts of Rpt6 and PCNA suggested that the rice proteasome played important roles in DNA replication involving PCNA. These findings indicate a proteasome-dependent degradation of PCNA.

Cell-cycle dependent dynamic change of 26S proteasome distribution in tobacco BY-2 cells.

The 26S proteasome is known to play pivotal roles in cell-cycle progression in various eukaryotic cells; however, little is known about its role in higher plants. Here we report that the subcellular distribution of the 26S proteasome is dynamically changed in a cell-cycle dependent manner in tobacco BY-2 cells as determined by immunostaining with anti-Rpn10 (a regulatory PA700 subunit) and anti-20S catalytic proteasome antibodies. The 26S proteasome was found to localize not only in nuclear envelopes and mitotic spindles but also in preprophase bands (PPBs) and phragmoplasts appearing in G(2) and M phases, respectively. MG132, a proteasome inhibitor, exclusively caused cell-cycle arrest not only at the metaphase but also the early stage of PPB formation at the G(2) phase and the collapse of the phragmoplast, which seems to be closely related to proteasome distribution in the cells.