Crime, neighborhood deprivation, and asthma: a GIS approach to define and assess neighborhoods

Components of Residential Neighborhood Deprivation and Their Impact on the Likelihood of Live-Donor and Preemptive Kidney Transplantation

INTRODUCTION

Adults residing in deprived neighborhoods face various socioeconomic stressors, hindering their likelihood of receiving live-donor kidney transplantation (LDKT) and preemptive kidney transplantation (KT). We quantified the association between residential neighborhood deprivation index (NDI) and the likelihood of LDKT/preemptive KT, testing for a differential impact by race and ethnicity.

METHODS

We studied 403 937 adults (age ≥ 18) KT candidates (national transplant registry; 2006-2021). NDI and its 10 components were averaged at the ZIP-code level. Cause-specific hazards models were used to quantify the adjusted hazard ratio (aHR) of LDKT and preemptive KT across tertiles of NDI and its 10 components.

RESULTS

Candidates residing in high-deprivation neighborhoods were more likely to be female (40.1% vs. 36.2%) and Black (41.9% vs. 17.7%), and were less likely to receive both LDKT (aHR = 0.66, 95% confidence interval [CI]: 0.64-0.67) and preemptive KT (aHR = 0.60, 95% CI: 0.59-0.62) than those in low-deprivation neighborhoods. These associations differedby race and ethnicity (Black: aHRLDKT = 0.58, 95% CI: 0.55-0.62; aHRpreemptive KT = 0.68, 95% CI: 0.63-0.73; Pinteractions: LDKT < 0.001; Preemptive KT = 0.002). All deprivation components were associated with the likelihood of both LDKT and preemptive KT (except median home value): for example, higher median household income (LDKT: aHR = 1.08, 95% CI: 1.07-1.09; Preemptive KT: aHR = 1.10, 95% CI: 1.08-1.11) and educational attainments (≥high school [LDKT: aHR = 1.17, 95% CI: 1.15-1.18; Preemptive KT: aHR = 1.23, 95% CI: 1.21-1.25]).

CONCLUSION

Residence in socioeconomically deprived neighborhoods is associated with a lower likelihood of LDKT and preemptive KT, differentially impacting minority candidates. Identifying and understanding which neighborhood-level socioeconomic status contributes to these racial disparities can be instrumental in tailoring interventions to achieve health equity in LDKT and preemptive KT.

A Geographical Analysis of Socioeconomic and Environmental Drivers of Physical Inactivity in Post Pandemic Cities: The Case Study of Chicago, IL, USA

The pandemic’s lockdown has made physical inactivity unavoidable, forcing many people to work from home and increasing the sedentary nature of their lifestyle. The link between spatial and socio-environmental dynamics and people’s levels of physical activity is critical for promoting healthy lifestyles and improving population health. Most studies on physical activity or sedentary behaviors have focused on the built environment, with less attention to social and natural environments. We illustrate the spatial distribution of physical inactivity using the space scan statistic to supplement choropleth maps of physical inactivity prevalence in Chicago, IL, USA. In addition, we employ geographically weighted regression (GWR) to address spatial non-stationarity of physical inactivity prevalence in Chicago per census tract. Lastly, we compare GWR to the traditional ordinary least squares (OLS) model to assess the effect of spatial dependency in the data. The findings indicate that, while access to green space, bike lanes, and living in a diverse environment, as well as poverty, unsafety, and disability, are associated with a lack of interest in physical activities, limited language proficiency is not a predictor of an inactive lifestyle. Our findings suggest that physical activity is related to socioeconomic and environmental factors, which may help guide future physical activity behavior research and intervention decisions, particularly in identifying vulnerable areas and people.

Differentiating kinds of systemic stressors with relation to psychotic-like experiences in late childhood and early adolescence: the stimulation, discrepancy, and deprivation model of psychosis

INTRODUCTION

Conceptualizations that distinguish systems-level stress exposures are lacking; the Stimulation (lack of safety and high attentional demands), Discrepancy (social exclusion and lack of belonging), and Deprivation (lack of environmental enrichment) (SDD) theory of psychosis and stressors occurring at the systems-level has not been directly tested.

METHODS

Exploratory factor analysis was conducted on 3,207 youth, and associations with psychotic-like experiences (PLEs) were explored.

RESULTS

Though model fit was suboptimal, five factors were defined, and four were consistent with the SDD theory, and related to PLEs. Objective and subjective/self-report exposures for deprivation showed significantly stronger PLE associations compared to discrepancy and objective stimulation factors. Objective and subjective/self-report measures converged overall, though self-report stimulation exhibited a significantly stronger association with PLEs compared to objective stimulation.

DISCUSSION

Considering distinct system-level exposures could help clarify putative mechanisms and psychosis vulnerability. The preliminary approach potentially informs health policy efforts aimed at psychopathology prevention and intervention.

A structural model of high crime neighborhoods as a driver of toxic stress leading to asthma diagnoses among children of a large medical practice

This study tested the relationship of neighborhood crime as a driver of pediatric asthma diagnoses via the mechanism of toxic stress utilizing data from a police department, and pediatric clinic in a large urban city in the southwestern United States. Using structural equation modeling, a full mediation model of neighborhood crime as a driver of toxic stress resulting in increased asthma diagnoses fit the data well (Χ² = 14.0, p =.371; df = 13; RMSEA = .028 [90% CI: 0.00, 0.102]; CFI: 0.995; SRMR = .053). Advocates should explore ways to reduce neighborhood crime to address toxic stress and asthma.

An external exposome-wide association study of COVID-19 mortality in the United States

The risk factors for severe COVID-19 beyond older age and certain underlying health conditions are largely unknown. Recent studies suggested that long-term environmental exposures may be important determinants of severe COVID-19. However, very few environmental factors have been studied, often separately, without considering the totality of the external environment (i.e., the external exposome). We conducted an external exposome-wide association study (ExWAS) using the nationwide county-level COVID-19 mortality data in the contiguous US. A total of 337 variables characterizing the external exposome from 8 data sources were integrated, harmonized, and spatiotemporally linked to each county. A two-phase procedure was used: (1) in Phase 1, a random 50:50 split divided the data into a discovery set and a replication set, and associations between COVID-19 mortality and individual factors were examined using mixed-effect negative binomial regression models, with multiple comparisons addressed, and (2) in Phase 2, a multivariable regression model including all variables that are significant from both the discovery and replication sets in Phase 1 was fitted. A total of 13 and 22 variables were significant in the discovery and replication sets in Phase 1, respectively. All the 4 variables that were significant in both sets in Phase 1 remained statistically significant in Phase 2, including two air toxicants (i.e., nitrogen dioxide or NO₂, and benzidine), one vacant land measure, and one food environment measure. This is the first external exposome study of COVID-19 mortality. It confirmed some of the previously reported environmental factors associated with COVID-19 mortality, but also generated unexpected predictors that may warrant more focused evaluation.

A geographic identifier assignment algorithm with Bayesian variable selection to identify neighborhood factors associated with emergency department visit disparities for asthma

BACKGROUND

Ecologic health studies often rely on outcomes from health service utilization data that are limited by relatively coarse spatial resolutions and missing geographic information, particularly neighborhood level identifiers. When fine-scale geographic data are missing, the ramifications and strategies for addressing them are not well researched or developed. This study illustrates a novel spatio-temporal framework that combines a geographic identifier assignment (i.e., geographic imputation) algorithm with predictive Bayesian variable selection to identify neighborhood factors associated with disparities in emergency department (ED) visits for asthma.

METHODS

ED visit records with missing fine-scale spatial identifiers (~ 20%) were geocoded using information from known, coarser, misaligned spatial units using an innovative geographic identifier assignment algorithm. We then employed systematic variable selection in a spatio-temporal Bayesian hierarchical model (BHM) predictive framework within the NIMBLE package in R. Our novel methodology is illustrated in an ecologic case study aimed at identifying neighborhood-level predictors of asthma ED visits in South Carolina, United States, from 1999 to 2015. The health outcome was annual ED visit counts in small areas (i.e., census tracts) with primary diagnoses of asthma (ICD9 codes 493.XX) among children ages 5 to 19 years.

RESULTS

We maintained 96% of ED visit records for this analysis. When the algorithm used areal proportions as probabilities for assignment, which addressed differential missingness of census tract identifiers in rural areas, variable selection consistently identified significant neighborhood-level predictors of asthma ED visit risk including pharmacy proximity, average household size, and carbon monoxide interactions. Contrasted with common solutions of removing geographically incomplete records or scaling up analyses, our methodology identified critical differences in parameters estimated, predictors selected, and inferences. We posit that the differences were attributable to improved data resolution, resulting in greater power and less bias. Importantly, without this methodology, we would have inaccurately identified predictors of risk for asthma ED visits, particularly in rural areas.

CONCLUSIONS

Our approach innovatively addressed several issues in ecologic health studies, including missing small-area geographic information, multiple correlated neighborhood covariates, and multiscale unmeasured confounding factors. Our methodology could be widely applied to other small-area studies, useful to a range of researchers throughout the world.

Individual and Neighborhood Factors Associated with Undiagnosed Asthma in a Large Cohort of Urban Adolescents

Undiagnosed asthma adds to the burden of asthma and is an especially significant public health concern among urban adolescents. While much is known about individual-level demographic and neighborhood-level factors that characterize those with diagnosed asthma, limited data exist regarding these factors and undiagnosed asthma. This observational study evaluated associations between undiagnosed asthma and individual and neighborhood factors among a large cohort of urban adolescents. We analyzed data from 10,295 New York City adolescents who reported on asthma symptoms and diagnosis; a subset (n = 6220) provided addresses that we were able to geocode into US Census tracts. Multivariable regression models estimated associations between undiagnosed asthma status and individual-level variables. Hierarchical linear modeling estimated associations between undiagnosed asthma status and neighborhood-level variables. Undiagnosed asthma prevalence was 20.2%. Females had higher odds of being undiagnosed (adjusted odds ratio (AOR) = 1.25; 95% confidence interval (CI) = 1.13-1.37). Compared to White, non-Hispanic adolescents, Asian-Americans had higher risk of being undiagnosed (AOR = 1.41; 95% CI = 1.01-1.95); Latinos (AOR = 0.67; 95% CI = 0.45-0.83); and African-Americans/Blacks (AOR = 0.66; 95% CI = 0.52-0.87) had lower risk; Latinos and African-Americans/Blacks did not differ significantly. Living in a neighborhood with a lower concentration of Latinos relative to White non-Latinos was associated with lower risk of being undiagnosed (AOR = 0.66; CI = 0.43-0.95). Living in a neighborhood with health care provider shortages was associated with lower risk of being undiagnosed (AOR = 0.80; 95% CI =0.69-0.93). Public health campaigns to educate adolescents and their caregivers about undiagnosed asthma, as well as education for health care providers to screen adolescent patients for asthma, are warranted.

The Use of Geographic Data to Improve Asthma Care Delivery and Population Health

The authors examine uses of geographic data to improve asthma care delivery and population health and describe potential practice changes and areas for future research.

Clinical and Geographic Characterization of 30-Day Readmissions in Pediatric Sickle Cell Crisis Patients

BACKGROUND AND OBJECTIVES

Sickle cell disease (SCD) is a blood disorder affecting many US children that is often associated with hospital readmission. Although previous studies have reported on the clinical factors that influence readmission risk, potential geographic factors have not been fully investigated. The goal of this study was to investigate the importance of geographic risk factors and to confirm previously derived clinical risk factors that influence readmissions for SCD pain crises.

METHODS

Retrospective analyses were performed on pediatric inpatients with sickle cell crises at a single center. Readmission rates and risk factors were assessed. Geospatial analysis was conducted on point variables that represented health service access, and multivariable logistic regression models were constructed.

RESULTS

The study identified 373 patients experiencing sickle cell crises, with 125 (33.5%) having at least one 30-day readmission. Age (mean difference: 2.2 years; P<0.001), length of stay (median difference: 1 day; P<.001), admission pain score>7 of 10 (odds ratio [OR]: 2.21; P<0.01), discharge pain score>4 of 10 (OR: 2.098; P<.01), living within 5 miles of the center's main hospital (OR: 0.573; P=.04), and >3 hospital utilizations in the previous 12 months (OR: 5.103; P<.001) were identified as potential indicators of 30-day readmission risk. Logistic regression models for 30-day readmissions yielded similar results.

CONCLUSIONS

Increased age, high admission and discharge pain scores, decreased length of stay, and increased hospital utilizations were found to be associated with an increased risk of readmission for sickle cell crisis. Patient's residence was also found to be a significant risk indicator, supporting the utility of geospatial analysis in assessing readmission risk.

Mapping the urban asthma experience: Using qualitative GIS to understand contextual factors affecting asthma control

Asthma is complex and connected to a number of factors including access to healthcare, crime and violence, and environmental triggers. A mixed method approach was used to examine the experiences of urban people with asthma in controlling their asthma symptoms. The study started with an initial phase of qualitative interviews in West Philadelphia, a primarily poor African American community. Data from qualitative, semi-structured interviews indicated that stress, environmental irritants, and environmental allergens were the most salient triggers of asthma. Based on the interviews, the team identified six neighborhood factors to map including crime, housing vacancy, illegal dumping, tree canopy and parks. These map layers were combined into a final composite map. These combined methodologies contextualized respondents' perceptions in the framework of the actual community and built environment which tells a more complete story about their experience with asthma.

Violent crime in San Antonio, Texas: an application of spatial epidemiological methods

Violent crimes are rarely considered a public health problem or investigated using epidemiological methods. But patterns of violent crime and other health conditions are often affected by similar characteristics of the built environment. In this paper, methods and perspectives from spatial epidemiology are used in an analysis of violent crimes in San Antonio, TX. Bayesian statistical methods are used to examine the contextual influence of several aspects of the built environment. Additionally, spatial regression models using Bayesian model specifications are used to examine spatial patterns of violent crime risk. Results indicate that the determinants of violent crime depend on the model specification, but are primarily related to the built environment and neighborhood socioeconomic conditions. Results are discussed within the context of a rapidly growing urban area with a diverse population.