Evaluating options for measurement of neighborhood socioeconomic context: evidence from a myocardial infarction case-control study

The impact of COVID-19 on racial and ethnic disparities in presentation with perforated appendicitis in children: A retrospective cohort study

Background

Children from racial and ethnic minority groups have higher prevalence of perforated appendicitis, and the COVID-19 pandemic worsened racial and ethnic health-related disparities. We hypothesized that the incidence of perforated appendicitis worsened for children from racial and ethnic minorities during the COVID-19 pandemic.

Methods

We performed a retrospective cohort study of the Pediatric Health Information System for children ages 2-18y undergoing appendectomy pre-pandemic (3/19/2019-3/18/2020) and intra-pandemic (3/19/2020-3/30/2021). The primary outcome was presentation with perforated appendicitis. Multivariable logistic regression with mixed effects estimated the likelihood of presentation with perforated appendicitis. Covariates included race, ethnicity, pandemic status, Child Opportunity Index, gender, insurance, age, and hospital region.

Results

Overall, 33,727 children underwent appendectomy: 16,048 (47.6 %) were Non-Hispanic White, 12,709 (37.7 %) were Hispanic, 2261 (6.7 %) were Non-Hispanic Black, 960 (2.8 %) were Asian, and 1749 (5.2 %) Other. Overall perforated appendicitis rates were unchanged during the pandemic (37.4 % intra-pandemic, 36.4 % pre-pandemic, p = 0.06). Hispanic children were more likely to present with perforated appendicitis intra-pandemic versus pre-pandemic (OR 1.18, 95%CI: 1.07, 1.13). Hispanic children had higher odds of perforated appendicitis versus Non-Hispanic White children pre-pandemic (OR 1.10, 95%CI: 1.00, 1.20) which increased intra-pandemic (OR 1.19, 95%CI: 1.09, 1.30). Publicly-insured children had increased odds of perforated appendicitis intra-pandemic versus pre-pandemic (OR 1.14, 95%CI: 1.03, 1.25), and had increased odds of perforated appendicitis versus privately-insured children (intra-pandemic OR 1.26, 95%CI: 1.16, 1.36; pre-pandemic OR 1.12, 95%CI: 1.04, 1.22).

Conclusions

During the COVID-19 pandemic, Hispanic and publicly-insured children were more likely to present with perforated appendicitis, suggesting that the pandemic exacerbated existing disparities in healthcare for children with appendicitis.

Key message

We found that Hispanic children and children with public insurance were more likely to present with perforated appendicitis during the COVID-19 pandemic. Public health efforts aimed at ameliorating racial and ethnic disparities created during the COVID-19 pandemic should consider increasing healthcare access for Hispanic children to address bias, racism, and systemic barriers that may prevent families from seeking care.

Association of neighborhood opportunity and pediatric hospitalization rates in the United States

We examined associations between a validated, multidimensional measure of social determinants of health and population-based hospitalization rates among children <18 years across 18 states from the 2017 Healthcare Cost and Utilization Project State Inpatient Databases and the US Census. The exposure was ZIP code-level Child Opportunity Index (COI), a composite measure of neighborhood resources and conditions that matter for children's health. The cohort included 614,823 hospitalizations among a population of 29,244,065 children (21.02 hospitalizations per 1000). Adjusted hospitalization rates decreased significantly and in a stepwise fashion as COI increased (p < .001 for each), from 26.56 per 1000 (95% confidence interval [CI] 26.41-26.71) in very low COI areas to 14.76 per 1000 (95% CI 14.66-14.87) in very high COI areas (incidence rate ratio 1.8; 95% CI 1.78-1.81). Decreasing neighborhood opportunity was associated with increasing hospitalization rates among children in 18 US states. These data underscore the importance of social context and community-engaged solutions for health systems aiming to eliminate care inequities.

Socio-spatial health disparities in Covid-19 cases and deaths in United States skilled nursing facilities over 30 months

BACKGROUND

This study investigated whether socio-spatial factors surrounding United States skilled nursing facilities related to Covid-19 case counts among residents, staff, and facility personnel and deaths among residents.

METHODS

With data on 12,403 United States skilled nursing facilities and Census data we estimated multilevel models to assess relationships between facility and surrounding area characteristics from June 2020 to September 2022 for cumulative resident and facility personnel case counts and resident deaths.

RESULTS

Facilities with more Black or Latino residents experienced more cases incident rate ratios (IRR = 1.005; 1.004) and deaths (IRR = 1.008) among residents during the first 6 months of the pandemic but were no different thereafter. Facilities with more racial and ethnic heterogeneity and percent Black or Latino in the surrounding buffer experienced more Covid-19 cases and deaths in the first 6 months, but no such differences were observed in the subsequent 24 months. Facilities surrounded by higher percent Latino consistently experienced more cases among staff and facility personnel over the study period (IRR = 1.006; 1.001).

CONCLUSIONS

Findings indicated socio-spatial health disparities in cases among residents, staff, and facility personnel in the first 6 months of the pandemic, with some disparities fading thereafter. This pattern likely suggests the importance of the adoption and adherence to pandemic-related safety measures in skilled nursing facilities nationwide.

Assessing performance of ZCTA-level and Census Tract-level social and environmental risk factors in a model predicting hospital events

Predictive analytics are used in primary care to efficiently direct health care resources to high-risk patients to prevent unnecessary health care utilization and improve health. Social determinants of health (SDOH) are important features in these models, but they are poorly measured in administrative claims data. Area-level SDOH can be proxies for unavailable individual-level indicators, but the extent to which the granularity of risk factors impacts predictive models is unclear. We examined whether increasing the granularity of area-based SDOH features from ZIP code tabulation area (ZCTA) to Census Tract strengthened an existing clinical prediction model for avoidable hospitalizations (AH events) in Maryland Medicare fee-for-service beneficiaries. We created a person-month dataset for 465,749 beneficiaries (59.4% female; 69.8% White; 22.7% Black) with 144 features indexing medical history and demographics using Medicare claims (September 2018 through July 2021). Claims data were linked with 37 SDOH features associated with AH events from 11 publicly-available sources (e.g., American Community Survey) based on the beneficiaries' ZCTA and Census Tract of residence. Individual AH risk was estimated using six discrete time survival models with different combinations of demographic, condition/utilization, and SDOH features. Each model used stepwise variable selection to retain only meaningful predictors. We compared model fit, predictive performance, and interpretation across models. Results showed that increasing the granularity of area-based risk factors did not dramatically improve model fit or predictive performance. However, it did affect model interpretation by altering which SDOH features were retained during variable selection. Further, the inclusion of SDOH at either granularity level meaningfully reduced the risk that was attributed to demographic predictors (e.g., race, dual-eligibility for Medicaid). Differences in interpretation are critical given that this model is used by primary care staff to inform the allocation of care management resources, including those available to address drivers of health beyond the bounds of traditional health care.

Construction and validation of CAPSES scale as a composite indicator of SES for health research: an application to modeling social determinants of cardiovascular diseases

BACKGROUND

The main objective of this study was to construct and validate a composite socioeconomic status indicator containing material capital, human capital, and social capital (CAPSES scale) and also appropriate it for CVDs in a large population-based study.

METHODS

This cross-sectional study, the Urban HEART-2 project, was conducted in Tehran, Iran, in 2011. A total of 34,116 households covering 118,542 individuals were assessed in this study. A 14-parts questionnaire was completed for all selected households. All the gathered data were based on the participants' self-reports. Literacy, wealth index, expenditure, skill level, and Townsend index were used as SES indexes. CVDs, including Hypertension, Myocardial infarction, and stroke, were considered the main outcomes. A structural equation model (SEM) was used to construct a CAPSES scale and a composition index of SES. Criterion validity and Construct validity were used to assess this scale.

RESULTS

A total of 91,830 subjects consisting of 33,884 (49%) men were included in this analysis. The mean age of the participants was 41.5 ± 11.37 years. Among the assessed participants, 5904(6.4%) reported hypertension, 1507(1.6%) myocardial infarction, and 407(0.4%) strokes. The overall weighted prevalence of self-reported cardiovascular events (hypertension, stroke, and MI) was 8.03% (95%CI: 7.8-8.2). Inverse associations were seen between the CAPSES scale and its domains with CVDs, adjusted for sex, age, BMI, smoking, and diabetes by a multiple logistic regression model.

CONCLUSION

The CAPSES scale was significantly associated with stroke and hypertension. Our findings showed that the CAPSES index could be useful for public health research.

Effect of Social Environments on Cardiovascular Disease in the United States

Background This study aims to examine the effect of time-variant perceived neighborhood social cohesion, perceived neighborhood physical disorder, and local crime on cardiovascular disease (CVD) incidence from 2006 through 2016. Methods and Results We obtained data from the Health & Retirement Study. Respondents aged ≥50 years and with no recorded history of CVD until 2006 (N=8826) were included and followed for 10 years. Cox proportional hazards models were estimated with CVD incidence as an outcome variable and time-variant social environment factors (perceived neighborhood social cohesion, perceived neighborhood physical disorder, and local crime) as exposures, after controlling for sociodemographic factors and CVD-related risk/protective factors. Our results showed that perceived neighborhood social cohesion was associated with CVD among Black respondents, but not Hispanic and White respondents. Perceived neighborhood physical disorder and local crime rates were not associated with CVD incidence across all racial and ethnic groups. Conclusions The results demonstrate that perceptions of favorable social environments need to be considered to reduce CVD risk among Black adults. Further research is needed to identify different pathways through which living in favorable social environments benefits cardiovascular health by racial and ethnic groups.

Neighbourhood Socioeconomic Processes and Dynamics and Healthy Ageing: A Scoping Review

Elderly citizens are concentrated in urban areas and are particularly affected by the immediate residential environment. Cities are unequal and segregated places, where there is an intensification of urban change processes such as gentrification and displacement. We aimed to understand how neighbourhood socioeconomic processes and dynamics influence older people’s health. Three bibliographic databases—PubMed, Web of Science, and Scopus—were used to identify evidence of the influence of neighbourhood socioeconomic deprivation, socio-spatial segregation, urban renewal, and gentrification on healthy ageing. We followed the method of Arksey and O’Malley, Levac and colleagues, the Joanna Briggs Institute, and the PRISMA-ScR. The included studies (n = 122) were published between 2001 and 2021. Most evaluated neighbourhood deprivation (n = 114), followed by gentrification (n = 5), segregation (n = 2), and urban renewal (n = 1). Overall, older people living in deprived neighbourhoods had worse healthy ageing outcomes than their counterparts living in more advantaged neighbourhoods. Older adults pointed out more negative comments than positive ones for gentrification and urban renewal. As to segregation, the direction of the association was not entirely clear. In conclusion, the literature has not extensively analysed the effects of segregation, gentrification, and urban renewal on healthy ageing, and more quantitative and longitudinal studies should be conducted to draw better inferences.

Neighborhood socioeconomic deprivation and psychiatric medication purchases. Different neighborhood delineations, different results? A nationwide register-based multilevel study

Previous studies of neighborhood socioeconomic deprivation and mental health treatment have shown mixed results. Multiple reviews have highlighted that the definition and measurement of neighborhoods can lead to different results, providing one explanation for these mixed findings. This study compares the use of micro-areas created using an automated redistricting algorithm and divided by physical barriers with the use of two administrative units, Danish parishes and postal codes. The geographical data are linked to Danish register data of the Danish population from age 16 in 2017, N = 4,347,001, to measure the association between the purchase of psychiatric medication and neighborhood socioeconomic deprivation using logistic multilevel models. Neighborhood socioeconomic deprivation is associated with a slightly increased probability of redeeming prescriptions for psychiatric medication after controlling for individual sociodemographic composition. However, this association was present only for micro-areas and not for parishes or postal codes. The results indicate that neighborhood effects on psychiatric medication purchases are affected by the neighborhood delineations used and that future studies should carefully consider how neighborhoods are defined and measured.

Neighborhood Ethnic Composition and Self-rated Health Among Chinese and Vietnamese American Immigrants

Immigrants tend to live in areas with higher co-ethnic density, and the effect of neighborhood ethnic composition could be particularly salient for health. This study explored associations between neighborhood ethnic composition and self-rated health among Asian immigrants. We analyzed data collected at baseline from 670 Chinese and Vietnamese immigrants enrolled in a lifestyle intervention trial. Residential addresses were geocoded and combined with neighborhood socio-demographic profiles based on census data. We used generalized estimating equations to examine neighborhood ethnic composition and self-rated health. Independent of individual-level factors, living in neighborhoods more densely populated by whites was associated with poor/fair self-rated health. Neighborhood household income and density of participants' own ethnic group were not associated with poor/fair self-rated health. More research is warranted to disentangle reasons why Chinese and Vietnamese immigrants living in white-concentrated neighborhoods reported poorer self-rated health, including investigating effects of discrimination, relative deprivation, and availability of social resources.

Neighborhood Socioeconomic Status and Stroke Incidence: A Systematic Review

OBJECTIVE

To summarize overall patterns of the impact of neighborhood socioeconomic status (nSES) on stroke incidence and uncover potential gaps in the literature, we conducted a systematic review of studies examining the association between nSES and stroke incidence, independent of individual SES.

METHODS

Four electronic databases and reference lists of included articles were searched, and corresponding authors were contacted to locate additional studies. A keyword search strategy included the 3 broad domains of neighborhood, SES, and stroke. Eight studies met our inclusion criteria (e.g., nSES as an exposure, individual SES as a covariate, and stroke incidence as an outcome). We coded study methodology and findings across the 8 studies.

RESULTS

The results provide evidence for the overall nSES and stroke incidence association in Sweden and Japan, but not within the United States. Findings were inconclusive when examining the nSES-stroke incidence association stratified by race. We found evidence for the mediating role of biological factors in the nSES-stroke incidence association.

CONCLUSIONS

Higher neighborhood disadvantage was found to be associated with higher stroke risk, but it was not significant in all the studies. The relationship between nSES and stroke risk within different racial groups in the United States was inconclusive. Inconsistencies may be driven by differences in covariate adjustment (e.g., individual-level sociodemographic characteristics and neighborhood-level racial composition). Additional research is needed to investigate potential intermediate and modifiable factors of the association between nSES and stroke incidence, which could serve as intervention points.

Comparisons of individual- and area-level socioeconomic status as proxies for individual-level measures: evidence from the Mortality Disparities in American Communities study

Background

Area-level measures are often used to approximate socioeconomic status (SES) when individual-level data are not available. However, no national studies have examined the validity of these measures in approximating individual-level SES.

Methods

Data came from ~ 3,471,000 participants in the Mortality Disparities in American Communities study, which links data from 2008 American Community Survey to National Death Index (through 2015). We calculated correlations, specificity, sensitivity, and odds ratios to summarize the concordance between individual-, census tract-, and county-level SES indicators (e.g., household income, college degree, unemployment). We estimated the association between each SES measure and mortality to illustrate the implications of misclassification for estimates of the SES-mortality association.

Results

Participants with high individual-level SES were more likely than other participants to live in high-SES areas. For example, individuals with high household incomes were more likely to live in census tracts (r = 0.232; odds ratio [OR] = 2.284) or counties (r = 0.157; OR = 1.325) whose median household income was above the US median. Across indicators, mortality was higher among low-SES groups (all p < .0001). Compared to county-level, census tract-level measures more closely approximated individual-level associations with mortality.

Conclusions

Moderate agreement emerged among binary indicators of SES across individual, census tract, and county levels, with increased precision for census tract compared to county measures when approximating individual-level values. When area level measures were used as proxies for individual SES, the SES-mortality associations were systematically underestimated. Studies using area-level SES proxies should use caution when selecting, analyzing, and interpreting associations with health outcomes.

Stroke Incidence and Outcome Disparity in Rural Regions of Southern West Virginia

INTRODUCTION

West Virginia has the highest incidence of obesity, smoking, and diabetes within the United States, placing its population at higher risk of stroke. In addition to these endemic risk factors, Appalachia faces various socioeconomic and health care access challenges that could negatively impact stroke incidence and outcomes. At present, there are limited data regarding geographic variables on stroke outcomes in rural Appalachia. We set out to quantify Appalachian geographic patterns of stroke incidence and outcomes.

METHODS

This is a retrospective analysis of all patients hospitalized with a diagnosis of stroke in West Virginia's largest tertiary hospital. During the study (2000-2018), 14,488 patients were analyzed, with an emphasis on those who died from stroke (n = 1022). We first used institutional ICD-9/10 data alongside demographics information and chart reviews to evaluate disease patterns while also exploring emerging hot spot pattern changes over time; we then exploited an emerging time series analysis using temporal trends to assess differing instances of stroke occurrence regionally with hot spots defined as higher than expected incidences of stroke and stroke death.

RESULTS

Data analysis revealed several hot spots of increasing stroke and mortality rates, many of which achieved statistically significant variance compared to expected norms (P = 0.001). Moreover, this study revealed high-risk zones in rural West Virginia wherein the incidence and mortality rates of stroke are suggestively higher and less resistance to economic change than urban centers.

CONCLUSIONS

Stroke incidence and mortality were found to be higher than expected in many areas of rural West Virginia. The higher stroke risk populations correlate with area that may be impacted by socioeconomic factors and limited access to primary care. These high-risk areas may therefore benefit from investments in infrastructure, patient education, and unrestricted primary care.

Disentangling individual and neighborhood differences in the intention to quit smoking in Asian American male smokers

Numerous studies have assessed individual-level factors associated with intention to quit smoking. However, fewer studies have assessed how neighborhood and built environment also contribute towards individual-level behavior. We used baseline data of 340 Chinese and Vietnamese male daily smokers from August 2015 to November 2017 living in the San Francisco Bay Area, who enrolled in a lifestyle intervention trial. The outcome variable was intention to quit in 30 days. To understand the role of contextual factors participants' residential addresses were geocoded, and neighborhood median income, ethnic composition, and tobacco retail density were computed. Individual level analysis suggested that Vietnamese American men had greater intention to quit smoking (OR = 2.90 CI = 1.59, 5.26) in comparison to Chinese Americans. However, after adding neighborhood level factors to the model, no ethnic group difference was observed. Neighborhood household median income (OR = 0.74, CI = 0.64, 0.86) and tobacco retail counts (OR = 0.79, CI = 0.67, 0.94) were negatively associated with intention to quit. Years lived in the U.S. was the only individual level factor associated with intention to quit. By comparing two Asian American groups that live in heterogeneous neighborhoods, we identify key environmental and policy drivers that are associated with quit intention. Future studies aimed at influencing individual-level behavior should take into consideration the neighborhood context and built environment characteristics.

Living in High-SES Neighborhoods Is Protective against Obesity among Higher-Income Children but Not Low-Income Children: Results from the Healthy Communities Study

Numerous studies have focused on the role of neighborhood socioeconomic status in childhood obesity and physical activity, but few studies have examined the effect of neighborhood socioeconomic changes over time and the interaction between family and neighborhood SES on childhood obesity and physical activity. This study measured neighborhood socioeconomic histories between 2000 and 2010 and examined the associations between neighborhood socioeconomic histories and childhood obesity, as well as physical activity. The moderating role of family poverty status was also examined. Using the Healthy Communities Study (2013-2015), we measured obesity indicators (objectively measured body mass index z-score and waist circumference) and a physical activity indicator (self-reported moderate-to-vigorous physical activity) for a cohort of 4114 children. Multilevel linear regression models were used to examine the associations between neighborhood socioeconomic histories between 2000 and 2009-2013 and body-mass index z-score, waist circumference, and moderate-to-vigorous physical activity. Results showed that higher-income children in consistently high socioeconomic neighborhoods had lower measured BMIz and WC and engaged in more moderate-to-vigorous physical activity than higher-income children in consistently low socioeconomic neighborhoods. Additionally, low-income children in consistently moderate socioeconomic neighborhoods reported a lower level of moderate-to-vigorous physical activity than low-income children in consistently low socioeconomic neighborhoods. The findings indicate that considering both family and neighborhood socioeconomic status may help elucidate the underlying differences in childhood obesity and physical activity levels by socioeconomic status.

Obesity and the Built Environment: A Reappraisal

The built environment (BE) has been viewed as an important determinant of health. Numerous studies have linked BE exposure, captured using a variety of methods, to diet quality and to area prevalence of obesity, diabetes, and cardiovascular disease. First-generation studies defined the neighborhood BE as the area around the home. Second-generation studies turned from home-centric to person-centric BE measures, capturing an individual's movements in space and time. Those studies made effective uses of global positioning system tracking devices and mobile phones, sometimes coupled with accelerometers and remote sensors. Activity space metrics explored travel paths, modes, and destinations to assess BE exposure that was both person and context specific. However, as measures of the contextual exposome have become ever more fine-grained and increasingly complex, connections to long-term chronic diseases with complex etiologies, such as obesity, are in danger of being lost. Furthermore, few studies on obesity and the BE have included intermediate energy balance behaviors, such as diet and physical activity, or explored the potential roles of social interactions or psychosocial pathways. Emerging survey-based applications that identify habitual destinations and associated travel patterns may become the third generation of tools to capture health-relevant BE exposures in the long term.

Examining the role of a retail density ordinance in reducing concentration of tobacco retailers

Neighborhood characteristics and the built environment are important determinants in shaping health inequalities. We evaluate the role of a retail density ordinance in reducing concentration of tobacco stores based on neighborhood characteristics and land use pattern in San Francisco. The study evaluated the spatial distribution of tobacco retailers before and after the ordinance to identify geographic pockets where the most significant reduction had occurred. A generalized additive model was applied to assess the association between the location of the closure of tobacco retailer and socio-demographic characteristics and land use pattern. We did not find a meaningful change in the overall concentration of retailers based on neighborhood income and ethnicity but found a significant association based on patterns of land use. Our findings suggest that future polices must account for the differential distribution of retailers based on land use mix to lower concentration in areas where it is needed the most.

Cross-sectional associations between residential environmental exposures and cardiovascular diseases

BACKGROUND

Prior research examining neighbourhood effects on cardiovascular diseases (CVDs) has focused on the impact of neighbourhood socio-economic status or a few selected environmental variables. No studies of cardiovascular disease outcomes have investigated a broad range of urban planning related environmental factors. This is the first study to combine multiple neighbourhood influences in an integrated approach to understanding the association between the built and social environment and CVDs. By modeling multiple neighbourhood level social and environmental variables simultaneously, the study improved the estimation of effects by accounting for potential contextual confounders.

METHODS

Data were collected using a cross-sectional survey (n = 2411) across 87 census tracts (CT) in Toronto, Canada, and commercial and census data were accessed to characterize the residential environment. Multilevel regressions were used to estimate the associations of neighbourhood factors on the risk of CVD.

RESULTS

Exposure to violent crimes, environmental noise, and proximity to a major road were independently associated with increased odds of CVDs (p < 0.05) in the fully adjusted model. While reduced access to food stores, parks/recreation, and increased access to fast food restaurants were associated with increased odds of CVDs in partially adjusted models (p < 0.05), these associations were fully attenuated after adjusting for BMI and physical activity. Housing disrepair was not associated with CVD risk.

CONCLUSIONS

These findings illustrate the importance of measuring and modeling a broad range of neighborhood factors--exposure to violent crimes, environmental noise, and traffic, and access to food stores, fast food, parks/recreation areas--to identify specific stressors in relation to adverse health outcomes. Further research to investigate the temporal order of events is needed to better understand the direction of causation for the observed associations.

Small and large size for gestational age and neighborhood deprivation measured within increasing proximity to homes

Neighborhood deprivation is consistently associated with greater risk of low birthweight. However, large birth size is increasingly relevant but overlooked in neighborhood health research, and proximity within which neighborhood deprivation may affect birth outcomes is unknown. We estimated race/ethnic-specific effects of neighborhood deprivation index (NDI) within 1, 3, 5, and 8km buffers around Oregon Pregnancy Risk Assessment Monitoring System (n=3716; 2004-2007) respondents׳ homes on small and large for gestational age (SGA, LGA). NDI was positively associated with LGA and SGA in most race/ethnic groups. The results varied little across the four buffer sizes.

Individual education, area income, and mortality and recurrence of myocardial infarction in a Medicare cohort: the National Longitudinal Mortality Study

Background

The Medicare program provides universal access to hospital care for the elderly; however, mortality disparities may still persist in this population. The association of individual education and area income with survival and recurrence post Myocardial Infarction (MI) was assessed in a national sample.

Methods

Individual level education from the National Longitudinal Mortality Study was linked to Medicare and National Death Index records over the period of 1991-2001 to test the association of individual education and zip code tabulation area median income with survival and recurrence post-MI. Survival was partitioned into 3 periods: in-hospital, discharge to 1 year, and 1 year to 5 years and recurrence was partitioned into two periods: 28 day to 1 year, and 1 year to 5 years.

Results

First MIs were found in 8,043 women and 7,929 men. In women and men 66-79 years of age, less than a high school education compared with a college degree or more was associated with 1-5 year mortality in both women (HRR 1.61, 95% confidence interval 1.03-2.50) and men (HRR 1.37, 1.06-1.76). Education was also associated with 1-5 year recurrence in men (HRR 1.68, 1.18-2.41, < High School compared with college degree or more), but not women. Across the spectrum of survival and recurrence periods median zip code level income was inconsistently associated with outcomes. Associations were limited to discharge-1 year survival (RR lowest versus highest quintile 1.31, 95% confidence interval 1.03-1.67) and 28 day-1 year recurrence (RR lowest versus highest quintile 1.72, 95% confidence interval 1.14-2.57) in older men.

Conclusions

Despite the Medicare entitlement program, disparities related to individual socioeconomic status remain. Additional research is needed to elucidate the barriers and mechanisms to eliminating health disparities among the elderly.

Aesthetic amenities and safety hazards associated with walking and bicycling for transportation in New York City

BACKGROUND

One strategy to address health problems related to insufficient physical activity is to examine modifiable neighborhood characteristics associated with active transportation.

PURPOSE

The aim of this study is to evaluate whether neighborhoods with more aesthetic amenities (sidewalk cafés, street trees, and clean sidewalks) and fewer safety hazards (pedestrian-auto fatalities and homicides) are associated with active transportation.

METHODS

The 2003 Community Health Survey in New York City, which asked about active transportation (walking or bicycling >10 blocks) in the past 30 days, was linked to ZIP-code population census and built environment characteristics. Adjusted associations were estimated for dichotomous (any active transportation versus none) and continuous (trip frequency) active transportation outcomes.

RESULTS

Among 8,034 adults, those living near sidewalk cafés were 10 % more likely to report active transportation (p = 0.01). Homicide rate was associated with less frequent active transportation among those reporting any active transportation (p = 0.002).

CONCLUSIONS

Investments in aesthetic amenities or homicide prevention may help to promote active transportation.

Air pollution and individual and neighborhood socioeconomic status: evidence from the Multi-Ethnic Study of Atherosclerosis (MESA)

BACKGROUND

Although research has shown that low socioeconomic status (SES) and minority communities have higher exposure to air pollution, few studies have simultaneously investigated the associations of individual and neighborhood SES with pollutants across multiple sites.

OBJECTIVES

We characterized the distribution of ambient air pollution by both individual and neighborhood SES using spatial regression methods.

METHODS

The study population comprised 6,140 participants from the Multi-Ethnic Study of Atherosclerosis (MESA). Year 2000 annual average ambient PM2.5 and NOx concentrations were calculated for each study participant's home address at baseline examination. We investigated individual and neighborhood (2000 U.S. Census tract level) SES measures corresponding to the domains of income, wealth, education, and occupation. We used a spatial intrinsic conditional autoregressive model for multivariable analysis and examined pooled and metropolitan area-specific models.

RESULTS

A 1-unit increase in the z-score for family income was associated with 0.03-μg/m3 lower PM2.5 (95% CI: -0.05, -0.01) and 0.93% lower NOx (95% CI: -1.33, -0.53) after adjustment for covariates. A 1-SD-unit increase in the neighborhood's percentage of persons with at least a high school degree was associated with 0.47-μg/m3 lower mean PM2.5 (95% CI: -0.55, -0.40) and 9.61% lower NOx (95% CI: -10.85, -8.37). Metropolitan area-specific results exhibited considerable heterogeneity. For example, in New York, high-SES neighborhoods were associated with higher concentrations of pollution.

CONCLUSIONS

We found statistically significant associations of SES measures with predicted air pollutant concentrations, demonstrating the importance of accounting for neighborhood- and individual-level SES in air pollution health effects research.

Air pollution and individual and neighborhood socioeconomic status: evidence from the Multi-Ethnic Study of Atherosclerosis (MESA)

BACKGROUND

Although research has shown that low socioeconomic status (SES) and minority communities have higher exposure to air pollution, few studies have simultaneously investigated the associations of individual and neighborhood SES with pollutants across multiple sites.

OBJECTIVES

We characterized the distribution of ambient air pollution by both individual and neighborhood SES using spatial regression methods.

METHODS

The study population comprised 6,140 participants from the Multi-Ethnic Study of Atherosclerosis (MESA). Year 2000 annual average ambient PM2.5 and NOx concentrations were calculated for each study participant's home address at baseline examination. We investigated individual and neighborhood (2000 U.S. Census tract level) SES measures corresponding to the domains of income, wealth, education, and occupation. We used a spatial intrinsic conditional autoregressive model for multivariable analysis and examined pooled and metropolitan area-specific models.

RESULTS

A 1-unit increase in the z-score for family income was associated with 0.03-μg/m3 lower PM2.5 (95% CI: -0.05, -0.01) and 0.93% lower NOx (95% CI: -1.33, -0.53) after adjustment for covariates. A 1-SD-unit increase in the neighborhood's percentage of persons with at least a high school degree was associated with 0.47-μg/m3 lower mean PM2.5 (95% CI: -0.55, -0.40) and 9.61% lower NOx (95% CI: -10.85, -8.37). Metropolitan area-specific results exhibited considerable heterogeneity. For example, in New York, high-SES neighborhoods were associated with higher concentrations of pollution.

CONCLUSIONS

We found statistically significant associations of SES measures with predicted air pollutant concentrations, demonstrating the importance of accounting for neighborhood- and individual-level SES in air pollution health effects research.

Neighbourhood, Route and Workplace-Related Environmental Characteristics Predict Adults' Mode of Travel to Work

Objective

Commuting provides opportunities for regular physical activity which can reduce the risk of chronic disease. Commuters' mode of travel may be shaped by their environment, but understanding of which specific environmental characteristics are most important and might form targets for intervention is limited. This study investigated associations between mode choice and a range of objectively assessed environmental characteristics.

Methods

Participants in the Commuting and Health in Cambridge study reported where they lived and worked, their usual mode of travel to work and a variety of socio-demographic characteristics. Using geographic information system (GIS) software, 30 exposure variables were produced capturing characteristics of areas around participants' homes and workplaces and their shortest modelled routes to work. Associations between usual mode of travel to work and personal and environmental characteristics were investigated using multinomial logistic regression.

Results

Of the 1124 respondents, 50% reported cycling or walking as their usual mode of travel to work. In adjusted analyses, home-work distance was strongly associated with mode choice, particularly for walking. Lower odds of walking or cycling rather than driving were associated with a less frequent bus service (highest versus lowest tertile: walking OR 0.61 [95% CI 0.20–1.85]; cycling OR 0.43 [95% CI 0.23–0.83]), low street connectivity (OR 0.22, [0.07–0.67]; OR 0.48 [0.26–0.90]) and free car parking at work (OR 0.24 [0.10–0.59]; OR 0.55 [0.32–0.95]). Participants were less likely to cycle if they had access to fewer destinations (leisure facilities, shops and schools) close to work (OR 0.36 [0.21–0.62]) and a railway station further from home (OR 0.53 [0.30–0.93]). Covariates strongly predicted travel mode (pseudo r-squared 0.74).

Conclusions

Potentially modifiable environmental characteristics, including workplace car parking, street connectivity and access to public transport, are associated with travel mode choice, and could be addressed as part of transport policy and infrastructural interventions to promote active commuting.

Body mass index, safety hazards, and neighborhood attractiveness

BACKGROUND

Neighborhood attractiveness and safety may encourage physical activity and help individuals maintain a healthy weight. However, these neighborhood characteristics may not be equally relevant to health across all settings and population subgroups.

PURPOSE

To evaluate whether potentially attractive neighborhood features are associated with lower BMI, whether safety hazards are associated with higher BMI, and whether environment-environment interactions are present such that associations for a particular characteristic are stronger in an otherwise supportive environment.

METHODS

Survey data and measured height and weight were collected from a convenience sample of 13,102 adult New York City (NYC) residents in 2000-2002; data analyses were completed 2008-2012. Built-environment measures based on municipal GIS data sources were constructed within 1-km network buffers to assess walkable urban form (density, land-use mix, transit access); attractiveness (sidewalk cafés, landmark buildings, street trees, street cleanliness); and safety (homicide rate, pedestrian-auto collision and fatality rate). Generalized linear models with cluster-robust SEs controlled for individual and area-based sociodemographic characteristics.

RESULTS

The presence of sidewalk cafés, density of landmark buildings, and density of street trees were associated with lower BMI, whereas the proportion of streets rated as clean was associated with higher BMI. Interactions were observed for sidewalk cafés with neighborhood poverty, for street-tree density with walkability, and for street cleanliness with safety. Safety hazard indicators were not independently associated with BMI.

CONCLUSIONS

Potentially attractive community and natural features were associated with lower BMI among adults in NYC, and there was some evidence of effect modification.

Measures of the consumer food store environment: a systematic review of the evidence 2000-2011

Description of the consumer food environment has proliferated in publication. However, there has been a lack of systematic reviews focusing on how the consumer food environment is associated with the following: (1) neighborhood characteristics; (2) food prices; (3) dietary patterns; and (4) weight status. We conducted a systematic review of primary, quantitative, observational studies, published in English that conducted an audit of the consumer food environment. The literature search included electronic, hand searches, and peer-reviewed from 2000 to 2011. Fifty six papers met the inclusion criteria. Six studies reported stores in low income neighborhoods or high minority neighborhoods had less availability of healthy food. While, four studies found there was no difference in availability between neighborhoods. The results were also inconsistent for differences in food prices, dietary patterns, and weight status. This systematic review uncovered several key findings. (1) Systematic measurement of determining availability of food within stores and store types is needed; (2) Context is relevant for understanding the complexities of the consumer food environment; (3) Interventions and longitudinal studies addressing purchasing habits, diet, and obesity outcomes are needed; and (4) Influences of price and marketing that may be linked with why people purchase certain items.

Use of community-level data in the National Children's Study to establish the representativeness of segment selection in the Queens Vanguard Site

BACKGROUND

The WHO Multiple Exposures Multiple Effects (MEME) framework identifies community contextual variables as central to the study of childhood health. Here we identify multiple domains of neighborhood context, and key variables describing the dimensions of these domains, for use in the National Children's Study (NCS) site in Queens. We test whether the neighborhoods selected for NCS recruitment, are representative of the whole of Queens County, and whether there is sufficient variability across neighborhoods for meaningful studies of contextual variables.

METHODS

Nine domains (demographic, socioeconomic, households, birth rated, transit, playground/greenspace, safety and social disorder, land use, and pollution sources) and 53 indicator measures of the domains were identified. Geographic information systems were used to create community-level indicators for US Census tracts containing the 18 study neighborhoods in Queens selected for recruitment, using US Census, New York City Vital Statistics, and other sources of community-level information. Mean and inter-quartile range values for each indicator were compared for Tracts in recruitment and non-recruitment neighborhoods in Queens.

RESULTS

Across the nine domains, except in a very few instances, the NCS segment-containing tracts (N=43) were not statistically different from those 597 populated tracts in Queens not containing portions of NCS segments; variability in most indicators was comparable in tracts containing and not containing segments.

CONCLUSIONS

In a diverse urban setting, the NCS segment selection process succeeded in identifying recruitment areas that are, as a whole, representative of Queens County, for a broad range of community-level variables.

Obesogenic environments in youth: concepts and methods from a longitudinal national sample

To effectively prevent and reduce childhood obesity through healthy community design, it is essential to understand which neighborhood environment features influence weight gain in various age groups. However, most neighborhood environment research is cross-sectional, focuses on adults, and is often carried out in small, nongeneralizable geographic areas. Thus, there is a great need for longitudinal neighborhood environment research in diverse populations across the life cycle. This paper describes (1) insights and challenges of longitudinal neighborhood environment research and (2) advancements and remaining gaps in measurement and study design that examine individuals and neighborhoods within the context of the broader community. Literature-based research and findings from the Obesity and Neighborhood Environment Database (ONEdata), a unique longitudinal GIS that is spatially and temporally linked to data in the National Longitudinal Study of Adolescent Health (N=20,745), provide examples of current limitations in this area of research. Findings suggest a need for longitudinal methodologic advancements to better control for dynamic sources of bias, investigate and capture appropriate temporal frameworks, and address complex residential location processes within families. Development of improved neighborhood environment measures that capture relevant geographic areas within complex communities and investigation of differences across urbanicity and sociodemographic composition are needed. Further longitudinal research is needed to identify, refine, and evaluate national and local policies to most effectively reduce childhood obesity.

Neighborhood disparities in stroke and myocardial infarction mortality: a GIS and spatial scan statistics approach

BACKGROUND

Stroke and myocardial infarction (MI) are serious public health burdens in the US. These burdens vary by geographic location with the highest mortality risks reported in the southeastern US. While these disparities have been investigated at state and county levels, little is known regarding disparities in risk at lower levels of geography, such as neighborhoods. Therefore, the objective of this study was to investigate spatial patterns of stroke and MI mortality risks in the East Tennessee Appalachian Region so as to identify neighborhoods with the highest risks.

METHODS

Stroke and MI mortality data for the period 1999-2007, obtained free of charge upon request from the Tennessee Department of Health, were aggregated to the census tract (neighborhood) level. Mortality risks were age-standardized by the direct method. To adjust for spatial autocorrelation, population heterogeneity, and variance instability, standardized risks were smoothed using Spatial Empirical Bayesian technique. Spatial clusters of high risks were identified using spatial scan statistics, with a discrete Poisson model adjusted for age and using a 5% scanning window. Significance testing was performed using 999 Monte Carlo permutations. Logistic models were used to investigate neighborhood level socioeconomic and demographic predictors of the identified spatial clusters.

RESULTS

There were 3,824 stroke deaths and 5,018 MI deaths. Neighborhoods with significantly high mortality risks were identified. Annual stroke mortality risks ranged from 0 to 182 per 100,000 population (median: 55.6), while annual MI mortality risks ranged from 0 to 243 per 100,000 population (median: 65.5). Stroke and MI mortality risks exceeded the state risks of 67.5 and 85.5 in 28% and 32% of the neighborhoods, respectively. Six and ten significant (p < 0.001) spatial clusters of high risk of stroke and MI mortality were identified, respectively. Neighborhoods belonging to high risk clusters of stroke and MI mortality tended to have high proportions of the population with low education attainment.

CONCLUSIONS

These methods for identifying disparities in mortality risks across neighborhoods are useful for identifying high risk communities and for guiding population health programs aimed at addressing health disparities and improving population health.

Identifying unique neighborhood characteristics to guide health planning for stroke and heart attack: fuzzy cluster and discriminant analyses approaches

BACKGROUND

Socioeconomic, demographic, and geographic factors are known determinants of stroke and myocardial infarction (MI) risk. Clustering of these factors in neighborhoods needs to be taken into consideration during planning, prioritization and implementation of health programs intended to reduce disparities. Given the complex and multidimensional nature of these factors, multivariate methods are needed to identify neighborhood clusters of these determinants so as to better understand the unique neighborhood profiles. This information is critical for evidence-based health planning and service provision. Therefore, this study used a robust multivariate approach to classify neighborhoods and identify their socio-demographic characteristics so as to provide information for evidence-based neighborhood health planning for stroke and MI.

METHODS AND FINDINGS

The study was performed in East Tennessee Appalachia, an area with one of the highest stroke and MI risks in USA. Robust principal component analysis was performed on neighborhood (census tract) socioeconomic and demographic characteristics, obtained from the US Census, to reduce the dimensionality and influence of outliers in the data. Fuzzy cluster analysis was used to classify neighborhoods into Peer Neighborhoods (PNs) based on their socioeconomic and demographic characteristics. Nearest neighbor discriminant analysis and decision trees were used to validate PNs and determine the characteristics important for discrimination. Stroke and MI mortality risks were compared across PNs. Four distinct PNs were identified and their unique characteristics and potential health needs described. The highest risk of stroke and MI mortality tended to occur in less affluent PNs located in urban areas, while the suburban most affluent PNs had the lowest risk.

CONCLUSIONS

Implementation of this multivariate strategy provides health planners useful information to better understand and effectively plan for the unique neighborhood health needs and is important in guiding resource allocation, service provision, and policy decisions to address neighborhood health disparities and improve population health.

A neighborhood wealth metric for use in health studies

BACKGROUND

Measures of neighborhood deprivation used in health research are typically based on conventional area-based SES.

PURPOSE

The aim of this study is to examine new data and measures of SES for use in health research. Specifically, assessed property values are introduced as a new individual-level metric of wealth and tested for their ability to substitute for conventional area-based SES as measures of neighborhood deprivation.

METHODS

The analysis was conducted in 2010 using data from 1922 participants in the 2008-2009 survey of the Seattle Obesity Study (SOS). It compared the relative strength of the association between the individual-level neighborhood wealth metric (assessed property values) and area-level SES measures (including education, income, and percentage above poverty as single variables, and as the composite Singh index) on the binary outcome fair/poor general health status. Analyses were adjusted for gender, categoric age, race, employment status, home ownership, and household income.

RESULTS

The neighborhood wealth measure was more predictive of fair/poor health status than area-level SES measures, calculated either as single variables or as indices (lower DIC measures for all models). The odds of having a fair/poor health status decreased by 0.85 (95% CI=0.77, 0.93) per $50,000 increase in neighborhood property values after adjusting for individual-level SES measures.

CONCLUSIONS

The proposed individual-level metric of neighborhood wealth, if replicated in other areas, could replace area-based SES measures, thus simplifying analyses of contextual effects on health.

What neighborhood area captures built environment features related to adolescent physical activity?

In research investigating built environment (BE) influences on physical activity (PA), inconsistent neighborhood definitions may contribute to inconsistent findings. Using data from the National Longitudinal Study of Adolescent Health (Wave I; 1994-95), we compared associations between moderate-vigorous PA (MVPA) and PA facility counts and street connectivity measures (intersection density and link:node ratio) within 1, 3, 5, and 8.05 km of each respondent's residence (Euclidean neighborhood buffers). BE-MVPA associations varied by BE characteristic, urbanicity, and sex. PA facilities within 3 km buffers and intersection density within 1 km buffers exhibited the most consistent associations with MVPA. Policy recommendations and corresponding research should address potential differences in relevant neighborhood areas across environment feature and population subgroup.

The effects of rurality on mental and physical health

The effects of rurality on physical and mental health are examined in analyses of a national dataset, the Community Tracking Survey, 2000-2001, that includes individual level observations from household interviews. We merge it with county level data reflecting community resources and use econometric methods to analyze this multi-level data. The statistical analysis of the impact of the choice of definition on outcomes and on the estimates and significance of explanatory variables in the model is presented using modern econometric methods, and differences in results for mental health and physical health are evaluated.

Chlorpyrifos exposure and urban residential environment characteristics as determinants of early childhood neurodevelopment

OBJECTIVES

We evaluated whether neighborhood characteristics correlated with early neurodevelopment and whether these characteristics confounded the previously reported association between exposure to chlorpyrifos (an organophosphate insecticide) and neurodevelopment.

METHODS

We obtained prenatal addresses, chlorpyrifos exposure data, and 36-month Psychomotor Development Index (PDI) and Mental Development Index (MDI) scores for a birth cohort in New York City (born 1998-2002). We used data from the 2000 US Census to estimate measures of physical infrastructure, socioeconomic status, crowding, demographic composition, and linguistic isolation for 1-kilometer network areas around each child's prenatal address. Generalized estimating equations were adjusted for demographics, maternal education and IQ, prenatal exposure to tobacco smoke, caretaking environment quality, and building dilapidation.

RESULTS

Of 266 children included as participants, 47% were male, 59% were Dominican, and 41% were African American. For each standard deviation higher in neighborhood percent poverty, the PDI score was 2.6 points lower (95% confidence interval [CI] = -3.7, -1.5), and the MDI score was 1.7 points lower (95% CI = -2.6, -0.8). Neighborhood-level confounding of the chlorpyrifos-neurodevelopment association was not apparent.

CONCLUSIONS

Neighborhood context and chlorpyrifos exposure were independently associated with neurodevelopment, thus providing distinct opportunities for health promotion.

Risky sexual behaviors and sexually transmitted diseases: a comparison study of cocaine-dependent individuals in treatment versus a community-matched sample

Cocaine users routinely engage in high-risk sexual behaviors that place them at an elevated risk of contracting HIV and other blood-borne infections. The purpose of the present study was to compare trading sex for drugs and/or money, having 10 or more sexual partners in 1 year, and sexually transmitted diseases (STDs) of cocaine-dependent individuals in treatment for their dependence across race and gender and against participants who live in their community. Cocaine-dependent individuals (n = 459) were identified through nine publicly and privately funded inpatient and outpatient chemical dependency treatment centers in the St. Louis area during 2001-2006. Community-based participants (n = 459) were matched to cocaine-dependent participants on age, ethnicity, gender, and zip code of residence. Mean age of the sample was 36 years old, 50% were Caucasians, 50% were African American, and 47% were male. Nearly half of cocaine-dependent participants in treatment had traded sex for drugs and/or money and over one-third had more than 10 sexual partners in 1 year with a risk concentrated among African Americans even after controlling for income and educational attainment. Participants recruited from the community with some exposure to cocaine reported similar rates of high risk sexual behaviors as the cocaine dependent subjects from treatment settings. It is important for clinicians to recognize that once released from treatment, cocaine-dependent individuals may be returning to high-risk environments where sexual risk behaviors are occurring in the context of cocaine use.

Individual and neighborhood socioeconomic status characteristics and prevalence of metabolic syndrome: the Atherosclerosis Risk in Communities (ARIC) Study

OBJECTIVE

The objective of this study was to examine the association of individual socioeconomic status (iSES) and neighborhood SES (nSES) on the prevalence of metabolic syndrome (MetS) in the Atherosclerosis Risk in Communities Study (1987-1999).

METHODS

Participants included 2932 black and 9777 white men and women aged 45 to 64 years without diabetes at baseline. Total combined family income for the past 12 months and six census tract socioeconomic measures combined into a composite index were used to quantify iSES and nSES, respectively. Poisson regression was used to assess associations of the joint contribution of iSES and nSES on the MetS, stratified by gender and race and adjusting for multiple covariates. For analyses that included nSES, hierarchical modeling techniques were used.

RESULTS

Using 2005 Third Report of the Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults criteria, MetS was identified in 40% of black women, 30% of white women, 28% of black men, and 35% of white men. Among black and white men, there was no association between MetS and iSES or nSES. In contrast, after adjustment for risk factors, black and white women with low (L)-iSES and medium (M)-iSES were more likely to have MetS than those with high (H)-iSES. Similar but weaker patterns were noted for L-nSES and M-nSES.

CONCLUSIONS

In summary, both iSES and nSES were independently associated with an increased prevalence of MetS among women but not men. Efforts aimed at understanding the causes of these gender differences may offer insight into avenues for reducing the prevalence of the MetS and its chronic disease sequelae.

Using built environment characteristics to predict walking for exercise

Background

Environments conducive to walking may help people avoid sedentary lifestyles and associated diseases. Recent studies developed walkability models combining several built environment characteristics to optimally predict walking. Developing and testing such models with the same data could lead to overestimating one's ability to predict walking in an independent sample of the population. More accurate estimates of model fit can be obtained by splitting a single study population into training and validation sets (holdout approach) or through developing and evaluating models in different populations. We used these two approaches to test whether built environment characteristics near the home predict walking for exercise. Study participants lived in western Washington State and were adult members of a health maintenance organization. The physical activity data used in this study were collected by telephone interview and were selected for their relevance to cardiovascular disease. In order to limit confounding by prior health conditions, the sample was restricted to participants in good self-reported health and without a documented history of cardiovascular disease.

Results

For 1,608 participants meeting the inclusion criteria, the mean age was 64 years, 90 percent were white, 37 percent had a college degree, and 62 percent of participants reported that they walked for exercise. Single built environment characteristics, such as residential density or connectivity, did not significantly predict walking for exercise. Regression models using multiple built environment characteristics to predict walking were not successful at predicting walking for exercise in an independent population sample. In the validation set, none of the logistic models had a C-statistic confidence interval excluding the null value of 0.5, and none of the linear models explained more than one percent of the variance in time spent walking for exercise. We did not detect significant differences in walking for exercise among census areas or postal codes, which were used as proxies for neighborhoods.

Conclusion

None of the built environment characteristics significantly predicted walking for exercise, nor did combinations of these characteristics predict walking for exercise when tested using a holdout approach. These results reflect a lack of neighborhood-level variation in walking for exercise for the population studied.