Community characteristics and mortality: the relative strength of association of different community characteristics

Decennial Ward-Level Influence of Demographic, Farming, and Economic Predictors on All-Cause Mortality

OBJECTIVE

This study has arisen in response to a lack of studies examining how farming affects mortality patterns across areas of Northern Ireland (NI) over a 10-year period. This paper aims to investigate whether agriculturally intensive electoral Wards have higher mortality rates compared to non-agriculturally based Wards, controlling for relevant factors.

METHODS

The population census and farm census information was downloaded from the Northern Ireland Neighbourhood Service (NINIS) website to construct two original mortality-based datasets. Linear regression was used for the analysis.

DESIGN

Decennial Ward-Level Influence of Demographic, Farming, and Economic Predictors on All-Cause Mortality.

SETTING

Five hundred and eighty-two Ward areas of Northern Ireland.

MAIN OUTCOME MEASURE

Mortality risk within Ward areas.

RESULTS

Findings showed larger amounts of natural log of the population, 65 to 100+ year-olds, limiting long-term illnesses, Farming Intensity Scores, residents living alone and full-time workers within Wards were predictive of mortality risk within those Wards.

CONCLUSIONS

This study is the first of its kind in NI to provide evidence for Farming Intensity Scores explaining the variation of mortality rates between areas, in addition to many of the usual predictors.

Assessing the impact of socio-demographics and farming activity on ward-level mortality patterns using farm and population decennial censuses

INTRODUCTION AND OBJECTIVE

Farmers experience a specific set of unique dangers, which increases their risk of mortality compared with any other occupation. This study hypothesised that Northern Ireland's (NIs) agriculturally saturated Wards have a higher risk of mortality compared against non-agriculturally based Wards.

DESIGN

The Population Census and Farm Census information were downloaded from the Northern Ireland Neighbourhood Service (NINIS) online depository to compile three mortality-based data sets (2001, 2011, pooled data sets). Assessing the impact of socio-demographics and farming activity on Ward-level mortality patterns using farm and population decennial censuses. This study analysed all 582 Ward areas of NI, which enclosed the entire populace of the country in 2001 and 2011.

FINDINGS

Path analysis was utilised to examine direct and indirect paths linked with mortality within two census years (2001; 2011), alongside testing pathways for invariance between census years (pooled data set). Ward-level results provided evidence for exogenous variables to mortality operating through three/four endogenous variables via: (i) direct effects (age), (ii) summed indirect effects (age; males; living alone; farming profit; and deprivation) and (iii) total effects (age; males; living alone; and deprivation). Multi-group results cross-validated these cause-and-effect relationships relating to mortality.

DISCUSSION AND CONCLUSION

This study demonstrated that farming intensity scores, farming profits and socio-demographics' influence on mortality risk in a Ward were dependent on the specific social-environmental characteristics within that area. In line with earlier area level research, results support the aggregated interpretation that higher levels of farming activity within a Ward increase the risk of mortality within those Wards of NI. This was an essential study to enable future tailoring of new strategies and upgrading of current policies to bring about significant mortality risk change at local level.

Patients admitted on weekends have higher in-hospital mortality than those admitted on weekdays: Analysis of national inpatient sample

Introduction

Since the 1999 Institute of Medicine report, hospitals have implemented a myriad of measures to protect patients from medical errors. At this point, looking beyond errors may bring additional safety benefits. This study aims to analyze predictors of in-hospital death regardless of underlying diagnoses in an effort to identify additional targets for improvement.

Methods

We performed a retrospective study of hospitalizations from the 2016-2019 National Inpatient Sample (NIS) database. Logistic regression analyses were used to calculate adjusted odds ratios (OR) for variables associated with in-hospital death.

Results

There were 121,026,484 adult hospital discharges in the database. Multivariable analysis showed the following variables were associated with higher in-hospital death: Age (OR, 1.04), Charlson Comorbidity Index (OR, 1.23), male (OR, 1.16), income Q1 (OR, 1.12), income Q2 (OR, 1.07), west region (OR, 1.07), non-elective admission (OR, 2.01), urban hospital location (OR, 1.17), and weekend admission (OR, 1.14). Percentage of deaths for weekend versus weekday admissions was 2.7% versus 2.1%. Fewer procedures (ICD-10-PCS) were done in first 24 hours of weekend admissions when compared to weekday admissions (34.8% vs 46.8%; p<0.001). Only 524,295 in-hospital deaths were expected for weekend admissions but 673,085 were observed.

Conclusion

Weekend hospital admissions were associated with higher adjusted mortality and a lower rate of procedures when compared to weekday admissions. Further studies should be done to further clarify and confirm if additional staffing and procedural availability on weekends could improve hospital outcomes.

The Association Between Social Determinants of Health and Population Health Outcomes: Ecological Analysis

BACKGROUND

With the increased availability of data, a growing number of studies have been conducted to address the impact of social determinants of health (SDOH) factors on population health outcomes. However, such an impact is either examined at the county level or the state level in the United States. The results of analysis at lower administrative levels would be useful for local policy makers to make informed health policy decisions.

OBJECTIVE

This study aimed to investigate the ecological association between SDOH factors and population health outcomes at the census tract level and the city level. The findings of this study can be applied to support local policy makers in efforts to improve population health, enhance the quality of care, and reduce health inequity.

METHODS

This ecological analysis was conducted based on 29,126 census tracts in 499 cities across all 50 states in the United States. These cities were grouped into 5 categories based on their population density and political affiliation. Feature selection was applied to reduce the number of SDOH variables from 148 to 9. A linear mixed-effects model was then applied to account for the fixed effect and random effects of SDOH variables at both the census tract level and the city level.

RESULTS

The finding reveals that all 9 selected SDOH variables had a statistically significant impact on population health outcomes for ≥2 city groups classified by population density and political affiliation; however, the magnitude of the impact varied among the different groups. The results also show that 4 SDOH risk factors, namely, asthma, kidney disease, smoking, and food stamps, significantly affect population health outcomes in all groups (P<.01 or P<.001). The group differences in health outcomes for the 4 factors were further assessed using a predictive margin analysis.

CONCLUSIONS

The analysis reveals that population density and political affiliation are effective delineations for separating how the SDOH affects health outcomes. In addition, different SDOH risk factors have varied effects on health outcomes among different city groups but similar effects within city groups. Our study has 2 policy implications. First, cities in different groups should prioritize different resources for SDOH risk mitigation to maximize health outcomes. Second, cities in the same group can share knowledge and enable more effective SDOH-enabled policy transfers for population health.

A Machine Learning Approach to Support Urgent Stroke Triage Using Administrative Data and Social Determinants of Health at Hospital Presentation: Retrospective Study

BACKGROUND

The key to effective stroke management is timely diagnosis and triage. Machine learning (ML) methods developed to assist in detecting stroke have focused on interpreting detailed clinical data such as clinical notes and diagnostic imaging results. However, such information may not be readily available when patients are initially triaged, particularly in rural and underserved communities.

OBJECTIVE

This study aimed to develop an ML stroke prediction algorithm based on data widely available at the time of patients' hospital presentations and assess the added value of social determinants of health (SDoH) in stroke prediction.

METHODS

We conducted a retrospective study of the emergency department and hospitalization records from 2012 to 2014 from all the acute care hospitals in the state of Florida, merged with the SDoH data from the American Community Survey. A case-control design was adopted to construct stroke and stroke mimic cohorts. We compared the algorithm performance and feature importance measures of the ML models (ie, gradient boosting machine and random forest) with those of the logistic regression model based on 3 sets of predictors. To provide insights into the prediction and ultimately assist care providers in decision-making, we used TreeSHAP for tree-based ML models to explain the stroke prediction.

RESULTS

Our analysis included 143,203 hospital visits of unique patients, and it was confirmed based on the principal diagnosis at discharge that 73% (n=104,662) of these patients had a stroke. The approach proposed in this study has high sensitivity and is particularly effective at reducing the misdiagnosis of dangerous stroke chameleons (false-negative rate <4%). ML classifiers consistently outperformed the benchmark logistic regression in all 3 input combinations. We found significant consistency across the models in the features that explain their performance. The most important features are age, the number of chronic conditions on admission, and primary payer (eg, Medicare or private insurance). Although both the individual- and community-level SDoH features helped improve the predictive performance of the models, the inclusion of the individual-level SDoH features led to a much larger improvement (area under the receiver operating characteristic curve increased from 0.694 to 0.823) than the inclusion of the community-level SDoH features (area under the receiver operating characteristic curve increased from 0.823 to 0.829).

CONCLUSIONS

Using data widely available at the time of patients' hospital presentations, we developed a stroke prediction model with high sensitivity and reasonable specificity. The prediction algorithm uses variables that are routinely collected by providers and payers and might be useful in underresourced hospitals with limited availability of sensitive diagnostic tools or incomplete data-gathering capabilities.

Data report on three datasets: Mortality patterns between agricultural and non-agricultural ward areas

The health of the farming community in Northern Ireland (NI) requires further research as previous mortality studies have reported contradictory results regarding farmers' health outcomes compared against other occupations and the general population. This study collated the NINIS area-level farm census with the population census information across 582 non-overlapping wards of NI to compile three mortality datasets (2001, 2011, and pooled dataset) (NISRA 2019). These datasets allow future researchers to investigate the influence of demographic, farming, and economic predictors on all-cause mortality at the ward level. The 2001 and 2011 mortality datasets were compiled for cross-sectional analyses and subsequently pooled for longitudinal analyses. Findings from these datasets will provide evidence of the influence of Farming Intensity scores influence on death risk within the wards for future researchers to utilise. This data report will aid in the understanding of socio-ecological variables' additive contribution to the risk of death at the ward level within NI. This data report is of interest to the One Health research community as it standardises the environment-human-animal data to pave the way towards a new One Health research paradigm. For example, future researchers can use this nationally representative data to investigate whether agriculturally saturated wards have a higher mortality risk than non-agriculturally based wards of NI.

The health impacts of eviction: Evidence from the national longitudinal study of adolescent to adult health

Eviction represents an urgent social and economic issue in the United States, with nearly two million evictions occurring annually in the U.S. Still, the population health impacts of evictions, as well as the pathways linking eviction to health, are not well documented or understood, particularly among young adults. Using nationally-representative, longitudinal data from the National Longitudinal Study of Adolescent to Adult Health (1994-2008) (n = 9029), the present study uses a combination of analytic methods-including prospective lagged dependent variable regression models, inverse probabilities of treatment weighting, longitudinal first difference models, causal mediation techniques-to comprehensively assess whether and how evictions relate to depressive risk and self-rated health across early adulthood, paying particular attention to the stress-related pathways linking eviction and health. Results provide robust evidence of positive longitudinal associations between eviction and depressive risk, in particular. In the prospective regression models, young adults who experienced recent eviction had more depressive symptoms and worse self-rated health than those who were not evicted, net a host of background characteristics. Using treatment weighting techniques, results showed that young adults who experienced eviction had more depressive symptoms than those who were not evicted (5.921 vs. 4.998 depressive symptoms, p = 0.003). Perceived social stress mediated nearly 18 percent of the associations between eviction and the depressive symptoms (p < 0.001). In the first difference models, young people who experienced eviction between survey waves experienced greater increases in depressive symptoms over time compared to those who were not evicted, net of changes in other indicators of socioeconomic status and residential instability. Taken together, our results suggest that the recent surges in evictions in the U.S. serve as a potent threat to population health during the emerging adult period, with especially devastating consequences for low-income individuals and communities of color.

Acute Air Pollution Exposure and the Risk of Violent Behavior in the United States

BACKGROUND

Violence is a leading cause of death and an important public health threat, particularly among adolescents and young adults. However, the environmental causes of violent behavior are not well understood. Emerging evidence suggests exposure to air pollution may be associated with aggressive or impulsive reactions in people.

METHODS

We applied a two-stage hierarchical time-series model to estimate change in risk of violent and nonviolent criminal behavior associated with short-term air pollution in U.S. counties (2000-2013). We used daily monitoring data for ozone and fine particulate matter (PM2.5) from the Environmental Protection Agency and daily crime counts from the Federal Bureau of Investigation. We evaluated the exposure-response relation and assessed differences in risk by community characteristics of poverty, urbanicity, race, and age.

RESULTS

Our analysis spans 301 counties in 34 states, representing 86.1 million people and 721,674 days. Each 10 µg/m change in daily PM2.5 was associated with a 1.17% (95% confidence interval [CI] = 0.90, 1.43) and a 10 ppb change in ozone with a 0.59% (95% CI = 0.41, 0.78) relative risk increase (RRI) for violent crime. However, we observed no risk increase for nonviolent property crime due to PM2.5 (RRI: 0.11%; 95% CI = -0.09, 0.31) or ozone (RRI: -0.05%; 95% CI = -0.22, 0.12). Our results were robust across all community types, except rural regions. Exposure-response curves indicated increased violent crime risk at concentrations below regulatory standards.

CONCLUSIONS

Our results suggest that short-term changes in ambient air pollution may be associated with a greater risk of violent behavior, regardless of community type.

Investigating improvements in premature death in two rural, majority-minority counties in the south

This exploratory study investigates counties in the southeast United States with mortality outcomes that were better than might be expected given their sociodemographic profiles (i.e., positive deviance). This study seeks to understand the community characteristics with the potential to moderate the negative health outcomes typically associated with social, geographic, or economic disadvantages. This article describes the process used to identify positive deviants and reports on the findings from key informant interviews in positive deviant counties to identify community factors or practices that might contribute to positive deviance in the observed outcomes. County Health Rankings and Roadmaps 2015 data and mortality trends were examined to identify positive deviant counties. The inclusion criteria were median household incomes in the lowest tertile of their state, ≥ 33% African American, and premature mortality rankings (as measured by Years of Potential Life Lost-YPLL) in the top quartile within their state. After benchmarking county rates against national figures and retaining counties with significant improvement trends, two counties emerged as positive deviants, Dooly County, Georgia and Washington County, North Carolina. Key informant interviews (n = 11) were conducted with community stakeholders in the study counties to better understand the community characteristics that could lead to the observed outcomes. Interview data were analyzed using qualitative methods. Key informant interviews revealed three emergent themes: 1. accessibility and availability of healthcare, 2. the provision of a robust EMS system, and 3. coordination of county-funded services targeting vulnerable populations. The positive deviance framework provides a foundation for the identification of community factors or practices with the potential to create a 'culture of health' in communities at the greatest risk for adverse health outcomes. Our findings suggest that healthcare supported by the coordination of non-emergency transportation and health and social services across numerous stakeholders may have contributed to observed outcomes in the study counties.

In vivo and in vitro inflammatory responses to fine particulate matter (PM2.5) from China and California

Ambient PM2.5 was collected during the winter season from Taiyuan, Shanxi, China; Jinan, Shandong, China; and Sacramento, California, USA, and used to create PMSX, PMSD, and PMCA extracts, respectively. Time-lag experiments were performed to explore the in vivo and in vitro toxicity of the PM extracts. In vivo inflammatory lung responses were assessed in BALB/c mice using a single oropharyngeal aspiration (OPA) of PM extract or vehicle (CTRL) on Day 0. Necropsies were performed on Days 1, 2, and 4 post-OPA, and pulmonary effects were determined using bronchoalveolar lavage (BAL) and histopathology. On Day 1, BAL neutrophils were significantly elevated in all PM- versus CTRL-exposed mice, with PMCA producing the strongest response. However, histopathological scoring showed greater alveolar and perivascular effects in PMSX-exposed mice compared to all three other groups. By Day 4, BAL neutrophilia and tissue inflammation were resolved, similar across all groups. In vitro effects were examined in human HepG2 hepatocytes, and U937 cells following 6, 24, or 48 h of exposure to PM extract or DMSO (control). Luciferase reporter and quantitative polymerase chain reaction assays were used to determine in vitro effects on aryl hydrocarbon receptor (AhR) activation and gene transcription, respectively. Though all three PM extracts activated AhR, PMSX produced the greatest increases in AhR activation, and mRNA levels of cyclooxygenase-2, cytochrome P450, interleukin (IL)-8, and interleukin (IL)-1β. These effects were assumed to result from a greater abundance of polycyclic aromatic hydrocarbons (PAHs) in PMSX compared to PMSD and PMCA.

Development and Validation of a County-Level Social Determinants of Health Risk Assessment Tool for Cardiovascular Disease

PURPOSE

Social determinants of health (SDoH) have been linked to a variety of health conditions, but there are no multivariate measures of these determinants to estimate the risk of morbidity or mortality in a community. We developed a score derived from multivariate measures of SDoH that predicts county-level cardiovascular disease (CVD) mortality.

METHODS

Using county-level data from 3,026 US counties, we developed a score considering variables of neighborhood socioeconomic status, food/lifestyle environment, and health care resource availability and accessibility to predict the 3-year average (2015-2017) age-adjusted county-level mortality rate for all CVD. We used one 50% random sample to develop the score and the other to validate the score. A Poisson regression model was developed to estimate parameters of variables while accounting for intrastate correlation.

RESULTS

The index score was based on 7 SDoH factors: percentage of the population of minority (nonwhite) race, poverty rate, percentage of the population without a high school diploma, grocery store ratio, fast-food restaurant ratio, after-tax soda price, and primary care physician supply. The area under the curve for the development and validation groups was similar, 0.851 (95% CI, 0.829-0.872) and 0.840 (95% CI, 0.817-0.863), respectively, indicating excellent discriminative ability. The index had better predictive performance for CVD burden than other area-level indexes: poverty only (area under the curve= 0.808, P <.001); the Centers for Disease Control and Prevention's Social Vulnerability Index (CDC-SVI) (area under the curve =0.786, P <.001); and the Agency for Healthcare Research and Quality's Socioeconomic Status (AHRQ-SES) index (area under the curve =0.835, P = .03).

CONCLUSIONS

Our validated multivariate SDoH index score accurately classifies counties with high CVD burden and therefore has the potential to improve CVD risk prediction for vulnerable populations and interventions for CVD at the county level.

Neighborhood Crime and Psychotropic Medications: A Longitudinal Data Linkage Study of 130,000 Scottish Adults

INTRODUCTION

Although neighborhood crime has been associated with mental health problems, longitudinal research utilizing objective measures of small-area crime and mental health service use is lacking. This study examines how local crime is associated with newly prescribed psychotropic medications in a large longitudinal sample of Scottish adults and explores whether the relationships vary between sociodemographic groups.

METHODS

Data from the Scottish Longitudinal Study, a 5.3% representative sample of the population, were linked with police-recorded crime in 2011 for residential locality and with psychotropic medications from 2009 to 2014, extracted from the prescription data set of National Health Service Scotland. Individuals receiving medication during the first 6 months of observation were excluded; the remaining sample was followed for 5.5 years. Covariate-adjusted, multilevel mixed-effects logistic models estimated associations between area crime and prescriptions for antidepressants, antipsychotics, and anxiolytics (analyzed in 2018-2019).

RESULTS

After adjustment for individual and neighborhood covariates, findings on 129,945 adults indicated elevated risk of antidepressant (OR=1.05, 95% CI=1.00, 1.10) and antipsychotic (OR=1.20, 95% CI=1.03, 1.39), but not anxiolytic (OR=0.99, 95% CI=0.93, 1.05) medication in high-crime areas. Crime showed stronger positive association with antidepressants among individuals (especially women) aged 24-53 years in 2009 and with antipsychotics among men aged 44-53 years in 2009. Skilled workers and people from lower nonmanual occupations had increased risk of medications in high-crime areas.

CONCLUSIONS

Local crime is an important predictor of mental health, independent of individual and other contextual risk factors. Place-based crime prevention and targeting vulnerable groups may have benefits for population mental health.

Cardiopulmonary Health Effects of Airborne Particulate Matter: Correlating Animal Toxicology to Human Epidemiology

The effects of particulate matter (PM) on cardiopulmonary health have been studied extensively over the past three decades. Particulate matter is the primary criteria air pollutant most commonly associated with adverse health effects on the cardiovascular and respiratory systems. The mechanisms by which PM exerts its effects are thought to be due to a variety of factors which may include, but are not limited to, concentration, duration of exposure, and age of exposed persons. Adverse effects of PM are strongly driven by their physicochemical properties, sites of deposition, and interactions with cells of the respiratory and cardiovascular systems. The direct translocation of particles, as well as neural and local inflammatory events, are primary drivers for the observed cardiopulmonary health effects. In this review, toxicological studies in animals, and clinical and epidemiological studies in humans are examined to demonstrate the importance of using all three approaches to better define potential mechanisms driving health outcomes upon exposure to airborne PM of diverse physicochemical compositions.

No Man is an Island: The Impact of Neighborhood Disadvantage on Mortality

This study’s purpose is to determine if neighborhood disadvantage, air quality, economic distress, and violent crime are associated with mortality among term life insurance policyholders, after adjusting for individual demographics, health, and socioeconomic characteristics. We used a sample of approximately 38,000 term life policyholders, from a large national life insurance company, who purchased a policy from 2002 to 2010. We linked this data to area-level data on neighborhood disadvantage, economic distress, violent crime, and air pollution. The hazard of dying for policyholders increased by 9.8% (CI: 6.0–13.7%) as neighborhood disadvantage increased by one standard deviation. Area-level poverty and mortgage delinquency were important predictors of mortality, even after controlling for individual personal income and occupational status. County level pollution and violent crime rates were positively, but not statistically significantly, associated with the hazard of dying. Our study provides evidence that neighborhood disadvantage and economic stress impact individual mortality independently from individual socioeconomic characteristics. Future studies should investigate pathways by which these area-level factors influence mortality. Public policies that reduce poverty rates and address economic distress can benefit everyone’s health.

Cancer mortality disparities among New York City's Upper Manhattan neighborhoods

The East Harlem (EH), Central Harlem (CH), and Upper East Side (UES) neighborhoods of New York City are geographically contiguous to tertiary medical care, but are characterized by cancer mortality rate disparities. This ecological study aims to disentangle the effects of race and neighborhood on cancer deaths. Mortality-to-incidence ratios were determined using neighborhood-specific data from the New York State Cancer Registry and Vital Records Office (2007-2011). Ecological data on modifiable cancer risk factors from the New York City Community Health Survey (2002-2006) were stratified by sex, age group, race/ethnicity, and neighborhood and modeled against stratified mortality rates to disentangle race/ethnicity and neighborhood using logistic regression. Significant gaps in mortality rates were observed between the UES and both CH and EH across all cancers, favoring UES. Mortality-to-incidence ratios of both CH and EH were similarly elevated in the range of 0.41-0.44 compared with UES (0.26-0.30). After covariate and multivariable adjustment, black race (odds ratio=1.68; 95% confidence interval: 1.46-1.93) and EH residence (odds ratio=1.20; 95% confidence interval: 1.07-1.35) remained significant risk factors in all cancers' combined mortality. Mortality disparities remain among EH, CH, and UES neighborhoods. Both neighborhood and race are significantly associated with cancer mortality, independent of each other. Multivariable adjusted models that include Community Health Survey risk factors show that this mortality gap may be avoidable through community-based public health interventions.

Cross-level interaction between individual socioeconomic status and regional deprivation on overall survival after onset of ischemic stroke: National health insurance cohort sample data from 2002 to 2013

INTRODUCTION

The literature on stroke mortality and neighborhood effect is characterized by studies that are often Western society-oriented, with a lack of racial and cultural diversity. We estimated the effect of cross-level interaction between individual and regional socioeconomic status on the survival after onset of ischemic stroke.

METHODS

We selected newly diagnosed ischemic stroke patients from 2002 to 2013 using stratified representative sampling data of 1,025,340 subjects. A total of 37,044 patients over the 10 years from 2004 to 2013 had newly diagnosed stroke. We calculated hazard ratios (HR) of 12- and 36-month mortality using the Cox proportional hazard model, with the reference group as stroke patients with high income in advantaged regions.

RESULTS

For the middle income level, the patients in advantaged regions showed low HRs for overall mortality (12-month HR 1.27; 95% confidence interval [CI], 1.13-1.44; 36-month HR 1.25; 95% CI, 1.14-1.37) compared to the others in disadvantaged regions (12-month HR 1.36; 95% CI, 1.19-1.56; 36-month HR 1.30; 95% CI, 1.17-1.44). Interestingly, for the low income level, the patients in advantaged regions showed high HRs for overall mortality (12-month HR 1.27; 95% CI, 1.13-1.44; 36-month HR 1.33; 95% CI, 1.22-1.46) compared to the others in disadvantaged regions (12-month HR 1.25; 95% CI, 1.09-1.43; 36-month HR 1.30; 95% CI, 1.18-1.44).

CONCLUSION

Although we need to perform further investigations to determine the exact mechanisms, regional deprivation, as well as medical factors, might be associated with survival after onset of ischemic stroke in low-income patients.

Housing Policies and Health Inequalities

A large body of literature shows the link between inadequate housing conditions and poor physical and mental health. The aim of this paper is to summarize the research on the impact of local housing policies on health inequalities, focusing on the issues of access to housing and fuel poverty as studied in the SOPHIE project. Our case studies in Spain showed that people facing housing insecurity, experienced intense levels of mental distress. We found that access to secure and adequate housing can improve the health of these populations, therefore, public policies that address housing instability and their consequences are urgently needed. Housing conditions related to fuel poverty are associated with poorer health and are unevenly distributed across Europe. We found possible positive effects of façade insulation interventions on cold-related mortality in women living in social housing; but not in men. Policies on housing energy efficiency can reduce the health consequences of fuel poverty, but need to be free to users, target the most vulnerable groups and be adaptable to their needs.

The influence of neighbourhood-level socioeconomic deprivation on cardiovascular disease mortality in older age: longitudinal multilevel analyses from a cohort of older British men

Background

Evidence from longitudinal studies on the influence of neighbourhood socioeconomic factors in older age on cardiovascular disease (CVD) mortality is limited. We aimed to investigate the prospective association of neighbourhood-level deprivation in later life with CVD mortality, and assess the underlying role of established cardiovascular risk factors.

Methods

A socially representative cohort of 3924 men, aged 60–79 years in 1998–2000, from 24 British towns, was followed up until 2012 for CVD mortality. Quintiles of the national Index of Multiple Deprivation (IMD), a composite score of neighbourhood-level factors (including income, employment, education, housing and living environment) were used. Multilevel logistic regression with discrete-time models (stratifying follow-up time into months) were used.

Results

Over 12 years, 1545 deaths occurred, including 580 from CVD. The risk of CVD mortality showed a graded increase from IMD quintile 1 (least deprived) to 5 (most deprived). Compared to quintile 1, the age-adjusted odds of CVD mortality in quintile 5 were 1.71 (95% CI 1.32 to 2.21), and 1.62 (95% CI 1.23 to 2.13) on further adjustment for individual social class, which was attenuated slightly to 1.44 (95% CI 1.09 to 1.89), but remained statistically significant after adjustment for smoking, body mass index, physical activity and use of alcohol. Further adjustment for blood pressure, high-density lipoprotein cholesterol and prevalent diabetes made little difference.

Conclusions

Neighbourhood-level deprivation was associated with an increased risk of CVD mortality in older people independent of individual-level social class and cardiovascular risk factors. The role of other specific neighbourhood-level factors merits further research.

A border versus non-border comparison of food environment, poverty, and ethnic composition in Texas urban settings

Purpose

The goal was to examine the relationship between the food environment and selected socioeconomic variables and ethnic/racial makeup in the eight largest urban settings in Texas so as to gain a better understanding of the relationships among Hispanic composition, poverty, and urban foodscapes, comparing border to non-border urban environments.

Methods

Census-tract level data on (a) socioeconomic factors, like percentage below the poverty line and number of households on foodstamps, and (b) ethnic variables, like percent of Mexican origin and percent foreign born, were obtained from the U.S. Census. Data at the census-tract level on the total number of healthy (e.g., supermarkets) and less-healthy (e.g., fast food outlets) food retailers were acquired from the CDC’s modified retail food environment index (mRFEI). Variation among urban settings in terms of the relationship between mRFEI scores and socioeconomic and ethnic context was tested using a mixed-effect model, and linear regression was used to identify significant factors for each urban location. A jackknife variance estimate was used to account for clustering and autocorrelation of adjacent census tracts.

Results

Average census-tract mRFEI scores exhibited comparatively small variation across Texas urban settings, while socioeconomic and ethnic factors varied significantly. The only covariates significantly associated with mRFEI score were percent foreign born and percent Mexican origin. Compared to the highest-population county (Harris, which incorporates most of Houston), the only counties that had significantly different mRFEI scores were Bexar, which is analogous to San Antonio (2.12 lower), El Paso (2.79 higher), and Neuces, which encompasses Corpus Christi (2.90 less). Significant interaction effects between mRFEI and percent foreign born (El Paso, Tarrant – Fort Worth, Travis – Austin), percent Mexican origin (Hidalgo – McAllen, El Paso, Tarrant, Travis), and percent living below the poverty line (El Paso) were observed for some urban settings. Percent foreign born and percent Mexican origin tended to be positively associated with mRFEI in some locations (Hidalgo, El Paso) and negatively associated in others (Tarrant, Travis).

Discussion

Findings are consistent with other studies that suggest the effects of Hispanic concentration on the foodscape may be positive (beneficially healthy) in border urban settings and negative in non-border. The evidence implies that the effects of Hispanic ethnic composition on the food environment are location-dependent, reflecting the unique attributes (e.g., culture, infrastructure, social networks) of specific urban settings.