Neighbourhood socioeconomic position and risks of major chronic diseases and all-cause mortality: a quasi-experimental study

Neighborhood deprivation, racial segregation and associations with cancer risk and outcomes across the cancer-control continuum

The racial/ethnic disparities in cancer incidence and outcome are partially due to the inequities in neighborhood advantage. Mounting evidences supported a link between neighborhood deprivation and cancer outcomes including higher mortality. In this review, we discuss some of the findings related to work on area-level neighborhood variables and cancer outcomes, and the potential biological and built/natural environmental mechanisms that might explain this link. Studies have also shown that residents of deprived neighborhoods or of racially or economically segregated neighborhoods have worse health outcomes than residents of more affluent neighborhoods and/or less racially or economically segregated neighborhoods, even after adjusting for the individual-level socioeconomic status. To date, little research has been conducted investigating the biological mediators that may play roles in the associations of neighborhood deprivation and segregation with cancer outcomes. The psychophysiological stress induced by neighborhood disadvantage among people living in these neighborhoods could be a potential underlying biological mechanism. We examined a number of chronic stress-related pathways that may potentially mediate the relationship between area-level neighborhood factors and cancer outcomes, including higher allostatic load, stress hormones, altered epigenome and telomere maintenance and biological aging. In conclusion, the extant evidence supports the notion that neighborhood deprivation and racial segregation have unfavorable impacts on cancer. Understanding how neighborhood factors influence the biological stress response has the potential to inform where and what types of resources are needed within the community to improve cancer outcomes and reduce disparities. More studies are warranted to directly assess the role of biological and social mechanisms in mediating the relationship between neighborhood factors and cancer outcomes.

The Structural Violence Trap: Disparities in Homicide, Chronic Disease Death, and Social Factors Across San Francisco Neighborhoods

BACKGROUND

On average, a person living in San Francisco can expect to live 83 years. This number conceals significant variation by sex, race, and place of residence. We examined deaths and area-based social factors by San Francisco neighborhood, hypothesizing that socially disadvantaged neighborhoods shoulder a disproportionate mortality burden across generations, especially deaths attributable to violence and chronic disease. These data will inform targeted interventions and guide further research into effective solutions for San Francisco's marginalized communities.

STUDY DESIGN

The San Francisco Department of Public Health provided data for the 2010-2014 top 20 causes of premature death by San Francisco neighborhood. Population-level demographic data were obtained from the US American Community Survey 2015 5-year estimate (2011-2015). The primary outcome was the association between years of life loss (YLL) and adjusted years of life lost (AYLL) for the top 20 causes of death in San Francisco and select social factors by neighborhood via linear regression analysis and heatmaps.

RESULTS

The top 20 causes accounted for N = 15,687 San Francisco resident deaths from 2010-2014. Eight neighborhoods (21.0%) accounted for 47.9% of city-wide YLLs, with 6 falling below the city-wide median household income and many having a higher percent population Black, and lower education and higher unemployment levels. For chronic diseases and homicides, AYLLs increased as a neighborhood's percent Black, below poverty level, unemployment, and below high school education increased.

CONCLUSIONS

Our study highlights the mortality inequity burdening socially disadvantaged San Francisco neighborhoods, which align with areas subjected to historical discriminatory policies like redlining. These data emphasize the need to address past injustices and move toward equal access to wealth and health for all San Franciscans.

The health potential of neighborhoods: A population-wide study in the Netherlands

BACKGROUND

While differences in population health across neighborhoods with different socioeconomic characteristics are well documented, health disparities across neighborhoods with similar socioeconomic characteristics are less well understood. We aimed to estimate population health inequalities, both within and between neighborhoods with similar socioeconomic status, and assessed the association of neighborhood characteristics and socioeconomic spillover effects from adjacent neighborhoods.

METHODS

Based on Dutch whole-population data we determined the percentage of inhabitants with good or very good self-assessed health (SAH) and the percentage of inhabitants with at least one chronic disease (CD) in 11,504 neighborhoods. Neighborhoods were classified by quintiles of a composite neighborhoods socioeconomic status score (NSES). A set of spatial models was estimated accounting for spatial effects in the dependent, independent, and error components of the model.

RESULTS

Substantial population health disparities in SAH and CD both within and between neighborhoods NSES quintiles were observed, with the largest SAH variance in the lowest NSES group. Neighborhoods adjacent to higher SES neighborhoods showed a higher SAH and a lower prevalence of CD. Projected impacts from the spatial regressions indicate how modest changes in NSES among the lowest socioeconomic neighborhoods can contribute to population health in both low- and high-SES neighborhoods.

CONCLUSION

Population health differs substantially among neighborhoods with similar socioeconomic characteristics, which can partially be explained by a spatial socio-economic spillover effect.

Blood pressure variability and microvascular dysfunction: the Maastricht Study

BACKGROUND

Microvascular dysfunction (MVD) contributes to stroke, dementia, depression, retinopathy and chronic kidney disease. However, the determinants of MVD are incompletely understood. Greater blood pressure variability (BPV) may be one such determinant.

METHODS AND RESULTS

We used cross-sectional data of The Maastricht Study (n = 2773, age 59.9 years; 51.9% men) to investigate whether greater very short- to mid-term BPV is associated with various MVD measures. We standardized and averaged within-visit, 24-h and 7-day BPV into a systolic and a diastolic BPV composite score. MVD measures included a composite score of MRI cerebral small vessel disease (CSVD) features (total brain parenchymal volume, white matter hyperintensity volume, lacunar infarcts and cerebral microbleeds), a composite score of flicker light-induced retinal arteriolar and venular dilation response, albuminuria, heat-induced skin hyperemia and a composite score of plasma biomarkers of MVD (sICAM-1, sVCAM-1, sE-selectin and von Willebrand Factor). We used linear regression adjusted for age, sex, glucose metabolism status, mean 24-h systolic or DBP, cardiovascular risk factors and antihypertensive medication. We found that higher systolic and diastolic BPV composite scores (per SD) were associated with higher albuminuria [higher ratio, 1.04 (95% CI 1.00-1.08) and 1.07 (1.03-1.11), respectively], but not with other measures of MVD tested.

CONCLUSION

Greater systolic and diastolic BPV was associated with higher albuminuria, but not with CSVD features, flicker light-induced retinal arteriolar and venular dilation response, heat-induced skin hyperemia and plasma biomarkers of MVD. This suggests that the microvasculature of the kidneys is most vulnerable to the detrimental effects of greater BPV.

Gender difference of geographic distribution of the stroke incidence affected by socioeconomic, clinical and urban-rural factors: an ecological study based on data from the Brest stroke registry in France

Background

Mapping the spatial distribution of disease occurrence is a strategy to identify contextual factors that could be useful for public health policies. The purpose of this ecological study was to examine to which extent the socioeconomic deprivation and the urbanization level can explain gender difference of geographic distribution in stroke incidence in Pays de Brest, France between 2008 and 2013.

Methods

Stroke cases aged 60 years or more were extracted from the Brest stroke registry and combined at the census block level. Contextual socioeconomic, demographic, and geographic variables at the census block level come from the 2013 national census. We used spatial and non-spatial regression models to study the geographic correlation between socioeconomic deprivation, degree or urbanization and stroke incidence. We generated maps using spatial geographically weighted models, after longitude and latitude smoothing and adjustment for covariates.

Results

Stroke incidence was comparable in women and men (6.26 ± 3.5 vs 6.91 ± 3.3 per 1000 inhabitants-year, respectively). Results showed different patterns of the distribution of stroke risk and its association with deprivation or urbanisation across gender. For women, stroke incidence was spatially homogeneous over the entire study area, but was associated with deprivation level in urban census blocks: age adjusted risk ratio of high versus low deprivation = 1.24, [95%CI 1.04–1.46]. For men, three geographic clusters were identified. One located in the northern rural and deprived census blocks with a 9–14% increase in the risk of stroke. Two others clusters located in the south-eastern (mostly urban part) and south-western (suburban and rural part) with low deprivation level and associated with higher risk of stroke incidence between (3 and 8%) and (8.5 and 19%) respectively. There were no differences in profile of cardiovascular risk factors, stroke type and stroke severity between clusters, or when comparing clusters cases to the rest of the study population.

Conclusions

Understanding whether and how neighborhood and patient’s characteristics influence stroke risk may be useful for both epidemiological research and healthcare service planning.

The Mortality Risk and Socioeconomic Vulnerability Associated with High and Low Temperature in Hong Kong

(1) Background: The adverse health effect associated with extreme temperature has been extensively reported in the current literature. Some also found that temperature effect may vary among the population with different socioeconomic status (SES), but found inconsistent results. Previous studies on the socioeconomic vulnerability of temperature effect were mainly achieved by multi-city or country analysis, but the large heterogeneity between cities may introduce additional bias to the estimation. The linkage between death registry and census in Hong Kong allows us to perform a city-wide analysis in which the study population shares virtually the same cultural, lifestyle and policy environment. This study aims to examine and compare the high and low temperature on morality in Hong Kong, a city with a subtropical climate and address a key research question of whether the extreme high and low temperature disproportionally affects population with lower SES. (2) Methods: Poisson-generalized additive models and distributed-lagged nonlinear models were used to examine the association between daily mortality and daily mean temperature between 2007–2015 with other meteorological and confounding factors controlled. Death registry was linked with small area census and area-level median household income was used as the proxy for socioeconomic status. (3) Results: 362,957 deaths during the study period were included in the analysis. The minimum mortality temperature was found to be 28.9 °C (82nd percentile). With a subtropical climate, the low temperature has a stronger effect than the high temperature on non-accidental, cardiovascular, respiratory and cancer deaths in Hong Kong. The hot effect was more pronounced in the first few days, while cold effect tended to last up to three weeks. Significant heat effect was only observed in the lower SES groups, whilst the extreme low temperature was associated with significantly higher mortality risk across all SES groups. The older population were susceptible to extreme temperature, especially for cold. (4) Conclusions: This study raised the concern of cold-related health impact in the subtropical region. Compared with high temperature, low temperature may be considered a universal hazard to the entire population in Hong Kong rather than only disproportionally affecting people with lower SES. Future public health policy should reconsider the strategy at both individual and community levels to reduce temperature-related mortality.

Social Determinants of Myocardial Infarction Risk and Survival: A Systematic Review

Social determinants of health that have been examined in relation to myocardial infarction incidence and survival include socioeconomic status (income, education), neighbourhood disadvantage, immigration status, social support, and social network. Other social determinants of health include geographic factors such as neighbourhood access to health services. Socioeconomic factors influence risk of myocardial infarction. Myocardial infarction incidence rates tend to be inversely associated with socioeconomic status. In addition, studies have shown that low socioeconomic status is associated with increased risk of poorer survival. There are well-documented disparities in myocardial infarction survival by socioeconomic status, race, education, and census-tract-level poverty. The results of this review indicate that social determinants such as neighbourhood disadvantage, immigration status, lack of social support, and social isolation also play an important role in myocardial infarction risk and survival. To address these social determinants and eliminate disparities, effective interventions are needed that account for the social and environmental contexts in which heart attack patients live and are treated.

How are lifestyle factors associated with socioeconomic differences in health care costs? Evidence from full population data in the Netherlands

INTRODUCTION

There is a strong socioeconomic gradient in health care costs. However, little is known about the role of lifestyle factors in the association between health care costs and socioeconomic status (SES). This study investigates variation in the association between lifestyle indicators and health care costs between and within neighborhoods with similar SES.

METHOD

Using 2016 whole-population data for all 790 neighborhoods of the Netherlands, we estimated the association between neighborhood average health care cost performance (i.e., health care costs adjusted for population age and gender) and neighborhood socioeconomic status (NSES) and four lifestyle indicators - smoking, alcohol consumption, exercise and sport club membership. Additionally, using regression analysis, we explored the multivariate relationship between average health care cost performance, NSES and lifestyle indicators.

RESULTS

Neighborhoods with proportionally fewer smokers and more sport club members had significantly lower average health care costs. Remarkably, neighborhoods with more people who complied with the recommended maximum alcohol consumption had significantly higher health care costs. These findings were consistent within and between neighborhoods with different SES levels. Neighborhoods with more compliance with exercise guidelines had lower health care costs. However, this relationship was inconsistent across different NSES levels, with the largest cost reductions found in the most deprived neighborhoods.

CONCLUSION

Our findings suggest that prevention policies aimed at reducing the number of smokers and increasing sport club membership may reduce health care costs across all NSES groups, while increasing compliance with physical exercise norms may be effective mainly in low SES neighborhoods.

Neighborhood Socioeconomic Status and Health Care Costs: A Population-Wide Study in the Netherlands

Objectives. To identify disparities in several types of insured health care costs in the Netherlands across neighborhoods with different socioeconomic statuses and to assess the room for improvement. Methods. We used 2015 Dutch whole-population registry data to estimate the age- and gender-specific cost structure by neighborhood for total, specialist, pharmaceutical, and mental health care. Classifying neighborhoods by the quintile of their neighborhood socioeconomic status (NSES), we determined differences in observed and expected health care costs for several scenarios of NSES improvement. Results. From low to high NSES, we found a clear downward gradient in health care costs. Total health care costs would drop by 7.3% if each neighborhood's cost structure was equal to that of the most affluent neighborhoods. The potential for cost reduction appeared highest for females, for age groups between 40 and 60 years, and for pharmaceutical care. Conclusions. Low NSES is associated with relatively high health care costs, and represents considerable potential for cost savings in health care. Public Health Implications. Our research suggests that policies aimed at improving the socioeconomic determinants of health locally may be pivotal in containing health care costs.

The parental co-immunization hypothesis: An observational competing risks analysis

The main interest of this study is the hypothesis that contact with small children may be beneficial for the parents’ later health and mortality (because of changes in their immune system). For this purpose, we document the relationship of a set of individual characteristics—including parenthood and marital state—and socioeconomic status with an individual’s cause of death. Using a novel and rich data set made available by the Office for National Statistics Longitudinal Study (ONS-LS), which follows 1% of the population of England and Wales along five census waves 1971, 1981, 1991, 2001, and 2011, our competing risks analysis yields several striking results: (1) Females with children have a 72.5% reduced risk of dying of cancer compared to childless females (for childless females of age 70, this corresponds to a risk of dying of cancer of 1.3% compared to a risk of about 1.3 × 0.275 = 0.4% for females with children). (2) Males have a 171% increased chance of dying of cancer when they are married (e.g., a baseline probability of 1.2% when 75 year old) compared to unmarried males. (3) Females with children have only a 34% risk of dying of heart disease (corresponding to a conditional probability of 0.3% when aged 65) relative to females without children and (4) a 53% chance of dying of infections (i.e., 0.1% at 65 years of age) compared to the risk for females without children. (5) At the same age, married men have an increased expectation of 123% of dying of heart disease (corresponding to an expected death probability of 0.7%) compared to unmarried men. (6) High income and house ownership is always associated with higher survival but less so than having children. While these results document a relationship between the presence of children and mortality, the specific transmission mechanisms remain unclear and we cannot make causality assertions.