Neighborhood socioeconomic status (NSES) is associated with cognitive function, independently of individual demographic, health, and socioeconomic characteristics. However, research has been largely cross-sectional, and mechanisms of the association are unknown. In 1992-1993, Cardiovascular Health Study participants (n = 3,595; mean age = 74.8 years; 15.7% black) underwent cognitive testing and magnetic resonance imaging of white matter hyperintensities (WMH), and their addresses were geocoded. NSES was calculated using 1990 US Census data (block groups; 6 measures of wealth, education, and occupation). The Modified Mini-Mental State Examination (3MS) was used to assess general cognition, and the Digit Symbol Substitution Test (DSST) was used to assess speed of processing annually for 6 years. Associations of race-specific NSES tertiles with 3MS, DSST, and WMH were estimated using linear mixed-effects models accounting for geographic clustering, stratified by race, and adjusted for demographic, health, and individual socioeconomic status (education, income, lifetime occupational status) variables. In fully adjusted models, higher NSES was associated with higher 3MS scores in blacks (mean difference between highest and lowest NSES = 2.4 points; P = 0.004) and whites (mean difference = 0.7 points; P = 0.02) at baseline but not with changes in 3MS over time. NSES was marginally associated with DSST and was not associated with WMH. Adjustment for WMH did not attenuate NSES-3MS associations. Associations of NSES with cognition in late adulthood differ by race, are not explained by WMH, and are evident only at baseline.
We sought to test whether the association between walkable environments and lower body mass index (BMI) was stronger within disadvantaged groups that may be particularly sensitive to environmental constraints.We measured height and weight in a diverse sample of 13 102 adults living throughout New York City from 2000-2002. Each participant's home address was geocoded and surrounded by a circular buffer with a 1-km radius. The composition and built environment characteristics of these areas were used to predict BMI through the use of generalized estimating equations. Indicators of individual or area disadvantage included low educational attainment, low household income, Black race, and Hispanic ethnicity.Higher population density, more mixed land use, and greater transit access were most consistently associated with a lower BMI among those with more education or higher incomes and among non-Hispanic Whites. Significant interactions were observed for education, income, race, and ethnicity.Contrary to expectations, built environment characteristics were less consistently associated with BMI among disadvantaged groups. This pattern may be explained by other barriers to maintaining a healthy weight encountered by disadvantaged groups.
Childhood obesity is a rising global health problem. The rapid urbanization experienced in Latin America might impact childhood obesity through different pathways involving urban built and social features of cities. We aimed to evaluate the association between built and social environment features of cities and childhood obesity across countries and cities in Latin America.Cross-sectional analysis of data from 20,040 children aged 1-5 years living in 159 large cities in six Latin American countries. We used individual-level anthropometric data for excess weight (overweight or obesity) from health surveys that could be linked to city-level data. City and sub-city level exposures included the social environment (living conditions, service provision and educational attainment) and the built environment (fragmentation, isolation, presence of mass transit, population density, intersection density and percent greenness). Multi-level logistic models were used to explore associations between city features and excess weight, adjusting for age, sex, and head of household education.The overall prevalence of excess weight among preschool children was 8% but varied substantially between and within countries, ranging from 4% to 25%. Our analysis showed that 97% of the variability was between individuals within sub-city units and around 3% of the variance in z-scores of weight for height was explained by the city and sub-city levels. At the city-level, a higher distance between urban patches (isolation, per 1 SD increase) was associated with lower odds of excess weight (OR 0.90, 95% CI 0.82-0.99). Higher sub-city education was also associated with lower odds of excess weight, but better sub-city living conditions were associated with higher odds of excess weight.Built and social environment features are related to excess weight in preschool children. Our evidence from a wide range of large Latin American cities suggests that urban health interventions may be suitable alternatives towards attaining the goal of reducing excess weight early in the life course.The SALURBAL project (Salud Urbana en América Latina, Urban Health in Latin America) is funded by Wellcome [205177/Z/16/Z].
Diarrhea is a leading cause of death in children globally, mostly due to inadequate sanitary conditions and overcrowding. Poor housing quality and lack of tenure security that characterize informal settlements are key underlying contributors to these risk factors for childhood diarrhea deaths. The objective of this study is to better understand the physical attributes of informal settlement households in Latin American cities that are associated with childhood diarrhea. We used data from a household survey (Encuesta CAF) conducted by the Corporación Andina de Fomento (CAF), using responses from sampled individuals in eleven cities. We created a household deprivation score based on household water and sewage infrastructure, overcrowding, flooring and wall material, and security of tenure. We fitted a multivariable logistic regression model to estimate odds ratios (OR) and 95% confidence intervals (95% CI) to test the association between the deprivation score and its individual components and childhood diarrhea during the prior 2 weeks. We included a total of 4732 households with children, out of which 12.2% had diarrhea in the 2-week period prior to completing the survey. After adjusting for respondent age, gender, and city, we found a higher risk of diarrhea associated with higher household deprivation scores. Specifically, we found that the odds of diarrhea for children living in a mild and severe deprived household were 1.04 (95% CI 0.84-1.28) and 3.19 times (95% CI 1.80-5.63) higher, respectively, in comparison to households with no deprivation. These results highlight the connections between childhood health and deprived living conditions common in informal settlements.
The growing availability of (non-)commercial historical datasets opens a new avenue of research on how long-term exposure to the neighbourhood environment affects health. However, traditional tools for longitudinal analysis (e.g. mixed models) are limited in their ability to operationalise long-term exposure. This study aims to summarise longitudinal exposure to the neighbourhood using latent class growth analysis (LCGA). Using the National Establishment Time-Series (NETS) 1990–2010, we analysed the trajectory of change in New York City (NYC) in the number of unhealthy food businesses – a potential indicator of an obesogenic environment.
Methods
The NETS is a commercial dataset providing retail business information in the United States. NYC data were acquired for the period 1990–2010. Businesses were grouped into researcher-defined categories based on Standard Industrial Classification codes and other fields such as business name. All businesses were re-geocoded to ensure accurate localisation. We defined access to BMI-unhealthy businesses (characterised as selling calorie-dense foods such as pizza and pastries) as the total number of BMI-unhealthy businesses present in each NYC census tract (n=2,167) in January of each year. We conducted LCGA in Mplus to identify census tracts with similar trajectories of BMI-unhealthy businesses. We used model fit statistics and interpretability to determine the number of classes. Using the final models, we assigned census tracts to latent classes. We predicted class membership with socio-demographic variables from the Census (population size, income, and ethnic composition) using multinomial logistic regressions and reported predicted probabilities with 95% CI. Sensitivity analyses were undertaken.
Results
The final models include 5 and 10 latent classes, respectively. The 5-class solution indicates an overall increase in the number of BMI-unhealthy businesses over time and shows a pattern of fanning out: the higher the value in 1990, the greater the increase over time. Classes are associated with 1990 population size, income, proportion of Black residents (all p<0.001), proportion of Hispanic residents (p=0.033), and 1990–2010 change in population size and income (p<0.001). The 10-class solution identifies two pairs of classes with similar 1990 values, but different trajectories. Differences in those trajectories are associated with population size and ethnic composition (p<0.001).
Conclusion
This study illustrates how LCGA contributes to the understanding of long-term exposure to the obesogenic environment. The technique can easily be applied to other aspects of the neighbourhood and to other geographies. When linked with health data, identified latent classes can be used to assess how longitudinal exposure to changing neighbourhoods affects health.
Worse neighborhood socioeconomic environment (NSEE) may contribute to an increased risk of type 2 diabetes (T2D). We examined whether the relationship between NSEE and T2D differs by sex and age in three study populations. We conducted a harmonized analysis using data from three independent longitudinal study samples in the US: 1) the Veteran Administration Diabetes Risk (VADR) cohort, 2) the REasons for Geographic and Racial Differences in Stroke (REGARDS) cohort, and 3) a case-control study of Geisinger electronic health records in Pennsylvania. We measured NSEE with a z-score sum of six census tract indicators within strata of community type (higher density urban, lower density urban, suburban/small town, and rural). Community type-stratified models evaluated the likelihood of new diagnoses of T2D in each study sample using restricted cubic splines and quartiles of NSEE. Across study samples, worse NSEE was associated with higher risk of T2D. We observed significant effect modification by sex and age, though evidence of effect modification varied by site and community type. Largely, stronger associations between worse NSEE and diabetes risk were found among women relative to men and among those less than age 45 in the VADR cohort. Similar modification by age group results were observed in the Geisinger sample in small town/suburban communities only and similar modification by sex was observed in REGARDS in lower density urban communities. The impact of NSEE on T2D risk may differ for males and females and by age group within different community types.
