Heavy precipitation and asthma exacerbation risk among children: A case-crossover study using electronic health records linked with geospatial data

Extreme precipitation events may be an important environmental trigger for asthma exacerbations in children. We used a time stratified case-crossover design and data from a large electronic health record database at the Children's Hospital of Philadelphia (CHOP) to estimate associations of daily heavy precipitation (defined as > 95th percentile of the summertime distribution) with asthma exacerbation among children. We defined control days as those falling on the same day of the week within the same month and year as the case. We restricted our primary analyses to the summer months in years 2011-2016 and used conditional logistic regression models to estimate associations between heavy precipitation and acute asthma exacerbations in both outpatient (primary care, specialty care, and emergency department) and inpatient settings. We investigated numerous individual-level (e.g., age, sex, eczema diagnosis) and environmental measures (e.g., greenspace, particulate matter) as potential effect modifiers. The analysis include 13,483 asthma exacerbations in 10,434 children. Odds of asthma exacerbation were 11% higher on heavy precipitation vs. no precipitation days (95% CI: 1.02-1.21). There was little evidence of effect modification by most measures. These results suggest that heavy summertime precipitation events may contribute to asthma exacerbations. Further research using larger datasets from other health systems is needed to confirm these results, and to explore underlying mechanisms.

Change in neighborhood environments and depressive symptoms in New York City: the Multi-Ethnic Study of Atherosclerosis

Physical and social features of neighborhoods, such as esthetic environments and social cohesion, change over time. The extent to which changes in neighborhood conditions are associated with changes in mental health outcomes has not been well-established. Using data from the MultiEthnic Study of Atherosclerosis, this study investigated the degree to which neighborhood social cohesion, stress, violence, safety and/or the esthetic environment changed between 2002 and 2007 in 103 New York City Census tracts and the associations of these changes with changes in depressive symptoms. Neighborhoods became less stressful, more socially cohesive, safer, and less violent. White, wealthy, highly educated individuals tended to live in neighborhoods with greater decreasing violence and stress and increasing social cohesion. Individuals living in neighborhoods with adverse changes were more likely to have increased CES-D scores, although due to limited sample size associations were imprecisely estimated (P>0.05). Changes in specific features of the neighborhood environment may be associated with changes in level of depressive symptoms among residents.

Physical activity and physiological cardiac remodelling in a community setting: the Multi-Ethnic Study of Atherosclerosis (MESA)

OBJECTIVE

To evaluate the association of physical activity with left ventricular structure and function in the general population in a community setting.

DESIGN

Cross-sectional study.

SETTING

The Multi-Ethnic Study of Atherosclerosis (MESA), a population-based study of subclinical atherosclerosis.

PARTICIPANTS

A multiethnic sample of 4992 participants (aged 45-84 years; 52% female) free of clinically apparent cardiovascular disease.

INTERVENTIONS

Physical activity induces beneficial physiological cardiac remodelling in a cross-sectional study of non-athlete individuals.

MAIN OUTCOME MEASURES

Left ventricular mass, volumes and function were assessed by cardiac magnetic resonance imaging. Physical activity, defined as intentional exercise and total moderate and vigorous physical activity, was assessed by a standard semiquantitative questionnaire.

RESULTS

Left ventricular mass and end-diastolic volume were positively associated with physical activity (eg, 1.4 g/m(2) (women) and 3.1 g/m(2) (men) greater left ventricular mass in the highest category of intentional exercise compared with individuals reporting no intentional exercise; p = 0.05 and p<0.001, respectively). Relationships were non-linear, with stronger positive associations at lower levels of physical activity (test for non-linearity; p = 0.02 and p = 0.03, respectively). Cardiac output and ejection fraction were unchanged with increased physical activity levels. Resting heart rate was lower in women and men with higher physical activity levels (eg, -2.6 beats/minute lower resting heart rate in the highest category of intentional exercise compared with individuals reporting no intentional exercise; p<0.001).

CONCLUSIONS

In a community-based population free of clinically apparent cardiovascular disease, higher physical activity levels were associated with proportionally greater left ventricular mass and end-diastolic volume and lower resting heart rate.

Next steps in understanding the multilevel determinants of health

This commentary briefly summarises past work that has used multilevel analysis to investigate the multilevel determinants of health and outlines possible new directions in this area. Topics discussed include the need to (1) examine contexts other than neighbourhoods; (2) improve measurement of group-level constructs; (3) apply techniques more appropriate for causal inference from observational data; (4) analyse data from "natural experiments" involving exogenous variations in contextual characteristics; (5) examine dependencies between groups (such as spatial dependencies) more broadly and allow for reciprocal relations between individuals and contexts; and (6) contrast multilevel statistical models (or regression models generally) and complex systems models in the study of multilevel effects.

Combining data from primary and ancillary surveys to assess the association between neighborhood-level characteristics and health outcomes: the Multi-Ethnic Study of Artherosclerosis

There is increasing interest in understanding the role of neighborhood-level factors on the health of individuals. Many large-scale epidemiological studies that accurately measure health status of individuals and individual risk factors exist. Sometimes these studies are linked to area-level databases (e.g. census) to assess the association between crude area-level characteristics and health. However, information from such databases may not measure the neighborhood-level constructs of interest. More recently, large-scale epidemiological studies have begun collecting data to measure specific features of neighborhoods using ancillary surveys. The ancillary surveys are composed of a separate, typically larger, set of individuals. The challenge is then to combine information from these two surveys to assess the role of neighborhood-level factors. We propose a method for combining information from the two data sources using a likelihood-based framework. We compare it with currently used ad hoc approaches via a simulation study. The simulation study shows that the proposed approach yields estimates with better sampling properties (less bias and better coverage probabilities) compared with the other approaches. However, there are cases where some ad hoc approaches may provide adequate estimates. We also compare the methods by applying them to the Multi-Ethnic Study of Atherosclerosis and its Neighborhood Ancillary Survey.

Are neighbourhood characteristics associated with depressive symptoms? A review of evidence

A review of published observational studies of neighbourhoods and depression/depressive symptoms was conducted to inform future directions for the field. Forty-five English-language cross-sectional and longitudinal studies that analysed the effect of at least one neighbourhood-level variable on either depression or depressive symptoms were analysed. Of the 45 studies, 37 reported associations of at least one neighbourhood characteristic with depression/depressive symptoms. Seven of the 10 longitudinal studies reported associations of at least one neighbourhood characteristic with incident depression. Socioeconomic composition was the most common neighbourhood characteristic investigated. The associations of depressive symptoms/depression with structural features (socioeconomic and racial composition, stability and built environment) were less consistent than with social processes (disorder, social interactions, violence). Among the structural features, measures of the built environment were the most consistently associated with depression but the number of studies was small. The extent to which these associations reflect causal processes remains to be determined. The large variability in studies across neighbourhood definitions and measures, adjustment variables and study populations makes it difficult to draw more than a few general qualitative conclusions. Improving the quality of observational work through improved measurement of neighbourhood attributes, more sophisticated consideration of spatial scale, longitudinal designs and evaluation of natural experiments will strengthen inferences regarding causal effects of area attributes on depression.

Using directed acyclic graphs to guide analyses of neighbourhood health effects: an introduction

BACKGROUND

Directed acyclic graphs, or DAGs, are a useful graphical tool in epidemiologic research that can help identify appropriate analytical strategies in addition to potential unintended consequences of commonly used methods such as conditioning on mediators. The use of DAGs can be particularly informative in the study of the causal effects of social factors on health.

METHODS

The authors consider four specific scenarios in which DAGs may be useful to neighbourhood health effects researchers: (1) identifying variables that need to be adjusted for in estimating neighbourhood health effects, (2) identifying the unintended consequences of estimating "direct" effects by conditioning on a mediator, (3) using DAGs to understand possible sources and consequences of selection bias in neighbourhood health effects research, and (4) using DAGs to identify the consequences of adjustment for variables affected by prior exposure.

CONCLUSIONS

The authors present simplified sample DAGs for each scenario and discuss the insights that can be gleaned from the DAGs in each case and the implications these have for analytical approaches.

Neighborhood environment and risk of ischemic stroke: the brain attack surveillance in Corpus Christi (BASIC) Project

The authors explored whether neighborhood-level characteristics are associated with ischemic stroke and whether the association differs by ethnicity, age, and gender. Using data from the Brain Attack Surveillance in Corpus Christi Project (January 2000-June 2003), they identified cases of ischemic stroke (n = 1,247) from both hospital and out-of-hospital sources. Census tracts served as proxies for neighborhoods, and neighborhood socioeconomic status scores were constructed from census variables (higher scores represented less disadvantage). In Poisson regression analyses comparing the 90th percentile of neighborhood score with the 10th, the relative risk of stroke was 0.49 (95% confidence interval (CI): 0.41, 0.58). After adjustment for age, gender, and ethnicity, this association was attenuated (relative risk (RR) = 0.79, 95% CI: 0.63, 1.00). There was no ethnic difference in the association of score with stroke (p for interaction = 0.79). Significant effect modification was found for age (p for interaction < 0.001) and gender (p for interaction = 0.04), with increasing scores being protective against stroke in men and younger persons. Associations were attenuated after adjustment for education (men: RR = 0.77, 95% CI: 0.55, 1.07; persons aged <65 years: RR = 0.65, 95% CI: 0.41, 1.02). Neighborhood characteristics may influence stroke risk in certain gender and age groups. Mechanisms for these associations should be examined.

Recent exposure to particulate matter and C-reactive protein concentration in the multi-ethnic study of atherosclerosis

Ambient levels of particulate matter have been linked to cardiovascular disease. The mechanisms mediating these associations are poorly understood. One candidate mechanism is inflammation. Using data from the Multi-Ethnic Study of Atherosclerosis (2000-2002), the authors investigated the relation between exposure to particulate matter of less than or equal to 2.5 microm in diameter (PM2.5) and C-reactive protein concentration in 5,634 persons aged 45-84 years who were free of cardiovascular disease. Data from US Environmental Protection Agency monitors were used to estimate PM2.5 exposures for the prior day, prior 2 days, prior week, prior 30 days, and prior 60 days. Only the 30-day and 60-day mean exposures showed a weak positive association with C-reactive protein, and confidence intervals were wide: relative increases in C-reactive protein per 10 microg/m3 of PM2.5 adjusted for person-level covariates were 3% (95% confidence interval (CI): -2, 10) for a 30-day mean and 4% (95% CI: -3, 11.0) for a 60-day mean. The means of 7-day, 30-day, and 60-day exposures were weakly, positively, and nonsignificantly associated with the odds of C-reactive protein of greater than or equal to 3 mg/liter: adjusted odds ratios were 1.05 (95% CI: 0.96, 1.15), 1.12 (95% CI: 0.98, 1.29), and 1.12 (95% CI: 0.96, 1.32), respectively. Slightly stronger associations were observed in persons without other risk factors for elevated C-reactive protein, but this heterogeneity was not statistically significant. The authors' results are not compatible with strong effects of particulate matter exposures on population levels of C-reactive protein.

Neighbourhood unemployment and all cause mortality: a comparison of six countries

STUDY OBJECTIVE

Studies have shown that living in more deprived neighbourhoods is related to higher mortality rates, independent of individual socioeconomic characteristics. One approach that contributes to understanding the processes underlying this association is to examine whether the relation is modified by the country context. In this study, the size of the association between neighbourhood unemployment rates and all cause mortality was compared across samples from six countries (United States, Netherlands, England, Finland, Italy, and Spain).

DESIGN

Data from three prospective cohort studies (ARIC (US), GLOBE (Netherlands), and Whitehall II (England)) and three population based register studies (Helsinki, Turin, Madrid) were analysed. In each study, neighbourhood unemployment rates were derived from census, register based data. Cox proportional hazard models, taking into account the possible correlation of outcomes among people of the same neighbourhood, were used to assess the associations between neighbourhood unemployment and all cause mortality, adjusted for education and occupation at the individual level.

RESULTS

In men, after adjustment for age, education, and occupation, living in the quartile of neighbourhoods with the highest compared with the lowest unemployment rates was associated with increased hazards of mortality (14%-46%), although for the Whitehall II study associations were not statistically significant. Similar patterns were found in women, but associations were not statistically significant in two of the five studies that included women.

CONCLUSIONS

Living in more deprived neighbourhoods is associated with increased all cause mortality in the US and five European countries, independent of individual socioeconomic characteristics. There is no evidence that country substantially modified this association.

Area characteristics, individual-level socioeconomic indicators, and smoking in young adults: the coronary artery disease risk development in young adults study

The 10-year follow-up examination in 1995-1996 to the population-based Coronary Artery Disease Risk Development in Young Adults Study was used to compare the strength with which socioeconomic indicators at the individual and area levels are related to smoking prevalence and to investigate contextual effects of area characteristics. When categories based on similar percentile cutoffs were compared, differences across area categories in the odds of smoking were smaller than differences across categories based on individual-level indicators. In Whites, there was evidence of a significant contextual effect of area characteristics on smoking: Living in the most disadvantaged area quartiles was associated with 50-110% higher odds of smoking, even after controlling for individual-level socioeconomic indicators. Clear contextual effects of area characteristics were not present in Blacks, but there was evidence that contextual effects may emerge at higher levels of individual-level socioeconomic position. Similar results were obtained for census tracts and block groups. Even in the presence of contextual effects, area measures may underestimate associations of individual-level variables with health outcomes. On the other hand, as illustrated by the presence of contextual effects, area- and individual-level measures are likely to tap into different constructs.

A glossary for multilevel analysis

Multilevel analysis has recently emerged as a useful analytical technique in several fields, including public health and epidemiology. This glossary defines key concepts and terms used in multilevel analysis.

Investigating neighborhood and area effects on health

The past few years have witnessed an explosion of interest in neighborhood or area effects on health. Several types of empiric studies have been used to examine possible area or neighborhood effects, including ecologic studies relating area characteristics to morbidity and mortality rates, contextual and multilevel analyses relating area socioeconomic context to health outcomes, and studies comparing small numbers of well-defined neighborhoods. Strengthening inferences regarding the presence and magnitude of neighborhood effects will require addressing a series of conceptual and methodological issues. Many of these issues relate to the need to develop theory and specific hypotheses on the processes through which neighborhood and individual factors may jointly influence specific health outcomes. Important challenges include defining neighborhoods or relevant geographic areas, identifying significant area or neighborhood characteristics, specifying the role of individual-level variables, incorporating life-course and longitudinal dimensions, combining a variety of research designs, and avoiding reductionism in the way in which "neighborhood" factors are incorporated into models of disease causation and quantitative analyses.analyses.

Neighborhood of residence and incidence of coronary heart disease

BACKGROUND

Where a person lives is not usually thought of as an independent predictor of his or her health, although physical and social features of places of residence may affect health and health-related behavior.

METHODS

Using data from the Atherosclerosis Risk in Communities Study, we examined the relation between characteristics of neighborhoods and the incidence of coronary heart disease. Participants were 45 to 64 years of age at base line and were sampled from four study sites in the United States: Forsyth County, North Carolina; Jackson, Mississippi; the northwestern suburbs of Minneapolis; and Washington County, Maryland. As proxies for neighborhoods, we used block groups containing an average of 1000 people, as defined by the U.S. Census. We constructed a summary score for the socioeconomic environment of each neighborhood that included information about wealth and income, education, and occupation.

RESULTS

During a median of 9.1 years of follow-up, 615 coronary events occurred in 13,009 participants. Residents of disadvantaged neighborhoods (those with lower summary scores) had a higher risk of disease than residents of advantaged neighborhoods, even after we controlled for personal income, education, and occupation. Hazard ratios for coronary events in the most disadvantaged group of neighborhoods as compared with the most advantaged group--adjusted for age, study site, and personal socioeconomic indicators--were 1.7 among whites (95 percent confidence interval, 1.3 to 2.3) and 1.4 among blacks (95 percent confidence interval, 0.9 to 2.0). Neighborhood and personal socioeconomic indicators contributed independently to the risk of disease. Hazard ratios for coronary heart disease among low-income persons living in the most disadvantaged neighborhoods, as compared with high-income persons in the most advantaged neighborhoods were 3.1 among whites (95 percent confidence interval, 2.1 to 4.8) and 2.5 among blacks (95 percent confidence interval, 1.4 to 4.5). These associations remained unchanged after adjustment for established risk factors for coronary heart disease.

CONCLUSIONS

Even after controlling for personal income, education, and occupation, we found that living in a disadvantaged neighborhood is associated with an increased incidence of coronary heart disease.