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.

Dietary intake, plasma homocysteine, and repetitive element DNA methylation in the Multi-Ethnic Study of Atherosclerosis (MESA).

BACKGROUND AND AIMS: DNA methylation of repetitive elements may explain the relations between dietary intake, hyperhomocysteinemia, and cardiovascular disease risk. We investigated associations of methyl micronutrient intake and plasma total homocysteine with LINE-1 and Alu methylation in a cross-sectional study of 987 adults aged 45-84 y who participated in the Multi-Ethnic Study of Atherosclerosis (MESA) Stress Study. METHODS AND RESULTS: DNA methylation was estimated using pyrosequencing technology. A 120-item food frequency questionnaire was used to ascertain daily intake of folate, vitamin B12, vitamin B6, zinc, and methionine. Plasma total homocysteine was quantified using a fluorescence polarization immunoassay. Associations of micronutrient intake and homocysteine with LINE-1 and Alu methylation were examined using linear regression. Adjusted differences in %5-methylated cytosines (%5 mC) were examined by categories of predictors using multivariable linear regression models. Intake of methyl-donor micronutrients was not associated with DNA methylation. After adjustment for covariates, each 3 µmol/L increment of homocysteine corresponded with 0.06 (-0.01, 0.13) %5 mC higher LINE-1 methylation. Additionally, BMI was positively associated with LINE-1 methylation (P trend = 0.03). Participants with BMI ≥ 40 kg/m² had 0.35 (0.03, 0.67) %5 mC higher LINE-1 than those with normal BMI. We also observed a 0.10 (0.02, 0.19) %5 mC difference in Alu methylation per 10 cm of height. These associations did not differ by sex. CONCLUSION: Dietary intake of methyl-donor micronutrients was not associated with measures of DNA methylation in our sample. However, higher BMI was related to higher LINE-1 methylation, and height was positively associated with Alu methylation.

Airborne particulate matter exposure and urinary albumin excretion: the Multi-Ethnic Study of Atherosclerosis.

OBJECTIVES: Understanding mechanistic pathways linking airborne particle exposure to cardiovascular health is important for causal inference and setting environmental standards. We evaluated whether urinary albumin excretion, a subclinical marker of microvascular function which predicts cardiovascular events, was associated with ambient particle exposure. METHODS: Urinary albumin and creatinine were measured among members of the Multi-Ethnic Study of Atherosclerosis at three visits during 2000-2004. Exposure to PM(2.5) and PM(10) (microg/m(3)) was estimated from ambient monitors for 1 month, 2 months and two decades before visit one. We regressed recent and chronic (20 year) particulate matter (PM) exposure on urinary albumin/creatinine ratio (UACR, mg/g) and microalbuminuria at first examination, controlling for age, race/ethnicity, sex, smoking, second-hand smoke exposure, body mass index and dietary protein (n = 3901). We also evaluated UACR changes and development of microalbuminuria between the first, and second and third visits which took place at 1.5- to 2-year intervals in relation to chronic PM exposure prior to baseline using mixed models. RESULTS: Chronic and recent particle exposures were not associated with current UACR or microalbuminuria (per 10 microg/m(3) increment of chronic PM(10) exposure, mean difference in log UACR = -0.02 (95% CI -0.07 to 0.03) and relative probability of having microalbuminuria = 0.92 (95% CI 0.77 to 1.08)) We found only weak evidence that albuminuria was accelerated among those chronically exposed to particles: each 10 microg/m(3) increment in chronic PM(10) exposure was associated with a 1.14 relative probability of developing microalbuminuria over 3-4 years, although 95% confidence intervals included the null (95% CI 0.96 to 1.36). CONCLUSIONS: UACR is not a strong mechanistic marker for the possible influence of air pollution on cardiovascular health in this sample.

Neighborhoods and health: where are we and were do we go from here?

In recent years there has been an explosion of interest in neighborhood health effects. Most existing work has relied on secondary data analyses and has used administrative areas and aggregate census data to characterize neighborhoods. Important questions remain regarding whether the associations reported by these studies reflect causal processes. This paper reviews the major limitations of existing work and discusses areas for future development including: (1) definition and measurement of area or ecologic attributes; (2) consideration of spatial scale; (3) cumulative exposures and lagged effects; (4) the complementary nature of observational, quasi-experimental, and experimental evidence. As is usually the case with complex research questions, consensus regarding the presence and magnitude of neighborhood health effects will emerge from the work of multiple disciplines, often with diverse methodological approaches, each with its strengths and its limitations. Partnership across disciplines, as well as among health researchers, communities, urban planners, and policy experts will be key.

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.

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.

Area characteristics and individual-level socioeconomic position indicators in three population-based epidemiologic studies.

PURPOSE: There is growing interest in incorporating area indicators into epidemiologic analyses. Using data from the 1990 U.S. Census linked to individual-level data from three epidemiologic studies, we investigated how different area indicators are interrelated, how measures for different sized areas compare, and the relation between area and individual-level social position indicators. METHODS: The interrelations between 13 area indicators of wealth/income, education, occupation, and other socioenvironmental characteristics were investigated using correlation coefficients and factor analyses. The extent to which block-group measures provide information distinct from census tract measures was investigated using intraclass correlation coefficients. Loglinear models were used to investigate associations between area and individual-level indicators. RESULTS: Correlations between area measures were generally in the 0.5--0.8 range. In factor analyses, six indicators of income/wealth, education, and occupation loaded on one factor in most geographic sites. Correlations between block-group and census tract measures were high (correlation coefficients 0.85--0.96). Most of the variability in block-group indicators was between census tracts (intraclass correlation coefficients 0.72--0.92). Although individual-level and area indicators were associated, there was evidence of important heterogeneity in area of residence within individual-level income or education categories. The strength of the association between individual and area measures was similar in the three studies and in whites and blacks, but blacks were much more likely to live in more disadvantaged areas than whites. CONCLUSIONS: Area measures of wealth/income, education, and occupation are moderately to highly correlated. Differences between using census tract or block-group measures in contextual investigations are likely to be relatively small. Area and individual-level indicators are far from perfectly correlated and provide complementary information on living circumstances. Differences in the residential environments of blacks and whites may need to be taken into account in interpreting race differences in epidemiologic studies.

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.

How do types of employment relate to health indicators? Findings from the second European survey on working conditions.

STUDY OBJECTIVE: To investigate the associations of various types of employment with six self reported health indicators, taking into account the part played by demographic variables, individual working conditions and four ecological indicators at the country level. DESIGN: Cross sectional survey (structured interview) of a sample of the active population of 15 European countries aged 15 years or over. Main independent variables were nine types of employment categorised as follows: small employers, full and part time permanent employees, full and part time fixed term employees, full and part time sole traders and full and part time temporary contracts. Main outcome measures were three self reported health related outcomes (job satisfaction, health related absenteeism, and stress) and three self reported health problems (overall fatigue, backache, and muscular pains). Logistic regression and multilevel models were used in the analyses. SETTING: 15 countries of the European Union. PARTICIPANTS: 15 146 employed persons aged 15 or over. MAIN RESULTS: Precarious employment was consistently and positively associated with job dissatisfaction but negatively associated with absenteeism and stress (as compared with full time permanent workers). Fatigue, backache and muscular pains also tended to be positively associated with precarious employment, particularly with full time precarious employment. Small employers reported high percentages of stress and fatigue, but absenteeism was relatively low. Sole traders generally reported high percentages of all outcomes, except for absenteeism, which was low. For each type of employment (except temporary contracts), full time workers tended to report worse health outcomes than part time workers. Patterns were generally consistent across countries. Associations persisted after adjustment for individual level working conditions and were not modified by country level variables. CONCLUSIONS: This study is the first to examine the relations between various types of employment and six health related indicators for all 15 member states of the European Union. Suggestive patterns worthy of further exploration have been found. Standardised definitions of types of underemployment and health related outcomes, more potent epidemiological designs and the inclusion of socioeconomic information (for example, social security systems, incapacity benefit schemes) at the regional level are proposed for inclusion in further research.

Neighbourhood differences in diet: the Atherosclerosis Risk in Communities (ARIC) Study.

STUDY OBJECTIVE: To investigate whether neighbourhood characteristics are related to dietary patterns independently of individual level variables. DESIGN: A cross sectional analysis of the relation between neighbourhood median household income and food and nutrient intakes, before and after adjustment for individual level variables. SETTING: Four United States communities (Washington Co, MD; Suburban Minneapolis, MN; Forsyth Co, NC, and Jackson, MS). PARTICIPANTS: 13,095 adults aged 45 to 64 years participating in the baseline examination of the Atherosclerosis Risk in Communities (ARIC) Study, a prospective study of atherosclerosis. MEASUREMENTS AND MAIN RESULTS: Information on diet and individual level income was obtained from the baseline examination of the ARIC Study. Diet was assessed using a semi-quantitative food frequency questionnaire. Information on neighbourhood (census defined block groups) median household income was obtained from the 1990 US Census. Multilevel models were used to account for the multilevel structure of the data. Living in lower income neighbourhoods was generally associated with decreased energy adjusted intake of fruits, vegetables, fish, and increased intake of meat. Patterns generally persisted after adjustment for individual level income, but were often not statistically significant. Inconsistent associations were recorded for the intake of saturated fat, polyunsaturated fat, and cholesterol. Overall, individual level income was a more consistent predictor of diet than neighbourhood income. CONCLUSION: Despite limitations in the definition and characterisation of neighbourhoods, this study found consistent (albeit small) differences across neighbourhoods in food intake, suggesting that more in depth research into potential neighbourhood level determinants of diet is warranted.

A multilevel analysis of income inequality and cardiovascular disease risk factors.

Recent research has suggested that inequality in the distribution of income is associated with increased mortality, even after accounting for average income levels. Using data from the Behavioral Risk Factor Surveillance System (BRFSS), we investigated whether inequality in the distribution of income within US states is related to the prevalence of four cardiovascular disease risk factors (body mass index (BMI), history of hypertension, sedentarism, and smoking). Multilevel models (including both state-level and individual-level variables) were used to examine associations of state inequality with risk factor levels before and after adjustment for individual-level income. For three of the four risk factors investigated (BMI, hypertension, and sedentarism), state inequality was associated with increased risk factor levels, particularly at low income levels (annual household incomes <$25,000), with associations persisting after adjustment for individual-level income. Inequality was also positively associated with smoking, but associations were either stronger or only present at higher income levels. Associations of inequality with the outcomes were statistically significant in women but not in men. Although not conclusive, findings for three of the four risk factors are suggestive of a contextual effect of income inequality, particularly among persons with lower incomes.

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.

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.

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.

Cumulative life course and adult socioeconomic status and markers of inflammation in adulthood.

OBJECTIVE: To examine the association between cumulative life course and adult socioeconomic status (SES) and adult levels of inflammatory risk markers (fibrinogen, white blood cell count (WBC), C-reactive protein (CRP), von Willebrand factor (vWF) and an overall inflammatory score). DESIGN: Retrospective cohort study. SETTING: 12,681 white and African-American participants in the Atherosclerosis Risk in Communities (ARIC) study and two ancillary studies. METHODS: Participants provided information on SES and place of residence in childhood and young (30-40 years) and mature (45+) adulthood. Residences were linked to census data for neighbourhood SES information. Multiple imputation (MI) was used for missing data. Linear regression and adjusted geometric means were used to estimate the effects of SES on inflammatory risk marker levels. RESULTS: Graded, statistically significant associations were observed between greater cumulative life-course exposure to low education and social class and elevated levels of fibrinogen and WBC among white participants. Stronger graded, statistically significant associations were observed between low adult education, social class and neighbourhood SES and elevated inflammatory levels. Associations were weaker and less consistent in African-Americans. Covariate adjustment attenuated results but many associations remained strong. CONCLUSIONS: Our results suggest that cumulative exposure to adverse SES conditions across the life course and low adult SES are associated with an elevated systemic inflammatory burden in adulthood. Chronic systemic inflammation may be one pathway linking low life-course SES and elevated cardiovascular disease risk.

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.

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.