Mujahid et al. Respond to "Beyond the Metrics for Measuring Neighborhood Effects"

In her commentary, Dr. Lynne Messer (1) recognizes the important contributions of our paper (2) to the discussion of methodological issues related to measurement of neighborhood or area-level properties. Dr. Messer reviews the many challenges involved in observational studies of neighborhood health effects, which we and other investigators have noted (3–8). A major challenge is developing theoretical models of the processes through which neighborhoods (or areas) may affect health. Messer argues that our paper "promises more, from a theoretical perspective, than it delivers" (1, p. 869). Our paper is merely a methodological illustration, with no grandiose theoretical aims. However, we do base the measures we explore on a theoretical model of the processes through which residential context may affect cardiovascular disease risk (1, 9). In her discussion of this model, Messer confuses inconsistent empirical support for aspects of the model with the absence of theory itself. Theorizing on the spatial scale at which different area processes operate is obviously important, but unfortunately there is very little information on which to base this theory. Additional qualitative research on the ways in which individuals interact with spaces may help us develop better theoretical models that may then be empirically tested. However, even if we were able to offer some crude hypotheses regarding spatial scales relevant to different processes, there are features of areas that could plausibly operate at multiple levels. Ultimately, we must rely on empirical research to uncover such relations rather than make a priori assertions under the guise of theory. For this, improving the validity of area-level measures and sensitivity analyses like the ones we present is crucial. Dr. Messer also alludes to the well-established challenges in estimating causal effects from observational data. Nonexchangeability (or its simpler and less fashionable synonym, "residual confounding") is always a concern. Messer implies that because of this, observational work in neighborhood health-effects research is meaningless. Firm believers in nonexchangeability will accept no defense of observational studies because it is impossible to categorically rule out residual confounding, except in the case of the ideal counterfactual experiment. However, claims of residual confounding also need to be subjected to empirical inquiry: What specific confounders have been omitted, and how strong are their effects expected to be? Careful observational work can empirically examine the sensitivity of results to different degrees of residual confounding and degrees of extrapolation. In this, neighborhood effects research is no different than the rest of epidemiology. Given the many limitations and logistical challenges of randomized trials (particularly for the study of neighborhood effects), reliance on observational and quasi-experimental data is likely to continue. Hence, anything we can do to improve the rigor of observational work is crucial. Our objective in the current paper was (merely) to contribute to emerging work on the measurement of area-level constructs, not to fully develop a theory on neighborhood causal effects or to resolve the issue of relevant spatial scale. Our objective was not even to estimate associations between neighborhood characteristics and health outcomes. Instead, we wanted to further develop and evaluate our ability to measure area-level constructs. Epidemiologists are very sophisticated at measuring individual-level characteristics but not as sophisticated at measuring features of ecologic settings. This seriously hampers their ability to examine contextual effects. Our analyses illustrate one approach to quantifying the measurement properties of area-based measures. This approach can be adapted to different constructs and different spatial scales, depending on the research problem and underlying theory. We firmly believe that improving the quality of measurement of area-level constructs is a prerequisite for more rigorous observational work. In fact, several of the inferential problems that arise when area socioeconomic status characteristics are used as proxies for features of areas may be reduced when specific features of areas are examined instead of aggregate socioeconomic status measures (which are, by definition, correlated with individual socioeconomic status, thus magnifying the extrapolation and exchangeability problems). We hope that the illustration we provide in our paper (2) will encourage other investigators to develop and test theoretically relevant area measures and to contrast different approaches to their measurement. Understanding if and how contexts (including neighborhoods) affect health is challenging and complex, but it is also enormously important from the point of view of public health and policy. In order to answer questions regarding these effects, we need to move beyond blanket (and sometimes facile) critiques, roll up our sleeves, and see if we can improve on the work that has been done to date. This means dealing with a messy, correlated, and confounded reality and doing the best we can to glean truth from our observations. As epidemiologists, this is our job, and also our responsibility to the public.http://deepblue.lib.umich.edu/bitstream/2027.42/58002/1/Mujahid et al Respond to Beyond the Metrics for Measuring Neighborhood Effects.pd

Understanding social disparities in hypertension prevalence, awareness, treatment, and control: The role of neighborhood context.

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/57187/1/Morenoff JD et al 2007 Understanding social disparities in hypertension prevalence awareness treatment and control The role of neighborhood context.pd

Neighborhood Characteristics and Hypertension

Background: The goal of this study was to investigate cross-sectional associations between features of neighborhoods and hypertension and to examine the sensitivity of results to various methods of estimating neighborhood conditions. Methods: We used data from the Multi-Ethnic Study of Atherosclerosis on 2612 individuals 45–85 years of age. Hypertension was defined as systolic blood pressure above 140 mm Hg, diastolic pressure above 90 mm Hg, or use of antihypertensive medications. Neighborhood (census tract) conditions potentially related to hypertension (walking environment, availability of healthy foods, safety, social cohesion) were measured using information from a separate phone survey conducted in the study neighborhoods. For each neighborhood we estimated scale scores by aggregating residents’ responses using simple aggregation (crude means) and empirical Bayes estimation (unconditional, conditional, and spatial). These estimates of neighborhood conditions were linked to each study participant based on the census tract of residence. Two-level binomial regression methods were used to estimate adjusted associations between neighborhood conditions and hypertension. Results: Residents of neighborhoods with better walkability, availability of healthy foods, greater safety, and more social cohesion were less likely to be hypertensive (relative prevalence [95% confidence interval] for 90th vs. 10th percentile of conditional empirical Bayes estimate = 0.75 [0.64–0.88], 0.72 [0.61–0.85], 0.74 [0.63–0.86], and 0.69 [0.57–0.83]), respectively, after adjusting for site, age, sex, income, and education. Associations were attenuated and often disappeared after additional adjustments for race/ethnicity. Conclusion: Neighborhood walkability, food availability, safety, and social cohesion may be mechanisms that link neighborhoods to hypertension.http://deepblue.lib.umich.edu/bitstream/2027.42/60338/1/Neighborhood Characteristics and Hypertension.pd

Constructing meaning about the delinquency of young girls in public-housing neighbourhoods

UID/SOC/04647/2013 SFRH/BPD/116119/2016Rooted in the theoretical approaches of social ecology and in childhood studies, the Ph.D. research project on which this paper is based aimed to achieve a better understanding of children’s socialization processes in multi-problematic spaces, particularly concerning their involvement in violence and delinquency. A case study based on ethnographic research and child-centred methods was carried out in six public-housing neighbourhoods in Portugal, which were chosen because they had relatively high levels of social deprivation, violence and crime. The specificity of the social group under study—children aged from 6 to 12 years old—and their living conditions, led us to extend the data collected by trying to learn, from the girls, the reasoning and the meanings they assign to their own actions in daily social practices. The intention was to study the features of girls’ socialization in the field through their own accounts of their lives and to examine their perspectives on offending behaviours. The genderized process of social learning in delinquency identified in the girls’ conversation is an important variable, as familial and social experiences tend to facilitate their entry into delinquency. The transmission of delinquent values takes place essentially within the female family circle or via female peers, rather than from the influence of male individuals.authorsversionpublishe

Using remote sensing to assess the relationship between crime and the urban layout

[EN] The link between place and crime is at the base of social ecology theories of crime that focus in the relationship of the characteristics of geographical areas and crime rates. The broken windows theory states that visible cues of physical and social disorder in a neighborhood can lead to an increase in more serious crime. The crime prevention through environmental design (CPTED) planning approach seeks to deter criminal behavior by creating defensible spaces. Based on the premise that a settlement's appearance is a reflection of the society, we ask whether a neighborhood's design has a quantifiable imprint when seen from space using urban fabric descriptors computed from very high spatial-resolution imagery. We tested which land cover, structure and texture descriptors were significantly related to intra-urban homicide rates in Medellin, Colombia, while controlling for socioeconomic confounders. The percentage of impervious surfaces other than clay roofs, the fraction of clay roofs to impervious surfaces, two structure descriptors related to the homogeneity of the urban layout, and the uniformity texture descriptor were all statistically significant. Areas with higher homicide rates tended to have higher local variation and less general homogeneity; that is, the urban layouts were more crowded and cluttered, with small dwellings with different roofing materials located in close proximity to one another, and these regions often lacked other homogeneous surfaces such as open green spaces, wide roads, or large facilities. These results seem to be in agreement with the broken windows theory and CPTED in the sense that more heterogeneous and disordered urban layouts are associated with higher homicide rates.This research was made possible by funding from EAFIT University (EAFIT-435-000060) and the Medellin City Hall EnlazaMundos program. The authors thank the anonymous reviewers and Hermilson Velazquez, Andr es Ramírez Hassan and Gustavo Canavire for their insightful observations and suggestions during the different stages of this projectPatiño Quinchía, JE.; Duque, JC.; Pardo Pascual, JE.; Ruiz Fernández, LÁ. (2014). Using remote sensing to assess the relationship between crime and the urban layout. Applied Geography. 55:48-60. https://doi.org/10.1016/j.apgeog.2014.08.016S48605

The joint influence of marital status, interpregnancy interval, and neighborhood on small for gestational age birth: a retrospective cohort study

<p>Abstract</p> <p>Background</p> <p>Interpregnancy interval (IPI), marital status, and neighborhood are independently associated with birth outcomes. The joint contribution of these exposures has not been evaluated. We tested for effect modification between IPI and marriage, controlling for neighborhood.</p> <p>Methods</p> <p>We analyzed a cohort of 98,330 live births in Montréal, Canada from 1997–2001 to assess IPI and marital status in relation to small for gestational age (SGA) birth. Births were categorized as subsequent-born with <it>short </it>(<12 months), <it>intermediate </it>(12–35 months), or <it>long </it>(36+ months) IPI, or as firstborn. The data had a 2-level hierarchical structure, with births nested in 49 neighborhoods. We used multilevel logistic regression to obtain adjusted effect estimates.</p> <p>Results</p> <p>Marital status modified the association between IPI and SGA birth. Being unmarried relative to married was associated with SGA birth for all IPI categories, particularly for subsequent births with <it>short </it>(odds ratio [OR] 1.60, 95% confidence interval [CI] 1.31–1.95) and <it>intermediate </it>(OR 1.48, 95% CI 1.26–1.74) IPIs. Subsequent births had a lower likelihood of SGA birth than firstborns. <it>Intermediate </it>IPIs were more protective for married (OR 0.50, 95% CI 0.47–0.54) than unmarried mothers (OR 0.65, 95% CI 0.56–0.76).</p> <p>Conclusion</p> <p>Being unmarried increases the likelihood of SGA birth as the IPI shortens, and the protective effect of <it>intermediate </it>IPIs is reduced in unmarried mothers. Marital status should be considered in recommending particular IPIs as an intervention to improve birth outcomes.</p

Ambient air pollution exposure and full-term birth weight in California

<p>Abstract</p> <p>Background</p> <p>Studies have identified relationships between air pollution and birth weight, but have been inconsistent in identifying individual pollutants inversely associated with birth weight or elucidating susceptibility of the fetus by trimester of exposure. We examined effects of prenatal ambient pollution exposure on average birth weight and risk of low birth weight in full-term births.</p> <p>Methods</p> <p>We estimated average ambient air pollutant concentrations throughout pregnancy in the neighborhoods of women who delivered term singleton live births between 1996 and 2006 in California. We adjusted effect estimates of air pollutants on birth weight for infant characteristics, maternal characteristics, neighborhood socioeconomic factors, and year and season of birth.</p> <p>Results</p> <p>3,545,177 singleton births had monitoring for at least one air pollutant within a 10 km radius of the tract or ZIP Code of the mother's residence. In multivariate models, pollutants were associated with decreased birth weight; -5.4 grams (95% confidence interval -6.8 g, -4.1 g) per ppm carbon monoxide, -9.0 g (-9.6 g, -8.4 g) per pphm nitrogen dioxide, -5.7 g (-6.6 g, -4.9 g) per pphm ozone, -7.7 g (-7.9 g, -6.6 g) per 10 <it>μ</it>g/m³particulate matter under 10 μm, -12.8 g (-14.3 g, -11.3 g) per 10 <it>μ</it>g/m³particulate matter under 2.5 μm, and -9.3 g (-10.7 g, -7.9 g) per 10 <it>μ</it>g/m³of coarse particulate matter. With the exception of carbon monoxide, estimates were largely unchanged after controlling for co-pollutants. Effect estimates for the third trimester largely reflect the results seen from full pregnancy exposure estimates; greater variation in results is seen in effect estimates specific to the first and second trimesters.</p> <p>Conclusions</p> <p>This study indicates that maternal exposure to ambient air pollution results in modestly lower infant birth weight. A small decline in birth weight is unlikely to have clinical relevance for individual infants, and there is debate about whether a small shift in the population distribution of birth weight has broader health implications. However, the ubiquity of air pollution exposures, the responsiveness of pollutant levels to regulation, and the fact that the highest pollution levels in California are lower than those regularly experienced in other countries suggest that precautionary efforts to reduce pollutants may be beneficial for infant health from a population perspective.</p

The Great American Crime Decline : Possible Explanations

This chapter examines the most important features of the crime decline in the United States during the 1990s-2010s but also takes a broader look at the violence declines of the last three centuries. The author argues that violent and property crime trends might have diverged in the 1990s, with property crimes increasingly happening in the online sphere and thus traditional property crime statistics not being reflective of the full picture. An important distinction is made between ‘contact crimes’ and crimes that do not require a victim and offender to be present in the same physical space. Contrary to the uncertainties engendered by property crime, the declines in violent (‘contact’) crime are rather general, and have been happening not only across all demographic and geographic categories within the United States but also throughout the developed world. An analysis of research literature on crime trends has identified twenty-four different explanations for the crime drop. Each one of them is briefly outlined and examined in terms of conceptual clarity and empirical support. Nine crime decline explanations are highlighted as the most promising ones. The majority of these promising explanations, being relative newcomers in the crime trends literature, have not been subjected to sufficient empirical scrutiny yet, and thus require further research. One potentially fruitful avenue for future studies is to examine the association of the most promising crime decline explanations with improvements in self-control

Spatial Random Slope Multilevel Modeling Using Multivariate Conditional Autoregressive Models: A Case Study of Subjective Travel Satisfaction in Beijing

This article explores how to incorporate a spatial dependence effect into the standard multilevel modeling (MLM). The proposed method is particularly well suited to the analysis of geographically clustered survey data where individuals are nested in geographical areas. Drawing on multivariate conditional autoregressive models, we develop a spatial random slope MLM approach to account for the within-group dependence among individuals in the same area and the spatial dependence between areas simultaneously. Our approach improves on recent methodological advances in the integrated spatial and MLM literature, offering greater flexibility in terms of model specification by allowing regression coefficients to be spatially varied. Bayesian Markov chain Monte Carlo (MCMC) algorithms are derived to implement the proposed model. Using two-level travel satisfaction data in Beijing, we apply the proposed approach as well as the standard nonspatial random slope MLM to investigate subjective travel satisfaction of residents and its determinants. Model comparison results show strong evidence that the proposed method produces a significant improvement against a nonspatial random slope MLM. A fairly large spatial correlation parameter suggests strong spatial dependence in district-level random effects. Moreover, spatial patterns of district-level random effects of locational variables have been identified, with high and low values clustering together