Characterizing the neighborhood risk environment in multisite clinic-based cohort studies: a practical geocoding and data linkages protocol for protected health information v1

Methods. This protocol demonstrates how to: (1) securely geocode patients’ residential addresses in a clinic setting and match geocoded addresses to census tracts using Geographic Information System software (Esri, Redlands, CA); (2) ascertain contextual variables of the risk environment from the American Community Survey and ArcGIS Business Analyst (Esri, Redlands, CA); (3) use geoidentifiers to link neighborhood risk data to census tracts containing geocoded addresses; and (4) assign randomly generated identifiers to census tracts and strip census tracts of their geoidentifiers to maintain patient confidentiality. Results. Completion of this protocol generates three neighborhood risk indices (i.e., a Neighborhood Disadvantage Index, a Murder Rate Index, and a Assault Rate Index) for patients’ coded census tract locations. Intended Usage. This protocol can be used by research personnel and clinic staff who do not have prior GIS experience to easily create objective indices of the neighborhood risk environment while upholding patient confidentiality. Future studies can adapt this protocol to fit their specific patient populations and analytic objectives.

Characterizing the neighborhood risk environment in multisite clinic-based cohort studies: a practical geocoding and data linkages protocol for protected health information v1

Methods. This protocol demonstrates how to: (1) securely geocode patients’ residential addresses in a clinic setting and match geocoded addresses to census tracts using Geographic Information System software (Esri, Redlands, CA); (2) ascertain contextual variables of the risk environment from the American Community Survey and ArcGIS Business Analyst (Esri, Redlands, CA); (3) use geoidentifiers to link neighborhood risk data to census tracts containing geocoded addresses; and (4) assign randomly generated identifiers to census tracts and strip census tracts of their geoidentifiers to maintain patient confidentiality. Results. Completion of this protocol generates three neighborhood risk indices (i.e., Neighborhood Disadvantage Index, Murder Rate Index, and Assault Rate Index) for patients’ coded census tract locations. Intended Usage. This protocol can be used by research personnel without prior GIS experience to easily create objective indices of the neighborhood risk environment while upholding patient confidentiality. Future studies can adapt this protocol to fit their specific patient populations and analytic objectives.

Disorder or Disadvantage: Investigating the Tension Between Neighborhood Social Structure and the Physical Environment on Local Violence

A renewed interest in understanding the relationship of the built environment with neighborhood crime patterns has encouraged researchers to utilize novel methods (e.g., risk terrain modeling) to better examine the influence of environmental risk factors on types of crime. The current study engages with this research by operationalizing neighborhoods using Hipp and Boessen’s egohood strategy and using Drawve’s aggregate neighborhood risk of crime measure to assess the relationship of a neighborhood’s physical environment with its spatial vulnerability of experiencing a homicide. Findings demonstrate that the physical environment was a significant predictor of neighborhood homicide; however, social structural neighborhood characteristics were more important. This suggests crime prevention strategies like crime prevention though environmental design or blight remediation may provide prudent and straightforward methods to inhibit lethal violence in a community in the short run, but that addressing a neighborhood’s social structural characteristics may be more effective at reducing homicides in the long term.

Cortisol and socioeconomic status in early childhood: A multidimensional assessment

The hypothalamic–pituitary–adrenal (HPA) axis is sensitive to early life stress, with enduring consequences for biological stress vulnerability and health (Gunnar & Talge, 2008). Low socioeconomic status (SES) is associated with dysregulation of the stress hormone cortisol in early childhood. However, a mechanistic understanding of this association is lacking. Multidimensional assessment of both SES and cortisol is needed to characterize the intricate relations between SES and cortisol function in early childhood. We assessed parent-reported family income, parent education, occupational prestige, neighborhood risk, food insecurity, and household chaos for 12-month-old infants (N = 90) and 3.5-year-old children (N = 91). Hair cortisol concentration (HCC) was obtained from parent and child, indexing chronic biological stress, and diurnal salivary cortisol was measured in the children. Controlling for parent HCC, parent education uniquely predicted infant and child HCC and, in addition, neighborhood risk uniquely predicted infant HCC. Household chaos predicted bedtime salivary cortisol concentration (SCC) for both infants and children, and infant daily cortisol output. Food insecurity was associated with flattened cortisol slope in 3.5-year-old children. Parental sensitivity did not mediate relations between SES and cortisol. Results highlight the utility of SES measures that index unpredictable and unsafe contexts, such as neighborhood risk, food insecurity, and household chaos.