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

Background Maintaining patient privacy when geocoding and linking residential address information with neighborhood-level data can create challenges during research. Challenges may arise when study staff have limited training in geocoding and linking data, or when non-study staff with appropriate expertise have limited availability, are unfamiliar with a study’s population or objectives, or are not affordable for the study team. Opportunities for data breaches may also arise when working with non-study staff who are not on-site. We detail a free, user-friendly protocol for constructing indices of the neighborhood risk environment during multisite, clinic-based cohort studies that rely on participants’ protected health information. This protocol can be implemented by study staff who do not have prior training in Geographic Information Systems (GIS) and can help minimize the operational costs of integrating geographic data into public health projects. 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. Conclusions 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

Multilevel Resilience and HIV Virologic Suppression Among African American/Black Adults in the Southeastern United States

Objective: To assess overall and by neighborhood risk environments whether multilevel resilience resources were associated with HIV virologic suppression among African American/Black adults in the Southeastern United States. Setting and Methods: This clinical cohort sub-study included 436 African American/Black participants enrolled in two parent HIV clinical cohorts. Resilience was assessed using the Multilevel Resilience Resource Measure (MRM) for African American/Black adults living with HIV, where endorsement of a MRM statement indicated agreement that a resilience resource helped a participant continue HIV care despite challenges or was present in a participant’s neighborhood. Modified Poisson regression models estimated adjusted prevalence ratios (aPRs) for virologic suppression as a function of categorical MRM scores, controlling for demographic, clinical, and behavioral characteristics at or prior to sub-study enrollment. We assessed for effect measure modification (EMM) by neighborhood risk environments. Results: Compared to participants with lesser endorsement of multilevel resilience resources, aPRs for virologic suppression among those with greater or moderate endorsement were 1.03 (95% confidence interval: 0.96–1.11) and 1.03 (0.96–1.11), respectively. Regarding multilevel resilience resource endorsement, there was no strong evidence for EMM by levels of neighborhood risk environments. Conclusions: Modest positive associations between higher multilevel resilience resource endorsement and virologic suppression were at times most compatible with the data. However, null findings were also compatible. There was no strong evidence for EMM concerning multilevel resilience resource endorsement, which could have been due to random error. Prospective studies assessing EMM by levels of the neighborhood risk environment with larger sample sizes are needed