Towards a geocomputational landscape epidemiology: surveillance, modelling, and interventions

Applications and Best Practices for Geospatial Analysis Research in Dermatology

Dermatologic diseases often exhibit distinct geographic patterns, underscoring the significant role of regional environmental, genetic, and sociocultural factors in driving their prevalence and manifestations. Geographic information and geospatial analysis enable researchers to investigate the spatial distribution of adverse health outcomes and their relationship with socioeconomic and environmental risk factors that are inherently geographic. Health geographers and spatial epidemiologists have developed numerous geospatial analytical tools to collect, process, visualize, and analyze geographic data. These tools help provide vital spatial context to the comprehension of the underlying dynamics behind health outcomes. By identifying areas with high rates of dermatologic disease and areas with barriers to access to quality dermatologic care, findings from studies utilizing geospatial analysis can inform the design and targeting of policy and intervention to help improve dermatologic healthcare outcomes and promote health equity. This article emphasizes the significance of geospatial data and analysis in dermatology research. We explore the common processes in data acquisition, harmonization, and geospatial analytics while conducting spatially and dermatologically relevant research. The article also highlights the practical application of geospatial analysis through instances drawn from the dermatology literature.

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.

COVID-19 in Toronto: A Spatial Exploratory Analysis

COVID-19 has had a significant impact on a global scale. Evident signs of spatial-explicit characteristics have been noted. Nevertheless, publicly available data are scarce, impeding a complete picture of the locational impacts of COVID-19. This paper aimed to assess, confirm, and validate several geographical attributes of the geography of the pandemic. A spatial modeling framework defined whether there was a clear spatial profile to COVID-19 and the key socio-economic characteristics of the distribution in Toronto. A stepwise backward regression model was generated within a geographical information systems framework to establish the key variables influencing the spread of COVID-19 in Toronto. Further to this analysis, spatial autocorrelation was performed at the global and local levels, followed by an error and lag spatial regression to understand which explanatory framework best explained disease spread. The findings support that COVID-19 is strongly spatially explicit and that geography matters in preventing spread. Social injustice, infrastructure, and neighborhood cohesion are evident characteristics of the increasing spread and incidence of COVID-19. Mitigation of incidents can be carried out by intertwining local policies with spatial monitoring strategies at the neighborhood level throughout large cities, ensuring open data and adequacy of information management within the knowledge chain.