Release a study package using GIS tools to gain a better understanding of health inequities

OHDSI / HealthEquityWG

We aim to engage critically and intentionally in all of our work, considering not only the results but the potential interpretation and impact of results, steering clear of work that reinforces health disparities and misinterpretations that generate stigma, and lifting up work which is likely to contribute to health equity.

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Release a study package using GIS tools to gain a better understanding of health inequities #11

Open Jake-Gillberg opened 2 years ago

Jake-Gillberg commented 2 years ago

Will discuss further on 10/5 workgroup meeting.

Three potential studies:

local emergency general surgery coverage impact on health outcomes
Social Vulnerability Index at home address as a predictor of ED utilization in aging and HIV positive patients
Area Deprivation Index at home address as a predictor of cancer outcomes

chandryou commented 2 years ago

There is an open-source software based on R/Shiny for GIS analysis of OMOP-CDM data: AEGIS.

Jaehyeong Cho, Seng Chan You, et al., “Application of Epidemiological Geographic Information System: An Open-Source Spatial Analysis Tool Based on the OMOP Common Data Model.” International Journal of Environmental Research and Public Health 17, no. 21 (January 2020): 7824. https://doi.org/10.3390/ijerph17217824.

Jake-Gillberg commented 2 years ago

@chandryou, thanks! This is a great demo and reference. Is there any currently ongoing work with AEGIS or studies that use it? Was this tool ever used in the context of a multi-database study?

chandryou commented 2 years ago

@Jake-Gillberg Unfortunately, the AEGIS has not been used for multi-database study. I don't know the current progress GIS WG....

Jake-Gillberg commented 2 years ago

@chandryou thanks, good to know. Right now the GIS workgroup has infrastructure for standing up a local geocoding pipeline, we are now focusing on standardizing the distribution / synchronization of GIS data sets in a network setting, transforming them to a more OMOP-friendly entity–attribute–value model.