The purpose of this project is to provide a framework for easy mapping to external data sets using demographic measures from EHR systems. This pilot project will look at external data sets for rurality that link to row-level patient data in a hypothetical demographic table to demonstrate a prototype for a software package capable of routinizing this task for clinical research data repositories or for project-specific requirements.

This prototype includes two location-based indices: RUCA and RUCC. These can be linked using Census Tract data, county (by FIPS), and ZIP Code.

The 2013 Rural-Urban Continuum Codes form a classification scheme that distinguishes metropolitan counties by the population size of their metro area, and nonmetropolitan counties by degree of urbanization and adjacency to a metro area. The official Office of Management and Budget (OMB) metro and nonmetro categories have been subdivided into three metro and six nonmetro categories. Each county in the U.S., municipio in Puerto Rico, and Census Bureau-designated county-equivalent area of the Virgin Islands/other inhabited island territories of the U.S. is assigned one of the 9 codes. This scheme allows researchers to break county data into finer residential groups, beyond metro and nonmetro, particularly for the analysis of trends in nonmetro areas that are related to population density and metro influence. The Rural-Urban Continuum Codes were originally developed in 1974. They have been updated each decennial since (1983, 1993, 2003, 2013), and slightly revised in 1988. Note that the 2013 Rural-Urban Continuum Codes are not directly comparable with the codes prior to 2000 because of the new methodology used in developing the 2000 metropolitan areas. See the Documentation for details and a map of the codes. Source: USDA ERS Rural-Urban Continuum Codes

The rural-urban commuting area (RUCA) codes, a detailed and flexible scheme for delineating sub-county components of rural and urban areas, have been updated using data from the 2010 decennial census and the 2006-10 American Community Survey (ACS). RUCA codes are based on the same theoretical concepts used by the Office of Management and Budget (OMB) to define county-level metropolitan and micropolitan areas. We applied similar criteria to measures of population density, urbanization, and daily commuting to identify urban cores and adjacent territory that is economically integrated with those cores. We adopted OMB's metropolitan and micropolitan terminology to highlight the underlying connectedness between the two classification systems. However, the use of census tracts instead of counties as building blocks for RUCA codes provides a different and more detailed geographic pattern of urban and rural areas. Source: USDA ERS 2010 Rural-Urban Commuting Area (RUCA) Codes

This project was created for UNO BMI8540-850, Spring 2021.

• Requirements
• Usage
• Optional Mapping
• References
• License

RIMPLES requires the following to run:

• MySQLdb
• Python 3.X
• Pandas
• Plotly Express

This RUMPLES pilot is built on a slimmed-down version of OMOP CDM v5.2.2. Please follow the instruction here to deploy the database locally.

After installing the OMOP database in MySQL, proceed to defining your cohort of interest. Alternatively, you can run the program against your entire database. The output creates a new table in your simple OMOP database as well as an output file with the mapped patient information with the included RUCC and RUCA information.

First download locationSDOH.py into your local directory:

wget https://raw.githubusercontent.com/jerrodanzalone/Rural-Urban-Mapping-Protocol-for-Linking-External-Sources/main/locationSDOH.py

From here, update information about your local database connection. This will vary depending on your underlying database settings. The package is flexible and can support multiple database models (i.e., MariaDB, PostgreSQL, SQLite, etc.):

# Connect to my SQL database
conn = MySQLdb.connect(db="DATABASE INFORMATION")
cursor = conn.cursor()

After defining a cohort of interest, the package runs through system arguments. It takes in two system arguments: one input file and one output file. The input file contains a column defining the person_id of interest, which is used to JOIN to the OMOP test database. The output file will automatically output into the filepath given as the second system argument. An example of an input file named cohort.csv with a desired path to file is provided below.

python3 locationSDOH.py cohort.csv /path/to/locationSDOH.csv

A table is also created in the database with the following variables:

To run the package against the entire database, run the following against the OMOP database and save the results as a csv:

SELECT DISTINCT(person_id)
FROM person;

To employ an optional mapping component based on the defined cohort, a fun package add-on is provided to create a cohort-mapped csv file with county code and an option to map it using Plotly Express.

First download cohortFips.py and cohortMapping.py to your local directory:

wget https://raw.githubusercontent.com/jerrodanzalone/Rural-Urban-Mapping-Protocol-for-Linking-External-Sources/main/cohortFIPS.py
wget https://raw.githubusercontent.com/jerrodanzalone/Rural-Urban-Mapping-Protocol-for-Linking-External-Sources/main/cohortMapping.py

The cohort defined in the previous step is used here as well. The output file will automatically output into the filepath given as the second system argument. The program also creates a table in your database corresponding to cohort_fips, which includes the patient cohort and the fips for the county associated with each person. An example of an input file named cohort.csv with a desired path to file is provided below.

python3 cohortFIPS.py cohort.csv /path/to/mappedCohort.csv

This input file is then used in the cohortMapping.py The output file will automatically output into the filepath given as the second system argument. An example of an input file named cohort.csv with a desired path to file is provided below.

python3 cohortMapping.py mappedCohort.csv /path/to/cohortMapped.html

This creates an html choropleth map of the full cohort by county. It is an interactive html map based on the MapBox add-on built within Plotly Express. It includes counts of the defined cohort mapped to county. Please view an example of the map here. Here is a sample of what you can expect from this process:

1. Gardner, B. J., Pedersen, J. G., Campbell, M. E., & McClay, J. C. (2019). Incorporating a location-based socioeconomic index into a de-identified i2b2 clinical data warehouse. Journal of the American Medical Informatics Association: JAMIA, 26(4), 286–293. https://doi.org/10.1093/jamia/ocy172
2. Gagalova, K. K., Elizalde, M. A. L., Portales-Casamar, E., & Görges, M. (2020). What You Need to Know Before Implementing a Clinical Research Data Warehouse: Comparative Review of Integrated Data Repositories in Health Care Institutions. JMIR formative research, 4(8), e17687.
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4. Source dataset: https://www.ers.usda.gov/data-products/rural-urban-continuum-codes.aspx
5. Source dataset: https://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes.aspx
6. Haendel, M. A., Chute, C. G., Bennett, T. D., Eichmann, D. A., Guinney, J., Kibbe, W. A., ... & N3C Consortium. (2020). The National COVID Cohort Collaborative (N3C): rationale, design, infrastructure, and deployment. Journal of the American Medical Informatics Association.
7. Ehrenstein V, Kharrazi H, Lehmann H, et al. Obtaining Data From Electronic Health Records. In: Gliklich RE, Leavy MB, Dreyer NA, editors. Tools and Technologies for Registry Interoperability, Registries for Evaluating Patient Outcomes: A User’s Guide, 3rd Edition, Addendum 2 [Internet]. Rockville (MD): Agency for Healthcare Research and Quality (US); 2019 Oct. Chapter 4. Available from: https://www.ncbi.nlm.nih.gov/books/NBK551878/
8. Rangel, J. C., Ranade, S., Sutcliffe, P., Mykhalovskiy, E., Gastaldo, D., & Eakin, J. (2020). COVID-19 policy measures-Advocating for the inclusion of the social determinants of health in modelling and decision making. Journal of evaluation in clinical practice, 26(4), 1078–1080. https://doi.org/10.1111/jep.13436
9. Onega, T., Weiss, J. E., Alford‐Teaster, J., Goodrich, M., Eliassen, M. S., & Kim, S. J. (2020). Concordance of rural‐urban self‐identity and ZIP code‐derived rural‐urban commuting area (RUCA) designation. The Journal of Rural Health, 36(2), 274-280.
10. Cho, J., You, S. C., Lee, S., Park, D., Park, B., Hripcsak, G., & Park, R. W. (2020). 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(21), 7824.
11. OHDSI. (2019). The Book of OHDSI.
12. Platt, R. W. (2021). Code Review: An Important Step Towards Reproducible Research. American journal of epidemiology.

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