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Geospatial and Socioeconomic Analysis of Direct Primary Care Practices in the United States

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Abstract Background Current literature on direct primary care (DPC) is largely composed of opinion-based arguments that the model may exacerbate healthcare inequity. Objective data are needed to assess how DPC practices are distributed and whether they contribute to disparities in access to care. Methods 1. Visited DPC Frontier website ( https://mapper.dpcfrontier.com ). 2. Extracted clinic information from the website using a custom web-socket script (ws.py) that requested each entry using its practice-key (scraped from raw html). 3. Duplicate entries removed by R script (No-Dups.R) unique.data.frame() (unique rows) (Data_Columns = “Postal”,“City”,“Region”,“Name”,“Lat”,“Lng”,“Website”) 3. Clinic data was then merged (by zipcode) with the metadata from the zipcodeR database and the Rural-Urban Commuting Area Codes (RUCA) coding table from the US Department of Agriculture to produce the Mergd-with-RUCA.csv spreadsheet (a comprehensive geospatial database of DPC practice locations and their associated demographic and socioeconomic characteristics). 4. Data Analysis: Data-Analysis-Recommendations.pdf Sections 2,4, and 5 (authored by Sr. Lugo Capera) Results DPC practices were more common in urban and suburban zip codes, positively correlated with higher total housing units, lower occupancy rates, and lower median household incomes. Family medicine was more prevalent in lower-income zip codes, while specialties such as dermatology and cardiology clustered in middle- and higher-income areas. Discussion While DPC practices appear to favor more commercial or suburban settings, their presence in lower-income zip codes suggests potential to serve populations with limited access to traditional care. However, specialty care appears less equitably distributed. Conclusion The GINI coefficient of 0.22 for DPC practice distribution indicates modest inequality, with most zip codes hosting one or no DPC practices. While geographic access to primary DPC appears relatively even, disparities in specialty DPC and potential quality differences merit further investigation.
Title: Geospatial and Socioeconomic Analysis of Direct Primary Care Practices in the United States
Description:
Abstract Background Current literature on direct primary care (DPC) is largely composed of opinion-based arguments that the model may exacerbate healthcare inequity.
Objective data are needed to assess how DPC practices are distributed and whether they contribute to disparities in access to care.
Methods 1.
Visited DPC Frontier website ( https://mapper.
dpcfrontier.
com ).
2.
Extracted clinic information from the website using a custom web-socket script (ws.
py) that requested each entry using its practice-key (scraped from raw html).
3.
Duplicate entries removed by R script (No-Dups.
R) unique.
data.
frame() (unique rows) (Data_Columns = “Postal”,“City”,“Region”,“Name”,“Lat”,“Lng”,“Website”) 3.
Clinic data was then merged (by zipcode) with the metadata from the zipcodeR database and the Rural-Urban Commuting Area Codes (RUCA) coding table from the US Department of Agriculture to produce the Mergd-with-RUCA.
csv spreadsheet (a comprehensive geospatial database of DPC practice locations and their associated demographic and socioeconomic characteristics).
4.
Data Analysis: Data-Analysis-Recommendations.
pdf Sections 2,4, and 5 (authored by Sr.
Lugo Capera) Results DPC practices were more common in urban and suburban zip codes, positively correlated with higher total housing units, lower occupancy rates, and lower median household incomes.
Family medicine was more prevalent in lower-income zip codes, while specialties such as dermatology and cardiology clustered in middle- and higher-income areas.
Discussion While DPC practices appear to favor more commercial or suburban settings, their presence in lower-income zip codes suggests potential to serve populations with limited access to traditional care.
However, specialty care appears less equitably distributed.
Conclusion The GINI coefficient of 0.
22 for DPC practice distribution indicates modest inequality, with most zip codes hosting one or no DPC practices.
While geographic access to primary DPC appears relatively even, disparities in specialty DPC and potential quality differences merit further investigation.

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