A data pipeline for secure extraction and sharing of social determinants of health.
<h4>Objectives</h4>Linking neighborhood- and patient-level data provides valuable information about the influence of upstream social determinants of health (SDOH). However, sharing of these data across health systems presents challenges. We set out to develop a pipeline to acquire, deide...
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| Format: | Article |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0317215 |
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| author | Tyler Schappe Lisa M McElroy Moronke Ogundolie Roland Matsouaka Ursula Rogers Nrupen A Bhavsar |
| author_facet | Tyler Schappe Lisa M McElroy Moronke Ogundolie Roland Matsouaka Ursula Rogers Nrupen A Bhavsar |
| author_sort | Tyler Schappe |
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| description | <h4>Objectives</h4>Linking neighborhood- and patient-level data provides valuable information about the influence of upstream social determinants of health (SDOH). However, sharing of these data across health systems presents challenges. We set out to develop a pipeline to acquire, deidentify, and share neighborhood-level SDOH data across multiple health systems.<h4>Methods</h4>We created a pipeline centered around Decentralized Geomarker Assessment for Multi-Site Studies (DeGAUSS) that utilizes containerization to geocode patient addresses and obtain neighborhood-level SDOH variables. We compared DeGAUSS to a third-party vendor geocoding tool available at Duke Health using a cohort of adult patients referred for abdominal transplant from January 1, 2016, to December 31, 2022. We calculated Cohen's Kappa and percent disagreement at census block group and tract levels, and by Area Deprivation Index, urbanicity, and year.<h4>Results</h4>The pipeline successfully generated SDOH data for 97.8% of addresses. There was high concordance between DeGAUSS and the vendor tool at the census block group (0.93) and tract levels (0.95). At the block group level, disagreement proportion differed by year and urbanicity, with larger disagreement in the rural category than in micropolitan and metropolitan categories (13%, 7%, 6.2%, respectively).<h4>Discussion and conclusion</h4>We describe a novel pipeline that can facilitate the secure acquisition and sharing of neighborhood-level SDOH without sharing PHI. The pipeline can be scaled to include additional social, climate, and environmental variables, and can be extended to an unlimited number of health systems. |
| format | Article |
| id | doaj-art-abd885711dd0450599ce149a55a68e6e |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
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| series | PLoS ONE |
| spelling | doaj-art-abd885711dd0450599ce149a55a68e6e2025-02-07T05:30:42ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e031721510.1371/journal.pone.0317215A data pipeline for secure extraction and sharing of social determinants of health.Tyler SchappeLisa M McElroyMoronke OgundolieRoland MatsouakaUrsula RogersNrupen A Bhavsar<h4>Objectives</h4>Linking neighborhood- and patient-level data provides valuable information about the influence of upstream social determinants of health (SDOH). However, sharing of these data across health systems presents challenges. We set out to develop a pipeline to acquire, deidentify, and share neighborhood-level SDOH data across multiple health systems.<h4>Methods</h4>We created a pipeline centered around Decentralized Geomarker Assessment for Multi-Site Studies (DeGAUSS) that utilizes containerization to geocode patient addresses and obtain neighborhood-level SDOH variables. We compared DeGAUSS to a third-party vendor geocoding tool available at Duke Health using a cohort of adult patients referred for abdominal transplant from January 1, 2016, to December 31, 2022. We calculated Cohen's Kappa and percent disagreement at census block group and tract levels, and by Area Deprivation Index, urbanicity, and year.<h4>Results</h4>The pipeline successfully generated SDOH data for 97.8% of addresses. There was high concordance between DeGAUSS and the vendor tool at the census block group (0.93) and tract levels (0.95). At the block group level, disagreement proportion differed by year and urbanicity, with larger disagreement in the rural category than in micropolitan and metropolitan categories (13%, 7%, 6.2%, respectively).<h4>Discussion and conclusion</h4>We describe a novel pipeline that can facilitate the secure acquisition and sharing of neighborhood-level SDOH without sharing PHI. The pipeline can be scaled to include additional social, climate, and environmental variables, and can be extended to an unlimited number of health systems.https://doi.org/10.1371/journal.pone.0317215 |
| spellingShingle | Tyler Schappe Lisa M McElroy Moronke Ogundolie Roland Matsouaka Ursula Rogers Nrupen A Bhavsar A data pipeline for secure extraction and sharing of social determinants of health. PLoS ONE |
| title | A data pipeline for secure extraction and sharing of social determinants of health. |
| title_full | A data pipeline for secure extraction and sharing of social determinants of health. |
| title_fullStr | A data pipeline for secure extraction and sharing of social determinants of health. |
| title_full_unstemmed | A data pipeline for secure extraction and sharing of social determinants of health. |
| title_short | A data pipeline for secure extraction and sharing of social determinants of health. |
| title_sort | data pipeline for secure extraction and sharing of social determinants of health |
| url | https://doi.org/10.1371/journal.pone.0317215 |
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