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1651. Describing the Tuberculosis Infection Cascade of Care Based on Electronic Health Record Data

BACKGROUND: Appropriate screening of individuals to detect latent tuberculosis infection (LTBI) is a critical step for achieving tuberculosis (TB) elimination in the US; >80% of TB cases are attributed to LTBI reactivation. TB infection testing and treatment must engage community health clinics w...

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Autores principales: Vonnahme, Laura A, Todd, Jonathan, Puro, Jon, Oakley, Jee, Jones, Matthew, Rivera, Pedro, Langer, Adam J, Ayers, Tracy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7777836/
http://dx.doi.org/10.1093/ofid/ofaa439.1829
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author Vonnahme, Laura A
Todd, Jonathan
Puro, Jon
Oakley, Jee
Jones, Matthew
Rivera, Pedro
Langer, Adam J
Ayers, Tracy
author_facet Vonnahme, Laura A
Todd, Jonathan
Puro, Jon
Oakley, Jee
Jones, Matthew
Rivera, Pedro
Langer, Adam J
Ayers, Tracy
author_sort Vonnahme, Laura A
collection PubMed
description BACKGROUND: Appropriate screening of individuals to detect latent tuberculosis infection (LTBI) is a critical step for achieving tuberculosis (TB) elimination in the US; >80% of TB cases are attributed to LTBI reactivation. TB infection testing and treatment must engage community health clinics where populations at risk seek care. However, there are significant data knowledge gaps in the current LTBI cascade of care (CoC) in this setting. We used an electronic health record (EHR) database from OCHIN, Inc., to characterize the LTBI CoC and identify potential future interventions. METHODS: We extracted a cohort of patients from 2012–2016 EHR data; we stratified by whether patients were at risk for TB based on meeting at least one of the following criteria: non-US born or non-English language preference, homelessness, encounter at correctional facility, history of close contact with a TB case, or being immunocompromised. Along each step of the LTBI CoC, we determined the proportions with a test for TB infection, with available test results, with a positive test, with an LTBI diagnosis, and with LTBI treatment prescribed. We used Χ (2) tests to compare the LTBI CoCs among patients at risk with those classified as not at risk. RESULTS: Of nearly 2.2 million patient records, 701,467 (32.0%) met criteria for being at risk for TB; 84,422 at risk (12.0%) were tested; 65,562 (77.7%) had available results, of whom 9,624 (14.7%) were positive. Among those with positive results, 6,958 (72.3%) had an LTBI diagnosis, of whom 1,732 (24.9%) were prescribed treatment. Among those classified as not at risk, fewer were tested (66,773 [4.5%], p< 0.001) and had positive results (2,500 [3.7%], p< 0.0001). Among those with positive results, 1,998 (80.0%) had an LTBI diagnosis, of whom 395 (19.8%) initiated treatment. CONCLUSION: This study highlights gaps in the LTBI CoC, and where interventions are most needed. The largest gaps were in testing patients at risk, as 88% were not tested, and treatment, as 75% diagnosed with LTBI were not treated. Just under half (44%) of all TB tests appeared to be performed in persons with little risk for TB; this is a substantial amount of testing given very few begin treatment. Resources could be redirected to increase screening and treatment among populations at risk. DISCLOSURES: All Authors: No reported disclosures
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spelling pubmed-77778362021-01-07 1651. Describing the Tuberculosis Infection Cascade of Care Based on Electronic Health Record Data Vonnahme, Laura A Todd, Jonathan Puro, Jon Oakley, Jee Jones, Matthew Rivera, Pedro Langer, Adam J Ayers, Tracy Open Forum Infect Dis Poster Abstracts BACKGROUND: Appropriate screening of individuals to detect latent tuberculosis infection (LTBI) is a critical step for achieving tuberculosis (TB) elimination in the US; >80% of TB cases are attributed to LTBI reactivation. TB infection testing and treatment must engage community health clinics where populations at risk seek care. However, there are significant data knowledge gaps in the current LTBI cascade of care (CoC) in this setting. We used an electronic health record (EHR) database from OCHIN, Inc., to characterize the LTBI CoC and identify potential future interventions. METHODS: We extracted a cohort of patients from 2012–2016 EHR data; we stratified by whether patients were at risk for TB based on meeting at least one of the following criteria: non-US born or non-English language preference, homelessness, encounter at correctional facility, history of close contact with a TB case, or being immunocompromised. Along each step of the LTBI CoC, we determined the proportions with a test for TB infection, with available test results, with a positive test, with an LTBI diagnosis, and with LTBI treatment prescribed. We used Χ (2) tests to compare the LTBI CoCs among patients at risk with those classified as not at risk. RESULTS: Of nearly 2.2 million patient records, 701,467 (32.0%) met criteria for being at risk for TB; 84,422 at risk (12.0%) were tested; 65,562 (77.7%) had available results, of whom 9,624 (14.7%) were positive. Among those with positive results, 6,958 (72.3%) had an LTBI diagnosis, of whom 1,732 (24.9%) were prescribed treatment. Among those classified as not at risk, fewer were tested (66,773 [4.5%], p< 0.001) and had positive results (2,500 [3.7%], p< 0.0001). Among those with positive results, 1,998 (80.0%) had an LTBI diagnosis, of whom 395 (19.8%) initiated treatment. CONCLUSION: This study highlights gaps in the LTBI CoC, and where interventions are most needed. The largest gaps were in testing patients at risk, as 88% were not tested, and treatment, as 75% diagnosed with LTBI were not treated. Just under half (44%) of all TB tests appeared to be performed in persons with little risk for TB; this is a substantial amount of testing given very few begin treatment. Resources could be redirected to increase screening and treatment among populations at risk. DISCLOSURES: All Authors: No reported disclosures Oxford University Press 2020-12-31 /pmc/articles/PMC7777836/ http://dx.doi.org/10.1093/ofid/ofaa439.1829 Text en © The Author 2020. Published by Oxford University Press on behalf of Infectious Diseases Society of America. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Poster Abstracts
Vonnahme, Laura A
Todd, Jonathan
Puro, Jon
Oakley, Jee
Jones, Matthew
Rivera, Pedro
Langer, Adam J
Ayers, Tracy
1651. Describing the Tuberculosis Infection Cascade of Care Based on Electronic Health Record Data
title 1651. Describing the Tuberculosis Infection Cascade of Care Based on Electronic Health Record Data
title_full 1651. Describing the Tuberculosis Infection Cascade of Care Based on Electronic Health Record Data
title_fullStr 1651. Describing the Tuberculosis Infection Cascade of Care Based on Electronic Health Record Data
title_full_unstemmed 1651. Describing the Tuberculosis Infection Cascade of Care Based on Electronic Health Record Data
title_short 1651. Describing the Tuberculosis Infection Cascade of Care Based on Electronic Health Record Data
title_sort 1651. describing the tuberculosis infection cascade of care based on electronic health record data
topic Poster Abstracts
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7777836/
http://dx.doi.org/10.1093/ofid/ofaa439.1829
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