scholarly journals Use of Electronic Health Records and Administrative Data for Public Health Surveillance of Eye Health and Vision-Related Conditions in the United States

2012 ◽  
Vol 154 (6) ◽  
pp. S63-S70 ◽  
Author(s):  
Amanda F. Elliott ◽  
Arthur Davidson ◽  
Flora Lum ◽  
Michael F. Chiang ◽  
Jinan B. Saaddine ◽  
...  
Author(s):  
Michael Klompas ◽  
Michael Murphy ◽  
Julie Lankiewicz ◽  
Jason McVetta ◽  
Ross Lazarus ◽  
...  

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S819-S820
Author(s):  
Jonathan Todd ◽  
Jon Puro ◽  
Matthew Jones ◽  
Jee Oakley ◽  
Laura A Vonnahme ◽  
...  

Abstract Background Over 80% of tuberculosis (TB) cases in the United States are attributed to reactivation of latent TB infection (LTBI). Eliminating TB in the United States requires expanding identification and treatment of LTBI. Centralized electronic health records (EHRs) are an unexplored data source to identify persons with LTBI. We explored EHR data to evaluate TB and LTBI screening and diagnoses within OCHIN, Inc., a U.S. practice-based research network with a high proportion of Federally Qualified Health Centers. Methods From the EHRs of patients who had an encounter at an OCHIN member clinic between January 1, 2012 and December 31, 2016, we extracted demographic variables, TB risk factors, TB screening tests, International Classification of Diseases (ICD) 9 and 10 codes, and treatment regimens. Based on test results, ICD codes, and treatment regimens, we developed a novel algorithm to classify patient records into LTBI categories: definite, probable or possible. We used multivariable logistic regression, with a referent group of all cohort patients not classified as having LTBI or TB, to identify associations between TB risk factors and LTBI. Results Among 2,190,686 patients, 6.9% (n=151,195) had a TB screening test; among those, 8% tested positive. Non-U.S. –born or non-English–speaking persons comprised 24% of our cohort; 11% were tested for TB infection, and 14% had a positive test. Risk factors in the multivariable model significantly associated with being classified as having LTBI included preferring non-English language (adjusted odds ratio [aOR] 4.20, 95% confidence interval [CI] 4.09–4.32); non-Hispanic Asian (aOR 5.17, 95% CI 4.94–5.40), non-Hispanic black (aOR 3.02, 95% CI 2.91–3.13), or Native Hawaiian/other Pacific Islander (aOR 3.35, 95% CI 2.92–3.84) race; and HIV infection (aOR 3.09, 95% CI 2.84–3.35). Conclusion This study demonstrates the utility of EHR data for understanding TB screening practices and as an important data source that can be used to enhance public health surveillance of LTBI prevalence. Increasing screening among high-risk populations remains an important step toward eliminating TB in the United States. These results underscore the importance of offering TB screening in non-U.S.–born populations. Disclosures All Authors: No reported disclosures


2021 ◽  
Vol 40 (1) ◽  
pp. 61-79
Author(s):  
Carmela Alcántara ◽  
Shakira F. Suglia ◽  
Irene Perez Ibarra ◽  
A. Louise Falzon ◽  
Elliot McCullough ◽  
...  

2018 ◽  
Vol 136 (2) ◽  
pp. 164 ◽  
Author(s):  
Michele C. Lim ◽  
Michael V. Boland ◽  
Colin A. McCannel ◽  
Arvind Saini ◽  
Michael F. Chiang ◽  
...  

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