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Development and Validation of an Electronic Medical Record (EMR) Based Algorithm to Identify Patient Milestones in the Hepatitis C Virus Care Cascade

Development and Validation of an Electronic Medical Record (EMR) Based Algorithm to Identify... Disease-specific care cascades are important public health and organizational tools to characterize gaps in care and target resources but are labor intensive to maintain. Using data available from the EMR, we developed an algorithm with high accuracy for correctly representing an individual’s status in the HCV care cascade. Main Text: The hepatitis C virus (HCV) is the most common blood-borne infection in the United States and remains 1,2 the leading cause of cirrhosis, hepatocellular carcinoma, and liver transplantation. HCV treatment with highly effective, well tolerated direct-acting antiviral agents (DAA) improves health outcomes, may be cost-effective at any time, and has the potential to eliminate HCV as part of a treatment as prevention 4,5 strategy. The care cascade is a framework for the continuum of care that was first developed for persons living with HIV. The care cascade model helps programs target gaps in care and develop interventions. The Hepatitis C Care Cascade (HCV-CC) starts with diagnosis and ends with a sustained virologic response. The Chronic Hepatitis Cohort Study (CHeCS) demonstrated that gaps in care exist at every step along this cascade. In the US it was estimated that only 50% of individuals were aware of their diagnosis, 16% had 6–9 been prescribed treatment, and only 9% reached SVR. We describe the development of an algorithm that using data routinely collected by the electronic medical record (EMR) automates generation of a care cascade for persons diagnosed with HCV. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy153/5047994 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript Methods: We sought to create an EMR-based algorithm to automatically establish an individual’s stage in the HCV- CC from routinely collected data. We limited our analysis to patients with a first anti-HCV antibody (HCV Ab) positive, at our institution, from 2013 to 2015, coinciding with the release of the DAAs. This was designed to mimic the process of a HCV testing and linkage to care team. This study was performed at an urban academic medical center consisting of inpatient services at two sites in Upper Manhattan in addition to a number of ambulatory care sites. The EMR is shared with another academic medical center with data viewable in the EMR but unavailable for analysis. The medical center serves a community in which 72% of residents as Hispanic and 10,11 25% of households are below the federal poverty line. Defining Milestones: To develop the stages in the cascade we started with the steps identified by Yehia, et al., and identified five milestones to represent the stages of care in our clinical environment (Figure 1). Stage 1 all patients with a first positive HCV Ab at our institution; Stage 2 confirmed infection with HCV RNA testing; Stage 3, linkage to care, was defined as a completed outpatient visit with either an infectious disease or gastroenterology provider; Stage 4, treatment initiation; and Stage 5, reaching sustained virologic response at 12 weeks after end of treatment (SVR12). We included linkage to care, as opposed to referral for care which has been used in other studies, since this represents a more meaningful outcome. Selection of Electronic Medical Record (EMR) data: From the cohort of all individuals with a first positive HCV Ab from 2013-2015, we collected basic demographic and HCV-related laboratory information (HCV Ab, HCV RNA qualitative and quantitative)) between January 1, 2013 and December 31, 2015. Information regarding all hospitalizations, affiliated outpatient visits, and clinical notes were obtained. Data were extracted from Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy153/5047994 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript the two different EMR systems used at our institution. (Allscripts Sunrise and Clinical Records Online Web Network (CROWN), Allscripts Corp., Chicago, IL) Algorithm: Using EMR data, we developed criteria to represent each milestone within the HCV-CC. Through a review of the hospital data dictionary we identified HCV laboratory tests that had been used including one anti-HCV Ab test and nine HCV RNA tests. Additionally, we identified fourteen clinical note headers used by HCV providers, and served as a marker of linkage to care, and seven different medication orders for HCV treatment. Steps in the HCV-CC did not have to be documented sequentially as it was noted that some patients who were linked to care and reached SVR had medications documented but did not have a visit with an HCV provider within our system. A manual review of these charts revealed that they were being treated by an unaffiliated physician. Therefore, after stage 2, all dependencies were removed, and patients were considered to have reached any stage of care preceding their final classification. Reference Standard: To evaluate the algorithm’s accuracy we created a reference standard to replicate a clinician’s review of the chart using all available medical records. This consisted of a manual review of the chart by a single researcher without access to the algorithm. Data unlikely to be used by clinicians (i.e. billing codes, pharmacy records) were not included. In all instances where the reference standard and algorithm were discordant the result was reviewed by the study team; if the manual review was thought to be incorrect this was confirmed with the manual reviewer. Determination of Algorithm performance: We applied the algorithm to all patients with a first positive anti-HCV Ab from 2013-2015. Using random sampling, we reviewed 125 patients for manual classification of their HCV stage of care. Results from the algorithm were compared to those of the reference standard. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy153/5047994 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript Results: st The algorithm identified 1825 patients with a 1 positive HCV Ab (stage 1). 58% of identified patients were male, 42% female, 26% White, 20% Hispanic, and 18% African American. 1311 (72%) had a viral load performed (stage 2). 600 (33%) patients had an HCV RNA viral load that was undetectable and removed from the cascade at this stage as they did not require linkage to care. The resulting 1225 patients, 711 (39%) who were HCV RNA positive and 514 (28%) without a viral load, were included in further analysis. The algorithm classified 330 (27%) patients as reaching stage 3, 100 (8%) reaching stage 4, 39 (3%) reaching stage 5. (Figure 1). Among patients linked to care, 30% received HCV treatment and 39% eceiving treatment reached SVR. There were no differences in progression through the care cascade by gender. Hispanic patients were less likely to receive treatment. 33% of new HCV diagnoses were outside the birth cohort with 9% born before 1945 and 25% born after 1965. Those born prior to 1945 were less likely to have a viral load performed, be linked to care, or prescribed treatment. (Figure 2). A random sample of 125 (10%) patients was selected to undergo the reference standard review. The algorithm correctly categorized 117 of 129 (90%) patients compared to 126 of 129 (98%) for the reference standard. 10/12 errors were related to care that was documented, but provided outside of the institution. Discussion: We developed an automated EMR algorithm to classify a patient’s stage in the HCV-CC using routinely collected electronic data. Several organizations have attempted to create an HCV-CC for their institutions 6–9 utilizing case registries, manual data collection, meta-analysis, and HCV care coordinators . To our knowledge, this is the first description of an algorithm using EMR data to automate identification of a patient’s stage in the HCV-CC. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy153/5047994 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript It is estimated that only 16% of HCV-infected individuals in the US have received antiviral therapy and only 9% reached SVR. The HCV-CC in the Veterans Health Administration (VHA), the largest provider of HCV care in the US, found higher rates of linkage, similar rates of treatment, and lower rates of SVR 7,13 when compared to our institution and CHeCS. Three community-based primary care clinics affiliated with Montefiore Medical Center found no difference in stage of care in patients identified by birth cohort versus risk-based screenings. The primary care clinics’ care cascade had higher rates of viral load testing when compared to our center but a similar rate of linkage to care and lower rates of treatment and SVR. The VHA and CHeCS data ended in 2013, and Montefiore data ended in 2011 representing the pre-DAA era. In the post DAA era, 2014 – 2016, the VHA, an integrated system found higher rates of linkage (93%), treatment (59%) and SVR (84%). The use of an EMR-based algorithm has several advantages. The HCV-CC allows providers and health care systems to assess and monitor the quality of HCV. For example, our electronic care cascade recognized that 33% of new HCV diagnoses were outside the birth cohort prompting a change in internal screening recommendations. Some healthcare systems utilize HCV care coordinators to track patient progress and this system could enhance the coordinator role. Our algorithm used commonly available data elements in order to be widely applicable. A major challenge for future research will be to identify clear and consistent milestones for the HCV-CC to allow for better comparisons across institutions and to share best practices. This algorithm is not without limitations. Our reviewer could view records from the outside, but affiliated hospital, however the algorithm could not use this information. Utilizing a regional health information organization could lead to a more complete HCV-CC. Future iterations may consider including any patient with a history of chronic HCV infection and not just new diagnosis, however this would require the identification of any additional test names and note types that may have been Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy153/5047994 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript previously used. Our definition of linkage to care relies on an outpatient visit with an infectious disease or gastroenterology provider and while the visit ocurred we have no way of ensuring that HCV care was provided. Our model ends at SVR and does not account for reinfection or relapse after 12 weeks which could be validated in future versions of this algorithm. Finally, in our model patients were assumed to have completed all stages prior to their final stage, our random sample validated this assumption, however it is possible that an individual could be misclassified. Conclusion: The dramatic improvements in HCV treatment have created both an individual and public health imperative to assist patients in completing the care cascade. Currently available HCV-CC demonstrate that gaps remain. Our HCV-CC offers healthcare systems the ability to measure and monitor performance to allow prioritization of resources to improve the overall quality of HCV care and help expanded HCV linkage to care and optimize treatment strategies. Funding: Jason Zucker is supported by the training grant “Training in Pediatric Infectious Diseases” (National Institute of Allergy and Infectious Diseases T32AI007531). This work was supported by a Gilead FOCUS program grant awarded to Peter Gordon. References: 1. Statistics and Surveillance | HCV | Division of Viral Hepatitis | CDC. 2. Chung R, Baumert T. Curing Chronic Hepatitis C — The Arc of a Medical Triumph. N Engl J Med. 2014;2014:1576-1578. doi:10.1056/NEJMp1401833. 3. Chahal HS, Marseille EA, Tice JA, et al. Cost-effectiveness of Early Treatment of Hepatitis C Virus Genotype 1 by Stage of Liver Fibrosis in a US Treatment-Naive Population. JAMA Intern Med. 2015;94118:1-9. doi:10.1001/jamainternmed.2015.6011. 4. Boerekamps A, Van den Berk GE, Fanny LN, et al. Declining HCV incidence in Dutch HIV Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy153/5047994 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript positive men who have sex with men after unrestricted access to HCV therapy. Clin Infect Dis. 2017;66(June). doi:10.1093/cid/cix1007. 5. Zelenev A, Li J, Mazhnaya A, Basu S, Altice FL. Hepatitis C virus treatment as prevention in an extended network of people who inject drugs in the USA: A modelling study. Lancet Infect Dis. 2017;18(2):215-224. doi:10.1016/S1473-3099(17)30676-X. 6. Yehia BR, Schranz AJ, Umscheid CA, Lo Re V. The treatment cascade for chronic hepatitis C virus infection in the United States: A systematic review and meta-analysis. PLoS One. 2014;9(7):3-9. doi:10.1371/journal.pone.0101554. 7. Maier MM, Ross DB, Chartier M, Belperio PS, Backus LI. Cascade of care for hepatitis C virus infection within the US veterans health administration. Am J Public Health. 2016;106(2):353-358. doi:10.2105/AJPH.2015.302927. 8. Jonas MC, Rodriguez C V., Redd J, Sloane DA, Winston BJ, Loftus BC. Streamlining Screening to Treatment: The Hepatitis C Cascade of Care at Kaiser Permanente Mid-Atlantic States. Clin Infect Dis. 2016;62:ciw086. doi:10.1093/cid/ciw086. 9. Norton B, Southern WN, Steinman M, et al. No Differences in Achieving Hepatitis C Virus Care Milestones Between Patients Identified by Birth Cohort and Risk-Based Screening. Clin Gastroenterol Hepatol. 2016;(May):1-5. doi:10.1016/j.cgh.2016.04.017. 10. Emory University RS of PH. AIDSVu (www.aidsvu.org). https://aidsvu.org/contact-us/. Accessed October 2, 2017. 11. Dinapoli TP, Bleiwas KB. An Economic Snapshot of Washington Heights and Inwood. 2016:1-4. 12. Felsen UR, Bellin EY, Cunningham CO, Zingman BS. Development of an electronic medical record-based algorithm to identify patients with unknown HIV status. AIDS Care. 2014;26(10):1318-1325. doi:10.1080/09540121.2014.911813. 13. Holmberg, Scott; Spralding, Philip; Moorman, Anne; Denniston M. Hepatitis C in the United States. N Engl J Med. 2013;368:1859-1861. doi:10.1056/NEJMp1303819. 14. Falade-Nwulia O, Sulkowski M. The HCV care continuum does not end with cure: A call to arms for the prevention of reinfection. J Hepatol. 2017;44(2):319-335. doi:10.1111/cdoe.12200.Factors. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy153/5047994 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript Figure 1: Hepatitis C Care Cascade for Patients Diagnosed 2013 – 2015 Figure 2: Hepatitis C Care Cascade by Demographics, 2013 - 2015 Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy153/5047994 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript Figure 1. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy153/5047994 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript Figure 2. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy153/5047994 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Open Forum Infectious Diseases Oxford University Press

Development and Validation of an Electronic Medical Record (EMR) Based Algorithm to Identify Patient Milestones in the Hepatitis C Virus Care Cascade

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Oxford University Press
Copyright
© The Author(s) 2018. Published by Oxford University Press on behalf of Infectious Diseases Society of America.
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2328-8957
DOI
10.1093/ofid/ofy153
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Abstract

Disease-specific care cascades are important public health and organizational tools to characterize gaps in care and target resources but are labor intensive to maintain. Using data available from the EMR, we developed an algorithm with high accuracy for correctly representing an individual’s status in the HCV care cascade. Main Text: The hepatitis C virus (HCV) is the most common blood-borne infection in the United States and remains 1,2 the leading cause of cirrhosis, hepatocellular carcinoma, and liver transplantation. HCV treatment with highly effective, well tolerated direct-acting antiviral agents (DAA) improves health outcomes, may be cost-effective at any time, and has the potential to eliminate HCV as part of a treatment as prevention 4,5 strategy. The care cascade is a framework for the continuum of care that was first developed for persons living with HIV. The care cascade model helps programs target gaps in care and develop interventions. The Hepatitis C Care Cascade (HCV-CC) starts with diagnosis and ends with a sustained virologic response. The Chronic Hepatitis Cohort Study (CHeCS) demonstrated that gaps in care exist at every step along this cascade. In the US it was estimated that only 50% of individuals were aware of their diagnosis, 16% had 6–9 been prescribed treatment, and only 9% reached SVR. We describe the development of an algorithm that using data routinely collected by the electronic medical record (EMR) automates generation of a care cascade for persons diagnosed with HCV. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy153/5047994 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript Methods: We sought to create an EMR-based algorithm to automatically establish an individual’s stage in the HCV- CC from routinely collected data. We limited our analysis to patients with a first anti-HCV antibody (HCV Ab) positive, at our institution, from 2013 to 2015, coinciding with the release of the DAAs. This was designed to mimic the process of a HCV testing and linkage to care team. This study was performed at an urban academic medical center consisting of inpatient services at two sites in Upper Manhattan in addition to a number of ambulatory care sites. The EMR is shared with another academic medical center with data viewable in the EMR but unavailable for analysis. The medical center serves a community in which 72% of residents as Hispanic and 10,11 25% of households are below the federal poverty line. Defining Milestones: To develop the stages in the cascade we started with the steps identified by Yehia, et al., and identified five milestones to represent the stages of care in our clinical environment (Figure 1). Stage 1 all patients with a first positive HCV Ab at our institution; Stage 2 confirmed infection with HCV RNA testing; Stage 3, linkage to care, was defined as a completed outpatient visit with either an infectious disease or gastroenterology provider; Stage 4, treatment initiation; and Stage 5, reaching sustained virologic response at 12 weeks after end of treatment (SVR12). We included linkage to care, as opposed to referral for care which has been used in other studies, since this represents a more meaningful outcome. Selection of Electronic Medical Record (EMR) data: From the cohort of all individuals with a first positive HCV Ab from 2013-2015, we collected basic demographic and HCV-related laboratory information (HCV Ab, HCV RNA qualitative and quantitative)) between January 1, 2013 and December 31, 2015. Information regarding all hospitalizations, affiliated outpatient visits, and clinical notes were obtained. Data were extracted from Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy153/5047994 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript the two different EMR systems used at our institution. (Allscripts Sunrise and Clinical Records Online Web Network (CROWN), Allscripts Corp., Chicago, IL) Algorithm: Using EMR data, we developed criteria to represent each milestone within the HCV-CC. Through a review of the hospital data dictionary we identified HCV laboratory tests that had been used including one anti-HCV Ab test and nine HCV RNA tests. Additionally, we identified fourteen clinical note headers used by HCV providers, and served as a marker of linkage to care, and seven different medication orders for HCV treatment. Steps in the HCV-CC did not have to be documented sequentially as it was noted that some patients who were linked to care and reached SVR had medications documented but did not have a visit with an HCV provider within our system. A manual review of these charts revealed that they were being treated by an unaffiliated physician. Therefore, after stage 2, all dependencies were removed, and patients were considered to have reached any stage of care preceding their final classification. Reference Standard: To evaluate the algorithm’s accuracy we created a reference standard to replicate a clinician’s review of the chart using all available medical records. This consisted of a manual review of the chart by a single researcher without access to the algorithm. Data unlikely to be used by clinicians (i.e. billing codes, pharmacy records) were not included. In all instances where the reference standard and algorithm were discordant the result was reviewed by the study team; if the manual review was thought to be incorrect this was confirmed with the manual reviewer. Determination of Algorithm performance: We applied the algorithm to all patients with a first positive anti-HCV Ab from 2013-2015. Using random sampling, we reviewed 125 patients for manual classification of their HCV stage of care. Results from the algorithm were compared to those of the reference standard. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy153/5047994 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript Results: st The algorithm identified 1825 patients with a 1 positive HCV Ab (stage 1). 58% of identified patients were male, 42% female, 26% White, 20% Hispanic, and 18% African American. 1311 (72%) had a viral load performed (stage 2). 600 (33%) patients had an HCV RNA viral load that was undetectable and removed from the cascade at this stage as they did not require linkage to care. The resulting 1225 patients, 711 (39%) who were HCV RNA positive and 514 (28%) without a viral load, were included in further analysis. The algorithm classified 330 (27%) patients as reaching stage 3, 100 (8%) reaching stage 4, 39 (3%) reaching stage 5. (Figure 1). Among patients linked to care, 30% received HCV treatment and 39% eceiving treatment reached SVR. There were no differences in progression through the care cascade by gender. Hispanic patients were less likely to receive treatment. 33% of new HCV diagnoses were outside the birth cohort with 9% born before 1945 and 25% born after 1965. Those born prior to 1945 were less likely to have a viral load performed, be linked to care, or prescribed treatment. (Figure 2). A random sample of 125 (10%) patients was selected to undergo the reference standard review. The algorithm correctly categorized 117 of 129 (90%) patients compared to 126 of 129 (98%) for the reference standard. 10/12 errors were related to care that was documented, but provided outside of the institution. Discussion: We developed an automated EMR algorithm to classify a patient’s stage in the HCV-CC using routinely collected electronic data. Several organizations have attempted to create an HCV-CC for their institutions 6–9 utilizing case registries, manual data collection, meta-analysis, and HCV care coordinators . To our knowledge, this is the first description of an algorithm using EMR data to automate identification of a patient’s stage in the HCV-CC. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy153/5047994 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript It is estimated that only 16% of HCV-infected individuals in the US have received antiviral therapy and only 9% reached SVR. The HCV-CC in the Veterans Health Administration (VHA), the largest provider of HCV care in the US, found higher rates of linkage, similar rates of treatment, and lower rates of SVR 7,13 when compared to our institution and CHeCS. Three community-based primary care clinics affiliated with Montefiore Medical Center found no difference in stage of care in patients identified by birth cohort versus risk-based screenings. The primary care clinics’ care cascade had higher rates of viral load testing when compared to our center but a similar rate of linkage to care and lower rates of treatment and SVR. The VHA and CHeCS data ended in 2013, and Montefiore data ended in 2011 representing the pre-DAA era. In the post DAA era, 2014 – 2016, the VHA, an integrated system found higher rates of linkage (93%), treatment (59%) and SVR (84%). The use of an EMR-based algorithm has several advantages. The HCV-CC allows providers and health care systems to assess and monitor the quality of HCV. For example, our electronic care cascade recognized that 33% of new HCV diagnoses were outside the birth cohort prompting a change in internal screening recommendations. Some healthcare systems utilize HCV care coordinators to track patient progress and this system could enhance the coordinator role. Our algorithm used commonly available data elements in order to be widely applicable. A major challenge for future research will be to identify clear and consistent milestones for the HCV-CC to allow for better comparisons across institutions and to share best practices. This algorithm is not without limitations. Our reviewer could view records from the outside, but affiliated hospital, however the algorithm could not use this information. Utilizing a regional health information organization could lead to a more complete HCV-CC. Future iterations may consider including any patient with a history of chronic HCV infection and not just new diagnosis, however this would require the identification of any additional test names and note types that may have been Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy153/5047994 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript previously used. Our definition of linkage to care relies on an outpatient visit with an infectious disease or gastroenterology provider and while the visit ocurred we have no way of ensuring that HCV care was provided. Our model ends at SVR and does not account for reinfection or relapse after 12 weeks which could be validated in future versions of this algorithm. Finally, in our model patients were assumed to have completed all stages prior to their final stage, our random sample validated this assumption, however it is possible that an individual could be misclassified. Conclusion: The dramatic improvements in HCV treatment have created both an individual and public health imperative to assist patients in completing the care cascade. Currently available HCV-CC demonstrate that gaps remain. Our HCV-CC offers healthcare systems the ability to measure and monitor performance to allow prioritization of resources to improve the overall quality of HCV care and help expanded HCV linkage to care and optimize treatment strategies. Funding: Jason Zucker is supported by the training grant “Training in Pediatric Infectious Diseases” (National Institute of Allergy and Infectious Diseases T32AI007531). This work was supported by a Gilead FOCUS program grant awarded to Peter Gordon. References: 1. Statistics and Surveillance | HCV | Division of Viral Hepatitis | CDC. 2. Chung R, Baumert T. Curing Chronic Hepatitis C — The Arc of a Medical Triumph. N Engl J Med. 2014;2014:1576-1578. doi:10.1056/NEJMp1401833. 3. Chahal HS, Marseille EA, Tice JA, et al. Cost-effectiveness of Early Treatment of Hepatitis C Virus Genotype 1 by Stage of Liver Fibrosis in a US Treatment-Naive Population. JAMA Intern Med. 2015;94118:1-9. doi:10.1001/jamainternmed.2015.6011. 4. Boerekamps A, Van den Berk GE, Fanny LN, et al. Declining HCV incidence in Dutch HIV Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy153/5047994 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript positive men who have sex with men after unrestricted access to HCV therapy. Clin Infect Dis. 2017;66(June). doi:10.1093/cid/cix1007. 5. Zelenev A, Li J, Mazhnaya A, Basu S, Altice FL. Hepatitis C virus treatment as prevention in an extended network of people who inject drugs in the USA: A modelling study. Lancet Infect Dis. 2017;18(2):215-224. doi:10.1016/S1473-3099(17)30676-X. 6. Yehia BR, Schranz AJ, Umscheid CA, Lo Re V. The treatment cascade for chronic hepatitis C virus infection in the United States: A systematic review and meta-analysis. PLoS One. 2014;9(7):3-9. doi:10.1371/journal.pone.0101554. 7. Maier MM, Ross DB, Chartier M, Belperio PS, Backus LI. Cascade of care for hepatitis C virus infection within the US veterans health administration. Am J Public Health. 2016;106(2):353-358. doi:10.2105/AJPH.2015.302927. 8. Jonas MC, Rodriguez C V., Redd J, Sloane DA, Winston BJ, Loftus BC. Streamlining Screening to Treatment: The Hepatitis C Cascade of Care at Kaiser Permanente Mid-Atlantic States. Clin Infect Dis. 2016;62:ciw086. doi:10.1093/cid/ciw086. 9. Norton B, Southern WN, Steinman M, et al. No Differences in Achieving Hepatitis C Virus Care Milestones Between Patients Identified by Birth Cohort and Risk-Based Screening. Clin Gastroenterol Hepatol. 2016;(May):1-5. doi:10.1016/j.cgh.2016.04.017. 10. Emory University RS of PH. AIDSVu (www.aidsvu.org). https://aidsvu.org/contact-us/. Accessed October 2, 2017. 11. Dinapoli TP, Bleiwas KB. An Economic Snapshot of Washington Heights and Inwood. 2016:1-4. 12. Felsen UR, Bellin EY, Cunningham CO, Zingman BS. Development of an electronic medical record-based algorithm to identify patients with unknown HIV status. AIDS Care. 2014;26(10):1318-1325. doi:10.1080/09540121.2014.911813. 13. Holmberg, Scott; Spralding, Philip; Moorman, Anne; Denniston M. Hepatitis C in the United States. N Engl J Med. 2013;368:1859-1861. doi:10.1056/NEJMp1303819. 14. Falade-Nwulia O, Sulkowski M. The HCV care continuum does not end with cure: A call to arms for the prevention of reinfection. J Hepatol. 2017;44(2):319-335. doi:10.1111/cdoe.12200.Factors. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy153/5047994 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript Figure 1: Hepatitis C Care Cascade for Patients Diagnosed 2013 – 2015 Figure 2: Hepatitis C Care Cascade by Demographics, 2013 - 2015 Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy153/5047994 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript Figure 1. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy153/5047994 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript Figure 2. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy153/5047994 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript

Journal

Open Forum Infectious Diseases – Oxford University Press

Published: Jul 3, 2018

There are no references for this article.