A Flow-Based Model of the HIV Care Continuum in the United States
BACKGROUND:Understanding the flow of patients through the continuum of HIV care is critical to determine how best to intervene so that the proportion of HIV-infected persons who are on antiretroviral treatment and virally suppressed is as large as possible. METHODS:Using immunological and virologica...
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Veröffentlicht in: | Journal of acquired immune deficiency syndromes (1999) 2017-08, Vol.75 (5), p.548-553 |
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container_title | Journal of acquired immune deficiency syndromes (1999) |
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creator | Gonsalves, Gregg S Paltiel, A David Cleary, Paul D Gill, Michael J Kitahata, Mari M Rebeiro, Peter F Silverberg, Michael J Horberg, Michael Abraham, Alison G Althoff, Keri N Moore, Richard Bosch, Ronald J Tang, Tian Hall, H Irene Kaplan, Edward H |
description | BACKGROUND:Understanding the flow of patients through the continuum of HIV care is critical to determine how best to intervene so that the proportion of HIV-infected persons who are on antiretroviral treatment and virally suppressed is as large as possible.
METHODS:Using immunological and virological data from the Centers for Disease Control and Prevention and the North American AIDS Cohort Collaboration on Research and Design from 2009 to 2012, we estimated the distribution of time spent in and dropout probability from each stage in the continuum of HIV care. We used these estimates to develop a queueing model for the expected number of patients found in each stage of the cascade.
RESULTS:HIV-infected individuals spend an average of about 3.1 months after HIV diagnosis before being linked to care, or dropping out of that stage of the continuum with a probability of 8%. Those who link to care wait an additional 3.7 months on average before getting their second set of laboratory results (indicating engagement in care) or dropping out of care with probability of almost 6%. Those engaged in care spent an average of almost 1 year before achieving viral suppression on antiretroviral therapy or dropping out with average probability 13%. For patients who achieved viral suppression, the average time suppressed on antiretroviral therapy was an average of 4.5 years.
CONCLUSIONS:Interventions should be targeted to more rapidly identifying newly infected individuals, and increasing the fraction of those engaged in care that achieves viral suppression. |
doi_str_mv | 10.1097/QAI.0000000000001429 |
format | Article |
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METHODS:Using immunological and virological data from the Centers for Disease Control and Prevention and the North American AIDS Cohort Collaboration on Research and Design from 2009 to 2012, we estimated the distribution of time spent in and dropout probability from each stage in the continuum of HIV care. We used these estimates to develop a queueing model for the expected number of patients found in each stage of the cascade.
RESULTS:HIV-infected individuals spend an average of about 3.1 months after HIV diagnosis before being linked to care, or dropping out of that stage of the continuum with a probability of 8%. Those who link to care wait an additional 3.7 months on average before getting their second set of laboratory results (indicating engagement in care) or dropping out of care with probability of almost 6%. Those engaged in care spent an average of almost 1 year before achieving viral suppression on antiretroviral therapy or dropping out with average probability 13%. For patients who achieved viral suppression, the average time suppressed on antiretroviral therapy was an average of 4.5 years.
CONCLUSIONS:Interventions should be targeted to more rapidly identifying newly infected individuals, and increasing the fraction of those engaged in care that achieves viral suppression.</description><identifier>ISSN: 1525-4135</identifier><identifier>EISSN: 1944-7884</identifier><identifier>DOI: 10.1097/QAI.0000000000001429</identifier><identifier>PMID: 28471841</identifier><language>eng</language><publisher>United States: Copyright Wolters Kluwer Health, Inc. All rights reserved</publisher><subject>Acquired immune deficiency syndrome ; AIDS ; AIDS/HIV ; Anti-HIV Agents - therapeutic use ; Antiretroviral agents ; Antiretroviral drugs ; Antiretroviral therapy ; CD4 Lymphocyte Count ; Continuity of Patient Care - statistics & numerical data ; Cross-Sectional Studies ; Disease control ; Female ; HIV ; HIV Infections - drug therapy ; HIV Infections - mortality ; HIV Infections - virology ; Human immunodeficiency virus ; Humans ; Immunology ; Male ; Models, Theoretical ; Patients ; Population Surveillance ; Probability ; Queues ; Therapy ; United States - epidemiology ; Viral Load ; Virology</subject><ispartof>Journal of acquired immune deficiency syndromes (1999), 2017-08, Vol.75 (5), p.548-553</ispartof><rights>Copyright © 2017 Wolters Kluwer Health, Inc. All rights reserved.</rights><rights>Copyright Lippincott Williams & Wilkins Aug 15, 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4859-eef82de53dac74aa1e18f908c01d47c54172a7957ef7d2deff95320d3e4d62923</citedby><cites>FETCH-LOGICAL-c4859-eef82de53dac74aa1e18f908c01d47c54172a7957ef7d2deff95320d3e4d62923</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,27923,27924</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28471841$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Gonsalves, Gregg S</creatorcontrib><creatorcontrib>Paltiel, A David</creatorcontrib><creatorcontrib>Cleary, Paul D</creatorcontrib><creatorcontrib>Gill, Michael J</creatorcontrib><creatorcontrib>Kitahata, Mari M</creatorcontrib><creatorcontrib>Rebeiro, Peter F</creatorcontrib><creatorcontrib>Silverberg, Michael J</creatorcontrib><creatorcontrib>Horberg, Michael</creatorcontrib><creatorcontrib>Abraham, Alison G</creatorcontrib><creatorcontrib>Althoff, Keri N</creatorcontrib><creatorcontrib>Moore, Richard</creatorcontrib><creatorcontrib>Bosch, Ronald J</creatorcontrib><creatorcontrib>Tang, Tian</creatorcontrib><creatorcontrib>Hall, H Irene</creatorcontrib><creatorcontrib>Kaplan, Edward H</creatorcontrib><title>A Flow-Based Model of the HIV Care Continuum in the United States</title><title>Journal of acquired immune deficiency syndromes (1999)</title><addtitle>J Acquir Immune Defic Syndr</addtitle><description>BACKGROUND:Understanding the flow of patients through the continuum of HIV care is critical to determine how best to intervene so that the proportion of HIV-infected persons who are on antiretroviral treatment and virally suppressed is as large as possible.
METHODS:Using immunological and virological data from the Centers for Disease Control and Prevention and the North American AIDS Cohort Collaboration on Research and Design from 2009 to 2012, we estimated the distribution of time spent in and dropout probability from each stage in the continuum of HIV care. We used these estimates to develop a queueing model for the expected number of patients found in each stage of the cascade.
RESULTS:HIV-infected individuals spend an average of about 3.1 months after HIV diagnosis before being linked to care, or dropping out of that stage of the continuum with a probability of 8%. Those who link to care wait an additional 3.7 months on average before getting their second set of laboratory results (indicating engagement in care) or dropping out of care with probability of almost 6%. Those engaged in care spent an average of almost 1 year before achieving viral suppression on antiretroviral therapy or dropping out with average probability 13%. For patients who achieved viral suppression, the average time suppressed on antiretroviral therapy was an average of 4.5 years.
CONCLUSIONS:Interventions should be targeted to more rapidly identifying newly infected individuals, and increasing the fraction of those engaged in care that achieves viral suppression.</description><subject>Acquired immune deficiency syndrome</subject><subject>AIDS</subject><subject>AIDS/HIV</subject><subject>Anti-HIV Agents - therapeutic use</subject><subject>Antiretroviral agents</subject><subject>Antiretroviral drugs</subject><subject>Antiretroviral therapy</subject><subject>CD4 Lymphocyte Count</subject><subject>Continuity of Patient Care - statistics & numerical data</subject><subject>Cross-Sectional Studies</subject><subject>Disease control</subject><subject>Female</subject><subject>HIV</subject><subject>HIV Infections - drug therapy</subject><subject>HIV Infections - mortality</subject><subject>HIV Infections - virology</subject><subject>Human immunodeficiency virus</subject><subject>Humans</subject><subject>Immunology</subject><subject>Male</subject><subject>Models, Theoretical</subject><subject>Patients</subject><subject>Population Surveillance</subject><subject>Probability</subject><subject>Queues</subject><subject>Therapy</subject><subject>United States - epidemiology</subject><subject>Viral Load</subject><subject>Virology</subject><issn>1525-4135</issn><issn>1944-7884</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kctOHDEQRa2IKLzyByhqiQ2bJi63PbY3kYZRCCMRIcRjazntaqaJpw22OyP-PoYBRFikNlVSnbq6pUvIHtBDoFp-PZ_OD-mbAs70B7IFmvNaKsU3yiyYqDk0YpNsp3RbmAnn-hPZZIpLUBy2yHRaHfuwqo9sQlf9DA59FboqL7A6mV9XMxuxmoUh98M4Lqt-eNpcDX0u9EW2GdMu-dhZn_Dzc98hV8ffL2cn9enZj_lselq3XAldI3aKORSNs63k1gKC6jRVLQXHZSs4SGalFhI76QrYdVo0jLoGuZswzZod8m2tezf-WqJrccjRenMX-6WNDybY3vy7GfqFuQl_jBBNAxNVBA6eBWK4HzFls-xTi97bAcOYDCg9oZw1QAu6_w69DWMcynsGNBMgtJaPjviaamNIKWL3agaoeczIlIzM-4zK2Ze3j7wevYRSALUGVsFnjOm3H1cYzQKtz4v_a_8FJKabtA</recordid><startdate>20170815</startdate><enddate>20170815</enddate><creator>Gonsalves, Gregg S</creator><creator>Paltiel, A David</creator><creator>Cleary, Paul D</creator><creator>Gill, Michael J</creator><creator>Kitahata, Mari M</creator><creator>Rebeiro, Peter F</creator><creator>Silverberg, Michael J</creator><creator>Horberg, Michael</creator><creator>Abraham, Alison G</creator><creator>Althoff, Keri N</creator><creator>Moore, Richard</creator><creator>Bosch, Ronald J</creator><creator>Tang, Tian</creator><creator>Hall, H Irene</creator><creator>Kaplan, Edward H</creator><general>Copyright Wolters Kluwer Health, Inc. All rights reserved</general><general>Lippincott Williams & Wilkins Ovid Technologies</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7T2</scope><scope>7T5</scope><scope>7TK</scope><scope>7U7</scope><scope>7U9</scope><scope>C1K</scope><scope>H94</scope><scope>K9.</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20170815</creationdate><title>A Flow-Based Model of the HIV Care Continuum in the United States</title><author>Gonsalves, Gregg S ; Paltiel, A David ; Cleary, Paul D ; Gill, Michael J ; Kitahata, Mari M ; Rebeiro, Peter F ; Silverberg, Michael J ; Horberg, Michael ; Abraham, Alison G ; Althoff, Keri N ; Moore, Richard ; Bosch, Ronald J ; Tang, Tian ; Hall, H Irene ; Kaplan, Edward H</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4859-eef82de53dac74aa1e18f908c01d47c54172a7957ef7d2deff95320d3e4d62923</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Acquired immune deficiency syndrome</topic><topic>AIDS</topic><topic>AIDS/HIV</topic><topic>Anti-HIV Agents - therapeutic use</topic><topic>Antiretroviral agents</topic><topic>Antiretroviral drugs</topic><topic>Antiretroviral therapy</topic><topic>CD4 Lymphocyte Count</topic><topic>Continuity of Patient Care - statistics & numerical data</topic><topic>Cross-Sectional Studies</topic><topic>Disease control</topic><topic>Female</topic><topic>HIV</topic><topic>HIV Infections - drug therapy</topic><topic>HIV Infections - mortality</topic><topic>HIV Infections - virology</topic><topic>Human immunodeficiency virus</topic><topic>Humans</topic><topic>Immunology</topic><topic>Male</topic><topic>Models, Theoretical</topic><topic>Patients</topic><topic>Population Surveillance</topic><topic>Probability</topic><topic>Queues</topic><topic>Therapy</topic><topic>United States - epidemiology</topic><topic>Viral Load</topic><topic>Virology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gonsalves, Gregg S</creatorcontrib><creatorcontrib>Paltiel, A David</creatorcontrib><creatorcontrib>Cleary, Paul D</creatorcontrib><creatorcontrib>Gill, Michael J</creatorcontrib><creatorcontrib>Kitahata, Mari M</creatorcontrib><creatorcontrib>Rebeiro, Peter F</creatorcontrib><creatorcontrib>Silverberg, Michael J</creatorcontrib><creatorcontrib>Horberg, Michael</creatorcontrib><creatorcontrib>Abraham, Alison G</creatorcontrib><creatorcontrib>Althoff, Keri N</creatorcontrib><creatorcontrib>Moore, Richard</creatorcontrib><creatorcontrib>Bosch, Ronald J</creatorcontrib><creatorcontrib>Tang, Tian</creatorcontrib><creatorcontrib>Hall, H Irene</creatorcontrib><creatorcontrib>Kaplan, Edward H</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Health and Safety Science Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of acquired immune deficiency syndromes (1999)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gonsalves, Gregg S</au><au>Paltiel, A David</au><au>Cleary, Paul D</au><au>Gill, Michael J</au><au>Kitahata, Mari M</au><au>Rebeiro, Peter F</au><au>Silverberg, Michael J</au><au>Horberg, Michael</au><au>Abraham, Alison G</au><au>Althoff, Keri N</au><au>Moore, Richard</au><au>Bosch, Ronald J</au><au>Tang, Tian</au><au>Hall, H Irene</au><au>Kaplan, Edward H</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Flow-Based Model of the HIV Care Continuum in the United States</atitle><jtitle>Journal of acquired immune deficiency syndromes (1999)</jtitle><addtitle>J Acquir Immune Defic Syndr</addtitle><date>2017-08-15</date><risdate>2017</risdate><volume>75</volume><issue>5</issue><spage>548</spage><epage>553</epage><pages>548-553</pages><issn>1525-4135</issn><eissn>1944-7884</eissn><abstract>BACKGROUND:Understanding the flow of patients through the continuum of HIV care is critical to determine how best to intervene so that the proportion of HIV-infected persons who are on antiretroviral treatment and virally suppressed is as large as possible.
METHODS:Using immunological and virological data from the Centers for Disease Control and Prevention and the North American AIDS Cohort Collaboration on Research and Design from 2009 to 2012, we estimated the distribution of time spent in and dropout probability from each stage in the continuum of HIV care. We used these estimates to develop a queueing model for the expected number of patients found in each stage of the cascade.
RESULTS:HIV-infected individuals spend an average of about 3.1 months after HIV diagnosis before being linked to care, or dropping out of that stage of the continuum with a probability of 8%. Those who link to care wait an additional 3.7 months on average before getting their second set of laboratory results (indicating engagement in care) or dropping out of care with probability of almost 6%. Those engaged in care spent an average of almost 1 year before achieving viral suppression on antiretroviral therapy or dropping out with average probability 13%. For patients who achieved viral suppression, the average time suppressed on antiretroviral therapy was an average of 4.5 years.
CONCLUSIONS:Interventions should be targeted to more rapidly identifying newly infected individuals, and increasing the fraction of those engaged in care that achieves viral suppression.</abstract><cop>United States</cop><pub>Copyright Wolters Kluwer Health, Inc. All rights reserved</pub><pmid>28471841</pmid><doi>10.1097/QAI.0000000000001429</doi><tpages>6</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Acquired immune deficiency syndrome AIDS AIDS/HIV Anti-HIV Agents - therapeutic use Antiretroviral agents Antiretroviral drugs Antiretroviral therapy CD4 Lymphocyte Count Continuity of Patient Care - statistics & numerical data Cross-Sectional Studies Disease control Female HIV HIV Infections - drug therapy HIV Infections - mortality HIV Infections - virology Human immunodeficiency virus Humans Immunology Male Models, Theoretical Patients Population Surveillance Probability Queues Therapy United States - epidemiology Viral Load Virology |
title | A Flow-Based Model of the HIV Care Continuum in the United States |
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