Choice of time horizon critical in estimating costs and effects of changes to HIV programmes
Uganda changed its antiretroviral therapy guidelines in 2014, increasing the CD4 threshold for antiretroviral therapy initiation from 350 cells/μl to 500 cells/μl. We investigate what effect this change in policy is likely to have on HIV incidence, morbidity, and programme costs, and estimate the co...
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| creator | McCreesh, Nicky Andrianakis, Ioannis Nsubuga, Rebecca N Strong, Mark Vernon, Ian McKinley, Trevelyan J Oakley, Jeremy E Goldstein, Michael Hayes, Richard White, Richard G |
| description | Uganda changed its antiretroviral therapy guidelines in 2014, increasing the CD4 threshold for antiretroviral therapy initiation from 350 cells/μl to 500 cells/μl. We investigate what effect this change in policy is likely to have on HIV incidence, morbidity, and programme costs, and estimate the cost-effectiveness of the change over different time horizons.
We used a complex individual-based model of HIV transmission and antiretroviral therapy scale-up in Uganda. 100 model fits were generated by fitting the model to 51 demographic, sexual behaviour, and epidemiological calibration targets, varying 96 input parameters, using history matching with model emulation. An additional 19 cost and disability weight parameters were varied during the analysis of the model results. For each model fit, the model was run to 2030, with and without the change in threshold to 500 cells/μl.
The change in threshold led to a 9.7% (90% plausible range: 4.3%-15.0%) reduction in incidence in 2030, and averted 278,944 (118,452-502,790) DALYs, at a total cost of $28M (-$142M to +$195M). The cost per disability adjusted life year (DALY) averted fell over time, from $3238 (-$125 to +$29,969) in 2014 to $100 (-$499 to +$785) in 2030. The change in threshold was cost-effective (cost |
| doi_str_mv | 10.1371/journal.pone.0196480 |
| format | Article |
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We used a complex individual-based model of HIV transmission and antiretroviral therapy scale-up in Uganda. 100 model fits were generated by fitting the model to 51 demographic, sexual behaviour, and epidemiological calibration targets, varying 96 input parameters, using history matching with model emulation. An additional 19 cost and disability weight parameters were varied during the analysis of the model results. For each model fit, the model was run to 2030, with and without the change in threshold to 500 cells/μl.
The change in threshold led to a 9.7% (90% plausible range: 4.3%-15.0%) reduction in incidence in 2030, and averted 278,944 (118,452-502,790) DALYs, at a total cost of $28M (-$142M to +$195M). The cost per disability adjusted life year (DALY) averted fell over time, from $3238 (-$125 to +$29,969) in 2014 to $100 (-$499 to +$785) in 2030. The change in threshold was cost-effective (cost <3×Uganda's per capita GDP per DALY averted) by 2018, and highly cost-effective (cost <Uganda's per capita GDP per DALY averted) by 2022, for more than 50% of parameter sets.
Model results suggest that the change in threshold is unlikely to have been cost-effective to date, but is likely to be highly cost-effective in Uganda by 2030. The time horizon needs to be chosen carefully when projecting intervention effects. Large amounts of uncertainty in our results demonstrates the need to comprehensively incorporate uncertainties in model parameterisation.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0196480</identifier><identifier>PMID: 29768457</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Acquired immune deficiency syndrome ; AIDS ; Analysis ; Anti-HIV Agents - economics ; Anti-HIV Agents - therapeutic use ; Antiretroviral agents ; Antiretroviral drugs ; Antiretroviral therapy ; Biology and Life Sciences ; Calibration ; CD4 antigen ; CD4 Lymphocyte Count ; Cost analysis ; Cost-Benefit Analysis ; Demographics ; Disease transmission ; Drug therapy ; Economic aspects ; Epidemiology ; Female ; Funding ; Health Policy - economics ; HIV ; HIV Infections - drug therapy ; HIV Infections - economics ; HIV Infections - epidemiology ; Human immunodeficiency virus ; Humans ; Hygiene ; Incidence ; Male ; Mathematical models ; Medical tests ; Medicine ; Medicine and Health Sciences ; Model matching ; Models, Economic ; Morbidity ; Mortality ; Parameters ; People and Places ; Population ; Quality-Adjusted Life Years ; Research and Analysis Methods ; Sexual behavior ; Sexually transmitted diseases ; Social Sciences ; Software ; STD ; Studies ; Therapy ; Time Factors ; Uganda - epidemiology ; Uncertainty</subject><ispartof>PloS one, 2018-05, Vol.13 (5), p.e0196480</ispartof><rights>COPYRIGHT 2018 Public Library of Science</rights><rights>2018 McCreesh et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2018 McCreesh et al 2018 McCreesh et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c585t-339e3e661db733a9f908f94d0970929faa4343282a0dfb375d50b0de0bdf5e643</citedby><cites>FETCH-LOGICAL-c585t-339e3e661db733a9f908f94d0970929faa4343282a0dfb375d50b0de0bdf5e643</cites><orcidid>0000-0003-1409-8531</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5955498/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5955498/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79569,79570</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29768457$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>McCreesh, Nicky</creatorcontrib><creatorcontrib>Andrianakis, Ioannis</creatorcontrib><creatorcontrib>Nsubuga, Rebecca N</creatorcontrib><creatorcontrib>Strong, Mark</creatorcontrib><creatorcontrib>Vernon, Ian</creatorcontrib><creatorcontrib>McKinley, Trevelyan J</creatorcontrib><creatorcontrib>Oakley, Jeremy E</creatorcontrib><creatorcontrib>Goldstein, Michael</creatorcontrib><creatorcontrib>Hayes, Richard</creatorcontrib><creatorcontrib>White, Richard G</creatorcontrib><title>Choice of time horizon critical in estimating costs and effects of changes to HIV programmes</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Uganda changed its antiretroviral therapy guidelines in 2014, increasing the CD4 threshold for antiretroviral therapy initiation from 350 cells/μl to 500 cells/μl. We investigate what effect this change in policy is likely to have on HIV incidence, morbidity, and programme costs, and estimate the cost-effectiveness of the change over different time horizons.
We used a complex individual-based model of HIV transmission and antiretroviral therapy scale-up in Uganda. 100 model fits were generated by fitting the model to 51 demographic, sexual behaviour, and epidemiological calibration targets, varying 96 input parameters, using history matching with model emulation. An additional 19 cost and disability weight parameters were varied during the analysis of the model results. For each model fit, the model was run to 2030, with and without the change in threshold to 500 cells/μl.
The change in threshold led to a 9.7% (90% plausible range: 4.3%-15.0%) reduction in incidence in 2030, and averted 278,944 (118,452-502,790) DALYs, at a total cost of $28M (-$142M to +$195M). The cost per disability adjusted life year (DALY) averted fell over time, from $3238 (-$125 to +$29,969) in 2014 to $100 (-$499 to +$785) in 2030. The change in threshold was cost-effective (cost <3×Uganda's per capita GDP per DALY averted) by 2018, and highly cost-effective (cost <Uganda's per capita GDP per DALY averted) by 2022, for more than 50% of parameter sets.
Model results suggest that the change in threshold is unlikely to have been cost-effective to date, but is likely to be highly cost-effective in Uganda by 2030. The time horizon needs to be chosen carefully when projecting intervention effects. Large amounts of uncertainty in our results demonstrates the need to comprehensively incorporate uncertainties in model parameterisation.</description><subject>Acquired immune deficiency syndrome</subject><subject>AIDS</subject><subject>Analysis</subject><subject>Anti-HIV Agents - economics</subject><subject>Anti-HIV Agents - therapeutic use</subject><subject>Antiretroviral agents</subject><subject>Antiretroviral drugs</subject><subject>Antiretroviral therapy</subject><subject>Biology and Life Sciences</subject><subject>Calibration</subject><subject>CD4 antigen</subject><subject>CD4 Lymphocyte Count</subject><subject>Cost analysis</subject><subject>Cost-Benefit Analysis</subject><subject>Demographics</subject><subject>Disease transmission</subject><subject>Drug therapy</subject><subject>Economic aspects</subject><subject>Epidemiology</subject><subject>Female</subject><subject>Funding</subject><subject>Health Policy - economics</subject><subject>HIV</subject><subject>HIV Infections - drug therapy</subject><subject>HIV Infections - economics</subject><subject>HIV Infections - epidemiology</subject><subject>Human immunodeficiency virus</subject><subject>Humans</subject><subject>Hygiene</subject><subject>Incidence</subject><subject>Male</subject><subject>Mathematical models</subject><subject>Medical tests</subject><subject>Medicine</subject><subject>Medicine and Health Sciences</subject><subject>Model matching</subject><subject>Models, Economic</subject><subject>Morbidity</subject><subject>Mortality</subject><subject>Parameters</subject><subject>People and Places</subject><subject>Population</subject><subject>Quality-Adjusted Life Years</subject><subject>Research and Analysis Methods</subject><subject>Sexual behavior</subject><subject>Sexually transmitted diseases</subject><subject>Social Sciences</subject><subject>Software</subject><subject>STD</subject><subject>Studies</subject><subject>Therapy</subject><subject>Time Factors</subject><subject>Uganda - epidemiology</subject><subject>Uncertainty</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><sourceid>DOA</sourceid><recordid>eNptkt-L1DAQx4so3rn6H4gGBPFl17T50eTl4FhOb-HAF_VJCGk6abO0yZp0Bf3rL-v2jl3xKSHzme_MdzJF8brEq5LU5cdt2Eevh9UueFjhUnIq8JPispSkWvIKk6cn94viRUpbjBkRnD8vLipZc0FZfVn8WPfBGUDBosmNgPoQ3Z_gkYluckYPyHkEKYf05HyHTEhTQtq3CKwFk-850fTad5DQFNDt5jvaxdBFPY6QXhbPrB4SvJrPRfHt083X9e3y7svnzfr6bmmYYNOSEAkEOC_bpiZESyuxsJK2WNZYVtJqTQkllag0bm1DatYy3OAWcNNaBpySRfH2qLsbQlLzYJLKxqWgWFR1JjZHog16q3Yx-4m_VdBO_X0IsVM6ZsMDKGZM2_DGEM01hdyPoEQaY6DipeC5_qK4mqvtmxFaA36KejgTPY9416su_FJMMkalyAIfZoEYfu7zdNXokoFh0B7C_tA3xXVdEYkz-u4f9P_uZqrT2YDzNuS65iCqrvOXS1YxyjL1_oTqQQ9Tn8Kwn1zw6RykR9DEkFIE--itxOqwew9NqMPuqXn3ctqb07k8Jj0sG7kHc73WZQ</recordid><startdate>20180516</startdate><enddate>20180516</enddate><creator>McCreesh, Nicky</creator><creator>Andrianakis, Ioannis</creator><creator>Nsubuga, Rebecca N</creator><creator>Strong, Mark</creator><creator>Vernon, Ian</creator><creator>McKinley, Trevelyan J</creator><creator>Oakley, Jeremy E</creator><creator>Goldstein, Michael</creator><creator>Hayes, Richard</creator><creator>White, Richard G</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</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>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PHGZM</scope><scope>PHGZT</scope><scope>PIMPY</scope><scope>PJZUB</scope><scope>PKEHL</scope><scope>PPXIY</scope><scope>PQEST</scope><scope>PQGLB</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-1409-8531</orcidid></search><sort><creationdate>20180516</creationdate><title>Choice of time horizon critical in estimating costs and effects of changes to HIV programmes</title><author>McCreesh, Nicky ; Andrianakis, Ioannis ; Nsubuga, Rebecca N ; Strong, Mark ; Vernon, Ian ; McKinley, Trevelyan J ; Oakley, Jeremy E ; Goldstein, Michael ; Hayes, Richard ; White, Richard G</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c585t-339e3e661db733a9f908f94d0970929faa4343282a0dfb375d50b0de0bdf5e643</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Acquired immune deficiency syndrome</topic><topic>AIDS</topic><topic>Analysis</topic><topic>Anti-HIV Agents - economics</topic><topic>Anti-HIV Agents - therapeutic use</topic><topic>Antiretroviral agents</topic><topic>Antiretroviral drugs</topic><topic>Antiretroviral therapy</topic><topic>Biology and Life Sciences</topic><topic>Calibration</topic><topic>CD4 antigen</topic><topic>CD4 Lymphocyte Count</topic><topic>Cost analysis</topic><topic>Cost-Benefit Analysis</topic><topic>Demographics</topic><topic>Disease transmission</topic><topic>Drug therapy</topic><topic>Economic aspects</topic><topic>Epidemiology</topic><topic>Female</topic><topic>Funding</topic><topic>Health Policy - economics</topic><topic>HIV</topic><topic>HIV Infections - drug therapy</topic><topic>HIV Infections - economics</topic><topic>HIV Infections - epidemiology</topic><topic>Human immunodeficiency virus</topic><topic>Humans</topic><topic>Hygiene</topic><topic>Incidence</topic><topic>Male</topic><topic>Mathematical models</topic><topic>Medical tests</topic><topic>Medicine</topic><topic>Medicine and Health Sciences</topic><topic>Model matching</topic><topic>Models, Economic</topic><topic>Morbidity</topic><topic>Mortality</topic><topic>Parameters</topic><topic>People and Places</topic><topic>Population</topic><topic>Quality-Adjusted Life Years</topic><topic>Research and Analysis Methods</topic><topic>Sexual behavior</topic><topic>Sexually transmitted diseases</topic><topic>Social Sciences</topic><topic>Software</topic><topic>STD</topic><topic>Studies</topic><topic>Therapy</topic><topic>Time Factors</topic><topic>Uganda - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>McCreesh, Nicky</au><au>Andrianakis, Ioannis</au><au>Nsubuga, Rebecca N</au><au>Strong, Mark</au><au>Vernon, Ian</au><au>McKinley, Trevelyan J</au><au>Oakley, Jeremy E</au><au>Goldstein, Michael</au><au>Hayes, Richard</au><au>White, Richard G</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Choice of time horizon critical in estimating costs and effects of changes to HIV programmes</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2018-05-16</date><risdate>2018</risdate><volume>13</volume><issue>5</issue><spage>e0196480</spage><pages>e0196480-</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Uganda changed its antiretroviral therapy guidelines in 2014, increasing the CD4 threshold for antiretroviral therapy initiation from 350 cells/μl to 500 cells/μl. We investigate what effect this change in policy is likely to have on HIV incidence, morbidity, and programme costs, and estimate the cost-effectiveness of the change over different time horizons.
We used a complex individual-based model of HIV transmission and antiretroviral therapy scale-up in Uganda. 100 model fits were generated by fitting the model to 51 demographic, sexual behaviour, and epidemiological calibration targets, varying 96 input parameters, using history matching with model emulation. An additional 19 cost and disability weight parameters were varied during the analysis of the model results. For each model fit, the model was run to 2030, with and without the change in threshold to 500 cells/μl.
The change in threshold led to a 9.7% (90% plausible range: 4.3%-15.0%) reduction in incidence in 2030, and averted 278,944 (118,452-502,790) DALYs, at a total cost of $28M (-$142M to +$195M). The cost per disability adjusted life year (DALY) averted fell over time, from $3238 (-$125 to +$29,969) in 2014 to $100 (-$499 to +$785) in 2030. The change in threshold was cost-effective (cost <3×Uganda's per capita GDP per DALY averted) by 2018, and highly cost-effective (cost <Uganda's per capita GDP per DALY averted) by 2022, for more than 50% of parameter sets.
Model results suggest that the change in threshold is unlikely to have been cost-effective to date, but is likely to be highly cost-effective in Uganda by 2030. The time horizon needs to be chosen carefully when projecting intervention effects. Large amounts of uncertainty in our results demonstrates the need to comprehensively incorporate uncertainties in model parameterisation.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>29768457</pmid><doi>10.1371/journal.pone.0196480</doi><orcidid>https://orcid.org/0000-0003-1409-8531</orcidid><oa>free_for_read</oa></addata></record> |
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| recordid | cdi_plos_journals_2039840827 |
| source | Public Library of Science (PLoS) Journals Open Access; MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Acquired immune deficiency syndrome AIDS Analysis Anti-HIV Agents - economics Anti-HIV Agents - therapeutic use Antiretroviral agents Antiretroviral drugs Antiretroviral therapy Biology and Life Sciences Calibration CD4 antigen CD4 Lymphocyte Count Cost analysis Cost-Benefit Analysis Demographics Disease transmission Drug therapy Economic aspects Epidemiology Female Funding Health Policy - economics HIV HIV Infections - drug therapy HIV Infections - economics HIV Infections - epidemiology Human immunodeficiency virus Humans Hygiene Incidence Male Mathematical models Medical tests Medicine Medicine and Health Sciences Model matching Models, Economic Morbidity Mortality Parameters People and Places Population Quality-Adjusted Life Years Research and Analysis Methods Sexual behavior Sexually transmitted diseases Social Sciences Software STD Studies Therapy Time Factors Uganda - epidemiology Uncertainty |
| title | Choice of time horizon critical in estimating costs and effects of changes to HIV programmes |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-21T17%3A52%3A31IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Choice%20of%20time%20horizon%20critical%20in%20estimating%20costs%20and%20effects%20of%20changes%20to%20HIV%20programmes&rft.jtitle=PloS%20one&rft.au=McCreesh,%20Nicky&rft.date=2018-05-16&rft.volume=13&rft.issue=5&rft.spage=e0196480&rft.pages=e0196480-&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0196480&rft_dat=%3Cgale_plos_%3EA538952545%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2039840827&rft_id=info:pmid/29768457&rft_galeid=A538952545&rft_doaj_id=oai_doaj_org_article_5ccdb6bc3a6a4e33a8439ccce26186fb&rfr_iscdi=true |