TIME Impact - a new user-friendly tuberculosis (TB) model to inform TB policy decisions.
Identifieur interne : 000720 ( PubMed/Corpus ); précédent : 000719; suivant : 000721TIME Impact - a new user-friendly tuberculosis (TB) model to inform TB policy decisions.
Auteurs : R M G J. Houben ; M. Lalli ; T. Sumner ; M. Hamilton ; D. Pedrazzoli ; F. Bonsu ; P. Hippner ; Y. Pillay ; M. Kimerling ; S. Ahmedov ; C. Pretorius ; R G WhiteSource :
- BMC medicine [ 1741-7015 ] ; 2016.
English descriptors
- KwdEn :
- MESH :
- epidemiology : South Africa, Tuberculosis.
- Health Policy, Health Resources, Humans, Models, Theoretical, Policy Making.
Abstract
Tuberculosis (TB) is the leading cause of death from infectious disease worldwide, predominantly affecting low- and middle-income countries (LMICs), where resources are limited. As such, countries need to be able to choose the most efficient interventions for their respective setting. Mathematical models can be valuable tools to inform rational policy decisions and improve resource allocation, but are often unavailable or inaccessible for LMICs, particularly in TB. We developed TIME Impact, a user-friendly TB model that enables local capacity building and strengthens country-specific policy discussions to inform support funding applications at the (sub-)national level (e.g. Ministry of Finance) or to international donors (e.g. the Global Fund to Fight AIDS, Tuberculosis and Malaria).TIME Impact is an epidemiological transmission model nested in TIME, a set of TB modelling tools available for free download within the widely-used Spectrum software. The TIME Impact model reflects key aspects of the natural history of TB, with additional structure for HIV/ART, drug resistance, treatment history and age. TIME Impact enables national TB programmes (NTPs) and other TB policymakers to better understand their own TB epidemic, plan their response, apply for funding and evaluate the implementation of the response.The explicit aim of TIME Impact's user-friendly interface is to enable training of local and international TB experts towards independent use. During application of TIME Impact, close involvement of the NTPs and other local partners also builds critical understanding of the modelling methods, assumptions and limitations inherent to modelling. This is essential to generate broad country-level ownership of the modelling data inputs and results. In turn, it stimulates discussions and a review of the current evidence and assumptions, strengthening the decision-making process in general.TIME Impact has been effectively applied in a variety of settings. In South Africa, it informed the first South African HIV and TB Investment Cases and successfully leveraged additional resources from the National Treasury at a time of austerity. In Ghana, a long-term TIME model-centred interaction with the NTP provided new insights into the local epidemiology and guided resource allocation decisions to improve impact.
DOI: 10.1186/s12916-016-0608-4
PubMed: 27012808
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pubmed:27012808Le document en format XML
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Houben</name><affiliation><nlm:affiliation>TB Modelling Group, TB Centre, London School of Hygiene and Tropical Medicine, Keppel Street, WC1E 7HT, London, UK. rein.houben@lshtm.ac.uk.</nlm:affiliation></affiliation></author><author><name sortKey="Lalli, M" sort="Lalli, M" uniqKey="Lalli M" first="M" last="Lalli">M. Lalli</name><affiliation><nlm:affiliation>TB Modelling Group, TB Centre, London School of Hygiene and Tropical Medicine, Keppel Street, WC1E 7HT, London, UK.</nlm:affiliation></affiliation></author><author><name sortKey="Sumner, T" sort="Sumner, T" uniqKey="Sumner T" first="T" last="Sumner">T. Sumner</name><affiliation><nlm:affiliation>TB Modelling Group, TB Centre, London School of Hygiene and Tropical Medicine, Keppel Street, WC1E 7HT, London, UK.</nlm:affiliation></affiliation></author><author><name sortKey="Hamilton, M" sort="Hamilton, M" uniqKey="Hamilton M" first="M" last="Hamilton">M. Hamilton</name><affiliation><nlm:affiliation>Avenir Health, Glastonbury, CT, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Pedrazzoli, D" sort="Pedrazzoli, D" uniqKey="Pedrazzoli D" first="D" last="Pedrazzoli">D. Pedrazzoli</name><affiliation><nlm:affiliation>TB Modelling Group, TB Centre, London School of Hygiene and Tropical Medicine, Keppel Street, WC1E 7HT, London, UK.</nlm:affiliation></affiliation></author><author><name sortKey="Bonsu, F" sort="Bonsu, F" uniqKey="Bonsu F" first="F" last="Bonsu">F. Bonsu</name><affiliation><nlm:affiliation>National Tuberculosis Control Programme, Ghana Health Service, Accra, Ghana.</nlm:affiliation></affiliation></author><author><name sortKey="Hippner, P" sort="Hippner, P" uniqKey="Hippner P" first="P" last="Hippner">P. Hippner</name><affiliation><nlm:affiliation>Aurum Institute, Johannesburg, South Africa.</nlm:affiliation></affiliation></author><author><name sortKey="Pillay, Y" sort="Pillay, Y" uniqKey="Pillay Y" first="Y" last="Pillay">Y. Pillay</name><affiliation><nlm:affiliation>National Department of Health, Pretoria, South Africa.</nlm:affiliation></affiliation></author><author><name sortKey="Kimerling, M" sort="Kimerling, M" uniqKey="Kimerling M" first="M" last="Kimerling">M. Kimerling</name><affiliation><nlm:affiliation>KNCV Tuberculosis Foundation, The Hague, The Netherlands.</nlm:affiliation></affiliation></author><author><name sortKey="Ahmedov, S" sort="Ahmedov, S" uniqKey="Ahmedov S" first="S" last="Ahmedov">S. Ahmedov</name><affiliation><nlm:affiliation>USAID, Washington, DC, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Pretorius, C" sort="Pretorius, C" uniqKey="Pretorius C" first="C" last="Pretorius">C. Pretorius</name><affiliation><nlm:affiliation>Avenir Health, Glastonbury, CT, USA.</nlm:affiliation></affiliation></author><author><name sortKey="White, R G" sort="White, R G" uniqKey="White R" first="R G" last="White">R G White</name><affiliation><nlm:affiliation>TB Modelling Group, TB Centre, London School of Hygiene and Tropical Medicine, Keppel Street, WC1E 7HT, London, UK.</nlm:affiliation></affiliation></author></analytic><series><title level="j">BMC medicine</title><idno type="eISSN">1741-7015</idno><imprint><date when="2016" type="published">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Health Policy</term><term>Health Resources</term><term>Humans</term><term>Models, Theoretical</term><term>Policy Making</term><term>South Africa (epidemiology)</term><term>Tuberculosis (epidemiology)</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>South Africa</term><term>Tuberculosis</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Health Policy</term><term>Health Resources</term><term>Humans</term><term>Models, Theoretical</term><term>Policy Making</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Tuberculosis (TB) is the leading cause of death from infectious disease worldwide, predominantly affecting low- and middle-income countries (LMICs), where resources are limited. As such, countries need to be able to choose the most efficient interventions for their respective setting. Mathematical models can be valuable tools to inform rational policy decisions and improve resource allocation, but are often unavailable or inaccessible for LMICs, particularly in TB. We developed TIME Impact, a user-friendly TB model that enables local capacity building and strengthens country-specific policy discussions to inform support funding applications at the (sub-)national level (e.g. Ministry of Finance) or to international donors (e.g. the Global Fund to Fight AIDS, Tuberculosis and Malaria).TIME Impact is an epidemiological transmission model nested in TIME, a set of TB modelling tools available for free download within the widely-used Spectrum software. The TIME Impact model reflects key aspects of the natural history of TB, with additional structure for HIV/ART, drug resistance, treatment history and age. TIME Impact enables national TB programmes (NTPs) and other TB policymakers to better understand their own TB epidemic, plan their response, apply for funding and evaluate the implementation of the response.The explicit aim of TIME Impact's user-friendly interface is to enable training of local and international TB experts towards independent use. During application of TIME Impact, close involvement of the NTPs and other local partners also builds critical understanding of the modelling methods, assumptions and limitations inherent to modelling. This is essential to generate broad country-level ownership of the modelling data inputs and results. In turn, it stimulates discussions and a review of the current evidence and assumptions, strengthening the decision-making process in general.TIME Impact has been effectively applied in a variety of settings. In South Africa, it informed the first South African HIV and TB Investment Cases and successfully leveraged additional resources from the National Treasury at a time of austerity. In Ghana, a long-term TIME model-centred interaction with the NTP provided new insights into the local epidemiology and guided resource allocation decisions to improve impact.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">27012808</PMID><DateCreated><Year>2016</Year><Month>03</Month><Day>25</Day></DateCreated><DateCompleted><Year>2016</Year><Month>11</Month><Day>14</Day></DateCompleted><DateRevised><Year>2017</Year><Month>09</Month><Day>02</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1741-7015</ISSN><JournalIssue CitedMedium="Internet"><Volume>14</Volume><PubDate><Year>2016</Year><Month>Mar</Month><Day>24</Day></PubDate></JournalIssue><Title>BMC medicine</Title><ISOAbbreviation>BMC Med</ISOAbbreviation></Journal><ArticleTitle>TIME Impact - a new user-friendly tuberculosis (TB) model to inform TB policy decisions.</ArticleTitle><Pagination><MedlinePgn>56</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/s12916-016-0608-4</ELocationID><Abstract><AbstractText>Tuberculosis (TB) is the leading cause of death from infectious disease worldwide, predominantly affecting low- and middle-income countries (LMICs), where resources are limited. As such, countries need to be able to choose the most efficient interventions for their respective setting. Mathematical models can be valuable tools to inform rational policy decisions and improve resource allocation, but are often unavailable or inaccessible for LMICs, particularly in TB. We developed TIME Impact, a user-friendly TB model that enables local capacity building and strengthens country-specific policy discussions to inform support funding applications at the (sub-)national level (e.g. Ministry of Finance) or to international donors (e.g. the Global Fund to Fight AIDS, Tuberculosis and Malaria).TIME Impact is an epidemiological transmission model nested in TIME, a set of TB modelling tools available for free download within the widely-used Spectrum software. The TIME Impact model reflects key aspects of the natural history of TB, with additional structure for HIV/ART, drug resistance, treatment history and age. TIME Impact enables national TB programmes (NTPs) and other TB policymakers to better understand their own TB epidemic, plan their response, apply for funding and evaluate the implementation of the response.The explicit aim of TIME Impact's user-friendly interface is to enable training of local and international TB experts towards independent use. During application of TIME Impact, close involvement of the NTPs and other local partners also builds critical understanding of the modelling methods, assumptions and limitations inherent to modelling. This is essential to generate broad country-level ownership of the modelling data inputs and results. In turn, it stimulates discussions and a review of the current evidence and assumptions, strengthening the decision-making process in general.TIME Impact has been effectively applied in a variety of settings. In South Africa, it informed the first South African HIV and TB Investment Cases and successfully leveraged additional resources from the National Treasury at a time of austerity. In Ghana, a long-term TIME model-centred interaction with the NTP provided new insights into the local epidemiology and guided resource allocation decisions to improve impact.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Houben</LastName><ForeName>R M G J</ForeName><Initials>RM</Initials><AffiliationInfo><Affiliation>TB Modelling Group, TB Centre, London School of Hygiene and Tropical Medicine, Keppel Street, WC1E 7HT, London, UK. rein.houben@lshtm.ac.uk.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK. rein.houben@lshtm.ac.uk.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Lalli</LastName><ForeName>M</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>TB Modelling Group, TB Centre, London School of Hygiene and Tropical Medicine, Keppel Street, WC1E 7HT, London, UK.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Sumner</LastName><ForeName>T</ForeName><Initials>T</Initials><AffiliationInfo><Affiliation>TB Modelling Group, TB Centre, London School of Hygiene and Tropical Medicine, Keppel Street, WC1E 7HT, London, UK.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Hamilton</LastName><ForeName>M</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Avenir Health, Glastonbury, CT, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Pedrazzoli</LastName><ForeName>D</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>TB Modelling Group, TB Centre, London School of Hygiene and Tropical Medicine, Keppel Street, WC1E 7HT, London, UK.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Bonsu</LastName><ForeName>F</ForeName><Initials>F</Initials><AffiliationInfo><Affiliation>National Tuberculosis Control Programme, Ghana Health Service, Accra, Ghana.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Hippner</LastName><ForeName>P</ForeName><Initials>P</Initials><AffiliationInfo><Affiliation>Aurum Institute, Johannesburg, South Africa.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Pillay</LastName><ForeName>Y</ForeName><Initials>Y</Initials><AffiliationInfo><Affiliation>National Department of Health, Pretoria, South Africa.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kimerling</LastName><ForeName>M</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>KNCV Tuberculosis Foundation, The Hague, The Netherlands.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Ahmedov</LastName><ForeName>S</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>USAID, Washington, DC, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Pretorius</LastName><ForeName>C</ForeName><Initials>C</Initials><AffiliationInfo><Affiliation>Avenir Health, Glastonbury, CT, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>White</LastName><ForeName>R G</ForeName><Initials>RG</Initials><AffiliationInfo><Affiliation>TB Modelling Group, TB Centre, London School of Hygiene and Tropical Medicine, Keppel Street, WC1E 7HT, London, UK.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>MRC_MR/J005088/1</GrantID><Agency>Medical Research Council</Agency><Country>United Kingdom</Country></Grant><Grant><Agency>Medical Research Council</Agency><Country>United Kingdom</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016422">Letter</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2016</Year><Month>03</Month><Day>24</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>BMC Med</MedlineTA><NlmUniqueID>101190723</NlmUniqueID><ISSNLinking>1741-7015</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><CommentsCorrectionsList><CommentsCorrections RefType="Cites"><RefSource>Diabetes Res Clin Pract. 2000 Nov;50 Suppl 3:S15-46</RefSource><PMID 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RefType="Cites"><RefSource>J Theor Biol. 2014 Oct 7;358:74-84</RefSource><PMID Version="1">24878110</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Lancet Glob Health. 2014 Aug;2(8):e453-9</RefSource><PMID Version="1">25103518</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Int J Infect Dis. 2016 Jan;42:17-23</RefSource><PMID Version="1">26546234</PMID></CommentsCorrections></CommentsCorrectionsList><MeshHeadingList><MeshHeading><DescriptorName UI="D006291" MajorTopicYN="Y">Health Policy</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006295" MajorTopicYN="N">Health Resources</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008962" MajorTopicYN="Y">Models, Theoretical</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011050" MajorTopicYN="Y">Policy Making</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D013019" MajorTopicYN="N">South Africa</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D014376" MajorTopicYN="N">Tuberculosis</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName></MeshHeading></MeshHeadingList><OtherID Source="NLM">PMC4806495</OtherID><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Capacity building</Keyword><Keyword MajorTopicYN="N">Mathematical modelling</Keyword><Keyword MajorTopicYN="N">Policy support</Keyword><Keyword MajorTopicYN="N">Tuberculosis</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2016</Year><Month>01</Month><Day>16</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2016</Year><Month>03</Month><Day>22</Day></PubMedPubDate><PubMedPubDate 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