Recommendations for the use of mathematical modelling to support decision‐making on integration of non‐communicable diseases into HIV care
Introduction Integrating services for non‐communicable diseases (NCDs) into existing primary care platforms such as HIV programmes has been recommended as a way of strengthening health systems, reducing redundancies and leveraging existing systems to rapidly scale‐up underdeveloped programmes. Mathe...
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Veröffentlicht in: | Journal of the International AIDS Society 2020-06, Vol.23 (SI1), p.e25505-n/a |
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creator | Kibachio, Joseph Mwenda, Valerian Ombiro, Oren Kamano, Jamima H Perez‐Guzman, Pablo N Mutai, Kennedy K Guessous, Idris Beran, David Kasaie, Paratsu Weir, Brian Beecroft, Blythe Kilonzo, Nduku Kupfer, Linda Smit, Mikaela |
description | Introduction
Integrating services for non‐communicable diseases (NCDs) into existing primary care platforms such as HIV programmes has been recommended as a way of strengthening health systems, reducing redundancies and leveraging existing systems to rapidly scale‐up underdeveloped programmes. Mathematical modelling provides a powerful tool to address questions around priorities, optimization and implementation of such programmes. In this study, we examine the case for NCD‐HIV integration, use Kenya as a case‐study to highlight how modelling has supported wider policy formulation and decision‐making in healthcare and to collate stakeholders’ recommendations on use of models for NCD‐HIV integration decision‐making.
Discussion
Across Africa, NCDs are increasingly posing challenges for health systems, which historically focused on the care of acute and infectious conditions. Pilot programmes using integrated care services have generated advantages for both provider and user, been cost‐effective, practical and achieve rapid coverage scale‐up. The shared chronic nature of NCDs and HIV means that many operational approaches and infrastructure developed for HIV programmes apply to NCDs, suggesting this to be a cost‐effective and sustainable policy option for countries with large HIV programmes and small, un‐resourced NCD programmes. However, the vertical nature of current disease programmes, policy financing and operations operate as barriers to NCD‐HIV integration. Modelling has successfully been used to inform health decision‐making across a number of disease areas and in a number of ways. Examples from Kenya include (i) estimating current and future disease burden to set priorities for public health interventions, (ii) forecasting the requisite investments by government, (iii) comparing the impact of different integration approaches, (iv) performing cost‐benefit analysis for integration and (v) evaluating health system capacity needs.
Conclusions
Modelling can and should play an integral part in the decision‐making processes for health in general and NCD‐HIV integration specifically. It is especially useful where little data is available. The successful use of modelling to inform decision‐making will depend on several factors including policy makers’ comfort with and understanding of models and their uncertainties, modellers understanding of national priorities, funding opportunities and building local modelling capacity to ensure sustainability. |
doi_str_mv | 10.1002/jia2.25505 |
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Integrating services for non‐communicable diseases (NCDs) into existing primary care platforms such as HIV programmes has been recommended as a way of strengthening health systems, reducing redundancies and leveraging existing systems to rapidly scale‐up underdeveloped programmes. Mathematical modelling provides a powerful tool to address questions around priorities, optimization and implementation of such programmes. In this study, we examine the case for NCD‐HIV integration, use Kenya as a case‐study to highlight how modelling has supported wider policy formulation and decision‐making in healthcare and to collate stakeholders’ recommendations on use of models for NCD‐HIV integration decision‐making.
Discussion
Across Africa, NCDs are increasingly posing challenges for health systems, which historically focused on the care of acute and infectious conditions. Pilot programmes using integrated care services have generated advantages for both provider and user, been cost‐effective, practical and achieve rapid coverage scale‐up. The shared chronic nature of NCDs and HIV means that many operational approaches and infrastructure developed for HIV programmes apply to NCDs, suggesting this to be a cost‐effective and sustainable policy option for countries with large HIV programmes and small, un‐resourced NCD programmes. However, the vertical nature of current disease programmes, policy financing and operations operate as barriers to NCD‐HIV integration. Modelling has successfully been used to inform health decision‐making across a number of disease areas and in a number of ways. Examples from Kenya include (i) estimating current and future disease burden to set priorities for public health interventions, (ii) forecasting the requisite investments by government, (iii) comparing the impact of different integration approaches, (iv) performing cost‐benefit analysis for integration and (v) evaluating health system capacity needs.
Conclusions
Modelling can and should play an integral part in the decision‐making processes for health in general and NCD‐HIV integration specifically. It is especially useful where little data is available. The successful use of modelling to inform decision‐making will depend on several factors including policy makers’ comfort with and understanding of models and their uncertainties, modellers understanding of national priorities, funding opportunities and building local modelling capacity to ensure sustainability.</description><identifier>ISSN: 1758-2652</identifier><identifier>EISSN: 1758-2652</identifier><identifier>DOI: 10.1002/jia2.25505</identifier><identifier>PMID: 32562338</identifier><language>eng</language><publisher>Switzerland: International AIDS Society</publisher><subject>Acquired immune deficiency syndrome ; AIDS ; Analysis ; Care and treatment ; Childrens health ; Chronic illnesses ; Communicable diseases ; Cost control ; Decision Making ; Delivery of Health Care ; Delivery of Health Care, Integrated ; Diabetes ; Disease prevention ; Expenditures ; Government Programs ; Health aspects ; HIV ; HIV infection ; HIV Infections - therapy ; Human immunodeficiency virus ; Humans ; Hypertension ; Infectious diseases ; Infrastructure ; Integrated delivery networks ; integration ; Kenya ; Laboratories ; Management ; Maternal & child health ; Mathematical models ; Medical research ; modelling ; Models, Biological ; Models, Theoretical ; Mortality ; Noncommunicable Diseases - therapy ; non‐communicable diseases ; Optimization ; policy ; Population ; Primary care ; Primary Health Care ; Risk factors ; Supplement ; Supply chains</subject><ispartof>Journal of the International AIDS Society, 2020-06, Vol.23 (SI1), p.e25505-n/a</ispartof><rights>2020 The Authors. Journal of the International AIDS Society published by John Wiley & Sons Ltd on behalf of the International AIDS Society.</rights><rights>COPYRIGHT 2020 International AIDS Society</rights><rights>COPYRIGHT 2020 John Wiley & Sons, Inc.</rights><rights>2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c7485-94d107a2a3958d5601ebe80724de36dcd3f206a95aa0dfd611a707a9e22faca03</citedby><cites>FETCH-LOGICAL-c7485-94d107a2a3958d5601ebe80724de36dcd3f206a95aa0dfd611a707a9e22faca03</cites><orcidid>0000-0001-8530-748X ; 0000-0002-6886-6818 ; 0000-0002-3744-9501</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/PMC7305412/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7305412/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,1417,11562,27924,27925,45574,45575,46052,46476,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32562338$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Kibachio, Joseph</creatorcontrib><creatorcontrib>Mwenda, Valerian</creatorcontrib><creatorcontrib>Ombiro, Oren</creatorcontrib><creatorcontrib>Kamano, Jamima H</creatorcontrib><creatorcontrib>Perez‐Guzman, Pablo N</creatorcontrib><creatorcontrib>Mutai, Kennedy K</creatorcontrib><creatorcontrib>Guessous, Idris</creatorcontrib><creatorcontrib>Beran, David</creatorcontrib><creatorcontrib>Kasaie, Paratsu</creatorcontrib><creatorcontrib>Weir, Brian</creatorcontrib><creatorcontrib>Beecroft, Blythe</creatorcontrib><creatorcontrib>Kilonzo, Nduku</creatorcontrib><creatorcontrib>Kupfer, Linda</creatorcontrib><creatorcontrib>Smit, Mikaela</creatorcontrib><title>Recommendations for the use of mathematical modelling to support decision‐making on integration of non‐communicable diseases into HIV care</title><title>Journal of the International AIDS Society</title><addtitle>J Int AIDS Soc</addtitle><description>Introduction
Integrating services for non‐communicable diseases (NCDs) into existing primary care platforms such as HIV programmes has been recommended as a way of strengthening health systems, reducing redundancies and leveraging existing systems to rapidly scale‐up underdeveloped programmes. Mathematical modelling provides a powerful tool to address questions around priorities, optimization and implementation of such programmes. In this study, we examine the case for NCD‐HIV integration, use Kenya as a case‐study to highlight how modelling has supported wider policy formulation and decision‐making in healthcare and to collate stakeholders’ recommendations on use of models for NCD‐HIV integration decision‐making.
Discussion
Across Africa, NCDs are increasingly posing challenges for health systems, which historically focused on the care of acute and infectious conditions. Pilot programmes using integrated care services have generated advantages for both provider and user, been cost‐effective, practical and achieve rapid coverage scale‐up. The shared chronic nature of NCDs and HIV means that many operational approaches and infrastructure developed for HIV programmes apply to NCDs, suggesting this to be a cost‐effective and sustainable policy option for countries with large HIV programmes and small, un‐resourced NCD programmes. However, the vertical nature of current disease programmes, policy financing and operations operate as barriers to NCD‐HIV integration. Modelling has successfully been used to inform health decision‐making across a number of disease areas and in a number of ways. Examples from Kenya include (i) estimating current and future disease burden to set priorities for public health interventions, (ii) forecasting the requisite investments by government, (iii) comparing the impact of different integration approaches, (iv) performing cost‐benefit analysis for integration and (v) evaluating health system capacity needs.
Conclusions
Modelling can and should play an integral part in the decision‐making processes for health in general and NCD‐HIV integration specifically. It is especially useful where little data is available. The successful use of modelling to inform decision‐making will depend on several factors including policy makers’ comfort with and understanding of models and their uncertainties, modellers understanding of national priorities, funding opportunities and building local modelling capacity to ensure sustainability.</description><subject>Acquired immune deficiency syndrome</subject><subject>AIDS</subject><subject>Analysis</subject><subject>Care and treatment</subject><subject>Childrens health</subject><subject>Chronic illnesses</subject><subject>Communicable diseases</subject><subject>Cost control</subject><subject>Decision Making</subject><subject>Delivery of Health Care</subject><subject>Delivery of Health Care, Integrated</subject><subject>Diabetes</subject><subject>Disease prevention</subject><subject>Expenditures</subject><subject>Government Programs</subject><subject>Health aspects</subject><subject>HIV</subject><subject>HIV infection</subject><subject>HIV Infections - therapy</subject><subject>Human immunodeficiency virus</subject><subject>Humans</subject><subject>Hypertension</subject><subject>Infectious diseases</subject><subject>Infrastructure</subject><subject>Integrated delivery networks</subject><subject>integration</subject><subject>Kenya</subject><subject>Laboratories</subject><subject>Management</subject><subject>Maternal & child health</subject><subject>Mathematical models</subject><subject>Medical research</subject><subject>modelling</subject><subject>Models, Biological</subject><subject>Models, Theoretical</subject><subject>Mortality</subject><subject>Noncommunicable Diseases - therapy</subject><subject>non‐communicable diseases</subject><subject>Optimization</subject><subject>policy</subject><subject>Population</subject><subject>Primary care</subject><subject>Primary Health Care</subject><subject>Risk factors</subject><subject>Supplement</subject><subject>Supply chains</subject><issn>1758-2652</issn><issn>1758-2652</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>WIN</sourceid><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNqNk1GL1DAQgIso3nn64g-QgCAi7JpMm7R9EZZDvZUDQdTXkE2mu1nbZK9plXvzF4i_0V9iunseXVmWo9CmnW--ScpMkjxldMoohddrq2AKnFN-LzllOS8mIDjcH61PkkchrCkVUGTlw-QkBS4gTYvT5Ncn1L5p0BnVWe8CqXxLuhWSPiDxFWlUfIk3q1VNGm-wrq1bks6T0G82vu2IQW1DTP3z83ejvg1B74h1HS7brXKwuG14KNS7aFrUSIwNqAKGAfXkYv6VaNXi4-RBpeqAT26eZ8mXd28_n19MLj--n5_PLic6zwo-KTPDaK5ApSUvDBeU4QILmkNmMBVGm7QCKlTJlaKmMoIxlUe-RIBKaUXTs-TNzrvpFw0aja5rVS03rW1Uey29snI_4uxKLv13maeUZwyi4OWNoPVXPYZONjbo-HeUQ98HCRnjUJZClBF9_h-69n3r4vEixaEQNMvEcYplJfBiTC1VjdK6ysfd6aG0nOVAIecZ0KOUgDIWFCmP1OQAtUSH8cDeYWXj5z3rXfixf3qAj5fBxuqDBe6UMK7wYpSwQlV3q-DrftvJ--aj4Nj4agfq1ofQYnXbEozKYdjkMGxyO2wRfjZuolv033RFgO2AH3Hj10dU8sN8BjvpX8qnMjY</recordid><startdate>202006</startdate><enddate>202006</enddate><creator>Kibachio, Joseph</creator><creator>Mwenda, Valerian</creator><creator>Ombiro, Oren</creator><creator>Kamano, Jamima H</creator><creator>Perez‐Guzman, Pablo N</creator><creator>Mutai, Kennedy K</creator><creator>Guessous, Idris</creator><creator>Beran, David</creator><creator>Kasaie, Paratsu</creator><creator>Weir, Brian</creator><creator>Beecroft, Blythe</creator><creator>Kilonzo, Nduku</creator><creator>Kupfer, Linda</creator><creator>Smit, Mikaela</creator><general>International AIDS Society</general><general>John Wiley & Sons, Inc</general><general>John Wiley and Sons Inc</general><scope>24P</scope><scope>WIN</scope><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>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-8530-748X</orcidid><orcidid>https://orcid.org/0000-0002-6886-6818</orcidid><orcidid>https://orcid.org/0000-0002-3744-9501</orcidid></search><sort><creationdate>202006</creationdate><title>Recommendations for the use of mathematical modelling to support decision‐making on integration of non‐communicable diseases into HIV care</title><author>Kibachio, Joseph ; Mwenda, Valerian ; Ombiro, Oren ; Kamano, Jamima H ; Perez‐Guzman, Pablo N ; Mutai, Kennedy K ; Guessous, Idris ; Beran, David ; Kasaie, Paratsu ; Weir, Brian ; Beecroft, Blythe ; Kilonzo, Nduku ; Kupfer, Linda ; Smit, Mikaela</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c7485-94d107a2a3958d5601ebe80724de36dcd3f206a95aa0dfd611a707a9e22faca03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Acquired immune deficiency syndrome</topic><topic>AIDS</topic><topic>Analysis</topic><topic>Care and treatment</topic><topic>Childrens health</topic><topic>Chronic illnesses</topic><topic>Communicable diseases</topic><topic>Cost control</topic><topic>Decision Making</topic><topic>Delivery of Health Care</topic><topic>Delivery of Health Care, Integrated</topic><topic>Diabetes</topic><topic>Disease prevention</topic><topic>Expenditures</topic><topic>Government Programs</topic><topic>Health aspects</topic><topic>HIV</topic><topic>HIV infection</topic><topic>HIV Infections - therapy</topic><topic>Human immunodeficiency virus</topic><topic>Humans</topic><topic>Hypertension</topic><topic>Infectious diseases</topic><topic>Infrastructure</topic><topic>Integrated delivery networks</topic><topic>integration</topic><topic>Kenya</topic><topic>Laboratories</topic><topic>Management</topic><topic>Maternal & child health</topic><topic>Mathematical models</topic><topic>Medical research</topic><topic>modelling</topic><topic>Models, Biological</topic><topic>Models, Theoretical</topic><topic>Mortality</topic><topic>Noncommunicable Diseases - therapy</topic><topic>non‐communicable diseases</topic><topic>Optimization</topic><topic>policy</topic><topic>Population</topic><topic>Primary care</topic><topic>Primary Health Care</topic><topic>Risk factors</topic><topic>Supplement</topic><topic>Supply chains</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kibachio, Joseph</creatorcontrib><creatorcontrib>Mwenda, Valerian</creatorcontrib><creatorcontrib>Ombiro, Oren</creatorcontrib><creatorcontrib>Kamano, Jamima H</creatorcontrib><creatorcontrib>Perez‐Guzman, Pablo N</creatorcontrib><creatorcontrib>Mutai, Kennedy K</creatorcontrib><creatorcontrib>Guessous, Idris</creatorcontrib><creatorcontrib>Beran, David</creatorcontrib><creatorcontrib>Kasaie, Paratsu</creatorcontrib><creatorcontrib>Weir, Brian</creatorcontrib><creatorcontrib>Beecroft, Blythe</creatorcontrib><creatorcontrib>Kilonzo, Nduku</creatorcontrib><creatorcontrib>Kupfer, Linda</creatorcontrib><creatorcontrib>Smit, Mikaela</creatorcontrib><collection>Wiley Online Library (Open Access Collection)</collection><collection>Wiley Online Library (Open Access Collection)</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of the International AIDS Society</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kibachio, Joseph</au><au>Mwenda, Valerian</au><au>Ombiro, Oren</au><au>Kamano, Jamima H</au><au>Perez‐Guzman, Pablo N</au><au>Mutai, Kennedy K</au><au>Guessous, Idris</au><au>Beran, David</au><au>Kasaie, Paratsu</au><au>Weir, Brian</au><au>Beecroft, Blythe</au><au>Kilonzo, Nduku</au><au>Kupfer, Linda</au><au>Smit, Mikaela</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Recommendations for the use of mathematical modelling to support decision‐making on integration of non‐communicable diseases into HIV care</atitle><jtitle>Journal of the International AIDS Society</jtitle><addtitle>J Int AIDS Soc</addtitle><date>2020-06</date><risdate>2020</risdate><volume>23</volume><issue>SI1</issue><spage>e25505</spage><epage>n/a</epage><pages>e25505-n/a</pages><issn>1758-2652</issn><eissn>1758-2652</eissn><abstract>Introduction
Integrating services for non‐communicable diseases (NCDs) into existing primary care platforms such as HIV programmes has been recommended as a way of strengthening health systems, reducing redundancies and leveraging existing systems to rapidly scale‐up underdeveloped programmes. Mathematical modelling provides a powerful tool to address questions around priorities, optimization and implementation of such programmes. In this study, we examine the case for NCD‐HIV integration, use Kenya as a case‐study to highlight how modelling has supported wider policy formulation and decision‐making in healthcare and to collate stakeholders’ recommendations on use of models for NCD‐HIV integration decision‐making.
Discussion
Across Africa, NCDs are increasingly posing challenges for health systems, which historically focused on the care of acute and infectious conditions. Pilot programmes using integrated care services have generated advantages for both provider and user, been cost‐effective, practical and achieve rapid coverage scale‐up. The shared chronic nature of NCDs and HIV means that many operational approaches and infrastructure developed for HIV programmes apply to NCDs, suggesting this to be a cost‐effective and sustainable policy option for countries with large HIV programmes and small, un‐resourced NCD programmes. However, the vertical nature of current disease programmes, policy financing and operations operate as barriers to NCD‐HIV integration. Modelling has successfully been used to inform health decision‐making across a number of disease areas and in a number of ways. Examples from Kenya include (i) estimating current and future disease burden to set priorities for public health interventions, (ii) forecasting the requisite investments by government, (iii) comparing the impact of different integration approaches, (iv) performing cost‐benefit analysis for integration and (v) evaluating health system capacity needs.
Conclusions
Modelling can and should play an integral part in the decision‐making processes for health in general and NCD‐HIV integration specifically. It is especially useful where little data is available. The successful use of modelling to inform decision‐making will depend on several factors including policy makers’ comfort with and understanding of models and their uncertainties, modellers understanding of national priorities, funding opportunities and building local modelling capacity to ensure sustainability.</abstract><cop>Switzerland</cop><pub>International AIDS Society</pub><pmid>32562338</pmid><doi>10.1002/jia2.25505</doi><tpages>7</tpages><orcidid>https://orcid.org/0000-0001-8530-748X</orcidid><orcidid>https://orcid.org/0000-0002-6886-6818</orcidid><orcidid>https://orcid.org/0000-0002-3744-9501</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Acquired immune deficiency syndrome AIDS Analysis Care and treatment Childrens health Chronic illnesses Communicable diseases Cost control Decision Making Delivery of Health Care Delivery of Health Care, Integrated Diabetes Disease prevention Expenditures Government Programs Health aspects HIV HIV infection HIV Infections - therapy Human immunodeficiency virus Humans Hypertension Infectious diseases Infrastructure Integrated delivery networks integration Kenya Laboratories Management Maternal & child health Mathematical models Medical research modelling Models, Biological Models, Theoretical Mortality Noncommunicable Diseases - therapy non‐communicable diseases Optimization policy Population Primary care Primary Health Care Risk factors Supplement Supply chains |
title | Recommendations for the use of mathematical modelling to support decision‐making on integration of non‐communicable diseases into HIV care |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-24T01%3A50%3A06IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Recommendations%20for%20the%20use%20of%20mathematical%20modelling%20to%20support%20decision%E2%80%90making%20on%20integration%20of%20non%E2%80%90communicable%20diseases%20into%20HIV%20care&rft.jtitle=Journal%20of%20the%20International%20AIDS%20Society&rft.au=Kibachio,%20Joseph&rft.date=2020-06&rft.volume=23&rft.issue=SI1&rft.spage=e25505&rft.epage=n/a&rft.pages=e25505-n/a&rft.issn=1758-2652&rft.eissn=1758-2652&rft_id=info:doi/10.1002/jia2.25505&rft_dat=%3Cgale_pubme%3EA629604635%3C/gale_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2414925846&rft_id=info:pmid/32562338&rft_galeid=A629604635&rfr_iscdi=true |