Cost-effectiveness of incorporating Ebola prediction score tools and rapid diagnostic tests into a screening algorithm: A decision analytic model
No distinctive clinical signs of Ebola virus disease (EVD) have prompted the development of rapid screening tools or called for a new approach to screening suspected Ebola cases. New screening approaches require evidence of clinical benefit and economic efficiency. As of now, no evidence or defined...
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| Veröffentlicht in: | PloS one 2023-10, Vol.18 (10), p.e0293077-e0293077 |
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| creator | Tshomba, Antoine Oloma Mukadi-Bamuleka, Daniel De Weggheleire, Anja Tshiani, Olivier M Kayembe, Charles T Mbala-Kingebeni, Placide Muyembe-Tamfum, Jean-Jacques Ahuka-Mundeke, Steve Chenge, Faustin M Jacobs, Bart Karl M Mumba, Dieudonné N Tshala-Katumbay, Désiré D Mulangu, Sabue |
| description | No distinctive clinical signs of Ebola virus disease (EVD) have prompted the development of rapid screening tools or called for a new approach to screening suspected Ebola cases. New screening approaches require evidence of clinical benefit and economic efficiency. As of now, no evidence or defined algorithm exists.
To evaluate, from a healthcare perspective, the efficiency of incorporating Ebola prediction scores and rapid diagnostic tests into the EVD screening algorithm during an outbreak.
We collected data on rapid diagnostic tests (RDTs) and prediction scores' accuracy measurements, e.g., sensitivity and specificity, and the cost of case management and RDT screening in EVD suspect cases. The overall cost of healthcare services (PPE, procedure time, and standard-of-care (SOC) costs) per suspected patient and diagnostic confirmation of EVD were calculated. We also collected the EVD prevalence among suspects from the literature. We created an analytical decision model to assess the efficiency of eight screening strategies: 1) Screening suspect cases with the WHO case definition for Ebola suspects, 2) Screening suspect cases with the ECPS at -3 points of cut-off, 3) Screening suspect cases with the ECPS as a joint test, 4) Screening suspect cases with the ECPS as a conditional test, 5) Screening suspect cases with the WHO case definition, then QuickNavi™-Ebola RDT, 6) Screening suspect cases with the ECPS at -3 points of cut-off and QuickNavi™-Ebola RDT, 7) Screening suspect cases with the ECPS as a conditional test and QuickNavi™-Ebola RDT, and 8) Screening suspect cases with the ECPS as a joint test and QuickNavi™-Ebola RDT. We performed a cost-effectiveness analysis to identify an algorithm that minimizes the cost per patient correctly classified. We performed a one-way and probabilistic sensitivity analysis to test the robustness of our findings.
Our analysis found dual ECPS as a conditional test with the QuickNavi™-Ebola RDT algorithm to be the most cost-effective screening algorithm for EVD, with an effectiveness of 0.86. The cost-effectiveness ratio was 106.7 USD per patient correctly classified. The following algorithms, the ECPS as a conditional test with an effectiveness of 0.80 and an efficiency of 111.5 USD per patient correctly classified and the ECPS as a joint test with the QuickNavi™-Ebola RDT algorithm with an effectiveness of 0.81 and a cost-effectiveness ratio of 131.5 USD per patient correctly classified. These findings were sensitive |
| doi_str_mv | 10.1371/journal.pone.0293077 |
| format | Article |
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To evaluate, from a healthcare perspective, the efficiency of incorporating Ebola prediction scores and rapid diagnostic tests into the EVD screening algorithm during an outbreak.
We collected data on rapid diagnostic tests (RDTs) and prediction scores' accuracy measurements, e.g., sensitivity and specificity, and the cost of case management and RDT screening in EVD suspect cases. The overall cost of healthcare services (PPE, procedure time, and standard-of-care (SOC) costs) per suspected patient and diagnostic confirmation of EVD were calculated. We also collected the EVD prevalence among suspects from the literature. We created an analytical decision model to assess the efficiency of eight screening strategies: 1) Screening suspect cases with the WHO case definition for Ebola suspects, 2) Screening suspect cases with the ECPS at -3 points of cut-off, 3) Screening suspect cases with the ECPS as a joint test, 4) Screening suspect cases with the ECPS as a conditional test, 5) Screening suspect cases with the WHO case definition, then QuickNavi™-Ebola RDT, 6) Screening suspect cases with the ECPS at -3 points of cut-off and QuickNavi™-Ebola RDT, 7) Screening suspect cases with the ECPS as a conditional test and QuickNavi™-Ebola RDT, and 8) Screening suspect cases with the ECPS as a joint test and QuickNavi™-Ebola RDT. We performed a cost-effectiveness analysis to identify an algorithm that minimizes the cost per patient correctly classified. We performed a one-way and probabilistic sensitivity analysis to test the robustness of our findings.
Our analysis found dual ECPS as a conditional test with the QuickNavi™-Ebola RDT algorithm to be the most cost-effective screening algorithm for EVD, with an effectiveness of 0.86. The cost-effectiveness ratio was 106.7 USD per patient correctly classified. The following algorithms, the ECPS as a conditional test with an effectiveness of 0.80 and an efficiency of 111.5 USD per patient correctly classified and the ECPS as a joint test with the QuickNavi™-Ebola RDT algorithm with an effectiveness of 0.81 and a cost-effectiveness ratio of 131.5 USD per patient correctly classified. These findings were sensitive to variations in the prevalence of EVD in suspected population and the sensitivity of the QuickNavi™-Ebola RDT.
Findings from this study showed that prediction scores and RDT could improve Ebola screening. The use of the ECPS as a conditional test algorithm and the dual ECPS as a conditional test and then the QuickNavi™-Ebola RDT algorithm are the best screening choices because they are more efficient and lower the number of confirmation tests and overall care costs during an EBOV epidemic.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0293077</identifier><identifier>PMID: 37847703</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Biology and life sciences ; Cost analysis ; Cost benefit analysis ; Costs ; Data collection ; Decision analysis ; Decision trees ; Diagnosis ; Diagnostic tests ; Diagnostic Tests, Routine - methods ; Distribution ; Ebola virus ; Ebola virus infections ; Ebolavirus ; Effectiveness ; Engineering and Technology ; Epidemics ; Evaluation ; FDA approval ; Fever ; Health care ; Health care industry ; Health services ; Hemorrhagic Fever, Ebola - diagnosis ; Hemorrhagic Fever, Ebola - epidemiology ; Humans ; Infections ; Mathematical models ; Medical care, Cost of ; Medical diagnosis ; Medical research ; Medical tests ; Medicine and Health Sciences ; Medicine, Experimental ; Oral contraceptives ; Patients ; Physical Sciences ; Predictions ; Probability ; Rapid Diagnostic Tests ; Research and Analysis Methods ; Sensitivity analysis ; Sensitivity and Specificity ; Social Sciences ; Surveillance ; Tropical diseases ; Viral diseases ; Viruses</subject><ispartof>PloS one, 2023-10, Vol.18 (10), p.e0293077-e0293077</ispartof><rights>Copyright: © 2023 Tshomba et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</rights><rights>COPYRIGHT 2023 Public Library of Science</rights><rights>2023 Tshomba 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>2023 Tshomba et al 2023 Tshomba et al</rights><rights>2023 Tshomba 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><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c642t-fc95065b6f662b20dcfb47e35348101295c95645bb9c94699a8dde8288712d9e3</cites><orcidid>0000-0003-2440-8468 ; 0000-0002-5886-2004</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/PMC10581462/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10581462/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,724,777,781,861,882,2096,2915,23847,27905,27906,53772,53774,79349,79350</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37847703$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Rychtář, Jan</contributor><creatorcontrib>Tshomba, Antoine Oloma</creatorcontrib><creatorcontrib>Mukadi-Bamuleka, Daniel</creatorcontrib><creatorcontrib>De Weggheleire, Anja</creatorcontrib><creatorcontrib>Tshiani, Olivier M</creatorcontrib><creatorcontrib>Kayembe, Charles T</creatorcontrib><creatorcontrib>Mbala-Kingebeni, Placide</creatorcontrib><creatorcontrib>Muyembe-Tamfum, Jean-Jacques</creatorcontrib><creatorcontrib>Ahuka-Mundeke, Steve</creatorcontrib><creatorcontrib>Chenge, Faustin M</creatorcontrib><creatorcontrib>Jacobs, Bart Karl M</creatorcontrib><creatorcontrib>Mumba, Dieudonné N</creatorcontrib><creatorcontrib>Tshala-Katumbay, Désiré D</creatorcontrib><creatorcontrib>Mulangu, Sabue</creatorcontrib><title>Cost-effectiveness of incorporating Ebola prediction score tools and rapid diagnostic tests into a screening algorithm: A decision analytic model</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>No distinctive clinical signs of Ebola virus disease (EVD) have prompted the development of rapid screening tools or called for a new approach to screening suspected Ebola cases. New screening approaches require evidence of clinical benefit and economic efficiency. As of now, no evidence or defined algorithm exists.
To evaluate, from a healthcare perspective, the efficiency of incorporating Ebola prediction scores and rapid diagnostic tests into the EVD screening algorithm during an outbreak.
We collected data on rapid diagnostic tests (RDTs) and prediction scores' accuracy measurements, e.g., sensitivity and specificity, and the cost of case management and RDT screening in EVD suspect cases. The overall cost of healthcare services (PPE, procedure time, and standard-of-care (SOC) costs) per suspected patient and diagnostic confirmation of EVD were calculated. We also collected the EVD prevalence among suspects from the literature. We created an analytical decision model to assess the efficiency of eight screening strategies: 1) Screening suspect cases with the WHO case definition for Ebola suspects, 2) Screening suspect cases with the ECPS at -3 points of cut-off, 3) Screening suspect cases with the ECPS as a joint test, 4) Screening suspect cases with the ECPS as a conditional test, 5) Screening suspect cases with the WHO case definition, then QuickNavi™-Ebola RDT, 6) Screening suspect cases with the ECPS at -3 points of cut-off and QuickNavi™-Ebola RDT, 7) Screening suspect cases with the ECPS as a conditional test and QuickNavi™-Ebola RDT, and 8) Screening suspect cases with the ECPS as a joint test and QuickNavi™-Ebola RDT. We performed a cost-effectiveness analysis to identify an algorithm that minimizes the cost per patient correctly classified. We performed a one-way and probabilistic sensitivity analysis to test the robustness of our findings.
Our analysis found dual ECPS as a conditional test with the QuickNavi™-Ebola RDT algorithm to be the most cost-effective screening algorithm for EVD, with an effectiveness of 0.86. The cost-effectiveness ratio was 106.7 USD per patient correctly classified. The following algorithms, the ECPS as a conditional test with an effectiveness of 0.80 and an efficiency of 111.5 USD per patient correctly classified and the ECPS as a joint test with the QuickNavi™-Ebola RDT algorithm with an effectiveness of 0.81 and a cost-effectiveness ratio of 131.5 USD per patient correctly classified. These findings were sensitive to variations in the prevalence of EVD in suspected population and the sensitivity of the QuickNavi™-Ebola RDT.
Findings from this study showed that prediction scores and RDT could improve Ebola screening. The use of the ECPS as a conditional test algorithm and the dual ECPS as a conditional test and then the QuickNavi™-Ebola RDT algorithm are the best screening choices because they are more efficient and lower the number of confirmation tests and overall care costs during an EBOV epidemic.</description><subject>Algorithms</subject><subject>Biology and life sciences</subject><subject>Cost analysis</subject><subject>Cost benefit analysis</subject><subject>Costs</subject><subject>Data collection</subject><subject>Decision analysis</subject><subject>Decision trees</subject><subject>Diagnosis</subject><subject>Diagnostic tests</subject><subject>Diagnostic Tests, Routine - methods</subject><subject>Distribution</subject><subject>Ebola virus</subject><subject>Ebola virus infections</subject><subject>Ebolavirus</subject><subject>Effectiveness</subject><subject>Engineering and Technology</subject><subject>Epidemics</subject><subject>Evaluation</subject><subject>FDA approval</subject><subject>Fever</subject><subject>Health care</subject><subject>Health care industry</subject><subject>Health services</subject><subject>Hemorrhagic Fever, Ebola - diagnosis</subject><subject>Hemorrhagic Fever, Ebola - epidemiology</subject><subject>Humans</subject><subject>Infections</subject><subject>Mathematical models</subject><subject>Medical care, Cost of</subject><subject>Medical diagnosis</subject><subject>Medical research</subject><subject>Medical tests</subject><subject>Medicine and Health Sciences</subject><subject>Medicine, Experimental</subject><subject>Oral contraceptives</subject><subject>Patients</subject><subject>Physical Sciences</subject><subject>Predictions</subject><subject>Probability</subject><subject>Rapid Diagnostic Tests</subject><subject>Research and Analysis Methods</subject><subject>Sensitivity analysis</subject><subject>Sensitivity and Specificity</subject><subject>Social Sciences</subject><subject>Surveillance</subject><subject>Tropical 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Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural Science Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Materials Science Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - 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>Tshomba, Antoine Oloma</au><au>Mukadi-Bamuleka, Daniel</au><au>De Weggheleire, Anja</au><au>Tshiani, Olivier M</au><au>Kayembe, Charles T</au><au>Mbala-Kingebeni, Placide</au><au>Muyembe-Tamfum, Jean-Jacques</au><au>Ahuka-Mundeke, Steve</au><au>Chenge, Faustin M</au><au>Jacobs, Bart Karl M</au><au>Mumba, Dieudonné N</au><au>Tshala-Katumbay, Désiré D</au><au>Mulangu, Sabue</au><au>Rychtář, Jan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Cost-effectiveness of incorporating Ebola prediction score tools and rapid diagnostic tests into a screening algorithm: A decision analytic model</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2023-10-17</date><risdate>2023</risdate><volume>18</volume><issue>10</issue><spage>e0293077</spage><epage>e0293077</epage><pages>e0293077-e0293077</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>No distinctive clinical signs of Ebola virus disease (EVD) have prompted the development of rapid screening tools or called for a new approach to screening suspected Ebola cases. New screening approaches require evidence of clinical benefit and economic efficiency. As of now, no evidence or defined algorithm exists.
To evaluate, from a healthcare perspective, the efficiency of incorporating Ebola prediction scores and rapid diagnostic tests into the EVD screening algorithm during an outbreak.
We collected data on rapid diagnostic tests (RDTs) and prediction scores' accuracy measurements, e.g., sensitivity and specificity, and the cost of case management and RDT screening in EVD suspect cases. The overall cost of healthcare services (PPE, procedure time, and standard-of-care (SOC) costs) per suspected patient and diagnostic confirmation of EVD were calculated. We also collected the EVD prevalence among suspects from the literature. We created an analytical decision model to assess the efficiency of eight screening strategies: 1) Screening suspect cases with the WHO case definition for Ebola suspects, 2) Screening suspect cases with the ECPS at -3 points of cut-off, 3) Screening suspect cases with the ECPS as a joint test, 4) Screening suspect cases with the ECPS as a conditional test, 5) Screening suspect cases with the WHO case definition, then QuickNavi™-Ebola RDT, 6) Screening suspect cases with the ECPS at -3 points of cut-off and QuickNavi™-Ebola RDT, 7) Screening suspect cases with the ECPS as a conditional test and QuickNavi™-Ebola RDT, and 8) Screening suspect cases with the ECPS as a joint test and QuickNavi™-Ebola RDT. We performed a cost-effectiveness analysis to identify an algorithm that minimizes the cost per patient correctly classified. We performed a one-way and probabilistic sensitivity analysis to test the robustness of our findings.
Our analysis found dual ECPS as a conditional test with the QuickNavi™-Ebola RDT algorithm to be the most cost-effective screening algorithm for EVD, with an effectiveness of 0.86. The cost-effectiveness ratio was 106.7 USD per patient correctly classified. The following algorithms, the ECPS as a conditional test with an effectiveness of 0.80 and an efficiency of 111.5 USD per patient correctly classified and the ECPS as a joint test with the QuickNavi™-Ebola RDT algorithm with an effectiveness of 0.81 and a cost-effectiveness ratio of 131.5 USD per patient correctly classified. These findings were sensitive to variations in the prevalence of EVD in suspected population and the sensitivity of the QuickNavi™-Ebola RDT.
Findings from this study showed that prediction scores and RDT could improve Ebola screening. The use of the ECPS as a conditional test algorithm and the dual ECPS as a conditional test and then the QuickNavi™-Ebola RDT algorithm are the best screening choices because they are more efficient and lower the number of confirmation tests and overall care costs during an EBOV epidemic.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>37847703</pmid><doi>10.1371/journal.pone.0293077</doi><tpages>e0293077</tpages><orcidid>https://orcid.org/0000-0003-2440-8468</orcidid><orcidid>https://orcid.org/0000-0002-5886-2004</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2023-10, Vol.18 (10), p.e0293077-e0293077 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_2878304659 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Public Library of Science (PLoS); PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Algorithms Biology and life sciences Cost analysis Cost benefit analysis Costs Data collection Decision analysis Decision trees Diagnosis Diagnostic tests Diagnostic Tests, Routine - methods Distribution Ebola virus Ebola virus infections Ebolavirus Effectiveness Engineering and Technology Epidemics Evaluation FDA approval Fever Health care Health care industry Health services Hemorrhagic Fever, Ebola - diagnosis Hemorrhagic Fever, Ebola - epidemiology Humans Infections Mathematical models Medical care, Cost of Medical diagnosis Medical research Medical tests Medicine and Health Sciences Medicine, Experimental Oral contraceptives Patients Physical Sciences Predictions Probability Rapid Diagnostic Tests Research and Analysis Methods Sensitivity analysis Sensitivity and Specificity Social Sciences Surveillance Tropical diseases Viral diseases Viruses |
| title | Cost-effectiveness of incorporating Ebola prediction score tools and rapid diagnostic tests into a screening algorithm: A decision analytic model |
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