Accuracy of different diagnostic techniques for Schistosoma haematobium to estimate treatment needs in Zimbabwe: Application of a hierarchical Bayesian egg count model
Treatment needs for Schistosoma haematobium are commonly evaluated using urine filtration with detection of parasite eggs under a microscope. A common symptom of S. haematobium is hematuria, the passing of blood in urine. Hence, the use of hematuria-based diagnostic techniques as a proxy for the ass...
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| Veröffentlicht in: | PLoS neglected tropical diseases 2020-08, Vol.14 (8), p.e0008451-e0008451 |
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| creator | Midzi, Nicholas Bärenbold, Oliver Manangazira, Portia Phiri, Isaac Mutsaka-Makuvaza, Masceline J Mhlanga, Gibson Utzinger, Jürg Vounatsou, Penelope |
| description | Treatment needs for Schistosoma haematobium are commonly evaluated using urine filtration with detection of parasite eggs under a microscope. A common symptom of S. haematobium is hematuria, the passing of blood in urine. Hence, the use of hematuria-based diagnostic techniques as a proxy for the assessment of treatment needs has been considered. This study evaluates data from a national survey in Zimbabwe, where three hematuria-based diagnostic techniques, that is microhematuria, macrohematuria, and an anamnestic questionnaire pertaining to self-reported blood in urine, have been included in addition to urine filtration in 280 schools across 70 districts.
We developed an egg count model, which evaluates the infection intensity-dependent sensitivity and the specificity of each diagnostic technique without relying on a 'gold' standard. Subsequently, we determined prevalence thresholds for each diagnostic technique, equivalent to a 10% urine filtration-based prevalence and compared classification of districts according to treatment strategy based on the different diagnostic methods.
A 10% urine filtration prevalence threshold corresponded to a 17.9% and 13.3% prevalence based on questionnaire and microhematuria, respectively. Both the questionnaire and the microhematuria showed a sensitivity and specificity of more than 85% for estimating treatment needs at the above thresholds. For diagnosis at individual level, the questionnaire showed the highest sensitivity (70.0%) followed by urine filtration (53.8%) and microhematuria (52.2%).
The high sensitivity and specificity of a simple questionnaire to estimate treatment needs of S. haematobium suggests that it can be used as a rapid, low-cost method to estimate district prevalence. Our modeling approach can be expanded to include setting-dependent specificity of the technique and should be assessed in relation to other diagnostic methods due to potential cross-reaction with other diseases. |
| doi_str_mv | 10.1371/journal.pntd.0008451 |
| format | Article |
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We developed an egg count model, which evaluates the infection intensity-dependent sensitivity and the specificity of each diagnostic technique without relying on a 'gold' standard. Subsequently, we determined prevalence thresholds for each diagnostic technique, equivalent to a 10% urine filtration-based prevalence and compared classification of districts according to treatment strategy based on the different diagnostic methods.
A 10% urine filtration prevalence threshold corresponded to a 17.9% and 13.3% prevalence based on questionnaire and microhematuria, respectively. Both the questionnaire and the microhematuria showed a sensitivity and specificity of more than 85% for estimating treatment needs at the above thresholds. For diagnosis at individual level, the questionnaire showed the highest sensitivity (70.0%) followed by urine filtration (53.8%) and microhematuria (52.2%).
The high sensitivity and specificity of a simple questionnaire to estimate treatment needs of S. haematobium suggests that it can be used as a rapid, low-cost method to estimate district prevalence. Our modeling approach can be expanded to include setting-dependent specificity of the technique and should be assessed in relation to other diagnostic methods due to potential cross-reaction with other diseases.</description><identifier>ISSN: 1935-2735</identifier><identifier>ISSN: 1935-2727</identifier><identifier>EISSN: 1935-2735</identifier><identifier>DOI: 10.1371/journal.pntd.0008451</identifier><identifier>PMID: 32817650</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adolescent ; Bayes Theorem ; Bayesian analysis ; Biology and Life Sciences ; Blood ; Care and treatment ; Child ; Child care ; Children & youth ; Comparative analysis ; Cross-reaction ; Cross-Sectional Studies ; Data collection ; Diagnosis ; Diagnostic systems ; Diagnostic techniques ; Education ; Eggs ; Evaluation ; Female ; Filtration ; Hematuria ; Humans ; Laboratories ; Male ; Medical research ; Medicine and Health Sciences ; Methods ; Molecular diagnostic techniques ; Morbidity ; Parasite Egg Count - methods ; Parasites ; People and Places ; Probability theory ; Public health ; Questionnaires ; Rural areas ; Sample size ; Schistosoma haematobium ; Schistosomiasis ; Schistosomiasis haematobia - diagnosis ; Schistosomiasis haematobia - urine ; Schools ; Sensitivity and Specificity ; Social Sciences ; Specificity ; Surveying ; Surveys and Questionnaires ; Teams ; Thresholds ; Tropical diseases ; Urinalysis ; Urine ; Zimbabwe - epidemiology</subject><ispartof>PLoS neglected tropical diseases, 2020-08, Vol.14 (8), p.e0008451-e0008451</ispartof><rights>COPYRIGHT 2020 Public Library of Science</rights><rights>2020 Midzi 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>2020 Midzi et al 2020 Midzi et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c624t-31a1241d85f345e67d6991ff96c93b52312958e7e95358855260dc9360e33c593</citedby><cites>FETCH-LOGICAL-c624t-31a1241d85f345e67d6991ff96c93b52312958e7e95358855260dc9360e33c593</cites><orcidid>0000-0002-4904-5352</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/PMC7462259/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7462259/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79343,79344</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32817650$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Midzi, Nicholas</creatorcontrib><creatorcontrib>Bärenbold, Oliver</creatorcontrib><creatorcontrib>Manangazira, Portia</creatorcontrib><creatorcontrib>Phiri, Isaac</creatorcontrib><creatorcontrib>Mutsaka-Makuvaza, Masceline J</creatorcontrib><creatorcontrib>Mhlanga, Gibson</creatorcontrib><creatorcontrib>Utzinger, Jürg</creatorcontrib><creatorcontrib>Vounatsou, Penelope</creatorcontrib><title>Accuracy of different diagnostic techniques for Schistosoma haematobium to estimate treatment needs in Zimbabwe: Application of a hierarchical Bayesian egg count model</title><title>PLoS neglected tropical diseases</title><addtitle>PLoS Negl Trop Dis</addtitle><description>Treatment needs for Schistosoma haematobium are commonly evaluated using urine filtration with detection of parasite eggs under a microscope. A common symptom of S. haematobium is hematuria, the passing of blood in urine. Hence, the use of hematuria-based diagnostic techniques as a proxy for the assessment of treatment needs has been considered. This study evaluates data from a national survey in Zimbabwe, where three hematuria-based diagnostic techniques, that is microhematuria, macrohematuria, and an anamnestic questionnaire pertaining to self-reported blood in urine, have been included in addition to urine filtration in 280 schools across 70 districts.
We developed an egg count model, which evaluates the infection intensity-dependent sensitivity and the specificity of each diagnostic technique without relying on a 'gold' standard. Subsequently, we determined prevalence thresholds for each diagnostic technique, equivalent to a 10% urine filtration-based prevalence and compared classification of districts according to treatment strategy based on the different diagnostic methods.
A 10% urine filtration prevalence threshold corresponded to a 17.9% and 13.3% prevalence based on questionnaire and microhematuria, respectively. Both the questionnaire and the microhematuria showed a sensitivity and specificity of more than 85% for estimating treatment needs at the above thresholds. For diagnosis at individual level, the questionnaire showed the highest sensitivity (70.0%) followed by urine filtration (53.8%) and microhematuria (52.2%).
The high sensitivity and specificity of a simple questionnaire to estimate treatment needs of S. haematobium suggests that it can be used as a rapid, low-cost method to estimate district prevalence. Our modeling approach can be expanded to include setting-dependent specificity of the technique and should be assessed in relation to other diagnostic methods due to potential cross-reaction with other diseases.</description><subject>Adolescent</subject><subject>Bayes Theorem</subject><subject>Bayesian analysis</subject><subject>Biology and Life Sciences</subject><subject>Blood</subject><subject>Care and treatment</subject><subject>Child</subject><subject>Child care</subject><subject>Children & youth</subject><subject>Comparative analysis</subject><subject>Cross-reaction</subject><subject>Cross-Sectional Studies</subject><subject>Data collection</subject><subject>Diagnosis</subject><subject>Diagnostic systems</subject><subject>Diagnostic techniques</subject><subject>Education</subject><subject>Eggs</subject><subject>Evaluation</subject><subject>Female</subject><subject>Filtration</subject><subject>Hematuria</subject><subject>Humans</subject><subject>Laboratories</subject><subject>Male</subject><subject>Medical research</subject><subject>Medicine and Health Sciences</subject><subject>Methods</subject><subject>Molecular diagnostic techniques</subject><subject>Morbidity</subject><subject>Parasite Egg Count - methods</subject><subject>Parasites</subject><subject>People and Places</subject><subject>Probability theory</subject><subject>Public health</subject><subject>Questionnaires</subject><subject>Rural areas</subject><subject>Sample size</subject><subject>Schistosoma haematobium</subject><subject>Schistosomiasis</subject><subject>Schistosomiasis haematobia - diagnosis</subject><subject>Schistosomiasis haematobia - urine</subject><subject>Schools</subject><subject>Sensitivity and Specificity</subject><subject>Social Sciences</subject><subject>Specificity</subject><subject>Surveying</subject><subject>Surveys and Questionnaires</subject><subject>Teams</subject><subject>Thresholds</subject><subject>Tropical diseases</subject><subject>Urinalysis</subject><subject>Urine</subject><subject>Zimbabwe - epidemiology</subject><issn>1935-2735</issn><issn>1935-2727</issn><issn>1935-2735</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><sourceid>DOA</sourceid><recordid>eNptkttu1DAQhiMEoqXwBggsISFudvEhzoELpKXiUKkSF8ANN9bEGe96ldiL7YD2iXhNnHZbdVHlizj2P9_M_J6ieM7okomavd36KTgYljuX-iWltCkle1CcslbIBa-FfHhnf1I8iXFLqWxlwx4XJ4I3rK4kPS3-rrSeAug98Yb01hgM6FLewdr5mKwmCfXG2V8TRmJ8IN_0xsbkox-BbABHSL6z00iSJ5j1-R9JCghpnDkOsY_EOvLTjh10f_AdWe12g9WQrHdzzkyxGCBkrIaBfIA9RguO4HpNtJ8yY_Q9Dk-LRwaGiM8O37Pix6eP38-_LC6_fr44X10udMXLtBAMGC9Z30gjSolV3Vdty4xpK92KTnLBeLYAa2ylkE0jJa9on68qikJo2Yqz4uU1dzf4qA4eR8XLUlRMcCmz4uJa0XvYql3ILYe98mDV1YEPawUhGzegajrGDWc1M5UuBaOdAWZaoA02PTcCMuv9IdvUjdjrbFmA4Qh6fOPsRq39b1WXFedX5b45AIKfnyip0UaNwwAO_TTXLaqSMlnXWfrqP-n93R1Ua8gNWGd8zqtnqFpVosw8Uc6s5T2qvHocrfYOjc3nRwGv7wRsEIa0iX6Y5imIx8LyWqiDjzGguTWDUTXP_U3Vap57dZj7HPbirpG3QTeDLv4BXjMBBg</recordid><startdate>20200801</startdate><enddate>20200801</enddate><creator>Midzi, Nicholas</creator><creator>Bärenbold, Oliver</creator><creator>Manangazira, Portia</creator><creator>Phiri, Isaac</creator><creator>Mutsaka-Makuvaza, Masceline J</creator><creator>Mhlanga, Gibson</creator><creator>Utzinger, Jürg</creator><creator>Vounatsou, Penelope</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>7QL</scope><scope>7SS</scope><scope>7T2</scope><scope>7T7</scope><scope>7U9</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8C1</scope><scope>8FD</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>H94</scope><scope>H95</scope><scope>H97</scope><scope>K9.</scope><scope>L.G</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-4904-5352</orcidid></search><sort><creationdate>20200801</creationdate><title>Accuracy of different diagnostic techniques for Schistosoma haematobium to estimate treatment needs in Zimbabwe: Application of a hierarchical Bayesian egg count model</title><author>Midzi, Nicholas ; Bärenbold, Oliver ; Manangazira, Portia ; Phiri, Isaac ; Mutsaka-Makuvaza, Masceline J ; Mhlanga, Gibson ; Utzinger, Jürg ; Vounatsou, Penelope</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c624t-31a1241d85f345e67d6991ff96c93b52312958e7e95358855260dc9360e33c593</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Adolescent</topic><topic>Bayes Theorem</topic><topic>Bayesian analysis</topic><topic>Biology and Life Sciences</topic><topic>Blood</topic><topic>Care and treatment</topic><topic>Child</topic><topic>Child care</topic><topic>Children & youth</topic><topic>Comparative analysis</topic><topic>Cross-reaction</topic><topic>Cross-Sectional Studies</topic><topic>Data collection</topic><topic>Diagnosis</topic><topic>Diagnostic systems</topic><topic>Diagnostic techniques</topic><topic>Education</topic><topic>Eggs</topic><topic>Evaluation</topic><topic>Female</topic><topic>Filtration</topic><topic>Hematuria</topic><topic>Humans</topic><topic>Laboratories</topic><topic>Male</topic><topic>Medical research</topic><topic>Medicine and Health Sciences</topic><topic>Methods</topic><topic>Molecular diagnostic techniques</topic><topic>Morbidity</topic><topic>Parasite Egg Count - methods</topic><topic>Parasites</topic><topic>People and Places</topic><topic>Probability theory</topic><topic>Public health</topic><topic>Questionnaires</topic><topic>Rural areas</topic><topic>Sample size</topic><topic>Schistosoma haematobium</topic><topic>Schistosomiasis</topic><topic>Schistosomiasis haematobia - diagnosis</topic><topic>Schistosomiasis haematobia - urine</topic><topic>Schools</topic><topic>Sensitivity and Specificity</topic><topic>Social Sciences</topic><topic>Specificity</topic><topic>Surveying</topic><topic>Surveys and Questionnaires</topic><topic>Teams</topic><topic>Thresholds</topic><topic>Tropical diseases</topic><topic>Urinalysis</topic><topic>Urine</topic><topic>Zimbabwe - epidemiology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Midzi, Nicholas</creatorcontrib><creatorcontrib>Bärenbold, Oliver</creatorcontrib><creatorcontrib>Manangazira, Portia</creatorcontrib><creatorcontrib>Phiri, Isaac</creatorcontrib><creatorcontrib>Mutsaka-Makuvaza, Masceline J</creatorcontrib><creatorcontrib>Mhlanga, Gibson</creatorcontrib><creatorcontrib>Utzinger, Jürg</creatorcontrib><creatorcontrib>Vounatsou, Penelope</creatorcontrib><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>Bacteriology Abstracts (Microbiology B)</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Health and Safety Science Abstracts (Full archive)</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Virology and AIDS Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Public Health Database</collection><collection>Technology Research Database</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 One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 1: Biological Sciences & Living Resources</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 3: Aquatic Pollution & Environmental Quality</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</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>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PLoS neglected tropical diseases</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Midzi, Nicholas</au><au>Bärenbold, Oliver</au><au>Manangazira, Portia</au><au>Phiri, Isaac</au><au>Mutsaka-Makuvaza, Masceline J</au><au>Mhlanga, Gibson</au><au>Utzinger, Jürg</au><au>Vounatsou, Penelope</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Accuracy of different diagnostic techniques for Schistosoma haematobium to estimate treatment needs in Zimbabwe: Application of a hierarchical Bayesian egg count model</atitle><jtitle>PLoS neglected tropical diseases</jtitle><addtitle>PLoS Negl Trop Dis</addtitle><date>2020-08-01</date><risdate>2020</risdate><volume>14</volume><issue>8</issue><spage>e0008451</spage><epage>e0008451</epage><pages>e0008451-e0008451</pages><issn>1935-2735</issn><issn>1935-2727</issn><eissn>1935-2735</eissn><abstract>Treatment needs for Schistosoma haematobium are commonly evaluated using urine filtration with detection of parasite eggs under a microscope. A common symptom of S. haematobium is hematuria, the passing of blood in urine. Hence, the use of hematuria-based diagnostic techniques as a proxy for the assessment of treatment needs has been considered. This study evaluates data from a national survey in Zimbabwe, where three hematuria-based diagnostic techniques, that is microhematuria, macrohematuria, and an anamnestic questionnaire pertaining to self-reported blood in urine, have been included in addition to urine filtration in 280 schools across 70 districts.
We developed an egg count model, which evaluates the infection intensity-dependent sensitivity and the specificity of each diagnostic technique without relying on a 'gold' standard. Subsequently, we determined prevalence thresholds for each diagnostic technique, equivalent to a 10% urine filtration-based prevalence and compared classification of districts according to treatment strategy based on the different diagnostic methods.
A 10% urine filtration prevalence threshold corresponded to a 17.9% and 13.3% prevalence based on questionnaire and microhematuria, respectively. Both the questionnaire and the microhematuria showed a sensitivity and specificity of more than 85% for estimating treatment needs at the above thresholds. For diagnosis at individual level, the questionnaire showed the highest sensitivity (70.0%) followed by urine filtration (53.8%) and microhematuria (52.2%).
The high sensitivity and specificity of a simple questionnaire to estimate treatment needs of S. haematobium suggests that it can be used as a rapid, low-cost method to estimate district prevalence. Our modeling approach can be expanded to include setting-dependent specificity of the technique and should be assessed in relation to other diagnostic methods due to potential cross-reaction with other diseases.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>32817650</pmid><doi>10.1371/journal.pntd.0008451</doi><orcidid>https://orcid.org/0000-0002-4904-5352</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1935-2735 |
| ispartof | PLoS neglected tropical diseases, 2020-08, Vol.14 (8), p.e0008451-e0008451 |
| issn | 1935-2735 1935-2727 1935-2735 |
| language | eng |
| recordid | cdi_pubmed_primary_32817650 |
| source | Public Library of Science (PLoS) Journals Open Access; MEDLINE; DOAJ Directory of Open Access Journals; EZB-FREE-00999 freely available EZB journals; PubMed Central; PubMed Central Open Access |
| subjects | Adolescent Bayes Theorem Bayesian analysis Biology and Life Sciences Blood Care and treatment Child Child care Children & youth Comparative analysis Cross-reaction Cross-Sectional Studies Data collection Diagnosis Diagnostic systems Diagnostic techniques Education Eggs Evaluation Female Filtration Hematuria Humans Laboratories Male Medical research Medicine and Health Sciences Methods Molecular diagnostic techniques Morbidity Parasite Egg Count - methods Parasites People and Places Probability theory Public health Questionnaires Rural areas Sample size Schistosoma haematobium Schistosomiasis Schistosomiasis haematobia - diagnosis Schistosomiasis haematobia - urine Schools Sensitivity and Specificity Social Sciences Specificity Surveying Surveys and Questionnaires Teams Thresholds Tropical diseases Urinalysis Urine Zimbabwe - epidemiology |
| title | Accuracy of different diagnostic techniques for Schistosoma haematobium to estimate treatment needs in Zimbabwe: Application of a hierarchical Bayesian egg count model |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-01T15%3A27%3A39IST&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=Accuracy%20of%20different%20diagnostic%20techniques%20for%20Schistosoma%20haematobium%20to%20estimate%20treatment%20needs%20in%20Zimbabwe:%20Application%20of%20a%20hierarchical%20Bayesian%20egg%20count%20model&rft.jtitle=PLoS%20neglected%20tropical%20diseases&rft.au=Midzi,%20Nicholas&rft.date=2020-08-01&rft.volume=14&rft.issue=8&rft.spage=e0008451&rft.epage=e0008451&rft.pages=e0008451-e0008451&rft.issn=1935-2735&rft.eissn=1935-2735&rft_id=info:doi/10.1371/journal.pntd.0008451&rft_dat=%3Cgale_plos_%3EA634243347%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=2443613255&rft_id=info:pmid/32817650&rft_galeid=A634243347&rft_doaj_id=oai_doaj_org_article_8b12f2171f6c4310bfa1f9a08e8d2f3a&rfr_iscdi=true |