<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Clark J</submitter><funding>European Research Council</funding><funding>Medical Research Council</funding><funding>Wellcome Trust</funding><funding>Engineering and Physical Sciences Research Council</funding><pagination>1557-1563</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC9070857</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>74(9)</volume><pubmed_abstract>&lt;h4>Background&lt;/h4>Despite decades of interventions, 240 million people have schistosomiasis. Infections cannot be directly observed, and egg-based Kato-Katz thick smears lack sensitivity, affected treatment efficacy and reinfection rate estimates. The point-of-care circulating cathodic antigen (referred to from here as POC-CCA+) test is advocated as an improvement on the Kato-Katz method, but improved estimates are limited by ambiguities in the interpretation of trace results.&lt;h4>Method&lt;/h4>We collected repeated Kato-Katz egg counts from 210 school-aged children and scored POC-CCA tests according to the manufacturer's guidelines (referred to from here as POC-CCA+) and the externally developed G score. We used hidden Markov models parameterized with Kato-Katz; Kato-Katz and POC-CCA+; and Kato-Katz and G-Scores, inferring latent clearance and reinfection probabilities at four timepoints over six-months through a more formal statistical reconciliation of these diagnostics than previously conducted. Our approach required minimal but robust assumptions regarding trace interpretations.&lt;h4>Results&lt;/h4>Antigen-based models estimated higher infection prevalence across all timepoints compared with the Kato-Katz model, corresponding to lower clearance and higher reinfection estimates. Specifically, pre-treatment prevalence estimates were 85% (Kato-Katz; 95% CI: 79%-92%), 99% (POC-CCA+; 97%-100%) and 98% (G-Score; 95%-100%). Post-treatment, 93% (Kato-Katz; 88%-96%), 72% (POC-CCA+; 64%-79%) and 65% (G-Score; 57%-73%) of those infected were estimated to clear infection. Of those who cleared infection, 35% (Kato-Katz; 27%-42%), 51% (POC-CCA+; 41%-62%) and 44% (G-Score; 33%-55%) were estimated to have been reinfected by 9-weeks.&lt;h4>Conclusions&lt;/h4>Treatment impact was shorter-lived than Kato-Katz-based estimates alone suggested, with lower clearance and rapid reinfection. At 3 weeks after treatment, longer-term clearance dynamics are captured. At 9 weeks after treatment, reinfection was captured, but failed clearance could not be distinguished from rapid reinfection. Therefore, frequent sampling is required to understand these important epidemiological dynamics.</pubmed_abstract><journal>Clinical infectious diseases : an official publication of the Infectious Diseases Society of America</journal><pubmed_title>Reconciling Egg- and Antigen-Based Estimates of Schistosoma mansoni Clearance and Reinfection: A Modeling Study.</pubmed_title><pmcid>PMC9070857</pmcid><funding_grant_id>204820/Z/16/Z</funding_grant_id><funding_grant_id>680088</funding_grant_id><funding_grant_id>MR/P025447/1</funding_grant_id><funding_grant_id>EP/R01437X/1</funding_grant_id><pubmed_authors>Clark J</pubmed_authors><pubmed_authors>Lamberton PHL</pubmed_authors><pubmed_authors>Tukahebwa EM</pubmed_authors><pubmed_authors>Francoeur R</pubmed_authors><pubmed_authors>Moses A</pubmed_authors><pubmed_authors>Faust CL</pubmed_authors><pubmed_authors>Wamboko A</pubmed_authors><pubmed_authors>Ajambo D</pubmed_authors><pubmed_authors>Nankasi A</pubmed_authors><pubmed_authors>Besigye F</pubmed_authors><pubmed_authors>Carruthers LV</pubmed_authors><pubmed_authors>Atuhaire A</pubmed_authors><pubmed_authors>Prada JM</pubmed_authors></additional><is_claimable>false</is_claimable><name>Reconciling Egg- and Antigen-Based Estimates of Schistosoma mansoni Clearance and Reinfection: A Modeling Study.</name><description>&lt;h4>Background&lt;/h4>Despite decades of interventions, 240 million people have schistosomiasis. Infections cannot be directly observed, and egg-based Kato-Katz thick smears lack sensitivity, affected treatment efficacy and reinfection rate estimates. The point-of-care circulating cathodic antigen (referred to from here as POC-CCA+) test is advocated as an improvement on the Kato-Katz method, but improved estimates are limited by ambiguities in the interpretation of trace results.&lt;h4>Method&lt;/h4>We collected repeated Kato-Katz egg counts from 210 school-aged children and scored POC-CCA tests according to the manufacturer's guidelines (referred to from here as POC-CCA+) and the externally developed G score. We used hidden Markov models parameterized with Kato-Katz; Kato-Katz and POC-CCA+; and Kato-Katz and G-Scores, inferring latent clearance and reinfection probabilities at four timepoints over six-months through a more formal statistical reconciliation of these diagnostics than previously conducted. Our approach required minimal but robust assumptions regarding trace interpretations.&lt;h4>Results&lt;/h4>Antigen-based models estimated higher infection prevalence across all timepoints compared with the Kato-Katz model, corresponding to lower clearance and higher reinfection estimates. Specifically, pre-treatment prevalence estimates were 85% (Kato-Katz; 95% CI: 79%-92%), 99% (POC-CCA+; 97%-100%) and 98% (G-Score; 95%-100%). Post-treatment, 93% (Kato-Katz; 88%-96%), 72% (POC-CCA+; 64%-79%) and 65% (G-Score; 57%-73%) of those infected were estimated to clear infection. Of those who cleared infection, 35% (Kato-Katz; 27%-42%), 51% (POC-CCA+; 41%-62%) and 44% (G-Score; 33%-55%) were estimated to have been reinfected by 9-weeks.&lt;h4>Conclusions&lt;/h4>Treatment impact was shorter-lived than Kato-Katz-based estimates alone suggested, with lower clearance and rapid reinfection. At 3 weeks after treatment, longer-term clearance dynamics are captured. At 9 weeks after treatment, reinfection was captured, but failed clearance could not be distinguished from rapid reinfection. Therefore, frequent sampling is required to understand these important epidemiological dynamics.</description><dates><release>2022-01-01T00:00:00Z</release><publication>2022 May</publication><modification>2025-04-04T09:10:26.173Z</modification><creation>2025-04-04T09:10:26.173Z</creation></dates><accession>S-EPMC9070857</accession><cross_references><pubmed>34358299</pubmed><doi>10.1093/cid/ciab679</doi></cross_references></HashMap>