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dc.contributor.author | Salvatore, Phillip P. | |
dc.contributor.author | Proano, Alvaro | |
dc.contributor.author | Kendall, Emily A. | |
dc.contributor.author | Gilman, Robert Hugh | |
dc.contributor.author | Dowdy, David W. | |
dc.date.accessioned | 2018-12-01T00:04:14Z | |
dc.date.available | 2018-12-01T00:04:14Z | |
dc.date.issued | 2018 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12866/4243 | |
dc.description.abstract | Background. Substantial individual heterogeneity exists in the clinical manifestations and duration of active tuberculosis. We sought to link the individual-level characteristics of tuberculosis disease to observed population-level outcomes. Methods. We developed an individual-based, stochastic model of tuberculosis disease in a hypothetical cohort of patients with smear-positive tuberculosis. We conceptualized the disease process as consisting of 2 states-progression and recovery-including transitions between the 2. We then used a Bayesian process to calibrate the model to clinical data from the prechemotherapy era, thus identifying the rates of progression and recovery (and probabilities of transition) consistent with observed population-level clinical outcomes. Results. Observed outcomes are consistent with slow rates of disease progression (median doubling time: 84 days, 95% uncertainty range 62-104) and a low, but nonzero, probability of transition from disease progression to recovery (median 16% per year, 95% uncertainty range 11%-21%). Other individual-level dynamics were less influential in determining observed outcomes. Conclusions. This simplified model identifies individual-level dynamics-including a long doubling time and low probability of immune recovery-that recapitulate population-level clinical outcomes of untreated tuberculosis patients. This framework may facilitate better understanding of the population-level impact of interventions acting at the individual host level. | en_US |
dc.language.iso | eng | |
dc.publisher | Oxford University Press | |
dc.relation.ispartofseries | Journal of Infectious Diseases | |
dc.rights | info:eu-repo/semantics/restrictedAccess | |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es | |
dc.subject | adult | en_US |
dc.subject | Bayes theorem | en_US |
dc.subject | calibration | en_US |
dc.subject | cohort analysis | en_US |
dc.subject | controlled study | en_US |
dc.subject | disease association | en_US |
dc.subject | disease course | en_US |
dc.subject | disease progression | en_US |
dc.subject | Disease progression | en_US |
dc.subject | disease transition | en_US |
dc.subject | female | en_US |
dc.subject | human | en_US |
dc.subject | immunocompetence | en_US |
dc.subject | intermethod comparison | en_US |
dc.subject | major clinical study | en_US |
dc.subject | male | en_US |
dc.subject | mathematical models | en_US |
dc.subject | Mathematical models | en_US |
dc.subject | medical history | en_US |
dc.subject | natural history | en_US |
dc.subject | Natural history | en_US |
dc.subject | outcome assessment | en_US |
dc.subject | priority journal | en_US |
dc.subject | process development | en_US |
dc.subject | spontaneous remission | en_US |
dc.subject | Spontaneous remission | en_US |
dc.subject | stochastic model | en_US |
dc.subject | tuberculosis | en_US |
dc.subject | Tuberculosis | en_US |
dc.title | Linking individual natural history to population outcomes in tuberculosis | en_US |
dc.type | info:eu-repo/semantics/article | |
dc.identifier.doi | https://doi.org/10.1093/infdis/jix555 | |
dc.subject.ocde | https://purl.org/pe-repo/ocde/ford#3.03.08 | |
dc.relation.issn | 1537-6613 |
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