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Effect of applying a treatment threshold in a population. An example of pulmonary tuberculosis in Rwanda

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dc.contributor.author Van den Ende, J.
dc.contributor.author Mugabekazi, J.
dc.contributor.author Moreira, J.
dc.contributor.author Seryange, E.
dc.contributor.author Basinga, P.
dc.contributor.author Bisoffi, Z.
dc.contributor.author Menten, J.
dc.contributor.author Boelaert, M.
dc.date.accessioned 2010-06-17T09:15:10Z
dc.date.available 2010-06-17T09:15:10Z
dc.date.issued 2010
dc.identifier.issn 1356-1294
dc.identifier.other http://dx.doi.org/10.1111/j.1365-2753.2009.01150.x
dc.identifier.other ITG-C1A
dc.identifier.other ITG-C3B
dc.identifier.other ITG-I7A
dc.identifier.other ITG-HLA
dc.identifier.other CLINIC
dc.identifier.other U-TROPIC
dc.identifier.other INTER
dc.identifier.other U-CTU
dc.identifier.other HEALTH
dc.identifier.other U-EPID
dc.identifier.other JIF
dc.identifier.other DOI
dc.identifier.other Abstract
dc.identifier.other UPD22
dc.identifier.uri http://hdl.handle.net/10390/6209
dc.description.abstract Purpose Clinicians often think treatment thresholds should be adapted to the setting. We intended to explore the effect in terms of harm because of false negatives and true and false positives of the application of a treatment threshold for pulmonary tuberculosis from a patient's perspective at different prevalence levels in a developing country. Methods In a cohort of 300 patients with chronic cough, we estimated the prevalence of pulmonary tuberculosis, and the sensitivity and specificity of key predictors with latent class analysis (LCA). We computed the post-test probability of individual patients based on these data. With disease- and treatment-related mortality and morbidity, and without cost or regret, we calculated the break-even point of disease probability where treating versus not treating resulted in similar total harm from the patient's perspective. We estimated the total harm of applying this threshold to the cohort, and to hypothetical settings with different disease prevalence. Results The threshold was computed at 0.026, suggesting treatment for all patients of the cohort. Hypothetically lowering the prevalence showed that the lowest total harm in the cohort always coincides with this threshold, but that numbers of treated patients drop considerably. Conclusion For pulmonary tuberculosis a decision threshold solely based on utilities without cost or regret leads to a very low threshold. The lowest total harm is found always at this disease probability, irrespective of the distribution of the patients. Although these findings might suggest an excess prescription at reference level, this is not the case in settings with lower prevalence en
dc.language English en
dc.subject Bacterial diseases en
dc.subject Tuberculosis en
dc.subject Pulmonary en
dc.subject Mycobacterium tuberculosis en
dc.subject Prevalence en
dc.subject Patient's perspective en
dc.subject Predictive value en
dc.subject Sensitivity en
dc.subject Specificity en
dc.subject Latent class analysis en
dc.subject LCA en
dc.subject Probabilities en
dc.subject Thresholds en
dc.subject Mathematical modeling en
dc.subject Treatment en
dc.subject Decision-tree en
dc.subject Decision making en
dc.subject Rwanda en
dc.subject Africa, Central en
dc.title Effect of applying a treatment threshold in a population. An example of pulmonary tuberculosis in Rwanda en
dc.type Article en
dc.citation.issue 3 en
dc.citation.jtitle Journal of Evaluation in Clinical Practice en
dc.citation.volume 16 en
dc.citation.pages 499-508 en
dc.identifier.pmid http://www.ncbi.nlm.nih.gov/pubmed/20074302
dc.citation.jabbreviation J Eval Clin Pract en


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