title: Effect of applying a treatment threshold in a population. An example of pulmonary tuberculosis in Rwanda.
abstract: OBJECTIVE
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Clinical Sciences, Institute of Tropical Medicine, Antwerp, Belgium. jvdende@itg.be
authors.forename: Jef
authors.initials: J
authors.lastname: Van den Ende
authors.forename: Julie
authors.initials: J
authors.lastname: Mugabekazi
authors.forename: Juan
authors.initials: J
authors.lastname: Moreira
authors.forename: Eric
authors.initials: E
authors.lastname: Seryange
authors.forename: Paulin
authors.initials: P
authors.lastname: Basinga
authors.forename: Zeno
authors.initials: Z
authors.lastname: Bisoffi
authors.forename: Joris
authors.initials: J
authors.lastname: Menten
authors.forename: Marleen
authors.initials: M
authors.lastname: Boelaert
mesh_terms: D000293:Adolescent; D000328:Adult; D016000:Cluster Analysis; D015331:Cohort Studies; D003657:Decision Making; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011446:Prospective Studies; D012432:Rwanda / Q000453:epidemiology; D012680:Sensitivity and Specificity; D014397:Tuberculosis, Pulmonary / Q000175:diagnosis* / Q000188:drug therapy / Q000453:epidemiology; D055815:Young Adult