Effect of applying a treatment threshold in a population. An example of pulmonary tuberculosis in Rwanda.
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.
Van den Ende
Jef
J
Department of Clinical Sciences, Institute of Tropical Medicine, Antwerp, Belgium. jvdende@itg.be
Mugabekazi
Julie
J
Moreira
Juan
J
Seryange
Eric
E
Basinga
Paulin
P
Bisoffi
Zeno
Z
Menten
Joris
J
Boelaert
Marleen
M
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