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Effect of applying a treatment threshold in a population. An example of pulmonary tuberculosis in Rwanda
Author(s) -
Van den Ende Jef,
Mugabekazi Julie,
Moreira Juan,
Seryange Eric,
Basinga Paulin,
Bisoffi Zeno,
Menten Joris,
Boelaert Marleen
Publication year - 2010
Publication title -
journal of evaluation in clinical practice
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.737
H-Index - 73
eISSN - 1365-2753
pISSN - 1356-1294
DOI - 10.1111/j.1365-2753.2009.01150.x
Subject(s) - medicine , cohort , regret , medical prescription , tuberculosis , population , disease , harm , statistics , environmental health , pathology , psychology , social psychology , mathematics , pharmacology
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.