The effect of diagnostic delays on the drop-out rate and the total delay to diagnosis of tuberculosis

Abstract

Background: Numerous patient and healthcare system-related delays contribute to the overall delay experienced by patients from onset of TB symptoms to diagnosis and treatment. Such delays are critical as infected individuals remain untreated in the community, providing more opportunities for transmission of the disease and adversely affecting the epidemic.

Methodology/principal findings: We present an analysis of the factors that contribute to the overall delay in TB diagnosis and treatment, in a resource-poor setting. Impact on the distribution of diagnostic delay times was assessed for various factors, the sensitivity of the diagnostic method being found to be the most significant. A linear relationship was found between the sensitivity of the test and the predicted mean delay time, with an increase in test sensitivity resulting in a reduced mean delay time and a reduction in the drop-out rate.

Conclusions/significance: The results show that in a developing country a number of delay factors, particularly the low sensitivity of the initial sputum smear microscopy test, potentially increase total diagnostic delay times experienced by TB patients significantly. The results reinforce the urgent need for novel diagnostic methods, both for smear positive and negative TB, that are highly sensitive, accessible and point of care, in order to reduce mean delay times.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1. Schematic of the model.

Figure 2. Distribution of delays since onset of symptoms to start of treatment for patients that do not drop out, using a SSM test sensitivity of 0.55.

Figure 3. Distribution of delays since onset of symptoms to start of treatment for patients that do not drop out, using a SSM test sensitivity of 0.99.

Figure 4. Distribution of delays since onset of symptoms to start of treatment for patients that do not drop out, using a SSM test sensitivity of 0.01.

Figure 5. Linear relationship between initial SSM test sensitivity and mean number of days delayed between onset of symptoms and start of treatment for those that did not drop out.

Figure 6. Distribution of delays since onset of symptoms to start of treatment for patients that do not drop out, when 0.1 (above) and 0.5 (below) smear negative patients with an abnormal CXR are treated presumptively.

Figure 7. Distribution of delays since onset of symptoms to start of treatment for patients that do not drop out, when culture takes 42 days (top) and 28 days (bottom).

Figure 8. Distribution of delays since onset of symptoms to start of treatment for patients that do not drop out, with a 0.05 (top) and 0.10 (bottom) probability that patient makes a clinic visit on a given day.

Figure 9. The effect of changing the probabilities of a patient making visit ‘1’ and ‘V’ on mean delay.

Figure 10. Distribution of delays since onset of symptoms to start of treatment for patients that do not drop out, with a 0.95 (above) and 0.10 (below) probability that sputum is requested on visit ‘V’.

Figure 11. Relationship between the probability of sputum being requested on visit ‘V’ and mean delay.

Figure 12. The effect of ss-CXR and SSM sensitivity on mean delay.

Figure 13. Linear relationship between initial SSM test sensitivity and percentage of patients that drop out.

Figure 14. The effect of parameter changes on mean drop-out rate.