The Effect of Diagnostic Delays on the Drop-Out Rate and the Total Delay to Diagnosis of Tuberculosis

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 an

A Threshold Value for the Time Delay to TB Diagnosis

The original publication is available at http:/www.plosone.orgIncludes bibliographyBackgound. In many communities where TB occurs at high incidence, the major force driving the epidemic is transmission. It is plausible that the typical long delay from the onset of infectious disease to diagnosis and commencement of treatment is almost certainly the major factor contributing to the high rate of transmission. Methodology/Principal Findings. This study is confined to communities which are epidemiologically relatively isolated and which have low HIV incidence. The consequences of delays to diagnosis are analyzed and the existence of a threshold delay value is demonstrated. It is shown that unless a sufficient number of cases are detected before this threshold, the epidemic will escalate. The method used for the analysis avoids the standard computer integration of systems of differential equations since the intention is to present a line of reasoning that reveals the essential dynamics of an epidemic in an intuitively clear way that is nevertheless quantitatively realistic. Conclusions/Significance. The analysis presented here shows that typical delays to diagnosis present a major obstacle to the control of a TB epidemic. Control can be achieved by optimizing the rapid identification of TB cases together with measures to increase the threshold value. A calculated and aggressive program is therefore necessary in order to bring about a reduction in the prevalence of TB in a community by decreasing the time to diagnosis in all its ramifications. Intervention strategies to increase the threshold value relative to the time to diagnosis and which thereby decrease disease incidence are discussed. © 2007 Uys et al.Publishers' Versio

Tuberculosis reinfection rate as a proportion of total infection rate correlates with the logarithm of the incidence rate: a mathematical model

In a significant number of instances, an episode of tuberculosis can be attributed to a reinfection event. Because reinfection is more likely in high incidence regions than in regions of low incidence, more tuberculosis (TB) cases due to reinfection could be expected in high-incidence regions than in low-incidence regions. Empirical data from regions with various incidence rates appear to confirm the conjecture that, in fact, the incidence rate due to reinfection only, as a proportion of all cases, correlates with the logarithm of the incidence rate, rather than with the incidence rate itself. A theoretical model that supports this conjecture is presented. A Markov model was used to obtain a relationship between incidence and reinfection rates. It was assumed in this model that the rate of reinfection is a multiple, ρ (the reinfection factor), of the rate of first-time infection, λ. The results obtained show a relationship between the proportion of cases due to reinfection and the rate of incidence that is approximately logarithmic for a range of values of the incidence rate typical of those observed in communities across the globe. A value of ρ is determined such that the relationship between the proportion of cases due to reinfection and the logarithm of the incidence rate closely correlates with empirical data. From a purely theoretical investigation, it is shown that a simple relationship can be expected between the logarithm of the incidence rates and the proportions of cases due to reinfection after a prior episode of TB. This relationship is sustained by a rate of reinfection that is higher than the rate of first-time infection and this latter consideration underscores the great importance of monitoring recovered TB cases for repeat disease episodes, especially in regions where TB incidence is high. Awareness of this may assist in attempts to control the epidemic

Percentage Change in Mean Delay Due to a 20% Increase in Individual Parameters.

<p><b>Note:</b> The average mean delay corresponding to a 20% increase in the particular parameter is compared to the standard mean delay of 45.7</p

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.

<p>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.</p

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.

<p>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.</p

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.

<p>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.</p

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.

<p>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.</p