Background

TB incidence in India continues to be high due, in large part, to long delays experienced by patients before successful diagnosis and treatment initiation, especially in the private sector. This diagnostic delay is driven by patients’ inclination to switch between different type of providers and providers’ inclination to delay ordering of accurate diagnostic tests relevant to TB. Our objective is to quantify the impact of changes in these behavioural characteristics of providers and patients on diagnostic delay experienced by pulmonary TB patients.

Methods/Findings

We develop a discrete event simulation model of patients’ diagnostic pathways that captures key behavioural characteristics of providers (time to order a test) and patients (time to switch to another provider). We use Expectation-Maximization algorithm to estimate the parameters underlying these behavioural characteristics with quantitative data encoded from detailed interviews of 76 and 64 pulmonary TB patients in two Indian cities of Mumbai and Patna, respectively, which were conducted in 2014. We employ the estimated model to simulate different counterfactual scenarios of diagnostic pathways under altered behavioural characteristics of providers and patients to predict their potential impact on the diagnostic delay.

Private healthcare providers including chemists are the first point of contact for majority of TB patients in Mumbai (70%) and Patna (94%). In Mumbai, 44.6% TB patients first approach less-than-fully qualified providers (LTFQs), who take 29 days on average for diagnosis. Consequently, about 60.7% of the patients switch to other providers without a diagnosis. Immediate testing for tuberculosis by LTFQs at the first visit (at the current level of diagnostic accuracy) could reduce the average diagnostic delay by 17 days (47.3% reduction). In Patna, 61% TB patients first approach fully qualified providers (FQs), who take 10 days on average for diagnosis. Similarly, immediate testing by FQs at the first visit (at the current level of diagnostic accuracy) could reduce the average diagnostic delay by 12 days (52.3% reduction). Improving diagnostic accuracy of providers, per se, without reducing the time to testing, is not predicted to lead to any reductions in diagnostic delay. Our study was limited because of its restricted geographic scope, small sample size, and possible recall bias, which are typically associated with study of patient pathways using patient interviews.

Conclusions

Encouraging private providers to order definitive TB diagnostic tests earlier during patient consultation may have greater impact on reducing diagnostic delay in these urban Indian settings. These results should be combined with disease transmission models to predict the impact of changes in provider behaviour on TB incidence.

Dataset was collected by conducting detailed patient interviews about the pathways followed by the patients until they received the correct tuberculosis diagnosis followed by quantitative codificaiton of their responses. The data collection was done in two cities - Mumbai and Patna. This particular dataset refers only to Patna. The data from Mumbai can be found at https://datadryad.org/stash/dataset/doi:10.5061/dryad.bt36t.

Each row in the the dataset corresponds to one patient and the columns correspond to the demographic details of the patients, followed by initial healthcare seeking behavior and finally, details of their each stage of consultation with a different provider. More details are available in the accompnied attachment "Pathways Data_Read_Me.txt".