Predicting the impact of patient and private provider behaviour on diagnostic delay for pulmonary tuberculosis patients in India: A simulation modelling approach

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.