Background Visceral leishmaniasis (VL) is a debilitating disease and without treatment, a fatal disease which burdens the most impoverished communities in northeastern India. Control and ultimately, elimination of VL depends heavily on prompt case detection. However, a proportion of VL cases remain undiagnosed many months after symptom onset. Delay to diagnosis increases the chance of onward transmission, and poses a risk of resurgence in populations with waning immunity. We checked the spatial variation of delayed diagnosis of VL in Bihar, India and aimed to understand the potential driving factors of delayed diagnosis. Methods The spatial distribution of diagnostic delays was explored using a Bayesian model fit to geo-located cases using the Integrated Nested Laplace Approximation (INLA) approach, assuming days of delay as Poisson-distributed and adjusting for individual- (age, sex, HIV) and local-level (recent incidence, vector control, health facility access) characteristics. Residual variance was modelled with an explicit spatial structure. Cumulative delays were estimated under different scenarios of active case detection coverage. Findings The 4,270 cases analysed were prone to excessive delays outside existing endemic 'hot spots', beyond the focus of interventions. Cases diagnosed within recently-affected blocks and villages experienced shorter delays on average (by 13% 95% Credible Interval [2.9% - 21.7%] and 7% [1.3% - 13.1%], respectively) than those in non-recently-affected areas. Interpretation Delays to VL diagnosis when incidence is low could influence whether transmission of the disease could be interrupted or resurges. Prioritising and narrowing surveillance to high-burden areas may increase the likelihood of excessive delays in diagnosis in peripheral areas. Active surveillance driven by observed incidence may lead to missing the risk posed by as-yet-undiagnosed cases in low-endemic areas, and such surveillance could be insufficient for achieving and sustaining elimination. Funding The Bill and Melinda Gates Foundation.

The authors have declared no competing interest.

This work was funded by the Bill and Melinda Gates Foundation (ESN, MCC: OPP1183986, GFM: OPP1184344). OJB was funded by a UK Medical Research Council Career Development Award (MR/V031112/1). TCDL was supported by the NIHR Applied Research Collaboration East Midlands (ARC EM). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. CB, JB, KP, AD and SS were funded by the Bill and Melinda Gates Foundation (OPP1196454).

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The ethics committee of the London School of Hygiene and Tropical Medicine gave ethical The ethics committee of the All India Institute of Medical Sciences-Patna approved the ACD effectiveness evaluation protocol for analysis of data from the Kala-Azar Management Information System (KAMIS); no new data were collected under the research protocol. The National Vector Borne Disease Control Programme in India (NVBDCP) gave permission for use of data from the Kala-Azar Management Information System (KAMIS)for the work of the SPEAK India research consortium.

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The full analysis dataset cannot be publicly shared as it contains both sensitive (HIV infection) and identifiable (age, sex, and GPS of resident village) information on individual patients. An altered dataset with GPS locations jittered in order to not correspond to unique villages can be made available upon request. It should be noted that from this it would not be possible to exactly replicate the presented results.

https://github.com/esnightingale/vl-diagnosis-delay