03260nas a2200373   4500000000100000008004100001260004400042653003200086653001000118653001600128653002600144653001800170653001200188653001700200100001900217700001400236700001200250700001700262700001000279700001100289700001700300700001200317700001500329700001400344700001300358700001400371700001300385245008500398856006500483300000700548490000600555520231100561022001402872       2025                            d  bSpringer Science and Business Media LLC10aVisceral leishmaniasis (VL)10aIndia10aElimination10aActive case detection10aGeostatistics10aSpatial10aSurveillance1 aNightingale ES1 aBindroo J1 aDubey P1 aPriyamvada K1 aDas A1 aBern C1 aSrikantiah S1 aKumar A1 aCameron MM1 aLucas TCD1 aSharma S1 aMedley GF1 aBrady OJ00aSpatial variation in time to diagnosis of visceral leishmaniasis in Bihar, India  uhttps://link.springer.com/article/10.1186/s44263-025-00169-3  a130 v33 a<p><strong>Background </strong>Visceral leishmaniasis (VL) is a debilitating and—without treatment—fatal parasitic 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 analysed the spatial variation of delayed diagnosis of VL in Bihar, India and aimed to understand the potential driving factors of these delays.</p>

<p><strong>Methods </strong>The spatial distribution of time to diagnosis was explored using a Bayesian hierarchical model fit to 4270 geo-located cases notified between January 2018 and July 2019 through routine surveillance. Days between symptoms meeting clinical criteria (14-day fever) and diagnosis were assumed to be Poisson-distributed, adjusting for individual- and village-level characteristics. Residual variance was modelled with an explicit spatial structure. Cumulative delays were estimated under different scenarios of active case detection coverage.</p>

<p><strong>Results</strong> The 4270 cases analysed were found to be prone to excessive delays in areas outside existing endemic ‘hot spots’. After accounting for differences associated with age, HIV status and mode of detection (active versus passive surveillance), cases diagnosed within recently affected (≥ 1 case reported in the previous year) blocks and villages experienced shorter delays on average (by 13% [2.9–21.7%] (95% credible interval) and 7% [1.3–13.1%], respectively) than those in non-recently-affected areas.</p>

<p><strong>Conclusions</strong> 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.</p>
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