Spatial variation in delayed diagnosis of visceral leishmaniasis in Bihar, India

Spatial variation in delayed diagnosis of visceral leishmaniasis in Bihar, India | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatial variation in delayed diagnosis of visceral leishmaniasis in Bihar, India Emily S. Nightingale, Joy Bindroo, Pushkar Dubey, Khushbu Priyamvada, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4268474/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract 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, 1 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. Full Text Additional Declarations No competing interests reported. Supplementary Files Supplementary.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 05 Jun, 2024 Reviews received at journal 03 Jun, 2024 Reviews received at journal 16 May, 2024 Reviewers agreed at journal 01 May, 2024 Reviewers agreed at journal 30 Apr, 2024 Reviewers invited by journal 24 Apr, 2024 Editor assigned by journal 16 Apr, 2024 Submission checks completed at journal 16 Apr, 2024 First submitted to journal 15 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4268474","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":291765287,"identity":"d3981da7-19bc-46aa-8c12-89b2da14a5eb","order_by":0,"name":"Emily S. 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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.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eThe spatial distribution of diagnostic delays was explored using a Bayesian model fit to geo-located cases using the Integrated Nested Laplace Approximation (INLA) approach, 1 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.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFindings \u003c/strong\u003eThe 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.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterpretation \u003c/strong\u003eDelays 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.\u003c/p\u003e","manuscriptTitle":"Spatial variation in delayed diagnosis of visceral leishmaniasis in Bihar, India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-18 02:49:06","doi":"10.21203/rs.3.rs-4268474/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-05T16:01:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-03T07:43:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-16T16:19:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"baa170e3-2972-4a5f-9f69-4c06e853d535","date":"2024-05-01T04:15:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"99585270669518794365585302968510503480","date":"2024-05-01T02:36:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-24T15:35:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-16T08:31:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-16T08:28:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Global and Public Health","date":"2024-04-15T08:55:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-global-and-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Global and Public Health](https://bmcglobalpublichealth.biomedcentral.com/)","snPcode":"44263","submissionUrl":"https://submission.springernature.com/new-submission/44263/3","title":"BMC Global and Public Health","twitterHandle":"@BMC_GPH","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dfb1e7fa-992d-4071-abdb-2cf141a4e229","owner":[],"postedDate":"April 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-05-13T16:08:29+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-18 02:49:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4268474","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4268474","identity":"rs-4268474","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}