Evidence map›Paper›PMID 39567715›Full record

ArticleCommunications medicine2024

Inferring the regional distribution of Visceral Leishmaniasis incidence from data at different spatial scales.

Emily S Nightingale, Swaminathan Subramanian, Ashley R Schwarzer, Lloyd A C Chapman, Purushothaman Jambulingam, Mary M Cameron, Oliver J Brady, Graham F Medley, Tim C D Lucas

Abstract read
In one paragraph

Article in Communications medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Emily S NightingaleDepartment of Infectious Disease Epidemiology and Dynamics, London School of Hygiene and Tropical Medicine, London, UK. emily.nightingale@lshtm.ac.uk.ORCID http://orcid.org/0000-0002-9265-1841
Swaminathan SubramanianICMR-Vector Control Research Centre, Puducherry, India.ORCID http://orcid.org/0000-0003-0804-0763
Ashley R SchwarzerLondon School of Hygiene and Tropical Medicine, London, UK.ORCID http://orcid.org/0009-0009-8915-8165
Lloyd A C ChapmanSchool of Mathematical Sciences, Lancaster University, Lancaster, UK.ORCID http://orcid.org/0000-0001-7727-7102
Purushothaman JambulingamICMR-Vector Control Research Centre, Puducherry, India.
Mary M CameronDepartment of Disease Control, London School of Hygiene and Tropical Medicine, London, UK.
Oliver J BradyDepartment of Infectious Disease Epidemiology and Dynamics, London School of Hygiene and Tropical Medicine, London, UK.ORCID http://orcid.org/0000-0002-3235-2129
Graham F MedleyCentre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK.ORCID http://orcid.org/0000-0002-0030-7278
Tim C D LucasDepartment of Population Health Sciences, University of Leicester, Leicester, UK.

Funding

Bill and Melinda Gates Foundation (Bill & Melinda Gates Foundation) OPP1183986Bill and Melinda Gates Foundation (Bill & Melinda Gates Foundation) OPP1184344
6 · The paper itself

Abstract

backgroundAs cases of visceral leishmaniasis (VL) in India dwindle, there is motivation to monitor elimination progress on a finer geographic scale than sub-district (block). Low-incidence projections across geographically- and demographically- heterogeneous communities are difficult to act upon, and equitable elimination cannot be achieved if local pockets of incidence are overlooked. However, maintaining consistent surveillance at this scale is resource-intensive and not sustainable in the long-term.

methodsWe analysed VL incidence across 45,000 villages in Bihar state, exploring spatial autocorrelation and associations with local environmental conditions in order to assess the feasibility of inference at this scale. We evaluated a statistical disaggregation approach to infer finer spatial variation from routinely-collected, block-level data, validating against observed village-level incidence.

resultsThis disaggregation approach does not estimate village-level incidence more accurately than a baseline assumption of block-homogeneity. Spatial auto-correlation is evident on a block-level but weak between neighbouring villages within the same block, possibly suggesting that longer-range transmission (e.g., due to population movement) may be an important contributor to village-level heterogeneity.

conclusionsIncreasing the range of reactive interventions to neighbouring villages may not improve their efficacy in suppressing transmission, but maintaining surveillance and diagnostic capacity in areas distant from recently observed cases - particularly along routes of population movement from endemic regions - could reduce reintroduction risk in currently unaffected villages. The reactive, spatially-targeted approach to VL surveillance limits interpretability of data observed at the village level, and hence the feasibility of routinely drawing and validating inference at this scale.

Identifiers

PMID39567715
PMCPMC11579291

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.