Evidence map›Paper›PMID 40303089›Full record

ArticleTransboundary and emerging diseases2024

Space-Time Clustering and Climatic Risk Factors for Lumpy Skin Disease of Cattle in Uttar Pradesh, India, 2022.

Isha Agrawal, Barkha Sharma, Csaba Varga

Abstract read
In one paragraph

Article in Transboundary and emerging diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–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

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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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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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

3 authors.

Isha AgrawalDepartment of Pathobiology, College of Veterinary Medicine, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.ORCID https://orcid.org/0009-0005-3219-9223
Barkha SharmaU.P. Pandit Deen Dayal Upadhyay Pashu Chikitsa Vigyan Vishwavidyalaya Evam Go Anusandhan Sansthan, Mathura, India.
Csaba VargaDepartment of Pathobiology, College of Veterinary Medicine, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.ORCID https://orcid.org/0000-0003-2751-3677

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lumpy skin disease (LSD), a transboundary infectious disease, negatively impacts cattle health and production. The first LSD outbreaks were reported in India in 2019, and since then, LSD spread to over 15 states, including Uttar Pradesh. This study evaluated LSD cases reported by veterinarians in Uttar Pradesh, India, during 2022. Using scan statistics, Poisson models that accounted for the background cattle population were constructed to identify spatial, temporal, and space-time clusters. A negative binomial regression model was built to assess the impact of temperature and humidity on the incidence rate (IR) of LSD. A total of 112,226 cases across 33 districts were reported in 2022. A purely temporal cluster with higher-than-expected LSD rates was identified between August and October 2022. Several purely spatial clusters were identified in the western part of the state. A primary space-time cluster was detected in west Utter Pradesh between August and October 2022 that overlapped with the spatial clusters. The secondary cluster occurred between September and October 2022 in the eastern part of the state. A rise in humidity (incidence rate ratio (IRR) = 1.39; 95% CI: 1.30-1.49) and temperature (IRR = 1.16; 95% CI: 1.06-1.27) increased the IR of LSD, suggesting a seasonality of the outbreaks. The results of this study can aid animal health authorities in developing effective LSD prevention, surveillance, and control strategies among cattle in India.

Indexed as

ClimateDisease OutbreaksLumpy Skin DiseaseAnimalsCattleHumidityIncidenceIndiaLumpy skin disease virusRisk FactorsSeasonsSpace-Time ClusteringTemperature

Identifiers

PMID40303089
PMCPMC12016720

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Registered trials

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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.