Evidence map›Paper›PMID 41387015›Full record

ArticleBMJ open2025

Spatiotemporal patterns of asthma in Bhutan: a Bayesian analysis.

Tsheten Tsheten, Dan Château, Erin Walsh, Ginny Sargent, Archie C A Clements, Darren Gray, Matthew Kelly, Nima Dorji, Phurpa Tenzin, Lila Adhikari and 3 more

Abstract read
In one paragraph

Article in BMJ open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

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4 · The record

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5 · Who and what money

Authors and funding

13 authors.

Tsheten TshetenNational Centre for Epidemiology and Population Health, Australian National University, Canberra, Australian Capital Territory, Australia tsheten.tsheten@anu.edu.au.ORCID http://orcid.org/0000-0002-8071-5721
Dan ChâteauNational Centre for Epidemiology and Population Health, Australian National University, Canberra, Australian Capital Territory, Australia.
Erin WalshNational Centre for Epidemiology and Population Health, Australian National University, Canberra, Australian Capital Territory, Australia.ORCID http://orcid.org/0000-0001-8941-0046
Ginny SargentNational Centre for Epidemiology and Population Health, Australian National University, Canberra, Australian Capital Territory, Australia.
Archie C A ClementsQueen's University Belfast, Northern Ireland, UK.
Darren GrayQIMR Berghofer Medical Research Institute, Herston, Queensland, Australia.
Matthew KellyNational Centre for Epidemiology and Population Health, Australian National University, Canberra, Australian Capital Territory, Australia.ORCID http://orcid.org/0000-0001-7963-2139
Nima DorjiMinistry of Health, Punakha District Hospital, Punakha, Bhutan.
Phurpa TenzinDepartment of Public Health, Ministry of Health, Thimphu, Bhutan.
Lila AdhikariRoyal Centre for Disease Control, Ministry of Health, Thimphu, Bhutan.
Kinley PenjorKhesar Gyalpo University of Medical Sciences of Bhutan, Thimphu, Bhutan.ORCID http://orcid.org/0000-0001-9529-4096
Nasser BagheriUniversity of Canberra, Canberra, Australian Capital Territory, Australia.
Kinley WangdiUniversity of Canberra, Canberra, Australian Capital Territory, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAsthma is a chronic respiratory disorder requiring ongoing medical management. This ecological study investigated the spatial and temporal patterns of notification rates for asthma from clinic visits and hospital discharges and identified demographic, meteorological and environmental factors that drive asthma in Bhutan.

methodsMonthly numbers of asthma notifications from 2016 to 2022 were obtained from the Bhutan Ministry of Health. Climatic variables (rainfall, relative humidity, minimum and maximum temperature) were obtained from the National Centre for Hydrology and Meteorology, Bhutan. The Normalised Difference Vegetation Index (NDVI) and surface particulate matter (PM2.5) were extracted from open sources. A multivariable zero-inflated Poisson regression (ZIP) model was developed in a Bayesian framework to quantify the relationship between risk of asthma and sociodemographic and environmental correlates, while also identifying the underlying spatial structure of the data.

resultsThere were 12 696 asthma notifications, with an annual average prevalence of 244/100 000 population between 2016 and 2022. In ZIP analysis, asthma notifications were 3.4 times (relative risk (RR)=3.39; 95% credible interval (CrI) 3.047 to 3.773) more likely in individuals aged >14 years than those aged ≤14 years, and 43% (RR=1.43; 95% CrI 36.5% to 49.2%) more likely for females than males. Asthma notification increased by 0.8% (RR=1.008, 95% CrI 0.2% to 1.5%) for every 10 cm increase in rainfall, and 1.7% (RR=1.017; 95% CrI 1.2% to 2.3%) for a 1°C increase in maximum temperature. An increase in one unit of NDVI and 10 µg/m

conclusionEnvironmental risk factors and spatial clusters of asthma notifications were identified. Identification of spatial clusters and environmental risk factors can help develop targeted interventions that maximise impact of limited public health resources for controlling asthma in Bhutan.

Indexed as

AsthmaAdolescentAdultAgedBayes TheoremBhutanChildChild, PreschoolFemaleHumansInfantMaleMiddle AgedPrevalenceRisk FactorsSpatio-Temporal AnalysisAsthmaEPIDEMIOLOGYLung Diseases, InterstitialPublic health

Identifiers

PMID41387015
PMCPMC12706219

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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.