Lagged effects of climate factors on bacillary dysentery in western China.

bacillary dysentery climate distributed lag non-linear model western China

Journal

Transactions of the Royal Society of Tropical Medicine and Hygiene
ISSN: 1878-3503
Titre abrégé: Trans R Soc Trop Med Hyg
Pays: England
ID NLM: 7506129

Informations de publication

Date de publication:
11 Oct 2024
Historique:
received: 20 02 2024
revised: 24 06 2024
accepted: 30 08 2024
medline: 11 10 2024
pubmed: 11 10 2024
entrez: 11 10 2024
Statut: aheadofprint

Résumé

Evidence has shown that the incidence of bacillary dysentery (BD) is associated with climatic factors. However, the lagged effects of climatic factors on BD are still unclear, especially lacking research evidence from arid and semi-arid regions. Therefore, this study aims to add new insights into this research field. Spatial autocorrelation, time series analysis and spatiotemporal scans were used to perform descriptive analyses of BD cases from 2009 to 2019. On the basis of monthly data from 2015 to 2019, multivariable distributed lag non-linear models were used to investigate the lagged effects of climatic factors on BD. The hot spots for BD incidence are gradually decreasing and becoming increasingly concentrated in the southern part of Gansu Province. The maximum cumulative relative risks for monthly average temperature, sunshine duration, average relative humidity and precipitation were 3.21, 1.64, 1.55 and 1.41, respectively. The lagged effects peaked either in the current month or with a 1-month lag, and the shape of the exposure-response curve changed with the increase in maximum lag time. After stratification by per capita gross domestic product, there were differences in the effects. Climatic factors can influence the incidence of BD, with effects varying across different lag times. It is imperative to vigilantly track the disparities in the incidence of BD attributable to economic factors.

Sections du résumé

BACKGROUND BACKGROUND
Evidence has shown that the incidence of bacillary dysentery (BD) is associated with climatic factors. However, the lagged effects of climatic factors on BD are still unclear, especially lacking research evidence from arid and semi-arid regions. Therefore, this study aims to add new insights into this research field.
METHODS METHODS
Spatial autocorrelation, time series analysis and spatiotemporal scans were used to perform descriptive analyses of BD cases from 2009 to 2019. On the basis of monthly data from 2015 to 2019, multivariable distributed lag non-linear models were used to investigate the lagged effects of climatic factors on BD.
RESULTS RESULTS
The hot spots for BD incidence are gradually decreasing and becoming increasingly concentrated in the southern part of Gansu Province. The maximum cumulative relative risks for monthly average temperature, sunshine duration, average relative humidity and precipitation were 3.21, 1.64, 1.55 and 1.41, respectively. The lagged effects peaked either in the current month or with a 1-month lag, and the shape of the exposure-response curve changed with the increase in maximum lag time. After stratification by per capita gross domestic product, there were differences in the effects.
CONCLUSIONS CONCLUSIONS
Climatic factors can influence the incidence of BD, with effects varying across different lag times. It is imperative to vigilantly track the disparities in the incidence of BD attributable to economic factors.

Identifiants

pubmed: 39392187
pii: 7818266
doi: 10.1093/trstmh/trae064
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : Natural Science Foundation of Gansu Province
ID : 20JR10RA598

Informations de copyright

© The Author(s) 2024. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene.

Auteurs

Rui Li (R)

Institute of Epidemiology and Health Statistics, School of Public Health, Lanzhou University, Lanzhou, Gansu 730000, China.

Dongpeng Liu (D)

Gansu Provincial Center for Disease Control and Prevention, Lanzhou, Gansu 730000, China.

Tingrong Wang (T)

Institute of Epidemiology and Health Statistics, School of Public Health, Lanzhou University, Lanzhou, Gansu 730000, China.

Donghua Li (D)

Institute of Epidemiology and Health Statistics, School of Public Health, Lanzhou University, Lanzhou, Gansu 730000, China.

Tianshan Shi (T)

Institute of Epidemiology and Health Statistics, School of Public Health, Lanzhou University, Lanzhou, Gansu 730000, China.

Xin Zhao (X)

Institute of Epidemiology and Health Statistics, School of Public Health, Lanzhou University, Lanzhou, Gansu 730000, China.

Hongmiao Zheng (H)

Institute of Epidemiology and Health Statistics, School of Public Health, Lanzhou University, Lanzhou, Gansu 730000, China.

Xiaowei Ren (X)

Institute of Epidemiology and Health Statistics, School of Public Health, Lanzhou University, Lanzhou, Gansu 730000, China.

Classifications MeSH