Spatio-temporal pattern and associate factors of intestinal infectious diseases in Zhejiang Province, China, 2008-2021: a Bayesian modeling study.
Climate factors
Hierarchical Bayesian model
Intestinal infectious diseases
Socioeconomic condition
Spatial clustering
Journal
BMC public health
ISSN: 1471-2458
Titre abrégé: BMC Public Health
Pays: England
ID NLM: 100968562
Informations de publication
Date de publication:
29 08 2023
29 08 2023
Historique:
received:
12
04
2023
accepted:
17
08
2023
medline:
31
8
2023
pubmed:
30
8
2023
entrez:
29
8
2023
Statut:
epublish
Résumé
Despite significant progress in sanitation status and public health awareness, intestinal infectious diseases (IID) have caused a serious disease burden in China. Little was known about the spatio-temporal pattern of IID at the county level in Zhejiang. Therefore, a spatio-temporal modelling study to identify high-risk regions of IID incidence and potential risk factors was conducted. Reported cases of notifiable IID from 2008 to 2021 were obtained from the China Information System for Disease Control and Prevention. Moran's I index and the local indicators of spatial association (LISA) were calculated using Geoda software to identify the spatial autocorrelation and high-risk areas of IID incidence. Bayesian hierarchical model was used to explore socioeconomic and climate factors affecting IID incidence inequities from spatial and temporal perspectives. From 2008 to 2021, a total of 101 cholera, 55,298 bacterial dysentery, 131 amoebic dysentery, 5297 typhoid, 2102 paratyphoid, 27,947 HEV, 1,695,925 hand, foot and mouth disease (HFMD), and 1,505,797 other infectious diarrhea (OID) cases were reported in Zhejiang Province. The hot spots for bacterial dysentery, OID, and HEV incidence were found mainly in Hangzhou, while high-high cluster regions for incidence of enteric fever and HFMD were mainly located in Ningbo. The Bayesian model showed that Areas with a high proportion of males had a lower risk of BD and enteric fever. People under the age of 18 may have a higher risk of IID. High urbanization rate was a protective factor against HFMD (RR = 0.91, 95% CI: 0.88, 0.94), but was a risk factor for HEV (RR = 1.06, 95% CI: 1.01-1.10). BD risk (RR = 1.14, 95% CI: 1.10-1.18) and enteric fever risk (RR = 1.18, 95% CI:1.10-1.27) seemed higher in areas with high GDP per capita. The greater the population density, the higher the risk of BD (RR = 1.29, 95% CI: 1.23-1.36), enteric fever (RR = 1.12, 95% CI: 1.00-1.25), and HEV (RR = 1.15, 95% CI: 1.09-1.21). Among climate variables, higher temperature was associated with a higher risk of BD (RR = 1.32, 95% CI: 1.23-1.41), enteric fever (RR = 1.41, 95% CI: 1.33-1.50), and HFMD (RR = 1.22, 95% CI: 1.08-1.38), and with lower risk of HEV (RR = 0.83, 95% CI: 0.78-0.89). Precipitation was positively correlated with enteric fever (RR = 1.04, 95% CI: 1.00-1.08), HFMD (RR = 1.03, 95% CI: 1.00-1.06), and HEV (RR = 1.05, 95% CI: 1.03-1.08). Higher HFMD risk was also associated with increasing relative humidity (RR = 1.20, 95% CI: 1.16-1.24) and lower wind velocity (RR = 0.88, 95% CI: 0.84-0.92). There was significant spatial clustering of IID incidence in Zhejiang Province from 2008 to 2021. Spatio-temporal patterns of IID risk could be largely explained by socioeconomic and meteorological factors. Preventive measures and enhanced monitoring should be taken in some high-risk counties in Hangzhou city and Ningbo city.
Sections du résumé
BACKGROUND
Despite significant progress in sanitation status and public health awareness, intestinal infectious diseases (IID) have caused a serious disease burden in China. Little was known about the spatio-temporal pattern of IID at the county level in Zhejiang. Therefore, a spatio-temporal modelling study to identify high-risk regions of IID incidence and potential risk factors was conducted.
METHODS
Reported cases of notifiable IID from 2008 to 2021 were obtained from the China Information System for Disease Control and Prevention. Moran's I index and the local indicators of spatial association (LISA) were calculated using Geoda software to identify the spatial autocorrelation and high-risk areas of IID incidence. Bayesian hierarchical model was used to explore socioeconomic and climate factors affecting IID incidence inequities from spatial and temporal perspectives.
RESULTS
From 2008 to 2021, a total of 101 cholera, 55,298 bacterial dysentery, 131 amoebic dysentery, 5297 typhoid, 2102 paratyphoid, 27,947 HEV, 1,695,925 hand, foot and mouth disease (HFMD), and 1,505,797 other infectious diarrhea (OID) cases were reported in Zhejiang Province. The hot spots for bacterial dysentery, OID, and HEV incidence were found mainly in Hangzhou, while high-high cluster regions for incidence of enteric fever and HFMD were mainly located in Ningbo. The Bayesian model showed that Areas with a high proportion of males had a lower risk of BD and enteric fever. People under the age of 18 may have a higher risk of IID. High urbanization rate was a protective factor against HFMD (RR = 0.91, 95% CI: 0.88, 0.94), but was a risk factor for HEV (RR = 1.06, 95% CI: 1.01-1.10). BD risk (RR = 1.14, 95% CI: 1.10-1.18) and enteric fever risk (RR = 1.18, 95% CI:1.10-1.27) seemed higher in areas with high GDP per capita. The greater the population density, the higher the risk of BD (RR = 1.29, 95% CI: 1.23-1.36), enteric fever (RR = 1.12, 95% CI: 1.00-1.25), and HEV (RR = 1.15, 95% CI: 1.09-1.21). Among climate variables, higher temperature was associated with a higher risk of BD (RR = 1.32, 95% CI: 1.23-1.41), enteric fever (RR = 1.41, 95% CI: 1.33-1.50), and HFMD (RR = 1.22, 95% CI: 1.08-1.38), and with lower risk of HEV (RR = 0.83, 95% CI: 0.78-0.89). Precipitation was positively correlated with enteric fever (RR = 1.04, 95% CI: 1.00-1.08), HFMD (RR = 1.03, 95% CI: 1.00-1.06), and HEV (RR = 1.05, 95% CI: 1.03-1.08). Higher HFMD risk was also associated with increasing relative humidity (RR = 1.20, 95% CI: 1.16-1.24) and lower wind velocity (RR = 0.88, 95% CI: 0.84-0.92).
CONCLUSIONS
There was significant spatial clustering of IID incidence in Zhejiang Province from 2008 to 2021. Spatio-temporal patterns of IID risk could be largely explained by socioeconomic and meteorological factors. Preventive measures and enhanced monitoring should be taken in some high-risk counties in Hangzhou city and Ningbo city.
Identifiants
pubmed: 37644452
doi: 10.1186/s12889-023-16552-4
pii: 10.1186/s12889-023-16552-4
pmc: PMC10464402
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1652Informations de copyright
© 2023. BioMed Central Ltd., part of Springer Nature.
Références
Lancet. 2018 Nov 10;392(10159):1736-1788
pubmed: 30496103
Int J Infect Dis. 2015 May;34:66-70
pubmed: 25770912
Sci Rep. 2016 May 05;6:25407
pubmed: 27146250
Sci Rep. 2017 Jul 18;7(1):5780
pubmed: 28720886
Epidemiol Infect. 2010 Dec;138(12):1765-74
pubmed: 20800009
Lancet Infect Dis. 2017 Jul;17(7):716-725
pubmed: 28412150
Lancet. 2018 Nov 10;392(10159):1789-1858
pubmed: 30496104
Nat Commun. 2021 Nov 26;12(1):6923
pubmed: 34836947
Front Public Health. 2021 Jul 22;9:679853
pubmed: 34368054
PLoS Med. 2007 Feb;4(2):e68
pubmed: 17326709
Int J Infect Dis. 2022 Mar;116:7-9
pubmed: 34973415
Proc Natl Acad Sci U S A. 2023 Jan 17;120(3):e2119409120
pubmed: 36623190
J Infect. 2021 Oct;83(4):424-432
pubmed: 34358582
Int J Environ Res Public Health. 2018 Jul 12;15(7):
pubmed: 30002344
Emerg Infect Dis. 2018 Feb;24(2):284-293
pubmed: 29350150
PLoS One. 2015 Sep 30;10(9):e0139109
pubmed: 26422015
Lancet. 2013 Apr 20;381(9875):1405-1416
pubmed: 23582727
Psychol Bull. 1995 Nov;118(3):392-404
pubmed: 7501743
Lancet. 2023 Dec 17;400(10369):2221-2248
pubmed: 36423648
Int J Infect Dis. 2019 Aug;85:188-194
pubmed: 31202907
Emerg Infect Dis. 2022 Aug;28(8):1722-1724
pubmed: 35876603
BMC Public Health. 2020 Jan 8;20(1):25
pubmed: 31914962
Lancet Reg Health West Pac. 2021 Nov;16:100268
pubmed: 34568854
Sci Rep. 2019 Jul 11;9(1):10042
pubmed: 31296895
Lancet Planet Health. 2022 Mar;6(3):e202-e218
pubmed: 35278387
Appl Environ Microbiol. 2016 Jun 30;82(14):4225-4231
pubmed: 27208095
BMJ. 2020 Apr 2;369:m1043
pubmed: 32241761
PLoS Biol. 2020 Nov 24;18(11):e3000938
pubmed: 33232316
Infect Dis Poverty. 2018 Jan 31;7(1):7
pubmed: 29391070
Environ Res. 2019 Jun;173:255-261
pubmed: 30928856
Sci Total Environ. 2019 Jan 15;648:550-560
pubmed: 30121533
BMC Infect Dis. 2019 Sep 2;19(1):766
pubmed: 31477044
BMJ Glob Health. 2018 Jan 26;3(1):e000442
pubmed: 29564154
BMJ. 2021 Feb 26;372:n437
pubmed: 33637488
Int J Hyg Environ Health. 2020 Mar;224:113432
pubmed: 31978729
Nat Commun. 2021 Apr 29;12(1):2464
pubmed: 33927201
Infect Dis Poverty. 2017 Jun 21;6(1):113
pubmed: 28637484
J Health Popul Nutr. 2013 Dec;31(4 Suppl 1):81-97
pubmed: 24992814
PLoS One. 2022 Sep 20;17(9):e0274421
pubmed: 36126038
Crit Rev Food Sci Nutr. 2022;62(15):4010-4035
pubmed: 33455435
Int J Environ Res Public Health. 2017 May 07;14(5):
pubmed: 28481286