Geographically Weighted Regression Modeling of Spatial Clustering and Determinants of Focal Typhoid Fever Incidence.
SaTScan clusters
geographically weighted regression
hotspots
spatial autocorrelation
typhoid incidence
Journal
The Journal of infectious diseases
ISSN: 1537-6613
Titre abrégé: J Infect Dis
Pays: United States
ID NLM: 0413675
Informations de publication
Date de publication:
23 11 2021
23 11 2021
Historique:
received:
08
07
2021
accepted:
16
07
2021
entrez:
3
3
2022
pubmed:
4
3
2022
medline:
10
5
2022
Statut:
ppublish
Résumé
Typhoid is known to be heterogenous in time and space, with documented spatiotemporal clustering and hotspots associated with environmental factors. This analysis evaluated spatial clustering of typhoid and modeled incidence rates of typhoid from active surveillance at 4 sites with child cohorts in India. Among approximately 24 000 children aged 0.5-15 years followed for 2 years, typhoid was confirmed by blood culture in all children with fever >3 days. Local hotspots for incident typhoid cases were assessed using SaTScan spatial cluster detection. Incidence of typhoid was modeled with sociodemographic and water, sanitation, and hygiene-related factors in smaller grids using nonspatial and spatial regression analyses. Hotspot households for typhoid were identified at Vellore and Kolkata. There were 4 significant SaTScan clusters (P < .05) for typhoid in Vellore. Mean incidence of typhoid was 0.004 per child-year with the highest incidence (0.526 per child-year) in Kolkata. Unsafe water and poor sanitation were positively associated with typhoid in Kolkata and Delhi, whereas drinking untreated water was significantly associated in Vellore (P = .0342) and Delhi (P = .0188). Despite decades of efforts to improve water and sanitation by the Indian government, environmental factors continue to influence the incidence of typhoid. Hence, administration of the conjugate vaccine may be essential even as efforts to improve water and sanitation continue.
Sections du résumé
BACKGROUND
Typhoid is known to be heterogenous in time and space, with documented spatiotemporal clustering and hotspots associated with environmental factors. This analysis evaluated spatial clustering of typhoid and modeled incidence rates of typhoid from active surveillance at 4 sites with child cohorts in India.
METHODS
Among approximately 24 000 children aged 0.5-15 years followed for 2 years, typhoid was confirmed by blood culture in all children with fever >3 days. Local hotspots for incident typhoid cases were assessed using SaTScan spatial cluster detection. Incidence of typhoid was modeled with sociodemographic and water, sanitation, and hygiene-related factors in smaller grids using nonspatial and spatial regression analyses.
RESULTS
Hotspot households for typhoid were identified at Vellore and Kolkata. There were 4 significant SaTScan clusters (P < .05) for typhoid in Vellore. Mean incidence of typhoid was 0.004 per child-year with the highest incidence (0.526 per child-year) in Kolkata. Unsafe water and poor sanitation were positively associated with typhoid in Kolkata and Delhi, whereas drinking untreated water was significantly associated in Vellore (P = .0342) and Delhi (P = .0188).
CONCLUSIONS
Despite decades of efforts to improve water and sanitation by the Indian government, environmental factors continue to influence the incidence of typhoid. Hence, administration of the conjugate vaccine may be essential even as efforts to improve water and sanitation continue.
Identifiants
pubmed: 35238357
pii: 6433800
doi: 10.1093/infdis/jiab379
pmc: PMC8892548
doi:
Substances chimiques
Typhoid-Paratyphoid Vaccines
0
Water
059QF0KO0R
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
S601-S611Subventions
Organisme : FIC NIH HHS
ID : D43 TW007392
Pays : United States
Informations de copyright
© The Author(s) 2021. Published by Oxford University Press for the Infectious Diseases Society of America.
Références
Pan Afr Med J. 2017 Oct 27;28:179
pubmed: 29541325
Epidemiol Infect. 1998 Mar;120(2):129-38
pubmed: 9593481
Int J Health Geogr. 2006 Mar 27;5:13
pubmed: 16566830
PLoS Negl Trop Dis. 2016 Jun 22;10(6):e0004785
pubmed: 27331909
BMC Public Health. 2007 Oct 12;7:289
pubmed: 17935611
PLoS One. 2018 Nov 29;13(11):e0208006
pubmed: 30496258
Zhonghua Liu Xing Bing Xue Za Zhi. 2017 Nov 10;38(11):1504-1508
pubmed: 29141338
BMC Public Health. 2016 Aug 22;16(1):849
pubmed: 27549095
JAMA. 2004 Jun 2;291(21):2607-15
pubmed: 15173152
Lancet. 2015 Mar 21;385(9973):1136-45
pubmed: 25458731
Int J Health Geogr. 2004 Jun 4;3(1):11
pubmed: 15176979
Health Sci Rep. 2020 Jul 27;3(3):e178
pubmed: 32728636
PLoS One. 2015 Dec 07;10(12):e0144010
pubmed: 26641642
PLoS Negl Trop Dis. 2016 Apr 15;10(4):e0004616
pubmed: 27082958
Soc Sci Med. 1996 Mar;42(6):843-55
pubmed: 8778997
PLoS Negl Trop Dis. 2013;7(1):e1998
pubmed: 23359825
Indian J Med Res. 2012 Nov;136(5):776-82
pubmed: 23287124
Rev Soc Bras Med Trop. 2016 Feb;49(1):74-82
pubmed: 27163567
BMC Infect Dis. 2016 Dec 5;16(1):732
pubmed: 27919235
Bull World Health Organ. 1985;63(5):899-904
pubmed: 3879201
BMC Public Health. 2018 May 3;18(1):594
pubmed: 29724223
Clin Infect Dis. 2019 Mar 7;68(Suppl 2):S105-S116
pubmed: 30845336
Malar J. 2012 Mar 05;11:63
pubmed: 22390636
Trop Med Int Health. 2001 Jun;6(6):484-90
pubmed: 11422963
Parasit Vectors. 2018 Jan 4;11(1):9
pubmed: 29301546
J Epidemiol Community Health. 2013 Nov 1;67(11):974-8
pubmed: 23963506
Trans R Soc Trop Med Hyg. 2010 Sep;104(9):601-12
pubmed: 20638091
Int J Health Geogr. 2013 Mar 16;12:13
pubmed: 23497202