Currently accepted at: JMIR Public Health and Surveillance
Date Submitted: Feb 8, 2024
Open Peer Review Period: Feb 9, 2024 - Apr 5, 2024
Date Accepted: May 7, 2024
(closed for review but you can still tweet)
This paper has been accepted and is currently in production.
It will appear shortly on 10.2196/57209
The final accepted version (not copyedited yet) is in this tab.
Pulmonary tuberculosis notification rate in Shenzhen, China during 2010-2019: spatial-temporal analysis
ABSTRACT
Background:
Pulmonary tuberculosis (PTB) is a chronic communicable disease of major public health and social concern. Though spatial-temporal analysis has been widely used to describe distribution characteristics and transmission patterns, few studies have been conducted to reveal the changes of small-scale clustering of PTB at the street level.
Objective:
This study analyzed the temporal and spatial distribution characteristics and clusters of PTB at the street level in Shenzhen municipality to provide reference of PTB prevention and control.
Methods:
A total of 58122 PTB cases from 2010 to 2019 were notified in Shenzhen. The annual notification rate of PTB decreased significantly from 64.97 per 100000 population in 2010 to 43.43 per 100000 population in 2019. PTB cases have shown seasonal variations with the peak in late spring and summer each year. The PTB notification rate was non-randomly distribution and spatially clustering with the Moran’s I value of 0.134 (P<.05). One most likely cluster and ten secondary clusters were detected, and the most likely clustering area was centered at Nanshan Street of Nanshan District covering six streets, with the clustering time from January 2010 to November 2012.
Results:
A total of 58122 PTB cases were notified in Shenzhen. The annual notification rate of PTB decreased significantly from 64.97 per 100000 population in 2010 to 43.43 per 100000 population in 2019. PTB cases have shown seasonal variations with the peak in late spring and summer each year. The PTB notification rate was non-randomly distribution and spatially clustering with the Moran’s I value of 0.134 (P<0.05). One most likely cluster and ten secondary clusters were detected, and the most likely clustering area was centered at Nanshan Street of Nanshan District covering six streets, with the clustering time from January 2010 to November 2012.
Conclusions:
This study identified seasonal patterns and spatial-temporal clusters at the street level in Shenzhen municipality. Resources should be prioritized to high-risk areas for PTB prevention and control.
Citation
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Copyright
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