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• W2345883982 abstract "Tuberculosis (TB) is the notifiable infectious disease with the second highest incidence in the Qinghai province, a province with poor primary health care infrastructure. Understanding the spatial distribution of TB and related environmental factors is necessary for developing effective strategies to control and further eliminate TB.Our TB incidence data and meteorological data were extracted from the China Information System of Disease Control and Prevention and statistical yearbooks, respectively. We calculated the global and local Moran's I by using spatial autocorrelation analysis to detect the spatial clustering of TB incidence each year. A spatial panel data model was applied to examine the associations of meteorological factors with TB incidence after adjustment of spatial individual effects and spatial autocorrelation.The Local Moran's I method detected 11 counties with a significantly high-high spatial clustering (average annual incidence: 294/100 000) and 17 counties with a significantly low-low spatial clustering (average annual incidence: 68/100 000) of TB annual incidence within the examined five-year period; the global Moran's I values ranged from 0.40 to 0.58 (all P-values < 0.05). The TB incidence was positively associated with the temperature, precipitation, and wind speed (all P-values < 0.05), which were confirmed by the spatial panel data model. Each 10 °C, 2 cm, and 1 m/s increase in temperature, precipitation, and wind speed associated with 9 % and 3 % decrements and a 7 % increment in the TB incidence, respectively.High TB incidence areas were mainly concentrated in south-western Qinghai, while low TB incidence areas clustered in eastern and north-western Qinghai. Areas with low temperature and precipitation and with strong wind speeds tended to have higher TB incidences." @default.
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• W2345883982 date "2016-06-02" @default.
• W2345883982 modified "2023-10-15" @default.
• W2345883982 title "Spatial transmission and meteorological determinants of tuberculosis incidence in Qinghai Province, China: a spatial clustering panel analysis" @default.
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• W2345883982 doi "https://doi.org/10.1186/s40249-016-0139-4" @default.
• W2345883982 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4890510" @default.
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