Spatial transmission and meteorological determinants of tuberculosis incidence in Qinghai Province, China: a spatial clustering panel analysis

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Keywords: Meteorological factors; Spatial clustering; Spatial panel data model; Tuberculosis incidence.

Fig. 1

Location of the study areas, Qinghai Province, China. The map was created using the ArcGIS software (version 10.0, ESRI Inc., Redlands, CA, USA)

Fig. 2

Annual incidence of tuberculosis in Qinghai Province, China, 2009-2013. The areas of high annual incidence of TB were mainly concentrated in south-western Qinghai, with the top three counties being Maduo, Jiuzhi, and Zaduo

Fig. 3

Monthly incidence rates of TB in Qinghai Province, China, from January 2009 to December 2013. The TB incidence rate showed significant periodicity and seasonality, reaching a seasonal peak around April and decreasing to a trough in December. TB, tuberculosis

Fig. 4

Moran scatter plot for the annual incidence of tuberculosis in Qinghai Province, China, 2009-2013. The horizontal axis shows the standardized incidence of the counties, and the vertical axis indicates the spatial lag factors; the linear slope is the Moran’s I

Fig. 5

LISA significance map and cluster map for annual tuberculosis incidence in Qinghai Province, China, 2009-2013. The high risk areas were mainly concentrated in the cities of Yushu and Guoluo, while the low incidence districts were mainly distributed in the cities of Xining and Haixi. LISA, local indicators of spatial association