Spatial epidemiology and spatial ecology study of worldwide drug-resistant tuberculosis

Drug-resistant tuberculosis (DR-TB) is a major public health problem caused by various factors. It is essential to systematically investigate the epidemiological and, in particular, the ecological factors of DR-TB for its prevention and control. Studies of the ecological factors can provide information on etiology, and assist in the effective prevention and control of disease. So it is of great significance for public health to explore the ecological factors of DR-TB, which can provide guidance for formulating regional prevention and control strategies.

Anti-TB drug resistance data were obtained from the World Health Organization/International Union Against Tuberculosis and Lung Disease (WHO/UNION) Global Project on Anti-Tuberculosis Drug Resistance Surveillance, and data on ecological factors were collected to explore the ecological factors for DR-TB. Partial least square path modeling (PLS-PM), in combination with ordinary least squares (OLS) regression, as well as geographically weighted regression (GWR), were used to build a global and local spatial regression model between the latent synthetic DR-TB factor ("DR-TB") and latent synthetic risk factors.

OLS regression and PLS-PM indicated a significant globally linear spatial association between "DR-TB" and its latent synthetic risk factors. However, the GWR model showed marked spatial variability across the study regions. The "TB Epidemic", "Health Service" and "DOTS (directly-observed treatment strategy) Effect" factors were all positively related to "DR-TB" in most regions of the world, while "Health Expenditure" and "Temperature" factors were negatively related in most areas of the world, and the "Humidity" factor had a negative influence on "DR-TB" in all regions of the world.

In summary, the influences of the latent synthetic risk factors on DR-TB presented spatial variability. We should formulate regional DR-TB monitoring planning and prevention and control strategies, based on the spatial characteristics of the latent synthetic risk factors and spatial variability of the local relationship between DR-TB and latent synthetic risk factors.