Local spatial variations analysis of smear-positive tuberculosis in Xinjiang using Geographically Weighted Regression model

Abstract

Background: The spatial interplay between socioeconomic factors and tuberculosis (TB) cases contributes to the understanding of regional tuberculosis burdens. Historically, local Poisson Geographically Weighted Regression (GWR) has allowed for the identification of the geographic disparities of TB cases and their relevant socioeconomic determinants, thereby forecasting local regression coefficients for the relations between the incidence of TB and its socioeconomic determinants. Therefore, the aims of this study were to: (1) identify the socioeconomic determinants of geographic disparities of smear positive TB in Xinjiang, China (2) confirm if the incidence of smear positive TB and its associated socioeconomic determinants demonstrate spatial variability (3) compare the performance of two main models: one is Ordinary Least Square Regression (OLS), and the other local GWR model. Methods: Reported smear-positive TB cases in Xinjiang were extracted from the TB surveillance system database during 2004-2010. The average number of smear-positive TB cases notified in Xinjiang was collected from 98 districts/counties. The population density (POPden), proportion of minorities (PROmin), number of infectious disease network reporting agencies (NUMagen), proportion of agricultural population (PROagr), and per capita annual gross domestic product (per capita GDP) were gathered from the Xinjiang Statistical Yearbook covering a period from 2004 to 2010. The OLS model and GWR model were then utilized to investigate socioeconomic determinants of smear-positive TB cases. Geoda 1.6.7, and GWR 4.0 software were used for data analysis. Results: Our findings indicate that the relations between the average number of smear-positive TB cases notified in Xinjiang and their socioeconomic determinants (POPden, PROmin, NUMagen, PROagr, and per capita GDP) were significantly spatially non-stationary. This means that in some areas more smear-positive TB cases could be related to higher socioeconomic determinant regression coefficients, but in some areas more smear-positive TB cases were found to do with lower socioeconomic determinant regression coefficients. We also found out that the GWR model could be better exploited to geographically differentiate the relationships between the average number of smear-positive TB cases and their socioeconomic determinants, which could interpret the dataset better (adjusted R 2 = 0.912, AICc = 1107.22) than the OLS model (adjusted R 2 = 0.768, AICc = 1196.74). Conclusions: POPden, PROmin, NUMagen, PROagr, and per capita GDP are socioeconomic determinants of smear-positive TB cases. Comprehending the spatial heterogeneity of POPden, PROmin, NUMagen, PROagr, per capita GDP, and smear-positive TB cases could provide valuable information for TB precaution and control strategies.