RESUMO
As a southwestern province of China, Sichuan is confronted with geographical disparities in access to healthcare professionals because of its complex terrain, uneven population distribution and huge economic gaps between regions. With 10-year data, this study aims to explore the county-level spatial disparities in access to different types of healthcare professionals (licensed doctors, registered nurses, pharmacists, technologists and interns) in Sichuan using temporal and spatial analysis methods. The time-series results showed that the quantity of all types of healthcare professionals increased, especially the registered nurses, while huge spatial disparities exist in the distribution of healthcare professionals in Sichuan. The local Moran's I calculations showed that high-high clusters (significantly high healthcare professional quantity in a group of counties) were detected in Chengdu (capital of Sichuan) and relatively rich areas, while low-low clusters (significantly low healthcare professional quantity in a group of counties) were usually found near the mountain areas, namely, Tsinling Mountains and Hengduan Mountains. The findings may deserve considerations in making region-oriented policies in educating and attracting more healthcare professionals to the disadvantaged areas.
RESUMO
In this paper, a sequential change point detection method is developed to monitor structural change in smoothly clipped absolute deviation (SCAD) penalized quantile regression (SPQR) models. The asymptotic properties of the test statistic are derived from the null and alternative hypotheses. In order to improve the performance of the SPQR method, we propose a post-SCAD penalized quantile regression estimator (P-SPQR) for high-dimensional data. We examined the finite sample properties of the proposed methods via Monte Carlo studies under different scenarios. A real data application is provided to demonstrate the effectiveness of the method.