Background
Methods
Materials
Bayesian space-time hierarchical model
Spatial and temporal matching OR index
Bayesian geo-detector model
Results
Descriptive statistics
Spatiotemporal trends
Overall spatial trends
Local temporal trends
Spatial and temporal matching between PAR and NMRTR
Overall analysis
The number of licensed certified physicians per thousand residents (NLCPTR) (95%CI) | The number of registered nurses per thousand residents (NRNTR) (95%CI) | The number of beds in hospital per thousand residents (NBHTR) (95%CI) | |
---|---|---|---|
Spatial | 0.16 (0.12, 0.19) | 0.15 (0.12, 0.18) | 0.42 (0.38, 0.49) |
Temporal | 0.22 (0.16, 0.28) | 0.17 (0.13, 0.21) | 0.37 (0.30, 0.44) |
Provincial analysis
Provincial regions | Spatial matching OR between PAR and NLCPTR (95% CI) | Spatial matching OR between PAR and NRNTR (95% CI) | Spatial matching OR between PAR and NBHTR (95% CI) |
---|---|---|---|
Beijing | 0.50 (0.48, 0.52) | 0.41 (0.39, 0.42) | 0.72 (0.69, 0.74) |
Tianjin | 0.91 (0.87, 0.94) | 0.93 (0.87, 0.98) | 1.10 (1.06, 1.14) |
Hebei | 1.08 (1.04, 1.13) | 1.31 (1.21, 1.42) | 1.12 (1.08, 1.17) |
Shanxi | 0.72 (0.69, 0.75) | 0.85 (0.79, 0.91) | 0.84 (0.80, 0.88) |
Inner Mongolia | 0.73 (0.70, 0.76) | 0.91 (0.85, 0.97) | 0.88 (0.84, 0.92) |
Liaoning | 1.00 (0.97, 1.04) | 1.03 (0.97, 1.09) | 0.92 (0.89, 0.95) |
Jilin | 0.85 (0.82, 0.88) | 1.03 (0.96, 1.10) | 0.89 (0.86, 0.92) |
Heilongjiang | 0.97 (0.93, 1.01) | 1.07 (1.00, 1.15) | 0.86 (0.83, 0.90) |
Shanghai | 0.86 (0.83, 0.89) | 0.64 (0.61, 0.67) | 0.96 (0.93, 1.00) |
Jiangsu | 1.25 (1.20, 1.29) | 1.20 (1.13, 1.27) | 1.28 (1.24, 1.32) |
Zhejiang | 0.87 (0.84, 0.90) | 0.86 (0.82, 0.91) | 1.02 (0.99, 1.06) |
Anhui | 1.59 (1.52, 1.65) | 1.50 (1.38, 1.62) | 1.42 (1.37, 1.48) |
Fujian | 1.04 (0.99, 1.09) | 0.95 (0.89, 1.02) | 1.13 (1.08, 1.18) |
Jiangxi | 1.21 (1.16, 1.27) | 1.13 (1.04, 1.23) | 1.28 (1.23, 1.34) |
Shandong | 1.07 (1.03, 1.12) | 1.08 (1.01, 1.15) | 1.16 (1.11, 1.20) |
Henan | 1.24 (1.18, 1.30) | 1.17 (1.08, 1.26) | 1.05 (1.00, 1.10) |
Hubei | 1.14 (1.10, 1.18) | 1.06 (1.00, 1.14) | 1.16 (1.12, 1.20) |
Hunan | 1.38 (1.33, 1.43) | 1.34 (1.24, 1.44) | 1.25 (1.20, 1.29) |
Guangdong | 0.86 (0.82, 0.91) | 0.70 (0.65, 0.75) | 0.96 (0.91, 1.02) |
Guangxi | 1.26 (1.21, 1.32) | 1.11 (1.03, 1.19) | 1.39 (1.33, 1.45) |
Hainan | 0.98 (0.93, 1.03) | 0.75 (0.70, 0.80) | 1.06 (1.01, 1.11) |
Chongqing | 1.67 (1.61, 1.74) | 1.63 (1.51, 1.76) | 1.45 (1.40, 1.50) |
Sichuan | 1.40 (1.35, 1.45) | 1.55 (1.45, 1.68) | 1.36 (1.32, 1.41) |
Guizhou | 1.37 (1.31, 1.44) | 1.33 (1.23, 1.46) | 1.07 (1.03, 1.12) |
Yunnan | 1.15 (1.09, 1.21) | 1.14 (1.04, 1.24) | 0.92 (0.88, 0.96) |
Tibet | 0.83 (0.78, 0.88) | 1.38 (1.21, 1.60) | 0.84 (0.79, 0.90) |
Shaanxi | 1.10 (1.05, 1.14) | 0.96 (0.90, 1.02) | 0.96 (0.92, 0.99) |
Gansu | 1.16 (1.11, 1.22) | 1.28 (1.19, 1.40) | 1.04 (0.99, 1.08) |
Qinghai | 0.70 (0.66, 0.74) | 0.76 (0.71, 0.82) | 0.64 (0.61, 0.67) |
Ningxia | 0.65 (0.62, 0.68) | 0.71 (0.66, 0.76) | 0.62 (0.60, 0.66) |
Xinjiang | 0.65 (0.61, 0.68) | 0.60 (0.56, 0.64) | 0.52 (0.49, 0.54) |
Provincial regions | Temporal matching OR between PAR and NLCPTR (95% CI) | Temporal matching OR between PAR and NRNTR (95% CI) | Temporal matching OR between PAR and NBHTR (95% CI) |
---|---|---|---|
Beijing | 0.89 (0.84, 0.94) | 0.60 (0.56, 0.65) | 0.71 (0.67, 0.76) |
Tianjin | 0.94 (0.88, 0.99) | 0.71 (0.64, 0.78) | 0.82 (0.77, 0.87) |
Hebei | 0.96 (0.89, 1.02) | 0.92 (0.81, 1.05) | 0.91 (0.85, 0.97) |
Shanxi | 0.88 (0.82, 0.94) | 0.93 (0.83, 1.04) | 0.87 (0.81, 0.94) |
Inner Mongolia | 0.83 (0.78, 0.89) | 0.96 (0.86, 1.07) | 0.94 (0.88, 1.01) |
Liaoning | 0.88 (0.83, 0.93) | 0.82 (0.74, 0.91) | 0.91 (0.86, 0.96) |
Jilin | 0.83 (0.78, 0.88) | 0.83 (0.74, 0.92) | 0.83 (0.77, 0.88) |
Heilongjiang | 0.82 (0.77, 0.88) | 0.82 (0.73, 0.93) | 0.87 (0.82, 0.93) |
Shanghai | 0.88 (0.82, 0.93) | 0.60 (0.55, 0.64) | 0.78 (0.74, 0.83) |
Jiangsu | 1.06 (1.01, 1.12) | 1.01 (0.91, 1.12) | 1.05 (1.00, 1.11) |
Zhejiang | 1.06 (1.00, 1.13) | 0.95 (0.87, 1.04) | 1.02 (0.95, 1.08) |
Anhui | 1.05 (0.98, 1.12) | 1.08 (0.96, 1.22) | 1.06 (0.99, 1.12) |
Fujian | 1.14 (1.06, 1.22) | 1.10 (1.00, 1.23) | 1.09 (1.02, 1.17) |
Jiangxi | 0.97 (0.90, 1.05) | 1.01 (0.90, 1.16) | 1.07 (0.99, 1.15) |
Shandong | 1.00 (0.94, 1.06) | 0.99 (0.90, 1.10) | 0.93 (0.88, 0.99) |
Henan | 1.03 (0.96, 1.11) | 1.12 (0.99, 1.26) | 1.03 (0.96, 1.10) |
Hubei | 1.03 (0.97, 1.09) | 1.13 (1.02, 1.25) | 1.10 (1.03, 1.17) |
Hunan | 1.11 (1.04, 1.19) | 1.13 (1.01, 1.27) | 1.18 (1.11, 1.25) |
Guangdong | 1.08 (1.00, 1.17) | 0.92 (0.82, 1.02) | 1.03 (0.95, 1.11) |
Guangxi | 1.12 (1.04, 1.20) | 1.21 (1.09, 1.36) | 1.11 (1.03, 1.19) |
Hainan | 1.15 (1.06, 1.26) | 1.10 (0.98, 1.23) | 1.12 (1.03, 1.21) |
Chongqing | 1.06 (0.99, 1.12) | 1.26 (1.12, 1.41) | 1.17 (1.11, 1.24) |
Sichuan | 1.02 (0.97, 1.08) | 1.22 (1.09, 1.37) | 1.17 (1.10, 1.23) |
Guizhou | 1.18 (1.10, 1.27) | 1.47 (1.29, 1.69) | 1.32 (1.24, 1.41) |
Yunnan | 1.08 (1.01, 1.18) | 1.33 (1.17, 1.54) | 1.12 (1.04, 1.20) |
Tibet | 1.18 (1.07, 1.30) | 1.21 (0.99, 1.47) | 1.20 (1.10, 1.32) |
Shaanxi | 1.00 (0.94, 1.07) | 1.14 (1.04, 1.27) | 1.03 (0.97, 1.10) |
Gansu | 1.00 (0.93, 1.08) | 1.07 (0.94, 1.20) | 1.00 (0.93, 1.07) |
Qinghai | 1.02 (0.94, 1.11) | 1.03 (0.91, 1.17) | 1.08 (1.00, 1.17) |
Ningxia | 0.96 (0.88, 1.04) | 1.01 (0.90, 1.15) | 0.91 (0.84, 0.99) |
Xinjiang | 0.96 (0.88, 1.04) | 0.94 (0.84, 1.05) | 0.89 (0.82, 0.96) |