Descriptive analysis of SS + PTB cases
A total of 2 380 233 SS+PTB cases were reported in China between 2011 and 2017, of which, 1 716 382 (72.11%) were male and 663 851 (27.89%) were female. The notification rate of SS+PTB decreased from 29.82 cases per 100 000 population in 2011 to 16.78 cases per 100 000 population in 2017, with an annual average rate of 21.42 per 100 000 population. Table 2 shows that the number of male cases was twice that of female cases. In addition, a significant proportion of the SS + PTB infections were aged > 60 years old (33.92%) and between 45 and 60 years old (27.35%). Among the reported cases, around two-thirds were peasants; the percentage of SS + PTB cases that were classified as retired or unemployed increased over the years of the study.
Table 3 shows the characteristics of internal migrants from 2011 to 2017. The sample consisted of 1 231 277 internal migrants, 53.62% of whom were male and 46.38% were female. Further, 78.52% of migrants were married, 84.53% had at least a middle school education, 76.11% had a monthly household per capita income of less than 7000 CNY (around 1000 US $), and 61.14% only had rural medical insurance. In addition, a significant proportion of the migrants were from rural households (84.53%). Among the total sample of internal migrants, 51.19% of whom were migrated across provinces and 30.73% migrated across municipal jurisdictions within a province. Over 85% of internal migrants had left their place of household registration for work or business purposes. Other reasons for migration included study and training, which only accounted for around 15% of internal migrants.
Figure 2 shows the spatial distribution of the annual average notification rate of SS + PTB and the proportions of internal emigrants and immigrants in China at the provincial level from 2011 to 2017. There were obvious spatial variations in the annual average notification rate of SS + PTB, with rates ranging from 8.37 to 41.38 per 100 000 population. The highest SS + PTB notification rates were found in Xinjiang, Qinghai, Hubei, Hunan, Jiangxi, and Guizhou provinces, primarily in the northwest and south of China.
Sichuan (10.59%), Fujian (9.81%), Anhui (9.19%), and Hubei (9.17%) provinces had the highest levels of internal emigrants. On the other hand, provinces with the highest levels of internal immigrants were located in eastern regions, such as Shanghai (58.31%), Beijing (51.99%), Tianjin (27.28%), Zhejiang (25.46%), and Guangdong (22.02%) provinces. Provinces with lower levels of immigrants were also located in southern areas close to Guangdong, Zhejiang, and Shanghai. However, those provinces had higher levels of internal emigrants.
Global and local spatial autocorrelations
The global Moran’s I statistics showed positive spatial autocorrelations in SS + PTB in China each year (as presented in Table 4). Further, there was an increasing trend in global Moran’s I and Z-scores. The highest spatial autocorrelations were observed in 2013–2017, ranging from 0.384 to 0.413. Furthermore, the proportions of internal emigrants and immigrants were also spatially auto-correlated each year (see Table 5).
Figures 3 and 4 show the local Moran’s I statistic results. Stability of spatial clusters was observed each year during the study period, and the clusters were stable within most provinces. Provinces such as Shaanxi, Henan, Chongqing, Guizhou, and Hubei showed a low-low type of relationship, indicating that these provinces had a low proportion of internal immigrants and the surrounding provinces also had low proportions of immigrants. Jiangsu province, which is located on the southeast coast of China, had a low-high type of relationship, meaning that a low proportion of immigrants were found in Jiangsu while the surrounding provinces had high proportions of immigrants. Anhui, Jiangxi, Chongqing, Shaanxi, Guizhou, Henan, Hubei, and Zhejiang exhibited high-high types of relationships in the proportion of internal emigrants.
Spatial variation in temporal trends
The spatial variation in temporal trend results showed that there was an 9.9% average annual decrease in the notification rate of SS + PTB from 2011 to 2017. One most likely cluster and seven secondary clusters were identified during the study period; one municipality showed increasing annual trends while 12 provinces/municipalities showed slower decreasing annual trends compared to the outside time trend (see Table 6). Ningxia showed an increasing annual average trend of 0.937%. Fujian, Zhejiang, Jiangxi, and Shanghai showed decreasing annual average trends of 3.846% compared to the outside time trend (10.818% annual decrease). Guizhou, Beijing, Tianjing, Jiangsu, Xinjiang, Tibet, Hainan and Guangxi showed decreasing annual average trends of 3.869%, 2.692%, 5.890%, 5.787%, 3.188%, and 9.327%, respectively. Figure 5 shows the spatial distribution of the most likely and secondary clusters. Most clusters were located in the southern provinces of China; although, Xinjiang, Ningxia, and Tibet are in west China and Beijing is in northeast China.
12.86% and 5.64% average annual decrease were found in the notification rate of SS + PTB from 2011 to 2013, 2014 to 2017. Beijing and Shaanxi showed an increasing annual average trend of 5.001% and 11.396% (see Table S1). 8 provinces/municipalities were located in western China; 5 provinces/municipalities were located in eastern China (see Figure S1)
The association between internal migration and SS + PTB
The fixed-effect and spatial autoregressive models were examined: one was the fixed-effect model (model 1 and 4), one was the spatial autoregressive model with continuity weights matrix (model 2 and 5), one was the spatial autoregressive model with distance decay weights matrix (model 3 and 6). The panel regression results indicated that POE, GDP per capita, population density, education level, and the ratio of males to females were significantly associated with the incidence of SS + PTB (see Table 7). Furthermore, population density and GDP per capita were significantly positively related to SS + PTB while the POE, education level and the ratio of males to females were significantly negatively related to SS + PTB. While POR was not significantly related to SS + PTB in model 4-6, POU was significantly negatively related to SS + PTB in model 6, and neither POR nor POU were significantly associated with the SS + PTB in model 4 and 5. Moreover, neither POE nor POI were significantly associated with the SS + PTB in model S1-S3, and model S1 had the highest R-square value.
Internal migration flow maps
Based on the SS + PTB spatial cluster results and panel data analysis, the most likely cluster and the six secondary clusters were chosen to produce internal migration flow maps. Among these clusters, Guangdong, Beijing, Shanghai, Fujian, Jiangsu, and Zhejiang are developed and prosperous provinces, while Guizhou and Jiangxi are located in southern China, near Guangdong, Fujian, and Zhejiang provinces, which have large immigrant populations. The proportion of emigrants was significantly higher than the proportion of immigrants in Guizhou (POE: 6.32% vs POI: 3.09%) and Jiangxi (POE: 5.08% vs POI: 1.42%). In contrast, the proportion of immigrants was obviously higher than the proportions of emigrants in Guangdong (POI: 22.02% vs POE: 2.59%), Beijing (POI: 51.99% vs POE: 0.78%), Shanghai (POI: 58.31% vs POE: 0.69%), Fujian (POI: 14.31% vs POE: 9.81%), Jiangsu (POI: 10.8% vs POE: 5.14%), and Zhejiang (POI: 25.46% vs POE: 7.6%).
Figure 6 shows the migration flows of internal migrants for the eight spatial clusters. The highest proportion of immigrants from Hebei (22.04%) flowed into Beijing, with immigrants from other spatial clusters accounting for 16.49% of all immigrants. Similarly, the highest proportion of immigrants from Anhui (29.96%) flowed into Shanghai, with the other spatial clusters accounting for 33.58% of immigrants. The highest proportion of immigrants from Anhui (21.55%) flowed into Zhejiang, with the other spatial clusters accounting for 28.92% of immigrants. The highest portion of immigrants from Hunan (21.87%) flowed into Guangdong, with immigrants from the other spatial clusters accounting for 20.57% immigrants. The highest proportion of immigrants from Anhui (38.75%) flowed into Jiangsu, with other spatial clusters accounting for 11.10% of immigrants. The highest proportion of immigrants from Sichuan (20.82%) flowed into Fujian, with other spatial clusters accounting for 31.22% of immigrants. In contrast, 37.91% and 13.25% of the emigrants in Guizhou flowed into Zhejiang and Guangdong, respectively. We also found that 25.63% and 15.04% of the emigrants in Jiangxi flowed into Zhejiang and Guangdong, respectively.