Determining the effects of social-environmental factors on the incidence and mortality of lung cancer in China based on remote sensing and GIS technology during 2007-2016

Abstract

Background: Lung cancer is the leading cause of cancer-related death in China. However, its relationship with social-environmental factors has not been revealed comprehensively. We are the first group to determine cold and hot spots associated with the incidence and mortality of lung cancer (IMLC) in both females and males and their spatiotemporal changes and to explore the social‒environmental burden of lung cancer in China between 2007 and 2016.

Methods: The explanatory powers of various social-environmental factors for the IMLC were evaluated through correlation analysis and the Geodetector tool. Spatial analysis models were applied to determine the relationships between the IMLC and social-environmental factors.

Results: The results are as follows: (1) The distribution of the IMLC exhibited significant spatial heterogeneity; the Global Moran's index values for incidence ranged from 0.04-0.2 and 0.09-0.33 in males and females, respectively, and the values for mortality ranged from 0.01-0.12 and 0.11-0.32 in males and females, respectively. (2) The IMLC was spatially clustered with an overall positive autocorrelation. Male population-related hot spots were observed in the central-southern region of China, and cold spots were observed in western China. Female population-related hot spots were observed primarily in northeastern China. The cold spots occurred primarily in southern and some western regions of China. (3) The effects of social-environmental factors on the IMLC showed significant spatial and temporal variability: in males, the interaction between terrain undulation and road area exhibited the highest explanatory power for the incidence and mortality, with a value of 0.22 for both; in females, the interaction between O₃ and road area and the interaction between O₃ and the number of medical beds exhibited the highest explanatory powers for the incidence and mortality, reaching 0.27 and 0.34, respectively. (4) The optimal model capturing the relationships between the IMLC and social-environmental factors was the GTWR model, which relies on reclassified data. The best R² value is 0.456.

Conclusions: The influence of each social‒environmental factor on the IMLC showed significant spatiotemporal variability, providing a systematic basis for governments to implement better targeted control of lung cancer.

Keywords: Cold and hot spots; GTWR; GWR; Lung cancer; OLS; Social–environmental factors; Spatial autocorrelation; Spatiotemporal moving trajectory; Standard deviation ellipse.

Fig. 1

Workflow of the whole study

Fig. 2

Study area and the distribution of tumor registries in 2016

Fig. 3

Temporal trends of the annual age-standardized IMLC (per 100, 000 population) nationwide from 2007 to 2016

Fig. 4

The IMLC in both males and females by province in 2016. The number in parenthesis indicates the number of tumor registries in each province. A incidence of lung cancer in males, (B) incidence of lung cancer in females, (C) mortality of lung cancer in males, (D) mortality of lung cancer in females

Fig. 5

The distribution of the cold and hot spots of IMLC in both males and females in 2016 and the moving trajectories of the centers of gravity of the cold and hot spots between 2012 to 2016. A incidence in males, (B) incidence in females, (C) mortality in males, (D) mortality in females. NM: Nei Mongol, TJ: Tianjin, NX: Ningxia, SX: Shanxi, HeB: Hebei, SD: Shandong, GS: Gansu, ShX: Shaanxi, HeN: Henan, JS: Jiangsu, SC: Sichuan, HuB: Hubei, AH: Anhui, SH: Shanghai, CQ: Chongqing, HuN: Hunan, JX: Jiangxi, FJ: Fujian, ZJ: Zhejiang, GZ: Guizhou, LN: Liaoning, BJ: Beijing

Fig. 6

Temporal trends of 15 social–environmental factors between 2007 to 2016. A PM_2.5, (B) PM₁₀, (C) SO₂, (D) NO₂, (E) O₃, (F) NDVI, the number of days with (G) blowing sand (BS), (H) floating dust (FD), (I) haze, (J) sandstorms, (K) annual average temperature (TEMP), (L) the number of hospitals, (M) the number of medical beds (MedBeds), (N) the number of doctors, and (O) road area

Fig. 7

Spatial distributions pattern of social–environmental factors, including concentrations of (A) PM_2.5, (B) PM₁₀, (C) SO₂, (D) NO₂ and (E) O₃, (F) NDVI, the number of days with (G) blowing sand (BS), (H) floating dust (FD), (I) haze and (J) sandstorms, (K) annual average temperature (TEMP), (L) economic density (ED), (M) population density, (N) terrain undulation (TU), (O) the number of hospitals, (P) the number of medical beds (MedBeds), (Q) the number of doctors, (R) road area, (S) geomorphic types (GT), (T) types of land uses (LT)

Fig. 8

Correlation coefficients between each social–environmental factor and the IMLC (A), as well as between the social–environmental factors themselves (B) based on the reclassified data by Person’s correlation analysis

Fig. 9

Results of interaction detection between the social–environmental factors for (A) the incidence in males, (B) the incidence in females, (C) the mortality in males, and (D) the mortality in females. All the values are listed in Supplementary Tables 1–4

Fig. 10

Distribution of coefficients of factors in GTWR model. Model coefficients of (A) terrain undulation (TU) and (B) road area for the incidence in males, (C) O₃ and (D) road area for the incidence in females, (E) TU and (F) road area for the mortality in males, (G) O₃ and (H) the number of medical beds (MedBeds) for the mortality in females