The prevalence of gastric cancer in GansuAge

A total of 75,522 gastric cancer patients with complete age information were ultimately included in our study. The mean age was 62.4 ± 10.8 years old and the median age was 63 years old, with most falling into the middle-aged to elderly range (50–70 years old). Fig. 1 displays the age distribution of these patients. As shown in Fig. 2, both mean and median ages varied across different years and exhibited an increasing trend from 2013 to 2021. In addition, we found that the proportion of gastric cancer patients whose age was younger than 50 years old and older than 65 years old increased, and the proportion of gastric cancer patients whose age ranged between 50 and 64 decreased in recent years.

Fig. 1

The age composition of patients included

Fig. 2

The mean age, median age and the trend of age proportion of patients included (A, mean age and median age. B, the trend of age proportion.)

Gender

A total of 74,906 patients with complete gender information were included in our study, comprising 57,761 males and 17,145 females at a male-to-female ratio of 3.4:1.0. According to the latest Chinese census data, we found that the city with the highest incidence for males was Zhangye while Wuwei had the highest incidence for females; Longnan had the lowest incidence for both genders (Fig. 3).

Fig. 3

The gastric cancer incidence for different gender in different cities (1/100,000)

Rural and urban regions

A total of 70,405 gastric cancer patients with complete residential address information were included in this study, and the rural–urban ratio of cases was 3.1:1.0. As illustrated in Fig. 4, both the incidence and prevalence of gastric cancer were significantly higher in rural regions than urban regions during recent years.

Fig. 4

The cases and incidence of gastric cancer in rural and urban regions(A, cases. B, incidence.)

GC incidence of gansu province

As shown in Fig. 5. Our analysis revealed a significant upward trend in the crude incidence rate and age standardized rate (ASR) of gastric cancer in Gansu province between 2013 and 2021, with an AAPC of 5.55% (95% CI 5.05, 9.01; p < 0.05). Joinpoint regression identified two distinct time segments with different APC values during this time frame. In segment 1 (2013–2016), the ASR showed a significant increase of 16.02% per year (APC = 14.31; 95% CI 8.33, 29.70; p < 0.05), while in segment 2 (2016–2021), it increased by only 0.61% per year, which was not statistically significant (APC = 0.61; 95% CI −4.42, 3.03; p > 0.05).

Fig. 5

Time trends of incidences of gastric cancer in Gansu province from 2013 to 2021 (A, Incidence. B, Joinpoint regression for ASR.)

Regional distribution of gastric cancer patients included

As presented in Table 1 and Fig. 6, Wuwei, Lanzhou, and Zhangye were identified as the top three cities with the highest cumulative number of gastric cancer patients in Gansu Province; whereas Jiayuguan, Gannan, and Jinchang were recognized as the last three cities with the smallest cumulative number of gastric cancer patients. The top three cities with the highest incidence were Wuwei, Zhangye and Jinchang; and the last three cities with the lowest incidence were Longnan, Qingyang and Jiayuguan.

Table 1 Regional distribution of gastric cancer patients included in GansuFig. 6

The prevalence map of gastric cancer in Gansu

Spatial epidemiological analysis of the gastric cancer in GansuSpatial analysis (SA)

We conducted both global and local auto-correlation analyses, revealing a clustered pattern of gastric cancer incidence in Gansu Province based on the global Moran's index (Moran's index = 0.38, Z = 2.46, p < 0.05, Fig. 7). We further completed cold-hot spot analysis and found that the spatial distribution of gastric cancer in Gansu province exhibited significant regularity, as shown in Fig. 8, the northern region of Gansu province represents a central hots pot area with higher incidence rates, while the southern region is characterized by lower incidence rates and serves as a leading cold spot area. Based on local Moran's index calculations, we identified a significant high-high cluster area in Wuwei and Jinchang; however, no significant low-low cluster areas were observed throughout Gansu province (Fig. 9).

Fig. 7

Global auto-correlation analyses (A, Moran`s index scatter plot. B, Permutation test.)

Fig. 8

Hot spot analysis of gastric cancer incidence in Gansu province

Fig. 9

Local auto-correlation analysis (A, Clustering map. B, Significance map)

Spatial scanning analysis based on SaTScan™

We used SaTScan™ software to identify significant spatial clusters of gastric cancer in Gansu. As shown in Table 2 and Fig. 10, two high risk clusters and one low risk cluster were observed in the purely spatial analysis.

Table 2 Gastric cancer cluster details based on purely spatial analysisFig. 10

Purely spatial clusters map of GC in Gansu province

The most likely high risk cluster was found in the north of Gansu province, Wuwei, Zhangye and Jinchang were included in this area. In this area, there were 20,473 observed cases of gastric cancer and 9434 expected cases, with a statistically significant 161% increased risk of GC (RR = 2.61, p < 0.01). Another secondary high risk cluster was found in the southwest of Gansu province, Linxia and Gannan were included in this area. There were a total of 9189 observed cases of GC and 8195 expected cases, with a RR of 1.14 (p < 0.01), which implied that there was a 14% increased risk in this area compared with the total population in Gansu province. The most likely low risk cluster was found in the south of Gansu, Longnan, Tianshui, Dingxi, Pingliang and Qingyang were included in this area, there were 25,897 observed cases of GC and 36,333 expected cases, with a RR of 0.56, implying that, there is a statistically significant 0.44% decreased risk of GC in this area.

We further complete space–time scanning with the data. As shown in Table 3, the high risk and low risk clusters identified through space–time analysis closely resemble those found in purely spatial clustering.

Table 3 Gastric cancer cluster details based on space–time analysisMacro determinants of gastric cancer based on GeodetectorSocial economic data of every city in Gansu

As presented in Table 4, we calculated the average values of five key economic indicators and three variables related to allocation of medical source in every city of Gansu province, in which, the units of five economic variables are 10,000 yuan.

Table 4 The mean values of multiple economic indicators across all cities in GansuNatural environmental data of every city in Gansu

In the study, we obtained the topography data, agrotype data, vegetational form data, altitude data, slope data, regional rainfall data, ambient temperature data, ambient humidity data and diurnal temperature data of Gansu province; these data were all extracted by mask from Chinese or global data which were downloaded from some official public database, including High-resolution gridded datasets, Geospatial Data Cloud and Resource and Environment Science and Data Center. The results are shown in Fig. 11.

Fig. 11

Natural environmental data of Gansu province. (A, regional rainfall. B, ambient temperature data. C, ambient humidity data. D, diurnal temperature variation data. E, altitude data. F, slope data. G, topography data. H, agrotype data. I, vegetational form data.)

Factor detector result of Geodetector

According to the aforementioned social-economic, medical source allocation and natural environmental data, we run Geodetector to explore the correlation between gastric cancer incidence and these factors. As shown in Table 5, all included factors exhibited statistically significant determinant power on gastric cancer in Gansu province (p < 0.05). Furthermore, we assessed the influential power of each factor and discovered that all except altitude, slope, and topography (q < 0.1) significantly impacted gastric cancer incidence. Among the remaining factors, three medical resource allocation variables, three economic indicators (Output value of second industry, GDP per capita and GDP), and diurnal temperature variation had a greater determinant power than others. Notably, the number of health technical staff per 10,000 people was the most influential factor that determined the gastric cancer distribution in Gansu province (q = 0.898). Except for these seven main factors, other factors such as the output value of the primary industry, regional rainfall, ambient humidity, output value of the tertiary industry, ambient temperature, agrotype and vegetational form also have a significant impact on gastric cancer distribution. However, their influence is all less than 50%.

Table 5 The factor detector result of GeodetectorInteractive detector result of Geodetector

In this study, we used the interactive detector to find out whether the two risk factors included acted separately or synergistically. As shown in Fig. 12, our findings indicated that any combination of paired risk factors could significantly amplify their impact on gastric cancer in Gansu province through various forms of interaction, including bivariate enhancement and non-linearity enhancement. We used the interaction of ambient temperature and GDP per capita, regional rainfall and altitude as examples to explain the interactive effect of two different risk factors. According to Fig. 12, we could find the independent influential power of these two factors; their q values were 0.413 and 0.725 respectively; The independent determinant power of regional rainfall was found to be significantly smaller than that of GDP per capita. However, the combinational influential power of these two factors exhibited a synergistic effect with a q value of 0.903, which surpassed their individual q values but fell short of their sum and thus suggesting a bivariate enhancement effect on gastric cancer in Gansu province. Meanwhile, the combination of regional precipitation and tertiary industry output significantly enhances their independent determinant power on the dependent variable with a value of 0.964, surpassing the significance of either individual q value (0.413 and 0.372); moreover, it exceeds their sum (sum(q) = 0.785), indicating a non-linear enhancement on gastric cancer in Gansu province.

Fig. 12

Interactive impact of risk factors on gastric cancer incidence in Gansu (X1, GDP; X2, Output value of first industry; X3, Output value of second industry; X4, Output value of third industry; X5, GDP per capita; X6, Number of medical institutions per 10,000 people; X7, Number of hospital beds per 10,000 people; X8, Number of health technical staffs per 10,000 people; X9, Altitude; X10, Slope; X11, Topography; X12, Agrotype; X13, Vegetational form; X14, Regional rainfall; X15, Ambient temperature; X16, Diurnal temperature variation; X17, Ambient humidity)