(2024) 22:364
Li et al. BMC Medicine
https://doi.org/10.1186/s12916-024-03594-7

BMC Medicine

RESEARCH

Open Access

The spatiotemporal associations
between esophageal and gastric cancers
provide evidence for its joint endoscopic
screening in China: a population‑based study
Peng Li1,2, Xin Qi2, Ruhai Bai3, Mei Yang1, Jing Jing4, Ruyi Xia2, Jizhao Wang1, Guangjian Zhang1* andGuihua Zhuang2,5*

Abstract
Background The spatiotemporal epidemiological evidence supporting joint endoscopic screening for esophageal
cancer (EC) and gastric cancer (GC) remains limited. This study aims to identify combined high-risk regions for EC
and GC and determine optimal areas for joint and separate endoscopic screening.
Methods We analyzed the association of incidence trends between EC and GC in cancer registry areas across China
from 2006 to 2016 using spatiotemporal statistical methods. Based on these analyses, we divided different combined
risk regions for EC and GC to implement joint endoscopic screening.
Results From 2006 to 2016, national incidence trends for both EC and GC showed a decline, with an average annual
percentage change of -3.15 (95% confidence interval [CI]: -5.33 to -0.92) for EC and -3.78 (95% CI: -4.98 to -2.56) for GC.
A grey comprehensive correlation analysis revealed a strong temporal association between the incidence trends of EC
and GC, with correlations of 79.00% (95% CI: 77.85 to 80.14) in males and 77.62% (95% CI: 76.50 to 78.73) in females.
Geographic patterns of EC and GC varied, demonstrating both homogeneity and heterogeneity across different
regions. The cancer registry areas were classified into seven distinct combined risk regions, with 33 areas identified
as high-risk for both EC and GC, highlighting these regions as priorities for joint endoscopic screening.
Conclusion This study demonstrates a significant spatiotemporal association between EC and GC. The identified
combined risk regions provide a valuable basis for optimizing joint endoscopic screening strategies for these cancers.
Keywords Esophageal cancer, Gastric cancer, Spatiotemporal association, Endoscopic screening, China*Correspondence:
Guihua Zhuang
zhuanggh@mail.xjtu.edu.cn
Guangjian Zhang
michael8039@xjtu.edu.cn
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as longas you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if
you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article orparts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicatedotherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To
view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Li et al. BMC Medicine(2024) 22:364

Background
Upper gastrointestinal cancer (UGIC), including esophageal cancer (EC) and gastric cancer (GC), impose a significant global burden [1]. In 2022, there were 511,054
new cases of EC and 968,764 new cases of GC worldwide, with China contributing to 37.02% of EC cases and43.83% of GC cases, respectively [2, 3]. Consequently,
China bears the heaviest burden of UGIC globally, withthe highest number of cases and deaths.
Higher incidences of both cancers are observed in the
Taihang Mountains and Huaihe Basin, while the Kazakhinhabited Xinjiang region has a higher incidence of EC.
In contrast, Northwest China (e.g., Gansu and Qinghai
provinces) and East Shandong show higher incidences of
GC [4]. These findings reveal distinct geographical patterns of EC and GC, with notable overlaps and associations, suggesting shared risk factors and the potential forjoint early prevention and screening.
The geographical distribution of EC and GC exhibits
both similarities and differences worldwide. East Asia
reports the highest incidence rates for both cancers, withelevated rates also observed in southeastern Africa andwestern South America [5]. The concentrating and deviating patterns of EC and GC vary by subsite and location across specific regions, demonstrating both positiveand inverse correlations [6]. For example, Mongolia andChina rank among the top five countries globally in termsof EC and GC incidence, with rates exceeding 10.8 per
100,000 persons. In contrast, Japan has a higher incidence of EC relative to GC, whereas South Korea exhibits
the opposite trend. Similarly, South Africa reports a significantly higher incidence of EC compared to GC, with adifference exceeding tenfold [6, 7]. Our previous research
identified common high-risk regions for EC and GC in
China and quantified the associated risk levels. However,regional disparities and similarities in risk persist [4].
Higher incidences of both cancers are observed in the
Taihang Mountains and Huaihe Basin, while the Kazakhinhabited Xinjiang region has a higher incidence of EC.
In contrast, Northwest China (eg, Gansu and Qinghai
provinces) and East Shandong have higher incidences of
GC [4]. These findings indicated distinct geographicalpatterns of EC and GC, with notable overlaps and associations, suggesting shared risk factors and the potential
for joint early prevention and screening.
Since the 1970s, China has implemented early endoscopic screening programs for EC and GC and intensified research on UGIC in high-risk areas [8]. Prior
studies have predominantly focused on high-risk regions
for EC or GC separately, neglecting their geographicalco-occurrence patterns [9, 10]. Despite being different
cancer types, EC and GC share anatomical proximity
and several analogous risk factors. Furthermore, early

Page 2 of 12

screening for both cancers can be conducted collectively
using endoscopy. In 2015, the National Cancer Center ofChina (NCCC) launched a multi-center joint screening
program for EC and GC to assess the cost-effectivenessof joint screening among residents aged 40–69 years in
six regions of China, reporting 23% and 57% reductionsin UGIC incidence and mortality rates, respectively [11].
Our previous studies have indicated that biennial joint
endoscopic screening for EC and GC could be an optimal strategy in common high-risk areas, supporting its
implementation in China [12, 13]. Nevertheless, limited
research has explored the temporal and spatial associations between EC and GC, leaving unanswered questions
regarding the necessity to expand screening regions andthe identification of priority areas for joint endoscopic
screening.
Therefore, this study aims to elucidate the temporaland spatial associations between EC and GC, and accurately divide different combined risk regions. The findings will provide robust evidence for targeting high-riskareas and inform the rational planning and implementation of joint endoscopic screening programs for thesecancers in China.

Methods
Data source

The data for this study were obtained from the China
Cancer Annual Report, issued by the NCCC. This reportprovides comprehensive and standardized annual dataon cancer incidence and mortality, stratified by sex andacross various regions of China. Since 2005, populationbased cancer statistics have been reported to the NCCC
by local cancer registries, which collect data from hospitals, community health centers, basic medical insurance
systems, the new rural cooperative medical system, anddeath surveillance centers [14]. The data collection process adheres to the Guidelines for China Cancer Registration, the standards of the International Agency for
Research on Cancer (IARC), and the Cancer Incidence
in Five Continents (CI5) series [15]. The NCCC ensuresdata quality through rigorous evaluation of comparability, completeness, and validity using multiple indicators,
including the mortality-to-incidence ratio, the percentage of morphologically verified cases, the percentage ofdeath certificate-only cases, and the stability of cancer
trends over time. Although the cancer registration system has improved over time, regional disparities anddiscontinuities in data remain due to the varying number of cancer registries. In 2006, there were 34 cancer
registration areas (CRAs), expanding to 487 by 2016,
with 31 CRAs providing continuous data throughout the
study period. Detailed information on the number andLi et al. BMC Medicine(2024) 22:364

population coverage of these CRAs is provided in Additional file 1: Table S1 and S2.
We extracted data on EC and GC using the International Classification of Diseases, 10th Revision (ICD-10)
codes C15 and C16, respectively. The dataset included
annual case counts, crude incidence rates, and age-standardized incidence rates (ASIRs) adjusted using Segi’sworld standard population, stratified by sex and urban–
rural classification. According to China’s urban–rural
administrative division guidelines, a CRA in a countylevel city is classified as rural, while a CRA in a prefecture-level city or municipal district is classified as urban.
To ensure consistency in administrative region names
over time, we merged and matched all CRAs based on
year and identical administrative division codes, enabling
accurate integration and analysis across different regionsin China.
To address potential deviations in geographical locations caused by administrative division changes, we calibrated these deviations by CRA location and year. First,
we matched CRA locations with administrative divisions
using identical codes and then extracted matched CRAs.
Unmatched CRAs were calibrated by revising names orcodes and re-matching them. For administrative division adjustments (e.g., separations or mergers) in specificyears, we aligned CRA locations using corresponding
codes. Name changes were addressed by updating codes
based on the latest administrative names and matchingthem again. Ultimately, we integrated the matched CRA
locations and administrative divisions to establish theGeographical Information Database of EC and GC in
China from 2006 to 2016. More details on this process
are provided in Additional file 2:Fig. S1 and S2.
Statistical analysis
Temporal association between EC and GC

To assess overall trends in ASIRs for EC and GC, we calculated the average annual percentage change (AAPC)
using linear logarithmic regression, with ASIR as thedependent variable and year as the independent variable. Additionally, the annual percentage change (APC), a
geometrically weighted average of the annual percentagechanges, was computed to identify the year with the mostsignificant changes during the study period. We used the
Joinpoint regression model to analyze both overall andsegmented trends in ASIRs for EC and GC, stratified bysex, at the national and CRA levels from 2006 to 2016
(Joinpoint Regression Program, Version 4.5.0.1, Statistical Methodology and Applications Branch, Surveillance
Research Program, National Cancer Institute, Rockville,
MD, USA). A significant upward trend was defined as
APC or AAPC > 0 with the lower bound of the 95% confidence interval (CI) > 0 (P < 0.05). Conversely, a significant

Page 3 of 12

downward trend was defined as APC or AAPC < 0 withthe upper bound of the 95% CI < 0 (P < 0.05). If neither
condition was met, the trend was considered non-significant. For instance, an APC or AAPC of 0.1 would indicate an annual or average annual increase of 0.1% overthe period. The principles and interpretations of APC
and AAPC are discussed in a previous study [16]. Subsequently, the temporal association between EC and GC
from 2006 to 2016 was examined using the grey comprehensive correlation (GCC), a summary indicator measuring temporal associations over a fixed period. GCC is aweighted measure of grey absolute correlation (GAC) andgrey relative correlation (GRC), indicating the concordance and fluctuation association between EC and GC,
with values ranging from 0 to 100%. Due to non-continuous CRA data between 2006 and 2016, we selected datawith at least three years for temporal association analysis
by sex. Detailed GCC calculations are provided in Additional file 2 (p3-p4) and are described elsewhere [17].
Spatial association between EC and GC

We mapped the geographical distribution of ASIRs for
EC and GC by sex from 2006 to 2016, using the ASIR
in 2016 as a reference. Moran’s I index was used to analyze the spatial patterns of EC and GC in adjacent areas,
determining whether the spatial distribution was clustered, dispersed, or random. A Moran’s I > 0 with P < 0.05
suggests a clustered pattern, while a Moran’s I < 0 with
P < 0.05 indicates a dispersed pattern. A random patternis indicated when neither condition is met. This analysis
was conducted using ArcGIS 10.6, with further details
provided in Additional file 2 (p5). Given the large population and extensive CRA coverage in 2016, we calculated
the relative risk (RR) of EC and GC for that year to assess
their spatial association. A concordant direction between­RREC and R­ RGC suggests a homogeneous association(both high-risk or low-risk regions) between EC andGC, while an opposite direction suggests a heterogeneous association (separately high-risk or low-risk regions).
Spatial scan statistics were employed using SatScanTM
9.6.1, with detailed methods described in Additional
file 2 (p6-p7).
Combined risk regions of EC and GC

We classified combined risk regions for EC and GC by
sex using the ratio of ASIR at each CRA to the national
level, providing insights into prioritizing areas for endoscopic screening. The combined risk regions were classified as follows: double high-risk EC and GC, single
high-risk EC, single high-risk GC, double middle-risk EC
and GC, middle-risk EC and low-risk GC, middle-risk
GC and low-risk EC, and double low-risk EC and GC.

Li et al. BMC Medicine(2024) 22:364

Classification methods and definitions are detailed in
Additional file 2: Fig. S3, and Table S1.

Results
Temporal trends between EC and GC

As shown in Fig. 1, the ASIR of EC and GC for both
sexes combined increased nationally from 2006 to 2010
(EC: APC = 6.75, 95% CI: 1.20 to 12.61; GC: APC = 1.45,
95% CI: -1.55 to 4.54). Following this period, there wasa significant decrease in EC (APC = -6.01, 95% CI: -7.41
to -4.57) and GC (APC = -5.45, 95% CI: -6.31 to -4.56).
When stratified by sex, this pattern remained consistent.
Notably, the incidence trends differed between urban andrural areas. In urban areas, the ASIR of EC and GC initially rose before declining, whereas in rural areas, a linear decrease was observed throughout the study period.
This trend persisted after further stratification by sex inboth urban and rural regions.
As presented in Fig. 2, the ASIR for both EC andGC combined showed a significant decline from 2006
to 2016. The ASIR for EC decreased from 13.11 per
100,000 persons in 2006 to 11.46 per 100,000 persons in 2016, with an AAPC of -3.15 (95% confidence

Page 4 of 12interval [CI]: -5.33 to -0.92). Similarly, the ASIR for
GC declined from 24.14 per 100,000 persons in 2006
to 17.62 per 100,000 persons in 2016, with an AAPCof -3.78 (95% CI: -4.98 to -2.56). This consistent downward trend was observed in both males and females
over the same period.
Table 1 presents the AAPC in ASIR for EC and GC
across 31 CRAs with continuous data from 2006 to
2016. Most CRAs exhibited a decline in ASIR for both
EC and GC (AAPC < 0), with the exception of Fusui.
Specifically, significant reductions in ASIR for EC
were observed in 25 CRAs for males and 23 CRAs forfemales, while GC rates significantly decreased in 23
CRAs for males and 20 CRAs for females. The mostnotable decrease in ASIR for EC among males wasrecorded in Jianhu, with an AAPC of -8.73 (95% CI:-11.38 to -6.00), while Nangang showed the largest
decrease among females, with an AAPC of -14.35 (95%
CI: -23.46 to -4.16). For GC, the largest declines wereobserved in Jianhu and Dafeng, with AAPCs of -7.85(95% CI: -9.59 to -6.07) for males and -10.29 (95% CI:-13.94 to -6.48) for females, respectively. Detailed data
can be found in Table 1.

Fig. 1 The annual percentage change (APC) in the ASIR of EC and GC by sex and urban–rural types from 2006 to 2016 in China (A, B and C
denote whole population, males and females at national level, respectively; D, E and F denote whole population, males and females in urban area,
respectively; G, H and I denote whole population, males and females in rural area, respectively)

Li et al. BMC Medicine(2024) 22:364

Page 5 of 12

Fig. 2 The average annual percentage change (AAPC) of the EC and GC in China between 2006 and 2016

Temporal association between EC and GC

As shown in Table 2, from 2006 to 2016, the average
GAC, GRC, GCC between EC and GC for males were
73.63% (95CI: 72.12 to 75.14), 84.37% (95CI: 83.19 to85.55) and 79.00% (95CI: 77.85 to 80.14). For females,
these correlation values were 73.81% (95% CI: 72.33 to
75.29), 81.42% (95% CI: 80.07 to 82.78), and 77.62% (95%
CI: 76.50 to 78.37), respectively. These findings indicate
significant associations in the trends of concordance,
fluctuation, and comprehensiveness between EC and GC.
Geographical patterns of between EC and GC

The geographical patterns of EC and GC over time arepresented in Additional File 2 (Fig. S4-S7). Figure 3
exhibits the geographical patterns of the ASIR for EC
and GC, both of which exhibit spatial clusters (Moran’s
I > 0 and P < 0.01), indicating a significant spatial association of EC and GC with that in adjacent areas.Additionally, the temporal variations in Moran’s I for both cancers
over the period suggest consistent spatial clustering
trends, reflecting temporal concordance in their spatial
associations.
Spatial association of EC and GC

Figure 4 shows substantial geographical variation in thespatial association between EC and GC, reflecting bothhomogeneous and heterogeneous risk patterns across
CRAs. In regions with homogeneous high-risk for both
EC and GC (­RREC and R­ RGC > 1), the ­RREC and R­ RGC

among males were 2.75 (95% CI: 2.73 to 2.77) and 2.44
(95% CI: 2.43 to 2.46), respectively, while among females
they were 4.54 (95% CI: 4.41 to 4.67) and 2.28 (95% CI:
2.22 to 2.33), respectively (Additional File 3: Table S1).
The differences in ­RREC between the highest- and lowest-risk areas for males and females were 10.60-fold and62.33-fold, respectively, while the differences for R­ RGC
were 6.71-fold and 9.82-fold, respectively (Additional File
3:Tables S2 and S3).
In heterogeneous risk regions, where ­RREC and R­ RGC
exhibited opposite trends, there were significant differences in risk magnitudes and patterns (Fig. 4). In areas
where ­RREC > 1 and ­RRGC < 1, the ­RREC and ­RRGC among
males were 1.71 (95% CI: 1.68 to 1.74) and 0.66 (95%
CI: 0.64 to 0.68), respectively, while among females they
were 1.92 (95% CI: 1.80 to 2.05) and 0.64 (95% CI: 0.59
to 0.70), respectively, indicating a higher risk for EC anda lower risk for GC. Conversely, in areas where ­RREC < 1and ­RRGC > 1, the ­RREC and ­RRGC among males were 0.57
(95% CI: 0.53 to 0.61) and 1.58 (95% CI: 1.56 to 1.60),
respectively, while among females they were 0.36 (95%
CI: 0.34 to 0.39) and 1.63 (95% CI: 1.59 to 1.69), respectively (Additional File 3: Table S1).
The combined risk regions of EC and GC

Table 3 summarizes the classification of combined
risk regions for EC and GC, highlighting areas thatshould be prioritized for joint endoscopic screening. The double high-risk regions, identified as top

Li et al. BMC Medicine(2024) 22:364

Page 6 of 12

Table 1 The average annual percentage change (AAPC) in 31 CRAs from 2006 to 2016
CRAs

Male

FemaleEC(95%CI)
Beijing
Shexian

GC(95%CI)-3.98(-4.73, -3.22)aa

EC(95%CI)

GC(95%CI)-2.05(-2.68, -1.41)a-7.78(-9.21, -6.33)a-1.46(-2.81, -0.09)aaa-6.62(-9.01, -4.17)a-7.89(-10.28, -5.43)-5.30(-7.42, -3.13)

a-6.40(-9.27, -3.44)

a-3.24(-5.74, -0.67)-7.52(-9.68, -5.30)

a-6.01(-7.63, -4.36)

a-5.54(-8.64, -2.33)

a

Shenyanga-3.53(-6.58, -0.38)

aAnshan-3.46(-6.15, -0.69)aBenxi-4.41(-8.25, -0.42)a

CixianYangcheng-6.78(-8.7, -4.82)

a-4.48(-7.48, -1.38)a-3.85(-7.37, -0.18)a-4.99(-8.16, -1.71)-3.08(-4.95, -1.18)

a-6.51(-10.06, -2.82)-1.70(-3.47, 0.11)-3.91(-5.85, -1.94)a-10.33(-14.16, -6.32)a-3.41(-6.35, -0.37)-4.97(-8.67, -1.11)a-3.67 (-15.4, 9.68)-3.55(-6.55, -0.45)a
a

Daoli

-1.82(-7.33, 4.03)-6.19(-9.25, -3.03)-10.89(-16.71, -4.66)-1.36(-5.04, 2.46)

Nangang-5.25(-7.38, -3.08)a-2.47(-4.45, -0.44)a-14.35(-23.46, -4.16)a-5.80(-8.31, -3.21)a-7.30(-9.28, -5.28)-6.51(-7.83, -5.16)

a-9.40(-12.52, -6.18)-5.04(-6.71, -3.35)a

Qidong-1.35(-3.97, 1.34)

a-0.03(-2.24, 2.24)

a-4.24(-7.62, -0.74)-0.94(-3.71, 1.91)

Haimen-5.12(-7.22, -2.97)a-4.85(-7.72, -1.88)a-5.37(-8.10, -2.55)a-2.08(-4.63, 0.54)

Jianhu-8.73(-11.38, -6.00)a-7.85(-9.59, -6.07)a-7.50(-10.34, -4.57)aSuzhouYangzhongaa-8.69(-12.02, -5.23)

a-5.50(-7.53, -3.42)aa-7.79(-10.49, -5.01)-12.50(-15.48, -9.41)

aa-8.85(-12.93, -4.58)aHangzhou-1.46(-4.09, 1.25)-2.78(-4.79, -0.72)-8.80(-10.12, -7.46)-3.32(-5.42, -1.17)

Jiaxing-4.50(-7.68, -1.21)a-4.31(-6.58, -1.98)a-4.25(-12.96, 5.33)-0.99(-5.33, 3.56)

Jiashan-7.02(-9.59, -4.38)a-4.89(-7.51, -2.20)a-4.82(-9.43, 0.03)-5.89(-8.54, -3.16)aHaininga-5.61(-8.17, -2.97)-2.53(-6.26, 1.34)-5.68(-9.99, -1.16)

Maanshan-4.50(-8.18, -0.68)a-1.60(-5.21, 2.14)-6.54(-12.42, -0.27)-3.69(-6.17, -1.15)

Feicheng-5.99(-9.11, -2.76)a-1.86(-4.38, 0.73)-7.46(-11.59, -3.14)a-4. 55(-9.11, 0.23)

a-2.58(-6.51, 1.51)

Linzhou-5.37(-8.99, -1.60)a-2.68(-5.74, 0.47)-6.22(-8.39, -4.00)a-3.80(-6.13, -1.41)aWuhan-3.25(-4.42, -2.07)a-2.89(-4.09, -1.67)a-4.86(-8.16, -1.45)a-1.77(-3.36, -0.15)aGuangzhou-8.62(-11.29, -5.88)a-5.44(-7.68, -3.14)a-12.86(-15.76, -9.86)a-5.42(-7.13, -3.68)aSihui-5.88(-10.40, -1.13)a-3.41(-9.16, 2.70)-5.61(-12.38, 1.67)

3.57(-2.56, 10.09)

Zhongshan-3.89(-6.32, -1.40)a-2.31(-5.45, 0.93)-10.31(-20.19, 0.80)-2.21(-6.21, 1.96)

Fusui

1.21(-7.14, 10.31)

9.38(5.07, 13.87)a

101.18(-10.64,352.92)

7.89(0.31, 16.05)aYanting-0.65(-1.90, 0.62)-1.11(-2.65, 0.46)-1.37(-2.89, 0.16)

Hangzhou-2.03(-3.3, -0.74)aaa-2.31(-3.96, -0.63)

a-2.49(-3.41, -1.57)
aDafeng-8.30(-10.69, -5.85)-8.22(-12.22, -4.04)

Changleaa

Huaian-5.21(-9.09, -1.17)-6.30(-14.04, 2.14)-7.09(-9.27, -4.87)-3.82(-10.55, 3.43)-3.01(-3.76, -2.26)a-6.85(-8.09, -5.60)
aa-11.85(-14.51, -9.11)-10.29(-13.94,-6.48)a-3.93(-8.00, 0.33)-6.57(-9.23, -3.84)aa-8.21(-15.52, -0.26)-3.04(-9.43, 3.79)a

CRAs cancer registration areasadenotes statistically significant

priorities for joint screening of both EC and GC,
encompass 33 areas. In these regions, the average
ASIR for EC and GC among males were 54.23 per
100,000 persons (ratio = 3.16) and 78.42 per 100,000
persons (ratio = 3.11), respectively. Among females,
the corresponding rates were 27.21 per 100,000 persons (ratio = 4.66) and 31.05 per 100,000 persons(ratio = 3.02), respectively. For regions classified as single high-risk for EC, where targeted screening for EC
is recommended, 38 areas were identified for males
and 27 for females. In these regions, the average ASIR
for EC and GC among males were 49.75 per 100,000
persons (ratio = 2.89) and 30.78 per 100,000 persons(ratio = 1.22), respectively, while among females, therates were 20.10 per 100,000 persons (ratio = 3.44)
and 12.66 per 100,000 persons (ratio = 1.23), respectively. For regions classified as single high-risk for GC,
where screening efforts should focus on GC, 25 areas
were identified for males and 15 areas for females. In
these regions, the average ASIRs for EC and GC among
males were 20.70 per 100,000 persons (ratio = 1.20) and63.71 per 100,000 persons (ratio = 2.53), respectively,
while among females, the rates were 5.97 per 100,000
persons (ratio = 1.02) and 27.96 per 100,000 persons(ratio = 2.72), respectively. Further details on regionalclassifications are depicted in Additional File 2: Fig. S9

Li et al. BMC Medicine(2024) 22:364

Page 7 of 12

Table 2 The temporal association magnitude of EC and GC from2006 to ­2016a
Temporalassociation

Min(%)

Max(%)

Mean(%)

SD(%)

95%CI

Male
GAC​

50.42

99.61

73.63

13.69

72.12 to 75.14

GRC​

52.17

99.96

84.37

10.75

83.19 to 85.55

GCC​

53.65

98.46

79.00

10.41

77.85 to 80.14

Female
GAC​

51.54

99.72

73.81

13.49

72.33 to 75.29

GRC​

50.00

99.99

81.42

12.31

80.07 to 82.78

GCC​

54.26

98.02

77.62

10.12

76.50 to 78.73a

322 cancer registration areas with EC and GC data not less than 3 years from2006 to 2016. GAC​grey absolute correlation, GRC​ grey relative correlation,
GCC​grey comprehensive correlation, Min Minimum value, Max Maximum value,
SD standard deviation, 95%CI 95% confidence interval

Fig. 3 The spatial autocorrelation of EC and GC by sex from 2006 to 2016

Fig. 4 The spatial association of EC and GC by sex in China

and S10. Comprehensive data on ASIRs and risk levels
across all categorized CRAs are available in Additional
File 2: Table S3.

Discussion
This study is the first to explore the spatiotemporal associations between EC and GC in China and to identify
their combined risk regions using population-reported
data and a series of spatiotemporal analyses. We identified seven distinct combined risk regions for EC and GC,
providing critical evidence for prioritizing joint endoscopic screening areas and understanding the overlapping and divergent risks associated with these cancers in
China.
Our findings indicate that ASIR of EC and GC initially
increased before showing a significant decline from 2006
to 2016. This trend is consistent with previous studies on the epidemiological profiles of EC and GC bothLi et al. BMC Medicine(2024) 22:364

Page 8 of 12

Table 3 The general information of the combined risk region between EC and GC by sex
Combined risk regions

Male

FemaleN

EC
ASIR

High-risk

Non-high risk

N

GC
Ratio

ASIR

Ratio

EC
ASIR

GC
Ratio

ASIR

Ratio

Region 1

33

54.42

3.16

78.42

3.11

33

27.21

4.66

31.05

3.02

Region 2

38

49.75

2.89

30.78

1.22

27

20.10

3.44

12.66

1.23

Region 3

25

20.70

1.20

63.71

2.53

15

5.97

1.02

27.96

2.72

Region 4

205

18.60

1.08

26.62

1.05

131

5.72

0.98

10.81

1.05

Region 5

39

14.83

0.86

9.41

0.37

17

4.98

0.85

4.06

0.39

Region 6

94

6.29

0.37

20.77

0.82

175

1.40

0.24

9.21

0.89

Region 7

53

5.29

0.31

9.42

0.37

59

0.98

0.17

3.81

0.37

N: the number of cancer registration areas;
ASIR: age-standardized incidence rate(per 100,000 persons);
Ratio: the ratio of ASIR at cancer registration area to national level;
Region1: double high-risk EC and GC;
Region2: single high-risk EC;
Region3: single high-risk GC;
Region4: double middle-risk EC and GC;
Region5: middle-risk EC and low-risk GC;
Region6: middle-risk GC and low-risk EC;
Region7: double low-risk EC and GC

in China and globally. For example, research has documented a decreasing annual trend in the ASIR of EC and
GC of -4.2% (95% CI: -4.6 to -3.7) and -3.0% (95% CI: -3.5
to -2.4) between 2005 and 2015, respectively, with projections of continued declines through 2030 [18]. The
Global Burden of Disease study also estimated that the
global ASIR of EC and GC decreased by 22% and 28%,
respectively, with China contributing to a significant proportion of these cases [19, 20].
The decline in ASIR for both EC and GC was more
pronounced in rural areas compared to urban areas,
regardless of sex. This difference is likely attributable
to the implementation of early screening programs inrural areas since the 1970s. A case–control study in
Linzhou, China, demonstrated that early endoscopic
screening reduced GC mortality by 28% [21]. Additionally, a nine-year screening program in rural areas led
to a 20% reduction in incidence and a 32% decrease inmortality risk for EC [22]. Between 2012 and 2018, over2.16 million individuals in 194 rural areas participatedin early screening programs, with a detection rate of
UGIC at 2.05% compared to 0.20% in urban areas [23].
Nutritional interventions with beta-carotene, vitamin
E, and selenium in rural areas have also been effective in reducing EC and GC incidence by 8% and 16%,
respectively [24]. Despite limited access to treatment
facilities in rural areas, China’s central government has
implemented early diagnosis and treatment projects
for UGIC in high-incidence areas since 2005. With

the enhancement of the medical insurance system andthe improvement of primary healthcare services, themedical care level for residents in rural areas has significantly improved. Policies and guidelines have been
issued, and medical resources have been allocated tofacilitate early diagnosis and treatment [25–27]. For
instance, in Cixian, a high-incidence area, early screening and treatment increased five-year survival rates by
14.11% for EC, 16.71% for cardia GC, and 14.92% fornoncardia GC due to early screening and treatment
from 2003 to 2013 [28]. These results highlight theimportance of ongoing and targeted screening efforts,
particularly in rural regions, to further mitigate theburden of EC and GC in China.
Our study found a significant temporal association
between EC and GC, with concordance rates of 79.00%(95% CI: 77.85 to 80.14) among males and 77.62% (95%
CI: 76.50 to 78.73) among females. This suggests thatboth cancers may share common risk factors and etiological pathways. Although the exact reasons for thisassociation are not fully understood, several factors
likely contribute, including the decreasing prevalence ofsmoking, alcohol use, and Helicobacter pylori (Hp) infection [5, 29, 30], as well as improved dietary habits andaccess to medical services have likely played a role [31].
The increased consumption of fruits and vegetables,
which are rich in antioxidants, has been associated witha reduced risk of both cancers, potentially explaining thedeclining incidence [32]. Enhanced access to medicalLi et al. BMC Medicine(2024) 22:364

facilities through health insurance and public health initiatives has likely improved early detection and treatment,
further contributing to the decrease in advanced cancers[33]. Although our study could not evaluate the specificeffects of these factors due to data limitations, previous
research supports their role in reducing cancer incidence[34].
Our analysis revealed that high-risk areas for EC andGC are primarily concentrated in the adjacent regionsof Henan, Hebei, Shanxi, Shandong, Jiangsu, and Anhui
provinces, as well as in parts of Xinjiang and Sichuanprovinces. Spatial scanning statistics identified homogeneous high-risk areas in the Taihang Mountains andHuaihe Basin, suggesting that environmental factors
may significantly contribute to the high incidence of EC
and GC in these regions. While excessive smoking anddrinking have been highlighted as risk factors in paststudies, case–control studies suggested their role incarcinogenesis may be limited [35, 36]. Therefore, further research is needed to clarify the influence of thesefactors across both high- and low-risk regions [37]. The
elevated incidence risks of EC and GC in these areas
may be associated with water pollution. Previous studies documented that approximately 80% of EC cases
occurred within 10 km of rivers, and rural residents
often used untreated surface water from rivers andlakes for drinking [38, 39]. Additionally, 76% and 88%
of cancer villages in high-risk areas located within 3 km
and 10 km of rivers, respectively, and the correlation
coefficient between sewage discharge and high-risk villages was 0.94 (P < 0.05) [40]. However, the incidencerisks significantly decreased with strict pollution control measures.
The heterogeneous risk patterns for EC and GC suggest that variations in geographical patterns are driven
by different risk exposures. Although specific risk factors
for this distribution are not fully identified, the higher
prevalence of Hp infection in Northwest China (e.g.,
Ningxia, Qinghai, and Gansu provinces) is associatedwith increased GC rates in these regions [41, 42]. This
indicates that Hp infection may play a significant role inthe spatial distribution of EC and GC. The common lowrisk areas in the southern Yangtze River regions, despite
being surrounded by high-risk areas, have notably lower
cancer rates compared to traditional high-risk areas. This
discrepancy highlights the need for further research to
uncover the protective factors and risk exposures contributing to lower incidence rates in these regions.
Our previous research demonstrated that joint
screening could be a cost-effective strategy in highrisk regions for both EC and GC [12, 43]. This
study further validates that areas with a high prevalence of EC often also have a high prevalence of GC,

Page 9 of 12

supporting the rationale for joint screening initiatives.
Identifying different combined risk regions for EC
and GC allows for prioritizing joint screening efforts
and guiding cancer prevention and control strategies.
These findings can inform cancer prevention and control plans by delineating various risk regions, including those without joint screening, and guide related
research on cancer burden and risk factor assessment.
The First National Death Survey (1973–1975) definedhigh-risk areas for EC as those with a crude mortality rate of ≥ 60 per 100,000 males and ≥ 30 per 100,000
females. Similarly, high-risk areas for GC were definedas ≥ 50 per 100,000 males and ≥ 25 per 100,000 females[44]. While this classification reflected the realities ofEC and GC in China at that time, it did not accountfor population structure and lacks comparability ata global level. Another study classified high-risk,
medium-risk, and low-risk areas of GC worldwide
by ASIR > 20 per 100,000 persons, 11–19 per 100,000
persons, and < 10 per 100,000 persons based on 2005
cancer statistics, respectively [45]. However, this classification is outdated for China, where GC incidence
has notably declined, though it remains higher than
the global average. Our study, in contrast, divided
classifies combined risk areas of EC and GC using the
RR in CRAs compared to the national level, based on
spatiotemporal associations in China. Areas with an
RR ≥ 2 are prioritized for joint endoscopic screening
for EC and GC, reflecting current realities in China
and helping policymakers understand the similarities and differences between these cancers. We recommend implementing joint endoscopic screening
in the following areas: Shexian, Cixian, Wu’an, Pingshun, Yangcheng, Jintan, Yandu, Jianhu, Danyang,
Yangzhong, Feixi, Lujiang, Chaohu, Yi’an, Tianchang,
Songxian, Luoning, Lushan, Linzhou, Neixiang, Jiyuan,
Yanting, Shangzhou, Liangzhou, and Xinyuan. By
focusing on high-risk regions, resources can be allocated more effectively to reduce the burden of EC
and GC. The geographic and epidemiological data
provided enhance the precision of identifying criticalareas, improving upon previous methods that relied onbroader regional data. Additionally, the effective joint
screening in rural areas faces substantial challenges
and barriers, including logistic and financial constraints. Our study acknowledges these challenges andsuggests that future research should explore innovative
solutions, such as mobile screening units and telemedicine, to overcome these barriers.
Public health implications

The classification of risk regions for EC and GC hassignificant public health implications. UnderstandingLi et al. BMC Medicine(2024) 22:364

the spatiotemporal patterns of these cancers is crucial for developing national screening standards. First,
many risk factors contributing to disparities in EC
and GC remain poorly understood despite decades ofresearch. It is essential to strengthen efforts to implement early screening strategies, improve compliancewith screening programs, and expand cancer registries
to reduce exposure to specific risks. Second, collaboration with local governments and research institutions
is vital to establish screening programs in uncovered
regions and to ensure effective diagnosis and treatment capacities. Finally, future research should focus
on environmental and screening-related factors to
explore potential etiological pathways between EC andGC. Expanding joint screening initiatives and conducting comprehensive cost-effectiveness analyses in identified high-risk areas will further optimize resource
allocation and policy decisions. Addressing these key
aspects will enhance early detection, improve prevention efforts, and ultimately reduce the burden of EC
and GC in high-risk populations.

Page 10 of 12

the need for further research. Future studies should
assess how access barriers influence screening outcomes and explore solutions such as mobile screening
units and telemedicine initiatives [46]. Third, due to
disparities in population density and economic conditions across CRAs, and the concentration of CRAs ineastern regions, potential biases may exist in the findings for western regions. However, the existing CRAs
in Western China still reflect real epidemiological process because the CRA settings are based on population
sampling data. Fourth, this study was conducted during the COVID-19 pandemic, which caused a significant lag in the latest data. Data spanning from 2006 to2016 only present profiles up to that time. However,given the typically stable geographical patterns of EC
and GC in China, these findings still offer valuableguidance for joint screening efforts. Lastly, the unavailability of combined data for both sexes within each
CRA limits our ability to examine sex-specific associations comprehensively. Despite this, the consistency
in results across sexes strengthens the robustness andvalidity of our conclusions.

Strengths and limitations

In this study, our primary contributions are being
the first to examine the spatiotemporal associations
between EC and GC and to divide different combined
risk areas for both cancers. This provides crucial evidence to support joint endoscopic screening and identifies high-risk areas for EC and GC in China. Second,
this study applies interdisciplinary and integrated
approaches, utilizing spatiotemporal techniques toanalyze the association between EC and GC. The combined application of these methods and models allows
us to explore potential etiological associations between
EC and GC, offering important references for specificcancers with previously unexplored relationships.
Third, our findings establish a theoretical frameworkand explicit analytical solutions for tailoring standardsof high- and low-risk areas for UGIC at the national
level. However, there are some limitations to thisstudy. First, EC and GC have different subsites thatwere not analyzed due to a lack of data, so the results
predominantly reflect the dominant subsites (esophageal squamous cell carcinoma and gastric cardia cancer) in China. Second, we acknowledge the potential
discrepancy between the locations where cancer
cases are identified and where patients receive treatment, particularly in rural areas with limited healthcare access. Although our study focuses on incidence
locations using data from the NCCC—which includes
diverse sources like hospitals, community health centers, and insurance systems—these challenges highlight

Conclusions
This study utilized spatiotemporal statistical approaches
to investigate the associations between EC and GC,
identifying various combined risk regions and supporting the implementation of joint endoscopic screening
efforts. From 2006 to 2016, the ASIRs of EC and GC
initially increased slightly but then experienced a significant decline, with a notable reduction observed particularly among females and in rural areas. Identifying
high-risk regions provides a foundation for targeted
screening and prevention strategies, which are crucial
for refining public health policies and enhancing cancer control efforts in China. The findings underscore theimportance of sustained, targeted screening programs
to further mitigate the burden of EC and GC, offeringvaluable insights for developing effective prevention
and control measures.
Abbreviations
AAPC	Average annual percentage change
APC	Annual percentage change
ASIR	Age-standardized incidence rate
CI	Confidence interval
CRAs	Cancer registry areas
EC	Esophageal cancer
GAC​	Grey absolute correlation
GC	Gastric cancer
GCC​	Grey comprehensive correlation
GRC​	Grey relative correlation
Hp	Helicobacter pylori
NCCC​	National Cancer Center of China
RR	Relative risk
UGIC	Upper gastrointestinal cancerLi et al. BMC Medicine(2024) 22:364

Page 11 of 12

Supplementary Information

Author details
1
Department of Thoracic Surgery, The First Affiliated Hospital of Xian Jiaotong
University, Xi’an, Shaanxi, China. 2 Department of Epidemiology and Health
Statistics, School of Public Health, Xi’an Jiaotong University Health Science
Center, Xi’an , Shaanxi, China. 3 Evidence‑Based Research Center of Social
Science and Health, School of Public Affairs, Nanjing University of Scienceand Technology, Nanjing, China. 4 College of Geography and Environment,
Baoji University of Arts and Sciences, Baoji, Shaanxi, China. 5 Key Laboratory
for Disease Prevention and Control and Health Promotion of Shaanxi Province,
Xi’an, Shaanxi, China.

The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​024-​03594-7.
Additional file 1: Table S1. The general information of China Cancer Registration between 2006 and 2016. Table S2. The number of cancerregistration areas in provicial regions from 2006 to 2016.
Additional file 2: Table S1. The division method of the combined risk
areas in EC and GC. Table S2. The division results of combined risk regions
between EC and GC by sexes in 487 cancer registration areas of China.
Fig. S1. Flow chart of geographical information database. Fig. S2. The geographical distribution of CRAs in 2006–2016. Fig. S3. The division diagramof combined risk regions of EC and GC. Fig. S4. The geographical patternsof EC ASIR among males in from 2006 to 2016. Fig. S5. The geographical patterns of EC ASIR among females in from 2006 to 2016. Fig. S6. The
geographical patterns of GC ASIR among males in from 2006 to 2016. Fig.
S7. The geographical patterns of GC ASIR among females in from 2006
to 2016. Fig. S8. The geographic patterns of the combined risk regions of
EC and GC by sex in China. Fig. S9. The different combination risk regions
between EC and GC among males in China. Fig. S10. The different combination risk regions between EC and GC among females in’ China.
Additional file 3: Table S1. The spatial association magnitude of EC and
GC in China. Table S2. The relative risk of EC and GC among males in eachcancer registration area. Table S3. The relative risk of EC and GC amongfemales in each cancer registration areas.
Acknowledgements
The authors thank National Cancer Center of China for providing all involved
data.
Authors’ contributions
PL, XQ, and GHZ conceptualized and designed the study. PL and XQ contributed to statistical analysis, interpretation of data, and PL drafted the manuscript. JJ, RUB, RYX revised critically the manuscript for important intellectual
content. MY, RYX, and JZW did administrative, technical, or material support.
This work was completed under the supervision of GHZ and GJZ. All authors
read and approved the final manuscript.
Funding
This work was supported provided by National funding program for postdoctoral researchers (Grant No. GZC20232085), National Key Research Development Program of China (Grant No. 2018YFC1313100), Shaanxi Provincial
Administration of Traditional Chinese Medicine (Grant NO.2022-SLRH-LJ-009),
and National Youth Science Foundation (Grant NO.82102976), Social Science
Foundation Project of Shaanxi Province (Grant NO.2020F009), and Basic
Research Program for Young Scientists of Shaanxi Province of Natural Science:(Grant NO.2021JQ-804).
Availability of data and materials
The source of esophageal and gastric cancers in this study can be obtained
from China Cancer Registry Annual Report, respectively. The important data
(e.g., the divided regions in Fig. 5) of this study were listed in Additional file 1,2
and 3. The whole analysis process can be obtained on reasonable request.

Declarations
Ethics approval and consent to participate
This study approved from the First Affiliated Hospital of Xi’an Jiaotong University Research Ethics Committee (No. LLSBPJ-2024–304).
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.

Received: 10 March 2024 Accepted: 27 August 2024

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